Blowfly Figure.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# beginning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import neo\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mne.filter import filter_data\n",
    "from mne.preprocessing.peak_finder import peak_finder\n",
    "from scipy.ndimage import measurements\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from scipy.signal import find_peaks_cwt\n",
    "from scipy.stats import linregress\n",
    "from pandas import get_dummies, read_csv\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# helper functions\n",
    "\n",
    "def download_data(data_file): \n",
    "    r = neo.AxonIO(filename=data_file)\n",
    "    block = r.read_block()\n",
    "    segment = block.segments[0]\n",
    "\n",
    "    h1, drum = segment.analogsignals\n",
    "\n",
    "    sampling_rate = float(h1.sampling_rate)\n",
    "    drum = np.squeeze(np.array(drum)).astype(np.float64)\n",
    "    h1 = np.squeeze(np.array(h1)).astype(np.float64)\n",
    "    \n",
    "    \n",
    "    return h1, drum, sampling_rate\n",
    "\n",
    "\n",
    "def find_oscillation_events(arr, direction, sfreq, h_freq=5, thresh=None):\n",
    "    '''\n",
    "    Idiosyncratic function designed to detect motion/null events in\n",
    "    botfly oscillation-motion recordings. Roughly, works by:\n",
    "        1. Filter: lowpass filters stimulus (drum) recording to smooth input signal.\n",
    "        2. Peak finding: identify minima and maxima of drum signal.\n",
    "        3. Events: Reorganize info as an [Nx3] array of [start, stop, condition], \n",
    "        where {CCW = 1, CW = 2}\n",
    "           \n",
    "   INPUTS\n",
    "   -- arr: stimulus (drum) signal\n",
    "   -- direction: CCW or CW\n",
    "   -- sfreq: sampling frequency\n",
    "   -- h_freq: lowpass frequency. If set to False, no lowpass applied.\n",
    "    '''\n",
    "    assert direction == 'CCW' or direction == 'CW'\n",
    "    \n",
    "    ## Filter data.\n",
    "    if h_freq: arr = filter_data(arr, sfreq, l_freq=None, h_freq=h_freq, phase='zero', verbose=False)\n",
    "\n",
    "    ## Identify extrema.\n",
    "    maxima, _ = peak_finder(arr, thresh=thresh, extrema=1)\n",
    "    minima, _ = peak_finder(arr, thresh=thresh, extrema=-1)\n",
    "\n",
    "    ## Re-organize into events.\n",
    "    events = np.sort(np.concatenate([maxima,minima]))\n",
    "    events = np.array([events[i:i+2] for i in range(events.size-1)])\n",
    "    if not events.size: raise ValueError('No events detected. Check paramaters.')\n",
    "\n",
    "    ## Demarcate null/motion events.\n",
    "    events = np.concatenate([events, np.zeros(events.shape[0], dtype=int).reshape(-1,1)], axis=-1)\n",
    "    if direction == 'CCW':\n",
    "        events[np.in1d(events[:,0], maxima), -1] = 1\n",
    "        events[np.in1d(events[:,0], minima), -1] = 2\n",
    "    else:\n",
    "        events[np.in1d(events[:,0], maxima), -1] = 2\n",
    "        events[np.in1d(events[:,0], minima), -1] = 1\n",
    "        \n",
    "    return events\n",
    "\n",
    "def amplitude_thresholding(x0, thresh):\n",
    "    '''Simple peak finding algorithm.'''\n",
    "    assert x0.ndim == 1\n",
    "    clusters, ix = measurements.label(x0 > thresh)\n",
    "    peak_loc = np.concatenate(measurements.maximum_position(x0, labels=clusters, index=np.arange(ix)+1))\n",
    "    peak_mag = measurements.maximum(x0, labels=clusters, index=np.arange(ix)+1)\n",
    "    return peak_loc, peak_mag\n",
    "\n",
    "\n",
    "def fix_events(events):\n",
    "    events_copy = np.copy(events)\n",
    "    for row_i, e in enumerate(events[:-1,:]):\n",
    "        if events[row_i, 1] != events[row_i + 1, 0]:\n",
    "            events_copy[row_i, 1] = events[row_i + 1, 0]\n",
    "            \n",
    "    return events_copy\n",
    "\n",
    "data_file = 'data/17o23009.abf'\n",
    "h1, drum, sampling_rate = download_data(data_file)\n",
    "DEGREES_PER_VOLTAGE = 360 / (np.max(drum) - np.min(drum))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# first pass "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_file = 'data/17o23000.abf'\n",
    "\n",
    "\n",
    "\n",
    "h1_sample, drum_sample, sampling_rate = download_data(data_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x120a98160>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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GSX4MuMkpiJ31deXspnKF9hEl6Ui9BZ3SuFesvBj0KedtE4Xj3J2HhweKyyrM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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12038f358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1,  figsize=(15, 5), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "plt.plot(h1_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fir_design defaults to \"firwin2\" in 0.15 but will change to \"firwin\" in 0.16, set it explicitly to avoid this warning.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-3-9293fb4dff84>:36: DeprecationWarning: fir_design defaults to \"firwin2\" in 0.15 but will change to \"firwin\" in 0.16, set it explicitly to avoid this warning.\n",
      "  if h_freq: arr = filter_data(arr, sfreq, l_freq=None, h_freq=h_freq, phase='zero', verbose=False)\n"
     ]
    }
   ],
   "source": [
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    ### Event detection.\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    \n",
    "direction = 'CCW' \n",
    "hfreq = 5.0\n",
    "events_sample = find_oscillation_events(drum_sample, direction, sampling_rate, hfreq)\n",
    "## Compute epoch lengths.\n",
    "lengths_sample = np.diff(events_sample[:,:-1], axis=1) / sampling_rate\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GM2XmUy1090e98bvk5VH5wHgj5iTK6QuXdfclN5opyY1m6u4bP3eX/G/b30aGBoiISFJa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1208f7898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1,  figsize=(15, 5), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "plt.plot(drum_sample)\n",
    "ax = plt.gca()\n",
    "## Add event markers.\n",
    "for n, color in zip(np.unique(events_sample[:,-1]), ['#ff7f0e', '#1f77b4']):\n",
    "    ax.vlines(np.arange(len(drum_sample))[events_sample[events_sample[:,-1]==n, 0]], 0, 6, color=color, alpha=0.8)    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "threshold = 55 # eyeballing the threshold\n",
    "peak_loc, peak_mag = amplitude_thresholding(h1_sample, threshold)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "    \n",
    "    \n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "### Epoching.\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "\n",
    "## Iteratively count spikes within epoch.\n",
    "data = []\n",
    "df = []\n",
    "for event in events_sample:\n",
    "\n",
    "    ## Count spikes in window.\n",
    "    n_spikes = np.logical_and( peak_loc >= event[0], peak_loc < event[1] ).sum()\n",
    "\n",
    "    ## Append information.\n",
    "    d = 'Stationary' if not event[-1] else 'CCW' if event[-1]==1 else 'CW'\n",
    "    length = (event[1] - event[0]) / sampling_rate\n",
    "    \n",
    "    n_spike_rate = n_spikes / length\n",
    "    data.append( [d, length, n_spikes, n_spike_rate] )\n",
    "\n",
    "## Convert to DataFrame. Append.\n",
    "data = pd.DataFrame(data, columns=('Direction','Duration','Spikes', 'Spike_rate'))\n",
    "df.append(data)\n",
    "\n",
    "df = pd.concat(df).reset_index(drop=True)\n",
    "df.to_csv('spike_counts.csv', index=False)\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1201a49b0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A5dStPdqPPvpoGhoast9++yVJHnzwwfzgBz8orZ81a9Z73vOb3/wmbW1tqa+vT11dXVat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\n25tbeECrOpQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a787b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Initialize canvas.\n",
    "fig = plt.figure(1,  figsize=(15, 5), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "\n",
    "\n",
    "## Plot difference in direction.\n",
    "sns.barplot('Direction', 'Spike_rate', data=df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Varying Speed and Determining Avg Spike Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fir_design defaults to \"firwin2\" in 0.15 but will change to \"firwin\" in 0.16, set it explicitly to avoid this warning.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-2-9293fb4dff84>:36: DeprecationWarning: fir_design defaults to \"firwin2\" in 0.15 but will change to \"firwin\" in 0.16, set it explicitly to avoid this warning.\n",
      "  if h_freq: arr = filter_data(arr, sfreq, l_freq=None, h_freq=h_freq, phase='zero', verbose=False)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fir_design defaults to \"firwin2\" in 0.15 but will change to \"firwin\" in 0.16, set it explicitly to avoid this warning.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-2-9293fb4dff84>:36: DeprecationWarning: fir_design defaults to \"firwin2\" in 0.15 but will change to \"firwin\" in 0.16, set it explicitly to avoid this warning.\n",
      "  if h_freq: arr = filter_data(arr, sfreq, l_freq=None, h_freq=h_freq, phase='zero', verbose=False)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fir_design defaults to \"firwin2\" in 0.15 but will change to \"firwin\" in 0.16, set it explicitly to avoid this warning.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-2-9293fb4dff84>:36: DeprecationWarning: fir_design defaults to \"firwin2\" in 0.15 but will change to \"firwin\" in 0.16, set it explicitly to avoid this warning.\n",
      "  if h_freq: arr = filter_data(arr, sfreq, l_freq=None, h_freq=h_freq, phase='zero', verbose=False)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n"
     ]
    },
    {
     "data": {
      "image/png": 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2cJc49li0mL3O1T47RAKxVYHd6ZZ7//Wt17JdU/vJ4E72Ur8HvPOM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\n25AM+Gv58uWyadMmWbdundSpU0cGDx4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t1b4NBtAw5tfdMvGPfZrHGo9bLPlmGnfQV3Ze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CYRkeJOVg43AlcPCrWGet7of7v+9mA0xues/SIiUqZkqMNjgbDuiKuHjL/tOOmjSPyfoijiqjln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\nXZ6vPbJH7/oSF1OyK7Ix+1hEnwfRFekYR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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114063ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datafile_speed = ['data/17o23001.abf', 'data/17o23000.abf', 'data/17o23002.abf']\n",
    "df = []\n",
    "fig, ax = plt.subplots(3, 1, figsize=(15, 15), dpi=80, facecolor='w', edgecolor='k')\n",
    "fix_offset = [-1, -1, 0]\n",
    "for i, datafile in enumerate(datafile_speed):\n",
    "    \n",
    "    #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    ### Download Data\n",
    "    #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    \n",
    "    h1, drum, sampling_rate = download_data(datafile)\n",
    "\n",
    "    ax[i].plot(drum)\n",
    "    #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    ### Event detection.\n",
    "    #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    \n",
    "    threshold = 55 # eyeballing the threshold\n",
    "    peak_loc, peak_mag = amplitude_thresholding(h1, threshold)\n",
    "\n",
    "    direction = 'CCW' \n",
    "    hfreq = 5.0\n",
    "    events = find_oscillation_events(drum, direction, sampling_rate, hfreq, thresh = 1)\n",
    "    ## Compute epoch lengths.\n",
    "    lengths = np.diff(events[:,:-1], axis=1) / sampling_rate\n",
    "    \n",
    "    problem_areas = np.argwhere(lengths < 0.5)[:,0] \n",
    "\n",
    "    speed_all = []\n",
    "    for row, event in enumerate(events):\n",
    "        speed_all.append(linregress(np.arange(0, lengths[row], 1/sampling_rate), drum[event[0]: event[1]])[0])\n",
