02-network.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ffee69a5-3370-4439-bab1-c0a24cc2c349",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-10T07:51:43.767593Z",
     "iopub.status.busy": "2022-02-10T07:51:43.767426Z",
     "iopub.status.idle": "2022-02-10T07:51:45.183336Z",
     "shell.execute_reply": "2022-02-10T07:51:45.182587Z",
     "shell.execute_reply.started": "2022-02-10T07:51:43.767524Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import contextily as cx\n",
    "from IPython.display import display\n",
    "from matplotlib import cm\n",
    "from matplotlib.colors import Normalize\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from src import largest_strongly_connected_component\n",
    "from run import filepath_network, filepath_un_locode_cleaned, filepath_ships_classification, filepath_portcalls_processed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7f2f0df-ccee-421b-b205-51ea43c928cb",
   "metadata": {},
   "source": [
    "# Mobility network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3b0bb69e-ba5c-47d7-9b22-209846cb0fc8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-10T07:51:45.184444Z",
     "iopub.status.busy": "2022-02-10T07:51:45.184243Z",
     "iopub.status.idle": "2022-02-10T07:52:04.136143Z",
     "shell.execute_reply": "2022-02-10T07:52:04.135360Z",
     "shell.execute_reply.started": "2022-02-10T07:51:45.184427Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "G = nx.read_gpickle(filepath_network)\n",
    "Gc = largest_strongly_connected_component(G)\n",
    "un_locode = pd.read_pickle(filepath_un_locode_cleaned)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "243a2195-1ecd-4809-b873-e4932f0b838e",
   "metadata": {},
   "source": [
    "## Figure\n",
    "### Obtain UNLOCODE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "35d50a08-f667-46c3-b24a-c55b4a80890b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-10T07:52:04.137128Z",
     "iopub.status.busy": "2022-02-10T07:52:04.137013Z",
     "iopub.status.idle": "2022-02-10T07:52:04.661615Z",
     "shell.execute_reply": "2022-02-10T07:52:04.660954Z",
     "shell.execute_reply.started": "2022-02-10T07:52:04.137114Z"
    },
    "tags": []
   },
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>strength</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BEANR</th>\n",
       "      <td>4.25</td>\n",
       "      <td>51.13</td>\n",
       "      <td>10405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEBON</th>\n",
       "      <td>4.14</td>\n",
       "      <td>51.06</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEBGS</th>\n",
       "      <td>3.14</td>\n",
       "      <td>51.13</td>\n",
       "      <td>812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEGNK</th>\n",
       "      <td>5.30</td>\n",
       "      <td>50.58</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEGNE</th>\n",
       "      <td>3.43</td>\n",
       "      <td>51.03</td>\n",
       "      <td>2821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEVST</th>\n",
       "      <td>16.33</td>\n",
       "      <td>59.37</td>\n",
       "      <td>548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEVVK</th>\n",
       "      <td>16.38</td>\n",
       "      <td>57.45</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEVBY</th>\n",
       "      <td>18.18</td>\n",
       "      <td>57.38</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEWAL</th>\n",
       "      <td>11.42</td>\n",
       "      <td>58.01</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEYST</th>\n",
       "      <td>13.49</td>\n",
       "      <td>55.25</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>774 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           x      y  strength\n",
       "label                        \n",
       "BEANR   4.25  51.13     10405\n",
       "BEBON   4.14  51.06        28\n",
       "BEBGS   3.14  51.13       812\n",
       "BEGNK   5.30  50.58        44\n",
       "BEGNE   3.43  51.03      2821\n",
       "...      ...    ...       ...\n",
       "SEVST  16.33  59.37       548\n",
       "SEVVK  16.38  57.45        48\n",
