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_theme(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": [
    "# Download the EEG data\n",
    "eeg_filepath = yasa.fetch_sample('full_6hrs_100Hz_Cz+Fz+Pz.npz') \n",
    "\n",
    "# Load EEG data\n",
    "npzfile = np.load(eeg_filepath)\n",
    "data, ch_names = npzfile['data'], npzfile['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": "stdout",
     "output_type": "stream",
     "text": [
      "(720,) Unique values = [0 1 2 3 4]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "28-Dec-24 15:13:42 | WARNING | Hypnogram is SHORTER than data by 10.58 seconds. Padding hypnogram with last value to match data.size.\n"
     ]
    }
   ],
   "source": [
    "# Load the hypnogram data\n",
    "hypno_filepath = yasa.fetch_sample('full_6hrs_100Hz_hypno_30s.txt') \n",
    "hypno = np.loadtxt(hypno_filepath).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://yasa-sleep.org/generated/yasa.sw_detect.html) allows us to replicate these exact same steps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<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",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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://yasa-sleep.org/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": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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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",
      "text/plain": [
       "<Figure size 640x480 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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",
      "text/plain": [
       "<Figure size 500x1000 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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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tensorpac import Pac\n",
    "sns.set_theme(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_0301f_row12_col0 {\n",
       "  background-color: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_0301f\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >FreqPhase</th>\n",
       "      <th id=\"T_0301f_level0_col0\" class=\"col_heading level0 col0\" >0.500000</th>\n",
       "      <th id=\"T_0301f_level0_col1\" class=\"col_heading level0 col1\" >0.750000</th>\n",
       "      <th id=\"T_0301f_level0_col2\" class=\"col_heading level0 col2\" >1.000000</th>\n",
       "      <th id=\"T_0301f_level0_col3\" class=\"col_heading level0 col3\" >1.250000</th>\n",
       "      <th id=\"T_0301f_level0_col4\" class=\"col_heading level0 col4\" >1.500000</th>\n",
       "      <th id=\"T_0301f_level0_col5\" class=\"col_heading level0 col5\" >1.750000</th>\n",
       "      <th id=\"T_0301f_level0_col6\" class=\"col_heading level0 col6\" >2.000000</th>\n",
       "      <th id=\"T_0301f_level0_col7\" class=\"col_heading level0 col7\" >2.250000</th>\n",
       "      <th id=\"T_0301f_level0_col8\" class=\"col_heading level0 col8\" >2.500000</th>\n",
       "      <th id=\"T_0301f_level0_col9\" class=\"col_heading level0 col9\" >2.750000</th>\n",
       "      <th id=\"T_0301f_level0_col10\" class=\"col_heading level0 col10\" >3.000000</th>\n",
       "      <th id=\"T_0301f_level0_col11\" class=\"col_heading level0 col11\" >3.250000</th>\n",
       "      <th id=\"T_0301f_level0_col12\" class=\"col_heading level0 col12\" >3.500000</th>\n",
       "      <th id=\"T_0301f_level0_col13\" class=\"col_heading level0 col13\" >3.750000</th>\n",
       "      <th id=\"T_0301f_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_0301f_level0_row0\" class=\"row_heading level0 row0\" >7.500000</th>\n",
       "      <td id=\"T_0301f_row0_col0\" class=\"data row0 col0\" >0.010621</td>\n",
       "      <td id=\"T_0301f_row0_col1\" class=\"data row0 col1\" >0.005415</td>\n",
       "      <td id=\"T_0301f_row0_col2\" class=\"data row0 col2\" >0.003180</td>\n",
       "      <td id=\"T_0301f_row0_col3\" class=\"data row0 col3\" >0.001964</td>\n",
       "      <td id=\"T_0301f_row0_col4\" class=\"data row0 col4\" >0.001374</td>\n",
