completed_tutorials/05-Electrophysiology Computed Tables.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with automated computations: Computed tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome back! In this session, we are going to continue working with the pipeline for the mouse electrophysiology example. \n",
    "\n",
    "In this session, we will learn to:\n",
    "\n",
    "* compute various statistics for each neuron by defining a `Computed` table\n",
    "* define a `Lookup` table to store parameters for computation\n",
    "* define another `Computed` table to perform spike detection and store the detected spikes\n",
    "* automatically trigger computations for all missing entries with `populate`\n",
    "* define a `Part` table to save the results computed with the master `Computed` table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First thing first, let's import `datajoint` again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datajoint as dj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we are going to perform some computations, let's go ahead and import NumPy as well as Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly as before, to continue working with the tables we defined in the previous notebook, we can either redefine the classes for each table `Mouse`, `Session`, `Neuron` and populate them. Or, again for your convenience, we can import them from the `tutorial_pipeline.ephys_cell_activity` module. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please enter DataJoint username: root\n",
      "Please enter DataJoint password: ········\n",
      "Connecting root@localhost:3306\n",
      "Populating neuron(s) for mouse_id=0 on session_date=2017-05-15\n",
      "\tPopulated neuron 0\n",
      "Populating neuron(s) for mouse_id=0 on session_date=2017-05-19\n",
      "\tPopulated neuron 0\n",
      "Populating neuron(s) for mouse_id=5 on session_date=2017-01-05\n",
      "\tPopulated neuron 0\n",
      "Populating neuron(s) for mouse_id=100 on session_date=2017-05-25\n",
      "\tPopulated neuron 0\n",
      "Populating neuron(s) for mouse_id=100 on session_date=2017-06-01\n",
      "\tPopulated neuron 0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "if os.path.basename(os.getcwd())!='notebooks': os.chdir('/home/notebooks')\n",
    "from tutorial_pipeline.ephys_cell_activity import schema, Mouse, Session, Neuron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "        \n",
       "        <b>Experiment session</b>\n",
       "            <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "            <table border=\"1\" class=\"Relation\">\n",
       "                <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">mouse_id</p>\n",
       "                                <span class=\"djtooltiptext\">Unique animal ID</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">session_date</p>\n",
       "                                <span class=\"djtooltiptext\">date</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">experiment_setup</p>\n",
       "                                <span class=\"djtooltiptext\">experiment setup ID</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">experimenter</p>\n",
       "                                <span class=\"djtooltiptext\">experimenter name</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">data_path</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>0</td>\n",
       "<td>2017-05-15</td>\n",
       "<td>0</td>\n",
       "<td>Edgar Y. Walker</td>\n",
       "<td>data</td></tr><tr><td>0</td>\n",
       "<td>2017-05-19</td>\n",
       "<td>0</td>\n",
       "<td>Edgar Y. Walker</td>\n",
       "<td>data</td></tr><tr><td>5</td>\n",
       "<td>2017-01-05</td>\n",
       "<td>1</td>\n",
       "<td>Fabian Sinz</td>\n",
       "<td>data</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>100</td>\n",
       "<td>Jacob Reimer</td>\n",
       "<td>data</td></tr><tr><td>100</td>\n",
       "<td>2017-06-01</td>\n",
       "<td>1</td>\n",
       "<td>Jacob Reimer</td>\n",
       "<td></td> </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 5</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*mouse_id    *session_date  experiment_set experimenter   data_path    \n",
       "+----------+ +------------+ +------------+ +------------+ +-----------+\n",
       "0            2017-05-15     0              Edgar Y. Walke data         \n",
       "0            2017-05-19     0              Edgar Y. Walke data         \n",
       "5            2017-01-05     1              Fabian Sinz    data         \n",
       "100          2017-05-25     100            Jacob Reimer   data         \n",
       "100          2017-06-01     1              Jacob Reimer                \n",
       " (Total: 5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Session()"
   ]
  },
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   "execution_count": 5,
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       "        <b></b>\n",
       "            <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "            <table border=\"1\" class=\"Relation\">\n",
       "                <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">mouse_id</p>\n",
       "                                <span class=\"djtooltiptext\">Unique animal ID</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">session_date</p>\n",
       "                                <span class=\"djtooltiptext\">date</span>\n",
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       "                                <span class=\"djtooltiptext\"></span>\n",
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       "                                <p id=\"nonprimary\">activity</p>\n",
       "                                <span class=\"djtooltiptext\">electric activity of the neuron</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>0</td>\n",
       "<td>2017-05-15</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td></tr><tr><td>0</td>\n",
       "<td>2017-05-19</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td></tr><tr><td>5</td>\n",
       "<td>2017-01-05</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td></tr><tr><td>100</td>\n",
       "<td>2017-06-01</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td> </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 5</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*mouse_id    *session_date  *neuron_id    activity  \n",
       "+----------+ +------------+ +-----------+ +--------+\n",
       "0            2017-05-15     0             =BLOB=    \n",
       "0            2017-05-19     0             =BLOB=    \n",
       "5            2017-01-05     0             =BLOB=    \n",
       "100          2017-05-25     0             =BLOB=    \n",
       "100          2017-06-01     0             =BLOB=    \n",
       " (Total: 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "Neuron()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `ephys_cell_activity.py` also fills each table with data and import the neuron activity data from `.npy` files to make sure we are all on the same page."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computations in data pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have successfully imported all data we have into our pipeline, it's time for us to start analyzing them! \n",
    "\n",
    "When you perform computations in the DataJoint data pipeline, you focus and design tables in terms of **what** is it that you are computing rather than the **how**. You should think in terms of the \"things\" that you are computing!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's say that we want to compute the satististics, such as mean, standard deviation, and maximum value of each of our neuron's activity traces. Hence we want to compute the neuron's **activity statistics** for each neuron!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the new \"thing\" or entity here is `ActivityStatistics`, where each entry corresponds to the statistics of a single `Neuron`. Let's start designing the table, paying special attention to the dependencies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistics of neuron activities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we create the table to store the statistics, let's think about how we might go about computing interesting statistics for a single neuron.\n",
    "\n",
    "Let's start by fetching one neuron to work with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "keys = Neuron().fetch('KEY')\n",
    "\n",
    "# pick one key\n",
    "key = keys[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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       "            <table border=\"1\" class=\"Relation\">\n",
       "                <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">mouse_id</p>\n",
       "                                <span class=\"djtooltiptext\">Unique animal ID</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">session_date</p>\n",
       "                                <span class=\"djtooltiptext\">date</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">neuron_id</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">activity</p>\n",
       "                                <span class=\"djtooltiptext\">electric activity of the neuron</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>0</td>\n",
       "<td>2017-05-15</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td> </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 1</p></div>\n",
       "            "
      ],
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       "*mouse_id    *session_date  *neuron_id    activity  \n",
       "+----------+ +------------+ +-----------+ +--------+\n",
       "0            2017-05-15     0             =BLOB=    \n",
       " (Total: 1)"
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     "execution_count": 7,
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   "source": [
    "Neuron() & key"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets go ahead and grab the `activity` data stored as numpy array. As we learned in the last session, we can `fetch` it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "activity = (Neuron() & key).fetch('activity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
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       "        4.87463624e-01,  3.59415382e-01,  1.79998644e-01,  3.03695719e-01,\n",
       "        1.62269198e-01,  1.88029365e-01,  7.70099213e-02,  1.03689795e+00,\n",
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       "        1.52493846e-01,  3.84866267e-01,  8.58807488e-02,  6.02818296e-02,\n",
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       "        3.91577648e-01,  2.21325503e-01,  3.04573167e-01,  1.56762722e-01,\n",
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       "       -1.72645376e-01,  1.35352750e-01,  2.53995982e-02,  3.90884089e-02,\n",
       "        5.71430012e-02,  1.80028326e-01,  2.13436539e-02,  1.80930730e-01,\n",
       "        1.03541707e-01,  8.41700237e-01,  8.41336519e-01,  6.15004166e-01,\n",
       "        1.66401572e+00,  1.24331519e+00,  1.10549354e+00,  7.18672227e-01,\n",
       "        7.10294950e-01,  4.68031444e-01,  3.07723842e-01,  3.85116194e-01,\n",
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       "       -5.71529176e-02,  6.48490146e-02, -1.23334789e-02,  2.30623539e-01,\n",
       "        5.12007823e-02,  8.73800247e-02, -9.04408350e-03, -6.99598011e-02,\n",
       "        6.78200687e-03,  1.15865457e-01, -4.18379703e-02, -1.09293334e-01,\n",
       "        3.59053268e-02, -8.22174747e-02,  5.11019451e-03, -6.16000717e-02,\n",
       "        5.44727222e-03, -9.78321940e-02,  7.70249179e-02, -5.88816408e-02,\n",
       "       -6.53482040e-02, -4.06193822e-02, -1.53111574e-01,  6.09304478e-02,\n",
       "        1.30095832e-01,  3.93270237e-02, -1.36006509e-02,  1.24505657e-01,\n",
       "       -8.51880316e-02, -1.04119637e-02,  6.79276317e-02,  4.43370379e-02,\n",
       "       -3.44310185e-02, -1.46022386e-01,  7.97515407e-02, -1.55983167e-02,\n",
       "        2.59952844e-02, -1.92743149e-01, -1.14546115e-01,  1.68661011e-01,\n",
       "        7.21492352e-02, -8.15528413e-02, -1.26248020e-01,  5.42392992e-04,\n",
       "       -3.40266491e-02, -2.67852136e-02, -1.86944094e-02,  1.51646655e-01,\n",
       "       -2.88587981e-02,  7.91561911e-02, -1.49274034e-01, -7.83390380e-02,\n",
       "       -6.39837999e-02,  7.98993185e-03,  5.00516600e-02, -8.89976239e-02])],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's a bit subtle, but `fetch` returns a NumPy array of the attribute, even if the attribute contains a NumPy array. So here, we actually got a NumPy array of NumPy array. We can of course just index into it,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.57887412e-01,  4.47531558e-01,  1.96412988e-01,  3.91114491e-01,\n",
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       "       -7.75270782e-02,  1.04298520e-01,  2.41849780e-03,  4.48712026e-02,\n",
       "        2.52274241e-01, -6.00374401e-02,  5.40367599e-02,  1.73639411e-01,\n",
       "       -7.86343607e-03,  6.22913351e-02,  2.53208560e-02, -1.69087628e-01,\n",
       "       -1.65132452e-01,  2.86023053e-02, -9.29146792e-02, -6.11378213e-02,\n",
       "        2.04881973e-01,  1.88225440e-01, -5.91635886e-02, -5.79665004e-02,\n",
       "        1.44294351e-01,  4.46050806e-02,  7.94540969e-03, -6.35426095e-02,\n",
       "       -1.15997227e-01,  1.83191753e-02,  5.28225263e-02, -1.08532350e-01,\n",
       "        1.44496792e-01,  1.43302657e-01, -1.46524350e-01,  1.18188356e+00,\n",
       "        9.03639320e-01,  6.04454661e-01,  4.49513478e-01,  3.83200563e-01,\n",
       "        5.06436477e-01,  3.49265581e-01,  3.99738778e-01,  2.45887692e-01,\n",
       "        2.81484301e-01,  1.77785305e-01,  1.09703265e-02,  3.70530398e-02,\n",
       "        1.31330082e-01,  8.71425640e-02,  3.33857832e-01, -7.12413968e-02,\n",
       "       -8.97154629e-02,  7.40730721e-02,  5.97128245e-02,  2.34869128e-01,\n",
       "       -3.20819416e-02,  2.65632944e-02, -6.07013335e-02, -1.27002758e-01,\n",
       "        2.16984986e-02,  9.03023410e-03,  2.20839362e-01, -8.91938650e-02,\n",
       "        2.33953850e-01,  1.43573346e-01,  2.75152230e-01,  1.28656643e-01,\n",
       "       -1.16480260e-02,  9.03293415e-04, -1.18839774e-01,  7.86330161e-02,\n",
       "        4.00007325e-02, -9.80397383e-02, -4.42695682e-02, -1.49883960e-02,\n",
       "       -1.69976818e-01, -7.78312668e-03, -1.94404123e-02, -8.03773902e-02,\n",
       "       -6.05579523e-02,  1.95069803e-02, -4.52126912e-02,  1.74422122e-01,\n",
       "       -1.99238260e-01, -3.91236052e-02,  5.89841644e-02, -6.67758085e-02,\n",
       "       -2.73644241e-02,  2.19098411e-01,  4.46983268e-02, -3.66252078e-02,\n",
       "        4.43283833e-02,  2.25697557e-02,  1.95433005e-01,  4.09668282e-02,\n",
       "       -5.43391320e-02, -4.33887655e-02,  1.47436220e-01,  1.65408829e-01,\n",
       "       -9.09196975e-02,  1.07774571e-01,  8.58332038e-02,  4.33849188e-03,\n",
       "        1.53411792e-01, -3.97305805e-02, -2.14871074e-02, -6.97095042e-02,\n",
       "        3.00432966e-02, -3.16463544e-02,  3.07840191e-03, -2.16733667e-02,\n",
       "       -3.18560339e-02,  3.66698288e-02, -3.43363193e-02,  2.06746714e-01,\n",
       "       -7.60502887e-02, -1.71189864e-01,  1.06735423e-01, -3.61907100e-02,\n",
       "       -1.88581197e-01,  1.55249578e-01,  6.59242205e-03, -4.46976133e-02,\n",
       "        1.86226099e-01, -1.49095939e-01, -2.47424070e-02,  1.07958794e-01,\n",
