{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0939fc66",
   "metadata": {},
   "source": [
    "# Making predictions of precisions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0ad07e9d",
   "metadata": {},
   "source": [
    "This notebook introduces the basic usage of the exoInfoMatrix class, used to make predictions about the precision of planetary parameters. For a more specific example aimed at predictions of planet candidates, see the TOIs notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7e99d29c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Import the class and lightkurve\n",
    "from pepita import exoInfoMatrix\n",
    "import lightkurve as lk\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import ldtk.filters as filters\n",
    "from ldtk import LDPSetCreator, BoxcarFilter, TabulatedFilter\n",
    "from astropy.time import Time\n",
    "import astropy.units as u"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d0b00a82",
   "metadata": {},
   "source": [
    "Now, suppose you want to make predictions for TOI-1601"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "57dfb3aa",
   "metadata": {},
   "source": [
    "Then we have two situations, suppose we have made observations with 1800s cadence (the case for TOI-1601). Then we can\n",
    "\n",
    "- Make a Markov Chain Monte Carlo fit to these observations to obtain some planetary and orbital parameters\n",
    "- Or find the maximum likelihood set of parameters for the observations.\n",
    "\n",
    "For our practical case, we will download the 1800s cadecence lightcurve that is available and use it to obtain the mean error in the flux measurements. This will later be used to estimate the errors in measurements made using different cadences.\n",
    "\n",
    "For the planetary parameters we'll use the values reported in the NASA Exoplanet Archive but we could've done any of the above too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "82acf503",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will use ligthcurves from the 'TESS-SPOC' pipeline\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "SearchResult containing 4 data products.\n",
       "\n",
       "<table id=\"table139730335768656\">\n",
       "<thead><tr><th>#</th><th>mission</th><th>year</th><th>author</th><th>exptime</th><th>target_name</th><th>distance</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th></th><th></th><th>s</th><th></th><th>arcsec</th></tr></thead>\n",
       "<tr><td>0</td><td>TESS Sector</td><td>2019</td><td>DIAMANTE</td><td>1800</td><td>139375960</td><td>0.0</td></tr>\n",
       "<tr><td>1</td><td>TESS Sector 18</td><td>2019</td><td><a href='https://archive.stsci.edu/hlsp/tess-spoc'>TESS-SPOC</a></td><td>1800</td><td>139375960</td><td>0.0</td></tr>\n",
       "<tr><td>2</td><td>TESS Sector 18</td><td>2019</td><td><a href='https://archive.stsci.edu/hlsp/qlp'>QLP</a></td><td>1800</td><td>139375960</td><td>0.0</td></tr>\n",
       "<tr><td>3</td><td>TESS Sector 18</td><td>2019</td><td><a href='https://archive.stsci.edu/hlsp/cdips'>CDIPS</a></td><td>1800</td><td>139375960</td><td>0.0</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "SearchResult containing 4 data products.\n",
       "\n",
       " #     mission     year   author  exptime target_name distance\n",
       "                                     s                 arcsec \n",
       "--- -------------- ---- --------- ------- ----------- --------\n",
       "  0   TESS Sector  2019  DIAMANTE    1800   139375960      0.0\n",
       "  1 TESS Sector 18 2019 TESS-SPOC    1800   139375960      0.0\n",
       "  2 TESS Sector 18 2019       QLP    1800   139375960      0.0\n",
       "  3 TESS Sector 18 2019     CDIPS    1800   139375960      0.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "search = lk.search_lightcurve(\"TOI-1601\", mission=\"TESS\")\n",
    "print(\"We will use ligthcurves from the 'TESS-SPOC' pipeline\")\n",
    "search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "93b92649",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "SearchResult containing 1 data products.\n",
       "\n",
       "<table id=\"table139730375507008\">\n",
       "<thead><tr><th>#</th><th>mission</th><th>year</th><th>author</th><th>exptime</th><th>target_name</th><th>distance</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th></th><th></th><th>s</th><th></th><th>arcsec</th></tr></thead>\n",
