doc/source/notebooks/07_frame_reports.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Frame Report\n",
    "## Introduction\n",
    "In this tutorial we cover how to inspect a frame 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": [
    "There are two different possible report types `'soma_report'` and `'compartment_report'`. In this tutorial, we only work with `'soma_report'` but they work exactly in the same way. Available frame reports can be shown with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'soma_report': <bluepysnap.frame_report.SomaReport at 0x2aaab6be5610>}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulation.reports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accessing the reports is straightforward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "soma_report = simulation.reports['soma_report']"
   ]
  },
  {
   "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) and time_units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "dt from the report differs from the global simulation dt.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 1000.0 0.1 ms\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    soma_report.time_start, \n",
    "    soma_report.time_stop, \n",
    "    soma_report.dt,\n",
    "    soma_report.time_units\n",
    ")  # Gives a warning in case the dt differs from simulation.dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Populations\n",
    "If we are interested in the populations in frame report, they can be shown with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hippocampus_neurons']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soma_report.population_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'bluepysnap.frame_report.PopulationSomaReport'>\n"
     ]
    }
   ],
   "source": [
    "soma_pop = soma_report['hippocampus_neurons']\n",
    "print(type(soma_pop))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accessing the actual NodePopulation from the report can be done with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hippocampus_neurons: <class 'bluepysnap.nodes.node_population.NodePopulation'>\n"
     ]
    }
   ],
   "source": [
    "node_population = soma_pop.nodes\n",
    "print(f'{node_population.name}: {type(node_population)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node IDs\n",
    "Accessing the node_ids in the report and getting their info is the same as in the SpikeReport"
   ]
  },
  {
   "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",
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       "    }\n",
       "\n",
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       "    }\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>0</th>\n",
       "      <td>SLM</td>\n",
       "      <td>INH</td>\n",
       "      <td>409.446689</td>\n",
       "      <td>525.218483</td>\n",
       "      <td>1074.146619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SLM</td>\n",
       "      <td>INH</td>\n",
       "      <td>692.961402</td>\n",
       "      <td>511.187277</td>\n",
       "      <td>573.179832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SLM</td>\n",
       "      <td>INH</td>\n",
       "      <td>624.142630</td>\n",
       "      <td>520.641475</td>\n",
       "      <td>976.924721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SR</td>\n",
       "      <td>INH</td>\n",
       "      <td>430.543380</td>\n",
       "      <td>411.999965</td>\n",
       "      <td>1180.185218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SR</td>\n",
       "      <td>INH</td>\n",
       "      <td>427.936977</td>\n",
       "      <td>302.239247</td>\n",
       "      <td>1013.453517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  layer synapse_class           x           y            z\n",
       "0   SLM           INH  409.446689  525.218483  1074.146619\n",
       "1   SLM           INH  692.961402  511.187277   573.179832\n",
       "2   SLM           INH  624.142630  520.641475   976.924721\n",
       "3    SR           INH  430.543380  411.999965  1180.185218\n",
       "4    SR           INH  427.936977  302.239247  1013.453517"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids = soma_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 the same way as in SpikeReport. Just as in SpikeReport, the index are different moments of time. Please note that this time, as there is more data in the output, the columns represent the population names and the ids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"21\" halign=\"left\">hippocampus_neurons</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>...</th>\n",
       "      <th>18028</th>\n",
       "      <th>18029</th>\n",
       "      <th>18030</th>\n",
       "      <th>18031</th>\n",
       "      <th>18032</th>\n",
       "      <th>18033</th>\n",
       "      <th>18034</th>\n",
       "      <th>18035</th>\n",
       "      <th>18036</th>\n",
       "      <th>18037</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>-64.495819</td>\n",
       "      <td>-63.987568</td>\n",
       "      <td>-62.878498</td>\n",
       "      <td>-63.299671</td>\n",
       "      <td>-65.634201</td>\n",
       "      <td>-69.895012</td>\n",
       "      <td>-60.940189</td>\n",
       "      <td>-64.212494</td>\n",
       "      <td>-65.128319</td>\n",
       "      <td>-65.810913</td>\n",
       "      <td>...</td>\n",
       "      <td>-62.824352</td>\n",
       "      <td>-69.909012</td>\n",
       "      <td>-58.932209</td>\n",
       "      <td>-69.869728</td>\n",
       "      <td>-63.633045</td>\n",
       "      <td>-62.883789</td>\n",
       "      <td>-67.959328</td>\n",
       "      <td>-67.936882</td>\n",
       "      <td>-63.578728</td>\n",
       "      <td>-65.507950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.1</th>\n",
       "      <td>-64.667297</td>\n",
       "      <td>-64.153236</td>\n",
       "      <td>-63.007919</td>\n",
       "      <td>-63.403816</td>\n",
       "      <td>-65.700539</td>\n",
       "      <td>-69.863594</td>\n",
       "      <td>-61.141182</td>\n",
       "      <td>-64.390297</td>\n",
       "      <td>-65.280983</td>\n",
       "      <td>-65.886642</td>\n",
