{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0d230a47",
   "metadata": {},
   "source": [
    "# White light CCF of HD 189733 - two nights\n",
    "\n",
    "In this notebook, we present a simple example of the application of the Doppler Shadow technique to two nights of ESPRESSO observations of HD 189733, a K1V star, extracting the white light CCFs provided by the DRS. The nights of observations were 11-08-2021 and 31-08-2021.\n",
    "\n",
    "We run two different instances of HECATE and then use the multi_night_analysis class to combine the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13a9ada1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from HECATE.HECATE import HECATE   # main class\n",
    "from HECATE.get_data import *\n",
    "from HECATE.multi_night_analysis import multi_night_analysis # to perform the multi-night analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b82fead8",
   "metadata": {},
   "outputs": [],
   "source": [
    "stellar_params = {\n",
    "                \"Teff\":4969, \"Teff_err\":43,   #effective temperature [K]\n",
    "                \"logg\":4.60, \"logg_err\":0.01, #superficial gravity [dex]\n",
    "                \"FeH\":-0.07, \"FeH_err\":0.02,  #metallicity [dex]\n",
    "                \"P_rot\":2.21857312,           #rotation period [d]\n",
    "                \"R_star\":0.766,               #radius [solar radii]\n",
    "                \"inc_star\":71.87              #stellar inclination [º]\n",
    "                }\n",
    "\n",
    "planet_params = {\n",
    "                \"P_orb\":2.21857312,           #orbital period [d]\n",
    "                \"a_R\":8.76863,                #system scale [stellar radii]\n",
    "                \"Rp_Rs\":0.1602,               #planet-to-star radius ratio \n",
    "                \"t0\":53988.30339,             #mid-transit time [d]\n",
    "                \"e\":0,                        #orbital eccentricity\n",
    "                \"w\":90,                       #argument of periastron [º]\n",
    "                \"inc_planet\":85.465,          #planet inclination [º]\n",
    "                \"lbda\":-1.00,                 #spin-orbit angle [º]\n",
    "                \"dfp\": -0.002424              #mid-transit phase shift, night of 2021-09-11\n",
    "                }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d29f76cd",
   "metadata": {},
   "source": [
    "Now we quickly run HECATE for the first night of observations, saving the local CCF parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2570398",
   "metadata": {},
   "outputs": [],
   "source": [
    "CCFs, time, airmass, berv, bervmax, snr, list_ccfs = get_CCFs(planet_params, stellar_params,\n",
    "                                                              day='2021-08-11', \n",
    "                                                              directory_path=\"HD189733_ESPRESSO_white_light_ccfs\",\n",
    "                                                              plot=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a99660da",
   "metadata": {},
   "outputs": [],
   "source": [
    "hecate11 = HECATE(planet_params, stellar_params, time, CCFs, spectra=None, plot_soap=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3066ee8",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot = {\"fits_initial_CCF\":False,         # plot the initial fits to the CCFs\n",
    "        \"sys_vel_ccf\":False,               # plot the systemic velocity of the star in the CCFs\n",
    "        \"avg_out_of_transit_CCF\":False,   # plot the average out-of-transit CCF\n",
    "        \"local_CCFs\":True,                # plot the local CCFs\n",
    "        \"photometrical_rescale\":False     # if the local CCFs are rescaled by the photometric transit model\n",
    "        }\n",
    "\n",
    "ccf_type = \"white light\"\n",
    "model_fit = \"modified Gaussian\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93c21737",
   "metadata": {},
   "outputs": [],
   "source": [
    "local_CCFs, CCFs_flux_corr, CCFs_sub_all, avg_out_of_transit_CCF = hecate11.extract_local_CCF(model_fit, plot, save=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61282d62",
   "metadata": {},
   "outputs": [],
   "source": [
    "master_results11 = hecate11.get_profile_parameters(profiles=avg_out_of_transit_CCF, \n",
    "                                               data_type=\"CCF\", \n",
    "                                               observation_type=\"master\", \n",
    "                                               model=\"modified Gaussian\", \n",
    "                                               print_output=False, \n",
    "                                               plot_fit=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "139a22bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "local_results11 = hecate11.get_profile_parameters(profiles=local_CCFs, \n",
    "                                              data_type=\"CCF\", \n",
    "                                              observation_type=\"local\", \n",
    "                                              model=\"modified Gaussian\", \n",
    "                                              print_output=False, \n",
    "                                              plot_fit=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c9cef81",
   "metadata": {},
   "outputs": [],
   "source": [
    "indices_final11 = np.array(list(set(np.where(local_results11['R2'] >= 0.95)[0]).intersection(set(np.where(hecate11.mu_in >= 0.3)[0]))))\n",
    "\n",
    "local_params11 = [local_results11['central_rv'], local_results11['width'], local_results11['intensity']]\n",
    "master_params11 = [master_results11['central_rv'], master_results11['width'], master_results11['intensity']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2627862a",
   "metadata": {},
   "source": [
    "Now we do the same for the second night."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63d628d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "planet_params[\"dfp\"] = -0.002300    # mid-transit phase shift, night of 2021-08-31"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2291a9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "CCFs, time, airmass, berv, bervmax, snr, list_ccfs = get_CCFs(planet_params, stellar_params,\n",
