completed_tutorials/03-Calcium Imaging 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": [
    "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 and 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`, `Scan`, `AverageFrame` and populate them. Or, again for your convenience, we can import them from the `tutorial_pipeline.imaging` module. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connecting shan@workshop-db.datajoint.io:3306\n"
     ]
    }
   ],
   "source": [
    "from tutorial_pipeline.imaging import schema, Mouse, Session, Scan, AverageFrame"
   ]
  },
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   "execution_count": 4,
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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> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 4</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",
       " (Total: 4)"
      ]
     },
     "execution_count": 4,
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   ],
   "source": [
    "Session()"
   ]
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       "        <b></b>\n",
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       "                                <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\">scan_idx</p>\n",
       "                                <span class=\"djtooltiptext\">scan index</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">depth</p>\n",
       "                                <span class=\"djtooltiptext\">depth of this scan</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">wavelength</p>\n",
       "                                <span class=\"djtooltiptext\">wavelength used</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">laser_power</p>\n",
       "                                <span class=\"djtooltiptext\">power of the laser used</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">fps</p>\n",
       "                                <span class=\"djtooltiptext\">frames per second</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">file_name</p>\n",
       "                                <span class=\"djtooltiptext\">name of the tif file</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>0</td>\n",
       "<td>2017-05-15</td>\n",
       "<td>1</td>\n",
       "<td>150.0</td>\n",
       "<td>920.0</td>\n",
       "<td>26.0</td>\n",
       "<td>15.0</td>\n",
       "<td>example_scan_01.tif</td></tr><tr><td>0</td>\n",
       "<td>2017-05-15</td>\n",
       "<td>2</td>\n",
       "<td>200.0</td>\n",
       "<td>920.0</td>\n",
       "<td>24.0</td>\n",
       "<td>15.0</td>\n",
       "<td>example_scan_02.tif</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>1</td>\n",
       "<td>150.0</td>\n",
       "<td>920.0</td>\n",
       "<td>25.0</td>\n",
       "<td>15.0</td>\n",
       "<td>example_scan_03.tif</td> </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 3</p></div>\n",
       "            "
      ],
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       "*mouse_id    *session_date  *scan_idx    depth     wavelength     laser_power    fps      file_name     \n",
       "+----------+ +------------+ +----------+ +-------+ +------------+ +------------+ +------+ +------------+\n",
       "0            2017-05-15     1            150.0     920.0          26.0           15.0     example_scan_0\n",
       "0            2017-05-15     2            200.0     920.0          24.0           15.0     example_scan_0\n",
       "100          2017-05-25     1            150.0     920.0          25.0           15.0     example_scan_0\n",
       " (Total: 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Scan()"
   ]
  },
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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",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">scan_idx</p>\n",
       "                                <span class=\"djtooltiptext\">scan index</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">average_frame</p>\n",
       "                                <span class=\"djtooltiptext\">average fluorescence across frames</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>0</td>\n",
       "<td>2017-05-15</td>\n",
       "<td>1</td>\n",
       "<td>=BLOB=</td></tr><tr><td>0</td>\n",
       "<td>2017-05-15</td>\n",
       "<td>2</td>\n",
       "<td>=BLOB=</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>1</td>\n",
       "<td>=BLOB=</td> </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 3</p></div>\n",
       "            "
      ],
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       "*mouse_id    *session_date  *scan_idx    average_fr\n",
       "+----------+ +------------+ +----------+ +--------+\n",
       "0            2017-05-15     1            =BLOB=    \n",
       "0            2017-05-15     2            =BLOB=    \n",
       "100          2017-05-25     1            =BLOB=    \n",
       " (Total: 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AverageFrame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `imaging.py` also fill each table by inserting manually and loading data from the external tiff files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computations in data pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it's time to perform more complicated analyses. \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, we would like to detect cells from the average image. The final product we would like is the binary **mask** for each individual cell, with the 1 in the region of interest (ROI) and 0 in other places."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the new \"thing\" or entity here is `Roi`, where each entry corresponds the mask of one ROI. Let's start designing the table, paying special attention to the dependencies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detect cells from the average fluorescence image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's perform the segmentation to isolate ROIs. Let's start by taking a look at one average fluoresence image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "keys = AverageFrame.fetch('KEY')\n",
    "\n",
    "# pick one key\n",
    "key = keys[0]"
   ]
  },
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   "execution_count": 8,
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       "                                <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\">scan_idx</p>\n",
       "                                <span class=\"djtooltiptext\">scan index</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">average_frame</p>\n",
       "                                <span class=\"djtooltiptext\">average fluorescence across frames</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>0</td>\n",
       "<td>2017-05-15</td>\n",
       "<td>1</td>\n",
       "<td>=BLOB=</td> </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 1</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*mouse_id    *session_date  *scan_idx    average_fr\n",
       "+----------+ +------------+ +----------+ +--------+\n",
       "0            2017-05-15     1            =BLOB=    \n",
       " (Total: 1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ENTER YOUR CODE! - preview an AverageFrame for a particular key\n",
