notebooks/demo-2_automind_inference_from_spikes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demo 2. Network burst inference from raw spike train data\n",
    "\n",
    "This demo is similar to the previous one, where we aim to do inference with a trained density estimator. But now, we target a real experimental recording. Specifically, we want to discover circuit models that can reproduce a multi-electrode array recording from the organoid dataset (unused in the publication), starting from spike times over 64 electrodes. \n",
    "\n",
    "We will walk through how to compute network burst summary statistics such that we can use them for inference, get samples from the trained density estimator, and then simulate and analyze a few of those discovered model configurations to check if they indeed reproduce the target observation.\n",
    "\n",
    "Many thanks to [Tereza Zuskinova](https://github.com/TerezaZuskinova) at IST Austria for her help in developing and testing this demo.\n",
    "\n",
    "For more details, visit the [AutoMIND preprint](https://www.biorxiv.org/content/10.1101/2024.08.21.608969v1).\n",
    "\n",
    "---\n",
    "Note: to run this notebook live, you will first need to download and unzip the trained DGMs and example dataset. You can find them from the data repository links on the [homepage](https://github.com/mackelab/automind), and you should place them in the appropriate directories by following the included instructions (or simply change the datapaths below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl_rc = \"../assets/matplotlibrc\"\n",
    "plt.style.use(mpl_rc)\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from torch import Tensor\n",
    "from time import time\n",
    "\n",
    "from automind.sim import b2_interface, runners\n",
    "from automind.inference import inferer\n",
    "from automind.utils import data_utils, analysis_utils, dist_utils, plot_utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Load trained DGM and meta info, and dataset\n",
    "We first load the trained deep generative model (Normalizing Flow) stored in the `posterior.pickle` file, along with some analysis parameters we need in `params_dict_analysis_updated.pickle`.\n",
    "\n",
    "As its name suggests, the posterior file contains the trained posterior estimator neural network, constructed using the `sbi` package. The one we use below is particular to the type of data feature we wish to target, i.e., network burst statistics.\n",
    "\n",
    "In addition, it contains some meta information, like the specific data features it was trained on---and hence must be passed during inference---as well as data preprocessing steps, which must also be applied to the target observation as well.\n",
    "\n",
    "Unlike the previous demo, we don't need to load the discovered samples dataset, since we're loading and composing the target data from scratch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "posterior.pickle ['5065886_posterior.pickle']\n",
      "params_dict_analysis_updated.pickle ['5065886_MK1_params_dict_analysis_updated.pickle']\n",
      "summary_data_merged.csv ['5065886_MK1_summary_data_merged.csv']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    /opt/miniconda3/envs/automind/lib/python3.11/site-packages/torch/storage.py:414: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  return torch.load(io.BytesIO(b))\n",
      " [py.warnings]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2502 samples per xo.\n",
      "Posterior conditional density p(θ|x) of type DirectPosterior. It samples the posterior network and rejects samples that\n",
      "            lie outside of the prior bounds.\n",
      "-------------------------\n",
      "Density estimator was trained on these features: ['burst_interval_mean', 'burst_interval_cv', 'burst_width_mean', 'burst_offset_time_mean']\n"
     ]
    }
   ],
   "source": [
    "data_dirs = \"../datasets/discovered_posterior_samples/organoids/\"\n",
    "path_dict = data_utils.extract_data_files(\n",
    "    data_dirs,\n",
    "    [\n",
    "        \"posterior.pickle\",\n",
    "        \"params_dict_analysis_updated.pickle\",\n",
    "        \"summary_data_merged.csv\",\n",
    "    ],\n",
    ")\n",
    "_, posterior, params_dict = data_utils.load_df_posteriors(path_dict)\n",
    "print(posterior)\n",
    "\n",
    "cols_features = list(posterior.x_bounds_and_transforms.keys())\n",
    "print(\"-------------------------\")\n",
    "print(f'Density estimator was trained on these features: {cols_features}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and process target data from raw spike times\n",
    "Here, we use another recording from the organoid dataset, and demonstrate how to start from raw spike times to computing summary features amenable to be used for inference.\n",
    "\n",
    "You can download the `.npz` file [here](https://figshare.com/s/3f1467f8fb0f328aed16) in `./observations`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recording LFP_Sp_161223.mat was recorded for 250.0 seconds, with a sampling rate of 12500 Hz\n"
     ]
    }
   ],
   "source": [
    "new_data = np.load(\"../datasets/observations/example_raw_data.npz\", allow_pickle=True)\n",
    "spikes_in_list, t_end, rec_name, fs = new_data['spikes'], new_data['t_end'], new_data['recording_name'], new_data['fs']\n",
    "print(f\"Recording {rec_name} was recorded for {t_end} seconds, with a sampling rate of {fs} Hz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spike train data can simply be in the form of a list, where each element contains the spike times of one cell. Timestamps can be in arbitrary units, such as integer indices, as long as the sampling rate to real-time is known (`fs`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  42779.,  327041.,  327082., ..., 2840183., 2844504., 2846592.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print spike times of one cell\n",
    "spikes_in_list[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we convert the list of spikes into the dictionary form, mimicing the simulated data, in order to use the analysis functions as is. Note that this is not necessary, as any custom processing can be used to analyze the data, as long as its final form is what the trained density estimator expects. Although in practice it's recommended to use the same or similar analysis functions and settings, as this will result in better matches to the simulations.\n",
