examples/TKS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>PLEASE NOTE:</b> this software was initially intended for quick & easy use via command-line interface (CLI). However, I know some folks love their jupyter notebook so I made copies of the more important classes and features that are compatible with Ipython for demonstrative purposes. Most of the functions used here are saved to the `sortasurvey.notebook` script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sortasurvey.notebook import Survey, rank"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# $\\rm \\textbf{Step #1: Create your Survey}$\n",
    "<br>\n",
    "We will use the TESS-Keck Survey (TKS) as an example, which motivated a lot of this work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "TKS = Survey(verbose=True, path_sample='info/TKS_sample.csv', path_survey='info/survey_info.csv',\n",
    "             path_priority='info/high_priority.csv', path_ignore='info/no_no.csv', iter=1, \n",
    "             hours=10., nights=50., save=True, note='', notebook=True, progress=True, )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is showing all of the default keyword arguments for the `Survey` class, with the exceptions of `path_priority` and `path_ignore`. These are both defaulted to `None` but necessary for this example (and are actually really helpful to use once you get the hang of them).\n",
    "\n",
    "## *The two <i><u>key</u></i> pieces to your successful `Survey` are the <u>sample</u> and the <u>programs</u>!*\n",
    "\n",
    "So let's start with the sample. The sample to select targets from is set as the `Survey.sample` attribute (convenient, I know). For target selection of individual `Survey` programs, selection criteria must match the column headers provided in the sample in order for this to work properly (i.e. the script uses the `pandas.Dataframe.query` [module](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.query.html) if anyone is familiar with that).<br>\n",
    "\n",
    "So, let's take a look at what columns are available for target selection purposes!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['toi', 'tic', 'evol', 'source', 'disp', 'photo_vetting', 'spec_vetting', 'ao_vetting', 'ra', 'dec', 'vmag', 'jmag', 'nobs', 'template', 'nobs_goal', 'tot_time', 'rem_nobs', 'rem_time', 'r_s', 'teff', 'teff_err', 't_eff', 'f_eh', 'ast_det_t', 'ruwe', 'rp', 'rp_err', 'mp', 'period', 'period_err', 'a_to_R', 'sinc', 'rt_5sig', 'drop', 'finish', 'npl', 'select_DG', 'in_other_programs', 'n_select', 'priority', 'in_SC1A', 'in_SC1B', 'in_SC1C', 'in_SC1D', 'in_SC1E', 'in_SC2A', 'in_SC2Bi', 'in_SC2Bii', 'in_SC2C', 'in_SC3', 'in_SC4', 'in_TOA', 'in_TOB', 'TSM', 'SC3_bin_rank']\n"
     ]
    }
   ],
   "source": [
    "candidates = TKS.sample\n",
    "\n",
    "print(candidates.columns.values.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the `Survey` sample, the `Survey` program information is stored to (yup, you guessed it) `Survey.programs`. This is also a pandas.DataFrame with program names as the index to make it easier to access specific information for a given program.\n",
    "\n",
    "### <b>The complete list of TKS programs is:</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['SC1A', 'SC1B', 'SC1C', 'SC1D', 'SC1E', 'SC2A', 'SC2Bi', 'SC2Bii', 'SC2C', 'SC3', 'SC4', 'TOA', 'TOB']\n"
     ]
    }
   ],
   "source": [
    "print(TKS.programs.index.values.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------\n",
    "## A crash course into the TESS-Keck Survey (TKS) science goals:\n",
    "\n",
    "<b>SC1A \\[Fulton Gap\\]:</b> Probing compositions across the planet radius gap to constrain the physical mechanism(s) causing the bimodal planet radius distribution <br>\n",
    "<b>SC1B \\[Stellar Flux \\& Gaseous Envelopes\\]:</b> Analyzing the diversity of gaseous envelopes and their dependence on properties like stellar mass, incident flux, activity level, and other properties <br>\n",
    "<b>SC1C \\[USPs\\]:</b> Using ultra-short period planet compositions as a window into the refractory cores of small planets <br>\n",
    "<b>SC1D \\[Habitable Zone\\]:</b> Identifying and characterizing planets orbiting in (or near) the habitable zone of of their host star <br>\n",
    "<b>SC1E \\[Planet-Star Correlations\\]:</b> Exploring dependencies of bulk planet properties (densities) with different stellar properties i.e. $M_{\\star}$ <br>\n",
    "<b>SC2A \\[Distant Giants\\]:</b> Understanding the occurrence and connection between close-in small planets and distant giant planets <br>\n",
    "<b>SC2Bi \\[Eccentricities\\]:</b> Characterizing the eccentricities of sub-Jovian planets to elucidate the possible formation and/or evolution pathways for dynamically 'hot' planets<br>\n",
    "<b>SC2Bii \\[Obliquities\\]:</b> Measuring spin-orbit (mis)alignments of previously unexplored parameter spaces (i.e. young stars) to trace formation histories and past dynamical interactions <br>\n",
    "<b>SC2C \\[Multis\\]:</b> Examining the diversity and/or uniformity of properties for planets in multi-planet systems <br>\n",
    "<b>SC3 \\[Atmospheres\\]:</b> Identifying intriguing planets amenable to atmospheric characterization using transmission and/or eclipse spectroscopy <br>\n",
    "<b>SC4 \\[Evolved\\]:</b> Investigating the role and degree to which stellar evolution plays in shaping post-main sequence planetary systems <br>\n",
    "<b>TOA \\[Stellar Properties\\]:</b> Homogeneously determining fundamental stellar properties derived from spectroscopy, asteroseismology, and astrometry <br>\n",
    "<b>TOB \\[Activity\\]:</b> Studying the relationship between Doppler jitter with various stellar astrophysical phenomena <br>\n",
    "\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a basic understanding of what these science keys mean, let's take a look at some examples. We'll start with the TKS multis (SC2C) program criteria:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name                                                            SC2C\n",
