notebooks/12_SO-sigma_coupling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase-Amplitude Coupling\n",
    "\n",
    "This notebook demonstrates how to use YASA to calculate phase-amplitude coupling (PAC) between two frequency bands.\n",
    "\n",
    "Please make sure to install the latest version of YASA first by typing the following line in your terminal or command prompt:\n",
    "\n",
    "`pip install --upgrade yasa`\n",
    "\n",
    "If you are not familiar with PAC methods, I highly recommend reading the two papers below:\n",
    "\n",
    "* Tort, A. B. L., Komorowski, R., Eichenbaum, H., & Kopell, N. (2010). Measuring phase-amplitude coupling between neuronal oscillations of different frequencies. *Journal of Neurophysiology, 104(2), 1195–1210.* https://doi.org/10.1152/jn.00106.2010\n",
    "\n",
    "* Aru, J., Aru, J., Priesemann, V., Wibral, M., Lana, L., Pipa, G., Singer, W., & Vicente, R. (2015). Untangling cross-frequency coupling in neuroscience. *Current Opinion in Neurobiology, 31, 51–61.*  https://doi.org/10.1016/j.conb.2014.08.002"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mne\n",
    "import yasa\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import pingouin as pg\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.signal import hilbert\n",
    "sns.set(style='ticks', font_scale=1.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*****\n",
    "\n",
    "## Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2161058,) [15.797 22.307 39.922 25.657 27.094]\n"
     ]
    }
   ],
   "source": [
    "# Load EEG data\n",
    "f = np.load('data_full_6hrs_100Hz_Cz+Fz+Pz.npz')\n",
    "data, ch_names = f['data'], f['chan']\n",
    "sf = 100.\n",
    "times = np.arange(data.size) / sf\n",
    "\n",
    "# Keep only Cz\n",
    "data = data[0, :].astype(np.float64)\n",
    "print(data.shape, np.round(data[0:5], 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "20-Feb-22 14:13:06 | WARNING | Hypnogram is SHORTER than data by 10.58 seconds. Padding hypnogram with last value to match data.size.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(720,) Unique values = [0 1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "# Load the hypnogram data\n",
    "hypno = np.loadtxt('data_full_6hrs_100Hz_hypno_30s.txt').astype(int)\n",
    "print(hypno.shape, 'Unique values =', np.unique(hypno))\n",
    "\n",
    "# Upsample to data\n",
    "hypno = yasa.hypno_upsample_to_data(hypno=hypno, sf_hypno=1/30, data=data, sf_data=sf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*******\n",
    "\n",
    "## Event-locked analyses\n",
    "\n",
    "One PAC approach that has been used in several recent publications is to first detect slow-waves, and then calculate the PAC based on epochs that are centered around the negative peak of the slow-waves.\n",
    "\n",
    "For example, this is from the Methods section of [Winer et al., J Neuro 2019](https://doi.org/10.1523/JNEUROSCI.0503-19.2019):\n",
    "\n",
    "> *For event-locked cross-frequency analyses (Dvorak and Fenton, 2014; Staresina et al.,2015; Helfrich et al., 2018), the normalized SO trough-locked data were first filtered into the SO component (0.1–1.25 Hz), and then the instantaneous phase angle was extracted after applying a Hilbert transform. Then the same trials were filtered between 12 and 16 Hz, and the instantaneous amplitude was extracted from the Hilbert transform. Only the time range from 2 to 2 s was considered, to avoid filter edge artifacts. For every subject, channel, and epoch, the maximal sleep spindle amplitude and corresponding SO phase angle were detected. The mean circular direction and resultant vector length across all NREM events were determined using the CircStat toolbox(Berens, 2009).*\n",
    "\n",
    "YASA provides some convenient tools to automatize these steps. Specifically, the ``coupling`` and ``coupling_params`` parameters of the [yasa.sw_detect](https://raphaelvallat.com/yasa/build/html/generated/yasa.sw_detect.html) allows us to replicate these exact same steps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start</th>\n",
       "      <th>NegPeak</th>\n",
       "      <th>MidCrossing</th>\n",
       "      <th>PosPeak</th>\n",
       "      <th>End</th>\n",
       "      <th>Duration</th>\n",
       "      <th>ValNegPeak</th>\n",
       "      <th>ValPosPeak</th>\n",
       "      <th>PTP</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>SigmaPeak</th>\n",
       "      <th>PhaseAtSigmaPeak</th>\n",
       "      <th>ndPAC</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Channel</th>\n",
       "      <th>IdxChannel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>644.28</td>\n",
       "      <td>644.54</td>\n",
       "      <td>644.76</td>\n",
       "      <td>644.96</td>\n",
       "      <td>645.15</td>\n",
       "      <td>0.87</td>\n",
       "      <td>-59.705</td>\n",
       "      <td>47.278</td>\n",
       "      <td>106.983</td>\n",
       "      <td>486.287</td>\n",
       "      <td>1.149</td>\n",
       "      <td>644.13</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.020</td>\n",
       "      <td>2</td>\n",
       "      <td>Cz</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>645.15</td>\n",
       "      <td>645.37</td>\n",
       "      <td>645.59</td>\n",
       "      <td>645.82</td>\n",
       "      <td>646.03</td>\n",
       "      <td>0.88</td>\n",
       "      <td>-48.099</td>\n",
       "      <td>48.927</td>\n",
       "      <td>97.026</td>\n",
       "      <td>441.027</td>\n",
       "      <td>1.136</td>\n",
       "      <td>644.13</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.106</td>\n",
       "      <td>2</td>\n",
       "      <td>Cz</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>646.03</td>\n",
       "      <td>646.26</td>\n",
       "      <td>646.45</td>\n",
       "      <td>646.68</td>\n",
       "      <td>646.88</td>\n",
       "      <td>0.85</td>\n",
       "      <td>-53.666</td>\n",
       "      <td>70.078</td>\n",
       "      <td>123.744</td>\n",
       "      <td>651.285</td>\n",
       "      <td>1.176</td>\n",
       "      <td>646.51</td>\n",
       "      <td>-1.171</td>\n",
       "      <td>0.135</td>\n",
       "      <td>2</td>\n",
       "      <td>Cz</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>646.88</td>\n",
       "      <td>647.08</td>\n",
       "      <td>647.29</td>\n",
       "      <td>647.48</td>\n",
       "      <td>647.71</td>\n",
       "      <td>0.83</td>\n",