    "\n",
    "The convenience function `convert_spike_array_to_dict` creates a dictionary from a list of spike \"times\" and given a sampling rate, resulting in a dictionary with a population `exc_spikes` and spike times in seconds. Then, we can call the analysis function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spikes_dict = data_utils.convert_spike_array_to_dict(spikes_in_list, fs, 'exc_spikes')\n",
    "spikes_dict['t_end'] = t_end # end recording time, you can take this as the last spike time plus a little more.\n",
    "\n",
    "# Analyze data with the same params_dict as the simulated data. \n",
    "# Note that depending on your own data, some settings may need to be adjusted in params_dict['params_analysis'].\n",
    "spikes_analyzed = analysis_utils.compute_spike_burst_features(spikes_dict, params_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This particular analysis computes the total population rate by collapsing across all cells / channels (elements in your original list), and calculates a range of statistics based on the detected network bursts, which uses `scipy.signal.find_peaks` with parameters given by settings in `param_dict`.\n",
    "\n",
    "We can plot the real data raster and population rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlims = [0, 250]\n",
    "fig, axs = plt.subplots(2,1,figsize=(2, 1))\n",
    "plot_utils._plot_raster_pretty(spikes_dict, plot_inh=False, XL=xlims, every_other=50, fontsize=5, ax=axs[0])\n",
    "plot_utils._plot_rates_pretty(\n",
    "        spikes_analyzed[1],\n",
    "        XL=xlims,        \n",
    "        pops_to_plot=[\"avgpop_rate\"],\n",
    "        fontsize=5,\n",
    "        ax=axs[1],\n",
    "        color=\"k\",        \n",
    "    )\n",
    "axs[1].set_ylabel(\"Rate\", labelpad=-5)\n",
    "[axs[1].axvline(bt, color='r', ls='--', lw=2, alpha=0.4) for bt in spikes_analyzed[2]['burst_times']];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define target observation data and apply preprocessing\n",
    "Element 0 of the output contains a dataframe with all the computed statistics. We further focus on just the ones required by the density estimator for inference.\n",
    "\n",
    "The target observation then has to undergo a few preprocessing steps, in particular, taking the log of the values to be put on the same scale. These transformations are tracked in the posterior network object (`posterior.x_bounds_and_transforms` and `posterior.x_standardizing_func`), and just need to be applied to the dataset.\n",
    "\n",
    "If you want to run this demo on a new dataset, you just need to replace `df_xos` with your own pandas dataframe, where each row must contain, among other features, the necessary burst timing features (names printed below). \n",
    "\n",
    "Alternatively, it can be supplied as a a numpy array (`xo`), where only the last step is necessary (standardizing and converting to torch tensor)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>burst_interval_mean</th>\n",
       "      <th>burst_interval_cv</th>\n",
       "      <th>burst_width_mean</th>\n",
       "      <th>burst_offset_time_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.39375</td>\n",
       "      <td>0.201014</td>\n",
       "      <td>1.524269</td>\n",
       "      <td>1.421244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   burst_interval_mean  burst_interval_cv  burst_width_mean  \\\n",
       "0             24.39375           0.201014          1.524269   \n",
       "\n",
       "   burst_offset_time_mean  \n",
       "0                1.421244  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_xos = spikes_analyzed[0]\n",
    "df_xos[cols_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed target observation has shape torch.Size([1, 4]): tensor([[-0.3947,  1.4649,  0.1235,  0.5578]])\n"
     ]
    }
   ],
   "source": [
    "df_xos_preproc, _, _, _ = dist_utils.preproc_dataframe(\n",
    "    df_xos,\n",
    "    posterior.x_bounds_and_transforms,\n",
    "    drop_nans=False,\n",
    "    replace_infs=True,\n",
    ")\n",
    "xo = df_xos_preproc.loc[0][cols_features].values\n",
    "xo_tensor = posterior.x_standardizing_func(Tensor(xo[None, :]))\n",
    "print(f\"Processed target observation has shape {xo_tensor.shape}: {xo_tensor}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Inference\n",
    "Same as last time, this is where the magic happens: we provide the (preprocessed) target observation and draw samples of model configurations that are likely to reproduce this target.\n",
    "\n",
    "Under the hood, the target is supplied as the conditioner to the conditional Normalizing Flow, which then approximates the posterior distribution given that observation, i.e., $p(parameters|target)$.\n",
    "\n",
    "Also, we apply the \"oversampling\" trick here: to acquire `n_samples=5` samples in the end, we first sample by a factor of `oversample_factor=200` more (so 1000 total), and then select the top 5 samples with the highest log-likelihood returned by the Normalizing Flow. This is a fast and cheap operation and encourages samples to be in high-density regions, which empirically helps with finding better data-consistent samples.\n",
    "\n",
    "For real data, we will be taking more final samples (20 here), since there is typically a 5-25% yield of models that actually result in good matches after simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "332026e114db48129bb0a59970f5749b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Drawing 4000 posterior samples:   0%|          | 0/4000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    /opt/miniconda3/envs/automind/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2190.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      " [py.warnings]\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>inference.type</th>\n",
       "      <th>infer.log_prob</th>\n",
       "      <th>params_net.exc_prop</th>\n",
       "      <th>params_net.p_e2e</th>\n",
       "      <th>params_net.p_e2i</th>\n",
       "      <th>params_net.p_i2e</th>\n",
       "      <th>params_net.p_i2i</th>\n",
