acq-success-trend-yearly.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Acquisition success yearly trending\n",
    "\n",
    "To run this standalone on the HEAD or GRETA networks:\n",
    "```\n",
    "# Get into Ska3 environment\n",
    "git clone https://github.com/sot/aca_stats.git\n",
    "cd aca_stats\n",
    "jupyter nbconvert --to notebook --execute acq-success-trend-yearly.ipynb\n",
    "ls -lrt\n",
    "```\n",
    "This will create the png and csv output files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from agasc import get_supplement_table\n",
    "%matplotlib inline\n",
    "from astropy.table import Table\n",
    "from mica.stats.acq_stats import get_stats\n",
    "from cxotime import CxoTime\n",
    "import matplotlib.pyplot as plt\n",
    "from chandra_aca.star_probs import binom_ppf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "290593"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats = Table(get_stats())\n",
    "len(stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make truncated year column for doing grouping. If current time is less than\n",
    "# half way through the year then slosh that data into the previous year.\n",
    "curr_year = CxoTime.now().decimalyear\n",
    "curr_year_trunc = np.trunc(curr_year)\n",
    "obs_year = CxoTime(stats[\"guide_tstart\"]).decimalyear\n",
    "if curr_year - curr_year_trunc < 0.5:\n",
    "    obs_year = obs_year.clip(None, curr_year_trunc - 1)\n",
    "stats[\"iyear\"] = np.trunc(obs_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240676\n"
     ]
    }
   ],
   "source": [
    "# Correct ACA mags using AGASC supplement\n",
    "obs_mags = get_supplement_table(\"mags\", as_dict=True)\n",
    "n_match = 0\n",
    "for row in stats:\n",
    "    try:\n",
    "        row[\"mag\"] = obs_mags[row[\"agasc_id\"]][\"mag_aca\"]\n",
    "        n_match += 1\n",
    "    except KeyError:\n",
    "        pass\n",
    "print(n_match)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select only faint stars.\n",
    "# Original version of notebook filtered on stats['halfw'] >= 120 but don't do that here.\n",
    "ok = (stats[\"mag\"] >= 9.6) & (stats[\"mag\"] < 10.6)\n",
    "sok = stats[ok]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68218"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sok)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "240676"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sok[\"imag\"] = np.trunc(sok[\"mag\"] * 5) / 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "sg = sok.group_by(keys=[\"imag\", \"iyear\"])[\"imag\", \"iyear\", \"star_tracked\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sg.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = []\n",
    "for grp in sg.groups:\n",
    "    n_tracked = np.sum(grp[\"star_tracked\"])\n",
    "    n_obs = len(grp)\n",
    "    f_track = n_tracked / n_obs\n",
    "    err_m, err_p = binom_ppf(n_tracked, n_obs, [0.16, 0.84]) - f_track\n",
    "    row = (\n",
    "        grp[\"imag\"][0],\n",
    "        grp[\"iyear\"][0],\n",
    "        n_tracked,\n",
    "        n_obs,\n",
    "        f_track.round(3),\n",
    "        err_m.round(4),\n",
    "        err_p.round(4),\n",
    "    )\n",
    "    rows.append(row)\n",
    "sga = Table(\n",
    "    rows=rows,\n",
    "    names=[\"imag\", \"iyear\", \"n_tracked\", \"n_obs\", \"frac_tracked\", \"err_m\", \"err_p\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=110</i>\n",
       "<table id=\"table5381926832\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>imag</th><th>iyear</th><th>n_tracked</th><th>n_obs</th><th>frac_tracked</th><th>err_m</th><th>err_p</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>9.6</td><td>2002.0</td><td>512</td><td>523</td><td>0.979</td><td>-0.0087</td><td>0.0041</td></tr>\n",
       "<tr><td>9.6</td><td>2003.0</td><td>920</td><td>935</td><td>0.984</td><td>-0.0057</td><td>0.0027</td></tr>\n",
       "<tr><td>9.6</td><td>2004.0</td><td>802</td><td>814</td><td>0.985</td><td>-0.006</td><td>0.0026</td></tr>\n",
       "<tr><td>9.6</td><td>2005.0</td><td>906</td><td>916</td><td>0.989</td><td>-0.0051</td><td>0.002</td></tr>\n",
       "<tr><td>9.6</td><td>2006.0</td><td>908</td><td>936</td><td>0.97</td><td>-0.0071</td><td>0.0041</td></tr>\n",
       "<tr><td>9.6</td><td>2007.0</td><td>947</td><td>972</td><td>0.974</td><td>-0.0066</td><td>0.0036</td></tr>\n",
       "<tr><td>9.6</td><td>2008.0</td><td>980</td><td>993</td><td>0.987</td><td>-0.0052</td><td>0.0022</td></tr>\n",
       "<tr><td>9.6</td><td>2009.0</td><td>1088</td><td>1103</td><td>0.986</td><td>-0.0049</td><td>0.0022</td></tr>\n",