       "SEVBY  18.18  57.38        38\n",
       "SEWAL  11.42  58.01       126\n",
       "SEYST  13.49  55.25        39\n",
       "\n",
       "[774 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_strength(x):\n",
    "    return [Gc.degree(node) for node in x.index]\n",
    "\n",
    "nodes = (\n",
    "    un_locode\n",
    "    .drop_duplicates(subset='label')\n",
    "    .set_index('label')\n",
    "    .loc[lambda x: x.index.isin(Gc)]\n",
    "    .assign(strength=get_strength)\n",
    "    .loc[lambda x: x.strength > 10]\n",
    ")\n",
    "nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24f16610-0776-428b-be8a-0ae5b2bdd4a2",
   "metadata": {},
   "source": [
    "### Select edges with known location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6ecb3e05-77d0-4af8-9ee2-62feb72300b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-10T07:52:04.663142Z",
     "iopub.status.busy": "2022-02-10T07:52:04.662937Z",
     "iopub.status.idle": "2022-02-10T07:52:04.725534Z",
     "shell.execute_reply": "2022-02-10T07:52:04.724850Z",
     "shell.execute_reply.started": "2022-02-10T07:52:04.663126Z"
    },
    "tags": []
   },
   "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>source</th>\n",
       "      <th>target</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GBLRP</td>\n",
       "      <td>GBBEL</td>\n",
       "      <td>3955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GBBEL</td>\n",
       "      <td>GBLRP</td>\n",
       "      <td>3954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FRBON</td>\n",
       "      <td>ITSTE</td>\n",
       "      <td>3101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ITSTE</td>\n",
       "      <td>FRBON</td>\n",
       "      <td>3099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FRCQF</td>\n",
       "      <td>FRCQF</td>\n",
       "      <td>1902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19038</th>\n",
       "      <td>NOARH</td>\n",
       "      <td>NOEKF</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19039</th>\n",
       "      <td>ESLPA</td>\n",
       "      <td>FRBOD</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19040</th>\n",
       "      <td>DEWVN</td>\n",
       "      <td>SEMMA</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19041</th>\n",
       "      <td>GBLIV</td>\n",
       "      <td>ESLCG</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19042</th>\n",
       "      <td>ITGAE</td>\n",
       "      <td>ITVCE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19043 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      source target  weight\n",
       "0      GBLRP  GBBEL    3955\n",
       "1      GBBEL  GBLRP    3954\n",
       "2      FRBON  ITSTE    3101\n",
       "3      ITSTE  FRBON    3099\n",
       "4      FRCQF  FRCQF    1902\n",
       "...      ...    ...     ...\n",
       "19038  NOARH  NOEKF       1\n",
       "19039  ESLPA  FRBOD       1\n",
       "19040  DEWVN  SEMMA       1\n",
       "19041  GBLIV  ESLCG       1\n",
       "19042  ITGAE  ITVCE       1\n",
       "\n",
       "[19043 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def select_only_edges_with_location(x):\n",
    "    values = nodes.index\n",
    "    return x['source'].isin(values) & x['target'].isin(values)\n",
    "    \n",
    "selected_edges = (\n",
    "    nx.to_pandas_edgelist(Gc)\n",
    "    .loc[select_only_edges_with_location]\n",
    "    .value_counts()\n",
    "    .reset_index()\n",
    "    .rename(columns={0:'weight'})\n",
    ")\n",
    "selected_edges"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdece140-af17-4eaa-880f-56600393452b",
   "metadata": {},
   "source": [
    "### Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "710e818f-dc0c-4502-bb7a-1f8a5082bcfe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-10T07:52:04.726373Z",
     "iopub.status.busy": "2022-02-10T07:52:04.726263Z",
     "iopub.status.idle": "2022-02-10T07:52:08.926296Z",
     "shell.execute_reply": "2022-02-10T07:52:08.925524Z",
     "shell.execute_reply.started": "2022-02-10T07:52:04.726359Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 347.04x231.12 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rasterized = False\n",