       "      <th>params_net.R_pe2e</th>\n",
       "      <th>params_net.R_Qe2e</th>\n",
       "      <th>params_Epop.p_igniters</th>\n",
       "      <th>...</th>\n",
       "      <th>params_Epop.tau_w</th>\n",
       "      <th>params_Epop.E_gi</th>\n",
       "      <th>params_Epop.Q_ge</th>\n",
       "      <th>params_Epop.Q_gi</th>\n",
       "      <th>params_Epop.tau_ge</th>\n",
       "      <th>params_Epop.tau_gi</th>\n",
       "      <th>params_Epop.poisson_rate</th>\n",
       "      <th>params_Ipop.Q_ge</th>\n",
       "      <th>params_Ipop.Q_gi</th>\n",
       "      <th>params_Ipop.poisson_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>demo_NPE_samples_prune_200</td>\n",
       "      <td>14.218349</td>\n",
       "      <td>0.941954</td>\n",
       "      <td>0.040212</td>\n",
       "      <td>0.089394</td>\n",
       "      <td>0.087514</td>\n",
       "      <td>0.199783</td>\n",
       "      <td>2.963229</td>\n",
       "      <td>1.753297</td>\n",
       "      <td>0.271752</td>\n",
       "      <td>...</td>\n",
       "      <td>4262.124512</td>\n",
       "      <td>-69.666931</td>\n",
       "      <td>1.538027</td>\n",
       "      <td>5.740131</td>\n",
       "      <td>11.845749</td>\n",
       "      <td>12.416341</td>\n",
       "      <td>3.139677</td>\n",
       "      <td>1.676930</td>\n",
       "      <td>15.464734</td>\n",
       "      <td>4.179419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>demo_NPE_samples_prune_200</td>\n",
       "      <td>14.114674</td>\n",
       "      <td>0.853909</td>\n",
       "      <td>0.023708</td>\n",
       "      <td>0.071285</td>\n",
       "      <td>0.074351</td>\n",
       "      <td>0.090198</td>\n",
       "      <td>1.101550</td>\n",
       "      <td>1.877630</td>\n",
       "      <td>0.274740</td>\n",
       "      <td>...</td>\n",
       "      <td>4636.706543</td>\n",
       "      <td>-68.850220</td>\n",
       "      <td>3.058450</td>\n",
       "      <td>1.553654</td>\n",
       "      <td>12.618026</td>\n",
       "      <td>6.843685</td>\n",
       "      <td>4.045559</td>\n",
       "      <td>4.875051</td>\n",
       "      <td>4.813944</td>\n",
       "      <td>4.294415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>demo_NPE_samples_prune_200</td>\n",
       "      <td>13.850510</td>\n",
       "      <td>0.717111</td>\n",
       "      <td>0.026991</td>\n",
       "      <td>0.084222</td>\n",
       "      <td>0.061285</td>\n",
       "      <td>0.050953</td>\n",
       "      <td>3.875781</td>\n",
       "      <td>1.584280</td>\n",
       "      <td>0.609745</td>\n",
       "      <td>...</td>\n",
       "      <td>5101.727539</td>\n",
       "      <td>-66.180862</td>\n",
       "      <td>1.111281</td>\n",
       "      <td>10.745667</td>\n",
       "      <td>12.867508</td>\n",
       "      <td>1.455210</td>\n",
       "      <td>2.866703</td>\n",
       "      <td>1.867696</td>\n",
       "      <td>4.001274</td>\n",
       "      <td>4.283347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>demo_NPE_samples_prune_200</td>\n",
       "      <td>13.373520</td>\n",
       "      <td>0.756532</td>\n",
       "      <td>0.047827</td>\n",
       "      <td>0.031827</td>\n",
       "      <td>0.101967</td>\n",
       "      <td>0.169729</td>\n",
       "      <td>3.535644</td>\n",
       "      <td>1.570758</td>\n",
       "      <td>0.270651</td>\n",
       "      <td>...</td>\n",
       "      <td>5171.982422</td>\n",
       "      <td>-80.073677</td>\n",
       "      <td>1.453532</td>\n",
       "      <td>15.231366</td>\n",
       "      <td>12.484603</td>\n",
       "      <td>7.595463</td>\n",
       "      <td>4.707281</td>\n",
       "      <td>1.057857</td>\n",
       "      <td>16.649380</td>\n",
       "      <td>4.794970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>demo_NPE_samples_prune_200</td>\n",
       "      <td>13.222931</td>\n",
       "      <td>0.918047</td>\n",
       "      <td>0.053465</td>\n",
       "      <td>0.015740</td>\n",
       "      <td>0.105973</td>\n",
       "      <td>0.106277</td>\n",
       "      <td>1.815602</td>\n",
       "      <td>1.830136</td>\n",
       "      <td>0.252458</td>\n",
       "      <td>...</td>\n",
       "      <td>3728.452393</td>\n",
       "      <td>-73.886818</td>\n",
       "      <td>1.481260</td>\n",
       "      <td>18.821224</td>\n",
       "      <td>11.179410</td>\n",
       "      <td>1.989216</td>\n",
       "      <td>2.167878</td>\n",
       "      <td>2.637160</td>\n",
       "      <td>6.563420</td>\n",
       "      <td>4.039377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>demo_NPE_samples_prune_200</td>\n",
       "      <td>13.172291</td>\n",
       "      <td>0.893356</td>\n",
       "      <td>0.020129</td>\n",
       "      <td>0.054569</td>\n",
       "      <td>0.093841</td>\n",
       "      <td>0.120805</td>\n",
       "      <td>2.729041</td>\n",
       "      <td>1.723387</td>\n",
       "      <td>0.145617</td>\n",
       "      <td>...</td>\n",
       "      <td>3678.786865</td>\n",
       "      <td>-77.044685</td>\n",
       "      <td>4.761185</td>\n",
       "      <td>13.836735</td>\n",
       "      <td>10.697940</td>\n",
       "      <td>4.987892</td>\n",
       "      <td>2.004332</td>\n",
       "      <td>3.340909</td>\n",
       "      <td>18.956047</td>\n",
       "      <td>3.646063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>demo_NPE_samples_prune_200</td>\n",
       "      <td>13.132172</td>\n",
       "      <td>0.939266</td>\n",
       "      <td>0.063758</td>\n",
       "      <td>0.015451</td>\n",
       "      <td>0.058424</td>\n",
       "      <td>0.125610</td>\n",
       "      <td>3.243870</td>\n",
       "      <td>1.500175</td>\n",
       "      <td>0.213150</td>\n",
       "      <td>...</td>\n",
       "      <td>2740.413574</td>\n",
       "      <td>-76.648811</td>\n",
       "      <td>2.128978</td>\n",
       "      <td>12.770164</td>\n",
       "      <td>11.787007</td>\n",
       "      <td>4.622208</td>\n",
       "      <td>3.621238</td>\n",
       "      <td>1.136560</td>\n",
       "      <td>1.814951</td>\n",
       "      <td>3.984406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>demo_NPE_samples_prune_200</td>\n",