       "      <td id=\"T_0301f_row0_col5\" class=\"data row0 col5\" >0.000995</td>\n",
       "      <td id=\"T_0301f_row0_col6\" class=\"data row0 col6\" >0.000763</td>\n",
       "      <td id=\"T_0301f_row0_col7\" class=\"data row0 col7\" >0.000691</td>\n",
       "      <td id=\"T_0301f_row0_col8\" class=\"data row0 col8\" >0.000580</td>\n",
       "      <td id=\"T_0301f_row0_col9\" class=\"data row0 col9\" >0.000519</td>\n",
       "      <td id=\"T_0301f_row0_col10\" class=\"data row0 col10\" >0.000498</td>\n",
       "      <td id=\"T_0301f_row0_col11\" class=\"data row0 col11\" >0.000562</td>\n",
       "      <td id=\"T_0301f_row0_col12\" class=\"data row0 col12\" >0.000623</td>\n",
       "      <td id=\"T_0301f_row0_col13\" class=\"data row0 col13\" >0.000655</td>\n",
       "      <td id=\"T_0301f_row0_col14\" class=\"data row0 col14\" >0.000686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0301f_level0_row1\" class=\"row_heading level0 row1\" >8.000000</th>\n",
       "      <td id=\"T_0301f_row1_col0\" class=\"data row1 col0\" >0.010731</td>\n",
       "      <td id=\"T_0301f_row1_col1\" class=\"data row1 col1\" >0.005380</td>\n",
       "      <td id=\"T_0301f_row1_col2\" class=\"data row1 col2\" >0.003420</td>\n",
       "      <td id=\"T_0301f_row1_col3\" class=\"data row1 col3\" >0.002132</td>\n",
       "      <td id=\"T_0301f_row1_col4\" class=\"data row1 col4\" >0.001509</td>\n",
       "      <td id=\"T_0301f_row1_col5\" class=\"data row1 col5\" >0.001140</td>\n",
       "      <td id=\"T_0301f_row1_col6\" class=\"data row1 col6\" >0.000848</td>\n",
       "      <td id=\"T_0301f_row1_col7\" class=\"data row1 col7\" >0.000737</td>\n",
       "      <td id=\"T_0301f_row1_col8\" class=\"data row1 col8\" >0.000597</td>\n",
       "      <td id=\"T_0301f_row1_col9\" class=\"data row1 col9\" >0.000540</td>\n",
       "      <td id=\"T_0301f_row1_col10\" class=\"data row1 col10\" >0.000507</td>\n",
       "      <td id=\"T_0301f_row1_col11\" class=\"data row1 col11\" >0.000572</td>\n",
       "      <td id=\"T_0301f_row1_col12\" class=\"data row1 col12\" >0.000626</td>\n",
       "      <td id=\"T_0301f_row1_col13\" class=\"data row1 col13\" >0.000660</td>\n",
       "      <td id=\"T_0301f_row1_col14\" class=\"data row1 col14\" >0.000690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0301f_level0_row2\" class=\"row_heading level0 row2\" >8.500000</th>\n",
       "      <td id=\"T_0301f_row2_col0\" class=\"data row2 col0\" >0.010951</td>\n",
       "      <td id=\"T_0301f_row2_col1\" class=\"data row2 col1\" >0.005300</td>\n",
       "      <td id=\"T_0301f_row2_col2\" class=\"data row2 col2\" >0.003325</td>\n",
       "      <td id=\"T_0301f_row2_col3\" class=\"data row2 col3\" >0.002183</td>\n",
       "      <td id=\"T_0301f_row2_col4\" class=\"data row2 col4\" >0.001527</td>\n",
       "      <td id=\"T_0301f_row2_col5\" class=\"data row2 col5\" >0.001167</td>\n",
       "      <td id=\"T_0301f_row2_col6\" class=\"data row2 col6\" >0.000842</td>\n",
       "      <td id=\"T_0301f_row2_col7\" class=\"data row2 col7\" >0.000734</td>\n",
       "      <td id=\"T_0301f_row2_col8\" class=\"data row2 col8\" >0.000605</td>\n",
       "      <td id=\"T_0301f_row2_col9\" class=\"data row2 col9\" >0.000539</td>\n",
       "      <td id=\"T_0301f_row2_col10\" class=\"data row2 col10\" >0.000513</td>\n",
       "      <td id=\"T_0301f_row2_col11\" class=\"data row2 col11\" >0.000578</td>\n",
       "      <td id=\"T_0301f_row2_col12\" class=\"data row2 col12\" >0.000619</td>\n",
       "      <td id=\"T_0301f_row2_col13\" class=\"data row2 col13\" >0.000655</td>\n",
       "      <td id=\"T_0301f_row2_col14\" class=\"data row2 col14\" >0.000685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0301f_level0_row3\" class=\"row_heading level0 row3\" >9.000000</th>\n",
       "      <td id=\"T_0301f_row3_col0\" class=\"data row3 col0\" >0.011446</td>\n",
       "      <td id=\"T_0301f_row3_col1\" class=\"data row3 col1\" >0.005553</td>\n",
