{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial: Multi-Population Recurrent Network (with BioNet) \n",
    "\n",
    "Here we will create a heterogenous yet relatively small network consisting of hundreds of recurrently-connected cells. All cells will belong to one of four \"cell-types\". Two of these cell types will be biophysically-detailed cells, i.e. containing a full morphology and somatic and dendritic channels and receptors. The other two will be point-neuron models, which lack a full morphology or channels but still provide inhibitory and excitatory activity.\n",
    "\n",
    "As input to drive the simulation, we will also create an external network of \"virtual cells\" that synapse directly onto our internal cells and provide spike trains as stimuli.\n",
    "\n",
    "**Note** - scripts and files for running this tutorial can be found in the directory [sources/chapter04/](https://github.com/AllenInstitute/bmtk/tree/develop/docs/tutorial/sources/chapter04).\n",
    "\n",
    "requirements:\n",
    "* bmtk\n",
    "* NEURON 7.4+\n",
    "\n",
    "For more information on the BioNet Simulator, please see the [BioNet Overview](https://alleninstitute.github.io/bmtk/bionet.html).\n",
    "\n",
    "For more information on BMTK and SONATA format, please see the [Overview of BMTK](https://alleninstitute.github.io/bmtk/user_guide.html) and [Network Builder](https://alleninstitute.github.io/bmtk/builder.html) pages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Building the network\n",
    "\n",
    "### Set nodes\n",
    "\n",
    "This network will loosely resemble a portion of one layer in the mouse primary visual cortex (area V1). (See Arkhipov et al., 2018 for an example of a scientific study using a similar system, although with many more cells). Along the center of the column will be a population of 50 biophysically detailed neurons: 80 excitatory Scnn1a cells and 20 inhibitory PV cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from bmtk.builder.networks import NetworkBuilder\n",
    "from bmtk.builder.auxi.node_params import positions_columinar, xiter_random\n",
    "\n",
    "net = NetworkBuilder(\"V1\")\n",
    "net.add_nodes(\n",
    "    N=80, pop_name='Scnn1a',\n",
    "    positions=positions_columinar(N=80, center=[0, 50.0, 0], max_radius=30.0, height=100.0),\n",
    "    rotation_angle_yaxis=xiter_random(N=80, min_x=0.0, max_x=2*np.pi),\n",
    "    rotation_angle_zaxis=xiter_random(N=80, min_x=0.0, max_x=2*np.pi),\n",
    "    tuning_angle=np.linspace(start=0.0, stop=360.0, num=80, endpoint=False),\n",
    "    location='VisL4',\n",
    "    ei='e',\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",
    ")\n",
    "\n",
    "net.add_nodes(\n",
    "    N=20, pop_name='PV',\n",
    "    positions=positions_columinar(N=20, center=[0, 50.0, 0], max_radius=30.0, height=100.0),\n",
    "    rotation_angle_yaxis=xiter_random(N=20, min_x=0.0, max_x=2*np.pi),\n",
    "    rotation_angle_zaxis=xiter_random(N=20, min_x=0.0, max_x=2*np.pi),\n",
    "    location='VisL4',\n",
    "    ei='i',\n",
    "    model_type='biophysical',\n",
    "    model_template='ctdb:Biophys1.hoc',\n",
    "    model_processing='aibs_perisomatic',\n",
    "    dynamics_params='472912177_fit.json',\n",
    "    morphology='Pvalb_470522102_m.swc'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To set the position and rotation of each cell, we use the builtiin functions `positions_columinar` and `xiter_random`, which return a list of position and rotation values, respectively. A user could set the values themselves using a list of size *N*, where *N* represents the number of cells. The parameters like *location*, *ei*, *model_type*, etc. are cell-type parameters, and will be used for all *N* cells of that type. See [tutorial 2](tutorial_02_single_cell_syn.ipynb) and [tutorial 3](tutorial_03_single_pop.ipynb) for more details.\n",
    "\n",
    "The excitatory cells are also given a *tuning_angle* parameter. The \"tuning angle\" is an intrinsic property found in some cells in the visual cortex. In this model, we will use this property to determine the strength of connections between subsets of cells by using custom functions. In general most models will not have or use a tuning angle, but they may require some other parameter. Users can assign whatever custom parameters they want to cells and cell-types and use them as properties for creating connections and running simulations.\n",
