Earth_TEST!_Copy_of_TOI199c_Detectability_and_Probability.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "PNcfqeNmJFUp",
        "ejcDSs5K7Ow1",
        "ce4IMPmaijKK",
        "nS8pCgcWiPK3"
      ],
      "authorship_tag": "ABX9TyOTlNYzmwsi5DRUqJvVfO8s",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/liamhall64/Habitable-Exomoons/blob/main/Earth_TEST!_Copy_of_TOI199c_Detectability_and_Probability.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "GK6yhXsRXupD",
        "outputId": "339b3782-1316-40dd-a013-3ba375dd6443"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pytransit\n",
            "  Downloading PyTransit-2.6.11-py3-none-any.whl.metadata (5.7 kB)\n",
            "Collecting celerite\n",
            "  Downloading celerite-0.4.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.9 kB)\n",
            "Collecting emcee\n",
            "  Downloading emcee-3.1.6-py2.py3-none-any.whl.metadata (3.0 kB)\n",
            "Collecting corner\n",
            "  Downloading corner-2.2.2-py3-none-any.whl.metadata (2.2 kB)\n",
            "Requirement already satisfied: arviz in /usr/local/lib/python3.10/dist-packages (from pytransit) (0.19.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pytransit) (1.26.4)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from pytransit) (1.13.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pytransit) (2.2.2)\n",
            "Requirement already satisfied: xarray in /usr/local/lib/python3.10/dist-packages (from pytransit) (2024.9.0)\n",
            "Requirement already satisfied: tables in /usr/local/lib/python3.10/dist-packages (from pytransit) (3.8.0)\n",
            "Collecting uncertainties (from pytransit)\n",
            "  Downloading uncertainties-3.2.2-py3-none-any.whl.metadata (6.9 kB)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.10/dist-packages (from pytransit) (0.60.0)\n",
            "Requirement already satisfied: astropy in /usr/local/lib/python3.10/dist-packages (from pytransit) (6.1.4)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pytransit) (3.7.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from pytransit) (4.66.5)\n",
            "Collecting semantic-version (from pytransit)\n",
            "  Downloading semantic_version-2.10.0-py2.py3-none-any.whl.metadata (9.7 kB)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from pytransit) (71.0.4)\n",
            "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pytransit) (1.2.14)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (from pytransit) (0.13.2)\n",
            "Collecting ldtk (from pytransit)\n",
            "  Downloading ldtk-1.8.4-py3-none-any.whl.metadata (992 bytes)\n",
            "Collecting pyopencl (from pytransit)\n",
            "  Downloading pyopencl-2024.2.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.7 kB)\n",
            "Collecting pyrr (from pytransit)\n",
            "  Downloading pyrr-0.10.3-py3-none-any.whl.metadata (832 bytes)\n",
            "Collecting meepmeep (from pytransit)\n",
            "  Downloading MeepMeep-0.6.3-py3-none-any.whl.metadata (714 bytes)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pytransit) (1.3.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pytransit) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pytransit) (4.54.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pytransit) (1.4.7)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pytransit) (24.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pytransit) (10.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pytransit) (3.1.4)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pytransit) (2.8.2)\n",
            "Requirement already satisfied: dm-tree>=0.1.8 in /usr/local/lib/python3.10/dist-packages (from arviz->pytransit) (0.1.8)\n",
            "Requirement already satisfied: h5netcdf>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from arviz->pytransit) (1.4.0)\n",
