Fix squidy.pl.ligrec's pandas usage

requirements.txt

@@ -6,7 +6,7 @@ dask[array]>=2021.02.0
 docrep>=0.3.1
 fsspec>=2021.11.0
 leidenalg>=0.8.2
-matplotlib>=3.3,!=3.6.1  # https://github.com/matplotlib/matplotlib/issues/24127
+matplotlib>=3.3
 matplotlib-scalebar>=0.8.0
 networkx>=2.6.0
 numba>=0.55.0,<0.56.0
@@ -20,7 +20,6 @@ scikit-learn>=0.24.0
 statsmodels>=0.12.0
 tifffile!=2022.4.22  # https://github.com/scverse/squidpy/issues/526
 tqdm>=4.50.2
-typing_extensions
 validators>=0.18.2
 xarray>=0.16.1
 zarr>=2.6.1

squidpy/pl/_ligrec.py

@@ -293,10 +293,6 @@ def get_dendrogram(adata: AnnData, linkage: str = "complete") -> Mapping[str, An
         means = means.T
 
     for cls, size in (pvals.groupby(level=0, axis=1)).size().to_dict().items():
-        size = np.unique(list(size.values()))
-        if len(size) != 1:
-            raise ValueError(f"Expected all groups to have the same number of interacting clusters, found `{size}`.")
-        size = size[0]
         label_ranges[cls] = (start, start + size - 1)
         start += size
     label_ranges = {k: label_ranges[k] for k in sorted(label_ranges.keys())}

squidpy/pl/_spatial_utils.py

@@ -460,7 +460,8 @@ def _set_color_source_vec(
     alpha: float = 1.0,
 ) -> Tuple[NDArrayA | pd.Series | None, NDArrayA, bool]:
     if value_to_plot is None:
-        return np.full(adata.n_obs, to_hex(na_color)), np.broadcast_to(np.nan, adata.n_obs), False
+        color = np.full(adata.n_obs, to_hex(na_color))
+        return color, color, False
 
     if alt_var is not None and value_to_plot not in adata.obs and value_to_plot not in adata.var_names:
         value_to_plot = adata.var_names[adata.var[alt_var] == value_to_plot][0]
@@ -699,7 +700,11 @@ def _map_color_seg(
         cols = colors.to_rgba_array(color_vector.categories)  # type: ignore
     else:
         val_im = map_array(seg, cell_id, cell_id)  # replace with same seg id to remove missing segs
-        cols = cmap_params.cmap(cmap_params.norm(color_vector))
+        try:
+            cols = cmap_params.cmap(cmap_params.norm(color_vector))
+        except TypeError:
+            assert all(colors.is_color_like(c) for c in color_vector), "Not all values are color-like."
+            cols = colors.to_rgba_array(color_vector)
 
     if seg_erosionpx is not None:
         val_im[val_im == erosion(val_im, square(seg_erosionpx))] = 0