ModesIF.ipynb

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   "source": [
    "### Modes of Information Flow analysis code\n",
    "\n",
    "In the first part I upgrade dit package for Python. This package has many functions to compute measures for discrete information theory problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
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   "outputs": [
   ],
   "source": [
    "# Install the upgrade from git\n",
    "#!pip install --user --upgrade git+git://github.com/dit/dit.git#egg=dit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "**Step 1**\n",
    "\n",
    "To work with this code correctly we need to import all the packages needed in Python. We also load the dataset which contains time series constructed as the average of the emotions expressed on Twitter in time slots of five minutes during the 24 hours after the earthquake, therefore, every time series has 288 values. Because we are using a package for discrete information theory (dit) the time series where dichotomized, the criteria used was 1 was assigned for values greater than the median, and 0 otherwise for each time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
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   "outputs": [
   ],
   "source": [
    "# Import the packages numpy, matplotlib, dit, collections, and pandas needed to run the code\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from collections import namedtuple\n",
    "from itertools import permutations\n",
    "\n",
    "import dit\n",
    "from dit.inference import binned, dist_from_timeseries\n",
    "from dit.multivariate import total_correlation as I, intrinsic_total_correlation as IMI\n",
    "\n",
    "dit.ditParams['repr.print'] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sadness</th>\n",
       "      <th>anger</th>\n",
       "      <th>fear</th>\n",
       "      <th>disgust</th>\n",
       "      <th>joy</th>\n",
       "      <th>sadness_dich</th>\n",
       "      <th>anger_dich</th>\n",
       "      <th>fear_dich</th>\n",
       "      <th>disgust_dich</th>\n",
       "      <th>joy_dich</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.162649</td>\n",
       "      <td>0.137207</td>\n",
       "      <td>0.065900</td>\n",
       "      <td>0.014342</td>\n",
       "      <td>0.178080</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.162913</td>\n",
       "      <td>0.061809</td>\n",
       "      <td>0.017641</td>\n",
       "      <td>0.145686</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.134369</td>\n",
       "      <td>0.131008</td>\n",
       "      <td>0.057596</td>\n",
       "      <td>0.027222</td>\n",
       "      <td>0.177402</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.185977</td>\n",
       "      <td>0.068172</td>\n",
       "      <td>0.045904</td>\n",
       "      <td>0.018430</td>\n",
       "      <td>0.205039</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.177835</td>\n",
       "      <td>0.042975</td>\n",
       "      <td>0.031071</td>\n",
       "      <td>0.039012</td>\n",
       "      <td>0.226750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>0.132210</td>\n",
       "      <td>0.045497</td>\n",
       "      <td>0.098556</td>\n",
       "      <td>0.030217</td>\n",
       "      <td>0.182622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>0.182335</td>\n",
       "      <td>0.101294</td>\n",
       "      <td>0.124704</td>\n",
       "      <td>0.031984</td>\n",
       "      <td>0.111595</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>0.180883</td>\n",
       "      <td>0.061389</td>\n",
       "      <td>0.067752</td>\n",
       "      <td>0.044968</td>\n",
       "      <td>0.119976</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>0.129512</td>\n",
       "      <td>0.043418</td>\n",
       "      <td>0.087818</td>\n",
       "      <td>0.033160</td>\n",
       "      <td>0.161766</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>0.187634</td>\n",
       "      <td>0.081746</td>\n",
       "      <td>0.017056</td>\n",
       "      <td>0.021080</td>\n",
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       "      <td>1</td>\n",