            "Requirement already satisfied: typing-extensions>=4.1.0 in /usr/local/lib/python3.10/dist-packages (from arviz->pytransit) (4.12.2)\n",
            "Requirement already satisfied: xarray-einstats>=0.3 in /usr/local/lib/python3.10/dist-packages (from arviz->pytransit) (0.8.0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->pytransit) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas->pytransit) (2024.2)\n",
            "Requirement already satisfied: pyerfa>=2.0.1.1 in /usr/local/lib/python3.10/dist-packages (from astropy->pytransit) (2.0.1.4)\n",
            "Requirement already satisfied: astropy-iers-data>=0.2024.8.27.10.28.29 in /usr/local/lib/python3.10/dist-packages (from astropy->pytransit) (0.2024.10.7.0.32.46)\n",
            "Requirement already satisfied: PyYAML>=3.13 in /usr/local/lib/python3.10/dist-packages (from astropy->pytransit) (6.0.2)\n",
            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pytransit) (1.16.0)\n",
            "Requirement already satisfied: traitlets in /usr/local/lib/python3.10/dist-packages (from ldtk->pytransit) (5.7.1)\n",
            "Requirement already satisfied: tenacity in /usr/local/lib/python3.10/dist-packages (from ldtk->pytransit) (9.0.0)\n",
            "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba->pytransit) (0.43.0)\n",
            "Requirement already satisfied: platformdirs>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from pyopencl->pytransit) (4.3.6)\n",
            "Collecting pytools>=2024.1.5 (from pyopencl->pytransit)\n",
            "  Downloading pytools-2024.1.14-py3-none-any.whl.metadata (3.0 kB)\n",
            "Requirement already satisfied: multipledispatch in /usr/local/lib/python3.10/dist-packages (from pyrr->pytransit) (1.0.0)\n",
            "Requirement already satisfied: cython>=0.29.21 in /usr/local/lib/python3.10/dist-packages (from tables->pytransit) (3.0.11)\n",
            "Requirement already satisfied: numexpr>=2.6.2 in /usr/local/lib/python3.10/dist-packages (from tables->pytransit) (2.10.1)\n",
            "Requirement already satisfied: blosc2~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from tables->pytransit) (2.0.0)\n",
            "Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.10/dist-packages (from tables->pytransit) (9.0.0)\n",
            "Requirement already satisfied: msgpack in /usr/local/lib/python3.10/dist-packages (from blosc2~=2.0.0->tables->pytransit) (1.0.8)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.10/dist-packages (from h5netcdf>=1.0.2->arviz->pytransit) (3.11.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pytransit) (1.16.0)\n",
            "Downloading PyTransit-2.6.11-py3-none-any.whl (10.9 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.9/10.9 MB\u001b[0m \u001b[31m34.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading celerite-0.4.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (307 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m308.0/308.0 kB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading emcee-3.1.6-py2.py3-none-any.whl (47 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m47.4/47.4 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading corner-2.2.2-py3-none-any.whl (15 kB)\n",
            "Downloading ldtk-1.8.4-py3-none-any.whl (38 kB)\n",
            "Downloading MeepMeep-0.6.3-py3-none-any.whl (33 kB)\n",
            "Downloading pyopencl-2024.2.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (698 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m698.1/698.1 kB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading pyrr-0.10.3-py3-none-any.whl (46 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.8/46.8 kB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading semantic_version-2.10.0-py2.py3-none-any.whl (15 kB)\n",