       "      <td id=\"T_0301f_row3_col2\" class=\"data row3 col2\" >0.003476</td>\n",
       "      <td id=\"T_0301f_row3_col3\" class=\"data row3 col3\" >0.002441</td>\n",
       "      <td id=\"T_0301f_row3_col4\" class=\"data row3 col4\" >0.001739</td>\n",
       "      <td id=\"T_0301f_row3_col5\" class=\"data row3 col5\" >0.001316</td>\n",
       "      <td id=\"T_0301f_row3_col6\" class=\"data row3 col6\" >0.000920</td>\n",
       "      <td id=\"T_0301f_row3_col7\" class=\"data row3 col7\" >0.000817</td>\n",
       "      <td id=\"T_0301f_row3_col8\" class=\"data row3 col8\" >0.000671</td>\n",
       "      <td id=\"T_0301f_row3_col9\" class=\"data row3 col9\" >0.000583</td>\n",
       "      <td id=\"T_0301f_row3_col10\" class=\"data row3 col10\" >0.000537</td>\n",
       "      <td id=\"T_0301f_row3_col11\" class=\"data row3 col11\" >0.000591</td>\n",
       "      <td id=\"T_0301f_row3_col12\" class=\"data row3 col12\" >0.000629</td>\n",
       "      <td id=\"T_0301f_row3_col13\" class=\"data row3 col13\" >0.000667</td>\n",
       "      <td id=\"T_0301f_row3_col14\" class=\"data row3 col14\" >0.000690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0301f_level0_row4\" class=\"row_heading level0 row4\" >9.500000</th>\n",
       "      <td id=\"T_0301f_row4_col0\" class=\"data row4 col0\" >0.011647</td>\n",
       "      <td id=\"T_0301f_row4_col1\" class=\"data row4 col1\" >0.005810</td>\n",
       "      <td id=\"T_0301f_row4_col2\" class=\"data row4 col2\" >0.003634</td>\n",
       "      <td id=\"T_0301f_row4_col3\" class=\"data row4 col3\" >0.002651</td>\n",
       "      <td id=\"T_0301f_row4_col4\" class=\"data row4 col4\" >0.001995</td>\n",
       "      <td id=\"T_0301f_row4_col5\" class=\"data row4 col5\" >0.001498</td>\n",
       "      <td id=\"T_0301f_row4_col6\" class=\"data row4 col6\" >0.001065</td>\n",
       "      <td id=\"T_0301f_row4_col7\" class=\"data row4 col7\" >0.000952</td>\n",
       "      <td id=\"T_0301f_row4_col8\" class=\"data row4 col8\" >0.000752</td>\n",
       "      <td id=\"T_0301f_row4_col9\" class=\"data row4 col9\" >0.000652</td>\n",
       "      <td id=\"T_0301f_row4_col10\" class=\"data row4 col10\" >0.000571</td>\n",
       "      <td id=\"T_0301f_row4_col11\" class=\"data row4 col11\" >0.000617</td>\n",
       "      <td id=\"T_0301f_row4_col12\" class=\"data row4 col12\" >0.000646</td>\n",
       "      <td id=\"T_0301f_row4_col13\" class=\"data row4 col13\" >0.000679</td>\n",
       "      <td id=\"T_0301f_row4_col14\" class=\"data row4 col14\" >0.000702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0301f_level0_row5\" class=\"row_heading level0 row5\" >10.000000</th>\n",
       "      <td id=\"T_0301f_row5_col0\" class=\"data row5 col0\" >0.011650</td>\n",
       "      <td id=\"T_0301f_row5_col1\" class=\"data row5 col1\" >0.005881</td>\n",
       "      <td id=\"T_0301f_row5_col2\" class=\"data row5 col2\" >0.003558</td>\n",
       "      <td id=\"T_0301f_row5_col3\" class=\"data row5 col3\" >0.002584</td>\n",
       "      <td id=\"T_0301f_row5_col4\" class=\"data row5 col4\" >0.001969</td>\n",
       "      <td id=\"T_0301f_row5_col5\" class=\"data row5 col5\" >0.001488</td>\n",
       "      <td id=\"T_0301f_row5_col6\" class=\"data row5 col6\" >0.001095</td>\n",
       "      <td id=\"T_0301f_row5_col7\" class=\"data row5 col7\" >0.000990</td>\n",
       "      <td id=\"T_0301f_row5_col8\" class=\"data row5 col8\" >0.000764</td>\n",
       "      <td id=\"T_0301f_row5_col9\" class=\"data row5 col9\" >0.000651</td>\n",
       "      <td id=\"T_0301f_row5_col10\" class=\"data row5 col10\" >0.000579</td>\n",
       "      <td id=\"T_0301f_row5_col11\" class=\"data row5 col11\" >0.000628</td>\n",
       "      <td id=\"T_0301f_row5_col12\" class=\"data row5 col12\" >0.000659</td>\n",
       "      <td id=\"T_0301f_row5_col13\" class=\"data row5 col13\" >0.000690</td>\n",
       "      <td id=\"T_0301f_row5_col14\" class=\"data row5 col14\" >0.000714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0301f_level0_row6\" class=\"row_heading level0 row6\" >10.500000</th>\n",