    "    \n",
    "    if fix_offset[i] > -1:\n",
    "        events = np.delete(events, problem_areas, axis = 0)\n",
    "        events = events[fix_offset[i]::2,:]\n",
    "        \n",
    "        speed_all = np.delete(speed_all, problem_areas, axis = 0)\n",
    "        speed_all = speed_all[fix_offset[i]::2]\n",
    "        \n",
    "        events = fix_events(events)\n",
    "        lengths = np.diff(events[:,:-1], axis=1) / sampling_rate\n",
    "\n",
    "    \n",
    "    speed = np.average(np.abs(speed_all)) * DEGREES_PER_VOLTAGE \n",
    "    for n, color in zip(np.unique(events[:,-1]), ['#ff7f0e', '#1f77b4']):\n",
    "        ax[i].vlines(np.arange(len(drum))[events[events[:,-1]==n, 0]], 0, 6, color=color, alpha=0.8)    \n",
    "\n",
    "\n",
    "    #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    ### Epoching.\n",
    "    #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "\n",
    "    ## Iteratively count spikes within epoch.\n",
    "    data = []\n",
    "    \n",
    "    for event in events:\n",
    "\n",
    "        ## Count spikes in window.\n",
    "        n_spikes = np.logical_and( peak_loc >= event[0], peak_loc < event[1] ).sum()\n",
    "\n",
    "\n",
    "        ## Append information.\n",
    "        d = 'Stationary' if not event[-1] else 'CCW' if event[-1]==1 else 'CW'\n",
    "        length = (event[1] - event[0]) / sampling_rate\n",
    "        \n",
    "        n_spike_rate = n_spikes / length\n",
    "        data.append( [speed, d, length, n_spikes, n_spike_rate] )\n",
    "    ## Convert to DataFrame. Append.\n",
    "    data = pd.DataFrame(data, columns=('Speed','Direction','Duration','Spikes', 'Spike_rate'))\n",
    "    df.append(data)\n",
    "\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "### Concatenate and save DataFrames.\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    \n",
    "## Concatenate DataFrames.\n",
    "df = pd.concat(df).reset_index(drop=True)\n",
    "df.to_csv('spike_counts_speed.csv', index=False)\n",
    "\n",
    "print('Done.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Speed</th>\n",
       "      <th>Direction</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Spikes</th>\n",
       "      <th>Spike_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.420010</td>\n",
       "      <td>CCW</td>\n",
       "      <td>2.0032</td>\n",
       "      <td>239</td>\n",
       "      <td>119.309105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.420010</td>\n",
       "      <td>CW</td>\n",
       "      <td>2.0020</td>\n",
       "      <td>6</td>\n",
       "      <td>2.997003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.420010</td>\n",
       "      <td>CCW</td>\n",
       "      <td>2.0008</td>\n",
       "      <td>230</td>\n",
       "      <td>114.954018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.420010</td>\n",
       "      <td>CW</td>\n",
       "      <td>2.0006</td>\n",
       "      <td>6</td>\n",
       "      <td>2.999100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.420010</td>\n",
       "      <td>CCW</td>\n",
       "      <td>2.0020</td>\n",
       "      <td>271</td>\n",
       "      <td>135.364635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35.420010</td>\n",
       "      <td>CW</td>\n",
       "      <td>1.9981</td>\n",
       "      <td>28</td>\n",
       "      <td>14.013313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>35.420010</td>\n",
       "      <td>CCW</td>\n",
       "      <td>2.0011</td>\n",
       "      <td>226</td>\n",
       "      <td>112.937884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35.420010</td>\n",
       "      <td>CW</td>\n",
       "      <td>2.0021</td>\n",
       "      <td>4</td>\n",
       "      <td>1.997902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>35.420010</td>\n",
       "      <td>CCW</td>\n",
       "      <td>2.0016</td>\n",
       "      <td>226</td>\n",
       "      <td>112.909672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>35.420010</td>\n",
       "      <td>CW</td>\n",
       "      <td>2.0012</td>\n",
       "      <td>6</td>\n",
       "      <td>2.998201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>35.420010</td>\n",
       "      <td>CCW</td>\n",
       "      <td>1.9999</td>\n",
       "      <td>211</td>\n",
       "      <td>105.505275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>35.420010</td>\n",
       "      <td>CW</td>\n",
       "      <td>2.0065</td>\n",
       "      <td>4</td>\n",
       "      <td>1.993521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>35.420010</td>\n",
       "      <td>CCW</td>\n",
       "      <td>1.9982</td>\n",
       "      <td>189</td>\n",
       "      <td>94.585127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>69.602511</td>\n",
       "      <td>CW</td>\n",
       "      <td>1.1155</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>69.602511</td>\n",
       "      <td>CCW</td>\n",
       "      <td>2.0762</td>\n",
       "      <td>128</td>\n",
       "      <td>61.651093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>69.602511</td>\n",
       "      <td>CW</td>\n",
       "      <td>1.9275</td>\n",
       "      <td>2</td>\n",
       "      <td>1.037613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>69.602511</td>\n",
       "      <td>CCW</td>\n",
       "      <td>2.0724</td>\n",
       "      <td>146</td>\n",
       "      <td>70.449720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>69.602511</td>\n",
       "      <td>CW</td>\n",
       "      <td>1.9280</td>\n",
       "      <td>5</td>\n",
       "      <td>2.593361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>69.602511</td>\n",
       "      <td>CCW</td>\n",
       "      <td>1.9295</td>\n",
       "      <td>155</td>\n",
       "      <td>80.331692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>69.602511</td>\n",
       "      <td>CW</td>\n",
       "      <td>2.0750</td>\n",
       "      <td>38</td>\n",
       "      <td>18.313253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>69.602511</td>\n",
       "      <td>CCW</td>\n",
       "      <td>1.9307</td>\n",
       "      <td>146</td>\n",
       "      <td>75.620241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>69.602511</td>\n",
       "      <td>CW</td>\n",
       "      <td>2.0708</td>\n",
       "      <td>9</td>\n",
       "      <td>4.346146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>69.602511</td>\n",
       "      <td>CCW</td>\n",
       "      <td>1.9286</td>\n",
       "      <td>135</td>\n",
       "      <td>69.998963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>98.051615</td>\n",
       "      <td>CW</td>\n",
       "      <td>1.6854</td>\n",
       "      <td>27</td>\n",
       "      <td>16.019936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>98.051615</td>\n",
       "      <td>CCW</td>\n",
       "      <td>2.0030</td>\n",
       "      <td>118</td>\n",
       "      <td>58.911633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>98.051615</td>\n",
       "      <td>CW</td>\n",
       "      <td>1.9996</td>\n",
       "      <td>6</td>\n",
       "      <td>3.000600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>98.051615</td>\n",
       "      <td>CCW</td>\n",
       "      <td>2.0051</td>\n",
       "      <td>126</td>\n",
       "      <td>62.839759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>98.051615</td>\n",
       "      <td>CW</td>\n",
       "      <td>1.9978</td>\n",
       "      <td>25</td>\n",
       "      <td>12.513765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>98.051615</td>\n",
       "      <td>CCW</td>\n",
       "      <td>2.0040</td>\n",
       "      <td>125</td>\n",
       "      <td>62.375250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>98.051615</td>\n",
       "      <td>CW</td>\n",
       "      <td>1.9992</td>\n",
       "      <td>5</td>\n",
       "      <td>2.501000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>98.051615</td>\n",
       "      <td>CCW</td>\n",
       "      <td>2.0022</td>\n",
       "      <td>109</td>\n",
       "      <td>54.440116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>98.051615</td>\n",
       "      <td>CW</td>\n",
       "      <td>2.0002</td>\n",
       "      <td>2</td>\n",
       "      <td>0.999900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>98.051615</td>\n",
       "      <td>CCW</td>\n",
       "      <td>2.0028</td>\n",
       "      <td>108</td>\n",
       "      <td>53.924506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>98.051615</td>\n",
       "      <td>CW</td>\n",
       "      <td>1.9994</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>98.051615</td>\n",
       "      <td>CCW</td>\n",
       "      <td>0.5209</td>\n",
       "      <td>35</td>\n",
       "      <td>67.191400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Speed Direction  Duration  Spikes  Spike_rate\n",
       "0   35.420010       CCW    2.0032     239  119.309105\n",
       "1   35.420010        CW    2.0020       6    2.997003\n",
       "2   35.420010       CCW    2.0008     230  114.954018\n",
       "3   35.420010        CW    2.0006       6    2.999100\n",
       "4   35.420010       CCW    2.0020     271  135.364635\n",
       "5   35.420010        CW    1.9981      28   14.013313\n",
       "6   35.420010       CCW    2.0011     226  112.937884\n",
       "7   35.420010        CW    2.0021       4    1.997902\n",
       "8   35.420010       CCW    2.0016     226  112.909672\n",
       "9   35.420010        CW    2.0012       6    2.998201\n",
       "10  35.420010       CCW    1.9999     211  105.505275\n",
       "11  35.420010        CW    2.0065       4    1.993521\n",
       "12  35.420010       CCW    1.9982     189   94.585127\n",
       "13  69.602511        CW    1.1155       0    0.000000\n",
       "14  69.602511       CCW    2.0762     128   61.651093\n",
       "15  69.602511        CW    1.9275       2    1.037613\n",
       "16  69.602511       CCW    2.0724     146   70.449720\n",
       "17  69.602511        CW    1.9280       5    2.593361\n",
       "18  69.602511       CCW    1.9295     155   80.331692\n",
       "19  69.602511        CW    2.0750      38   18.313253\n",
       "20  69.602511       CCW    1.9307     146   75.620241\n",
       "21  69.602511        CW    2.0708       9    4.346146\n",
       "22  69.602511       CCW    1.9286     135   69.998963\n",
       "23  98.051615        CW    1.6854      27   16.019936\n",
       "24  98.051615       CCW    2.0030     118   58.911633\n",
       "25  98.051615        CW    1.9996       6    3.000600\n",
       "26  98.051615       CCW    2.0051     126   62.839759\n",
       "27  98.051615        CW    1.9978      25   12.513765\n",
       "28  98.051615       CCW    2.0040     125   62.375250\n",
       "29  98.051615        CW    1.9992       5    2.501000\n",
       "30  98.051615       CCW    2.0022     109   54.440116\n",
       "31  98.051615        CW    2.0002       2    0.999900\n",
       "32  98.051615       CCW    2.0028     108   53.924506\n",
       "33  98.051615        CW    1.9994       2    1.000300\n",
       "34  98.051615       CCW    0.5209      35   67.191400"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sasha/anaconda/envs/biomath/lib/python3.5/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x153d5bcc0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H00yIH//4xzEFKyIiIiKZZ9SoUbz55psUFBTwL//yL6kOR0QqnT17FpfLxfe+971UhyIi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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x152891550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('notebook', font_scale=1.75)\n",
    " \n",
    "## Open canvas for plotting.\n",
    "fig = plt.figure(figsize=(20,8))\n",
    "\n",
