Analytics notebook example

.gitattributes

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 docs/images/** filter=lfs diff=lfs merge=lfs -text
 pan3d/utils/data/** filter=lfs diff=lfs merge=lfs -text
+examples/data/tas_Amon_ACCESS-ESM1-5_historical_r10i1p1f1_gn_185001-201412.nc filter=lfs diff=lfs merge=lfs -text

examples/jupyter/xarray-analytics.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cd15ae3c-235b-4c53-a32e-76e4e9a1dca2",
+   "metadata": {},
+   "source": [
+    "### Open Xarray data set with xCDAT for Analytics Explorer\n",
+    "\n",
+    "The following code open the xarray dataset with xCDAT and prepares it for processing with the Analytics Explorer from pan3D\n",
+    "\n",
+    "The data is a full resolution example used for [xCDAT tutorials](https://github.com/xCDAT/xcdat-data.git)\n",
+    "\n",
+    "After opening the dataset, we convert the unit of \"average surface temperature\" from Kelvin to Celsius."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5866dd3b-1dbd-4b3b-b8ba-b61aae2d71fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import the necessary packages, we only need xcdat to open local data file\n",
+    "import xcdat as xc\n",
+    "\n",
+    "# specify local data file\n",
+    "# The example xcdat datasets can be downloaded from\n",
+    "# https://github.com/xCDAT/xcdat-data\n",
+    "filepath = \"../data/tas_Amon_ACCESS-ESM1-5_historical_r10i1p1f1_gn_185001-201412.nc\"\n",
+    "\n",
+    "# open dataset\n",
+    "ds = xc.open_dataset(filepath).sel(time=slice(\"1980-01-01\", \"2005-01-01\"))\n",
+    "\n",
+    "# Unit adjust (-273.15, K to C)\n",
+    "ds[\"tas\"] = ds.tas - 273.15"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6eea4c25-3e81-4fb8-a61a-c5019b3972ea",
+   "metadata": {},
+   "source": [
+    "### Launch the explorer with the Xarray data set\n",
+    "\n",
+    "The following code configures the Analytics Explorer, which can later be used interactively for data analysis through plots generated using xCDAT and plotly.\n",
+    "The interaction is facilitated by the trame widget library [trame-plotly](https://github.com/Kitware/trame-plotly.git)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2aeb1cd-f058-4a77-a2e9-ecab0b2494ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import the installed pan3d explorer\n",
+    "from pan3d.explorers.analytics import AnalyticsExplorer\n",
+    "\n",
+    "# Instantiate the explorer and wait until it finishes setting up\n",
+    "explorer = AnalyticsExplorer(xarray=ds, server=\"esgf-slicer\")\n",
+    "await explorer.ui.ready\n",
+    "\n",
+    "# Launch the interactive explorer within jypyter notebook\n",
+    "explorer.ui"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ed417cf-5ba2-48ab-99e7-7f0974e624c2",
+   "metadata": {},
+   "source": [
+    "### Get the link to launch the explorer in the browser"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9cc244a4-2f42-4fc9-b70a-2d31ca6af913",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "explorer.ui.url"
+   ]
+  }
+ ],
+ "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": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}