spikeforest_analysis.ipynb

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  "metadata": {
    "colab": {
      "name": "spikeforest_analysis.ipynb",
      "version": "0.3.2",
      "provenance": [],
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      "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/github/jamesjun/ironclust/blob/master/spikeforest_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "xfQAJFPFgbzE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## SpikeForest bionet analysis"
      ]
    },
    {
      "metadata": {
        "id": "Fcx4kQoVt374",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "This notebook represents a complete spikeforest analysis of the bionet studies. You should execute the first few cells and then skip down to the section of interest below."
      ]
    },
    {
      "metadata": {
        "id": "vtsE5P3EgXYr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# JJJ was here\n",
        "# JFM was here too\n",
        "# Only run this cell if you are running this on a hosted runtime that does not have these packages installed\n",
        "# %%capture is used to suppress the output... this should take up to a minute to complete\n",
        "%%capture\n",
        "!pip install spikeforest\n",
        "!pip install git+https://github.com/magland/spikeforest_batch_run"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "P3-FpNWUgWln",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Import the python packages -- autoreload is used for development purposes\n",
        "%load_ext autoreload\n",
        "%autoreload 2\n",
        "\n",
        "import spikeforest as sf\n",
        "from kbucket import client as kb"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_JHpizoTul8M",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "## Configure readonly access to kbucket -- use this if you only want to browse the results ---\n",
        "sf.kbucketConfigRemote(name='spikeforest1-readonly')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3pHRiMtggkaT",
        "colab_type": "code",
        "outputId": "d8b2b93b-c7b4-4a15-db29-bd690ad4815c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "## Configure read/write access to kbucket -- use this if you are preparing the studies or the processing batches\n",
        "sf.kbucketConfigRemote(name='spikeforest1-readwrite',ask_password=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter password: ··········\n",
            "Pairio user set to spikeforest. Test succeeded.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "blWcqWOIhzkc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Prepare recordings"
      ]
    },
    {
      "metadata": {
        "id": "HtbjJUirgm0s",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def read_text_file(path):\n",
        "  path2=kb.realizeFile(path)\n",
        "  if path2 is None:\n",
        "    raise Exception('Unable to realize file: '+path)\n",
        "  with open(path2,'r') as f:\n",
        "    return f.read()\n",
        "  \n",
        "def prepare_bionet_studies(*,basedir,channels):\n",
        "  study_set_name='bionet'\n",
        "  studies=[]\n",
        "  recordings=[]\n",
        "  names=['bionet_drift','bionet_shuffle','bionet_static']\n",
        "  for name in names:\n",
        "    study_name=name\n",
        "    study_dir=basedir+'/bionet/'+name\n",
        "    description=read_text_file(study_dir+'/readme.txt')\n",
        "    study0=dict(\n",
        "        name=study_name,\n",
        "        study_set=study_set_name,\n",
        "        directory=study_dir,\n",
        "        description=description\n",
        "    )\n",
        "    studies.append(study0)\n",
        "    dd=kb.readDir(study_dir)\n",
        "    for dsname in dd['dirs']:\n",
        "        dsdir='{}/{}'.format(study_dir,dsname)\n",
        "        rec0=dict(\n",
        "            name=dsname,\n",
        "            study=study_name,\n",
        "            description='',\n",
        "            directory=dsdir,\n",
        "            channels=channels\n",
        "        )\n",
        "        if len(rec0['channels'])>0:\n",
        "          units=sf.sf_batch.select_units_on_channels(\n",
        "              recording_dir=dsdir,\n",
        "              firings=dsdir+'/firings_true.mda',\n",
        "              channels=rec0['channels']\n",
        "          )\n",
        "          rec0['units_true']=units\n",
        "        recordings.append(rec0)\n",
        "  return studies, recordings\n",
        "\n",
        "def prepare_magland_synth_studies(*,basedir):\n",
        "  study_set_name='magland_synth'\n",
        "  studies=[]\n",
        "  recordings=[]\n",
        "  names=[]\n",
        "  names=names+['datasets_noise10_K10_C4','datasets_noise10_K10_C8']\n",
        "  names=names+['datasets_noise10_K20_C4','datasets_noise10_K20_C8']\n",
        "  names=names+['datasets_noise20_K10_C4','datasets_noise20_K10_C8']\n",
        "  names=names+['datasets_noise20_K20_C4','datasets_noise20_K20_C8']\n",
        "  description=read_text_file(basedir+'/magland_synth/readme.txt')\n",
        "  for name in names:\n",
        "    study_name='magland_synth_'+name[9:]\n",
        "    study_dir=basedir+'/magland_synth/'+name\n",
        "    study0=dict(\n",
        "        name=study_name,\n",
        "        study_set=study_set_name,\n",
        "        directory=study_dir,\n",
        "        description=description\n",
        "    )\n",