    "## Initialize top-half of plots.\n",
    "gs = gridspec.GridSpec(1, 1)\n",
    "gs.update(left=0.125, right=0.975, bottom=0.6,  top=0.98, hspace=0.05)\n",
    "\n",
    "\n",
    "\n",
    "ax = plt.subplot(gs[0])\n",
    "ax.plot(h1_sample, color = 'k', alpha = 0.8)\n",
    "\n",
    "plt.xticks(np.arange(0, len(h1_sample) + 25000, 25000), ['0', '2.5', '5','7.5',  '10', '12.5', '15', '17.5', '20'])\n",
    "plt.xlabel('Time (Seconds)') \n",
    "plt.ylabel('Extracellular Recording ($\\mu$V)')\n",
    "## Add event markers.\n",
    "for n, color in zip(np.unique(events_sample[:,-1]), ['#1f77b4', '#ff7f0e']):\n",
    "    ax.vlines(np.arange(len(h1))[events_sample[events_sample[:,-1]==n, 0]], -150, 150, color=color)    \n",
    "\n",
    "ax.legend()\n",
    "    \n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "### Plot statistics.\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "\n",
    "\n",
    "## Initialize bottom-half of plots.\n",
    "gs = gridspec.GridSpec(1, 2)\n",
    "gs.update(left=0.125, right=0.975, bottom=0.01, top=0.45, hspace=0.0, wspace=0.225)\n",
    "\n",
    "## Plot difference in direction \n",
    "ax = plt.subplot(gs[0])\n",
    "sns.barplot('Direction', 'Spike_rate', data=df)\n",
    "plt.ylabel('Spike rate (Spikes/second)')\n",
    "ax.text(-0.48, 100, 'B', va='top', ha='left', fontsize=24)\n",
    "ax.text(-0.48, 220, 'A', va='top', ha='left', fontsize=24)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "## Dummy-code Speed\n",
    "_df = df.loc[df['Direction'] == 'CCW']\n",
    "dummies = pd.get_dummies(_df.Speed)\n",
    "_df = _df.merge(dummies, left_index=True, right_index=True)\n",
    "\n",
    "\n",
    "## Plot difference in speed \n",
    "ax = plt.subplot(gs[1])\n",
    "sns.barplot('Speed', 'Spike_rate', data=_df, palette=['#4c72b0', '#c44e52', '#55a868'])\n",
    "plt.xticks(np.arange(3), ['35.42', '69.60', '98.05'])\n",
    "plt.xlabel('Speed (Degrees/$\\mu$s)')\n",
    "plt.ylabel('Spike rate (Spikes/second)')\n",
    "ax.text(-0.48, 127, 'C', va='top', ha='left', fontsize=24)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     0,  50000, 100000, 150000, 200000])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Turning curves\n",
    "##  Tuning Curves for changing bar length\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_file_width = 'data/17o23033.abf'\n",
    "h1, drum, sampling_rate = download_data(data_file_width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1195dc710>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FqKWuXDdG1YqZ21Hv6G9+\nYxp1Zmor/Kn1uMqFjKh5nUcCGQf3b3GctZotl2+YYvux2+e3a/9xs3ReGhcfgq7Oxr6WbuZuVWmx\nqnzM0O5uqtL39q7z7fULXezx7uD+Lbpq2FlCA5LzOuPpny3Lmhv++YIkGWNmSNopaaakYUnvNsbk\nx1lJwKQqXOvE0t4m9QbiinTqE7FMU3jX2RjwBj3LJ5u8SoV36tIs1vyoDcAqMdmeCuNE5Appc01Z\nVurF5Cqve5FIn33hqsmOg1iXrOlNegK6bV7yKX+rslQ/bd304Na+SPcw8eWYlefi1cTI5Bg0ZUKN\noyme8VSUuj9si7SpurxYx6XYv6LZ+Sgyfb9uitfedDL+jpozUT0eF6NPZX54xa1kRfF3LGjX9VuS\n12qK9o3L7V/kTPd7UBoz1a6pOrtZJulIlCETTyhktHOwUyUu19xKZkZrra8Xz0aKi49IdzZWtob2\niV4mm0Gvk4c69eU3pF7ltJACL8M2S2PEmunB1NF85cdUu62SPmFZ1iuWZf1S0jOSBn1oB9IwJ3zl\ne22cIui5pL9tpJPYMCO9TsZpccZclwthtqEe+0Was2ldnO+KG5+nm/PcnXAWwDjSxsqoE9WSIqPy\nkuwfft5/+oAn2w3YTITD2uor9M2rV+vyDc6nml6+YarOSnLVOFVxZ7cDo7nQB8WyO0XFj/fmpKh9\nPJWlqU+QgjRFJ9P366WQkRam0b5k+42XJ9AnDR7JMowN4MTzlh1ztHmW/QsQPePjB+ES7U8tDldr\nO2d5t/49pl7Ww1eu0g+uT51pUulBwDVffOK8RSrNYqArmVCeXRz2YmXB5pqypPV637Fzrvas6dWK\n8IrgbsnkrXg9bkx34ak829085XUPcYkx5jFjzAeMMZFc1jZJv456zG/CtyEHTGup1TO3bdLGHK1R\nFNHbXKNnbtuUduG7dDuZeE/75LmL0tuYz9w+DjY4LGqdKzY4+K4k+kgDXf/G5ndhU1Tm097N03X9\nlvjZNLdt6/esplB9ZfLpJvm4ukl7Q6XrQcpnbtvk6PEPXLzM1df301Ee1KwoREEKSvl90vDMbZtT\nP8ghLz/fdLMCvPKta9boqmH7wfW9W2aoL6bodnVZscalyHrad8zMwNTnPGEgOMvbRzg9ac+HAu+n\nDo2tiZUvJo2r0mUbpno+w+P8lZP1zyfOSbly37tPmW8rQwvBltG33hjzNWPMT+L8bJX0Hkk9kuZK\nek7SW9PY/rnGmO8bY77/wgsvZNLUguL1gM7t5XLtWjllvKvvLfp9rPBhulgktXuox36tjiBy45B0\nzJyJ+tGNGzLahpM6NH78vaOle/W9OBxAiNQ/SRaQ8vME2c7X9NQ4RUwl6ZShSb5NZexpOvJ5zshC\nQe2U47moD3JcnBotiYoGx9usW31nT1OViotCh4tP25GsoPaCScGtHxWPk9pKbgX8AhSjCaR4ZQX8\nDiYlE9veeAX97Zg50d3FUezUzgmiUMhkJcPl9CVdaf+tMvX1mOmHjQ4KjWfKyfQ5J33Vf121Wp+7\n2L3C9yEzelW7m7fOTDklPHaqttPdKPp4EFkV0O4m5nXaO/bN9mgRpFTlWLK1p7fUlmnbvPaU363N\ns1o1aVz2phYvzvFzs6DKKIJgWdY6y7L64/zcb1nW7yzLet2yrEOS3qsj0+meldQRtZn28G3xtn+P\nZVkDlmUNjB8fjDoyuSTIA69o05KkekbLdFCUrJO98Sj79Qfc8vXLV+rb167J+utKUlP12JNYP4Mx\nrRkWKD927kRbUz+y0RY79jqodxFPJAD12SSrFS13OT36MAcjy1zpgyL1iCbUHRnMf+HS+IsU+KGn\nqUrfuGLVqNsm1JZlvej3xat7dX/4RKGkKJRwlT8n3rAuecHcY+dO1KmLOrU6xdVQv8Xb17cv8D4r\nYUf4NSKD9obKUlcCCJHp5Jt9ym6JzUjJd04Dw1994wqdNNiR+oEOpDpJ94MXU4ycqPGx0LSkw8u7\nF9k4mHp1zG9v8GYxnAm15Zrd7t4x7I7ts4/8x0i7Fnfpn09MnsV82uIu117/lKFJOmWoc8z4LlKz\nLLbuod16TqfHWUAgE+umT9BJg53anYXV2uJ9fROVa7jt2H6PW+PM2gDXj8xlXq5qF325fZukn4R/\n/5ykncaYMmNMt6Q+SQe8ageC74QBdwdP6YjuHFNdOTtpsFOdjakPxLsWx8noiNp0XUWJozoobpzk\nRcSr3fIBj+rf2OJjgOLJm4d17aZph1PXz1zapSf2bfSsaGt1WXHigp8Ox9heL8tthxd/umyfanzw\njIV6Yt9GNdek9x2ze3I0Nrhr79ObUFvueTZCsuLAEa315apJswZCIqmygt6+c55uPXZW0iK5S3vd\nuTLZlcFqo/FO2stLQmq2sTR2ugHaJ28e1v7wyVZdRYlu3zZLHz17SDXlJfrpzSMLBjtZzSxac225\nnti3Ueeu6EmvcUpeDy5Vrbje5mo9sW9jIKcUOeVkGphdfRNqfFvkZHrrSFDwFBenGUXqbgbFbdtG\nToL7Jvg7zb0rnOFhZ2XYrXO9CRx+9Owhvfm4WWNu3z6/3ffAXMTOhR1qqSt3vIiGm9nVpcUh3bZt\n1pgpmz3jq/WzW4b1vl3pjbFNTCZXuiLnJBes6tGbj5vl6fgxncWtmh3WavOa3zMj8pWXc6buNMY8\nbox5TNJqSW+UJMuynpD0KUk/lfQlSRdZlvW6h+3ILzZ6n3xOy8/GeytPsXLIm4+bpTu2z055ELl5\na79u2TrTtXYt6Y1/NSte8epUjp3bpptj2hZvCmUksyZ6gPvFS5frK29coT1re9VcU6ahbvfSUYvT\nTGM/d0X6SwZXlBbpvJWTD9cnMDKqKitOOI3JD8mCYA9esky3bxs7KPTLQ1essj1IujP6CqWPjDG2\nrj7emuKKXKoTwYFJDUnvl6QTBzo1rqpUx85rc9TfvfuU+arKoPDtJ89brPvOHkrruX4WlE03WBhP\nJlOXpreMPXF2c2XZeCpKi0YF7k4e6jycqVRZWqxPnLtIX8wgc6+qrNjXFVyryoq175h+3XPagqQL\nethtYvTxJVurwR3cv0UXruodfaOLH+nwzBY1VZdqQm32pl4115Tr4P4trtZ6evCS0fvpf1y0VA/H\nZHkmDWS6PDi0m4mfysaZE3Rw/5ZRt334rEHbtWr+5eR5+uAZC9USc+Ex3j6/aup4NVWX6Zzl6QeL\n42mrr9BJg51jpoa99YQ5enzfxlG3ZTNBbevciQnvs9v3zuts0N2nLXD8PLsiXU55SZGjMiWx+4wb\nbt7ar4P7t2jBJHulHrzq+tPZRz52ztixSSbjHTumFFjWbbZ4Nlq0LOs0y7JmWZY127KsYyzLei7q\nvtssy5psWdZUy7K+6FUbCl2OzHJRmQ+rW8Vy2g9WlRVr3QznAZ9IcKPcxaKKU1JckYu3JHom9RCm\nt9ZqyoQazW6v14G961RXWaKHrljlKCNjQxqfXTIz8ngp0wcuXpZ0RaD+tjqdPNSZ8H43RAcWUn1X\nupuOnNCl2stOWNgRd/8MKq+me0VfNe4cV6kf3LB+1PQGO1/X6H0kXrAg1d+tqbpMSxMEt1N56MpV\naT1POpLxVFVWrMEuZ7XPHrxkmfqj6l9kGgBzerVcGjn5+dCZC7U7wQqA6Z6HXbKm9/Bge+/m9Kbm\nLuoZpwkBuYq8de5EXbS6d8ztd506P+nzKkqLHC3QEO849J5TRl7jkatXH77t385bbHub2ZHentJc\nW67vX7/+cLCho3EkQJfOIX4oXHuw36OaMk7M7ahXV5Pz4GD0intBcMmasat/rpwyPulqYtFqy0ts\nH3eaqsv0/evXebY4x727h8bUmhq5fVCXJljltKGyJG5ph1SyuXK0l4sl2Z1OF0TJsoyzKWSkJZPH\njk3yOckinwVjr0JBy9ZUO7sDsQo3o+jhF90+v1171vbpy29coT1r+3RB7BXQGO0NFbanfUS/Lzup\noddvmT7m6lmmupuqtCRO0d14Vw3vOnWB/vWU+aOKJXudGZDLkhVmzpYfvymzwu+OxYwo3Lr6bEc6\nQbzYAVCiIESsyHf3wN51eiLmynGh6Gmq0rWbpumuUxfoo2cP6bGb7O9rsSfI0VeuMz0B/ZjN7K93\n7JynVVObXS86fPmGqYcH20GbgmBX9JXtd+ycp4aoPj9yX3XZkUBRUbiIsZ2p7NGij4EbZ469qBG7\nmENFSVHKFcy85EV/tntZt354w/qExXc7wp9pcSjxsH/TrFb94Pp1aS9+EQS1Fe6c6EemuGXSjzRV\nl2qmg4tiqaYdpxMYd1N1WbEmjx97oXN533i9cX38Wn3pZEz+8Ib1h/dXp4K0Wqab/HhbQanRGe+C\nhV3ZmrnAOYx9BJ7guyBE1SMHq77mapU6yEY6zmYxzvKSkC5bP0XVZcW6bP2UlMGtT523eMzJhp0D\nqp2u72yX07Aj4rVv58LRQcXOxkoN97eopCikWo9r1jhZicVN9ZXpv6/IwHJHGkWJdy7sSLjU85Ub\nR9cYcZoZkmx/jT+wdHeY5NZVQzt/m9u3zUo7zT3ySdxw1AxH26goLcrpK6OJPHzFqrgp8tF1NYwx\nOm/lZLXUlau0OJR0Se5bUkx1jD6WxKtJ4kSiqc1ObInKQjtvZU/WpncFRaS+VLI6U9HdR215ie4+\nbYE+eMbCMY+7fsv0tOuxxAaZ/Fy9rbQo5Ki2o13GmKSf8wdOH9Ddpy1QXYo+MF5ALhsLbngt0h/b\nHd+Nqy7Twf1btCODC6Pnr5xsO/DidKqk0ejvTnkAZg4k8qajZ6q+siTl8bchfH+6demipR008Sl+\ncPn6KYGrc+aVZX1NKikythZHaawq1eUbRo9fp7XWqLwkpD0xWXbx/ubZXoAFqQW3p4Itl64bm97q\n9yogXvL6rcXruB6+YpWu2JB85SW3ja8pkxer9sYuHZuKlx+3l/vp5/cs00fOclav5tyVPVre16Qd\nGRazTVVzy06R0HQGTfu3z9ZdURkf0VOvYjNDMhmkOpmm6WZ9mEeuOjJN5sQ0TwbiXa1N1/oZEzSt\nNbPMhVSfjtOvyMZwzZV4QVc/DgtdTVVxU+Td5tZ7i2znpqNHT/9Mp8D1J89dpM6orNXdS7v1jctX\nxX2sne/J1cPTtO8Y92oGZsPk5pHv2/UOVvHcOLMlbvBj06xWPXZTdrIC/VhGO9E+fPyCdn109+hj\n2RobU69is8bGVZelNaXo385frOV9RzKp7Ywh9qzt1fK+Jh0950gNnupw0NDpSlFu9ltfu2yl/vtq\nf1YSTmVFX/qFjL/6xhX6Zhrv650nzdN5CRYP6Gis0A1pTIOPl5V19JyJevTGDSkvMn/tspX66hvt\n1b4KkrtOna8zEqw8Z3cEdMnavjF1zvzmVRZPc025nr5ts85c2iVJmhqnPmIyteUl+tktm7TSYfFv\nJ8cheIfAUw6bNK5SE5MW3CT1L1pkdaHmOFeWkqUwdzVV5exUh1j/ekr8ehofOvPIFebHb9qgT567\naNT9dvakcWnM4/fKzIl1h6cz2A0kNdeU697dQ6qvzOx9pCqQ/qnzFiW93y3Rq49FH6Cv3TRN/35h\n+unHQ92Nunh1r+7YPpJR0mgzwyvTQrTRqfe3buv3fTWd9+4aODKQ9iEgHi2SeXnH9tk6sHetylIs\nkOC3Jo+mOSX7nN64zv7Fg9j+Pl6dltRtcff4e8Gqya4vq+258Pci3gnnseF91o0sMLdPkD501tiM\nq2ypKCnSm6ICn/+0Y46W9R0J3n7/+nV6T4q6WJJcm/q5MKru2vevX5dwDBGtta5C9+4eGpW9WFte\nogPXrdVNR6cfPK0pyyxTrbe5WuNtrDLphe/tXefZtiePr06rTz1mzkRdG1M/LlK79K5TF9ieMu6W\ncdVl6svBgs7D/a26yeOLApkEYOOVwQiCrXPb9K1r1mhxmu2b1lKTsLZYPH5muuIIAk8oGDcePVNv\n3TFHbz9xbsLHJBrAJirQ2h51Mux1qnO68/uj0+QTnZBGn9TXlJektWT69Vtm6PZts9K6WuxFjLSh\nqlQH92/RmUvHDp7mhusm7V7mzbTDZHqSZN1ETngT1emIDC670yi6GnHeysnqbU4/8ycUMrpi41Sd\nMNCh27fN0jWbjgxc+8LbjTdIWj9jgk5fnHz57cgqJZHVcxK9z5KikNoa0puu4mW9KLv7cWR1u3md\nqVe5S+Xg/i16W7hPKykKubrKm1ciV9KdFhNf5fAK58H9W3RWnO9/InYG98kyE/wOhibS0+TvkvDx\nvhcnDHTo4P4tnkw7y5apHpwkz5hYG/eYFdFUXeZbYDnT126uLXe0slesY2NKGySampTNVTZTrXQa\nYSfg5TQL3Iss1pVTxuvg/i0ZrfAZzc2hXU94PGAnyBb9uk6C226198FLliW9P1nSQCqpjmnnregZ\nVSP2Y+cs8mSVPDdk8jkYY0bVFiPVIjcQeMox0ceZf9oxJ+5jIleFlvd5P8XBjgtWpb/UfazoefBL\ne1MHOCaGgy6XrZ+iopDR9gXtcTNaUh3AE6V0nhpViDjZ1aF0xgeD3e5cpUiVadLXXO3KFKSK0iKd\nPNSpoZ7RJ5PfumaN7omaAhaxzMb++a6T5425bfeybn3n2rXpN1Qj88YP7t9iqw3ZtHpqsz5y1qDO\nXxn/O7Owu1H37h7UZQkKeWaTMUYnD3Umr//kYHtnLOnSEzcPSxqpSXXv7kHdefxsx9tJ5UtvWKEZ\nHq36k0ok6HXWsm4d3L8lq1ff4wXc/BqMRgKKsX1FKh2NlXHbnKz/3r28W7Pa6rTNZj0+Kf7+FhnI\nJ2qDJD2+b6MWdmUeTHTbG+JMyccRRQHIDo9kyKUqMJ3Pbjm2X6ckWdyhKGRGrXZmN/DitFi9E6cu\nmqRHrlqtr102drW3WN+6Zo2+/AZ3ppJF77IB2H0P83JK96Vr+3Tv7kGdGK4dOrej/nBJisj3J17w\nLp0LqZnqaEi+z22dOzIN9arhqY7PkSLHtIoEZRuu3TxdD1+5Ou592XLFxima01HveIwdW0bBTn8Y\nORfMJKiN7OGvlKNOWzRpVAp0tLrKEj1y1WrdvNXelRiv9WSQnRErOhPk/acv1LeuST6vvW9CjR6+\nYpUutrkqgtMDeKrpFJmMBy5d26eHr1g15vZ4AcdkzUjVxkUu17PYs6ZPN4YzGuZ1NmhifUXcpYPf\ndPRMve2EkfcSLxW4r7laR80+UiPiwvDB2Uiur8rnVJOHAYMVU8YnPdgu7xuf9wfY4qLQqLoiblgW\nVSz60xcszjh4mY4NM1v0jctXjqq/lYybqxjZXZI7XQeus/95djVV6eErVukNDqa/2RHvW9NWX6EH\nLlmmxgynAj+4Z7m+6+A9Bolf/UXWVuGK+sNH6h/F1rWTjpzExE4Lf8O6Pu1KkY0ZsX1+/KnbVw9P\nG3Nbg4OFJuZ11Ouc5d1JM7Kzbf2M5PUK3Xbaokm6bVviRQFWT3V+TDhw3Vp94VJva+d0NFbayiJO\nNBZKV6R8RB6XdR0lMi6Y19mgh65YpTOWdOnYeW06ffEkXT08ugC138G48tKRPndnzKqIkQtAkdkV\nF67qjdt3JLJnTe/hY5rXU+gy+Qx7m2t0/0VLMw76fTzO4iSx3n/6Qp002KH7XVrBrq6ixHH2st/7\nWy4JZl44MpbuUqS5pLykyFaaZpeNwJedA/cnz12UdBjdkkEdqKrSIv3t1ddH3VYUMupqqtLdpy1Q\n17gq3f1f/5P29uOJnNgfO29iwsdUlhbpxZdfc5SqHAoZnbWsWycPdSYtpF1SFNJx89u1eVZr0sdN\nGlepX/3+72rIsPaSm+47e0iPPP1/uurTjyV8TFPUyY0x/gwO7z5tgSaNy35f4NZbTfaZOf08P3zW\n4OHfK0uLVVnq3uHPyQl2sqmWieTCUr2p6uBdsGqy3vPwkT7MTr+cbScPdepLT/xWc8NTPdsbKvSb\nP74kaWQp8Wobg9GgngSePNSprzzx21G33XXq/LSyXZ3Wk8nm/jvc36qf3TIc95jSXFOuO7bP0uqp\no4OwtRUlunhNnz7y7V+l3P6stlp95odjb4/XBzip8xUKGe3d4ryYcyL1GdYz+fmtmwKVfdVWX2E7\ngDq1pUaPP/tnGZm8qc8Za1N/q3Yu7NT3Dv7BtXpeXvDqhDySNVtWXKR9Ni6yf/3ylXryub/o4o/9\nKOnjUn1nJ9SW2SpqXVZcpKduHVZJyJ2gf9aC+B6yU2cx+n3OaK1Vb3PqQG15SZHefNxsG9s+ojnJ\nxeMfXO9dPTYQeMqq0uKQXn3tkN/NQJqGUmQGnTI0STfc/0Ra237i5mGdcNe3deDgH8bcd3glGpeP\nO91NVSmn2rTVV+jazdO1YJLz6SN2Vm+z87jPXLBEv3jhb/rxr//kuA2ZqC4t1qy2ujFLtkojxVNP\nGOhIGHj69PmLR6X3//fVa7Rk/zc8a2sidlcxqikr1ouvvOb669sZdHaHg5rJai+5MXjNxklUcIf/\n2XHZ+il621d/Hve+U4Y69cCP/1fHL8hs1ch43BqUrwjXN3FDsn22K5y569X3Lp7bt83S7THZJMP9\n9jLvon36/MWjVusLomTHlBMXJp7KlcgpQ52677v/L+3VNP2Qaf240mJvs+T2rOnVC399VR8/8P8c\nPS96KlWiIO9HzhrUk7/9i2vvoaOxQr/+w0tjbr8wwzIS9+4e1GnvP3D4/8v7xus/f/5CyudVlBQd\nzugL2kXmyJ8kZKRDPsRKEu0Tk8ePlJRIFXiK9vXLV+q110dv8LvX2Q9KeFKLLeCpNQ9cvEz1CTI9\ng5KoX1tenHTqfb7PKPAbn24WVSWphYIRiZYk9VpkpbtNDgfikZPZnqaqjK862T15MhqdTeO1lVPG\n27rS74Z4J6VN1WUa7G7UKYs6tWNBe1orTKUjFDJ64JJlaU03GOhqHHWlNZMCim65fdssvTvBikSP\nXG2vHoAXxZNXT23WF/YsP1y3AclFLqDW+lC3IpVkmTDtDZX65tVrvP0upNkFZ3u1m6W9TfrCnuU6\nZZG96V1BMtDVeLiAfVuKv+WqKSOZRX0TvC1unq1Tsf720dP3asq5dpuuyzZM1ZuPSzytLpaT8+2G\nqlItmexe/cbP71muR64ae4y8ysEUqXhip5PfderYWpi56vN7vJ3emEqiLMsts5OP8UuixvGTx1e7\nOjXSK+/bNeB3Ew6b1V43JhgaKQsz30EwvKw4lHC8mqkNM1tY9d1HHDURKGUeX2VLpKm6TI/dtEHV\nDqffNFaV6uatM7V2evLghNvTL7559Rq96xvP6F0PPTNq+0EIcGTitm2ztGOgQ9vf860x91WWFust\nUfWtusZV6uDv/57N5mVkzbRmPZrlrK1oJycp2hqv4H6sNx83y3Y9MKer88yY6LzYd0djpZ763YuO\nn+cFr6ZXRVbNOimqTkRzTbn2HTPT1fore9Ykr4EXWdShviI4013d5GXtpq5xlXrhxVfGXEWdMbFW\nn/vx/3r2utnwjStW6lCSJO4dA+3a2N8S+GWsI/3fOcu79d5Hfmn7ee8+Zb4Gb/96wvs5tckPteUl\nowL95SWhlGO+dFSUFqmuokR/fukfrm872yJj0UQLpfgl1Xdyscd1k9K1dtoEvf1rT2ttilqNl6Q4\nlnshVdbdqqnN+vGbNtg6DkTGUjdvnRnI6fjIHIEneM7JOVk2V3mKlW4Gwa7FXQnvcxJUv2z9VJ30\n3u/Yemx5SZHKS8YG6W48aobj1PUgKQoZ21eRP3X+Yg3elnjQH+vBS5bp5gd/qgO/HDuiPr6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11962d828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,8))\n",
