{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9ad12793",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Data processing and analysis - Building on top of Neo\n",
    "Based on tutorial material by Cristiano Koehler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfea7784",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Until now we have discussed the advantages gained by using Neo for standardized data storage via conversion to standardized formats and as data representation to develop custom visualizations and analysis. However, the most important aspect is that for classic anlysis it not necessary any more to implement these as other software packages are building on top of the Neo data representation!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3babf2c3",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "![neo_as_interface](./neo_material/neo_as_interface.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f41ea5b0",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Elephant - the Electrophysiology Analysis Toolkit\n",
    "![](elephant_material/elephant_logo_sidebar.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "989b705b",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "\n",
    "provides analysis on different levels\n",
    "- data preprocessing\n",
    "    - filtering, z-score\n",
    "    - cross-correlation\n",
    "- classic analysis\n",
    "    - spike train statistics & correlation\n",
    "    - phase calculation\n",
    "    - spectral analysis\n",
    "    - kernel convolution\n",
    "    - spike-triggered LFP & phase\n",
    "- advanced:\n",
    "    - surrogate generation\n",
    "    - detection of synchronous spike patterns: CAD, Unitary Events, **SPADE**, ASSET, CuBIC\n",
    "    - detection of non-stationary processes\n",
    "    - Gaussian-Process Factor Analysis (GPFA)\n",
    "- ...\n",
    "\n",
    "see also [https://elephant.readthedocs.io/](https://elephant.readthedocs.io/)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae7cba1a",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Elephant\n",
    "- works on `Neo`, `Numpy` or `Quantities` objects\n",
    "- optimal metadata usage for `Neo` objects\n",
    "- open source & community project\n",
    "- extension suggestions -> open an issue & PR on [github](https://github.com/NeuralEnsemble/elephant)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b758a848",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Viziphant\n",
    "![](./elephant_material/viziphant_logo_sidebar.png)\n",
    "\n",
    "Used to provide simple visualizations of analysis results\n",
    " \n",
    "see also [https://viziphant.readthedocs.io/](https://viziphant.readthedocs.io/)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33b82088",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Imports and preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "798d8686",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import quantities as pq\n",
    "import neo.utils\n",
    "import elephant\n",
    "import viziphant\n",
    "import nixio\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1337e17f",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "On Linux you can download the compiled nix file via the command below. On other systems, please download the file manually from here and save it in the same folder as this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "de3739e0",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "!wget -O i140703-001.nix https://gin.g-node.org/sprenger/multielectrode_grasp/raw/dataset_nix/datasets_nix/i140703-001_cut_74sec.nix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35a52d48",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Loading and preparing the data for the analysis\n",
    "\n",
    "We start by loading the data file and extracting the trials of interest.\n",
    "Our objective is to retain only the trials where the monkey performed correctly, for one of the four trial types in the experiment.\n",
    "\n",
    "Let's start by reading the dataset `i140703-001.nix` into a `neo.Block`. Then, we will select the proper `neo.Segment` object\n",
    "with the recording data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "05567b46",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "with neo.io.NixIO('i140703-001.nix', 'ro') as io:\n",
    "    block = io.read_block()\n",
    "\n",
    "# Use first segment in the file\n",
    "segment = block.segments[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea87c0c8",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The events in `segment.events[0]` correspond to inputs from the digital port of the acquisition system,\n",
    "with the important time points during the trial. They have labels stored as the `trial_event_labels` annotation.\n",
    "In previous sessions, you learned the details about the experiment.\n",
    "\n",
    "The start of the trial is identified by the `TS-ON` label. We will use it as a reference to cut our data from `segment`.\n",