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       "<p>288 rows × 10 columns</p>\n",
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       "      sadness     anger      fear   disgust       joy  sadness_dich  \\\n",
       "0    0.162649  0.137207  0.065900  0.014342  0.178080             1   \n",
       "1    0.154200  0.162913  0.061809  0.017641  0.145686             0   \n",
       "2    0.134369  0.131008  0.057596  0.027222  0.177402             0   \n",
       "3    0.185977  0.068172  0.045904  0.018430  0.205039             1   \n",
       "4    0.177835  0.042975  0.031071  0.039012  0.226750             1   \n",
       "..        ...       ...       ...       ...       ...           ...   \n",
       "283  0.132210  0.045497  0.098556  0.030217  0.182622             0   \n",
       "284  0.182335  0.101294  0.124704  0.031984  0.111595             1   \n",
       "285  0.180883  0.061389  0.067752  0.044968  0.119976             1   \n",
       "286  0.129512  0.043418  0.087818  0.033160  0.161766             0   \n",
       "287  0.187634  0.081746  0.017056  0.021080  0.114416             1   \n",
       "\n",
       "     anger_dich  fear_dich  disgust_dich  joy_dich  \n",
       "0             1          1             0         1  \n",
       "1             1          0             0         1  \n",
       "2             1          0             0         1  \n",
       "3             1          0             0         1  \n",
       "4             0          0             1         1  \n",
       "..          ...        ...           ...       ...  \n",
       "283           0          1             0         1  \n",
       "284           1          1             0         0  \n",
       "285           1          1             1         0  \n",
       "286           0          1             0         1  \n",
       "287           1          0             0         0  \n",
       "\n",
       "[288 rows x 10 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {
     },
     "output_type": "execute_result"
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   ],
   "source": [
    "#Import the dataset\n",
    "dataset = pd.read_csv('earthquakedata_dich.csv', encoding='utf-8')\n",
    "\n",
    "#Print dataset\n",
    "dataset"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "**Step 2**\n",
    "\n",
    "The package dit has defined functions to compute the intrinsic mutual information (IMI), the time-delayed mutual information, and the transfer entropy. Shared and synergistic flows are defined using algebra and the previous measures."
   ]
  },
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   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
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   ],
   "source": [
    "#Definition of the tuple to save the Modes of Information Flow\n",
    "Flows = namedtuple('Flows', ['intrinsic', 'shared', 'synergistic'])\n",
    "\n",
    "#Function to compute the Modes of Information Flow\n",
    "def get_flows(ts, length):\n",
    "\n",
    "    d = dist_from_timeseries(ts, history_length=length)\n",
    "\n",
    "    i_past, j_past, i_pres, j_pres = [0], [1], [2], [3]\n",
    "\n",
    "    intrinsic = IMI(d, [i_past, j_pres], j_past)\n",
    "    shared = I(d, [i_past, j_pres]) - intrinsic\n",
    "    synergistic = I(d, [i_past, j_pres], j_past) - intrinsic\n",
    "\n",
    "    return Flows(intrinsic, shared, synergistic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(288, 5)"
      ]
     },
     "execution_count": 60,
     "metadata": {
     },
     "output_type": "execute_result"
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   ],
   "source": [
    "#Creation of numpy array to compute flows of information\n",
    "emotions = dataset[['sadness_dich','anger_dich','fear_dich','disgust_dich', 'joy_dich']].to_numpy()\n",
    "\n",
    "#Checking shape of new dataset\n",
    "np.shape(emotions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "#Creation of the list of all possible permutations to compute all pairwise information flows for the five emotions\n",
    "# 0=sadness, 1=anger, 2=fear, 3=disgust, 4=joy\n",