       "        7.10083147e-02,  5.70557055e-02,  8.96181297e-03, -2.11594738e-01,\n",
       "       -5.03600676e-02, -2.56128830e-02, -4.02507671e-02, -1.99935220e-01,\n",
       "        1.62758261e-02, -2.10041565e-02,  3.83952841e-02, -1.20534363e-02,\n",
       "       -5.10847887e-02, -4.33253439e-02, -1.88213705e-02,  4.58028139e-02,\n",
       "       -1.80158629e-02,  1.43620301e-01, -1.70544931e-01,  1.77761139e-03,\n",
       "       -2.08095349e-02,  3.34326742e-02,  4.40900132e-02,  6.77012418e-02,\n",
       "       -9.25022511e-02,  8.56213543e-02, -1.64681050e-02,  1.51338327e-02,\n",
       "        5.40416644e-02,  1.33405086e-02, -6.50942134e-02, -4.35238219e-03,\n",
       "        6.74543989e-02,  1.83795805e-02, -8.89084565e-03, -8.38116481e-02,\n",
       "       -8.85186574e-02, -8.22233688e-02,  1.63380941e-01,  1.93323312e-01,\n",
       "       -2.52080190e-03,  1.55618202e-01, -3.48718916e-02, -4.39154177e-03,\n",
       "       -1.50650829e-01, -3.85879559e-02,  1.23910600e-01,  1.26416145e-01,\n",
       "        4.57916224e-02,  8.45197602e-02,  6.91956186e-02,  1.29578141e-01,\n",
       "       -3.42267972e-02,  6.38280360e-02, -1.71677496e-02, -1.92718154e-01,\n",
       "        1.16080879e+00,  9.21546435e-01,  5.16635067e-01,  7.23468589e-01,\n",
       "        3.09147097e-01,  4.49592539e-01,  2.62198787e-01,  1.89167293e-01,\n",
       "        6.80711173e-02,  1.16969270e-01,  1.50577864e-01,  3.10725745e-02,\n",
       "       -9.74933170e-03, -1.04268235e-01,  6.56952998e-02,  1.40981993e-01,\n",
       "       -5.38736937e-02,  9.89558899e-02, -4.52553764e-02, -7.47028216e-02,\n",
       "       -2.45633731e-02,  4.91456086e-03,  2.24777465e-02,  5.19085415e-02,\n",
       "       -7.12450557e-02,  8.96366331e-02,  5.53691200e-02, -7.74717503e-02,\n",
       "       -6.71054350e-02, -6.39028129e-02,  3.12679383e-02, -1.84819956e-01,\n",
       "        2.07870708e-02, -1.31116132e-02,  2.76283840e-02,  4.87966338e-02,\n",
       "        9.84081330e-02, -6.95627406e-02,  2.36105975e-01,  1.45560427e-02,\n",
       "        8.64798612e-02, -3.32754493e-03,  4.40784273e-02,  8.92873175e-03,\n",
       "       -7.64205487e-03,  6.77330030e-02, -6.38034776e-02,  2.32453845e-02,\n",
       "       -1.76792583e-02, -1.22653199e-01,  2.60944692e-01, -8.19576302e-03,\n",
       "        1.21502703e-01, -1.17436798e-01,  5.91152919e-02,  1.45069334e-02,\n",
       "       -2.77748199e-02,  8.70198108e-02,  1.09750973e+00,  7.82291035e-01,\n",
       "        7.21941782e-01,  4.34205229e-01,  4.95265468e-01,  4.64622108e-01,\n",
       "        2.74796291e-01,  2.18068847e-01,  3.26694544e-01,  1.82281257e-01,\n",
       "        5.71407916e-02,  1.35026995e-01,  6.95359173e-02,  1.19570987e-01,\n",
       "        2.85917443e-02,  1.68845114e-01,  2.88802329e-02,  1.38069548e-01,\n",
       "        4.18650358e-02, -1.33298268e-01, -5.40673322e-02, -2.12729776e-02,\n",
       "       -6.04156952e-02,  1.01418967e-01,  6.41443313e-02,  3.63175454e-02,\n",
       "        8.56384845e-02, -1.15120677e-02,  1.10539191e-01, -1.24283472e-01,\n",
       "       -1.81287322e-01, -1.05492936e-01, -1.39418774e-01, -8.96223894e-02,\n",
       "        5.29831286e-02, -9.06341080e-02,  1.11775639e-01,  2.52898789e-01,\n",
       "        1.37988335e-01, -1.99189622e-03,  7.86036381e-02,  6.69568198e-02,\n",
       "       -1.39980384e-02, -8.60498166e-02,  9.52417978e-02,  1.19438557e-02,\n",
       "        3.25813543e-02,  6.52716977e-02, -1.01625274e-02,  1.38016286e-01,\n",
       "        1.68098374e-01,  2.08439386e-02,  5.07228859e-02,  1.64968265e-01,\n",
       "       -1.94238088e-01, -2.26970816e-01,  1.90859273e-01,  1.55852829e-02,\n",
       "        9.92850491e-02,  4.34517009e-02, -6.93774354e-03,  2.37587998e-02,\n",
       "       -1.10832949e-01,  1.28747269e-01, -8.28914026e-02,  1.84212900e-01,\n",
       "        3.88899986e-02,  2.75027529e-02, -4.41476612e-02,  9.83779242e-03,\n",
       "       -7.59881145e-02, -5.62181424e-02,  4.71012725e-02, -2.83273040e-02,\n",
       "       -8.95039250e-02, -1.73206060e-01,  8.04575793e-02,  4.89516889e-02,\n",
       "       -5.36636280e-02,  1.11401613e-01,  7.41568054e-02, -1.58587913e-02,\n",
       "        1.38660466e-02,  9.15840051e-01,  9.35520924e-01,  8.20295289e-01,\n",
       "        4.16726598e-01,  4.10924305e-01,  4.29348959e-01,  1.88910731e-01,\n",
       "        2.71345351e-01,  3.36252176e-01,  7.99709853e-02,  1.18817313e+00,\n",
       "        1.12547896e+00,  7.10084698e-01,  5.19828662e-01,  4.90804498e-01,\n",
       "        3.38881990e-01,  3.76448904e-01,  1.18645994e+00,  1.06435236e+00,\n",
       "        1.94919081e+00,  1.41683039e+00,  1.43564448e+00,  1.04081010e+00,\n",
       "        7.90455203e-01,  5.63441762e-01,  4.12188514e-01,  4.14429943e-01,\n",
       "        3.94528788e-01,  1.34387624e+00,  9.47544414e-01,  1.02847705e+00,\n",
       "        7.18964295e-01,  5.60308504e-01,  5.55145624e-01,  3.72224551e-01,\n",
       "        2.14687813e-01,  3.13142646e-01,  2.50581325e-01,  2.09818408e-02,\n",
       "        1.49008637e-01,  2.03570380e-01,  5.05068803e-02,  1.20428405e-01,\n",
       "        1.11461852e+00,  8.81700992e-01,  1.81056078e+00,  1.52936936e+00,\n",
       "        1.16525616e+00,  8.56957933e-01,  9.04530004e-01,  5.83086508e-01,\n",
       "        6.42397646e-01,  3.49120161e-01,  3.95190201e-01,  3.04481134e-01,\n",
       "        3.28985466e-01,  3.22194130e-01, -4.78729019e-02,  3.06232461e-01,\n",
       "       -4.02626838e-02,  1.23166186e-01,  4.93381103e-02,  1.10558060e-01,\n",
       "        2.07875455e-02,  2.31902493e-01, -1.12148445e-01, -1.28066781e-02,\n",
       "       -4.42326632e-02, -2.45775081e-02, -1.04753587e-01,  8.97585073e-02,\n",
       "       -2.27588842e-02,  8.57118601e-02,  5.94292811e-03,  1.68235395e-01,\n",
       "        1.74473514e-01,  8.15705527e-02, -1.62542119e-01, -1.50153441e-01,\n",
       "       -3.82194543e-01,  7.89967120e-02,  1.24402821e-01, -7.45099363e-02,\n",
       "        1.02042814e-01, -1.54368942e-02,  1.51022438e-01,  5.45528677e-02,\n",
       "        2.11612947e-01,  3.05863774e-02,  1.13577596e-01, -9.28138132e-02,\n",
       "        1.05944320e+00,  8.22694192e-01,  6.86775834e-01,  5.26383175e-01,\n",
       "        4.87463624e-01,  3.59415382e-01,  1.79998644e-01,  3.03695719e-01,\n",
       "        1.62269198e-01,  1.88029365e-01,  7.70099213e-02,  1.03689795e+00,\n",
       "        8.59575605e-01,  6.84051568e-01,  4.93971211e-01,  5.43776616e-01,\n",
       "        3.61937201e-01,  2.07496283e-01,  2.38354097e-01,  2.53068762e-01,\n",
       "        1.52493846e-01,  3.84866267e-01,  8.58807488e-02,  6.02818296e-02,\n",
       "        1.24288796e-01,  2.27447449e-01,  2.01378209e-02, -7.01849363e-02,\n",
       "        1.23180928e-01, -4.31365196e-02, -1.05023995e-01,  1.57140085e-02,\n",
       "        7.78643730e-02,  1.16315021e-01, -1.95906107e-01, -9.75358646e-03,\n",
       "       -1.03878208e-01,  3.84609155e-02,  1.72760630e-01, -3.90920446e-02,\n",
       "        2.92722474e-03, -1.35533373e-02,  1.13720826e-02, -5.35428824e-02,\n",
       "       -1.76178376e-01, -7.24951113e-02, -1.95280857e-02,  1.25993789e-01,\n",
       "        9.37632205e-02,  8.89435244e-02,  6.00251479e-02, -4.69134844e-02,\n",
       "       -4.78302608e-02, -9.85106634e-02,  2.43915467e-01,  1.59116000e-01,\n",
       "       -1.77494253e-01,  7.37983421e-02, -8.42027434e-02, -1.41031791e-01,\n",
       "       -1.17614325e-01, -1.72387392e-01,  1.13488435e+00,  7.94030330e-01,\n",
       "        7.05362490e-01,  4.25140173e-01,  3.77941998e-01,  4.51229738e-01,\n",
       "        3.91577648e-01,  2.21325503e-01,  3.04573167e-01,  1.56762722e-01,\n",
       "        1.35208947e-01,  1.72394809e-01,  8.77570143e-04, -4.35287122e-02,\n",
       "       -1.72645376e-01,  1.35352750e-01,  2.53995982e-02,  3.90884089e-02,\n",
       "        5.71430012e-02,  1.80028326e-01,  2.13436539e-02,  1.80930730e-01,\n",
       "        1.03541707e-01,  8.41700237e-01,  8.41336519e-01,  6.15004166e-01,\n",
       "        1.66401572e+00,  1.24331519e+00,  1.10549354e+00,  7.18672227e-01,\n",
       "        7.10294950e-01,  4.68031444e-01,  3.07723842e-01,  3.85116194e-01,\n",
       "        3.39574633e-01,  8.18889780e-02,  1.33506454e-01,  1.14196508e-02,\n",
       "       -5.71529176e-02,  6.48490146e-02, -1.23334789e-02,  2.30623539e-01,\n",
       "        5.12007823e-02,  8.73800247e-02, -9.04408350e-03, -6.99598011e-02,\n",
       "        6.78200687e-03,  1.15865457e-01, -4.18379703e-02, -1.09293334e-01,\n",
       "        3.59053268e-02, -8.22174747e-02,  5.11019451e-03, -6.16000717e-02,\n",
       "        5.44727222e-03, -9.78321940e-02,  7.70249179e-02, -5.88816408e-02,\n",
       "       -6.53482040e-02, -4.06193822e-02, -1.53111574e-01,  6.09304478e-02,\n",
       "        1.30095832e-01,  3.93270237e-02, -1.36006509e-02,  1.24505657e-01,\n",
       "       -8.51880316e-02, -1.04119637e-02,  6.79276317e-02,  4.43370379e-02,\n",
       "       -3.44310185e-02, -1.46022386e-01,  7.97515407e-02, -1.55983167e-02,\n",
       "        2.59952844e-02, -1.92743149e-01, -1.14546115e-01,  1.68661011e-01,\n",
       "        7.21492352e-02, -8.15528413e-02, -1.26248020e-01,  5.42392992e-04,\n",
       "       -3.40266491e-02, -2.67852136e-02, -1.86944094e-02,  1.51646655e-01,\n",
       "       -2.88587981e-02,  7.91561911e-02, -1.49274034e-01, -7.83390380e-02,\n",
       "       -6.39837999e-02,  7.98993185e-03,  5.00516600e-02, -8.89976239e-02])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "but if we knew that there was only one item, we can use `fetch1` instead to save some trouble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "activity = (Neuron() & key).fetch1('activity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.57887412e-01,  4.47531558e-01,  1.96412988e-01,  3.91114491e-01,\n",
       "        1.76695176e-01,  3.80088116e-01,  9.36353656e-02,  1.10668001e-02,\n",
       "        1.14509759e-01,  1.65947705e-01,  1.02058476e-01,  1.15037690e-01,\n",
       "        2.18115225e-01,  1.34076355e-01,  7.74560696e-02,  9.19639027e-02,\n",
       "       -7.60109670e-03,  1.12814011e+00,  9.48711628e-01,  7.77781245e-01,\n",
       "        5.46847747e-01,  3.91930226e-01,  2.19542222e-01,  3.92001858e-01,\n",
       "        3.17729202e-01,  2.97934834e-01,  1.85589403e-01,  3.04215179e-01,\n",
       "        8.75626287e-02, -1.76018214e-02,  1.43719730e-01, -1.28348781e-01,\n",
       "       -4.38344653e-02,  1.57370375e-01,  1.70233992e-01,  8.98556797e-01,\n",
       "        9.36896773e-01,  1.67418776e+00,  2.48160598e+00,  1.89737445e+00,\n",
       "        1.45746470e+00,  1.32137967e+00,  1.05349900e+00,  1.88503636e+00,\n",
       "        1.53324759e+00,  1.34566597e+00,  1.93924029e+00,  1.58839106e+00,\n",
       "        1.26074176e+00,  1.21973014e+00,  9.80495008e-01,  7.66163859e-01,\n",
       "        1.59531686e+00,  1.10087015e+00,  1.15513304e+00,  8.56314992e-01,\n",
       "        7.37593259e-01,  6.50912336e-01,  3.78222011e-01,  3.33381961e-01,\n",
       "        3.36530722e-01,  2.72070063e-01,  2.21307773e-02,  1.27166159e-01,\n",
       "        1.02442286e-01,  7.17979754e-02,  1.24111799e-01,  2.95003486e-02,\n",
       "        5.81101732e-02,  7.83296477e-02,  1.07236342e-01, -7.71500819e-02,\n",
       "       -4.03102332e-02,  9.32218744e-02, -1.39751021e-01,  4.34990973e-02,\n",
       "       -3.62238166e-02,  5.65840332e-02,  5.83848056e-03, -1.86444246e-04,\n",
       "        5.48341851e-02,  5.23098255e-03, -3.18617746e-02, -1.09971236e-01,\n",
       "       -1.19861696e-01,  8.11585903e-02,  5.25812482e-02, -5.30601889e-02,\n",
       "        5.57417328e-02,  1.53998941e-02, -3.64440351e-02, -2.59108524e-01,\n",
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       "        4.16726598e-01,  4.10924305e-01,  4.29348959e-01,  1.88910731e-01,\n",
       "        2.71345351e-01,  3.36252176e-01,  7.99709853e-02,  1.18817313e+00,\n",
       "        1.12547896e+00,  7.10084698e-01,  5.19828662e-01,  4.90804498e-01,\n",
       "        3.38881990e-01,  3.76448904e-01,  1.18645994e+00,  1.06435236e+00,\n",
       "        1.94919081e+00,  1.41683039e+00,  1.43564448e+00,  1.04081010e+00,\n",
       "        7.90455203e-01,  5.63441762e-01,  4.12188514e-01,  4.14429943e-01,\n",
       "        3.94528788e-01,  1.34387624e+00,  9.47544414e-01,  1.02847705e+00,\n",
       "        7.18964295e-01,  5.60308504e-01,  5.55145624e-01,  3.72224551e-01,\n",
       "        2.14687813e-01,  3.13142646e-01,  2.50581325e-01,  2.09818408e-02,\n",
       "        1.49008637e-01,  2.03570380e-01,  5.05068803e-02,  1.20428405e-01,\n",
       "        1.11461852e+00,  8.81700992e-01,  1.81056078e+00,  1.52936936e+00,\n",
       "        1.16525616e+00,  8.56957933e-01,  9.04530004e-01,  5.83086508e-01,\n",
       "        6.42397646e-01,  3.49120161e-01,  3.95190201e-01,  3.04481134e-01,\n",
       "        3.28985466e-01,  3.22194130e-01, -4.78729019e-02,  3.06232461e-01,\n",
       "       -4.02626838e-02,  1.23166186e-01,  4.93381103e-02,  1.10558060e-01,\n",
       "        2.07875455e-02,  2.31902493e-01, -1.12148445e-01, -1.28066781e-02,\n",
       "       -4.42326632e-02, -2.45775081e-02, -1.04753587e-01,  8.97585073e-02,\n",
       "       -2.27588842e-02,  8.57118601e-02,  5.94292811e-03,  1.68235395e-01,\n",
       "        1.74473514e-01,  8.15705527e-02, -1.62542119e-01, -1.50153441e-01,\n",
       "       -3.82194543e-01,  7.89967120e-02,  1.24402821e-01, -7.45099363e-02,\n",
       "        1.02042814e-01, -1.54368942e-02,  1.51022438e-01,  5.45528677e-02,\n",
       "        2.11612947e-01,  3.05863774e-02,  1.13577596e-01, -9.28138132e-02,\n",
       "        1.05944320e+00,  8.22694192e-01,  6.86775834e-01,  5.26383175e-01,\n",
       "        4.87463624e-01,  3.59415382e-01,  1.79998644e-01,  3.03695719e-01,\n",
       "        1.62269198e-01,  1.88029365e-01,  7.70099213e-02,  1.03689795e+00,\n",
       "        8.59575605e-01,  6.84051568e-01,  4.93971211e-01,  5.43776616e-01,\n",
       "        3.61937201e-01,  2.07496283e-01,  2.38354097e-01,  2.53068762e-01,\n",
       "        1.52493846e-01,  3.84866267e-01,  8.58807488e-02,  6.02818296e-02,\n",
       "        1.24288796e-01,  2.27447449e-01,  2.01378209e-02, -7.01849363e-02,\n",
       "        1.23180928e-01, -4.31365196e-02, -1.05023995e-01,  1.57140085e-02,\n",
       "        7.78643730e-02,  1.16315021e-01, -1.95906107e-01, -9.75358646e-03,\n",
       "       -1.03878208e-01,  3.84609155e-02,  1.72760630e-01, -3.90920446e-02,\n",
       "        2.92722474e-03, -1.35533373e-02,  1.13720826e-02, -5.35428824e-02,\n",
       "       -1.76178376e-01, -7.24951113e-02, -1.95280857e-02,  1.25993789e-01,\n",
       "        9.37632205e-02,  8.89435244e-02,  6.00251479e-02, -4.69134844e-02,\n",
       "       -4.78302608e-02, -9.85106634e-02,  2.43915467e-01,  1.59116000e-01,\n",
       "       -1.77494253e-01,  7.37983421e-02, -8.42027434e-02, -1.41031791e-01,\n",
       "       -1.17614325e-01, -1.72387392e-01,  1.13488435e+00,  7.94030330e-01,\n",
       "        7.05362490e-01,  4.25140173e-01,  3.77941998e-01,  4.51229738e-01,\n",
       "        3.91577648e-01,  2.21325503e-01,  3.04573167e-01,  1.56762722e-01,\n",
       "        1.35208947e-01,  1.72394809e-01,  8.77570143e-04, -4.35287122e-02,\n",
       "       -1.72645376e-01,  1.35352750e-01,  2.53995982e-02,  3.90884089e-02,\n",
       "        5.71430012e-02,  1.80028326e-01,  2.13436539e-02,  1.80930730e-01,\n",
       "        1.03541707e-01,  8.41700237e-01,  8.41336519e-01,  6.15004166e-01,\n",
       "        1.66401572e+00,  1.24331519e+00,  1.10549354e+00,  7.18672227e-01,\n",