       "      <td>-66.642</td>\n",
       "      <td>52.305</td>\n",
       "      <td>118.947</td>\n",
       "      <td>566.414</td>\n",
       "      <td>1.205</td>\n",
       "      <td>646.51</td>\n",
       "      <td>-1.171</td>\n",
       "      <td>0.353</td>\n",
       "      <td>2</td>\n",
       "      <td>Cz</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>648.74</td>\n",
       "      <td>649.06</td>\n",
       "      <td>649.30</td>\n",
       "      <td>649.51</td>\n",
       "      <td>649.73</td>\n",
       "      <td>0.99</td>\n",
       "      <td>-136.856</td>\n",
       "      <td>97.897</td>\n",
       "      <td>234.753</td>\n",
       "      <td>978.139</td>\n",
       "      <td>1.010</td>\n",
       "      <td>649.34</td>\n",
       "      <td>-1.365</td>\n",
       "      <td>0.398</td>\n",
       "      <td>2</td>\n",
       "      <td>Cz</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2392</th>\n",
       "      <td>20356.67</td>\n",
       "      <td>20357.26</td>\n",
       "      <td>20357.66</td>\n",
       "      <td>20358.23</td>\n",
       "      <td>20358.65</td>\n",
       "      <td>1.98</td>\n",
       "      <td>-42.348</td>\n",
       "      <td>43.948</td>\n",
       "      <td>86.296</td>\n",
       "      <td>215.740</td>\n",
       "      <td>0.505</td>\n",
       "      <td>20357.41</td>\n",
       "      <td>-2.399</td>\n",
       "      <td>0.235</td>\n",
       "      <td>2</td>\n",
       "      <td>Cz</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2393</th>\n",
       "      <td>20377.58</td>\n",
       "      <td>20377.93</td>\n",
       "      <td>20378.42</td>\n",
       "      <td>20378.70</td>\n",
       "      <td>20378.97</td>\n",
       "      <td>1.39</td>\n",
       "      <td>-83.200</td>\n",
       "      <td>114.139</td>\n",
       "      <td>197.339</td>\n",
       "      <td>402.732</td>\n",
       "      <td>0.719</td>\n",
       "      <td>20378.95</td>\n",
       "      <td>1.379</td>\n",
       "      <td>0.242</td>\n",
       "      <td>2</td>\n",
       "      <td>Cz</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2394</th>\n",
       "      <td>20499.68</td>\n",
       "      <td>20500.06</td>\n",
       "      <td>20500.33</td>\n",
       "      <td>20500.70</td>\n",
       "      <td>20501.00</td>\n",
       "      <td>1.32</td>\n",
       "      <td>-111.219</td>\n",
       "      <td>142.628</td>\n",
       "      <td>253.847</td>\n",
       "      <td>940.174</td>\n",
       "      <td>0.758</td>\n",
       "      <td>20498.61</td>\n",
       "      <td>2.165</td>\n",
       "      <td>0.302</td>\n",
       "      <td>2</td>\n",
       "      <td>Cz</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2395</th>\n",
       "      <td>20501.00</td>\n",
       "      <td>20501.27</td>\n",
       "      <td>20501.52</td>\n",
       "      <td>20501.74</td>\n",
       "      <td>20502.01</td>\n",
       "      <td>1.01</td>\n",
       "      <td>-139.083</td>\n",
       "      <td>107.707</td>\n",
       "      <td>246.789</td>\n",
       "      <td>987.157</td>\n",
       "      <td>0.990</td>\n",
       "      <td>20499.46</td>\n",
       "      <td>0.297</td>\n",
       "      <td>0.211</td>\n",
       "      <td>2</td>\n",
       "      <td>Cz</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2396</th>\n",
       "      <td>20525.88</td>\n",
       "      <td>20526.22</td>\n",
       "      <td>20526.50</td>\n",
       "      <td>20526.74</td>\n",
       "      <td>20527.36</td>\n",
       "      <td>1.48</td>\n",
       "      <td>-146.799</td>\n",
       "      <td>94.584</td>\n",
       "      <td>241.383</td>\n",
       "      <td>862.082</td>\n",
       "      <td>0.676</td>\n",
       "      <td>20525.08</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.410</td>\n",
       "      <td>2</td>\n",
       "      <td>Cz</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2397 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Start   NegPeak  MidCrossing   PosPeak       End  Duration  \\\n",
       "0       644.28    644.54       644.76    644.96    645.15      0.87   \n",
       "1       645.15    645.37       645.59    645.82    646.03      0.88   \n",
       "2       646.03    646.26       646.45    646.68    646.88      0.85   \n",
       "3       646.88    647.08       647.29    647.48    647.71      0.83   \n",
       "4       648.74    649.06       649.30    649.51    649.73      0.99   \n",
       "...        ...       ...          ...       ...       ...       ...   \n",
       "2392  20356.67  20357.26     20357.66  20358.23  20358.65      1.98   \n",
       "2393  20377.58  20377.93     20378.42  20378.70  20378.97      1.39   \n",
       "2394  20499.68  20500.06     20500.33  20500.70  20501.00      1.32   \n",
       "2395  20501.00  20501.27     20501.52  20501.74  20502.01      1.01   \n",
       "2396  20525.88  20526.22     20526.50  20526.74  20527.36      1.48   \n",
       "\n",
       "      ValNegPeak  ValPosPeak      PTP    Slope  Frequency  SigmaPeak  \\\n",
       "0        -59.705      47.278  106.983  486.287      1.149     644.13   \n",
       "1        -48.099      48.927   97.026  441.027      1.136     644.13   \n",
       "2        -53.666      70.078  123.744  651.285      1.176     646.51   \n",
       "3        -66.642      52.305  118.947  566.414      1.205     646.51   \n",
       "4       -136.856      97.897  234.753  978.139      1.010     649.34   \n",
       "...          ...         ...      ...      ...        ...        ...   \n",
       "2392     -42.348      43.948   86.296  215.740      0.505   20357.41   \n",
       "2393     -83.200     114.139  197.339  402.732      0.719   20378.95   \n",
       "2394    -111.219     142.628  253.847  940.174      0.758   20498.61   \n",
       "2395    -139.083     107.707  246.789  987.157      0.990   20499.46   \n",
       "2396    -146.799      94.584  241.383  862.082      0.676   20525.08   \n",
       "\n",
       "      PhaseAtSigmaPeak  ndPAC  Stage Channel  IdxChannel  \n",
       "0                0.584  0.020      2      Cz           0  \n",
       "1                0.584  0.106      2      Cz           0  \n",
       "2               -1.171  0.135      2      Cz           0  \n",
       "3               -1.171  0.353      2      Cz           0  \n",
       "4               -1.365  0.398      2      Cz           0  \n",
       "...                ...    ...    ...     ...         ...  \n",
