Astropy.ipynb

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   "source": [
    "%matplotlib inline\n",
    "from astropy.io import fits\n",
    "from astroquery.mast import Observations\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
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   "source": [
    "#filter data\n",
    "obsCount = Observations.query_criteria_count(\n",
    "                                             objectname = \"K2-3d\",\n",
    "                                             radius=\".00033 deg\",\n",
    "                                             obs_collection = \"K2\",\n",
    "                                             dataproduct_type = [\"timeseries\"]\n",
    "                                             )\n",
    "print(\"Number of Observations: %i\" % obsCount)\n",
    "\n",
    "obsTable = Observations.query_criteria(\n",
    "                                         objectname = \"K2-3d\",\n",
    "                                         \n",
    "                                         obs_collection = \"K2\",\n",
    "                                         dataproduct_type = [\"timeseries\"]\n",
    "                                     )\n",
    "#print(obsTable)\n",
    "\n",
    "#obsTable[0:5]['obsid','proposal_id','obs_id']\n",
    "#print(obsTable)\n",
    "\n",
    "#astroquery to retrieve the list of data products\n",
    "dataProducts = Observations.get_product_list(obsTable[0:])\n",
    "dataProducts.colnames\n",
    "\n",
    "dataProducts['obsID']\n",
    "\n",
    "print(dataProducts)\n",
    "\n",
    "want = dataProducts['description'] == \"Light curves\"\n",
    "#print(dataProducts[want])\n",
    "\n",
    "manifest = Observations.download_products(dataProducts)\n",
    "print(manifest)"
   ]
  },
  {
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   "source": [
    "# Query Information\n",
    "mission = \"TESS\"\n",
    "dataProdType = [\"timeseries\"]\n",
    "GiProgram = \"*G011183*\"\n",
    "fileType = \"Light curves\"\n",
    "sector = 1\n",
    "\n",
    "# Query Mast Holdings\n",
    "obsTable = Observations.query_criteria(obs_collection = mission,\n",
    "                                       dataproduct_type = dataProdType,\n",
    "                                       sequence_number = sector,\n",
    "                                       proposal_id = GiProgram)\n",
    "\n",
    "# Get Product List\n",
    "dataProducts = Observations.get_product_list(obsTable[0:1])\n",
    "want = dataProducts['description'] == fileType\n",
    "\n",
    "# Download Data\n",
    "#manifest = Observations.download_products(dataProducts[want])\n",
    "print(manifest)"
   ]
  },
  {
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   "source": [
    "# For the purposes of this tutorial, we just know the MAST URL location of the file we want to examine.\n",
    "#fits_file = \"https://archive.stsci.edu/missions/tess/tid/s0001/0000/0000/2515/5310/tess2018206045859-s0001-0000000025155310-0120-s_lc.fits\"\n",
    "fits_file = \"./mastDownload/K2/ktwo201367065-c01_lc/ktwo201367065-c01_llc.fits\"\n",
    "#abs_pathname = os.path.abspath(\"tess2018206045859-s0001-0000000025153007-0120-s_lc.fits\")\n",
    "\n",
    "#print(abs_pathname)\n",
    "#file=open(path, \"r\")\n",
    "#os.listdir()\n",
    "\n",
    "#nb_full_path = os.path.join(os.getcwd(),\"LR_ML.xlsx\" )\n",
    "#print(nb_full_path)"
   ]
  },
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   "source": [
    "fits.info(fits_file)\n",
    "fits.getdata(fits_file, ext=1).columns\n",
    "\n",
    "with fits.open(fits_file, mode=\"readonly\") as hdulist:\n",
    "    tess_bjds = hdulist[1].data['TIME']\n",
    "    sap_fluxes = hdulist[1].data['SAP_FLUX']\n",
    "    pdcsap_fluxes = hdulist[1].data['PDCSAP_FLUX']"
   ]
  },
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   "source": [