       "      <td>...</td>\n",
       "      <td>-63.159550</td>\n",
       "      <td>-70.017754</td>\n",
       "      <td>-59.412067</td>\n",
       "      <td>-69.980461</td>\n",
       "      <td>-63.979340</td>\n",
       "      <td>-63.340405</td>\n",
       "      <td>-68.112579</td>\n",
       "      <td>-68.085899</td>\n",
       "      <td>-63.905514</td>\n",
       "      <td>-65.812584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.2</th>\n",
       "      <td>-64.836189</td>\n",
       "      <td>-64.316284</td>\n",
       "      <td>-63.137890</td>\n",
       "      <td>-63.507111</td>\n",
       "      <td>-65.766029</td>\n",
       "      <td>-69.833801</td>\n",
       "      <td>-61.339825</td>\n",
       "      <td>-64.564308</td>\n",
       "      <td>-65.428932</td>\n",
       "      <td>-65.961830</td>\n",
       "      <td>...</td>\n",
       "      <td>-63.481346</td>\n",
       "      <td>-70.124634</td>\n",
       "      <td>-59.878464</td>\n",
       "      <td>-70.089333</td>\n",
       "      <td>-64.307793</td>\n",
       "      <td>-63.783619</td>\n",
       "      <td>-68.260345</td>\n",
       "      <td>-68.229614</td>\n",
       "      <td>-64.216225</td>\n",
       "      <td>-66.099930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.3</th>\n",
       "      <td>-65.002808</td>\n",
       "      <td>-64.477020</td>\n",
       "      <td>-63.268269</td>\n",
       "      <td>-63.609692</td>\n",
       "      <td>-65.830833</td>\n",
       "      <td>-69.805328</td>\n",
       "      <td>-61.536880</td>\n",
       "      <td>-64.734985</td>\n",
       "      <td>-65.572899</td>\n",
       "      <td>-66.036568</td>\n",
       "      <td>...</td>\n",
       "      <td>-63.791656</td>\n",
       "      <td>-70.229721</td>\n",
       "      <td>-60.331379</td>\n",
       "      <td>-70.196411</td>\n",
       "      <td>-64.620468</td>\n",
       "      <td>-64.214165</td>\n",
       "      <td>-68.403145</td>\n",
       "      <td>-68.368538</td>\n",
       "      <td>-64.512779</td>\n",
       "      <td>-66.372002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.4</th>\n",
       "      <td>-65.167358</td>\n",
       "      <td>-64.635651</td>\n",
       "      <td>-63.398903</td>\n",
       "      <td>-63.711647</td>\n",
       "      <td>-65.895073</td>\n",
       "      <td>-69.777985</td>\n",
       "      <td>-61.732819</td>\n",
       "      <td>-64.902641</td>\n",
       "      <td>-65.713432</td>\n",
       "      <td>-66.110916</td>\n",
       "      <td>...</td>\n",
       "      <td>-64.091980</td>\n",
       "      <td>-70.333076</td>\n",
       "      <td>-60.771034</td>\n",
       "      <td>-70.301758</td>\n",
       "      <td>-64.919052</td>\n",
       "      <td>-64.632706</td>\n",
       "      <td>-68.541420</td>\n",
       "      <td>-68.503098</td>\n",
       "      <td>-64.796761</td>\n",
       "      <td>-66.630463</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 18002 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    hippocampus_neurons                                              \\\n",
       "                  36         37         38         39         40      \n",
       "1.0          -64.495819 -63.987568 -62.878498 -63.299671 -65.634201   \n",
       "1.1          -64.667297 -64.153236 -63.007919 -63.403816 -65.700539   \n",
       "1.2          -64.836189 -64.316284 -63.137890 -63.507111 -65.766029   \n",
       "1.3          -65.002808 -64.477020 -63.268269 -63.609692 -65.830833   \n",
       "1.4          -65.167358 -64.635651 -63.398903 -63.711647 -65.895073   \n",
       "\n",
       "                                                            ...             \\\n",
       "         41         42         43         44         45     ...      18028   \n",
       "1.0 -69.895012 -60.940189 -64.212494 -65.128319 -65.810913  ... -62.824352   \n",
       "1.1 -69.863594 -61.141182 -64.390297 -65.280983 -65.886642  ... -63.159550   \n",
       "1.2 -69.833801 -61.339825 -64.564308 -65.428932 -65.961830  ... -63.481346   \n",
       "1.3 -69.805328 -61.536880 -64.734985 -65.572899 -66.036568  ... -63.791656   \n",
       "1.4 -69.777985 -61.732819 -64.902641 -65.713432 -66.110916  ... -64.091980   \n",
       "\n",
       "                                                                       \\\n",
       "         18029      18030      18031      18032      18033      18034   \n",
       "1.0 -69.909012 -58.932209 -69.869728 -63.633045 -62.883789 -67.959328   \n",
       "1.1 -70.017754 -59.412067 -69.980461 -63.979340 -63.340405 -68.112579   \n",
       "1.2 -70.124634 -59.878464 -70.089333 -64.307793 -63.783619 -68.260345   \n",
       "1.3 -70.229721 -60.331379 -70.196411 -64.620468 -64.214165 -68.403145   \n",
       "1.4 -70.333076 -60.771034 -70.301758 -64.919052 -64.632706 -68.541420   \n",
       "\n",
       "                                      \n",
       "         18035      18036      18037  \n",
       "1.0 -67.936882 -63.578728 -65.507950  \n",
       "1.1 -68.085899 -63.905514 -65.812584  \n",
       "1.2 -68.229614 -64.216225 -66.099930  \n",
       "1.3 -68.368538 -64.512779 -66.372002  \n",
       "1.4 -68.503098 -64.796761 -66.630463  \n",
       "\n",
       "[5 rows x 18002 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered = soma_report.filter(group={'layer':'SP'}, t_start=1, t_stop=100)\n",
    "filtered.report.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "Frame reports can only do trace plots. Again, we can only plot filtered data"
   ]
  },
  {
   "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.trace();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And of course, calling `filter` without arguments provides all available data"
   ]
  },
  {
   "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": [
    "soma_report.filter().trace();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading the log\n",
    "Not applicable to `FrameReport`s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "We introduced how frame reports can be accessed and plotted with `bluepysnap`. This concludes the basic usage tutorials."
   ]
  }
 ],
 "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
}