       "        7.10294950e-01,  4.68031444e-01,  3.07723842e-01,  3.85116194e-01,\n",
       "        3.39574633e-01,  8.18889780e-02,  1.33506454e-01,  1.14196508e-02,\n",
       "       -5.71529176e-02,  6.48490146e-02, -1.23334789e-02,  2.30623539e-01,\n",
       "        5.12007823e-02,  8.73800247e-02, -9.04408350e-03, -6.99598011e-02,\n",
       "        6.78200687e-03,  1.15865457e-01, -4.18379703e-02, -1.09293334e-01,\n",
       "        3.59053268e-02, -8.22174747e-02,  5.11019451e-03, -6.16000717e-02,\n",
       "        5.44727222e-03, -9.78321940e-02,  7.70249179e-02, -5.88816408e-02,\n",
       "       -6.53482040e-02, -4.06193822e-02, -1.53111574e-01,  6.09304478e-02,\n",
       "        1.30095832e-01,  3.93270237e-02, -1.36006509e-02,  1.24505657e-01,\n",
       "       -8.51880316e-02, -1.04119637e-02,  6.79276317e-02,  4.43370379e-02,\n",
       "       -3.44310185e-02, -1.46022386e-01,  7.97515407e-02, -1.55983167e-02,\n",
       "        2.59952844e-02, -1.92743149e-01, -1.14546115e-01,  1.68661011e-01,\n",
       "        7.21492352e-02, -8.15528413e-02, -1.26248020e-01,  5.42392992e-04,\n",
       "       -3.40266491e-02, -2.67852136e-02, -1.86944094e-02,  1.51646655e-01,\n",
       "       -2.88587981e-02,  7.91561911e-02, -1.49274034e-01, -7.83390380e-02,\n",
       "       -6.39837999e-02,  7.98993185e-03,  5.00516600e-02, -8.89976239e-02])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can compute some statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2073565345570553"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ENTER YOUR CODE! - compute the mean of the activity\n",
    "mean_activity = activity.mean()\n",
    "mean_activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4008667662130083"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ENTER YOUR CODE! - compute the standard deviation of the activity\n",
    "std_activity = activity.std()\n",
    "std_activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.481605975684647"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ENTER YOUR CODE! - compute the maximum of the activity\n",
    "max_activity = activity.max()\n",
    "max_activity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives us a good idea on how to:\n",
    "1. fetch the activity of a neuron knowing its primary key, and\n",
    "2. compute interesting statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Armed with this knowledge, let's go ahead and define the table for `ActivityStatistics`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining `ActivityStatistics` table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's go ahead and work out the definition of the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "@schema\n",
    "class ActivityStatistics(dj.Computed):\n",
    "    definition = \"\"\"\n",
    "    -> Neuron\n",
    "    ---\n",
    "    mean: float    # mean activity\n",
    "    stdev: float   # standard deviation of activity\n",
    "    max: float     # maximum activity\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Did you notice that we are now inheriting from `dj.Computed`?  `Computed` is yet another table tier that signifies that **the entries of this table are computed using data in other tables**. `Computed` tables are represented as red circles in the Diagram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": "<svg height=\"247pt\" viewBox=\"0.00 0.00 109.00 247.00\" width=\"109pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 243)\">\n<title>%3</title>\n<polygon fill=\"white\" points=\"-4,4 -4,-243 105,-243 105,4 -4,4\" stroke=\"none\"/>\n<!-- Mouse -->\n<g class=\"node\" id=\"node1\"><title>Mouse</title>\n<g id=\"a_node1\"><a xlink:title=\"mouse_id             \r\n------------------------------\r\ndob=null             \r\nsex=&quot;unknown&quot;        \r\n\">\n<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"76.5,-239 24.5,-239 24.5,-204 76.5,-204 76.5,-239\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"32.5\" y=\"-219.4\">Mouse</text>\n</a>\n</g>\n</g>\n<!-- Session -->\n<g class=\"node\" id=\"node2\"><title>Session</title>\n<g id=\"a_node2\"><a xlink:title=\"→ Mouse\r\nsession_date         \r\n------------------------------\r\nexperiment_setup     \r\nexperimenter         \r\ndata_path=&quot;&quot;         \r\n\">\n<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"80,-168 21,-168 21,-133 80,-133 80,-168\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"29\" y=\"-148.4\">Session</text>\n</a>\n</g>\n</g>\n<!-- Mouse&#45;&gt;Session -->\n<g class=\"edge\" id=\"edge1\"><title>Mouse-&gt;Session</title>\n<path d=\"M50.5,-203.797C50.5,-192.949 50.5,-178.867 50.5,-168.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n</g>\n<!-- Neuron -->\n<g class=\"node\" id=\"node4\"><title>Neuron</title>\n<g id=\"a_node4\"><a xlink:title=\"→ Session\r\nneuron_id            \r\n------------------------------\r\nactivity             \r\n\">\n<ellipse cx=\"50.5\" cy=\"-79.5\" fill=\"#00007f\" fill-opacity=\"0.250980\" rx=\"33.5018\" ry=\"17.5\" stroke=\"#00007f\" stroke-opacity=\"0.250980\"/>\n<text fill=\"#00007f\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"31.5\" y=\"-77.4\">Neuron</text>\n</a>\n</g>\n</g>\n<!-- Session&#45;&gt;Neuron -->\n<g class=\"edge\" id=\"edge2\"><title>Session-&gt;Neuron</title>\n<path d=\"M50.5,-132.797C50.5,-121.949 50.5,-107.867 50.5,-97.0492\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n</g>\n<!-- ActivityStatistics -->\n<g class=\"node\" id=\"node3\"><title>ActivityStatistics</title>\n<g id=\"a_node3\"><a xlink:title=\"→ Neuron\r\n------------------------------\r\nmean                 \r\nstdev                \r\nmax                  \r\n\">\n<ellipse cx=\"50.5\" cy=\"-13\" fill=\"#ff0000\" fill-opacity=\"0.125490\" rx=\"13\" ry=\"13\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\"/>\n<text fill=\"#7f0000\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"50.5\" y=\"-9.9\">ActivityStatistics</text>\n</a>\n</g>\n</g>\n<!-- Neuron&#45;&gt;ActivityStatistics -->\n<g class=\"edge\" id=\"edge3\"><title>Neuron-&gt;ActivityStatistics</title>\n<path d=\"M50.5,-61.8896C50.5,-50.6919 50.5,-36.1749 50.5,-26.0728\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n</g>\n</g>\n</svg>",
      "text/plain": [
       "<datajoint.diagram.Diagram at 0x1bb3fea2908>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj.Diagram(schema)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just like the `Imported` tables, `Computed` tables make use of the same `make` and `populate` logic for defining new entries in the table. Let's go ahead and implement `make` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ENTER YOUR CODE! - complete the `make` method\n",
    "\n",
    "@schema\n",
    "class ActivityStatistics(dj.Computed):\n",
    "    definition = \"\"\"\n",
    "    -> Neuron\n",
    "    ---\n",
    "    mean: float    # mean activity\n",
    "    stdev: float   # standard deviation of activity\n",
    "    max: float     # maximum activity\n",
    "    \"\"\"\n",
    "    \n",
    "    def make(self, key):\n",
    "        activity = (Neuron() & key).fetch1('activity')    # fetch activity as NumPy array\n",
    "\n",
    "        # compute various statistics on activity\n",
    "        key['mean'] = activity.mean()  # compute mean\n",
    "        key['stdev'] = activity.std()  # compute standard deviation\n",
    "        key['max'] = activity.max()    # compute max\n",
    "        self.insert1(key)\n",
    "        print('Computed statistics for neuron_id {neuron_id} mouse_id {mouse_id} session_date {session_date}'.format(**key))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's go ahead and populate the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed statistics for neuron_id 0 mouse_id 0 session_date 2017-05-15\n",
      "Computed statistics for neuron_id 0 mouse_id 0 session_date 2017-05-19\n",
      "Computed statistics for neuron_id 0 mouse_id 5 session_date 2017-01-05\n",
      "Computed statistics for neuron_id 0 mouse_id 100 session_date 2017-05-25\n",
      "Computed statistics for neuron_id 0 mouse_id 100 session_date 2017-06-01\n"
     ]
    }
   ],
   "source": [
    "ActivityStatistics.populate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        \n",
       "        <style type=\"text/css\">\n",
       "            .Relation{\n",
       "                border-collapse:collapse;\n",
       "            }\n",
       "            .Relation th{\n",
       "                background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
       "                font-weight: normal; font-family: monospace; font-size: 100%;\n",
       "            }\n",
       "            .Relation td{\n",
       "                padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
       "            }\n",
       "            .Relation tr:nth-child(odd){\n",
       "                background: #ffffff;\n",
       "            }\n",
       "            .Relation tr:nth-child(even){\n",
       "                background: #f3f1ff;\n",
       "            }\n",
       "            /* Tooltip container */\n",
       "            .djtooltip {\n",
       "            }\n",
       "            /* Tooltip text */\n",
       "            .djtooltip .djtooltiptext {\n",
       "                visibility: hidden;\n",
       "                width: 120px;\n",
       "                background-color: black;\n",
       "                color: #fff;\n",
       "                text-align: center;\n",
       "                padding: 5px 0;\n",
       "                border-radius: 6px;\n",
       "                /* Position the tooltip text - see examples below! */\n",
       "                position: absolute;\n",
       "                z-index: 1;\n",
       "            }\n",
       "            #primary {\n",
       "                font-weight: bold;\n",
       "                color: black;\n",
       "            }\n",
       "\n",
       "            #nonprimary {\n",
       "                font-weight: normal;\n",
       "                color: white;\n",
       "            }\n",
       "\n",
       "            /* Show the tooltip text when you mouse over the tooltip container */\n",
       "            .djtooltip:hover .djtooltiptext {\n",
       "                visibility: visible;\n",
       "            }\n",
       "        </style>\n",
       "        \n",
       "        <b></b>\n",
       "            <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "            <table border=\"1\" class=\"Relation\">\n",
       "                <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">mouse_id</p>\n",
       "                                <span class=\"djtooltiptext\">Unique animal ID</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">session_date</p>\n",
       "                                <span class=\"djtooltiptext\">date</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">neuron_id</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">mean</p>\n",
       "                                <span class=\"djtooltiptext\">mean activity</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">stdev</p>\n",
       "                                <span class=\"djtooltiptext\">standard deviation of activity</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">max</p>\n",
       "                                <span class=\"djtooltiptext\">maximum activity</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>0</td>\n",
       "<td>2017-05-15</td>\n",
       "<td>0</td>\n",
       "<td>0.207357</td>\n",
       "<td>0.400867</td>\n",
       "<td>2.48161</td></tr><tr><td>0</td>\n",
       "<td>2017-05-19</td>\n",
       "<td>0</td>\n",
       "<td>0.13274</td>\n",
       "<td>0.291462</td>\n",
       "<td>1.82805</td></tr><tr><td>5</td>\n",
       "<td>2017-01-05</td>\n",
       "<td>0</td>\n",
       "<td>0.0891786</td>\n",
       "<td>0.236412</td>\n",
       "<td>1.37389</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>0</td>\n",
       "<td>0.21907</td>\n",
       "<td>0.328783</td>\n",
       "<td>1.76383</td></tr><tr><td>100</td>\n",
       "<td>2017-06-01</td>\n",
       "<td>0</td>\n",
       "<td>0.0873266</td>\n",
       "<td>0.237858</td>\n",
       "<td>1.32454</td> </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 5</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*mouse_id    *session_date  *neuron_id    mean          stdev        max        \n",
       "+----------+ +------------+ +-----------+ +-----------+ +----------+ +---------+\n",
       "0            2017-05-15     0             0.207357      0.400867     2.48161    \n",
       "0            2017-05-19     0             0.13274       0.291462     1.82805    \n",
       "5            2017-01-05     0             0.0891786     0.236412     1.37389    \n",
       "100          2017-05-25     0             0.21907       0.328783     1.76383    \n",
       "100          2017-06-01     0             0.0873266     0.237858     1.32454    \n",
       " (Total: 5)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ActivityStatistics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voila!! You have computed statistics for each neuron activity!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spike detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's go ahead and tackle a more challenging computation. While having raw neural traces in itself can be quite interesting, nothing is as exciting as spikes! Let's take a look at the neurons activities and plot them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all keys\n",
    "keys = Neuron.fetch('KEY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fetch all activities - returned as NumPy array of NumPy arrays\n",
    "activities = (Neuron & keys).fetch('activity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, len(activities), figsize=(16, 4))\n",
    "for activity, ax in zip(activities, axs.ravel()):\n",
    "    ax.plot(activity)\n",
    "    ax.set_xlabel('Time')\n",
    "    ax.set_ylabel('Activity')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now focus on one trace instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "key = keys[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "activity = (Neuron & key).fetch1('activity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 300)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(activity)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Activity')\n",
    "plt.xlim([0, 300])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perhaps we can use threshold to detect when a spike occurs. Threshold of `0.5` may be a good start."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 300)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold = 0.5\n",
    "\n",
    "# find activity above threshold\n",
    "above_thrs = (activity > threshold).astype(np.int)   \n",
    "\n",
    "plt.plot(activity)\n",
    "plt.plot(above_thrs)\n",
    "plt.xlabel('Time')\n",