       "2392            -2.399  0.235      2      Cz           0  \n",
       "2393             1.379  0.242      2      Cz           0  \n",
       "2394             2.165  0.302      2      Cz           0  \n",
       "2395             0.297  0.211      2      Cz           0  \n",
       "2396             0.216  0.410      2      Cz           0  \n",
       "\n",
       "[2397 rows x 17 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sw = yasa.sw_detect(data, sf, ch_names=[\"Cz\"], hypno=hypno, include=(2, 3), coupling=True,\n",
    "                    coupling_params=dict(freq_sp=(12, 16), time=2, p=None))\n",
    "events = sw.summary()\n",
    "events.round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As explained in the documentation of the [yasa.sw_detect](https://raphaelvallat.com/yasa/build/html/generated/yasa.sw_detect.html), we get some additional columns in the output dataframe:\n",
    "\n",
    "1) The ``SigmaPeak`` column contains the timestamp (in seconds from the beginning of the recording) where the sigma-filtered (12 - 16 Hz, see ``freq_sp``) amplitude is at its maximal. This is calculated separately for each detected slow-wave, using a **4-seconds epoch centered around the negative peak of the slow-wave**. Note that the duration of the epoch can be changed by specifying ``coupling_params['time']``.\n",
    "\n",
    "2) The ``PhaseAtSigmaPeak`` column contains the phase (in radians) of the slow-wave filtered signal (0.3 - 1.5 Hz, see ``freq_sw``) at ``SigmaPeak``. Using the [Pingouin package](https://pingouin-stats.org/), we can then easily extract and visualize the direction and strength of coupling across all channels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Circular mean: -0.232 rad\n",
      "Vector length: 0.172\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pingouin as pg\n",
    "pg.plot_circmean(events['PhaseAtSigmaPeak'])\n",
    "print('Circular mean: %.3f rad' % pg.circ_mean(events['PhaseAtSigmaPeak']))\n",
    "print('Vector length: %.3f' % pg.circ_r(events['PhaseAtSigmaPeak']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) The ``ndPAC`` columns contains the normalized mean vector length (also called the normalized direct PAC, see [Ozkurt 2012](https://doi.org/10.1109/TBME.2012.2194783)). Note that ``ndPAC`` should be highly correlated the vector length of the ``PhaseAtSigmaPeak``. It may be more accurate though since it is calculated on the entire 4-seconds epoch.\n",
    "\n",
    "For more details, please refer to:\n",
    "\n",
    "* Özkurt, T. E. (2012). Statistically Reliable and Fast Direct Estimation of Phase-Amplitude Cross-Frequency Coupling. IEEE Transactions on Biomedical Engineering, 59(7), 1943–1950. https://doi.org/10.1109/TBME.2012.2194783"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of ndPAC value: \n",
    "events['ndPAC'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22130789113606647"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This should be close to the vector length that we calculated above\n",
    "events['ndPAC'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Example for one slow-wave**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the SO phase and sigma amplitude signals\n",
    "\n",
    "# First, filter the data in the sigma range (12-16 Hz)\n",
    "data_sigma = mne.filter.filter_data(\n",
    "    sw._data, sf, 12, 16, method='fir',\n",
    "    l_trans_bandwidth=1.5, h_trans_bandwidth=1.5, \n",
    "    verbose=0)\n",
    "\n",
    "# Now extract the instantaneous phase/amplitude using Hilbert transform\n",
    "sw_pha = np.angle(hilbert(sw._data_filt))  # data_filt is filtered between 0.3 to 1.5 Hz\n",
    "sp_amp = np.abs(hilbert(data_sigma))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phase at sigma peak: 1.14\n",
      "ndPAC: 0.30\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx_sw = 301\n",
    "\n",
    "print(\"Phase at sigma peak: %.2f\" % events.loc[idx_sw, \"PhaseAtSigmaPeak\"])\n",
    "print(\"ndPAC: %.2f\" % events.loc[idx_sw, \"ndPAC\"])\n",
    "\n",
    "idx_start = int(events['Start'].loc[idx_sw] * sf) - 10\n",
    "idx_stop = int(events['End'].loc[idx_sw] * sf) + 10\n",
    "\n",
    "idx_sw = np.arange(idx_start, idx_stop + 1)\n",
    "time = np.arange(idx_sw.size) / sf\n",
    "\n",
    "# Plot\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(5, 10), sharex=True)\n",
    "\n",
    "ax1.plot(time, np.squeeze(sw._data_filt[:, idx_sw]), \n",
    "         lw=2, color=\"k\", label=\"Data (filtered)\")\n",
    "ax2.plot(time, np.squeeze(sw_pha[:, idx_sw]), \n",
    "         lw=2, color=\"tab:blue\", label=\"SO (phase)\")\n",
    "ax3.plot(time, np.squeeze(sp_amp[:, idx_sw]), \n",
    "         lw=2, color=\"tab:orange\", label=\"Sigma (amp)\")\n",
    "\n",
    "ax1.axhline(0, color=\"tab:grey\", lw=1, ls=\":\")\n",
    "ax2.axhline(0, color=\"tab:grey\", lw=1, ls=\":\")\n",
    "\n",
    "ax1.set_ylabel(\"Amplitude\")\n",
    "ax2.set_ylabel(\"Phase (radians)\")\n",
    "ax3.set_ylabel(\"Amplitude\")\n",
    "\n",
    "ax1.set_xlim(0, None)\n",
    "\n",
    "ax3.set_xlabel(\"Time (seconds)\")\n",
    "\n",
    "ax1.legend(frameon=False)\n",
    "ax2.legend(frameon=False)\n",
    "ax3.legend(frameon=False)\n",
    "\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*************************\n",
    "\n",
    "## Data-driven PAC\n",
    "\n",
    "Here, rather than focusing on event-locked coupling (e.g. based on slow-waves detection), we'll simply use epochs of 15-seconds of N2 sleep to estimate PAC across a range of phase and amplitude frequencies.\n",
    "\n",
    "To calculate formal phase-amplitude coupling, we'll be using the [tensorpac](https://etiennecmb.github.io/tensorpac/) package. Make sure to install it using: \n",
    "\n",
    "`pip install -U tensorpac`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(636, 1500)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Segment N2 sleep into 15-seconds non-overlapping epochs\n",
    "_, data_N2 = yasa.sliding_window(data[hypno == 2], sf, window=15)\n",
    "\n",
    "# We end up with 636 epochs of 15-seconds\n",
    "data_N2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.375, 0.625, 0.875, 1.125, 1.375, 1.625, 1.875, 2.125, 2.375,\n",
       "        2.625, 2.875, 3.125, 3.375, 3.625, 3.875, 4.125]),\n",
       " array([ 7.25,  7.75,  8.25,  8.75,  9.25,  9.75, 10.25, 10.75, 11.25,\n",
       "        11.75, 12.25, 12.75, 13.25, 13.75, 14.25, 14.75, 15.25, 15.75,\n",