            "Downloading uncertainties-3.2.2-py3-none-any.whl (58 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading pytools-2024.1.14-py3-none-any.whl (89 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89.9/89.9 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: uncertainties, semantic-version, pytools, pyrr, emcee, celerite, pyopencl, meepmeep, ldtk, corner, pytransit\n",
            "Successfully installed celerite-0.4.3 corner-2.2.2 emcee-3.1.6 ldtk-1.8.4 meepmeep-0.6.3 pyopencl-2024.2.7 pyrr-0.10.3 pytools-2024.1.14 pytransit-2.6.11 semantic-version-2.10.0 uncertainties-3.2.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pytools/persistent_dict.py:63: RecommendedHashNotFoundWarning: Unable to import recommended hash 'siphash24.siphash13', falling back to 'hashlib.sha256'. Run 'python3 -m pip install siphash24' to install the recommended hash.\n",
            "  warn(\"Unable to import recommended hash 'siphash24.siphash13', \"\n"
          ]
        }
      ],
      "source": [
        "# @title\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from astropy import units as u\n",
        "from astropy import constants as const\n",
        "\n",
        "!pip install pytransit celerite emcee corner\n",
        "from pytransit import QuadraticModel\n",
        "# from pytransit import UniformModel\n",
        "from scipy.optimize import minimize"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "abc = 0 # VALUE OF DISTANCE BETWEEN ROCHE AND HILL SPHERE\n",
        "xyz = 10 # DISTANCE TO SYSTEM IN PC"
      ],
      "metadata": {
        "id": "_oLgINRKJO89"
      },
      "execution_count": 1024,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Functions"
      ],
      "metadata": {
        "id": "PNcfqeNmJFUp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ROCHE LIMIT AND HILL SPHERE CALUCLATIONS\n",
        "def hillsphere(Mp, Ms, a):\n",
        "  hill = a*((Mp/(3*Ms)))**(1/3)\n",
        "  return hill\n",
        "\n",
        "def roche_lim(Rp, P_dens, M_dens):\n",
        "  roche = Rp * (2*(dens_p/dens_m))**(1/3)\n",
        "  return roche\n",
        "\n",
        "def CHZ(L_star, S_eff):\n",
        "  habitable_zone = np.sqrt(L_star/(const.L_sun*S_eff))*u.AU\n",
        "  return habitable_zone\n",
        "\n",
        "def per(tot_mass, distance):\n",
        "  period = ((4*np.pi**2)/(const.G*tot_mass)*distance**3)**0.5 # KEPLER'S THIRD LAW\n",
        "  return period.to(u.day)\n",
        "\n",
        "def barycentre(Mp, Mm, dpm, moon_period):\n",
        "  dp = dpm/(1+(Mp/Mm)) # DIST BARYCENTRE TO PLANET\n",
        "  dm = dpm/(1+(Mm/Mp)) # DIST BARYCENTRE TO MOON\n",
        "  #print(f'Planet-barycentre distance = {dp:.3f}\\nMoon-barycentre distance = {dm:.3f}')\n",
        "  return dp, dm"
      ],
      "metadata": {
        "id": "c36bSWSTIP0Y"
      },
      "execution_count": 973,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Parameters"
      ],
      "metadata": {
        "id": "ejcDSs5K7Ow1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# STELLAR PARAMETERS\n",
        "dist_to_sys = (xyz*u.parsec).to(u.m)\n",
        "stellar_mass = 1*const.M_sun\n",
        "Rs = 1*const.R_sun\n",
        "temp = 5772*u.K\n",
        "stellar_lum = 4*np.pi*Rs**2*const.sigma_sb*temp**4\n",
        "\n",
        "separation = CHZ(stellar_lum, 1).to(u.m)\n",
        "\n",
        "# PLANET PARAMETERS\n",
        "plan_mass = 1*const.M_earth\n",
        "Rp = 1*const.R_earth\n",
        "dens_p = 5513*u.kg/u.m**3\n",
        "P = per((stellar_mass + plan_mass), separation)\n",
        "\n",
        "# MOON PARAMETERS\n",
        "moon_mass = 0.0123*const.M_earth # ASSUMING EARTH MASS FOR MOON\n",
        "dens_m = 3344*u.kg/u.m**3\n",
        "\n",
        "roche, hill = roche_lim(Rp, dens_p, dens_m), hillsphere(plan_mass, stellar_mass, separation)\n",
        "\n",
        "distance = np.linspace(roche, hill, 10)\n",
        "# SIMULATED DISTANCE\n",
        "dpm = distance[abc]\n",
        "#dpm = 384400000*u.m\n",
        "\n",
