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    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Spike Train Features\n\nDetect spike train features\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
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      "source": [
        "import os\nfrom ipfx.dataset.create import create_ephys_data_set\nfrom ipfx.feature_extractor import (\n    SpikeFeatureExtractor, SpikeTrainFeatureExtractor\n)\n\n# Download and access the experimental data from DANDI archive per instructions in the documentation\n# Example below will use an nwb file provided with the package\n\nnwb_file = os.path.join(\n    os.path.dirname(os.getcwd()),\n    \"data\",\n    \"nwb2_H17.03.008.11.03.05.nwb\"\n)\n\n# Create data set from the nwb file and choose a sweeep\ndataset = create_ephys_data_set(nwb_file=nwb_file)\nsweep = dataset.sweep(sweep_number=39)\n\n# Instantiate feature extractor for spikes\nstart, end = 1.02, 2.02\nsfx = SpikeFeatureExtractor(start=start, end=end)\n\n# Run feature extractor returning a table of spikes and their features\nspikes_df = sfx.process(t=sweep.t, v=sweep.v, i=sweep.i)\n\n# Instantiate Spike Train feature extractor\nstfx = SpikeTrainFeatureExtractor(start=start, end=end)\n\n# Run to produce features of a spike train\nspike_train_results = stfx.process(\n    t=sweep.t,\n    v=sweep.v,\n    i=sweep.i,\n    spikes_df=spikes_df\n)\n\nprint(spike_train_results)"
      ]
    }
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