    "plt.xlim([0, 300])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to find out **when** it crossed the threshold. That is, find time bins where `above_thrs` goes from 0 (`False`) to 1 (`True`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 300)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CBgB8HoAKAJTSuwC8C8BHCCE6gDSAG6kzbrMFscNVLVPSCiueenAqwzQGiaArzDQGiRCE/dzHMFdjaLWetQIG1xiaxpTk0Bh4Vd92gj9z8aBql7NXZYKeqB9PHmgdwaCb1M5jAJi1wa0Vq27QRSu82RDBQCl9d5nX7wBwRyPO1SzY4aqWE6mvIwQAGJxKQzNMy/nMNIZkVkfYx01J+R2A3bhdaAwtid5kGh83b0YCCoZbyLRSKfzzdYV9OD6eAgAoMvMxJLM60jnDttk3M06NAbDM0B4+hpRmgFIKy+CyYIjM5xrhu31eXZVrDKem0tBNClUm6OaCIaMh5GcT1qkx8B2e0BhaE73JnM9cUMUC6pzyK+0AN73yaD+AhYv3WL+3gp+BUloQlQQwB7S7KYmC0sUJThGCoUb4JOWZz0tjAUiEaQy6YUKRJNv5nMzkNYYZoTG0DXmNoTkEAxdQyzsCmEjlWqqwXCVoBis+x/ODAKYx8HpJvChlM8Mjm50agzM51gnfeCxG50chGGokH5VErP8lLIsFcGoqDc2KSuqyBYOGgCqBkCKNge84hcbQkjSbj4FvNPo7Q6AUTdlkvh5Yj3WCjlC+qoAiE9ccoWaFR47JBYJBci1hwu/nYnwuIRhqRC+qrgqw9PxTk2nopglVyvsYMpoJQohVL8mhMZiiiF4r03RRSYYJQoA+y6x5err5TSvVoFs91rmGADAfX9AlR6hZMWxLQ6GPwW0N4KZmIRhaCB46pziiC/o6gxicTtt5DFErEmn90igAIOyXXaOSmiUOXlAdRpNlrmtWOefeKBMMwy2UDVwJOYOFgXcWaQxhf3P2NHCDbybkYudzCY1hMQReQ6KSXonYzmepUGN4aOcQgqoMVWa9oB/8+FXo72QRS2GfUhCXnC+iJwRDK8If3GbSGFSJYGmMCYZWcMZWg6abUGWCDqePQSIIqa1jSjKMuRpDwDc3Kok7qYHFEXhCMNSIXlRdFQCWxwPQTYpkVrdv/EX9HfbrIb9cmPnMo5KEKakl0Zst89kwoSoSusI+KBJpW1NSh8OURAhxlJVoflOSrTE4TNABRZ4TeeTs370YTYiEKalGuMPRKfljgfyEdSawcEJWTwYOFy66SQuarAhag2YTDDmDQpEkSBIrE9GepiSCuENjAICQ7WNo/g2Wq4/BN9f57CyhvhgluYVgqJHi6qoAEAvmFTCniYkT9smF1VXbvH5+u8N9DM1iStINEz5rQ9IbC7SpKUkqiEoC8hVKW8GUZEclkdI+Bs3RN352EXJShGCoEd1k7TudTqRowBlfPVdjCPuVwuqqjpsvBEPr0Yw+BkXmeTV+DCfaTDDYzudCjUGSiJU93PymJKOE87mgu6Nj0yjyGFqInOMh5DhNSao896uNB1WMJrJ2FEuhxtD8ux1BIc1WXVWzMu4BlnDZnj4GYmsITkJF2nizYpuSHBtHvyqDFnV3dJqWRbhqC6EbFL6ixT8ayJuSnDZEzpvPX4ZkVscv9gxbYzh79DbH4iKonKbLY7BMLQDQG/UjkdHbasOR09lmzK1uULDFBEOxxgAUBqFoplNjEIKhZdANc465KBYsTNUv5up13ejrCOL+LSfZGKbQGFoZvcnyGHjUDpA3a7ZCmYhK4SUx3Aj7lEUxuVSL7up85oLBS2MQpqSWgUeAOAk7KjuqLj4GSSJ4x4YVePrwGHK6WdijV2gMLYfRlD4GNu+49jrTRoJBd5jKimk9jcFRRE+dm7mtGUJjaEmcESAcp4pbLDQ4y+MBUMo6UDkdTCKXofXQm6xWEnfOAu2pMeQcprJiQi5JYs2Im8awxGoBPDGbcxwnNIaWRHNxPjtxi0oCYNd5mU5rIiqpxTGarLoqK4nB5l3EKseSzLRPwx6n4HvpL96E5z93rf1ayCc3ZR7DlqMTWPPZh7BzYApAPsTZ6WPgmerOKDJdaAytiWZS18Wfq4VeKq9TMGgGtSeI0Bhaj2bTGPQCjcESDIvUAWw+cJqSOsM+e0EFgKBPacpw1acOjgIAHt83AiC/4CsFgoFVYXYmJDr9j4vRW0MIhhrRPRxhPAvTy5SUFww56KZp7+xu/u6L+N5zx+fpagXzgd5kZdOdfq9YG5qStBKmpOLk0WYhwgW0dR/copLiQRU+RcJIgcbA5pRPlhaliN4rQjDMR3tp3nOhmECVGgMXDABw2493Nfw6BfNHviRGcyxIulUyAnAuSO1jSsoZ1NN8G2xSHwP39fAgAN0lj4EQVsJkJJnXGLjzOR5SF0W4t71gGJpO47y/fBS7Tk03dFzN6tJWTMUaQ0qDbph2yWAAuLAv3tBrFMwvzZbgZph502S0aKfaDmguAR8c5mPQ52UTWA98PUhmmYB2i0oCmJ+hwMdg+SLWdIUwNJ1e8FpqrwDBkEFaM7B3KNHQcd0S3IB8THI55/NUWoNuFmoMzlpLgubGWRa5aQQDzQsGVZYQUCXMtJOPwSgVlaTApM1zL4pJFmsMRQmwxSVMuC9iXW8EmkExtMBZ7G0vGEzrRow2uM2h5pLgBuSdz7JL5jPAEt8ifsU2JTnrK7VTzHm74/ANNo2PwakxAEDEr7aVKUkrYUoK+ZqzWQ9f4PM+hrlRSQDQGw1gJOE0JbHj1vWyJl/Hxmfn/VqdtL1g4BJ6LJkrc2R1aI4sUydcYyi1WMSDqpXHYCLkk/HYp6/Bm85b2lYRJO2OMzmxqQSDI5cmFlCQaJPNBqUUuTKmJABINVl0Hzcdcc3NS2PojfmRzOp2zgI/bl1vBABwbDy1INfLaXvBYMyTxuB09DmxJ2iJnUs8qDIfg8l2QOt6I+gK+4TG0EIYDpWhHufzm776a9z0zecbcUlzNIZoQGkbHwP/vr1MSUGf1cWtyTZXfIHnmptbVBIALLXasXKtgW88+joCCKgSjo8JjaGh2IKhwbXpvZzPy2JBAN6mJCCvMfBWjABLSGone3C744wzryeP4eDIDH5zcKwRlwSTUkhOU1JAwUybmJLync88NIYm7cnATUd2VJKdxzDX+Qzkk9yc/V7WdIVbU2MghHyLEDJCCHGNtySMrxFCDhFCdhJCLmnEeSuBC4axmcaaknSDFrT15HzmLetx29vOxVvOX+b53rwpKR/yGvYrSOWMgp2ooHlx3qdmNSVF/YsT6jgfcMHg1gALqExTbwTTKQ0HhpMVH8+vm2dlGx4CrpcnuVkhqzwqSZElrO4K4XiL+hi+A+C6Eq//FoCzrH83A7izQectS/eDP8BTd74f/3vr64E1a4B77qlvwHvuAdaswWOfeQO++MdvmzNe0CfjQ1evLasxTFklMZSiTNXFSGYRlMC635CkgvmjO5yINUfC3HMPnrrz/Tjyd9fXPzfvuQcPffUmfOmdF9ljtZMpSbr3Xjx15/vxoU3rXL+rsBXdZ7fO5feNEEBR2P8N+I5zK1dh3bI46OrV5ce65x6863dfiyN/dz2euvP9ML77Pe+oJNuUxDQGnsegSgQrO0M4ObmwGkND4iMppU8SQtaUOOQGAP9FWZDxc4SQDkLIckrpUCPO78k99+Dcz/8JlEya/X78OPBHHwT0LPAHH6hpPNx8M5BKQQKwZGyIjTd2EPjtN1c8zAXGcZxMD8GnyDgzNQwcT+KM2WFcRo4gdzgERPxAfCXQsbL6axQ0Dsf9BpCfP2MHIb3xGlxG9iGkykilDfz3Dybxu5f2MRMBkYDlGwA1UHbs/uKxZ0aAW/5vTde5omist73/AxjuPhs4HgRkP7DiYibgWo177kHwYx9Bf3rufeDPXfdUGpeRffAP6sBj24E///v8fTOM/Ptuvpn9vHlz1deAm29GDx/zxAngjz7kPZZ1fNQ6vj8xCnrzh3D+xz+My5SV8A9GgMl8RGKMUrxG2Q//YAI4fhpLxoZwGTmGwFAE52rDuFA/idyRKHzhTmDp+dVdew2QRiWEWILhZ5TSC1xe+xmAv6WUPmX9/ksAf0Yp3VpqzI0bN9KtW0seUpo1a9hkKKYnCozUkNfgNV6cAJ+KVj9eKWJ9wKf3NHZMQXXUc79ffxtwzWdqGFsCxlKA4m/8df7+PcC5b6983Gah2vvwz0lgusS6tno1cOxYY66hfwVw8lTlxzdirfjENqDrTM+XCSEvUko31nOKhcqocrOruN45QsjNYOYmrFq1qr6znjjh/vexym2EFY2XAPC+n1Q8zGN7h/Gtp48BAN5+4XK854pV2H5yCn//6H584frzcfbJ7wMHf1HbNQoaR4n7PXTDf+PW7+/A8lgAQ5b6/6dvWY8NKzuAe28EMlO1jT1tAnqmOsHgMRZNAJtzn8N//O5qhH56S/lralYqeO4ymoEP/OdWvPuylbh+uow24DVeLe855WH0KHdP3rfR9otwvvjgbsgywW1vOw8P7hjE/S+cxLf/4DJsOTaBO399GN94HUXnc18G0vN/HxdKMAwAcNpF+gEMuh1IKb0bwN0A0xjqOuuqVe5SuyvU2PFWrQLWbqp4mOzMEJ4xWXzyxUvOBNaeA0OewDOmiqGuy3H2zAuA2R624ZamxP2e7bsKz5gGunU/xkzmMDwe34ANa/vYol7u/nmNHSeAWaUD1WOs1NIVeMa8AJPd5yIEtO6cquC581OK55HFxaG1uD4uMQFbarxGXcOKpVUdP9O7HM+YF4Cs3QQUCYaRnhj2nk7gtrWbcPzoQTxjxiGt24SMMYpnzCCmOkx0AgtyHxfK4PgggPdZ0UlXApied/8CANx+Owy/r/BvPgn4vYtqHg/BIrtxKMT+XgWdIUcLUMvmG/E7im1JSus+xO3E7bcDgaKdu3W/3ZyI02krNLSS+3f77WwsJwEfcG0FQsVlLFo8ViiEsT//PABg97AV0dKqc+r222EGg4V/K3ruCCEs5DujA2+OA36PPW8Nzyu/hjn3SwXwpx+u+HjNH8CzH7wVgHs4e2/Mn89jcMwvOzCF375WEQyEkPsAPAtgPSFkgBDyQULIhwkh/Ft7GMARAIcA/DuAjzbivGXZvBn7Pv5HQJzABJDoXQG892zgNf01j4d/+AIQJ6B8vLvvrtqR1RHKCyvVDldlu4eZrMYWFmoATVYQ7BXH5s3Alz7BdvGEMNu0db95nPln3rIe3/7DywCwUEYAlQmGzZuBu++GHldAAWRW9AP/7w+BC33VP/ibN4P+290w4jKzz1rX2fexDyLiV/DCCcufVq0m0ixs3oyxv/lr9twRFNwHJxG/wqoHXBoDPvFmoNN6zvgivGplTc8rvwbcfTeynSFQAKOxDuD6APCON5Y8PtXVCQAY6+jCd/7gc9j7+usBzI1KAlguw0xWx0xWZz3lJQJCHIJBs96zAIKhUVFJ7y7zOgXwsUacq1pGNl2J8yPfxTuzX8KrrrwWXxz9VH1f7G+/BRj5G3wodyvec9MtuPZcD1WyBLyVH4B8uCrXGLIGW1gA9iDLorDeovK2q4HZu4EPPw0sy8dVcI2hM6zi9ef0IuSTMVWNxgCAvuc9GDlwG35jXITQ/7kT15tPAD+5v6b5qd/4biQOfhZDK96MC2/5JgD2cG9c04nnj4+zg1pVYwCQePtb0Tv1Rey65K9x4Ts+4XpMNKDkNe5rzgZWjwKSDPRvBLb9F/DZXUAgVvtFbN6M7af/B8unt+NPtA/jAf9flf5ON2/Gz7URvPPYF/Dts/8T3z2g4CbDACEoSETk9EaZdjqSyFhVEdgxvLfGjG5tFFtFY2hmqMG+xGDAx8pi1GumsXZdndEgNq3vrWmIjgJTUpHGkNHZZAZa+kFuG/g9kAoFdL4YGnuEOqykRXasXNHu3DApFJgwILH32huC6u+7SSkUGKBF13nl2i4cGE3XPG6zYGjsuyUlNkp29QD+jNv/rHvRgM8vUQM6ZOio8Bk12XVftKoLiYyOPYMJV20BcGY/Z62qCMU5TnzM+df82l4wwBIMHZEQRpOWYDBqnyCTM8xee/XZy0smsZWCN/MB8rVfFFlCUJXzpiSgpR/ktsFDMOiOBCQAiBUIBgUwypei0E0KGWyhSWS0/IaghvnJxjLzY1hc1B+HwRexCq6pWTGNCgQDT+jjz7ipsfvHP3dDBIMOAzIMvnSWGZNYr1+0qgsA6wHttW7wFp8jyUxBVQRemn+G3z5z/u9j20VZ+m8AACAASURBVAsGat2YzkiQlcWQ5LomSDbHxHZnNFjmyNLwXYOzdHck4NjxAAsyAQSlGZhg9vkHthXGShQXQ+OFEQFUrJVqhgkVRkM0BsOkUKEDpHDhjAVU6PYi1qI+BgCmJSyJrHoeEw2o1vMj5zUFrjUADREMhBrQIVWhMbDXe+MsCnE2Z3g28VoSZoJhYjZXUBVBkSWEfDKSOWFKahhcMCyJco1BreuLNXT28MslJmglcPXQWfsl6rd2PHzsFn6Q24XZNIsS+afHjyLjKOlc3KKxI6RWF5UEpnXYGkO9piRLY6BFGkM0oIBCAgVpaQ3U1LnG4P3cRfjzw59xU7c0h8YJBokyc121gkFWfPBbtdW8NIaYtSbwXi1qUaVcu9ybEAwNwNppLImFMJPVYZD6NAbdsnVKSn2CgffkdWoM0aCKqZQmfAzNhDV/dEh4dPdp+896kY8h7jQlyUpFQl0zTSgwoUNm77U3BLVoDCZUYuTHsOA1hEzS2iHQ3JQklTAlRQNK3hQ7Tz4GQplGX7FgoNa5JdnR9te7iVc0oGDKavvrbEoU8StI2KYk4WOoH+vGdUfDAICsWUMCkXM4a4LK9QoGKwrJefP7O4OsWJbwMTQNhm513oKMcUeF3nz55LwpaSptvV6DxjCcyGK2jgff4PWAinwhEVsw1LchWmyo9fnKOZ8zmqU1Oc1ItsZQ/4IqmSxq0KjUPOfwUYWsnhGlfJNc89QcUUkAM5MlhCmpcXBTUleMJZtkDKkxpqQ6BUPelJS/+auXhHBqMp13Frbwg9wu8I2ADqmgUqlRZEqKB1VkNJM17alUMOgmFMKikl48PonP/WSvddIaNAZrXhb7GPyKBEUilmBoXdNkRc5nSwgacPoYjMb6GMAFQ2XPKCkQDKXb/gLWBiOVY43AnGbmgIJp3mtMCIb6IdbDsCTKBUN9tla+g5TrzC+IWpOYOOrnr+4KQTcppjJWOn8LP8jtAnd6GpAL+icXZz7HraRF24lckcbAxrtkTQ+uPacXp5O1L2D8OiEX+hgIIQj7lfxi2aLwsPNypiSACwaNRSOZej6Io0E+BpPI8KkVmv3MvCYX8legMQR9c3q1ACyIYDrL1wUhGOrHmhRdtmDI/60WqPUwK4qvzJGl4T6GlKP/wqolzNw1Ots4m6igPqjBzEM6ZFeNweljAKzs5woj3/guvycexnUXLHNED1U/P/mOulhjANhO2l4sW5S8j6FUVFKxxlDkY2hAuK5MdVAiw+fzVzSmZGsMst1lzsvHAADxEOvVolltfzlMY7BMSXWE21fKK0AwWOGq0SAIAVJ6fT4Gg/sY1MaYkpztPFdbxf1GUtbfWjjuvF3gO/FY0I9ktoTGwAVDNRqDxoQOkRQsjwcd5okafAy6t6kl4leY0GnhjQatQDBwv50OiS2etDhctQE+Bktj8PMabOXGpDrzRxBSkSmpg/eDd7T9BbhgEBpD47BunKr6EQuodfsYTMuUpNTpY1i/jKXmO+smLYsF4FMkDM8IjaFZ4CaMWDhYoDHoRr6DG5AvjMhyZSoTDNwsKckKlsX9Do2h+vvOF85i5zPAsurZLrp1TZOmyb+rEoLB2mzpkAHDMshTo7EJbjBgEgV+HxcM5XwMBnSr8hA3JXnlMQCO7o7GXOdzy9VKamaczp+OkIp0nT4G0zYl1ffVvfeKVVjZGcQ1Z/fYf5MkgpWdQQwnG7fDEdSHaejQqYSuaAAJp2Ao0hjW9UYgEWDP4DSuq1QwWGYqIitYGgvUFXSQTwBz0RgCKjTa2hoDN5/IJZ477nzWqMS6NNrvtaLFGvD5ZWqAEiVvSionGKjOHP+AbUoqF5VkmBRT6ZxdIgNgAqPSbOtG8ArQGKwvkcjoCKpI66hTMFgTVK2ikYoLhBBsWt9b4HwGgNVdYQwmG7fDEdSJyUogdIV9mHE4n/NRSewRCvkUnLMshpdOTlkaQ3mhrmv5XXA0oEJVK9uFul6mrTHM3VFH/DK0Fnc+m0Z5jYEXp8yaEmt2xOE/N0pjkGQE/RUKBtOAaQn8kFUPzakJFNMRZJ9hbCZX4Is4sycCje/jRR5D/RDKSg5AkhAP+ZAy6vMxcNOCWqePwYuzl0ZxcrpxOxxBfVBDgw4J8aBaaEoqKokBABev6sD2E1OskF0VzmdimSU7eJmVGuYn1b3j/MM+peU1BlqBxrAk7MPSmJ9F9Tk1Bo0LhvoXVJkaoJARqNDHQKiVVAtUFq5qmSQnZnMFzufzVsRgYuFMSW0vGNiOL18BM1W3xsBNSfMjGM5bEUPWWDiVUVAaahowICPKC7RZ8OqqSoFg6EQyq7OaNhWZkgqzeZdErMYuteQxGHlHdjGRgALNlFraNMnzkcpVHLiwrwOTGRPQ0vk/6o2rLitZFWxD/sq0O+6sBmAnuJWKSuoI5j+f6tAsloR9+QAFIRjqh1imAIDZ71Ia6or2ofMtGJbHKk+3F8w/hgaDyIj4VaQ1A5rldNYMd40BAMbTZkUhhaadE8Pm0hIrCbOW+ckXTrf+HRE/0xhoK0e58WjAMjXKLuqPYzpLQXWnYLC0hwaE6yrUACUyQn4VOpXKjilRPW9KqkJjAOY6qe21QVRXrR9CHYIhqGJWJ3V1R7NNSb768hi8OKM7nFeXW3iH1zZYGws7vNjSGgyX1p6rlrCFfVZDdaYkazHvtnJtzBri1O2SEa5RSay2j9nCgqFijaE/Dh0yqOYwJTXYx0AlBREr0ouWuVfEoTGEfeWjkriPAZjrizh/RQwalaDrQjDUDTEN28YXD/mg0dpjxYH8BJ0vjUGWCPq64uyXFk5IahdokWDg5iQ3H4MqS+gMVW6uzNfdYmPzJMzZTMbzPZ5j6fkIp2J4HoOxAAvKvFHhc3dRXxw6lSFRx/ffwDwGGSwqKeRXoEEuu0gzUxK7J8FK8hgcGsOr+jsKXlu/LMZKtM+mi9/WcNo/XJUaBRrDmNNMU0tZC2tnptYZrlqK1T1RYArClNQEEJPt+KJWe8WEFZlkmPmevE56on5mrqxEMOiFSVvclDSbziJa5XXSElE7PPOZ5020IvbnKyMYuiJ+77IZjQhXhQFIslVmRIKm5VDqiiSqw7SqJfMujaV8DAFVxh+/YR3O7I3ghg19Ba91hlTokKFp8y/gXwGCIW9KigfVupKIAICaGnJUhk+eP2UrHmLRKdTQ4T2FBAuCyeLQY0WZ6rpJXXd+3RE/kpOVRb7ZIZhWeZUlMVYSZTZdvcbANVnXqCS/Ao22timpUo0BsKoSuD3eDcljMEElBWEfa+9JKtIY2PoTVMvXSgKAT795vevfuUnQWADB8IowJfEb0xFS665cSh3O7PkiGGAx0rxbnGDx4BsLrjHYpiSDuu78eqJ+1oKxIlMSD8FkY3MfQyqT9XyPF3xH7RqVZGkMtfgumga74U35vaydD+IxRj0oYM5nlnAmI1fmGZUsZzWQdz6XymMoBRcMwsfQACSaTzDpCDWgzaFhzLtgCAeZYMhkq18gBA3GZA923sfATUneGsNMheGqxT6GbktjqEswlPAxlHOUNjO8SjJxSeArRvXSKhpmSlLQ1xmEDqnsveIlNIC8KUku4XwuRcSvwKASDFFEr34kR4JJPOhzaAw1Sl1Ts8ebL0IBLhiExrDYEFODSRQX57MJ1cWc2BP1I20Q0Arml60xWH6BcKD2DYFdfdRlUYwEuMbQwvOJf58uGlExqs9DMNS7oJomZEIBSUF/ZwgGlZEuIxhkqtvtVoMV5DGUIuxn5iseaDCftL1gcNYqYT6GOnMEHAlz80U4yGqkZIQpadEhlGsMbLE5Pp7C3qEEDg7PeGoMBpVBqAlYSXBe0OJugJIEA1JtmmIJH8PyeMCKSmpdjSHf16D8psw/T6YkLuxZuKoCKsll75UE0zYlhSuISipFyGcJ+AW4j23vfGamJPYxfYqUT5CpQzCY82xKClmCoZz9UjD/SNSAKSnwKRLeftFyfPe5Y/juc8egGRTL44E5x/dE/TjK5wc1UGrv5VbmwYRUk6Zox/m7mFoCqgxV9bV8uKpGZaik/KLq83vUMatTMJiGta3kWouslr1XMjVgEjZPgmV6PpdDtjrxLUSiYttrDM6oAACV11H3wJkXMV9ELR9DVvgYFh1i5p2Hf3XDBeiJ+BG3kpCGpudGD/VE/BVXwXQrr2ISpbagA7N068uA39fSUUnE1GGQypYrv1fyaZ2CQdcdPb3BTIDl7hVPiAMAn8zarNaqMQAAJQsTRNAQwUAIuY4Qsp8QcogQ8lmX1zcRQqYJIdutf3/ZiPNWQrFg4Pb7micJnX+NIRpk4ao5TWgMi41Eddt52Bn24Ze3bsKvPrPJ8/juqK9ic6Vb7gEl5SNdSo3lFbUT8vtb2vkMWnk0YMBDY6D1agxWoUKe/yQrCjRNg2l6V1GQqWF31SOEoCPkQ9hfu6GGSkrdn6MS6jYlEUJkAN8A8CYAAwBeIIQ8SCndU3Tobyilb6/3fNXiTDABgGgwACRRs2BgO5f5tcBFQsKU1CwQK6GJw80B7758FdK5uXOoK+zPa5TlBAN3qDoFg6RAz2nI6SZ8SuX7tnweg/tuORgMgEwamM3qdS1MiwUxK48GtCufFpHOZBGq4xoMvbBQoaL6IFEdI8kslrmYFSmlLFPaMX++8/7LXI+tFCopCyLgGzFDLgdwiFJ6BAAIIfcDuAFAsWBYFIgjJR1gnbgA1C4YijSQ+YBP7IXIcBSURrIasxTz5Xde6Hq8LBGE/H7AQFlzJa9vVBBpIylQYGB0Jou+jmDlF2qNJXuYkiKBAGQYOD6ewnkrYpWP2yxUkT/E84CKma1bMOR7uwCs9L4MHScnU66LvWFSyA7nMwBc0Bev4wpQcXfAemmEKakPwEnH7wPW34p5NSFkByHkEULI+V6DEUJuJoRsJYRsHR0drfvieG0TTizMbmCt9nti6vMuGPiORBempEVHpnpFkTBO+BwrVyU1344zP76iqFBg4JGXh6o6J18svEpGREJ+qDDw1q/9Bg+8cNL1mGZGqsKEG/IwJdWSUe6kuB6V3+eDCgOHR2Zcj9dNChV6RSG2FSMpLVNd1c2TUmx02wZgNaX0VQC+DuDHXoNRSu+mlG6klG7s6enxOqxi5KIdftzSGCaSqZrGc4a/zhuEQIcErZWjSNoEiRpVP9gVa6XmXI1BVX1YHlNx95NHkNWrCJCweyK7X2s0FIAMFj5715OHQWusLrxomEbFzmcvU1ItiYOFl1BY2tzv88Enmdh3Oul6vK0xNFAwEEmxk/3mk0YIhgEAKx2/9wMYdB5AKU1QSmesnx8GoBJCussNTNnxdV0ca8WXvzEdVjOUyZnaKhSyKJX5t9EaUBakJoqgNMxGXF0l3Q5LMJQV7I5+5DaSjPU9QYwks3jm8HjF5+Q+Bq9+BT7Vh5BC8fHXr8OR0Vkc9NjlNisS1WFUaPmWFW8fQz3YpSis+UBkFWEV2O8hGHSDQiFGYwWD3DqNel4AcBYh5AxCiA/AjQAedB5ACFlGrDKUhJDLrfOWnfV7hxJ4qFqVughnrRIA6LTaJ07O1KYxFI83X5hkYWqiCLyhlFoaQ3X3u9OqeTQ6NVt6fFfBoKAjwJTwkxNVzFHufPYqByEpkKmB971mNQgBHn55CPtPJ/H9rS1iVqomTNxjIU7XGf5tFtejkhQmGIaTrhtY3TSZltZQwaCyOTnP1C0YKKU6gI8DeBTAXgAPUEp3E0I+TAj5sHXYuwDsIoTsAPA1ADfSClQBw6Q4VOfOptjH0GlpDFO1agwL4HwGmGBo6YSkNkAzKBSYnrkBXnRG2Obj9FTpuUtcfAyQFPglCr8iVSUYSLkic5bTsjcawEV9cWw5OoHvPHMMt/14V8XnWEykaky4Hgtxts4SMzwPxJ4PkoKQQjExm8Ob/+lJPLSzcBNrmBQKqjdFlkKSFbYGlQiRbQQNuWLLPPRw0d/ucvx8B4A7ahk7ka5PbZJoYbgYz2OYnq3NEcXGqz3crFIoke16/YLFIWeYkEn1DzZvuDMyXWZTQ901BmIa6O8MYmCyis1LubLUkgKAAqaJ/iUh7B1MwK9IyOomNMO97lMzQRzFMMvioeHVW2LGLkVhawwyAjJboA+OzODeLcfxtouW28frJkUA1WucpZBkFQQGZnO6XaZlPmju2QBgOq0hkdGQztWmPskoNP1wNTAxm0ZGM6pT18F2LgvhY6BEae0yyW2ApptMY6hSMHBT0shU6blFbeez4wGX2c6+vzNUk2Bwa9Rjj2sdtywWwNB0xs7cns02/zwrTlQtfbD7/dK0XF07bZ53Yn/HkoKAlB+vuDQW0xgaa0qSFBUyDMxm8+vhZ3+4E5+6/6WGnQNoAcGQyGj4wLdfwBd/uhunptLYemyiqvfPUeWsn5OpNL751FG85Z+frEroFCeszBuSDNPU511lFHiTM0wo0Ks2JXHn53iytI+BGO4aA0wNK5cEcXKyGh+DAYMSyLLH3OTnMDUsjweQ1gwcHWPXxyvGNjONMCXJVMdEqnatobhHN/Pb6Nj7petww4YVc+6XbpmSqt1YlEKWVajEsBtGmSbFI7tO48UTkw07B9ACgmE6reHo2CyOjc/ijscP4qP3bKvq/XJRghufNLOZLE6Mp5DKGdg1OF3xeF4JTw1HZolOJ6rUaASNI1ejxsBNB2Xj5m1TUqGPgWsMUynN7v9QDmLq0CHDswyPlNcYlsasXB6dbXFnXTK4m41GaAwyTMzUIQSpXlTBVlIA00DQJ2NlZwhD0xncv+WEnSdimKbdv6FRyIoCGaat5R0cmcF0WsPwdLahm8imFwxTqRwmUzlMzmoYTWYxlarO7j7nxlhqYCqdwegMi1LYfmKq4vGkBtsMvQj6WabqT3cMlj9YMC/kDPZge0b6eGHNt1SmzO6Ul3B3Vgy1FpuVncwcVak5iZeMKO5BXXxNMI05VWFbwZRUXMGgJB4anmLZ5mvFLDbXObKQ+zuDMEyKL/50D7751FEADo3By7xXA7KVAMnv2QuWBSVnmHVpQ8U0tWAIqjJOTqRhUmAylcP4bA45w6wq8YclmDh3ZOxnQ9dxbJyp0i+drFwNk2tIeKoFRVHRG1bwg20DeGDrSeT00rX9BY1HM7jGUJtgyGRLawyu9X8k2dIYWGRTxX4GWqZPCH8GHBoDpzVMSY3RGFI1+ioBR6Z6gcbABQMT5GnNwMBkCpRS6LphN/ZpFKrqgwwDU2l2LS84TOunXar91kpTC4awX0FaYzdyKqVhYpZJxGomsox8dUMA+ZK5JG9jfakKjaHRqqEnkoJVHSqOj6fwpz/YiYfrzOcQVE9OZxqDVzaxJ9b8SGdz+MYTh/De/3je9TDXLHprsVndxRaaI6OVhWuTcrWEXExJHKcjs1lxVrktf7C3xjBTh3Zkt2J1EQwrl+TrWs3mDEymNLsFZ7U+qlLEwwEoMLFnMAFKKZ4/MmGf260MfK00tWBw2ktzhomhKfbBK7UTUspUOVrs3AOgwASlrKvb0HQGTx0cAwCMJrPQDffduWlSSzAsjPO5L6biRx99DWIBBc8dqTwLVtAYNN2AQqrPY+DzI5fL4cXjk9hybMI1AYq4NX2yFpuOkA8r4gHsHUpUdEpC9Xy5b9drygsGnyKhO5LPDp7JNn9YdHF0YUn4Z3WU0KCSAhkGUnUIwXxfbacpiY23PB4ssAgOTKbsonuNFAyKokIlJnYMTGH/cBKnExn83qWs8MTp6dpys9xoasFQ3NAiZy3YlUr9fIJJkXMPgAI2xh9dfQbO6o3g4/dtw8mJFDZ95Qn84MUB1/E007QEzfzFDzuvk5gGLlnVicvP6BKCYRHg/TA8Q0C9sOaHTHUcHZtFTjcxnZ67+ErUmFvCXVLs3sTnrYhhT6WCoVxmMJ+z1q53WTxgb7xmWkJjqEEwSKr9M5X9UIlRlz+Fm5JsDVKS7YJ2PkVCX0fQrog7MJnOF91rpIXBikp6+dQ0ntjHioz+7qX9UCTyytEYvDodJSqM1NANEwopiiO2hIRiFRRb0x3G7b9zIaZSGn607RRmcwaOe0QCaQbTGBq5A/DEoaZeuXYJjo2nMNTAHYGgPLrGK5bWZkqSYeK45cc6nZj70Lpm0Tvu+3nLYzg8OouMZiCdM5DRvBdwUq6Rje1jYGOsWhLCGd1hAJVr4IuJjCqcz/yzSkr+2VdYEcG6nM/FpqGiEth3br4U//6+jQCYxjAnU7oRSAokamAqpeHeLcdxzrIoVnQEsTQWeOX4GLwEQ6UT2fSoRQMAKmGCoSfix/qlUQDAkweZBJ6cdffu65YzcqF8DHnB0AUA2HK0uhwOQX1olsbgVZjOE1srNcAjCIcTc+v0lPIxAExjMEyKA8NJfPzebfj0A9u9z1mukY3DlAQAf/H28/BvN21EQJVaJly1eo0hLxiI4i+I5qmpOGdxocIiwXBhfxznrYghHlRZ0ExxbaVGICmQYILAxMmJNN52Icu0XhYPvII0Bo/Qu0qdz7wIXaGPgd3UqGVi7Yn6EQ+p6AypeMlKEpn0CPuywxcXQjDIefsld0SOuCwugvmDzx+vHgeeWDtWXuYaAIaLNAZKKSSznMbAmrrsGUxgx8A0jo5557RIZaOSCgXD8ngQ63ojiPiVlohKkmkVVUq52UyS7XtB1CAUYmI2ZyCV03H53/wSP99VXUCHWexjkFWAmnNSnlk5k5QjIa6Bpmfr87znsn58+Z0X4qOvXweACQY3rbTm0zRspHlAltjlxYOFX2zFPgarbDVx0RhifiZ0eqKsdtKa7rC9u5v0yJWotahaTTgWiLBPgUQqN6EJGkPSqqcl15jHoJC86WfE8dDqholN//ArZLWch2Bg7+vvDCLqV/D80QmMzWQxVSJOndByPoZCwcCJ+JWWyGOQqqk4wD+rnPcxQA1CJSZSWR0Hh2cwmszi8GjpzPQ5FBcq5NdTVO20vzOIk5Npu7ZS1T6qUlif5/Z3nIN3X77KtqosjwUwNJ1uWJ+NJhcM7P+1PeECj79XNujYTLbADGS6lhxgNzPmIwioEiJW/9s1XWH7EK8HUF9IjcEhGCSJIBZUXR2YgvmBUoqfbmcZrEuiVTaEdPgYOM7d3HRaw/HxFBSYLlFJcsF9P3d5DL/YMwzAW5MFKigy50hwcxIJKHWFcC4UxVWSS1LgY7AWZcUPHzExkzXsis2pKk1oPCrJ3ih4CNulsQBGk9m5PolG4HHOZfEAMpp7kENNp2nIKPMEl4Y9ET9iViVBVSZIekzkj3zvRdz6/R3270ZxQgrAskyJjJ6wjPXLYnamqFMweGsMppXJuFCCIX8dsYAQDAvJthOT2D/ITIu15jEoYIuwX5EKfAzcdNMfV+2CewXvdTz0562I2Qt3RjMLHNCzWR23fHcrTk2lIZl6GY0hn+DmJOwrFAwP7Ryqep596ad78In7GlvErZiq8occ1U+dzmeFmEjldBwa5YKhymgss2ih91ikl4R9mE5r0HQe1Tb/gmF5vLG5DE0tGCRCoMoEXRE/OkMqYgEF8aDqahOllGL3YALbTkza6pRt4yueUJKCV6+J4/u3vNr+05pu9oASwjQGN5Usp7FMxoURDHLB7i4eVJEQgmHBePrQOBTionFWgnV8SGYaw7nLYwWmJL4Qr4gq8Knq3Pc6NgTnLY8VvOzUGvadTuDR3cN4+tBY+Z7IjiJ6TqIBBROzOTy0cwhHRmfwsXu34cHtp+zXnz08jvu2nCj5cXcPTmNfhWG1taLU7Hy23qOwntczWT2vMVQbpmsnuPkKz1PU27srzF6fstoHz4vGYMzVGIDGZT83tWAAgD9+w1n4nYv70Bn2YUnYh4hfcY1KOp3IIJVjYVyD1pdD7VZ8cwWDRA34lPzHP7MnAgA4uzcKzaCu6rVhNwNfmDwG564gLkxJC0oirSHKb3O1gsFaCCIqgU+RsK434qoxqMQEinNiZLVgQ3DeikLBMOEwlU7OsvkwkshYPoYS18nnbLHG4FdwaIQJhO88cwwAMDaTP8d/PnMMf/fzfSU+LBN0mWr6U9dAbRqDUqQxGEjlHKakEuG/bvAy6bJarDEUjtMVYX7LqVkWXj4fPoa5GgMTDK8IjQEAPnHtWbj8jCU4Z1kM5yyLIRpQbR9DTjdx+0N7MDiVxuGRvCNp1ylWLdUzJV1W5nyx56+I4V9u3ID3vno1ALgW63vxGMuOrjp8sRYkteAaY0EFiRaIHmkXEhkNMb/1eNSoMYR9QG/UjxXxAEaSGXve8v/n5NgABT4GAFjXG4EiEQRVtvN1zkuuPYwks+VrCXn5GPz58/PEzqlUDu/71hb84//ux+lEBlMpraQ9PpnRkdGqq+U1MJnCH3xrS8UBFazmWYX3QfYQDDAxmcrZuSXpan0MdhG9Io3BxZQE5NsHVx3VVgqPc/ZE/ZBI47Kfm14wcL78zgtx102XMo3B2s0/eWAU//6bo/jvF07i0Ei+IffuQabW8sxDFC/k0lzBQAjBDRv6sMyqIzOZykE3WHcrgKnU//DIbgBAf3e04Z9vDi2oMTz88hCePjS22JdRMamcjjt/dbgg2ODJA6P4+a4hJNI64n7rjzUKhg4/QX9nENes74FJgUd3Mycyn7+uOTFF9z2gyrjx8pV4zxWrABSakriQGE5kmGCoKMGtcN77lfx7uM19Kq1h+4lJbD02aZvABqe8d6IzWR2ZKu31W45O4NcHRu2aP3/+o5fx6wOj+NMf7HD1V9StMahByGD10Xj0oZeP4dRUGjd983kMJzJ4578+jUd3n2Yv2FFJpZ3P3JQ0Os0EkE/1oWF4nFOVJfRE/a8cjaGYaCAfd81v2HNHxnF4dBbRgIJ1vRHssformAa78VKZh89JZ4jd9MmUhtt+vAt/9F9bAQAHR5J2lImqNPBGe1HkY4gFmt/H8NF7tmGzR8G4heD4+Cy+8cShikL2plI53Hj3c/i7n+/DT7bnS5v/668OzDDxAQAAIABJREFU4au/OIBkVkPUV5/G8JZze/DV39uAS1Z1YuWSIH5i2e5twUBc6m7xuen4DH/92xfiltetBVAYGMGFxHAiW74XuceCctDaUHVH/PbfRhJZJDI6Tk6mMJJkJrBSWfczmepNSdwkNjGbw/7hJO7bcgK3PrAd+04nceB0suBY03SpeVYKDx+DDMP+WrvCPk/B8On/3o7fHBzDT3cMYtuJqXxiqaHDpASK7Ih6Ajw1hhOj7HNEQw1sBeyh+QHAsniwYbkMLScYIpZg0A0Tj+0dBiGsOuruwWmc2RPBRf1xbDsxBdOk+aikKgRDR8hyHKVy2DuUsCuwjs/k7CiThSmipxSZklRkdROb/+M5/PmPXgYAPH1oDH/RJM3cx2cWP/nue88dx1ce3V+2VPXOgSm87u+fsE2Oux2NmkaSWUymNCTSOmJc/lcrGKzibVEfsKIjCEIIfmdDH54+NIaRRMbe2LiWcOe/00LTDJ+Xd/3qMN5xx1OglNpCYsTSGGrJY7hhQx8A4MPXrLX/dtiK2hmYTEO3tteDU+7faUYzkDNMaAa1i09OpXL44/te8qwgAADj1mvjszk8uotpUmf2RDCZys3RjA3TCvqoR2OQ/ez7BgswOb8vbnduPDWVts1LGc3A85Yg4Lvv0WQW9205gYOnp6BDgsQrMngs0h0hHwhxFt1rpPPZXfMDeC7DK1QwxAIqZrI6dgxMYzKl4Z0X9yNnmNh2Ygrrl0bxurN6MDGbw85T03bHpTnhYkW7cSe2xjCbw3Aiaz/Ek6kcOgM17iBrwUUwACxa5r4tJ/DUwTHc9evD+O5zx+1F2TApvvHEIZwYX/iub3uHkuUPmmd2nGQL/JGx0olL33vuOCgFHv7k1di0vgc7B/KCYTTJcmESGQ1RH18AqtwIEDLn/t1wcR9MCjy4YxDJjA6fLEFyFQzuD75PkRD2yTg1lcbOgWkk0rptAhudybJe5DXkMbzr0n4c/fJb8dsX9+HKtUtwyaoOW0twcsoyJVFK8ZuDo3ZPFGeQRsbqGfL1xw/hwR2D+OG2Ac/LmbAc3OMzWVvzV2UJUyltjt+BVymt+D4QZx6DwgS1rLImW2AJaEtCKlJWLayr/vZxXPOVXwEAfr7rtD3MgWE2p0eTWfz7k0cwnkzBgAxFKpoXRfdKlgg6Qz7IPMGxwSUx3M4JsMikanvYe56mIaMsICyFX8N+S9380NVnIOyTcUFfDJ9601l43dk9IAT47rPH8dhuNjHnSGxJmRNixuFZ1uOzOYzOZJHMaKCUYnw2h57QAgsGo9DH4ORz//OyXXH1wDDb4X1/60l85dH9uOvJwwCAX+0fWbA+DnuG2OIa9i2ANuWCbph42dIADo949zAwTIpf7h3BpnN6cc6yGC7qi+PAcNIuUpfM6NBNiqHpDKI+y+5Qy/0ummNcm/3x9lNIZjREAlZYqpfG4DI/Y445cGoqbZuSNIMCZpmeyHwRcxmXEILuiB/33/xqXNTf4fr2IUtj+NtH9uGmb27J+0scARE8x+KYJZh57pEbXGM4NDJjV5Adm2EbsVTOsH17AKBzX2HFGoPEhIEk57OfJcXWGM7qjSLoU5DOGQVhxADww20D6O8MIuSTbcEwksxgcDoNFQZ0yPkabh4hwAAzVamYT8Ew95zXrO9BR6gxju6WEwxL4wGYFHjq0Cj8ioT1S6N4/E824UcfuQrL40EsCfvwqv4O/HDbAH6xi9mO3fIYvExJiiyhI6Ri/+kkDJNCMygymonJ2RyWBBdSMMhznM+cj246EycmUmxBANvZzGR1/NNjBwAAPitl/I7HD+FfHjtY1WlPTqTwge+8MOeBKccey+GvyIszpQ4Mz9hNnQ67NLcZTWYxmszipROTGJ/N4c3nLQUAXNTfAZMyc5KzFlVONxFRixaAanCUtuD89oY+7DqVwMunplk0kOnhYwBc56fTfjw0ncZUSrMXKWroMEvtqEuM68RtYVkeD2BwOo1DI0n825NHAMB2NiddBAOvTuyViAoAE7Psu+ZNsjpCrCkVx+lPM7Qa8km4tsCT3CQFEtUBUKzrjSDkk5HKGfjF3mH7LcOJDJ4+NIbfubgPPVG/HWJ8YiKFjMaqHhiQXATD3M+5JMw6rbHjGrhZKuFjeP36Xjz/uTc25jQNGWUBucCK635s7wjW9kQgSQRLY4GCnISbrmQhp9wnMKdnb1EoaDFrusIFLfOSGdY9rjNo3eCFCFeVi8JVA/mH4pbXnYlLVnWgO+JHPKhi3+kEbn1gux1/zndjp6bSc+y1pUo3A0zLeHzfCD5uRYZQSss6cw2T2uaYmazesHotLx6fxPu/vaV0VVGLHQNsgemJ+l0Fw2d+sAOffmA7njw4Bomw3RUAXLSSFar7zjPH5jhYI/w213K/XTYfl5+xBAALp44GrNfnRMzxfIO598n5tQ5OpTExm7NLZ5ftcCa55zEU01GkmRICXNQfx9BUBv+7J7+IZnQDN9zxFP7Z2owAbG7phmmbMktF0U045igAXNgXtwU7fy+lFMOJjJ0/5NXL2RVJdQiHvK9BAsW6HiYY0pphlxsBgPu3nIRJmd+FRxYBsDdgCgxokPPFPWXve9UV8Tl8kg1cL+TKBHy9tJxgOHd5DLJEkNNNnNkTdj3mdy/tx0t/8SbIVmntOQkmJXwMAKvN5Iz+SGR0jBdoDAvvfOYaQ3fEh3hIxd3v24gHbrkS65dG8cNtp/Do7mH8+W+dg42rOzGWzEIzTAwnMphK56AZJkaTWew7ncAFn38U26wqsjfe/Sy+8cShgtNmLTvxlqMTODCcxBv+8df2LtGLv/jJLhwZm8XanjAMk1Yd0+7FbT/ehScPjuFH207h2cPj+OGLAzDMuUKHUopnD4+jK+zDNWf3uBZHG5rK4OjYLAYmU1gWC9hmjt5oALe+6Wz8bOcQ/uqhPQXvCdsJbjXcbxfBsHIJy643qZU/YOoV+xgANvcBVhZmYIppDOuXsdBpmZhlBENlC0qntSD6FAmdIRXdET9Wd4UxMJXG43tH7DaS6ZyBvaeTeMoRnpzRTBwcmbEbak2XqO00XuSYvqAvXvD7dFrDv/zyIK74m1/i9NRs4WeoBEkpMCPxBVWBgfP7Ygj6ZFAKvDwwDVVmC/2zR8YQD6pY1xuxk9ScyDBhQHY4n73vFdMYzOqvu5LP5XHORtIQwUAIuY4Qsp8QcogQ8lmX1wkh5GvW6zsJIZfUeq6AKmOdlaXMs5Xd6AznJbarj6HEF1s8biKjYXI2h45AHaaFapEUVrXR2iZy+/LabnZt3RE/1vZEcPayCHK6iUtXd+KDrz0D3RE/xmezOD2dgUnZw/qdp49h01eewJMHRqGbFA9uH8SR0Rk8d2QC9zx3vGCH73xgH9o5hKNjs/jWU0cLbL5OptMa7n3+BDZfsQrvv+oMAECyglaRP981hLt+fdjz9emUhn2nE7jpytVQZYI/+PYW3Pr9Hfj64wfxh9/eYtt/P37vNvz+3c/hl3uH8ebzl2JdbwSjyewcB+ZUOoeRRBaDU2m7fADnE9eehVet7MCuU4VlHcL8NtdsSiqcY/GgyjQFIK8xePkYXObnA7dciS3/71osiwdweGQWOcO0e4ko5VpfFjXq8YJHP3WHfejrDKI36scbz12KnG5i6/FJvGF9LwDW1zinm/ZGArAiehydBr00hpxuFpigloR9WNERLDgmkdHt+TE8xe51VdE9vOR2kcbwyCdeg/NXxBH2sd/HZ3O2wN05MI3+TnYdTo2Bo1g+hvw5vO/V+qXR+TE9t4pgIITIAL4B4LcAnAfg3YSQ84oO+y0AZ1n/bgZwZz3nPL+P3cgze70FAwDbxjc3KqmcYCjURIamMtBNusBRSYUPMtcYzuwtvLaL+jogEeC2t50LQgi6Ij6MzeQKQja3n5zCbM7Aj7axOPpHd5/GL/eOAAAGpzPY4YjKmZjJoTviR0CV8Mt9TM0eSWbt44sZtSJYLj9jCaJWFm0ljZTu3XISd/36MHK6WRAuynnh2AQoBa67YBmuPYctTPGgin9+7CB+tX/U7tH9s51D2HJ0ArM5A2+9cDlWW7vy4uiM6bSGnGFi71DSLjjm5NJVnXP+FlLrdD67LMIrO9n1RQNq1T6GaEBFbzSAFfGgnauzNOZHNKCwzOAawlWL4aak7qgf//eNZ+NTbzwbl63pxIaVzCn92rN64FMk11DUtGbgZzuHcM6yKC7si2PKQzBwMxJffPs7g3Y0IGcqlbM1z4kkm8ukGpOMi48BAM7sYppA0BEkcY6ldaVyRl4wWD2xncEUMjFhUMeSWeI73XzFanzh7eut4+bDxzC/gqERK9zlAA5RSo8AACHkfgA3AHDq5TcA+C/KtqbPEUI6CCHLKaWlQ2ZyKWBg65w/vz4yiCPkKC6gYWBg0OWNjEuDw2Atn10EQ3rCdWwAOM+cxQaSN7Gkj+awgZzAytwCawwAcPJ5qwAY8IVLUrhi5WjBdb9zqYlrbopjqXQYGADOpyewJ30Ss4dz9mdQhgaxgaSAYeBiCaAJ4Nkn9+MtcQljMzlsfzaFDYTt9uMTe3FVIAMSJDg2OIsNhD1ELz49ges6zsX/vDSA/3zmOH7wkVdDkSRkTk1jAzmE1Wk/crqJDeQQjJMhHDlF8Pi+EXzwqjPsCrZOImMvYU06gwcfmsW9W07iW3+4ER3B/C5tYNdRbFSGcLHUjRWvyuBNcQ0XrAjiH/73CEaTWYRGczBPjNmfMRpQ8Gp/D/aOJ9k1nPADZgce2TWEs5dGcY5+ACAAMsAl8gpgoFADekN0FNvIIXSGVExndJgmRWcqUXgvqrp/MjBzes4cuzp4DD4ygXP0GUBLe2sMgy8Bs6OuQ7/afxTZxCh6CbA6reDq4DH4U1plpqTJY57zHgCWJjLYQA7hUqUD10YtzerUSXzx0jT+MzuE1wSO4jLlMGLj49hACvuQJw8Z0E8cxYeuXIVdp6aRShjAgISvPLoPac3Ah69Zh96oH6mxGWwgh3B+Rwy7UwlcFezCylSm4Jkb3JXEBsI2JtJpNi+qKnfvzGFw/jywFQjEsWJm1D7f1UEDB8hRAMDlagoYAM7RB7GBHMX6rgj2n2Y+q25Mu2sMI3sBX+EmVQIgTR4pPK4RcCEzsg8IuEeQNQJSr6OQEPIuANdRSj9k/X4TgCsopR93HPMzAH9LKX3K+v2XAP6MUuo9QwFsXCHTrTeX1goq4fnrH8MVl16W/8O9vw8c+HntA37gUWDVlXVfV0le+Cbw0Kfn9xyCyvj0XiC2orr33PVa4PTL5Y97zR8Db/6r/O/7HwHuu7G6c1k83PlevPWT33B/0dCBL/cBeuO6fC00u66+Exdc+57KDr7zKmD1VWxnPfACcOVHgR9/uO5r2GGuxau+ZJXsGNkH/OsVZd5BgM8cBsJddZ8bADB2CLjj0tJn/GLiRUrpxnpO0whR5tZ/s1jaVHIMO5CQm8HMTVi3shfY/L2aL2wkkcE3X5zEJy+8uPCF678GnN5Z8r1/+oMdiIdUHByewdlLozgwnMTnrz8PZ6xYCvRfXvM1VczF7wWWnFHWJlzMC8cm8I0nDmFNd9iOJ3fy3itW4w3n9OL4RAorOgI4MDyDf/zf/fjQa9fitWd147M/3ImVXSEsjwXw4I5B9HUEsfmKVfj7R/fjE29Yx8ozj83i5tetxWvO7MZje4bxveeP419uvBiTqRy+8OBufOIN6/DkgTHsGJhi31l3BNNpDZ+8/yW87uwevPOSPnzq/sJIo1uuWYsL++L4/+2daXBc1ZWAv9Ob1N1Sq7WvtiXZsryvsvGCwVjYgIsAk4UwSQiTpJLAxEASqASSKoYwf8gkYSaVzAbJ1GRCFpLJZEJlSFgSFMhqHPCC8YpjwJu8yrIsW4t158d7r9UttVqSu+VefL6qrn799Pr1PbrvvXPPPcu9+wevRaJv7l4zjcVTSobJ8PmfbGVKaYD1c6t5+OntbLhmGi311nEnunq478db+JsV9cyfFOYzT222l1ocnFq7a/VUrmgYfqN+9qnNTC4NcOxMDwc7zvFP719AuLRq/EoB4P3fg+O7h+1+7o0jfP9Pb/PexXXcOK8WJg25lprWwR0/T/gA37T/JN98cS/z6oq4a/U0vvXSPl55u4Oy0qtZP9KX3B74+IvQeXCkIwDLkX/nk3/mutlVvHtRXdxjPvffW/C6XZGIIodQvpezvf1868Mt/Ncf9rPprVN85b3zufPJPwPWuhTLp5bStsuyhD60bApP/vEtbl8+hQV1Ye778RYK8j12dJMhHPBytqefgjwvR7oNj8y6IWHbY/jgj8EXtHx0fecgUAqh6kgex47DnZGqsY/cNIfHXthFR3cf97Q2sWhyMdsPneYrz+7ilgU1/HrnMSaV+Nl+qJM9A7X8zvmN8mb46HPQk6DkeLAsdUoBoGwafOx5OD98+jXCl9Yl/TOpUAwHgElRn+uAofM7YzkGAGPM48DjAC0tLYamtRfdsArgwXjKtbASChOf931/vZigz8ON33iZnWfzOTJwnr9vvgZKxrma18XiyYOpa8b9NeM5SduvCuCoFcHkhLCKWPfInXOX4WospcE+fs4Mw4FXX+Kx/R6uXL+SX/QYbq6ooXZyMW2vbeb66iqarlzIa20v8P2TlXSWTaXtaDtT+xtY0TSLrX/ZxUummII5N9Bxqpu2AS/vKp7PT7v28PZANx8ILaahqYq3DnTQNmDo7StlQbiZtiG+7CW+ZvAX86IdGnj/uuksXtMUV8a9RUHewcWMcANtA17ubV4Bto/A39tP21PCssIZVJSX0zYAvg4XvVHr8t7dvBziKJwP37GEgjwPX/jpNjaePEnezOvhYhP2iqdYryFIXzttf9hEa81saKof/j2XGxpWJTx1SxM8sWYAr50z0r57Gy/tf5ubPaNcm5WzrFcCBPj0XcuoLfbDCAlqW/15VgLogOVfcgpbyjkrKEKmX0v7vp08s2sf91euom3gAssaS2jbd5Lf7fPQNWCV4Xho+dXseWMjNS0tBEqDtD0lNAaCdJ7v53hXD6try9nT3hVRQNUl4yheGa3M/faUS+PqyK7e/A7aBqypsvxZrex9pYTXuzr53MxVUBPCXdhJ2y/8tE6ewx1XFFLk9/HPj/1myD9LYPJoFsMEMHQwMQGkQjG8AjSJSANwELgNGGrvPQ1ssP0PVwCnR/UvpJkl9gi0IM8TSSxyHFKZTHQbF08pjmSo3jy/hv4BE3EiOogIa2dV8sRL++jq6ef0uT5Kgj4abQd8Q3kQr9vFwslhdrefIeS3LplX3z7Fy3uOcayr1wrNc0mkhPOJsz28c8py/rZHqnNaN/c7p7rj1jI6cKo74ujb+IVWKkIjFx4rCfp452Q3R05bD6boKCO/143XLXR093HCTqLqHRJRVRXH+QxE/jclAR9et7X0a6px8g7ihUOOB29UImFp0DpXitJHIlE6I+H3uTl1dND5XF6YZ+evDDqvwwEvFwYM++2chtYZlfxx30m6evr53PXN3LKglpqwn989MDj4yfNYyaUiVhb0jKoQJ8/2crDjHIX5npgS4ckSsK81Eet6cooI1trO52nlBXxkZT3XzqyIG6yQ6yR95Rtj+oENwLPADuBHxpjtInKniDiTes8A+4C9wBPA3yb7u5eKQnvUZKXJXwKnc5KUBQcfOB9f1RjJ0lxcX8I3P7CIfO/wEfD8ujD9A4bf2zHpJUEf0ysLmVdXxKqmMmtfwMep7t5ImOFrb3dw+7c38sy2w5EHU4Edivn6wc7IQ8op6uXU2jnUcZ637Cmu6ZWW/8gl8M7Jc+xqP0M44KW8MPFDszTo48TZXo50nkcktjKoiFDk93H6XC8numIjZ5ya9RWjnL86nE9FYX5cp3myTKso4Psfv4K1duZ1Kiiz5blUZdnzPe5IcT2wLFMHJ3PaiaJzFsW5orEksqbE8sbSYeGpznfDAV8kNHtmdWEkcqkmxQ9nJyqpOODD43ZRFcqnOOCNtNvjdvF375p9WSoFSI3FgDHmGayHf/S+f4vaNsCnUvFblxon7nx2TeJRVKbgjOjBshhC+R5OdfdRFicu22G+nf3bttua+y0J+sj3unl6w5WRY4oCXjq6+/C6XXhcEnkwnD7Xxxw7fDjPY43WnSxkEauMQ+f5vkitnQsDho37T1Ia9DGjKsTu9i4WTS7mwKluzvb201xZOOoDuSTos4ocnj5PWUFezOgZoMjv4fS5vmFJVDOrQ+w+cmbY8UO5t7WJDy+vT3hMMqyYWpbS8zl925EgoSyVRId6hvI91Ib9vObqoH/ARPIgiuwIMycLvTKUz5zaENsPdQ5LZnO4ZWEtDaVBfmkX1ZtRFaIkaA1WqsMpLF0NkUGeo9Tubm3ifS3xfSqXI5k/BE4zLvshNbsm/sWcaYgIj757LnNqixARwgGfpRgSjJKrQvmUF+bRttPKVSgJDFcixQEfXT39uARuXTKJB26YwU3f+C37T3RHLAawpt7eOtGNiHVjv7znOAsfeT6yABLAy3uOM6+uiKUNJbxxuJPF9cV8++W/0N7Zw61juDlLgj76Bwx7jp6hMjRcrnDAZ00lRZUCF4GHbpw1pvLg4YAv8oDLBpy+PRVn1cGJwB9ldf7nR5fSUBrkhR1H6erpj0wlDbUYSoI+Nqxp4lDHuREV84M3zATg92+ewOsWGsuDkQd3qkfuzlRSZAop7Kc2jhVzuaKKYRQcx1e2WAwAty2dHNl2btB4mZwOIsL8uiJesJPYSuL4Upwpgs7z/YTyvYTyvcytC7P/RHfMVE6BbaFMLS+goSzADrty5sGOczSWBSMlsVdOK+NDy6bwoWVT+N6f3qJ/wNA/cIHmqtH/z44f5Y3DnVw5bfjoO+z3cqTzfMz6yKF8q9TBtFGSIrMRp28TrX+QSqKnI+tLgxQHfeR7XXT1DF4nzvu+Y10UB7x43S6unl4+pvPfsrCGaRUFeN2uSF/XFKXWYsjzuHANmYZUBsm6WkmXGmfeNlsshqE4N2giiwHgJnvBFiDGAhg8z6CycKbX5tpTSNEO7347qmjdrEoqhziQF00ZzC7+yIr6yPbCSdaU16qmMlpnViRsJ0CJ3b7zfQNxR3lFfmva63hXb6QOTqrKEWciTt8mqmaaSvy+wceGM/J2lIVznTh+nON2Jv14WDOjkntarYg0p6+HljFJFhGr+GZ96dijDP/w4Bpe+OzVKW1HpqIWwyjcsqCG/918KO6URTZQ5Pfic7si5SpG4qb5NTSUBtlxuDOu8ze66mYoohisKJ7yqBvfcTZfN7sqsl6EYylMKg4wv66IhrJgTNTRrJoQWx++bswyRVs/62ZXDft7UcBaH/vk2R6mVRSy43DnsPUscgmnb51yDhONM5UkYo28IVox2BZqQR4zq0PsONyZ1KjcmYKcNAFh4j/71MpIcMlYuJwc0aoYRuFrty7g0ffMm5AIlUvByqllGMOY2j+3roi5dfEto+IYi8G6mVrqi7mntYlroyJsnDr38+qKON7Vg0vgkZvn8MnvbmJ2TYh7WqclKdHgmroAyxqHJw8V+a1V/to7e1g4Ocye9jM5rRhEhB99cjlTxjH6TQZHCfi97sh15YT2Rpc1uaa5nB2HO5MK+10xtZR/v30xVzQMzztJlkQh0Zc7qhhGwe0S3JeizPYEceuSSdy6ZNLoB45C9FSME/nkdbv47NrpMcc9++mrON93ARFhzYwKXv78GmrDfl59aC0+tyslCtaZurp6evngoinRbbWVwMGOc6ybbU1pJfKx5AJLJ+DBORKOYghERSf5h1gMANfMqOBf2t5k55GLX/bV5RKui2MVKhOLKgZlTETf8InM72iTX0QiPoA8T+qUa57HzYv3rx4xiqQoqq2lQR9fv22BOhlTiKMEosNWHWURbZktnBSmubKQzwwZPCiZjyoGZUwU5Hki+QuF+em/bJwM4nhET2c0lBVE6igpqcFRCNFhq47iL46yzDxuF89+5qpL2zglJWhUkjImrJwIazSYaJH3TKDCDhRY1VTG+rk6DZFqBi2GwQGCoyyGLg2qZCfpH/opWUM4YBXlywSLIRGzqkP8/O4rmVkdytqggUzGcSYHoiyGfI8Lr1ti/A5K9pLZd7iSUYT9VoGzYIbXjBKREcsuKMmTH8fH0FheQHPV6OVMlOwgs+9wJaMIB3wU5HkGF0NXLkviOZ/vWj2VO69uTFeTlBSjikEZM1PLg5Ey2srlSzznM4wtV0bJDlQxKGPmvnXN3DswMPqBSk7jj5PHoOQWqhiUMePzuPBpINtlTzwfg5Jb6F2uKMq4iC6JoeQmqhgURRkXhfkeAj53zBobSm6hU0mKooyLfK+bX9+3OivWQFcuDlUMiqKMm1Svj6BkFjqVpCiKosSgikFRFEWJQRWDoiiKEoMqBkVRFCUGVQyKoihKDKoYFEVRlBhUMSiKoigxJJXHICIlwFNAPbAfuNUYcyrOcfuBM8AFoN8Y05LM7yqKoigTR7IWwwPAr4wxTcCv7M8jcY0xZoEqBUVRlMwmWcVwM/Ade/s7wC1Jnk9RFEVJM8kqhkpjzGEA+71ihOMM8JyI/FlEPpHohCLyCRHZJCKbjh07lmTzFEVRlPEyqo9BRF4AquL86Yvj+J2VxphDIlIBPC8iO40xL8U70BjzOPA4QEtLixnHbyiKoigpQIy5+GeviOwCVhtjDotINdBmjGke5TsPA13GmK+O4fxngF0X3cDMpgw4nu5GTCAqX3aj8mUvzcaYwmROkGx11aeBO4BH7fefDT1ARIKAyxhzxt5eBzwyxvPvylVntYhsylXZQOXLdlS+7EVENiV7jmR9DI8Ca0VkD7DW/oyI1IjIM/YxlcBvRWQLsBH4P2PML5P8XUVRFGWCSMpiMMacAFrj7D8ErLe39wHzk/kdRVEU5dKR6ZnPj6e7ARNILssGKl+2o/JlL0nLlpTzWVEURclY4G6QAAAEaUlEQVQ9Mt1iUBRFUS4xqhgURVGUGDJSMYjI9SKyS0T2ikii+ktZg4jsF5FtIrLZCScTkRIReV5E9tjvxelu51gRkf8QkaMi8nrUvhHlEZEH7f7cJSLXpafVY2cE+R4WkYN2H24WkfVRf8sa+URkkoi8KCI7RGS7iNxr78+J/ksgX670X76IbBSRLbZ8X7L3p67/jDEZ9QLcwJtAI+ADtgCz0t2uFMi1Hygbsu8fgAfs7QeAL6e7neOQ5ypgEfD6aPIAs+x+zAMa7P51p1uGi5DvYeD+OMdmlXxANbDI3i4Edtsy5ET/JZAvV/pPgAJ72wv8CViWyv7LRIthKbDXGLPPGNML/BCrWF8ukrVFCI1V0uTkkN0jyXMz8ENjTI8x5i/AXqx+zlhGkG8ksko+Y8xhY8yr9vYZYAdQS470XwL5RiLb5DPGmC77o9d+GVLYf5moGGqBd6I+HyBxp2YL8QoJjrUIYbYwkjy51KcbRGSrPdXkmOpZK5+I1AMLsUadOdd/Q+SDHOk/EXGLyGbgKPC8MSal/ZeJikHi7MuFmNqVxphFwA3Ap0TkqnQ36BKSK336r8BUYAFwGPiavT8r5RORAuAnwKeNMZ2JDo2zLxvly5n+M8ZcMMYsAOqApSIyJ8Hh45YvExXDAWBS1Oc64FCa2pIyjJUNjjHmKPBTLFOu3S4+iP1+NH0tTAkjyZMTfWqMabdvyAHgCQbN8ayTT0S8WA/N7xlj/sfenTP9F0++XOo/B2NMB9AGXE8K+y8TFcMrQJOINIiID7gNq1hf1iIiQREpdLaxCgm+zmARQhihCGGWMZI8TwO3iUieiDQATVh1s7IK56az+SusPoQsk09EBPg2sMMY81jUn3Ki/0aSL4f6r1xEwva2H7gW2Ekq+y/dHvYRvO7rsSIJ3gS+mO72pECeRqyogC3AdkcmoBRrSdQ99ntJuts6Dpl+gGWO92GNSD6WSB6s9TvexCqjfkO623+R8n0X2AZstW+26myUD7gSayphK7DZfq3Plf5LIF+u9N884DVbjteBh+z9Kes/LYmhKIqixJCJU0mKoihKGlHFoCiKosSgikFRFEWJQRWDoiiKEoMqBkVRFCWGpJb2VJRcQUScUD+AKuACcMz+3G2MWZGWhilKGtBwVUUZgog8DHQZY76a7rYoSjrQqSRFGQUR6bLfV4vIb0TkRyKyW0QeFZEP2rXxt4nIVPu4chH5iYi8Yr9WplcCRRkfqhgUZXzMB+4F5gK3A9ONMUuBbwF328d8HfhHY8wS4D323xQla1Afg6KMj1eMXdpYRN4EnrP3bwOusbevBWZZJXsACIlIobHWBlCUjEcVg6KMj56o7YGozwMM3k8uYLkx5tylbJiipAqdSlKU1PMcsMH5ICIL0tgWRRk3qhgUJfXcA7TYK4W9AdyZ7gYpynjQcFVFURQlBrUYFEVRlBhUMSiKoigxqGJQFEVRYlDFoCiKosSgikFRFEWJQRWDoiiKEoMqBkVRFCWG/wf5JlMlGB3w5AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rising = (np.diff(above_thrs) > 0).astype(np.int)   # find rising edge of crossing threshold\n",
    "spikes = np.hstack((0, rising))    # prepend 0 to account for shortening due to np.diff\n",
    "\n",
    "plt.plot(activity)\n",
    "plt.plot(above_thrs)\n",
    "plt.plot(np.where(spikes>0), 1,  'ro'); # plot only spike points\n",
    "plt.xlabel('Time')\n",
    "plt.xlim([0, 300])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's also compute the spike counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = spikes.sum()   # compute total spike counts\n",
    "count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is our complete spike detection algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ENTER YOUR CODE! - try different values of threshold!\n",
    "\n",
    "threshold = 0.7    # enter different threshold values here\n",
    "\n",
    "# find activity above threshold\n",
    "above_thrs = (activity > threshold).astype(np.int) \n",
    "\n",
    "rising = (np.diff(above_thrs) > 0).astype(np.int)   # find rising edge of crossing threshold\n",
    "spikes = np.hstack((0, rising))    # prepend 0 to account for shortening due to np.diff\n",
    "\n",
    "count = spikes.sum()   # compute total spike counts\n",
    "\n",
    "\n",
    "plt.plot(activity)\n",
    "plt.plot(above_thrs)\n",
    "plt.plot(np.where(spikes>0), 1,  'ro'); # plot only spike points\n",
    "plt.xlabel('Time')\n",
    "plt.title('Total spike counts: {}'.format(count));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now notice that the exact spikes you detect depend on the value of the `threshold`. Therefore, the `threshold` is a parameter for our spike detection computation. Rather than fixing the value of the threshold, we might want to try different values and see what works well.\n",
    "\n",
    "In other words, you want to compute `Spikes` for a **combination** of `Neuron`s and different `threshold` values. To do this while still taking advantage of the `make` and `populate` logic, you would want to define a table to house parameters for spike detection in a `Lookup` table!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter `Lookup` table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define `SpikeDetectionParam` table to hold different parameter configuration for our spike detection algorithm. We are going to define this table as a `Lookup` table, rather than a `Manual` table. By now, you know that `Lookup` must be yet another **table tier** in DataJoint. `Lookup` tables are depicted by gray boxes in the Diagram.\n",
