doc/source/notebooks/06_spike_reports.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spike Report\n",
    "## Introduction\n",
    "In this tutorial we cover how to inspect a spike report using BlueBrain SNAP and access its properties."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by importing the `bluepysnap` package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bluepysnap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading\n",
    "In order to load the simulation data, we need the path to the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulation_path = \"/gpfs/bbp.cscs.ch/project/proj12/NSE/bluepysnap-functional-tests/simulation_config_bluepy_examples.json\"\n",
    "simulation = bluepysnap.Simulation(simulation_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accessing simulation spike report is straightforward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "spikes = simulation.spikes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Timing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with the `Simulation` instance, it's easy to access report start and end time, as well as the time step (dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1000.0 0.025\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    spikes.time_start, \n",
    "    spikes.time_stop, \n",
    "    spikes.dt\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Populations\n",
    "If we are interested in the populations in spike report, they can be shown with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hippocampus_neurons']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spikes.population_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And when we know the population name, accessing it in the report is done as in the case of node/edge populations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'bluepysnap.spike_report.PopulationSpikeReport'>\n"
     ]
    }
   ],
   "source": [
    "spike_pop = spikes['hippocampus_neurons']\n",
    "print(type(spike_pop))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to access the actual NodePopulation instance from the report, it can also be done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hippocampus_neurons: <class 'bluepysnap.nodes.node_population.NodePopulation'>\n"
     ]
    }
   ],
   "source": [
    "node_population = spike_pop.nodes\n",
    "print(f'{node_population.name}: {type(node_population)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node IDs\n",
    "Now that we are familiar with the basics, accessing the node_ids in the report and getting their info is as easy as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>layer</th>\n",
       "      <th>synapse_class</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>SP</td>\n",
       "      <td>EXC</td>\n",
       "      <td>475.613040</td>\n",
       "      <td>214.946340</td>\n",
       "      <td>418.594196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>SP</td>\n",
       "      <td>EXC</td>\n",
       "      <td>102.885407</td>\n",
       "      <td>205.698670</td>\n",
       "      <td>820.509814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>SP</td>\n",
       "      <td>EXC</td>\n",
       "      <td>151.772716</td>\n",
       "      <td>170.433333</td>\n",
       "      <td>808.895026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>SP</td>\n",
       "      <td>EXC</td>\n",
       "      <td>175.807845</td>\n",
       "      <td>215.114387</td>\n",
       "      <td>620.637111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>SP</td>\n",
       "      <td>EXC</td>\n",
       "      <td>569.932009</td>\n",
       "      <td>173.500045</td>\n",
       "      <td>760.627625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    layer synapse_class           x           y           z\n",
       "145    SP           EXC  475.613040  214.946340  418.594196\n",
       "277    SP           EXC  102.885407  205.698670  820.509814\n",
       "427    SP           EXC  151.772716  170.433333  808.895026\n",
       "602    SP           EXC  175.807845  215.114387  620.637111\n",
       "669    SP           EXC  569.932009  173.500045  760.627625"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids = spike_pop.node_ids\n",
    "node_population.get(ids, properties=['layer','synapse_class','x','y','z']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering\n",
    "We can filter the report to only access the data we are interested in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>ids</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>times</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.700</th>\n",
       "      <td>7447</td>\n",
       "      <td>hippocampus_neurons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.150</th>\n",
       "      <td>1006</td>\n",
       "      <td>hippocampus_neurons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.150</th>\n",