       "        16.25, 16.75, 17.25, 17.75, 18.25, 18.75, 19.25, 19.75, 20.25,\n",
       "        20.75, 21.25, 21.75, 22.25, 22.75, 23.25, 23.75, 24.25, 24.75,\n",
       "        25.25]))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, let's define our array of frequencies for phase and amplitude\n",
    "f_pha = np.arange(0.375, 4.25, 0.25)  # Frequency for phase\n",
    "f_amp = np.arange(7.25, 25.5, 0.5)  # Frequency for amplitude\n",
    "\n",
    "f_pha, f_amp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now calculate the comodulogram. Please refer to the [main API of tensorpac](https://etiennecmb.github.io/tensorpac/generated/tensorpac.Pac.html#tensorpac.Pac) for more details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tensorpac import Pac\n",
    "sns.set(font_scale=1.1, style='white')\n",
    "\n",
    "# Define a PAC object\n",
    "p = Pac(idpac=(2, 0, 0), f_pha=f_pha, f_amp=f_amp, verbose='WARNING')\n",
    "\n",
    "# Filter the data and extract the PAC values\n",
    "xpac = p.filterfit(sf, data_N2)\n",
    "\n",
    "# Plot the comodulogram\n",
    "p.comodulogram(xpac.mean(-1), title=str(p), vmin=0, plotas='imshow');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_40393_row12_col0 {\n",
       "  background-color: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_40393\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >FreqPhase</th>\n",
       "      <th id=\"T_40393_level0_col0\" class=\"col_heading level0 col0\" >0.500000</th>\n",
       "      <th id=\"T_40393_level0_col1\" class=\"col_heading level0 col1\" >0.750000</th>\n",
       "      <th id=\"T_40393_level0_col2\" class=\"col_heading level0 col2\" >1.000000</th>\n",
       "      <th id=\"T_40393_level0_col3\" class=\"col_heading level0 col3\" >1.250000</th>\n",
       "      <th id=\"T_40393_level0_col4\" class=\"col_heading level0 col4\" >1.500000</th>\n",
       "      <th id=\"T_40393_level0_col5\" class=\"col_heading level0 col5\" >1.750000</th>\n",
       "      <th id=\"T_40393_level0_col6\" class=\"col_heading level0 col6\" >2.000000</th>\n",
       "      <th id=\"T_40393_level0_col7\" class=\"col_heading level0 col7\" >2.250000</th>\n",
       "      <th id=\"T_40393_level0_col8\" class=\"col_heading level0 col8\" >2.500000</th>\n",
       "      <th id=\"T_40393_level0_col9\" class=\"col_heading level0 col9\" >2.750000</th>\n",
       "      <th id=\"T_40393_level0_col10\" class=\"col_heading level0 col10\" >3.000000</th>\n",
       "      <th id=\"T_40393_level0_col11\" class=\"col_heading level0 col11\" >3.250000</th>\n",
       "      <th id=\"T_40393_level0_col12\" class=\"col_heading level0 col12\" >3.500000</th>\n",
       "      <th id=\"T_40393_level0_col13\" class=\"col_heading level0 col13\" >3.750000</th>\n",
       "      <th id=\"T_40393_level0_col14\" class=\"col_heading level0 col14\" >4.000000</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >FreqAmplitude</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "      <th class=\"blank col12\" >&nbsp;</th>\n",
       "      <th class=\"blank col13\" >&nbsp;</th>\n",
       "      <th class=\"blank col14\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_40393_level0_row0\" class=\"row_heading level0 row0\" >7.500000</th>\n",
       "      <td id=\"T_40393_row0_col0\" class=\"data row0 col0\" >0.010621</td>\n",
       "      <td id=\"T_40393_row0_col1\" class=\"data row0 col1\" >0.005415</td>\n",
       "      <td id=\"T_40393_row0_col2\" class=\"data row0 col2\" >0.003180</td>\n",
       "      <td id=\"T_40393_row0_col3\" class=\"data row0 col3\" >0.001964</td>\n",
       "      <td id=\"T_40393_row0_col4\" class=\"data row0 col4\" >0.001374</td>\n",
       "      <td id=\"T_40393_row0_col5\" class=\"data row0 col5\" >0.000995</td>\n",
       "      <td id=\"T_40393_row0_col6\" class=\"data row0 col6\" >0.000763</td>\n",
       "      <td id=\"T_40393_row0_col7\" class=\"data row0 col7\" >0.000691</td>\n",
       "      <td id=\"T_40393_row0_col8\" class=\"data row0 col8\" >0.000580</td>\n",
       "      <td id=\"T_40393_row0_col9\" class=\"data row0 col9\" >0.000519</td>\n",
       "      <td id=\"T_40393_row0_col10\" class=\"data row0 col10\" >0.000498</td>\n",
       "      <td id=\"T_40393_row0_col11\" class=\"data row0 col11\" >0.000562</td>\n",
       "      <td id=\"T_40393_row0_col12\" class=\"data row0 col12\" >0.000623</td>\n",
       "      <td id=\"T_40393_row0_col13\" class=\"data row0 col13\" >0.000655</td>\n",
       "      <td id=\"T_40393_row0_col14\" class=\"data row0 col14\" >0.000686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40393_level0_row1\" class=\"row_heading level0 row1\" >8.000000</th>\n",
       "      <td id=\"T_40393_row1_col0\" class=\"data row1 col0\" >0.010731</td>\n",
       "      <td id=\"T_40393_row1_col1\" class=\"data row1 col1\" >0.005380</td>\n",
       "      <td id=\"T_40393_row1_col2\" class=\"data row1 col2\" >0.003420</td>\n",
       "      <td id=\"T_40393_row1_col3\" class=\"data row1 col3\" >0.002132</td>\n",
       "      <td id=\"T_40393_row1_col4\" class=\"data row1 col4\" >0.001509</td>\n",
       "      <td id=\"T_40393_row1_col5\" class=\"data row1 col5\" >0.001140</td>\n",
       "      <td id=\"T_40393_row1_col6\" class=\"data row1 col6\" >0.000848</td>\n",
       "      <td id=\"T_40393_row1_col7\" class=\"data row1 col7\" >0.000737</td>\n",
       "      <td id=\"T_40393_row1_col8\" class=\"data row1 col8\" >0.000597</td>\n",
       "      <td id=\"T_40393_row1_col9\" class=\"data row1 col9\" >0.000540</td>\n",
       "      <td id=\"T_40393_row1_col10\" class=\"data row1 col10\" >0.000507</td>\n",
       "      <td id=\"T_40393_row1_col11\" class=\"data row1 col11\" >0.000572</td>\n",
       "      <td id=\"T_40393_row1_col12\" class=\"data row1 col12\" >0.000626</td>\n",
       "      <td id=\"T_40393_row1_col13\" class=\"data row1 col13\" >0.000660</td>\n",
       "      <td id=\"T_40393_row1_col14\" class=\"data row1 col14\" >0.000690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40393_level0_row2\" class=\"row_heading level0 row2\" >8.500000</th>\n",
       "      <td id=\"T_40393_row2_col0\" class=\"data row2 col0\" >0.010951</td>\n",
       "      <td id=\"T_40393_row2_col1\" class=\"data row2 col1\" >0.005300</td>\n",
       "      <td id=\"T_40393_row2_col2\" class=\"data row2 col2\" >0.003325</td>\n",
       "      <td id=\"T_40393_row2_col3\" class=\"data row2 col3\" >0.002183</td>\n",
       "      <td id=\"T_40393_row2_col4\" class=\"data row2 col4\" >0.001527</td>\n",