    "\n",
    "cmap = cm.get_cmap('viridis')\n",
    "colors = [cmap(i/5, alpha=i/5) for i in nodes.strength]\n",
    "\n",
    "xs_strong = list()\n",
    "ys_strong = list()\n",
    "xs_weak = list()\n",
    "ys_weak = list()\n",
    "for edge in selected_edges.itertuples():\n",
    "    source_location = nodes.loc[edge.source]\n",
    "    target_location = nodes.loc[edge.target]\n",
    "    if edge.weight > 100:\n",
    "        xs_strong.append((source_location.x, target_location.x))\n",
    "        ys_strong.append((source_location.y, target_location.y))\n",
    "    elif edge.weight > 10:\n",
    "        xs_weak.append((source_location.x, target_location.x))\n",
    "        ys_weak.append((source_location.y, target_location.y))\n",
    "\n",
    "with plt.rc_context({'font.size': 8}):\n",
    "    fig, ax = plt.subplots(figsize=(4.82,3.21), constrained_layout=True)\n",
    "    ax.plot(xs_strong, ys_strong, color='darkblue', alpha=.2, lw=1, rasterized=rasterized)\n",
    "    ax.plot(xs_weak, ys_weak, color='darkblue', alpha=.1, lw=.5, rasterized=rasterized)\n",
    "    ax.scatter(x=nodes.x, y=nodes.y, c=np.log10(nodes.strength), s=np.log10(nodes.strength)*10, cmap='viridis', norm=Normalize(0, 5), alpha=1)\n",
    "    fig.colorbar(cm.ScalarMappable(norm=Normalize(0, 5), cmap='viridis'), ax=ax, shrink=0.80, label=r'$\\log(\\mathrm{node}\\ \\mathrm{strength})$')\n",
    "    ax.xaxis.set_ticks_position('both')\n",
    "    ax.xaxis.set_minor_locator(MultipleLocator(1))\n",
    "    ax.yaxis.set_ticks_position('both')\n",
    "    ax.yaxis.set_minor_locator(MultipleLocator(1))\n",
    "    ax.set_xlim(-30, 40)\n",
    "    ax.set_ylim(25, 70)\n",
    "    ax.set_xlabel('Longitude')\n",
    "    ax.set_ylabel('Latitude')\n",
    "    cx.add_basemap(ax, crs='WGS84', zoom=3, interpolation='none', source=cx.providers.CartoDB.Voyager, attribution=False)\n",
    "\n",
    "    fig.savefig('fig/network.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf9f814c-ec23-474c-a762-c811fcff6081",
   "metadata": {},
   "source": [
    "## Stats\n",
    "### Calculate distance distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ff408d2b-aea8-49f3-97d4-8256143dc635",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-10T07:52:08.927413Z",
     "iopub.status.busy": "2022-02-10T07:52:08.927257Z",
     "iopub.status.idle": "2022-02-10T07:52:11.445025Z",
     "shell.execute_reply": "2022-02-10T07:52:11.444134Z",
     "shell.execute_reply.started": "2022-02-10T07:52:08.927397Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Welcome to teexGraph ***\n",
      "- Use standard input (cin) to give commands\n",
      "- Read standard output (cout) to catch the result\n",
      "- Observe standard log (clog) and (cerr) for status and error messages\n",
      "- Graphs up to MAXN = 10000000 nodes are accepted\n",
      "Input a command: Loading an undirected graph. Enter filename: \n",
      "Loading graph from cache/network.edges ...\n",
      "- 28638 edges added (m = 28638) in total\n",
      "- 0 edges skipped\n",
      "- 612 self-edges added\n",
      "\n",
      "Sorting edge list...\n",
      "Sorting done.\n",
      "Loading done.\n",
      "\n",
      "Making graph undirected (m = 28638)...\n",
      "Sorting edge list...\n",
      "Sorting done.\n",
      "  WARNING: number of edges is not equal to (twice the) number of input lines.\n",
      "  Verify that the graph is actually undirected.\n",
      "Undirected-making done (m = 56664).\n",
      "Loading file succeeded.\n",
      "WCC computed.\n",
      "\n",
      "> Computing distance distribution (based on a 100% sample of 1445 nodes) with 256 CPUs...\n",
      "        0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0  0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%   0% 0% 0% 0% 0% 0% 0% 0%  0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%0%0%0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%  0  0% % 0% 0% 0% 0% 0% 0% 0%  00  0%0% 0% 0% 5% 10% 0% 0% 0% 0% 0% 0 %0  0% 0  0% 0% 0%  0%  0% 0% 15% 0% 0% 0% 0% 0% 20% 0% 25% 30% 0% 0%  0 0 0% 0%  0% 36% 0% 0% 0%   0% 0% 0% 0% 0%    00%% 0%  41 0% 0% 0% 0% 0% 46% 51% 0% 0% 0% 0 0% 56% 0% 61% 66% 72% 0% 77% 82% 0% 0% 0% 0% 0% 0% 0% 0% 0%0%%0%0% 0% 0% 0% 0%0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%0%0% 0%0% 0% 0% 0%0%0% 0%0% 0%0% 0%%0%0%0% 0% 0% 0% 0% 0% 0% 0% 0% 0%0%% 0%% 0% 0% 0%0% 0% 0%0%0% 0%0%0%0% 0 0%%0%% 0%   0% 0% 0% 0% 0% 0% 0% 0% 0% 0%%% 0%% 0%0% 0% 0% 0%0%% Done.\n",
