Fix shared Normalize causing out-of-range values in multi-channel rendering

The root cause of matplotlib's "Clipping input data" warning was that a
single Normalize instance was shared across all channels. It auto-ranged
on the first channel's min/max, causing subsequent channels with different
value ranges to normalize outside [0, 1].

Fix: when the shared norm has auto-ranging (vmin=None or vmax=None), copy
it per channel so each channel normalizes independently. This replaces
the previous approach of clipping the final array.

Additive compositing paths (palette, categorical) still clip after
summing, since summing RGBA values can inherently exceed [0, 1].

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: timtreis

src/spatialdata_plot/pl/render.py

@@ -1201,6 +1201,10 @@ def _render_images(
                 ch_norm = render_params.cmap_params[ch_idx].norm
             else:
                 ch_norm = render_params.cmap_params.norm
+                # When a single auto-ranging norm is shared across channels, copy it so
+                # each channel normalizes independently based on its own value range.
+                if isinstance(ch_norm, Normalize) and (ch_norm.vmin is None or ch_norm.vmax is None):
+                    ch_norm = copy(ch_norm)
 
             if ch_norm is not None:
                 layers[ch] = ch_norm(layers[ch])
@@ -1229,7 +1233,7 @@ def _render_images(
                 )
 
             _ax_show_and_transform(
-                np.clip(stacked, 0, 1),
+                stacked,
                 trans_data,
                 ax,
                 render_params.alpha,
@@ -1246,15 +1250,15 @@ def _render_images(
                     [channel_cmaps[ch_ind](layers[ch]) for ch_ind, ch in enumerate(channels)],
                     0,
                 ).sum(0)
-                colored = colored[:, :, :3]
+                colored = np.clip(colored[:, :, :3], 0, 1)
             elif n_channels == 3:
                 seed_colors = _get_colors_for_categorical_obs(list(range(n_channels)))
                 channel_cmaps = [_get_linear_colormap([c], "k")[0] for c in seed_colors]
                 colored = np.stack(
                     [channel_cmaps[ind](layers[ch]) for ind, ch in enumerate(channels)],
                     0,
                 ).sum(0)
-                colored = colored[:, :, :3]
+                colored = np.clip(colored[:, :, :3], 0, 1)
             else:
                 if isinstance(render_params.cmap_params, list):
                     cmap_is_default = render_params.cmap_params[0].cmap_is_default
@@ -1291,7 +1295,7 @@ def _render_images(
                 )  # TODO: update when pca is added as strategy
 
             _ax_show_and_transform(
-                np.clip(colored, 0, 1),
+                colored,
                 trans_data,
                 ax,
                 render_params.alpha,
@@ -1305,10 +1309,10 @@ def _render_images(
 
             channel_cmaps = [_get_linear_colormap([c], "k")[0] for c in palette if isinstance(c, str)]
             colored = np.stack([channel_cmaps[i](layers[c]) for i, c in enumerate(channels)], 0).sum(0)
-            colored = colored[:, :, :3]
+            colored = np.clip(colored[:, :, :3], 0, 1)
 
             _ax_show_and_transform(
-                np.clip(colored, 0, 1),
+                colored,
                 trans_data,
                 ax,
                 render_params.alpha,
@@ -1327,7 +1331,7 @@ def _render_images(
             colored = colored[:, :, :3]
 
             _ax_show_and_transform(
-                np.clip(colored, 0, 1),
+                colored,
                 trans_data,
                 ax,
                 render_params.alpha,