    "a = [0, 1, 2, 3, 4]\n",
    "perms = list(permutations(a,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "#Computation of all flows of information\n",
    "intrinsic_calc = []\n",
    "shared_calc = []\n",
    "synergistic_calc = []\n",
    "\n",
    "#For cycle for all the pairwise information flows\n",
    "for pair in perms:\n",
    "    timeseries = emotions[:,pair]\n",
    "    flows = get_flows(timeseries, 1)\n",
    "    intrinsic_calc.append(flows[0])\n",
    "    shared_calc.append(flows[1])\n",
    "    synergistic_calc.append(flows[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "**Step 3**\n",
    "\n",
    "Here we calculate all the pairwise modes of information flow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
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   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Pairs</th>\n",
       "      <th>Intrinsic</th>\n",
       "      <th>Shared</th>\n",
       "      <th>Synergistic</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>0.000219</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.933463e-04</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(0, 2)</td>\n",
       "      <td>0.032841</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>1.542320e-02</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(0, 3)</td>\n",
       "      <td>0.001481</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.657495e-04</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(0, 4)</td>\n",
       "      <td>0.006392</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.478007e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(1, 0)</td>\n",
       "      <td>0.000219</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.370048e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(1, 2)</td>\n",
       "      <td>0.003839</td>\n",
       "      <td>4.591370e-03</td>\n",
       "      <td>-1.110223e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(1, 3)</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>5.636770e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(1, 4)</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>-2.220446e-16</td>\n",
       "      <td>6.649098e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(2, 0)</td>\n",
       "      <td>0.013359</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.471128e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(2, 1)</td>\n",
       "      <td>0.006351</td>\n",
       "      <td>3.209198e-03</td>\n",
       "      <td>-1.110223e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(2, 3)</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>1.113887e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(2, 4)</td>\n",
       "      <td>0.018255</td>\n",
       "      <td>8.398211e-03</td>\n",
       "      <td>1.261866e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(3, 0)</td>\n",
       "      <td>0.013287</td>\n",
       "      <td>4.523547e-03</td>\n",
       "      <td>-1.110223e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(3, 1)</td>\n",
       "      <td>0.000710</td>\n",
       "      <td>-2.220446e-16</td>\n",
       "      <td>9.382345e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(3, 2)</td>\n",
       "      <td>0.006851</td>\n",
       "      <td>1.579307e-03</td>\n",
       "      <td>1.235155e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(3, 4)</td>\n",
       "      <td>0.005482</td>\n",
       "      <td>-4.440892e-16</td>\n",
       "      <td>2.654533e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(4, 0)</td>\n",
       "      <td>0.017811</td>\n",
       "      <td>-1.249001e-16</td>\n",
       "      <td>3.519902e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(4, 1)</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>-3.203428e-16</td>\n",
       "      <td>4.651767e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(4, 2)</td>\n",
       "      <td>0.014013</td>\n",
       "      <td>8.884119e-03</td>\n",
       "      <td>5.634006e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(4, 3)</td>\n",
       "      <td>0.010753</td>\n",
       "      <td>-2.220446e-16</td>\n",
