{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1f827cb6",
   "metadata": {},
   "source": [
    "# Constructing Simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "437c82a7",
   "metadata": {},
   "source": [
    ":::{note}\n",
    "This tool extensively use NeuroH5 for simulation data structure. It is recommended to check this {ref}`discussion <discussion/neuroh5:neuroh5 structure>`.\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1212d37b",
   "metadata": {},
   "source": [
    "```{mermaid}\n",
    "graph LR\n",
    "D(Design</br>Experiment) -->|yaml| C(Construct</br>Simulation) -->|h5| R(Run) -->|h5| P(Post Process)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90d798a3",
   "metadata": {},
   "source": [
    "The experiment is designed within `YAML` configurations. To run the first example simulation, we provide the set of configuration files below:\n",
    "\n",
    "- [Repository](https://github.com/GazzolaLab/MiV-Simulator-Cases/tree/master/1-construction)\n",
    "  1. __config__: configuration YAML files to construct simulation\n",
    "  2. __templates__: collection of cell parameters (.hoc files)\n",
    "  3. __datasets__: directory to construct simulation in h5 format. It also contains (.swc) files.\n",
    "\n",
    "In the remaining, we demonstrate how to construct the simulation and how to run the simulation.\n",
    "\n",
    ":::{note}\n",
    "The detail description and configurability of each file is included [here](configuration_description.md).\n",
    ":::\n",
    "\n",
    ":::{note}\n",
    "Add the option `--use-hwthread-cpus` for `mpirun` to use thread-wise MPI instead of core.\n",
    ":::\n",
    "\n",
    ":::{note}\n",
    "Run each cells only once.\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3afdd7c9",
   "metadata": {},
   "source": [
    "## Reset\n",
    "\n",
    "The configuration of the simuulation environment (soma coordinate, dendrite connection, cell parameters, etc.) are built in NeuroH5 format in `datasets` directory. To reset the configuration steps in this tutorial, simply remove all `*.h5` files inside the directory.\n",
    "\n",
    ":::{admonition} Click to toggle\n",
    ":class: dropdown\n",
    "```sh\n",
    "!rm -rf datasets/*.h5\n",
    "```\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6b13bb",
   "metadata": {},
   "source": [
    "## Creating H5Types definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7424be0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "datapath = \"datasets\"\n",
    "os.makedirs(datapath, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ececd0c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!make-h5types --output-path datasets/MiV_Small_h5types.h5  # If config path is not specified, use default.\n",
    "!make-h5types --config-prefix config -c Microcircuit_Small.yaml --output-path datasets/MiV_Small_h5types.h5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f823fc14",
   "metadata": {},
   "source": [
    "You can use HDF5 utilities `h5ls` and `h5dump` to examine the contents of an HDF5 file as we build the case study.\n",
    "\n",
    "- h5ls: List each objects of an HDF5 file name and objects within the file. Apply method recursively with `-r` flag.\n",
    "- h5dump: Display h5 contents in dictionary format, in human readable form.\n",
    "\n",
    "For more detail, checkout [this page](https://www.asc.ohio-state.edu/wilkins.5/computing/HDF/hdf5tutorial/util.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a4192d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "!h5ls -r ./datasets/MiV_Small_h5types.h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "878ccfa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "!h5dump -d /H5Types/Populations ./datasets/MiV_Small_h5types.h5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97ee4c78",
   "metadata": {},
   "source": [
