{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visual Inspection of an Object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the [Aperture Photometry tutorial](Aperture-Photometry.html) we learned how to make our own apertures and ensure that they covered the right object. Now we need to understand how to interpret and examine our lightcurves.  \n",
    "\n",
    "In this tutorial we learn the following,\n",
    "- How to interactively inspect our object of interest using the *Lightcurve* interact tool.\n",
    "- What features in a lightcurve may be caused by certain anomalies or by contamination from another star.\n",
    "- How to create and store aperture arrays via an interactive tool.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports\n",
    "This tutorial requires:\n",
    "- [**Lightkurve**](https://docs.lightkurve.org) \n",
    "- [**Matplotlib**](https://matplotlib.org/) \n",
    "- [**Numpy**](https://numpy.org)\n",
    "- [**astrowidgets**](https://pypi.org/project/astrowidgets/)\n",
    "- [**opencv-python**](https://pypi.org/project/opencv-python/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "import lightkurve as lk\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining Terms\n",
    "\n",
    "- Target Pixel File (TPF): A file containing the original CCD pixel observations from which light curves are extracted. \n",
    "\n",
    "- LightCurve Object: Obtained from a TPF and contains lightcurve information derived using simple aperture photometry.\n",
    "\n",
    "- LightCurveFile Object: Obtained from MAST and contains both SAP flux and PSDCSAP flux.\n",
    "    \n",
    "- SAP: Simple aperture photometry\n",
    "\n",
    "- PDCSAP: Pre-search Data Conditioning SAP\n",
    "\n",
    "- Cadence: The rate at which TESS photometric observations are stored. \n",
    "\n",
    "- Sector: One of TESS's 27 (to date) observing periods, approximately ~27 days in duration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downloading data\n",
    "\n",
    "We will be using [Gaia object DR25290850609994130560](https://arxiv.org/pdf/2005.12281.pdf) in this tutorial. This object was observed in TESS FFI data. We'll use the [`search_tesscut`](https://docs.lightkurve.org/api/lightkurve.search.search_tesscut.html) function to download a cut out of the target in all sectors observed. You can determine which sectors the target was observed in using the [MAST TESS portal](https://mast.stsci.edu/portal/Mashup/Clients/Mast/Portal.html).\n",
    "\n",
    "Lets grab our data for Gaia object DR25290850609994130560."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SearchResult containing 5 data products.\n",
      "\n",
      " #   observation   author         target_name         productFilename distance\n",
      "--- -------------- ------ --------------------------- --------------- --------\n",
      "  0  TESS Sector 4   MAST Gaia DR25290850609994130560         TESSCut      0.0\n",
      "  1  TESS Sector 7   MAST Gaia DR25290850609994130560         TESSCut      0.0\n",
      "  2  TESS Sector 8   MAST Gaia DR25290850609994130560         TESSCut      0.0\n",
      "  3  TESS Sector 9   MAST Gaia DR25290850609994130560         TESSCut      0.0\n",
      "  4 TESS Sector 10   MAST Gaia DR25290850609994130560         TESSCut      0.0\n"
     ]
    }
   ],
   "source": [
    "search_result = lk.search_tesscut('Gaia DR25290850609994130560')\n",
    "print(search_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see  that this object has been observed in 5 sectors. Lets download the data for sector 8, and cut out a region of 10 x 10 pixels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TessTargetPixelFile(TICID: Gaia DR25290850609994130560)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_result = lk.search_tesscut('Gaia DR25290850609994130560', sector=8)\n",
    "tpfs = search_result.download(cutout_size=10)\n",
    "tpfs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot and Inspect the Data \n",
    "FFI cut outs do not have a SPOC defined *optimal* apertures, we must therefore define our own as in the previous [Aperture Photometry Tutorial](Aperture-Photometry.html). We can define our mask initially using a threshold cut as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 848.5x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_mask = tpfs.create_threshold_mask(threshold=10, reference_pixel='center')\n",
    "tpfs.plot(aperture_mask=target_mask, mask_color='r');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh no! It looks like the bright object next to our object of interest was covered. We have to correct this by specifying a mask array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffa71fad710>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 848.5x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aper_new = np.zeros(tpfs.shape[1:], dtype=bool)\n",