       "<tr><td>0</td><td>TESS Sector 18</td><td>2019</td><td><a href='https://archive.stsci.edu/hlsp/tess-spoc'>TESS-SPOC</a></td><td>1800</td><td>139375960</td><td>0.0</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "SearchResult containing 1 data products.\n",
       "\n",
       " #     mission     year   author  exptime target_name distance\n",
       "                                     s                 arcsec \n",
       "--- -------------- ---- --------- ------- ----------- --------\n",
       "  0 TESS Sector 18 2019 TESS-SPOC    1800   139375960      0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Filter to get only the TESS-SPOC results\n",
    "\n",
    "mask = [author == \"TESS-SPOC\" for author in search.author]\n",
    "search[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "04bd71bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Now download the ligthcurve\n",
    "\n",
    "plt.close()\n",
    "\n",
    "# We have to normalize so that the lightcurve is centred around 0 and flux errors are scaled properly\n",
    "lc = search[mask].download().remove_nans().normalize().remove_outliers(sigma_lower=np.inf)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "lc.plot(ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e9269299",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# The median error of the flux measurements is\n",
    "\n",
    "sigma1800s = float(np.median(lc.flux_err.value))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "388a60f2",
   "metadata": {},
   "source": [
    "\n",
    "**__IMPORTANT: It is required that the errors of the measurements are those corresponding to a scaling of the flux measurements such that the value of the flux outside the transits is 1 (that is, the lightcurve and the errors in the flux measurement must be normalized).__**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "61e8b47b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# From the exoplanet archive (https://exoplanetarchive.ipac.caltech.edu/overview/TOI-1601) (Rodriguez et al. 2021) we have\n",
    "\n",
    "period = 5.331751 # [days]\n",
    "period_sd = 0.000011 # error of period value [days]\n",
    "t0 = 2458990.55202 # transit mid-point [Barycentric Julian Date (BJD)]\n",
    "t0_sd = 0.00081 # error of transit mid-point value [BJD]\n",
    "ror = 0.05827 # radius ratio between planet and star\n",
    "ror_sd = 0.00071 # error of radius ratio\n",
    "b = 0.136 # the impact parameter\n",
    "b_sd = 0.13 # error of impact parameter\n",
    "m_star = 1.517 # mass of the star [solar mass]\n",
    "m_star_sd = 0.053 # error of stellar mass\n",
    "r_star = 2.186 # radius of the star [solar radii]\n",
    "r_star_sd = 0.074 # error of stellar radius\n",
    "teff = 5948 # effective temperature of the star [K]\n",
    "teff_sd = 89 # error of effective temperature [K]\n",
    "logg = 3.940 # stellar surface gravity [cm/s^2]\n",
    "logg_sd = 0.025 # error of stellar surface gravity [cm/s^2]\n",
    "z = 0.329 # metallicity [Fe/H]\n",
    "z_sd = 0.083 # error in metallicity [Fe/H]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4c399ced",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Since there are no values for the limb-darkening parameters we estimate them. See the TOIs notebook for a more in-depth procedure\n",
    "\n",
    "filt = filters.create_tess()\n",
    "\n",
    "sc = LDPSetCreator(teff=(teff, teff_sd), logg=(logg, logg_sd), z=(z, z_sd), filters=[filt])\n",
    "\n",
    "ps = sc.create_profiles(nsamples=1000)\n",
    "\n",
    "qc, qe = ps.coeffs_qd(do_mc=True)\n",
    "\n",
    "u1 = qc[0][0] # quadratic limb-darkening coefficient 1\n",
    "u2 = qc[0][1] # quadratic limb-darkening coefficient 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57bd29d2",
   "metadata": {},
   "source": [
    "\n",
    "Suppose we want to see what would happen to the precision of the radius ratio if we make observations with 20s cadence during 40 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "859a0479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2459928.5       , 2459928.50023148, 2459928.50046296, ...,\n",