       "      <td id=\"T_40393_row2_col5\" class=\"data row2 col5\" >0.001167</td>\n",
       "      <td id=\"T_40393_row2_col6\" class=\"data row2 col6\" >0.000842</td>\n",
       "      <td id=\"T_40393_row2_col7\" class=\"data row2 col7\" >0.000734</td>\n",
       "      <td id=\"T_40393_row2_col8\" class=\"data row2 col8\" >0.000605</td>\n",
       "      <td id=\"T_40393_row2_col9\" class=\"data row2 col9\" >0.000539</td>\n",
       "      <td id=\"T_40393_row2_col10\" class=\"data row2 col10\" >0.000513</td>\n",
       "      <td id=\"T_40393_row2_col11\" class=\"data row2 col11\" >0.000578</td>\n",
       "      <td id=\"T_40393_row2_col12\" class=\"data row2 col12\" >0.000619</td>\n",
       "      <td id=\"T_40393_row2_col13\" class=\"data row2 col13\" >0.000655</td>\n",
       "      <td id=\"T_40393_row2_col14\" class=\"data row2 col14\" >0.000685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40393_level0_row3\" class=\"row_heading level0 row3\" >9.000000</th>\n",
       "      <td id=\"T_40393_row3_col0\" class=\"data row3 col0\" >0.011446</td>\n",
       "      <td id=\"T_40393_row3_col1\" class=\"data row3 col1\" >0.005553</td>\n",
       "      <td id=\"T_40393_row3_col2\" class=\"data row3 col2\" >0.003476</td>\n",
       "      <td id=\"T_40393_row3_col3\" class=\"data row3 col3\" >0.002441</td>\n",
       "      <td id=\"T_40393_row3_col4\" class=\"data row3 col4\" >0.001739</td>\n",
       "      <td id=\"T_40393_row3_col5\" class=\"data row3 col5\" >0.001316</td>\n",
       "      <td id=\"T_40393_row3_col6\" class=\"data row3 col6\" >0.000920</td>\n",
       "      <td id=\"T_40393_row3_col7\" class=\"data row3 col7\" >0.000817</td>\n",
       "      <td id=\"T_40393_row3_col8\" class=\"data row3 col8\" >0.000671</td>\n",
       "      <td id=\"T_40393_row3_col9\" class=\"data row3 col9\" >0.000583</td>\n",
       "      <td id=\"T_40393_row3_col10\" class=\"data row3 col10\" >0.000537</td>\n",
       "      <td id=\"T_40393_row3_col11\" class=\"data row3 col11\" >0.000591</td>\n",
       "      <td id=\"T_40393_row3_col12\" class=\"data row3 col12\" >0.000629</td>\n",
       "      <td id=\"T_40393_row3_col13\" class=\"data row3 col13\" >0.000667</td>\n",
       "      <td id=\"T_40393_row3_col14\" class=\"data row3 col14\" >0.000690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40393_level0_row4\" class=\"row_heading level0 row4\" >9.500000</th>\n",
       "      <td id=\"T_40393_row4_col0\" class=\"data row4 col0\" >0.011647</td>\n",
       "      <td id=\"T_40393_row4_col1\" class=\"data row4 col1\" >0.005810</td>\n",
       "      <td id=\"T_40393_row4_col2\" class=\"data row4 col2\" >0.003634</td>\n",
       "      <td id=\"T_40393_row4_col3\" class=\"data row4 col3\" >0.002651</td>\n",
       "      <td id=\"T_40393_row4_col4\" class=\"data row4 col4\" >0.001995</td>\n",
       "      <td id=\"T_40393_row4_col5\" class=\"data row4 col5\" >0.001498</td>\n",
       "      <td id=\"T_40393_row4_col6\" class=\"data row4 col6\" >0.001065</td>\n",
       "      <td id=\"T_40393_row4_col7\" class=\"data row4 col7\" >0.000952</td>\n",
       "      <td id=\"T_40393_row4_col8\" class=\"data row4 col8\" >0.000752</td>\n",
       "      <td id=\"T_40393_row4_col9\" class=\"data row4 col9\" >0.000652</td>\n",
       "      <td id=\"T_40393_row4_col10\" class=\"data row4 col10\" >0.000571</td>\n",
       "      <td id=\"T_40393_row4_col11\" class=\"data row4 col11\" >0.000617</td>\n",
       "      <td id=\"T_40393_row4_col12\" class=\"data row4 col12\" >0.000646</td>\n",
       "      <td id=\"T_40393_row4_col13\" class=\"data row4 col13\" >0.000679</td>\n",
       "      <td id=\"T_40393_row4_col14\" class=\"data row4 col14\" >0.000702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40393_level0_row5\" class=\"row_heading level0 row5\" >10.000000</th>\n",
       "      <td id=\"T_40393_row5_col0\" class=\"data row5 col0\" >0.011650</td>\n",
       "      <td id=\"T_40393_row5_col1\" class=\"data row5 col1\" >0.005881</td>\n",
       "      <td id=\"T_40393_row5_col2\" class=\"data row5 col2\" >0.003558</td>\n",
       "      <td id=\"T_40393_row5_col3\" class=\"data row5 col3\" >0.002584</td>\n",
       "      <td id=\"T_40393_row5_col4\" class=\"data row5 col4\" >0.001969</td>\n",
       "      <td id=\"T_40393_row5_col5\" class=\"data row5 col5\" >0.001488</td>\n",
       "      <td id=\"T_40393_row5_col6\" class=\"data row5 col6\" >0.001095</td>\n",
       "      <td id=\"T_40393_row5_col7\" class=\"data row5 col7\" >0.000990</td>\n",
       "      <td id=\"T_40393_row5_col8\" class=\"data row5 col8\" >0.000764</td>\n",
       "      <td id=\"T_40393_row5_col9\" class=\"data row5 col9\" >0.000651</td>\n",
       "      <td id=\"T_40393_row5_col10\" class=\"data row5 col10\" >0.000579</td>\n",
       "      <td id=\"T_40393_row5_col11\" class=\"data row5 col11\" >0.000628</td>\n",
       "      <td id=\"T_40393_row5_col12\" class=\"data row5 col12\" >0.000659</td>\n",
       "      <td id=\"T_40393_row5_col13\" class=\"data row5 col13\" >0.000690</td>\n",
       "      <td id=\"T_40393_row5_col14\" class=\"data row5 col14\" >0.000714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40393_level0_row6\" class=\"row_heading level0 row6\" >10.500000</th>\n",
       "      <td id=\"T_40393_row6_col0\" class=\"data row6 col0\" >0.011957</td>\n",
       "      <td id=\"T_40393_row6_col1\" class=\"data row6 col1\" >0.006034</td>\n",
       "      <td id=\"T_40393_row6_col2\" class=\"data row6 col2\" >0.003667</td>\n",
       "      <td id=\"T_40393_row6_col3\" class=\"data row6 col3\" >0.002653</td>\n",
       "      <td id=\"T_40393_row6_col4\" class=\"data row6 col4\" >0.002095</td>\n",
       "      <td id=\"T_40393_row6_col5\" class=\"data row6 col5\" >0.001626</td>\n",
       "      <td id=\"T_40393_row6_col6\" class=\"data row6 col6\" >0.001234</td>\n",
       "      <td id=\"T_40393_row6_col7\" class=\"data row6 col7\" >0.001102</td>\n",
       "      <td id=\"T_40393_row6_col8\" class=\"data row6 col8\" >0.000852</td>\n",
       "      <td id=\"T_40393_row6_col9\" class=\"data row6 col9\" >0.000711</td>\n",
       "      <td id=\"T_40393_row6_col10\" class=\"data row6 col10\" >0.000641</td>\n",
       "      <td id=\"T_40393_row6_col11\" class=\"data row6 col11\" >0.000677</td>\n",
       "      <td id=\"T_40393_row6_col12\" class=\"data row6 col12\" >0.000722</td>\n",
       "      <td id=\"T_40393_row6_col13\" class=\"data row6 col13\" >0.000732</td>\n",