        "    studies.append(study0)\n",
        "    dd=kb.readDir(study_dir)\n",
        "    for dsname in dd['dirs']:\n",
        "        dsdir='{}/{}'.format(study_dir,dsname)\n",
        "        recordings.append(dict(\n",
        "            name=dsname,\n",
        "            study=study_name,\n",
        "            directory=dsdir,\n",
        "            description='One of the recordings in the {} study'.format(study_name)\n",
        "        ))\n",
        "  return studies, recordings"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BpXkEoS8gaMn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "basedir='kbucket://15734439d8cf/groundtruth'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "b92joharg0G5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "channels=[0,1,2,3,4,5,6,7]\n",
        "studies,recordings=prepare_bionet_studies(basedir=basedir,channels=channels)\n",
        "kb.saveObject(dict(studies=studies,recordings=recordings),key=dict(name='spikeforest_bionet_recordings'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XCUMPH8JgXK7",
        "colab_type": "code",
        "outputId": "98e5a738-2d09-4a88-e29c-01211964fb92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "studies,recordings=prepare_magland_synth_studies(basedir=basedir)\n",
        "kb.saveObject(dict(studies=studies,recordings=recordings),key=dict(name='spikeforest_magland_synth_recordings'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Already on server.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "rvCvUJ1JiZX1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Create summarize recordings batches"
      ]
    },
    {
      "metadata": {
        "id": "MWIJU5Gxic_f",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def create_summarize_recordings_batch(*,recordings_name,batch_name):\n",
        "  print('Creating summarize_recordings batch: '+batch_name)\n",
        "  SF=sf.SFData()\n",
        "  SF.loadRecordings(key=dict(name=recordings_name))\n",
        "  \n",
        "  jobs=[]\n",
        "  for name in SF.studyNames():\n",
        "    study=SF.study(name)\n",
        "    for recname in study.recordingNames():\n",
        "      R=study.recording(recname)\n",
        "      job=dict(\n",
        "          command='summarize_recording',\n",
        "          label=R.name(),\n",
        "          recording=R.getObject()\n",
        "      )\n",
        "      jobs.append(job)\n",
        "  batch=dict(jobs=jobs)\n",
        "  print('Number of jobs: {}'.format(len(jobs)))\n",
        "  kb.saveObject(key=dict(batch_name=batch_name),object=batch)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Vrw88QA5hACU",
        "colab_type": "code",
        "outputId": "71ece4cc-ad87-4c7e-c7f8-3aed3a64d1ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "cell_type": "code",
      "source": [
        "create_summarize_recordings_batch(recordings_name='spikeforest_bionet_recordings',batch_name='summarize_recordings_bionet')\n",
        "create_summarize_recordings_batch(recordings_name='spikeforest_magland_synth_recordings',batch_name='summarize_recordings_magland_synth')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Creating summarize_recordings batch: summarize_recordings_bionet\n",
            "Number of jobs: 36\n",
            "Already on server.\n",
            "Creating summarize_recordings batch: summarize_recordings_magland_synth\n",
            "Number of jobs: 80\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "weikHp2gjbwJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "To run these batches, go to a computer with resources somewhere and run something like:\n",
        "\n",
        "```\n",
        "bin/sf_run_batch [name_of_batch] --run_prefix \"srun -c 2 -n 40\"\n",
        "```\n",
        "\n",
        "where bin/sf_run_batch is found in the spikeforest_batch_run repository.\n",
        "\n",
        "Alternatively, you can test run it in this notebook using the following commands:"
      ]
    },
    {
      "metadata": {
        "id": "z5k_8AHGk0mu",
        "colab_type": "code",
        "outputId": "416499f3-7ffc-467e-b994-34f66e226d2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        }
      },
      "cell_type": "code",
      "source": [
        "## Note: usually you would not run this cell -- see the note above.\n",
        "\n",
        "import spikeforest_batch_run as sbr\n",
        "# Execute prepareBatch once (serially)\n",
        "sbr.prepareBatch(batch_name='summarize_recordings_bionet')\n",
        "\n",
        "# Execute runBatch many times in parallel\n",
        "sbr.runBatch(batch_name='summarize_recordings_bionet')\n",
        "\n",
        "# Execute assembleBatchResults once (serially)\n",
        "sbr.assembleBatchResults(batch_name='summarize_recordings_bionet')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Preparing batch summarize_recordings_bionet with 36 jobs\n",
            "Running batch summarize_recordings_bionet with 36 jobs\n",
            "Assembling results for batch summarize_recordings_bionet with 36 jobs\n",
            "ASSEMBLING: drift_8x_A_2A\n",
            "ASSEMBLING: drift_8x_A_2B\n",
            "ASSEMBLING: drift_8x_A_4A\n",
            "ASSEMBLING: drift_8x_A_4B\n",
            "ASSEMBLING: drift_8x_B_2A\n",
            "ASSEMBLING: drift_8x_B_2B\n",
            "ASSEMBLING: drift_8x_B_4A\n",
            "ASSEMBLING: drift_8x_B_4B\n",
            "ASSEMBLING: drift_8x_C_2A\n",
