Reading Plexon data with Neo

reading_plx_spike_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import neo\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fname = 'data/DBS_patient 001.plx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "reader = neo.PlexonIO(fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pearsonlab/projects/python-neo/neo/io/plexonio.py:190: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  sigarrays[chan] = np.zeros(nb_samples[chan])\n",
      "/home/pearsonlab/projects/python-neo/neo/io/plexonio.py:254: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  sample_positions[chan]+data.size] = data\n"
     ]
    }
   ],
   "source": [
    "blks = reader.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get first (and only) data segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "seg = blks[0].segments[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(AnalogSignal with 1 channels of length 698324; units V; datatype float64 \n",
       " annotations: {'channel_index': 0, 'channel_name': 'ContinuousChannel005'}\n",
       " sampling rate: 48000.0 Hz\n",
       " time: 0.0 s to 14.548416666666666 s,\n",
       " AnalogSignal with 1 channels of length 7335; units V; datatype float64 \n",
       " annotations: {'channel_index': 1, 'channel_name': 'Aux11Input1Channel1'}\n",
       " sampling rate: 500.0 Hz\n",
       " time: 0.0 s to 14.67 s,\n",
       " AnalogSignal with 1 channels of length 7335; units V; datatype float64 \n",
       " annotations: {'channel_index': 2, 'channel_name': 'Aux11Input2Channel2'}\n",
       " sampling rate: 500.0 Hz\n",
       " time: 0.0 s to 14.67 s,\n",
       " Event annotations: {'channel_index': 1, 'channel_name': 'EvtContChannel005'},\n",
       " Event annotations: {'channel_index': 2, 'channel_name': 'EvtAuxChannel011'},\n",
       " Event annotations: {'channel_index': 3, 'channel_name': 'EvtAuxChannel000'},\n",
       " Event annotations: {'channel_index': 4, 'channel_name': 'EvtStimChannel005'},\n",
       " Event\n",
       " annotations: {'channel_index': 257, 'channel_name': 'StrobedSerialData'},\n",
       " Event annotations: {'channel_index': 258, 'channel_name': 'StartdSerialData'},\n",
       " Event annotations: {'channel_index': 259, 'channel_name': 'StopdSerialData'})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg.data_children"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get first (and only) analog signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sig = seg.analogsignals[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnalogSignal with 1 channels of length 698324; units V; datatype float64 \n",
       "annotations: {'channel_index': 0, 'channel_name': 'ContinuousChannel005'}\n",
       "sampling rate: 48000.0 Hz\n",
       "time: 0.0 s to 14.548416666666666 s"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48000.0 Hz\n"
     ]
    }
   ],
   "source": [
    "sr = sig.sampling_rate\n",
    "print(sr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract data and timestamps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dat = sig.data\n",
    "t_axis = sig.times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f18fcf410f0>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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klLzSGGRu/2+yAywrvAqFvdK50V6FAqDWiBOL3BFprjbZKklOy2oHp/GsYzfq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\nOlgwMAzDMDr+H64AoPLGLlBkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18fcc6e048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slc = slice(0, 10000)\n",
    "plt.plot(t_axis[slc], dat[slc])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Event annotations: {'channel_index': 1, 'channel_name': 'EvtContChannel005'},\n",
       " Event annotations: {'channel_index': 2, 'channel_name': 'EvtAuxChannel011'},\n",
       " Event annotations: {'channel_index': 3, 'channel_name': 'EvtAuxChannel000'},\n",
       " Event annotations: {'channel_index': 4, 'channel_name': 'EvtStimChannel005'},\n",
       " Event\n",
       " annotations: {'channel_index': 257, 'channel_name': 'StrobedSerialData'},\n",
       " Event annotations: {'channel_index': 258, 'channel_name': 'StartdSerialData'},\n",
       " Event annotations: {'channel_index': 259, 'channel_name': 'StopdSerialData'}]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg.events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([  0.        ,  14.54837513], dtype=float32) * s,\n",
       " array([  0.        ,  14.54800034], dtype=float32) * s,\n",
       " array([], dtype=float32) * s,\n",
       " array([], dtype=float32) * s,\n",
       " array([], dtype=float32) * s,\n",
       " array([], dtype=float32) * s,\n",
       " array([], dtype=float32) * s]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[e.times for e in seg.events]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Prepare to save data\n",
    "\n",