        "print(f'Roche = {roche.to(u.km):.3f}\\nHill = {hill.to(u.au):.6f}\\nEarth\\'s Roche limit and Hill sphere from the literature is given by 9497 km and 0.01 AU respectively.')\n",
        "print(f'Semi-major axis for the planet = {separation.to(u.AU):.3f}')"
      ],
      "metadata": {
        "id": "8l9GZ_K27NoB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b8356ec3-9975-4051-855f-cbcd976fd7f9"
      },
      "execution_count": 1025,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Roche = 9493.111 km\n",
            "Hill = 0.010004 AU\n",
            "Earth's Roche limit and Hill sphere from the literature is given by 9497 km and 0.01 AU respectively.\n",
            "Semi-major axis for the planet = 1.000 AU\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "earth_years = 5*365.25*u.day\n",
        "orbits = (earth_years / P)"
      ],
      "metadata": {
        "id": "uUUqYPiwJXnK"
      },
      "execution_count": 1026,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "period = per(plan_mass + moon_mass, dpm)\n",
        "\n",
        "print(f'Test for Earth-Moon Period = {per(const.M_earth+0.0123*const.M_earth, 384.4e6*u.m):.2f}') # TEST ON EARTH-MOON\n",
        "print(f'Planet-moon period = {period:.2f}') # PLANET-MOON CALC ASSUMING MOON HALFWAY BETWEEN ROCHE AND HILL"
      ],
      "metadata": {
        "id": "kw3pViesiUgC",
        "outputId": "a74e7f29-8adb-402e-879a-65ee18dbdfdc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 1027,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test for Earth-Moon Period = 27.28 d\n",
            "Planet-moon period = 0.11 d\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dp, dm = barycentre(plan_mass, moon_mass, dpm, period)"
      ],
      "metadata": {
        "id": "YRrTetmCkCvM"
      },
      "execution_count": 1028,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TTV calculations"
      ],
      "metadata": {
        "id": "ce4IMPmaijKK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "N_trials = 100 # NUMBER OF ANGLES\n",
        "N_obs = 50 # NUMBER OF OBSERVATIONS/TRANSITS (SHOULD BE WITHIN 5 YEARS AT LEAST)"
      ],
      "metadata": {
        "id": "Xe-pLTr_kWXn"
      },
      "execution_count": 1029,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "t = np.linspace(0, 2*P, 1000)\n",
        "\n",
        "# BARYCENTRE\n",
        "ab = separation # SEPARATION\n",
        "Pb = P.to(u.s)  # PERIOD\n",
        "wb = (2*np.pi/Pb) *u.rad # ANGULAR FREQ\n",
        "alphab = wb*t\n",
        "xb = ab*np.sin(alphab)\n",
        "\n",
        "angle = []\n",
        "def position(N_trials):\n",
        "  for i in range(N_trials):\n",
        "    angle.append(np.random.uniform(0, 2*np.pi)*u.rad)\n",
        "    #print(f\"Start angle #{i+1} = {angle[i]:.3f}\") # IF WE WANT TO PRINT THE LIST OF ALL STARTING ANGLES OF 'N' REALISATIONS\n",
        "\n",
        "    # PLANET\n",
        "    wp = 2*np.pi/period *u.rad\n",
        "    alphap = wp*t + angle[i]\n",
        "    xp = dp * np.sin(alphap) + xb\n",
        "\n",
        "    # MOON\n",
        "    wm = 2*np.pi/period *u.rad\n",
        "    alpham = wm*t + np.pi*u.rad + angle[i]\n",
        "    xm = dm*np.sin(alpham) + xb\n",
        "  return angle, xb, xp, xm, wb, wp, wm, ab, alphap, alpham\n",
        "\n",
        "\n",
        "angle, xb, xp, xm, wb, wp, wm, ab, alphap, alpham = position(N_trials)\n",
        "\n",
        "plt.plot(t, xb.to(u.AU), label = 'Barycentre')\n",
        "plt.plot(t, xp.to(u.AU), label = 'Planet')\n",
        "plt.plot(t, xm.to(u.AU), label = 'Moon')\n",
        "plt.title('Position of barycentre, planet and moon WRT line of sight (LOS)')\n",
        "plt.xlabel('Time (days)')\n",
        "plt.ylabel('Distance between LOS and object (AU)')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.show()\n",
        "print('Done')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5nsaVyZbiqOA",