      "Weighted total: 5.50581e+06, count: 2.08802e+06, average: 2.63685\n",
      "Min-index: 0, max-index: 6, min-value: 122, max-value: 963380\n",
      "> \n",
      "End of program.\n"
     ]
    },
    {
     "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>distance</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>56052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>860418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>963380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>194988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>11620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance  counts\n",
       "0         0    1445\n",
       "1         1   56052\n",
       "2         2  860418\n",
       "3         3  963380\n",
       "4         4  194988\n",
       "5         5   11620\n",
       "6         6     122"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!printf '%s\\n' 'load_undirected cache/network.edges' 'dist_distri' > \"cache/input.txt\"\n",
    "nx.write_edgelist(nx.convert_node_labels_to_integers(nx.Graph(Gc)), 'cache/network.edges', data=False)\n",
    "! /data/bruingjde/teexgraph/teexgraph < cache/input.txt > cache/output.txt\n",
    "output_teexgraph = pd.read_csv('cache/output.txt', sep='\\t', names=['distance', 'counts'])\n",
    "os.remove('cache/network.edges')\n",
    "os.remove('cache/input.txt')\n",
    "os.remove('cache/output.txt')\n",
    "output_teexgraph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58b4714b-5df3-4ec0-8ae0-85dab788085c",
   "metadata": {},
   "source": [
    "### Missing ports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "00d3ee23-1f4c-45bd-9827-340e48df5aad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-10T07:52:11.446769Z",
     "iopub.status.busy": "2022-02-10T07:52:11.446292Z",
     "iopub.status.idle": "2022-02-10T07:52:11.562088Z",
     "shell.execute_reply": "2022-02-10T07:52:11.561685Z",
     "shell.execute_reply.started": "2022-02-10T07:52:11.446732Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ports in ML observed in weakly connected component of cargo ship network: [ 525 1427] (no/yes)\n",
      "[0.26895492 0.73104508]\n"
     ]
    }
   ],
   "source": [
    "ships_classification = np.load(filepath_ships_classification)\n",
    "portcalls_processed = pd.read_pickle(filepath_portcalls_processed)\n",
    "portcalls_classification = portcalls_processed.loc[lambda x: x['ship'].isin(ships_classification)]\n",
    "ports_ml = portcalls_classification['port'].unique()\n",
    "observed = [port in Gc for port in ports_ml]\n",
    "result = np.bincount(observed)\n",
    "message = \"Ports in ML observed in weakly connected component of cargo ship network\"\n",
    "print(f\"{message}: {result} (no/yes)\")\n",
    "print(f\"{result/len(observed)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "296924f6-88d6-48ba-8abe-94ac5b188cb9",
   "metadata": {},
   "source": [
    "### Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "49bff1cb-427e-48e7-bf47-6b3fc9d21003",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-10T07:52:11.562859Z",
     "iopub.status.busy": "2022-02-10T07:52:11.562750Z",
     "iopub.status.idle": "2022-02-10T07:52:15.422958Z",
     "shell.execute_reply": "2022-02-10T07:52:15.422299Z",
     "shell.execute_reply.started": "2022-02-10T07:52:11.562845Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'number of nodes': 1459,\n",
       " 'number of nodes in GC': 1445,\n",
       " 'number of edges': 28653,\n",
       " 'fraction of edges in GC': 28638,\n",
       " 'density': 0.027449702383805077,\n",
       " 'diameter': 6,\n",
       " 'average distance': 2.636851570263766,\n",
       " 'clustering coefficient': 0.4756258478731693,\n",
       " 'degree assortativity': -0.07604151737724668,\n",