       "      <td id=\"T_40393_row6_col14\" class=\"data row6 col14\" >0.000755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40393_level0_row7\" class=\"row_heading level0 row7\" >11.000000</th>\n",
       "      <td id=\"T_40393_row7_col0\" class=\"data row7 col0\" >0.012387</td>\n",
       "      <td id=\"T_40393_row7_col1\" class=\"data row7 col1\" >0.005993</td>\n",
       "      <td id=\"T_40393_row7_col2\" class=\"data row7 col2\" >0.003625</td>\n",
       "      <td id=\"T_40393_row7_col3\" class=\"data row7 col3\" >0.002611</td>\n",
       "      <td id=\"T_40393_row7_col4\" class=\"data row7 col4\" >0.002040</td>\n",
       "      <td id=\"T_40393_row7_col5\" class=\"data row7 col5\" >0.001558</td>\n",
       "      <td id=\"T_40393_row7_col6\" class=\"data row7 col6\" >0.001199</td>\n",
       "      <td id=\"T_40393_row7_col7\" class=\"data row7 col7\" >0.001079</td>\n",
       "      <td id=\"T_40393_row7_col8\" class=\"data row7 col8\" >0.000849</td>\n",
       "      <td id=\"T_40393_row7_col9\" class=\"data row7 col9\" >0.000715</td>\n",
       "      <td id=\"T_40393_row7_col10\" class=\"data row7 col10\" >0.000664</td>\n",
       "      <td id=\"T_40393_row7_col11\" class=\"data row7 col11\" >0.000695</td>\n",
       "      <td id=\"T_40393_row7_col12\" class=\"data row7 col12\" >0.000759</td>\n",
       "      <td id=\"T_40393_row7_col13\" class=\"data row7 col13\" >0.000761</td>\n",
       "      <td id=\"T_40393_row7_col14\" class=\"data row7 col14\" >0.000787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40393_level0_row8\" class=\"row_heading level0 row8\" >11.500000</th>\n",
       "      <td id=\"T_40393_row8_col0\" class=\"data row8 col0\" >0.013275</td>\n",
       "      <td id=\"T_40393_row8_col1\" class=\"data row8 col1\" >0.006133</td>\n",
       "      <td id=\"T_40393_row8_col2\" class=\"data row8 col2\" >0.003738</td>\n",
       "      <td id=\"T_40393_row8_col3\" class=\"data row8 col3\" >0.002741</td>\n",
       "      <td id=\"T_40393_row8_col4\" class=\"data row8 col4\" >0.002137</td>\n",
       "      <td id=\"T_40393_row8_col5\" class=\"data row8 col5\" >0.001665</td>\n",
       "      <td id=\"T_40393_row8_col6\" class=\"data row8 col6\" >0.001311</td>\n",
       "      <td id=\"T_40393_row8_col7\" class=\"data row8 col7\" >0.001181</td>\n",
       "      <td id=\"T_40393_row8_col8\" class=\"data row8 col8\" >0.000919</td>\n",
       "      <td id=\"T_40393_row8_col9\" class=\"data row8 col9\" >0.000790</td>\n",
       "      <td id=\"T_40393_row8_col10\" class=\"data row8 col10\" >0.000729</td>\n",
       "      <td id=\"T_40393_row8_col11\" class=\"data row8 col11\" >0.000756</td>\n",
       "      <td id=\"T_40393_row8_col12\" class=\"data row8 col12\" >0.000815</td>\n",
       "      <td id=\"T_40393_row8_col13\" class=\"data row8 col13\" >0.000817</td>\n",
       "      <td id=\"T_40393_row8_col14\" class=\"data row8 col14\" >0.000840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40393_level0_row9\" class=\"row_heading level0 row9\" >12.000000</th>\n",
       "      <td id=\"T_40393_row9_col0\" class=\"data row9 col0\" >0.014303</td>\n",
       "      <td id=\"T_40393_row9_col1\" class=\"data row9 col1\" >0.006224</td>\n",
       "      <td id=\"T_40393_row9_col2\" class=\"data row9 col2\" >0.003688</td>\n",
       "      <td id=\"T_40393_row9_col3\" class=\"data row9 col3\" >0.002685</td>\n",
       "      <td id=\"T_40393_row9_col4\" class=\"data row9 col4\" >0.002065</td>\n",
       "      <td id=\"T_40393_row9_col5\" class=\"data row9 col5\" >0.001664</td>\n",
       "      <td id=\"T_40393_row9_col6\" class=\"data row9 col6\" >0.001329</td>\n",
       "      <td id=\"T_40393_row9_col7\" class=\"data row9 col7\" >0.001188</td>\n",
       "      <td id=\"T_40393_row9_col8\" class=\"data row9 col8\" >0.000911</td>\n",
       "      <td id=\"T_40393_row9_col9\" class=\"data row9 col9\" >0.000799</td>\n",
       "      <td id=\"T_40393_row9_col10\" class=\"data row9 col10\" >0.000741</td>\n",
       "      <td id=\"T_40393_row9_col11\" class=\"data row9 col11\" >0.000779</td>\n",
       "      <td id=\"T_40393_row9_col12\" class=\"data row9 col12\" >0.000843</td>\n",
       "      <td id=\"T_40393_row9_col13\" class=\"data row9 col13\" >0.000850</td>\n",
       "      <td id=\"T_40393_row9_col14\" class=\"data row9 col14\" >0.000872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40393_level0_row10\" class=\"row_heading level0 row10\" >12.500000</th>\n",
       "      <td id=\"T_40393_row10_col0\" class=\"data row10 col0\" >0.015098</td>\n",
       "      <td id=\"T_40393_row10_col1\" class=\"data row10 col1\" >0.006340</td>\n",
       "      <td id=\"T_40393_row10_col2\" class=\"data row10 col2\" >0.003643</td>\n",
       "      <td id=\"T_40393_row10_col3\" class=\"data row10 col3\" >0.002578</td>\n",
       "      <td id=\"T_40393_row10_col4\" class=\"data row10 col4\" >0.001982</td>\n",
       "      <td id=\"T_40393_row10_col5\" class=\"data row10 col5\" >0.001643</td>\n",
       "      <td id=\"T_40393_row10_col6\" class=\"data row10 col6\" >0.001315</td>\n",
       "      <td id=\"T_40393_row10_col7\" class=\"data row10 col7\" >0.001160</td>\n",
       "      <td id=\"T_40393_row10_col8\" class=\"data row10 col8\" >0.000883</td>\n",
       "      <td id=\"T_40393_row10_col9\" class=\"data row10 col9\" >0.000795</td>\n",
       "      <td id=\"T_40393_row10_col10\" class=\"data row10 col10\" >0.000748</td>\n",
       "      <td id=\"T_40393_row10_col11\" class=\"data row10 col11\" >0.000797</td>\n",
       "      <td id=\"T_40393_row10_col12\" class=\"data row10 col12\" >0.000858</td>\n",
       "      <td id=\"T_40393_row10_col13\" class=\"data row10 col13\" >0.000870</td>\n",
       "      <td id=\"T_40393_row10_col14\" class=\"data row10 col14\" >0.000894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40393_level0_row11\" class=\"row_heading level0 row11\" >13.000000</th>\n",
       "      <td id=\"T_40393_row11_col0\" class=\"data row11 col0\" >0.015359</td>\n",
       "      <td id=\"T_40393_row11_col1\" class=\"data row11 col1\" >0.006512</td>\n",
       "      <td id=\"T_40393_row11_col2\" class=\"data row11 col2\" >0.003799</td>\n",
       "      <td id=\"T_40393_row11_col3\" class=\"data row11 col3\" >0.002636</td>\n",
       "      <td id=\"T_40393_row11_col4\" class=\"data row11 col4\" >0.002086</td>\n",
       "      <td id=\"T_40393_row11_col5\" class=\"data row11 col5\" >0.001747</td>\n",
       "      <td id=\"T_40393_row11_col6\" class=\"data row11 col6\" >0.001390</td>\n",