       "      <td id=\"T_0301f_row6_col0\" class=\"data row6 col0\" >0.011957</td>\n",
       "      <td id=\"T_0301f_row6_col1\" class=\"data row6 col1\" >0.006034</td>\n",
       "      <td id=\"T_0301f_row6_col2\" class=\"data row6 col2\" >0.003667</td>\n",
       "      <td id=\"T_0301f_row6_col3\" class=\"data row6 col3\" >0.002653</td>\n",
       "      <td id=\"T_0301f_row6_col4\" class=\"data row6 col4\" >0.002095</td>\n",
       "      <td id=\"T_0301f_row6_col5\" class=\"data row6 col5\" >0.001626</td>\n",
       "      <td id=\"T_0301f_row6_col6\" class=\"data row6 col6\" >0.001234</td>\n",
       "      <td id=\"T_0301f_row6_col7\" class=\"data row6 col7\" >0.001102</td>\n",
       "      <td id=\"T_0301f_row6_col8\" class=\"data row6 col8\" >0.000852</td>\n",
       "      <td id=\"T_0301f_row6_col9\" class=\"data row6 col9\" >0.000711</td>\n",
       "      <td id=\"T_0301f_row6_col10\" class=\"data row6 col10\" >0.000641</td>\n",
       "      <td id=\"T_0301f_row6_col11\" class=\"data row6 col11\" >0.000677</td>\n",
       "      <td id=\"T_0301f_row6_col12\" class=\"data row6 col12\" >0.000722</td>\n",
       "      <td id=\"T_0301f_row6_col13\" class=\"data row6 col13\" >0.000732</td>\n",
       "      <td id=\"T_0301f_row6_col14\" class=\"data row6 col14\" >0.000755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0301f_level0_row7\" class=\"row_heading level0 row7\" >11.000000</th>\n",
       "      <td id=\"T_0301f_row7_col0\" class=\"data row7 col0\" >0.012387</td>\n",
       "      <td id=\"T_0301f_row7_col1\" class=\"data row7 col1\" >0.005993</td>\n",
       "      <td id=\"T_0301f_row7_col2\" class=\"data row7 col2\" >0.003625</td>\n",
       "      <td id=\"T_0301f_row7_col3\" class=\"data row7 col3\" >0.002611</td>\n",
       "      <td id=\"T_0301f_row7_col4\" class=\"data row7 col4\" >0.002040</td>\n",
       "      <td id=\"T_0301f_row7_col5\" class=\"data row7 col5\" >0.001558</td>\n",
       "      <td id=\"T_0301f_row7_col6\" class=\"data row7 col6\" >0.001199</td>\n",
       "      <td id=\"T_0301f_row7_col7\" class=\"data row7 col7\" >0.001079</td>\n",
       "      <td id=\"T_0301f_row7_col8\" class=\"data row7 col8\" >0.000849</td>\n",
       "      <td id=\"T_0301f_row7_col9\" class=\"data row7 col9\" >0.000715</td>\n",
       "      <td id=\"T_0301f_row7_col10\" class=\"data row7 col10\" >0.000664</td>\n",
       "      <td id=\"T_0301f_row7_col11\" class=\"data row7 col11\" >0.000695</td>\n",
       "      <td id=\"T_0301f_row7_col12\" class=\"data row7 col12\" >0.000759</td>\n",
       "      <td id=\"T_0301f_row7_col13\" class=\"data row7 col13\" >0.000761</td>\n",
       "      <td id=\"T_0301f_row7_col14\" class=\"data row7 col14\" >0.000787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0301f_level0_row8\" class=\"row_heading level0 row8\" >11.500000</th>\n",
       "      <td id=\"T_0301f_row8_col0\" class=\"data row8 col0\" >0.013275</td>\n",
       "      <td id=\"T_0301f_row8_col1\" class=\"data row8 col1\" >0.006133</td>\n",
       "      <td id=\"T_0301f_row8_col2\" class=\"data row8 col2\" >0.003738</td>\n",
       "      <td id=\"T_0301f_row8_col3\" class=\"data row8 col3\" >0.002741</td>\n",
       "      <td id=\"T_0301f_row8_col4\" class=\"data row8 col4\" >0.002137</td>\n",
       "      <td id=\"T_0301f_row8_col5\" class=\"data row8 col5\" >0.001665</td>\n",
       "      <td id=\"T_0301f_row8_col6\" class=\"data row8 col6\" >0.001311</td>\n",
       "      <td id=\"T_0301f_row8_col7\" class=\"data row8 col7\" >0.001181</td>\n",
       "      <td id=\"T_0301f_row8_col8\" class=\"data row8 col8\" >0.000919</td>\n",
       "      <td id=\"T_0301f_row8_col9\" class=\"data row8 col9\" >0.000790</td>\n",
       "      <td id=\"T_0301f_row8_col10\" class=\"data row8 col10\" >0.000729</td>\n",
       "      <td id=\"T_0301f_row8_col11\" class=\"data row8 col11\" >0.000756</td>\n",
       "      <td id=\"T_0301f_row8_col12\" class=\"data row8 col12\" >0.000815</td>\n",
       "      <td id=\"T_0301f_row8_col13\" class=\"data row8 col13\" >0.000817</td>\n",