            "ASSEMBLING: drift_8x_C_2B\n",
            "ASSEMBLING: drift_8x_C_4A\n",
            "ASSEMBLING: drift_8x_C_4B\n",
            "ASSEMBLING: shuffle_8x_A_2A\n",
            "ASSEMBLING: shuffle_8x_A_2B\n",
            "ASSEMBLING: shuffle_8x_A_4A\n",
            "ASSEMBLING: shuffle_8x_A_4B\n",
            "ASSEMBLING: shuffle_8x_B_2A\n",
            "ASSEMBLING: shuffle_8x_B_2B\n",
            "ASSEMBLING: shuffle_8x_B_4A\n",
            "ASSEMBLING: shuffle_8x_B_4B\n",
            "ASSEMBLING: shuffle_8x_C_2A\n",
            "ASSEMBLING: shuffle_8x_C_2B\n",
            "ASSEMBLING: shuffle_8x_C_4A\n",
            "ASSEMBLING: shuffle_8x_C_4B\n",
            "ASSEMBLING: static_8x_A_2A\n",
            "ASSEMBLING: static_8x_A_2B\n",
            "ASSEMBLING: static_8x_A_4A\n",
            "ASSEMBLING: static_8x_A_4B\n",
            "ASSEMBLING: static_8x_B_2A\n",
            "ASSEMBLING: static_8x_B_2B\n",
            "ASSEMBLING: static_8x_B_4A\n",
            "ASSEMBLING: static_8x_B_4B\n",
            "ASSEMBLING: static_8x_C_2A\n",
            "ASSEMBLING: static_8x_C_2B\n",
            "ASSEMBLING: static_8x_C_4A\n",
            "ASSEMBLING: static_8x_C_4B\n",
            "Saving results...\n",
            "Already on server.\n",
            "Done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "TKwgGlPKh5uE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Browse recordings"
      ]
    },
    {
      "metadata": {
        "id": "-qq56y38hJpv",
        "colab_type": "code",
        "outputId": "43ad502f-c6b8-407f-90a9-90c1e9429cc6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "SF=sf.SFData()\n",
        "SF.loadRecordings(key=dict(name='spikeforest_bionet_recordings'))\n",
        "SF.loadRecordings(key=dict(name='spikeforest_magland_synth_recordings'))\n",
        "SF.loadProcessingBatch(key=dict(batch_name='summarize_recordings_bionet',name='job_results'))\n",
        "SF.loadProcessingBatch(key=dict(batch_name='summarize_recordings_magland_synth',name='job_results'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loaded 0 sorting results and 36 recording summary results\n",
            "Downloading file --- (0.0 MB): http://132.249.245.245:24351/7317cea8265b/download/1/e5/1e55d734f322497acaee334660f373bd270cf2cd -> /home/magland/kbucket_cache/1/e5/1e55d734f322497acaee334660f373bd270cf2cd\n",
            "Loaded 0 sorting results and 80 recording summary results\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "6o8vClJ9hrRa",
        "colab_type": "code",
        "outputId": "0379a936-5617-446d-ed25-3d1e8dde8cd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 97
        }
      },
      "cell_type": "code",
      "source": [
        "X=sf.SFSelectWidget(sfdata=SF,mode='recording')\n",
        "display(X)"
      ],
      "execution_count": 0,
      "outputs": [
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              "document.getElementById('36ac824a-3272-477e-a44a-928437712a6f').innerHTML=atob('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'); console.log('test1');"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "document.getElementById('20ee1f45-6597-4cce-8d0c-27c94df06a27').innerHTML=atob('PHNlbGVjdCBvbmNoYW5nZT0iZ29vZ2xlLmNvbGFiLmtlcm5lbC5pbnZva2VGdW5jdGlvbigmI3gyNztvbmNoYW5nZWNhbGxiYWNrLWU3YTVmNmU4LTg1NzMtNGJhZS05ZTczLTczNzhjZDQ5OGQ5NyYjeDI3OywgW10sIHt2YWx1ZTp0aGlzLnZhbHVlfSkiPjwvc2VsZWN0Pg=='); console.log('test1');"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<spikeforest.sfselectwidget.SFSelectWidget at 0x7f353ac56860>"
            ],
            "text/html": [
              "<div id=4f704f07-146c-4225-a0df-23a624138169><div><hr></hr><table><tr><td>Study **: </td><td><div id=78c13603-b05b-4eeb-a633-a293398aeef9><select onchange=\"google.colab.kernel.invokeFunction(&#x27;onchangecallback-5a340658-f199-43be-8cce-f68abb99c625&#x27;, [], {value:this.value})\"><option selected=\"selected\">bionet_drift</option><option>bionet_shuffle</option><option>bionet_static</option><option>magland_synth_noise10_K10_C4</option><option>magland_synth_noise10_K10_C8</option><option>magland_synth_noise10_K20_C4</option><option>magland_synth_noise10_K20_C8</option><option>magland_synth_noise20_K10_C4</option><option>magland_synth_noise20_K10_C8</option><option>magland_synth_noise20_K20_C4</option><option>magland_synth_noise20_K20_C8</option></select></div></td></tr><tr><td>Recording: </td><td><div id=36ac824a-3272-477e-a44a-928437712a6f><select onchange=\"google.colab.kernel.invokeFunction(&#x27;onchangecallback-e142c448-ec85-4190-8865-d6d47a9b8df6&#x27;, [], {value:this.value})\"><option selected=\"selected\">drift_8x_A_2A</option><option>drift_8x_A_2B</option><option>drift_8x_A_4A</option><option>drift_8x_A_4B</option><option>drift_8x_B_2A</option><option>drift_8x_B_2B</option><option>drift_8x_B_4A</option><option>drift_8x_B_4B</option><option>drift_8x_C_2A</option><option>drift_8x_C_2B</option><option>drift_8x_C_4A</option><option>drift_8x_C_4B</option></select></div></td></tr></table><hr></hr></div></div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "CwiDbAZbiF48",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "R=X.recording()\n",
        "display(R.plot('timeseries'))\n",
        "display(R.plot('waveforms_true'))\n",
        "display(R.trueUnitsInfo())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WBdqbkSNiJyS",
        "colab_type": "code",
