Adding Figures to Quickstart (#403)

* added plots to TAPE quickstart. Including a small routine for plotting multi-band light curves

* added new markdown cell for light curve searching

* Added brief doc on fetching the source table and plotting a light curve

* Made edits to quickstart tutorial by improving plots and using ens.to_timeseries() for light curve plotting

* final changes

* added matplotlib import

docs/gettingstarted/quickstart.ipynb

@@ -120,13 +120,68 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The result is a table of delta times (dts) and structure function (sf2) for each unique lightcurve (labeled by lc_id)."
+    "The result is a table of delta times (dts) and structure function (sf2) for each unique lightcurve (labeled by lc_id). We can now visualize our delta times versus the computed structure function for each unique object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import rcParams\n",
+    "\n",
+    "%matplotlib inline\n",
+    "%config InlineBackend.figure_format = \"retina\"\n",
+    "rcParams[\"savefig.dpi\"] = 550\n",
+    "rcParams[\"font.size\"] = 20\n",
+    "plt.rc(\"font\", family=\"serif\")\n",
+    "\n",
+    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(5, 4))\n",
+    "plt.scatter(result[\"dt\"], result[\"sf2\"], s=20, alpha=1, color=\"#353935\")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.ylabel(\"Log(SF) (mag)\")\n",
+    "plt.xlabel(\"Time Lag (days)\")\n",
+    "plt.ylim(1e-3, 1e1)\n",
+    "plt.xlim(0, 2e3)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "Finally, suppose we want to select the ID with the maximum sf2 value from the computed feature. Using the available `ens.to_timeseries()` that creates a TimeSeries object, we can access the light curve for the target ID."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_id = result.compute()[\"sf2\"].idxmax()[0]\n",
+    "lc = ens.to_timeseries(max_id)\n",
+    "lc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filter_r = lc.band == \"r\"  # select filter\n",
+    "\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "plt.errorbar(\n",
+    "    lc.time[filter_r], lc.flux[filter_r], lc.flux_err[filter_r], fmt=\"o\", color=\"red\", alpha=0.8, label=\"r\"\n",
+    ")\n",
+    "plt.minorticks_on()\n",
+    "plt.ylabel(\"Flux (mJy)\")\n",
+    "plt.xlabel(\"Time (MJD)\")\n",
+    "plt.legend(title=\"Band\", loc=\"upper left\")"
+   ]
   }
  ],
  "metadata": {
@@ -145,7 +200,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.10.13"
   },
   "vscode": {
    "interpreter": {