    "We will cut a slice of total 2 s duration after `TS-ON`.\n",
    "\n",
    "We can also use the `performance_in_trial_str` annotation to select the trials where the monkey performed\n",
    "correctly, and `belongs_to_trialtype` to select one of the four trial types. Here, we choose `SGHF`, i.e., side-grips (SG)\n",
    "with a high force (HF).\n",
    "\n",
    "Instead of the manual approach to selecting these events as used in tutorial 2, we use the convenience function `get_events()`\n",
    "from the `neo.utils` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5f4da019",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "start_events = neo.utils.get_events(\n",
    "    segment,\n",
    "    trial_event_labels='TS-ON',\n",
    "    belongs_to_trialtype='SGHF',\n",
    "    performance_in_trial_str='correct_trial')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a7be32",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Having the `TS-ON` events as the start time points, we now define epochs with the desired duration of 2 s and store them as the variable\n",
    "`trial_epochs`. When fetching the events, we consider only the first item returned `start_events[0]` which corresponds\n",
    "to the events recorded through the digital port of the acquisition system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aa7ed590",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Create epochs between the events\n",
    "trial_epochs = neo.utils.add_epoch(\n",
    "    segment,\n",
    "    event1=start_events[0],     # first Neo Event contains relevant data\n",
    "    pre=0*pq.ms,\n",
    "    post=2000*pq.ms,\n",
    "    array_annotations=start_events[0].array_annotations)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d64c62c5",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "As next step, let's cut our original segment of continuous data into separate segments, one for each epoch (trial).\n",
    "To this end, we create a new block called `trials` to store the new segments.\n",
    "\n",
    "Use the `reset_time` parameter to set the start times of each individual trial to zero, so that they are aligned to `TS-ON`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "745a54f4",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Create the new block\n",
    "trials = neo.Block()\n",
    "\n",
    "# Cut the recording segment into the trials, as defined by the epochs\n",
    "trials.segments = neo.utils.cut_segment_by_epoch(segment, trial_epochs, reset_time=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cbfe78e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Selecting a single trial\n",
    "\n",
    "The first analyses will be for a single trial. Let's then select the first trial available as the variable `trial`.\n",
    "\n",
    "We will also use the events from the digital input port. Like before, they are the first `neo.Event` object in `trial.events`.\n",
    "Let's select the digital events of the trial in a variable called `trial_events` and inspect the annotations, to check if we\n",
    "got the correct data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1be3b0d3",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['TS-ON' 'WS-ON' 'CUE-ON' 'CUE-OFF']\n",
      "{'trial_id': array([1, 1, 1, 1]), 'trial_timestamp_id': array([39424, 39424, 39424, 39424]), 'performance_in_trial': array([255, 255, 255, 255]), 'performance_in_trial_str': array(['correct_trial', 'correct_trial', 'correct_trial', 'correct_trial'],\n",
      "      dtype='<U13'), 'belongs_to_trialtype': array(['SGHF', 'SGHF', 'SGHF', 'SGHF'], dtype='<U4'), 'trial_event_labels': array(['TS-ON', 'WS-ON', 'CUE-ON', 'CUE-OFF'], dtype='<U11'), 'trial_reject_HFC': array([False, False, False, False]), 'trial_reject_LFC': array([False, False, False, False]), 'trial_reject_IFC': array([ True,  True,  True,  True])}\n"
     ]
    }
   ],
   "source": [
    "# Select first segment as the trial for analysis\n",
    "trial = trials.segments[0]\n",
    "\n",
    "# Select first event of the trial\n",
    "trial_events = trial.events[0]\n",
    "\n",
    "# Inspect\n",
    "print(trial_events.labels)\n",
    "print(trial_events.array_annotations)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cad8dab1",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Selecting spike trains with single-unit activity (SUA)\n",
    "\n",
    "An electrode may record spike trains originated from several neurons in the vicinity. After the process of spike sorting,\n",
    "spike trains that contain the activity from only a single neuron are obtained. These are referred to as single-unit activity (SUA)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "76edc67d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'nix_name': 'neo.spiketrain.22a41c20c0614a238d32dea2d46673ca',\n",
       " 'id': 'Unit 1000',\n",
       " 'channel_id': 1,\n",
       " 'unit_id': 0,\n",
