Add join catalogs notebook (#481)

* Add joining catalogs notebook

* Remove kernel metadata

* Change differences to ratios

docs/tutorials.rst

@@ -14,6 +14,7 @@ An introduction to LSDB's core features and functionality
 
     Getting data into LSDB <tutorials/getting_data>
     Filtering large catalogs <tutorials/filtering_large_catalogs>
+    Joining catalogs <tutorials/join_catalogs>
     Exporting results <tutorials/exporting_results>
 
 Advanced Topics

docs/tutorials/join_catalogs.ipynb

@@ -0,0 +1,137 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Joining catalogs\n",
+    "\n",
+    "In this tutorial we join a small cone region of Gaia with Gaia Early Data Release 3 (EDR3) and compute the ratio between the distances given by their `parallax` and `r_med_geo` columns, respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import lsdb\n",
+    "from lsdb.core.search import ConeSearch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we load Gaia with its objects `source_id`, their positions and `parallax` columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gaia = lsdb.read_hats(\n",
+    "    \"https://data.lsdb.io/hats/gaia_dr3/gaia\",\n",
+    "    margin_cache=\"https://data.lsdb.io/hats/gaia_dr3/gaia_10arcs\",\n",
+    "    columns=[\"source_id\", \"ra\", \"dec\", \"parallax\"],\n",
+    "    search_filter=ConeSearch(ra=0, dec=0, radius_arcsec=10 * 3600),\n",
+    ")\n",
+    "gaia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will do the same with Gaia EDR3 but the distance column we will use is called `r_med_geo`, the median of the geometric distance estimate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gaia_edr3 = lsdb.read_hats(\n",
+    "    \"https://data.lsdb.io/hats/gaia_dr3/gaia_edr3_distances\",\n",
+    "    margin_cache=\"https://data.lsdb.io/hats/gaia_dr3/gaia_edr3_distances_10arcs\",\n",
+    "    columns=[\"source_id\", \"ra\", \"dec\", \"r_med_geo\"],\n",
+    "    search_filter=ConeSearch(ra=0, dec=0, radius_arcsec=10 * 3600),\n",
+    ")\n",
+    "gaia_edr3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are now able to join both catalogs on the `source_id` column, as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "joined = gaia.join(gaia_edr3, left_on=\"source_id\", right_on=\"source_id\")\n",
+    "joined"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's calculate a histogram with the ratio in catalog distances."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = (1e3 / joined[\"parallax_gaia\"]) / joined[\"r_med_geo_gaia_edr3_distances\"]\n",
+    "ratios = results.compute().to_numpy()\n",
+    "ratios"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.hist(ratios, bins=np.linspace(0.8, 1.2, 100))\n",
+    "plt.title(\"Histogram of Gaia distance / Gaia EDR3 distance\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "demo",
+   "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}