      "method                                    hires-nobs=100-counts=ramp\n",
      "filter             photo_vetting == 'passed' and spec_vetting != ...\n",
      "prioritize_by                                     [npl, actual_cost]\n",
      "ascending_by                                           [False, True]\n",
      "remaining_hours                                               47.619\n",
      "n_maximum                                                          4\n",
      "total_time                                                    47.619\n",
      "high_priority                                                     []\n",
      "n_targets_left                                                     4\n",
      "pick_number                                                        0\n",
      "Name: SC2C, dtype: object\n"
     ]
    }
   ],
   "source": [
    "case = 'SC2C'\n",
    "\n",
    "print(TKS.programs.loc[case])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multi-planet systems are more challenging to characterize through RVs thus requiring more observations than the default ($n=60$) observations for other TKS programs (e.g., shown above with $nobs=100$). SC2C is actually an excellent example since it also has more than one prioritization metric, which first sorts by the planet multiplicity. Then for systems with the same number of planets, the selected target will be the cheapest target at that specific iteration (which may not necessarily be the brightest, but also could be a system that is shared amongst several TKS science programs). \n",
    "\n",
    "<br>\n",
    "\n",
    "SC2A is probing more long-term characteristics in the system (looking for distant companions) and therefore requires less precision and lower-cadence observations. You will see the entirely different observing `method` printed below. Additionally, SC2A gets to use borrowed time from another program that doesn't require all of its allocation. Therefore, you'll see that even though it has the same allocation as SC2C, the total time is incremented by +14.1 hours (which can be done by specifying this in the `info/survey_info.csv`, under the `remaining_hours` column)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name                                                            SC2A\n",
      "method                                       hires-nobs=15-counts=60\n",
      "filter             select_DG == 1 and toi != 1469.01 and toi != 1...\n",
      "prioritize_by                                                 [vmag]\n",
      "ascending_by                                                  [True]\n",
      "remaining_hours                                               61.719\n",
      "n_maximum                                                         50\n",
      "total_time                                                    61.719\n",
      "high_priority      [1469.01, 1773.01, 1611.01, 509.01, 1435.01, 1...\n",
      "n_targets_left                                                    50\n",
      "pick_number                                                        0\n",
      "Name: SC2A, dtype: object\n"
     ]
    }
   ],
   "source": [
    "case = 'SC2A'\n",
    "\n",
    "print(TKS.programs.loc[case])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distant giants science case (SC2A) is also special and dissimilar to other TKS science cases for a couple other reasons:\n",
    "1) it is the only TKS science that does not care about discounts for shared targets, as it is a magnitude-limited 3-year survey. You can see that the prioritization metric for this program only cares about the visual magnitude.\n",
    "\n",
    "2) the target selection process for SC2A was very complex s.t. it could not be automated through the same means as the other programs (via `filter`). Therefore, this program is a great demonstration for how to \"force\" your target list, which essentially can be done by providing the entire ranked list as `high_priority` targets. High priority targets are hand-selected in the order in which they are provided and therefore does not care about or know prioritization metrics/preferences.\n",
    "\n",
    "See the long list of high priority targets for SC2A below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1469.01, 1773.01, 1611.01, 509.01, 1435.01, 1742.01, 1339.01, 1247.01, 1437.01, 1797.01, 1471.01, 1751.01, 1716.01, 1710.01, 1451.01, 1723.01, 1824.01, 1416.01, 1255.01, 1691.01, 1669.01, 1794.01, 1288.01, 1411.01, 1422.01, 1443.01, 1279.01, 1823.01, 1758.01, 1444.01, 1438.01, 1174.01, 1180.01, 1173.01, 1706.01, 1249.01, 1410.01, 1194.01, 1472.01, 1694.01, 1235.01, 465.01, 1269.01, 1246.01, 1775.01, 1272.01, 1248.01, 1753.01, 1244.01, 1759.01]\n"
     ]
    }
   ],
   "source": [
    "print(TKS.programs.loc['SC2A','high_priority'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Essentially the way this is set up now, each science case in the survey needs:\n",
    "\n",
    "1) an observing strategy (via `method`) <br>\n",
    "2) selection criteria (via `filter`) <br>\n",
    "3) prioritization metrics (via `prioritize_by` and `ascending_by`) <br>\n",
    "4) fractional allocation <br>\n",
    "\n",
    "#### As well as the optional:\n",
    "\n",
    "5) maximum number of selections (via `n_maximum`) <br>\n",
    "6) additional allocation (via `remaining_hours`) <br>\n",
    "7) high priority targets (in the `info/high_priority.csv`) <br>\n",
    "8) targets to ignore (in the `info/no_no.csv`) <br>\n",
    "\n",
    "<b>NOTE: a program's `remaining_hours` and `pick_number` are updated during the target selection process, providing ways to identify when the selection process would otherwise crash (i.e. if a selected program cannot afford their selected target or if the program runs out of relevant selections, the latter of which happened in the early days of TESS for the habitable zone science case).</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------\n",
    "# $\\rm \\textbf{Step #2: Rank your Targets}$\n",
    "<br>\n",
