Coloring labels by a continuous variable fixed

CHANGELOG.md

@@ -8,6 +8,22 @@ and this project adheres to [Semantic Versioning][].
 [keep a changelog]: https://keepachangelog.com/en/1.0.0/
 [semantic versioning]: https://semver.org/spec/v2.0.0.html
 
+## [0.2.6] - tbd
+
+### Added
+
+-
+
+### Changed
+
+- Lowered RMSE-threshold for plot-based tests from 45 to 15 (#344)
+- When subsetting to `groups`, `NA` isn't automatically added to legend (#344)
+
+### Fixed
+
+- Filtering with `groups` now preserves original cmap (#344)
+- Non-selected `groups` are now not shown in `na_color` (#344)
+
 ## [0.2.5] - 2024-08-23
 
 ### Added

src/spatialdata_plot/pl/render.py

@@ -855,7 +855,7 @@ def _draw_labels(seg_erosionpx: int | None, seg_boundaries: bool, alpha: float)
  legend_fontweight=legend_params.legend_fontweight,
  legend_loc=legend_params.legend_loc,
  legend_fontoutline=legend_params.legend_fontoutline,
- na_in_legend=legend_params.na_in_legend,
+ na_in_legend=legend_params.na_in_legend if groups is None else len(groups) == len(set(color_vector)),
  colorbar=legend_params.colorbar,
  scalebar_dx=scalebar_params.scalebar_dx,
  scalebar_units=scalebar_params.scalebar_units,

src/spatialdata_plot/pl/utils.py

@@ -567,9 +567,6 @@ def _get_subplots(num_images: int, ncols: int = 4, width: int = 4, height: int =
  Union[plt.Figure, plt.Axes]
  Matplotlib figure and axes object.
  """
- # if num_images <= 1:
- # raise ValueError("Number of images must be greater than 1.")
-
  if num_images < ncols:
  nrows = 1
  ncols = num_images
@@ -733,8 +730,6 @@ def _set_color_source_vec(
  color = np.full(len(element), na_color)
  return color, color, False
 
- # model = get_model(sdata[element_name])
-
  # Figure out where to get the color from
  origins = _locate_value(value_key=value_to_plot, sdata=sdata, element_name=element_name, table_name=table_name)
 
@@ -778,16 +773,13 @@ def _set_color_source_vec(
  palette=palette,
  na_color=na_color,
  )
+
  color_source_vector = color_source_vector.set_categories(color_mapping.keys())
  if color_mapping is None:
  raise ValueError("Unable to create color palette.")
 
  # do not rename categories, as colors need not be unique
  color_vector = color_source_vector.map(color_mapping)
- if color_vector.isna().any():
- if (na_cat_color := to_hex(na_color)) not in color_vector.categories:
- color_vector = color_vector.add_categories([na_cat_color])
- color_vector = color_vector.fillna(to_hex(na_color))
 
  return color_source_vector, color_vector, True
 
@@ -808,44 +800,43 @@ def _map_color_seg(
  seg_boundaries: bool = False,
 ) -> ArrayLike:
  cell_id = np.array(cell_id)
- if color_vector is not None and isinstance(color_vector.dtype, pd.CategoricalDtype):
- # users wants to plot a categorical column
+
+ if pd.api.types.is_categorical_dtype(color_vector.dtype):
+ # Case A: users wants to plot a categorical column
  if np.any(color_source_vector.isna()):
  cell_id[color_source_vector.isna()] = 0
- val_im: ArrayLike = map_array(seg, cell_id, color_vector.codes + 1)
+ val_im: ArrayLike = map_array(seg.copy(), cell_id, color_vector.codes + 1)
  cols = colors.to_rgba_array(color_vector.categories)
-
  elif pd.api.types.is_numeric_dtype(color_vector.dtype):
- # user wants to plot a continous column
+ # Case B: user wants to plot a continous column
  if isinstance(color_vector, pd.Series):
  color_vector = color_vector.to_numpy()
- val_im = map_array(seg, cell_id, color_vector)
  cols = cmap_params.cmap(cmap_params.norm(color_vector))
-
+ val_im = map_array(seg.copy(), cell_id, cell_id)
  else:
- val_im = map_array(seg.copy(), cell_id, cell_id) # replace with same seg id to remove missing segs
-
- if val_im.shape[0] == 1:
- val_im = np.squeeze(val_im, axis=0)
- if "#" in str(color_vector[0]):
- # we have hex colors
- assert all(_is_color_like(c) for c in color_vector), "Not all values are color-like."
- cols = colors.to_rgba_array(color_vector)
+ # Case C: User didn't specify any colors
+ if color_source_vector is not None and (
+ set(color_vector) == set(color_source_vector)
+ and len(set(color_vector)) == 1
+ and set(color_vector) == {na_color}
+ and not na_color_modified_by_user
+ ):
+ val_im = map_array(seg.copy(), cell_id, cell_id)
+ RNG = default_rng(42)
+ cols = RNG.random((len(color_vector), 3))
  else:
- cols = cmap_params.cmap(cmap_params.norm(color_vector))
+ # Case D: User didn't specify a column to color by, but modified the na_color
+ val_im = map_array(seg.copy(), cell_id, cell_id)
+ if "#" in str(color_vector[0]):
+ # we have hex colors
+ assert all(_is_color_like(c) for c in color_vector), "Not all values are color-like."
+ cols = colors.to_rgba_array(color_vector)
+ else:
+ cols = cmap_params.cmap(cmap_params.norm(color_vector))
 
  if seg_erosionpx is not None:
  val_im[val_im == erosion(val_im, square(seg_erosionpx))] = 0
 
- if color_source_vector is not None and (
- set(color_vector) == set(color_source_vector)
- and len(set(color_vector)) == 1
- and set(color_vector) == {na_color}
- and not na_color_modified_by_user
- ):
- RNG = default_rng(42)
- cols = RNG.random((len(cols), 3))
-
  seg_im: ArrayLike = label2rgb(
  label=val_im,
  colors=cols,
@@ -948,7 +939,7 @@ def _get_categorical_color_mapping(
  else:
  base_mapping = _generate_base_categorial_color_mapping(adata, cluster_key, color_source_vector, na_color)
 
- return _modify_categorical_color_mapping(base_mapping, groups, palette)
+ return _modify_categorical_color_mapping(mapping=base_mapping, groups=groups, palette=palette)
 
 
 def _maybe_set_colors(
@@ -1587,19 +1578,14 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
 
  palette = param_dict["palette"]
 
- if (groups := param_dict.get("groups")) is not None and palette is None:
- warnings.warn(
- "Groups is specified but palette is not. Setting palette to default 'lightgray'", UserWarning, stacklevel=2
- )
- param_dict["palette"] = ["lightgray" for _ in range(len(groups))]
-
  if isinstance((palette := param_dict["palette"]), list):
  if not all(isinstance(p, str) for p in palette):
  raise ValueError("If specified, parameter 'palette' must contain only strings.")
  elif isinstance(palette, (str, type(None))) and "palette" in param_dict:
  param_dict["palette"] = [palette] if palette is not None else None
 
  if element_type in ["shapes", "points", "labels"] and (palette := param_dict.get("palette")) is not None:
+ groups = param_dict.get("groups")
  if groups is None:
  raise ValueError("When specifying 'palette', 'groups' must also be specified.")
  if len(groups) != len(palette):

tests/conftest.py

@@ -33,7 +33,7 @@
 
 EXPECTED = HERE / "_images"
 ACTUAL = HERE / "figures"
-TOL = 45
+TOL = 15
 DPI = 80
 
 RNG = np.random.default_rng(seed=42)

tests/pl/test_render_labels.py

@@ -5,9 +5,8 @@
 import pandas as pd
 import pytest
 import scanpy as sc
-from anndata import AnnData
 from spatial_image import to_spatial_image
-from spatialdata import SpatialData, deepcopy, get_element_instances
+from spatialdata import SpatialData, deepcopy
 from spatialdata.models import TableModel
 
 import spatialdata_plot # noqa: F401
@@ -82,26 +81,31 @@ def test_plot_can_stack_render_labels(self, sdata_blobs: SpatialData):
  def test_plot_can_color_labels_by_continuous_variable(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_labels("blobs_labels", color="channel_0_sum").pl.show()
 
+ def test_plot_can_color_labels_by_categorical_variable(self, sdata_blobs: SpatialData):
+ max_col = sdata_blobs.table.to_df().idxmax(axis=1)
+ max_col = pd.Categorical(max_col, categories=sdata_blobs.table.to_df().columns, ordered=True)
+ sdata_blobs.table.obs["which_max"] = max_col
+
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show()
+
  @pytest.mark.parametrize(
  "label",
  [
  "blobs_labels",
  "blobs_multiscale_labels",
  ],
  )
- def test_plot_can_color_labels_by_categorical_variable(self, sdata_blobs: SpatialData, label: str):
+ def test_plot_can_color_labels_by_categorical_variable_in_other_table(self, sdata_blobs: SpatialData, label: str):
 
  def _make_tablemodel_with_categorical_labels(sdata_blobs, label):
 
- n_obs = len(get_element_instances(sdata_blobs[label]))
- vals = np.arange(n_obs) + 1
- adata = AnnData(vals.reshape(-1, 1), obs=pd.DataFrame({"instance_id": vals}))
- adata.obs["category"] = pd.Categorical(
- list(["a", "b", "c"] * ((n_obs // 3) + 1))[:n_obs],
- categories=["a", "b", "c"],
- ordered=True,
- )
+ adata = sdata_blobs.tables["table"].copy()
+ max_col = adata.to_df().idxmax(axis=1)
+ max_col = max_col.str.replace("channel_", "ch").str.replace("_sum", "")
+ max_col = pd.Categorical(max_col, categories=set(max_col), ordered=True)
+ adata.obs["which_max"] = max_col
  adata.obs["region"] = label
+ del adata.uns["spatialdata_attrs"]
  table = TableModel.parse(
  adata=adata,
  region_key="region",
@@ -110,7 +114,17 @@ def _make_tablemodel_with_categorical_labels(sdata_blobs, label):
  )
  sdata_blobs.tables["other_table"] = table
 
- sdata_blobs.pl.render_labels(label, color="category", table="other_table", scale="scale0").pl.show()
+ _, axs = plt.subplots(nrows=1, ncols=3, layout="tight")
+
+ sdata_blobs.pl.render_labels(label, color="channel_1_sum", table="other_table", scale="scale0").pl.show(
+ ax=axs[0], title="ch_1_sum", colorbar=False
+ )
+ sdata_blobs.pl.render_labels(label, color="channel_2_sum", table="other_table", scale="scale0").pl.show(
+ ax=axs[1], title="ch_2_sum", colorbar=False
+ )
+ sdata_blobs.pl.render_labels(label, color="which_max", table="other_table", scale="scale0").pl.show(
+ ax=axs[2], legend_fontsize=6
+ )
 
  # we're modifying the data here, so we need an independent copy
  sdata_blobs_local = deepcopy(sdata_blobs)
@@ -176,7 +190,7 @@ def test_plot_subset_categorical_label_maintains_order(self, sdata_blobs: Spatia
 
  _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 
- sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0])
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0], legend_fontsize=6)
  sdata_blobs.pl.render_labels(
  "blobs_labels",
  color="which_max",
@@ -190,7 +204,7 @@ def test_plot_subset_categorical_label_maintains_order_when_palette_overwrite(se
 
  _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 
- sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0])
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0], legend_fontsize=6)
  sdata_blobs.pl.render_labels(
  "blobs_labels", color="which_max", groups=["channel_0_sum"], palette="red"
  ).pl.show(ax=axs[1])

tests/pl/test_render_points.py

@@ -1,4 +1,5 @@
 import matplotlib
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import scanpy as sc
@@ -27,15 +28,25 @@ class TestPoints(PlotTester, metaclass=PlotTesterMeta):
  def test_plot_can_render_points(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_points(element="blobs_points").pl.show()
 
- def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
+ def test_plot_can_filter_with_groups_default_palette(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_points"
- sdata_blobs.pl.render_points(color="genes", groups="gene_b", palette="red").pl.show()
 
- def test_plot_can_filter_with_groups_default_palette(self, sdata_blobs: SpatialData):
+ sdata_blobs.pl.render_points(color="genes", size=10).pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_points(color="genes", groups="gene_b", size=10).pl.show(ax=axs[1], legend_fontsize=6)
+
+ def test_plot_can_filter_with_groups_custom_palette(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_points"
- sdata_blobs.pl.render_points(color="genes", groups="gene_b").pl.show()
+
+ sdata_blobs.pl.render_points(color="genes", size=10).pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_points(color="genes", groups="gene_b", size=10, palette="red").pl.show(
+ ax=axs[1], legend_fontsize=6
+ )
 
  def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs

tests/pl/test_render_shapes.py

@@ -1,6 +1,7 @@
 import anndata
 import geopandas as gpd
 import matplotlib
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import scanpy as sc
@@ -107,6 +108,8 @@ def test_plot_can_scale_shapes(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_shapes(element="blobs_circles", scale=0.5).pl.show()
 
  def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  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"] = "c1"
@@ -115,7 +118,10 @@ def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
  "category"
  )
 
- sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups="c1").pl.show()
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster").pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups="c1").pl.show(
+ ax=axs[1], legend_fontsize=6
+ )
 
  def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs

tests/pl/test_utils.py

@@ -58,25 +58,23 @@ def test_plot_can_set_zero_in_cmap_to_transparent(self, sdata_blobs: SpatialData
  from spatialdata_plot.pl.utils import set_zero_in_cmap_to_transparent
 
  # set up figure and modify the data to add 0s
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
- table = sdata_blobs.table.copy()
- x = table.X.todense()
- x[:10, 0] = 0
- table.X = x
- sdata_blobs.tables["modified_table"] = table
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+ sdata_blobs.tables["table"].obs["my_var"] = list(range(len(sdata_blobs.tables["table"].obs)))
+ sdata_blobs.tables["table"].obs["my_var"] += 2 # shift the values to not have 0s
 
- # create a new cmap with 0 as transparent
- new_cmap = set_zero_in_cmap_to_transparent(cmap="plasma")
+ new_cmap = set_zero_in_cmap_to_transparent(cmap="viridis")
 
  # baseline img
- sdata_blobs.pl.render_labels("blobs_labels", color="channel_0_sum", cmap="viridis", table="table").pl.show(
+ sdata_blobs.pl.render_labels("blobs_labels", color="my_var", cmap="viridis", table="table").pl.show(
  ax=axs[0], colorbar=False
  )
 
+ sdata_blobs.tables["table"].obs.iloc[8:12, 2] = 0
+
  # image with 0s as transparent, so some labels are "missing"
- sdata_blobs.pl.render_labels(
-"blobs_labels", color="channel_0_sum", cmap=new_cmap, table="modified_table"
- ).pl.show(ax=axs[1], colorbar=False)
+ sdata_blobs.pl.render_labels("blobs_labels", color="my_var", cmap=new_cmap, table="table").pl.show(
+ax=axs[1], colorbar=False
+ )
 