    "# Define the epoch of primary transit in TBJD.  Our timestamps are also already in TBJD.\n",
    "t0 = 2405\n",
    "\n",
    "# Start figure and axis.\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Plot the timeseries in black circles.\n",
    "ax.plot(tess_bjds, pdcsap_fluxes, 'ko')\n",
    "\n",
    "# Center the x-axis on where we expect a transit to be (time = T0), and set\n",
    "# the x-axis range within +/- 1 day of T0.\n",
    "#ax.set_xlim(t0 - 10, t0 + 5)\n",
    "#plt.axis([0, 82, 0, 4900])\n",
    "\n",
    "# Overplot a red vertical line that should be where the transit occurs.\n",
    "#ax.axvline(x=t0, color=\"red\")\n",
    "\n",
    "# Let's label the axes and define a title for the figure.\n",
    "fig.suptitle(\"WASP-126 b Light Curve - Sector 1\")\n",
    "ax.set_ylabel(\"PDCSAP Flux (e-/s)\")\n",
    "ax.set_xlabel(\"Time (TBJD)\")\n",
    "\n",
    "# Adjust the left margin so the y-axis label shows up.\n",
    "plt.subplots_adjust(left=0.15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "#print(tess_bjds)\n",
    "#for test in pdcsap_fluxes:\n",
    "#    print(test)\n",
    "print(len(pdcsap_fluxes))\n",
    "print(len(tess_bjds))\n",
    "#test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edf074b2-b46f-4457-aa7c-491d2d787b4f",
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   "source": [
    "with fits.open(fits_file, mode=\"readonly\") as hdulist:\n",
    "    aperture = hdulist[2].data\n",
    "\n",
    "# Start figure and axis.\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Display the pixels as an image.\n",
    "cax = ax.imshow(aperture, cmap=plt.cm.YlGnBu_r, origin=\"lower\")\n",
    "\n",
    "# Add a color bar.\n",
    "cbar = fig.colorbar(cax)\n",
    "\n",
    "# Add a title to the plot.\n",
    "fig.suptitle(\"WASP-126 b Aperture - Sector 1\")\n",
    "plt.show()   \n",
    "\n",
    "# Break down a pixel value of 267 (yellow pixels displayed above) into its\n",
    "# constituent bits.\n",
    "bitmask = np.binary_repr(267)\n",
    "print(bitmask)"
   ]
  },
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   "source": [
    "# First we need to read in the array of cadence quality flags, let's do\n",
    "# that now.\n",
    "with fits.open(fits_file, mode=\"readonly\") as hdulist:\n",
    "    qual_flags = hdulist[1].data['QUALITY']"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Start figure and axis.\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Plot the timeseries in black circles.\n",
    "ax.plot(tess_bjds, pdcsap_fluxes, 'ko')\n",
    "\n",
    "# Locate quality flags greater than zero.\n",
    "where_gt0 = np.where(qual_flags > 0)[0]\n",
    "\n",
    "# Overplot the fluxes with quality flags greater than zero in red.\n",
    "ax.plot(tess_bjds[where_gt0], pdcsap_fluxes[where_gt0], 'ro')\n",
    "\n",
    "# Let's label the axes and define a title for the figure.\n",
    "fig.suptitle(\"WASP-126 b Light Curve - Sector 1\")\n",
    "ax.set_ylabel(\"PDCSAP Flux (e-/s)\")\n",
    "ax.set_xlabel(\"Time (TBJD)\")\n",
    "plt.show()"
   ]
  },
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   "source": [
    "with fits.open(fits_file, mode=\"readonly\") as hdulist:\n",
    "    aperture = hdulist[2].data\n",
    "\n",
    "# Start figure and axis.\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Display the pixels as an image.\n",
    "cax = ax.imshow(aperture, cmap=plt.cm.YlGnBu_r, origin=\"lower\")\n",
    "\n",
    "# Add a color bar.\n",
    "cbar = fig.colorbar(cax)\n",
    "\n",
    "# Add a title to the plot.\n",
    "fig.suptitle(\"WASP-126 b Aperture - Sector 1\")\n",
    "plt.show()   \n",
    "\n",
    "# Break down a pixel value of 267 (yellow pixels displayed above) into its\n",
    "# constituent bits.\n",
    "bitmask = np.binary_repr(267)\n",
    "print(bitmask)"
   ]
  },
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