spikeinterface_demo.ipynb

spikeinterface_demo.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "spikeinterface_demo.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/magland/e43542fe2dfe856fd04903b9ff1f8e4e/spikeinterface_demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "NX2p0yPv0uwk",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# SpikeInterface, SpikeWidgets, and SpikeTookit"
      ]
    },
    {
      "metadata": {
        "id": "UdA3ioZN2Awd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "SpikeInterface is a module that enables easy creation and deployment of tools for extracting raw or spike sorted extracellular data from any file format. More information can be found at the [git repository](https://github.com/colehurwitz31/spikeinterface). \n",
        "\n",
        "This notebook demonstrates the usage and basic features of this package as well as its two companion packages, spikewidgets and spiketoolkit."
      ]
    },
    {
      "metadata": {
        "id": "dOEyIRQc2r1B",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "## First install the packages using pip (%%capture will suppress the console output)\n",
        "%%capture\n",
        "!pip install spikeinterface==0.1.15 spiketoolkit==0.1.4 spikewidgets==0.1.18\n",
        "# The following are needed for the Mda (MountainSort) format below\n",
        "!pip install mountainlab_pytools==0.7.3 kbucket==0.12.0 ml_ms4alg==0.1.16"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oOasH_0x29Uz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#%load_ext autoreload\n",
        "#%autoreload 2\n",
        "\n",
        "## Import some modules\n",
        "import spikeinterface as si\n",
        "import spikewidgets as sw\n",
        "import spiketoolkit as st\n",
        "import numpy as np\n",
        "import os"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zwqqrJTm6v5r",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Recording extractors"
      ]
    },
    {
      "metadata": {
        "id": "d7jWCv0_7SB0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Recording extractors represent electrophysiology recordings, including the raw traces and information about the probe and electrode channels.\n",
        "\n",
        "The simplest kind of recording extractor is the NumpyRecordingExtractor. It represents an electrophysiology recording in memory and is initialized by passing numpy arrays as follows."
      ]
    },
    {
      "metadata": {
        "id": "6a6zqIg-3EJa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Properties of the in-memory dataset\n",
        "num_channels=7\n",
        "samplerate=30000\n",
        "duration=20\n",
        "num_timepoints=int(samplerate*duration)\n",
        "\n",
        "# Generate a pure-noise timeseries dataset and a linear geometry\n",
        "timeseries=np.random.normal(0,10,(num_channels,num_timepoints))\n",
        "geom=np.zeros((num_channels,2))\n",
        "geom[:,0]=range(num_channels)\n",
        "\n",
        "recording=si.NumpyRecordingExtractor(\n",
        "    timeseries=timeseries,\n",
        "    geom=geom,\n",
        "    samplerate=samplerate\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PgaQTs3v3FmV",
        "colab_type": "code",
        "outputId": "0f3b90f0-895b-4fff-9a70-f64773beb3f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "cell_type": "code",
      "source": [
        "## Print out various properties of this recording\n",
        "print('Channels = {}'.format(\n",
        "    recording.getChannelIds()\n",
        "))\n",
        "print('Sampling frequency = {} Hz'.format(\n",
        "    recording.getSamplingFrequency()\n",
        "))\n",
        "print('Num. timepoints = {}'.format(\n",
        "    recording.getNumFrames()\n",
        "))\n",
        "print('Stdev. on third channel = {}'.format(\n",
        "    np.std(recording.getTraces(channel_ids=2))\n",
        "))\n",
        "print('Location of third electrode = {}'.format(recording.getChannelProperty(2,'location')))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Channels = [0, 1, 2, 3, 4, 5, 6]\n",
            "Sampling frequency = 30000.0 Hz\n",
            "Num. timepoints = 600000\n",
            "Stdev. on third channel = 9.99562836232269\n",
            "Location of third electrode = [2. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "btekH7Iy7v0_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "MdaRecordingExtractor is another example of a recording extractor (subclass of RecordingExtractor). This is meant to read/write the format used by MountainSort. Let's write out the above recording in the Mda format and then read it back in using this extractor:"