 
 @pytest.mark.parametrize(

CHANGELOG.md

@@ -8,6 +8,22 @@ and this project adheres to [Semantic Versioning][].
 [keep a changelog]: https://keepachangelog.com/en/1.0.0/
 [semantic versioning]: https://semver.org/spec/v2.0.0.html
 
+## [0.2.6] - tbd
+
+### Added
+
+-
+
+### Changed
+
+- Lowered RMSE-threshold for plot-based tests from 45 to 15 (#344)
+- When subsetting to `groups`, `NA` isn't automatically added to legend (#344)
+
+### Fixed
+
+- Filtering with `groups` now preserves original cmap (#344)
+- Non-selected `groups` are now not shown in `na_color` (#344)
+
 ## [0.2.5] - 2024-08-23
 
 ### Added

src/spatialdata_plot/pl/render.py

@@ -855,7 +855,7 @@ def _draw_labels(seg_erosionpx: int | None, seg_boundaries: bool, alpha: float)
  legend_fontweight=legend_params.legend_fontweight,
  legend_loc=legend_params.legend_loc,
  legend_fontoutline=legend_params.legend_fontoutline,
- na_in_legend=legend_params.na_in_legend,
+ na_in_legend=legend_params.na_in_legend if groups is None else len(groups) == len(set(color_vector)),
  colorbar=legend_params.colorbar,
  scalebar_dx=scalebar_params.scalebar_dx,
  scalebar_units=scalebar_params.scalebar_units,

src/spatialdata_plot/pl/utils.py

@@ -567,9 +567,6 @@ def _get_subplots(num_images: int, ncols: int = 4, width: int = 4, height: int =
  Union[plt.Figure, plt.Axes]
  Matplotlib figure and axes object.
  """
- # if num_images <= 1:
- # raise ValueError("Number of images must be greater than 1.")
-
  if num_images < ncols:
  nrows = 1
  ncols = num_images
@@ -733,8 +730,6 @@ def _set_color_source_vec(
  color = np.full(len(element), na_color)
  return color, color, False
 
- # model = get_model(sdata[element_name])
-
  # Figure out where to get the color from
  origins = _locate_value(value_key=value_to_plot, sdata=sdata, element_name=element_name, table_name=table_name)
 
@@ -778,16 +773,13 @@ def _set_color_source_vec(
  palette=palette,
  na_color=na_color,
  )
+
  color_source_vector = color_source_vector.set_categories(color_mapping.keys())
  if color_mapping is None:
  raise ValueError("Unable to create color palette.")
 
  # do not rename categories, as colors need not be unique
  color_vector = color_source_vector.map(color_mapping)
- if color_vector.isna().any():
- if (na_cat_color := to_hex(na_color)) not in color_vector.categories:
- color_vector = color_vector.add_categories([na_cat_color])
- color_vector = color_vector.fillna(to_hex(na_color))
 
  return color_source_vector, color_vector, True
 
@@ -808,44 +800,43 @@ def _map_color_seg(
  seg_boundaries: bool = False,
 ) -> ArrayLike:
  cell_id = np.array(cell_id)
- if color_vector is not None and isinstance(color_vector.dtype, pd.CategoricalDtype):
- # users wants to plot a categorical column
+
+ if pd.api.types.is_categorical_dtype(color_vector.dtype):
+ # Case A: users wants to plot a categorical column
  if np.any(color_source_vector.isna()):
  cell_id[color_source_vector.isna()] = 0
- val_im: ArrayLike = map_array(seg, cell_id, color_vector.codes + 1)
+ val_im: ArrayLike = map_array(seg.copy(), cell_id, color_vector.codes + 1)
  cols = colors.to_rgba_array(color_vector.categories)
-
  elif pd.api.types.is_numeric_dtype(color_vector.dtype):
- # user wants to plot a continous column
+ # Case B: user wants to plot a continous column
  if isinstance(color_vector, pd.Series):
  color_vector = color_vector.to_numpy()
- val_im = map_array(seg, cell_id, color_vector)
  cols = cmap_params.cmap(cmap_params.norm(color_vector))
-
+ val_im = map_array(seg.copy(), cell_id, cell_id)
  else:
- val_im = map_array(seg.copy(), cell_id, cell_id) # replace with same seg id to remove missing segs
-
- if val_im.shape[0] == 1:
- val_im = np.squeeze(val_im, axis=0)
- if "#" in str(color_vector[0]):
- # we have hex colors
- assert all(_is_color_like(c) for c in color_vector), "Not all values are color-like."
- cols = colors.to_rgba_array(color_vector)
+ # Case C: User didn't specify any colors
+ if color_source_vector is not None and (
+ set(color_vector) == set(color_source_vector)
+ and len(set(color_vector)) == 1
+ and set(color_vector) == {na_color}
+ and not na_color_modified_by_user
+ ):
+ val_im = map_array(seg.copy(), cell_id, cell_id)
+ RNG = default_rng(42)
+ cols = RNG.random((len(color_vector), 3))
  else:
- cols = cmap_params.cmap(cmap_params.norm(color_vector))
+ # Case D: User didn't specify a column to color by, but modified the na_color
+ val_im = map_array(seg.copy(), cell_id, cell_id)
+ if "#" in str(color_vector[0]):
+ # we have hex colors
+ assert all(_is_color_like(c) for c in color_vector), "Not all values are color-like."
+ cols = colors.to_rgba_array(color_vector)
+ else:
+ cols = cmap_params.cmap(cmap_params.norm(color_vector))
 
  if seg_erosionpx is not None:
  val_im[val_im == erosion(val_im, square(seg_erosionpx))] = 0
 
- if color_source_vector is not None and (
- set(color_vector) == set(color_source_vector)
- and len(set(color_vector)) == 1
- and set(color_vector) == {na_color}
- and not na_color_modified_by_user
- ):
- RNG = default_rng(42)
- cols = RNG.random((len(cols), 3))
-
  seg_im: ArrayLike = label2rgb(
  label=val_im,
  colors=cols,
@@ -948,7 +939,7 @@ def _get_categorical_color_mapping(
  else:
  base_mapping = _generate_base_categorial_color_mapping(adata, cluster_key, color_source_vector, na_color)
 
- return _modify_categorical_color_mapping(base_mapping, groups, palette)
+ return _modify_categorical_color_mapping(mapping=base_mapping, groups=groups, palette=palette)
 
 
 def _maybe_set_colors(
@@ -1587,19 +1578,14 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
 
  palette = param_dict["palette"]
 
- if (groups := param_dict.get("groups")) is not None and palette is None:
- warnings.warn(
- "Groups is specified but palette is not. Setting palette to default 'lightgray'", UserWarning, stacklevel=2
- )
- param_dict["palette"] = ["lightgray" for _ in range(len(groups))]
-
  if isinstance((palette := param_dict["palette"]), list):
  if not all(isinstance(p, str) for p in palette):
  raise ValueError("If specified, parameter 'palette' must contain only strings.")
  elif isinstance(palette, (str, type(None))) and "palette" in param_dict:
  param_dict["palette"] = [palette] if palette is not None else None
 
  if element_type in ["shapes", "points", "labels"] and (palette := param_dict.get("palette")) is not None:
+ groups = param_dict.get("groups")
  if groups is None:
  raise ValueError("When specifying 'palette', 'groups' must also be specified.")
  if len(groups) != len(palette):

tests/conftest.py

@@ -33,7 +33,7 @@
 
 EXPECTED = HERE / "_images"
 ACTUAL = HERE / "figures"
-TOL = 45
+TOL = 15
 DPI = 80
 
 RNG = np.random.default_rng(seed=42)

tests/pl/test_render_labels.py

@@ -5,9 +5,8 @@
 import pandas as pd
 import pytest
 import scanpy as sc
-from anndata import AnnData
 from spatial_image import to_spatial_image
-from spatialdata import SpatialData, deepcopy, get_element_instances
+from spatialdata import SpatialData, deepcopy
 from spatialdata.models import TableModel
 
 import spatialdata_plot # noqa: F401
@@ -82,26 +81,31 @@ def test_plot_can_stack_render_labels(self, sdata_blobs: SpatialData):
  def test_plot_can_color_labels_by_continuous_variable(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_labels("blobs_labels", color="channel_0_sum").pl.show()
 
+ def test_plot_can_color_labels_by_categorical_variable(self, sdata_blobs: SpatialData):
+ max_col = sdata_blobs.table.to_df().idxmax(axis=1)
+ max_col = pd.Categorical(max_col, categories=sdata_blobs.table.to_df().columns, ordered=True)
+ sdata_blobs.table.obs["which_max"] = max_col
+
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show()
+
  @pytest.mark.parametrize(
  "label",
  [
  "blobs_labels",
  "blobs_multiscale_labels",
  ],
  )
- def test_plot_can_color_labels_by_categorical_variable(self, sdata_blobs: SpatialData, label: str):
+ def test_plot_can_color_labels_by_categorical_variable_in_other_table(self, sdata_blobs: SpatialData, label: str):
 
  def _make_tablemodel_with_categorical_labels(sdata_blobs, label):
 
- n_obs = len(get_element_instances(sdata_blobs[label]))
- vals = np.arange(n_obs) + 1
- adata = AnnData(vals.reshape(-1, 1), obs=pd.DataFrame({"instance_id": vals}))
- adata.obs["category"] = pd.Categorical(
- list(["a", "b", "c"] * ((n_obs // 3) + 1))[:n_obs],
- categories=["a", "b", "c"],
- ordered=True,
- )
+ adata = sdata_blobs.tables["table"].copy()
+ max_col = adata.to_df().idxmax(axis=1)
+ max_col = max_col.str.replace("channel_", "ch").str.replace("_sum", "")
+ max_col = pd.Categorical(max_col, categories=set(max_col), ordered=True)
+ adata.obs["which_max"] = max_col
  adata.obs["region"] = label
+ del adata.uns["spatialdata_attrs"]
  table = TableModel.parse(
  adata=adata,
  region_key="region",
@@ -110,7 +114,17 @@ def _make_tablemodel_with_categorical_labels(sdata_blobs, label):
  )
  sdata_blobs.tables["other_table"] = table
 
- sdata_blobs.pl.render_labels(label, color="category", table="other_table", scale="scale0").pl.show()
+ _, axs = plt.subplots(nrows=1, ncols=3, layout="tight")
+
+ sdata_blobs.pl.render_labels(label, color="channel_1_sum", table="other_table", scale="scale0").pl.show(
+ ax=axs[0], title="ch_1_sum", colorbar=False
+ )
+ sdata_blobs.pl.render_labels(label, color="channel_2_sum", table="other_table", scale="scale0").pl.show(
+ ax=axs[1], title="ch_2_sum", colorbar=False
+ )
+ sdata_blobs.pl.render_labels(label, color="which_max", table="other_table", scale="scale0").pl.show(
+ ax=axs[2], legend_fontsize=6
+ )
 
  # we're modifying the data here, so we need an independent copy
  sdata_blobs_local = deepcopy(sdata_blobs)
@@ -176,7 +190,7 @@ def test_plot_subset_categorical_label_maintains_order(self, sdata_blobs: Spatia
 
  _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 
- sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0])
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0], legend_fontsize=6)
  sdata_blobs.pl.render_labels(
  "blobs_labels",
  color="which_max",
@@ -190,7 +204,7 @@ def test_plot_subset_categorical_label_maintains_order_when_palette_overwrite(se
 
  _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 
- sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0])
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0], legend_fontsize=6)
  sdata_blobs.pl.render_labels(
  "blobs_labels", color="which_max", groups=["channel_0_sum"], palette="red"
  ).pl.show(ax=axs[1])

tests/pl/test_render_points.py

@@ -1,4 +1,5 @@
 import matplotlib
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import scanpy as sc
@@ -27,15 +28,25 @@ class TestPoints(PlotTester, metaclass=PlotTesterMeta):
  def test_plot_can_render_points(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_points(element="blobs_points").pl.show()
 
- def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
+ def test_plot_can_filter_with_groups_default_palette(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_points"
- sdata_blobs.pl.render_points(color="genes", groups="gene_b", palette="red").pl.show()
 
- def test_plot_can_filter_with_groups_default_palette(self, sdata_blobs: SpatialData):
+ sdata_blobs.pl.render_points(color="genes", size=10).pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_points(color="genes", groups="gene_b", size=10).pl.show(ax=axs[1], legend_fontsize=6)
+
+ def test_plot_can_filter_with_groups_custom_palette(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_points"
- sdata_blobs.pl.render_points(color="genes", groups="gene_b").pl.show()
+
+ sdata_blobs.pl.render_points(color="genes", size=10).pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_points(color="genes", groups="gene_b", size=10, palette="red").pl.show(
+ ax=axs[1], legend_fontsize=6
+ )
 
  def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs

tests/pl/test_render_shapes.py

@@ -1,6 +1,7 @@
 import anndata
 import geopandas as gpd
 import matplotlib
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import scanpy as sc
@@ -107,6 +108,8 @@ def test_plot_can_scale_shapes(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_shapes(element="blobs_circles", scale=0.5).pl.show()
 
  def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  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"] = "c1"
@@ -115,7 +118,10 @@ def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
  "category"
  )
 
- sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups="c1").pl.show()
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster").pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups="c1").pl.show(
+ ax=axs[1], legend_fontsize=6
+ )
 
  def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs

tests/pl/test_utils.py

@@ -58,25 +58,23 @@ def test_plot_can_set_zero_in_cmap_to_transparent(self, sdata_blobs: SpatialData
  from spatialdata_plot.pl.utils import set_zero_in_cmap_to_transparent
 
  # set up figure and modify the data to add 0s
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
- table = sdata_blobs.table.copy()
- x = table.X.todense()
- x[:10, 0] = 0
- table.X = x
- sdata_blobs.tables["modified_table"] = table
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+ sdata_blobs.tables["table"].obs["my_var"] = list(range(len(sdata_blobs.tables["table"].obs)))
+ sdata_blobs.tables["table"].obs["my_var"] += 2 # shift the values to not have 0s
 
- # create a new cmap with 0 as transparent
- new_cmap = set_zero_in_cmap_to_transparent(cmap="plasma")
+ new_cmap = set_zero_in_cmap_to_transparent(cmap="viridis")
 
  # baseline img
- sdata_blobs.pl.render_labels("blobs_labels", color="channel_0_sum", cmap="viridis", table="table").pl.show(
+ sdata_blobs.pl.render_labels("blobs_labels", color="my_var", cmap="viridis", table="table").pl.show(
  ax=axs[0], colorbar=False
  )
 
+ sdata_blobs.tables["table"].obs.iloc[8:12, 2] = 0
+
  # image with 0s as transparent, so some labels are "missing"
- sdata_blobs.pl.render_labels(
-"blobs_labels", color="channel_0_sum", cmap=new_cmap, table="modified_table"
- ).pl.show(ax=axs[1], colorbar=False)
+ sdata_blobs.pl.render_labels("blobs_labels", color="my_var", cmap=new_cmap, table="table").pl.show(
+ax=axs[1], colorbar=False
+ )
 
 
 @pytest.mark.parametrize(

CHANGELOG.md

@@ -8,6 +8,22 @@ and this project adheres to [Semantic Versioning][].
 [keep a changelog]: https://keepachangelog.com/en/1.0.0/
 [semantic versioning]: https://semver.org/spec/v2.0.0.html
 
+## [0.2.6] - tbd
+
+### Added
+
+-
+
+### Changed
+
+- Lowered RMSE-threshold for plot-based tests from 45 to 15 (#344)
+- When subsetting to `groups`, `NA` isn't automatically added to legend (#344)
+
+### Fixed
+
+- Filtering with `groups` now preserves original cmap (#344)
+- Non-selected `groups` are now not shown in `na_color` (#344)
+
 ## [0.2.5] - 2024-08-23
 
 ### Added

src/spatialdata_plot/pl/render.py

@@ -855,7 +855,7 @@ def _draw_labels(seg_erosionpx: int | None, seg_boundaries: bool, alpha: float)
  legend_fontweight=legend_params.legend_fontweight,
  legend_loc=legend_params.legend_loc,
  legend_fontoutline=legend_params.legend_fontoutline,
- na_in_legend=legend_params.na_in_legend,
+ na_in_legend=legend_params.na_in_legend if groups is None else len(groups) == len(set(color_vector)),
  colorbar=legend_params.colorbar,
  scalebar_dx=scalebar_params.scalebar_dx,
  scalebar_units=scalebar_params.scalebar_units,

src/spatialdata_plot/pl/utils.py

@@ -567,9 +567,6 @@ def _get_subplots(num_images: int, ncols: int = 4, width: int = 4, height: int =
  Union[plt.Figure, plt.Axes]
  Matplotlib figure and axes object.
  """
- # if num_images <= 1:
- # raise ValueError("Number of images must be greater than 1.")
-
  if num_images < ncols:
  nrows = 1
  ncols = num_images
@@ -733,8 +730,6 @@ def _set_color_source_vec(
  color = np.full(len(element), na_color)
  return color, color, False
 
- # model = get_model(sdata[element_name])
-
  # Figure out where to get the color from
  origins = _locate_value(value_key=value_to_plot, sdata=sdata, element_name=element_name, table_name=table_name)
 
@@ -778,16 +773,13 @@ def _set_color_source_vec(
  palette=palette,
  na_color=na_color,
  )
+
  color_source_vector = color_source_vector.set_categories(color_mapping.keys())
  if color_mapping is None:
  raise ValueError("Unable to create color palette.")
 
