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docs(normalization): fix LogNorm image attribution, real datashader-percentile demo, RGB composite note
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‎tutorials/normalization_and_contrast.ipynb‎

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"source": [
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"## 1. The default — per-channel min/max\n",
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"\n",
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"With no `norm`, every channel is scaled independently to its own min and max. This is the historical default."
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"With no `norm`, every channel is scaled independently to its own min and max. This is the historical default.\n",
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"\n",
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"`blobs_image` has three channels, so with the default colormap they are composited into a single\n",
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"RGB image — channel 0 → red, 1 → green, 2 → blue."
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]
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"## 3. Clipping — what happens outside `[vmin, vmax]`\n",
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"\n",
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"With `clip=False` (the default), values below `vmin` or above `vmax` are drawn in the colormap's **under/over**\n",
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"colors. With `clip=True`, they are clamped to the ends instead. We use a cmap with distinct under/over colors\n",
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"colors (the special colors a colormap shows for values past each end, set via `set_under` / `set_over`). With `clip=True`, they are clamped to the ends instead. We use a cmap with distinct under/over colors\n",
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"to make the difference obvious.\n",
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"\n",
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"First, `clip=False` — out-of-range pixels show magenta (under) and red (over):"
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"source": [
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"## 4. Logarithmic scaling — `LogNorm`\n",
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"\n",
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"For data spanning orders of magnitude. `spatialdata-plot` guards the log domain: limits autoscale from the\n",
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"**strictly-positive** values only, so a non-positive `vmin` never raises a late \"Invalid vmin or vmax\"."
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"For data spanning orders of magnitude. On the non-image renderers (shapes, points, labels),\n",
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"`spatialdata-plot` resolves `LogNorm` limits from the **strictly-positive** data and falls back to a valid\n",
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"domain when none exist, so a non-positive `vmin` never raises a late \"Invalid vmin or vmax\". On images,\n",
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"as below, `LogNorm` is applied directly by matplotlib."
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"ignored — the contrast follows the aggregate range instead."
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"source": [
838-
"sdata.pl.render_points(\"blobs_points\", color=\"instance_id\", method=\"datashader\", norm=Normalize()).pl.show()"
846+
"sdata.pl.render_points(\"blobs_points\", color=\"instance_id\", method=\"datashader\", norm=PercentileNormalize(5, 95)).pl.show()"
839847
]
840848
},
841849
{
@@ -859,14 +867,14 @@
859867
},
860868
{
861869
"cell_type": "code",
862-
"execution_count": 22,
870+
"execution_count": 21,
863871
"id": "015066fb96f841e5be1e03a9eaadc3b6",
864872
"metadata": {
865873
"execution": {
866-
"iopub.execute_input": "2026-08-18T20:28:59.253659Z",
867-
"iopub.status.busy": "2026-08-18T20:28:59.253536Z",
868-
"iopub.status.idle": "2026-08-18T20:28:59.256245Z",
869-
"shell.execute_reply": "2026-08-18T20:28:59.255764Z"
874+
"iopub.execute_input": "2026-08-19T03:27:25.984887Z",
875+
"iopub.status.busy": "2026-08-19T03:27:25.984762Z",
876+
"iopub.status.idle": "2026-08-19T03:27:25.987400Z",
877+
"shell.execute_reply": "2026-08-19T03:27:25.986972Z"
870878
}
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},
872880
"outputs": [
@@ -895,14 +903,14 @@
895903
},
896904
{
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"cell_type": "code",
898-
"execution_count": 23,
906+
"execution_count": 22,
899907
"id": "9075f00cfa8d463f84130041b1e44ca7",
900908
"metadata": {
901909
"execution": {
902-
"iopub.execute_input": "2026-08-18T20:28:59.257685Z",
903-
"iopub.status.busy": "2026-08-18T20:28:59.257561Z",
904-
"iopub.status.idle": "2026-08-18T20:28:59.260002Z",
905-
"shell.execute_reply": "2026-08-18T20:28:59.259452Z"
910+
"iopub.execute_input": "2026-08-19T03:27:25.989054Z",
911+
"iopub.status.busy": "2026-08-19T03:27:25.988938Z",
912+
"iopub.status.idle": "2026-08-19T03:27:25.991305Z",
913+
"shell.execute_reply": "2026-08-19T03:27:25.990752Z"
906914
}
907915
},
908916
"outputs": [

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