    "AverageFrame & key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_image = AverageFrame.fetch('average_frame')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([array([[32.73, 30.77, 31.74, ..., 65.76, 62.77, 58.65],\n",
       "       [30.07, 27.96, 28.74, ..., 60.11, 58.1 , 54.59],\n",
       "       [28.91, 27.02, 27.12, ..., 58.01, 56.1 , 53.25],\n",
       "       ...,\n",
       "       [35.17, 35.56, 34.37, ..., 21.08, 21.45, 21.09],\n",
       "       [33.14, 35.73, 37.64, ..., 20.42, 21.24, 21.51],\n",
       "       [33.52, 37.65, 41.71, ..., 19.82, 20.29, 21.36]]),\n",
       "       array([[ 29.53,  30.8 ,  30.96, ..., 230.09, 230.78, 228.19],\n",
       "       [ 29.61,  29.69,  30.71, ..., 224.86, 227.21, 227.22],\n",
       "       [ 29.61,  28.28,  30.19, ..., 220.51, 224.43, 224.85],\n",
       "       ...,\n",
       "       [ 23.67,  23.05,  24.06, ...,  32.54,  32.07,  35.28],\n",
       "       [ 24.03,  22.45,  22.4 , ...,  31.67,  30.75,  32.72],\n",
       "       [ 25.78,  23.27,  21.92, ...,  31.12,  31.29,  32.94]]),\n",
       "       array([[41.55, 40.2 , 38.3 , ..., 55.67, 57.17, 58.65],\n",
       "       [43.66, 42.2 , 38.59, ..., 50.35, 54.83, 57.9 ],\n",
       "       [42.07, 41.46, 38.3 , ..., 49.46, 52.67, 55.61],\n",
       "       ...,\n",
       "       [30.04, 31.05, 30.92, ..., 45.04, 44.93, 44.81],\n",
       "       [28.88, 29.52, 29.93, ..., 46.5 , 45.65, 45.13],\n",
       "       [27.84, 27.34, 28.5 , ..., 42.75, 43.28, 45.17]])], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_image"
   ]
  },
  {
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[32.73, 30.77, 31.74, ..., 65.76, 62.77, 58.65],\n",
       "       [30.07, 27.96, 28.74, ..., 60.11, 58.1 , 54.59],\n",
       "       [28.91, 27.02, 27.12, ..., 58.01, 56.1 , 53.25],\n",
       "       ...,\n",
       "       [35.17, 35.56, 34.37, ..., 21.08, 21.45, 21.09],\n",
       "       [33.14, 35.73, 37.64, ..., 20.42, 21.24, 21.51],\n",
       "       [33.52, 37.65, 41.71, ..., 19.82, 20.29, 21.36]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_image[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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_image = (AverageFrame & key).fetch1('average_frame')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[32.73, 30.77, 31.74, ..., 65.76, 62.77, 58.65],\n",
       "       [30.07, 27.96, 28.74, ..., 60.11, 58.1 , 54.59],\n",
       "       [28.91, 27.02, 27.12, ..., 58.01, 56.1 , 53.25],\n",
       "       ...,\n",
       "       [35.17, 35.56, 34.37, ..., 21.08, 21.45, 21.09],\n",
       "       [33.14, 35.73, 37.64, ..., 20.42, 21.24, 21.51],\n",
       "       [33.52, 37.65, 41.71, ..., 19.82, 20.29, 21.36]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot to take a quick look:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c18312d30>"
      ]
     },
     "execution_count": 14,
     "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.imshow(avg_image, cmap=plt.cm.gray)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are multiple ways to perform the segmentation. To keep it simple, we just detect the cells by setting up the threshold on the average image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c184ffb00>"
      ]
     },
     "execution_count": 15,
     "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 = 50\n",
    "mask = avg_image > threshold\n",
    "plt.imshow(mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The outcome is different across different thresholds we set. Therefore, this threshold is a parameter we could potentially tweak."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c185e4ba8>"
      ]
     },
     "execution_count": 16,
     "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 = 60\n",
    "mask = avg_image > threshold\n",
    "plt.imshow(mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we could use scipy.ndimage to detect the blobs from this binary mask\n",
    "\n",
    "For the detailed tutorial, please refer to https://scipy-lectures.org/advanced/image_processing/index.html#segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import ndimage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method `label` marks each blob with different number, `label_im` is the image with different blobs marked with different number and `nb_labels` is the number of blobs that are detected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c186e2978>"
      ]
     },
     "execution_count": 18,
     "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": [
    "label_im, nb_labels = ndimage.label(mask)\n",
    "print(nb_labels)\n",
    "plt.imshow(label_im) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10], dtype=int32)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(label_im)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0 marks the background and 1 to 10 mark the detected blobs. Some of these are too small to be cells. We could set a cutoff on size to filter out the small blobs. Here the cutoff is another parameter that could be tweaked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c18765a90>"
      ]
     },
     "execution_count": 20,
     "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": [
    "size_cutoff = 50\n",
    "sizes = np.array([np.sum(label_im==i) for i in np.unique(label_im)])\n",
    "\n",
    "small_size_filter = sizes < size_cutoff\n",
    "pixel_to_remove = small_size_filter[label_im]\n",
    "\n",
    "label_im[pixel_to_remove] = 0\n",
    "plt.imshow(label_im)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now there are only two blobs left."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next let's separate out each mask."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "rois = []\n",
    "for i in np.unique(label_im)[1:]: # 0 is the background\n",
    "    rois.append(label_im == i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two special notes about the analyses we performed above.\n",
    "1. The segmentation results are dependent on the parameters `threshold` and `size_cutoff`. Rather than fixing the value of the threshold, we might want to try different values and see what works well.\n",
    "2. For the segemtation analyses of each `AverageFrame`, the result is a number of ROIs instead of one. We need to perform the analyses on the level of each `AverageFrame`, but save the result on the granularity of each `Roi`.\n",
    "\n",
    "Next we would like to introduce two DataJoint table tiers to help handling these two needs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter `Lookup` table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would like to perform the segmentation for a **combination** of `AverageFrame`s and different set of parameters of `threshold` and `size_cutoff` 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 segmentation in a `Lookup` table!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define `Param` 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": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "@schema\n",
    "class SegmentationParam(dj.Lookup):\n",
    "    definition = \"\"\"\n",
    "    seg_param_id        : int      # unique id for cell segmentation parameter set\n",
    "    ---\n",
    "    threshold           : float\n",
    "    size_cutoff         : float\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"256pt\" viewBox=\"0.00 0.00 220.39 256.00\" width=\"220pt\" 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(4 252)\">\n",