      ]
    },
    {
      "metadata": {
        "id": "OHIrOGOj3E6O",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "## The following should write a dataset to the sample_mountainsort_dataset directory\n",
        "si.MdaRecordingExtractor.writeRecording(\n",
        "    recording=recording,\n",
        "    save_path='sample_mountainsort_dataset'\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "IuesDWxo5w9L",
        "colab_type": "code",
        "outputId": "da1888ea-7a10-458a-c8be-d93fcf0033b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "cell_type": "code",
      "source": [
        "## Verify that we will get the same results if we load it back in using the MdaRecordingExtractor:\n",
        "recording2=si.MdaRecordingExtractor(\n",
        "    dataset_directory='sample_mountainsort_dataset'\n",
        ")\n",
        "\n",
        "print('Channels = {}'.format(\n",
        "    recording2.getChannelIds()\n",
        "))\n",
        "print('Sampling frequency = {} Hz'.format(\n",
        "    recording2.getSamplingFrequency()\n",
        "))\n",
        "print('Num. timepoints = {}'.format(\n",
        "    recording2.getNumFrames()\n",
        "))\n",
        "print('Stdev. on third channel = {}'.format(\n",
        "    np.std(recording2.getTraces(channel_ids=2))\n",
        "))\n",
        "print('Location of third electrode = {}'.format(\n",
        "    recording2.getChannelProperty(2,'location')\n",
        "))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Channels = [0, 1, 2, 3, 4, 5, 6]\n",
            "Sampling frequency = 30000.0 Hz\n",
            "Num. timepoints = 600000\n",
            "Stdev. on third channel = 9.995628356933594\n",
            "Location of third electrode = [2. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "pJDbQw1n64zl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "There's a lot more to explore about recording extractors! But first let's introduce sorting extractors..."
      ]
    },
    {
      "metadata": {
        "id": "lu0G3M__7Eza",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Sorting extractors"
      ]
    },
    {
      "metadata": {
        "id": "HY7afAXu8Fni",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Sorting extractors represent the output of a spike sorting run. Similar to above, the simplest kind of sorting extractor is the NumpySortingExtractor. It is defined by passing in spike trains via numpy arrays as follows."
      ]
    },
    {
      "metadata": {
        "id": "1Nbz1yUQ3Ij2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Generate some random events\n",
        "num_events=300\n",
        "num_timepoints=30000*20\n",
        "num_units=8\n",
        "times=np.sort(np.random.uniform(0,num_timepoints,num_events))\n",
        "labels=np.random.randint(1,num_units+1,size=num_events)\n",
        "    \n",
        "# Define the in-memory sorting extractor\n",
        "sorting=si.NumpySortingExtractor()\n",
        "for k in range(1,num_units+1):\n",
        "    times_k=times[np.where(labels==k)[0]]\n",
        "    sorting.addUnit(unit_id=k,times=times_k)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Kvz-UeUq9S9h",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Next, to demonstrate the API, we can print out some information about this sorting."
      ]
    },
    {
      "metadata": {
        "id": "aPXp1yKs9EOZ",
        "colab_type": "code",
        "outputId": "13b6e20a-954f-4dc1-baed-2ee76272bc55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "cell_type": "code",
      "source": [
        "print('Unit IDs: {}'.format(\n",
        "    sorting.getUnitIds()\n",
        "))\n",
        "\n",
        "train=sorting.getUnitSpikeTrain(unit_id=1)\n",
        "print('Num. events for unit #1: {}'.format(\n",
        "    len(train)\n",
        "))\n",
        "\n",
        "train1=sorting.getUnitSpikeTrain(unit_id=1,start_frame=0,end_frame=30000*2)\n",
        "print('Num. events for first two seconds of unit #1: {}'.format(\n",
        "    len(train1)\n",
        "))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unit IDs: [1, 2, 3, 4, 5, 6, 7, 8]\n",
            "Num. events for unit #1: 28\n",
            "Num. events for first two seconds of unit #1: 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "IlrhgXfB9n5H",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The MdaSortingExtractor allows us to read and write using the firings.mda format used by MountainSort."