       "      <td id=\"T_0301f_row8_col14\" class=\"data row8 col14\" >0.000840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0301f_level0_row9\" class=\"row_heading level0 row9\" >12.000000</th>\n",
       "      <td id=\"T_0301f_row9_col0\" class=\"data row9 col0\" >0.014303</td>\n",
       "      <td id=\"T_0301f_row9_col1\" class=\"data row9 col1\" >0.006224</td>\n",
       "      <td id=\"T_0301f_row9_col2\" class=\"data row9 col2\" >0.003688</td>\n",
       "      <td id=\"T_0301f_row9_col3\" class=\"data row9 col3\" >0.002685</td>\n",
       "      <td id=\"T_0301f_row9_col4\" class=\"data row9 col4\" >0.002065</td>\n",
       "      <td id=\"T_0301f_row9_col5\" class=\"data row9 col5\" >0.001664</td>\n",
       "      <td id=\"T_0301f_row9_col6\" class=\"data row9 col6\" >0.001329</td>\n",
       "      <td id=\"T_0301f_row9_col7\" class=\"data row9 col7\" >0.001188</td>\n",
       "      <td id=\"T_0301f_row9_col8\" class=\"data row9 col8\" >0.000911</td>\n",
       "      <td id=\"T_0301f_row9_col9\" class=\"data row9 col9\" >0.000799</td>\n",
       "      <td id=\"T_0301f_row9_col10\" class=\"data row9 col10\" >0.000741</td>\n",
       "      <td id=\"T_0301f_row9_col11\" class=\"data row9 col11\" >0.000779</td>\n",
       "      <td id=\"T_0301f_row9_col12\" class=\"data row9 col12\" >0.000843</td>\n",
       "      <td id=\"T_0301f_row9_col13\" class=\"data row9 col13\" >0.000850</td>\n",
       "      <td id=\"T_0301f_row9_col14\" class=\"data row9 col14\" >0.000872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0301f_level0_row10\" class=\"row_heading level0 row10\" >12.500000</th>\n",
       "      <td id=\"T_0301f_row10_col0\" class=\"data row10 col0\" >0.015098</td>\n",
       "      <td id=\"T_0301f_row10_col1\" class=\"data row10 col1\" >0.006340</td>\n",
       "      <td id=\"T_0301f_row10_col2\" class=\"data row10 col2\" >0.003643</td>\n",
       "      <td id=\"T_0301f_row10_col3\" class=\"data row10 col3\" >0.002578</td>\n",
       "      <td id=\"T_0301f_row10_col4\" class=\"data row10 col4\" >0.001982</td>\n",
       "      <td id=\"T_0301f_row10_col5\" class=\"data row10 col5\" >0.001643</td>\n",
       "      <td id=\"T_0301f_row10_col6\" class=\"data row10 col6\" >0.001315</td>\n",
       "      <td id=\"T_0301f_row10_col7\" class=\"data row10 col7\" >0.001160</td>\n",
       "      <td id=\"T_0301f_row10_col8\" class=\"data row10 col8\" >0.000883</td>\n",
       "      <td id=\"T_0301f_row10_col9\" class=\"data row10 col9\" >0.000795</td>\n",
       "      <td id=\"T_0301f_row10_col10\" class=\"data row10 col10\" >0.000748</td>\n",
       "      <td id=\"T_0301f_row10_col11\" class=\"data row10 col11\" >0.000797</td>\n",
       "      <td id=\"T_0301f_row10_col12\" class=\"data row10 col12\" >0.000858</td>\n",
       "      <td id=\"T_0301f_row10_col13\" class=\"data row10 col13\" >0.000870</td>\n",
       "      <td id=\"T_0301f_row10_col14\" class=\"data row10 col14\" >0.000894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0301f_level0_row11\" class=\"row_heading level0 row11\" >13.000000</th>\n",
       "      <td id=\"T_0301f_row11_col0\" class=\"data row11 col0\" >0.015359</td>\n",
       "      <td id=\"T_0301f_row11_col1\" class=\"data row11 col1\" >0.006512</td>\n",
       "      <td id=\"T_0301f_row11_col2\" class=\"data row11 col2\" >0.003799</td>\n",
       "      <td id=\"T_0301f_row11_col3\" class=\"data row11 col3\" >0.002636</td>\n",
       "      <td id=\"T_0301f_row11_col4\" class=\"data row11 col4\" >0.002086</td>\n",
       "      <td id=\"T_0301f_row11_col5\" class=\"data row11 col5\" >0.001747</td>\n",
       "      <td id=\"T_0301f_row11_col6\" class=\"data row11 col6\" >0.001390</td>\n",
       "      <td id=\"T_0301f_row11_col7\" class=\"data row11 col7\" >0.001222</td>\n",
       "      <td id=\"T_0301f_row11_col8\" class=\"data row11 col8\" >0.000922</td>\n",
       "      <td id=\"T_0301f_row11_col9\" class=\"data row11 col9\" >0.000835</td>\n",
       "      <td id=\"T_0301f_row11_col10\" class=\"data row11 col10\" >0.000784</td>\n",