    "\n",
    "Next we continue to create our point (integrate-and-fire) neurons. Notice they don't have properties like *rotation_angle_yaxis* or *morphology*, as these don't apply to point neurons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.add_nodes(\n",
    "    N=200, pop_name='LIF_exc',\n",
    "    positions=positions_columinar(N=200, center=[0, 50.0, 0], min_radius=30.0, max_radius=60.0, height=100.0),\n",
    "    tuning_angle=np.linspace(start=0.0, stop=360.0, num=200, endpoint=False),\n",
    "    location='VisL4',\n",
    "    ei='e',\n",
    "    model_type='point_process',\n",
    "    model_template='nrn:IntFire1',\n",
    "    dynamics_params='IntFire1_exc_1.json'\n",
    ")\n",
    "\n",
    "net.add_nodes(\n",
    "    N=100, pop_name='LIF_inh',\n",
    "    positions=positions_columinar(N=100, center=[0, 50.0, 0], min_radius=30.0, max_radius=60.0, height=100.0),\n",
    "    location='VisL4',\n",
    "    ei='i',\n",
    "    model_type='point_process',\n",
    "    model_template='nrn:IntFire1',\n",
    "    dynamics_params='IntFire1_inh_1.json'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set edges\n",
    "\n",
    "Now we want to create connections between the cells. Depending on the model type, and whether or not the presynaptic \"source\" cell is excitory or inhibitory, we will use different synaptic models and parameters. Using the *source* and *target* filter parameters, we can create different connection types.\n",
    "\n",
    "To determine the excitatory-to-excitatory connection matrix we want to use the *distance* and *tuning_angle* properties. To do this we create a customized function `dist_tuning_connector`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import math\n",
    "\n",
    "def dist_tuning_connector(source, target, d_weight_min, d_weight_max, d_max, t_weight_min, t_weight_max, nsyn_min,\n",
    "                          nsyn_max):\n",
    "    if source['node_id'] == target['node_id']:\n",
    "        # Avoid self-connections.n_nodes\n",
    "        return None\n",
    "\n",
    "    r = np.linalg.norm(np.array(source['positions']) - np.array(target['positions']))\n",
    "    if r > d_max:\n",
    "        dw = 0.0\n",
    "    else:\n",
    "        t = r / d_max\n",
    "        dw = d_weight_max * (1.0 - t) + d_weight_min * t\n",
    "\n",
    "    if dw <= 0:\n",
    "        # drop the connection if the weight is too low\n",
    "        return None\n",
    "\n",
    "    # next create weights by orientation tuning [ aligned, misaligned ] --> [ 1, 0 ], Check that the orientation\n",
    "    # tuning property exists for both cells; otherwise, ignore the orientation tuning.\n",
    "    if 'tuning_angle' in source and 'tuning_angle' in target:\n",
    "\n",
    "        # 0-180 is the same as 180-360, so just modulo by 180\n",
    "        delta_tuning = math.fmod(abs(source['tuning_angle'] - target['tuning_angle']), 180.0)\n",
    "\n",
    "        # 90-180 needs to be flipped, then normalize to 0-1\n",
    "        delta_tuning = delta_tuning if delta_tuning < 90.0 else 180.0 - delta_tuning\n",
    "\n",
    "        t = delta_tuning / 90.0\n",
    "        tw = t_weight_max * (1.0 - t) + t_weight_min * t\n",
    "    else:\n",
    "        tw = dw\n",
    "\n",
    "    # drop the connection if the weight is too low\n",
    "    if tw <= 0:\n",
    "        return None\n",
    "\n",
    "    # filter out nodes by treating the weight as a probability of connection\n",
    "    if random.random() > tw:\n",
    "        return None\n",
    "\n",
    "    # Add the number of synapses for every connection.\n",
    "    # It is probably very useful to take this out into a separate function.\n",
    "    return random.randint(nsyn_min, nsyn_max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first two parameters of this function are \"source\" and \"target\" and are required for all custom connector functions. These are node objects which give a representation of a single source and target cell, with properties that can be accessed like a python dictionary. When The Network Builder creates the connection matrix, it will call this function for all possible source-target pairs. The user doesn't call this function directly.\n",
    "\n",
    "The remaining parameters are optional. Using these parameters, plus the distance and angles between source and target cells, this function determines the number of connections between each given source and target cell. If there are none you can return either None or 0.\n",