        "outputId": "91e60e18-03d8-49a2-b780-a1bbdb1117a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "R.plotNames()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['timeseries', 'waveforms_true']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "metadata": {
        "id": "1vvFYgXjmetF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Create spike sorting batches"
      ]
    },
    {
      "metadata": {
        "id": "7toleMsBnMy6",
        "colab_type": "code",
        "outputId": "0c2d92e4-6754-4f8d-8edb-866ea1dae07a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "SF=sf.SFData()\n",
        "SF.loadRecordings(key=dict(name='spikeforest_bionet_recordings'))\n",
        "SF.loadRecordings(key=dict(name='spikeforest_magland_synth_recordings'))\n",
        "SF.loadProcessingBatch(key=dict(batch_name='summarize_recordings_bionet',name='job_results'))\n",
        "SF.loadProcessingBatch(key=dict(batch_name='summarize_recordings_magland_synth',name='job_results'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loaded 0 sorting results and 36 recording summary results\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2PHEdeuBnVJl",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sorter_ms4_thr3=dict(\n",
        "    name='MountainSort4-thr3',\n",
        "    processor_name='MountainSort4',\n",
        "    params=dict(\n",
        "        detect_sign=-1,\n",
        "        adjacency_radius=100,\n",
        "        detect_threshold=3\n",
        "    )\n",
        ")\n",
        "\n",
        "sorter_irc_tetrode=dict(\n",
        "    name='IronClust-tetrode',\n",
        "    processor_name='IronClust',\n",
        "    params=dict(\n",
        "        detect_sign=-1,\n",
        "        adjacency_radius=100,\n",
        "        detect_threshold=5,\n",
        "        prm_template_name=\"tetrode_template.prm\"\n",
        "    )\n",
        ")\n",
        "\n",
        "sorter_irc_drift=dict(\n",
        "    name='IronClust-drift',\n",
        "    processor_name='IronClust',\n",
        "    params=dict(\n",
        "        detect_sign=-1,\n",
        "        adjacency_radius=100,\n",
        "        prm_template_name=\"drift_template.prm\"\n",
        "    )\n",
        ")\n",
        "\n",
        "sorter_sc=dict(\n",
        "    name='SpykingCircus',\n",
        "    processor_name='SpykingCircus',\n",
        "    params=dict(\n",
        "        detect_sign=-1,\n",
        "        adjacency_radius=100\n",
        "    )\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NhvWMXK7mSYu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def create_sorting_batch(*,recordings_name,batch_name,sorters):\n",
        "  print('Creating sorting batch: '+batch_name)\n",
        "  SF=sf.SFData()\n",
        "  SF.loadRecordings(key=dict(name=recordings_name))\n",
        "  \n",
        "  jobs=[]\n",
        "  for name in SF.studyNames():\n",
        "    study=SF.study(name)\n",
        "    for rname in study.recordingNames():\n",
        "      R=study.recording(rname)\n",
        "      for sorter in sorters:\n",
        "        job=dict(\n",
        "          command='sort_recording',\n",
        "          label=sorter['name']+': '+R.name(),\n",
        "          recording=R.getObject(),\n",
        "          sorter=sorter\n",
        "        )\n",
        "        jobs.append(job)\n",
        "\n",
        "  batch=dict(jobs=jobs)\n",
        "  print('Number of jobs: {}'.format(len(jobs)))\n",
        "  kb.saveObject(key=dict(batch_name=batch_name),object=batch)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yb-zTMQgnK98",
        "colab_type": "code",
        "outputId": "b6bb6fbc-f252-4cd3-ddb2-0ad124f17828",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "cell_type": "code",
      "source": [
        "create_sorting_batch(recordings_name='spikeforest_magland_synth_recordings',batch_name='ms4_magland_synth',sorters=[sorter_ms4_thr3])\n",
        "create_sorting_batch(recordings_name='spikeforest_magland_synth_recordings',batch_name='irc_magland_synth',sorters=[sorter_irc_tetrode])\n",
        "create_sorting_batch(recordings_name='spikeforest_magland_synth_recordings',batch_name='sc_magland_synth',sorters=[sorter_sc])\n",
        "\n",
        "create_sorting_batch(recordings_name='spikeforest_bionet_recordings',batch_name='ms4_bionet',sorters=[sorter_ms4_thr3])\n",
        "create_sorting_batch(recordings_name='spikeforest_bionet_recordings',batch_name='irc_bionet',sorters=[sorter_irc_drift])\n",
        "create_sorting_batch(recordings_name='spikeforest_bionet_recordings',batch_name='sc_bionet',sorters=[sorter_sc])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Creating sorting batch: ms4_magland_synth\n",
            "Number of jobs: 80\n",
            "Already on server (*)\n",
            "Creating sorting batch: irc_magland_synth\n",
            "Number of jobs: 80\n",
            "Already on server.\n",
            "Creating sorting batch: sc_magland_synth\n",
            "Number of jobs: 80\n",
            "Already on server (*)\n",
            "Creating sorting batch: ms4_bionet\n",
            "Number of jobs: 36\n",
            "Already on server.\n",
            "Creating sorting batch: irc_bionet\n",
            "Number of jobs: 36\n",
            "Already on server.\n",
            "Creating sorting batch: sc_bionet\n",
            "Number of jobs: 36\n",
            "Already on server.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "W2VTozxFnoE3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "To run these sorting batches, follow the instructions above."
