{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aXvrQraBOZpl"
   },
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/predictive-clinical-neuroscience/BigDataCourse/blob/main/practicals/Big_data_mouse_practical_2020.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E82tdUEX7lU8"
   },
   "source": [
    "# Big data : Mouse\n",
    "\n",
    "The objectives of this exercise are to familiarize the students with the Allen Institute for Brain Science ([AIBS](https://portal.brain-map.org/)) Software Development Kit ([SDK](https://allensdk.readthedocs.io/en/latest/)).  \n",
    "<br>\n",
    "The Allen Institute for Brain Science is a research centre specialized in the generation and distribution of large datasets in neuroscience. In this exercise, we will explore one dataset: the [Mouse Brain Connectivity](https://connectivity.brain-map.org/).   \n",
    "<br>\n",
    "**References:**\n",
    "1. This exercise is heavily influenced by the [AIBS SDK example](https://allensdk.readthedocs.io/en/latest/examples.html)\n",
    "\n",
    "2. *Julia M. Huntenburg, Ling Yun Yeow, Francesca Mandino, Joanes Grandjean, Gradients of functional connectivity in the mouse cortex reflect neocortical evolution, Neuorimage, 2020, https://doi.org/10.1016/j.neuroimage.2020.117528.*\n",
    "\n",
    "3. *Julia M. Huntenburg, Gradients of functional connectivity in the mouse cortex reflect neocortical evolution, Github, https://github.com/juhuntenburg/mouse_gradients*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pkUlCjXcM-tS"
   },
   "source": [
    "## Foreword and environment\n",
    "This is a Jupyter notebook. This document combines both text (e.g. *this*), and executable code. These are organized into cells. This is a **text** cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "alNQlF0qNis1",
    "outputId": "8116275b-a2f7-45a0-b8db-c5bfa95f46f0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a code cell\n"
     ]
    }
   ],
   "source": [
    "print('This is a code cell')\n",
    "# This is a comment. The line above will be executed when you click on the 'play' icon on the left. The comment will not be executed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lKkEa-yuOABV"
   },
   "source": [
    "## Environment basics\n",
    "The default environment is Python3. You can check the version by running the\n",
    "code in the following cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YLJUiPefOHss",
    "outputId": "bcb57584-fe8e-4499-b72c-72fbb0f14f5f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.10.7\n"
     ]
    }
   ],
   "source": [
    "import platform  #the import function loads packages (a collection of functions) that isn't available within the default python environment.\n",
    "\n",
    "print(platform.python_version()) #the print function *prints* on screen the content therein. In this particular instance, it *prints* the content from the function `platform.python_version()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dwr-VUN_Pzz2"
   },
   "source": [
    "## Editing cells.\n",
    "You can *Double-click* to edit the content of a cell.\n",
    "<br>\n",
    "## Adding new cells.\n",
    "By hovering your mouse above or below a cell, you can add either a *code* cell or a *text* cell\n",
    "<br>\n",
    "## Formating text.\n",
    "You can add emphasis to your text using either the icons on top of the cell while editing, or using [Markdown](https://www.markdownguide.org/cheat-sheet/), a simple text-formating language.\n",
    "<br><br>\n",
    "##### 100% optional (but so good!!)\n",
    "While in Google Colab: Tools -> Settings -> Miscellaneous -> Corgi mode [x]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NHK8v4nuY8vy"
   },
   "source": [
    "### Question 1\n",
    "**Create a code cell, and have it print your name**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eyJYHwAl5Dc9",
    "outputId": "6315efef-bea5-4843-83f1-edd5acef274b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My name is  Jo\n"
     ]
    }
   ],
   "source": [
    "name = 'Jo'\n",
    "print('My name is ', name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hkNDxb1zRMl4"
   },
   "source": [
    "## Environment: packages\n",
    "By default, Google Colab will have a few packages installed. You can check them using the following cell.\n",
    "\n",
    "Please note the following.\n",
    "1. \"!\" This indicates that the following instructions need to be run outside python. This will be used to run commands in [bash](https://en.wikipedia.org/wiki/Bash_(Unix_shell))\n",
    "2. \"pip\" is a python package installer\n",
    "3. \"pip list\" instructs to list all packages currently installed in this session\n",
    "4. \"| tail\" instructs to only list the last few packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ac_IJtBVQ-ck",
    "outputId": "a28a94be-d4e9-4de4-ac6c-6d526c60f2dc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrapt                         1.12.1         \n",
      "xarray                        0.15.1         \n",
      "xgboost                       0.90           \n",
      "xkit                          0.0.0          \n",
      "xlrd                          1.1.0          \n",
      "xlwt                          1.3.0          \n",
      "yarl                          1.6.3          \n",
      "yellowbrick                   0.9.1          \n",
      "zict                          2.0.0          \n",
      "zipp                          3.4.0          \n"
     ]
    }
   ],
   "source": [
    "!pip list | tail\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R0wX9ybnTWtf"
   },
   "source": [
    "## Environment: install Allen Institute for Brain Science Software Development Kit\n",
    "\n",
    "Using \"pip\" we can install the \"allensdk\" package. While at it, we will also upgrade \"pandas\" a package to handle tables. Have a look at this [cheatsheet](http://datacamp-community-prod.s3.amazonaws.com/dbed353d-2757-4617-8206-8767ab379ab3).\n",
    "\n",
    "Following this, you will be asked to \"restart runtime\". Click on the corresponding icon at the bottom of the cell to do so.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SJ1ZvDpV7zRp",
    "outputId": "1f0f2cae-79f3-453e-9bd1-9556998bbb2f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: msgpack in c:\\users\\aswen\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from blosc2>=2.3.0->tables->allensdk) (1.0.4)\n",
      "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in c:\\users\\aswen\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from jsonschema>=2.6.0->hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (2023.12.1)\n",
      "Requirement already satisfied: referencing>=0.28.4 in c:\\users\\aswen\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from jsonschema>=2.6.0->hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (0.32.1)\n",
      "Requirement already satisfied: rpds-py>=0.7.1 in c:\\users\\aswen\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from jsonschema>=2.6.0->hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (0.17.1)\n",