    "\n",
    "This tier indicates that the table will contain information:\n",
    "* that will be referenced by other tables\n",
    "* that doesn't change much - usually contains a few pre-known entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "@schema\n",
    "class SpikeDetectionParam(dj.Lookup):\n",
    "    definition = \"\"\"\n",
    "    sdp_id: int      # unique id for spike detection parameter set\n",
    "    ---\n",
    "    threshold: float   # threshold for spike detection\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": "<svg height=\"247pt\" viewBox=\"0.00 0.00 214.00 247.00\" width=\"214pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 243)\">\n<title>%3</title>\n<polygon fill=\"white\" points=\"-4,4 -4,-243 210,-243 210,4 -4,4\" stroke=\"none\"/>\n<!-- Neuron -->\n<g class=\"node\" id=\"node1\"><title>Neuron</title>\n<g id=\"a_node1\"><a xlink:title=\"→ Session\r\nneuron_id            \r\n------------------------------\r\nactivity             \r\n\">\n<ellipse cx=\"50.5\" cy=\"-79.5\" fill=\"#00007f\" fill-opacity=\"0.250980\" rx=\"33.5018\" ry=\"17.5\" stroke=\"#00007f\" stroke-opacity=\"0.250980\"/>\n<text fill=\"#00007f\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"31.5\" y=\"-77.4\">Neuron</text>\n</a>\n</g>\n</g>\n<!-- ActivityStatistics -->\n<g class=\"node\" id=\"node2\"><title>ActivityStatistics</title>\n<g id=\"a_node2\"><a xlink:title=\"→ Neuron\r\n------------------------------\r\nmean                 \r\nstdev                \r\nmax                  \r\n\">\n<ellipse cx=\"50.5\" cy=\"-13\" fill=\"#ff0000\" fill-opacity=\"0.125490\" rx=\"13\" ry=\"13\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\"/>\n<text fill=\"#7f0000\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"50.5\" y=\"-9.9\">ActivityStatistics</text>\n</a>\n</g>\n</g>\n<!-- Neuron&#45;&gt;ActivityStatistics -->\n<g class=\"edge\" id=\"edge1\"><title>Neuron-&gt;ActivityStatistics</title>\n<path d=\"M50.5,-61.8896C50.5,-50.6919 50.5,-36.1749 50.5,-26.0728\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n</g>\n<!-- Mouse -->\n<g class=\"node\" id=\"node3\"><title>Mouse</title>\n<g id=\"a_node3\"><a xlink:title=\"mouse_id             \r\n------------------------------\r\ndob=null             \r\nsex=&quot;unknown&quot;        \r\n\">\n<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"76.5,-239 24.5,-239 24.5,-204 76.5,-204 76.5,-239\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"32.5\" y=\"-219.4\">Mouse</text>\n</a>\n</g>\n</g>\n<!-- Session -->\n<g class=\"node\" id=\"node5\"><title>Session</title>\n<g id=\"a_node5\"><a xlink:title=\"→ Mouse\r\nsession_date         \r\n------------------------------\r\nexperiment_setup     \r\nexperimenter         \r\ndata_path=&quot;&quot;         \r\n\">\n<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"80,-168 21,-168 21,-133 80,-133 80,-168\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"29\" y=\"-148.4\">Session</text>\n</a>\n</g>\n</g>\n<!-- Mouse&#45;&gt;Session -->\n<g class=\"edge\" id=\"edge2\"><title>Mouse-&gt;Session</title>\n<path d=\"M50.5,-203.797C50.5,-192.949 50.5,-178.867 50.5,-168.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n</g>\n<!-- SpikeDetectionParam -->\n<g class=\"node\" id=\"node4\"><title>SpikeDetectionParam</title>\n<g id=\"a_node4\"><a xlink:title=\"sdp_id               \r\n------------------------------\r\nthreshold            \r\n\">\n<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"206,-239 95,-239 95,-204 206,-204 206,-239\" stroke=\"none\"/>\n<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"103\" y=\"-220\">SpikeDetectionParam</text>\n</a>\n</g>\n</g>\n<!-- Session&#45;&gt;Neuron -->\n<g class=\"edge\" id=\"edge3\"><title>Session-&gt;Neuron</title>\n<path d=\"M50.5,-132.797C50.5,-121.949 50.5,-107.867 50.5,-97.0492\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n</g>\n</g>\n</svg>",
      "text/plain": [
       "<datajoint.diagram.Diagram at 0x1bb4106c278>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj.Diagram(schema)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining `Spikes` table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's take everything together and define the `Spikes` table. Here each entry of the table will be *a set of spikes* for a single neuron, using a particular value of the `SpikeDetectionParam`. In other words, any particular entry of the `Spikes` table is determined by **a combination of a neuron and spike detection parameters**.\n",
    "\n",
    "We capture this by depending on both `Neuron` and `SpikeDetectionParam`. For each spike set, we want to store the detected spikes and the total number of spikes. The table definition will look something like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "@schema\n",
    "class Spikes(dj.Computed):\n",
    "    definition = \"\"\"\n",
    "    -> Neuron\n",
    "    -> SpikeDetectionParam\n",
    "    ---\n",
    "    spikes: longblob     # detected spikes\n",
    "    count: int           # total number of detected spikes\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the `Waveform` part table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would also like to store the waveform at each detected spike, for a particular neuron. For simplicity, let's define the waveform to be 40 sample points before and after the onset of the detected spike. \n",
    "To accomplish this, we will define a `dj.Part` table `Waveform` which will contain the waveform per spike. The table definition is something like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "@schema\n",
    "class Spikes(dj.Computed):\n",
    "    definition = \"\"\"\n",
    "    -> Neuron\n",
    "    -> SpikeDetectionParam\n",
    "    ---\n",
    "    spikes: longblob     # detected spikes\n",
    "    count: int           # total number of detected spikes\n",
    "    \"\"\"\n",
    "    \n",
    "    class Waveform(dj.Part):\n",
    "        definition = \"\"\"\n",
    "        -> master\n",
    "        spike_id: int\n",
    "        ---\n",
    "        waveform: longblob  # waveform extracted from this spike\n",
    "        \"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `-> master` syntax denotes that the `Waveform` part table is foreign key constrained by `Spike` table - i.e. the master table. The master table drives the ***populate*** logic, and the content of the part table is generally ingested together with the content of the master table, all in one step (i.e. one `make()` call)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": "<svg height=\"302pt\" viewBox=\"0.00 0.00 221.00 302.00\" width=\"221pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 298)\">\n<title>%3</title>\n<polygon fill=\"white\" points=\"-4,4 -4,-298 217,-298 217,4 -4,4\" stroke=\"none\"/>\n<!-- Spikes.Waveform -->\n<g class=\"node\" id=\"node1\"><title>Spikes.Waveform</title>\n<g id=\"a_node1\"><a xlink:title=\"→ Spikes\r\nspike_id             \r\n------------------------------\r\nwaveform             \r\n\">\n<polygon fill=\"none\" points=\"197.5,-19 105.5,-19 105.5,-0 197.5,-0 197.5,-19\" stroke=\"none\"/>\n<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"113.5\" y=\"-8\">Spikes.Waveform</text>\n</a>\n</g>\n</g>\n<!-- Neuron -->\n<g class=\"node\" id=\"node2\"><title>Neuron</title>\n<g id=\"a_node2\"><a xlink:title=\"→ Session\r\nneuron_id            \r\n------------------------------\r\nactivity             \r\n\">\n<ellipse cx=\"50.5\" cy=\"-134.5\" fill=\"#00007f\" fill-opacity=\"0.250980\" rx=\"33.5018\" ry=\"17.5\" stroke=\"#00007f\" stroke-opacity=\"0.250980\"/>\n<text fill=\"#00007f\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"31.5\" y=\"-132.4\">Neuron</text>\n</a>\n</g>\n</g>\n<!-- ActivityStatistics -->\n<g class=\"node\" id=\"node3\"><title>ActivityStatistics</title>\n<g id=\"a_node3\"><a xlink:title=\"→ Neuron\r\n------------------------------\r\nmean                 \r\nstdev                \r\nmax                  \r\n\">\n<ellipse cx=\"50.5\" cy=\"-68\" fill=\"#ff0000\" fill-opacity=\"0.125490\" rx=\"13\" ry=\"13\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\"/>\n<text fill=\"#7f0000\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"50.5\" y=\"-64.9\">ActivityStatistics</text>\n</a>\n</g>\n</g>\n<!-- Neuron&#45;&gt;ActivityStatistics -->\n<g class=\"edge\" id=\"edge1\"><title>Neuron-&gt;ActivityStatistics</title>\n<path d=\"M50.5,-116.89C50.5,-105.692 50.5,-91.1749 50.5,-81.0728\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n</g>\n<!-- Spikes -->\n<g class=\"node\" id=\"node4\"><title>Spikes</title>\n<g id=\"a_node4\"><a xlink:title=\"→ Neuron\r\n→ SpikeDetectionParam\r\n------------------------------\r\nspikes               \r\ncount                \r\n\">\n<ellipse cx=\"151.5\" cy=\"-68\" fill=\"#ff0000\" fill-opacity=\"0.125490\" rx=\"13\" ry=\"13\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\"/>\n<text fill=\"#7f0000\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"151.5\" y=\"-64.9\">Spikes</text>\n</a>\n</g>\n</g>\n<!-- Neuron&#45;&gt;Spikes -->\n<g class=\"edge\" id=\"edge2\"><title>Neuron-&gt;Spikes</title>\n<path d=\"M70.9389,-120.447C92.151,-106.901 124.489,-86.2498 141.001,-75.705\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n</g>\n<!-- Spikes&#45;&gt;Spikes.Waveform -->\n<g class=\"edge\" id=\"edge3\"><title>Spikes-&gt;Spikes.Waveform</title>\n<path d=\"M151.5,-54.7403C151.5,-43.9652 151.5,-28.5623 151.5,-19.0639\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n</g>\n<!-- Mouse -->\n<g class=\"node\" id=\"node5\"><title>Mouse</title>\n<g id=\"a_node5\"><a xlink:title=\"mouse_id             \r\n------------------------------\r\ndob=null             \r\nsex=&quot;unknown&quot;        \r\n\">\n<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"76.5,-294 24.5,-294 24.5,-259 76.5,-259 76.5,-294\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"32.5\" y=\"-274.4\">Mouse</text>\n</a>\n</g>\n</g>\n<!-- Session -->\n<g class=\"node\" id=\"node7\"><title>Session</title>\n<g id=\"a_node7\"><a xlink:title=\"→ Mouse\r\nsession_date         \r\n------------------------------\r\nexperiment_setup     \r\nexperimenter         \r\ndata_path=&quot;&quot;         \r\n\">\n<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"80,-223 21,-223 21,-188 80,-188 80,-223\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n<text fill=\"darkgreen\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"29\" y=\"-203.4\">Session</text>\n</a>\n</g>\n</g>\n<!-- Mouse&#45;&gt;Session -->\n<g class=\"edge\" id=\"edge4\"><title>Mouse-&gt;Session</title>\n<path d=\"M50.5,-258.797C50.5,-247.949 50.5,-233.867 50.5,-223.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n</g>\n<!-- SpikeDetectionParam -->\n<g class=\"node\" id=\"node6\"><title>SpikeDetectionParam</title>\n<g id=\"a_node6\"><a xlink:title=\"sdp_id               \r\n------------------------------\r\nthreshold            \r\n\">\n<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"213,-152 102,-152 102,-117 213,-117 213,-152\" stroke=\"none\"/>\n<text font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"110\" y=\"-133\">SpikeDetectionParam</text>\n</a>\n</g>\n</g>\n<!-- SpikeDetectionParam&#45;&gt;Spikes -->\n<g class=\"edge\" id=\"edge5\"><title>SpikeDetectionParam-&gt;Spikes</title>\n<path d=\"M155.955,-116.89C154.913,-105.692 153.563,-91.1749 152.623,-81.0728\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n</g>\n<!-- Session&#45;&gt;Neuron -->\n<g class=\"edge\" id=\"edge6\"><title>Session-&gt;Neuron</title>\n<path d=\"M50.5,-187.797C50.5,-176.949 50.5,-162.867 50.5,-152.049\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"0.75\"/>\n</g>\n</g>\n</svg>",
      "text/plain": [
       "<datajoint.diagram.Diagram at 0x1bb437100f0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj.Diagram(schema)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the Diagram, we see that `Spikes` is a computed table (red circle) that depends on **both Neuron and SpikeDetectionParam**. Finally, let's go ahead and implement the `make` method for the `Spikes` table. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "@schema\n",
    "class Spikes(dj.Computed):\n",
    "    definition = \"\"\"\n",
    "    -> Neuron\n",
    "    -> SpikeDetectionParam\n",
    "    ---\n",
    "    spikes: longblob     # detected spikes\n",
    "    count: int           # total number of detected spikes\n",
    "    \"\"\"\n",
    "    \n",
    "    class Waveform(dj.Part):\n",
    "        definition = \"\"\"\n",
    "        -> master\n",
    "        spike_id: int\n",
    "        ---\n",
    "        waveform: longblob  # waveform extracted from this spike\n",
    "        \"\"\"\n",
    "        \n",
    "    def make(self, key):\n",
    "        print('Populating for: ', key)\n",
    "\n",
    "        activity = (Neuron() & key).fetch1('activity')\n",
    "        threshold = (SpikeDetectionParam() & key).fetch1('threshold')\n",
    "\n",
    "        above_thrs = (activity > threshold).astype(np.int)   # find activity above threshold\n",
    "        rising = (np.diff(above_thrs) > 0).astype(np.int)   # find rising edge of crossing threshold\n",
    "        spikes = np.hstack((0, rising))    # prepend 0 to account for shortening due to np.diff\n",
    "\n",
    "        count = spikes.sum()   # compute total spike counts\n",
    "        print('Detected {} spikes!\\n'.format(count))\n",
    "\n",
    "        # create and insert a new dictionary containing `key` and additionally `spikes` and `count`\n",
    "        self.insert1(dict(key, spikes=spikes, count=count))  \n",
    "        \n",
    "        # extract waveform for the `Waveform` part-table\n",
    "        before_spk, after_spk = 40, 40  # extract 40 sample points before and after a spike as the waveform\n",
    "        for spk_id, spk in enumerate(np.where(spikes==1)[0]):\n",
    "            \n",
    "            # For simplicity, skip the spikes too close to the beginning or the end\n",
    "            if spk - before_spk < 0 or spk + after_spk > len(activity) + 1: \n",
    "                continue\n",
    "                \n",
    "            wf = activity[spk - before_spk: spk + after_spk] \n",
    "            \n",
    "            # create and insert a new dictionary containing `key` and additionally `spike_id` and `waveform`\n",
    "            self.Waveform.insert1(dict(key, spike_id=spk_id, waveform=wf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The implementation of the spike detection is pretty much what we had above, except that we now fetch the value of `threshold` from the `SpikeDetectionParam` table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the `Spikes` table, we see that it indeed inherits the primary key attributes from **both Neuron (`mouse_id`, `session_date`) and SpikeDetectionParam (`sdp_id`)**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            .djtooltip .djtooltiptext {\n",
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       "                visibility: visible;\n",
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       "        </style>\n",
       "        \n",
       "        <b></b>\n",
       "            <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "            <table border=\"1\" class=\"Relation\">\n",
       "                <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">mouse_id</p>\n",
       "                                <span class=\"djtooltiptext\">Unique animal ID</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">session_date</p>\n",
       "                                <span class=\"djtooltiptext\">date</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">neuron_id</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">sdp_id</p>\n",