  # do not rename categories, as colors need not be unique
  color_vector = color_source_vector.map(color_mapping)
- if color_vector.isna().any():
- if (na_cat_color := to_hex(na_color)) not in color_vector.categories:
- color_vector = color_vector.add_categories([na_cat_color])
- color_vector = color_vector.fillna(to_hex(na_color))
 
  return color_source_vector, color_vector, True
 
@@ -808,44 +800,43 @@ def _map_color_seg(
  seg_boundaries: bool = False,
 ) -> ArrayLike:
  cell_id = np.array(cell_id)
- if color_vector is not None and isinstance(color_vector.dtype, pd.CategoricalDtype):
- # users wants to plot a categorical column
+
+ if pd.api.types.is_categorical_dtype(color_vector.dtype):
+ # Case A: users wants to plot a categorical column
  if np.any(color_source_vector.isna()):
  cell_id[color_source_vector.isna()] = 0
- val_im: ArrayLike = map_array(seg, cell_id, color_vector.codes + 1)
+ val_im: ArrayLike = map_array(seg.copy(), cell_id, color_vector.codes + 1)
  cols = colors.to_rgba_array(color_vector.categories)
-
  elif pd.api.types.is_numeric_dtype(color_vector.dtype):
- # user wants to plot a continous column
+ # Case B: user wants to plot a continous column
  if isinstance(color_vector, pd.Series):
  color_vector = color_vector.to_numpy()
- val_im = map_array(seg, cell_id, color_vector)
  cols = cmap_params.cmap(cmap_params.norm(color_vector))
-
+ val_im = map_array(seg.copy(), cell_id, cell_id)
  else:
- val_im = map_array(seg.copy(), cell_id, cell_id) # replace with same seg id to remove missing segs
-
- if val_im.shape[0] == 1:
- val_im = np.squeeze(val_im, axis=0)
- if "#" in str(color_vector[0]):
- # we have hex colors
- assert all(_is_color_like(c) for c in color_vector), "Not all values are color-like."
- cols = colors.to_rgba_array(color_vector)
+ # Case C: User didn't specify any colors
+ if color_source_vector is not None and (
+ set(color_vector) == set(color_source_vector)
+ and len(set(color_vector)) == 1
+ and set(color_vector) == {na_color}
+ and not na_color_modified_by_user
+ ):
+ val_im = map_array(seg.copy(), cell_id, cell_id)
+ RNG = default_rng(42)
+ cols = RNG.random((len(color_vector), 3))
  else:
- cols = cmap_params.cmap(cmap_params.norm(color_vector))
+ # Case D: User didn't specify a column to color by, but modified the na_color
+ val_im = map_array(seg.copy(), cell_id, cell_id)
+ if "#" in str(color_vector[0]):
+ # we have hex colors
+ assert all(_is_color_like(c) for c in color_vector), "Not all values are color-like."
+ cols = colors.to_rgba_array(color_vector)
+ else:
+ cols = cmap_params.cmap(cmap_params.norm(color_vector))
 
  if seg_erosionpx is not None:
  val_im[val_im == erosion(val_im, square(seg_erosionpx))] = 0
 
- if color_source_vector is not None and (
- set(color_vector) == set(color_source_vector)
- and len(set(color_vector)) == 1
- and set(color_vector) == {na_color}
- and not na_color_modified_by_user
- ):
- RNG = default_rng(42)
- cols = RNG.random((len(cols), 3))
-
  seg_im: ArrayLike = label2rgb(
  label=val_im,
  colors=cols,
@@ -948,7 +939,7 @@ def _get_categorical_color_mapping(
  else:
  base_mapping = _generate_base_categorial_color_mapping(adata, cluster_key, color_source_vector, na_color)
 
- return _modify_categorical_color_mapping(base_mapping, groups, palette)
+ return _modify_categorical_color_mapping(mapping=base_mapping, groups=groups, palette=palette)
 
 
 def _maybe_set_colors(
@@ -1587,19 +1578,14 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
 
  palette = param_dict["palette"]
 
- if (groups := param_dict.get("groups")) is not None and palette is None:
- warnings.warn(
- "Groups is specified but palette is not. Setting palette to default 'lightgray'", UserWarning, stacklevel=2
- )
- param_dict["palette"] = ["lightgray" for _ in range(len(groups))]
-
  if isinstance((palette := param_dict["palette"]), list):
  if not all(isinstance(p, str) for p in palette):
  raise ValueError("If specified, parameter 'palette' must contain only strings.")
  elif isinstance(palette, (str, type(None))) and "palette" in param_dict:
  param_dict["palette"] = [palette] if palette is not None else None
 
  if element_type in ["shapes", "points", "labels"] and (palette := param_dict.get("palette")) is not None:
+ groups = param_dict.get("groups")
  if groups is None:
  raise ValueError("When specifying 'palette', 'groups' must also be specified.")
  if len(groups) != len(palette):

tests/conftest.py

@@ -33,7 +33,7 @@
 
 EXPECTED = HERE / "_images"
 ACTUAL = HERE / "figures"
-TOL = 45
+TOL = 15
 DPI = 80
 
 RNG = np.random.default_rng(seed=42)

tests/pl/test_render_labels.py

@@ -5,9 +5,8 @@
 import pandas as pd
 import pytest
 import scanpy as sc
-from anndata import AnnData
 from spatial_image import to_spatial_image
-from spatialdata import SpatialData, deepcopy, get_element_instances
+from spatialdata import SpatialData, deepcopy
 from spatialdata.models import TableModel
 
 import spatialdata_plot # noqa: F401
@@ -82,26 +81,31 @@ def test_plot_can_stack_render_labels(self, sdata_blobs: SpatialData):
  def test_plot_can_color_labels_by_continuous_variable(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_labels("blobs_labels", color="channel_0_sum").pl.show()
 
+ def test_plot_can_color_labels_by_categorical_variable(self, sdata_blobs: SpatialData):
+ max_col = sdata_blobs.table.to_df().idxmax(axis=1)
+ max_col = pd.Categorical(max_col, categories=sdata_blobs.table.to_df().columns, ordered=True)
+ sdata_blobs.table.obs["which_max"] = max_col
+
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show()
+
  @pytest.mark.parametrize(
  "label",
  [
  "blobs_labels",
  "blobs_multiscale_labels",
  ],
  )
- def test_plot_can_color_labels_by_categorical_variable(self, sdata_blobs: SpatialData, label: str):
+ def test_plot_can_color_labels_by_categorical_variable_in_other_table(self, sdata_blobs: SpatialData, label: str):
 
  def _make_tablemodel_with_categorical_labels(sdata_blobs, label):
 
- n_obs = len(get_element_instances(sdata_blobs[label]))
- vals = np.arange(n_obs) + 1
- adata = AnnData(vals.reshape(-1, 1), obs=pd.DataFrame({"instance_id": vals}))
- adata.obs["category"] = pd.Categorical(
- list(["a", "b", "c"] * ((n_obs // 3) + 1))[:n_obs],
- categories=["a", "b", "c"],
- ordered=True,
- )
+ adata = sdata_blobs.tables["table"].copy()
+ max_col = adata.to_df().idxmax(axis=1)
+ max_col = max_col.str.replace("channel_", "ch").str.replace("_sum", "")
+ max_col = pd.Categorical(max_col, categories=set(max_col), ordered=True)
+ adata.obs["which_max"] = max_col
  adata.obs["region"] = label
+ del adata.uns["spatialdata_attrs"]
  table = TableModel.parse(
  adata=adata,
  region_key="region",
@@ -110,7 +114,17 @@ def _make_tablemodel_with_categorical_labels(sdata_blobs, label):
  )
  sdata_blobs.tables["other_table"] = table
 
- sdata_blobs.pl.render_labels(label, color="category", table="other_table", scale="scale0").pl.show()
+ _, axs = plt.subplots(nrows=1, ncols=3, layout="tight")
+
+ sdata_blobs.pl.render_labels(label, color="channel_1_sum", table="other_table", scale="scale0").pl.show(
+ ax=axs[0], title="ch_1_sum", colorbar=False
+ )
+ sdata_blobs.pl.render_labels(label, color="channel_2_sum", table="other_table", scale="scale0").pl.show(
+ ax=axs[1], title="ch_2_sum", colorbar=False
+ )
+ sdata_blobs.pl.render_labels(label, color="which_max", table="other_table", scale="scale0").pl.show(
+ ax=axs[2], legend_fontsize=6
+ )
 
  # we're modifying the data here, so we need an independent copy
  sdata_blobs_local = deepcopy(sdata_blobs)
@@ -176,7 +190,7 @@ def test_plot_subset_categorical_label_maintains_order(self, sdata_blobs: Spatia
 
  _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 
- sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0])
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0], legend_fontsize=6)
  sdata_blobs.pl.render_labels(
  "blobs_labels",
  color="which_max",
@@ -190,7 +204,7 @@ def test_plot_subset_categorical_label_maintains_order_when_palette_overwrite(se
 
  _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 
- sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0])
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0], legend_fontsize=6)
  sdata_blobs.pl.render_labels(
  "blobs_labels", color="which_max", groups=["channel_0_sum"], palette="red"
  ).pl.show(ax=axs[1])

tests/pl/test_render_points.py

@@ -1,4 +1,5 @@
 import matplotlib
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import scanpy as sc
@@ -27,15 +28,25 @@ class TestPoints(PlotTester, metaclass=PlotTesterMeta):
  def test_plot_can_render_points(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_points(element="blobs_points").pl.show()
 
- def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
+ def test_plot_can_filter_with_groups_default_palette(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_points"
- sdata_blobs.pl.render_points(color="genes", groups="gene_b", palette="red").pl.show()
 
- def test_plot_can_filter_with_groups_default_palette(self, sdata_blobs: SpatialData):
+ sdata_blobs.pl.render_points(color="genes", size=10).pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_points(color="genes", groups="gene_b", size=10).pl.show(ax=axs[1], legend_fontsize=6)
+
+ def test_plot_can_filter_with_groups_custom_palette(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_points"
- sdata_blobs.pl.render_points(color="genes", groups="gene_b").pl.show()
+
+ sdata_blobs.pl.render_points(color="genes", size=10).pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_points(color="genes", groups="gene_b", size=10, palette="red").pl.show(
+ ax=axs[1], legend_fontsize=6
+ )
 
  def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs

tests/pl/test_render_shapes.py

@@ -1,6 +1,7 @@
 import anndata
 import geopandas as gpd
 import matplotlib
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import scanpy as sc
@@ -107,6 +108,8 @@ def test_plot_can_scale_shapes(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_shapes(element="blobs_circles", scale=0.5).pl.show()
 
  def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  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"] = "c1"
@@ -115,7 +118,10 @@ def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
  "category"
  )
 
- sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups="c1").pl.show()
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster").pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups="c1").pl.show(
+ ax=axs[1], legend_fontsize=6
+ )
 
  def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs

tests/pl/test_utils.py

@@ -58,25 +58,23 @@ def test_plot_can_set_zero_in_cmap_to_transparent(self, sdata_blobs: SpatialData
  from spatialdata_plot.pl.utils import set_zero_in_cmap_to_transparent
 
  # set up figure and modify the data to add 0s
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
- table = sdata_blobs.table.copy()
- x = table.X.todense()
- x[:10, 0] = 0
- table.X = x
- sdata_blobs.tables["modified_table"] = table
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+ sdata_blobs.tables["table"].obs["my_var"] = list(range(len(sdata_blobs.tables["table"].obs)))
+ sdata_blobs.tables["table"].obs["my_var"] += 2 # shift the values to not have 0s
 
- # create a new cmap with 0 as transparent
- new_cmap = set_zero_in_cmap_to_transparent(cmap="plasma")
+ new_cmap = set_zero_in_cmap_to_transparent(cmap="viridis")
 
  # baseline img
- sdata_blobs.pl.render_labels("blobs_labels", color="channel_0_sum", cmap="viridis", table="table").pl.show(
+ sdata_blobs.pl.render_labels("blobs_labels", color="my_var", cmap="viridis", table="table").pl.show(
  ax=axs[0], colorbar=False
  )
 
+ sdata_blobs.tables["table"].obs.iloc[8:12, 2] = 0
+
  # image with 0s as transparent, so some labels are "missing"
- sdata_blobs.pl.render_labels(
-"blobs_labels", color="channel_0_sum", cmap=new_cmap, table="modified_table"
- ).pl.show(ax=axs[1], colorbar=False)
+ sdata_blobs.pl.render_labels("blobs_labels", color="my_var", cmap=new_cmap, table="table").pl.show(
+ax=axs[1], colorbar=False
+ )
 
 
 @pytest.mark.parametrize(

CHANGELOG.md

@@ -8,6 +8,22 @@ and this project adheres to [Semantic Versioning][].
 [keep a changelog]: https://keepachangelog.com/en/1.0.0/
 [semantic versioning]: https://semver.org/spec/v2.0.0.html
 
+## [0.2.6] - tbd
+
+### Added
+
+-
+
+### Changed
+
+- Lowered RMSE-threshold for plot-based tests from 45 to 15 (#344)
+- When subsetting to `groups`, `NA` isn't automatically added to legend (#344)
+
+### Fixed
+
+- Filtering with `groups` now preserves original cmap (#344)
+- Non-selected `groups` are now not shown in `na_color` (#344)
+
 ## [0.2.5] - 2024-08-23
 
 ### Added

src/spatialdata_plot/pl/render.py

@@ -855,7 +855,7 @@ def _draw_labels(seg_erosionpx: int | None, seg_boundaries: bool, alpha: float)
  legend_fontweight=legend_params.legend_fontweight,
  legend_loc=legend_params.legend_loc,
  legend_fontoutline=legend_params.legend_fontoutline,
- na_in_legend=legend_params.na_in_legend,
+ na_in_legend=legend_params.na_in_legend if groups is None else len(groups) == len(set(color_vector)),
  colorbar=legend_params.colorbar,
  scalebar_dx=scalebar_params.scalebar_dx,
  scalebar_units=scalebar_params.scalebar_units,

src/spatialdata_plot/pl/utils.py

@@ -567,9 +567,6 @@ def _get_subplots(num_images: int, ncols: int = 4, width: int = 4, height: int =
  Union[plt.Figure, plt.Axes]
  Matplotlib figure and axes object.
  """
- # if num_images <= 1:
- # raise ValueError("Number of images must be greater than 1.")
-
  if num_images < ncols:
  nrows = 1
  ncols = num_images
@@ -733,8 +730,6 @@ def _set_color_source_vec(
  color = np.full(len(element), na_color)
  return color, color, False
 
- # model = get_model(sdata[element_name])
-
  # Figure out where to get the color from
  origins = _locate_value(value_key=value_to_plot, sdata=sdata, element_name=element_name, table_name=table_name)
 
@@ -778,16 +773,13 @@ def _set_color_source_vec(
  palette=palette,
  na_color=na_color,
  )
+
  color_source_vector = color_source_vector.set_categories(color_mapping.keys())
  if color_mapping is None:
  raise ValueError("Unable to create color palette.")
 