       " 'strength assortativity': 0.3521541970827181}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\n",
    "    'number of nodes': G.number_of_nodes(),\n",
    "    'number of nodes in GC': Gc.number_of_nodes(),\n",
    "    'number of edges': nx.Graph(G).number_of_edges(),\n",
    "    'fraction of edges in GC': nx.Graph(Gc).number_of_edges(),\n",
    "    'density': nx.density(nx.Graph(Gc)),\n",
    "    'diameter': output_teexgraph['distance'].max(),\n",
    "    'average distance': np.average(output_teexgraph['distance'], weights=output_teexgraph['counts']),\n",
    "    'clustering coefficient': nx.average_clustering(nx.Graph(Gc)),\n",
    "    'degree assortativity': nx.degree_assortativity_coefficient(nx.Graph(Gc)),\n",
    "    'strength assortativity': nx.degree_assortativity_coefficient(Gc)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "35cd99f4-0a15-4965-ae7b-58d6d8bf4aa7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-10T07:52:15.424002Z",
     "iopub.status.busy": "2022-02-10T07:52:15.423875Z",
     "iopub.status.idle": "2022-02-10T07:52:15.538395Z",
     "shell.execute_reply": "2022-02-10T07:52:15.537992Z",
     "shell.execute_reply.started": "2022-02-10T07:52:15.423984Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('DEPUT', 25312),\n",
       " ('NLRTM', 23592),\n",
       " ('ESALG', 16191),\n",
       " ('GBBEL', 13310),\n",
       " ('GBPME', 11622),\n",
       " ('BEANR', 10405),\n",
       " ('ESLPA', 9961),\n",
       " ('DEHAM', 8383),\n",
       " ('ESBCN', 8261),\n",
       " ('ITGOA', 8025),\n",
       " ('GBLRP', 7972),\n",
       " ('FRBON', 7135),\n",
       " ('ESVLC', 6909),\n",
       " ('ESIBZ', 6795),\n",
       " ('ITSTE', 6697),\n",
       " ('ESTRF', 6628),\n",
       " ('ITLIV', 5901),\n",
       " ('GBIMM', 5866),\n",
       " ('GRRAF', 5653),\n",
       " ('GBLON', 5544),\n",
       " ('DEBRV', 5387),\n",
       " ('GBABD', 5288),\n",
       " ('GIGIB', 5233),\n",
       " ('ITPMO', 5195),\n",
       " ('NLAMS', 5016),\n",
       " ('ITCVV', 4998),\n",
       " ('GRTSO', 4780),\n",
       " ('SEGOT', 4651),\n",
       " ('GRIGO', 4651),\n",
       " ('ESSCT', 4572),\n",
       " ('ESPMI', 4566),\n",
       " ('FRCQF', 4559),\n",
       " ('GRKER', 4354),\n",
       " ('GBSOU', 4278),\n",
       " ('GRPIR', 4055),\n",
       " ('ITNAP', 3939),\n",
       " ('GBPHD', 3912),\n",
       " ('ITOLB', 3436),\n",
       " ('GBHUL', 3429),\n",
       " ('ITAOI', 3409),\n",
       " ('ITBRI', 3274),\n",
       " ('ITCTA', 3270),\n",
       " ('GBBRK', 3068),\n",
       " ('FRFOS', 3036),\n",
       " ('FRMRS', 2981),\n",
       " ('ESCBS', 2954),\n",
       " ('ITRAN', 2929),\n",
       " ('LTKLJ', 2851),\n",
       " ('GBHRW', 2850),\n",
       " ('BEGNE', 2821),\n",
       " ('FRLEH', 2773),\n",
       " ('ESMOT', 2756),\n",
       " ('LVRIX', 2690),\n",
       " ('BEZEE', 2677),\n",
       " ('GBLIV', 2676),\n",
       " ('NOOSL', 2673),\n",
       " ('ESCEU', 2599),\n",
       " ('GRGPA', 2554),\n",
       " ('DERSK', 2497),\n",
       " ('ITVCE', 2463),\n",
       " ('FIKTK', 2434),\n",
       " ('ROCND', 2323),\n",
       " ('FILAN', 2314),\n",
       " ('PLSZZ', 2296),\n",
       " ('MTMAR', 2265),\n",
       " ('PTFNC', 2203),\n",
       " ('IEDFC', 2170),\n",
       " ('PLNOW', 2160),\n",
       " ('NLDHR', 2147),\n",
       " ('NOBGO', 2132),\n",
       " ('ESTAR', 2120),\n",
       " ('FINLI', 2111),\n",
       " ('ITCAG', 2092),\n",
       " ('NOAES', 2076),\n",
       " ('NLEEM', 2047),\n",
       " ('ITTRS', 2011),\n",
       " ('ESCAR', 1999),\n",
       " ('ITBDS', 1962),\n",
       " ('PTPXO', 1927),\n",
       " ('DKCPH', 1915),\n",
       " ('ESBIO', 1912),\n",
       " ('FRURO', 1889),\n",
       " ('PTLEI', 1842),\n",
       " ('FRTLN', 1794),\n",
       " ('FRDKK', 1780),\n",
       " ('NLVLI', 1768),\n",
       " ('ITSAL', 1733),\n",
       " ('GBHLY', 1717),\n",
       " ('DKAAR', 1715),\n",
       " ('GBESH', 1711),\n",
       " ('NOSVV', 1708),\n",
       " ('IEDFB', 1694),\n",
       " ('ESHUV', 1687),\n",
       " ('PLGDY', 1670),\n",
       " ('CYLMS', 1632),\n",
       " ('DKEBJ', 1576),\n",
       " ('ESVGO', 1557),\n",