       "                                <span class=\"djtooltiptext\">unique id for spike detection parameter set</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">spikes</p>\n",
       "                                <span class=\"djtooltiptext\">detected spikes</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">count</p>\n",
       "                                <span class=\"djtooltiptext\">total number of detected spikes</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr>  </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 0</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*mouse_id    *session_date  *neuron_id    *sdp_id    spikes     count    \n",
       "+----------+ +------------+ +-----------+ +--------+ +--------+ +-------+\n",
       "\n",
       " (Total: 0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spikes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Populating `Spikes` table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to populate! When we call `populate` on `Spikes`, DataJoint will automatically call `make` on **every valid combination of the parent tables - Neuron and SpikeDetectionParam**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ENTER YOUR CODE! - populate the Spikes table\n",
    "Spikes.populate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hm... `populate` doesn't seem to be doing anything... What could be the cause?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at `SpikeDetectionParam` reveals the issue:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                background: #f3f1ff;\n",
       "            }\n",
       "            /* Tooltip container */\n",
       "            .djtooltip {\n",
       "            }\n",
       "            /* Tooltip text */\n",
       "            .djtooltip .djtooltiptext {\n",
       "                visibility: hidden;\n",
       "                width: 120px;\n",
       "                background-color: black;\n",
       "                color: #fff;\n",
       "                text-align: center;\n",
       "                padding: 5px 0;\n",
       "                border-radius: 6px;\n",
       "                /* Position the tooltip text - see examples below! */\n",
       "                position: absolute;\n",
       "                z-index: 1;\n",
       "            }\n",
       "            #primary {\n",
       "                font-weight: bold;\n",
       "                color: black;\n",
       "            }\n",
       "\n",
       "            #nonprimary {\n",
       "                font-weight: normal;\n",
       "                color: white;\n",
       "            }\n",
       "\n",
       "            /* Show the tooltip text when you mouse over the tooltip container */\n",
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       "                visibility: visible;\n",
       "            }\n",
       "        </style>\n",
       "        \n",
       "        <b></b>\n",
       "            <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "            <table border=\"1\" class=\"Relation\">\n",
       "                <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">sdp_id</p>\n",
       "                                <span class=\"djtooltiptext\">unique id for spike detection parameter set</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">threshold</p>\n",
       "                                <span class=\"djtooltiptext\">threshold for spike detection</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr>  </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 0</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*sdp_id    threshold    \n",
       "+--------+ +-----------+\n",
       "\n",
       " (Total: 0)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SpikeDetectionParam()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's right! We have not added a detection parameter set yet. Let's go ahead and add one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "SpikeDetectionParam.insert1((0, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
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       "                color: black;\n",
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       "\n",
       "            #nonprimary {\n",
       "                font-weight: normal;\n",
       "                color: white;\n",
       "            }\n",
       "\n",
       "            /* Show the tooltip text when you mouse over the tooltip container */\n",
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       "                visibility: visible;\n",
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       "        </style>\n",
       "        \n",
       "        <b></b>\n",
       "            <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "            <table border=\"1\" class=\"Relation\">\n",
       "                <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">sdp_id</p>\n",
       "                                <span class=\"djtooltiptext\">unique id for spike detection parameter set</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">threshold</p>\n",
       "                                <span class=\"djtooltiptext\">threshold for spike detection</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>0</td>\n",
       "<td>0.5</td> </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 1</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*sdp_id    threshold    \n",
       "+--------+ +-----------+\n",
       "0          0.5          \n",
       " (Total: 1)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SpikeDetectionParam()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we should really be ready to perform the computation..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating for:  {'mouse_id': 0, 'session_date': datetime.date(2017, 5, 15), 'neuron_id': 0, 'sdp_id': 0}\n",
      "Detected 26 spikes!\n",
      "\n",
      "Populating for:  {'mouse_id': 0, 'session_date': datetime.date(2017, 5, 19), 'neuron_id': 0, 'sdp_id': 0}\n",
      "Detected 24 spikes!\n",
      "\n",
      "Populating for:  {'mouse_id': 5, 'session_date': datetime.date(2017, 1, 5), 'neuron_id': 0, 'sdp_id': 0}\n",
      "Detected 18 spikes!\n",
      "\n",
      "Populating for:  {'mouse_id': 100, 'session_date': datetime.date(2017, 5, 25), 'neuron_id': 0, 'sdp_id': 0}\n",
      "Detected 41 spikes!\n",
      "\n",
      "Populating for:  {'mouse_id': 100, 'session_date': datetime.date(2017, 6, 1), 'neuron_id': 0, 'sdp_id': 0}\n",
      "Detected 18 spikes!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# ENTER YOUR CODE! - populate the Spikes table for real!\n",
    "Spikes.populate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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       "        <style type=\"text/css\">\n",
       "            .Relation{\n",
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       "            #primary {\n",
       "                font-weight: bold;\n",
       "                color: black;\n",
       "            }\n",
       "\n",
       "            #nonprimary {\n",
       "                font-weight: normal;\n",
       "                color: white;\n",
       "            }\n",
       "\n",
       "            /* Show the tooltip text when you mouse over the tooltip container */\n",
       "            .djtooltip:hover .djtooltiptext {\n",
       "                visibility: visible;\n",
       "            }\n",
       "        </style>\n",
       "        \n",
       "        <b></b>\n",
       "            <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "            <table border=\"1\" class=\"Relation\">\n",
       "                <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">mouse_id</p>\n",
       "                                <span class=\"djtooltiptext\">Unique animal ID</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">session_date</p>\n",
       "                                <span class=\"djtooltiptext\">date</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">neuron_id</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">sdp_id</p>\n",
       "                                <span class=\"djtooltiptext\">unique id for spike detection parameter set</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">spikes</p>\n",
       "                                <span class=\"djtooltiptext\">detected spikes</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">count</p>\n",
       "                                <span class=\"djtooltiptext\">total number of detected spikes</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>0</td>\n",
       "<td>2017-05-15</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>26</td></tr><tr><td>0</td>\n",
       "<td>2017-05-19</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>24</td></tr><tr><td>5</td>\n",
       "<td>2017-01-05</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>18</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>41</td></tr><tr><td>100</td>\n",
       "<td>2017-06-01</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>18</td> </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 5</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*mouse_id    *session_date  *neuron_id    *sdp_id    spikes     count    \n",
       "+----------+ +------------+ +-----------+ +--------+ +--------+ +-------+\n",
       "0            2017-05-15     0             0          =BLOB=     26       \n",
       "0            2017-05-19     0             0          =BLOB=     24       \n",
       "5            2017-01-05     0             0          =BLOB=     18       \n",
       "100          2017-05-25     0             0          =BLOB=     41       \n",
       "100          2017-06-01     0             0          =BLOB=     18       \n",
       " (Total: 5)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spikes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...and we now have spike detection running!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking the waveform(s) from the Waveform part table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        \n",
       "        <style type=\"text/css\">\n",
       "            .Relation{\n",
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       "            .Relation th{\n",
       "                background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
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       "            .djtooltip {\n",
       "            }\n",
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       "                padding: 5px 0;\n",
       "                border-radius: 6px;\n",
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       "                position: absolute;\n",
       "                z-index: 1;\n",
       "            }\n",
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       "\n",
       "            #nonprimary {\n",
       "                font-weight: normal;\n",
       "                color: white;\n",
       "            }\n",
       "\n",
       "            /* Show the tooltip text when you mouse over the tooltip container */\n",
       "            .djtooltip:hover .djtooltiptext {\n",
       "                visibility: visible;\n",
       "            }\n",
       "        </style>\n",
       "        \n",
       "        <b></b>\n",
       "            <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "            <table border=\"1\" class=\"Relation\">\n",
       "                <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">mouse_id</p>\n",
       "                                <span class=\"djtooltiptext\">Unique animal ID</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">session_date</p>\n",
       "                                <span class=\"djtooltiptext\">date</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">neuron_id</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">sdp_id</p>\n",
       "                                <span class=\"djtooltiptext\">unique id for spike detection parameter set</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">spike_id</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">waveform</p>\n",
       "                                <span class=\"djtooltiptext\">waveform extracted from this spike</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>2</td>\n",
       "<td>=BLOB=</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>3</td>\n",
       "<td>=BLOB=</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>4</td>\n",
       "<td>=BLOB=</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>5</td>\n",
       "<td>=BLOB=</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>6</td>\n",
       "<td>=BLOB=</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>7</td>\n",
       "<td>=BLOB=</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>8</td>\n",
       "<td>=BLOB=</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>9</td>\n",
       "<td>=BLOB=</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>10</td>\n",
       "<td>=BLOB=</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>11</td>\n",
       "<td>=BLOB=</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>12</td>\n",
       "<td>=BLOB=</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>13</td>\n",
       "<td>=BLOB=</td> </tr> </tbody>\n",
       "            </table>\n",
       "            <p>...</p>\n",
       "            <p>Total: 36</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*mouse_id    *session_date  *neuron_id    *sdp_id    *spike_id    waveform  \n",
       "+----------+ +------------+ +-----------+ +--------+ +----------+ +--------+\n",
       "100          2017-05-25     0             0          2            =BLOB=    \n",
       "100          2017-05-25     0             0          3            =BLOB=    \n",
       "100          2017-05-25     0             0          4            =BLOB=    \n",
       "100          2017-05-25     0             0          5            =BLOB=    \n",
       "100          2017-05-25     0             0          6            =BLOB=    \n",
       "100          2017-05-25     0             0          7            =BLOB=    \n",
       "100          2017-05-25     0             0          8            =BLOB=    \n",
       "100          2017-05-25     0             0          9            =BLOB=    \n",
       "100          2017-05-25     0             0          10           =BLOB=    \n",
       "100          2017-05-25     0             0          11           =BLOB=    \n",
       "100          2017-05-25     0             0          12           =BLOB=    \n",
       "100          2017-05-25     0             0          13           =BLOB=    \n",
       "   ...\n",
       " (Total: 36)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ENTER YOUR CODE! - Now, build a query for the waveforms from mouse 100, session on \"2017-05-25\", with detection param 0\n",
    "Spikes.Waveform & 'mouse_id = 100' & 'session_date = \"2017-05-25\"' & 'sdp_id = 0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ENTER YOUR CODE! - try fetching all the waveforms\n",
    "waveforms = (Spikes.Waveform & 'mouse_id = 100' & 'session_date = \"2017-05-25\"' & 'sdp_id = 0').fetch('waveform')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1bb436bcbe0>]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ENTER YOUR CODE! - and plot the average waveform\n",