       " 'unit_tag': 'unclassified',\n",
       " 'electrode_reject_HFC': False,\n",
       " 'electrode_reject_LFC': False,\n",
       " 'electrode_reject_IFC': False,\n",
       " 'connector_aligned_id': 93,\n",
       " 'coordinate_x': array(0.8) * mm,\n",
       " 'coordinate_y': array(3.6) * mm,\n",
       " 'sua': False,\n",
       " 'mua': False,\n",
       " 'noise': True}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trial.spiketrains[0].annotations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "011b2bf7",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For our analysis, we will consider only the spike trains in the trial that are classified as SUA. This information has been recorded\n",
    "in the boolean annotation `sua` of each spike train\n",
    "\n",
    "We will also consider only the SUA spike trains that fire more than 10000 spikes in the complete recording session, as well as without any noise.\n",
    "We can do that by checking the relevant annotations. Let's store this in a final list called `sua_spiketrains_with_spikes`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3bd6bf2d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Select only SUA spike trains with spikes and certain quality criteria\n",
    "\n",
    "sua_spiketrains_with_spikes = [spiketrain for spiketrain in trial.spiketrains if\n",
    "                                   spiketrain.annotations['sua'] and                          # Is SUA\n",
    "                                   not spiketrain.annotations['electrode_reject_HFC'] and     # Bad quality is marked as True\n",
    "                                   not spiketrain.annotations['electrode_reject_IFC'] and\n",
    "                                   not spiketrain.annotations['electrode_reject_LFC'] and\n",
    "                                   not spiketrain.annotations['noise'] and                    # If noisy, is market as True\n",
    "                                   spiketrain.annotations['spike_count'] > 10000]             # Spike count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57264808",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Plotting a raster plot and firing rates across SUAs\n",
    "\n",
    "As a first step in the analysis of spike trains, it is good to have an overview of the spiking activity of all\n",
    "neurons along the duration of the trial, with respect to the events. We can do that by plotting the raster plot,\n",
    "population histogram and mean firing rate for the SUAs in the trial:\n",
    "\n",
    "* The raster plot shows the time points where each spike occurred, for the individual neurons.\n",
    "* The mean firing rate is the temporal average of the number of spikes of an individual neuron in the trial.\n",
    "* The population histogram shows the number of spikes that occurred during small intervals along the trial, considering all neurons.\n",
    "\n",
    "Viziphant provides the function `rasterplot_rates` to easily produce the plots.\n",
    "\n",
    "The immediate output of the plotting function does not have the events. We will add them manually using\n",
    "Viziphant's `add_event` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4755fe82",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the raster plot of the SUA spike trains\n",
    "#   `axes` is the main raster plot.\n",
    "#   `axes_histx`: population histogram (above the raster plot)\n",
    "#   `axes_histy`: mean firing rates for each neuron (right)\n",
    "fig, axes = plt.subplots(1, 1, sharey=True, figsize=(10,5))\n",
    "axes, axes_histx, axes_histy = viziphant.rasterplot.rasterplot_rates(\n",
    "    sua_spiketrains_with_spikes, ax=fig.axes[0])\n",
    "# Label the mean firing rate histogram\n",
    "axes_histy.set_xlabel('Rate (Hz)')\n",
    "# Add vertical lines for all important trial events\n",
    "# (remove the last element in case there is overlap with the STOP event)\n",
    "viziphant.events.add_event([axes_histx, axes], trial_events, key='trial_event_labels')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfc4b817",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Visualize the instantaneous firing rate of one neuron in the trial\n",
    "\n",
    "The mean firing rate is just the temporal average of the number of spikes per time in the spiking activity of the neuron for the\n",
    "whole trial. We can have a time-varying estimate of the firing rate along the duration of the trial by computing the instantaneous rate.\n",
    "\n",
    "For this estimation, Elephant's `statistics` module provides the `instantaneous_rate` function. The function performs kernel\n",
    "convolution: each spike that ocurred during the trial is blurred with a kernel function. The bandwidth of the kernel function is\n",
    "controlled by the parameter `sigma`. Different types of kernel and bandwidths can be selected for more or less smooth estimates.\n",
    "\n",