        "outputId": "c7d9da11-ae13-4fcb-e78a-2c43ec392378"
      },
      "execution_count": 1030,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t = np.arange(0,N_obs*P.value,P.value)*P.unit # TIME\n",
        "\n",
        "# BARYCENTRE\n",
        "ab = separation # SEPARATION\n",
        "Pb = P.to(u.s)  # PERIOD\n",
        "wb = (2*np.pi/Pb) *u.rad # ANGULAR FREQ\n",
        "alphab = wb*t\n",
        "xb = separation*np.sin(alphab)\n",
        "\n",
        "ttvp = []\n",
        "det = 0\n",
        "\n",
        "for i in range(N_trials):\n",
        "  angle = np.random.uniform(0, 2*np.pi)*u.rad\n",
        "\n",
        "  # PLANET\n",
        "  wp = 2*np.pi/period *u.rad\n",
        "  alphap = wp*t + angle\n",
        "  xp = dp * np.sin(alphap) + xb\n",
        "  diffp = xb - xp\n",
        "\n",
        "  vb = (2*np.pi*ab)/P.to(u.s) # BARYCENTRE VELOCITY\n",
        "  #print(f'Velocity of system around star = {vb:.3f}')\n",
        "  ttvp.append(diffp/vb)\n",
        "  if N_trials <=10:\n",
        "    print(ttvp[i])\n",
        "else:\n",
        "  print('Too many to print!')\n",
        "ttvp = ttvp[i].to(u.day)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S94uWWAHuoSV",
        "outputId": "accc5e0e-c75a-47f3-8f16-6d83e5da0be7"
      },
      "execution_count": 1031,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Too many to print!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Light curve"
      ],
      "metadata": {
        "id": "nS8pCgcWiPK3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# SWAPPED THE t0 FOR ttvp FOR THE TIME OF INFERIOR CONJUNCTION IN DAYS TO RUN IN A LOOP TO GET MULTIPLE LIGHT CURVES\n",
        "\n",
        "T14 = (2*Rs+2*Rp)/vb\n",
        "T14 = T14.to(u.day)\n",
        "\n",
        "rp_rs = Rp/Rs        #planet radius / stellar radius ratio\n",
        "ars =  separation/Rs              #semi-major axis / stellar radius ratio\n",
        "inc =  (90*u.deg).to(u.rad).value    #orbital inclination (in radians)\n",
        "ecc = 0.                             #eccentricity\n",
        "w = (90*u.deg).to(u.rad).value       #longitude of periastron (in radians)\n",
        "gamma = [0.231, 0.226]                 #limb darkening coefficients [u1, u2]\n",
        "\n",
        "texp = 100*u.s.to(u.day)             # the exposure time : we convert seconds to days\n",
        "t = np.arange(-0.5, 0.5, texp)       # new timegrid #note we use np.arange rather than np.linspace (DAYS) MANDEL AND........\n",
        "tm = QuadraticModel()                # a model that uses two limb-darkening coefficients\n",
        "tm.set_data(t)\n",
        "\n",
        "lc_Earth = []\n",
        "for j in range(len(ttvp)):\n",
        "  # ttvp[j] is a scalar Quantity, so we directly use it as t0_val\n",
        "  t0_val = ttvp[j].value\n",
        "  lc_Earth.append(tm.evaluate(k=rp_rs, ldc=gamma, t0=t0_val, p=np.float64(P), a=ars, i=inc, e=ecc, w=w))\n",
        "  plt.plot(t, lc_Earth[j], '-o') # Plot the last added light curve\n",
        "print('Done')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "wLmFYK49iNY1",
        "outputId": "52393e97-ab9b-4759-c64c-eb0ecea83cbc"
      },
      "execution_count": 1032,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def planck(wl, T):\n",
        "  a = np.float64(1.191042768e8)*u.um**5 *u.W/ u.m**2 /u.sr/u.um\n",
        "  b = np.float64(14387.7516)*1*u.um * 1*u.K\n",
        "  try:\n",
        "    x = b/(wl*T)\n",
        "    bb = a/wl**5 / (np.exp(x) - 1.0)\n",
        "  except ArithmeticError:\n",
        "    bb = np.zeros(np.size(wl))\n",
        "  return bb\n",
        "\n",
        "wl = np.linspace(0.6, 5.3, 1000)*u.um                    # JWST NIRSPEC PRISM WAVELENGTH RANGE 0.6-5.3 (OR HUBBLE 1.1-1.7)\n",
        "wl_full = np.linspace(0,6, 1000)*u.um\n",
        "BB_flux = np.pi*u.sr*planck(wl,temp)                      # FLUX DENSITY - W/M^2/MICRONS\n",