       "      <td id=\"T_40393_row11_col7\" class=\"data row11 col7\" >0.001222</td>\n",
       "      <td id=\"T_40393_row11_col8\" class=\"data row11 col8\" >0.000922</td>\n",
       "      <td id=\"T_40393_row11_col9\" class=\"data row11 col9\" >0.000835</td>\n",
       "      <td id=\"T_40393_row11_col10\" class=\"data row11 col10\" >0.000784</td>\n",
       "      <td id=\"T_40393_row11_col11\" class=\"data row11 col11\" >0.000830</td>\n",
       "      <td id=\"T_40393_row11_col12\" class=\"data row11 col12\" >0.000878</td>\n",
       "      <td id=\"T_40393_row11_col13\" class=\"data row11 col13\" >0.000887</td>\n",
       "      <td id=\"T_40393_row11_col14\" class=\"data row11 col14\" >0.000909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40393_level0_row12\" class=\"row_heading level0 row12\" >13.500000</th>\n",
       "      <td id=\"T_40393_row12_col0\" class=\"data row12 col0\" >0.015365</td>\n",
       "      <td id=\"T_40393_row12_col1\" class=\"data row12 col1\" >0.006613</td>\n",
       "      <td id=\"T_40393_row12_col2\" class=\"data row12 col2\" >0.003927</td>\n",
       "      <td id=\"T_40393_row12_col3\" class=\"data row12 col3\" >0.002631</td>\n",
       "      <td id=\"T_40393_row12_col4\" class=\"data row12 col4\" >0.002125</td>\n",
       "      <td id=\"T_40393_row12_col5\" class=\"data row12 col5\" >0.001773</td>\n",
       "      <td id=\"T_40393_row12_col6\" class=\"data row12 col6\" >0.001381</td>\n",
       "      <td id=\"T_40393_row12_col7\" class=\"data row12 col7\" >0.001217</td>\n",
       "      <td id=\"T_40393_row12_col8\" class=\"data row12 col8\" >0.000915</td>\n",
       "      <td id=\"T_40393_row12_col9\" class=\"data row12 col9\" >0.000834</td>\n",
       "      <td id=\"T_40393_row12_col10\" class=\"data row12 col10\" >0.000787</td>\n",
       "      <td id=\"T_40393_row12_col11\" class=\"data row12 col11\" >0.000839</td>\n",
       "      <td id=\"T_40393_row12_col12\" class=\"data row12 col12\" >0.000877</td>\n",
       "      <td id=\"T_40393_row12_col13\" class=\"data row12 col13\" >0.000882</td>\n",
       "      <td id=\"T_40393_row12_col14\" class=\"data row12 col14\" >0.000911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40393_level0_row13\" class=\"row_heading level0 row13\" >14.000000</th>\n",
       "      <td id=\"T_40393_row13_col0\" class=\"data row13 col0\" >0.014872</td>\n",
       "      <td id=\"T_40393_row13_col1\" class=\"data row13 col1\" >0.006688</td>\n",
       "      <td id=\"T_40393_row13_col2\" class=\"data row13 col2\" >0.004131</td>\n",
       "      <td id=\"T_40393_row13_col3\" class=\"data row13 col3\" >0.002677</td>\n",
       "      <td id=\"T_40393_row13_col4\" class=\"data row13 col4\" >0.002202</td>\n",
       "      <td id=\"T_40393_row13_col5\" class=\"data row13 col5\" >0.001815</td>\n",
       "      <td id=\"T_40393_row13_col6\" class=\"data row13 col6\" >0.001392</td>\n",
       "      <td id=\"T_40393_row13_col7\" class=\"data row13 col7\" >0.001246</td>\n",
       "      <td id=\"T_40393_row13_col8\" class=\"data row13 col8\" >0.000947</td>\n",
       "      <td id=\"T_40393_row13_col9\" class=\"data row13 col9\" >0.000848</td>\n",
       "      <td id=\"T_40393_row13_col10\" class=\"data row13 col10\" >0.000794</td>\n",
       "      <td id=\"T_40393_row13_col11\" class=\"data row13 col11\" >0.000835</td>\n",
       "      <td id=\"T_40393_row13_col12\" class=\"data row13 col12\" >0.000864</td>\n",
       "      <td id=\"T_40393_row13_col13\" class=\"data row13 col13\" >0.000863</td>\n",
       "      <td id=\"T_40393_row13_col14\" class=\"data row13 col14\" >0.000897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40393_level0_row14\" class=\"row_heading level0 row14\" >14.500000</th>\n",
       "      <td id=\"T_40393_row14_col0\" class=\"data row14 col0\" >0.014036</td>\n",
       "      <td id=\"T_40393_row14_col1\" class=\"data row14 col1\" >0.006767</td>\n",
       "      <td id=\"T_40393_row14_col2\" class=\"data row14 col2\" >0.004350</td>\n",
       "      <td id=\"T_40393_row14_col3\" class=\"data row14 col3\" >0.002812</td>\n",
       "      <td id=\"T_40393_row14_col4\" class=\"data row14 col4\" >0.002335</td>\n",
       "      <td id=\"T_40393_row14_col5\" class=\"data row14 col5\" >0.001896</td>\n",
       "      <td id=\"T_40393_row14_col6\" class=\"data row14 col6\" >0.001440</td>\n",
       "      <td id=\"T_40393_row14_col7\" class=\"data row14 col7\" >0.001305</td>\n",
       "      <td id=\"T_40393_row14_col8\" class=\"data row14 col8\" >0.001015</td>\n",
       "      <td id=\"T_40393_row14_col9\" class=\"data row14 col9\" >0.000887</td>\n",
       "      <td id=\"T_40393_row14_col10\" class=\"data row14 col10\" >0.000820</td>\n",
       "      <td id=\"T_40393_row14_col11\" class=\"data row14 col11\" >0.000835</td>\n",
       "      <td id=\"T_40393_row14_col12\" class=\"data row14 col12\" >0.000850</td>\n",
       "      <td id=\"T_40393_row14_col13\" class=\"data row14 col13\" >0.000843</td>\n",
       "      <td id=\"T_40393_row14_col14\" class=\"data row14 col14\" >0.000872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40393_level0_row15\" class=\"row_heading level0 row15\" >15.000000</th>\n",
       "      <td id=\"T_40393_row15_col0\" class=\"data row15 col0\" >0.013263</td>\n",
       "      <td id=\"T_40393_row15_col1\" class=\"data row15 col1\" >0.006705</td>\n",
       "      <td id=\"T_40393_row15_col2\" class=\"data row15 col2\" >0.004417</td>\n",
       "      <td id=\"T_40393_row15_col3\" class=\"data row15 col3\" >0.002979</td>\n",
       "      <td id=\"T_40393_row15_col4\" class=\"data row15 col4\" >0.002506</td>\n",
       "      <td id=\"T_40393_row15_col5\" class=\"data row15 col5\" >0.002075</td>\n",
       "      <td id=\"T_40393_row15_col6\" class=\"data row15 col6\" >0.001588</td>\n",
       "      <td id=\"T_40393_row15_col7\" class=\"data row15 col7\" >0.001439</td>\n",
       "      <td id=\"T_40393_row15_col8\" class=\"data row15 col8\" >0.001151</td>\n",
       "      <td id=\"T_40393_row15_col9\" class=\"data row15 col9\" >0.001028</td>\n",
       "      <td id=\"T_40393_row15_col10\" class=\"data row15 col10\" >0.000941</td>\n",
       "      <td id=\"T_40393_row15_col11\" class=\"data row15 col11\" >0.000919</td>\n",
       "      <td id=\"T_40393_row15_col12\" class=\"data row15 col12\" >0.000908</td>\n",
       "      <td id=\"T_40393_row15_col13\" class=\"data row15 col13\" >0.000890</td>\n",