       "      <td>13.037655</td>\n",
       "      <td>0.769722</td>\n",
       "      <td>0.072576</td>\n",
       "      <td>0.093747</td>\n",
       "      <td>0.083795</td>\n",
       "      <td>0.066592</td>\n",
       "      <td>4.258139</td>\n",
       "      <td>1.276656</td>\n",
       "      <td>0.206821</td>\n",
       "      <td>...</td>\n",
       "      <td>5150.429199</td>\n",
       "      <td>-69.936218</td>\n",
       "      <td>1.192999</td>\n",
       "      <td>1.968454</td>\n",
       "      <td>11.477825</td>\n",
       "      <td>4.009518</td>\n",
       "      <td>2.237082</td>\n",
       "      <td>4.054039</td>\n",
       "      <td>13.057570</td>\n",
       "      <td>4.122100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>demo_NPE_samples_prune_200</td>\n",
       "      <td>13.030533</td>\n",
       "      <td>0.933765</td>\n",
       "      <td>0.033606</td>\n",
       "      <td>0.013959</td>\n",
       "      <td>0.099130</td>\n",
       "      <td>0.093464</td>\n",
       "      <td>2.971474</td>\n",
       "      <td>1.388139</td>\n",
       "      <td>0.205837</td>\n",
       "      <td>...</td>\n",
       "      <td>2924.523193</td>\n",
       "      <td>-82.657913</td>\n",
       "      <td>4.562043</td>\n",
       "      <td>3.974713</td>\n",
       "      <td>9.465996</td>\n",
       "      <td>7.515447</td>\n",
       "      <td>2.706392</td>\n",
       "      <td>2.603636</td>\n",
       "      <td>5.767857</td>\n",
       "      <td>4.774770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>demo_NPE_samples_prune_200</td>\n",
       "      <td>12.986698</td>\n",
       "      <td>0.884204</td>\n",
       "      <td>0.031422</td>\n",
       "      <td>0.082973</td>\n",
       "      <td>0.152994</td>\n",
       "      <td>0.195906</td>\n",
       "      <td>3.053780</td>\n",
       "      <td>1.332489</td>\n",
       "      <td>0.230948</td>\n",
       "      <td>...</td>\n",
       "      <td>4063.808105</td>\n",
       "      <td>-80.490997</td>\n",
       "      <td>3.871441</td>\n",
       "      <td>7.749259</td>\n",
       "      <td>12.700874</td>\n",
       "      <td>8.084185</td>\n",
       "      <td>3.333295</td>\n",
       "      <td>1.663790</td>\n",
       "      <td>18.584270</td>\n",
       "      <td>4.379348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>demo_NPE_samples_prune_200</td>\n",
       "      <td>12.916065</td>\n",
       "      <td>0.858867</td>\n",
       "      <td>0.036523</td>\n",
       "      <td>0.015330</td>\n",
       "      <td>0.104437</td>\n",
       "      <td>0.077066</td>\n",
       "      <td>4.890635</td>\n",
       "      <td>1.532762</td>\n",
       "      <td>0.231095</td>\n",
       "      <td>...</td>\n",
       "      <td>4059.470947</td>\n",
       "      <td>-77.611870</td>\n",
       "      <td>1.576795</td>\n",
       "      <td>1.233919</td>\n",
       "      <td>11.742482</td>\n",
       "      <td>2.076792</td>\n",
       "      <td>2.132282</td>\n",
       "      <td>4.646295</td>\n",
       "      <td>2.994199</td>\n",
       "      <td>4.664698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>demo_NPE_samples_prune_200</td>\n",
       "      <td>12.870522</td>\n",
       "      <td>0.735329</td>\n",
       "      <td>0.034429</td>\n",
       "      <td>0.046476</td>\n",
       "      <td>0.054337</td>\n",
       "      <td>0.061358</td>\n",
       "      <td>4.349137</td>\n",
       "      <td>1.714934</td>\n",
       "      <td>0.187308</td>\n",
       "      <td>...</td>\n",
       "      <td>6407.967773</td>\n",
       "      <td>-75.831291</td>\n",
       "      <td>1.530120</td>\n",
       "      <td>2.995511</td>\n",
       "      <td>13.961203</td>\n",
       "      <td>2.208848</td>\n",
       "      <td>2.646500</td>\n",
       "      <td>4.565966</td>\n",
       "      <td>9.774639</td>\n",
       "      <td>4.580254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>demo_NPE_samples_prune_200</td>\n",
       "      <td>12.803070</td>\n",
       "      <td>0.926422</td>\n",
       "      <td>0.081388</td>\n",
       "      <td>0.057631</td>\n",
       "      <td>0.132104</td>\n",
       "      <td>0.114805</td>\n",
       "      <td>4.118057</td>\n",
       "      <td>1.202418</td>\n",
       "      <td>0.969420</td>\n",
       "      <td>...</td>\n",
       "      <td>2380.655762</td>\n",
       "      <td>-81.393951</td>\n",
       "      <td>1.140215</td>\n",
       "      <td>6.293014</td>\n",
       "      <td>10.790934</td>\n",
       "      <td>4.667439</td>\n",
       "      <td>1.791489</td>\n",
       "      <td>2.161114</td>\n",
       "      <td>11.651899</td>\n",
       "      <td>4.719689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>demo_NPE_samples_prune_200</td>\n",
       "      <td>12.796158</td>\n",
       "      <td>0.737805</td>\n",
       "      <td>0.039048</td>\n",
       "      <td>0.090788</td>\n",
       "      <td>0.083636</td>\n",
       "      <td>0.099527</td>\n",
       "      <td>1.975813</td>\n",
       "      <td>1.195736</td>\n",
       "      <td>0.232498</td>\n",
       "      <td>...</td>\n",
       "      <td>3306.258789</td>\n",
       "      <td>-73.164238</td>\n",
       "      <td>3.480698</td>\n",
       "      <td>1.364852</td>\n",
       "      <td>2.147285</td>\n",
       "      <td>2.516879</td>\n",
       "      <td>4.781724</td>\n",
       "      <td>2.853611</td>\n",
       "      <td>12.489593</td>\n",
       "      <td>4.910177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>demo_NPE_samples_prune_200</td>\n",
       "      <td>12.664261</td>\n",
       "      <td>0.916152</td>\n",
       "      <td>0.016761</td>\n",
       "      <td>0.060136</td>\n",
       "      <td>0.087552</td>\n",
       "      <td>0.057496</td>\n",
       "      <td>2.444399</td>\n",
       "      <td>1.962491</td>\n",
       "      <td>0.448491</td>\n",
       "      <td>...</td>\n",
       "      <td>2772.190918</td>\n",
       "      <td>-81.537033</td>\n",
       "      <td>4.227221</td>\n",
       "      <td>4.692933</td>\n",
       "      <td>9.680085</td>\n",
       "      <td>4.294734</td>\n",
       "      <td>3.309908</td>\n",
       "      <td>2.577863</td>\n",
       "      <td>8.541971</td>\n",