    "aper_new[4:6, 5:7] = True\n",
    "tpfs.plot(aperture_mask=aper_new, mask_color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK phew! Our object is now covered by the aperture. Lets take a look at the lightcurve data using this aperture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffa91274240>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 848.5x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_lc = tpfs.to_lightcurve(aperture_mask=aper_new)\n",
    "target_lc.scatter(label='Target + background')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we see that the object looks to have some sort of variability. There also appears to be some sort of data gap. What is this caused by? Lets check the [TESS Data Release Notes](https://archive.stsci.edu/missions/tess/doc/tess_drn/tess_sector_08_drn10_v02.pdf) for sector 8. If we look at this we see that there was an instrument anomaly starting on 1531 and ending 1535.  This explains some of issues we are seeing.\n",
    "\n",
    "What about the two bright peaks though, this is unlikely from our object of interest. It might be useful to visually inspect these cadences to better understand what is happening. \n",
    "\n",
    "One tool provided by *Lightkurve* to investigate such an event is [`interact`](https://docs.lightkurve.org/api/lightkurve.targetpixelfile.TessTargetPixelFile.html?highlight=interact#lightkurve.targetpixelfile.TessTargetPixelFile.interact). Lets use this tool and see what happens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No pixels in `aperture_mask`, finding optimum aperture using `tpf.create_threshold_mask`.\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(null);\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\": \"kLr4fYcqcSpbuI95brIH3vnnYCquzzSxHPU6XGQCIkQRGJwhg0StNbj1eegrHs12\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\": \"xIGPmVtaOm+z0BqfSOMn4lOR6ciex448GIKG4eE61LsAvmGj48XcMQZtKcE/UXZe\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\": \"Dc9u1wF/0zApGIWoBbH77iWEHtdmkuYWG839Uzmv8y8yBLXebjO9ZnERsde5Ln/P\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\": \"cT9JaBz7GiRXdENrJLZNSC6eMNF3nh3fa5fTF51Svp+ukxPdwcU5kGXGPBgDCa2j\"};\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      if (url in hashes) {\n",
       "        element.crossOrigin = \"anonymous\";\n",
       "        element.integrity = \"sha384-\" + hashes[url];\n",
       "      }\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(null);\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\": \"kLr4fYcqcSpbuI95brIH3vnnYCquzzSxHPU6XGQCIkQRGJwhg0StNbj1eegrHs12\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\": \"xIGPmVtaOm+z0BqfSOMn4lOR6ciex448GIKG4eE61LsAvmGj48XcMQZtKcE/UXZe\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\": \"Dc9u1wF/0zApGIWoBbH77iWEHtdmkuYWG839Uzmv8y8yBLXebjO9ZnERsde5Ln/P\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\": \"cT9JaBz7GiRXdENrJLZNSC6eMNF3nh3fa5fTF51Svp+ukxPdwcU5kGXGPBgDCa2j\"};\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.bokehjs_exec.v0+json": "",
      "text/html": [
       "\n",
       "<script id=\"1001\">\n",
       "  var xhr = new XMLHttpRequest()\n",
       "  xhr.responseType = 'blob';\n",
       "  xhr.open('GET', \"http://localhost:63594/autoload.js?bokeh-autoload-element=1001&bokeh-absolute-url=http://localhost:63594&resources=none\", true);\n",
       "  \n",
       "  xhr.onload = function (event) {\n",
       "    var script = document.createElement('script'),\n",
       "    src = URL.createObjectURL(event.target.response);\n",
       "    script.src = src;\n",
       "    document.body.appendChild(script);\n",
       "  };\n",
       "xhr.send();\n",
       "</script>"
      ]
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "server_id": "d78883ecb2434cbd95d5f4d792a92aec"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tpfs.interact()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above you will see a lightcurve plot to the left, and a TPF postage stamp to the right. \n",
    "\n",
    "In the lightcurve plot you can move the large bottom left slider to change the location of the vertical red bar, which indicates which cadence is being shown in the TPF postage stamp image. \n",
    "\n",
    "The slider beneath the TPF postage stamp image on the right controls the screen stretch, which defaults to logarithmic scaling initialized to 1% and 95% lower and upper limits respectively.\n",
    "\n",
    "You can move your cursor over individual data points to show hover-over tool-tips indicating additional information about that data. Currently the tool tips list the cadence, time, flux, and quality flags. \n",
    "\n",
    "The tools on the right hand side of the plots enable zooming, and pixel selection.\n",
    "\n",