    "plt.plot(h1)\n",
    "plt.plot(drum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fir_design defaults to \"firwin2\" in 0.15 but will change to \"firwin\" in 0.16, set it explicitly to avoid this warning.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-3-9293fb4dff84>:36: DeprecationWarning: fir_design defaults to \"firwin2\" in 0.15 but will change to \"firwin\" in 0.16, set it explicitly to avoid this warning.\n",
      "  if h_freq: arr = filter_data(arr, sfreq, l_freq=None, h_freq=h_freq, phase='zero', verbose=False)\n"
     ]
    }
   ],
   "source": [
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    ### Event detection.\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    \n",
    "direction = 'CCW' \n",
    "hfreq = 5.0\n",
    "events = find_oscillation_events(drum, direction, sampling_rate, hfreq)\n",
    "## Compute epoch lengths.\n",
    "\n",
    "# get the CCW starts \n",
    "events = events[::2]\n",
    "events = fix_events(events)\n",
    "lengths = np.diff(events[:,:-1], axis=1) / sampling_rate\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pChNBllDT+xszVdhIBaKCr9MUgi/psqlqG7JmQPBtmIZP3KgmSD7j9kPOJpVK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\nh2oiIiIiIiKiKP0P8nuupcdMoDgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119724668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1,  figsize=(15, 5), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "plt.plot(drum)\n",
    "ax = plt.gca()\n",
    "## Add event markers.\n",
    "for n, color in zip(np.unique(events[:,-1]), ['#ff7f0e', '#1f77b4']):\n",
    "    ax.vlines(np.arange(len(drum))[events[events[:,-1]==n, 0]], 0, 6, color=color, alpha=0.8)    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Bin size; take the minimum of the length (not including the last one because it's not a full cycle)\n",
    "sample_size = int(min(lengths[:-1]) * sampling_rate) \n",
    "bar_length_spikes = np.zeros((len(lengths[:-1]), sample_size), float)\n",
    "threshold = 55 # eyeballing the threshold\n",
    "\n",
    "spike_count = np.zeros((sample_size, 1), int)\n",
    "for event_i, event in enumerate(events[:-1,:]):\n",
    "    ## Takes the spike_count in every event bucket \n",
    "    bar_length_spikes[event_i, :] = h1[event[0]: event[0] + sample_size]\n",
    "    peak_loc, peak_mag = amplitude_thresholding(bar_length_spikes[event_i, :], threshold)\n",
    "    spike_count[peak_loc] = spike_count[peak_loc] + 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_bins = 50 \n",
    "bin_size = int(sample_size / num_bins ) \n",
    "\n",
    "spike_rate_per_bin = np.zeros((int(num_bins * 0.80), 1), float)\n",
    "for b in np.arange(int(num_bins * 0.80)): # take out last 20% \n",
    "    spike_rate_per_bin[b] = sum(spike_count[b * bin_size : (b + 1) * bin_size])/bin_size * sampling_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AgF0gqKNR+/tl4QrwddfK3Ue1ODHD6HIAAAAAgKCOxs3Xw1XPXl0xWO6F73ao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\nhBBCCCGEEKJE/h/EB/qgQwO3gAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a775e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = plt.figure(1,  figsize=(15, 5), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "plt.plot(spike_rate_per_bin)\n",
    "# bar length ranges from 1.6 inches to 0.016 inches in forty bins \n",
    "receptive_field_angle = np.round(2 * np.arctan(np.arange(0.016, 1.6396, 0.0396)/10), 3).astype(str)\n",
    "\n",
    "plt.xticks(np.arange(0, 41, 5), receptive_field_angle[::5]);\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.05617978],\n",
       "       [ 0.07022472],\n",
       "       [ 0.05430712],\n",
       "       [ 0.04494382],\n",
       "       [ 0.03651685]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spike_rate_per_bin[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### first plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sasha/anaconda/envs/biomath/lib/python3.5/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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jcVWmONsHPqf69+/PO++8wz//+U/cbjdfffUV8PW8ewDHjx9nzpw5EasUb7zx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\ntd65Dpm94447kJWV5Qzk7Nu3D++9957mxCIlSpTAmTNnnOv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2cm4EUu/2VZf3JCJolWX/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\nzczMzMzMzMzMrGX8fwALbmx139qWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120d0e748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('notebook', font_scale=1.75)\n",
    " \n",
    "## Open canvas for plotting.\n",
    "fig = plt.figure(figsize=(20,8))\n",
    "\n",
    "## Initialize top-half of plots.\n",
    "gs = gridspec.GridSpec(1, 1)\n",
    "gs.update(left=0.125, right=0.975, bottom=0.6,  top=0.98, hspace=0.05)\n",
    "\n",
    "\n",
    "\n",
    "ax = plt.subplot(gs[0])\n",
    "ax.plot(h1_sample, color = 'k', alpha = 0.8)\n",
    "\n",
    "plt.xticks(np.arange(0, len(h1_sample) + 25000, 25000), ['0', '2.5', '5','7.5',  '10', '12.5', '15', '17.5', '20'])\n",
    "plt.xlabel('Time (Seconds)') \n",
    "plt.ylabel('Extracellular Recording ($\\mu$V)')\n",
    "## Add event markers.\n",
    "for n, color in zip(np.unique(events_sample[:,-1]), ['#1f77b4', '#ff7f0e']):\n",
    "    ax.vlines(np.arange(len(h1))[events_sample[events_sample[:,-1]==n, 0]], -150, 150, color=color)    \n",
    "\n",
    "ax.legend()\n",
    "    \n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "### Plot statistics.\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "\n",
    "\n",
    "## Initialize bottom-half of plots.\n",
    "gs = gridspec.GridSpec(1, 2, width_ratios=[1,2])\n",
    "gs.update(left=0.125, right=0.975, bottom=0.01, top=0.45, hspace=0.0, wspace=0.225)\n",
    "\n",
    "## Plot difference in direction \n",
    "ax = plt.subplot(gs[0])\n",
    "sns.barplot('Direction', 'Spike_rate', data=df)\n",
    "plt.ylabel('Spike rate (Hz)')\n",
    "ax.text(-0.48, 90, 'B', va='top', ha='left', fontsize=24)\n",
    "ax.text(-0.48, 190, 'A', va='top', ha='left', fontsize=24)\n",
    "ax.text(2.2, 90, 'C', va='top', ha='left', fontsize=24)\n",
    "\n",
    "\n",
    "ax = plt.subplot(gs[1])\n",
    "plt.plot(spike_rate_per_bin)\n",
    "# bar length ranges from 1.6 inches to 0.016 inches in forty bins \n",
    "receptive_field_angle = np.round(2 * np.arctan(np.arange(0.016, 1.6396, 0.0396)/10), 3).astype(str)\n",
    "\n",
    "plt.xticks(np.arange(0, 41, 5), receptive_field_angle[::5]);\n",
    "plt.xlabel('Receptive Field Size (Degrees)')\n",
    "plt.ylabel('Average Spike Response (Hz)')\n",
    "\n",
    "plt.savefig('first_fig.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Turning Curve for changing contrast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1162becf8>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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60XNHPam67Ogb0gf+sCNwf2aIHwmlJBKNmr6smGnuGtB7b9uuJVMkVypimLhw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\nRvnsL/KpeKek+UlHP30JMOw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RERERkcUYUDLRRwcPWd0E1bweN2YG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QAAAc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\nRERERESK/H8zUsjUMZG3/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116307cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_file_contrast = 'data/17o23034.abf'\n",
    "h1, drum, sampling_rate = download_data(data_file_contrast)\n",
    "\n",
    "fig = plt.figure(figsize=(20,8))\n",
    "plt.plot(h1)\n",
    "plt.plot(drum)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fir_design defaults to \"firwin2\" in 0.15 but will change to \"firwin\" in 0.16, set it explicitly to avoid this warning.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-2-9293fb4dff84>:36: DeprecationWarning: fir_design defaults to \"firwin2\" in 0.15 but will change to \"firwin\" in 0.16, set it explicitly to avoid this warning.\n",
      "  if h_freq: arr = filter_data(arr, sfreq, l_freq=None, h_freq=h_freq, phase='zero', verbose=False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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hMrICCmfeiohIXEyUFIoLvDUdaxPNVKwg65nhy+PxyKhb6/k9/8HcFOVLkEyl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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1160808d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    ### Event detection.\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    \n",
    "direction = 'CCW' \n",
    "hfreq = 5.0\n",
    "events = find_oscillation_events(drum, direction, sampling_rate, hfreq)\n",
    "## Compute epoch lengths.\n",
    "\n",
    "# get the CCW starts \n",
    "events = events[::2]\n",
    "events = fix_events(events)\n",
    "lengths = np.diff(events[:,:-1], axis=1) / sampling_rate\n",
    "\n",
    "fig = plt.figure(1,  figsize=(15, 5), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "plt.plot(drum)\n",
    "ax = plt.gca()\n",
    "## Add event markers.\n",
    "for n, color in zip(np.unique(events[:,-1]), ['#ff7f0e', '#1f77b4']):\n",
    "    ax.vlines(np.arange(len(drum))[events[events[:,-1]==n, 0]], 0, 6, color=color, alpha=0.8)    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116948080>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Hj37Y8b6cPm0tu9webS273B5t63ug9vZoa/nl9mlL2bl6tKX3bzmOoy1l5ziG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\nAgzSAAAAUIBBGgAAAAr4f7A5I8ugERSdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1169526a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    ### Count Spikes Per Trial\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "\n",
    "## Bin size; take the minimum of the length (not including the last one because it's not a full cycle)\n",
    "trial_size = int(min(lengths[:-1]) * sampling_rate) \n",
    "bar_length_spikes = np.zeros((len(lengths[:-1]), trial_size), float)\n",
    "threshold = 55 # eyeballing the threshold\n",
    "\n",
    "spike_count = np.zeros((events.shape[0], trial_size), int)\n",
    "for event_i, event in enumerate(events[:-1,:]):\n",
    "\n",
    "\n",
    "    ## Average spikes in the window \n",
    "    bar_length_spikes[event_i, :] = h1[event[0]: event[0] + trial_size]\n",
    "    peak_loc, peak_mag = amplitude_thresholding(bar_length_spikes[event_i, :], threshold)\n",
    "\n",
    "    spike_count[event_i, peak_loc] = 1 \n",
    "\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    ### Spike Rates in Bins\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "num_bins = 50 \n",
    "bin_size = int(trial_size / num_bins ) \n",
    "\n",
    "spike_rate_per_bin_contrast = np.zeros((events.shape[0], int(num_bins * 0.80)), float)\n",
    "for event_i, event in enumerate(events[:-1,:]):\n",
    "    for b in np.arange(int(num_bins * 0.80)): # take out last 20% \n",
    "        spike_rate_per_bin_contrast[event_i, b] = sum(spike_count[event_i, b * bin_size : (b + 1) * bin_size])/bin_size * sampling_rate\n",
    "\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    ### Plot\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "\n",
    "    \n",
