From 01d828d470e69372a89b9265219e908a9df30ce9 Mon Sep 17 00:00:00 2001
From: Carifio24 <carifio24@gmail.com>
Date: Tue, 12 Nov 2024 15:52:52 -0500
Subject: [PATCH] Update example notebook.

---
 docs/PlotlyViewerExample.ipynb            | 139 ++------
 docs/assets/img/PlotlyViewerExample.ipynb | 375 ----------------------
 2 files changed, 25 insertions(+), 489 deletions(-)
 delete mode 100644 docs/assets/img/PlotlyViewerExample.ipynb

diff --git a/docs/PlotlyViewerExample.ipynb b/docs/PlotlyViewerExample.ipynb
index 1bc281c..c0fe49c 100644
--- a/docs/PlotlyViewerExample.ipynb
+++ b/docs/PlotlyViewerExample.ipynb
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "75a31f1b-a32a-42ba-a016-f324faa9f3a7",
    "metadata": {},
    "outputs": [],
@@ -40,28 +40,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "0746adc6-dbe1-4f85-8690-da24458667c0",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['ipyvolume:lasso', 'ipyvolume:circle', 'ipyvolume:rectangle']\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jon/opt/mambaforge/envs/glue-plotly/lib/python3.11/site-packages/glue_qt/viewers/common/tool.py:5: UserWarning:\n",
-      "\n",
-      "glue.viewers.common.tool is deprecated, use glue.viewers.common.tool instead\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "app = jglue()\n",
     "session = app.session\n",
@@ -78,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "95d749da-73c8-432a-9a23-85d37e1b6fb5",
    "metadata": {},
    "outputs": [],
@@ -86,7 +68,7 @@
     "from random import randint\n",
     "x = [randint(0, 100) for _ in range(100)]\n",
     "y = [randint(300, 400) for _ in range(100)]\n",
-    "data = app.load_data(\"~/dev/glue/data/airplane/planes_truncated.csv\")\n",
+    "data = Data(label='data', x=x, y=y)\n",
     "dc.append(data)\n",
     "data.style.color = \"#0000ff\"\n",
     "data.style.markersize = 6"
@@ -94,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "92745ee0-25bf-455a-b022-045059fa8e19",
    "metadata": {},
    "outputs": [],
@@ -119,25 +101,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "id": "cf42768e-6d31-4375-90ee-facd8d19800a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a086592611844e8d94a1c6623d35cb5a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "LayoutWidget(controls={'toolbar_selection_tools': BasicJupyterToolbar(template=Template(template='<template>\\n…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "scatter_viewer = app.new_data_viewer(\"plotly_scatter\", data=data)\n",
     "# Alternatively, you can use the viewer class:\n",
@@ -146,33 +113,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "id": "d94774d4-3a68-4b7a-b7f2-7e79afead2b5",
+   "execution_count": null,
+   "id": "cbeeb763-daf0-4828-8535-f05f97ec856f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "scatter_viewer.state.x_axislabel = \"Longitude\"\n",
-    "scatter_viewer.state.y_axislabel = \"Latitude\"\n",
-    "scatter_viewer.figure.layout.width = 900"
+    "scatter_viewer.add_data(data2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "1355c76d-ad3d-4202-a08f-ee17b1facbd4",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(PlotlyScatterLayerArtist for My Data, PlotlyScatterLayerArtist for data2)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "scatter_viewer.layers"
    ]
@@ -187,46 +141,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "da0a70ee-f299-47cd-9eb0-9a634d983c02",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "scatter_viewer.layers[1].state.zorder"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "3e66be74-929f-4cfa-910c-9d3b65b4ac91",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e72117c63af0431a91224959cfdbf10b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "LayoutWidget(controls={'toolbar_selection_tools': BasicJupyterToolbar(template=Template(template='<template>\\n…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "histogram_viewer = app.new_data_viewer(\"plotly_histogram\", data=data)\n",
     "# Alternatively, you can use the viewer class:\n",
@@ -243,15 +171,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "821b6397-80e2-443c-b6c3-50bf954abbf2",
    "metadata": {},
    "outputs": [],
    "source": [
     "histogram_viewer.state.gaps = True\n",
-    "histogram_viewer.state.gap_fraction = 0.13\n",
-    "histogram_viewer.state.x_axislabel = \"Value\"\n",
-    "histogram_viewer.figure.layout.width = 900"
+    "histogram_viewer.state.gap_fraction = 0.15"
    ]
   },
   {
