eleonor_photometry.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/astrobatty/eleonor_photometry/blob/master/eleonor_photometry.ipynb\" target=\"_parent\\\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lGwCqdEicmkj"
   },
   "source": [
    "# Install packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "PYEB9V2WT9Iz",
    "outputId": "8d48885d-0ac8-4b6c-c567-bdde5f51db52"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "  Created wheel for eleanor: filename=eleanor-0.2.9-py2.py3-none-any.whl size=40313 sha256=957ed58ec7cb57eb777841744e1a8716f6aabd7f3a1e6932b7ef7246bf9d98cd\n",
      "  Stored in directory: /root/.cache/pip/wheels/80/40/63/f8b83cc2ee3559a0b17f255a9ea7884f8ff11b1a769bea92d3\n",
      "  Building wheel for lightkurve (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for lightkurve: filename=lightkurve-1.2.0-cp36-none-any.whl size=498126 sha256=bad2d347e0cf31a6fcb6c374e3c1a3e9a4c6e486b4e2e023e59bbff3f7aa27e3\n",
      "  Stored in directory: /root/.cache/pip/wheels/c9/b0/6a/30512b5211e9a2c91fbcc088b4151b9dbc46f80d0464c2fb34\n",
      "  Building wheel for astroquery (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for astroquery: filename=astroquery-0.3.10-cp36-none-any.whl size=3508444 sha256=92c5fdae42859ed1357de64afea912b1564deb098221dea773e7c6c5ffce70ee\n",
      "  Stored in directory: /root/.cache/pip/wheels/cb/2f/df/06562d703b7f0577221c87174ae12bf4dcab0d40e8a02a8988\n",
      "  Building wheel for muchbettermoments (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for muchbettermoments: filename=muchbettermoments-1.0.0-py2.py3-none-any.whl size=2491 sha256=ec8dba71b2dc529487f0171a06762e97d278c3b7305c1b6e4be2adfc9b2266c4\n",
      "  Stored in directory: /root/.cache/pip/wheels/0d/0e/a8/03e897b86885e6d13b6f71b1e8b3cdc09e4ae9a4d14d732b61\n",
      "  Building wheel for fitsio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for fitsio: filename=fitsio-1.0.5-cp36-cp36m-linux_x86_64.whl size=1668341 sha256=dc7fb611a0fd053aa916d4c751a10bff4f6f9751985724d42f49e098a742fcff\n",
      "  Stored in directory: /root/.cache/pip/wheels/7d/23/2f/53e62dabda9ef78b0204a10a095e9dd89837e08a0f5c2865c6\n",
      "  Building wheel for vaneska (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for vaneska: filename=vaneska-0.0.dev0-cp36-none-any.whl size=2567 sha256=f6514c9e271eed8af0954960cf486071904b171a3ec47b353700380bd8d59b0b\n",
      "  Stored in directory: /root/.cache/pip/wheels/44/d9/6e/f371b89ebd7487b96e363c9d738e8865d6a208cc1b7deb3926\n",
      "  Building wheel for tess-point (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for tess-point: filename=tess_point-0.3.7-cp36-none-any.whl size=19617 sha256=f9dc343084f79bd523c5d763262fcff99e3451ebb8a4ebb081da68c1013441b5\n",
      "  Stored in directory: /root/.cache/pip/wheels/63/f9/e9/f4dd028f856f025d7159ef09e899df35d4357bd03049f81d1d\n",
      "  Building wheel for oktopus (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for oktopus: filename=oktopus-0.1.2-cp36-none-any.whl size=12779 sha256=e56eb240f7fba9939ea83fb40d016e5c232d44aef7eea21083681322bb61ddd7\n",
      "  Stored in directory: /root/.cache/pip/wheels/9b/90/81/098fc66ee56166d63c9a8fc0a9672ae7b3423396a588ec952a\n",
      "  Building wheel for uncertainties (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for uncertainties: filename=uncertainties-3.1.2-cp36-none-any.whl size=96457 sha256=0c30bd816c0467c157a3644d07cee7364614051d6f97d19bdc23aa8c5051c67c\n",
      "  Stored in directory: /root/.cache/pip/wheels/d9/d3/0e/5b0b743a8abd50373705427438456da5dc2621891138d7a618\n",
      "Successfully built eleanor lightkurve astroquery muchbettermoments fitsio vaneska tess-point oktopus uncertainties\n",
      "Installing collected packages: matplotlib, mplcursors, cryptography, jeepney, secretstorage, keyring, astroquery, oktopus, uncertainties, lightkurve, muchbettermoments, fitsio, tensorflow-estimator, tensorboard, tensorflow, vaneska, tess-point, eleanor\n",
      "  Found existing installation: matplotlib 3.0.3\n",
      "    Uninstalling matplotlib-3.0.3:\n",
      "      Successfully uninstalled matplotlib-3.0.3\n",
      "  Found existing installation: tensorflow-estimator 1.15.1\n",
      "    Uninstalling tensorflow-estimator-1.15.1:\n",
      "      Successfully uninstalled tensorflow-estimator-1.15.1\n",
      "  Found existing installation: tensorboard 1.15.0\n",
      "    Uninstalling tensorboard-1.15.0:\n",
      "      Successfully uninstalled tensorboard-1.15.0\n",