    "Let's plot the instantaneous rate for one neuron that we are analyzing. Let's use a Gaussian kernel with two different `sigma`\n",
    "parameters: `20 ms` or `100 ms`. Use `.1 ms` as `sampling_period`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6f41f351",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(9, 'ch14#1'), (64, 'ch85#1')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(idx, val.name) for idx, val in enumerate(\n",
    "    sua_spiketrains_with_spikes) if val.annotations['SNR'] > 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4d75849e",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ch14#1\n"
     ]
    }
   ],
   "source": [
    "# Select one neuron from the SUA list\n",
    "neuron = sua_spiketrains_with_spikes[9]\n",
    "print(neuron.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "719195e3",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Define kernels\n",
    "kernel_20 = elephant.kernels.GaussianKernel(sigma=20*pq.ms)\n",
    "kernel_100 = elephant.kernels.GaussianKernel(sigma=100*pq.ms)\n",
    "\n",
    "# Define the sampling period for the instantaneous rate function\n",
    "sampling_period = .1*pq.ms\n",
    "\n",
    "rates_20 = elephant.statistics.instantaneous_rate(\n",
    "    neuron, kernel=kernel_20, sampling_period=sampling_period)\n",
    "rates_100 = elephant.statistics.instantaneous_rate(\n",
    "    neuron, kernel=kernel_100, sampling_period=sampling_period)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58976214",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Execute the code below to plot the spike train together with the two rate profiles that we calculated.\n",
    "The spike times will be added to the bottom of each plot. Note that the parameters of the rate computations\n",
    "are retained in the resulting rate variables, which in turn are Neo `AnalogSignal` objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "10f902c4",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, sharey=True, figsize=(10,5))\n",
    "for plot, rates in enumerate([rates_20, rates_100]):\n",
    "    axes[plot].plot(rates.times.rescale(pq.s), rates, linewidth=2)\n",
    "    axes[plot].eventplot(neuron.magnitude, color='black', linelengths=5)\n",
    "    axes[plot].set_ylabel(\"Firing rate (Hz)\")\n",
    "    axes[plot].set_title(f\"$\\sigma$ = {rates.annotations['kernel']['sigma']}\")\n",
    "viziphant.events.add_event(axes, trial_events, key='trial_event_labels')\n",
    "axes[-1].set_xlabel(\"Time (s)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a0b489f",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We can see that the smoothness of the rate curve is affected by the `sigma` parameter.\n",
    "Elephant can try to estimate the best kernel bandwidth for a Gaussian kernel by using `'auto'` as the value of the\n",
    "`kernel` parameter. Let's try this option."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "520c2a6b",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "rates_auto = elephant.statistics.instantaneous_rate(\n",
    "    neuron, kernel='auto', sampling_period=sampling_period)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58218597",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Execute the code below to plot the instantaneous firing rate obtained from the automatic kernel bandwidth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fc3257d6",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(8,4))\n",
    "axes.plot(rates_auto.times.rescale(pq.s), rates_auto)\n",
    "axes.eventplot(neuron.magnitude, color='black', linelengths=5)\n",
    "axes.set_ylabel(\"Firing rate [Hz]\")\n",
    "axes.set_xlabel(\"Time (s)\")\n",
    "axes.set_ylim([0,70])\n",
    "viziphant.events.add_event(axes, trial_events[:-1], key='trial_event_labels')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90f51dfc",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Finally, let's compute and plot the instantaneous firing rates for all the SUAs in the trial.\n",
    "\n",
    "You can pass the list with all spike trains for the `instantaneous_rate` function, to generate a 2D-matrix.\n",
    "Use `kernel_100` as the `kernel` parameter and `.1 ms` as `sampling_period`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "40554790",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "all_rates = elephant.statistics.instantaneous_rate(\n",
    "    sua_spiketrains_with_spikes, kernel=kernel_100, sampling_period=sampling_period)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3a92069",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Now use the Viziphant function `plot_instantaneous_rates_colormesh` to plot the rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "214efc27",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the axes, setting the figure size\n",
    "fig, axes = plt.subplots(figsize=(10,5))\n",
    "# Plot the rates using the axes\n",
    "viziphant.statistics.plot_instantaneous_rates_colormesh(all_rates, axes=axes)\n",