    "Well not necessarily <b><i>you</i></b> per se, but execute the script so that your computer can select your targets for you!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, so now that all of the boring stuff is out of the way, let's select some targets...\n",
    "\n",
    "For each iteration, the `Survey.sample` and `Survey.programs` are copies to `Survey.candidates` and `Survey.sciences` respectively, which are the survey attributes that get modified during the target selection (just an FYI) in order to preserve the original survey information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " -----------------------------\n",
      " -- prioritization starting --\n",
      " -----------------------------\n",
      "\n",
      "   - loading sample and survey science information\n",
      "   - 785 targets make the standard survey cuts\n",
      "   - 242 have also passed various vetting steps\n",
      "   - ranking algorithm initialized using 50.0 nights (10.0 hr/n)\n",
      "   - algorithm took 44 seconds to run\n",
      "   - 86 targets were selected, containing a total of 103 planets\n",
      "   - Making data products, including:\n",
      "     - a copy of the updated sample\n",
      "     - algorithm history (via ranking steps)\n",
      "     - the final prioritized list (via observing priorities)\n",
      "     - final costs saved\n",
      "     - program overlap\n",
      "     - txt file w/ run info\n",
      "\n",
      " ------------------------------\n",
      " ----- process - complete -----\n",
      " ------------------------------\n",
      "\n",
      "Thu 06/10/21 03:20PM\n",
      "\n",
      "Out of the 86 total targets:\n",
      "  - SC1A has 31 targets\n",
      "  - SC1B has 30 targets\n",
      "  - SC1C has 6 targets\n",
      "  - SC1D has 4 targets\n",
      "  - SC1E has 4 targets\n",
      "  - SC2A has 44 targets\n",
      "  - SC2Bi has 5 targets\n",
      "  - SC2Bii has 2 targets\n",
      "  - SC2C has 4 targets\n",
      "  - SC3 has 21 targets\n",
      "  - SC4 has 16 targets\n",
      "  - TOA has 86 targets\n",
      "  - TOB has 8 targets\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rank(TKS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, so let's decompress. I'm not sure if this was mentioned yet but there is a feature in the software to include archival data and/or existing observations, which was folded in for this survey. <b>To summarize: Using 50 Keck/HIRES nights (assuming 10 hours per night) and existing data, a total of 86 targets and 103 planets were selected.</b> This was all done in under a minute!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------\n",
    "# $\\rm \\textbf{Step #3: Peep your Results}$\n",
    "### $\\rm aka\\,the\\, data\\, products \\, \\& \\, what \\, they \\, mean$\n",
    "$\\rm [this \\,is\\, all\\, conveniently\\, saved\\, to\\, the\\, Survey\\, class\\, object \\, for \\,you]$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $\\rm \\textbf{Data product #1: the final, prioritized (selected) target list}$\n",
    "\n",
    "(via `args.outdir/observing_priorities.csv`) <br>\n",
    "\n",
    "Well let's start with the reason you came here for, a survey sample. This synthesizes all steps of the selection process into a single, prioritized list and is saved to `Survey.observed` (which should really be \"to-be-observed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>overall_priority</th>\n",
       "      <th>tic</th>\n",
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       "      <td>[SC1D, SC3, SC1E]</td>\n",
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       "      <td>3</td>\n",
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       "      <th>4</th>\n",
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       "      <td>[TOB, SC1C, SC1E]</td>\n",
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       "   overall_priority        tic   toi                       programs\n",
       "0                 1  269701147  1339  [SC3, SC2A, SC1A, SC1B, SC2C]\n",
       "1                 2  441765914  2088              [SC1D, SC3, SC1E]\n",
       "2                 3  317548889   480              [SC4, SC1A, SC1B]\n",
       "3                 4  199444169  1410        [SC1B, SC1A, SC2A, SC3]\n",
       "4                 5  154383539  1436              [TOB, SC1C, SC1E]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TKS.observed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $\\rm \\textbf{Data product #2: the entire selection process history}$\n",
    "\n",
    "(via `args.outdir/ranking_steps.csv`) <br>\n",
    "\n",
    "This is actually my most favorite output, which was initially intended for debugging purposes but was so useful that it ended up sticking around. This data product shows how the target selection happened on a step-by-step bases, but only records when selections were made (and therefore doesn't show when programs could not afford a certain target). For me, it's fun to sort by the program column, where you can see how individual programs made their selections. 12/10 recommend playing around with this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "      <td>SC3</td>\n",
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       "      <td>23.810</td>\n",
       "      <td>47.619</td>\n",
       "      <td>61.719</td>\n",
       "      <td>33.519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.619</td>\n",
       "      <td>46.702</td>\n",
       "      <td>47.619</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.619</td>\n",
       "      <td>499.083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SC1D</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>441765914.0</td>\n",
       "      <td>2088.01</td>\n",
       "      <td>47.619</td>\n",
       "      <td>47.619</td>\n",
       "      <td>47.619</td>\n",
       "      <td>7.676</td>\n",
       "      <td>47.619</td>\n",
       "      <td>61.719</td>\n",
       "      <td>33.519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.619</td>\n",
       "      <td>46.702</td>\n",