       "      <td id=\"T_0301f_row11_col11\" class=\"data row11 col11\" >0.000830</td>\n",
       "      <td id=\"T_0301f_row11_col12\" class=\"data row11 col12\" >0.000878</td>\n",
       "      <td id=\"T_0301f_row11_col13\" class=\"data row11 col13\" >0.000887</td>\n",
       "      <td id=\"T_0301f_row11_col14\" class=\"data row11 col14\" >0.000909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0301f_level0_row12\" class=\"row_heading level0 row12\" >13.500000</th>\n",
       "      <td id=\"T_0301f_row12_col0\" class=\"data row12 col0\" >0.015365</td>\n",
       "      <td id=\"T_0301f_row12_col1\" class=\"data row12 col1\" >0.006613</td>\n",
       "      <td id=\"T_0301f_row12_col2\" class=\"data row12 col2\" >0.003927</td>\n",
       "      <td id=\"T_0301f_row12_col3\" class=\"data row12 col3\" >0.002631</td>\n",
       "      <td id=\"T_0301f_row12_col4\" class=\"data row12 col4\" >0.002125</td>\n",
       "      <td id=\"T_0301f_row12_col5\" class=\"data row12 col5\" >0.001773</td>\n",
       "      <td id=\"T_0301f_row12_col6\" class=\"data row12 col6\" >0.001381</td>\n",
       "      <td id=\"T_0301f_row12_col7\" class=\"data row12 col7\" >0.001217</td>\n",
       "      <td id=\"T_0301f_row12_col8\" class=\"data row12 col8\" >0.000915</td>\n",
       "      <td id=\"T_0301f_row12_col9\" class=\"data row12 col9\" >0.000834</td>\n",
       "      <td id=\"T_0301f_row12_col10\" class=\"data row12 col10\" >0.000787</td>\n",
       "      <td id=\"T_0301f_row12_col11\" class=\"data row12 col11\" >0.000839</td>\n",
       "      <td id=\"T_0301f_row12_col12\" class=\"data row12 col12\" >0.000877</td>\n",
       "      <td id=\"T_0301f_row12_col13\" class=\"data row12 col13\" >0.000882</td>\n",
       "      <td id=\"T_0301f_row12_col14\" class=\"data row12 col14\" >0.000911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0301f_level0_row13\" class=\"row_heading level0 row13\" >14.000000</th>\n",
       "      <td id=\"T_0301f_row13_col0\" class=\"data row13 col0\" >0.014872</td>\n",
       "      <td id=\"T_0301f_row13_col1\" class=\"data row13 col1\" >0.006688</td>\n",
       "      <td id=\"T_0301f_row13_col2\" class=\"data row13 col2\" >0.004131</td>\n",
       "      <td id=\"T_0301f_row13_col3\" class=\"data row13 col3\" >0.002677</td>\n",
       "      <td id=\"T_0301f_row13_col4\" class=\"data row13 col4\" >0.002202</td>\n",
       "      <td id=\"T_0301f_row13_col5\" class=\"data row13 col5\" >0.001815</td>\n",
       "      <td id=\"T_0301f_row13_col6\" class=\"data row13 col6\" >0.001392</td>\n",
       "      <td id=\"T_0301f_row13_col7\" class=\"data row13 col7\" >0.001246</td>\n",
       "      <td id=\"T_0301f_row13_col8\" class=\"data row13 col8\" >0.000947</td>\n",
       "      <td id=\"T_0301f_row13_col9\" class=\"data row13 col9\" >0.000848</td>\n",
       "      <td id=\"T_0301f_row13_col10\" class=\"data row13 col10\" >0.000794</td>\n",
       "      <td id=\"T_0301f_row13_col11\" class=\"data row13 col11\" >0.000835</td>\n",
       "      <td id=\"T_0301f_row13_col12\" class=\"data row13 col12\" >0.000864</td>\n",
       "      <td id=\"T_0301f_row13_col13\" class=\"data row13 col13\" >0.000863</td>\n",
       "      <td id=\"T_0301f_row13_col14\" class=\"data row13 col14\" >0.000897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0301f_level0_row14\" class=\"row_heading level0 row14\" >14.500000</th>\n",
       "      <td id=\"T_0301f_row14_col0\" class=\"data row14 col0\" >0.014036</td>\n",
       "      <td id=\"T_0301f_row14_col1\" class=\"data row14 col1\" >0.006767</td>\n",
       "      <td id=\"T_0301f_row14_col2\" class=\"data row14 col2\" >0.004350</td>\n",
       "      <td id=\"T_0301f_row14_col3\" class=\"data row14 col3\" >0.002812</td>\n",
       "      <td id=\"T_0301f_row14_col4\" class=\"data row14 col4\" >0.002335</td>\n",
       "      <td id=\"T_0301f_row14_col5\" class=\"data row14 col5\" >0.001896</td>\n",
       "      <td id=\"T_0301f_row14_col6\" class=\"data row14 col6\" >0.001440</td>\n",
       "      <td id=\"T_0301f_row14_col7\" class=\"data row14 col7\" >0.001305</td>\n",