@@ -265,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "4f9d9ffe-ec2e-4d7f-aa8b-8b2a7524b9c2",
    "metadata": {},
    "outputs": [],
@@ -280,25 +206,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "174cd945-0744-43dc-b23e-bcef244a0bff",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4aef2c287aca4be08ac525f96db1510e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "LayoutWidget(controls={'toolbar_selection_tools': BasicJupyterToolbar(template=Template(template='<template>\\n…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dotplot_viewer = app.new_data_viewer(DotplotViewer, data=data)"
    ]
@@ -314,9 +225,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "glue-plotly",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "glue-plotly"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -328,7 +239,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
diff --git a/docs/assets/img/PlotlyViewerExample.ipynb b/docs/assets/img/PlotlyViewerExample.ipynb
deleted file mode 100644
index d231199..0000000
--- a/docs/assets/img/PlotlyViewerExample.ipynb
+++ /dev/null
@@ -1,375 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "d5103242-a264-4f98-a168-ab40087f8c0c",
-   "metadata": {},
-   "source": [
-    "# Plotly Viewer Examples\n",
-    "This notebook contains examples of how to use the viewers provided by the `glue-plotly` package"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9101e7af-1a3a-4152-b43f-829c52a3d166",
-   "metadata": {},
-   "source": [
-    "### Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "75a31f1b-a32a-42ba-a016-f324faa9f3a7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from glue.core import Data\n",
-    "from glue_jupyter import jglue\n",
-    "from glue_plotly.viewers.histogram.viewer import PlotlyHistogramView\n",
-    "from glue_plotly.viewers.scatter.viewer import PlotlyScatterView"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c015dc70-746f-415b-853f-c31ea92cff67",
-   "metadata": {},
-   "source": [
-    "Create a glue-jupyter application and session"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "0746adc6-dbe1-4f85-8690-da24458667c0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "app = jglue()\n",
-    "session = app.session\n",
-    "dc = app.session.data_collection"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7d6adbc6-93b8-4a0e-8505-83ef23fb2f6b",
-   "metadata": {},
-   "source": [
-    "Create some random data to use, and do a bit of styling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "95d749da-73c8-432a-9a23-85d37e1b6fb5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from random import randint\n",
-    "x = [randint(0, 100) for _ in range(100)]\n",
-    "y = [randint(300, 400) for _ in range(100)]\n",
-    "data = Data(label='data', x=x, y=y)\n",
-    "dc.append(data)\n",
-    "data.style.color = \"#0000ff\"\n",
-    "data.style.markersize = 6"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "92745ee0-25bf-455a-b022-045059fa8e19",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x2 = [randint(0, 100) for _ in range(100)]\n",
-    "y2 = [randint(300, 400) for _ in range(100)]\n",
-    "data2 = Data(label='data2', x=x2, y=y2)\n",
-    "dc.append(data2)\n",
-    "app.add_link(data, 'x', data2, 'x')\n",
-    "app.add_link(data, 'y', data2, 'y')\n",
-    "data.style.color = \"#0000ff\"\n",
-    "data.style.markersize = 6"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2c520488-472c-4c43-8f29-3ae91fe41c13",
-   "metadata": {},
-   "source": [
-    "### Create a Plotly Scatter Viewer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "cf42768e-6d31-4375-90ee-facd8d19800a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "29dfa73fb7554c97a66b58cd2cafee9d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "LayoutWidget(controls={'toolbar_selection_tools': BasicJupyterToolbar(template=Template(template='<template>\\n…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "scatter_viewer = app.new_data_viewer(\"plotly_scatter\", data=data)\n",
-    "# Alternatively, you can use the viewer class:\n",
-    "# viewer = app.new_data_viewer(PlotlyScatterView, data=data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "cbeeb763-daf0-4828-8535-f05f97ec856f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scatter_viewer.add_data(data2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "1355c76d-ad3d-4202-a08f-ee17b1facbd4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(PlotlyScatterLayerArtist for data, PlotlyScatterLayerArtist for data2)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scatter_viewer.layers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "143236d3-446b-4286-b24c-a9511204b970",