       "      <td>47.619</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.619</td>\n",
       "      <td>482.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SC4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>317548889.0</td>\n",
       "      <td>480.01</td>\n",
       "      <td>47.619</td>\n",
       "      <td>47.619</td>\n",
       "      <td>47.619</td>\n",
       "      <td>7.676</td>\n",
       "      <td>47.619</td>\n",
       "      <td>61.719</td>\n",
       "      <td>33.519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.619</td>\n",
       "      <td>46.702</td>\n",
       "      <td>45.869</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.619</td>\n",
       "      <td>481.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SC3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>199444169.0</td>\n",
       "      <td>1410.01</td>\n",
       "      <td>47.619</td>\n",
       "      <td>47.619</td>\n",
       "      <td>47.619</td>\n",
       "      <td>7.676</td>\n",
       "      <td>47.619</td>\n",
       "      <td>61.719</td>\n",
       "      <td>33.519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.619</td>\n",
       "      <td>39.271</td>\n",
       "      <td>45.869</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.619</td>\n",
       "      <td>473.769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  program  program_pick  overall_priority          tic      toi    SC1A  \\\n",
       "0      --           0.0               0.0          0.0     0.00  47.619   \n",
       "1     SC3           1.0               1.0  269701147.0  1339.01  47.619   \n",
       "2    SC1D           1.0               2.0  441765914.0  2088.01  47.619   \n",
       "3     SC4           1.0               3.0  317548889.0   480.01  47.619   \n",
       "4     SC3           2.0               4.0  199444169.0  1410.01  47.619   \n",
       "\n",
       "     SC1B    SC1C    SC1D    SC1E    SC2A   SC2Bi  SC2Bii    SC2C     SC3  \\\n",
       "0  47.619  47.619  23.810  47.619  61.719  33.519     0.0  47.619  47.619   \n",
       "1  47.619  47.619  23.810  47.619  61.719  33.519     0.0  47.619  46.702   \n",
       "2  47.619  47.619   7.676  47.619  61.719  33.519     0.0  47.619  46.702   \n",
       "3  47.619  47.619   7.676  47.619  61.719  33.519     0.0  47.619  46.702   \n",
       "4  47.619  47.619   7.676  47.619  61.719  33.519     0.0  47.619  39.271   \n",
       "\n",
       "      SC4  TOA     TOB  total_time  \n",
       "0  47.619  0.0  47.619     500.000  \n",
       "1  47.619  0.0  47.619     499.083  \n",
       "2  47.619  0.0  47.619     482.950  \n",
       "3  45.869  0.0  47.619     481.200  \n",
       "4  45.869  0.0  47.619     473.769  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TKS.ranking_steps.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $\\rm \\textbf{Data product #3: the updated sample}$\n",
    "\n",
    "(via `args.outdir/path_sample_final.csv`) <br>\n",
    "\n",
    "The updated sample is only a subset of the original sample, which was filtered on basic survey cuts. For TKS, the main requirements depended on Keck observability ($\\delta>-30$) and the Gaia RUWE metric (RUWE<2). It also has some new, perhaps important columns that you would like to know about. For starters, it has added columns for all survey programs, which are booleans for if a target is in a given program or not (this is an easy way to filter out which targets were selected). The same overall priority from the prioritized list discussed above is also copied over here, to keep everything in one place."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>toi</th>\n",
       "      <th>tic</th>\n",
       "      <th>disp</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>vmag</th>\n",
       "      <th>evol</th>\n",
       "      <th>r_s</th>\n",
       "      <th>t_eff</th>\n",
       "      <th>rp</th>\n",
       "      <th>period</th>\n",
       "      <th>sinc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>103.01</td>\n",
       "      <td>336732616</td>\n",
       "      <td>KP</td>\n",
       "      <td>312.4574</td>\n",
       "      <td>-24.4288</td>\n",
       "      <td>12.433</td>\n",
       "      <td>MS</td>\n",
       "      <td>1.28</td>\n",
       "      <td>6371.0</td>\n",
       "      <td>14.58</td>\n",
       "      <td>3.547854</td>\n",
       "      <td>1212.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>108.01</td>\n",
       "      <td>289793076</td>\n",
       "      <td>KP</td>\n",
       "      <td>316.9615</td>\n",
       "      <td>-26.0967</td>\n",
       "      <td>13.911</td>\n",
       "      <td>MS</td>\n",
       "      <td>0.81</td>\n",
       "      <td>5445.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>3.043839</td>\n",
       "      <td>400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>109.01</td>\n",
       "      <td>29344935</td>\n",
       "      <td>KP</td>\n",
       "      <td>313.2155</td>\n",
       "      <td>-25.6873</td>\n",
       "      <td>14.052</td>\n",
       "      <td>MS</td>\n",
       "      <td>0.98</td>\n",
       "      <td>5361.0</td>\n",
       "      <td>11.66</td>\n",
       "      <td>2.766737</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>123.01</td>\n",
       "      <td>290131778</td>\n",
       "      <td>P</td>\n",
       "      <td>319.6994</td>\n",
       "      <td>-26.6161</td>\n",
       "      <td>8.324</td>\n",
       "      <td>MS</td>\n",
       "      <td>2.62</td>\n",
       "      <td>6356.0</td>\n",
       "      <td>7.72</td>\n",
       "      <td>3.308754</td>\n",
       "      <td>1078.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>143.01</td>\n",
       "      <td>25375553</td>\n",
       "      <td>KP</td>\n",
       "      <td>328.7677</td>\n",
       "      <td>-22.6126</td>\n",
       "      <td>10.251</td>\n",
       "      <td>MS</td>\n",
       "      <td>1.95</td>\n",
       "      <td>6510.0</td>\n",
       "      <td>17.63</td>\n",
       "      <td>2.310894</td>\n",