       "<tr><td>9.6</td><td>2010.0</td><td>1053</td><td>1073</td><td>0.981</td><td>-0.0056</td><td>0.0028</td></tr>\n",
       "<tr><td>9.6</td><td>2011.0</td><td>1163</td><td>1185</td><td>0.981</td><td>-0.0053</td><td>0.0026</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>10.4</td><td>2014.0</td><td>139</td><td>163</td><td>0.853</td><td>-0.0324</td><td>0.0229</td></tr>\n",
       "<tr><td>10.4</td><td>2015.0</td><td>130</td><td>162</td><td>0.802</td><td>-0.0352</td><td>0.0269</td></tr>\n",
       "<tr><td>10.4</td><td>2016.0</td><td>106</td><td>153</td><td>0.693</td><td>-0.0399</td><td>0.0339</td></tr>\n",
       "<tr><td>10.4</td><td>2017.0</td><td>118</td><td>191</td><td>0.618</td><td>-0.0365</td><td>0.0331</td></tr>\n",
       "<tr><td>10.4</td><td>2018.0</td><td>119</td><td>241</td><td>0.494</td><td>-0.0323</td><td>0.0314</td></tr>\n",
       "<tr><td>10.4</td><td>2019.0</td><td>137</td><td>212</td><td>0.646</td><td>-0.0344</td><td>0.0307</td></tr>\n",
       "<tr><td>10.4</td><td>2020.0</td><td>39</td><td>65</td><td>0.6</td><td>-0.0631</td><td>0.0561</td></tr>\n",
       "<tr><td>10.4</td><td>2021.0</td><td>24</td><td>46</td><td>0.522</td><td>-0.0731</td><td>0.0703</td></tr>\n",
       "<tr><td>10.4</td><td>2022.0</td><td>81</td><td>140</td><td>0.579</td><td>-0.0428</td><td>0.0396</td></tr>\n",
       "<tr><td>10.4</td><td>2023.0</td><td>42</td><td>62</td><td>0.677</td><td>-0.0643</td><td>0.0522</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=110>\n",
       "  imag   iyear  n_tracked n_obs frac_tracked  err_m   err_p \n",
       "float64 float64   int64   int64   float64    float64 float64\n",
       "------- ------- --------- ----- ------------ ------- -------\n",
       "    9.6  2002.0       512   523        0.979 -0.0087  0.0041\n",
       "    9.6  2003.0       920   935        0.984 -0.0057  0.0027\n",
       "    9.6  2004.0       802   814        0.985  -0.006  0.0026\n",
       "    9.6  2005.0       906   916        0.989 -0.0051   0.002\n",
       "    9.6  2006.0       908   936         0.97 -0.0071  0.0041\n",
       "    9.6  2007.0       947   972        0.974 -0.0066  0.0036\n",
       "    9.6  2008.0       980   993        0.987 -0.0052  0.0022\n",
       "    9.6  2009.0      1088  1103        0.986 -0.0049  0.0022\n",
       "    9.6  2010.0      1053  1073        0.981 -0.0056  0.0028\n",
       "    9.6  2011.0      1163  1185        0.981 -0.0053  0.0026\n",
       "    ...     ...       ...   ...          ...     ...     ...\n",
       "   10.4  2014.0       139   163        0.853 -0.0324  0.0229\n",
       "   10.4  2015.0       130   162        0.802 -0.0352  0.0269\n",
       "   10.4  2016.0       106   153        0.693 -0.0399  0.0339\n",
       "   10.4  2017.0       118   191        0.618 -0.0365  0.0331\n",
       "   10.4  2018.0       119   241        0.494 -0.0323  0.0314\n",
       "   10.4  2019.0       137   212        0.646 -0.0344  0.0307\n",
       "   10.4  2020.0        39    65          0.6 -0.0631  0.0561\n",
       "   10.4  2021.0        24    46        0.522 -0.0731  0.0703\n",
       "   10.4  2022.0        81   140        0.579 -0.0428  0.0396\n",
       "   10.4  2023.0        42    62        0.677 -0.0643  0.0522"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sga"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The mean of the star_tracked 0's and 1's is the tracking fraction.\n",
    "# sga = sg.groups.aggregate(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make the plot\n",
    "fig, ax = plt.subplots()\n",
    "imags = sorted(set(sga[\"imag\"]))\n",
    "for imag in imags:\n",
    "    ok = sga[\"imag\"] == imag\n",
    "    ax.errorbar(\n",
    "        sga[\"iyear\"][ok],\n",
    "        sga[\"frac_tracked\"][ok],\n",
    "        yerr=[-sga[\"err_m\"][ok], sga[\"err_p\"][ok]],\n",
    "        fmt=\"-o\",\n",
    "        ms=3,\n",
    "        label=f\"{imag:.1f} - {imag+0.2:.1f} mag\",\n",
    "    )\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "ax.set_title(\"Acquisition success rate trend\")\n",
    "ax.set_ylabel(\"Success fraction\")\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylim(0, 1)\n",
    "plt.savefig(\"acq_success_yearly.png\", dpi=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sga.write(\"acq_success_yearly.csv\", overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "d2df0004ee630a46de2935730c9c65ee0c09bd3f3b85f07c44dd36ceff3dbd5e"
  },
  "kernelspec": {
   "display_name": "Python 3.8.3 64-bit ('base': conda)",
   "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": "3.10.8"
  },
  "orig_nbformat": 2
 },
 "nbformat": 4,
 "nbformat_minor": 2
}