    "## Copying and compiling NMODL mechanisms\n",
    "\n",
    ":::{note}\n",
    "`nrnivmodl` is a command to compile NEURON NMODL scripts. For more detail of NEURON NMODL, checkout [this page](http://web.mit.edu/neuron_v7.4/nrntuthtml/tutorial/tutD.html)\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2efe5ef7",
   "metadata": {},
   "outputs": [],
   "source": [
    "!nrnivmodl mechanisms/* ."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac3f74f5",
   "metadata": {},
   "source": [
    "## Generating soma coordinates and measuring distances\n",
    "\n",
    "Here, we create `Microcircuit_Small_coords.h5` file that stores soma coordinate information. To see the contents in the file, try to use `h5dump`/`h5ls` like above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06269de8",
   "metadata": {},
   "outputs": [],
   "source": [
    "!generate-soma-coordinates -v \\\n",
    "    --config=Microcircuit_Small.yaml \\\n",
    "    --config-prefix=config \\\n",
    "    --types-path=datasets/MiV_Small_h5types.h5 \\\n",
    "    --output-path=datasets/Microcircuit_Small_coords.h5 \\\n",
    "    --output-namespace='Generated Coordinates'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a42080fb",
   "metadata": {},
   "source": [
    "The tool `generate-soma-coordinates` generate and store soma location for each population. If you check the `h5` file, you should see new datasets `/Populations/PYR/Generated Coordinates/X Coordinate` etc. For more detail, you can checkout [this](miv_simulator.simulator.generate_soma_coordinates)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3c2d784",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mpirun -np 1 measure-distances -v \\\n",
    "             -i PYR -i PVBC -i OLM -i STIM \\\n",
    "             --config=Microcircuit_Small.yaml \\\n",
    "             --config-prefix=config \\\n",
    "             --coords-path=datasets/Microcircuit_Small_coords.h5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1babea6",
   "metadata": {},
   "source": [
    "The tool `measure-distances` computes arc distances of soma, and store in the field `Arc Distances`. You should see the data now contains the group `/Populations/PYR/Arc Distance`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07adc01a",
   "metadata": {},
   "source": [
    "### Visualize (Soma Location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64796249",
   "metadata": {},
   "outputs": [],
   "source": [
    "from miv_simulator import plotting as plot\n",
    "from miv_simulator import utils\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbda4aaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "utils.config_logging(True)\n",
    "fig = plot.plot_coords_in_volume(\n",
    "    populations=(\"PYR\", \"PVBC\", \"OLM\"),\n",
    "    coords_path=\"datasets/Microcircuit_Small_coords.h5\",\n",
    "    config=\"config/Microcircuit_Small.yaml\",\n",
    "    coords_namespace=\"Generated Coordinates\",\n",
    "    scale=25.0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33258c6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "utils.config_logging(True)\n",
    "fig = plot.plot_coords_in_volume(\n",
    "    populations=(\"STIM\",),\n",
    "    coords_path=\"datasets/Microcircuit_Small_coords.h5\",\n",
    "    config=\"config/Microcircuit_Small.yaml\",\n",
    "    coords_namespace=\"Generated Coordinates\",\n",
    "    scale=25.0,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bec5dd17",
   "metadata": {},
   "source": [
    "## Creating dendritic trees in NeuroH5 format\n",
    "\n",
    "`*.swc` file contains 3D point structure of the cell model. The tree model `*_tree.h5` can be created using `neurotree_import` feature from `neuroh5`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd4eaca8",
   "metadata": {},
   "outputs": [],
   "source": [
    "!neurotrees_import PVBC datasets/PVBC_tree.h5 morphology/PVBC.swc\n",