-   "metadata": {},
-   "source": [
-    "### Create a Plotly Histogram Viewer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "da0a70ee-f299-47cd-9eb0-9a634d983c02",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scatter_viewer.layers[1].state.zorder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "3e66be74-929f-4cfa-910c-9d3b65b4ac91",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7ea50b1df9e445f9a1411deb973b0a2d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "LayoutWidget(controls={'toolbar_selection_tools': BasicJupyterToolbar(template=Template(template='<template>\\n…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "histogram_viewer = app.new_data_viewer(\"plotly_histogram\", data=data)\n",
-    "# Alternatively, you can use the viewer class:\n",
-    "# viewer = app.new_data_viewer(PlotlyHistogramView, data=data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "26be6106-c61a-4d84-82e3-136ccfcee588",
-   "metadata": {},
-   "source": [
-    "You can adjust whether there are gaps between the bars and, if so, what fraction of the plot they occupy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "821b6397-80e2-443c-b6c3-50bf954abbf2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "histogram_viewer.state.gaps = True\n",
-    "histogram_viewer.state.gap_fraction = 0.15"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "588347a7-8f4d-4f47-9838-a4a2e4106b02",
-   "metadata": {},
-   "source": [
-    "### Dotplot Viewer\n",
-    "`glue-plotly` provides an alternate layer artist for the histogram viewer that displays data as a dotplot. While we don't offer a built-in viewer that uses this functionality, you can use the simple implementation below as a starting point if you're interested in using dotplots."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "id": "9e25e894-bab1-4b21-a744-dcc5f9bc435d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from echo import delay_callback\n",
-    "from glue.config import viewer_tool\n",
-    "from glue_plotly.viewers.common.tools import PlotlyHZoomMode\n",
-    "\n",
-    "@viewer_tool\n",
-    "class BinningHZoom(PlotlyHZoomMode):\n",
-    "    tool_id = \"plotly:binhzoom\"\n",
-    "\n",
-    "    def _on_selection(self, trace, points, selector):\n",
-    "        xmin, xmax = selector.xrange\n",
-    "        viewer_state = self.viewer.state\n",
-    "        with self.viewer.figure.batch_update(), delay_callback(viewer_state, 'x_min', 'x_max'):\n",
-    "            viewer_state.x_min = xmin\n",
-    "            viewer_state.x_max = xmax\n",
-    "        self.viewer.state.update_bins_to_view()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "4f9d9ffe-ec2e-4d7f-aa8b-8b2a7524b9c2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from glue_plotly.viewers.histogram.dotplot_layer_artist import PlotlyDotplotLayerArtist\n",
-    "\n",
-    "class DotplotViewer(PlotlyHistogramView):\n",
-    "\n",
-    "    tools = [\"plotly:binhzoom\"]\n",
-    "    _data_artist_cls = PlotlyDotplotLayerArtist\n",
-    "    _subset_artist_cls = PlotlyDotplotLayerArtist"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "id": "174cd945-0744-43dc-b23e-bcef244a0bff",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7c795aca0ca3468fabb86f8d090f4edf",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "LayoutWidget(controls={'toolbar_selection_tools': BasicJupyterToolbar(template=Template(template='<template>\\n…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dotplot_viewer = app.new_data_viewer(DotplotViewer, data=data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "id": "ed78d034-9ec2-4e56-a481-f7578f0f7e3a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "27.638810597519733 33.05031003382187\n",
-      "[30 31 29 31 30 33]\n",
-      "6\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Check that the dotplot is displaying the correct values\n",
-    "values = dotplot_viewer.layers[0].layer[dotplot_viewer.state.x_att]\n",
-    "mask = (values >= dotplot_viewer.state.hist_x_min) & (values <= dotplot_viewer.state.hist_x_max)\n",
-    "print(dotplot_viewer.state.hist_x_min, dotplot_viewer.state.hist_x_max)\n",
-    "print(values[mask])\n",
-    "print(len(values[mask]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5abe0321-b28a-4ff7-a19a-9093eb8efa93",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}