{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 3: Multi-cell, single population network (with BioNet)\n",
    "\n",
    "In this tutorial, we will create a more complex network that contains multiple biophysical cells, but all of them having the same cell-type (we will cover hetergenous networks in the next tutorial). The network will contain recurrent connections, as well as external input that provide the next with stimululation.\n",
    "\n",
    "**Note** - scripts and files for running this tutorial can be found in the directory [sources/chapter03/](sources/chapter03)\n",
    "\n",
    "requirements:\n",
    "* bmtk\n",
    "* NEURON 7.4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Building the Network\n",
    "\n",
    "First we will build our internal network, which consists of 100 different cells. All the cells are of the same type (we'll show how to build a heterogeneous network in the next tutorial), however they all have a different location and y-axis rotation.\n",
    "\n",
    "#### nodes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from bmtk.builder.networks import NetworkBuilder\n",
    "from bmtk.builder.auxi.node_params import positions_columinar, xiter_random\n",
    "\n",
    "cortex = NetworkBuilder('mcortex')\n",
    "cortex.add_nodes(N=100,\n",
    "                 pop_name='Scnn1a',\n",
    "                 positions=positions_columinar(N=100, center=[0, 50.0, 0], max_radius=30.0, height=100.0),\n",
    "                 rotation_angle_yaxis=xiter_random(N=100, min_x=0.0, max_x=2*np.pi),\n",
    "                 rotation_angle_zaxis=3.646878266,\n",
    "                 potental='exc',\n",
    "                 model_type='biophysical',\n",
    "                 model_template='ctdb:Biophys1.hoc',\n",
    "                 model_processing='aibs_perisomatic',\n",
    "                 dynamics_params='472363762_fit.json',\n",
    "                 morphology='Scnn1a_473845048_m.swc')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameter N is used to indicate the number of cells in our population. The positions of each cell is defined by the columinar built-in method, which will random place our cells in a column (users can define their own positions as shown here). The rotation_angel_yaxis is similarl defined by a built-in function that will randomly assign each cell a given y angle.\n",
    "\n",
    "One thing to note is that while yaxis is defined by a function which returns a lists of values, the zaxis is defined by a single value. This means that all cells will share the zaxis. we could alteratively give all cells the same y-axis rotation:\n",
    "```python\n",
    "    rotation_angle_yaxis=rotation_value\n",
    "```\n",
    "or give all cells a unique z-rotation angle\n",
    "```python\n",
    "    rotation_angle_zaxis=xiter_random(N=100, min_x=0.0, max_x=2*np.pi)\n",
    "```\n",
    "and in general, it is at the discretion of the modeler to choose what parameters are unqiue to each cell, and what parameters are global to the cell-type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### edges\n",
    "\n",
    "Next we want to add recurrent edges. To create the connections we will use the built-in distance_connector function, which will assign the number of connections between two cells randomly (between range nsyn_min and nsysn_max) but weighted by distance. The other parameters, including the synaptic model (AMPA_ExcToExc) will be shared by all connections.\n",
    "\n",
    "To use this, or even customized, connection functions, we must pass in the name of our connection function using the \"connection_rule\" parameter, and the function parameters through \"connection_params\" as a dictionary, which will looks something like:\n",
    "```python\n",
    "    connection_rule=<name_of_function>\n",
    "    connection_params={'param_arg1': val1, 'param_arg2': val2, ...}\n",
    "```\n",
    "The connection_rule method isn't explicitly called by the script. Rather when the build() method is called, the connection_rule will iterate through every source/target node pair, and use the rule and build a connection matrix.\n",
    "\n",
    "\n",
    "After building the connections based on our connection function, we will save the nodes and edges files into the network/ directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bmtk.builder.connection_map.ConnectionMap at 0x7f62f3911090>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bmtk.builder.auxi.edge_connectors import distance_connector\n",
    "\n",
    "cortex.add_edges(source={'pop_name': 'Scnn1a'}, target={'pop_name': 'Scnn1a'},\n",
    "                 connection_rule=distance_connector,\n",
    "                 connection_params={'d_weight_min': 0.0, 'd_weight_max': 0.34, 'd_max': 50.0, 'nsyn_min': 0, 'nsyn_max': 10},\n",
    "                 syn_weight=2.0e-04,\n",
    "                 distance_range=[30.0, 150.0],\n",
    "                 target_sections=['basal', 'apical', 'soma'],\n",
    "                 delay=2.0,\n",
    "                 dynamics_params='AMPA_ExcToExc.json',\n",
    "                 model_template='exp2syn')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cortex.build()\n",
    "cortex.save_nodes(output_dir='sim_ch03/network')\n",
    "cortex.save_edges(output_dir='sim_ch03/network')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### External network\n",