      "Requirement already satisfied: ruamel.yaml.clib>=0.2.6 in c:\\users\\aswen\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from ruamel-yaml>=0.16->hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (0.2.6)\n",
      "Installing collected packages: pandas\n",
      "  Attempting uninstall: pandas\n",
      "    Found existing installation: pandas 2.2.0\n",
      "    Uninstalling pandas-2.2.0:\n",
      "      Successfully uninstalled pandas-2.2.0\n",
      "Successfully installed pandas-1.5.3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  WARNING: Failed to remove contents in a temporary directory 'C:\\Users\\aswen\\AppData\\Local\\Programs\\Python\\Python310\\Lib\\site-packages\\~andas.libs'.\n",
      "  You can safely remove it manually.\n",
      "  WARNING: Failed to remove contents in a temporary directory 'C:\\Users\\aswen\\AppData\\Local\\Programs\\Python\\Python310\\Lib\\site-packages\\~andas'.\n",
      "  You can safely remove it manually.\n"
     ]
    }
   ],
   "source": [
    "!pip install allensdk\n",
    "!pip install --upgrade pandas\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z_hi5mLXYlYx"
   },
   "source": [
    "### Question 2\n",
    "**Check that package 'matplotlib' is installed. (Hint: use !pip again).**\n",
    "Write a code cell to do so.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "s8GmdE4f5Mio",
    "outputId": "d7682ae8-7bfa-437b-f593-464360e19015"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'grep' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    }
   ],
   "source": [
    "# using the grep function only shows the line that contains the desired package name\n",
    "!pip list | grep matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xIoOPkM2Ye8A"
   },
   "source": [
    "## Demonstrating the AIBS connectivity database\n",
    "To show the connectivity database, we are going to import the Allen SDK connectivity related functions.\n",
    "\n",
    "1. The get_experiments() function will download a table that lists all the experiments in that database. This is saved into the variable \"all_experiments\".\n",
    "\n",
    "2. To determine the number of experiments in that database, we use the **len()** function.\n",
    "\n",
    "3. Each experiment consists of a 3D reconstruction of the viral tracer injection and the areas of the brain where the tracer has been transported to, as well as meta-data.  \n",
    "\n",
    "4. The table (all_experiments) contains the meta-data for all these experiments.\n",
    "\n",
    "5. Using the **iloc()** function, we can determine the n<sup>th</sup> item in the table. In python, the first item has an index at 0.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "42jWlTckVrye",
    "outputId": "2bc9b3da-0219-4429-dbef-4de84d0cf2f7"
   },
   "outputs": [
    {
     "ename": "JSONDecodeError",
     "evalue": "Expecting value: line 1 column 1 (char 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 6\u001b[0m\n\u001b[0;32m      3\u001b[0m mcc \u001b[38;5;241m=\u001b[39m MouseConnectivityCache(resolution\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m)\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# open up a list of all of the experiments\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m all_experiments \u001b[38;5;241m=\u001b[39m \u001b[43mmcc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_experiments\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataframe\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m total experiments\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mlen\u001b[39m(all_experiments))\n\u001b[0;32m      9\u001b[0m all_experiments\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m0\u001b[39m]\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\allensdk\\core\\mouse_connectivity_cache.py:329\u001b[0m, in \u001b[0;36mMouseConnectivityCache.get_experiments\u001b[1;34m(self, dataframe, file_name, cre, injection_structure_ids)\u001b[0m\n\u001b[0;32m    295\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    296\u001b[0m \u001b[38;5;124;03mRead a list of experiments that match certain criteria.  If caching is\u001b[39;00m\n\u001b[0;32m    297\u001b[0m \u001b[38;5;124;03menabled,\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    324\u001b[0m \n\u001b[0;32m    325\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    327\u001b[0m file_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_cache_path(file_name, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mEXPERIMENTS_KEY)\n\u001b[1;32m--> 329\u001b[0m experiments \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapi\u001b[38;5;241m.\u001b[39mget_experiments_api(\n\u001b[0;32m    330\u001b[0m     path\u001b[38;5;241m=\u001b[39mfile_name, strategy\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlazy\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mCache\u001b[38;5;241m.\u001b[39mcache_json()\n\u001b[0;32m    331\u001b[0m )\n\u001b[0;32m    333\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m e \u001b[38;5;129;01min\u001b[39;00m experiments:\n\u001b[0;32m    334\u001b[0m     \u001b[38;5;66;03m# renaming id\u001b[39;00m\n\u001b[0;32m    335\u001b[0m     e[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mid\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m e[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata_set_id\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\allensdk\\api\\warehouse_cache\\cache.py:661\u001b[0m, in \u001b[0;36mcacheable.<locals>.decor.<locals>.w\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    658\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m decor\u001b[38;5;241m.\u001b[39mpost \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpost in kwargs\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m    659\u001b[0m     kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m decor\u001b[38;5;241m.\u001b[39mpost\n\u001b[1;32m--> 661\u001b[0m result \u001b[38;5;241m=\u001b[39m Cache\u001b[38;5;241m.\u001b[39mcacher(func,\n\u001b[0;32m    662\u001b[0m                       \u001b[38;5;241m*\u001b[39margs,\n\u001b[0;32m    663\u001b[0m                       \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    664\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\allensdk\\api\\warehouse_cache\\cache.py:383\u001b[0m, in \u001b[0;36mCache.cacher\u001b[1;34m(fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m    380\u001b[0m Manifest\u001b[38;5;241m.\u001b[39msafe_make_parent_dirs(path)\n\u001b[0;32m    382\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m writer:\n\u001b[1;32m--> 383\u001b[0m     data \u001b[38;5;241m=\u001b[39m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    384\u001b[0m     data \u001b[38;5;241m=\u001b[39m pre(data)\n\u001b[0;32m    385\u001b[0m     writer(path, data)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\allensdk\\api\\queries\\mouse_connectivity_api.py:98\u001b[0m, in \u001b[0;36mMouseConnectivityApi.get_experiments_api\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     88\u001b[0m \u001b[38;5;129m@cacheable\u001b[39m()\n\u001b[0;32m     89\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_experiments_api\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m     90\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m'''\u001b[39;00m\n\u001b[0;32m     91\u001b[0m \u001b[38;5;124;03m    Fetch experiment metadata from the Mouse Brain Connectivity Atlas via the ApiConnectivity table.