       "      <td>5.687911e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "     Pairs  Intrinsic        Shared   Synergistic\n",
       "0   (0, 1)   0.000219  0.000000e+00  5.933463e-04\n",
       "1   (0, 2)   0.032841  2.220446e-16  1.542320e-02\n",
       "2   (0, 3)   0.001481  0.000000e+00  8.657495e-04\n",
       "3   (0, 4)   0.006392  0.000000e+00  4.478007e-03\n",
       "4   (1, 0)   0.000219  0.000000e+00  7.370048e-04\n",
       "5   (1, 2)   0.003839  4.591370e-03 -1.110223e-16\n",
       "6   (1, 3)   0.000079  2.220446e-16  5.636770e-03\n",
       "7   (1, 4)   0.000009 -2.220446e-16  6.649098e-04\n",
       "8   (2, 0)   0.013359  0.000000e+00  2.471128e-02\n",
       "9   (2, 1)   0.006351  3.209198e-03 -1.110223e-16\n",
       "10  (2, 3)   0.000009  2.220446e-16  1.113887e-02\n",
       "11  (2, 4)   0.018255  8.398211e-03  1.261866e-04\n",
       "12  (3, 0)   0.013287  4.523547e-03 -1.110223e-16\n",
       "13  (3, 1)   0.000710 -2.220446e-16  9.382345e-05\n",
       "14  (3, 2)   0.006851  1.579307e-03  1.235155e-03\n",
       "15  (3, 4)   0.005482 -4.440892e-16  2.654533e-03\n",
       "16  (4, 0)   0.017811 -1.249001e-16  3.519902e-02\n",
       "17  (4, 1)   0.000009 -3.203428e-16  4.651767e-04\n",
       "18  (4, 2)   0.014013  8.884119e-03  5.634006e-04\n",
       "19  (4, 3)   0.010753 -2.220446e-16  5.687911e-03"
      ]
     },
     "execution_count": 63,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculation of Modes of Information Flow\n",
    "df = pd.DataFrame({'Pairs':perms, 'Intrinsic':intrinsic_calc, 'Shared':shared_calc, 'Synergistic':synergistic_calc})\n",
    "\n",
    "#If you want to save the results\n",
    "#df.to_csv('results.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "**Step 4**\n",
    "\n",
    "Finally to have a visual perspective we plot the modes of information flow for the five emotions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "#Extraction of Sadness data\n",
    "x_int_sadness = list(df['Intrinsic'][0:4])\n",
    "y_int_sadness = [df['Intrinsic'][4],df['Intrinsic'][8],df['Intrinsic'][12],df['Intrinsic'][16]]\n",
    "x_shared_sadness = list(df['Shared'][0:4])\n",
    "y_shared_sadness = [df['Shared'][4],df['Shared'][8],df['Shared'][12],df['Shared'][16]]\n",
    "x_synergistic_sadness = list(df['Synergistic'][0:4])\n",
    "y_synergistic_sadness = [df['Synergistic'][4],df['Synergistic'][8],df['Synergistic'][12],df['Synergistic'][16]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "execution_count": 65,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Plots of Sadness\n",
    "\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "#plt.figure(figsize=(10,5))\n",
    "\n",
    "n = ['A','F','D','J']\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharex = True, sharey = True, figsize=(12,5))\n",
    "\n",
    "fig.text(0.5, 0.04, r'$Sadness \\rightarrow *$', ha='center', va='center', fontsize=20)\n",
    "fig.text(0.06, 0.5, r'$* \\rightarrow Sadness$', ha='center', va='center', rotation='vertical', fontsize=20)\n",
    "\n",
    "ax1.title.set_text('Intrinsic')\n",
    "ax1.title.set_color('red')\n",
    "ax1.title.set_fontsize(20)\n",
    "ax1.scatter(x_int_sadness, y_int_sadness, color='red')\n",
    "ax1.axes.xaxis.set_ticklabels([])\n",
    "ax1.axes.yaxis.set_ticklabels([])\n",
    "for i, txt in enumerate(n):\n",
    "    ax1.annotate(txt, (x_int_sadness[i], y_int_sadness[i]))\n",
    "\n",
    "ax2.title.set_text('Shared')\n",
    "ax2.title.set_color('green')\n",
    "ax2.title.set_fontsize(20)\n",
    "ax2.scatter(x_shared_sadness, y_shared_sadness, color='green')\n",
    "for i, txt in enumerate(n):\n",
    "    ax2.annotate(txt, (x_shared_sadness[i], y_shared_sadness[i]))\n",
    "\n",
    "ax3.title.set_text('Synergistic')\n",
    "ax3.title.set_color('blue')\n",
    "ax3.title.set_fontsize(20)\n",
    "ax3.scatter(x_synergistic_sadness, y_synergistic_sadness, color='blue')\n",
    "for i, txt in enumerate(n):\n",
    "    ax3.annotate(txt, (x_synergistic_sadness[i], y_synergistic_sadness[i]))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "#Extraction of Anger data\n",
    "x_int_anger = list(df['Intrinsic'][4:8])\n",
