From 732608f7647342d4dd9e005b8ccb7b359e9d13c7 Mon Sep 17 00:00:00 2001
From: Bryan Paget <8212170+bryanpaget@users.noreply.github.com>
Date: Tue, 12 Dec 2023 14:22:00 +0000
Subject: [PATCH] feat(rerun): add more comments

---
 .../en/1-Experiments/Notebooks/ReRun_EN.ipynb | 71 +++++++++++++++++--
 1 file changed, 65 insertions(+), 6 deletions(-)

diff --git a/docs/en/1-Experiments/Notebooks/ReRun_EN.ipynb b/docs/en/1-Experiments/Notebooks/ReRun_EN.ipynb
index 7923ab390..375169b71 100644
--- a/docs/en/1-Experiments/Notebooks/ReRun_EN.ipynb
+++ b/docs/en/1-Experiments/Notebooks/ReRun_EN.ipynb
@@ -124,7 +124,40 @@
    "id": "759d2169",
    "metadata": {},
    "source": [
-    "But that's not that impressive. ReRun shines when we view multi-model time-series data. Run the following code and interact with the ReRun Viewer. You can rotate the image and scan forward and backward in time."
+    "## DNA\n",
+    "\n",
+    "This is a minimal example that logs synthetic 3D data in the shape of a double helix. The underlying data is generated\n",
+    "using numpy and visualized using Rerun.\n",
+    "\n",
+    "### How it was made\n",
+    "\n",
+    "The full source code for this example is available\n",
+    "[on GitHub](https://github.com/rerun-io/rerun/blob/latest/examples/python/dna/main.py).\n",
+    "\n",
+    "#### Colored 3D points\n",
+    "\n",
+    "The colored 3D points were added to the scene by logging the\n",
+    "[rr.Points3D archetype](https://www.rerun.io/docs/reference/types/archetypes/points3d) to the\n",
+    "[helix/structure/left](recording://helix/structure/left) and [helix/structure/right](recording://helix/structure/right)\n",
+    "entities.\n",
+    "\n",
+    "#### 3D line strips\n",
+    "\n",
+    "The 3D line strips connecting the 3D point pairs are logged as an\n",
+    "[rr.LineStrips3D archetype](https://www.rerun.io/docs/reference/types/archetypes/line_strips3d) to the\n",
+    "[helix/structure/scaffolding entity](recording://helix/structure/scaffolding).\n",
+    "\n",
+    "#### Rotation\n",
+    "\n",
+    "The whole structure is rotated over time by logging a\n",
+    "[rr.Transform3D archetype](https://www.rerun.io/docs/reference/types/archetypes/transform3d) to the\n",
+    "[helix/structure entity](recording://helix/structure.Transform3D) that changes over time. This transform determines the rotation of\n",
+    "the [structure entity](recording://helix/structure) relative to the [helix](recording://helix) entity. Since all other\n",
+    "entities are children of [helix/structure](recording://helix/structure) they will also rotate based on this transform.\n",
+    "\n",
+    "You can visualize this rotation by selecting the two entities on the left-hand side and activating `Show transform` in\n",
+    "the Blueprint settings on the right-hand side. You will see one static frame (i.e., the frame of\n",
+    "[helix](recording://helix)) and the rotating frame (i.e., the frame of [structure](recording://helix/structure)).\n"
    ]
   },
   {
@@ -212,6 +245,37 @@
     "rec.show(width=1024, height=768)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "cb0cbca9",
+   "metadata": {},
+   "source": [
+    "## Plots\n",
+    "\n",
+    "But that's not that impressive. ReRun shines when we view multi-model time-series data. The following code example shows rerun displaying simple plots corresponding to different reading on the same time-series data. This example shows various plot types that you can create using Rerun. Common usecases for such plots would be logging\n",
+    "losses or metrics over time, histograms, or general function plots.\n",
+    "\n",
+    "### How it was made\n",
+    "The full source code for this example is available [on GitHub](https://github.com/rerun-io/rerun/blob/latest/examples/python/plots/main.py).\n",
+    "\n",
+    "#### Bar charts\n",
+    "The [bar chart](recording://bar_chart) is created by logging the [rr.BarChart archetype](https://www.rerun.io/docs/reference/types/archetypes/bar_chart).\n",
+    "\n",
+    "#### Time series\n",
+    "All other plots are created using the\n",
+    "[rr.TimeSeriesScalar archetype](https://www.rerun.io/docs/reference/types/archetypes/bar_chart)\n",
+    "with different settings. Each plot is created by logging scalars at different time steps (i.e., the x-axis).\n",
+    "\n",
+    "For the [parabola](recording://curves/parabola) the radius and color is changed over time.\n",
+    "\n",
+    "[sin](recording://trig/sin) and [cos](recording://trig/cos) are logged with the same parent entity (i.e.,\n",
+    "`trig/{cos,sin}`) which will put them in the same view by default.\n",
+    "\n",
+    "For the [classification samples](recording://classification/samples) `rr.TimeSeriesScalar(..., scatter=True)` is used to\n",
+    "create separate points that do not connect over time. Note, that in the other plots the logged scalars are connected\n",
+    "over time by lines."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -219,11 +283,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#!/usr/bin/env python3\n",
-    "\"\"\"\n",
-    "Demonstrates how to log simple plots with the Rerun SDK.\n",
-    "\n",
-    "\"\"\"\n",
     "from __future__ import annotations\n",
     "\n",
     "import argparse\n",