       " ('ITPTO', 1555),\n",
       " ('ITAMA', 1552),\n",
       " ('GBTEE', 1538),\n",
       " ('NOKRS', 1530),\n",
       " ('SEKPS', 1505),\n",
       " ('PTLIS', 1487),\n",
       " ('ITPIO', 1485),\n",
       " ('ITCPA', 1482),\n",
       " ('SIKOP', 1481),\n",
       " ('FRBIA', 1480),\n",
       " ('ITPNZ', 1469),\n",
       " ('GBSHS', 1467),\n",
       " ('BGVAR', 1455),\n",
       " ('PTSIE', 1416),\n",
       " ('MTMLA', 1409),\n",
       " ('FRPRP', 1369),\n",
       " ('ITSVN', 1368),\n",
       " ('ESCAS', 1364),\n",
       " ('ITAUG', 1355),\n",
       " ('FRCER', 1346),\n",
       " ('ITSPE', 1329),\n",
       " ('PLSWI', 1302),\n",
       " ('NLTNZ', 1298),\n",
       " ('GBFXT', 1250),\n",
       " ('FRNTE', 1242),\n",
       " ('GRLAV', 1234),\n",
       " ('GBHYM', 1234),\n",
       " ('LVLPX', 1216),\n",
       " ('GBGSY', 1214),\n",
       " ('GRMJT', 1207),\n",
       " ('GBLSH', 1188),\n",
       " ('ESACE', 1168),\n",
       " ('ESSVQ', 1131),\n",
       " ('FIHEL', 1127),\n",
       " ('EEMUG', 1117),\n",
       " ('GRHER', 1113),\n",
       " ('FIRAU', 1110),\n",
       " ('BGBOJ', 1106),\n",
       " ('FISKV', 1096),\n",
       " ('DEBRE', 1080),\n",
       " ('DKFRC', 1072),\n",
       " ('GBNPT', 1057),\n",
       " ('GRSKG', 1056),\n",
       " ('DETRV', 1048),\n",
       " ('DEWVN', 1045),\n",
       " ('BEOST', 1044),\n",
       " ('NLDZL', 1035),\n",
       " ('FRAJA', 1006),\n",
       " ('SEMMA', 986),\n",
       " ('NOFRO', 983),\n",
       " ('PTSET', 977),\n",
       " ('ESGIJ', 968),\n",
       " ('DEEME', 964),\n",
       " ('GBDVR', 935),\n",
       " ('ESLCG', 904),\n",
       " ('GBGTY', 899),\n",
       " ('NOHSO', 880),\n",
       " ('SENYN', 869),\n",
       " ('ITPFX', 854),\n",
       " ('NOKSU', 846),\n",
       " ('DEMUK', 835),\n",
       " ('ESSDR', 829),\n",
       " ('GBAVO', 825),\n",
       " ('ESSAG', 820),\n",
       " ('SEHEL', 818),\n",
       " ('DEBKE', 813),\n",
       " ('BEBGS', 812),\n",
       " ('NOFRK', 808),\n",
       " ('IEDUB', 806),\n",
       " ('LVVNT', 787),\n",
       " ('FRSET', 786),\n",
       " ('NOTAE', 785),\n",
       " ('DECUX', 784),\n",
       " ('NOLAR', 783),\n",
       " ('SEOXE', 777),\n",
       " ('NOMAY', 776),\n",
       " ('DELBC', 776),\n",
       " ('DEWIS', 745),\n",
       " ('FITOR', 727),\n",
       " ('NODRM', 720),\n",
       " ('ITTAR', 718),\n",
       " ('NOJEL', 710),\n",
       " ('ITGIT', 710),\n",
       " ('FIHKO', 696),\n",
       " ('NOTOS', 695),\n",
       " ('ITPFE', 692),\n",
       " ('NOSVG', 690),\n",
       " ('GBGRG', 682),\n",
       " ('DKHIR', 680),\n",
       " ('GBMLF', 677),\n",
       " ('ESFUE', 672),\n",
       " ('GBCDF', 671),\n",
       " ('GRKEA', 668),\n",
       " ('FIKOK', 663),\n",
       " ('SESOE', 657),\n",
       " ('GRCFU', 655),\n",
       " ('ITCAS', 647),\n",
       " ('SESTO', 642),\n",
       " ('ESAGP', 638),\n",
       " ('DEBRB', 636),\n",
       " ('ESPAS', 636),\n",
       " ('ITATX', 625),\n",
       " ('NOMON', 621),\n",
       " ('FRBOD', 616),\n",
       " ('PLGDN', 615),\n",
       " ('GRKAS', 614),\n",
       " ('GBMLG', 613),\n",
       " ('GBGRK', 606),\n",
       " ('SENRK', 606),\n",
       " ('SEHAD', 600),\n",
       " ('DKAAL', 593),\n",
       " ('ESAVS', 582),\n",
       " ('GBKGH', 580),\n",
       " ('SEBRO', 577),\n",
       " ('PTAVE', 574),\n",
       " ('ESMLN', 572),\n",
       " ('EEPRN', 569),\n",
       " ('SEGVX', 568),\n",
       " ('FIRAA', 563),\n",
       " ('SEKAN', 561),\n",
       " ('FRLRH', 554),\n",
       " ('DKTYB', 552),\n",
       " ('NOTRD', 551),\n",
       " ('GREEU', 550),\n",
       " ('DKKAL', 548),\n",
       " ('SEVST', 548),\n",
       " ('ITANZ', 546),\n",
       " ('ESCRS', 544),\n",
       " ('SETRG', 541),\n",
       " ('NOTAU', 537),\n",
       " ('HRDBV', 532),\n",
       " ('ESFRO', 525),\n",
       " ('ROMID', 523),\n",
       " ('FIKEM', 521),\n",
       " ('ESCAD', 496),\n",
       " ('ITMNF', 496),\n",
       " ('FIUKI', 493),\n",
       " ('SESTE', 491),\n",
       " ('SEJAT', 484),\n",
       " ('GBPRU', 483),\n",
       " ('NOBVK', 482),\n",
       " ('GRVOL', 475),\n",