\u001b[39;00m\n\u001b[0;32m     92\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     96\u001b[0m \u001b[38;5;124;03m        The constructed URL\u001b[39;00m\n\u001b[0;32m     97\u001b[0m \u001b[38;5;124;03m    '''\u001b[39;00m\n\u001b[1;32m---> 98\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_query\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mApiConnectivity\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_rows\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mall\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\allensdk\\api\\queries\\rma_api.py:257\u001b[0m, in \u001b[0;36mRmaApi.model_query\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    217\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmodel_query\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m    218\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m'''Construct and execute a model stage of an RMA query string.\u001b[39;00m\n\u001b[0;32m    219\u001b[0m \n\u001b[0;32m    220\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    255\u001b[0m \u001b[38;5;124;03m    response, including the normalized query.\u001b[39;00m\n\u001b[0;32m    256\u001b[0m \u001b[38;5;124;03m    '''\u001b[39;00m\n\u001b[1;32m--> 257\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjson_msg_query\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    258\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_query_url\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    259\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_stage\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\allensdk\\api\\api.py:164\u001b[0m, in \u001b[0;36mApi.json_msg_query\u001b[1;34m(self, url, dataframe)\u001b[0m\n\u001b[0;32m    147\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mjson_msg_query\u001b[39m(\u001b[38;5;28mself\u001b[39m, url, dataframe\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[0;32m    148\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m''' Common case where the url is fully constructed\u001b[39;00m\n\u001b[0;32m    149\u001b[0m \u001b[38;5;124;03m        and the response data is stored in the 'msg' field.\u001b[39;00m\n\u001b[0;32m    150\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    161\u001b[0m \u001b[38;5;124;03m        returned data; type depends on dataframe option\u001b[39;00m\n\u001b[0;32m    162\u001b[0m \u001b[38;5;124;03m    '''\u001b[39;00m\n\u001b[1;32m--> 164\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_query\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    165\u001b[0m \u001b[43m                         \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    167\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m dataframe \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m    168\u001b[0m         warnings\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdataframe argument is deprecated\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\allensdk\\api\\api.py:204\u001b[0m, in \u001b[0;36mApi.do_query\u001b[1;34m(self, url_builder_fn, json_traversal_fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m    200\u001b[0m api_url \u001b[38;5;241m=\u001b[39m url_builder_fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    202\u001b[0m post \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m--> 204\u001b[0m json_parsed_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mretrieve_parsed_json_over_http\u001b[49m\u001b[43m(\u001b[49m\u001b[43mapi_url\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpost\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    206\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m json_traversal_fn(json_parsed_data)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\allensdk\\api\\api.py:369\u001b[0m, in \u001b[0;36mApi.retrieve_parsed_json_over_http\u001b[1;34m(self, url, post)\u001b[0m\n\u001b[0;32m    366\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_log\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDownloading URL: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, url)\n\u001b[0;32m    368\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m post \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[1;32m--> 369\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mjson_utilities\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_url_get\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    370\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrequests\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquote\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    371\u001b[0m \u001b[43m                             \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m;/?:@&=+$,\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    372\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    373\u001b[0m     data \u001b[38;5;241m=\u001b[39m json_utilities\u001b[38;5;241m.\u001b[39mread_url_post(url)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\allensdk\\core\\json_utilities.py:118\u001b[0m, in \u001b[0;36mread_url_get\u001b[1;34m(url)\u001b[0m\n\u001b[0;32m    115\u001b[0m response \u001b[38;5;241m=\u001b[39m urllib_request\u001b[38;5;241m.\u001b[39murlopen(url)\n\u001b[0;32m    116\u001b[0m json_string \u001b[38;5;241m=\u001b[39m response\u001b[38;5;241m.\u001b[39mread()\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mjson\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloads\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjson_string\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\simplejson\\__init__.py:525\u001b[0m, in \u001b[0;36mloads\u001b[1;34m(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw)\u001b[0m\n\u001b[0;32m    477\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Deserialize ``s`` (a ``str`` or ``unicode`` instance containing a JSON\u001b[39;00m\n\u001b[0;32m    478\u001b[0m \u001b[38;5;124;03mdocument) to a Python object.\u001b[39;00m\n\u001b[0;32m    479\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    519\u001b[0m \n\u001b[0;32m    520\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    521\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m encoding \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[0;32m    522\u001b[0m         parse_int \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m parse_float \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[0;32m    523\u001b[0m         parse_constant \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_pairs_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    524\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m use_decimal \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kw):\n\u001b[1;32m--> 525\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_default_decoder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    526\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    527\u001b[0m     \u001b[38;5;28mcls\u001b[39m \u001b[38;5;241m=\u001b[39m JSONDecoder\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\simplejson\\decoder.py:370\u001b[0m, in \u001b[0;36mJSONDecoder.decode\u001b[1;34m(self, s, _w, _PY3)\u001b[0m\n\u001b[0;32m    368\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _PY3 \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(s, \u001b[38;5;28mbytes\u001b[39m):\n\u001b[0;32m    369\u001b[0m     s \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(s, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mencoding)\n\u001b[1;32m--> 370\u001b[0m obj, end \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraw_decode\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    371\u001b[0m end \u001b[38;5;241m=\u001b[39m _w(s, end)\u001b[38;5;241m.