    "\n",
    "After building our internal network, we will build the external thalamic network which will provide input (see previous tutorial for more detail). Our thalamic network will consist of 100 \"filter\" cells, which aren't actual cells by just place holders for spike-trains."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "thalamus = NetworkBuilder('mthalamus')\n",
    "thalamus.add_nodes(N=100,\n",
    "                   pop_name='tON',\n",
    "                   potential='exc',\n",
    "                   model_type='virtual')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The external network doesn't have recurrent connections. Rather all the cells are feedforward onto the internal network. To do this is in a separate script which must reload the saved mcortex cell files using the import function. Then we create an edge with the thalamus nodes as the sources and the cortext nodes as the targets. This time we use the built-in connect_random connection rule, which will randomly assign each thalamus --> cortex connection between 0 and 12 synaptic connections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bmtk.builder.auxi.edge_connectors import connect_random\n",
    "\n",
    "thalamus.add_edges(source=thalamus.nodes(), target=cortex.nodes(),\n",
    "                   connection_rule=connect_random,\n",
    "                   connection_params={'nsyn_min': 0, 'nsyn_max': 12},\n",
    "                   syn_weight=1.0e-04,\n",
    "                   distance_range=[0.0, 150.0],\n",
    "                   target_sections=['basal', 'apical'],\n",
    "                   delay=2.0,\n",
    "                   dynamics_params='AMPA_ExcToExc.json',\n",
    "                   model_template='exp2syn')\n",
    "\n",
    "thalamus.build()\n",
    "thalamus.save_nodes(output_dir='sim_ch03/network')\n",
    "thalamus.save_edges(output_dir='sim_ch03/network')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Spike Trains\n",
    "\n",
    "We next need to create the individual spike trains for our thalamic filter cells. We will use a Poission distrubition to create a random distribution of spikes for our 300 hundred cells each firing at ~ 15 Hz over a 3 second window. Then we can save our spike trains as a [SONATA file](https://github.com/AllenInstitute/sonata/blob/master/docs/SONATA_DEVELOPER_GUIDE.md#spike-file) under **sim_ch03/inputs** directory. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node_id</th>\n",
       "      <th>population</th>\n",
       "      <th>timestamps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.001397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.099114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.145707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.147085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.162860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.208985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.209418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.225538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.232885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.320728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.435443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.456890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.484684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.523754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.542172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.542389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.549574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.686661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.744817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.745667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.814251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.891552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.911168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>0.923863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>1.206938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>1.234080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>1.284708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>1.361369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>1.376251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>1.441294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4461</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>1.637971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4462</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>1.686438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4463</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>1.834531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4464</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>1.892833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4465</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>1.896758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4466</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>1.929408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4467</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>1.954290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4468</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