       " ('NOMSS', 472),\n",
       " ('GBGOO', 465),\n",
       " ('GBLER', 462),\n",
       " ('GBIPS', 459),\n",
       " ('FIPOR', 458),\n",
       " ('NOKKN', 453),\n",
       " ('GRKLL', 448),\n",
       " ('DEBUZ', 448),\n",
       " ('NOSLG', 443),\n",
       " ('GBFAL', 439),\n",
       " ('DKNBG', 437),\n",
       " ('FIOUL', 436),\n",
       " ('NOHRY', 430),\n",
       " ('ESLEI', 427),\n",
       " ('NODUS', 426),\n",
       " ('NLHAR', 421),\n",
       " ('ROGAL', 420),\n",
       " ('IEORK', 416),\n",
       " ('ISREY', 413),\n",
       " ('FRBAY', 411),\n",
       " ('NLANK', 408),\n",
       " ('ESMPG', 407),\n",
       " ('EEKND', 406),\n",
       " ('SEHUS', 406),\n",
       " ('NOSAS', 406),\n",
       " ('SEKLR', 397),\n",
       " ('GRAGT', 394),\n",
       " ('DKRAN', 392),\n",
       " ('NOARH', 390),\n",
       " ('GRJMK', 389),\n",
       " ('FRBES', 389),\n",
       " ('HRSPU', 389),\n",
       " ('EEPLS', 383),\n",
       " ('SELLA', 380),\n",
       " ('GBFAW', 380),\n",
       " ('SEAHU', 377),\n",
       " ('FIIMA', 377),\n",
       " ('GBSCR', 374),\n",
       " ('GRRHO', 372),\n",
       " ('PTCNL', 372),\n",
       " ('NORAF', 365),\n",
       " ('GBCYN', 360),\n",
       " ('NOHAU', 358),\n",
       " ('ESSPC', 358),\n",
       " ('FIKAS', 355),\n",
       " ('NOBOO', 355),\n",
       " ('SEIGG', 352),\n",
       " ('ITMLZ', 348),\n",
       " ('DKKOG', 347),\n",
       " ('HRRJK', 346),\n",
       " ('HRGNA', 346),\n",
       " ('NOHOG', 343),\n",
       " ('NOSSJ', 342),\n",
       " ('SEHLD', 338),\n",
       " ('DKSKA', 336),\n",
       " ('NOFOR', 331),\n",
       " ('EESLM', 330),\n",
       " ('NOFRS', 329),\n",
       " ('SETUN', 328),\n",
       " ('GBIVG', 328),\n",
       " ('DKFDH', 325),\n",
       " ('GBLEI', 325),\n",
       " ('DKFDV', 325),\n",
       " ('NOEGE', 324),\n",
       " ('GRJTR', 323),\n",
       " ('ITPEM', 320),\n",
       " ('ESALC', 316),\n",
       " ('EEVIR', 316),\n",
       " ('GBMON', 313),\n",
       " ('DKAVN', 311),\n",
       " ('NOHFT', 310),\n",
       " ('NOEKF', 310),\n",
       " ('SEVAG', 306),\n",
       " ('GRNEA', 304),\n",
       " ('ITSPA', 303),\n",
       " ('SEVAL', 303),\n",
       " ('NLSCE', 302),\n",
       " ('FRNCE', 302),\n",
       " ('FRLRT', 293),\n",
       " ('FITKU', 290),\n",
       " ('NOSKX', 290),\n",
       " ('GBPLY', 287),\n",
       " ('NOHRD', 284),\n",
       " ('GBPTL', 280),\n",
       " ('NORIV', 278),\n",
       " ('GBINV', 277),\n",
       " ('ITPNG', 273),\n",
       " ('ESSCI', 273),\n",
       " ('FRSML', 272),\n",
       " ('DKKOL', 272),\n",
       " ('SEHAK', 272),\n",
       " ('GBLAR', 272),\n",
       " ('CYVAS', 269),\n",
       " ('PLPLC', 268),\n",
       " ('IEAUG', 265),\n",
       " ('LVSKU', 264),\n",
       " ('NOKVD', 263),\n",
       " ('NOSLV', 263),\n",
       " ('GBDUN', 263),\n",
       " ('EEVAN', 261),\n",
       " ('DENHA', 261),\n",
       " ('EEPLN', 252),\n",
       " ('GRKAK', 251),\n",
       " ('PTFDF', 251),\n",
       " ('NOAVE', 250),\n",
       " ('NOREK', 250),\n",
       " ('DKSTT', 250),\n",
       " ('GRJSY', 249),\n",
       " ('NOMJF', 247),\n",
       " ('ITMDC', 247),\n",
       " ('ITLMP', 245),\n",
       " ('NOMOB', 244),\n",
       " ('CYLCA', 243),\n",
       " ('FILPP', 241),\n",
       " ('NOSAU', 241),\n",
       " ('FIINK', 241),\n",
       " ('GRNKV', 238),\n",
       " ('NOMQN', 236),\n",
       " ('NOKAR', 234),\n",
       " ('ITCHI', 233),\n",
       " ('DEKEL', 230),\n",
       " ('GRAIO', 225),\n",
       " ('GRASS', 225),\n",
       " ('NOHOA', 224),\n",
       " ('ITRMA', 224),\n",
       " ('ISAKU', 221),\n",
       " ('NOBJF', 220),\n",
       " ('SEHAH', 220),\n",
       " ('ITMSN', 219),\n",
       " ('NOPOR', 218),\n",
       " ('HRSIB', 218),\n",
       " ('DKSVV', 217),\n",
       " ('NOLAO', 216),\n",
       " ('NORYF', 216),\n",
       " ('GBSUN', 215),\n",
       " ('NOZAA', 214),\n",
       " ('GBSWA', 213),\n",
       " ('FRPOV', 209),\n",
       " ('ISMJH', 209),\n",
       " ('DKODE', 208),\n",
       " ('PTPDL', 206),\n",
       " ('IEFOV', 204),\n",
       " ('GBGRW', 204),\n",
       " ('ITTPS', 203),\n",