    "\n",
    "To create these connections we call the `add_edges` method of the builder. We use the source and target parameter to filter out only excitatory-to-excitatory connections. We must also take into consideration the model type (biophysical or integrate-and-fire) of the target when setting parameters. We pass in the function through the `connection_rule` parameter, and the function parameters (except source and target) through `connection_params`. (If our `dist_tuning_connector` function didn't have any parameters other than source and target, we could just not set `connection_params`).\n",
    "\n",
    "The parameter weight_function will be described in detail in Step 3. \n",
    "\n",
    "See [tutorial 2](tutorial_02_single_cell_syn.ipynb) and [tutorial 3](tutorial_03_single_pop.ipynb) for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bmtk.builder.connection_map.ConnectionMap at 0x7f56031b5130>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.add_edges(\n",
    "    source={'ei': 'e'}, target={'pop_name': 'Scnn1a'},\n",
    "    connection_rule=dist_tuning_connector,\n",
    "    connection_params={'d_weight_min': 0.0, 'd_weight_max': 0.34, 'd_max': 300.0, 't_weight_min': 0.5,\n",
    "                       't_weight_max': 1.0, 'nsyn_min': 3, 'nsyn_max': 7},\n",
    "    syn_weight=5e-05,\n",
    "    weight_function='gaussianLL',\n",
    "    weight_sigma=50.0,\n",
    "    distance_range=[30.0, 150.0],\n",
    "    target_sections=['basal', 'apical'],\n",
    "    delay=2.0,\n",
    "    dynamics_params='AMPA_ExcToExc.json',\n",
    "    model_template='exp2syn'\n",
    ")\n",
    "\n",
    "net.add_edges(\n",
    "    source={'ei': 'e'}, target={'pop_name': 'LIF_exc'},\n",
    "    connection_rule=dist_tuning_connector,\n",
    "    connection_params={'d_weight_min': 0.0, 'd_weight_max': 0.34, 'd_max': 300.0, 't_weight_min': 0.5,\n",
    "                       't_weight_max': 1.0, 'nsyn_min': 3, 'nsyn_max': 7},\n",
    "    syn_weight=0.0019,\n",
    "    weight_function='gaussianLL',\n",
    "    weight_sigma=50.0,\n",
    "    delay=2.0,\n",
    "    dynamics_params='instantaneousExc.json',\n",
    "    model_template='exp2syn'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create the other types of connections in a similar manner. But since either the source, target, or both cells will not have the *tuning_angle* parameter, we don't want to use `dist_tuning_connector`. Instead we can use the built-in `distance_connector` function which just creates connections determined by distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bmtk.builder.connection_map.ConnectionMap at 0x7f5602eec400>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bmtk.builder.auxi.edge_connectors import distance_connector\n",
    "\n",
    "### Generating I-to-I connections\n",
    "net.add_edges(\n",
    "    source={'ei': 'i'}, target={'ei': 'i', 'model_type': 'biophysical'},\n",
    "    connection_rule=distance_connector,\n",
    "    connection_params={'d_weight_min': 0.0, 'd_weight_max': 1.0, 'd_max': 160.0, 'nsyn_min': 3, 'nsyn_max': 7},\n",
    "    syn_weight=0.0002,\n",
    "    weight_function='wmax',\n",
    "    distance_range=[0.0, 1e+20],\n",
    "    target_sections=['somatic', 'basal'],\n",
    "    delay=2.0,\n",
    "    dynamics_params='GABA_InhToInh.json',\n",
    "    model_template='exp2syn'\n",
    ")\n",
    "\n",
    "net.add_edges(\n",
    "    source={'ei': 'i'}, target={'ei': 'i', 'model_type': 'point_process'},\n",
    "    connection_rule=distance_connector,\n",
    "    connection_params={'d_weight_min': 0.0, 'd_weight_max': 1.0, 'd_max': 160.0, 'nsyn_min': 3, 'nsyn_max': 7},\n",
    "    syn_weight=0.001,\n",
    "    weight_function='wmax',\n",
    "    delay=2.0,\n",
    "    dynamics_params='instantaneousInh.json',\n",
    "    model_template='exp2syn'\n",
    ")\n",
    "\n",
    "### Generating I-to-E connections\n",
    "net.add_edges(\n",
    "    source={'ei': 'i'}, target={'ei': 'e', 'model_type': 'biophysical'},\n",
    "    connection_rule=distance_connector,\n",
    "    connection_params={'d_weight_min': 0.0, 'd_weight_max': 1.0, 'd_max': 160.0, 'nsyn_min': 3, 'nsyn_max': 7},\n",
    "    syn_weight=0.0001,\n",
    "    weight_function='wmax',\n",
    "    distance_range=[0.0, 50.0],\n",
    "    target_sections=['somatic', 'basal', 'apical'],\n",