      ]
    },
    {
      "metadata": {
        "id": "KCNl8ngJ9D4U",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "if not os.path.exists('sample_mountainsort_dataset'): os.mkdir('sample_mountainsort_dataset')\n",
        "si.MdaSortingExtractor.writeSorting(sorting=sorting,save_path='sample_mountainsort_dataset/firings_true.mda')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6Oyiplaw9EE_",
        "colab_type": "code",
        "outputId": "4bf28083-1188-4210-9553-6cbe4c5946f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "cell_type": "code",
      "source": [
        "sorting2=si.MdaSortingExtractor(firings_file='sample_mountainsort_dataset/firings_true.mda')\n",
        "\n",
        "## Verify that we get the same outputs as above\n",
        "print('Unit IDs: {}'.format(sorting2.getUnitIds()))\n",
        "train=sorting2.getUnitSpikeTrain(unit_id=1)\n",
        "print('Num. events for unit #1: {}'.format(len(train)))\n",
        "train1=sorting2.getUnitSpikeTrain(unit_id=1,start_frame=0,end_frame=30000*2)\n",
        "print('Num. events for first two seconds of unit #1: {}'.format(len(train1)))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unit IDs: [1 2 3 4 5 6 7 8]\n",
            "Num. events for unit #1: 28\n",
            "Num. events for first two seconds of unit #1: 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "SJDMLV_v0wnw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Visualization and analysis using SpikeWidgets and SpikeToolkit"
      ]
    },
    {
      "metadata": {
        "id": "wbSs1Alt-bYp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Spike interface allows us to develop visualization tools that can be applied to recordings and sortings, independent of the underlying file format. Let's try out the spikewidgets package:"
      ]
    },
    {
      "metadata": {
        "id": "y5sg57wqCOGp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "For convenience, SpikeWidgets can generate a toy example for us:"
      ]
    },
    {
      "metadata": {
        "id": "B7khZGqNArnH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Generate a toy example recording (R1) and ground truth sorting (S1)\n",
        "R1,S1=sw.example_datasets.toy_example1(\n",
        "    duration=100,\n",
        "    num_channels=4,\n",
        "    samplerate=30000,\n",
        "    K=10\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bq59gYm5D0Ld",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now we can plot the average spike waveforms and a collection of representative spikes by passing this recording/sorting extractor pair to the UnitWaveformsWidget:"
      ]
    },
    {
      "metadata": {
        "id": "58B5PxffDYlW",
        "colab_type": "code",
        "outputId": "16d55094-4813-4b03-9449-cec551eec30e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        }
      },
      "cell_type": "code",
      "source": [
        "sw.UnitWaveformsWidget(\n",
        "    recording = R1,\n",
        "    sorting = S1,\n",
        "    unit_ids = [1,2,3,4],\n",
        "    channels = [0,1,2,3]\n",
        ").plot()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqYAAADACAYAAADWSaHxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npoQQQgghZCX8H8GyyxHntPG0AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f8dfaaae1d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "axRdlNz1EKzd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "A similar method gives us the autocorrelograms:"
      ]
    },
    {
      "metadata": {
        "id": "vMSq_eEfEGOs",
        "colab_type": "code",
        "outputId": "ba9a6e59-1147-487e-f2b6-82202c2827b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        }
      },
      "cell_type": "code",
      "source": [
        "sw.CrossCorrelogramsWidget(\n",
        "    sorting = S1,\n",
        "    samplerate = R1.getSamplingFrequency(),\n",
        "    unit_ids=[1,2,3,4,5]\n",
        ").plot()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1MAAADOCAYAAADbqFicAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQAZhCgAAIIMwBQAAkOHfyZvA5kTYZ+MAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f8df79866a0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "DsvWSRCOElKk",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "A section of the timeseries data can be viewed using the TimeseriesWidget:"
      ]
    },
    {
      "metadata": {
        "id": "lY_bwmYwCrwU",
        "colab_type": "code",
        "outputId": "046df443-5587-4583-bdf5-93e957082158",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        }
      },
      "cell_type": "code",
      "source": [
        "# The following is interactive only when using Jupyter and the ipywidgets extension\n",
        "sw.TimeseriesWidget(recording=R1,channels=[0,1,2,3],trange=[1000,8000]).display()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqsAAAFzCAYAAAAZnkAuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niYiIiMhm/X/XvQ7SguG8nQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f8dfe0fc518>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "885ff154e4694a8fa6216ef2ddb6be44",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "VBox(children=(HBox(children=(Button(icon='plus-square', layout=Layout(width='40px'), style=ButtonStyle(), too…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "QlTjJKFP0w3z",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Spike sorting"
      ]
    },
    {
      "metadata": {
        "id": "6bYX-gdWGYLx",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Since we have encapsulated recordings and sortings in abstract python objects, it is natural to define a spike sorter to be a function that takes as input a recording extractor and outputs a sorting extractor. This completely separates the issue of file formats from the spike sorting procedure, and ensures that all algorithms operate within the same framework.\n",
        "\n",
        "We have wrapped several open-source spike sorting algorithms in a package called SpikeToolkit."