    "The file format for input to klusta is detailed [here](https://github.com/kwikteam/klusta/blob/b06776551c5329bd547592d6d65eb56b1a9fbef1/docs/sorting.md#raw-data). Basically, we need flat bytes stored from numpy in an array of `(n_samples, n_channels)`. The number of channels, sampling rate, and data type are given in the `.prm` file described [here](https://github.com/kwikteam/klusta/blob/b06776551c5329bd547592d6d65eb56b1a9fbef1/docs/sorting.md#parameters-file)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(698324, 1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.shape  # one column per channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('data/dots_001.dat', 'wb') as output:\n",
    "    output.write(dat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test file write\n",
    "\n",
    "Can we read back in the `.dat` file?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dat_check = np.fromfile('data/dots_001.dat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f18fce89160>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3svxXI6nklcYgc+uHiQGw7PArFHZL+0b7FQqAWiOOT3JHJFttoleSXJbdCk7z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\nGIZhDLBgYBiGYQywYGAYhmEM/D+8+OqgNZI2EAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18fcf88a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dat_check[slc])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Write clustering configuration files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Probe definition (electrode configuration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting fhc.prb\n"
     ]
    }
   ],
   "source": [
    "%%file fhc.prb\n",
    "\n",
    "channel_groups = {\n",
    "    0: {\n",
    "        'channels': [0],\n",
    "        'graph': []\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting fhc.prm\n"
     ]
    }
   ],
   "source": [
    "%%file fhc.prm\n",
    "\n",
    "experiment_name = 'dots'\n",
    "prb_file = 'fhc.prb'\n",
    "\n",
    "traces = dict(\n",
    "    raw_data_files=['data/dots_001.dat', ],  # path to your .dat file(s)\n",
    "    sample_rate=48000,  # sampling rate in Hz\n",
    "    n_channels=1,  # number of channels in the .dat files\n",
    "    dtype='float64',  # the data type used in the .dat files\n",
    ")\n",
    "\n",
    "# Parameters for the spike detection process.\n",
    "spikedetekt = dict(\n",
    "    threshold_strong_std_factor = 4.0,\n",
    "    detect_spikes = 'positive',\n",
    "    extract_s_before = 10,\n",
    "    extract_s_after = 10,\n",
    ")\n",
    "\n",
    "# Parameters for the automatic clustering process.\n",
    "klustakwik2 = dict(\n",
    "    num_starting_clusters=10,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Klusta!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# need to:\n",
    "# source activate klusta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15:22:54 [I] creator:446          Using kwik file `/home/pearsonlab/projects/dots_analysis/dots.kwik`.\n",
      "15:22:54 [I] launch:206           Starting spike detection.\n",
      "15:22:54 [I] launch:53            SpikeDetekt parameters:\n",
      "15:22:54 [I] launch:55            n_features_per_channel = 3\n",
      "15:22:54 [I] launch:55            extract_s_before = 10\n",
      "15:22:54 [I] launch:55            chunk_size_seconds = 1.0\n",
      "15:22:54 [I] launch:55            experiment_name = dots.kwik\n",
      "15:22:54 [I] launch:55            connected_component_join_size = 1\n",
      "15:22:54 [I] launch:55            excerpt_size_seconds = 1.0\n",
      "15:22:54 [I] launch:55            chunk_overlap_seconds = 0.015\n",
      "15:22:54 [I] launch:55            pca_n_waveforms_max = 10000\n",
      "15:22:54 [I] launch:55            filter_butter_order = 3\n",
      "15:22:54 [I] launch:55            filter_low = 500.0\n",
      "15:22:54 [I] launch:55            n_channels = 1\n",
      "15:22:54 [I] launch:55            use_single_threshold = 1\n",
      "15:22:54 [I] launch:55            extract_s_after = 10\n",
      "15:22:54 [I] launch:55            detect_spikes = positive\n",
      "15:22:54 [I] launch:55            n_excerpts = 50\n",
      "15:22:54 [I] launch:55            filter_high_factor = 0.475\n",
      "15:22:54 [I] launch:55            prb_file = fhc.prb\n",
      "15:22:54 [I] launch:55            raw_data_files = data/dots_001.dat\n",
      "15:22:54 [I] launch:55            threshold_strong_std_factor = 4.0\n",
      "15:22:54 [I] launch:55            dtype = float64\n",
      "15:22:54 [I] launch:55            sample_rate = 48000\n",
      "15:22:54 [I] launch:55            threshold_weak_std_factor = 2.0\n",
      "15:22:54 [I] launch:55            weight_power = 2\n",
      "15:22:54 [I] spikedetekt:212      Found 1 live channels in group 0.\n",
      "15:22:54 [I] spikedetekt:285      Finding the thresholds...\n",
      "Detecting spikes        : 100%|█████████████████| 15/15 [00:00<00:00, 36.46it/s]\n",