       "<title>%3</title>\n",
       "<polygon fill=\"#ffffff\" points=\"-4,4 -4,-252 216.3895,-252 216.3895,4 -4,4\" stroke=\"transparent\"/>\n",
       "<!-- Mouse -->\n",
       "<g class=\"node\" id=\"node1\">\n",
       "<title>Mouse</title>\n",
       "<g id=\"a_node1\"><a xlink:title=\"mouse_id             \r\n",
       "------------------------------\r\n",
       "dob=null             \r\n",
       "sex=&quot;unknown&quot;        \r\n",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"86.826,-248 34.7908,-248 34.7908,-213 86.826,-213 86.826,-248\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"#006400\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"42.7996\" y=\"-228.7994\">Mouse</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- Session -->\n",
       "<g class=\"node\" id=\"node2\">\n",
       "<title>Session</title>\n",
       "<g id=\"a_node2\"><a xlink:title=\"→ Mouse\r\n",
       "session_date         \r\n",
       "------------------------------\r\n",
       "experiment_setup     \r\n",
       "experimenter         \r\n",
       "data_path=&quot;&quot;         \r\n",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"90.0006,-177 31.6162,-177 31.6162,-142 90.0006,-142 90.0006,-177\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"#006400\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"39.4627\" y=\"-157.7994\">Session</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- Mouse&#45;&gt;Session -->\n",
       "<g class=\"edge\" id=\"edge1\">\n",
       "<title>Mouse-&gt;Session</title>\n",
       "<path d=\"M60.8084,-212.9494C60.8084,-202.1083 60.8084,-188.2366 60.8084,-177.3418\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\".75\"/>\n",
       "</g>\n",
       "<!-- Scan -->\n",
       "<g class=\"node\" id=\"node5\">\n",
       "<title>Scan</title>\n",
       "<g id=\"a_node5\"><a xlink:title=\"→ Session\r\n",
       "scan_idx             \r\n",
       "------------------------------\r\n",
       "depth                \r\n",
       "wavelength           \r\n",
       "laser_power          \r\n",
       "fps                  \r\n",
       "file_name            \r\n",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"82.6614,-106 38.9554,-106 38.9554,-71 82.6614,-71 82.6614,-106\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"#006400\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"47.1326\" y=\"-86.7994\">Scan</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- Session&#45;&gt;Scan -->\n",
       "<g class=\"edge\" id=\"edge2\">\n",
       "<title>Session-&gt;Scan</title>\n",
       "<path d=\"M60.8084,-141.9494C60.8084,-131.1083 60.8084,-117.2366 60.8084,-106.3418\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\".75\"/>\n",
       "</g>\n",
       "<!-- SegmentationParam -->\n",
       "<g class=\"node\" id=\"node3\">\n",
       "<title>SegmentationParam</title>\n",
       "<g id=\"a_node3\"><a xlink:title=\"seg_param_id         \r\n",
       "------------------------------\r\n",
       "threshold            \r\n",
       "size_cutoff          \r\n",
       "\">\n",
       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" points=\"212.4707,-248 105.1462,-248 105.1462,-213 212.4707,-213 212.4707,-248\" stroke=\"transparent\"/>\n",
       "<text fill=\"#000000\" font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"113.2274\" y=\"-229.2495\">SegmentationParam</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- AverageFrame -->\n",
       "<g class=\"node\" id=\"node4\">\n",
       "<title>AverageFrame</title>\n",
       "<g id=\"a_node4\"><a xlink:title=\"→ Scan\r\n",
       "------------------------------\r\n",
       "average_frame        \r\n",
       "\">\n",
       "<ellipse cx=\"60.8084\" cy=\"-17.5\" fill=\"#00007f\" fill-opacity=\"0.250980\" rx=\"60.6175\" ry=\"17.5\" stroke=\"#00007f\" stroke-opacity=\"0.250980\"/>\n",
       "<text fill=\"#00007f\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"60.8084\" y=\"-14.7994\">AverageFrame</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- Scan&#45;&gt;AverageFrame -->\n",
       "<g class=\"edge\" id=\"edge3\">\n",
       "<title>Scan-&gt;AverageFrame</title>\n",
       "<path d=\"M60.8084,-70.9494C60.8084,-60.1083 60.8084,-46.2366 60.8084,-35.3418\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<datajoint.diagram.Diagram at 0x1c189a4470>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj.Diagram(schema)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far the `SegmentationParam` is an extra table that did not relate with any of the existing, but the `Segmentation` will depend on this table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Master `Computed` table `Segmentation` and `Part` table for `Roi`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned above, we performed the segmentation processing on each `AverageFrame`, but the product is the masks of each ROI. In this case, we could create a `Computed` table `Segmentation` containing the `make` method to drive the processing, while using a `Part` table to save results into `Roi`.\n",
    "\n",
    "`Computed` table and `Part` table are another two table tiers like `Manual`, `Lookup`, and `Imported` we introduced previously.  \n",
    "\n",
    "`Computed` table is very similar to the `Imported` table, which also supports the definition of `make` function and `populate`. The only difference is that the computation in an `Imported` table is dependent on external data, while the computation in a `Computed` table only depends on data inside the database.\n",
    "\n",
    "Contents in a `Part` table is dependent on its **master** table and the master table could be any type of table. This current example is a very typical usage of Part table. The master `Computed` serves as the driver for computation and the major results with a smaller granularity are saved in the `Part` table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "@schema\n",
    "class Segmentation(dj.Computed):\n",
    "    definition = \"\"\"\n",
    "    -> AverageFrame\n",
    "    -> SegmentationParam\n",
    "    ---\n",
    "    segmented_masks         : longblob   # overview of segmented masks\n",
    "    \"\"\"\n",
    "    class Roi(dj.Part):\n",
    "        definition = \"\"\"\n",
    "        -> master\n",
    "        roi_idx             : int        # index of an roi\n",
    "        ---\n",
    "        mask                : longblob   # mask of this roi\n",
    "        \"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that in the definition of `Roi`, apart from inheriting the primary from its master table `Segmentation`, the Roi has another primary key attribute `roi_idx`. The relationship between `Segmentation` and `Roi` is **one-to-many**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<title>Session</title>\n",
       "<g id=\"a_node2\"><a xlink:title=\"→ Mouse\r\n",
       "session_date         \r\n",
       "------------------------------\r\n",
       "experiment_setup     \r\n",
       "experimenter         \r\n",
       "data_path=&quot;&quot;         \r\n",
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       "</g>\n",
       "<!-- Mouse&#45;&gt;Session -->\n",
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       "</g>\n",
       "<!-- Scan -->\n",