    "    delay=2.0,\n",
    "    dynamics_params='GABA_InhToExc.json',\n",
    "    model_template='exp2syn'\n",
    ")\n",
    "\n",
    "net.add_edges(\n",
    "    source={'ei': 'i'}, target={'ei': 'e', 'model_type': 'point_process'},\n",
    "    connection_rule=distance_connector,\n",
    "    connection_params={'d_weight_min': 0.0, 'd_weight_max': 1.0, 'd_max': 160.0, 'nsyn_min': 3, 'nsyn_max': 7},\n",
    "    syn_weight=0.009,\n",
    "    weight_function='wmax',\n",
    "    delay=2.0,\n",
    "    dynamics_params='instantaneousInh.json',\n",
    "    model_template='exp2syn'\n",
    ")\n",
    "\n",
    "### Generating E-to-I connections\n",
    "net.add_edges(\n",
    "    source={'ei': 'e'}, target={'pop_name': 'PV'},\n",
    "    connection_rule=distance_connector,\n",
    "    connection_params={'d_weight_min': 0.0, 'd_weight_max': 0.26, 'd_max': 300.0, 'nsyn_min': 3, 'nsyn_max': 7},\n",
    "    syn_weight=0.004,\n",
    "    weight_function='wmax',\n",
    "    distance_range=[0.0, 1e+20],\n",
    "    target_sections=['somatic', 'basal'],\n",
    "    delay=2.0,\n",
    "    dynamics_params='AMPA_ExcToInh.json',\n",
    "    model_template='exp2syn'\n",
    ")\n",
    "\n",
    "net.add_edges(\n",
    "    source={'ei': 'e'}, target={'pop_name': 'LIF_inh'},\n",
    "    connection_rule=distance_connector,\n",
    "    connection_params={'d_weight_min': 0.0, 'd_weight_max': 0.26, 'd_max': 300.0, 'nsyn_min': 3, 'nsyn_max': 7},\n",
    "    syn_weight=0.006,\n",
    "    weight_function='wmax',\n",
    "    delay=2.0,\n",
    "    dynamics_params='instantaneousExc.json',\n",
    "    model_template='exp2syn'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we build the network (this may take a bit of time since there are many possible connection combinations), and save the nodes and edges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.build()\n",
    "net.save_nodes(output_dir='sim_ch04/network')\n",
    "net.save_edges(output_dir='sim_ch04/network')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build external network\n",
    "\n",
    "Next we want to create an external network consisting of virtual cells that form a feedforward network onto our V1, which will provide input during the simulation. We will call this LGN, since the LGN is the primary input to layer 4 cells of the V1 (if we wanted to we could also create multiple external networks and run simulations on any number of them). \n",
    "\n",
    "First we build our LGN nodes. Then we must import the V1 network nodes and create connections from LGN to V1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bmtk.builder.networks import NetworkBuilder\n",
    "\n",
    "lgn = NetworkBuilder('LGN')\n",
    "lgn.add_nodes(\n",
    "    N=500,\n",
    "    pop_name='tON',\n",
    "    potential='exc',\n",
    "    model_type='virtual'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, we will use a customized function to determine the number of connections between each source and target pair, however this time our connection rule is a bit different.\n",
    "\n",
    "In the previous example our connection rule function's first two arguments were the presynaptic and postsynaptic cells, which allowed us to choose the number of synaptic connections between pairs based on individual properties:\n",
    "```python\n",
    "def connection_fnc(source, target, ...):\n",
    "    source['param'] # presynaptic cell params\n",
    "    target['param'] # postsynaptic cell params\n",
    "    ...\n",
    "    return nsyns # number of connections between pair\n",
    "```\n",
    "\n",
    "But for our LGN --> V1 connection, we do things a bit differently. We want to make sure that for every source cell, there are a limited number of presynaptic targets. This is a not really possible with a function that iterates on a one-to-one basis. So instead we have a connector function for which the first parameter is a list of all N source cell while the second parameter is a single target cell. We return an array of integers, size *N*, with each element representing the number of synapses between the sources and the target. \n",
    "\n",
    "To tell the builder to use this schema, we must set `iterator='all_to_one'` in the `add_edges` method. (By default this is set to 'one_to_one'. You can also use 'one_to_all' iterator which will pass in a single source and all possible targets)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_source_cells(sources, target, nsources_min=10, nsources_max=30, nsyns_min=3, nsyns_max=12):\n",