       "      <td>4218.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      toi        tic disp        ra      dec    vmag evol   r_s   t_eff  \\\n",
       "0  103.01  336732616   KP  312.4574 -24.4288  12.433   MS  1.28  6371.0   \n",
       "1  108.01  289793076   KP  316.9615 -26.0967  13.911   MS  0.81  5445.0   \n",
       "2  109.01   29344935   KP  313.2155 -25.6873  14.052   MS  0.98  5361.0   \n",
       "3  123.01  290131778    P  319.6994 -26.6161   8.324   MS  2.62  6356.0   \n",
       "4  143.01   25375553   KP  328.7677 -22.6126  10.251   MS  1.95  6510.0   \n",
       "\n",
       "      rp    period    sinc  \n",
       "0  14.58  3.547854  1212.0  \n",
       "1  14.00  3.043839   400.0  \n",
       "2  11.66  2.766737    -1.0  \n",
       "3   7.72  3.308754  1078.0  \n",
       "4  17.63  2.310894  4218.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TKS.final[['toi','tic','disp','ra','dec','vmag','evol','r_s','t_eff','rp','period','sinc']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see which targets were selected for a given program. We haven't talked about the evolved stars (SC4) science case yet, let's take a look at that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>toi</th>\n",
       "      <th>tic</th>\n",
       "      <th>evol</th>\n",
       "      <th>disp</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>vmag</th>\n",
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       "      <th>sinc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>329.01</td>\n",
       "      <td>169765334</td>\n",
       "      <td>SG</td>\n",
       "      <td>LEPC</td>\n",
       "      <td>351.320900</td>\n",
       "      <td>-15.634700</td>\n",
       "      <td>11.262</td>\n",
       "      <td>1.61</td>\n",
       "      <td>5560.0</td>\n",
       "      <td>10.840000</td>\n",
       "      <td>5.701800</td>\n",
       "      <td>3576.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>480.01</td>\n",
       "      <td>317548889</td>\n",
       "      <td>MS</td>\n",
       "      <td>CPC</td>\n",
       "      <td>88.378700</td>\n",
       "      <td>-16.265000</td>\n",
       "      <td>7.280</td>\n",
       "      <td>1.50</td>\n",
       "      <td>6212.0</td>\n",
       "      <td>3.080000</td>\n",
       "      <td>6.866196</td>\n",
       "      <td>368.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>1181.01</td>\n",
       "      <td>229510866</td>\n",
       "      <td>SG</td>\n",
       "      <td>VPC+</td>\n",
       "      <td>297.215900</td>\n",
       "      <td>64.354400</td>\n",
       "      <td>10.582</td>\n",
       "      <td>1.93</td>\n",
       "      <td>6122.0</td>\n",
       "      <td>16.660000</td>\n",
       "      <td>2.103240</td>\n",
       "      <td>4026.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>1294.01</td>\n",
       "      <td>219015370</td>\n",
       "      <td>SG</td>\n",
       "      <td>VPC+</td>\n",
       "      <td>223.092900</td>\n",
       "      <td>70.476600</td>\n",
       "      <td>11.314</td>\n",
       "      <td>1.58</td>\n",
       "      <td>5714.0</td>\n",
       "      <td>9.300000</td>\n",
       "      <td>3.915000</td>\n",
       "      <td>325.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>1296.01</td>\n",
       "      <td>219854185</td>\n",
       "      <td>SG</td>\n",
       "      <td>VPC</td>\n",
       "      <td>256.770900</td>\n",
       "      <td>70.238500</td>\n",
       "      <td>11.370</td>\n",
       "      <td>1.75</td>\n",
       "      <td>5494.0</td>\n",
       "      <td>14.860000</td>\n",
       "      <td>3.944420</td>\n",
       "      <td>843.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>1298.01</td>\n",
       "      <td>237104103</td>\n",
       "      <td>SG</td>\n",
       "      <td>VPC</td>\n",
       "      <td>241.323400</td>\n",
       "      <td>70.189900</td>\n",
       "      <td>11.889</td>\n",
       "      <td>1.49</td>\n",
       "      <td>5731.0</td>\n",
       "      <td>10.150000</td>\n",
       "      <td>4.537150</td>\n",
       "      <td>509.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>1439.01</td>\n",
       "      <td>232982558</td>\n",
       "      <td>SG</td>\n",
       "      <td>PC</td>\n",
       "      <td>241.763900</td>\n",
       "      <td>67.877700</td>\n",
       "      <td>10.550</td>\n",
       "      <td>1.62</td>\n",
       "      <td>5873.0</td>\n",
       "      <td>3.430000</td>\n",
       "      <td>27.643040</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>1601.01</td>\n",
       "      <td>139375960</td>\n",
       "      <td>SG</td>\n",
       "      <td>VPC?</td>\n",
       "      <td>38.361400</td>\n",
       "      <td>41.013400</td>\n",
       "      <td>10.660</td>\n",
       "      <td>2.22</td>\n",
       "      <td>5917.0</td>\n",
       "      <td>14.610000</td>\n",
       "      <td>5.331550</td>\n",
       "      <td>1071.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>1736.01</td>\n",
       "      <td>408618999</td>\n",
       "      <td>SG</td>\n",
       "      <td>PC</td>\n",
       "      <td>43.435000</td>\n",
       "      <td>69.101400</td>\n",
       "      <td>8.953</td>\n",
       "      <td>1.41</td>\n",
       "      <td>5656.0</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>7.073490</td>\n",
       "      <td>201.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>1836.01</td>\n",
       "      <td>207468071</td>\n",
       "      <td>SG</td>\n",
       "      <td>PC</td>\n",
       "      <td>245.908200</td>\n",
       "      <td>54.689800</td>\n",
       "      <td>9.765</td>\n",
       "      <td>1.62</td>\n",
       "      <td>6351.0</td>\n",
       "      <td>7.880000</td>\n",
       "      <td>20.381840</td>\n",
       "      <td>108.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>1842.01</td>\n",
       "      <td>404505029</td>\n",
       "      <td>SG</td>\n",
       "      <td>VPC</td>\n",
       "      <td>201.962800</td>\n",
       "      <td>9.030700</td>\n",
       "      <td>9.815</td>\n",
       "      <td>2.05</td>\n",
