{
“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/](https://github.com/AllenInstitute/bmtk/tree/develop/docs/tutorial/sources/chapter03)n”, “n”, “requirements:n”, “* bmtkn”, “* NEURON 7.4+”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“## 1. Building the Networkn”, “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 npn”, “from bmtk.builder.networks import NetworkBuildern”, “from bmtk.builder.auxi.node_params import positions_columinar, xiter_randomn”, “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 anglen”, “`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”: [

“#### edgesn”, “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 0x7fb9ab955860>”

]

}, “execution_count”: 2, “metadata”: {}, “output_type”: “execute_result”

}

], “source”: [

“from bmtk.builder.auxi.edge_connectors import distance_connectorn”, “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 networkn”, “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_randomn”, “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 Trainsn”, “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”: {
“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>node_ids</th>n”, ” <th>timestamps</th>n”, ” <th>population</th>n”, ” </tr>n”, ” </thead>n”, ” <tbody>n”, ” <tr>n”, ” <th>0</th>n”, ” <td>0</td>n”, ” <td>41.735876</td>n”, ” <td>mthalamus</td>n”, ” </tr>n”, ” <tr>n”, ” <th>1</th>n”, ” <td>0</td>n”, ” <td>71.347642</td>n”, ” <td>mthalamus</td>n”, ” </tr>n”, ” <tr>n”, ” <th>2</th>n”, ” <td>0</td>n”, ” <td>206.996984</td>n”, ” <td>mthalamus</td>n”, ” </tr>n”, ” <tr>n”, ” <th>3</th>n”, ” <td>0</td>n”, ” <td>316.232099</td>n”, ” <td>mthalamus</td>n”, ” </tr>n”, ” <tr>n”, ” <th>4</th>n”, ” <td>0</td>n”, ” <td>479.833100</td>n”, ” <td>mthalamus</td>n”, ” </tr>n”, ” </tbody>n”, “</table>n”, “</div>”

], “text/plain”: [

” node_ids timestamps populationn”, “0 0 41.735876 mthalamusn”, “1 0 71.347642 mthalamusn”, “2 0 206.996984 mthalamusn”, “3 0 316.232099 mthalamusn”, “4 0 479.833100 mthalamus”

]

}, “execution_count”: 6, “metadata”: {}, “output_type”: “execute_result”

}

], “source”: [

“from bmtk.utils.reports.spike_trains import PoissonSpikeGeneratorn”, “n”, “psg = PoissonSpikeGenerator(population=’mthalamus’)n”, “psg.add(node_ids=range(100), # Have 10 nodes to match mthalamusn”, ” firing_rate=15.0, # 15 Hz, we can also pass in a nonhomoegenous function/arrayn”, ” times=(0.0, 3.0)) # Firing starts at 0 s up to 3 sn”, “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 = 4500n”, “# spikesn”, “psg.to_dataframe().head()n”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“## 2. Setting up BioNetn”, “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”: 7, “metadata”: {}, “outputs”: [], “source”: [

“from bmtk.utils.sim_setup import build_env_bionetn”, “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 forn”, ” ‘sim_ch03/inputs/mthalamus_spikes.h5’)],n”, ” include_examples=True, # Copies components filesn”, ” compile_mechanisms=True # Will try to compile NEURON mechanismsn”, ” )”

]

}, {

“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", "{\n", "\n", "    \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", "}\n", "`

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“## 3. Running the simulationn”, “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”: 8, “metadata”: {

“scrolled”: true

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“2020-08-25 13:50:49,354 [INFO] Created log filen”

]

}, {

“name”: “stderr”, “output_type”: “stream”, “text”: [

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“2020-08-25 13:50:49,485 [INFO] Building cells.n”

]

}, {

“name”: “stderr”, “output_type”: “stream”, “text”: [

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“2020-08-25 13:51:01,008 [INFO] Building recurrent connectionsn”

]

}, {

“name”: “stderr”, “output_type”: “stream”, “text”: [

INFO:NEURONIOUtils:Building recurrent connectionsn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“2020-08-25 13:51:02,693 [INFO] Building virtual cell stimulations for mthalamus_spikesn”

]