    "# Add the events (need to rescale to the same units as the sampling period in `all_rates`)\n",
    "viziphant.events.add_event(\n",
    "    axes, trial_events[:-1].rescale(all_rates.sampling_period.units),\n",
    "    key='trial_event_labels')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "276c7ec7",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Investigate the spike train correlations in the selected trial\n",
    "\n",
    "From the rasterplot that we generated above, it is difficult to assess if there are correlations between the spike trains.\n",
    "\n",
    "To investigate that, Elephant provides functions to calculate and plot the cross-correlation matrix. This matrix\n",
    "quantifies the similarity for each pair of spike trains in the trial. We start by binning the spike trains\n",
    "that we selected in the trial (SUAs with spiking activity in `sua_spiketrains_with_spikes`). Therefore, we are\n",
    "obtaining the number of spikes that occurred during small intervals along the trial, for each individual neuron.\n",
    "Let's use a bin size of 3 ms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ae94370d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/denker/miniconda3/envs/2021_nix_workshop/lib/python3.8/site-packages/elephant/utils.py:290: UserWarning: Correcting 1 rounding errors by shifting the affected spikes into the following bin. You can set tolerance=None to disable this behaviour.\n",
      "  warnings.warn(f'Correcting {num_corrections} rounding errors by '\n",
      "/home/denker/miniconda3/envs/2021_nix_workshop/lib/python3.8/site-packages/elephant/utils.py:290: UserWarning: Correcting 2 rounding errors by shifting the affected spikes into the following bin. You can set tolerance=None to disable this behaviour.\n",
      "  warnings.warn(f'Correcting {num_corrections} rounding errors by '\n",
      "/home/denker/miniconda3/envs/2021_nix_workshop/lib/python3.8/site-packages/elephant/conversion.py:1168: UserWarning: Binning discarded 2 last spike(s) of the input spiketrain\n",
      "  warnings.warn(\"Binning discarded {} last spike(s) of the \"\n"
     ]
    }
   ],
   "source": [
    "binned_spiketrains = elephant.conversion.BinnedSpikeTrain(\n",
    "    sua_spiketrains_with_spikes, binsize=3*pq.ms)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a464e9cc",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "If we inspect the spike times of one neuron, we can see that if the neuron fired in a particular bin,\n",
    "the `BinnedSpikeTrain` object will store the value 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "27de5c56",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.06746667 0.0734     0.08793333 0.096      0.1015     0.1333\n",
      " 0.1863     0.2388     0.39636667 0.46973333 0.4999     0.68813333\n",
      " 0.73756667 0.77723333 1.25816667 1.46953333 1.69226667 1.75393333\n",
      " 1.76566667 1.84573333 1.97663333] s\n"
     ]
    }
   ],
   "source": [
    "print(sua_spiketrains_with_spikes[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cd83ed3f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 22.48888889,  24.46666667,  29.31111111,  32.        ,\n",
       "        33.83333333,  44.43333333,  62.1       ,  79.6       ,\n",
       "       132.12222222, 156.57777778, 166.63333333, 229.37777778,\n",
       "       245.85555556, 259.07777778, 419.38888889, 489.84444444,\n",
       "       564.08888889, 584.64444444, 588.55555556, 615.24444444,\n",
       "       658.87777778]) * dimensionless"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Index of the 3 ms bin that the spike occurred\n",
    "sua_spiketrains_with_spikes[0].times / (0.003 * pq.s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1c3847a8",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 1 1 0 0 0\n",
      " 0 0 0 0 0 0 0 1 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(binned_spiketrains.to_array()[0,0:50])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d58f1ad8",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The binning above showed several warnings that happened due to the machine error precision of floating point computation. Since now we're aware of it, let's filter them out throughout the rest of the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8d4b6d0d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c49d5f8",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Then we calculate the cross-correlation matrix of the binned spikes using the `correlation_coefficient`\n",
    "function from the `statistics` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c39653af",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "cross_corr_matrix = elephant.spike_train_correlation.correlation_coefficient(\n",
    "    binned_spiketrains)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dae1287a",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We visualize the matrix using the `plot_corrcoef` function of Viziphant. To better visualize the\n",