       "      <td>6115.0</td>\n",
       "      <td>12.680000</td>\n",
       "      <td>19.148095</td>\n",
       "      <td>243.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>1898.01</td>\n",
       "      <td>91987762</td>\n",
       "      <td>SG</td>\n",
       "      <td>STPC</td>\n",
       "      <td>144.555600</td>\n",
       "      <td>23.546900</td>\n",
       "      <td>7.870</td>\n",
       "      <td>1.61</td>\n",
       "      <td>6303.0</td>\n",
       "      <td>7.160000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>201.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>2019.01</td>\n",
       "      <td>159781361</td>\n",
       "      <td>SG</td>\n",
       "      <td>PC</td>\n",
       "      <td>234.431700</td>\n",
       "      <td>48.955400</td>\n",
       "      <td>10.263</td>\n",
       "      <td>1.80</td>\n",
       "      <td>5588.0</td>\n",
       "      <td>6.090000</td>\n",
       "      <td>15.347453</td>\n",
       "      <td>194.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>2045.01</td>\n",
       "      <td>347013211</td>\n",
       "      <td>SG</td>\n",
       "      <td>PC</td>\n",
       "      <td>1.119100</td>\n",
       "      <td>54.934500</td>\n",
       "      <td>11.304</td>\n",
       "      <td>1.72</td>\n",
       "      <td>6125.0</td>\n",
       "      <td>12.970000</td>\n",
       "      <td>9.077880</td>\n",
       "      <td>535.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>2114.01</td>\n",
       "      <td>9828416</td>\n",
       "      <td>SG</td>\n",
       "      <td>PC</td>\n",
       "      <td>261.096400</td>\n",
       "      <td>33.205100</td>\n",
       "      <td>10.265</td>\n",
       "      <td>2.10</td>\n",
       "      <td>6382.0</td>\n",
       "      <td>13.870000</td>\n",
       "      <td>6.210750</td>\n",
       "      <td>528.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>2145.01</td>\n",
       "      <td>88992642</td>\n",
       "      <td>SG</td>\n",
       "      <td>VPC?</td>\n",
       "      <td>263.758112</td>\n",
       "      <td>40.695057</td>\n",
       "      <td>9.070</td>\n",
       "      <td>2.78</td>\n",
       "      <td>6202.0</td>\n",
       "      <td>12.410589</td>\n",
       "      <td>10.260773</td>\n",
       "      <td>1067.375334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         toi        tic evol  disp          ra        dec    vmag   r_s  \\\n",
       "38    329.01  169765334   SG  LEPC  351.320900 -15.634700  11.262  1.61   \n",
       "81    480.01  317548889   MS   CPC   88.378700 -16.265000   7.280  1.50   \n",
       "252  1181.01  229510866   SG  VPC+  297.215900  64.354400  10.582  1.93   \n",
       "300  1294.01  219015370   SG  VPC+  223.092900  70.476600  11.314  1.58   \n",
       "302  1296.01  219854185   SG   VPC  256.770900  70.238500  11.370  1.75   \n",
       "303  1298.01  237104103   SG   VPC  241.323400  70.189900  11.889  1.49   \n",
       "372  1439.01  232982558   SG    PC  241.763900  67.877700  10.550  1.62   \n",
       "466  1601.01  139375960   SG  VPC?   38.361400  41.013400  10.660  2.22   \n",
       "558  1736.01  408618999   SG    PC   43.435000  69.101400   8.953  1.41   \n",
       "648  1836.01  207468071   SG    PC  245.908200  54.689800   9.765  1.62   \n",
       "653  1842.01  404505029   SG   VPC  201.962800   9.030700   9.815  2.05   \n",
       "688  1898.01   91987762   SG  STPC  144.555600  23.546900   7.870  1.61   \n",
       "718  2019.01  159781361   SG    PC  234.431700  48.955400  10.263  1.80   \n",
       "735  2045.01  347013211   SG    PC    1.119100  54.934500  11.304  1.72   \n",
       "798  2114.01    9828416   SG    PC  261.096400  33.205100  10.265  2.10   \n",
       "826  2145.01   88992642   SG  VPC?  263.758112  40.695057   9.070  2.78   \n",
       "\n",
       "      t_eff         rp     period         sinc  \n",
       "38   5560.0  10.840000   5.701800  3576.000000  \n",
       "81   6212.0   3.080000   6.866196   368.000000  \n",
       "252  6122.0  16.660000   2.103240  4026.000000  \n",
       "300  5714.0   9.300000   3.915000   325.000000  \n",
       "302  5494.0  14.860000   3.944420   843.000000  \n",
       "303  5731.0  10.150000   4.537150   509.000000  \n",
       "372  5873.0   3.430000  27.643040    39.000000  \n",
       "466  5917.0  14.610000   5.331550  1071.000000  \n",
       "558  5656.0   2.740000   7.073490   201.000000  \n",
       "648  6351.0   7.880000  20.381840   108.000000  \n",
       "653  6115.0  12.680000  19.148095   243.000000  \n",
       "688  6303.0   7.160000        NaN   201.000000  \n",
       "718  5588.0   6.090000  15.347453   194.000000  \n",
       "735  6125.0  12.970000   9.077880   535.000000  \n",
       "798  6382.0  13.870000   6.210750   528.000000  \n",
       "826  6202.0  12.410589  10.260773  1067.375334  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "case = 'SC4'    # TKS evolved\n",
    "df = TKS.final[TKS.final['in_%s'%case] == 1]\n",
    "\n",
    "df[['toi','tic','evol','disp','ra','dec','vmag','r_s','t_eff','rp','period','sinc']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, with the exception of one asteroseismic candidate, all target hosts are subgiant stars (i.e. `evol == SG`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $\\rm \\textbf{Data product #4: Survey program overlap}$\n",
    "\n",
    "(via `outdir/program_overlap.csv`) <br>\n",
    "\n",
    "It's exactly how it sounds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tic</th>\n",
       "      <th>toi</th>\n",
       "      <th>priority</th>\n",
       "      <th>in_SC1A</th>\n",
       "      <th>in_SC1B</th>\n",
       "      <th>in_SC1C</th>\n",
       "      <th>in_SC1D</th>\n",
       "      <th>in_SC1E</th>\n",
       "      <th>in_SC2A</th>\n",
       "      <th>in_SC2Bi</th>\n",
       "      <th>in_SC2Bii</th>\n",
       "      <th>in_SC2C</th>\n",
       "      <th>in_SC3</th>\n",
       "      <th>in_SC4</th>\n",
       "      <th>in_TOA</th>\n",
       "      <th>in_TOB</th>\n",