>1.977157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4469</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>1.978290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4470</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>1.984737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4471</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>2.104143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4472</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>2.111367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4473</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>2.112607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4474</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>2.192592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4475</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>2.276194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4476</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>2.284277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4477</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>2.306376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4478</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>2.306636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4479</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>2.307304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4480</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>2.334148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4481</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>2.370546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4482</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>2.648120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4483</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>2.661539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4484</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>2.748451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4485</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>2.803204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4486</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>2.845400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4487</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>2.874962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4488</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>2.923604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4489</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>2.943302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4490</th>\n",
       "      <td>99</td>\n",
       "      <td>mthalamus</td>\n",
       "      <td>2.987504</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4491 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      node_id population  timestamps\n",
       "0           0  mthalamus    0.001397\n",
       "1           0  mthalamus    0.099114\n",
       "2           0  mthalamus    0.145707\n",
       "3           0  mthalamus    0.147085\n",
       "4           0  mthalamus    0.162860\n",
       "5           0  mthalamus    0.208985\n",
       "6           0  mthalamus    0.209418\n",
       "7           0  mthalamus    0.225538\n",
       "8           0  mthalamus    0.232885\n",
       "9           0  mthalamus    0.320728\n",
       "10          0  mthalamus    0.435443\n",
       "11          0  mthalamus    0.456890\n",
       "12          0  mthalamus    0.484684\n",
       "13          0  mthalamus    0.523754\n",
       "14          0  mthalamus    0.542172\n",
       "15          0  mthalamus    0.542389\n",
       "16          0  mthalamus    0.549574\n",
       "17          0  mthalamus    0.686661\n",
       "18          0  mthalamus    0.744817\n",
       "19          0  mthalamus    0.745667\n",
       "20          0  mthalamus    0.814251\n",
       "21          0  mthalamus    0.891552\n",
       "22          0  mthalamus    0.911168\n",
       "23          0  mthalamus    0.923863\n",
       "24          0  mthalamus    1.206938\n",
       "25          0  mthalamus    1.234080\n",
       "26          0  mthalamus    1.284708\n",
       "27          0  mthalamus    1.361369\n",
       "28          0  mthalamus    1.376251\n",
       "29          0  mthalamus    1.441294\n",
       "...       ...        ...         ...\n",
       "4461       99  mthalamus    1.637971\n",
       "4462       99  mthalamus    1.686438\n",
       "4463       99  mthalamus    1.834531\n",
       "4464       99  mthalamus    1.892833\n",
       "4465       99  mthalamus    1.896758\n",
       "4466       99  mthalamus    1.929408\n",
       "4467       99  mthalamus    1.954290\n",
       "4468       99  mthalamus    1.977157\n",
       "4469       99  mthalamus    1.978290\n",
       "4470       99  mthalamus    1.984737\n",
       "4471       99  mthalamus    2.104143\n",
       "4472       99  mthalamus    2.111367\n",
       "4473       99  mthalamus    2.112607\n",
       "4474       99  mthalamus    2.192592\n",
       "4475       99  mthalamus    2.276194\n",
       "4476       99  mthalamus    2.284277\n",
       "4477       99  mthalamus    2.306376\n",
       "4478       99  mthalamus    2.306636\n",
       "4479       99  mthalamus    2.307304\n",
       "4480       99  mthalamus    2.334148\n",