    "The gif below illustrates these features and more,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://docs.lightkurve.org/_images/20180925_interact_EB_contam.gif\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(url='https://docs.lightkurve.org/_images/20180925_interact_EB_contam.gif')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets move the cadence slider to a the peak flux date, so somewhere around 1435 days. If you do this you see that the entire TPF becomes completely yellow indicating saturation! What could be causing this?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scattered Light\n",
    "\n",
    "Given the repetative nature of the lightcurve and the saturation observed upon inspection it is likely that this issue is caused by scattered light. Each camera has a lens hood to reduce the scattered light from the Earth and the Moon. Due to TESS's wide field of view and the physical restrictions of the Sun shade the lens hood is not 100% efficient. The effect of the scattered light on the CCD's can be seen in the video below, typically the patchy brightness is 2-6 times that of the nominal sky background and covers approximately 10-15% of the FoV. When the Earth is below the level of the sun shade there is no scattered light. When the Earth or Moon is directly in the FoV of a camera the data is no longer viable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/https://www.youtube.com/watch?v=p_B85Lot8iU\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7ffa50a8ff60>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.lib.display import YouTubeVideo\n",
    "YouTubeVideo('https://www.youtube.com/watch?v=p_B85Lot8iU')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have demonstrated one use for the interact tool, but there are several different modes that you can access,\n",
    "\n",
    "Interaction modes:\n",
    "\n",
    "- Clicking on a single pixel shows the time-series lightcurve of that pixel alone.\n",
    "\n",
    "- Shift-clicking on multiple pixels shows the lightcurve using that pixel mask.\n",
    "\n",
    "- Shift-clicking on an already-selected pixel will de-select that pixel.\n",
    "\n",
    "- Clicking and dragging a box will make a rectangular aperture mask– individual pixels can be deselected from this mask by shift-clicking (box de-selecting does not work).\n",
    "\n",
    "- The screen stretch high and low limits can be changed independently by clicking and dragging each end, or simultaneously by clicking and dragging in the middle.\n",
    "\n",
    "- The cadence slider updates the postage stamp image at the position of the vertical red bar in the lightcurve.\n",
    "\n",
    "- Clicking on a position in the lightcurve automatically seeks to that Cadence Number.\n",
    "\n",
    "- The left and right arrows can be clicked to increment the cadence number by one.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the most useful applications of the interact tool is the ability to select pixels that make up your aperture. You can do this via shift-clicking on multiple pixels until you have created your pixel mask. Once satisfied you can then save your aperture and subsequent lightcurve as fits file by clicking the green `save lightcurve` button.\n",
    "\n",
    "A limitation to *Lightkurve* is that each TPF is inspected one at a time, this can be difficult when you want to create multiple custom apertures and obtain a lightcurve over many sectors for a given object. A work around to this is described below. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An alternative aperture selection tool\n",
    "\n",
    "Below we illustrate how one can load in a TPF, select pixels for an aperture mask, and save these pixels in an array to be applied at a later point. This avoids having to create a fits file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def image_inspect(tess_cut, epoch):\n",
    "    import astrowidgets\n",
    "    from astrowidgets import ImageWidget\n",
    "    \n",
    "    iw = ImageWidget()\n",
    "    iw.load_array(np.array(tess_cut.flux[epoch]))\n",
    "    \n",
    "    gv = iw._viewer\n",
    "    # set a color map on the viewer \n",
    "    gv.set_color_map('jet')\n",
    "    # Set color distribution algorithm\n",
    "    # choices: linear, log, power, sqrt, squared, asinh, sinh, histeq, \n",
    "    gv.set_color_algorithm('linear')\n",
    "    gv.auto_levels()\n",
    "    \n",
    "    canvas = gv.add_canvas()\n",
    "    canvas.delete_all_objects()\n",
    "    canvas.set_drawtype('point', color='black')\n",
    "    \n",
    "    return iw, canvas\n",
    "    \n",
    "def inter_mask(canvas):\n",
    "\n",
    "    mask_arrayx=[]\n",
    "    mask_arrayy=[]\n",
    "    \n",
    "    aper_out = np.zeros([10,10], dtype=bool)\n",
    "\n",
    "    for a in range(len(canvas.objects)):\n",
    "        p = canvas.objects[a]\n",
    "        print(np.round(p.x,0),np.round(p.y,0))\n",