    "!neurotrees_import PYR datasets/PYR_tree.h5 morphology/PYR.swc\n",
    "!neurotrees_import OLM datasets/OLM_tree.h5 morphology/OLM.swc\n",
    "\n",
    "!h5copy -p -s '/H5Types' -d '/H5Types' -i datasets/MiV_Small_h5types.h5 -o datasets/PVBC_tree.h5\n",
    "!h5copy -p -s '/H5Types' -d '/H5Types' -i datasets/MiV_Small_h5types.h5 -o datasets/PYR_tree.h5\n",
    "!h5copy -p -s '/H5Types' -d '/H5Types' -i datasets/MiV_Small_h5types.h5 -o datasets/OLM_tree.h5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd6323d7",
   "metadata": {},
   "source": [
    "## Distributing synapses along dendritic trees\n",
    "\n",
    ":::{note}\n",
    "`neurotrees_copy` is a CLI command for NeuroH5.\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "078ad103",
   "metadata": {},
   "outputs": [],
   "source": [
    "!neurotrees_copy --fill --output datasets/PYR_forest_Small.h5 datasets/PYR_tree.h5 PYR 10\n",
    "!neurotrees_copy --fill --output datasets/PVBC_forest_Small.h5 datasets/PVBC_tree.h5 PVBC 90\n",
    "!neurotrees_copy --fill --output datasets/OLM_forest_Small.h5 datasets/OLM_tree.h5 OLM 143"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c477bdf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mpirun -np 1 distribute-synapse-locs \\\n",
    "              --template-path templates \\\n",
    "              --config=Microcircuit_Small.yaml \\\n",
    "              --config-prefix=config \\\n",
    "              --populations PYR \\\n",
    "              --forest-path=./datasets/PYR_forest_Small.h5 \\\n",
    "              --output-path=./datasets/PYR_forest_Small.h5 \\\n",
    "              --distribution=poisson \\\n",
    "              --io-size=1 --write-size=0 -v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a64e9a6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mpirun -np 1 distribute-synapse-locs \\\n",
    "              --template-path templates \\\n",
    "              --config=Microcircuit_Small.yaml \\\n",
    "              --config-prefix=config \\\n",
    "              --populations PVBC \\\n",
    "              --forest-path=./datasets/PVBC_forest_Small.h5 \\\n",
    "              --output-path=./datasets/PVBC_forest_Small.h5 \\\n",
    "              --distribution=poisson \\\n",
    "              --io-size=1 --write-size=0 -v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccd8a52e",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mpirun -np 1 distribute-synapse-locs \\\n",
    "             --template-path templates \\\n",
    "              --config=Microcircuit_Small.yaml \\\n",
    "              --config-prefix=config \\\n",
    "              --populations OLM \\\n",
    "              --forest-path=./datasets/OLM_forest_Small.h5 \\\n",
    "              --output-path=./datasets/OLM_forest_Small.h5 \\\n",
    "              --distribution=poisson \\\n",
    "              --io-size=1 --write-size=0 -v"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8afaec3",
   "metadata": {},
   "source": [
    "## Generating connections\n",
    "\n",
    "Here, we generate distance connection network and store it in `Microcircuit_Small_connections.h5` file.\n",
    "\n",
    "> The schematic of the data structure can be found {ref}`here <discussion/neuroh5:neuroh5 structure>`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1afdccfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mpirun -np 4 generate-distance-connections \\\n",
    "    --config=Microcircuit_Small.yaml \\\n",
    "    --config-prefix=config \\\n",
    "    --forest-path=datasets/PYR_forest_Small.h5 \\\n",
    "    --connectivity-path=datasets/Microcircuit_Small_connections.h5 \\\n",
    "    --connectivity-namespace=Connections \\\n",
    "    --coords-path=datasets/Microcircuit_Small_coords.h5 \\\n",
    "    --coords-namespace='Generated Coordinates' \\\n",