    "                                                              day='2021-08-31', \n",
    "                                                              directory_path=\"HD189733_ESPRESSO_white_light_ccfs\",\n",
    "                                                              plot=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4bbfa80",
   "metadata": {},
   "outputs": [],
   "source": [
    "hecate31 = HECATE(planet_params, stellar_params, time, CCFs, None)\n",
    "\n",
    "plot = {\"fits_initial_CCF\":False, \n",
    "        \"sys_vel_ccf\":False, \n",
    "        \"avg_out_of_transit_CCF\":False, \n",
    "        \"local_CCFs\":True, \n",
    "        \"photometrical_rescale\":False}\n",
    "\n",
    "ccf_type = \"white light\"\n",
    "model_fit = \"modified Gaussian\"\n",
    "\n",
    "local_CCFs, CCFs_flux_corr, CCFs_sub_all, avg_out_of_transit_CCF = hecate31.extract_local_CCF(model_fit, plot, save=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58849e09",
   "metadata": {},
   "outputs": [],
   "source": [
    "master_results31 = hecate31.get_profile_parameters(profiles=avg_out_of_transit_CCF, data_type=\"CCF\", \n",
    "                                                    observation_type=\"master\", model=\"modified Gaussian\", \n",
    "                                                    print_output=False, plot_fit=False)\n",
    "\n",
    "local_results31 = hecate31.get_profile_parameters(profiles=local_CCFs, data_type=\"CCF\", \n",
    "                                                    observation_type=\"local\", model=\"modified Gaussian\", \n",
    "                                                    print_output=False, plot_fit=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddd0031a",
   "metadata": {},
   "outputs": [],
   "source": [
    "indices_final31 = np.array(list(set(np.where(local_results31['R2'] >= 0.9)[0]).intersection(set(np.where(hecate31.mu_in >= 0.3)[0]))))\n",
    "\n",
    "local_params31 = [local_results31['central_rv'], local_results31['width'], local_results31['intensity']]\n",
    "master_params31 = [master_results31['central_rv'], master_results31['width'], master_results31['intensity']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac82f66b",
   "metadata": {},
   "source": [
    "Having the results for each night, we build the following dictionaries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c228c164",
   "metadata": {},
   "outputs": [],
   "source": [
    "night_data_11 = {\n",
    "    'hecate': hecate11,                             # HECATE instance\n",
    "    'indices': indices_final11,                     # indices of points to retain\n",
    "    'local_params': np.array(local_params11),       # local CCF parameters \n",
    "    'master_params': np.array(master_params11),     # master CCF parameters\n",
    "    'color': \"blue\",                                # color to use in the plots for this night\n",
    "    'label': \"2021-08-11\"                           # label to use in the plots for this night\n",
    "        }\n",
    "\n",
    "night_data_31 = {\n",
    "    'hecate': hecate31,\n",
    "    'indices': indices_final31,\n",
    "    'local_params': np.array(local_params31),\n",
    "    'master_params': np.array(master_params31),\n",
    "    'color': \"red\",\n",
    "    'label': \"2021-08-31\"\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5e3a642",
   "metadata": {},
   "outputs": [],
   "source": [
    "night_data = {\"2021-08-11\": night_data_11,\n",
    "              \"2021-08-31\": night_data_31}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21ff6402",
   "metadata": {},
   "outputs": [],
   "source": [
    "mult_night = multi_night_analysis(night_data, data_type='CCF')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b9c24a8",
   "metadata": {},
   "source": [
    "We can just plot the two nights data in one single plot for each position parameter ($\\phi$ or $\\mu$):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e043cba",
   "metadata": {},
   "outputs": [],
   "source": [
    "mult_night.plot_parameters(param_type='phases', \n",
    "                            fit_each_night=False, \n",
    "                            fit_combined=False, \n",
    "                            combined_night_names=None, \n",
    "                            plot_nested=False, \n",
    "                            suptitle=\"Local white light CCF parameters\")\n",
    "\n",
    "\n",
    "mult_night.plot_parameters(param_type='mu', \n",
    "                            fit_each_night=False, \n",
    "                            fit_combined=False, \n",
    "                            combined_night_names=None, \n",
    "                            plot_nested=False, \n",
    "                            suptitle=\"Local white light CCF parameters\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc509f19",
   "metadata": {},
   "source": [
    "Or fit a linear model to each night:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "478f4f6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_results = mult_night.plot_parameters(param_type='phases', \n",
    "                                      fit_each_night=True, \n",
    "                                      fit_combined=False, \n",
    "                                      fit_param_indices = np.array([0]),\n",
    "                                      suptitle=\"Local white light CCF parameters\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19b0c924",
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_results = mult_night.plot_parameters(param_type='mu', \n",
    "                                      fit_each_night=True, \n",
    "                                      fit_combined=False, \n",
    "                                      fit_param_indices = np.array([1, 2]),\n",
    "                                      suptitle=\"Local white light CCF parameters\")"
   ]
  }
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