      ]
    },
    {
      "metadata": {
        "id": "shsKZEZMHr2L",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Let's generate a new toy example\n",
        "rec, sorting_true = sw.example_datasets.toy_example1(\n",
        "    duration = 120,\n",
        "    num_channels = 4,\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XTeXtK9aIAnA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Spike sorting is now a one-liner:"
      ]
    },
    {
      "metadata": {
        "id": "W3hUazZaIEYq",
        "colab_type": "code",
        "outputId": "76f3f60e-52f3-4ec6-8718-7166651aa6fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 907
        }
      },
      "cell_type": "code",
      "source": [
        "sorting_ms4=st.sorters.mountainsort4(\n",
        "    recording=rec,\n",
        "    detect_sign=-1,\n",
        "    adjacency_radius=-1,\n",
        "    detect_threshold=3,\n",
        "    noise_overlap_threshold=0.2\n",
        ")"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using tmpdir: /tmp/tmp7ae2r7z_\n",
            "Preparing /tmp/tmp7ae2r7z_/timeseries.hdf5...\n",
            "Preparing neighborhood sorters (M=4, N=3600000)...\n",
            "Detecting events on channel 1 (phase1)...\n",
            "Num events detected on channel 1 (phase1): 3513\n",
            "Computing PCA features for channel 1 (phase1)...\n",
            "Clustering for channel 1 (phase1)...\n",
            "Found 7 clusters for channel 1 (phase1)...\n",
            "Computing templates for channel 1 (phase1)...\n",
            "Re-assigning events for channel 1 (phase1)...\n",
            "Re-assigning 2 events from 1 to 4 with dt=-10 (k=3)\n",
            "Detecting events on channel 2 (phase1)...\n",
            "Num events detected on channel 2 (phase1): 1984\n",
            "Computing PCA features for channel 2 (phase1)...\n",
            "Clustering for channel 2 (phase1)...\n",
            "Found 10 clusters for channel 2 (phase1)...\n",
            "Computing templates for channel 2 (phase1)...\n",
            "Re-assigning events for channel 2 (phase1)...\n",
            "Re-assigning 2 events from 2 to 3 with dt=-2 (k=2)\n",
            "Re-assigning 41 events from 2 to 1 with dt=1 (k=3)\n",
            "Re-assigning 30 events from 2 to 1 with dt=3 (k=4)\n",
            "Re-assigning 1 events from 2 to 4 with dt=-7 (k=9)\n",
            "Detecting events on channel 3 (phase1)...\n",
            "Num events detected on channel 3 (phase1): 3851\n",
            "Computing PCA features for channel 3 (phase1)...\n",
            "Clustering for channel 3 (phase1)...\n",
            "Found 9 clusters for channel 3 (phase1)...\n",
            "Computing templates for channel 3 (phase1)...\n",
            "Re-assigning events for channel 3 (phase1)...\n",
            "Detecting events on channel 4 (phase1)...\n",
            "Num events detected on channel 4 (phase1): 1404\n",
            "Computing PCA features for channel 4 (phase1)...\n",
            "Clustering for channel 4 (phase1)...\n",
            "Found 6 clusters for channel 4 (phase1)...\n",
            "Computing templates for channel 4 (phase1)...\n",
            "Re-assigning events for channel 4 (phase1)...\n",
            "Re-assigning 29 events from 4 to 3 with dt=1 (k=4)\n",
            "Computing PCA features for channel 1 (phase2)...\n",
            "Clustering for channel 1 (phase2)...\n",
            "Found 5 clusters for channel 1 (phase2)...\n",
            "Computing PCA features for channel 2 (phase2)...\n",
            "Clustering for channel 2 (phase2)...\n",
            "Found 2 clusters for channel 2 (phase2)...\n",
            "Computing PCA features for channel 3 (phase2)...\n",
            "Clustering for channel 3 (phase2)...\n",
            "Found 6 clusters for channel 3 (phase2)...\n",
            "Computing PCA features for channel 4 (phase2)...\n",
            "Clustering for channel 4 (phase2)...\n",
            "Found 2 clusters for channel 4 (phase2)...\n",
            "Preparing output...\n",
            "Done.\n",
            "Cleaning tmpdir: /tmp/tmp7ae2r7z_\n",