    "    --io-size=1 --cache-size=20 --write-size=100 -v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "127356cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mpirun -np 4 generate-distance-connections \\\n",
    "    --config=Microcircuit_Small.yaml \\\n",
    "    --config-prefix=config \\\n",
    "    --forest-path=datasets/PVBC_forest_Small.h5 \\\n",
    "    --connectivity-path=datasets/Microcircuit_Small_connections.h5 \\\n",
    "    --connectivity-namespace=Connections \\\n",
    "    --coords-path=datasets/Microcircuit_Small_coords.h5 \\\n",
    "    --coords-namespace='Generated Coordinates' \\\n",
    "    --io-size=1 --cache-size=20 --write-size=100 -v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0aea05b",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mpirun -np 4 generate-distance-connections \\\n",
    "    --config=Microcircuit_Small.yaml \\\n",
    "    --config-prefix=config \\\n",
    "    --forest-path=datasets/OLM_forest_Small.h5 \\\n",
    "    --connectivity-path=datasets/Microcircuit_Small_connections.h5 \\\n",
    "    --connectivity-namespace=Connections \\\n",
    "    --coords-path=datasets/Microcircuit_Small_coords.h5 \\\n",
    "    --coords-namespace='Generated Coordinates' \\\n",
    "    --io-size=1 --cache-size=20 --write-size=100 -v"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1615bb8",
   "metadata": {},
   "source": [
    "## Creating input features and spike trains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ec4cad0",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mpirun -np 1 generate-input-features \\\n",
    "        -p STIM \\\n",
    "        --config=Microcircuit_Small.yaml \\\n",
    "        --config-prefix=config \\\n",
    "        --coords-path=datasets/Microcircuit_Small_coords.h5 \\\n",
    "        --output-path=datasets/Microcircuit_Small_input_features.h5 \\\n",
    "        -v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da74f76b",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mpirun -np 2 generate-input-spike-trains \\\n",
    "             --config=Microcircuit_Small.yaml \\\n",
    "             --config-prefix=config \\\n",
    "             --selectivity-path=datasets/Microcircuit_Small_input_features.h5 \\\n",
    "             --output-path=datasets/Microcircuit_Small_input_spikes.h5 \\\n",
    "             --n-trials=3 -p STIM -v"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c21b31d2",
   "metadata": {},
   "source": [
    "# Finalizing\n",
    "\n",
    "In the following steps, we collapse all H5 files into two files: __cell__ configuration, and __connectivity__ configuration. The simulator takes these two files to run the experiment.\n",
    "\n",
    "## Define path and variable names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7911cc82",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import h5py, pathlib\n",
    "\n",
    "\n",
    "def h5_copy_dataset(f_src, f_dst, dset_path):\n",
    "    print(f\"Copying {dset_path} from {f_src} to {f_dst} ...\")\n",
    "    target_path = str(pathlib.Path(dset_path).parent)\n",
    "    f_src.copy(f_src[dset_path], f_dst[target_path])\n",
    "\n",
    "\n",
    "h5types_file = \"MiV_Small_h5types.h5\"\n",
    "\n",
    "MiV_populations = [\"PYR\", \"OLM\", \"PVBC\", \"STIM\"]\n",
    "MiV_IN_populations = [\"OLM\", \"PVBC\"]\n",
    "MiV_EXT_populations = [\"STIM\"]\n",
    "\n",
    "MiV_cells_file = \"MiV_Cells_Microcircuit_Small_20220410.h5\"\n",
    "MiV_connections_file = \"MiV_Connections_Microcircuit_Small_20220410.h5\"\n",
    "\n",
    "MiV_coordinate_file = \"Microcircuit_Small_coords.h5\"\n",
    "\n",
    "MiV_PYR_forest_file = \"PYR_forest_Small.h5\"\n",
    "MiV_PVBC_forest_file = \"PVBC_forest_Small.h5\"\n",
    "MiV_OLM_forest_file = \"OLM_forest_Small.h5\"\n",
    "\n",
    "MiV_PYR_forest_syns_file = \"PYR_forest_Small.h5\"\n",
    "MiV_PVBC_forest_syns_file = \"PVBC_forest_Small.h5\"\n",
