From 069d7ee4b22fb9473b39709991629eba00129aed Mon Sep 17 00:00:00 2001
From: wbalmer <williamobalmer@gmail.com>
Date: Fri, 30 Aug 2024 16:45:42 -0400
Subject: [PATCH] adding another example to docs

---
 docs/additional-examples.ipynb | 172 +++++++++++++++++++++++++++++++++
 docs/index.rst                 |   1 +
 tests/0602cc1.csv              |   4 +
 3 files changed, 177 insertions(+)
 create mode 100644 docs/additional-examples.ipynb
 create mode 100644 tests/0602cc1.csv

diff --git a/docs/additional-examples.ipynb b/docs/additional-examples.ipynb
new file mode 100644
index 0000000..c5158d7
--- /dev/null
+++ b/docs/additional-examples.ipynb
@@ -0,0 +1,172 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7eed1901",
+   "metadata": {},
+   "source": [
+    "# Additional examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09b2a5cd",
+   "metadata": {},
+   "source": [
+    "Following our analysis of the curious case of HD 131339 ``b\" in the tutorial, below are additional examples of published data that could be analyzed with `backtracks`. If you use the package in your work (or want to and need help), please reach out to us, and we can include your case here!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "63e0eeac-442a-4fe2-a2fe-9f3756165ade",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# start by importing the System object from backtracks:\n",
+    "from backtracks import System"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00426a32-29cd-41fe-9a9b-8a022c2c5850",
+   "metadata": {},
+   "source": [
+    "## Sanghi et al. (2024), CC1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0d42b6e-864f-4e74-81ce-9b9179146816",
+   "metadata": {},
+   "source": [
+    "[Sanghi et al. (2024)](https://ui.adsabs.harvard.edu/abs/2024arXiv240814268S/abstract) found a companion candidate around the M-dwarf 2MASS J06022455-1634494 using an optimized reference differential imaging library; they concluded that the candidate was a likely background source."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "761c1ee9-3a54-4df9-a04f-47fb80f2f716",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!wget https://raw.githubusercontent.com/wbalmer/backtracks/main/tests/0602cc1.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "67e29928-e01b-4919-8638-dcc4e27957bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[BACKTRACK INFO]: Resolved the target star '2MASS J06022455-1634494' in Simbad!\n",
+      "[BACKTRACK INFO]: Resolved target's Gaia ID from Simbad, Gaia DR3 2991362056431878912\n",
+      "INFO: Query finished. [astroquery.utils.tap.core]\n",
+      "[BACKTRACK INFO]: gathered Gaia DR3 data for 2MASS J06022455-1634494\n",
+      "   * Gaia source ID = 2991362056431878912\n",
+      "   * Reference epoch = 2016.0\n",
+      "   * RA = 90.6023 deg\n",
+      "   * Dec = -16.5807 deg\n",
+      "   * PM RA = -8.22 mas/yr\n",
+      "   * PM Dec = -67.49 mas/yr\n",
+      "   * Parallax = 24.87 mas\n",
+      "   * RV = -10.48 km/s\n",
+      "INFO: Query finished. [astroquery.utils.tap.core]\n",
+      "[BACKTRACK INFO]: gathered 3565 Gaia objects from the 0.5 sq. deg. nearby 2MASS J06022455-1634494\n",
+      "[BACKTRACK INFO]: Finished nearby background gaia statistics\n",
+      "[BACKTRACK INFO]: Queried distance prior parameters, L=1.60, alpha=0.38, beta=4.12\n",
+      "[BACKTRACK INFO]: Estimating candidate position if stationary in RA,Dec @ 2016.0 from observation #0\n",
+      "[BACKTRACK INFO]: Opened ephemeris file\n"
+     ]
+    }
+   ],
+   "source": [
+    "track = System(target_name=\"2MASS J06022455-1634494\",\n",
+    "               candidate_file=\"0602cc1.csv\",\n",
+    "               nearby_window=0.5,\n",
+    "               fileprefix='./')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a001f568-ac7f-4740-b6c7-6737f502819f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[BACKTRACK INFO]: Generating Stationary plot\n",
+      "[BACKTRACK INFO]: Stationary plot saved to ./\n"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 4800x2400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig_stationary = track.generate_stationary_plot(days_backward=1*365.,\n",
+    "                                                days_forward=2*365,\n",
+    "                                                step_size=10.,\n",
+    "                                                filepost='.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b21da56-5fa4-4dc9-9da7-3ec8f0c20519",
+   "metadata": {},
+   "source": [
+    "It seems like the stationary background track fits the available data well!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ffc8d23-326a-401a-a02a-2a2176509dca",
+   "metadata": {},
+   "source": [
+    "## more to come..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dde1a8d0-ec19-4161-b296-3ec86865204f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "backtracks",
+   "language": "python",
+   "name": "backtracks"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/index.rst b/docs/index.rst
index d248fb4..3876880 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -10,5 +10,6 @@ Documentation for *backtracks*
 
    installation
    tutorial.ipynb
+   additional-examples.ipynb
    modules
    about
diff --git a/tests/0602cc1.csv b/tests/0602cc1.csv
new file mode 100644
index 0000000..ccf12af
--- /dev/null
+++ b/tests/0602cc1.csv
@@ -0,0 +1,4 @@
+epoch,object,quant1,quant1_err,quant2,quant2_err,quant12_corr,quant_type,instrument
+57703,1,145,19,108.3,6.1,nan,seppa,NIRC2
+57768,1,179,11,103.8,4.4,nan,seppa,NIRC2
+58063,1,160,18,91.3,3.9,nan,seppa,NIRC2
\ No newline at end of file