        "BB_flux_full = np.pi*u.sr*planck(wl_full,temp)\n",
        "\n",
        "####\n",
        "\n",
        "R_s = Rs # host star radius in Rsun (code converts this to m)\n",
        "d = dist_to_sys\n",
        "\n",
        "Flux = BB_flux*(R_s/d)**2\n",
        "\n",
        "D = 6.5*u.m; Atel = np.pi*(D/2)**2 # APERTURE OF SPECIFIC TELESCOPE\n",
        "trans = 0.5; QE = 0.8 # QUANTUM EFFICIENCY\n",
        "Power_per_micron = Flux*Atel*trans*QE # WATTS / MICRON\n",
        "Power = Power_per_micron*np.gradient(wl) # WATTS\n",
        "\n",
        "Photons_per_second = Power/(const.h*const.c/wl.to(u.m)) # CONVERT WATTS TO PHOTONS\n",
        "Photons_per_second = np.sum(Photons_per_second).value*1/u.s # TOTAL ELECTRONS PER SECOND\n",
        "\n",
        "exposure_time = 100*u.s # GENRALISED EXPOSURE\n",
        "Electrons = (Photons_per_second*exposure_time).value\n",
        "std = Electrons**0.5\n",
        "\n",
        "Noise = Electrons**0.5 # ASSUMING A NOISE LIMITED INSTRUMENT\n",
        "Noise = np.random.normal(0, Noise, len(lc_Earth[0]))\n",
        "\n",
        "'''\n",
        "for i in range(len(lc_Earth)):\n",
        "  lc = lc_Earth[0]*Electrons\n",
        "  lc = lc + Noise\n",
        "  plt.plot(t, lc, '-o', label = f'transit {i+1}')\n",
        "  plt.grid()\n",
        "  plt.title('Noise light curves for each ')\n",
        "  plt.ylabel('Relative signal')\n",
        "  plt.xlabel('Time (days)')\n",
        "\n",
        "  if N_obs <=10:\n",
        "    plt.legend()\n",
        "'''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "b7EIeasS7nKW",
        "outputId": "8658a13c-4c37-46d6-a23f-4cb8a62074eb"
      },
      "execution_count": 1033,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\nfor i in range(len(lc_Earth)):\\n  lc = lc_Earth[0]*Electrons\\n  lc = lc + Noise\\n  plt.plot(t, lc, '-o', label = f'transit {i+1}')\\n  plt.grid()\\n  plt.title('Noise light curves for each ')\\n  plt.ylabel('Relative signal')\\n  plt.xlabel('Time (days)')\\n\\n  if N_obs <=10:\\n    plt.legend()\\n\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 1033
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def chi_squared(X, lc, Noise):\n",
        "    rp_rs = X[0]\n",
        "    S = X[1]\n",
        "    t_0 = X[2]\n",
        "    model =tm.evaluate(k=rp_rs, ldc=gamma, t0=t_0, p=np.float64(P), a=ars, i=inc, e=ecc, w=w) *S\n",
        "    return np.sum(((model-lc))**2/Noise**2)\n",
        "\n",
        "t0 = ttvp\n",
        "data_lc = lc_Earth[0]\n",
        "S= Electrons\n",
        "\n",
        "fit_init = [((lc_Earth[0].max()-lc_Earth[0].min())/ lc_Earth[0].max())**0.5, np.mean(lc_Earth[0][0:40]), t0[0].value+np.random.normal(0,0.001)] # these are initial guesses - we'll cheat a bit by putting in the known values but you can try starting with different initial values too\n",
        "# Access the first element of t0.value to ensure it's a scalar\n",
        "bounds =((rp_rs*0.99,rp_rs*1.1), (S*0.99,S*1.1), (np.min(ttvp.value),np.max(ttvp.value))) # the bounds over which the algorithm will vary the parameters\n",
        "# Use std instead of Noise\n",
        "fit  = minimize(chi_squared, fit_init, args=(data_lc, std), method='Nelder-Mead', jac=None, hess=None, hessp=None, bounds=bounds, constraints=(), tol=None, callback=None, options=None)\n",
        "final_fit = [fit['x'][0], fit['x'][1], fit['x'][2]]\n",
        "if N_obs <= 10:\n",
        "  print ('Fitted Rp/Rs', final_fit[0], 'Actual Rp/Rs', rp_rs)\n",
        "  print ('Fitted S', final_fit[1], 'Actual S', S)\n",
        "  print ('Fitted t0', final_fit[2], 'Actual t0', t0)\n",
        "\n",
        "model_fit = tm.evaluate(k=fit['x'][0], ldc=gamma, t0=fit['x'][2], p=np.float64(P), a=ars, i=inc, e=ecc, w=w) *fit['x'][1]\n",
        "'''\n",
        "plt.figure('lc')\n",
        "plt.plot(t,data_lc, 'o')\n",
        "plt.plot(t,model_fit, '-')\n",
        "plt.show();\n",
        "'''\n",