    "MiV_OLM_forest_syns_file = \"OLM_forest_Small.h5\"\n",
    "\n",
    "MiV_PYR_connectivity_file = \"Microcircuit_Small_connections.h5\"\n",
    "MiV_PVBC_connectivity_file = \"Microcircuit_Small_connections.h5\"\n",
    "MiV_OLM_connectivity_file = \"Microcircuit_Small_connections.h5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0728dfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "connectivity_files = {\n",
    "    \"PYR\": MiV_PYR_connectivity_file,\n",
    "    \"PVBC\": MiV_PVBC_connectivity_file,\n",
    "    \"OLM\": MiV_OLM_connectivity_file,\n",
    "}\n",
    "\n",
    "\n",
    "coordinate_files = {\n",
    "    \"PYR\": MiV_coordinate_file,\n",
    "    \"PVBC\": MiV_coordinate_file,\n",
    "    \"OLM\": MiV_coordinate_file,\n",
    "    \"STIM\": MiV_coordinate_file,\n",
    "}\n",
    "\n",
    "distances_ns = \"Arc Distances\"\n",
    "input_coordinate_ns = \"Generated Coordinates\"\n",
    "coordinate_ns = \"Coordinates\"\n",
    "coordinate_namespaces = {\n",
    "    \"PYR\": input_coordinate_ns,\n",
    "    \"OLM\": input_coordinate_ns,\n",
    "    \"PVBC\": input_coordinate_ns,\n",
    "    \"STIM\": input_coordinate_ns,\n",
    "}\n",
    "\n",
    "\n",
    "forest_files = {\n",
    "    \"PYR\": MiV_PYR_forest_file,\n",
    "    \"PVBC\": MiV_PVBC_forest_file,\n",
    "    \"OLM\": MiV_OLM_forest_file,\n",
    "}\n",
    "\n",
    "forest_syns_files = {\n",
    "    \"PYR\": MiV_PYR_forest_syns_file,\n",
    "    \"PVBC\": MiV_PVBC_forest_syns_file,\n",
    "    \"OLM\": MiV_OLM_forest_syns_file,\n",
    "}\n",
    "\n",
    "\n",
    "vecstim_file_dict = {\"A Diag\": \"Microcircuit_Small_input_spikes.h5\"}\n",
    "\n",
    "vecstim_dict = {\n",
    "    f\"Input Spikes {stim_id}\": stim_file\n",
    "    for stim_id, stim_file in vecstim_file_dict.items()\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efb355c8",
   "metadata": {},
   "source": [
    "## Collapse files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d239fad",
   "metadata": {},
   "outputs": [],
   "source": [
    "%cd datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64d436fa",
   "metadata": {},
   "source": [
    "## Creates H5Types entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7502387",
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File(MiV_cells_file, \"w\") as f:\n",
    "    input_file = h5py.File(h5types_file, \"r\")\n",
    "    h5_copy_dataset(input_file, f, \"/H5Types\")\n",
    "    input_file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57adc005",
   "metadata": {},
   "source": [
    "## Creates coordinates entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "427d9c93",
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File(MiV_cells_file, \"a\") as f_dst:\n",
    "\n",
    "    grp = f_dst.create_group(\"Populations\")\n",
    "\n",
    "    for p in MiV_populations:\n",
    "        grp.create_group(p)\n",
    "\n",
    "    for p in MiV_populations:\n",
    "        coords_file = coordinate_files[p]\n",
    "        coords_ns = coordinate_namespaces[p]\n",
    "        coords_dset_path = f\"/Populations/{p}/{coords_ns}\"\n",
    "        coords_output_path = f\"/Populations/{p}/Coordinates\"\n",
    "        distances_dset_path = f\"/Populations/{p}/Arc Distances\"\n",
    "        with h5py.File(coords_file, \"r\") as f_src:\n",
    "            h5_copy_dataset(f_src, f_dst, coords_dset_path)\n",
    "            h5_copy_dataset(f_src, f_dst, distances_dset_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a24ce2d1",
   "metadata": {},
   "source": [
    "## Creates forest entries and synapse attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27865885",
   "metadata": {},
   "outputs": [],
   "source": [
    "for p in MiV_populations:\n",
    "    if p in forest_files:\n",
    "        forest_file = forest_files[p]\n",
    "        forest_syns_file = forest_syns_files[p]\n",