  # do not rename categories, as colors need not be unique
  color_vector = color_source_vector.map(color_mapping)
- if color_vector.isna().any():
- if (na_cat_color := to_hex(na_color)) not in color_vector.categories:
- color_vector = color_vector.add_categories([na_cat_color])
- color_vector = color_vector.fillna(to_hex(na_color))
 
  return color_source_vector, color_vector, True
 
@@ -808,44 +800,43 @@ def _map_color_seg(
  seg_boundaries: bool = False,
 ) -> ArrayLike:
  cell_id = np.array(cell_id)
- if color_vector is not None and isinstance(color_vector.dtype, pd.CategoricalDtype):
- # users wants to plot a categorical column
+
+ if pd.api.types.is_categorical_dtype(color_vector.dtype):
+ # Case A: users wants to plot a categorical column
  if np.any(color_source_vector.isna()):
  cell_id[color_source_vector.isna()] = 0
- val_im: ArrayLike = map_array(seg, cell_id, color_vector.codes + 1)
+ val_im: ArrayLike = map_array(seg.copy(), cell_id, color_vector.codes + 1)
  cols = colors.to_rgba_array(color_vector.categories)
-
  elif pd.api.types.is_numeric_dtype(color_vector.dtype):
- # user wants to plot a continous column
+ # Case B: user wants to plot a continous column
  if isinstance(color_vector, pd.Series):
  color_vector = color_vector.to_numpy()
- val_im = map_array(seg, cell_id, color_vector)
  cols = cmap_params.cmap(cmap_params.norm(color_vector))
-
+ val_im = map_array(seg.copy(), cell_id, cell_id)
  else:
- val_im = map_array(seg.copy(), cell_id, cell_id) # replace with same seg id to remove missing segs
-
- if val_im.shape[0] == 1:
- val_im = np.squeeze(val_im, axis=0)
- if "#" in str(color_vector[0]):
- # we have hex colors
- assert all(_is_color_like(c) for c in color_vector), "Not all values are color-like."
- cols = colors.to_rgba_array(color_vector)
+ # Case C: User didn't specify any colors
+ if color_source_vector is not None and (
+ set(color_vector) == set(color_source_vector)
+ and len(set(color_vector)) == 1
+ and set(color_vector) == {na_color}
+ and not na_color_modified_by_user
+ ):
+ val_im = map_array(seg.copy(), cell_id, cell_id)
+ RNG = default_rng(42)
+ cols = RNG.random((len(color_vector), 3))
  else:
- cols = cmap_params.cmap(cmap_params.norm(color_vector))
+ # Case D: User didn't specify a column to color by, but modified the na_color
+ val_im = map_array(seg.copy(), cell_id, cell_id)
+ if "#" in str(color_vector[0]):
+ # we have hex colors
+ assert all(_is_color_like(c) for c in color_vector), "Not all values are color-like."
+ cols = colors.to_rgba_array(color_vector)
+ else:
+ cols = cmap_params.cmap(cmap_params.norm(color_vector))
 
  if seg_erosionpx is not None:
  val_im[val_im == erosion(val_im, square(seg_erosionpx))] = 0
 
- if color_source_vector is not None and (
- set(color_vector) == set(color_source_vector)
- and len(set(color_vector)) == 1
- and set(color_vector) == {na_color}
- and not na_color_modified_by_user
- ):
- RNG = default_rng(42)
- cols = RNG.random((len(cols), 3))
-
  seg_im: ArrayLike = label2rgb(
  label=val_im,
  colors=cols,
@@ -948,7 +939,7 @@ def _get_categorical_color_mapping(
  else:
  base_mapping = _generate_base_categorial_color_mapping(adata, cluster_key, color_source_vector, na_color)
 
- return _modify_categorical_color_mapping(base_mapping, groups, palette)
+ return _modify_categorical_color_mapping(mapping=base_mapping, groups=groups, palette=palette)
 
 
 def _maybe_set_colors(
@@ -1587,19 +1578,14 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
 
  palette = param_dict["palette"]
 
- if (groups := param_dict.get("groups")) is not None and palette is None:
- warnings.warn(
- "Groups is specified but palette is not. Setting palette to default 'lightgray'", UserWarning, stacklevel=2
- )
- param_dict["palette"] = ["lightgray" for _ in range(len(groups))]
-
  if isinstance((palette := param_dict["palette"]), list):
  if not all(isinstance(p, str) for p in palette):
  raise ValueError("If specified, parameter 'palette' must contain only strings.")
  elif isinstance(palette, (str, type(None))) and "palette" in param_dict:
  param_dict["palette"] = [palette] if palette is not None else None
 
  if element_type in ["shapes", "points", "labels"] and (palette := param_dict.get("palette")) is not None:
+ groups = param_dict.get("groups")
  if groups is None:
  raise ValueError("When specifying 'palette', 'groups' must also be specified.")
  if len(groups) != len(palette):

tests/conftest.py

@@ -33,7 +33,7 @@
 
 EXPECTED = HERE / "_images"
 ACTUAL = HERE / "figures"
-TOL = 45
+TOL = 15
 DPI = 80
 
 RNG = np.random.default_rng(seed=42)

tests/pl/test_render_labels.py

@@ -5,9 +5,8 @@
 import pandas as pd
 import pytest
 import scanpy as sc
-from anndata import AnnData
 from spatial_image import to_spatial_image
-from spatialdata import SpatialData, deepcopy, get_element_instances
+from spatialdata import SpatialData, deepcopy
 from spatialdata.models import TableModel
 
 import spatialdata_plot # noqa: F401
@@ -82,26 +81,31 @@ def test_plot_can_stack_render_labels(self, sdata_blobs: SpatialData):
  def test_plot_can_color_labels_by_continuous_variable(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_labels("blobs_labels", color="channel_0_sum").pl.show()
 
+ def test_plot_can_color_labels_by_categorical_variable(self, sdata_blobs: SpatialData):
+ max_col = sdata_blobs.table.to_df().idxmax(axis=1)
+ max_col = pd.Categorical(max_col, categories=sdata_blobs.table.to_df().columns, ordered=True)
+ sdata_blobs.table.obs["which_max"] = max_col
+
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show()
+
  @pytest.mark.parametrize(
  "label",
  [
  "blobs_labels",
  "blobs_multiscale_labels",
  ],
  )
- def test_plot_can_color_labels_by_categorical_variable(self, sdata_blobs: SpatialData, label: str):
+ def test_plot_can_color_labels_by_categorical_variable_in_other_table(self, sdata_blobs: SpatialData, label: str):
 
  def _make_tablemodel_with_categorical_labels(sdata_blobs, label):
 
- n_obs = len(get_element_instances(sdata_blobs[label]))
- vals = np.arange(n_obs) + 1
- adata = AnnData(vals.reshape(-1, 1), obs=pd.DataFrame({"instance_id": vals}))
- adata.obs["category"] = pd.Categorical(
- list(["a", "b", "c"] * ((n_obs // 3) + 1))[:n_obs],
- categories=["a", "b", "c"],
- ordered=True,
- )
+ adata = sdata_blobs.tables["table"].copy()
+ max_col = adata.to_df().idxmax(axis=1)
+ max_col = max_col.str.replace("channel_", "ch").str.replace("_sum", "")
+ max_col = pd.Categorical(max_col, categories=set(max_col), ordered=True)
+ adata.obs["which_max"] = max_col
  adata.obs["region"] = label
+ del adata.uns["spatialdata_attrs"]
  table = TableModel.parse(
  adata=adata,
  region_key="region",
@@ -110,7 +114,17 @@ def _make_tablemodel_with_categorical_labels(sdata_blobs, label):
  )
  sdata_blobs.tables["other_table"] = table
 
- sdata_blobs.pl.render_labels(label, color="category", table="other_table", scale="scale0").pl.show()
+ _, axs = plt.subplots(nrows=1, ncols=3, layout="tight")
+
+ sdata_blobs.pl.render_labels(label, color="channel_1_sum", table="other_table", scale="scale0").pl.show(
+ ax=axs[0], title="ch_1_sum", colorbar=False
+ )
+ sdata_blobs.pl.render_labels(label, color="channel_2_sum", table="other_table", scale="scale0").pl.show(
+ ax=axs[1], title="ch_2_sum", colorbar=False
+ )
+ sdata_blobs.pl.render_labels(label, color="which_max", table="other_table", scale="scale0").pl.show(
+ ax=axs[2], legend_fontsize=6
+ )
 
  # we're modifying the data here, so we need an independent copy
  sdata_blobs_local = deepcopy(sdata_blobs)
@@ -176,7 +190,7 @@ def test_plot_subset_categorical_label_maintains_order(self, sdata_blobs: Spatia
 
  _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 
- sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0])
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0], legend_fontsize=6)
  sdata_blobs.pl.render_labels(
  "blobs_labels",
  color="which_max",
@@ -190,7 +204,7 @@ def test_plot_subset_categorical_label_maintains_order_when_palette_overwrite(se
 
  _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 
- sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0])
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0], legend_fontsize=6)
  sdata_blobs.pl.render_labels(
  "blobs_labels", color="which_max", groups=["channel_0_sum"], palette="red"
  ).pl.show(ax=axs[1])

tests/pl/test_render_points.py

@@ -1,4 +1,5 @@
 import matplotlib
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import scanpy as sc
@@ -27,15 +28,25 @@ class TestPoints(PlotTester, metaclass=PlotTesterMeta):
  def test_plot_can_render_points(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_points(element="blobs_points").pl.show()
 
- def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
+ def test_plot_can_filter_with_groups_default_palette(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_points"
- sdata_blobs.pl.render_points(color="genes", groups="gene_b", palette="red").pl.show()
 
- def test_plot_can_filter_with_groups_default_palette(self, sdata_blobs: SpatialData):
+ sdata_blobs.pl.render_points(color="genes", size=10).pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_points(color="genes", groups="gene_b", size=10).pl.show(ax=axs[1], legend_fontsize=6)
+
+ def test_plot_can_filter_with_groups_custom_palette(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_points"
- sdata_blobs.pl.render_points(color="genes", groups="gene_b").pl.show()
+
+ sdata_blobs.pl.render_points(color="genes", size=10).pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_points(color="genes", groups="gene_b", size=10, palette="red").pl.show(
+ ax=axs[1], legend_fontsize=6
+ )
 
  def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs

tests/pl/test_render_shapes.py

@@ -1,6 +1,7 @@
 import anndata
 import geopandas as gpd
 import matplotlib
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import scanpy as sc
@@ -107,6 +108,8 @@ def test_plot_can_scale_shapes(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_shapes(element="blobs_circles", scale=0.5).pl.show()
 
  def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  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"] = "c1"
@@ -115,7 +118,10 @@ def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
  "category"
  )
 
- sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups="c1").pl.show()
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster").pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups="c1").pl.show(
+ ax=axs[1], legend_fontsize=6
+ )
 
  def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs

tests/pl/test_utils.py

@@ -58,25 +58,23 @@ def test_plot_can_set_zero_in_cmap_to_transparent(self, sdata_blobs: SpatialData
  from spatialdata_plot.pl.utils import set_zero_in_cmap_to_transparent
 
  # set up figure and modify the data to add 0s
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
- table = sdata_blobs.table.copy()
- x = table.X.todense()
- x[:10, 0] = 0
- table.X = x
- sdata_blobs.tables["modified_table"] = table
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+ sdata_blobs.tables["table"].obs["my_var"] = list(range(len(sdata_blobs.tables["table"].obs)))
+ sdata_blobs.tables["table"].obs["my_var"] += 2 # shift the values to not have 0s
 
- # create a new cmap with 0 as transparent
- new_cmap = set_zero_in_cmap_to_transparent(cmap="plasma")
+ new_cmap = set_zero_in_cmap_to_transparent(cmap="viridis")
 
  # baseline img
- sdata_blobs.pl.render_labels("blobs_labels", color="channel_0_sum", cmap="viridis", table="table").pl.show(
+ sdata_blobs.pl.render_labels("blobs_labels", color="my_var", cmap="viridis", table="table").pl.show(
  ax=axs[0], colorbar=False
  )
 
+ sdata_blobs.tables["table"].obs.iloc[8:12, 2] = 0
+
  # image with 0s as transparent, so some labels are "missing"
- sdata_blobs.pl.render_labels(
-"blobs_labels", color="channel_0_sum", cmap=new_cmap, table="modified_table"
- ).pl.show(ax=axs[1], colorbar=False)
+ sdata_blobs.pl.render_labels("blobs_labels", color="my_var", cmap=new_cmap, table="table").pl.show(
+ax=axs[1], colorbar=False
+ )
 
 
 @pytest.mark.parametrize(

CHANGELOG.md

@@ -8,6 +8,22 @@ and this project adheres to [Semantic Versioning][].
 [keep a changelog]: https://keepachangelog.com/en/1.0.0/
 [semantic versioning]: https://semver.org/spec/v2.0.0.html
 
+## [0.2.6] - tbd
+
+### Added
+
+-
+
+### Changed
+
+- Lowered RMSE-threshold for plot-based tests from 45 to 15 (#344)
+- When subsetting to `groups`, `NA` isn't automatically added to legend (#344)
+
+### Fixed
+
+- Filtering with `groups` now preserves original cmap (#344)
+- Non-selected `groups` are now not shown in `na_color` (#344)
+
 ## [0.2.5] - 2024-08-23
 
 ### Added

src/spatialdata_plot/pl/render.py

@@ -855,7 +855,7 @@ def _draw_labels(seg_erosionpx: int | None, seg_boundaries: bool, alpha: float)
  legend_fontweight=legend_params.legend_fontweight,
  legend_loc=legend_params.legend_loc,
  legend_fontoutline=legend_params.legend_fontoutline,
- na_in_legend=legend_params.na_in_legend,
+ na_in_legend=legend_params.na_in_legend if groups is None else len(groups) == len(set(color_vector)),
  colorbar=legend_params.colorbar,
  scalebar_dx=scalebar_params.scalebar_dx,
  scalebar_units=scalebar_params.scalebar_units,

src/spatialdata_plot/pl/utils.py

@@ -567,9 +567,6 @@ def _get_subplots(num_images: int, ncols: int = 4, width: int = 4, height: int =
  Union[plt.Figure, plt.Axes]
  Matplotlib figure and axes object.
  """
- # if num_images <= 1:
- # raise ValueError("Number of images must be greater than 1.")
-
  if num_images < ncols:
  nrows = 1
  ncols = num_images
@@ -733,8 +730,6 @@ def _set_color_source_vec(
  color = np.full(len(element), na_color)
  return color, color, False
 
- # model = get_model(sdata[element_name])
-
  # Figure out where to get the color from
  origins = _locate_value(value_key=value_to_plot, sdata=sdata, element_name=element_name, table_name=table_name)
 
@@ -778,16 +773,13 @@ def _set_color_source_vec(
  palette=palette,
  na_color=na_color,
  )
+
  color_source_vector = color_source_vector.set_categories(color_mapping.keys())
  if color_mapping is None:
  raise ValueError("Unable to create color palette.")
 