    "plt.plot(waveforms.mean(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trying out other parameter values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how different thresholds affect the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "SpikeDetectionParam.insert1((1, 0.9))  # add another threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        \n",
       "        <style type=\"text/css\">\n",
       "            .Relation{\n",
       "                border-collapse:collapse;\n",
       "            }\n",
       "            .Relation th{\n",
       "                background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
       "                font-weight: normal; font-family: monospace; font-size: 100%;\n",
       "            }\n",
       "            .Relation td{\n",
       "                padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
       "            }\n",
       "            .Relation tr:nth-child(odd){\n",
       "                background: #ffffff;\n",
       "            }\n",
       "            .Relation tr:nth-child(even){\n",
       "                background: #f3f1ff;\n",
       "            }\n",
       "            /* Tooltip container */\n",
       "            .djtooltip {\n",
       "            }\n",
       "            /* Tooltip text */\n",
       "            .djtooltip .djtooltiptext {\n",
       "                visibility: hidden;\n",
       "                width: 120px;\n",
       "                background-color: black;\n",
       "                color: #fff;\n",
       "                text-align: center;\n",
       "                padding: 5px 0;\n",
       "                border-radius: 6px;\n",
       "                /* Position the tooltip text - see examples below! */\n",
       "                position: absolute;\n",
       "                z-index: 1;\n",
       "            }\n",
       "            #primary {\n",
       "                font-weight: bold;\n",
       "                color: black;\n",
       "            }\n",
       "\n",
       "            #nonprimary {\n",
       "                font-weight: normal;\n",
       "                color: white;\n",
       "            }\n",
       "\n",
       "            /* Show the tooltip text when you mouse over the tooltip container */\n",
       "            .djtooltip:hover .djtooltiptext {\n",
       "                visibility: visible;\n",
       "            }\n",
       "        </style>\n",
       "        \n",
       "        <b></b>\n",
       "            <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "            <table border=\"1\" class=\"Relation\">\n",
       "                <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">sdp_id</p>\n",
       "                                <span class=\"djtooltiptext\">unique id for spike detection parameter set</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">threshold</p>\n",
       "                                <span class=\"djtooltiptext\">threshold for spike detection</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>0</td>\n",
       "<td>0.5</td></tr><tr><td>1</td>\n",
       "<td>0.9</td> </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 2</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*sdp_id    threshold    \n",
       "+--------+ +-----------+\n",
       "0          0.5          \n",
       "1          0.9          \n",
       " (Total: 2)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SpikeDetectionParam()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating for:  {'mouse_id': 0, 'session_date': datetime.date(2017, 5, 15), 'neuron_id': 0, 'sdp_id': 1}\n",
      "Detected 27 spikes!\n",
      "\n",
      "Populating for:  {'mouse_id': 0, 'session_date': datetime.date(2017, 5, 19), 'neuron_id': 0, 'sdp_id': 1}\n",
      "Detected 21 spikes!\n",
      "\n",
      "Populating for:  {'mouse_id': 5, 'session_date': datetime.date(2017, 1, 5), 'neuron_id': 0, 'sdp_id': 1}\n",
      "Detected 14 spikes!\n",
      "\n",
      "Populating for:  {'mouse_id': 100, 'session_date': datetime.date(2017, 5, 25), 'neuron_id': 0, 'sdp_id': 1}\n",
      "Detected 35 spikes!\n",
      "\n",
      "Populating for:  {'mouse_id': 100, 'session_date': datetime.date(2017, 6, 1), 'neuron_id': 0, 'sdp_id': 1}\n",
      "Detected 15 spikes!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# ENTER YOUR CODE! - populate the \"missing\" entry in Spikes table\n",
    "Spikes.populate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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       "\n",
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       "        </style>\n",
       "        \n",
       "        <b></b>\n",
       "            <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "            <table border=\"1\" class=\"Relation\">\n",
       "                <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">mouse_id</p>\n",
       "                                <span class=\"djtooltiptext\">Unique animal ID</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">session_date</p>\n",
       "                                <span class=\"djtooltiptext\">date</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">neuron_id</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">sdp_id</p>\n",
       "                                <span class=\"djtooltiptext\">unique id for spike detection parameter set</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">spikes</p>\n",
       "                                <span class=\"djtooltiptext\">detected spikes</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">count</p>\n",
       "                                <span class=\"djtooltiptext\">total number of detected spikes</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>0</td>\n",
       "<td>2017-05-15</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>26</td></tr><tr><td>0</td>\n",
       "<td>2017-05-15</td>\n",
       "<td>0</td>\n",
       "<td>1</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>27</td></tr><tr><td>0</td>\n",
       "<td>2017-05-19</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>24</td></tr><tr><td>0</td>\n",
       "<td>2017-05-19</td>\n",
       "<td>0</td>\n",
       "<td>1</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>21</td></tr><tr><td>5</td>\n",
       "<td>2017-01-05</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>18</td></tr><tr><td>5</td>\n",
       "<td>2017-01-05</td>\n",
       "<td>0</td>\n",
       "<td>1</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>14</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>41</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>0</td>\n",
       "<td>1</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>35</td></tr><tr><td>100</td>\n",
       "<td>2017-06-01</td>\n",
       "<td>0</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>18</td></tr><tr><td>100</td>\n",
       "<td>2017-06-01</td>\n",
       "<td>0</td>\n",
       "<td>1</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>15</td> </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 10</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*mouse_id    *session_date  *neuron_id    *sdp_id    spikes     count    \n",
       "+----------+ +------------+ +-----------+ +--------+ +--------+ +-------+\n",
       "0            2017-05-15     0             0          =BLOB=     26       \n",
       "0            2017-05-15     0             1          =BLOB=     27       \n",
       "0            2017-05-19     0             0          =BLOB=     24       \n",
       "0            2017-05-19     0             1          =BLOB=     21       \n",
       "5            2017-01-05     0             0          =BLOB=     18       \n",
       "5            2017-01-05     0             1          =BLOB=     14       \n",
       "100          2017-05-25     0             0          =BLOB=     41       \n",
       "100          2017-05-25     0             1          =BLOB=     35       \n",
       "100          2017-06-01     0             0          =BLOB=     18       \n",
       "100          2017-06-01     0             1          =BLOB=     15       \n",
       " (Total: 10)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spikes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the results of spike detection under different parameter settings can live happily next to each other, without any confusion as to what is what."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deleting entries \"upstream\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's say that we decided that we don't like the first spike threshold of `0.5`. While there is really nothing wrong keeping those results around, you might decide that you'd rather delete all computations performed with that threshold to keep your tables clean."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While you can restrict `Spikes` table to the specific parameter id (i.e. `sdp_id = 0`) and delete the entries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "About to delete:\n",
      "`root_tutorial_pipeline`.`__spikes__waveform`: 115 items\n",
      "`root_tutorial_pipeline`.`__spikes`: 5 items\n",
      "Proceed? [yes, No]: no\n",
      "Cancelled deletes.\n"
     ]
    }
   ],
   "source": [
    "(Spikes & 'sdp_id = 0').delete()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can simply delete the unwanted parameter from the `SpikeDetectionParam` table, and let DataJoint cascade the deletion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            .djtooltip .djtooltiptext {\n",
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       "                padding: 5px 0;\n",
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       "                position: absolute;\n",
       "                z-index: 1;\n",
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       "            #primary {\n",
       "                font-weight: bold;\n",
       "                color: black;\n",
       "            }\n",
       "\n",
       "            #nonprimary {\n",
       "                font-weight: normal;\n",
       "                color: white;\n",
       "            }\n",
       "\n",
       "            /* Show the tooltip text when you mouse over the tooltip container */\n",
       "            .djtooltip:hover .djtooltiptext {\n",
       "                visibility: visible;\n",
       "            }\n",
       "        </style>\n",
       "        \n",
       "        <b></b>\n",
       "            <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "            <table border=\"1\" class=\"Relation\">\n",
       "                <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">sdp_id</p>\n",
       "                                <span class=\"djtooltiptext\">unique id for spike detection parameter set</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">threshold</p>\n",
       "                                <span class=\"djtooltiptext\">threshold for spike detection</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>0</td>\n",
       "<td>0.5</td> </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 1</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*sdp_id    threshold    \n",
       "+--------+ +-----------+\n",
       "0          0.5          \n",
       " (Total: 1)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SpikeDetectionParam() & 'sdp_id = 0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "About to delete:\n",
      "`root_tutorial_pipeline`.`__spikes__waveform`: 115 items\n",
      "`root_tutorial_pipeline`.`__spikes`: 5 items\n",
      "`root_tutorial_pipeline`.`#spike_detection_param`: 1 items\n",
      "Proceed? [yes, No]: yes\n",
      "Committed.\n"
     ]
    }
   ],
   "source": [
    "(SpikeDetectionParam() & 'sdp_id = 0').delete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                position: absolute;\n",
       "                z-index: 1;\n",
       "            }\n",
       "            #primary {\n",
       "                font-weight: bold;\n",
       "                color: black;\n",
       "            }\n",
       "\n",
       "            #nonprimary {\n",
       "                font-weight: normal;\n",
       "                color: white;\n",
       "            }\n",
       "\n",
       "            /* Show the tooltip text when you mouse over the tooltip container */\n",
       "            .djtooltip:hover .djtooltiptext {\n",
       "                visibility: visible;\n",
       "            }\n",
       "        </style>\n",
       "        \n",
       "        <b></b>\n",
       "            <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "            <table border=\"1\" class=\"Relation\">\n",
       "                <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">mouse_id</p>\n",
       "                                <span class=\"djtooltiptext\">Unique animal ID</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">session_date</p>\n",
       "                                <span class=\"djtooltiptext\">date</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">neuron_id</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">sdp_id</p>\n",
       "                                <span class=\"djtooltiptext\">unique id for spike detection parameter set</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">spikes</p>\n",
       "                                <span class=\"djtooltiptext\">detected spikes</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">count</p>\n",
       "                                <span class=\"djtooltiptext\">total number of detected spikes</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>0</td>\n",
       "<td>2017-05-15</td>\n",
       "<td>0</td>\n",
       "<td>1</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>27</td></tr><tr><td>0</td>\n",
       "<td>2017-05-19</td>\n",
       "<td>0</td>\n",
       "<td>1</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>21</td></tr><tr><td>5</td>\n",
       "<td>2017-01-05</td>\n",
       "<td>0</td>\n",
       "<td>1</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>14</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>0</td>\n",
       "<td>1</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>35</td></tr><tr><td>100</td>\n",
       "<td>2017-06-01</td>\n",
       "<td>0</td>\n",
       "<td>1</td>\n",
       "<td>=BLOB=</td>\n",
       "<td>15</td> </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 5</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*mouse_id    *session_date  *neuron_id    *sdp_id    spikes     count    \n",
       "+----------+ +------------+ +-----------+ +--------+ +--------+ +-------+\n",
       "0            2017-05-15     0             1          =BLOB=     27       \n",
       "0            2017-05-19     0             1          =BLOB=     21       \n",
       "5            2017-01-05     0             1          =BLOB=     14       \n",
       "100          2017-05-25     0             1          =BLOB=     35       \n",
       "100          2017-06-01     0             1          =BLOB=     15       \n",
       " (Total: 5)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spikes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations! You have successfully extended your pipeline with a table to represent recorded data (`Neuron` as `Imported` table), tables that performs and represents computation results (`ActivityStatistics` and `Spikes` as `Computed` tables) and a table to hold computation parameters (`SpikeDetectionParam` as `Lookup` table)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj.Diagram(schema)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our pipeline is still fairly simple but completely capable of handling analysis!"
   ]
  }
 ],
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