       "4481       99  mthalamus    2.370546\n",
       "4482       99  mthalamus    2.648120\n",
       "4483       99  mthalamus    2.661539\n",
       "4484       99  mthalamus    2.748451\n",
       "4485       99  mthalamus    2.803204\n",
       "4486       99  mthalamus    2.845400\n",
       "4487       99  mthalamus    2.874962\n",
       "4488       99  mthalamus    2.923604\n",
       "4489       99  mthalamus    2.943302\n",
       "4490       99  mthalamus    2.987504\n",
       "\n",
       "[4491 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bmtk.utils.reports.spike_trains import PoissonSpikeGenerator\n",
    "\n",
    "psg = PoissonSpikeGenerator(population='mthalamus')\n",
    "psg.add(node_ids=range(100),  # Have 10 nodes to match mthalamus\n",
    "        firing_rate=15.0,    # 15 Hz, we can also pass in a nonhomoegenous function/array\n",
    "        times=(0.0, 3.0))    # Firing starts at 0 s up to 3 s\n",
    "psg.to_sonata('sim_ch03/inputs/mthalamus_spikes.h5')\n",
    "\n",
    "# Let's do a quick check that we have reasonable results. Should see somewhere on the order of 15*3*100 = 4500\n",
    "# spikes\n",
    "psg.to_dataframe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Setting up BioNet\n",
    "\n",
    "#### file structure.\n",
    "\n",
    "Before running a simulation, we will need to create the runtime environment, including parameter files, run-script and configuration files. You've already completed Chapter 02 tutorial you can just copy the files to **sim_ch03** (just make sure not to overwrite the **network** and **inputs** directory).\n",
    "\n",
    "Or create them from scracth by either running the command:\n",
    "```bash\n",
    "$ python -m bmtk.utils.sim_setup  \\\n",
    "   --report-vars v,cai            \\                             \n",
    "   --network sim_ch03/network     \\                              \n",
    "   --spikes-inputs mthalamus:sim_ch03/inputs/mthalamus_spikes.h5 \\\n",
    "   --dt 0.1             \\\n",
    "   --tstop 3000.0       \\  \n",
    "   --include-examples   \\\n",
    "   --compile-mechanisms \\ \n",
    "   bionet sim_ch03\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bmtk.utils.sim_setup import build_env_bionet\n",
    "\n",
    "build_env_bionet(base_dir='sim_ch03',      \n",
    "                 network_dir='sim_ch03/network',\n",
    "                 tstop=3000.0, dt=0.1,\n",
    "                 report_vars=['v', 'cai'],     # Record membrane potential and calcium (default soma)\n",
    "                 spikes_inputs=[('mthalamus',   # Name of population which spikes will be generated for\n",
    "                                'sim_ch03/inputs/mthalamus_spikes.h5')],\n",
    "                 include_examples=True,    # Copies components files\n",
    "                 compile_mechanisms=True   # Will try to compile NEURON mechanisms\n",
    "                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's a good idea to check the configuration files **sim_ch03/circuit_config.json** and **sim_ch03/simulation_config.json**, especially to make sure that bmtk will know to use our generated spikes file (if you don't see the below section in the simulation_config.json file go ahead and add it). \n",
    "\n",
    "```json\n",
    "\"inputs\": {\n",
    "    \"tc_spikes\": {\n",
    "      \"input_type\": \"spikes\",\n",
    "      \"module\": \"csv\",\n",
    "      \"input_file\": \"${BASE_DIR}/mthalamus_spikes.csv\",\n",
    "      \"node_set\": \"mthalamus\"\n",
    "    }\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Running the simulation\n",
    "\n",
    "Once our config file is setup we can run a simulation either through the command line:\n",
    "```bash\n",
    "$ python run_bionet.py simulation_config.json\n",
    "```\n",
    "\n",
    "or through the script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-07-22 09:45:15,914 [INFO] Created log file\n",
      "2019-07-22 09:45:16,009 [INFO] Building cells.\n",
      "2019-07-22 09:45:23,968 [INFO] Building recurrent connections\n",
      "2019-07-22 09:45:24,609 [INFO] Building virtual cell stimulations for mthalamus_spikes\n",
      "2019-07-22 09:45:33,506 [INFO] Running simulation for 3000.000 ms with the time step 0.100 ms\n",
      "2019-07-22 09:45:33,507 [INFO] Starting timestep: 0 at t_sim: 0.000 ms\n",
      "2019-07-22 09:45:33,508 [INFO] Block save every 5000 steps\n",
      "2019-07-22 09:46:03,198 [INFO]     step:5000 t_sim:500.00 ms\n",
      "2019-07-22 09:46:33,324 [INFO]     step:10000 t_sim:1000.00 ms\n",
      "2019-07-22 09:47:03,400 [INFO]     step:15000 t_sim:1500.00 ms\n",
      "2019-07-22 09:47:33,230 [INFO]     step:20000 t_sim:2000.00 ms\n",
      "2019-07-22 09:48:03,147 [INFO]     step:25000 t_sim:2500.00 ms\n",
      "2019-07-22 09:48:33,519 [INFO]     step:30000 t_sim:3000.00 ms\n",
      "2019-07-22 09:48:36,802 [INFO] Simulation completed in 3.0 minutes, 3.297 seconds \n"
     ]
    }
   ],
   "source": [
    "from bmtk.simulator import bionet\n",
    "\n",
    "\n",
    "conf = bionet.Config.from_json('sim_ch03/simulation_config.json')\n",
    "conf.build_env()\n",
    "net = bionet.BioNetwork.from_config(conf)\n",
    "sim = bionet.BioSimulator.from_config(conf, network=net)\n",
    "sim.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Analyzing the run.\n",