\u001b[39mend()\n\u001b[0;32m    372\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m end \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(s):\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\simplejson\\decoder.py:400\u001b[0m, in \u001b[0;36mJSONDecoder.raw_decode\u001b[1;34m(self, s, idx, _w, _PY3)\u001b[0m\n\u001b[0;32m    398\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m ord0 \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0xef\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m s[idx:idx \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m3\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\xef\u001b[39;00m\u001b[38;5;130;01m\\xbb\u001b[39;00m\u001b[38;5;130;01m\\xbf\u001b[39;00m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m    399\u001b[0m         idx \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3\u001b[39m\n\u001b[1;32m--> 400\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscan_once\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_w\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mend\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)"
     ]
    }
   ],
   "source": [
    "from allensdk.core.mouse_connectivity_cache import MouseConnectivityCache\n",
    "\n",
    "mcc = MouseConnectivityCache(resolution=100)\n",
    "\n",
    "# open up a list of all of the experiments\n",
    "all_experiments = mcc.get_experiments(dataframe=True)\n",
    "print(\"%d total experiments\" % len(all_experiments))\n",
    "\n",
    "all_experiments.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cCw0_ppNOAod"
   },
   "source": [
    "### Question 3\n",
    "**What is the sex of the mouse used in the 10<sup>th</sup> experiment listed in this table? What is the structure name where the tracer was injected into?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RvPuJ52P5rRC",
    "outputId": "94d706d9-629b-4d7e-fc24-19dd06bb6355"
   },
   "outputs": [],
   "source": [
    "# because python index starts at 0, the 10th element in a vector or an array is 9\n",
    "all_experiments.iloc[9]\n",
    "\n",
    "# The injection is in a male, the injection was done in the anteroventral nuc of the thalamus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N0OGpVIAxHxR"
   },
   "source": [
    "### Advanced user question (optional)\n",
    "**What is the sex of the mouse in experiment ID 100142655 and into what brain structure was the tracer injected into? Hint: use the loc() function.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2jP-UgvO6CDQ",
    "outputId": "a084a93d-f652-494c-9ca9-03b21f56c766"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                                                                M\n",
       "injection_structures                         [329, 361, 369, 182305689]\n",
       "injection_volume                                                0.17592\n",
       "injection_x                                                        6920\n",
       "injection_y                                                        1660\n",
       "injection_z                                                        8020\n",
       "product_id                                                            5\n",
       "specimen_name                                                   378-850\n",
       "strain                                                         C57BL/6J\n",
       "structure_abbrev                                                SSp-bfd\n",
       "structure_id                                                        329\n",
       "structure_name                 Primary somatosensory area, barrel field\n",
       "transgenic_line                                                    None\n",
       "transgenic_line_id                                                  NaN\n",
       "id                                                            100142655\n",
       "primary_injection_structure                                         329\n",
       "Name: 100142655, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_experiments.loc[100142655]\n",
    "#Male, Primary S1, Barrel field"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BVcMncTyrygN"
   },
   "source": [
    "## Let's download one tracer map\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "XyZx_rDCqz91"
   },
   "outputs": [],
   "source": [
    "experiment_id = 100142655\n",
    "\n",
    "# projection density: number of projecting pixels / voxel volume\n",
    "tracer = mcc.get_projection_density(experiment_id)\n",
    "# We also download the reference template.\n",
    "template = mcc.get_template_volume()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M5G828BrxeHn"
   },
   "source": [
    "## Ploting the tracer\n",
    "We can use packages numpy and matplotlib to represent the images into a picture in the axial plane."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 210
    },
    "id": "YGC7kuI5MrFB",
    "outputId": "adfe0555-004c-4c69-a9be-242e341d32e3"
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "MouseConnectivityCache.get_affine_parameters() missing 1 required positional argument: 'section_data_set_id'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-f0e5e9182702>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maffinem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmcc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_affine_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maffinem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: MouseConnectivityCache.get_affine_parameters() missing 1 required positional argument: 'section_data_set_id'"
     ]
    }
   ],
   "source": [
    "\n",
    "affinem = mcc.get_affine_parameters()\n",
    "print(affinem)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "x1EsefeIODCG"
   },
   "outputs": [],
   "source": [
    "# Assuming template is a 3D numpy array\n",
    "# Create a NIfTI image from the numpy array\n",
    "nii_img = nib.Nifti1Image(template[0], affine=affinem)\n",
    "\n",
    "# Save the NIfTI image to a file\n",
    "nib.save(nii_img, 'template.nii.gz')\n",
    "\n",
    "# Download the saved NIfTI file to your local PC\n",
    "files.download('template.nii.gz')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 322
    },
    "id": "vk5cknSvr38W",