       "<g class=\"node\" id=\"node7\">\n",
       "<title>Scan</title>\n",
       "<g id=\"a_node7\"><a xlink:title=\"→ Session\r\n",
       "scan_idx             \r\n",
       "------------------------------\r\n",
       "depth                \r\n",
       "wavelength           \r\n",
       "laser_power          \r\n",
       "fps                  \r\n",
       "file_name            \r\n",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"82.6614,-223.499 38.9554,-223.499 38.9554,-188.499 82.6614,-188.499 82.6614,-223.499\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
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       "</g>\n",
       "</g>\n",
       "<!-- Session&#45;&gt;Scan -->\n",
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       "<title>Session-&gt;Scan</title>\n",
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       "</g>\n",
       "<!-- Segmentation -->\n",
       "<g class=\"node\" id=\"node3\">\n",
       "<title>Segmentation</title>\n",
       "<g id=\"a_node3\"><a xlink:title=\"→ AverageFrame\r\n",
       "→ SegmentationParam\r\n",
       "------------------------------\r\n",
       "segmented_masks      \r\n",
       "\">\n",
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       "</a>\n",
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       "</g>\n",
       "<!-- Segmentation.Roi -->\n",
       "<g class=\"node\" id=\"node5\">\n",
       "<title>Segmentation.Roi</title>\n",
       "<g id=\"a_node5\"><a xlink:title=\"→ Segmentation\r\n",
       "roi_idx              \r\n",
       "------------------------------\r\n",
       "mask                 \r\n",
       "\">\n",
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       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- Segmentation&#45;&gt;Segmentation.Roi -->\n",
       "<g class=\"edge\" id=\"edge3\">\n",
       "<title>Segmentation-&gt;Segmentation.Roi</title>\n",
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       "</g>\n",
       "<!-- SegmentationParam -->\n",
       "<g class=\"node\" id=\"node4\">\n",
       "<title>SegmentationParam</title>\n",
       "<g id=\"a_node4\"><a xlink:title=\"seg_param_id         \r\n",
       "------------------------------\r\n",
       "threshold            \r\n",
       "size_cutoff          \r\n",
       "\">\n",
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       "</g>\n",
       "<!-- SegmentationParam&#45;&gt;Segmentation -->\n",
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       "</g>\n",
       "<!-- AverageFrame -->\n",
       "<g class=\"node\" id=\"node6\">\n",
       "<title>AverageFrame</title>\n",
       "<g id=\"a_node6\"><a xlink:title=\"→ Scan\r\n",
       "------------------------------\r\n",
       "average_frame        \r\n",
       "\">\n",
       "<ellipse cx=\"60.8084\" cy=\"-134.999\" fill=\"#00007f\" fill-opacity=\"0.250980\" rx=\"60.6175\" ry=\"17.5\" stroke=\"#00007f\" stroke-opacity=\"0.250980\"/>\n",
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       "</g>\n",
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       "<title>AverageFrame-&gt;Segmentation</title>\n",
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       "<!-- Scan&#45;&gt;AverageFrame -->\n",
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       "<title>Scan-&gt;AverageFrame</title>\n",
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       "</g>\n",
       "</g>\n",
       "</svg>"
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      "text/plain": [
       "<datajoint.diagram.Diagram at 0x1c189eb160>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj.Diagram(schema)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Computed` table is labeled as a pink oval and the `Part` table is bare text. We see that `Segmentation` is a `Computed` table that depends on **both AverageFrame and SegmentationParam**. Finally, let's go ahead and implement the `make` method for the `Segmentation` table. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "@schema\n",
    "class Segmentation(dj.Computed):\n",
    "    definition = \"\"\"\n",
    "    -> AverageFrame\n",
    "    -> SegmentationParam\n",
    "    ---\n",
    "    segmented_masks         : longblob   # overview of segmented masks\n",
    "    \"\"\"\n",
    "    class Roi(dj.Part):\n",
    "        definition = \"\"\"\n",
    "        -> master\n",
    "        roi_idx             : int        # index of an roi\n",
    "        ---\n",
    "        mask                : longblob   # mask of this roi\n",
    "        \"\"\"\n",
    "    \n",
    "    def make(self, key): # key is one of the primary keys of the join product of AverageFrame and ParameterSet\n",
    "        \n",
    "        print('Populating for: ', key)\n",
    "        \n",
    "        # fetch average image from the previous table AverageFrame\n",
    "        avg_image = (AverageFrame & key).fetch1('average_frame')\n",
    "        \n",
    "        # fetch the parameters threshold and size_cutoff\n",
    "        threshold, size_cutoff = (SegmentationParam & key).fetch1(\n",
    "            'threshold', 'size_cutoff')\n",
    "        \n",
    "        # perform the thresholding and blob detection\n",
    "        mask = avg_image > threshold\n",
    "        label_im, nb_labels = ndimage.label(mask)\n",
    "        sizes = np.array([np.sum(label_im==i) for i in np.unique(label_im)])\n",
    "\n",
    "        small_size_filter = sizes < size_cutoff\n",
    "        pixel_to_remove = small_size_filter[label_im]\n",
    "\n",
    "        label_im[pixel_to_remove] = 0\n",
    "        \n",
    "        rois = []\n",
    "        for i in np.unique(label_im)[1:]: # 0 is the background\n",
    "            rois.append(\n",
    "                dict(**key,       # inherit primary key from master table\n",
    "                     roi_idx=i,    \n",
    "                     mask=label_im==i))\n",
    "        \n",
    "        # insert into the master table first\n",
    "        self.insert1(\n",
    "            dict(**key, segmented_masks=label_im)\n",
    "        )\n",
    "        print('Detected {} ROIs!\\n'.format(len(rois)))\n",
    "        # then insert into the part table\n",
    "        self.Roi.insert(rois)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The implementation of the segmentation is pretty much what we had above, except that we now fetch the value of `threshold` and `size_cutoff` from the `SegmentationParam` table.\n",
    "\n",
    "**Important note: always insert into the master table first and then insert the corresponding entries in the part table.** If a master table entry does not exist, its corresponding entries would not be valid."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the `Segmentation` table, we see that it indeed inherits the primary key attributes from **both AverageFrame (`mouse_id`, `session_date`, `scan_idx`) and SegmentationParam (`seg_param_id`)**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        </style>\n",
       "        \n",
       "        <b></b>\n",
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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\">scan_idx</p>\n",
       "                                <span class=\"djtooltiptext\">scan index</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">seg_param_id</p>\n",