       "      <td id=\"T_40393_row15_col14\" class=\"data row15 col14\" >0.000896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40393_level0_row16\" class=\"row_heading level0 row16\" >15.500000</th>\n",
       "      <td id=\"T_40393_row16_col0\" class=\"data row16 col0\" >0.012310</td>\n",
       "      <td id=\"T_40393_row16_col1\" class=\"data row16 col1\" >0.006489</td>\n",
       "      <td id=\"T_40393_row16_col2\" class=\"data row16 col2\" >0.004370</td>\n",
       "      <td id=\"T_40393_row16_col3\" class=\"data row16 col3\" >0.003107</td>\n",
       "      <td id=\"T_40393_row16_col4\" class=\"data row16 col4\" >0.002609</td>\n",
       "      <td id=\"T_40393_row16_col5\" class=\"data row16 col5\" >0.002155</td>\n",
       "      <td id=\"T_40393_row16_col6\" class=\"data row16 col6\" >0.001656</td>\n",
       "      <td id=\"T_40393_row16_col7\" class=\"data row16 col7\" >0.001494</td>\n",
       "      <td id=\"T_40393_row16_col8\" class=\"data row16 col8\" >0.001208</td>\n",
       "      <td id=\"T_40393_row16_col9\" class=\"data row16 col9\" >0.001080</td>\n",
       "      <td id=\"T_40393_row16_col10\" class=\"data row16 col10\" >0.000979</td>\n",
       "      <td id=\"T_40393_row16_col11\" class=\"data row16 col11\" >0.000942</td>\n",
       "      <td id=\"T_40393_row16_col12\" class=\"data row16 col12\" >0.000919</td>\n",
       "      <td id=\"T_40393_row16_col13\" class=\"data row16 col13\" >0.000897</td>\n",
       "      <td id=\"T_40393_row16_col14\" class=\"data row16 col14\" >0.000892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40393_level0_row17\" class=\"row_heading level0 row17\" >16.000000</th>\n",
       "      <td id=\"T_40393_row17_col0\" class=\"data row17 col0\" >0.011497</td>\n",
       "      <td id=\"T_40393_row17_col1\" class=\"data row17 col1\" >0.006146</td>\n",
       "      <td id=\"T_40393_row17_col2\" class=\"data row17 col2\" >0.004207</td>\n",
       "      <td id=\"T_40393_row17_col3\" class=\"data row17 col3\" >0.003124</td>\n",
       "      <td id=\"T_40393_row17_col4\" class=\"data row17 col4\" >0.002595</td>\n",
       "      <td id=\"T_40393_row17_col5\" class=\"data row17 col5\" >0.002134</td>\n",
       "      <td id=\"T_40393_row17_col6\" class=\"data row17 col6\" >0.001670</td>\n",
       "      <td id=\"T_40393_row17_col7\" class=\"data row17 col7\" >0.001498</td>\n",
       "      <td id=\"T_40393_row17_col8\" class=\"data row17 col8\" >0.001204</td>\n",
       "      <td id=\"T_40393_row17_col9\" class=\"data row17 col9\" >0.001061</td>\n",
       "      <td id=\"T_40393_row17_col10\" class=\"data row17 col10\" >0.000955</td>\n",
       "      <td id=\"T_40393_row17_col11\" class=\"data row17 col11\" >0.000920</td>\n",
       "      <td id=\"T_40393_row17_col12\" class=\"data row17 col12\" >0.000903</td>\n",
       "      <td id=\"T_40393_row17_col13\" class=\"data row17 col13\" >0.000883</td>\n",
       "      <td id=\"T_40393_row17_col14\" class=\"data row17 col14\" >0.000874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40393_level0_row18\" class=\"row_heading level0 row18\" >16.500000</th>\n",
       "      <td id=\"T_40393_row18_col0\" class=\"data row18 col0\" >0.010967</td>\n",
       "      <td id=\"T_40393_row18_col1\" class=\"data row18 col1\" >0.005851</td>\n",
       "      <td id=\"T_40393_row18_col2\" class=\"data row18 col2\" >0.004022</td>\n",
       "      <td id=\"T_40393_row18_col3\" class=\"data row18 col3\" >0.003054</td>\n",
       "      <td id=\"T_40393_row18_col4\" class=\"data row18 col4\" >0.002491</td>\n",
       "      <td id=\"T_40393_row18_col5\" class=\"data row18 col5\" >0.002039</td>\n",
       "      <td id=\"T_40393_row18_col6\" class=\"data row18 col6\" >0.001617</td>\n",
       "      <td id=\"T_40393_row18_col7\" class=\"data row18 col7\" >0.001446</td>\n",
       "      <td id=\"T_40393_row18_col8\" class=\"data row18 col8\" >0.001167</td>\n",
       "      <td id=\"T_40393_row18_col9\" class=\"data row18 col9\" >0.001011</td>\n",
       "      <td id=\"T_40393_row18_col10\" class=\"data row18 col10\" >0.000891</td>\n",
       "      <td id=\"T_40393_row18_col11\" class=\"data row18 col11\" >0.000859</td>\n",
       "      <td id=\"T_40393_row18_col12\" class=\"data row18 col12\" >0.000848</td>\n",
       "      <td id=\"T_40393_row18_col13\" class=\"data row18 col13\" >0.000839</td>\n",
       "      <td id=\"T_40393_row18_col14\" class=\"data row18 col14\" >0.000837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40393_level0_row19\" class=\"row_heading level0 row19\" >17.000000</th>\n",
       "      <td id=\"T_40393_row19_col0\" class=\"data row19 col0\" >0.010610</td>\n",
       "      <td id=\"T_40393_row19_col1\" class=\"data row19 col1\" >0.005825</td>\n",
       "      <td id=\"T_40393_row19_col2\" class=\"data row19 col2\" >0.004052</td>\n",
       "      <td id=\"T_40393_row19_col3\" class=\"data row19 col3\" >0.003148</td>\n",
       "      <td id=\"T_40393_row19_col4\" class=\"data row19 col4\" >0.002591</td>\n",
       "      <td id=\"T_40393_row19_col5\" class=\"data row19 col5\" >0.002145</td>\n",
       "      <td id=\"T_40393_row19_col6\" class=\"data row19 col6\" >0.001735</td>\n",
       "      <td id=\"T_40393_row19_col7\" class=\"data row19 col7\" >0.001562</td>\n",
       "      <td id=\"T_40393_row19_col8\" class=\"data row19 col8\" >0.001295</td>\n",
       "      <td id=\"T_40393_row19_col9\" class=\"data row19 col9\" >0.001158</td>\n",
       "      <td id=\"T_40393_row19_col10\" class=\"data row19 col10\" >0.001001</td>\n",
       "      <td id=\"T_40393_row19_col11\" class=\"data row19 col11\" >0.000969</td>\n",
       "      <td id=\"T_40393_row19_col12\" class=\"data row19 col12\" >0.000946</td>\n",
       "      <td id=\"T_40393_row19_col13\" class=\"data row19 col13\" >0.000938</td>\n",
       "      <td id=\"T_40393_row19_col14\" class=\"data row19 col14\" >0.000925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x13fb63850>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract PAC values into a DataFrame\n",
    "df_pac = pd.DataFrame(xpac.mean(-1), columns=p.xvec, index=p.yvec)\n",
    "df_pac.columns.name = 'FreqPhase'\n",
    "df_pac.index.name = 'FreqAmplitude'\n",
    "df_pac.head(20).style.highlight_max(axis=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the Pandas DataFrame above, we see that the maximal coupling (mean vector length) across all epochs is between the 0.5 Hz frequency for phase and 13.5 hz frequency for amplitude."
   ]
  }
 ],
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