{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to perform aperture photometry with custom apertures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning Goals\n",
    "\n",
    "We have discussed in [previous tutorials](XX) how Simple Aperture Photometry works (SAP). A set of pixels in the image are chosen and we sum those to produce a single flux value. We sum the same pre-selected pixels for every image at each time slice to produce a light curve.\n",
    "\n",
    "The [SPOC](https://github.com/nasa/kepler-pipeline) produces an aperture, which is used by default by *Lightkurve*. However, there are some cases where you might want to produce your own aperture. The field may be crowded, or you may wish to change the aperture size to change the relative contribution of the background. Lightkurve offers tools to select pixels programmatically.\n",
    "\n",
    "First, let's load a target pixel file. Let's choose Gaia object DR25290850609994130560. 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 a single sector. 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)."
   ]
  },
  {
   "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)"
   ]
  },
  {
   "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": [
    "## Downloading data\n",
    "Lets see when Gaia object DR25290850609994130560 was observed."
   ]
  },
  {
   "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 can see that this object is detected in Sectors  4, 7, 8, 9, and 10. You can download data from just one sector and specify the cutout_size in number of TESS pixels on a side as an argument to `.download()`. The default is a meager 5 × 5 square. Let’s go with 10 pixels square."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TessTargetPixelFile(TICID: Gaia DR25290850609994130560)\n"
     ]
    }
   ],
   "source": [
    "search_result = lk.search_tesscut('Gaia DR25290850609994130560', sector=8)\n",
    "tpfs = search_result.download(cutout_size=10)\n",
    "print(tpfs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a TessTargetPixelFile object that we can examine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine the data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets plot the data."
   ]
  },
  {
   "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": [
    "tpfs.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our object of interest should be located at the center of the image. There however appears to be a bright star near by, is this our object? We can check by using the [interact_sky](https://docs.lightkurve.org/api/lightkurve.targetpixelfile.KeplerTargetPixelFile.html?highlight=interact_sky#lightkurve.targetpixelfile.KeplerTargetPixelFile.interact_sky) function which can overlay Gaia objects. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "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=\"17166\">\n",
       "  var xhr = new XMLHttpRequest()\n",
       "  xhr.responseType = 'blob';\n",
       "  xhr.open('GET', \"http://localhost:61087/autoload.js?bokeh-autoload-element=17166&bokeh-absolute-url=http://localhost:61087&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": "69cdccd899ae4750adab9f9a4eeb52fe"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tpfs.interact_sky(magnitude_limit=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By adjusting the screen stretch below and moving our pointer over the red circles we can see that our object of interest is indeed the center object and not the bright object. \n",
    "\n",
    "Because we created this TPF from a FFI there is no predefined SPOC aperture and as such we have to make our own. We can do this via the [`create_threshold_mask`](https://docs.lightkurve.org/api/lightkurve.targetpixelfile.TessTargetPixelFile.html?highlight=create_threshold_mask#lightkurve.targetpixelfile.TessTargetPixelFile.create_threshold_mask). This method will identify the pixels in the TPF which show a median flux that is brighter than threshold times the standard deviation above the overall median. The standard deviation is estimated in a robust way by multiplying the Median Absolute Deviation (MAD) with 1.4826. In this example we will pick 10 as our threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_mask = tpfs.create_threshold_mask(threshold=10, reference_pixel='center')\n",
    "n_target_pixels = target_mask.sum()\n",
    "n_target_pixels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets now plot this up and see what it looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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=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, not our object. We have to correct this. We can do this K it looks like our object is covered nicely by the aperture, lets see what the light curve looks like!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specify an Aperture Array\n",
    "We need to define a new aperture array such that our aperture will cover our object of interest. In our previous tutorial (XXX) we learn that aperture arrays are boolean, based on this lets make up a new array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa9d0b49278>"
      ]
     },
     "execution_count": 16,
     "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 that is much better. Lets take a look at the lightcurve data using this aperture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       time               flux            flux_err      ... cadenceno quality\n",
      "                      electron / s      electron / s    ...                  \n",
      "------------------ ----------------- ------------------ ... --------- -------\n",
      "1517.3666381835938   8007.5966796875 2.5630364418029785 ...         0    2048\n",
      "1517.3875122070312  7244.67626953125  2.450223445892334 ...         1    2048\n",
      "1517.4083251953125    6630.228515625  2.352027654647827 ...         2       0\n",
      "1517.4291381835938    6195.158203125  2.283012628555298 ...         3       0\n",
      "1517.4500122070312     5870.74609375 2.2279577255249023 ...         4       0\n",
      "1517.4708251953125  5619.29345703125 2.1846823692321777 ...         5       0\n",
      "1517.4916381835938    5409.947265625 2.1488730907440186 ...         6       0\n",
      "1517.5125122070312    5226.427734375 2.1181600093841553 ...         7       0\n",
      "1517.5333251953125   5080.0048828125 2.0899178981781006 ...         8       0\n",
      "1517.5541381835938   4946.3740234375  2.065858840942383 ...         9       0\n",
      "               ...               ...                ... ...       ...     ...\n",
      " 1541.804443359375   3837.7802734375 1.8543916940689087 ...       953       0\n",
      "1541.8252563476562 3843.681396484375 1.8543570041656494 ...       954       0\n",
      "1541.8461303710938   3844.8037109375  1.854981780052185 ...       955       0\n",
      " 1541.866943359375  3844.24755859375 1.8543776273727417 ...       956       0\n",
      "1541.8877563476562 3846.120849609375  1.854745864868164 ...       957       0\n",
      "1541.9086303710938 3848.494384765625  1.854871392250061 ...       958       0\n",
      " 1541.929443359375   3849.5419921875 1.8557531833648682 ...       959       0\n",
      "1541.9502563476562  3847.31689453125 1.8546956777572632 ...       960       0\n",
      "1541.9711303710938  3848.44873046875 1.8580995798110962 ...       961       0\n",
      " 1541.991943359375   3847.9404296875 1.8589082956314087 ...       962       0\n",
      "Length = 963 rows\n"
     ]
    }
   ],
   "source": [
    "target_lc = tpfs.to_lightcurve(aperture_mask=target_mask)\n",
    "print(target_lc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Now lets plot this lightcurve. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa9a0840630>"
      ]
     },
     "execution_count": 18,
     "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": [
    "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. We can learn how to do this in the [next tutorial!](Visual-inspection.h)"
   ]
  }
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