       "      <th>total_programs</th>\n",
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       "      <th>0</th>\n",
       "      <td>269701147</td>\n",
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       "      <td>5</td>\n",
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       "      <th>1</th>\n",
       "      <td>441765914</td>\n",
       "      <td>2088</td>\n",
       "      <td>2</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "      <td>-</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>317548889</td>\n",
       "      <td>480</td>\n",
       "      <td>3</td>\n",
       "      <td>X</td>\n",
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       "      <th>3</th>\n",
       "      <td>199444169</td>\n",
       "      <td>1410</td>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>154383539</td>\n",
       "      <td>1436</td>\n",
       "      <td>5</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>X</td>\n",
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       "      <td>3</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         tic   toi priority in_SC1A in_SC1B in_SC1C in_SC1D in_SC1E in_SC2A  \\\n",
       "0  269701147  1339        1       X       X       -       -       -       X   \n",
       "1  441765914  2088        2       -       -       -       X       X       -   \n",
       "2  317548889   480        3       X       X       -       -       -       -   \n",
       "3  199444169  1410        4       X       X       -       -       -       X   \n",
       "4  154383539  1436        5       -       -       X       -       X       -   \n",
       "\n",
       "  in_SC2Bi in_SC2Bii in_SC2C in_SC3 in_SC4 in_TOA in_TOB total_programs  \n",
       "0        -         -       X      X      -      -      -              5  \n",
       "1        -         -       -      X      -      -      -              3  \n",
       "2        -         -       -      -      X      -      -              3  \n",
       "3        -         -       -      X      -      -      -              4  \n",
       "4        -         -       -      -      -      -      X              3  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TKS.overlap.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\rm We \\;can\\; also \\;check \\;the \\; amount\\; of\\; overlap\\; for\\; targets \\; selected\\; from\\; a\\; given\\; program.$ <br>\n",
    "$\\rm Let's \\; use\\; SC2A\\; (Distant \\; Giants) \\; as \\; an\\; example:$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 picked targets had large overlap (>= 4 programs) with other programs\n"
     ]
    }
   ],
   "source": [
    "case = 'SC2A'\n",
    "\n",
    "program = TKS.overlap.query(\"in_%s == 'X'\"%case)\n",
    "\n",
    "# how many have 4 or more programs?\n",
    "query = program.query('total_programs >= 4')\n",
    "print('%d picked targets had large overlap (>= 4 programs) with other programs'%len(query))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $\\rm \\textbf{Data product #5: the final costs per target per program}$\n",
    "\n",
    "(via `outdir/total_costs.csv`) <br>\n",
    "\n",
    "Think of this like an itemized receipt... but sorry, this is somewhat special/specific to TKS since it is fairly nitpick-y. However, if you can hack it to work for your survey, it's quite helpful to break down the final costs that each target costs for a given program. For TKS, this also includes the incorporation of archival or already existing observations for a target (and is why the charged time doesn't necessarily equal to the total time)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>priority</th>\n",
       "      <th>tic</th>\n",
       "      <th>toi</th>\n",
       "      <th>SC1A</th>\n",
       "      <th>SC1B</th>\n",
       "      <th>SC1C</th>\n",
       "      <th>SC1D</th>\n",
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       "      <th>SC4</th>\n",
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       "      <td>0.000</td>\n",
       "      <td>100</td>\n",
       "      <td>4.250</td>\n",
       "      <td>8.419</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>2088</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.378</td>\n",
       "      <td>5.378</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.378</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>60</td>\n",
       "      <td>16.133</td>\n",
       "      <td>17.626</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
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       "      <td>480</td>\n",
       "      <td>1.762</td>\n",
       "      <td>1.762</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>60</td>\n",
       "      <td>4.250</td>\n",
       "      <td>5.083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>199444169</td>\n",
       "      <td>1410</td>\n",
       "      <td>2.477</td>\n",
       "      <td>2.477</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.477</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>60</td>\n",
       "      <td>7.431</td>\n",
       "      <td>14.342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>154383539</td>\n",
       "      <td>1436</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>9.077</td>\n",
       "      <td>0.000</td>\n",
       "      <td>9.077</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.991</td>\n",
       "      <td>100</td>\n",
       "      <td>33.146</td>\n",
       "      <td>33.473</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   priority        tic   toi   SC1A   SC1B   SC1C   SC1D   SC1E  SC2A  SC2Bi  \\\n",
       "0       1.0  269701147  1339  0.557  0.557  0.000  0.000  0.000   0.0    0.0   \n",
       "1       2.0  441765914  2088  0.000  0.000  0.000  5.378  5.378   0.0    0.0   \n",
       "2       3.0  317548889   480  1.762  1.762  0.000  0.000  0.000   0.0    0.0   \n",
       "3       4.0  199444169  1410  2.477  2.477  0.000  0.000  0.000   0.0    0.0   \n",
       "4       5.0  154383539  1436  0.000  0.000  9.077  0.000  9.077   0.0    0.0   \n",