       "      <td id=\"T_0301f_row14_col8\" class=\"data row14 col8\" >0.001015</td>\n",
       "      <td id=\"T_0301f_row14_col9\" class=\"data row14 col9\" >0.000887</td>\n",
       "      <td id=\"T_0301f_row14_col10\" class=\"data row14 col10\" >0.000820</td>\n",
       "      <td id=\"T_0301f_row14_col11\" class=\"data row14 col11\" >0.000835</td>\n",
       "      <td id=\"T_0301f_row14_col12\" class=\"data row14 col12\" >0.000850</td>\n",
       "      <td id=\"T_0301f_row14_col13\" class=\"data row14 col13\" >0.000843</td>\n",
       "      <td id=\"T_0301f_row14_col14\" class=\"data row14 col14\" >0.000872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0301f_level0_row15\" class=\"row_heading level0 row15\" >15.000000</th>\n",
       "      <td id=\"T_0301f_row15_col0\" class=\"data row15 col0\" >0.013263</td>\n",
       "      <td id=\"T_0301f_row15_col1\" class=\"data row15 col1\" >0.006705</td>\n",
       "      <td id=\"T_0301f_row15_col2\" class=\"data row15 col2\" >0.004417</td>\n",
       "      <td id=\"T_0301f_row15_col3\" class=\"data row15 col3\" >0.002979</td>\n",
       "      <td id=\"T_0301f_row15_col4\" class=\"data row15 col4\" >0.002506</td>\n",
       "      <td id=\"T_0301f_row15_col5\" class=\"data row15 col5\" >0.002075</td>\n",
       "      <td id=\"T_0301f_row15_col6\" class=\"data row15 col6\" >0.001588</td>\n",
       "      <td id=\"T_0301f_row15_col7\" class=\"data row15 col7\" >0.001439</td>\n",
       "      <td id=\"T_0301f_row15_col8\" class=\"data row15 col8\" >0.001151</td>\n",
       "      <td id=\"T_0301f_row15_col9\" class=\"data row15 col9\" >0.001028</td>\n",
       "      <td id=\"T_0301f_row15_col10\" class=\"data row15 col10\" >0.000941</td>\n",
       "      <td id=\"T_0301f_row15_col11\" class=\"data row15 col11\" >0.000919</td>\n",
       "      <td id=\"T_0301f_row15_col12\" class=\"data row15 col12\" >0.000908</td>\n",
       "      <td id=\"T_0301f_row15_col13\" class=\"data row15 col13\" >0.000890</td>\n",
       "      <td id=\"T_0301f_row15_col14\" class=\"data row15 col14\" >0.000896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0301f_level0_row16\" class=\"row_heading level0 row16\" >15.500000</th>\n",
       "      <td id=\"T_0301f_row16_col0\" class=\"data row16 col0\" >0.012310</td>\n",
       "      <td id=\"T_0301f_row16_col1\" class=\"data row16 col1\" >0.006489</td>\n",
       "      <td id=\"T_0301f_row16_col2\" class=\"data row16 col2\" >0.004370</td>\n",
       "      <td id=\"T_0301f_row16_col3\" class=\"data row16 col3\" >0.003107</td>\n",
       "      <td id=\"T_0301f_row16_col4\" class=\"data row16 col4\" >0.002609</td>\n",
       "      <td id=\"T_0301f_row16_col5\" class=\"data row16 col5\" >0.002155</td>\n",
       "      <td id=\"T_0301f_row16_col6\" class=\"data row16 col6\" >0.001656</td>\n",
       "      <td id=\"T_0301f_row16_col7\" class=\"data row16 col7\" >0.001494</td>\n",
       "      <td id=\"T_0301f_row16_col8\" class=\"data row16 col8\" >0.001208</td>\n",
       "      <td id=\"T_0301f_row16_col9\" class=\"data row16 col9\" >0.001080</td>\n",
       "      <td id=\"T_0301f_row16_col10\" class=\"data row16 col10\" >0.000979</td>\n",
       "      <td id=\"T_0301f_row16_col11\" class=\"data row16 col11\" >0.000942</td>\n",
       "      <td id=\"T_0301f_row16_col12\" class=\"data row16 col12\" >0.000919</td>\n",
       "      <td id=\"T_0301f_row16_col13\" class=\"data row16 col13\" >0.000897</td>\n",
       "      <td id=\"T_0301f_row16_col14\" class=\"data row16 col14\" >0.000892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0301f_level0_row17\" class=\"row_heading level0 row17\" >16.000000</th>\n",
       "      <td id=\"T_0301f_row17_col0\" class=\"data row17 col0\" >0.011497</td>\n",
       "      <td id=\"T_0301f_row17_col1\" class=\"data row17 col1\" >0.006146</td>\n",
       "      <td id=\"T_0301f_row17_col2\" class=\"data row17 col2\" >0.004207</td>\n",
       "      <td id=\"T_0301f_row17_col3\" class=\"data row17 col3\" >0.003124</td>\n",