      ]
    },
    {
      "metadata": {
        "id": "dfDRhV7jpVgy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Browse sorting results"
      ]
    },
    {
      "metadata": {
        "id": "4ga6PepCpXKF",
        "colab_type": "code",
        "outputId": "13268d3e-76a9-4ff0-e888-88f9c2e70897",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "cell_type": "code",
      "source": [
        "SF=sf.SFData()\n",
        "SF.loadRecordings(key=dict(name='spikeforest_bionet_recordings'))\n",
        "SF.loadRecordings(key=dict(name='spikeforest_magland_synth_recordings'))\n",
        "SF.loadProcessingBatch(key=dict(batch_name='summarize_recordings_bionet',name='job_results'))\n",
        "SF.loadProcessingBatch(key=dict(batch_name='summarize_recordings_magland_synth',name='job_results'))\n",
        "\n",
        "SF.loadProcessingBatch(key=dict(batch_name='ms4_magland_synth',name='job_results'))\n",
        "SF.loadProcessingBatch(key=dict(batch_name='sc_magland_synth',name='job_results'))\n",
        "SF.loadProcessingBatch(key=dict(batch_name='irc_magland_synth',name='job_results'))\n",
        "\n",
        "SF.loadProcessingBatch(key=dict(batch_name='ms4_bionet',name='job_results'))\n",
        "#SF.loadProcessingBatch(key=dict(batch_name='sc_bionet',name='job_results')) ## Spyking circus not working yet -- need to put into singularity container\n",
        "SF.loadProcessingBatch(key=dict(batch_name='irc_bionet',name='job_results'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loaded 0 sorting results and 36 recording summary results\n",
            "Loaded 0 sorting results and 80 recording summary results\n",
            "Loaded 80 sorting results and 0 recording summary results\n",
            "Downloading file --- (0.1 MB): http://132.249.245.245:24351/7317cea8265b/download/f/f8/ff85038b9726cf723cddfe72c607cc34f13fe811 -> /home/magland/kbucket_cache/f/f8/ff85038b9726cf723cddfe72c607cc34f13fe811\n",
            "Loaded 80 sorting results and 0 recording summary results\n",
            "Downloading file --- (0.1 MB): http://132.249.245.245:24351/7317cea8265b/download/6/af/6af92f0f8e99625c1bacaee907d45998a6767e27 -> /home/magland/kbucket_cache/6/af/6af92f0f8e99625c1bacaee907d45998a6767e27\n",
            "Loaded 80 sorting results and 0 recording summary results\n",
            "Loaded 36 sorting results and 0 recording summary results\n",
            "Loaded 36 sorting results and 0 recording summary results\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "D7Ilcyb0pki5",
        "colab_type": "code",
        "outputId": "febd1b8a-828d-40b0-8fa7-23fcdf93b7b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120
        }
      },
      "cell_type": "code",
      "source": [
        "X=sf.SFSelectWidget(sfdata=SF,mode='sorting_result')\n",
        "display(X)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "document.getElementById('8b9c17f3-2b04-4427-b0c2-424ef5192fc1').innerHTML=atob('PHNlbGVjdCBvbmNoYW5nZT0iZ29vZ2xlLmNvbGFiLmtlcm5lbC5pbnZva2VGdW5jdGlvbigmI3gyNztvbmNoYW5nZWNhbGxiYWNrLTVhNzI3MTU2LTgzNTMtNDU3NC1iZWUxLTViMDUzZGVjNTA5OCYjeDI3OywgW10sIHt2YWx1ZTp0aGlzLnZhbHVlfSkiPjwvc2VsZWN0Pg=='); console.log('test1');"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "document.getElementById('ccb0bd7b-60e6-40f5-9c55-36f60061a97a').innerHTML=atob('PHNlbGVjdCBvbmNoYW5nZT0iZ29vZ2xlLmNvbGFiLmtlcm5lbC5pbnZva2VGdW5jdGlvbigmI3gyNztvbmNoYW5nZWNhbGxiYWNrLTI1NDNhNzNmLTU2ZDItNDJjMy1iNWEwLWVmMmI0YmJlMjkwYyYjeDI3OywgW10sIHt2YWx1ZTp0aGlzLnZhbHVlfSkiPjwvc2VsZWN0Pg=='); console.log('test1');"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "document.getElementById('e0e23c3b-22e4-4029-8f2f-fcf373007245').innerHTML=atob('PHNlbGVjdCBvbmNoYW5nZT0iZ29vZ2xlLmNvbGFiLmtlcm5lbC5pbnZva2VGdW5jdGlvbigmI3gyNztvbmNoYW5nZWNhbGxiYWNrLThiZjA0YjZhLTVmNjItNGEyNC1iMDFhLTBhNTc3NzE1ODM0ZSYjeDI3OywgW10sIHt2YWx1ZTp0aGlzLnZhbHVlfSkiPjwvc2VsZWN0Pg=='); console.log('test1');"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "document.getElementById('8b9c17f3-2b04-4427-b0c2-424ef5192fc1').innerHTML=atob('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'); console.log('test1');"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            "application/javascript": [