       "      <td>4.813415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>demo_NPE_samples_prune_200</td>\n",
       "      <td>12.606071</td>\n",
       "      <td>0.713363</td>\n",
       "      <td>0.037435</td>\n",
       "      <td>0.040754</td>\n",
       "      <td>0.081546</td>\n",
       "      <td>0.123833</td>\n",
       "      <td>4.555400</td>\n",
       "      <td>1.308392</td>\n",
       "      <td>0.259726</td>\n",
       "      <td>...</td>\n",
       "      <td>4354.932129</td>\n",
       "      <td>-73.206383</td>\n",
       "      <td>1.691243</td>\n",
       "      <td>1.700901</td>\n",
       "      <td>12.226908</td>\n",
       "      <td>16.334698</td>\n",
       "      <td>4.879207</td>\n",
       "      <td>3.946023</td>\n",
       "      <td>10.591513</td>\n",
       "      <td>4.678785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>demo_NPE_samples_prune_200</td>\n",
       "      <td>12.599506</td>\n",
       "      <td>0.948499</td>\n",
       "      <td>0.031353</td>\n",
       "      <td>0.082558</td>\n",
       "      <td>0.115036</td>\n",
       "      <td>0.125195</td>\n",
       "      <td>1.277942</td>\n",
       "      <td>1.980581</td>\n",
       "      <td>0.137554</td>\n",
       "      <td>...</td>\n",
       "      <td>3701.529297</td>\n",
       "      <td>-71.815811</td>\n",
       "      <td>3.612992</td>\n",
       "      <td>7.095530</td>\n",
       "      <td>10.809875</td>\n",
       "      <td>4.103291</td>\n",
       "      <td>4.210105</td>\n",
       "      <td>1.024559</td>\n",
       "      <td>19.856514</td>\n",
       "      <td>4.361209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>demo_NPE_samples_prune_200</td>\n",
       "      <td>12.589104</td>\n",
       "      <td>0.931660</td>\n",
       "      <td>0.021445</td>\n",
       "      <td>0.017539</td>\n",
       "      <td>0.133694</td>\n",
       "      <td>0.161152</td>\n",
       "      <td>2.544046</td>\n",
       "      <td>1.621964</td>\n",
       "      <td>0.190239</td>\n",
       "      <td>...</td>\n",
       "      <td>3780.726807</td>\n",
       "      <td>-81.249832</td>\n",
       "      <td>4.787728</td>\n",
       "      <td>6.732985</td>\n",
       "      <td>10.917697</td>\n",
       "      <td>15.375001</td>\n",
       "      <td>3.738635</td>\n",
       "      <td>3.373682</td>\n",
       "      <td>17.071510</td>\n",
       "      <td>3.625474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>demo_NPE_samples_prune_200</td>\n",
       "      <td>12.563438</td>\n",
       "      <td>0.731857</td>\n",
       "      <td>0.073844</td>\n",
       "      <td>0.032003</td>\n",
       "      <td>0.055172</td>\n",
       "      <td>0.153164</td>\n",
       "      <td>2.994542</td>\n",
       "      <td>1.193698</td>\n",
       "      <td>0.348310</td>\n",
       "      <td>...</td>\n",
       "      <td>3520.888916</td>\n",
       "      <td>-80.848129</td>\n",
       "      <td>1.729886</td>\n",
       "      <td>2.124394</td>\n",
       "      <td>13.408924</td>\n",
       "      <td>16.314816</td>\n",
       "      <td>2.576452</td>\n",
       "      <td>3.662679</td>\n",
       "      <td>13.253002</td>\n",
       "      <td>3.865971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>demo_NPE_samples_prune_200</td>\n",
       "      <td>12.543190</td>\n",
       "      <td>0.916131</td>\n",
       "      <td>0.032316</td>\n",
       "      <td>0.011386</td>\n",
       "      <td>0.073655</td>\n",
       "      <td>0.115832</td>\n",
       "      <td>2.280476</td>\n",
       "      <td>1.980327</td>\n",
       "      <td>0.152734</td>\n",
       "      <td>...</td>\n",
       "      <td>3167.627686</td>\n",
       "      <td>-78.684944</td>\n",
       "      <td>3.307084</td>\n",
       "      <td>3.509687</td>\n",
       "      <td>12.546023</td>\n",
       "      <td>10.387537</td>\n",
       "      <td>2.502075</td>\n",
       "      <td>4.116404</td>\n",
       "      <td>13.097081</td>\n",
       "      <td>3.729257</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                inference.type  infer.log_prob  params_net.exc_prop  \\\n",
       "0   demo_NPE_samples_prune_200       14.218349             0.941954   \n",
       "1   demo_NPE_samples_prune_200       14.114674             0.853909   \n",
       "2   demo_NPE_samples_prune_200       13.850510             0.717111   \n",
       "3   demo_NPE_samples_prune_200       13.373520             0.756532   \n",
       "4   demo_NPE_samples_prune_200       13.222931             0.918047   \n",
       "5   demo_NPE_samples_prune_200       13.172291             0.893356   \n",
       "6   demo_NPE_samples_prune_200       13.132172             0.939266   \n",
       "7   demo_NPE_samples_prune_200       13.037655             0.769722   \n",
       "8   demo_NPE_samples_prune_200       13.030533             0.933765   \n",
       "9   demo_NPE_samples_prune_200       12.986698             0.884204   \n",
       "10  demo_NPE_samples_prune_200       12.916065             0.858867   \n",
       "11  demo_NPE_samples_prune_200       12.870522             0.735329   \n",
       "12  demo_NPE_samples_prune_200       12.803070             0.926422   \n",
       "13  demo_NPE_samples_prune_200       12.796158             0.737805   \n",
       "14  demo_NPE_samples_prune_200       12.664261             0.916152   \n",
       "15  demo_NPE_samples_prune_200       12.606071             0.713363   \n",
       "16  demo_NPE_samples_prune_200       12.599506             0.948499   \n",
       "17  demo_NPE_samples_prune_200       12.589104             0.931660   \n",
       "18  demo_NPE_samples_prune_200       12.563438             0.731857   \n",
       "19  demo_NPE_samples_prune_200       12.543190             0.916131   \n",
       "\n",
       "    params_net.p_e2e  params_net.p_e2i  params_net.p_i2e  params_net.p_i2i  \\\n",
       "0           0.040212          0.089394          0.087514          0.199783   \n",
       "1           0.023708          0.071285          0.074351          0.090198   \n",
       "2           0.026991          0.084222          0.061285          0.050953   \n",
       "3           0.047827          0.031827          0.101967          0.169729   \n",
       "4           0.053465          0.015740          0.105973          0.106277   \n",