  # do not rename categories, as colors need not be unique
  color_vector = color_source_vector.map(color_mapping)
- if color_vector.isna().any():
- if (na_cat_color := to_hex(na_color)) not in color_vector.categories:
- color_vector = color_vector.add_categories([na_cat_color])
- color_vector = color_vector.fillna(to_hex(na_color))
 
  return color_source_vector, color_vector, True
 
@@ -808,44 +800,43 @@ def _map_color_seg(
  seg_boundaries: bool = False,
 ) -> ArrayLike:
  cell_id = np.array(cell_id)
- if color_vector is not None and isinstance(color_vector.dtype, pd.CategoricalDtype):
- # users wants to plot a categorical column
+
+ if pd.api.types.is_categorical_dtype(color_vector.dtype):
+ # Case A: users wants to plot a categorical column
  if np.any(color_source_vector.isna()):
  cell_id[color_source_vector.isna()] = 0
- val_im: ArrayLike = map_array(seg, cell_id, color_vector.codes + 1)
+ val_im: ArrayLike = map_array(seg.copy(), cell_id, color_vector.codes + 1)
  cols = colors.to_rgba_array(color_vector.categories)
-
  elif pd.api.types.is_numeric_dtype(color_vector.dtype):
- # user wants to plot a continous column
+ # Case B: user wants to plot a continous column
  if isinstance(color_vector, pd.Series):
  color_vector = color_vector.to_numpy()
- val_im = map_array(seg, cell_id, color_vector)
  cols = cmap_params.cmap(cmap_params.norm(color_vector))
-
+ val_im = map_array(seg.copy(), cell_id, cell_id)
  else:
- val_im = map_array(seg.copy(), cell_id, cell_id) # replace with same seg id to remove missing segs
-
- if val_im.shape[0] == 1:
- val_im = np.squeeze(val_im, axis=0)
- if "#" in str(color_vector[0]):
- # we have hex colors
- assert all(_is_color_like(c) for c in color_vector), "Not all values are color-like."
- cols = colors.to_rgba_array(color_vector)
+ # Case C: User didn't specify any colors
+ if color_source_vector is not None and (
+ set(color_vector) == set(color_source_vector)
+ and len(set(color_vector)) == 1
+ and set(color_vector) == {na_color}
+ and not na_color_modified_by_user
+ ):
+ val_im = map_array(seg.copy(), cell_id, cell_id)
+ RNG = default_rng(42)
+ cols = RNG.random((len(color_vector), 3))
  else:
- cols = cmap_params.cmap(cmap_params.norm(color_vector))
+ # Case D: User didn't specify a column to color by, but modified the na_color
+ val_im = map_array(seg.copy(), cell_id, cell_id)
+ if "#" in str(color_vector[0]):
+ # we have hex colors
+ assert all(_is_color_like(c) for c in color_vector), "Not all values are color-like."
+ cols = colors.to_rgba_array(color_vector)
+ else:
+ cols = cmap_params.cmap(cmap_params.norm(color_vector))
 
  if seg_erosionpx is not None:
  val_im[val_im == erosion(val_im, square(seg_erosionpx))] = 0
 
- if color_source_vector is not None and (
- set(color_vector) == set(color_source_vector)
- and len(set(color_vector)) == 1
- and set(color_vector) == {na_color}
- and not na_color_modified_by_user
- ):
- RNG = default_rng(42)
- cols = RNG.random((len(cols), 3))
-
  seg_im: ArrayLike = label2rgb(
  label=val_im,
  colors=cols,
@@ -948,7 +939,7 @@ def _get_categorical_color_mapping(
  else:
  base_mapping = _generate_base_categorial_color_mapping(adata, cluster_key, color_source_vector, na_color)
 
- return _modify_categorical_color_mapping(base_mapping, groups, palette)
+ return _modify_categorical_color_mapping(mapping=base_mapping, groups=groups, palette=palette)
 
 
 def _maybe_set_colors(
@@ -1587,19 +1578,14 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
 
  palette = param_dict["palette"]
 
- if (groups := param_dict.get("groups")) is not None and palette is None:
- warnings.warn(
- "Groups is specified but palette is not. Setting palette to default 'lightgray'", UserWarning, stacklevel=2
- )
- param_dict["palette"] = ["lightgray" for _ in range(len(groups))]
-
  if isinstance((palette := param_dict["palette"]), list):
  if not all(isinstance(p, str) for p in palette):
  raise ValueError("If specified, parameter 'palette' must contain only strings.")
  elif isinstance(palette, (str, type(None))) and "palette" in param_dict:
  param_dict["palette"] = [palette] if palette is not None else None
 
  if element_type in ["shapes", "points", "labels"] and (palette := param_dict.get("palette")) is not None:
+ groups = param_dict.get("groups")
  if groups is None:
  raise ValueError("When specifying 'palette', 'groups' must also be specified.")
  if len(groups) != len(palette):

tests/conftest.py

@@ -33,7 +33,7 @@
 
 EXPECTED = HERE / "_images"
 ACTUAL = HERE / "figures"
-TOL = 45
+TOL = 15
 DPI = 80
 
 RNG = np.random.default_rng(seed=42)

tests/pl/test_render_labels.py

@@ -5,9 +5,8 @@
 import pandas as pd
 import pytest
 import scanpy as sc
-from anndata import AnnData
 from spatial_image import to_spatial_image
-from spatialdata import SpatialData, deepcopy, get_element_instances
+from spatialdata import SpatialData, deepcopy
 from spatialdata.models import TableModel
 
 import spatialdata_plot # noqa: F401
@@ -82,26 +81,31 @@ def test_plot_can_stack_render_labels(self, sdata_blobs: SpatialData):
  def test_plot_can_color_labels_by_continuous_variable(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_labels("blobs_labels", color="channel_0_sum").pl.show()
 
+ def test_plot_can_color_labels_by_categorical_variable(self, sdata_blobs: SpatialData):
+ max_col = sdata_blobs.table.to_df().idxmax(axis=1)
+ max_col = pd.Categorical(max_col, categories=sdata_blobs.table.to_df().columns, ordered=True)
+ sdata_blobs.table.obs["which_max"] = max_col
+
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show()
+
  @pytest.mark.parametrize(
  "label",
  [
  "blobs_labels",
  "blobs_multiscale_labels",
  ],
  )
- def test_plot_can_color_labels_by_categorical_variable(self, sdata_blobs: SpatialData, label: str):
+ def test_plot_can_color_labels_by_categorical_variable_in_other_table(self, sdata_blobs: SpatialData, label: str):
 
  def _make_tablemodel_with_categorical_labels(sdata_blobs, label):
 
- n_obs = len(get_element_instances(sdata_blobs[label]))
- vals = np.arange(n_obs) + 1
- adata = AnnData(vals.reshape(-1, 1), obs=pd.DataFrame({"instance_id": vals}))
- adata.obs["category"] = pd.Categorical(
- list(["a", "b", "c"] * ((n_obs // 3) + 1))[:n_obs],
- categories=["a", "b", "c"],
- ordered=True,
- )
+ adata = sdata_blobs.tables["table"].copy()
+ max_col = adata.to_df().idxmax(axis=1)
+ max_col = max_col.str.replace("channel_", "ch").str.replace("_sum", "")
+ max_col = pd.Categorical(max_col, categories=set(max_col), ordered=True)
+ adata.obs["which_max"] = max_col
  adata.obs["region"] = label
+ del adata.uns["spatialdata_attrs"]
  table = TableModel.parse(
  adata=adata,
  region_key="region",
@@ -110,7 +114,17 @@ def _make_tablemodel_with_categorical_labels(sdata_blobs, label):
  )
  sdata_blobs.tables["other_table"] = table
 
- sdata_blobs.pl.render_labels(label, color="category", table="other_table", scale="scale0").pl.show()
+ _, axs = plt.subplots(nrows=1, ncols=3, layout="tight")
+
+ sdata_blobs.pl.render_labels(label, color="channel_1_sum", table="other_table", scale="scale0").pl.show(
+ ax=axs[0], title="ch_1_sum", colorbar=False
+ )
+ sdata_blobs.pl.render_labels(label, color="channel_2_sum", table="other_table", scale="scale0").pl.show(
+ ax=axs[1], title="ch_2_sum", colorbar=False
+ )
+ sdata_blobs.pl.render_labels(label, color="which_max", table="other_table", scale="scale0").pl.show(
+ ax=axs[2], legend_fontsize=6
+ )
 
  # we're modifying the data here, so we need an independent copy
  sdata_blobs_local = deepcopy(sdata_blobs)
@@ -176,7 +190,7 @@ def test_plot_subset_categorical_label_maintains_order(self, sdata_blobs: Spatia
 
  _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 
- sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0])
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0], legend_fontsize=6)
  sdata_blobs.pl.render_labels(
  "blobs_labels",
  color="which_max",
@@ -190,7 +204,7 @@ def test_plot_subset_categorical_label_maintains_order_when_palette_overwrite(se
 
  _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 
- sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0])
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0], legend_fontsize=6)
  sdata_blobs.pl.render_labels(
  "blobs_labels", color="which_max", groups=["channel_0_sum"], palette="red"
  ).pl.show(ax=axs[1])

tests/pl/test_render_points.py

@@ -1,4 +1,5 @@
 import matplotlib
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import scanpy as sc
@@ -27,15 +28,25 @@ class TestPoints(PlotTester, metaclass=PlotTesterMeta):
  def test_plot_can_render_points(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_points(element="blobs_points").pl.show()
 
- def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
+ def test_plot_can_filter_with_groups_default_palette(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_points"
- sdata_blobs.pl.render_points(color="genes", groups="gene_b", palette="red").pl.show()
 
- def test_plot_can_filter_with_groups_default_palette(self, sdata_blobs: SpatialData):
+ sdata_blobs.pl.render_points(color="genes", size=10).pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_points(color="genes", groups="gene_b", size=10).pl.show(ax=axs[1], legend_fontsize=6)
+
+ def test_plot_can_filter_with_groups_custom_palette(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_points"
- sdata_blobs.pl.render_points(color="genes", groups="gene_b").pl.show()
+
+ sdata_blobs.pl.render_points(color="genes", size=10).pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_points(color="genes", groups="gene_b", size=10, palette="red").pl.show(
+ ax=axs[1], legend_fontsize=6
+ )
 
  def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs

tests/pl/test_render_shapes.py

@@ -1,6 +1,7 @@
 import anndata
 import geopandas as gpd
 import matplotlib
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import scanpy as sc
@@ -107,6 +108,8 @@ def test_plot_can_scale_shapes(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_shapes(element="blobs_circles", scale=0.5).pl.show()
 
  def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  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"] = "c1"
@@ -115,7 +118,10 @@ def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
  "category"
  )
 
- sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups="c1").pl.show()
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster").pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups="c1").pl.show(
+ ax=axs[1], legend_fontsize=6
+ )
 
  def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs

tests/pl/test_utils.py

@@ -58,25 +58,23 @@ def test_plot_can_set_zero_in_cmap_to_transparent(self, sdata_blobs: SpatialData
  from spatialdata_plot.pl.utils import set_zero_in_cmap_to_transparent
 
  # set up figure and modify the data to add 0s
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
- table = sdata_blobs.table.copy()
- x = table.X.todense()
- x[:10, 0] = 0
- table.X = x
- sdata_blobs.tables["modified_table"] = table
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+ sdata_blobs.tables["table"].obs["my_var"] = list(range(len(sdata_blobs.tables["table"].obs)))
+ sdata_blobs.tables["table"].obs["my_var"] += 2 # shift the values to not have 0s
 
- # create a new cmap with 0 as transparent
- new_cmap = set_zero_in_cmap_to_transparent(cmap="plasma")
+ new_cmap = set_zero_in_cmap_to_transparent(cmap="viridis")
 
  # baseline img
- sdata_blobs.pl.render_labels("blobs_labels", color="channel_0_sum", cmap="viridis", table="table").pl.show(
+ sdata_blobs.pl.render_labels("blobs_labels", color="my_var", cmap="viridis", table="table").pl.show(
  ax=axs[0], colorbar=False
  )
 
+ sdata_blobs.tables["table"].obs.iloc[8:12, 2] = 0
+
  # image with 0s as transparent, so some labels are "missing"
- sdata_blobs.pl.render_labels(
-"blobs_labels", color="channel_0_sum", cmap=new_cmap, table="modified_table"
- ).pl.show(ax=axs[1], colorbar=False)
+ sdata_blobs.pl.render_labels("blobs_labels", color="my_var", cmap=new_cmap, table="table").pl.show(
+ax=axs[1], colorbar=False
+ )
 
 
 @pytest.mark.parametrize(

CHANGELOG.md

@@ -8,6 +8,22 @@ and this project adheres to [Semantic Versioning][].
 [keep a changelog]: https://keepachangelog.com/en/1.0.0/
 [semantic versioning]: https://semver.org/spec/v2.0.0.html
 
+## [0.2.6] - tbd
+
+### Added
+
+-
+
+### Changed
+
+- Lowered RMSE-threshold for plot-based tests from 45 to 15 (#344)
+- When subsetting to `groups`, `NA` isn't automatically added to legend (#344)
+
+### Fixed
+
+- Filtering with `groups` now preserves original cmap (#344)
+- Non-selected `groups` are now not shown in `na_color` (#344)
+
 ## [0.2.5] - 2024-08-23
 
 ### Added

src/spatialdata_plot/pl/render.py

@@ -855,7 +855,7 @@ def _draw_labels(seg_erosionpx: int | None, seg_boundaries: bool, alpha: float)
  legend_fontweight=legend_params.legend_fontweight,
  legend_loc=legend_params.legend_loc,
  legend_fontoutline=legend_params.legend_fontoutline,
- na_in_legend=legend_params.na_in_legend,
+ na_in_legend=legend_params.na_in_legend if groups is None else len(groups) == len(set(color_vector)),
  colorbar=legend_params.colorbar,
  scalebar_dx=scalebar_params.scalebar_dx,
  scalebar_units=scalebar_params.scalebar_units,

src/spatialdata_plot/pl/utils.py

@@ -567,9 +567,6 @@ def _get_subplots(num_images: int, ncols: int = 4, width: int = 4, height: int =
  Union[plt.Figure, plt.Axes]
  Matplotlib figure and axes object.
  """
- # if num_images <= 1:
- # raise ValueError("Number of images must be greater than 1.")
-
  if num_images < ncols:
  nrows = 1
  ncols = num_images
@@ -733,8 +730,6 @@ def _set_color_source_vec(
  color = np.full(len(element), na_color)
  return color, color, False
 
- # model = get_model(sdata[element_name])
-
  # Figure out where to get the color from
  origins = _locate_value(value_key=value_to_plot, sdata=sdata, element_name=element_name, table_name=table_name)
 
@@ -778,16 +773,13 @@ def _set_color_source_vec(
  palette=palette,
  na_color=na_color,
  )
+
  color_source_vector = color_source_vector.set_categories(color_mapping.keys())
  if color_mapping is None:
  raise ValueError("Unable to create color palette.")
 