    "outputId": "4fb27107-7be3-4fd0-ff83-21407cb0fb5e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "slice_idx = 60\n",
    "\n",
    "f, ccf_axes = plt.subplots(1, 3, figsize=(15, 6))\n",
    "\n",
    "#print(template[0][slice_idx,:,:])\n",
    "ccf_axes[0].imshow(template[0][slice_idx,:,:], cmap='gray', aspect='equal', vmin=template[0].min(), vmax=template[0].max())\n",
    "ccf_axes[0].set_title(\"registration template\")\n",
    "\n",
    "ccf_axes[1].imshow(tracer[0][slice_idx,:,:], cmap='hot', aspect='equal', vmin=0, vmax=tracer[0].max())\n",
    "ccf_axes[1].set_title(\"tracer projection density\")\n",
    "\n",
    "ccf_axes[2].imshow(template[0][slice_idx,:,:], cmap='gray', aspect='equal', vmin=template[0].min(), vmax=template[0].max())\n",
    "ccf_axes[2].imshow(tracer[0][slice_idx,:,:], cmap='hot', alpha=0.5, vmin=0, vmax=tracer[0].max())\n",
    "ccf_axes[2].set_title(\"overlay\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uv2o6mHjw-Vs"
   },
   "source": [
    "Or we can do the same, but with a coronal plane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 349
    },
    "id": "1gQh9S87xCE8",
    "outputId": "33ee2fc9-25bc-46aa-ccfe-8ccb739cd2fd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "slice_idx = 15\n",
    "\n",
    "f, ccf_axes = plt.subplots(1, 3, figsize=(15, 6))\n",
    "\n",
    "#print(template[0][slice_idx,:,:])\n",
    "ccf_axes[0].imshow(template[0][:,slice_idx,:], cmap='gray', aspect='equal', vmin=template[0].min(), vmax=template[0].max())\n",
    "ccf_axes[0].set_title(\"registration template\")\n",
    "\n",
    "ccf_axes[1].imshow(tracer[0][:,slice_idx,:], cmap='hot', aspect='equal', vmin=0, vmax=tracer[0].max())\n",
    "ccf_axes[1].set_title(\"tracer projection density\")\n",
    "\n",
    "ccf_axes[2].imshow(template[0][:,slice_idx,:], cmap='gray', aspect='equal', vmin=template[0].min(), vmax=template[0].max())\n",
    "ccf_axes[2].imshow(tracer[0][:,slice_idx,:], cmap='hot', alpha=0.5, vmin=0, vmax=tracer[0].max())\n",
    "ccf_axes[2].set_title(\"overlay\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yM3xkmEJ2c9I"
   },
   "source": [
    "### Question 4\n",
    "**Can you change the code above to render an image in the sagittal plane instead? Hint: [slice_idx,:,:] is the  axial plane and [:,slice_idx,:] is the coronal plane.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 390
    },
    "id": "oqcKR4QB6RiX",
    "outputId": "6949255f-8ecf-4447-9d29-f5c1172335a4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "slice_idx = 80\n",
    "\n",
    "f, ccf_axes = plt.subplots(1, 3, figsize=(15, 6))\n",
    "\n",
    "#print(template[0][slice_idx,:,:])\n",
    "ccf_axes[0].imshow(template[0][:,:,slice_idx], cmap='gray', aspect='equal', vmin=template[0].min(), vmax=template[0].max())\n",
    "ccf_axes[0].set_title(\"registration template\")\n",
    "\n",
    "ccf_axes[1].imshow(tracer[0][:,:,slice_idx], cmap='hot', aspect='equal', vmin=0, vmax=tracer[0].max())\n",
    "ccf_axes[1].set_title(\"tracer projection density\")\n",
    "\n",
    "ccf_axes[2].imshow(template[0][:,:,slice_idx], cmap='gray', aspect='equal', vmin=template[0].min(), vmax=template[0].max())\n",
    "ccf_axes[2].imshow(tracer[0][:,:,slice_idx], cmap='hot', alpha=0.5, vmin=0, vmax=tracer[0].max())\n",
    "ccf_axes[2].set_title(\"overlay\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jot5DMghyY8n"
   },
   "source": [
    "## Let's examine the tracer data in detail using the AIBS regions-of-interest\n",
    "You can find the brain annotation maps [here](https://atlas.brain-map.org/)\n",
    "\n",
    "The structure tree is summarized using ontology ID codes. These define sub-sets of the regions-of-interest (ROI) tree for specific structures (e.g. isocortex), or at different level of granularity.\n",
    "\n",
    "Here are three examples.   \n",
    "ID = 688152357 corresponds to summary ROIs in the isocortex.    \n",
    "ID = 687527670 to major divisions in the brain.   \n",
    "ID = 167587189 returns summary structures across the whole brain.\n",
    "\n",
    "Presently we will use the isocortex structures only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "97mHZFNuxkz2",
    "outputId": "910ee8ab-1cda-430f-8cae-81a1a907feb2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acronym</th>\n",
       "      <th>graph_id</th>\n",
       "      <th>graph_order</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>structure_id_path</th>\n",
       "      <th>structure_set_ids</th>\n",
       "      <th>rgb_triplet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FRP</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>184</td>\n",
       "      <td>Frontal pole, cerebral cortex</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 184]</td>\n",
       "      <td>[3, 112905828, 688152357, 691663206, 687527945...</td>\n",
       "      <td>[38, 143, 69]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MOp</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>985</td>\n",
       "      <td>Primary motor area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 500, 985]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[31, 157, 90]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MOs</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>993</td>\n",
       "      <td>Secondary motor area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 500, 993]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[31, 157, 90]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SSp-n</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>353</td>\n",
       "      <td>Primary somatosensory area, nose</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 453, 322, 353]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[24, 128, 100]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SSp-bfd</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>329</td>\n",
       "      <td>Primary somatosensory area, barrel field</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 453, 322, 329]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[24, 128, 100]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SSp-ll</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>337</td>\n",
       "      <td>Primary somatosensory area, lower limb</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 453, 322, 337]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[24, 128, 100]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SSp-m</td>\n",
       "      <td>1</td>\n",
       "      <td>72</td>\n",
       "      <td>345</td>\n",
       "      <td>Primary somatosensory area, mouth</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 453, 322, 345]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[24, 128, 100]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SSp-ul</td>\n",
       "      <td>1</td>\n",
       "      <td>79</td>\n",
       "      <td>369</td>\n",
       "      <td>Primary somatosensory area, upper limb</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 453, 322, 369]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[24, 128, 100]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SSp-tr</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>361</td>\n",