       "       2459955.49930556, 2459955.49953704, 2459955.49976852])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Supposing no interruptions during the 40 days our array of timestamps would be\n",
    "\n",
    "t = np.arange(0, 27, 20 / (3600 * 24))\n",
    "\n",
    "# With 0 corresponding to when we start observations. Now, imagine we start the observations on the 15th of December 2022. That means day 0 in JD (approximately ~BJD by max error of about ~8 mins) is\n",
    "\n",
    "day0 = Time('2022-12-15T00:00:00').jd\n",
    "\n",
    "# So our timestamps are\n",
    "\n",
    "t = t + day0\n",
    "\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2f855c50",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Now we can initalize the information analysis class. The oversample parameter measures how many points are used in each bin when binning the lightcurve to the needed cadence\n",
    "\n",
    "infomatrix = exoInfoMatrix(exptime=20, oversample=1000)\n",
    "\n",
    "# Then we set the values for the fiducial model\n",
    "\n",
    "infomatrix.set_data(\n",
    "    time_array = t,\n",
    "    period_val = period,\n",
    "    t0_val = t0,\n",
    "    ror_val = ror,\n",
    "    b_val = b,\n",
    "    u1_val = u1,\n",
    "    u2_val = u2,\n",
    "    m_star_val = m_star,\n",
    "    r_star_val = r_star\n",
    ")\n",
    "\n",
    "# And we can use the errors of our current values as priors\n",
    "\n",
    "infomatrix.set_priors(\n",
    "    period_prior = period_sd,\n",
    "    t0_prior = t0_sd,\n",
    "    ror_prior = ror_sd,\n",
    "    b_prior = b_sd,\n",
    "    m_star_prior = m_star_sd,\n",
    "    r_star_prior = r_star_sd,\n",
    ")\n",
    "\n",
    "# Now, we approximate the errors in 20s cadence measurements by scaling the 1800s mean errors\n",
    "\n",
    "sigma20s = sigma1800s * np.sqrt(1800/20)\n",
    "\n",
    "# And we get the covariance matrix\n",
    "\n",
    "covmatrix = infomatrix.eval_cov(sigma = sigma20s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0e91573f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'t': 0,\n",
       " 'period': 1,\n",
       " 't0': 2,\n",
       " 'ror': 3,\n",
       " 'b': 4,\n",
       " 'u1': 5,\n",
       " 'u2': 6,\n",
       " 'm_star': 7,\n",
       " 'r_star': 8}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# If we look at the legend\n",
    "\n",
    "infomatrix.legend\n",
    "\n",
    "# t is not included in the covariance matrix, so if we shift all variables to one value less we see that the row and column corresponding to the radius ratio is the second one. Therefore, the variance predicted for the radius ratio is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eb4e96ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "period sd = 5.064056611849475e-06\n",
      "t0 sd = 0.0007535295007894165\n",
      "ror sd = 0.0004713626767968396\n",
      "b sd = 0.11241866890854353\n",
      "u_star1 sd = 0.17692205571293404\n",
      "u_star2 sd = 0.3069646421996968\n",
      "m_star sd = 0.05076611684564301\n",
      "r_star sd = 0.04071672706234801\n"
     ]
    }
   ],
   "source": [
    "\n",
    "predict_ror_var = covmatrix[2, 2]\n",
    "\n",
    "# And the standard deviation, which is our predicted error is\n",
    "\n",
    "predict_ror_sd = np.sqrt(predict_ror_var)\n",
    "\n",
    "# We can print the predictions for all variables too\n",
    "\n",
    "for i, value1 in enumerate([\"period\", \"t0\", \"ror\", \"b\", \"u_star1\", \"u_star2\", \"m_star\", \"r_star\"]):\n",
    "    for j, value2 in enumerate([\"period\", \"t0\", \"ror\", \"b\", \"u_star1\", \"u_star2\", \"m_star\", \"r_star\"]):\n",
    "        if value1 == value2:\n",
    "            std = np.sqrt(np.abs(covmatrix[i,j]))\n",
    "            print(f\"{value1} sd = {std}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c1ab7df5",
   "metadata": {},
   "source": [
    "\n",
    "So we predict that by obtaining 27 days (~1 TESS sector) of 20s cadence observations we can improve the precision in the radius ratio by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7758ceef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33.6%\n"
     ]
    }
   ],
   "source": [
    "improv = ( 1 - predict_ror_sd / ror_sd ) * 100\n",
    "\n",
    "print(f\"{improv:.1f}%\")"
   ]
  }
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