       "5           0.020129          0.054569          0.093841          0.120805   \n",
       "6           0.063758          0.015451          0.058424          0.125610   \n",
       "7           0.072576          0.093747          0.083795          0.066592   \n",
       "8           0.033606          0.013959          0.099130          0.093464   \n",
       "9           0.031422          0.082973          0.152994          0.195906   \n",
       "10          0.036523          0.015330          0.104437          0.077066   \n",
       "11          0.034429          0.046476          0.054337          0.061358   \n",
       "12          0.081388          0.057631          0.132104          0.114805   \n",
       "13          0.039048          0.090788          0.083636          0.099527   \n",
       "14          0.016761          0.060136          0.087552          0.057496   \n",
       "15          0.037435          0.040754          0.081546          0.123833   \n",
       "16          0.031353          0.082558          0.115036          0.125195   \n",
       "17          0.021445          0.017539          0.133694          0.161152   \n",
       "18          0.073844          0.032003          0.055172          0.153164   \n",
       "19          0.032316          0.011386          0.073655          0.115832   \n",
       "\n",
       "    params_net.R_pe2e  params_net.R_Qe2e  params_Epop.p_igniters  ...  \\\n",
       "0            2.963229           1.753297                0.271752  ...   \n",
       "1            1.101550           1.877630                0.274740  ...   \n",
       "2            3.875781           1.584280                0.609745  ...   \n",
       "3            3.535644           1.570758                0.270651  ...   \n",
       "4            1.815602           1.830136                0.252458  ...   \n",
       "5            2.729041           1.723387                0.145617  ...   \n",
       "6            3.243870           1.500175                0.213150  ...   \n",
       "7            4.258139           1.276656                0.206821  ...   \n",
       "8            2.971474           1.388139                0.205837  ...   \n",
       "9            3.053780           1.332489                0.230948  ...   \n",
       "10           4.890635           1.532762                0.231095  ...   \n",
       "11           4.349137           1.714934                0.187308  ...   \n",
       "12           4.118057           1.202418                0.969420  ...   \n",
       "13           1.975813           1.195736                0.232498  ...   \n",
       "14           2.444399           1.962491                0.448491  ...   \n",
       "15           4.555400           1.308392                0.259726  ...   \n",
       "16           1.277942           1.980581                0.137554  ...   \n",
       "17           2.544046           1.621964                0.190239  ...   \n",
       "18           2.994542           1.193698                0.348310  ...   \n",
       "19           2.280476           1.980327                0.152734  ...   \n",
       "\n",
       "    params_Epop.tau_w  params_Epop.E_gi  params_Epop.Q_ge  params_Epop.Q_gi  \\\n",
       "0         4262.124512        -69.666931          1.538027          5.740131   \n",
       "1         4636.706543        -68.850220          3.058450          1.553654   \n",
       "2         5101.727539        -66.180862          1.111281         10.745667   \n",
       "3         5171.982422        -80.073677          1.453532         15.231366   \n",
       "4         3728.452393        -73.886818          1.481260         18.821224   \n",
       "5         3678.786865        -77.044685          4.761185         13.836735   \n",
       "6         2740.413574        -76.648811          2.128978         12.770164   \n",
       "7         5150.429199        -69.936218          1.192999          1.968454   \n",
       "8         2924.523193        -82.657913          4.562043          3.974713   \n",
       "9         4063.808105        -80.490997          3.871441          7.749259   \n",
       "10        4059.470947        -77.611870          1.576795          1.233919   \n",
       "11        6407.967773        -75.831291          1.530120          2.995511   \n",
       "12        2380.655762        -81.393951          1.140215          6.293014   \n",
       "13        3306.258789        -73.164238          3.480698          1.364852   \n",
       "14        2772.190918        -81.537033          4.227221          4.692933   \n",
       "15        4354.932129        -73.206383          1.691243          1.700901   \n",
       "16        3701.529297        -71.815811          3.612992          7.095530   \n",
       "17        3780.726807        -81.249832          4.787728          6.732985   \n",
       "18        3520.888916        -80.848129          1.729886          2.124394   \n",
       "19        3167.627686        -78.684944          3.307084          3.509687   \n",
       "\n",
       "    params_Epop.tau_ge  params_Epop.tau_gi  params_Epop.poisson_rate  \\\n",
       "0            11.845749           12.416341                  3.139677   \n",
       "1            12.618026            6.843685                  4.045559   \n",
       "2            12.867508            1.455210                  2.866703   \n",
       "3            12.484603            7.595463                  4.707281   \n",
       "4            11.179410            1.989216                  2.167878   \n",
       "5            10.697940            4.987892                  2.004332   \n",
       "6            11.787007            4.622208                  3.621238   \n",
       "7            11.477825            4.009518                  2.237082   \n",
       "8             9.465996            7.515447                  2.706392   \n",
       "9            12.700874            8.084185                  3.333295   \n",
       "10           11.742482            2.076792                  2.132282   \n",
       "11           13.961203            2.208848                  2.646500   \n",
       "12           10.790934            4.667439                  1.791489   \n",
       "13            2.147285            2.516879                  4.781724   \n",