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "document.getElementById('e0e23c3b-22e4-4029-8f2f-fcf373007245').innerHTML=atob('PHNlbGVjdCBvbmNoYW5nZT0iZ29vZ2xlLmNvbGFiLmtlcm5lbC5pbnZva2VGdW5jdGlvbigmI3gyNztvbmNoYW5nZWNhbGxiYWNrLTY0ZjgxYjQzLTE1MTAtNDYzOS1hYTE5LTczZDczMmQ2NDM2YyYjeDI3OywgW10sIHt2YWx1ZTp0aGlzLnZhbHVlfSkiPjxvcHRpb24gc2VsZWN0ZWQ9InNlbGVjdGVkIj5Nb3VudGFpblNvcnQ0LXRocjM8L29wdGlvbj48b3B0aW9uPklyb25DbHVzdC1kcmlmdDwvb3B0aW9uPjwvc2VsZWN0Pg=='); console.log('test1');"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<spikeforest.sfselectwidget.SFSelectWidget at 0x7f353abe25c0>"
            ],
            "text/html": [
              "<div id=3308e37a-0991-495e-9ef7-7a2fdfd8ee8f><div><hr></hr><table><tr><td>Study **: </td><td><div id=8b9c17f3-2b04-4427-b0c2-424ef5192fc1><select onchange=\"google.colab.kernel.invokeFunction(&#x27;onchangecallback-7ae7d866-ef31-4c11-bee5-40f9cb77537b&#x27;, [], {value:this.value})\"><option selected=\"selected\">bionet_drift</option><option>bionet_shuffle</option><option>bionet_static</option><option>magland_synth_noise10_K10_C4</option><option>magland_synth_noise10_K10_C8</option><option>magland_synth_noise10_K20_C4</option><option>magland_synth_noise10_K20_C8</option><option>magland_synth_noise20_K10_C4</option><option>magland_synth_noise20_K10_C8</option><option>magland_synth_noise20_K20_C4</option><option>magland_synth_noise20_K20_C8</option></select></div></td></tr><tr><td>Recording: </td><td><div id=ccb0bd7b-60e6-40f5-9c55-36f60061a97a><select onchange=\"google.colab.kernel.invokeFunction(&#x27;onchangecallback-14c28cab-b204-42cf-b233-1dfd58317dc6&#x27;, [], {value:this.value})\"><option selected=\"selected\">drift_8x_A_2A</option><option>drift_8x_A_2B</option><option>drift_8x_A_4A</option><option>drift_8x_A_4B</option><option>drift_8x_B_2A</option><option>drift_8x_B_2B</option><option>drift_8x_B_4A</option><option>drift_8x_B_4B</option><option>drift_8x_C_2A</option><option>drift_8x_C_2B</option><option>drift_8x_C_4A</option><option>drift_8x_C_4B</option></select></div></td></tr><tr><td>Sorting result: </td><td><div id=e0e23c3b-22e4-4029-8f2f-fcf373007245><select onchange=\"google.colab.kernel.invokeFunction(&#x27;onchangecallback-e32b3ae9-417a-488b-b198-9e206c778377&#x27;, [], {value:this.value})\"><option selected=\"selected\">MountainSort4-thr3</option><option>IronClust-drift</option></select></div></td></tr></table><hr></hr></div></div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Sganjg9cp56c",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "R=X.sortingResult()\n",
        "display(R.plot('unit_waveforms'))\n",
        "display(R.plot('autocorrelograms'))\n",
        "display(R.comparisonWithTruth())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fL2QjjBPqXS7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Aggregate sorting results"
      ]
    },
    {
      "metadata": {
        "id": "bBl4NBQCrXl3",
        "colab_type": "code",
        "outputId": "4531b9bd-7cda-436f-c963-d9fbfaa90c33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "cell_type": "code",
      "source": [
        "SF=sf.SFData()\n",
        "SF.loadRecordings(key=dict(name='spikeforest_bionet_recordings'))\n",
        "SF.loadProcessingBatch(key=dict(batch_name='summarize_recordings_bionet',name='job_results'))\n",
        "SF.loadProcessingBatch(key=dict(batch_name='ms4_bionet',name='job_results'))\n",
        "#SF.loadProcessingBatch(key=dict(batch_name='sc_bionet',name='job_results')) ## Spyking circus not working yet -- need to put into singularity container\n",
        "SF.loadProcessingBatch(key=dict(batch_name='irc_bionet',name='job_results'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading file --- (0.0 MB): http://132.249.245.245:24351/7317cea8265b/download/3/b6/3b673117cec90f6e040220b4da908dd22cc20957 -> /tmp/sha1-cache/3/b6/3b673117cec90f6e040220b4da908dd22cc20957\n",
            "Downloading file --- (0.0 MB): http://132.249.245.245:24351/7317cea8265b/download/1/ab/1abdcd3ef2ec66da41a759ca3a2a580a114c2321 -> /tmp/sha1-cache/1/ab/1abdcd3ef2ec66da41a759ca3a2a580a114c2321\n",
            "Loaded 0 sorting results and 36 recording summary results\n",
            "Downloading file --- (0.0 MB): http://132.249.245.245:24351/7317cea8265b/download/c/0f/c0f25b500d9f436bed530f52214d6ea6498bc4f9 -> /tmp/sha1-cache/c/0f/c0f25b500d9f436bed530f52214d6ea6498bc4f9\n",
            "Loaded 36 sorting results and 0 recording summary results\n",
            "Downloading file --- (0.0 MB): http://132.249.245.245:24351/7317cea8265b/download/c/b6/cb67707c116546cf6aff907c7bd73df00dc9e8ff -> /tmp/sha1-cache/c/b6/cb67707c116546cf6aff907c7bd73df00dc9e8ff\n",