      "Extracting waveforms    : 100%|█████████████████| 15/15 [00:01<00:00,  7.06it/s]\n",
      "Performing PCA          : 100%|██████████████████| 1/1 [00:00<00:00, 908.64it/s]\n",
      "Computing features      : 100%|█████████████████| 15/15 [00:01<00:00,  8.81it/s]\n",
      "15:22:58 [I] launch:218           Spike detection done! 775 spikes found in shank 0\n",
      "15:22:58 [I] launch:249           Starting clustering on shank 0/1.\n",
      "15:22:58 [I] launch:252           Clustering group 0 (775 spikes).\n",
      "15:22:58 [I] launch:123           Starting KK...\n",
      "15:22:58 [I] klustakwik KlustaKwik2 version 0.2.6\n",
      "15:22:58 [I] klustakwik.initial_parameters penalty_k_log_n = 1.0\n",
      "15:22:58 [I] klustakwik.initial_parameters full_step_every = 1\n",
      "15:22:58 [I] klustakwik.initial_parameters num_changed_threshold = 0.05\n",
      "15:22:58 [I] klustakwik.initial_parameters fast_split = False\n",
      "15:22:58 [I] klustakwik.initial_parameters points_for_cluster_mask = 100\n",
      "15:22:58 [I] klustakwik.initial_parameters max_iterations = 1000\n",
      "15:22:58 [I] klustakwik.initial_parameters penalty_k = 0.0\n",
      "15:22:58 [I] klustakwik.initial_parameters max_quick_step_candidates_fraction = 0.4\n",
      "15:22:58 [I] klustakwik.initial_parameters mua_point = 2\n",
      "15:22:58 [I] klustakwik.initial_parameters break_fraction = 0.0\n",
      "15:22:58 [I] klustakwik.initial_parameters max_quick_step_candidates = 100000000\n",
      "15:22:58 [I] klustakwik.initial_parameters subset_break_fraction = 0.01\n",
      "15:22:58 [I] klustakwik.initial_parameters prior_point = 1\n",
      "15:22:58 [I] klustakwik.initial_parameters max_possible_clusters = 1000\n",
      "15:22:58 [I] klustakwik.initial_parameters num_cpus = None\n",
      "15:22:58 [I] klustakwik.initial_parameters split_first = 20\n",
      "15:22:58 [I] klustakwik.initial_parameters noise_point = 1\n",
      "15:22:58 [I] klustakwik.initial_parameters use_mua_cluster = True\n",
      "15:22:58 [I] klustakwik.initial_parameters use_noise_cluster = True\n",
      "15:22:58 [I] klustakwik.initial_parameters dist_thresh = 9.21034037198\n",
      "15:22:58 [I] klustakwik.initial_parameters split_every = 40\n",
      "15:22:58 [I] klustakwik.initial_parameters always_split_bimodal = False\n",
      "15:22:58 [I] klustakwik.initial_parameters consider_cluster_deletion = True\n",
      "15:22:58 [I] klustakwik.initial_parameters max_split_iterations = None\n",
      "15:22:58 [I] klustakwik.initial_parameters num_starting_clusters = 10\n",
      "15:22:58 [I] klustakwik Using mask starts with 10 clusters, data has 1 unique masks\n",
      "15:22:58 [W] klustakwik Not enough masks (1) for specified number of starting clusters (10)\n",
      "15:22:58 [I] klustakwik Clustering data set of 775 points, 3 features\n",
      "15:22:58 [I] klustakwik Starting iteration 0 with 3 clusters\n",
      "15:22:58 [I] klustakwik Iteration 1F: 3 clusters, 7 changed, score=-1724.292027\n",
      "15:22:58 [I] klustakwik Iteration 2F: 3 clusters, 1 changed, score=-1740.820940 (decreased by 16.528914)\n",
      "15:22:58 [I] klustakwik Iteration 3F: 3 clusters, 0 changed, score=-1740.929134 (decreased by 0.108194)\n",
      "15:22:58 [I] klustakwik No points changed and last step was full, so trying to split.\n",
      "15:22:58 [I] klustakwik Trying to split clusters\n",
      "15:22:58 [I] klustakwik No points changed, previous step was full and did not split, so finishing.\n",
      "15:22:58 [I] launch:131           The automatic clustering process has finished.\n",
      "15:22:58 [I] launch:268           Save the clustering to `dots.kwik`.\n",
      "15:22:58 [I] model:1085           Save the Kwik file at `/home/pearsonlab/projects/dots_analysis/dots.kwik`.\n",
      "15:22:58 [I] launch:290           Clustering done!\n"
     ]
    }
   ],
   "source": [
    "!klusta fhc.prm --overwrite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GLib-GIO-\u001b[1;32mMessage\u001b[0m: Using the 'memory' GSettings backend.  Your settings will not be saved or shared with other applications.\n",
      "/home/pearsonlab/.local/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n",
      "/home/pearsonlab/.local/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "!phy kwik-gui dots.kwik"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in Klusta output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kfname = 'dots.kwik'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import h5py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "spkfile = h5py.File(kfname, 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "channel = '0'\n",
    "spktimes = spkfile['channel_groups'][channel]['spikes']['time_samples'].value\n",
    "clusters = spkfile['channel_groups'][channel]['spikes']['clusters']['main'].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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