}, {

“name”: “stderr”, “output_type”: “stream”, “text”: [

INFO:NEURONIOUtils:Building virtual cell stimulations for mthalamus_spikesn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“2020-08-25 13:51:18,534 [INFO] Running simulation for 3000.000 ms with the time step 0.100 msn”

]

}, {

“name”: “stderr”, “output_type”: “stream”, “text”: [

INFO:NEURONIOUtils:Running simulation for 3000.000 ms with the time step 0.100 msn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“2020-08-25 13:51:18,535 [INFO] Starting timestep: 0 at t_sim: 0.000 msn”

]

}, {

“name”: “stderr”, “output_type”: “stream”, “text”: [

INFO:NEURONIOUtils:Starting timestep: 0 at t_sim: 0.000 msn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“2020-08-25 13:51:18,537 [INFO] Block save every 5000 stepsn”

]

}, {

“name”: “stderr”, “output_type”: “stream”, “text”: [

INFO:NEURONIOUtils:Block save every 5000 stepsn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“2020-08-25 13:52:17,714 [INFO] step:5000 t_sim:500.00 msn”

]

}, {

“name”: “stderr”, “output_type”: “stream”, “text”: [

INFO:NEURONIOUtils: step:5000 t_sim:500.00 msn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“2020-08-25 13:53:12,883 [INFO] step:10000 t_sim:1000.00 msn”

]

}, {

“name”: “stderr”, “output_type”: “stream”, “text”: [

INFO:NEURONIOUtils: step:10000 t_sim:1000.00 msn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“2020-08-25 13:54:07,377 [INFO] step:15000 t_sim:1500.00 msn”

]

}, {

“name”: “stderr”, “output_type”: “stream”, “text”: [

INFO:NEURONIOUtils: step:15000 t_sim:1500.00 msn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“2020-08-25 13:55:01,072 [INFO] step:20000 t_sim:2000.00 msn”

]

}, {

“name”: “stderr”, “output_type”: “stream”, “text”: [

INFO:NEURONIOUtils: step:20000 t_sim:2000.00 msn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“2020-08-25 13:55:54,108 [INFO] step:25000 t_sim:2500.00 msn”

]

}, {

“name”: “stderr”, “output_type”: “stream”, “text”: [

INFO:NEURONIOUtils: step:25000 t_sim:2500.00 msn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“2020-08-25 13:56:47,955 [INFO] step:30000 t_sim:3000.00 msn”

]

}, {

“name”: “stderr”, “output_type”: “stream”, “text”: [

INFO:NEURONIOUtils: step:30000 t_sim:3000.00 msn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“2020-08-25 13:56:48,032 [INFO] Simulation completed in 5.0 minutes, 29.5 seconds n”

]

}, {

“name”: “stderr”, “output_type”: “stream”, “text”: [

INFO:NEURONIOUtils:Simulation completed in 5.0 minutes, 29.5 seconds n”

]

}

], “source”: [

“from bmtk.simulator import bionetn”, “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”: 10, “metadata”: {

“scrolled”: true

}, “outputs”: [

{
“data”: {

“image/png”: 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”, “text/plain”: [

“<Figure size 432x288 with 2 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“from bmtk.analyzer.spike_trains import plot_rastern”, “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”: 11, “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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”, “text/plain”: [

“<Figure size 432x288 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“from bmtk.analyzer.compartment import plot_tracesn”, “n”, “_ = plot_traces(config_file=’sim_ch01/simulation_config.json’, report_name=’v_report’)n”, “_ = plot_traces(config_file=’sim_ch01/simulation_config.json’, report_name=’cai_report’)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“## 5. Modifying the network”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“#### Customized node paramsn”, “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”: 12, “metadata”: {}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“cell 0: pop_name: Scnn1a, positions: [ -8.09139216 60.66966008 -18.96498613], angle_yaxis: 3.0000200067928224n”, “cell 1: pop_name: Scnn1a, positions: [ 4.49907709 78.44333228 2.94240925], angle_yaxis: 5.93260443315686n”

]

}

], “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 functionsn”, “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”: []

}

], “metadata”: {

“anaconda-cloud”: {}, “kernelspec”: {

“display_name”: “Python 3”, “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”: “3.6.10”

}

}, “nbformat”: 4, “nbformat_minor”: 2

}