       "      <td>Primary somatosensory area, trunk</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 453, 322, 361]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[24, 128, 100]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SSp-un</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>182305689</td>\n",
       "      <td>Primary somatosensory area, unassigned</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 453, 322, 182305689]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[24, 128, 100]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SSs</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>378</td>\n",
       "      <td>Supplemental somatosensory area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 453, 378]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[24, 128, 100]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GU</td>\n",
       "      <td>1</td>\n",
       "      <td>107</td>\n",
       "      <td>1057</td>\n",
       "      <td>Gustatory areas</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 1057]</td>\n",
       "      <td>[3, 112905828, 688152357, 691663206, 687527945...</td>\n",
       "      <td>[0, 156, 117]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>VISC</td>\n",
       "      <td>1</td>\n",
       "      <td>114</td>\n",
       "      <td>677</td>\n",
       "      <td>Visceral area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 677]</td>\n",
       "      <td>[3, 112905828, 688152357, 691663206, 687527945...</td>\n",
       "      <td>[17, 173, 131]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>AUDd</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>1011</td>\n",
       "      <td>Dorsal auditory area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 247, 1011]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[1, 147, 153]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>AUDp</td>\n",
       "      <td>1</td>\n",
       "      <td>136</td>\n",
       "      <td>1002</td>\n",
       "      <td>Primary auditory area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 247, 1002]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[1, 147, 153]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>AUDpo</td>\n",
       "      <td>1</td>\n",
       "      <td>143</td>\n",
       "      <td>1027</td>\n",
       "      <td>Posterior auditory area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 247, 1027]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[1, 147, 153]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AUDv</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>1018</td>\n",
       "      <td>Ventral auditory area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 247, 1018]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[1, 147, 153]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>VISal</td>\n",
       "      <td>1</td>\n",
       "      <td>164</td>\n",
       "      <td>402</td>\n",
       "      <td>Anterolateral visual area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 669, 402]</td>\n",
       "      <td>[396673091, 112905828, 688152357, 691663206, 6...</td>\n",
       "      <td>[8, 133, 140]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>VISam</td>\n",
       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "      <td>394</td>\n",
       "      <td>Anteromedial visual area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 669, 394]</td>\n",
       "      <td>[396673091, 112905828, 688152357, 691663206, 6...</td>\n",
       "      <td>[8, 133, 140]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>VISl</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>409</td>\n",
       "      <td>Lateral visual area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 669, 409]</td>\n",
       "      <td>[396673091, 112905828, 688152357, 691663206, 6...</td>\n",
       "      <td>[8, 133, 140]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>VISp</td>\n",
       "      <td>1</td>\n",
       "      <td>185</td>\n",
       "      <td>385</td>\n",
       "      <td>Primary visual area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 669, 385]</td>\n",
       "      <td>[396673091, 112905828, 688152357, 691663206, 6...</td>\n",
       "      <td>[8, 133, 140]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>VISpl</td>\n",
       "      <td>1</td>\n",
       "      <td>192</td>\n",
       "      <td>425</td>\n",
       "      <td>Posterolateral visual area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 669, 425]</td>\n",
       "      <td>[396673091, 112905828, 688152357, 691663206, 6...</td>\n",
       "      <td>[8, 133, 140]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>VISpm</td>\n",
       "      <td>1</td>\n",
       "      <td>199</td>\n",
       "      <td>533</td>\n",
       "      <td>posteromedial visual area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 669, 533]</td>\n",
       "      <td>[396673091, 112905828, 688152357, 691663206, 6...</td>\n",
       "      <td>[8, 133, 140]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>VISli</td>\n",
       "      <td>1</td>\n",
       "      <td>206</td>\n",
       "      <td>312782574</td>\n",
       "      <td>Laterointermediate area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 669, 312782574]</td>\n",
       "      <td>[396673091, 112905828, 688152357, 691663206, 6...</td>\n",
       "      <td>[8, 133, 140]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>VISpor</td>\n",
       "      <td>1</td>\n",
       "      <td>213</td>\n",
       "      <td>312782628</td>\n",
       "      <td>Postrhinal area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 669, 312782628]</td>\n",
       "      <td>[396673091, 112905828, 688152357, 691663206, 6...</td>\n",
       "      <td>[8, 133, 140]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ACAd</td>\n",
       "      <td>1</td>\n",
       "      <td>226</td>\n",
       "      <td>39</td>\n",
       "      <td>Anterior cingulate area, dorsal part</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 31, 39]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[64, 166, 102]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ACAv</td>\n",
       "      <td>1</td>\n",
       "      <td>232</td>\n",
       "      <td>48</td>\n",
       "      <td>Anterior cingulate area, ventral part</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 31, 48]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[64, 166, 102]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>PL</td>\n",
       "      <td>1</td>\n",
       "      <td>238</td>\n",
       "      <td>972</td>\n",
       "      <td>Prelimbic area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 972]</td>\n",
       "      <td>[3, 112905828, 688152357, 691663206, 687527945...</td>\n",
       "      <td>[47, 168, 80]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ILA</td>\n",
       "      <td>1</td>\n",
       "      <td>245</td>\n",
       "      <td>44</td>\n",
       "      <td>Infralimbic area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 44]</td>\n",