       "\n",
       "   SC2Bii  SC2C    SC3    SC4  TOA     TOB nobs_goal  charged_time  total_time  \n",
       "0     0.0  2.58  0.557  0.000  0.0   0.000       100         4.250       8.419  \n",
       "1     0.0  0.00  5.378  0.000  0.0   0.000        60        16.133      17.626  \n",
       "2     0.0  0.00  0.000  0.726  0.0   0.000        60         4.250       5.083  \n",
       "3     0.0  0.00  2.477  0.000  0.0   0.000        60         7.431      14.342  \n",
       "4     0.0  0.00  0.000  0.000  0.0  14.991       100        33.146      33.473  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TKS.costs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------\n",
    "# $\\rm \\textbf{Step #4: Plt your Sample}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TKS hosts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = TKS.final.copy()\n",
    "n_all = len(df.drop_duplicates(subset='tic'))\n",
    "vetted = TKS.final.query(\"photo_vetting == 'passed' and spec_vetting != 'failed' and spec_vetting != 'do not observe'\")\n",
    "n_vetted = len(vetted.drop_duplicates(subset='tic'))\n",
    "sample = TKS.final[TKS.final['in_other_programs'] != 0]\n",
    "n_prog = len(sample.drop_duplicates(subset='tic'))\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "plt.rcParams['mathtext.fontset'] = 'stix'\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "ax1 = plt.subplot(1,1,1)\n",
    "\n",
    "ax1.scatter(df.t_eff.values, df.r_s.values, c = '0.75', s = 100., zorder = 0, label = r'$\\rm Full \\,\\, Sample \\,\\, [%d]$'%n_all)\n",
    "ax1.scatter(vetted.t_eff.values, vetted.r_s.values, c = 'k', s = 100., zorder = 1, label = r'$\\rm Vetted \\,\\, Sample \\,\\, [%d]$'%n_vetted)\n",
    "ax1.scatter(sample.t_eff.values, sample.r_s.values, c = 'k', s = 100., zorder = 2)\n",
    "ax1.scatter(sample.t_eff.values, sample.r_s.values, c = 'none', s = 100., lw = 2.5, edgecolor = '#fd8d49', zorder = 3, label = r'$\\rm TKS \\,\\, Hosts \\,\\, [%d]$'%n_prog)\n",
    "ax1.set_ylabel(r'$\\rm R_{\\star} \\,\\, [R_{\\odot}]$', fontsize = 28)\n",
    "ax1.set_xlabel(r'$\\rm Teff \\,\\, [K]$', fontsize = 28)\n",
    "plt.yscale(\"log\")\n",
    "ax1.xaxis.set_major_locator(MultipleLocator(1000))\n",
    "ax1.xaxis.set_minor_locator(MultipleLocator(250))\n",
    "ax1.tick_params(axis = 'both', which = 'both', direction = 'inout')\n",
    "ax1.tick_params(labelsize = 22)\n",
    "ax1.tick_params(axis = 'both', which = 'minor', length = 10, width = 1.25, direction = 'inout')\n",
    "ax1.tick_params(axis = 'both', which = 'major', length = 15, width = 1.25, direction = 'inout')\n",
    "leg = ax1.legend(fontsize=20, scatteryoffsets=[0.5], framealpha=1.0, handletextpad=0.25, markerscale=1.25, handlelength=0.75, labelspacing=0.3, columnspacing=0.1)\n",
    "ax1.set_xticks([7000., 6000., 5000., 4000., 3000.])\n",
    "ax1.set_xticklabels([r'$7000$', r'$6000$', r'$5000$', r'$4000$', r'$3000$'])\n",
    "ax1.set_yticks([0.3, 1., 3.])\n",
    "ax1.set_yticklabels([r'$0.3$', r'$1$', r'$3$'])\n",
    "ax1.set_xlim([7800., 2900.])\n",
    "ax1.set_ylim([0.2, 9.])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TKS planets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,7))\n",
    "\n",
    "ax1 = plt.subplot(1,2,1)\n",
    "\n",
    "ax1.scatter(df.period.values, df.rp.values, c = '0.75', s = 100, zorder = 0, label = r'$\\rm Full \\,\\, Sample \\,\\, [%d]$'%len(df))\n",
    "ax1.scatter(vetted.period.values, vetted.rp.values, c = 'k', s = 100, zorder = 1, label = r'$\\rm Vetted \\,\\, Sample \\,\\, [%d]$'%len(vetted))\n",
    "ax1.scatter(sample.period.values, sample.rp.values, c = 'k', s = 100, zorder = 2)\n",
    "ax1.scatter(sample.period.values, sample.rp.values, c = 'none', s = 100, lw = 2.5, edgecolor = '#fd8d49', zorder = 3, label = r'$\\rm TKS \\,\\, Planets \\,\\, [%d]$'%len(sample))\n",
    "ax1.set_ylabel(r\"$\\rm R_{p}\\,\\, [R_{\\oplus}]$\", fontsize = 28)\n",
    "ax1.set_xlabel(r\"$\\rm Period\\,\\, [days]$\", fontsize = 28)\n",
    "ax1.tick_params(axis = 'both', which = 'minor', length = 10, width = 1.25, direction = 'inout')\n",
    "ax1.tick_params(axis = 'both', which = 'major', length = 15, width = 1.25, direction = 'inout')\n",
    "plt.yscale(\"log\")\n",
    "plt.xscale(\"log\")\n",
    "ax1.set_xticks([1, 10, 100])\n",
    "ax1.set_xticklabels([r'$1$', r'$10$', r'$100$'])\n",
    "ax1.set_yticks([1, 3, 10])\n",
    "ax1.set_yticklabels([r'$1$', r'$3$', r'$10$'])\n",
    "leg = ax1.legend(fontsize=20, scatteryoffsets=[0.5], framealpha=1.0, columnspacing=0.1, labelspacing=0.3, markerscale=1.25, handletextpad=0.25, handlelength=0.75)\n",
    "plt.tick_params(labelsize = 22)\n",
    "plt.ylim([0.9, 22.])\n",
    "\n",
    "ax2 = plt.subplot(1,2,2)\n",
    "\n",
    "ax2.scatter(df.sinc.values, df.rp.values, c = '0.75', s = 100, zorder = 0)\n",
    "ax2.scatter(vetted.sinc.values, vetted.rp.values, c = 'k', s = 100, zorder = 1)\n",
    "ax2.scatter(sample.sinc.values, sample.rp.values, c = 'k', s = 100, zorder = 2)\n",
    "ax2.scatter(sample.sinc.values, sample.rp.values, c = 'none', s = 100, lw = 2.5, edgecolor = '#fd8d49', zorder = 3)\n",
    "ax2.set_xlabel(r\"$\\rm Incident\\,\\, Flux\\,\\, [F_{\\oplus}]$\", fontsize = 28)\n",
    "ax2.tick_params(labelsize = 22)\n",
    "ax2.tick_params(axis = 'both', which = 'minor', length = 10, width = 1.25, direction = 'inout')\n",
    "ax2.tick_params(axis = 'both', which = 'major', length = 15, width = 1.25, direction = 'inout')\n",
    "plt.yscale(\"log\")\n",
    "plt.xscale(\"log\")\n",
    "ax2.set_xticks([1, 10, 100, 1000, 10000, 100000])\n",
    "ax2.set_xticklabels([r'$10^{0}$', r'$10^{1}$', r'$10^{2}$', r'$10^{3}$', r'$10^{4}$', r'$10^{5}$'])\n",
    "ax2.set_yticks([1, 3, 10])\n",
    "ax2.set_yticklabels([])\n",
    "plt.tick_params(labelsize = 22)\n",
    "plt.xlim([190000., 0.5])\n",
    "plt.ylim([0.9, 22.])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*** <b>Check the paper out [(here)](add link) for more details about the figures!<b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}