       "                                <span class=\"djtooltiptext\">unique id for cell segmentation parameter set</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">segmented_masks</p>\n",
       "                                <span class=\"djtooltiptext\">overview of segmented masks</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  *scan_idx    *seg_param_id  segmented_\n",
       "+----------+ +------------+ +----------+ +------------+ +--------+\n",
       "\n",
       " (Total: 0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Segmentation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And for the part table `Segmentation.Roi`, there was an additional primary key attribute `roi_idx`:`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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       "\n",
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       "                color: white;\n",
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       "\n",
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       "                visibility: visible;\n",
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       "        \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\">scan_idx</p>\n",
       "                                <span class=\"djtooltiptext\">scan index</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">seg_param_id</p>\n",
       "                                <span class=\"djtooltiptext\">unique id for cell segmentation parameter set</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">roi_idx</p>\n",
       "                                <span class=\"djtooltiptext\">index of an roi</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">mask</p>\n",
       "                                <span class=\"djtooltiptext\">mask of this roi</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  *scan_idx    *seg_param_id  *roi_idx    mask      \n",
       "+----------+ +------------+ +----------+ +------------+ +---------+ +--------+\n",
       "\n",
       " (Total: 0)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Segmentation.Roi()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Populating `Segmentation` table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to populate! When we call `populate` on `Segmentation`, DataJoint will automatically call `make` on **every valid combination of the parent tables - AverageFrame and SegmentationParam**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ENTER YOUR CODE! - populate the Segmentation table\n",
    "Segmentation.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 `SegmentationParam` reveals the issue:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "            #nonprimary {\n",
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       "\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\">seg_param_id</p>\n",
       "                                <span class=\"djtooltiptext\">unique id for cell segmentation parameter set</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">threshold</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">size_cutoff</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr>  </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 0</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*seg_param_id  threshold     size_cutoff   \n",
       "+------------+ +-----------+ +------------+\n",
       "\n",
       " (Total: 0)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SegmentationParam()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's right! We have not added a parameter set yet. Let's go ahead and add one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "SegmentationParam.insert1((0, 50, 50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">seg_param_id</p>\n",
       "                                <span class=\"djtooltiptext\">unique id for cell segmentation parameter set</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">threshold</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">size_cutoff</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>0</td>\n",
       "<td>50.0</td>\n",
       "<td>50.0</td> </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 1</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*seg_param_id  threshold     size_cutoff   \n",
       "+------------+ +-----------+ +------------+\n",
       "0              50.0          50.0          \n",
       " (Total: 1)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SegmentationParam()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we should really be ready to perform the computation..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating for:  {'mouse_id': 0, 'session_date': datetime.date(2017, 5, 15), 'scan_idx': 1, 'seg_param_id': 0}\n",
      "Detected 6 ROIs!\n",
      "\n",
      "Populating for:  {'mouse_id': 0, 'session_date': datetime.date(2017, 5, 15), 'scan_idx': 2, 'seg_param_id': 0}\n",
      "Detected 6 ROIs!\n",
      "\n",
      "Populating for:  {'mouse_id': 100, 'session_date': datetime.date(2017, 5, 25), 'scan_idx': 1, 'seg_param_id': 0}\n",
      "Detected 9 ROIs!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# ENTER YOUR CODE! - populate the Segmentation table for real!\n",
    "Segmentation.populate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                padding: 5px 0;\n",
       "                border-radius: 6px;\n",
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       "                position: absolute;\n",
       "                z-index: 1;\n",
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       "                color: black;\n",
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       "\n",
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       "                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\">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\">scan_idx</p>\n",
       "                                <span class=\"djtooltiptext\">scan index</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">seg_param_id</p>\n",
       "                                <span class=\"djtooltiptext\">unique id for cell segmentation parameter set</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">segmented_masks</p>\n",
       "                                <span class=\"djtooltiptext\">overview of segmented masks</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>0</td>\n",
       "<td>2017-05-15</td>\n",
       "<td>1</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td></tr><tr><td>0</td>\n",
       "<td>2017-05-15</td>\n",
       "<td>2</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>1</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td> </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 3</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*mouse_id    *session_date  *scan_idx    *seg_param_id  segmented_\n",
       "+----------+ +------------+ +----------+ +------------+ +--------+\n",
       "0            2017-05-15     1            0              =BLOB=    \n",
       "0            2017-05-15     2            0              =BLOB=    \n",
       "100          2017-05-25     1            0              =BLOB=    \n",
       " (Total: 3)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Segmentation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...and we now have spike detection running!"