    "y_int_anger = [df['Intrinsic'][0],df['Intrinsic'][9],df['Intrinsic'][13],df['Intrinsic'][17]]\n",
    "x_shared_anger = list(df['Shared'][4:8])\n",
    "y_shared_anger = [df['Shared'][0],df['Shared'][9],df['Shared'][13],df['Shared'][17]]\n",
    "x_synergistic_anger = list(df['Synergistic'][4:8])\n",
    "y_synergistic_anger = [df['Synergistic'][0],df['Synergistic'][9],df['Synergistic'][13],df['Synergistic'][17]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "execution_count": 67,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Plots of Anger\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "\n",
    "n = ['S','F','D','J']\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharex = True, sharey = True, figsize=(12,5))\n",
    "\n",
    "fig.text(0.5, 0.04, r'$Anger \\rightarrow *$', ha='center', va='center', fontsize=20)\n",
    "fig.text(0.06, 0.5, r'$* \\rightarrow Anger$', ha='center', va='center', rotation='vertical', fontsize=20)\n",
    "\n",
    "ax1.title.set_text('Intrinsic')\n",
    "ax1.title.set_color('red')\n",
    "ax1.title.set_fontsize(20)\n",
    "ax1.scatter(x_int_anger, y_int_anger, color='red')\n",
    "ax1.axes.xaxis.set_ticklabels([])\n",
    "ax1.axes.yaxis.set_ticklabels([])\n",
    "for i, txt in enumerate(n):\n",
    "    ax1.annotate(txt, (x_int_anger[i], y_int_anger[i]))\n",
    "\n",
    "ax2.title.set_text('Shared')\n",
    "ax2.title.set_color('green')\n",
    "ax2.title.set_fontsize(20)\n",
    "ax2.scatter(x_shared_anger, y_shared_anger, color='green')\n",
    "for i, txt in enumerate(n):\n",
    "    ax2.annotate(txt, (x_shared_anger[i], y_shared_anger[i]))\n",
    "\n",
    "ax3.title.set_text('Synergistic')\n",
    "ax3.title.set_color('blue')\n",
    "ax3.title.set_fontsize(20)\n",
    "ax3.scatter(x_synergistic_anger, y_synergistic_anger, color='blue')\n",
    "for i, txt in enumerate(n):\n",
    "    ax3.annotate(txt, (x_synergistic_anger[i], y_synergistic_anger[i]))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "#Extraction of Fear data\n",
    "x_int_fear = list(df['Intrinsic'][8:12])\n",
    "y_int_fear = [df['Intrinsic'][1],df['Intrinsic'][5],df['Intrinsic'][14],df['Intrinsic'][18]]\n",
    "x_shared_fear = list(df['Shared'][8:12])\n",
    "y_shared_fear = [df['Shared'][1],df['Shared'][5],df['Shared'][14],df['Shared'][18]]\n",
    "x_synergistic_fear = list(df['Synergistic'][8:12])\n",
    "y_synergistic_fear = [df['Synergistic'][1],df['Synergistic'][5],df['Synergistic'][14],df['Synergistic'][18]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "execution_count": 69,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fear plots\n",
    "\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "\n",
    "n = ['S','A','D','J']\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharex = True, sharey = True, figsize=(12,5))\n",
    "\n",
    "fig.text(0.5, 0.04, r'$Fear \\rightarrow *$', ha='center', va='center', fontsize=20)\n",
    "fig.text(0.06, 0.5, r'$* \\rightarrow Fear$', ha='center', va='center', rotation='vertical', fontsize=20)\n",
    "\n",
    "ax1.title.set_text('Intrinsic')\n",
    "ax1.title.set_color('red')\n",
    "ax1.title.set_fontsize(20)\n",
    "ax1.scatter(x_int_fear, y_int_fear, color='red')\n",
    "ax1.axes.xaxis.set_ticklabels([])\n",
    "ax1.axes.yaxis.set_ticklabels([])\n",
    "for i, txt in enumerate(n):\n",
    "    ax1.annotate(txt, (x_int_fear[i], y_int_fear[i]))\n",
    "\n",
    "ax2.title.set_text('Shared')\n",
    "ax2.title.set_color('green')\n",
    "ax2.title.set_fontsize(20)\n",
    "ax2.scatter(x_shared_fear, y_shared_fear, color='green')\n",
    "for i, txt in enumerate(n):\n",
    "    ax2.annotate(txt, (x_shared_fear[i], y_shared_fear[i]))\n",
    "\n",
    "ax3.title.set_text('Synergistic')\n",
    "ax3.title.set_color('blue')\n",
    "ax3.title.set_fontsize(20)\n",
    "ax3.scatter(x_synergistic_fear, y_synergistic_fear, color='blue')\n",
    "for i, txt in enumerate(n):\n",
    "    ax3.annotate(txt, (x_synergistic_fear[i], y_synergistic_fear[i]))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "#Extraction of Disgust data\n",
    "x_int_disgust = list(df['Intrinsic'][12:16])\n",
    "y_int_disgust = [df['Intrinsic'][2],df['Intrinsic'][6],df['Intrinsic'][10],df['Intrinsic'][19]]\n",