            "Elapsed time:  25.230539321899414\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "jex7rsXHFvp3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's take a look at the auto-correlograms for our sorting:"
      ]
    },
    {
      "metadata": {
        "id": "8lFRI6YmLY13",
        "colab_type": "code",
        "outputId": "5ec3dd44-5ae7-430b-996d-0c61aee33591",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "cell_type": "code",
      "source": [
        "sw.CrossCorrelogramsWidget(sorting=sorting_ms4,samplerate=rec.getSamplingFrequency()).plot()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1MAAAFxCAYAAACWQ/KIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAChZKw+OWGzuAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f8df6062ba8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "u3dcFX0YMdvX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "To compare with ground truth, we can create a sorting comparison object and then get a pandas dataframe summarizing the comparison:"
      ]
    },
    {
      "metadata": {
        "id": "62KZRvKyMi2E",
        "colab_type": "code",
        "outputId": "4c7bc106-df69-4233-8e5e-1942d6bf73a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "cell_type": "code",
      "source": [
        "comparison = st.comparison.SortingComparison(\n",
        "    sorting_true,\n",
        "    sorting_ms4\n",
        ")\n",
        "\n",
        "df = sw.SortingComparisonTable(\n",
        "    comparison = comparison\n",
        ").getDataframe()\n",
        "\n",
        "display(df)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "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>Unit ID</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Best unit</th>\n",
              "      <th>Matched unit</th>\n",
              "      <th>f.n.</th>\n",
              "      <th>f.p.</th>\n",
              "      <th># matches</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.99</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.93</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.03</td>\n",
              "      <td>268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1.00</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.79</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.20</td>\n",
              "      <td>230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0.96</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.04</td>\n",
              "      <td>268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0.71</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.29</td>\n",
              "      <td>203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0.97</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "      <td>269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>0.96</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "      <td>279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>0.99</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>294</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unit ID Accuracy  Best unit  Matched unit  f.n.  f.p.  # matches\n",
              "0        1     1.00          3             3  0.00  0.00        281\n",
              "1        2     0.99          5             5  0.00  0.00        284\n",
              "2        3     0.93          4             4  0.04  0.03        268\n",
              "3        4     1.00          6             6  0.00  0.00        289\n",
              "4        5     0.79          2             2  0.01  0.20        230\n",
              "5        6     0.96          8             8  0.00  0.04        268\n",
              "6        7     0.71         11            11  0.00  0.29        203\n",
              "7        8     0.97         13            13  0.00  0.03        269\n",
              "8        9     0.96         12            12  0.00  0.03        279\n",
              "9       10     0.99         15            15  0.00  0.00        294"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "9DAj9vPA8prl",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}