       "14            9.680085            4.294734                  3.309908   \n",
       "15           12.226908           16.334698                  4.879207   \n",
       "16           10.809875            4.103291                  4.210105   \n",
       "17           10.917697           15.375001                  3.738635   \n",
       "18           13.408924           16.314816                  2.576452   \n",
       "19           12.546023           10.387537                  2.502075   \n",
       "\n",
       "    params_Ipop.Q_ge  params_Ipop.Q_gi  params_Ipop.poisson_rate  \n",
       "0           1.676930         15.464734                  4.179419  \n",
       "1           4.875051          4.813944                  4.294415  \n",
       "2           1.867696          4.001274                  4.283347  \n",
       "3           1.057857         16.649380                  4.794970  \n",
       "4           2.637160          6.563420                  4.039377  \n",
       "5           3.340909         18.956047                  3.646063  \n",
       "6           1.136560          1.814951                  3.984406  \n",
       "7           4.054039         13.057570                  4.122100  \n",
       "8           2.603636          5.767857                  4.774770  \n",
       "9           1.663790         18.584270                  4.379348  \n",
       "10          4.646295          2.994199                  4.664698  \n",
       "11          4.565966          9.774639                  4.580254  \n",
       "12          2.161114         11.651899                  4.719689  \n",
       "13          2.853611         12.489593                  4.910177  \n",
       "14          2.577863          8.541971                  4.813415  \n",
       "15          3.946023         10.591513                  4.678785  \n",
       "16          1.024559         19.856514                  4.361209  \n",
       "17          3.373682         17.071510                  3.625474  \n",
       "18          3.662679         13.253002                  3.865971  \n",
       "19          4.116404         13.097081                  3.729257  \n",
       "\n",
       "[20 rows x 30 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set batch and random seeds\n",
    "batch_seed = 20241121\n",
    "data_utils.set_all_seeds(batch_seed)\n",
    "\n",
    "n_samples = 20\n",
    "cfg_sample = {\n",
    "    \"name\": \"demo_NPE\",\n",
    "    \"oversample_factor\": 200,\n",
    "    \"do_sample_map\": False,\n",
    "    \"do_sample_pmean\": False,\n",
    "}\n",
    "\n",
    "df_samples, samples_dict = inferer.sample_from_posterior(\n",
    "    posterior=posterior,  # Posterior network\n",
    "    prior=posterior,  # Need for extracting bounds\n",
    "    num_samples=n_samples,  # Number of samples to draw\n",
    "    x_o=xo_tensor,  # Target observation\n",
    "    cfg_algorithm=cfg_sample,  # Sampling options\n",
    ")\n",
    "\n",
    "df_samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize model parameters\n",
    "What do these model configurations look like? \n",
    "\n",
    "We can plot their values for each of the 28 different parameters in the clustered-AdEx network, where just these 5 different models already span a large range of values for most of the parameters (each dot below is one of the above sampled configurations). In other words, they exhibit parameter degeneracy / invariance.\n",
    "\n",
    "For details about the network model and what the parameters mean, see [here](https://www.biorxiv.org/content/10.1101/2024.08.21.608969v1.full.pdf#page=14.17)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1050x300 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from automind.sim.default_configs import MKI_pretty_param_names\n",
    "\n",
    "_, theta_minmax = dist_utils.standardize_theta(posterior.prior.sample((1,)), posterior)\n",
    "fig, axs = plt.subplots(10, 3, figsize=(3.5, 1))\n",
    "plot_utils.plot_params_1D(\n",
    "    df_samples[posterior.names].values,\n",
    "    theta_minmax,\n",
    "    MKI_pretty_param_names,\n",
    "    (fig, axs),\n",
    "    color=\"k\",\n",
    "    draw_canvas=True,\n",
    "    draw_kde=False,\n",
    "    draw_samples=True,\n",
    "    labelpad=12,\n",
    "    draw_median=False,\n",
    "    sample_ms=3,\n",
    "    sample_alpha=0.5,\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Simulate discovered configurations\n",
    "Now, we can take those model parameter configurations and simulate them using the clustered AdEx spiking neural network, saving the spike train data and some meta-info.\n",
    "\n",
    "For the purpose of the demo, we run the simulations in the notebook. However, this does take a long time, especially when multiprocessing doesn't place nice with Jupyter, i.e., as reported for Mac and Windows. Practically, this step is done on a compute cluster, and is highly parallelizeable (1 cpu per simulation). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "params_settings.batch_seed has no prior. Copied as bare value without unit.\n",
      "params_settings.random_seed has no prior. Copied as bare value without unit.\n"
     ]
    }
   ],
   "source": [
    "# Some housekeeping to set random seed identifiers and set up simulation configurations\n",
    "# params_dict['params_settings']['batch_seed'] = batch_seed\n",
    "random_seeds = np.sort(\n",
    "    np.random.choice(a=int(n_samples * 100), size=n_samples, replace=False)\n",
    ")\n",
    "df_samples.insert(loc=0, column=\"params_settings.batch_seed\", value=batch_seed)\n",
    "df_samples.insert(loc=1, column=\"params_settings.random_seed\", value=random_seeds)\n",
    "\n",
    "# NOTE: This sets the correct early-stopping condition, i.e., to assess the simulation from 0.1 to 10.1 seconds.\n",
    "params_dict['params_analysis']['analysis_window'] = [0.1, None] \n",
    "\n",
    "params_dict_run = data_utils.fill_params_dict(\n",
    "    params_dict, df_samples, posterior.as_dict, n_samples\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cache non-existent.\n",
      "20241121-211|20241121-1121|20241121-794|20241121-565|20241121-1938|20241121-1242|20241121-85|20241121-1429|20241121-1975|20241121-1829|20241121-5|20241121-1401|20241121-375|20241121-360|20241121-1629|20241121-343|20241121-1479|20241121-1791|20241121-1430|20241121-1002|Simulations took 158.26 seconds.\n",