  # do not rename categories, as colors need not be unique
  color_vector = color_source_vector.map(color_mapping)
- if color_vector.isna().any():
- if (na_cat_color := to_hex(na_color)) not in color_vector.categories:
- color_vector = color_vector.add_categories([na_cat_color])
- color_vector = color_vector.fillna(to_hex(na_color))
 
  return color_source_vector, color_vector, True
 
@@ -808,44 +800,43 @@ def _map_color_seg(
  seg_boundaries: bool = False,
 ) -> ArrayLike:
  cell_id = np.array(cell_id)
- if color_vector is not None and isinstance(color_vector.dtype, pd.CategoricalDtype):
- # users wants to plot a categorical column
+
+ if pd.api.types.is_categorical_dtype(color_vector.dtype):
+ # Case A: users wants to plot a categorical column
  if np.any(color_source_vector.isna()):
  cell_id[color_source_vector.isna()] = 0
- val_im: ArrayLike = map_array(seg, cell_id, color_vector.codes + 1)
+ val_im: ArrayLike = map_array(seg.copy(), cell_id, color_vector.codes + 1)
  cols = colors.to_rgba_array(color_vector.categories)
-
  elif pd.api.types.is_numeric_dtype(color_vector.dtype):
- # user wants to plot a continous column
+ # Case B: user wants to plot a continous column
  if isinstance(color_vector, pd.Series):
  color_vector = color_vector.to_numpy()
- val_im = map_array(seg, cell_id, color_vector)
  cols = cmap_params.cmap(cmap_params.norm(color_vector))
-
+ val_im = map_array(seg.copy(), cell_id, cell_id)
  else:
- val_im = map_array(seg.copy(), cell_id, cell_id) # replace with same seg id to remove missing segs
-
- if val_im.shape[0] == 1:
- val_im = np.squeeze(val_im, axis=0)
- if "#" in str(color_vector[0]):
- # we have hex colors
- assert all(_is_color_like(c) for c in color_vector), "Not all values are color-like."
- cols = colors.to_rgba_array(color_vector)
+ # Case C: User didn't specify any colors
+ if color_source_vector is not None and (
+ set(color_vector) == set(color_source_vector)
+ and len(set(color_vector)) == 1
+ and set(color_vector) == {na_color}
+ and not na_color_modified_by_user
+ ):
+ val_im = map_array(seg.copy(), cell_id, cell_id)
+ RNG = default_rng(42)
+ cols = RNG.random((len(color_vector), 3))
  else:
- cols = cmap_params.cmap(cmap_params.norm(color_vector))
+ # Case D: User didn't specify a column to color by, but modified the na_color
+ val_im = map_array(seg.copy(), cell_id, cell_id)
+ if "#" in str(color_vector[0]):
+ # we have hex colors
+ assert all(_is_color_like(c) for c in color_vector), "Not all values are color-like."
+ cols = colors.to_rgba_array(color_vector)
+ else:
+ cols = cmap_params.cmap(cmap_params.norm(color_vector))
 
  if seg_erosionpx is not None:
  val_im[val_im == erosion(val_im, square(seg_erosionpx))] = 0
 
- if color_source_vector is not None and (
- set(color_vector) == set(color_source_vector)
- and len(set(color_vector)) == 1
- and set(color_vector) == {na_color}
- and not na_color_modified_by_user
- ):
- RNG = default_rng(42)
- cols = RNG.random((len(cols), 3))
-
  seg_im: ArrayLike = label2rgb(
  label=val_im,
  colors=cols,
@@ -948,7 +939,7 @@ def _get_categorical_color_mapping(
  else:
  base_mapping = _generate_base_categorial_color_mapping(adata, cluster_key, color_source_vector, na_color)
 
- return _modify_categorical_color_mapping(base_mapping, groups, palette)
+ return _modify_categorical_color_mapping(mapping=base_mapping, groups=groups, palette=palette)
 
 
 def _maybe_set_colors(
@@ -1587,19 +1578,14 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
 
  palette = param_dict["palette"]
 
- if (groups := param_dict.get("groups")) is not None and palette is None:
- warnings.warn(
- "Groups is specified but palette is not. Setting palette to default 'lightgray'", UserWarning, stacklevel=2
- )
- param_dict["palette"] = ["lightgray" for _ in range(len(groups))]
-
  if isinstance((palette := param_dict["palette"]), list):
  if not all(isinstance(p, str) for p in palette):
  raise ValueError("If specified, parameter 'palette' must contain only strings.")
  elif isinstance(palette, (str, type(None))) and "palette" in param_dict:
  param_dict["palette"] = [palette] if palette is not None else None
 
  if element_type in ["shapes", "points", "labels"] and (palette := param_dict.get("palette")) is not None:
+ groups = param_dict.get("groups")
  if groups is None:
  raise ValueError("When specifying 'palette', 'groups' must also be specified.")
  if len(groups) != len(palette):

tests/conftest.py

@@ -33,7 +33,7 @@
 
 EXPECTED = HERE / "_images"
 ACTUAL = HERE / "figures"
-TOL = 45
+TOL = 15
 DPI = 80
 
 RNG = np.random.default_rng(seed=42)

tests/pl/test_render_labels.py

@@ -5,9 +5,8 @@
 import pandas as pd
 import pytest
 import scanpy as sc
-from anndata import AnnData
 from spatial_image import to_spatial_image
-from spatialdata import SpatialData, deepcopy, get_element_instances
+from spatialdata import SpatialData, deepcopy
 from spatialdata.models import TableModel
 
 import spatialdata_plot # noqa: F401
@@ -82,26 +81,31 @@ def test_plot_can_stack_render_labels(self, sdata_blobs: SpatialData):
  def test_plot_can_color_labels_by_continuous_variable(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_labels("blobs_labels", color="channel_0_sum").pl.show()
 
+ def test_plot_can_color_labels_by_categorical_variable(self, sdata_blobs: SpatialData):
+ max_col = sdata_blobs.table.to_df().idxmax(axis=1)
+ max_col = pd.Categorical(max_col, categories=sdata_blobs.table.to_df().columns, ordered=True)
+ sdata_blobs.table.obs["which_max"] = max_col
+
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show()
+
  @pytest.mark.parametrize(
  "label",
  [
  "blobs_labels",
  "blobs_multiscale_labels",
  ],
  )
- def test_plot_can_color_labels_by_categorical_variable(self, sdata_blobs: SpatialData, label: str):
+ def test_plot_can_color_labels_by_categorical_variable_in_other_table(self, sdata_blobs: SpatialData, label: str):
 
  def _make_tablemodel_with_categorical_labels(sdata_blobs, label):
 
- n_obs = len(get_element_instances(sdata_blobs[label]))
- vals = np.arange(n_obs) + 1
- adata = AnnData(vals.reshape(-1, 1), obs=pd.DataFrame({"instance_id": vals}))
- adata.obs["category"] = pd.Categorical(
- list(["a", "b", "c"] * ((n_obs // 3) + 1))[:n_obs],
- categories=["a", "b", "c"],
- ordered=True,
- )
+ adata = sdata_blobs.tables["table"].copy()
+ max_col = adata.to_df().idxmax(axis=1)
+ max_col = max_col.str.replace("channel_", "ch").str.replace("_sum", "")
+ max_col = pd.Categorical(max_col, categories=set(max_col), ordered=True)
+ adata.obs["which_max"] = max_col
  adata.obs["region"] = label
+ del adata.uns["spatialdata_attrs"]
  table = TableModel.parse(
  adata=adata,
  region_key="region",
@@ -110,7 +114,17 @@ def _make_tablemodel_with_categorical_labels(sdata_blobs, label):
  )
  sdata_blobs.tables["other_table"] = table
 
- sdata_blobs.pl.render_labels(label, color="category", table="other_table", scale="scale0").pl.show()
+ _, axs = plt.subplots(nrows=1, ncols=3, layout="tight")
+
+ sdata_blobs.pl.render_labels(label, color="channel_1_sum", table="other_table", scale="scale0").pl.show(
+ ax=axs[0], title="ch_1_sum", colorbar=False
+ )
+ sdata_blobs.pl.render_labels(label, color="channel_2_sum", table="other_table", scale="scale0").pl.show(
+ ax=axs[1], title="ch_2_sum", colorbar=False
+ )
+ sdata_blobs.pl.render_labels(label, color="which_max", table="other_table", scale="scale0").pl.show(
+ ax=axs[2], legend_fontsize=6
+ )
 
  # we're modifying the data here, so we need an independent copy
  sdata_blobs_local = deepcopy(sdata_blobs)
@@ -176,7 +190,7 @@ def test_plot_subset_categorical_label_maintains_order(self, sdata_blobs: Spatia
 
  _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 
- sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0])
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0], legend_fontsize=6)
  sdata_blobs.pl.render_labels(
  "blobs_labels",
  color="which_max",
@@ -190,7 +204,7 @@ def test_plot_subset_categorical_label_maintains_order_when_palette_overwrite(se
 
  _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 
- sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0])
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0], legend_fontsize=6)
  sdata_blobs.pl.render_labels(
  "blobs_labels", color="which_max", groups=["channel_0_sum"], palette="red"
  ).pl.show(ax=axs[1])

tests/pl/test_render_points.py

@@ -1,4 +1,5 @@
 import matplotlib
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import scanpy as sc
@@ -27,15 +28,25 @@ class TestPoints(PlotTester, metaclass=PlotTesterMeta):
  def test_plot_can_render_points(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_points(element="blobs_points").pl.show()
 
- def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
+ def test_plot_can_filter_with_groups_default_palette(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_points"
- sdata_blobs.pl.render_points(color="genes", groups="gene_b", palette="red").pl.show()
 
- def test_plot_can_filter_with_groups_default_palette(self, sdata_blobs: SpatialData):
+ sdata_blobs.pl.render_points(color="genes", size=10).pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_points(color="genes", groups="gene_b", size=10).pl.show(ax=axs[1], legend_fontsize=6)
+
+ def test_plot_can_filter_with_groups_custom_palette(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_points"
- sdata_blobs.pl.render_points(color="genes", groups="gene_b").pl.show()
+
+ sdata_blobs.pl.render_points(color="genes", size=10).pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_points(color="genes", groups="gene_b", size=10, palette="red").pl.show(
+ ax=axs[1], legend_fontsize=6
+ )
 
  def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs

tests/pl/test_render_shapes.py

@@ -1,6 +1,7 @@
 import anndata
 import geopandas as gpd
 import matplotlib
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import scanpy as sc
@@ -107,6 +108,8 @@ def test_plot_can_scale_shapes(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_shapes(element="blobs_circles", scale=0.5).pl.show()
 
  def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  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"] = "c1"
@@ -115,7 +118,10 @@ def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
  "category"
  )
 
- sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups="c1").pl.show()
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster").pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups="c1").pl.show(
+ ax=axs[1], legend_fontsize=6
+ )
 
  def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs

tests/pl/test_utils.py

@@ -58,25 +58,23 @@ def test_plot_can_set_zero_in_cmap_to_transparent(self, sdata_blobs: SpatialData
  from spatialdata_plot.pl.utils import set_zero_in_cmap_to_transparent
 
  # set up figure and modify the data to add 0s
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
- table = sdata_blobs.table.copy()
- x = table.X.todense()
- x[:10, 0] = 0
- table.X = x
- sdata_blobs.tables["modified_table"] = table
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+ sdata_blobs.tables["table"].obs["my_var"] = list(range(len(sdata_blobs.tables["table"].obs)))
+ sdata_blobs.tables["table"].obs["my_var"] += 2 # shift the values to not have 0s
 
- # create a new cmap with 0 as transparent
- new_cmap = set_zero_in_cmap_to_transparent(cmap="plasma")
+ new_cmap = set_zero_in_cmap_to_transparent(cmap="viridis")
 
  # baseline img
- sdata_blobs.pl.render_labels("blobs_labels", color="channel_0_sum", cmap="viridis", table="table").pl.show(
+ sdata_blobs.pl.render_labels("blobs_labels", color="my_var", cmap="viridis", table="table").pl.show(
  ax=axs[0], colorbar=False
  )
 
+ sdata_blobs.tables["table"].obs.iloc[8:12, 2] = 0
+
  # image with 0s as transparent, so some labels are "missing"
- sdata_blobs.pl.render_labels(
-"blobs_labels", color="channel_0_sum", cmap=new_cmap, table="modified_table"
- ).pl.show(ax=axs[1], colorbar=False)
+ sdata_blobs.pl.render_labels("blobs_labels", color="my_var", cmap=new_cmap, table="table").pl.show(
+ax=axs[1], colorbar=False
+ )
 
 
 @pytest.mark.parametrize(

CHANGELOG.md

@@ -8,6 +8,22 @@ and this project adheres to [Semantic Versioning][].
 [keep a changelog]: https://keepachangelog.com/en/1.0.0/
 [semantic versioning]: https://semver.org/spec/v2.0.0.html
 
+## [0.2.6] - tbd
+
+### Added
+
+-
+
+### Changed
+
+- Lowered RMSE-threshold for plot-based tests from 45 to 15 (#344)
+- When subsetting to `groups`, `NA` isn't automatically added to legend (#344)
+
+### Fixed
+
+- Filtering with `groups` now preserves original cmap (#344)
+- Non-selected `groups` are now not shown in `na_color` (#344)
+
 ## [0.2.5] - 2024-08-23
 
 ### Added

src/spatialdata_plot/pl/render.py

@@ -855,7 +855,7 @@ def _draw_labels(seg_erosionpx: int | None, seg_boundaries: bool, alpha: float)
  legend_fontweight=legend_params.legend_fontweight,
  legend_loc=legend_params.legend_loc,
  legend_fontoutline=legend_params.legend_fontoutline,
- na_in_legend=legend_params.na_in_legend,
+ na_in_legend=legend_params.na_in_legend if groups is None else len(groups) == len(set(color_vector)),
  colorbar=legend_params.colorbar,
  scalebar_dx=scalebar_params.scalebar_dx,
  scalebar_units=scalebar_params.scalebar_units,

src/spatialdata_plot/pl/utils.py

@@ -567,9 +567,6 @@ def _get_subplots(num_images: int, ncols: int = 4, width: int = 4, height: int =
  Union[plt.Figure, plt.Axes]
  Matplotlib figure and axes object.
  """
- # if num_images <= 1:
- # raise ValueError("Number of images must be greater than 1.")
-
  if num_images < ncols:
  nrows = 1
  ncols = num_images
@@ -733,8 +730,6 @@ def _set_color_source_vec(
  color = np.full(len(element), na_color)
  return color, color, False
 
- # model = get_model(sdata[element_name])
-
  # Figure out where to get the color from
  origins = _locate_value(value_key=value_to_plot, sdata=sdata, element_name=element_name, table_name=table_name)
 
@@ -778,16 +773,13 @@ def _set_color_source_vec(
  palette=palette,
  na_color=na_color,
  )
+
  color_source_vector = color_source_vector.set_categories(color_mapping.keys())
  if color_mapping is None:
  raise ValueError("Unable to create color palette.")
 