    "coefficients, we will set `correlation_range` to `'auto'`, to use the color bar only in the range of the\n",
    "obtained coefficients. We will also not plot the values along the main diagonal, as those are equal to 1, by\n",
    "setting `remove_diagonal` to True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4053bad3",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the axes, setting the figure size\n",
    "fig, axes = plt.subplots(figsize=(10,5))\n",
    "# Plot the correlation matrix\n",
    "viziphant.spike_train_correlation.plot_corrcoef(\n",
    "    cross_corr_matrix, axes=axes,correlation_range='auto',remove_diagonal=True)\n",
    "# Set labels and title\n",
    "axes.set_xlabel('Neuron')\n",
    "axes.set_ylabel('Neuron')\n",
    "axes.set_title(\"Correlation coefficient matrix\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f280744",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Save results to NIX\n",
    "\n",
    "Now that we generated several analysis results, we'd like to save them in a NIX file.\n",
    "\n",
    "Let's look at the rate estimates first. These are essentially time series that are represented as `AnalogSignal`\n",
    "objects. The advantage of using such an object instead of a `numpy` array is that it can capture information\n",
    "such as physical units or additional annotations indicating parameters of the analysis. As we've seen above,\n",
    "Elephant recorded such parameters automatically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8452906b",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hz\n",
      "2.0 s\n",
      "{'t_stop': array(2.) * s, 'kernel': {'type': 'GaussianKernel', 'sigma': '100.0 ms', 'invert': False}}\n"
     ]
    }
   ],
   "source": [
    "print(rates_100.dimensionality)\n",
    "print(rates_100.t_stop)\n",
    "print(rates_100.annotations)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "900273f2",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "This way, we can save analysis results to disk with important information indicating how these were generated.\n",
    "We can save these results using the Neo NIX IO. To this end, we compile results into a Neo `Block`, and all\n",
    "rate results in particular into a `Segment`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4c912637",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "results_block=neo.Block(name=\"Analysis\")\n",
    "results_segment=neo.Segment(name=\"Rate Analyses\")\n",
    "results_block.segments.append(results_segment)\n",
    "results_segment.analogsignals.append(rates_100)\n",
    "results_segment.analogsignals.append(rates_20)\n",
    "results_segment.analogsignals.append(rates_auto)\n",
    "\n",
    "with neo.io.NixIO('results.nix', 'ow') as io:\n",
    "    io.write(results_block)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9784961b",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "If we think about a collaborator who may want to build upon your results, maybe it would be good to also supply him/her with the data used to calculate these results. Luckily, these are available as a Neo `Block` in the variable `trials` which we created in the beginning of this tutorial. Saving this data to the same NIX file, we can combine analysis input and output in a single, compact file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6eb11db9",
   "metadata": {},
   "outputs": [],
   "source": [
    "with neo.io.NixIO('results.nix', 'rw') as io:\n",
    "    io.write(trials)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcc6568a",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Some analysis results are represented in structures that do not fit Neo structures, for example, the correlation matrix\n",
    "we calculated above. While efforts to create fitting representations for such analysis results are underway, we can use\n",
    "NIX nevertheless to save these results into the same file as a separate NIX Block. This way, we end up with one single\n",
    "file containing all analysis results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "36d59a83",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "nixfile = nixio.File.open(\"results.nix\", nixio.FileMode.ReadWrite)\n",
    "nixblock = nixfile.create_block(\"correlation_results\", \"nix.session\")\n",
    "data_array = nixblock.create_data_array(\n",
    "    \"correlation_matrix\", \"nix.sampled.multichannel\", \n",
    "    data=cross_corr_matrix,\n",
    "    label=\"cross-correlation coefficient\", \n",
    "    unit=\"dimensionless\")\n",
    "data_array.append_set_dimension(\n",
    "    labels=[\"neuron %i\" % i for i in range(cross_corr_matrix.shape[0])])\n",
    "data_array.append_set_dimension(\n",
    "    labels=[\"neuron %i\" % i for i in range(cross_corr_matrix.shape[0])])\n",
    "nixfile.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0fddd06",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
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}