   ]
  },
  {
   "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": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "SegmentationParam.insert1((1, 60, 50))  # add another threshold and size cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "\n",
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       "        \n",
       "        <b></b>\n",
       "            <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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       "                <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">seg_param_id</p>\n",
       "                                <span class=\"djtooltiptext\">unique id for cell segmentation parameter set</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">threshold</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">size_cutoff</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>0</td>\n",
       "<td>50.0</td>\n",
       "<td>50.0</td></tr><tr><td>1</td>\n",
       "<td>60.0</td>\n",
       "<td>50.0</td> </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 2</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*seg_param_id  threshold     size_cutoff   \n",
       "+------------+ +-----------+ +------------+\n",
       "0              50.0          50.0          \n",
       "1              60.0          50.0          \n",
       " (Total: 2)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SegmentationParam()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating for:  {'mouse_id': 0, 'session_date': datetime.date(2017, 5, 15), 'scan_idx': 1, 'seg_param_id': 1}\n",
      "Detected 3 ROIs!\n",
      "\n",
      "Populating for:  {'mouse_id': 0, 'session_date': datetime.date(2017, 5, 15), 'scan_idx': 2, 'seg_param_id': 1}\n",
      "Detected 2 ROIs!\n",
      "\n",
      "Populating for:  {'mouse_id': 100, 'session_date': datetime.date(2017, 5, 25), 'scan_idx': 1, 'seg_param_id': 1}\n",
      "Detected 3 ROIs!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# ENTER YOUR CODE! - populate the \"missing\" entry in Segmentation table\n",
    "Segmentation.populate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                                <p id=\"primary\">mouse_id</p>\n",
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       "                                <span class=\"djtooltiptext\">date</span>\n",
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       "                                <p id=\"nonprimary\">segmented_masks</p>\n",
       "                                <span class=\"djtooltiptext\">overview of segmented masks</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>0</td>\n",
       "<td>2017-05-15</td>\n",
       "<td>1</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td></tr><tr><td>0</td>\n",
       "<td>2017-05-15</td>\n",
       "<td>2</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>1</td>\n",
       "<td>0</td>\n",
       "<td>=BLOB=</td></tr><tr><td>0</td>\n",
       "<td>2017-05-15</td>\n",
       "<td>1</td>\n",
       "<td>1</td>\n",
       "<td>=BLOB=</td></tr><tr><td>0</td>\n",
       "<td>2017-05-15</td>\n",
       "<td>2</td>\n",
       "<td>1</td>\n",
       "<td>=BLOB=</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>1</td>\n",
       "<td>1</td>\n",
       "<td>=BLOB=</td> </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 6</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*mouse_id    *session_date  *scan_idx    *seg_param_id  segmented_\n",
       "+----------+ +------------+ +----------+ +------------+ +--------+\n",
       "0            2017-05-15     1            0              =BLOB=    \n",
       "0            2017-05-15     2            0              =BLOB=    \n",
       "100          2017-05-25     1            0              =BLOB=    \n",
       "0            2017-05-15     1            1              =BLOB=    \n",
       "0            2017-05-15     2            1              =BLOB=    \n",
       "100          2017-05-25     1            1              =BLOB=    \n",
       " (Total: 6)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Segmentation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the results of segmentation 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 from \"upstream\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's say that we decided that we don't like the first threshold of `50`. 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 `Segmentation` table to the specific parameter id (i.e. `seg_param_id = 0`) and delete the entries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "About to delete:\n",
      "`shan_tutorial_pipeline`.`__segmentation__roi`: 21 items\n",
      "`shan_tutorial_pipeline`.`__segmentation`: 3 items\n",
      "Proceed? [yes, No]: No\n",
      "Cancelled deletes.\n"
     ]
    }
   ],
   "source": [
    "# Select 'No' when it pops up\n",
    "(Segmentation & 'seg_param_id = 0').delete()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can simply delete the unwanted parameter from the `SegmentationParam` table, and let DataJoint cascade the deletion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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       "            .Relation{\n",
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       "            .Relation th{\n",
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       "                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\">seg_param_id</p>\n",
       "                                <span class=\"djtooltiptext\">unique id for cell segmentation parameter set</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">threshold</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">size_cutoff</p>\n",
       "                                <span class=\"djtooltiptext\"></span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>0</td>\n",
       "<td>50.0</td>\n",
       "<td>50.0</td> </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 1</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*seg_param_id  threshold     size_cutoff   \n",
       "+------------+ +-----------+ +------------+\n",
       "0              50.0          50.0          \n",
       " (Total: 1)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SegmentationParam & 'seg_param_id = 0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "About to delete:\n",
      "`shan_tutorial_pipeline`.`__segmentation__roi`: 21 items\n",
      "`shan_tutorial_pipeline`.`__segmentation`: 3 items\n",
      "`shan_tutorial_pipeline`.`#segmentation_param`: 1 items\n",
      "Proceed? [yes, No]: yes\n",
      "Committed.\n"
     ]
    }
   ],
   "source": [
    "(SegmentationParam() & 'seg_param_id = 0').delete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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",
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       "            .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\">scan_idx</p>\n",
       "                                <span class=\"djtooltiptext\">scan index</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"primary\">seg_param_id</p>\n",
       "                                <span class=\"djtooltiptext\">unique id for cell segmentation parameter set</span>\n",
       "                            </div></th><th><div class=\"djtooltip\">\n",
       "                                <p id=\"nonprimary\">segmented_masks</p>\n",
       "                                <span class=\"djtooltiptext\">overview of segmented masks</span>\n",
       "                            </div> </th> </tr> </thead>\n",