    "    total_sources = len(sources)\n",
    "    nsources = np.random.randint(nsources_min, nsources_max)\n",
    "    selected_sources = np.random.choice(total_sources, nsources, replace=False)\n",
    "    syns = np.zeros(total_sources)\n",
    "    syns[selected_sources] = np.random.randint(nsyns_min, nsyns_max, size=nsources)\n",
    "    return syns\n",
    "\n",
    "lgn.add_edges(\n",
    "    source=lgn.nodes(), target=net.nodes(pop_name='Scnn1a'),\n",
    "    iterator='all_to_one',\n",
    "    connection_rule=select_source_cells,\n",
    "    connection_params={'nsources_min': 10, 'nsources_max': 25},\n",
    "    syn_weight=1e-03,\n",
    "    weight_function='wmax',\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",
    "\n",
    "lgn.add_edges(\n",
    "    source=lgn.nodes(), target=net.nodes(pop_name='PV1'),\n",
    "    connection_rule=select_source_cells,\n",
    "    connection_params={'nsources_min': 15, 'nsources_max': 30},\n",
    "    iterator='all_to_one',\n",
    "    syn_weight=0.015,\n",
    "    weight_function='wmax',\n",
    "    distance_range=[0.0, 1.0e+20],\n",
    "    target_sections=['somatic', 'basal'],\n",
    "    delay=2.0,\n",
    "    dynamics_params='AMPA_ExcToInh.json',\n",
    "    model_template='exp2syn'\n",
    ")\n",
    "\n",
    "lgn.add_edges(\n",
    "    source=lgn.nodes(),  target=net.nodes(pop_name='LIF_exc'),\n",
    "    connection_rule=select_source_cells,\n",
    "    connection_params={'nsources_min': 10, 'nsources_max': 25},\n",
    "    iterator='all_to_one',\n",
    "    syn_weight= 0.07,\n",
    "    weight_function='wmax',\n",
    "    delay=2.0,\n",
    "    dynamics_params='instantaneousExc.json',\n",
    "    model_template='exp2syn'\n",
    ")\n",
    "\n",
    "lgn.add_edges(\n",
    "    source=lgn.nodes(),  target=net.nodes(pop_name='LIF_inh'),\n",
    "    connection_rule=select_source_cells,\n",
    "    connection_params={'nsources_min': 15, 'nsources_max': 30},\n",
    "    iterator='all_to_one',\n",
    "    syn_weight=0.05,\n",
    "    weight_function='wmax',\n",
    "    delay=2.0,\n",
    "    dynamics_params='instantaneousExc.json',\n",
    "    model_template='exp2syn'\n",
    ")\n",
    "\n",
    "lgn.build()\n",
    "lgn.save_nodes(output_dir='sim_ch04/network')\n",
    "lgn.save_edges(output_dir='sim_ch04/network')"
   ]
  },
  {
   "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 can copy the files from an existing simulation and execute with the following command:\n",
    "\n",
    "```bash\n",
    "$ python -m bmtk.utils.sim_setup  \\\n",
    "   --report-vars v                \\\n",
    "   --report-nodes 10,80           \\\n",
    "   --network sim_ch04/network     \\\n",
    "   --dt 0.1                       \\\n",
    "   --tstop 3000.0                 \\  \n",
    "   --include-examples             \\\n",
    "   --compile-mechanisms           \\ \n",
    "   bionet sim_ch04\n",
    "```\n",
    "\n",
    "$ python -m bmtk.utils.sim_setup --report-vars v --report-nodes 0,80,100,300 --network sim_ch04/network --dt 0.1 --tstop 3000.0  --include-examples --compile-mechanisms bionet sim_ch04\n",
    "\n",
    "or run it directly in python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bmtk.utils.sim_setup import build_env_bionet\n",
    "\n",
    "build_env_bionet(\n",
    "    base_dir='sim_ch04',\n",
    "    config_file='config.json',\n",
    "    network_dir='sim_ch04/network',\n",
    "    tstop=3000.0, dt=0.1,\n",
    "    report_vars=['v'],     # Record membrane potential (default soma)\n",
    "    include_examples=True,    # Copies components files\n",
    "    compile_mechanisms=True   # Will try to compile NEURON mechanisms\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will fill out the **sim_ch04** with all the files we need to get started to run the simulation. Of interest includes\n",
    "\n",
    "* **circuit_config.json** - A configuration file that contains the location of the network files we created above. Plus location of neuron and synaptic models, templates, morphologies and mechanisms required to build our circuit and instantiate individual cell models.\n",
    "\n",
    "\n",