      "  Found existing installation: tensorflow 1.15.0rc3\n",
      "    Uninstalling tensorflow-1.15.0rc3:\n",
      "      Successfully uninstalled tensorflow-1.15.0rc3\n",
      "Successfully installed astroquery-0.3.10 cryptography-2.8 eleanor-0.2.9 fitsio-1.0.5 jeepney-0.4.1 keyring-19.2.0 lightkurve-1.2.0 matplotlib-3.1.1 mplcursors-0.3 muchbettermoments-1.0.0 oktopus-0.1.2 secretstorage-3.1.1 tensorboard-1.14.0 tensorflow-1.14.0 tensorflow-estimator-1.14.0 tess-point-0.3.7 uncertainties-3.1.2 vaneska-0.0.dev0\n"
     ]
    },
    {
     "data": {
      "application/vnd.colab-display-data+json": {
       "pip_warning": {
        "packages": [
         "matplotlib",
         "mpl_toolkits"
        ]
       }
      }
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "!pip install imgaug==0.2.5\n",
    "!pip install git+https://github.com/astropy/photutils.git\n",
    "!pip install eleanor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0mYXEWoMc3uR"
   },
   "source": [
    "# Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "mfV6_LscW-i8"
   },
   "outputs": [],
   "source": [
    "import eleanor\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uFeBIBLtc7FS"
   },
   "source": [
    "# Extract photometry from one specific TESS sector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105
    },
    "colab_type": "code",
    "id": "IQD1bCZxXkfB",
    "outputId": "661c252c-f659-4e71-ecc8-6143c80cbfa7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/eleanor/source.py:396: UserWarning: Warning: unable to create .. Downloading TessCut to the current working directory instead.\n",
      "  'working directory instead.'.format(download_dir))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading URL https://mast.stsci.edu/tesscut/api/v0.1/astrocut?ra=68.9597092399964&dec=-64.0270368810381&y=31&x=31&units=px&sector=6 to ./tesscut_20191017205307.zip ... [Done]\n",
      "Inflating...\n"
     ]
    }
   ],
   "source": [
    "# Get data\n",
    "star = eleanor.Source(tic=38846515, sector=6, tc=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "aD37S26UXo_X"
   },
   "outputs": [],
   "source": [
    "# Do PSF photometry\n",
    "data = eleanor.TargetData(star, height=11, width=11, bkg_size=31, do_psf=True, do_pca=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "id": "PqqsvgeEXzcL",
    "outputId": "991677b5-99ad-4ef8-9952-511289b1c01e"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot TPF\n",
    "plt.imshow(data.tpf[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "id": "CC3D1JACX6vX",
    "outputId": "3c5a01f1-f1ef-4659-d7ef-ccaa144cf31b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot light curve\n",
    "q = data.quality == 0\n",
    "plt.plot(data.time[q], data.psf_flux[q], '.')\n",
    "#plt.ylim(14300,14600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "48CFEUyFcgCx"
   },
   "outputs": [],
   "source": [
    "# Save light curve to ASCII file\n",
    "np.savetxt('lc.txt', np.c_[data.time[q], data.psf_flux[q], data.flux_err[q]] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4q15lHC7dG_V"
   },
   "source": [
    "# Extract photometry from all available TESS sectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "vkbGRv6gYDf9",
    "outputId": "b669a095-631d-466d-af4a-bba69fa71833"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found star in Sector(s) 2 5 8 9 10 11 12\n"
     ]
    }
   ],
   "source": [
    "# Get data from all sectors\n",
    "star = eleanor.multi_sectors(tic=307210830, sectors='all', tc=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "eKPbhb40YY6u"
   },
   "outputs": [],
   "source": [
    "# Plot and save light curve\n",
    "plt.figure(figsize=(15,5))\n",
    "for i in range(len(star)):\n",
    "    print(i)\n",
    "    data0 = eleanor.TargetData(star[i], height=11, width=11, bkg_size=31, do_psf=True, do_pca=True)\n",
    "\n",
    "    q0 = data0.quality == 0\n",
    "\n",
    "    plt.plot(data0.time[q0], data0.psf_flux[q0]/np.median(data0.psf_flux[q0]), 'k.')\n",
    "    \n",
    "    np.savetxt('lc_%d.txt' % i, np.c_[data0.time[q0], data0.psf_flux[q0], data0.flux_err[q0]] )\n",
    "\n",
    "plt.ylabel('Normalized Flux', fontsize=24)\n",
    "plt.xlabel('Time', fontsize=24)\n",
    "plt.yticks(fontsize=18)\n",
    "plt.xticks(fontsize=18)\n",
    "\n",
    "plt.ylim(0.995, 1.002)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "cL-kwuxwf0Ka"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "Untitled5.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}