       "                <tbody> <tr> <td>0</td>\n",
       "<td>2017-05-15</td>\n",
       "<td>1</td>\n",
       "<td>1</td>\n",
       "<td>=BLOB=</td></tr><tr><td>0</td>\n",
       "<td>2017-05-15</td>\n",
       "<td>2</td>\n",
       "<td>1</td>\n",
       "<td>=BLOB=</td></tr><tr><td>100</td>\n",
       "<td>2017-05-25</td>\n",
       "<td>1</td>\n",
       "<td>1</td>\n",
       "<td>=BLOB=</td> </tr> </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <p>Total: 3</p></div>\n",
       "            "
      ],
      "text/plain": [
       "*mouse_id    *session_date  *scan_idx    *seg_param_id  segmented_\n",
       "+----------+ +------------+ +----------+ +------------+ +--------+\n",
       "0            2017-05-15     1            1              =BLOB=    \n",
       "0            2017-05-15     2            1              =BLOB=    \n",
       "100          2017-05-25     1            1              =BLOB=    \n",
       " (Total: 3)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Segmentation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize ROIs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have all ROI masks saved in the table `Segmentation.Roi`, let's quickly look at an example by fetching the `mask` from the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c18d53e48>"
      ]
     },
     "execution_count": 51,
     "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": [
    "# show one example ROI\n",
    "masks = (Segmentation.Roi).fetch('mask')\n",
    "plt.imshow(masks[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fluorescence trace of each segmented ROI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we got masks of ROIs in the table `Segmetation.Roi` obtained with different parameter combinations. We would like to extract the fluorescence trace of each segmentation. \n",
    "\n",
    "The table design is similar to the `Segmentation` and `Roi`. The master table `Fluorescence` is the driver for the computation, with a secondary attribute `time` shared across traces of all ROIs. The part table `Trace` saves the extracted trace for each ROI over time (frame). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage import io\n",
    "import os\n",
    "@schema\n",
    "class Fluorescence(dj.Imported): # imported table because it also rely on the external tiff file.\n",
    "    definition = \"\"\"\n",
    "    -> Segmentation\n",
    "    ---\n",
    "    time    : longblob    # time for each frame\n",
    "    \"\"\"\n",
    "    \n",
    "    class Trace(dj.Part):\n",
    "        definition = \"\"\"\n",
    "        -> master\n",
    "        -> Segmentation.Roi\n",
    "        ---\n",
    "        trace      :  longblob    # fluorescence trace of each ROI\n",
    "        \"\"\"\n",
    "          \n",
    "    # the master table is mainly to perform the computation, while the part table contains the result\n",
    "    def make(self, key):\n",
    "        \n",
    "        print('Populating: {}'.format(key))\n",
    "        # fetch data directory from table Session\n",
    "        data_path = '../01-Calcium_Imaging/' + (Session & key).fetch1('data_path')\n",
    "        \n",
    "        # fetch data file name from table Scan\n",
    "        file_name = (Scan & key).fetch1('file_name')\n",
    "        \n",
    "        # load the file\n",
    "        im = io.imread(os.path.join(data_path, file_name))\n",
    "        \n",
    "        # get dimensions of the image and reshape\n",
    "        n, w, h = np.shape(im)\n",
    "        im_reshaped = np.reshape(im, [n, w*h])\n",
    "        \n",
    "        # get frames per second to compute time\n",
    "        fps = (Scan & key).fetch1('fps')\n",
    "        \n",
    "        # insert into master table first\n",
    "        self.insert1(dict(**key, time=np.array(range(n))/fps))\n",
    "        \n",
    "        \n",
    "        # extract traces\n",
    "        roi_keys, masks = (Segmentation.Roi & key).fetch('KEY', 'mask')\n",
    "        \n",
    "        traces = []\n",
    "        for roi_key, mask in zip(roi_keys, masks):\n",
    "            \n",
    "            # reshape mask\n",
    "            mask_reshaped = np.reshape(mask, [w*h])\n",
    "            trace = np.mean(im_reshaped[:, mask_reshaped], axis=1)\n",
    "            \n",
    "            traces.append(dict(**roi_key, trace=trace))\n",
    "        \n",
    "        self.Trace.insert(traces)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating: {'mouse_id': 0, 'session_date': datetime.date(2017, 5, 15), 'scan_idx': 1, 'seg_param_id': 1}\n",
      "Populating: {'mouse_id': 0, 'session_date': datetime.date(2017, 5, 15), 'scan_idx': 2, 'seg_param_id': 1}\n",
      "Populating: {'mouse_id': 100, 'session_date': datetime.date(2017, 5, 25), 'scan_idx': 1, 'seg_param_id': 1}\n"
     ]
    }
   ],
   "source": [
    "# ENTER YOUR CODE! - populate the Fluorescence table\n",
    "Fluorescence.populate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we could plot the traces of an example scan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Fluorescence')"
      ]
     },
     "execution_count": 61,
     "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": [
    "key = dict(mouse_id=0, session_number=1, scan_idx=1, seg_param_id=1)\n",
    "\n",
    "# ENTER YOUR CODE! - fetch 'time' from the Fluorescence table using fetch1()\n",
    "time = (Fluorescence & key).fetch1('time')\n",
    "\n",
    "# ENTER YOUR CODE! - fetch 'trace' from the Fluorescence.Trace table using fetch()\n",
    "traces = (Fluorescence.Trace & key).fetch('trace')\n",
    "\n",
    "plt.plot(time, np.vstack(traces).T)\n",
    "plt.xlabel('Time (s)')\n",
    "plt.ylabel('Fluorescence')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations! You have successfully extended your pipeline with all types of tables. This pipeline is simple, but reflecting a typical preprocessin pipeline of calcium imaging. We have all the table tiers and major dependencies between DataJoint tables.\n",
    "\n",
    "**Table tiers**:  \n",
    "Manual table: green box  \n",
    "Lookup table: gray box  \n",
    "Imported table: blue oval  \n",
    "Computed table: red circle  \n",
    "Part table: plain text\n",
    "\n",
    "**Dependencies**:  \n",
    "One-to-one primary: thick solid line, share the exact same primary key  \n",
    "One-to-many primary: thin solid line, inherit the primary key from the parent table, but have additional field(s) as part of the primary key as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"444pt\" viewBox=\"0.00 0.00 313.07 444.50\" width=\"313pt\" 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(4 440.499)\">\n",
       "<title>%3</title>\n",
       "<polygon fill=\"#ffffff\" points=\"-4,4 -4,-440.499 309.0708,-440.499 309.0708,4 -4,4\" stroke=\"transparent\"/>\n",
       "<!-- Fluorescence.Trace -->\n",
       "<g class=\"node\" id=\"node1\">\n",
       "<title>Fluorescence.Trace</title>\n",
       "<g id=\"a_node1\"><a xlink:title=\"→ Fluorescence\r\n",
       "→ Segmentation.Roi\r\n",
       "------------------------------\r\n",
       "trace                \r\n",
       "\">\n",
       "<polygon fill=\"transparent\" points=\"108.2604,-19.7551 4.2644,-19.7551 4.2644,.2561 108.2604,.2561 108.2604,-19.7551\" stroke=\"transparent\"/>\n",
       "<text fill=\"#000000\" font-family=\"arial\" font-size=\"10.00\" text-anchor=\"middle\" x=\"56.2624\" y=\"-7.499\">Fluorescence.Trace</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- Fluorescence -->\n",
       "<g class=\"node\" id=\"node2\">\n",