       "      <td>11119</td>\n",
       "      <td>hippocampus_neurons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.150</th>\n",
       "      <td>11654</td>\n",
       "      <td>hippocampus_neurons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.225</th>\n",
       "      <td>10574</td>\n",
       "      <td>hippocampus_neurons</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ids           population\n",
       "times                            \n",
       "1.700   7447  hippocampus_neurons\n",
       "2.150   1006  hippocampus_neurons\n",
       "2.150  11119  hippocampus_neurons\n",
       "2.150  11654  hippocampus_neurons\n",
       "2.225  10574  hippocampus_neurons"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered = spikes.filter(group={'layer':'SP'}, t_start=1, t_stop=100)\n",
    "filtered.report.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "We may also want to visualize data. Plotting raster plot of filtered spikes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtered.raster();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting a firing rate histogram of the filtered spikes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtered.firing_rate_histogram();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The filtered report's `firing_rate_histogram` only takes into account the nodes that are present in the filtered report and thus the Y-axis frequency values do not represent the whole `group` the filtered report was created with.\n",
    "\n",
    "Creating the same plot and computing frequencies based on the whole `group` can be done as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.set_xlabel(\"Time [ms]\")\n",
    "ax.set_ylabel(\"PSTH [Hz]\")\n",
    "ax.set_title(f\"PSTH for group: {filtered.group}\")\n",
    "\n",
    "times = filtered.report.index\n",
    "\n",
    "time_start = np.min(times)\n",
    "time_stop = np.max(times)\n",
    "\n",
    "# heuristic for a nice bin size (~100 spikes per bin on average)\n",
    "time_binsize = min(50.0, (time_stop - time_start) / ((len(times) / 100.0) + 1.0))\n",
    "\n",
    "bins = np.append(np.arange(time_start, time_stop, time_binsize), time_stop)\n",
    "hist, bin_edges = np.histogram(times, bins=bins)\n",
    "node_count = len(simulation.circuit.nodes.ids(filtered.group))  # Get length of node ids for whole `group`\n",
    "freq = 1.0 * hist / node_count / (0.001 * time_binsize)\n",
    "\n",
    "# use the middle of the bins instead of the start of the bin\n",
    "ax.plot(0.5 * (bin_edges[1:] + bin_edges[:-1]), freq, label=\"PSTH\", drawstyle=\"steps-mid\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only filtered reports have plotting cababilities. However, filters can be called without arguments which leads to the filtered object having all the available data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spikes.filter().raster();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It might also be useful to change the y-axis to e.g., m-type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spikes.filter().raster(y_axis='etype');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can also plot interspike interval plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spikes.filter().isi(binsize=100);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading the log\n",
    "The `SpikeReport.log()` yields a file handler for the log file (raises error if log file not found). It can be read like any file in python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ...\n",
      "SimulatorMode set to 'CORENEURON'\n",
      " CoreNEURON requires Random123 generator, setting RNGMode = Random123\n",
      " [\u001b[0m\u001b[34m INFO \u001b[0m] \u001b[0m\u001b[39mUsing morphology path: /gpfs/bbp.cscs.ch/project/proj42/circuits/CA1.O1/mooc-circuit/morphologies/swc/\u001b[0m\n",
      " [\u001b[0m\u001b[34m INFO \u001b[0m] \u001b[0m\u001b[39mRunning Neurodamus with config from BlueConfig\u001b[0m\n",
      " [\u001b[1m\u001b[39m STEP \u001b[0m] \u001b[1m\u001b[39mLoading Targets\u001b[0m\n",
      " Reading target file /gpfs/bbp.cscs.ch/project/proj42/circuits/CA1.O1/mooc-circuit/start.target\n",
      " Reading target file /gpfs/bbp.cscs.ch/project/proj42/circuits/CA1.O1/mooc-circuit/user.target\n",
      "   * Target AllCompartments  18198 cells [1 subtargets]\n",
      "   * Target PV                 470 cells [3 subtargets]\n",
      "   * Target CCK                231 cells [3 subtargets]\n",
      "\n",
      " ...\n"
     ]
    }
   ],
   "source": [
    "with spikes.log() as l:\n",
    "    print(\" ...\")\n",
    "    print(*l.readlines()[20:30])\n",
    "    print(\" ...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "We introduced how spike reports can be accessed and plotted with `bluepysnap`. Next we'll take a look into the frame reports."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python3",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}