       "      <td>[3, 112905828, 688152357, 691663206, 687527945...</td>\n",
       "      <td>[89, 179, 99]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ORBl</td>\n",
       "      <td>1</td>\n",
       "      <td>258</td>\n",
       "      <td>723</td>\n",
       "      <td>Orbital area, lateral part</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 714, 723]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[36, 138, 94]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>ORBm</td>\n",
       "      <td>1</td>\n",
       "      <td>264</td>\n",
       "      <td>731</td>\n",
       "      <td>Orbital area, medial part</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 714, 731]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[36, 138, 94]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>ORBvl</td>\n",
       "      <td>1</td>\n",
       "      <td>272</td>\n",
       "      <td>746</td>\n",
       "      <td>Orbital area, ventrolateral part</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 714, 746]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[36, 138, 94]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>AId</td>\n",
       "      <td>1</td>\n",
       "      <td>279</td>\n",
       "      <td>104</td>\n",
       "      <td>Agranular insular area, dorsal part</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 95, 104]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[33, 152, 102]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>AIp</td>\n",
       "      <td>1</td>\n",
       "      <td>285</td>\n",
       "      <td>111</td>\n",
       "      <td>Agranular insular area, posterior part</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 95, 111]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[33, 152, 102]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>AIv</td>\n",
       "      <td>1</td>\n",
       "      <td>291</td>\n",
       "      <td>119</td>\n",
       "      <td>Agranular insular area, ventral part</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 95, 119]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[33, 152, 102]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>RSPagl</td>\n",
       "      <td>1</td>\n",
       "      <td>298</td>\n",
       "      <td>894</td>\n",
       "      <td>Retrosplenial area, lateral agranular part</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 254, 894]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[26, 166, 152]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>RSPd</td>\n",
       "      <td>1</td>\n",
       "      <td>325</td>\n",
       "      <td>879</td>\n",
       "      <td>Retrosplenial area, dorsal part</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 254, 879]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[26, 166, 152]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>RSPv</td>\n",
       "      <td>1</td>\n",
       "      <td>332</td>\n",
       "      <td>886</td>\n",
       "      <td>Retrosplenial area, ventral part</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 254, 886]</td>\n",
       "      <td>[112905828, 688152357, 691663206, 687527945, 1...</td>\n",
       "      <td>[26, 166, 152]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>VISa</td>\n",
       "      <td>1</td>\n",
       "      <td>346</td>\n",
       "      <td>312782546</td>\n",
       "      <td>Anterior area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 22, 312782546]</td>\n",
       "      <td>[396673091, 112905828, 688152357, 691663206, 6...</td>\n",
       "      <td>[0, 159, 172]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>VISrl</td>\n",
       "      <td>1</td>\n",
       "      <td>353</td>\n",
       "      <td>417</td>\n",
       "      <td>Rostrolateral visual area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 22, 417]</td>\n",
       "      <td>[396673091, 112905828, 688152357, 691663206, 6...</td>\n",
       "      <td>[0, 159, 172]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>TEa</td>\n",
       "      <td>1</td>\n",
       "      <td>360</td>\n",
       "      <td>541</td>\n",
       "      <td>Temporal association areas</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 541]</td>\n",
       "      <td>[3, 112905828, 688152357, 691663206, 687527945...</td>\n",
       "      <td>[21, 176, 179]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>PERI</td>\n",
       "      <td>1</td>\n",
       "      <td>367</td>\n",
       "      <td>922</td>\n",
       "      <td>Perirhinal area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 922]</td>\n",
       "      <td>[3, 112905828, 688152357, 691663206, 687527945...</td>\n",
       "      <td>[14, 150, 132]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>ECT</td>\n",
       "      <td>1</td>\n",
       "      <td>373</td>\n",
       "      <td>895</td>\n",
       "      <td>Ectorhinal area</td>\n",
       "      <td>[997, 8, 567, 688, 695, 315, 895]</td>\n",
       "      <td>[3, 112905828, 688152357, 691663206, 687527945...</td>\n",
       "      <td>[13, 159, 145]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    acronym  ...     rgb_triplet\n",
       "0       FRP  ...   [38, 143, 69]\n",
       "1       MOp  ...   [31, 157, 90]\n",
       "2       MOs  ...   [31, 157, 90]\n",
       "3     SSp-n  ...  [24, 128, 100]\n",
       "4   SSp-bfd  ...  [24, 128, 100]\n",
       "5    SSp-ll  ...  [24, 128, 100]\n",
       "6     SSp-m  ...  [24, 128, 100]\n",
       "7    SSp-ul  ...  [24, 128, 100]\n",
       "8    SSp-tr  ...  [24, 128, 100]\n",
       "9    SSp-un  ...  [24, 128, 100]\n",
       "10      SSs  ...  [24, 128, 100]\n",
       "11       GU  ...   [0, 156, 117]\n",
       "12     VISC  ...  [17, 173, 131]\n",
       "13     AUDd  ...   [1, 147, 153]\n",
       "14     AUDp  ...   [1, 147, 153]\n",
       "15    AUDpo  ...   [1, 147, 153]\n",
       "16     AUDv  ...   [1, 147, 153]\n",
       "17    VISal  ...   [8, 133, 140]\n",
       "18    VISam  ...   [8, 133, 140]\n",
       "19     VISl  ...   [8, 133, 140]\n",
       "20     VISp  ...   [8, 133, 140]\n",
       "21    VISpl  ...   [8, 133, 140]\n",
       "22    VISpm  ...   [8, 133, 140]\n",
       "23    VISli  ...   [8, 133, 140]\n",
       "24   VISpor  ...   [8, 133, 140]\n",
       "25     ACAd  ...  [64, 166, 102]\n",
       "26     ACAv  ...  [64, 166, 102]\n",
       "27       PL  ...   [47, 168, 80]\n",
       "28      ILA  ...   [89, 179, 99]\n",
       "29     ORBl  ...   [36, 138, 94]\n",
       "30     ORBm  ...   [36, 138, 94]\n",
       "31    ORBvl  ...   [36, 138, 94]\n",
       "32      AId  ...  [33, 152, 102]\n",
       "33      AIp  ...  [33, 152, 102]\n",
       "34      AIv  ...  [33, 152, 102]\n",
       "35   RSPagl  ...  [26, 166, 152]\n",
       "36     RSPd  ...  [26, 166, 152]\n",
       "37     RSPv  ...  [26, 166, 152]\n",
       "38     VISa  ...   [0, 159, 172]\n",
       "39    VISrl  ...   [0, 159, 172]\n",
       "40      TEa  ...  [21, 176, 179]\n",
       "41     PERI  ...  [14, 150, 132]\n",
       "42      ECT  ...  [13, 159, 145]\n",
       "\n",
       "[43 rows x 8 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pandas for nice tables\n",