            "Loaded 36 sorting results and 0 recording summary results\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "XyBRKILPp-0h",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "import altair as alt\n",
        "alt.renderers.enable('colab')\n",
        "\n",
        "# Accumulate the sorting results\n",
        "def accumulate_comparison_with_ground_truth(*,SF,studies,sorter_name,fieldnames):\n",
        "  ret=[]\n",
        "  for study in studies:\n",
        "    recordings=[study.recording(name) for name in study.recordingNames()]\n",
        "    for R in recordings:\n",
        "      result=R.sortingResult(sorter_name)\n",
        "      A=result.comparisonWithTruth(format='json')\n",
        "      B=R.trueUnitsInfo(format='json')\n",
        "      snr_by_true_unit=dict()\n",
        "      for b in B:\n",
        "        snr_by_true_unit[b['unit_id']]=b['snr']\n",
        "      for i in A:\n",
        "        a=A[i]\n",
        "        rec=dict()\n",
        "        rec['recording_name']=R.name()\n",
        "        rec['unit_id']=a['Unit ID']\n",
        "        rec['snr']=snr_by_true_unit[rec['unit_id']]\n",
        "        for fieldname in fieldnames:\n",
        "          rec[fieldname]=float(a[fieldname])\n",
        "        ret.append(rec)\n",
        "  return ret\n",
        "\n",
        "def show_accuracy_plot(*,SF,study_name,sorter_name,title):\n",
        "  \n",
        "  study=SF.study(study_name)\n",
        "  X=accumulate_comparison_with_ground_truth(\n",
        "      SF=SF,\n",
        "      studies=[study],\n",
        "      sorter_name=sorter_name,\n",
        "      fieldnames=['Accuracy']\n",
        "  )\n",
        "  \n",
        "  # Display the accumulated sorting results\n",
        "  cc=alt.Chart(pd.DataFrame(X),title=title).mark_point().encode(\n",
        "      x='snr',\n",
        "      y='Accuracy',\n",
        "      color='recording_name',\n",
        "      tooltip='recording_name'\n",
        "  ).interactive()\n",
        "  display(cc)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8-bIDoH1rkYW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import vdomr as vd\n",
        "\n",
        "class SelectBox(vd.Component):\n",
        "    def __init__(self,options=[]):\n",
        "        vd.Component.__init__(self)\n",
        "        self._on_change_handlers=[]\n",
        "        self._value=None\n",
        "        self.setOptions(options)\n",
        "        \n",
        "    def setOptions(self,options):\n",
        "        self._options=options\n",
        "        if self._value not in options:\n",
        "          self._value=options[0] if options else None\n",
        "        self.refresh()\n",
        "        \n",
        "    def value(self):\n",
        "        return self._value\n",
        "    \n",
        "    def setValue(self,value):\n",
        "        self._value=value\n",
        "        self.refresh()\n",
        "        \n",
        "    def onChange(self,handler):\n",
        "        self._on_change_handlers.append(handler)\n",
        "        \n",
        "    def _on_change(self,value):\n",
        "        self._value=value\n",
        "        for handler in self._on_change_handlers:\n",
        "            handler(value=value)\n",
        "        \n",
        "    def render(self):\n",
        "        opts=[]\n",
        "        for option in self._options:\n",
        "            if option==self._value:\n",
        "              opts.append(vd.option(option,selected='selected'))\n",
        "            else:\n",
        "              opts.append(vd.option(option))\n",
        "        X=vd.select(opts,onchange=self._on_change)\n",
        "        return X"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "THJyQ4V4rGT6",
        "colab_type": "code",
        "outputId": "b6cc5a40-723e-4064-a1ab-0c73b285e754",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "cell_type": "code",
      "source": [
        "STUDY=SelectBox(options=SF.studyNames())\n",
        "SORTER=SelectBox(options=['MountainSort4-thr3','IronClust-drift'])\n",
        "display(STUDY)\n",