    "fig = plt.figure(1,  figsize=(15, 5), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "sns.barplot(data=spike_rate_per_bin_contrast)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Turning Curve for changing frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11af155f8>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zR0DJ4MUrl5o9BCIiIiIiInIYBpAM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\nA0hERERERERERCTr/wOszgmkhRMAkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f29c7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_file_f = 'data/17o23035.abf'\n",
    "h1, drum, sampling_rate = download_data(data_file_f)\n",
    "\n",
    "fig = plt.figure(figsize=(20,8))\n",
    "plt.plot(h1)\n",
    "plt.plot(drum)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fir_design defaults to \"firwin2\" in 0.15 but will change to \"firwin\" in 0.16, set it explicitly to avoid this warning.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-2-9293fb4dff84>:36: DeprecationWarning: fir_design defaults to \"firwin2\" in 0.15 but will change to \"firwin\" in 0.16, set it explicitly to avoid this warning.\n",
      "  if h_freq: arr = filter_data(arr, sfreq, l_freq=None, h_freq=h_freq, phase='zero', verbose=False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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IO7YfOK4dwXc2DbW/p5rESC/j3K4bczbuMZgEXpKlDvHkyZPl2muvze4syKTU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\nAABwJQZiAAAAAIAr/QEEM39XVpiiRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12654c438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    ### Event detection.\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    \n",
    "direction = 'CCW' \n",
    "hfreq = 5.0\n",
    "events = find_oscillation_events(drum, direction, sampling_rate, hfreq)\n",
    "## Compute epoch lengths.\n",
    "\n",
    "# get the CCW starts \n",
    "events = events[::2]\n",
    "events = fix_events(events)\n",
    "lengths = np.diff(events[:,:-1], axis=1) / sampling_rate\n",
    "\n",
    "fig = plt.figure(1,  figsize=(15, 5), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "plt.plot(drum)\n",
    "ax = plt.gca()\n",
    "## Add event markers.\n",
    "for n, color in zip(np.unique(events[:,-1]), ['#ff7f0e', '#1f77b4']):\n",
    "    ax.vlines(np.arange(len(drum))[events[events[:,-1]==n, 0]], 0, 6, color=color, alpha=0.8)    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x127e36b38>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W7fF9lXm0QjsLTpgdBwAAAGjXUvOPy21ISTxmy3QUazSr+iFmKQwxAwAAAFpS\ncs7ZwWWcrzZbo4t1VlaWxowZo5iYGCUmJmrfvn3nXLd+/XrFxsZq4MCBmjRpksrKyr615te//rUs\nFot27dp18cnhkYaEB+kSaze9s+ewKqqdZscBAAAA2q2U3BIFBvgotk+Q2VE6vEYX69mzZ2vWrFnK\nzMzUvHnzNGPGjG+tqaio0MyZM7Vu3TplZWUpPDxcCxYsaLAmJSVF27dvV2RkZJPDwzPdOSpCp2pc\nenfPYbOjAAAAAO3SqRqn9jhOanRUsLy9OF9ttkYV66KiIqWmpmr69OmSpMmTJ6ugoEDZ2dkN1m3Y\nsEHx8fGKjY2VJM2ZM0erVq2q//lTp07pZz/7mZYsWdJc+eGBbhkRrgBfL/2bZ1oDAAAALWKn/YSc\nbkOjecyWR2hUsS4oKFBYWJh8fHwkSRaLRTabTXa7vcE6u93eYCc6KipKhYWFcjrrbgl+7LHH9MAD\nDygiIuJ732/hwoWyWq31r4qKigu6KJgrKMBXN8aFa6f9hDKPlpsdBwAAAGh3knNKJElJ/Rlc5gla\nbXjZRx99pPz8fP34xz8+79q5c+fK4XDUv7p25Zlsbc3UxDNDzNi1BgAAAJpdcm6pOvt5a2g456s9\nQaOKdURERIOdZ8MwZLfbZbPZGqyz2WzKz8+v/zgvL69+p/vTTz9VWlqaoqKiFBUVJYfDoRtuuEHv\nvPNOM14OPEViVA/179VFb+08pGqny+w4AAAAQLtRVevSzoITGhnZQ77ePOjJEzTqTyE0NFQJCQla\nsWKFJGnt2rWyWq2Kjo5usG7ChAlKS0tTenq6JGnRokWaNm2aJOn3v/+9Dh06pLy8POXl5clqteq9\n997TzTff3JzXAw9hsVg0NTFCpZU1+nh/kdlxAAAAgHZjj+OkapxuJXG+2mM0+ssbS5Ys0ZIlSxQT\nE6NnnnlGy5YtkyTNnz9fixcvliQFBgZq6dKluu222xQdHS2Hw6Enn3yyZZLD401KsMrHy6J/b7ef\nfzEAAACARuF8teexGIZhmB3ifKxWqxwOh9kxcBFm/ytVH+4/qi2PjZe1R2ez4wAAAABt3vSlydqe\nV6o9v7lO/j7eZsdpty6kh3JDPlrU1MQIGYa0JpUvjAAAAABNVetya0f+ccXbulOqPQjFGi3qyoEh\n6hMUoDWpBXK5Pf7mCAAAAMCj7T10UqdrXUrqx23gnoRijRbl4+2lKaOsOnyySp9nHzM7DgAAANCm\npeSWShKL2oaaAAAgAElEQVSDyzwMxRotbsrIs8+0ZogZAAAA0BTJOSXy9bYo3tbD7Cj4Goo1Wpyt\nZ2ddHt1TH+0/qpKKarPjAAAAAG2Sy20oNe+4LrF2Vyc/zld7Eoo1WsWdoyJU6zL01s5DZkcBAAAA\n2qQDhWUqr3ZyG7gHolijVVw/tI+6dfLVv7cXqA084Q0AAADwOMlnzlePplh7HIo1WkWAr7duj++r\n7KIKpdlPmB0HAAAAaHOSc0rkZZFGRVGsPQ3FGq1maiJDzAAAAICL4XYb2p5XqmF9u6mrv4/ZcfAN\nFGu0msFhQRpu7ab1ewpVUe00Ow4AAADQZmQVVej4qVrOV3soijVa1Z2JETpV49KbaQ6zowAAAABt\nRnJuiSQpqV9Pk5PgXCjWaFW3juir0EB/Lfwok0dvAQAAAI2UnFsqi0VK5Hy1R6JYo1V19ffR/JuH\n6MSpWj39XrrZcQAAAACPZxiGUnJLFdsnSN06+5odB+dAsUaruzEuTFfGhGhtmkNfHiwxOw4AAADg\n0T7Yd0TF5dW6cmAvs6PgO1Cs0eosFosW3DpUfj5e+t91e1XjdJsdCQAAAPBINU63ntmQri5+3vrJ\nFf3NjoPvQLGGKSJ7dtGD46N1sLhSf9+SY3YcAAAAwCO9lpyvvJJTemDcAIUE+psdB9+BYg3TzLqq\nvwaEdNFfPsmSveSU2XEAAAAAj1JWVas/f5KlPkEBmjmW3WpPRrGGafx9vPW72+JU7XTryf98JcMw\nzI4EAAAAeIxFGw/q+Kla/eK6GHXy8zY7Dr4HxRqmumxAT01K6KvNmcV6b+8Rs+MAAAAAHuHQidP6\nx9ZcxfYJ1KQEq9lxcB4Ua5juVzcMVrdOvvq/d/apvKrW7DgAAACA6Z77IEM1TreeuHGwvL0sZsfB\neVCsYbpeXf31y4mxKiqv1vMfZpodBwAAADDVXsdJvbXzkK6MCdEVA0PMjoNGoFjDI9w5KkIjI3vo\nn1/maa/jpNlxPILLbajWxaPIAAAAOhLDMPTUe/vlZZF+dUOs2XHQSBRreAQvL4ueun2YLBaLnli3\nVy53xxxkdqyiWm+mOfTQqp0a9buPNPz/PtQbOxxmxwIAAEAr+TS9SNtySnXHSKti+wSZHQeN5GN2\nAOCs2D5B+snYflryWY5WbMvXvWOizI7U4lxuQ7sdJ7Qpo1ibMoq099BJnR2OPjQ8SCUVNXpkzW5t\nzy3V/906VAG+TIMEAABor5wut36/IV2dfL0199pBZsfBBaBYw6P8zzUDtX5PoZ79IEMThvVR76AA\nsyM1u2MV1foss1ibMoq1JatYx0/VDWwLCvDRDXFhGhcToqsGhSg0MEAlFdV6ePVuvZ5aoD2HTmrR\n3Qnq16uLyVcAAACAlvB6aoGyiyr00A8Gqk+39vfv4PbMYrSBhwdbrVY5HNwO21F8vP+ofvLPVN10\nSZheuivB7DhN5nIb2lVwQpszirQps7jBrvSwvkEaFxOqcYNCNCKiu3y8v306w+029NLGbL3wcaa6\n+Pno2Tsu0cS4sFa+CgAAALSkimqnxj27SZK0+dFx6uLPHqjZLqSHNvpPKysrS/fee6+OHTumbt26\n6dVXX9XQoUO/tW79+vV65JFH5HK5FBcXp1dffVVBQUGqrKzU1VdfraqqKklSWFiYFi9erKioqMZG\nQAdxzZDeum5Ib63fU6gpo4p1VUzbm4T49V3pz7KKdeJ7dqXPx8vLood+MFAjI3vof/69Uw+sTNOP\nL4/SLycOlp8PYxIAAADag79tPqhjFdV6+vY4SnUb1Ogd66uvvlr33HOPZsyYoTfeeEN/+MMftH37\n9gZrKioqNGDAAG3evFmxsbH62c9+pk6dOunZZ5+V2+1WZWWlAgMDJUkvvPCCNm3apP/85z/nfW92\nrDueQydO69qFmxUS6K8Pfn6lx58t/uau9J6vTTZvzK50Yx0tq9KDr+1USl6pRkR018t3J6hv907N\ncQkAAAAwyZGTVRr33EZZe3TW+/9zRZP+vYjmcyE9tFHFuqioSNHR0SotLZWPj48Mw1BYWJg+//xz\nRUdH169bs2aNXnnlFb3//vuSpP379+u66677VhjDMPTb3/5WO3fu1Lp165r1gtB+/P2zHD313gE9\neHW0fnGd5w1vOLsrvfHMWemzu9LdOvnqioG9NG5QqK6M6dWoXekL4XS59dyHmVq8+aC6d/bVC1NH\naPyg0GZ9DwAAALSex97YrdWpDv1jxihdHdvb7Dg4o9lvBS8oKFBYWJh8fOqWWywW2Ww22e32BsXa\nbrcrMjKy/uOoqCgVFhbK6XTWf+4111yjvXv3KiQkRB988EGjLwodz48vj9KbOw9p8eaDunVEX0WH\ndjU7kqS6M88LP8rUy5uy689Kx/Xtph9dGqJxg0I03Nq0Xenz8fH20uMTYzUqsofmrt6lHy/brp+O\nH6CHr4nhq5sAAABtzIHCMq3Z4dBl/XuyWdKGtfq/wj/++GMVFhZq6tSpeuqpp865ZuHChbJarfWv\nioqKVk4JT+Dj7aWnbh8mp9vQ/67bK0+Ys1dV69KD/96plzZma1h4Nz03Zbi2P3GN3nlwrH5x3SCN\njAxutXJ7zZDeevehK3SJtZte3nhQ019JVlF5Vau8NwAAAJrH7zekyzCkJ24cLIvFYnYcXCRTbgWX\npCNHjmjgwIEqLy8/b0huBe/YfvXWXr2WbNfCO4drUoLVtBzHKqp13z9TtdN+QjfE9dHCO0d4xNnv\naqdLv1t/QP/alq+QQH+9+MN4Xdq/p9mxcAGcLrc+yyrWZ5nHdMXAXro6NpS/WCXVON0qqaxWUVm1\nisurVVxx5vsVVfLx8tLY6F4aE91Tnf0Y8AIAaJs+yyzWPf9I0e3xffXC1BFmx8E3NPsZa0kaN26c\nZsyYUT+87JlnnlFqamqDNeXl5RowYIA+++yz+uFlAQEBeu6553TkyBH5+/urR48ekqQ///nPWr16\ntbZu3dqsF4T25+SpWl39/CZJ0ie/uErdO/u1eobsonL9+NXtKig9rQfGDdCj1w2Sl5dnFZ+3dx/W\n42v3qKrWpV9cN0gPXDXA4zKioYPFFVqT6tCbaQ4VlVfX/3hsn0DNGR+tG+PC5N3O/gwNw1BZlVPF\n5VUqKj9TmL/2qv+ximqVVtac99fz8/bS6H7BGjeo7ijGgJCufFECANAmuNyGbvzLFuUcq9TGR8Yx\nkNYDtUixzsjI0IwZM1RSUqKgoCAtW7ZMcXFxmj9/vsLDw3X//fdLkt5++2099thjcjqdGjZsmJYv\nX65u3bopJSVFs2fPlsvlkmEYGjBggF544QX169evWS8I7dNbOx16+PXd+uHoCP1+0iWt+t5bs4/p\n/hU7dLrGpd/dNkzTRtta9f0vRHZRhX66Mk0ZR8s1flCIFt45Qj26tP4XIvDdyqtq9e6eQq3Z4dCO\n/OOSJGuPTpoyMkLjY0P09q7DWpls1+lal6J6dtbsqwZoUkJf+fuYf3dEY5w8XavUvFIdrd9lrjqz\ny/zf8lztdH/n53f191FIoL9CuvorJOjMt4H+Cg2s+7bu+wEqq6rV5oxibcos1racEtWc+TWtPTrp\nqpgQjR8Uym42AMCjrU4t0GNv7NH9Vw3Q4xNjzY6Dc2iRYm0mijUMw9DdS5P1xcESrX3gMo2MDG6V\n9319u11PvPWVOvl56693j9TYgb1a5X2b4nSNS/+77iutTXOob/dOevnuBI2I6G52rA7N7TaUnFuq\nNTsKtGHvEZ2udSnA10sTh4VpyiirLu3Xs8HdBaWVNXr1izy9ujVXZVVO9Q7y131X9NddSTaPLIpu\nt6GtB49pTapD7+87Ul9yz/KySL2+oyD/9/v+6tXV/6Ke23m6xqUvc45pU0bds+PtpacksZsNAPBc\np2tcGvfcRtU43dr82HgFBfiaHQnnQLFGu3SwuEIT/7RF/UO66J0Hx8q3BYeEud2Gnv0wQ3/ddFDW\nHp20bEaiBvYObLH3a26GYWh1aoHm/2ef3IahJ24YrHvHRFEqWtmhE6e1dodDb+xw1Je9eFt33Tkq\nQjdeEnbev0Qrqp1auS1fSz/PVXF5tXp09tWPL++ney+LUrfO5v8FXFB6Smt2OLR2h0OHTpyWJI2O\nCtbNw8Nk69mlfrc5uItfq93SbhiGco9V1pXsb+xm9+3e6UzJDtWYAT0vqsQDANAcXvwkS89/lKn/\nu2Wo7h0TZXYcfAeKNdqthR9l6i+fZOlXN8Rq1pUDWuQ9qmpd+sXq3Xp3b6FGRHTX3+8ZpZBA/xZ5\nr5a2/3CZ5qzcobySU7oxLkzPTI5TIF8RbVFVtS59uP+o1qQW6PPsYzKMut3ayQl9NWWUVdGhF/4F\nmqpal97Y4dDizQflOH5aXfy8Nf2ySM0c26/Zn5N+PqdrXNrwVaHWpDr0ZU6JJKlPUIDuGGnVHSOt\niurVpVXznM/37WYn9uuhcTGhGjcoRNGh7GYDAFpHcXm1xj27UaFBAfrw4StbdLMITUOxRrtVVevS\nhD99pqNl1fr4F1c1+5CHr0/+vjEuTM/fOdwjJn83RVlVrea9sUcbvjqifr26aNHdCRocFmR2rHbF\nMAztPXRSa1Id+s+uQyqrcsrHy6IfDA7VlJERumpQSLP8pel0ufXOnsNatPGgsooq5OfjpTtHWTX7\nygGKCO7cDFdyboZhKM1+Qm/sKNA7uwtVUe2Un7eXrh3aW3eOitDY6F5tYsja+XazrxoUonExIbo8\nuhe72QCAFvPEW3u1MtmuxdNHasKwPmbHwfegWKNd25JVrB+9kqJrBvfW0ntHNduvm3W0bvK347jn\nTv6+WIZh6NUv8vTUuwfk7WXRgluH6c7ECLNjtXklFdV6a+chvbHDofQjdY8OHNQ7UFNGWXVbfF/1\n6toydzq43YY+OnBUizZma7fjpLy9LLp1eLgeGDegWY8sFJVV6c2dh7QmtUAHiyslScP6BmnKyAjd\nOiLclAn9zel0jUvbckq0MaOowW62r7dFo/sF67YRfTU5wdpu/j8AADBfdlG5rv/TFsVHdNea+y/j\nbikPR7FGu/fQqp16e/dh/e1HI3Xd0KZ/pe/zrGN6YGXd5O+nbh+mqYmeO/m7KdLsx/WzlWk6fLJK\nd4y0asGtw9TJr23vyLc2p8utTRnFWrOjQJ8cKJLTbSgowEe3jqi71Tuub7dW+0vSMAx9cbBEL2/M\n1hcH627Lvn5ob80ZF63hFzmwrsbp1qfpRVqTWqBNmcVyuQ316Oyr2+L7asrICA0Jb593O3zXbvaY\nAT31h8mXtOgdAQCAjuMny7fr4wNFemvOGMXbepgdB+dBsUa7V1RepR88v1mB/j76aO5VTbpt898p\ndv3vurrJ34unj9Tl0Z4/+bspjlfW6OHVu7Qpo1ixfQL18t0JGhDS1exYHu9gcYVWpxbozbRDKi6v\nlsUijY3upSmjInTdkN6mHxlIsx/Xoo0H9fGBo5Lqss0ZP0CX9e/ZqKKffqRMa1IdemvnIZVW1sjL\nIl0VE6I7R0Xo6sGhbeZxX82lrKpWv38vXatS7Ors561f3jBYd4+2sXsNALhoXx4s0Q//vk03XhKm\nl+9KMDsOGoFijQ7hX1/m6cn/7NOsK/vrVzcMvuDPd7sN/fGDDC3e3DYnfzeF223or5sP6vkPMxTg\n663HJ8ZqelIkpeEcqmpdeuGjTP19S47chmQL7qw7Rlo1eaS12c/4N4eMI+X666Zsvb37sNxG3RTy\nn46L1tWxod/68z15qlZv7zmsNakF2uM4KUnq36uL7hhl1eQEq3oHte5gNE+0JatYj6/dq0MnTuuy\n/j31xzvYvQYAXDi329CtL29V+pEyfTJ3nGw9+bukLaBYo0NwuQ1NWrRVXx0u0/oHx17QQK6qWpfm\nrt6l9/YeUbytbvJ3S52H9WTbckr0yJrdchw/raR+wXr2juH8j/5r0uzH9eia3TpYXKnh1m56fOJg\nJfULbhNfgMgvqdSSz3L0RqpDNS63YvsE6oFxAzRxWJiSc0saPHO6i5+3brokXFNGWTUysgfnvb6h\nvKpWT7N7DQBognU7D+nnr+/SzLH99ORNQ8yOg0aiWKPD+OrQSd3y0ucaHtFda+8f06h/6BaX103+\n3lXQfiZ/N0VltVPPbEjXv7blq9OZ3esfXdqxd6+ral1a+FGmlm7JkY+Xlx6+Nkb3XdFPPm3wcRhH\ny6r0yue5WrEtX6dqXPLz8aqfhD26X7DuHBWhicP6MAW7ET7POqZ5a/ewew0AuCBVtS794PnNKq+q\n1WePjW/zwz87Eoo1OpTfvrNf/9iaq6dvj9NdSd8/dOzrk7/njBugR9rR5O+m+iL7mB5bu6fD716n\n2Y/rkTW7lVNcqeER3fXcHZe0iyMCxytrtPzLPG3KKNbY6F4e+czptuBbu9cTY3U3xygAAN9j8eaD\nemZDup64YbDuu7K/2XFwASjW6FAqqp265vnNOlXj1KePjPvOW7o/zzqmB1bs0Olal56+PY7HTZ1D\nR969bk+71Gh57F4DABqjtLJGV/1xo7p19tUnv7iqww0DbesupIfyL0a0eV39ffTrm4eorMqpp989\ncM41/06xa8ayFMkiLf9/oynV36GLv48W3DZMr92XpJ5d/fTrt/fph3/fpvySSrOjtagd+cd1w1+2\n6G+f5SjO2l3vPjRWD4wbQKnGdxo7sJc+ePhK3ZVk05c5Jbr+T5/pX1/mye32+K9VAwBa0V8+yVJ5\ntVPzJsRSqts5dqzRLhiGoZnLU/VpepFeuy9JYwbUPTLL7Tb0hw/StWRzjiKC6yZ/R4e2/dt6W8M3\nd6/nTRikey6Lale71+xSozmwew0AOJfcY5W6duFmDe3bTevmjGE4aBvEreDokApKT+naFzYrvFsn\nbfj5FXK7pbmrd2nDVx178ndTfXHwmB57o+7s9eh+wXr2jksU2bPtn83dkX9cj77R/s5SwxwV1U79\n/r0DWplcd/aaR9gBAB5YsUMbvjqi1bMv0+h+wWbHwUWgWKPDWrQpW398P0M/vjxKafYT2l1wQjde\nEqbnp3Tsyd9NVVnt1B/eT9c/v2z7u9cNdqm9vTT32hj9ZCy71GgeW7PrvhB16MRpXdq/bgggu9cA\n0PGk5pXqjsVf6rohvfW3e0aZHQcXiWKNDqvG6daNf9mirKIKSdJPxw/QL65l8ndz+eJg3S2vBaVt\nc/f6m7vUz0+5hKMBaHbsXgNAx2YYhib99QvtdZzUhw9fqf4hXc2OhItEsUaHlmY/rkdW79b9Vw1g\nSFkL+Obu9WMTBuleD9+9rqp16fkPM7T081z5skuNVvLN3es/Tu6Yj7ADgI7m3T2F+ulrabrnskj9\n9tZhZsdBE1CsAbS4BrvXUcH64x2XeORzkXfkl+rRNXuUc6xSIyK66zl2qdGKzNi9rqp1qbi8WkXl\n1Tpxqkaj+wUrMMC3xd4PAPBf1U6Xrl34mUora7T50XHqyXyfNo1iDaBVVFY79cf307X8y3wF+Hpp\n3oRYj9m9/uYu9S+ujdFPrugvbw/Iho6nqbvXbreh46dqVFRereIzr6L6b6vqfqyiWsVl1Sqvdjb4\n3Ng+gXpzzhh19vNp7ssCAHzDK5/nasH6/Xr0+kH66fhos+OgiSjWAFrVlwdL9Nja3R6ze80uNTxR\nRbVTz2w4oBXb7OrkW7d7PWWUVcfKa1RcUaWisjPluLy64ffLq3Ssokau73lGdvfOvgoN9FdIoL9C\nuvorNChAIV39lV9aqRXb7LoxLkwv3RXPo14AoAWdPFWrK5/dqM5+3tr4yDgG57YDFGsAre6bu9eP\nXR+rGWNad/e6qtal5z7I0Ctb2aWG5/oi+5gePbN7/X38vL3qivKZV2iD7wfU/1jPrn7y9zn3P94M\nw9CclWna8NURdk8AoIU9/d4B/e2zHD0/Zbgmj7SaHQfNgGINwDRm7V6zS422pKLaqUUbs1V4supr\nu8xf/zZAQZ18mmWHubLaqcl//UIZR8v1yr2jdHVs72a4AgDA1xWUntIPnt+s6NCuWv/gWI84Foem\no1gDMNU3d69viAuTr1fLTeAur67Vhq+OsEsNfAd7ySnd8vLncrkMrfvZ5RrAo18AoFk9tGqn3t59\nWCt/kqTLo3uZHQfNhGINwCNsyynRvLV7lF9yqsXfK8HWXX+8Y7iiQykMwLl8nnVM9/wjWf16ddG6\nn17OpHAAaCa7C07o1pe3atygEL3649Fmx0EzolgD8Bhut6GKGuf5FzZRoH/z3DYLtGdLt+Tod+8e\n0DWDQ/W3H43iVkUAaCLDMDT1b9uUmleq939+pWJ6cwytPbmQHsqzNwC0KC8vi4LYGQM8wsyx/bT/\ncJne3HlIL3ycqV9cN8jsSADQpn20/6hSckv1w9ERlOoOruUOPQIAAI9isVj09KQ4XWLtphc/zdaG\nvYVmRwKANqvW5dYzG9LV2c9bD18TY3YcmKzRxTorK0tjxoxRTEyMEhMTtW/fvnOuW79+vWJjYzVw\n4EBNmjRJZWVlkqTDhw/r+uuv16BBg3TJJZdo8uTJKi4ubp6rAAAAjRLg663F00eqV1c//WLNbqUf\nKTM7EgC0Sf9OsSvnWKVmXdlfoUEBZseByRpdrGfPnq1Zs2YpMzNT8+bN04wZM761pqKiQjNnztS6\ndeuUlZWl8PBwLViwQJLk7e2tJ598UhkZGdqzZ4/69++vRx99tNkuBAAANE5490766/SRqnW5dd8/\nU3W8ssbsSADQppRX1epPH2cpNNBfs67sb3YceIBGFeuioiKlpqZq+vTpkqTJkyeroKBA2dnZDdZt\n2LBB8fHxio2NlSTNmTNHq1atkiT17t1bY8eOrV+blJSkvLy85rgGAABwgRKjgvWbW4aqoPS0frYq\nTU6X2+xIANBmLN58UCWVNZp7bYw6+zG2Co0s1gUFBQoLC5OPT91/NBaLRTabTXa7vcE6u92uyMjI\n+o+joqJUWFgop7PhRGCXy6WXXnpJt9566znfb+HChbJarfWvioqKC7ooAABwfncnRequJJu2Zpfo\n9xvSzY4DAG3C4ROntXRLrgb1DtSUURFmx4GHaPXhZYZhaM6cOerRo4f+53/+55xr5s6dK4fDUf/q\n2pXn0gIA0BJ+c/NQJUb10Cuf5+rNNB5tCQDn8/yHmap2uvXLG2LlzWMLcUajinVERESDnWfDMGS3\n22Wz2Rqss9lsys/Pr/84Ly+vwU63JD300EMqKCjQ66+/Li8vhpIDAGAmPx8vLbp7pMK6BejxN/dq\nj+OE2ZEAwGPtO3xSb+50aGx0L10VE2J2HHiQRjXb0NBQJSQkaMWKFZKktWvXymq1Kjo6usG6CRMm\nKC0tTenpdbeTLVq0SNOmTav/+YceekjZ2dl666235Ofn11zXAAAAmiAk0F9LfjRSFkmz/7VDxeXV\nZkcCAI9jGIaefu+AJOmXN8TKYmG3Gv9lMQzDaMzCjIwMzZgxQyUlJQoKCtKyZcsUFxen+fPnKzw8\nXPfff78k6e2339Zjjz0mp9OpYcOGafny5erWrZu2bt2qsWPHKjY2Vv7+/pKkfv366a233jrve1ut\nVjkc3J4GAEBLemunQw+/vlujInvotfsulZ8Pd5YBwFkbM4r042XbNTnBqufvHG52HLSCC+mhjS7W\nZqJYAwDQOn63fr+Wfp6ru5Jsevr2OLPjAIBHcLrcuuEvW5RfckqbHh2nsG6dzI6EVnAhPZQvRQMA\ngHqPT4zV2Oheei3ZrpXJ+ef/BADoAN7Y4VDm0Qr95Ip+lGqcE8UaAADU8/H20os/jFdEcCf9+j/7\ntD2v1OxIAGCqymqnnv8oUz27+On+qwaYHQceimINAAAa6NHFT3+/Z5T8fLz0wIodOnzitNmRAMA0\nf9+So+Lyav382hgFBviaHQceimINAAC+JbZPkJ6fMlzHKmo0+187VFXrMjsSALS6orIqLdmco/4h\nXTQtMcLsOPBgFGsAAHBOE+PC9ODV0dp76KR+9eZetYF5pwDQrF74OFOna1365cTB8vWmOuG78V8H\nAAD4Tg9fE6NrBofqzZ2H9MrnuWbHAYBWk3m0XK9vL9DofsG6ZnCo2XHg4SjWAADgO3l5WfTC1BEa\nENJFT793QJ9nHTM7EgC0it+/d0BuQ3rihsGyWCxmx4GHo1gDAIDvFRjgq7/fM0pd/H30s1Vpspec\nMjsSALSordnHtDGjWLcMD9fwiO5mx0EbQLEGAADn1T+kq/4yLV4nT9fqvn+mqrLaaXYkAGgRbreh\np949ID9vLz16/SCz46CNoFgDAIBGGR8bqkeuG6SMo+V6ZM1uhpkBaJfe2nlI+wvLNOPyKEUEdzY7\nDtoIH7MDAACAtmPOuAHaX1imd/cU6qVPs/XgDwaaHcljud2G9heWaVNGkTZlFKui2qm//WiUbD35\nhzrgqapqXf+/vXuPjro+9z3+npAbITfIBTKZCRGSEJFLJIkKbgi0ShHrliLB7m3RWCxQdfd42m7d\nu2tV3VuP7WkR9XipVikqtFQCQq1UrK1c6wUCASVIEgwhMyEhQyBkBnKbzO/8EcwGuZiYyy+ZfF5r\nsWYm85uZZ3zWV3jy/H7Pl6V/LSY6LIj7pqeYHY70IyqsRUREpMMsFgu/njeBMtdpnnyvhCsTIrlh\n7HCzw+ozTp1pYVupiy3FLraWuDjuaQJgSPAgzrS0csfyj1izeDIJUYNNjlRELmb5jsNUnWrk4W+P\nJSosyOxwpB+xGP3gPC6bzYbT6TQ7DBERETnLceIM//zcDlpaDR7956uYPiaO2PAQs8PqdV/uSu+p\nOInv7L+s0oaHM31MPNPT4shKHsaGwkoeXPcJo+KG8MaiycRFDLz/XiJ92XFPE9N/vYWY8GDe+985\nBAfqqtmBrjN1qDrWIiIi0mn2YWE8f8ck8lbs4qf5+wCYYItielocOWPiybBHMyjAP7enuVxX+oYr\nhzN9TDw5Y+JIjD6/Kz0/205DSyuPvFXEguUf88dF1xEdFmzGVxCRi/h/fy/F0+TlV/MmqKiWTlPH\nWtWK65cAABiUSURBVERERL42l7uJrSUuNhfXsL3ERX1j27Tw6LAgpqbGnS20+3c3+9yu9OZiF4Xn\ndKXHDI9g+pi275g1cliH/jH+my2f8383HWSiLYpV91xLRKhONxUx2+cuDzOf2sZEWxTrfjhF+1YL\n0Lk6VIW1iIiIdAtvq4+9jjq2FLvYUlLD/sr69ufGJ0YxfUwc0/tJN/tyXenrU2LbTvEeE4c1+utd\nK/3kX4t59v1DXJM8jNe+fw2Dgwd1Z/gi0kmLXi/grweOse6Hk8kcOczscKSPUGEtIiIipqtxN7Kt\n5DhbimvYXnqcUw0twPnd7GlpcX3iWuPu7kp/FcMweHzjZyzfcZipqbG8clcWIYEqrkXMsPPwCea/\n9CE3jRvBb76XaXY40oeosBYREZE+xdvqY5/zbDe72MWnlafan/ufbnYcGfah3d7NbvUZ1HqaqHE3\n4fI04ao/e+tuosbdiMvdxOHjpznuaQYgPCSQ61Ni2q6VTvv6XemvYhgGP1u/n9U7K7hx7HBeuGMS\nQYN0XadIb/L5DL7zwj8oOlrP336cQ3LsELNDkj5EhbWIiIj0aS53E9tKXGwpcbGtxNXezY4aHMTU\n1Nj2ovZy3WxPkxeX+/wCue3++bcnTje1d58vJmZIMAnRoVw/uu1zM0cO7bXBRT6fwU/y97G+sJJb\nJlp5+vaMPn+avIg/eWvfUX60upC7r0/mkVuuMjsc6WNUWIuIiEi/cblu9rjESCaPiqHZ68PlaaLm\nnG7zmebWS75nSGAA8ZEhxEeEEhceQlxECPERZ28jQ4gLDyUuIoSY8GDTu8TeVh/3/6GQTUXVzM+y\n8cu5EwhQcS3S45q8rXzzya2camhh27/PYOgQTemX86mwFhERkX7r3G729lIXdWfautkWCwwLCybu\nbIHcViyHnnP/f27DQwL71VTfZq+PRSsL2FLsIm9KMo/cMrZfxS/SH728rYz/85fP+M+b0lmcM9rs\ncKQPUmEtIiIifqHVZ3D4uIeI0CCGDTG/u9yTGltayVuxk4/KTnDv9NE8OCvd7JBE/FbdmWam/Woz\nEaFB/P0nOYQGaXigXKgzdaj//u0kIiIi/d6gAAsp8REMjwz166IaIDRoEK/clc3VSdG8sOVznnu/\n1OyQRPzWs+8for7Ry4Ozxqiolm7h339DiYiIiPQj4SGBvHr3NYxNiGTpX0tYvuOw2SGJ+J0jtad5\n/cNyxidGccsEq9nhiJ9QYS0iIiLSh0QNDmLlwmtIiQ/nsbcPsHpnhdkhifiVX71bTEurwc9mX6lB\ngdJtVFiLiIiI9DEx4SH8/p5rGRkTxs/Wf8qGwkqzQxLxC3sqTrLxkypuuHI4k0fHmB2O+JEOF9al\npaVMmTKFtLQ0srOzKSoquuhxb7/9Nunp6aSmpjJ37lzq6+vbn5s3bx5WqxWLxUJdXV3XoxcRERHx\nU8MjQ/n9PdeSEBnKT/L3sWl/tanx+HwGBeUn2FNxkn4w+1bkAoZh8MTGzxgUYOE/btJwQOleHS6s\nFy9ezKJFiygpKeGhhx4iLy/vgmM8Hg8LFy5kw4YNlJaWYrVaeeyxx9qfX7JkCXv37u2WwEVERET8\nnW1oGKvuuZahYcH82+o9bCmu6fUYnCfP8MzfSslZupl5L37I3Bc+4JtPbuX5zYeoPtXY6/GIfF3v\nFlVTcOQk/3KNnZT4cLPDET/Toe22ampqSElJ4cSJEwQGBmIYBgkJCezYsYOUlJT24/Lz81m+fDmb\nNm0C4MCBA8ycOfOCEeUWi4WTJ08SHR3doSC13ZaIiIgMZMXVbm7/7Yc0NLfy6t3X9PgprI0trbxb\nVE1+gZN/fH4cw4C4iBDmTkqktdVgfWEltaebCbDAtLQ45mfZ+eaV8YQEarqy9E3NXh8zn9qKy93E\nln+fQVxEiNkhST/QmTo0sCMHORwOEhISCAxsO9xisZCUlERFRcV5hXVFRQUjR45sf5ycnExVVRVe\nr7f9tSIiIiLSOWNGRLDy+9fyry9/xMLXdrHqnmuZlDS0Wz/DMAw+cZ4if7eDP+09irvRS9AgC98a\nO4L52TampcYReHbLswdnpbO5uIb8Aiebi2vYUuwiOiyIORmJ5GbZuMoa1a2xiXTVHz4+QnntGX46\nM01FtfSIPlntLlu2jGXLlrU/9ng8JkYjIiIiYr7xtihW3J3NguU7yfvdTlYvuq5bCtjjniY2FFaS\nX+Ck+JgbgPQREczPsjPn6kSGDQm+4DXBgQF866oRfOuqEdS4G9lQWMmaAievflDOqx+Uc5U1ktxM\nG7dmJDL0Iq8X6U2nGlp45u+ljIgMZeE/jTI7HPFTOhVcREREpB/5x6Hj3P3qLsJDAlmz+DpS4iM6\n/R4trT62FLvIL3Dw/sEavD6DqMFB3JphZX6WnauskVgsnduGyDAM9jrqWFPg5O19R3E3eQkeFMCN\nY4eTm2Vjamocg7S1kfQywzD4rz8f4NUPyvn1vAnkZtnNDkn6kc7UoR0qrAGmT59OXl4eeXl5rF27\nll/+8pcUFBScd4zb7Wb06NFs27aN9PR07r//fkJDQ1m6dOn5H6rCWkRERORr+/tnx1i8cjfDhgST\nv2QyI2OGdOh1pcfc5O928uaeSo57mrBYYGpqHLmZNm4cO5zQoO65RrqhuZVNRVXkFzj54PNaAEZE\nhjJ3UiK5WXauiO1YvCJd4W318chbRfz+4wom2qJ4897r9csd6ZQeKayLi4vJy8ujtraWyMhIVqxY\nwfjx43n44YexWq0sWbIEgLfeeosHH3wQr9fLuHHjeO2114iKajtN6eabb2bfvn1UVlZitVpJTU1l\ny5Yt3fqFRERERAaCjZ9U8W+r95AQNZj8JZOxRg++6HH1jS28va+KNQUO9jratjsdGRNGbqaNuZNs\nl3xdd3GcOMPa3U7W7nZSWdcAQHbyUHKz7Nw8PoEhIX3yykTp59yNLdz3h0K2lbi4PiWGF+7IJGpw\nkNlhST/TI4W1mVRYi4iIiFxo7W4nP83fxxWxQ3hj8XXER4QCbXtOf1RWS/5uJ+/sr6KxxcfgoEHM\nHp/A/Cwb11wxrNOneneVz2fwYVktawocbNpfTZPXR1jwIG4en0Bulp3s5KG9HpP4p8q6Bha+uouD\n1W5uz7Lz+HfGETSow7sMi7RTYS0iIiIyQKz8sJyf/6mIMcMjeHL+RP722THW7nbiPNnWHc4aOZTc\nLBs3T7AS3ke6w6caWvjzvqPk73ay72wXPTkmjNwsO3MnJZIQ1bNddPFfnzjrWPhaAS53Ew/NSmdJ\nzij9wka+NhXWIiIiIgPIb7d9zhN/Odj+OD4ihNsybczLtDE6LtzEyL5ayTE3+QUO1hdWctzTtjd2\ncswQYiNCiIsIIb79NpS4iBDiwkOIjwxhWFgwAbpeVs7xblE1/+uPhRgGLJufwc0TEswOSfo5FdYi\nIiIiA8zyHYfZ56jjO1cnMjU1tn3P6f6ipdXH5oM1rC+s5HOXB5e7iZNnWi55/KAACzFDgomPbCu2\nzyu+v1SQDw7unqFs0jcZhsEr2w/zxDufETMkmN/emdXt+7zLwKTCWkRERET6vWavj+OeJlzuJmrc\nbbdt9xvb7nuaqKlvu232+i75PuEhge0F97ld7y8X5MOGBGtqdD/jbfXx8FtF/OHjClLiw1mRl419\nWJjZYYmf6Ewd2jcutBERERER+ZLgwACs0YO/cnK5YRjUN3hxeRrPK8DPLchr3I2UHnOz8/CJS77P\nF13wczve5xbe5/4sLFj/jDabJn9LX6L/I4iIiIhIv2axWIgKCyIqLIiU+IjLHntuF/y8Trinsb37\n7XI3UVrj6VgXPDyEuMgLu+AjokJJi4/o19eBt7T6cDd6GTYk2OxQLlBZ18D3V+yi+Jib72bbeWyO\nJn+LuVRYi4iIiMiA0akueKMXl/vCLvi5Bfkhl4ed5RfvgidGD2be2SFy/en05C8PlLvhynjunZHS\nZ65bPnfy93/clM7iaZr8LebTNdYiIiIiIl3Q7PVRe/pswX22613m8vDWvqMcq28CYMroGOZn2fnW\nVSP65DC1+sa2LdDWFJy/BVpy7BC2FLsAmDwqhvtmpHB9Soxpheym/dU88Ebb5O+nbs9g9nhN/pae\no+FlIiIiIiIm87b62H7oOPkFDt47cIyWVoOIkEBuybCSm2kjwx5taqfV5zP4sKyW/AIH7+yvpsnr\nIyx4ELPHJzA/y0528lAsFguHatz8ZksZG/ZW0uozmGiL4ofTU5g5dnivnepuGAYvby/jF+8cJGZI\nMC/fmcXVfaSDLv5LhbWIiIiISB9y4nQzf9pbSX6BkwNV9QCkxoeTm2XjO1fbiIsI6bVYHCfOsHa3\nk7W7nVTWNQCQnTyU3Ew7syckEB5y8atFHSfO8PL2Mv64y0Gz10dqfDg/nD6aWyZae/T65pZWH4+c\nnfydGh/O7zT5W3qJCmsRERERkT5qf+Up1u52smFvJXVnWhgUYGHGmHhys2x8Iz2+R4rUhuZW3i2q\nZk2Bgw8+rwVgeGQIt01quwZ8VFx4h9+rxt3I73aUs+qjI3iavNiGDmZxzmhyM22EBnXvae71jS3c\n9/s9bC89zj+lxPL8HZM0+Vt6jQprEREREZE+rsnbyt8O1JC/28G2Ehc+A2LDg5mTkcj8bDtpwy8/\n4fyrGIbBXkcd+bud/HnvUdxNXoIHBXDj2OHMy7IxLTWuS/t2n2poYeWH5fzuH+WcON1MbHgIP5h6\nBXdcN/KSXe/OcJ48w/df3UXJMQ//co2d/75Vk7+ld6mwFhERERHpR6pPNbJuj5P8AgfltWcAmGiL\nIjfLzi0TrZ3q0rrcTawvdJJf4KS0xgPA2IRI5mfZuDUjkaHdvH3WmWYvf9zp4LfbyqiubyQyNJC8\nKcnkXX/F196qa5+jbfL3cU8T/3lTOos0+VtMoMJaRERERKQfMgyDgiMnWbPLwcZPqzjT3EpIYACz\nxo0gN9POlNExFx0Y1tLq4/2DNeQXONlcXEOrzyA6LIg5GYnMy7QxLjGqx2Nv9vpYX+jkxa1lHD5+\nmsFBg/jXa5P4wdRRjIgK7fD7bNpfxQNv7MUw4OnbM7hJk7/FJCqsRURERET6udNNXv7yaRX5Bc72\nvbITowdzW6aN3LN7Y5ccc7Nml4MNe9v2nA6wwLS0OHIz7dwwNp6QwN7f2qvVZ/DO/iqe3/w5n1XV\nEzTIwrxMG4unjSY5dsglX3f+5O8QXrkriwx7dC9GLnI+FdYiIiIiIn7k8PHTrN3tYN3uSqrrGwEY\nGRPGkbOnjV8RO4R5mTZum2TrVHe4JxmGwZZiF89vPkTBkZMEWODmCVbunT6aKxMizzu2pdXHw38q\nYvXOCtKGt03+tg3V5G8xlwprERERERE/1Ooz2F7qIn+3k93lJ5mWFktulp2skUP79DXIOw+f4PnN\nh9ha4gLgm+nx3DsjhcyRQ8+b/D01tW3yd2SoJn+L+VRYi4iIiIhIn7O/8hQvbDnEO/urMQy49oph\nnDzTfHbydxL/fetVmvwtfUZn6tCuz8EXERERERHpgHGJUbxwRyaHajy8uPVzNhRW0moY/Gx2Oj+Y\nqsnf0n+pYy0iIiIiIqY4WtfAidPNvTK1XKSz1LEWEREREZE+zxo9GGv0YLPDEOkyXcAgIiIiIiIi\n0gUqrEVERERERES6QIW1iIiIiIiISBeosBYRERERERHpAhXWIiIiIiIiIl2gwlpERERERESkCzpc\nWJeWljJlyhTS0tLIzs6mqKjoose9/fbbpKenk5qayty5c6mvr29/7uOPP2bixImkpaXxjW98g8rK\nyq5/AxERERERERETdbiwXrx4MYsWLaKkpISHHnqIvLy8C47xeDwsXLiQDRs2UFpaitVq5bHHHgPA\n5/Nxxx138PTTT1NSUsLs2bN54IEHuu2LiIiIiIiIiJjBYhiG8VUH1dTUkJKSwokTJwgMDMQwDBIS\nEtixYwcpKSntx+Xn57N8+XI2bdoEwIEDB5g5cyZOp5Ndu3axYMECDh48CIDb7SYuLo66ujpCQ0Mv\n+/k2mw2n09mV7ykiIiIiIiLSYZ2pQzvUsXY4HCQkJBAYGAiAxWIhKSmJioqK846rqKhg5MiR7Y+T\nk5OpqqrC6/Ve8FxERASRkZEcPXr0gs9btmwZNput/Y/H4+nQlxERERERERHpbX1yeNmPf/xjnE5n\n+5/w8HCzQxIRERERERG5qMCOHGS329s7z1+cCl5RUUFSUtJ5xyUlJfHee++1Py4vL2/vdCclJXHk\nyJH259xuN6dOncJqtX7l57tcLmw2W0e/k2k8Ho9+CTCAKN8Dj3I+sCjfA4vyPfAo5wOL8j2wdFe+\nXS5Xh4/tUGEdHx/PpEmTWLVqFXl5eaxbtw6bzXbe9dUAs2bN4r777uPgwYOkp6fzwgsv8N3vfheA\nzMxMWlpa2Lx5MzNmzOCll17illtu+crrqwGampo6/IXMpGvBBxble+BRzgcW5XtgUb4HHuV8YFG+\nBxYz8t2hwhrgpZdeIi8vjyeeeILIyEhWrFgBwMMPP4zVamXJkiVERETwyiuvMGfOHLxeL+PGjeO1\n114DICAggFWrVrF48WIaGxuxWq2sXLmyZ76ViIiIiIiISC/p0FRw6Rj9JmxgUb4HHuV8YFG+Bxbl\ne+BRzgcW5XtgMSPfgx599NFHe/UT/dzkyZPNDkF6kfI98CjnA4vyPbAo3wOPcj6wKN8DS2/nWx1r\nERERERERkS7ok9ttiYiIiIiIiPQXKqxFREREREREukCFdTcoLS1lypQppKWlkZ2dTVFRkdkhSQ9L\nTk5mzJgxZGRkkJGRwRtvvGF2SNKNfvSjH5GcnIzFYmHv3r3tP6+pqWHWrFmkpqYybtw4tm3bZmKU\n0l0ule/p06dzxRVXtK/zp556ysQopbs0NjYyZ84c0tLSmDhxIjfeeCOHDh0CtMb90eXyrTXuv2bO\nnMmECRPIyMhg6tSpFBYWAlrj/upS+e71NW5Il82YMcNYsWKFYRiGkZ+fb2RlZZkbkPS4kSNHGoWF\nhWaHIT1k69athsPhuCDPd999t/HII48YhmEYO3fuNBITE43m5maTopTucql85+TkGOvXrzcxMukJ\nDQ0NxsaNGw2fz2cYhmE8++yzRk5OjmEYWuP+6HL51hr3XydPnmy//+abbxoTJkwwDENr3F9dKt+9\nvcbVse6impoaCgoK+N73vgfAbbfdhsPhaP9tqIj0P9OmTcNms13w8zVr1rBkyRIAsrOzsVqtbN26\ntbfDk252qXyLfwoNDWX27NlYLBYArrvuOsrLywGtcX90uXyL/4qOjm6/f+rUqfb8a437p0vlu7ep\nsO4ih8NBQkICgYGBAFgsFpKSkqioqDA5MulpCxYsYPz48SxcuBCXy2V2ONLDamtraWlpYcSIEe0/\nS05O1lr3cw8++CDjx4/n9ttvp6yszOxwpAc888wz3HrrrVrjA8QX+f6C1rj/uvPOO7Hb7fz85z9n\n5cqVWuN+7sv5/kJvrnEV1iJfw7Zt2/j000/Zs2cPsbGx3HXXXWaHJCLdbOXKlZSUlPDJJ58wdepU\nvv3tb5sdknSzJ554gkOHDvGLX/zC7FCkF3w531rj/u3111/H4XDw+OOP89BDD5kdjvSwi+W7t9e4\nCusustvtVFVV4fV6ATAMg4qKCpKSkkyOTHrSF/kNCgrigQceYPv27SZHJD0tJiaGwMBAqqur239W\nXl6ute7H7HY70HYm0v33309ZWRm1tbUmRyXdZenSpbz55pu88847hIWFaY37uS/nG7TGB4q77rqL\nzZs3A2iNDwBf5Lu2trbX17gK6y6Kj49n0qRJrFq1CoB169Zhs9lISUkxOTLpKadPn6aurq798erV\nq7n66qtNjEh6S25uLi+++CIAu3btorKykpycHJOjkp7g9Xo5duxY++N169YxfPhwYmJiTIxKusuy\nZctYvXo177333nnX5mmN+6eL5Vtr3H/V1dVx9OjR9scbNmwgJiaGYcOGaY37oUvlOzIystfXuMUw\nDKPH3n2AKC4uJi8vj9raWiIjI1mxYgXjx483OyzpIWVlZdx22220trZiGAajRo3imWeeITk52ezQ\npJssXryYjRs3Ul1dTUxMDBERERw6dIhjx46xYMECDh8+THBwMM899xwzZswwO1zpoovle9++feTk\n5NDU1ERAQACxsbEsW7aMiRMnmh2udJHT6cRutzNq1CgiIiIACAkJ4eOPP9Ya90OXyvf777+vNe6n\njhw5Qm5uLg0NDQQEBBAXF8fSpUvJyMjQGvdDl8p3ampqr69xFdYiIiIiIiIiXaBTwUVERERERES6\nQIW1iIiIiIiISBeosBYRERERERHpAhXWIiIiIiIiIl2gwlpERERERESkC1RYi4iIiIiIiHSBCmsR\nERERERGRLlBhLSIiIiIiItIFKqxFREREREREuuD/A5k9OzouR7Q6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12655e780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    ### Count Spikes Per Trial\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "\n",