       "<title>Fluorescence</title>\n",
       "<g id=\"a_node2\"><a xlink:title=\"→ Segmentation\r\n",
       "------------------------------\r\n",
       "time                 \r\n",
       "\">\n",
       "<ellipse cx=\"56.2624\" cy=\"-72.999\" fill=\"#00007f\" fill-opacity=\"0.250980\" rx=\"56.0258\" ry=\"17.5\" stroke=\"#00007f\" stroke-opacity=\"0.250980\"/>\n",
       "<text fill=\"#00007f\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"56.2624\" y=\"-70.2984\">Fluorescence</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- Fluorescence&#45;&gt;Fluorescence.Trace -->\n",
       "<g class=\"edge\" id=\"edge1\">\n",
       "<title>Fluorescence-&gt;Fluorescence.Trace</title>\n",
       "<path d=\"M56.2624,-55.3805C56.2624,-43.8298 56.2624,-29.1552 56.2624,-19.6127\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\".75\"/>\n",
       "</g>\n",
       "<!-- Mouse -->\n",
       "<g class=\"node\" id=\"node3\">\n",
       "<title>Mouse</title>\n",
       "<g id=\"a_node3\"><a xlink:title=\"mouse_id             \r\n",
       "------------------------------\r\n",
       "dob=null             \r\n",
       "sex=&quot;unknown&quot;        \r\n",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"270.28,-436.499 218.2448,-436.499 218.2448,-401.499 270.28,-401.499 270.28,-436.499\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"#006400\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"226.2536\" y=\"-417.2984\">Mouse</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- Session -->\n",
       "<g class=\"node\" id=\"node4\">\n",
       "<title>Session</title>\n",
       "<g id=\"a_node4\"><a xlink:title=\"→ Mouse\r\n",
       "session_date         \r\n",
       "------------------------------\r\n",
       "experiment_setup     \r\n",
       "experimenter         \r\n",
       "data_path=&quot;&quot;         \r\n",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"273.4546,-365.499 215.0702,-365.499 215.0702,-330.499 273.4546,-330.499 273.4546,-365.499\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"#006400\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"222.9167\" y=\"-346.2984\">Session</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- Mouse&#45;&gt;Session -->\n",
       "<g class=\"edge\" id=\"edge2\">\n",
       "<title>Mouse-&gt;Session</title>\n",
       "<path d=\"M244.2624,-401.4485C244.2624,-390.6074 244.2624,-376.7356 244.2624,-365.8408\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\".75\"/>\n",
       "</g>\n",
       "<!-- Scan -->\n",
       "<g class=\"node\" id=\"node9\">\n",
       "<title>Scan</title>\n",
       "<g id=\"a_node9\"><a xlink:title=\"→ Session\r\n",
       "scan_idx             \r\n",
       "------------------------------\r\n",
       "depth                \r\n",
       "wavelength           \r\n",
       "laser_power          \r\n",
       "fps                  \r\n",
       "file_name            \r\n",
       "\">\n",
       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" points=\"266.1154,-294.499 222.4094,-294.499 222.4094,-259.499 266.1154,-259.499 266.1154,-294.499\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\"/>\n",
       "<text fill=\"#006400\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"230.5866\" y=\"-275.2984\">Scan</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- Session&#45;&gt;Scan -->\n",
       "<g class=\"edge\" id=\"edge3\">\n",
       "<title>Session-&gt;Scan</title>\n",
       "<path d=\"M244.2624,-330.4485C244.2624,-319.6074 244.2624,-305.7356 244.2624,-294.8408\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\".75\"/>\n",
       "</g>\n",
       "<!-- Segmentation -->\n",
       "<g class=\"node\" id=\"node5\">\n",
       "<title>Segmentation</title>\n",
       "<g id=\"a_node5\"><a xlink:title=\"→ AverageFrame\r\n",
       "→ SegmentationParam\r\n",
       "------------------------------\r\n",
       "segmented_masks      \r\n",
       "\">\n",
       "<ellipse cx=\"178.2624\" cy=\"-139.499\" fill=\"#ff0000\" fill-opacity=\"0.125490\" rx=\"13.0073\" ry=\"13\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\"/>\n",
       "<text fill=\"#7f0000\" font-family=\"arial\" font-size=\"12.00\" text-anchor=\"middle\" x=\"178.2624\" y=\"-136.7984\">Segmentation</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- Segmentation&#45;&gt;Fluorescence -->\n",
       "<g class=\"edge\" id=\"edge4\">\n",
       "<title>Segmentation-&gt;Fluorescence</title>\n",
       "<path d=\"M166.5645,-133.1227C147.8376,-122.915 110.5943,-102.6143 84.5895,-88.4396\" fill=\"none\" stroke=\"#000000\" stroke-opacity=\"0.250980\" stroke-width=\"2\"/>\n",
       "</g>\n",
       "<!-- Segmentation.Roi -->\n",
       "<g class=\"node\" id=\"node7\">\n",
       "<title>Segmentation.Roi</title>\n",
       "<g id=\"a_node7\"><a xlink:title=\"→ Segmentation\r\n",
       "roi_idx              \r\n",
       "------------------------------\r\n",
       "mask                 \r\n",
       "\">\n",
       "<polygon fill=\"transparent\" points=\"226.2559,-83.0046 130.2689,-83.0046 130.2689,-62.9934 226.2559,-62.9934 226.2559,-83.0046\" stroke=\"transparent\"/>\n",
       "<text fill=\"#000000\" font-family=\"arial\" font-size=\"10.00\" text-anchor=\"start\" text-decoration=\"underline\" x=\"138.5163\" y=\"-71.7485\">Segmentation.Roi</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- Segmentation&#45;&gt;Segmentation.Roi -->\n",
       "<g class=\"edge\" id=\"edge5\">\n",
       "<title>Segmentation-&gt;Segmentation.Roi</title>\n",
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       "</g>\n",
       "<!-- SegmentationParam -->\n",
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       "<title>SegmentationParam</title>\n",
       "<g id=\"a_node6\"><a xlink:title=\"seg_param_id         \r\n",
       "------------------------------\r\n",
       "threshold            \r\n",
       "size_cutoff          \r\n",
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       "</g>\n",
       "</g>\n",
       "<!-- SegmentationParam&#45;&gt;Segmentation -->\n",
       "<g class=\"edge\" id=\"edge6\">\n",
       "<title>SegmentationParam-&gt;Segmentation</title>\n",
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       "<!-- Segmentation.Roi&#45;&gt;Fluorescence.Trace -->\n",
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       "<!-- AverageFrame -->\n",
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       "<title>AverageFrame</title>\n",
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       "------------------------------\r\n",
       "average_frame        \r\n",
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       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- AverageFrame&#45;&gt;Segmentation -->\n",
       "<g class=\"edge\" id=\"edge8\">\n",
       "<title>AverageFrame-&gt;Segmentation</title>\n",
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       "<!-- Scan&#45;&gt;AverageFrame -->\n",
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       "<title>Scan-&gt;AverageFrame</title>\n",
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       "</g>\n",
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      "text/plain": [
       "<datajoint.diagram.Diagram at 0x1c1993c3c8>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj.Diagram(schema)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have covered most of the building elements of data pipeline design. Using these elements, we could design more sophiscated pipelines that facillitates your experimental recordings and data analyses."
   ]
  }
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