    "x_shared_disgust = list(df['Shared'][12:16])\n",
    "y_shared_disgust = [df['Shared'][2],df['Shared'][6],df['Shared'][10],df['Shared'][19]]\n",
    "x_synergistic_disgust = list(df['Synergistic'][12:16])\n",
    "y_synergistic_disgust = [df['Synergistic'][2],df['Synergistic'][6],df['Synergistic'][10],df['Synergistic'][19]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "execution_count": 71,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Plots of Disgust\n",
    "\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "\n",
    "n = ['S','A','F','J']\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharex = True, sharey = True, figsize=(12,5))\n",
    "\n",
    "fig.text(0.5, 0.04, r'$Disgust \\rightarrow *$', ha='center', va='center', fontsize=20)\n",
    "fig.text(0.06, 0.5, r'$* \\rightarrow Disgust$', ha='center', va='center', rotation='vertical', fontsize=20)\n",
    "\n",
    "ax1.title.set_text('Intrinsic')\n",
    "ax1.title.set_color('red')\n",
    "ax1.title.set_fontsize(20)\n",
    "ax1.scatter(x_int_disgust, y_int_disgust, color='red')\n",
    "ax1.axes.xaxis.set_ticklabels([])\n",
    "ax1.axes.yaxis.set_ticklabels([])\n",
    "for i, txt in enumerate(n):\n",
    "    ax1.annotate(txt, (x_int_disgust[i], y_int_disgust[i]))\n",
    "\n",
    "ax2.title.set_text('Shared')\n",
    "ax2.title.set_color('green')\n",
    "ax2.title.set_fontsize(20)\n",
    "ax2.scatter(x_shared_disgust, y_shared_disgust, color='green')\n",
    "for i, txt in enumerate(n):\n",
    "    ax2.annotate(txt, (x_shared_disgust[i], y_shared_disgust[i]))\n",
    "\n",
    "ax3.title.set_text('Synergistic')\n",
    "ax3.title.set_color('blue')\n",
    "ax3.title.set_fontsize(20)\n",
    "ax3.scatter(x_synergistic_disgust, y_synergistic_disgust, color='blue')\n",
    "for i, txt in enumerate(n):\n",
    "    ax3.annotate(txt, (x_synergistic_disgust[i], y_synergistic_disgust[i]))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "#Extraction of Joy data\n",
    "x_int_joy = list(df['Intrinsic'][16:20])\n",
    "y_int_joy = [df['Intrinsic'][3],df['Intrinsic'][7],df['Intrinsic'][11],df['Intrinsic'][15]]\n",
    "x_shared_joy = list(df['Shared'][16:20])\n",
    "y_shared_joy = [df['Shared'][3],df['Shared'][7],df['Shared'][11],df['Shared'][15]]\n",
    "x_synergistic_joy = list(df['Synergistic'][16:20])\n",
    "y_synergistic_joy = [df['Synergistic'][3],df['Synergistic'][7],df['Synergistic'][11],df['Synergistic'][15]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "execution_count": 73,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Plots of Joy\n",
    "\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "\n",
    "n = ['S','A','F','D']\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharex = True, sharey = True, figsize=(12,5))\n",
    "\n",
    "fig.text(0.5, 0.04, r'$Joy \\rightarrow *$', ha='center', va='center', fontsize=20)\n",
    "fig.text(0.06, 0.5, r'$* \\rightarrow Joy$', ha='center', va='center', rotation='vertical', fontsize=20)\n",
    "\n",
    "ax1.title.set_text('Intrinsic')\n",
    "ax1.title.set_color('red')\n",
    "ax1.title.set_fontsize(20)\n",
    "ax1.scatter(x_int_joy, y_int_joy, color='red')\n",
    "ax1.axes.xaxis.set_ticklabels([])\n",
    "ax1.axes.yaxis.set_ticklabels([])\n",
    "for i, txt in enumerate(n):\n",
    "    ax1.annotate(txt, (x_int_joy[i], y_int_joy[i]))\n",
    "\n",
    "ax2.title.set_text('Shared')\n",
    "ax2.title.set_color('green')\n",
    "ax2.title.set_fontsize(20)\n",
    "ax2.scatter(x_shared_joy, y_shared_joy, color='green')\n",
    "for i, txt in enumerate(n):\n",
    "    ax2.annotate(txt, (x_shared_joy[i], y_shared_joy[i]))\n",
    "\n",
    "ax3.title.set_text('Synergistic')\n",
    "ax3.title.set_color('blue')\n",
    "ax3.title.set_fontsize(20)\n",
    "ax3.scatter(x_synergistic_joy, y_synergistic_joy, color='blue')\n",
    "for i, txt in enumerate(n):\n",
    "    ax3.annotate(txt, (x_synergistic_joy[i], y_synergistic_joy[i]))\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3",
   "resource_dir": "/srv/conda/envs/notebook/share/jupyter/kernels/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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}