  # do not rename categories, as colors need not be unique
  color_vector = color_source_vector.map(color_mapping)
- if color_vector.isna().any():
- if (na_cat_color := to_hex(na_color)) not in color_vector.categories:
- color_vector = color_vector.add_categories([na_cat_color])
- color_vector = color_vector.fillna(to_hex(na_color))
 
  return color_source_vector, color_vector, True
 
@@ -808,44 +800,43 @@ def _map_color_seg(
  seg_boundaries: bool = False,
 ) -> ArrayLike:
  cell_id = np.array(cell_id)
- if color_vector is not None and isinstance(color_vector.dtype, pd.CategoricalDtype):
- # users wants to plot a categorical column
+
+ if pd.api.types.is_categorical_dtype(color_vector.dtype):
+ # Case A: users wants to plot a categorical column
  if np.any(color_source_vector.isna()):
  cell_id[color_source_vector.isna()] = 0
- val_im: ArrayLike = map_array(seg, cell_id, color_vector.codes + 1)
+ val_im: ArrayLike = map_array(seg.copy(), cell_id, color_vector.codes + 1)
  cols = colors.to_rgba_array(color_vector.categories)
-
  elif pd.api.types.is_numeric_dtype(color_vector.dtype):
- # user wants to plot a continous column
+ # Case B: user wants to plot a continous column
  if isinstance(color_vector, pd.Series):
  color_vector = color_vector.to_numpy()
- val_im = map_array(seg, cell_id, color_vector)
  cols = cmap_params.cmap(cmap_params.norm(color_vector))
-
+ val_im = map_array(seg.copy(), cell_id, cell_id)
  else:
- val_im = map_array(seg.copy(), cell_id, cell_id) # replace with same seg id to remove missing segs
-
- if val_im.shape[0] == 1:
- val_im = np.squeeze(val_im, axis=0)
- if "#" in str(color_vector[0]):
- # we have hex colors
- assert all(_is_color_like(c) for c in color_vector), "Not all values are color-like."
- cols = colors.to_rgba_array(color_vector)
+ # Case C: User didn't specify any colors
+ if color_source_vector is not None and (
+ set(color_vector) == set(color_source_vector)
+ and len(set(color_vector)) == 1
+ and set(color_vector) == {na_color}
+ and not na_color_modified_by_user
+ ):
+ val_im = map_array(seg.copy(), cell_id, cell_id)
+ RNG = default_rng(42)
+ cols = RNG.random((len(color_vector), 3))
  else:
- cols = cmap_params.cmap(cmap_params.norm(color_vector))
+ # Case D: User didn't specify a column to color by, but modified the na_color
+ val_im = map_array(seg.copy(), cell_id, cell_id)
+ if "#" in str(color_vector[0]):
+ # we have hex colors
+ assert all(_is_color_like(c) for c in color_vector), "Not all values are color-like."
+ cols = colors.to_rgba_array(color_vector)
+ else:
+ cols = cmap_params.cmap(cmap_params.norm(color_vector))
 
  if seg_erosionpx is not None:
  val_im[val_im == erosion(val_im, square(seg_erosionpx))] = 0
 
- if color_source_vector is not None and (
- set(color_vector) == set(color_source_vector)
- and len(set(color_vector)) == 1
- and set(color_vector) == {na_color}
- and not na_color_modified_by_user
- ):
- RNG = default_rng(42)
- cols = RNG.random((len(cols), 3))
-
  seg_im: ArrayLike = label2rgb(
  label=val_im,
  colors=cols,
@@ -948,7 +939,7 @@ def _get_categorical_color_mapping(
  else:
  base_mapping = _generate_base_categorial_color_mapping(adata, cluster_key, color_source_vector, na_color)
 
- return _modify_categorical_color_mapping(base_mapping, groups, palette)
+ return _modify_categorical_color_mapping(mapping=base_mapping, groups=groups, palette=palette)
 
 
 def _maybe_set_colors(
@@ -1587,19 +1578,14 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
 
  palette = param_dict["palette"]
 
- if (groups := param_dict.get("groups")) is not None and palette is None:
- warnings.warn(
- "Groups is specified but palette is not. Setting palette to default 'lightgray'", UserWarning, stacklevel=2
- )
- param_dict["palette"] = ["lightgray" for _ in range(len(groups))]
-
  if isinstance((palette := param_dict["palette"]), list):
  if not all(isinstance(p, str) for p in palette):
  raise ValueError("If specified, parameter 'palette' must contain only strings.")
  elif isinstance(palette, (str, type(None))) and "palette" in param_dict:
  param_dict["palette"] = [palette] if palette is not None else None
 
  if element_type in ["shapes", "points", "labels"] and (palette := param_dict.get("palette")) is not None:
+ groups = param_dict.get("groups")
  if groups is None:
  raise ValueError("When specifying 'palette', 'groups' must also be specified.")
  if len(groups) != len(palette):

tests/conftest.py

@@ -33,7 +33,7 @@
 
 EXPECTED = HERE / "_images"
 ACTUAL = HERE / "figures"
-TOL = 45
+TOL = 15
 DPI = 80
 
 RNG = np.random.default_rng(seed=42)

tests/pl/test_render_labels.py

@@ -5,9 +5,8 @@
 import pandas as pd
 import pytest
 import scanpy as sc
-from anndata import AnnData
 from spatial_image import to_spatial_image
-from spatialdata import SpatialData, deepcopy, get_element_instances
+from spatialdata import SpatialData, deepcopy
 from spatialdata.models import TableModel
 
 import spatialdata_plot # noqa: F401
@@ -82,26 +81,31 @@ def test_plot_can_stack_render_labels(self, sdata_blobs: SpatialData):
  def test_plot_can_color_labels_by_continuous_variable(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_labels("blobs_labels", color="channel_0_sum").pl.show()
 
+ def test_plot_can_color_labels_by_categorical_variable(self, sdata_blobs: SpatialData):
+ max_col = sdata_blobs.table.to_df().idxmax(axis=1)
+ max_col = pd.Categorical(max_col, categories=sdata_blobs.table.to_df().columns, ordered=True)
+ sdata_blobs.table.obs["which_max"] = max_col
+
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show()
+
  @pytest.mark.parametrize(
  "label",
  [
  "blobs_labels",
  "blobs_multiscale_labels",
  ],
  )
- def test_plot_can_color_labels_by_categorical_variable(self, sdata_blobs: SpatialData, label: str):
+ def test_plot_can_color_labels_by_categorical_variable_in_other_table(self, sdata_blobs: SpatialData, label: str):
 
  def _make_tablemodel_with_categorical_labels(sdata_blobs, label):
 
- n_obs = len(get_element_instances(sdata_blobs[label]))
- vals = np.arange(n_obs) + 1
- adata = AnnData(vals.reshape(-1, 1), obs=pd.DataFrame({"instance_id": vals}))
- adata.obs["category"] = pd.Categorical(
- list(["a", "b", "c"] * ((n_obs // 3) + 1))[:n_obs],
- categories=["a", "b", "c"],
- ordered=True,
- )
+ adata = sdata_blobs.tables["table"].copy()
+ max_col = adata.to_df().idxmax(axis=1)
+ max_col = max_col.str.replace("channel_", "ch").str.replace("_sum", "")
+ max_col = pd.Categorical(max_col, categories=set(max_col), ordered=True)
+ adata.obs["which_max"] = max_col
  adata.obs["region"] = label
+ del adata.uns["spatialdata_attrs"]
  table = TableModel.parse(
  adata=adata,
  region_key="region",
@@ -110,7 +114,17 @@ def _make_tablemodel_with_categorical_labels(sdata_blobs, label):
  )
  sdata_blobs.tables["other_table"] = table
 
- sdata_blobs.pl.render_labels(label, color="category", table="other_table", scale="scale0").pl.show()
+ _, axs = plt.subplots(nrows=1, ncols=3, layout="tight")
+
+ sdata_blobs.pl.render_labels(label, color="channel_1_sum", table="other_table", scale="scale0").pl.show(
+ ax=axs[0], title="ch_1_sum", colorbar=False
+ )
+ sdata_blobs.pl.render_labels(label, color="channel_2_sum", table="other_table", scale="scale0").pl.show(
+ ax=axs[1], title="ch_2_sum", colorbar=False
+ )
+ sdata_blobs.pl.render_labels(label, color="which_max", table="other_table", scale="scale0").pl.show(
+ ax=axs[2], legend_fontsize=6
+ )
 
  # we're modifying the data here, so we need an independent copy
  sdata_blobs_local = deepcopy(sdata_blobs)
@@ -176,7 +190,7 @@ def test_plot_subset_categorical_label_maintains_order(self, sdata_blobs: Spatia
 
  _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 
- sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0])
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0], legend_fontsize=6)
  sdata_blobs.pl.render_labels(
  "blobs_labels",
  color="which_max",
@@ -190,7 +204,7 @@ def test_plot_subset_categorical_label_maintains_order_when_palette_overwrite(se
 
  _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 
- sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0])
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0], legend_fontsize=6)
  sdata_blobs.pl.render_labels(
  "blobs_labels", color="which_max", groups=["channel_0_sum"], palette="red"
  ).pl.show(ax=axs[1])

tests/pl/test_render_points.py

@@ -1,4 +1,5 @@
 import matplotlib
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import scanpy as sc
@@ -27,15 +28,25 @@ class TestPoints(PlotTester, metaclass=PlotTesterMeta):
  def test_plot_can_render_points(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_points(element="blobs_points").pl.show()
 
- def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
+ def test_plot_can_filter_with_groups_default_palette(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_points"
- sdata_blobs.pl.render_points(color="genes", groups="gene_b", palette="red").pl.show()
 
- def test_plot_can_filter_with_groups_default_palette(self, sdata_blobs: SpatialData):
+ sdata_blobs.pl.render_points(color="genes", size=10).pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_points(color="genes", groups="gene_b", size=10).pl.show(ax=axs[1], legend_fontsize=6)
+
+ def test_plot_can_filter_with_groups_custom_palette(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_points"
- sdata_blobs.pl.render_points(color="genes", groups="gene_b").pl.show()
+
+ sdata_blobs.pl.render_points(color="genes", size=10).pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_points(color="genes", groups="gene_b", size=10, palette="red").pl.show(
+ ax=axs[1], legend_fontsize=6
+ )
 
  def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs

tests/pl/test_render_shapes.py

@@ -1,6 +1,7 @@
 import anndata
 import geopandas as gpd
 import matplotlib
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import scanpy as sc
@@ -107,6 +108,8 @@ def test_plot_can_scale_shapes(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_shapes(element="blobs_circles", scale=0.5).pl.show()
 
  def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  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"] = "c1"
@@ -115,7 +118,10 @@ def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
  "category"
  )
 
- sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups="c1").pl.show()
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster").pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups="c1").pl.show(
+ ax=axs[1], legend_fontsize=6
+ )
 
  def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs

tests/pl/test_utils.py

@@ -58,25 +58,23 @@ def test_plot_can_set_zero_in_cmap_to_transparent(self, sdata_blobs: SpatialData
  from spatialdata_plot.pl.utils import set_zero_in_cmap_to_transparent
 
  # set up figure and modify the data to add 0s
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
- table = sdata_blobs.table.copy()
- x = table.X.todense()
- x[:10, 0] = 0
- table.X = x
- sdata_blobs.tables["modified_table"] = table
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+ sdata_blobs.tables["table"].obs["my_var"] = list(range(len(sdata_blobs.tables["table"].obs)))
+ sdata_blobs.tables["table"].obs["my_var"] += 2 # shift the values to not have 0s
 
- # create a new cmap with 0 as transparent
- new_cmap = set_zero_in_cmap_to_transparent(cmap="plasma")
+ new_cmap = set_zero_in_cmap_to_transparent(cmap="viridis")
 
  # baseline img
- sdata_blobs.pl.render_labels("blobs_labels", color="channel_0_sum", cmap="viridis", table="table").pl.show(
+ sdata_blobs.pl.render_labels("blobs_labels", color="my_var", cmap="viridis", table="table").pl.show(
  ax=axs[0], colorbar=False
  )
 
+ sdata_blobs.tables["table"].obs.iloc[8:12, 2] = 0
+
  # image with 0s as transparent, so some labels are "missing"
- sdata_blobs.pl.render_labels(
-"blobs_labels", color="channel_0_sum", cmap=new_cmap, table="modified_table"
- ).pl.show(ax=axs[1], colorbar=False)
+ sdata_blobs.pl.render_labels("blobs_labels", color="my_var", cmap=new_cmap, table="table").pl.show(
+ax=axs[1], colorbar=False
+ )
 
 
 @pytest.mark.parametrize(

CHANGELOG.md

@@ -8,6 +8,22 @@ and this project adheres to [Semantic Versioning][].
 [keep a changelog]: https://keepachangelog.com/en/1.0.0/
 [semantic versioning]: https://semver.org/spec/v2.0.0.html
 
+## [0.2.6] - tbd
+
+### Added
+
+-
+
+### Changed
+
+- Lowered RMSE-threshold for plot-based tests from 45 to 15 (#344)
+- When subsetting to `groups`, `NA` isn't automatically added to legend (#344)
+
+### Fixed
+
+- Filtering with `groups` now preserves original cmap (#344)
+- Non-selected `groups` are now not shown in `na_color` (#344)
+
 ## [0.2.5] - 2024-08-23
 
 ### Added

src/spatialdata_plot/pl/render.py

@@ -855,7 +855,7 @@ def _draw_labels(seg_erosionpx: int | None, seg_boundaries: bool, alpha: float)
  legend_fontweight=legend_params.legend_fontweight,
  legend_loc=legend_params.legend_loc,
  legend_fontoutline=legend_params.legend_fontoutline,
- na_in_legend=legend_params.na_in_legend,
+ na_in_legend=legend_params.na_in_legend if groups is None else len(groups) == len(set(color_vector)),
  colorbar=legend_params.colorbar,
  scalebar_dx=scalebar_params.scalebar_dx,
  scalebar_units=scalebar_params.scalebar_units,

src/spatialdata_plot/pl/utils.py

@@ -567,9 +567,6 @@ def _get_subplots(num_images: int, ncols: int = 4, width: int = 4, height: int =
  Union[plt.Figure, plt.Axes]
  Matplotlib figure and axes object.
  """
- # if num_images <= 1:
- # raise ValueError("Number of images must be greater than 1.")
-
  if num_images < ncols:
  nrows = 1
  ncols = num_images
@@ -733,8 +730,6 @@ def _set_color_source_vec(
  color = np.full(len(element), na_color)
  return color, color, False
 
- # model = get_model(sdata[element_name])
-
  # Figure out where to get the color from
  origins = _locate_value(value_key=value_to_plot, sdata=sdata, element_name=element_name, table_name=table_name)
 
@@ -778,16 +773,13 @@ def _set_color_source_vec(
  palette=palette,
  na_color=na_color,
  )
+
  color_source_vector = color_source_vector.set_categories(color_mapping.keys())
  if color_mapping is None:
  raise ValueError("Unable to create color palette.")
 