    "## Bin size; take the minimum of the length (not including the last one because it's not a full cycle)\n",
    "trial_size = int(min(lengths[:-1]) * sampling_rate) \n",
    "bar_length_spikes = np.zeros((len(lengths[:-1]), trial_size), float)\n",
    "threshold = 55 # eyeballing the threshold\n",
    "\n",
    "spike_count = np.zeros((trial_size, 1), int)\n",
    "for event_i, event in enumerate(events[:-1,:]):\n",
    "\n",
    "\n",
    "    ## Average spikes in the window \n",
    "    bar_length_spikes[event_i, :] = h1[event[0]: event[0] + trial_size]\n",
    "    peak_loc, peak_mag = amplitude_thresholding(bar_length_spikes[event_i, :], threshold)\n",
    "    spike_count[peak_loc] = spike_count[peak_loc] + 1 \n",
    "\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    ### Spike Rates in Bins\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "\n",
    "bin_size = 1500\n",
    "num_bins = trial_size/1500\n",
    "\n",
    "spike_rate_per_bin = np.zeros((int(num_bins), 1), float)\n",
    "for b in np.arange(int(num_bins)):\n",
    "    spike_rate_per_bin[b] = sum(spike_count[b * bin_size : (b + 1) * bin_size])/bin_size\n",
    "    \n",
    "\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    ### Plot\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "\n",
    "    \n",
    "fig = plt.figure(1,  figsize=(15, 5), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "plt.plot(spike_rate_per_bin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Varying Step Size (Time) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fir_design defaults to \"firwin2\" in 0.15 but will change to \"firwin\" in 0.16, set it explicitly to avoid this warning.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-2-9293fb4dff84>:36: DeprecationWarning: fir_design defaults to \"firwin2\" in 0.15 but will change to \"firwin\" in 0.16, set it explicitly to avoid this warning.\n",
      "  if h_freq: arr = filter_data(arr, sfreq, l_freq=None, h_freq=h_freq, phase='zero', verbose=False)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fir_design defaults to \"firwin2\" in 0.15 but will change to \"firwin\" in 0.16, set it explicitly to avoid this warning.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-2-9293fb4dff84>:36: DeprecationWarning: fir_design defaults to \"firwin2\" in 0.15 but will change to \"firwin\" in 0.16, set it explicitly to avoid this warning.\n",
      "  if h_freq: arr = filter_data(arr, sfreq, l_freq=None, h_freq=h_freq, phase='zero', verbose=False)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fir_design defaults to \"firwin2\" in 0.15 but will change to \"firwin\" in 0.16, set it explicitly to avoid this warning.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-2-9293fb4dff84>:36: DeprecationWarning: fir_design defaults to \"firwin2\" in 0.15 but will change to \"firwin\" in 0.16, set it explicitly to avoid this warning.\n",
      "  if h_freq: arr = filter_data(arr, sfreq, l_freq=None, h_freq=h_freq, phase='zero', verbose=False)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n"
     ]
    },
    {
     "data": {
      "image/png": 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UKt/asOfPfJcr5ucf0zYoIj5ZOw8G5nvOkG/WKCI+WX//cpXXz3HX+4vV+ZU5ARsaMzOt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\nc6iHOC0tTa5cuWJ9/P3330uLFi3cFhQAAAAAAO7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57rLPgX/SW8vmxIx++S6g5ajwysEy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\n7ccuqfbd2KZRavXRCtohgAXQIQ7BliFta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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1112e4b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datafile_speed = ['data/17o23006.abf', 'data/17o23000.abf', 'data/17o23003.abf']\n",
    "df = []\n",
    "fig, ax = plt.subplots(3, 1, figsize=(15, 15), dpi=80, facecolor='w', edgecolor='k')\n",
    "fix_offset = [-1, -1, 1]\n",
    "step_size = [0.5, 2, 5]\n",
    "for i, datafile in enumerate(datafile_speed):\n",
    "    \n",
    "    #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    ### Download Data\n",
    "    #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    \n",
    "    h1, drum, sampling_rate = download_data(datafile)\n",
    "\n",
    "    ax[i].plot(drum)\n",
    "    #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    ### Event detection.\n",
    "    #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    \n",
    "    threshold = 55 # eyeballing the threshold\n",
    "    peak_loc, peak_mag = amplitude_thresholding(h1, threshold)\n",
    "\n",
    "    direction = 'CCW' \n",
    "    hfreq = 5.0\n",
    "    if fix_offset[i] > -1:\n",
    "        events = find_oscillation_events(drum, direction, sampling_rate, hfreq, thresh = 1)\n",
    "    else:\n",
    "        events = find_oscillation_events(drum, direction, sampling_rate, hfreq)\n",
    " \n",
    "    ## Compute epoch lengths.\n",
    "    lengths = np.diff(events[:,:-1], axis=1) / sampling_rate\n",
    "    \n",
    "    problem_areas = np.argwhere(lengths < 0.5)[:,0] \n",
    "\n",
    "    speed_all = []\n",
    "    for row, event in enumerate(events):\n",
    "        speed_all.append(linregress(np.arange(0, lengths[row], 1/sampling_rate), drum[event[0]: event[1]])[0])\n",
    "    \n",
    "    if fix_offset[i] > -1:\n",
    "        events = np.delete(events, problem_areas, axis = 0)\n",
    "        events = events[fix_offset[i]::2,:]\n",
    "        \n",
    "        speed_all = np.delete(speed_all, problem_areas, axis = 0)\n",
    "        speed_all = speed_all[fix_offset[i]::2]\n",
    "        \n",
    "        events = fix_events(events)\n",
    "        lengths = np.diff(events[:,:-1], axis=1) / sampling_rate\n",
    "\n",
    "    speed = np.average(np.abs(speed_all)) * DEGREES_PER_VOLTAGE \n",
    "    \n",
    "    for n, color in zip(np.unique(events[:,-1]), ['#ff7f0e', '#1f77b4']):\n",
    "        ax[i].vlines(np.arange(len(drum))[events[events[:,-1]==n, 0]], 0, 6, color=color, alpha=0.8)    \n",
    "\n",
    "\n",
    "    #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    ### Epoching.\n",
    "    #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "\n",
    "    ## Iteratively count spikes within epoch.\n",
    "    data = []\n",
    "    \n",
    "    for event in events:\n",
    "\n",
    "        ## Count spikes in window.\n",
    "        n_spikes = np.logical_and( peak_loc >= event[0], peak_loc < event[1] ).sum()\n",
    "\n",
    "        ## Append information.\n",
    "        d = 'Stationary' if not event[-1] else 'CCW' if event[-1]==1 else 'CW'\n",
    "        length = (event[1] - event[0]) / sampling_rate\n",
    "        \n",
    "        n_spike_rate = n_spikes / length\n",
    "        data.append( [speed, d, length, n_spikes, step_size[i], n_spike_rate] )\n",
    "    ## Convert to DataFrame. Append.\n",
    "    data = pd.DataFrame(data, columns=('Speed','Direction','Duration','Spikes', 'Step_size', 'Spike_rate'))\n",
    "    df.append(data)\n",
    "\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "### Concatenate and save DataFrames.\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "    \n",
    "## Concatenate DataFrames.\n",
    "df = pd.concat(df).reset_index(drop=True)\n",
    "df.to_csv('spike_counts_speed.csv', index=False)\n",
    "\n",
    "print('Done.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Speed</th>\n",
       "      <th>Direction</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Spikes</th>\n",
       "      <th>Step_size</th>\n",
       "      <th>Spike_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CCW</td>\n",
       "      <td>0.4804</td>\n",
       "      <td>34</td>\n",
       "      <td>0.5</td>\n",
       "      <td>70.774355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CW</td>\n",
       "      <td>0.4949</td>\n",
       "      <td>3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.061831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CCW</td>\n",
       "      <td>0.5039</td>\n",
       "      <td>53</td>\n",
       "      <td>0.5</td>\n",
       "      <td>105.179599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CW</td>\n",
       "      <td>0.4985</td>\n",
       "      <td>3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.018054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CCW</td>\n",
       "      <td>0.5034</td>\n",
       "      <td>50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>99.324593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CW</td>\n",
       "      <td>0.4991</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CCW</td>\n",
       "      <td>0.5019</td>\n",
       "      <td>45</td>\n",
       "      <td>0.5</td>\n",
       "      <td>89.659295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CW</td>\n",
       "      <td>0.4991</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CCW</td>\n",
       "      <td>0.5006</td>\n",
       "      <td>42</td>\n",
       "      <td>0.5</td>\n",
       "      <td>83.899321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CW</td>\n",
       "      <td>0.4969</td>\n",
       "      <td>3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.037432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CCW</td>\n",
       "      <td>0.5048</td>\n",
       "      <td>40</td>\n",
       "      <td>0.5</td>\n",
       "      <td>79.239303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CW</td>\n",
       "      <td>0.4966</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CCW</td>\n",
       "      <td>0.5035</td>\n",
       "      <td>44</td>\n",
       "      <td>0.5</td>\n",
       "      <td>87.388282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CW</td>\n",
       "      <td>0.5007</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CCW</td>\n",
       "      <td>0.5011</td>\n",
       "      <td>39</td>\n",
       "      <td>0.5</td>\n",
       "      <td>77.828777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CW</td>\n",
       "      <td>0.4972</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.022526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CCW</td>\n",
       "      <td>0.5023</td>\n",
       "      <td>45</td>\n",
       "      <td>0.5</td>\n",
       "      <td>89.587896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CW</td>\n",
       "      <td>0.4986</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.005616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CCW</td>\n",
       "      <td>0.5033</td>\n",
       "      <td>41</td>\n",
       "      <td>0.5</td>\n",
       "      <td>81.462348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CW</td>\n",
       "      <td>0.4981</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.015258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CCW</td>\n",
       "      <td>0.5013</td>\n",
       "      <td>42</td>\n",
       "      <td>0.5</td>\n",
       "      <td>83.782166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CW</td>\n",
       "      <td>0.5013</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.989627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CCW</td>\n",
       "      <td>0.4980</td>\n",
       "      <td>40</td>\n",
       "      <td>0.5</td>\n",
       "      <td>80.321285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CW</td>\n",
       "      <td>0.5007</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.994408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CCW</td>\n",
       "      <td>0.5013</td>\n",
       "      <td>35</td>\n",
       "      <td>0.5</td>\n",
       "      <td>69.818472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CW</td>\n",
       "      <td>0.5014</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.994416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CCW</td>\n",