    "* **simulation_config.json** - contains information about the simulation. Including initial conditions and run-time configuration (_run_ and _conditions_). In the _inputs_ section we define what external sources we will use to drive the network (in this case a current clamp). And in the _reports_ section we define the variables (soma membrane potential and calcium) that will be recorded during the simulation \n",
    "\n",
    "\n",
    "* **run_bionent.py** - A script for running our simulation. Usually this file doesn't need to be modified.\n",
    "\n",
    "\n",
    "* **components/biophysical_neuron_models/** - The parameter file for the cells we're modeling. Originally [downloaded from the Allen Cell Types Database](http://celltypes.brain-map.org/neuronal_model/download/482934212). These files were automatically copied over when we used the _include-examples_ directive. If using a differrent or extended set of cell models place them here\n",
    "\n",
    "\n",
    "* **components/biophysical_neuron_models/** - The morphology file for our cells. Originally [downloaded from the Allen Cell Types Database](http://celltypes.brain-map.org/neuronal_model/download/482934212) and copied over using the _include_examples_.\n",
    "\n",
    "\n",
    "* **components/point_neuron_models/** - The parameter file for our LIF_exc and LIF_inh cells.\n",
    "\n",
    "\n",
    "* **components/synaptic_models/** - Parameter files used to create different types of synapses.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LGN Input\n",
    "\n",
    "We need to provide our LGN external network cells with spike-trains so they can activate our recurrent network. Previously we showed how to do this by generating CSV files. We can also use [NWB files](https://www.nwb.org/), which are a common format for saving electrophysiological data in neuroscience.\n",
    "\n",
    "We can use any NWB file generated experimentally or computationally, but for this example we will use a pre-existing file. First download the file:\n",
    "```bash\n",
    " $ wget https://github.com/AllenInstitute/bmtk/blob/develop/examples/spikes_inputs/lgn_spikes.h5?raw=true -O lgn_spikes.h5\n",
    "```\n",
    "or copy from [here](https://github.com/AllenInstitute/bmtk/tree/develop/examples/spikes_inputs/lgn_spikes.nwb).\n",
    "\n",
    "\n",
    "Then we must edit the **simulation_config.json** file to tell the simulator where to find the spike file and which network to associate it with:\n",
    "\n",
    "```json\n",
    "{\n",
    "  \"inputs\": {\n",
    "    \"LGN_spikes\": {\n",
    "      \"input_type\": \"spikes\",\n",
    "      \"module\": \"sonata\",\n",
    "      \"input_file\": \"$BASE_DIR/lgn_spikes.h5\",\n",
    "      \"node_set\": \"LGN\"\n",
    "    }\n",
    "  }\n",
    "}\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Running the simulation\n",
    "\n",
    "\n",
    "We are close to running our simulation, however unlike previous chapters we need a little more programming before we can begin. \n",
    "\n",
    "For most of the connections we added the parameter `weight_function='wmax'`. This is a built-in function that tells the simulator to use the 'weight_max' value assigned to that edge-type when creating a connection between two cells. \n",
    "\n",
    "However, when creating excitatory-to-excitatory connections we want to use the  `weight_function='gaussianLL'`. This is because we want to use the 'tuning_angle' parameter, when available, to determine the synaptic strength between two connections. First we create the function which takes in target, source and connection properties (which are just the edge-type and properties set in the `add_edges` method). Then we must register the function with the BioNet simulator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "from bmtk.simulator.bionet.pyfunction_cache import add_weight_function\n",
    "\n",
    "def gaussianLL(edge_props, source, target):\n",
    "    src_tuning = source['tuning_angle']\n",
    "    tar_tuning = target['tuning_angle']\n",
    "    w0 = edge_props[\"syn_weight\"]\n",
    "    sigma = edge_props[\"weight_sigma\"]\n",
    "\n",
    "    delta_tuning = abs(abs(abs(180.0 - abs(float(tar_tuning) - float(src_tuning)) % 360.0) - 90.0) - 90.0)\n",
    "    return w0 * math.exp(-(delta_tuning / sigma) ** 2)\n",
    "\n",
    "add_weight_function(gaussianLL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The weights will be adjusted before each simulation, and the function can be changed between different runs. Simply opening the **edge_types.csv** file with a text editor and altering the weight_function column allows users to take an existing network and readjust weights as needed in each new simulation runtime.\n",