      "cache non-existent.\n"
     ]
    }
   ],
   "source": [
    "cache_path = \"./.cache/\"\n",
    "b2_interface.clear_b2_cache(cache_path)\n",
    "b2_interface.set_b2_cache(cache_path, True)\n",
    "\n",
    "sim_parallel = True\n",
    "if sim_parallel:\n",
    "    from multiprocessing import Pool\n",
    "\n",
    "    start_time = time()\n",
    "    with Pool(n_samples) as pool:\n",
    "        sim_collector = pool.map(runners.adex_simulator, params_dict_run)\n",
    "    print(f\"Simulations took {time()-start_time:.2f} seconds.\")\n",
    "else:\n",
    "    sim_collector = []\n",
    "    for i_sim in range(n_samples):\n",
    "        start_time = time()\n",
    "        sim_collector.append(runners.adex_simulator(params_dict_run[i_sim]))\n",
    "        print(f\"Simulation {i_sim+1}/{n_samples} took {time()-start_time:.2f} seconds.\")\n",
    "\n",
    "b2_interface.clear_b2_cache(cache_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute summary features\n",
    "From the simulation data (spike trains), we can analyze them and extract the same bursting features, as well as smoothed population firing rate over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_collector = [\n",
    "    analysis_utils.compute_summary_features(sims[1], sims[0]) for sims in sim_collector\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the discovered model simulations\n",
    "Finally, we plot the simulated spike trains and the population firing rate of each discovered model. \n",
    "\n",
    "Note that we will first re-order the simulations based on their similarity to the real data (to show the best models first). We will plot the real data to the right for comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_stats = np.ones((n_samples, len(cols_features))) * np.inf\n",
    "for i_sim, rc in enumerate(results_collector):\n",
    "    if not rc['summary_bursts'][cols_features].empty:\n",
    "        model_stats[i_sim, :] = rc['summary_bursts'][cols_features].values\n",
    "\n",
    "ordered = np.argsort(np.abs(np.log10(model_stats) - xo).sum(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x225 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x225 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x225 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x225 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x225 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x225 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x225 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x225 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x225 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x225 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the models from best to worst, just plot the first 10\n",
    "xlims = [0,200]\n",
    "every_other = 200\n",
    "for i_, i_sim in enumerate(ordered[:10]):\n",
    "    fig, axs = plt.subplots(2, 2, figsize=(2, 0.75))\n",
    "    ax1, ax3, ax2, ax4 = axs.flatten()\n",
    "\n",
    "    # Plot raster, rates, and PSD\n",
    "    plot_utils._plot_raster_pretty(\n",
    "        sim_collector[i_sim][1], XL=xlims, every_other=every_other, fontsize=5, ax=ax1\n",
    "    )\n",
    "    plot_utils._plot_rates_pretty(\n",
    "        results_collector[i_sim][\"pop_rates\"],\n",
    "        XL=xlims,\n",
    "        pops_to_plot=[\"avgpop_rate\"],\n",
    "        ylim_past=10 * 1000,\n",
    "        fontsize=5,\n",
    "        ax=ax2,\n",
    "        color=\"k\",\n",
    "    )    \n",
    "    ax2.set_ylabel(\"Rate\", labelpad=-5)\n",
    "\n",
    "    # plot real data next to it\n",
    "    plot_utils._plot_raster_pretty(spikes_dict, XL=xlims, plot_inh=False, every_other=50, fontsize=5, ax=ax3)\n",
    "    plot_utils._plot_rates_pretty(\n",
    "            spikes_analyzed[1],\n",
    "            XL=xlims,        \n",
    "            pops_to_plot=[\"avgpop_rate\"],\n",
    "            fontsize=5,\n",
    "            ax=ax4,\n",
    "            color=\"k\",        \n",
    "        )\n",
    "    ax3.set_ylabel(None); ax4.set_ylabel(None);\n",
    "    if i_ == 0:\n",
    "        ax1.set_title(f\"Model\", fontsize=5, pad=0)\n",
    "        ax3.set_title(f\"Real data\", fontsize=5, pad=0)"
   ]
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    "Typically, for real experimental data, we expect a match of about 5-25% rate. Meaning, out of 20 posterior samples, about 2-5 should look reasonably close to the data.\n",
    "\n",
    "In the above, you can see that 7/20 produced bursts, and about 5 with a similar number of bursts, while the others still produced bursts at roughly the same frequency. The rest of the models produced either asynchronous activity, or was stopped early due to excessively high (or low) network activity (plot all 20 to see).\n",
    "\n",
    "Note that compared to traditional model fitting parameters, this is extremely efficient---especially for a 28-dimensional parameter space---as they typically expect a yield of much lower than 10% since most of the simulations at inference time are discarded in the process of, e.g., MCMC sampling or crossovers in genetic algorithms. Here, we had to make many simulations upfront at training time (1-million), but this cost has already been paid (by me) when generating the training simulation dataset. This is `amortized inference`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# That's all for now.\n",
    "Hopefully you got an overview of the workflow, key processing steps, and general capabilities of AutoMIND. \n",
    "\n",
    "We emphasize here again that, in a single Jupyter Notebook cell above, we could literally acquire hundreds or even thousands of **different** model configurations consistent with the **same** target observation here. Doing so for a different target observation simply requires loading a different data vector. This is the power of deep generative models combined with an interpretable mechanistic circuit model.\n",
    "\n",
    "In the following demos, we will explore how to train a posterior network from scratch, and using different data features."
   ]
  }
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