    "        mask_arrayx.append(int(np.round(p.x,0)))\n",
    "        mask_arrayy.append(int(np.round(p.y,0)))\n",
    "        \n",
    "        aper_out = np.zeros([10,10], dtype=bool)\n",
    "        aper_out[mask_arrayy,mask_arrayx] = True\n",
    "        \n",
    "    return aper_out\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following lines of code will bring up a *canvas* displaying the TPF. The user can place their mouse on the canvas and select pixels via clicking on them, this will bring up a black cross. The pixels selected will then be stored in an array that can be applied as an aperture mask."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "61c9ed4f914e4c0d8ed86f4670947bef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ImageWidget(children=(Image(value=b'\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x01\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\xff\\x…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iw, canvas = image_inspect(tpfs, 1)\n",
    "iw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0 6.0\n",
      "5.0 5.0\n",
      "4.0 5.0\n",
      "6.0 5.0\n",
      "5.0 4.0\n",
      "6.0 4.0\n",
      "4.0 6.0\n",
      "6.0 6.0\n",
      "4.0 4.0\n"
     ]
    }
   ],
   "source": [
    "aper2 = inter_mask(canvas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great we now have these pixels stored in an array called aper2. Lets plot the aperture on top of the TPF as we did in the past and make sure it falls where we expect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 848.5x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tpfs.plot(aperture_mask=aper2, mask_color='r');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our interactive aperture is exactly where we expect it to be. Lets now look at the flux in that aperture and plot up its light curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       time               flux            flux_err      ... cadenceno quality\n",
      "                      electron / s      electron / s    ...                  \n",
      "------------------ ----------------- ------------------ ... --------- -------\n",
      "1517.3666381835938   11895.255859375  3.193638801574707 ...         0    2048\n",
      "1517.3875122070312   10188.751953125  2.984729290008545 ...         1    2048\n",
      "1517.4083251953125   8872.3232421875 2.8149912357330322 ...         2       0\n",
      "1517.4291381835938  7873.26806640625 2.6775903701782227 ...         3       0\n",
      "1517.4500122070312   7127.3603515625  2.568751335144043 ...         4       0\n",
      "1517.4708251953125  6547.95947265625  2.483670473098755 ...         5       0\n",
      "1517.4916381835938  6070.17919921875 2.4083080291748047 ...         6       0\n",
      "1517.5125122070312  5667.85791015625  2.345278024673462 ...         7       0\n",
      "1517.5333251953125            5325.0 2.2904396057128906 ...         8       0\n",
      "1517.5541381835938   5023.4169921875  2.240952730178833 ...         9       0\n",
      "               ...               ...                ... ...       ...     ...\n",
      " 1541.804443359375   2373.7041015625 1.7375272512435913 ...       953       0\n",
      "1541.8252563476562 2372.584716796875 1.7389320135116577 ...       954       0\n",
      "1541.8461303710938 2373.234130859375  1.738275170326233 ...       955       0\n",
      " 1541.866943359375 2373.723876953125 1.7375884056091309 ...       956       0\n",
      "1541.8877563476562 2375.097900390625 1.7388625144958496 ...       957       0\n",
      "1541.9086303710938   2373.6474609375 1.7365226745605469 ...       958       0\n",
      " 1541.929443359375    2378.212890625 1.7380168437957764 ...       959       0\n",
      "1541.9502563476562 2374.004638671875  1.737522006034851 ...       960       0\n",
      "1541.9711303710938   2377.5498046875  1.745447039604187 ...       961       0\n",
      " 1541.991943359375 2375.447509765625 1.7382348775863647 ...       962       0\n",
      "Length = 963 rows\n"
     ]
    }
   ],
   "source": [
    "target_lc= tpfs.to_lightcurve(aperture_mask=aper2)\n",
    "print(target_lc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffa62c79cf8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 848.5x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_lc.scatter(label='Target + background')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have now demonstrated how to interactively inspect TPF files and select apertures in multiple ways. \n",
    "We have learned about scattered light and how it can dominate a light curve. \n",
    "In our [next tutorial](Removing-Scattered-light.html) we will learn how to remove such sources of noise that might affect our lightcurve."
   ]
  }
 ],
 "metadata": {
  "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}