        "print('Done')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZJC48-4y77F-",
        "outputId": "b04f19ed-8c25-41c7-a30c-b342dcdb39f0"
      },
      "execution_count": 1034,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t0_list = []\n",
        "\n",
        "for i in range(N_obs):\n",
        "  Noise = Electrons**0.5 # ASSUMING A NOISE LIMITED INSTRUMENT\n",
        "  Noise = (np.random.normal(0, Noise, len(lc_Earth[0])))\n",
        "\n",
        "  lc = lc_Earth[0]*Electrons\n",
        "  lc = lc + Noise\n",
        "\n",
        "  if N_obs <=10:\n",
        "    plt.plot(t, lc, '-o')\n",
        "    plt.grid()\n",
        "    plt.title('Noise light curves for each ttv')\n",
        "    plt.ylabel('Relative signal')\n",
        "    plt.xlabel('Time (days)')\n",
        "\n",
        "  t0 = ttvp\n",
        "  data_lc = lc\n",
        "  S = Electrons\n",
        "\n",
        "  fit_init = [((lc.max()-lc.min())/ lc.max())**0.5, np.mean(lc[0:40]), t0[0].value+np.random.normal(0,0.001)] # these are initial guesses - we'll cheat a bit by putting in the known values but you can try starting with different initial values too\n",
        "  # Access the first element of t0.value to ensure it's a scalar\n",
        "  bounds =((rp_rs*0.99,rp_rs*1.1), (S*0.99,S*1.1), (-0.01,0.01)) # the bounds over which the algorithm will vary the parameters\n",
        "  # Use std instead of Noise\n",
        "  fit  = minimize(chi_squared, fit_init, args=(data_lc, std), method='Nelder-Mead', jac=None, hess=None, hessp=None, bounds=bounds, constraints=(), tol=None, callback=None, options=None)\n",
        "  final_fit = [fit['x'][0], fit['x'][1], fit['x'][2]]\n",
        "  #print ('Fitted Rp/Rs', final_fit[0], 'Actual Rp/Rs', rp_rs_Earth)\n",
        "  #print ('Fitted S', final_fit[1], 'Actual S', S)\n",
        "  #print ('Fitted t0', final_fit[2], 'Actual t0', t0)\n",
        "\n",
        "  model_fit = tm.evaluate(k=fit['x'][0], ldc=gamma, t0=fit['x'][2], p=np.float64(P), a=ars, i=inc, e=ecc, w=w) * fit['x'][1]\n",
        "\n",
        "  #plt.figure('lc')\n",
        "  plt.plot(t,data_lc, 'o')\n",
        "  #plt.plot(t,model_fit, '-')\n",
        "\n",
        "  t0_list.append(final_fit[2])\n",
        "\n",
        "#plt.show()"
      ],
      "metadata": {
        "id": "-YBuKINOFcKb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "966f6ac7-e308-424a-9ba0-9e6b3f6d7172"
      },
      "execution_count": 1035,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Probability\n",
        "\n",
        "This calculates the SNR of all light curves and we should expect that for Earth, each transit or observation there should be a detection of a transit timing variation due to size of the Moon and separation."
      ],
      "metadata": {
        "id": "-WzUFPNKbGUL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "t0_list_sec = [t*u.day.to(u.s) for t in t0_list]\n",
        "stddev = np.std(t0_list_sec)*u.s\n",
        "\n",
        "det = 0\n",
        "\n",
        "for i in range(len(ttvp)):\n",
        "  SNR = np.abs(ttvp[i]/stddev)\n",
        "  #print(SNR) # IF ANY ARE GREATER THAN 5*SIGMA THEN SUCCESSFUL DETECTION\n",
        "\n",
        "  if SNR >= 5:\n",
        "    #if N_obs <=10:\n",
        "      #print('Detection')\n",
        "    det = det+1\n",
        "  #else:\n",
        "    #print(f'Obs {i+1} = No detection')\n",
        "\n",
        "prob = 100*det/N_obs\n",
        "print(f'Probability of detection out of {N_obs} transits = {prob:.1f}%')\n",
        "print(stddev, dpm.to(u.km))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JX9GzPEx7Bhu",
        "outputId": "da5c65f7-869e-42a4-f471-00a8d79933f4"
      },
      "execution_count": 1036,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Probability of detection out of 50 transits = 0.0%\n",
            "1.6762609266340982 s 9493.111107764946 km\n"
          ]
        }
      ]
    }
  ]
}