    "\n",
    "If successful, we should have our results in the **output** directory. We can use the analyzer to plot a raster of the spikes over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bmtk.analyzer.spike_trains import plot_raster\n",
    "\n",
    "plot_raster(config_file='sim_ch03/simulation_config.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our config file we used the cell_vars and node_id_selections parameters to save the calcium influx and membrane potential of selected cells. We can also use the analyzer to display these traces:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bmtk.analyzer.cell_vars import plot_report\n",
    "\n",
    "plot_report(config_file='sim_ch03/simulation_config.json', node_ids=[0, 49, 99])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Modifying the network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customized node params\n",
    "\n",
    "When building our cortex nodes, we used some built-in functions to set certain parameters like positions and y-axis rotations:\n",
    "```python\n",
    "cortex.add_nodes(N=100,\n",
    "                 pop_name='Scnn1a',\n",
    "                 positions=positions_columinar(N=100, center=[0, 50.0, 0], max_radius=30.0, height=100.0),\n",
    "                 rotation_angle_yaxis=xiter_random(N=100, min_x=0.0, max_x=2*np.pi),\n",
    "                 ...\n",
    "```\n",
    "\n",
    "These functions will assign every cell a unique value in the positions and rotation_angle_yaxis parameters, unlike the pop_name parameter which will be the same for all 100 cells. We can verify by the following code:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cell 0: pop_name: Scnn1a, positions: [ -1.99484437  41.49527042  22.33923077], angle_yaxis: 5.29759513272\n",
      "cell 1: pop_name: Scnn1a, positions: [-25.72073426  36.01835631   2.43526216], angle_yaxis: 2.94311607964\n"
     ]
    }
   ],
   "source": [
    "cortex_nodes = list(cortex.nodes())\n",
    "n0 = cortex_nodes[0]\n",
    "n1 = cortex_nodes[1]\n",
    "print('cell 0: pop_name: {}, positions: {}, angle_yaxis: {}'.format(n0['pop_name'], n0['positions'], n0['rotation_angle_yaxis']))\n",
    "print('cell 1: pop_name: {}, positions: {}, angle_yaxis: {}'.format(n1['pop_name'], n1['positions'], n1['rotation_angle_yaxis']))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Network Builder contains a growing number of built-in functions. However for advanced networks a modeler will probably want to assign parameters using their own functions. To do so, a modeler only needs to passes in, or alternatively create a function that returns, a list of size N. When saving the network, each individual position will be saved in the nodes.h5 file assigned to each cell by gid.\n",
    "\n",
    "```python\n",
    "def cortex_positions(N):\n",
    "    # codex to create a list/numpy array of N (x, y, z) positions.\n",
    "    return [...]\n",
    "\n",
    "cortex.add_nodes(N=100,\n",
    "                 positions=cortex_positions(100),\n",
    "                 ...\n",
    "```\n",
    "\n",
    "or if we wanted we could give all cells the same position (The builder has no restrictions on this, however this may cause issues if you're trying to create connections based on distance). When saving the network, the same position is assigned as a global cell-type property, and thus saved in the node_types.csv file.\n",
    "```python\n",
    "cortex.add_nodes(N=100,\n",
    "                 positions=np.ndarray([100.23, -50.67, 89.01]),\n",
    "                 ...\n",
    "```\n",
    "\n",
    "We can use the same logic not just for positions and rotation_angle, but for any parameter we choose."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customized connector functions\n",
    "\n",
    "When creating edges, we used the built-in distance_connector function to help create the connection matrix. There are a number of built-in connection functions, but we also allow modelers to create their own. To do so, the modeler must create a function that takes in a source, target, and a variable number of parameters, and pass back a natural number representing the number of connections.\n",
    "\n",
    "The Builder will iterate over that function passing in every source/target node pair (filtered by the source and target parameters in add_edges()). The source and target parameters are essentially dictionaries that can be used to fetch properties of the nodes. A typical example would look like:\n",
    "\n",
    "```python\n",
    "def customized_connector(source, target, param1, param2, param3):\n",
    "    if source.node_id == target.node_id:\n",
    "        # necessary if we don't want autapses\n",
    "        return 0\n",
    "    source_pot = source['potential']\n",
    "    target_pot = target['potential']\n",
    "    # some code to determine number of connections\n",
    "    return n_synapses\n",
    "    \n",
    "...\n",
    "cortex.add_edges(source=<source_nodes>, target=<target_nodes>,\n",
    "                 connection_rule=customized_connector,\n",
    "                 connection_params={'param1': <p1>, 'param2': <p2>, 'param3': <p3>},\n",
    "                 ...\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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