    "import pandas as pd\n",
    "\n",
    "# grab the StructureTree instance\n",
    "structure_tree = mcc.get_structure_tree()\n",
    "\n",
    "# Download the structure tree for the isocortex areas\n",
    "summary_structures = pd.DataFrame(structure_tree.get_structures_by_set_id([688152357]))\n",
    "summary_structures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kQUhnYb614-H"
   },
   "source": [
    "### Advanced user question (optional)\n",
    "**How many ROIs are there in the \"summary structures\" ontology?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LcCWIYwo6pJ6",
    "outputId": "7725720a-89c9-4e37-b3f0-aaed5a7a2efd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "316"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_structures2 = pd.DataFrame(structure_tree.get_structures_by_set_id([167587189]))\n",
    "len(summary_structures2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hpDKBBwiza6g"
   },
   "source": [
    "## Download the ROI-wise projection density\n",
    "In the cell above, we have defined a set of ROIs. This is saved into the \"summary_structures\" table.\n",
    "\n",
    "Below, we use the \"get_projection_matrix\" function to download the projection density in this sub-set of ROIs for the experiment \"experiment_id\". Note we can select one hemisphere or both. Most experiments in this database consists of tracers injected into the left hemisphere.\n",
    "\n",
    "We use matplotlib to represent the projection density in a color-coded format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 437
    },
    "id": "4LwSwDHh2yBN",
    "outputId": "d70ef4ce-7a12-460b-8e19-05d937fe4f5b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/allensdk/core/reference_space_cache.py:328: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  structure_ids[ii] = cls.validate_structure_id(sid)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm = mcc.get_projection_matrix(experiment_ids = [experiment_id],\n",
    "                               projection_structure_ids = summary_structures['id'],\n",
    "                               hemisphere_ids= [2], # hemispheres. Left = 1, Right = 2, Both = 3\n",
    "                               parameter = 'projection_density')\n",
    "\n",
    "row_labels = pm['rows'] # these are just experiment ids\n",
    "column_labels = [ c['label'] for c in pm['columns'] ]\n",
    "column_id = [ c['structure_id'] for c in pm['columns'] ]\n",
    "matrix = pm['matrix']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(30,6))\n",
    "heatmap = ax.pcolor(matrix, cmap=plt.cm.afmhot)\n",
    "\n",
    "# put the major ticks at the middle of each cell\n",
    "ax.set_xticks(np.arange(matrix.shape[1])+0.5, minor=False)\n",
    "ax.set_yticks(np.arange(matrix.shape[0])+0.5, minor=False)\n",
    "\n",
    "ax.set_xlim([0, matrix.shape[1]])\n",
    "ax.set_ylim([0, matrix.shape[0]])\n",
    "\n",
    "# want a more natural, table-like display\n",
    "ax.invert_yaxis()\n",
    "ax.xaxis.tick_top()\n",
    "\n",
    "ax.set_xticklabels(column_labels, minor=False,rotation=90)\n",
    "ax.set_yticklabels(row_labels, minor=False)\n",
    "\n",
    "ax.set_title(\"Projection density\")\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PaTqbPd23ZLP"
   },
   "source": [
    "### Advanced user question (optional)\n",
    "**Can you represent the distribution of projection intensity as a histogram? Hint: use the plt.hist() function and transpose the matrix with matrix.T**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "-GB39Tss66xq",
    "outputId": "5184f992-b964-4d4c-ff57-2d1877fe4ea3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([31.,  3.,  2.,  2.,  0.,  3.,  0.,  1.,  0.,  1.]),\n",
       " array([3.60822667e-04, 4.21014710e-02, 8.38421193e-02, 1.25582768e-01,\n",
       "        1.67323416e-01, 2.09064064e-01, 2.50804712e-01, 2.92545361e-01,\n",
       "        3.34286009e-01, 3.76026657e-01, 4.17767306e-01]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(matrix.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wHQdCrxA3cxU"
   },
   "source": [
    "### Question 5\n",
    "**Which ROI presents the highest projection density?**\n",
    "Can you tell from the image?\n",
    "Can you extract the \"projection_density\" value from the \"pm\" table?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fcIddF6U41b9",
    "outputId": "d4a62639-282a-41d9-d00f-e68037bc4d3b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area expressing the highest projection density relative to injection site 100142655\n",
      "9    SSp-un-R\n",
      "Name: label, dtype: object\n"
     ]
    }
   ],
   "source": [
    "d =  {'label': column_labels, 'density': pm['matrix'][0]}\n",
    "\n",
    "df = pd.DataFrame(data = d)\n",
    "\n",
    "print('Area expressing the highest projection density relative to injection site' , experiment_id)\n",
    "print(df.label[df.density == df.density.max()])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7PQbnrLP4ap4"
   },
   "source": [
    "### Advanced user question (optional)\n",
    "**Can you extract the name of the top 10% ROIs with the highest projection density? Hint: [here](https://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.DataFrame.nlargest.html)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "id": "GgKAh0qK7SUb",
    "outputId": "37840a4c-80e4-4f4e-8ec7-5aaa718c9f96"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SSp-bfd-R</td>\n",
       "      <td>0.308198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SSp-tr-R</td>\n",
       "      <td>0.225015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SSp-un-R</td>\n",
       "      <td>0.417767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>VISa-R</td>\n",
       "      <td>0.243052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        label   density\n",
       "4   SSp-bfd-R  0.308198\n",
       "8    SSp-tr-R  0.225015\n",
       "9    SSp-un-R  0.417767\n",
       "38     VISa-R  0.243052"
      ]
     },
     "execution_count": 41,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find how many entries correspond to 10%\n",
    "n10 = round(len(df)/10)\n",
    "# find the top 10% density values\n",
    "t10density = df.density.nlargest(n=n10)\n",
    "\n",
    "# get the label\n",
    "df.loc[df['density'].isin(t10density)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6vjbQ4Hk8VBa"
   },
   "source": [
    "## Congratulations.\n",
    "This is the end of the exercise. You should have learnt the following:\n",
    "1. Install and import a package with \"pip\"   \n",
    "2. Basics of Numpy, Pandas, Matplotlib    \n",
    "3. Interface with the AIBS SDK   \n",
    "4. Download an experiment and represent it into an image or a plot   \n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "dwr-VUN_Pzz2"
   ],
   "name": "Big_data_mouse_practical_2020_answer.ipynb",
   "provenance": [],
   "toc_visible": true
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  "kernelspec": {
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   "language": "python",
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