    "        forest_dset_path = f\"/Populations/{p}/Trees\"\n",
    "        forest_syns_dset_path = f\"/Populations/{p}/Synapse Attributes\"\n",
    "        cmd = (\n",
    "            f\"h5copy -p -s '{forest_dset_path}' -d '{forest_dset_path}' \"\n",
    "            f\"-i {forest_file} -o {MiV_cells_file}\"\n",
    "        )\n",
    "        print(cmd)\n",
    "        os.system(cmd)\n",
    "        cmd = (\n",
    "            f\"h5copy -p -s '{forest_syns_dset_path}' -d '{forest_syns_dset_path}' \"\n",
    "            f\"-i {forest_syns_file} -o {MiV_cells_file}\"\n",
    "        )\n",
    "        print(cmd)\n",
    "        os.system(cmd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5b3aca4",
   "metadata": {},
   "source": [
    "## Creates vector stimulus entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a996724b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for (vecstim_ns, vecstim_file) in vecstim_dict.items():\n",
    "    for p in MiV_EXT_populations:\n",
    "        vecstim_dset_path = f\"/Populations/{p}/{vecstim_ns}\"\n",
    "        cmd = (\n",
    "            f\"h5copy -p -s '{vecstim_dset_path}' -d '{vecstim_dset_path}' \"\n",
    "            f\"-i {vecstim_file} -o {MiV_cells_file}\"\n",
    "        )\n",
    "        print(cmd)\n",
    "        os.system(cmd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "437b0540",
   "metadata": {},
   "source": [
    "## Copy coordinates for STIM cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f591406c",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = \"STIM\"\n",
    "cmd = f\"h5copy -p -s '/Populations/{p}/Generated Coordinates' -d '/Populations/{p}/Coordinates' -i {MiV_cells_file} -o {MiV_cells_file}\"\n",
    "print(cmd)\n",
    "os.system(cmd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8b91182",
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File(MiV_connections_file, \"w\") as f:\n",
    "    input_file = h5py.File(h5types_file, \"r\")\n",
    "    h5_copy_dataset(input_file, f, \"/H5Types\")\n",
    "    input_file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e20203f7",
   "metadata": {},
   "source": [
    "## Creates connectivity entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbf1fede",
   "metadata": {},
   "outputs": [],
   "source": [
    "for p in MiV_populations:\n",
    "    if p in connectivity_files:\n",
    "        connectivity_file = connectivity_files[p]\n",
    "        projection_dset_path = f\"/Projections/{p}\"\n",
    "        cmd = (\n",
    "            f\"h5copy -p -s {projection_dset_path} -d {projection_dset_path} \"\n",
    "            f\"-i {connectivity_file} -o {MiV_connections_file}\"\n",
    "        )\n",
    "        print(cmd)\n",
    "        os.system(cmd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df985729",
   "metadata": {},
   "outputs": [],
   "source": [
    "%cd .."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fa75efb",
   "metadata": {},
   "source": [
    "# Run Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6266e0b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mpirun -np 8 run-network \\\n",
    "    --config-file=Microcircuit_Small.yaml  \\\n",
    "    --config-prefix=config \\\n",
    "    --arena-id=A \\\n",
    "    --stimulus-id=Diag \\\n",
    "    --template-paths=templates \\\n",
    "    --dataset-prefix=\"./datasets\" \\\n",
    "    --results-path=results \\\n",
    "    --io-size=1 \\\n",
    "    --tstop=500 \\\n",
    "    --v-init=-75 \\\n",
    "    --results-write-time=60 \\\n",
    "    --stimulus-onset=0.0 \\\n",
    "    --max-walltime-hours=0.49 \\\n",
    "    --dt 0.025 \\\n",
    "    --verbose\n"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "file_format": "mystnb",
  "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": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}