       "      <td id=\"T_0301f_row17_col4\" class=\"data row17 col4\" >0.002595</td>\n",
       "      <td id=\"T_0301f_row17_col5\" class=\"data row17 col5\" >0.002134</td>\n",
       "      <td id=\"T_0301f_row17_col6\" class=\"data row17 col6\" >0.001670</td>\n",
       "      <td id=\"T_0301f_row17_col7\" class=\"data row17 col7\" >0.001498</td>\n",
       "      <td id=\"T_0301f_row17_col8\" class=\"data row17 col8\" >0.001204</td>\n",
       "      <td id=\"T_0301f_row17_col9\" class=\"data row17 col9\" >0.001061</td>\n",
       "      <td id=\"T_0301f_row17_col10\" class=\"data row17 col10\" >0.000955</td>\n",
       "      <td id=\"T_0301f_row17_col11\" class=\"data row17 col11\" >0.000920</td>\n",
       "      <td id=\"T_0301f_row17_col12\" class=\"data row17 col12\" >0.000903</td>\n",
       "      <td id=\"T_0301f_row17_col13\" class=\"data row17 col13\" >0.000883</td>\n",
       "      <td id=\"T_0301f_row17_col14\" class=\"data row17 col14\" >0.000874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0301f_level0_row18\" class=\"row_heading level0 row18\" >16.500000</th>\n",
       "      <td id=\"T_0301f_row18_col0\" class=\"data row18 col0\" >0.010967</td>\n",
       "      <td id=\"T_0301f_row18_col1\" class=\"data row18 col1\" >0.005851</td>\n",
       "      <td id=\"T_0301f_row18_col2\" class=\"data row18 col2\" >0.004022</td>\n",
       "      <td id=\"T_0301f_row18_col3\" class=\"data row18 col3\" >0.003054</td>\n",
       "      <td id=\"T_0301f_row18_col4\" class=\"data row18 col4\" >0.002491</td>\n",
       "      <td id=\"T_0301f_row18_col5\" class=\"data row18 col5\" >0.002039</td>\n",
       "      <td id=\"T_0301f_row18_col6\" class=\"data row18 col6\" >0.001617</td>\n",
       "      <td id=\"T_0301f_row18_col7\" class=\"data row18 col7\" >0.001446</td>\n",
       "      <td id=\"T_0301f_row18_col8\" class=\"data row18 col8\" >0.001167</td>\n",
       "      <td id=\"T_0301f_row18_col9\" class=\"data row18 col9\" >0.001011</td>\n",
       "      <td id=\"T_0301f_row18_col10\" class=\"data row18 col10\" >0.000891</td>\n",
       "      <td id=\"T_0301f_row18_col11\" class=\"data row18 col11\" >0.000859</td>\n",
       "      <td id=\"T_0301f_row18_col12\" class=\"data row18 col12\" >0.000848</td>\n",
       "      <td id=\"T_0301f_row18_col13\" class=\"data row18 col13\" >0.000839</td>\n",
       "      <td id=\"T_0301f_row18_col14\" class=\"data row18 col14\" >0.000837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0301f_level0_row19\" class=\"row_heading level0 row19\" >17.000000</th>\n",
       "      <td id=\"T_0301f_row19_col0\" class=\"data row19 col0\" >0.010610</td>\n",
       "      <td id=\"T_0301f_row19_col1\" class=\"data row19 col1\" >0.005825</td>\n",
       "      <td id=\"T_0301f_row19_col2\" class=\"data row19 col2\" >0.004052</td>\n",
       "      <td id=\"T_0301f_row19_col3\" class=\"data row19 col3\" >0.003148</td>\n",
       "      <td id=\"T_0301f_row19_col4\" class=\"data row19 col4\" >0.002591</td>\n",
       "      <td id=\"T_0301f_row19_col5\" class=\"data row19 col5\" >0.002145</td>\n",
       "      <td id=\"T_0301f_row19_col6\" class=\"data row19 col6\" >0.001735</td>\n",
       "      <td id=\"T_0301f_row19_col7\" class=\"data row19 col7\" >0.001562</td>\n",
       "      <td id=\"T_0301f_row19_col8\" class=\"data row19 col8\" >0.001295</td>\n",
       "      <td id=\"T_0301f_row19_col9\" class=\"data row19 col9\" >0.001158</td>\n",
       "      <td id=\"T_0301f_row19_col10\" class=\"data row19 col10\" >0.001001</td>\n",
       "      <td id=\"T_0301f_row19_col11\" class=\"data row19 col11\" >0.000969</td>\n",
       "      <td id=\"T_0301f_row19_col12\" class=\"data row19 col12\" >0.000946</td>\n",
       "      <td id=\"T_0301f_row19_col13\" class=\"data row19 col13\" >0.000938</td>\n",
       "      <td id=\"T_0301f_row19_col14\" class=\"data row19 col14\" >0.000925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1e9844b5410>"
      ]
     },
     "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."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}