       "      <td>0.4994</td>\n",
       "      <td>41</td>\n",
       "      <td>0.5</td>\n",
       "      <td>82.098518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CW</td>\n",
       "      <td>0.4984</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.012841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CCW</td>\n",
       "      <td>0.5031</td>\n",
       "      <td>36</td>\n",
       "      <td>0.5</td>\n",
       "      <td>71.556351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>67.100726</td>\n",
       "      <td>CW</td>\n",
       "      <td>0.3581</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.792516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>68.690901</td>\n",
       "      <td>CW</td>\n",
       "      <td>1.1155</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>68.690901</td>\n",
       "      <td>CCW</td>\n",
       "      <td>2.0762</td>\n",
       "      <td>128</td>\n",
       "      <td>2.0</td>\n",
       "      <td>61.651093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>68.690901</td>\n",
       "      <td>CW</td>\n",
       "      <td>1.9275</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.037613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>68.690901</td>\n",
       "      <td>CCW</td>\n",
       "      <td>2.0724</td>\n",
       "      <td>146</td>\n",
       "      <td>2.0</td>\n",
       "      <td>70.449720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>68.690901</td>\n",
       "      <td>CW</td>\n",
       "      <td>1.9280</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.593361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>68.690901</td>\n",
       "      <td>CCW</td>\n",
       "      <td>1.9295</td>\n",
       "      <td>155</td>\n",
       "      <td>2.0</td>\n",
       "      <td>80.331692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>68.690901</td>\n",
       "      <td>CW</td>\n",
       "      <td>2.0750</td>\n",
       "      <td>38</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.313253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>68.690901</td>\n",
       "      <td>CCW</td>\n",
       "      <td>1.9307</td>\n",
       "      <td>146</td>\n",
       "      <td>2.0</td>\n",
       "      <td>75.620241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>68.690901</td>\n",
       "      <td>CW</td>\n",
       "      <td>2.0708</td>\n",
       "      <td>9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.346146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>68.690901</td>\n",
       "      <td>CCW</td>\n",
       "      <td>1.9286</td>\n",
       "      <td>135</td>\n",
       "      <td>2.0</td>\n",
       "      <td>69.998963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>68.690901</td>\n",
       "      <td>CW</td>\n",
       "      <td>0.9137</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.755609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>69.012885</td>\n",
       "      <td>CCW</td>\n",
       "      <td>5.0004</td>\n",
       "      <td>344</td>\n",
       "      <td>5.0</td>\n",
       "      <td>68.794496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>69.012885</td>\n",
       "      <td>CW</td>\n",
       "      <td>5.0047</td>\n",
       "      <td>10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.998122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>69.012885</td>\n",
       "      <td>CCW</td>\n",
       "      <td>5.0033</td>\n",
       "      <td>335</td>\n",
       "      <td>5.0</td>\n",
       "      <td>66.955809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>69.012885</td>\n",
       "      <td>CW</td>\n",
       "      <td>5.0023</td>\n",
       "      <td>10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.999080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>69.012885</td>\n",
       "      <td>CCW</td>\n",
       "      <td>5.0035</td>\n",
       "      <td>316</td>\n",
       "      <td>5.0</td>\n",
       "      <td>63.155791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>69.012885</td>\n",
       "      <td>CW</td>\n",
       "      <td>5.0062</td>\n",
       "      <td>28</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.593065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>69.012885</td>\n",
       "      <td>CCW</td>\n",
       "      <td>5.0024</td>\n",
       "      <td>399</td>\n",
       "      <td>5.0</td>\n",
       "      <td>79.761714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>69.012885</td>\n",
       "      <td>CW</td>\n",
       "      <td>5.0015</td>\n",
       "      <td>24</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.798560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>69.012885</td>\n",
       "      <td>CCW</td>\n",
       "      <td>5.0035</td>\n",
       "      <td>469</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93.734386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>69.012885</td>\n",
       "      <td>CW</td>\n",
       "      <td>5.0020</td>\n",
       "      <td>8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.599360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>69.012885</td>\n",
       "      <td>CCW</td>\n",
       "      <td>5.0046</td>\n",
       "      <td>352</td>\n",
       "      <td>5.0</td>\n",
       "      <td>70.335292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>69.012885</td>\n",
       "      <td>CW</td>\n",
       "      <td>0.8709</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.148237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Speed Direction  Duration  Spikes  Step_size  Spike_rate\n",
       "0   67.100726       CCW    0.4804      34        0.5   70.774355\n",
       "1   67.100726        CW    0.4949       3        0.5    6.061831\n",
       "2   67.100726       CCW    0.5039      53        0.5  105.179599\n",
       "3   67.100726        CW    0.4985       3        0.5    6.018054\n",
       "4   67.100726       CCW    0.5034      50        0.5   99.324593\n",
       "5   67.100726        CW    0.4991       0        0.5    0.000000\n",
       "6   67.100726       CCW    0.5019      45        0.5   89.659295\n",
       "7   67.100726        CW    0.4991       0        0.5    0.000000\n",
       "8   67.100726       CCW    0.5006      42        0.5   83.899321\n",
       "9   67.100726        CW    0.4969       3        0.5    6.037432\n",
       "10  67.100726       CCW    0.5048      40        0.5   79.239303\n",
       "11  67.100726        CW    0.4966       0        0.5    0.000000\n",
       "12  67.100726       CCW    0.5035      44        0.5   87.388282\n",
       "13  67.100726        CW    0.5007       0        0.5    0.000000\n",
       "14  67.100726       CCW    0.5011      39        0.5   77.828777\n",
       "15  67.100726        CW    0.4972       2        0.5    4.022526\n",
       "16  67.100726       CCW    0.5023      45        0.5   89.587896\n",
       "17  67.100726        CW    0.4986       1        0.5    2.005616\n",
       "18  67.100726       CCW    0.5033      41        0.5   81.462348\n",
       "19  67.100726        CW    0.4981       2        0.5    4.015258\n",
       "20  67.100726       CCW    0.5013      42        0.5   83.782166\n",
       "21  67.100726        CW    0.5013       2        0.5    3.989627\n",
       "22  67.100726       CCW    0.4980      40        0.5   80.321285\n",
       "23  67.100726        CW    0.5007       2        0.5    3.994408\n",
       "24  67.100726       CCW    0.5013      35        0.5   69.818472\n",
       "25  67.100726        CW    0.5014       1        0.5    1.994416\n",
       "26  67.100726       CCW    0.4994      41        0.5   82.098518\n",
       "27  67.100726        CW    0.4984       2        0.5    4.012841\n",
       "28  67.100726       CCW    0.5031      36        0.5   71.556351\n",
       "29  67.100726        CW    0.3581       1        0.5    2.792516\n",
       "30  68.690901        CW    1.1155       0        2.0    0.000000\n",
       "31  68.690901       CCW    2.0762     128        2.0   61.651093\n",
       "32  68.690901        CW    1.9275       2        2.0    1.037613\n",
       "33  68.690901       CCW    2.0724     146        2.0   70.449720\n",
       "34  68.690901        CW    1.9280       5        2.0    2.593361\n",
       "35  68.690901       CCW    1.9295     155        2.0   80.331692\n",
       "36  68.690901        CW    2.0750      38        2.0   18.313253\n",
       "37  68.690901       CCW    1.9307     146        2.0   75.620241\n",
       "38  68.690901        CW    2.0708       9        2.0    4.346146\n",
       "39  68.690901       CCW    1.9286     135        2.0   69.998963\n",
       "40  68.690901        CW    0.9137       8        2.0    8.755609\n",
       "41  69.012885       CCW    5.0004     344        5.0   68.794496\n",
       "42  69.012885        CW    5.0047      10        5.0    1.998122\n",
       "43  69.012885       CCW    5.0033     335        5.0   66.955809\n",
       "44  69.012885        CW    5.0023      10        5.0    1.999080\n",
       "45  69.012885       CCW    5.0035     316        5.0   63.155791\n",
       "46  69.012885        CW    5.0062      28        5.0    5.593065\n",
       "47  69.012885       CCW    5.0024     399        5.0   79.761714\n",
       "48  69.012885        CW    5.0015      24        5.0    4.798560\n",
       "49  69.012885       CCW    5.0035     469        5.0   93.734386\n",
       "50  69.012885        CW    5.0020       8        5.0    1.599360\n",
       "51  69.012885       CCW    5.0046     352        5.0   70.335292\n",
       "52  69.012885        CW    0.8709       1        5.0    1.148237"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d5a4940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "### Load and prepare data.\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "\n",
    "\n",
    "\n",
    "## Dummy-code direction.\n",
    "_df = df.loc[df['Direction'] == 'CCW']\n",
    "dummies = pd.get_dummies(_df.Step_size)\n",
    "df = df.merge(dummies, left_index=True, right_index=True)\n",
    "\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "### Plot.\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\n",
    "\n",
    "\n",
    "## Plot difference in direction.\n",
    "sns.barplot('Step_size', 'Spike_rate', data=_df)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fix_drum_data(data, events_raw, length_raw):\n",
    "    \n",
    "    _data = np.copy(data)\n",
    "    problem_areas = np.argwhere(length_raw < 0.5)[:,0] + 1\n",
    "    \n",
    "    if np.mod(len(problem_areas), 2) == 0:\n",
    "        problem_areas = np.reshape(problem_areas, (int(len(problem_areas)/2), 2))\n",
    "\n",
    "    else:\n",
    "        import pdb; pdb.set_trace()\n",
    "    for problem_area in (problem_areas):\n",
    "        import pdb; pdb.set_trace()\n",
    "        last_value = events[problem_area[0], 0]\n",
    "        time_start = events[problem_area[0], 1]\n",
    "        time_end = events[problem_area[1], 1]\n",
    "        \n",
    "        if data[last_value] > data[time_start]:\n",
    "            _data[time_start:time_end] = data[time_start:time_end] + data[last_value]\n",
    "        else:\n",
    "            _data[time_start:time_end] = data[time_start:time_end] - data[last_value]\n",
    "\n",
    "    return _data\n",
    "            "
   ]
  }
 ],
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   "display_name": "Python 3 (Biomath)",
   "language": "python",
   "name": "biomath"
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    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  },
  "toc": {
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    "height": "570px",
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