3_Simulate_samples.ipynb

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   "source": [
    "import argparse\n",
    "from os import makedirs, listdir\n",
    "from fsbi.utils import save_data_to_hdf5, read_monitor_weights_files, _forward\n",
    "import numpy as np\n",
    "import yaml\n",
    "import h5py\n",
    "import matplotlib\n",
    "\n",
    "import pickle\n",
    "from concurrent import futures\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd052aab",
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   "source": [
    "#### 1/ Simulation parameters, hardware options for polynomial rules"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "save_dir = \"data_synapsesbi/bg_IF_EEEIIEII_6pPol/\"\n",
    "runs_path = \"runs_synapsesbi/bg_IF_EEEIIEII_6pPol/\"\n",
    "task_name = \"bg_IF_EEEIIEII_6pPol\"\n",
    "round_name = \"pi3_r5to10Hz\"\n",
    "from fsbi.simulator import Simulator_bg_IF_EEEIIEII_6pPol as Simulator\n",
    "\n",
    "#For parallel execution on local computer\n",
    "hardware = \"local\"\n",
    "parallel_args = dict()\n",
    "parallel_args[\"max_workers\"]= 10 #depending on how many cpus are available\n",
    "\n",
    "#Get simulator params and make simulator\n",
    "with open(\"tasks_configs/%s.yaml\" % task_name, \"r\") as f:\n",
    "    simulator_params = yaml.load(f, Loader=yaml.Loader)\n",
    "simulator = Simulator(simulator_params)"
   ]
  },
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   "source": [
    "#### 1 bis/ Simulation parameters, hardware options for MLP rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "save_dir = \"data_synapsesbi/bg_CVAIF_EEIE_T4wvceciMLP/\"\n",
    "runs_path = \"runs_synapsesbi/bg_CVAIF_EEIE_T4wvceciMLP/\"\n",
    "task_name = \"bg_CVAIF_EEIE_T4wvceciMLP\"\n",
    "round_name = \"pi3_r5to10Hz\"\n",
    "from synapsbi.simulator import Simulator_bg_CVAIF_EEIE_T4wvceciMLP as Simulator\n",
    "\n",
    "#For parallel execution on local computer\n",
    "hardware = \"local\"\n",
    "parallel_args = dict()\n",
    "parallel_args[\"max_workers\"]= 10 #depending on how many cpus are available\n",
    "\n",
    "#Get simulator params and make simulator\n",
    "with open(\"tasks_configs/%s.yaml\" % task_name, \"r\") as f:\n",
    "    simulator_params = yaml.load(f, Loader=yaml.Loader)\n",
    "simulator = Simulator(simulator_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfc58737",
   "metadata": {},
   "source": [
    "#### Use samples generated in previous notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d96cbb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "thetas = np.load(save_dir + \"post_samples_\" + round_name + \".npz\")[\"thetas\"]\n",
    "num_samples = len(thetas)\n",
    "\n",
    "thetas, seeds = _make_unique_samples(num_samples, \n",
    "                                     prior=None,\n",
    "                                     thetas=thetas,\n",
    "                                     saved_seeds=[])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b381e80",
   "metadata": {},
   "source": [
    "#### Simulate the samples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "245f89cf",
   "metadata": {},
   "source": [
    "Refer to readme to make sure the spiking network simulator Auryn is installed on your machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "### prepare thetas for the simulation\n",
    "parameters = [dict(simulator=simulator,\n",
    "                   thetas=th.reshape(1, -1),\n",
    "                   seeds=[seed],\n",
    "                   return_data=False)\n",
    "              for th, seed in zip(thetas, seeds)][:num_samples]"
   ]
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   "source": [
    "## Quick look at the samples\n",
    "try:\n",
    "    a = np.array([i['thetas'].numpy() for i in parameters])\n",
    "except:\n",
    "    a = np.array([i['thetas'] for i in parameters])\n",
    "print(a.shape)\n",
    "\n",
    "fig = plt.figure(figsize=(3, 2)), plt.scatter(a[:,0,2],a[:,0,3], s = 1); plt.show()"
   ]
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   "source": [
    "start = time.time()\n",
    "if hardware == \"local\":\n",
    "    with futures.ThreadPoolExecutor(**parallel_args) as executor:\n",
    "        jobs = [executor.submit(_forward, parameter)\\\n",
    "                for parameter in parameters]\n",
    "print(time.time()-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46d481e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Collect the results\n",
    "outputs = []\n",
    "for job in jobs:\n",
    "    outputs.append(job.result())\n",
    "#     print(job.result())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eed449ca",
   "metadata": {},
   "source": [
    "#### Store simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "893a5746",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"fraction of blown up simulations:\", sum(np.array(outputs) > 0) / len(outputs))"
   ]
  },
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   "outputs": [],
   "source": [
    "save_data_to_hdf5(save_dir + round_name + \".h5\",\n",
    "                  thetas=thetas,\n",
    "                  seeds=seeds,\n",
    "                  spiketimes_workdir=simulator_params[\"workdir\"],\n",
    "                  con_type=simulator_params[\"con_type\"],\n",
    "                  outputs=outputs,\n",
    "                  params=simulator_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9e0712c",
   "metadata": {},
   "source": [
    "Congratulations, you have now simulated your rules in spiking networks and stored the raw weight traces and spike trains.\n",
    "Now head over to Compute_metrics to compute start the analysis of your simulations"
   ]
  }
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