        "display(SORTER)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "document.getElementById('165de563-e0e3-4a57-91e9-8ad7ed5dfb08').innerHTML=atob('PHNlbGVjdCBvbmNoYW5nZT0iZ29vZ2xlLmNvbGFiLmtlcm5lbC5pbnZva2VGdW5jdGlvbigmI3gyNztvbmNoYW5nZWNhbGxiYWNrLWQ0YmU1M2Y4LWE5YTUtNDJmZi04NmMyLTAwY2U2MDg4MWYyNCYjeDI3OywgW10sIHt2YWx1ZTp0aGlzLnZhbHVlfSkiPjxvcHRpb24gc2VsZWN0ZWQ9InNlbGVjdGVkIj5iaW9uZXRfZHJpZnQ8L29wdGlvbj48b3B0aW9uPmJpb25ldF9zaHVmZmxlPC9vcHRpb24+PG9wdGlvbj5iaW9uZXRfc3RhdGljPC9vcHRpb24+PC9zZWxlY3Q+'); console.log('test1');"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "document.getElementById('68fe3d85-753e-47cf-aba4-6c5cae3e8dea').innerHTML=atob('PHNlbGVjdCBvbmNoYW5nZT0iZ29vZ2xlLmNvbGFiLmtlcm5lbC5pbnZva2VGdW5jdGlvbigmI3gyNztvbmNoYW5nZWNhbGxiYWNrLWZjMzkyOWFjLWVmZmItNDlmNi05ZjVmLWQzN2Y1OWU4NDRkMSYjeDI3OywgW10sIHt2YWx1ZTp0aGlzLnZhbHVlfSkiPjxvcHRpb24gc2VsZWN0ZWQ9InNlbGVjdGVkIj5Nb3VudGFpblNvcnQ0LXRocjM8L29wdGlvbj48b3B0aW9uPklyb25DbHVzdC1kcmlmdDwvb3B0aW9uPjwvc2VsZWN0Pg=='); console.log('test1');"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div id=165de563-e0e3-4a57-91e9-8ad7ed5dfb08><select onchange=\"google.colab.kernel.invokeFunction(&#x27;onchangecallback-33e34f55-ef8c-4146-a6ee-42c14285652c&#x27;, [], {value:this.value})\"><option selected=\"selected\">bionet_drift</option><option>bionet_shuffle</option><option>bionet_static</option></select></div>"
            ],
            "text/plain": [
              "<__main__.SelectBox at 0x7f123e753630>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div id=68fe3d85-753e-47cf-aba4-6c5cae3e8dea><select onchange=\"google.colab.kernel.invokeFunction(&#x27;onchangecallback-8e6470ec-3728-4b1b-97a0-7e9e0a25eced&#x27;, [], {value:this.value})\"><option selected=\"selected\">MountainSort4-thr3</option><option>IronClust-drift</option></select></div>"
            ],
            "text/plain": [
              "<__main__.SelectBox at 0x7f123eee6470>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "fI6RItt-q5pM",
        "colab_type": "code",
        "outputId": "5df12ff5-914d-415e-ffa0-797bf9c147f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        }
      },
      "cell_type": "code",
      "source": [
        "show_accuracy_plot(\n",
        "    SF=SF,\n",
        "    study_name=STUDY.value(),\n",
        "    sorter_name=SORTER.value(),\n",
        "    title=SORTER.value()+' '+STUDY.value()\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading file --- (0.0 MB): http://132.249.245.245:24351/7317cea8265b/download/f/e4/fe4ee41e93b3602f9141059642859016e39892b0 -> /tmp/sha1-cache/f/e4/fe4ee41e93b3602f9141059642859016e39892b0\n",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Chart({\n",
              "  data:       Accuracy recording_name        snr  unit_id\n",
              "  0         0.00  drift_8x_A_2A   3.343870        1\n",
              "  1         0.00  drift_8x_A_2A   3.443811        3\n",
              "  2         0.84  drift_8x_A_2A  52.485138        4\n",
              "  3         0.92  drift_8x_A_2A  27.300165        6\n",
              "  4         0.00  drift_8x_A_2A   2.593275       16\n",
              "  5         0.00  drift_8x_A_2A   4.721575       21\n",
              "  6         0.01  drift_8x_A_2A  14.430388       26\n",
              "  7         0.00  drift_8x_A_2A   2.585488       28\n",
              "  8         0.53  drift_8x_A_2A  36.712619       50\n",
              "  9         0.00  drift_8x_A_2A   3.441622       76\n",
              "  10        0.00  drift_8x_A_2A   2.688525       83\n",
              "  11        0.00  drift_8x_A_2A   2.651218       84\n",
              "  12        0.00  drift_8x_A_2A   4.311581       85\n",
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              "    var embed_opt = {\"mode\": \"vega-lite\"};\n",
              "\n",
              "    function showError(el, error){\n",
              "        el.innerHTML = ('<div class=\"error\">'\n",
              "                        + '<p>JavaScript Error: ' + error.message + '</p>'\n",
              "                        + \"<p>This usually means there's a typo in your chart specification. \"\n",
              "                        + \"See the javascript console for the full traceback.</p>\"\n",
              "                        + '</div>');\n",
              "        throw error;\n",
              "    }\n",
              "    const el = document.getElementById('vis');\n",
              "    vegaEmbed(\"#vis\", spec, embed_opt)\n",
              "      .catch(error => showError(el, error));\n",
              "  </script>\n",
              "</body>\n",
              "</html>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Opz2K0VPrZAm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}