  # do not rename categories, as colors need not be unique
  color_vector = color_source_vector.map(color_mapping)
- if color_vector.isna().any():
- if (na_cat_color := to_hex(na_color)) not in color_vector.categories:
- color_vector = color_vector.add_categories([na_cat_color])
- color_vector = color_vector.fillna(to_hex(na_color))
 
  return color_source_vector, color_vector, True
 
@@ -808,44 +800,43 @@ def _map_color_seg(
  seg_boundaries: bool = False,
 ) -> ArrayLike:
  cell_id = np.array(cell_id)
- if color_vector is not None and isinstance(color_vector.dtype, pd.CategoricalDtype):
- # users wants to plot a categorical column
+
+ if pd.api.types.is_categorical_dtype(color_vector.dtype):
+ # Case A: users wants to plot a categorical column
  if np.any(color_source_vector.isna()):
  cell_id[color_source_vector.isna()] = 0
- val_im: ArrayLike = map_array(seg, cell_id, color_vector.codes + 1)
+ val_im: ArrayLike = map_array(seg.copy(), cell_id, color_vector.codes + 1)
  cols = colors.to_rgba_array(color_vector.categories)
-
  elif pd.api.types.is_numeric_dtype(color_vector.dtype):
- # user wants to plot a continous column
+ # Case B: user wants to plot a continous column
  if isinstance(color_vector, pd.Series):
  color_vector = color_vector.to_numpy()
- val_im = map_array(seg, cell_id, color_vector)
  cols = cmap_params.cmap(cmap_params.norm(color_vector))
-
+ val_im = map_array(seg.copy(), cell_id, cell_id)
  else:
- val_im = map_array(seg.copy(), cell_id, cell_id) # replace with same seg id to remove missing segs
-
- if val_im.shape[0] == 1:
- val_im = np.squeeze(val_im, axis=0)
- if "#" in str(color_vector[0]):
- # we have hex colors
- assert all(_is_color_like(c) for c in color_vector), "Not all values are color-like."
- cols = colors.to_rgba_array(color_vector)
+ # Case C: User didn't specify any colors
+ if color_source_vector is not None and (
+ set(color_vector) == set(color_source_vector)
+ and len(set(color_vector)) == 1
+ and set(color_vector) == {na_color}
+ and not na_color_modified_by_user
+ ):
+ val_im = map_array(seg.copy(), cell_id, cell_id)
+ RNG = default_rng(42)
+ cols = RNG.random((len(color_vector), 3))
  else:
- cols = cmap_params.cmap(cmap_params.norm(color_vector))
+ # Case D: User didn't specify a column to color by, but modified the na_color
+ val_im = map_array(seg.copy(), cell_id, cell_id)
+ if "#" in str(color_vector[0]):
+ # we have hex colors
+ assert all(_is_color_like(c) for c in color_vector), "Not all values are color-like."
+ cols = colors.to_rgba_array(color_vector)
+ else:
+ cols = cmap_params.cmap(cmap_params.norm(color_vector))
 
  if seg_erosionpx is not None:
  val_im[val_im == erosion(val_im, square(seg_erosionpx))] = 0
 
- if color_source_vector is not None and (
- set(color_vector) == set(color_source_vector)
- and len(set(color_vector)) == 1
- and set(color_vector) == {na_color}
- and not na_color_modified_by_user
- ):
- RNG = default_rng(42)
- cols = RNG.random((len(cols), 3))
-
  seg_im: ArrayLike = label2rgb(
  label=val_im,
  colors=cols,
@@ -948,7 +939,7 @@ def _get_categorical_color_mapping(
  else:
  base_mapping = _generate_base_categorial_color_mapping(adata, cluster_key, color_source_vector, na_color)
 
- return _modify_categorical_color_mapping(base_mapping, groups, palette)
+ return _modify_categorical_color_mapping(mapping=base_mapping, groups=groups, palette=palette)
 
 
 def _maybe_set_colors(
@@ -1587,19 +1578,14 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
 
  palette = param_dict["palette"]
 
- if (groups := param_dict.get("groups")) is not None and palette is None:
- warnings.warn(
- "Groups is specified but palette is not. Setting palette to default 'lightgray'", UserWarning, stacklevel=2
- )
- param_dict["palette"] = ["lightgray" for _ in range(len(groups))]
-
  if isinstance((palette := param_dict["palette"]), list):
  if not all(isinstance(p, str) for p in palette):
  raise ValueError("If specified, parameter 'palette' must contain only strings.")
  elif isinstance(palette, (str, type(None))) and "palette" in param_dict:
  param_dict["palette"] = [palette] if palette is not None else None
 
  if element_type in ["shapes", "points", "labels"] and (palette := param_dict.get("palette")) is not None:
+ groups = param_dict.get("groups")
  if groups is None:
  raise ValueError("When specifying 'palette', 'groups' must also be specified.")
  if len(groups) != len(palette):

tests/conftest.py

@@ -33,7 +33,7 @@
 
 EXPECTED = HERE / "_images"
 ACTUAL = HERE / "figures"
-TOL = 45
+TOL = 15
 DPI = 80
 
 RNG = np.random.default_rng(seed=42)

tests/pl/test_render_labels.py

@@ -5,9 +5,8 @@
 import pandas as pd
 import pytest
 import scanpy as sc
-from anndata import AnnData
 from spatial_image import to_spatial_image
-from spatialdata import SpatialData, deepcopy, get_element_instances
+from spatialdata import SpatialData, deepcopy
 from spatialdata.models import TableModel
 
 import spatialdata_plot # noqa: F401
@@ -82,26 +81,31 @@ def test_plot_can_stack_render_labels(self, sdata_blobs: SpatialData):
  def test_plot_can_color_labels_by_continuous_variable(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_labels("blobs_labels", color="channel_0_sum").pl.show()
 
+ def test_plot_can_color_labels_by_categorical_variable(self, sdata_blobs: SpatialData):
+ max_col = sdata_blobs.table.to_df().idxmax(axis=1)
+ max_col = pd.Categorical(max_col, categories=sdata_blobs.table.to_df().columns, ordered=True)
+ sdata_blobs.table.obs["which_max"] = max_col
+
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show()
+
  @pytest.mark.parametrize(
  "label",
  [
  "blobs_labels",
  "blobs_multiscale_labels",
  ],
  )
- def test_plot_can_color_labels_by_categorical_variable(self, sdata_blobs: SpatialData, label: str):
+ def test_plot_can_color_labels_by_categorical_variable_in_other_table(self, sdata_blobs: SpatialData, label: str):
 
  def _make_tablemodel_with_categorical_labels(sdata_blobs, label):
 
- n_obs = len(get_element_instances(sdata_blobs[label]))
- vals = np.arange(n_obs) + 1
- adata = AnnData(vals.reshape(-1, 1), obs=pd.DataFrame({"instance_id": vals}))
- adata.obs["category"] = pd.Categorical(
- list(["a", "b", "c"] * ((n_obs // 3) + 1))[:n_obs],
- categories=["a", "b", "c"],
- ordered=True,
- )
+ adata = sdata_blobs.tables["table"].copy()
+ max_col = adata.to_df().idxmax(axis=1)
+ max_col = max_col.str.replace("channel_", "ch").str.replace("_sum", "")
+ max_col = pd.Categorical(max_col, categories=set(max_col), ordered=True)
+ adata.obs["which_max"] = max_col
  adata.obs["region"] = label
+ del adata.uns["spatialdata_attrs"]
  table = TableModel.parse(
  adata=adata,
  region_key="region",
@@ -110,7 +114,17 @@ def _make_tablemodel_with_categorical_labels(sdata_blobs, label):
  )
  sdata_blobs.tables["other_table"] = table
 
- sdata_blobs.pl.render_labels(label, color="category", table="other_table", scale="scale0").pl.show()
+ _, axs = plt.subplots(nrows=1, ncols=3, layout="tight")
+
+ sdata_blobs.pl.render_labels(label, color="channel_1_sum", table="other_table", scale="scale0").pl.show(
+ ax=axs[0], title="ch_1_sum", colorbar=False
+ )
+ sdata_blobs.pl.render_labels(label, color="channel_2_sum", table="other_table", scale="scale0").pl.show(
+ ax=axs[1], title="ch_2_sum", colorbar=False
+ )
+ sdata_blobs.pl.render_labels(label, color="which_max", table="other_table", scale="scale0").pl.show(
+ ax=axs[2], legend_fontsize=6
+ )
 
  # we're modifying the data here, so we need an independent copy
  sdata_blobs_local = deepcopy(sdata_blobs)
@@ -176,7 +190,7 @@ def test_plot_subset_categorical_label_maintains_order(self, sdata_blobs: Spatia
 
  _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 
- sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0])
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0], legend_fontsize=6)
  sdata_blobs.pl.render_labels(
  "blobs_labels",
  color="which_max",
@@ -190,7 +204,7 @@ def test_plot_subset_categorical_label_maintains_order_when_palette_overwrite(se
 
  _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
 
- sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0])
+ sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show(ax=axs[0], legend_fontsize=6)
  sdata_blobs.pl.render_labels(
  "blobs_labels", color="which_max", groups=["channel_0_sum"], palette="red"
  ).pl.show(ax=axs[1])

tests/pl/test_render_points.py

@@ -1,4 +1,5 @@
 import matplotlib
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import scanpy as sc
@@ -27,15 +28,25 @@ class TestPoints(PlotTester, metaclass=PlotTesterMeta):
  def test_plot_can_render_points(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_points(element="blobs_points").pl.show()
 
- def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
+ def test_plot_can_filter_with_groups_default_palette(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_points"
- sdata_blobs.pl.render_points(color="genes", groups="gene_b", palette="red").pl.show()
 
- def test_plot_can_filter_with_groups_default_palette(self, sdata_blobs: SpatialData):
+ sdata_blobs.pl.render_points(color="genes", size=10).pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_points(color="genes", groups="gene_b", size=10).pl.show(ax=axs[1], legend_fontsize=6)
+
+ def test_plot_can_filter_with_groups_custom_palette(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs
  sdata_blobs["table"].uns["spatialdata_attrs"]["region"] = "blobs_points"
- sdata_blobs.pl.render_points(color="genes", groups="gene_b").pl.show()
+
+ sdata_blobs.pl.render_points(color="genes", size=10).pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_points(color="genes", groups="gene_b", size=10, palette="red").pl.show(
+ ax=axs[1], legend_fontsize=6
+ )
 
  def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_points"] * sdata_blobs["table"].n_obs

tests/pl/test_render_shapes.py

@@ -1,6 +1,7 @@
 import anndata
 import geopandas as gpd
 import matplotlib
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import scanpy as sc
@@ -107,6 +108,8 @@ def test_plot_can_scale_shapes(self, sdata_blobs: SpatialData):
  sdata_blobs.pl.render_shapes(element="blobs_circles", scale=0.5).pl.show()
 
  def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+
  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"] = "c1"
@@ -115,7 +118,10 @@ def test_plot_can_filter_with_groups(self, sdata_blobs: SpatialData):
  "category"
  )
 
- sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups="c1").pl.show()
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster").pl.show(ax=axs[0], legend_fontsize=6)
+ sdata_blobs.pl.render_shapes("blobs_polygons", color="cluster", groups="c1").pl.show(
+ ax=axs[1], legend_fontsize=6
+ )
 
  def test_plot_coloring_with_palette(self, sdata_blobs: SpatialData):
  sdata_blobs["table"].obs["region"] = ["blobs_polygons"] * sdata_blobs["table"].n_obs

tests/pl/test_utils.py

@@ -58,25 +58,23 @@ def test_plot_can_set_zero_in_cmap_to_transparent(self, sdata_blobs: SpatialData
  from spatialdata_plot.pl.utils import set_zero_in_cmap_to_transparent
 
  # set up figure and modify the data to add 0s
- fig, axs = plt.subplots(ncols=2, figsize=(6, 3))
- table = sdata_blobs.table.copy()
- x = table.X.todense()
- x[:10, 0] = 0
- table.X = x
- sdata_blobs.tables["modified_table"] = table
+ _, axs = plt.subplots(nrows=1, ncols=2, layout="tight")
+ sdata_blobs.tables["table"].obs["my_var"] = list(range(len(sdata_blobs.tables["table"].obs)))
+ sdata_blobs.tables["table"].obs["my_var"] += 2 # shift the values to not have 0s
 
- # create a new cmap with 0 as transparent
- new_cmap = set_zero_in_cmap_to_transparent(cmap="plasma")
+ new_cmap = set_zero_in_cmap_to_transparent(cmap="viridis")
 
  # baseline img
- sdata_blobs.pl.render_labels("blobs_labels", color="channel_0_sum", cmap="viridis", table="table").pl.show(
+ sdata_blobs.pl.render_labels("blobs_labels", color="my_var", cmap="viridis", table="table").pl.show(
  ax=axs[0], colorbar=False
  )
 
+ sdata_blobs.tables["table"].obs.iloc[8:12, 2] = 0
+
  # image with 0s as transparent, so some labels are "missing"
- sdata_blobs.pl.render_labels(
-"blobs_labels", color="channel_0_sum", cmap=new_cmap, table="modified_table"
- ).pl.show(ax=axs[1], colorbar=False)
+ sdata_blobs.pl.render_labels("blobs_labels", color="my_var", cmap=new_cmap, table="table").pl.show(
+ax=axs[1], colorbar=False
+ )
 
 
 @pytest.mark.parametrize(