    "\n",
    "Finally we are ready to run the simulation. Note that because this is a 400 cell simulation, this may be computationally intensive for some older computers and may take anywhere between a few minutes to half-an-hour to complete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-11-01 16:23:09,716 [INFO] Created log file\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:NEURONIOUtils:Created log file\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numprocs=1\n",
      "2022-11-01 16:23:09,800 [INFO] Building cells.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:NEURONIOUtils:Building cells.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-11-01 16:23:17,484 [INFO] Building recurrent connections\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:NEURONIOUtils:Building recurrent connections\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-11-01 16:23:36,637 [INFO] Building virtual cell stimulations for LGN_spikes\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:NEURONIOUtils:Building virtual cell stimulations for LGN_spikes\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-11-01 16:23:38,276 [INFO] Running simulation for 3000.000 ms with the time step 0.100 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:NEURONIOUtils:Running simulation for 3000.000 ms with the time step 0.100 ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-11-01 16:23:38,277 [INFO] Starting timestep: 0 at t_sim: 0.000 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:NEURONIOUtils:Starting timestep: 0 at t_sim: 0.000 ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-11-01 16:23:38,278 [INFO] Block save every 5000 steps\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:NEURONIOUtils:Block save every 5000 steps\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-11-01 16:24:19,186 [INFO]     step:5000 t_sim:500.00 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:NEURONIOUtils:    step:5000 t_sim:500.00 ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-11-01 16:25:02,737 [INFO]     step:10000 t_sim:1000.00 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:NEURONIOUtils:    step:10000 t_sim:1000.00 ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-11-01 16:25:44,100 [INFO]     step:15000 t_sim:1500.00 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:NEURONIOUtils:    step:15000 t_sim:1500.00 ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-11-01 16:26:30,819 [INFO]     step:20000 t_sim:2000.00 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:NEURONIOUtils:    step:20000 t_sim:2000.00 ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-11-01 16:27:13,897 [INFO]     step:25000 t_sim:2500.00 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:NEURONIOUtils:    step:25000 t_sim:2500.00 ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-11-01 16:27:56,117 [INFO]     step:30000 t_sim:3000.00 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:NEURONIOUtils:    step:30000 t_sim:3000.00 ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-11-01 16:27:56,176 [INFO] Simulation completed in 4.0 minutes, 17.9 seconds \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:NEURONIOUtils:Simulation completed in 4.0 minutes, 17.9 seconds \n"
     ]
    }
   ],
   "source": [
    "from bmtk.simulator import bionet\n",
    "\n",
    "\n",
    "conf = bionet.Config.from_json('sim_ch04/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 results\n",
    "\n",
    "Results of the simulation are saved into the output directory, as specified in the config file. Using the analyzer functions, we can do things like plot the raster plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bmtk.analyzer.spike_trains import plot_raster, plot_rates_boxplot\n",
    "\n",
    "_ = plot_raster(config_file='sim_ch04/config.json', group_by='pop_name')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and the rates of each node:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_ = plot_rates_boxplot(config_file='sim_ch04/config.json', group_by='pop_name')"
   ]
  },
  {
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bmtk.analyzer.compartment import plot_traces\n",
    "\n",
    "_ = plot_traces(config_file='sim_ch04/config.json', report_name='v_report', node_ids=[50,70])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}