       " ('IERDT', 203),\n",
       " ('SESOL', 203),\n",
       " ('GBSSS', 203),\n",
       " ('GBBOS', 202),\n",
       " ('NOVER', 200),\n",
       " ('DKVEJ', 200),\n",
       " ('GBNHO', 200),\n",
       " ('IEMTL', 200),\n",
       " ('ROAGI', 199),\n",
       " ('FILOV', 197),\n",
       " ('CYZYY', 197),\n",
       " ('NORVK', 195),\n",
       " ('GBWPT', 195),\n",
       " ('GBKLN', 194),\n",
       " ('IEWAT', 193),\n",
       " ('NOSRS', 192),\n",
       " ('GBLDY', 192),\n",
       " ('IETRP', 191),\n",
       " ('NOZFR', 190),\n",
       " ('DKKRR', 186),\n",
       " ('DESTL', 185),\n",
       " ('IEGWY', 184),\n",
       " ('IEWHI', 184),\n",
       " ('FIVAA', 183),\n",
       " ('GRKMI', 180),\n",
       " ('ITGAI', 180),\n",
       " ('GBLOW', 180),\n",
       " ('GRTSI', 179),\n",
       " ('NOSVJ', 178),\n",
       " ('FRFEC', 176),\n",
       " ('GRJKH', 174),\n",
       " ('FRLSO', 173),\n",
       " ('DKAVE', 173),\n",
       " ('ESGAN', 172),\n",
       " ('NONVK', 169),\n",
       " ('ESMAH', 168),\n",
       " ('GBBLY', 168),\n",
       " ('NOAGO', 167),\n",
       " ('GBBFB', 167),\n",
       " ('NOVAK', 166),\n",
       " ('GBSSH', 166),\n",
       " ('SEOER', 163),\n",
       " ('SELAA', 163),\n",
       " ('NOSTU', 163),\n",
       " ('NOSIP', 161),\n",
       " ('NOSPG', 161),\n",
       " ('EEBEK', 161),\n",
       " ('ESGAR', 160),\n",
       " ('DEHUS', 159),\n",
       " ('ESSSG', 158),\n",
       " ('NOIKR', 158),\n",
       " ('HRKOR', 154),\n",
       " ('GBCOW', 153),\n",
       " ('ITGAE', 152),\n",
       " ('ESCBZ', 151),\n",
       " ('GRLFK', 150),\n",
       " ('DKAAB', 150),\n",
       " ('NOALF', 150),\n",
       " ('NOLVG', 148),\n",
       " ('EEVEB', 148),\n",
       " ('NOORK', 146),\n",
       " ('GRAGE', 146),\n",
       " ('GBKWL', 144),\n",
       " ('ITPVE', 144),\n",
       " ('FIPRS', 144),\n",
       " ('NOFNE', 142),\n",
       " ('GRCLK', 142),\n",
       " ('DKGRE', 142),\n",
       " ('ROBRA', 141),\n",
       " ('NOHLL', 140),\n",
       " ('NOORS', 140),\n",
       " ('ITOTN', 140),\n",
       " ('GBGTN', 140),\n",
       " ('ITPSR', 140),\n",
       " ('ITCVX', 138),\n",
       " ('EERMS', 136),\n",
       " ('NOHLE', 136),\n",
       " ('GBTNM', 136),\n",
       " ('NOMAN', 135),\n",
       " ('NOFDE', 135),\n",
       " ('NOHSA', 134),\n",
       " ('ITPTF', 132),\n",
       " ('NOSKE', 132),\n",
       " ('LVMRX', 132),\n",
       " ('ITFAL', 131),\n",
       " ('GBSEA', 130),\n",
       " ('ITCRV', 130),\n",
       " ('SESUS', 130),\n",
       " ('NOMOL', 128),\n",
       " ('DEPAP', 128),\n",
       " ('NOELN', 128),\n",
       " ('GRKGS', 127),\n",
       " ('FIKNT', 127),\n",
       " ('NOKRV', 126),\n",
       " ('PTPRV', 126),\n",
       " ('ISISA', 126),\n",
       " ('SEWAL', 126),\n",
       " ('HRPMN', 126),\n",
       " ('NOOLN', 125),\n",
       " ('DKLIP', 125),\n",
       " ('NOSLX', 124),\n",
       " ('ESRIB', 124),\n",
       " ('IETIV', 124),\n",
       " ('SEKAS', 124),\n",
       " ('NOKMO', 123),\n",
       " ('FRRCO', 123),\n",
       " ('ITPVT', 123),\n",
       " ('GBSTO', 122),\n",
       " ('EEMRS', 122),\n",
       " ('FRILR', 122),\n",
       " ('ITBLT', 121),\n",
       " ('SEUDD', 121),\n",
       " ('IEKBS', 121),\n",
       " ('FIEJO', 120),\n",
       " ('NOFIS', 120),\n",
       " ('NOBOE', 120),\n",
       " ('FRSBK', 119),\n",
       " ('GRSDH', 117),\n",
       " ('DKHAN', 117),\n",
       " ('ITPZL', 117),\n",
       " ('ITNTA', 117),\n",
       " ('ITGIA', 116),\n",
       " ('NOSKN', 116),\n",
       " ('ITSIR', 116),\n",
       " ('GRKLX', 116),\n",
       " ('NOTON', 116),\n",
       " ('GBINK', 116),\n",
       " ('FIKJO', 116),\n",
       " ('GRAXD', 116),\n",
       " ('ITLIP', 114),\n",
       " ('PTHOR', 114),\n",
       " ('NOJOS', 114),\n",
       " ('HRHVA', 114),\n",
       " ('ITQOS', 114),\n",
       " ('NOSKY', 113),\n",
       " ('SESLI', 112),\n",
       " ('NOBVR', 112),\n",
       " ('NOSVE', 111),\n",
       " ('NOGLO', 111),\n",
       " ('NOZOS', 111),\n",
       " ('NODYD', 111),\n",
       " ('GRPMS', 110),\n",
       " ('GRARM', 110),\n",
       " ('NOMVV', 110),\n",
       " ('HRPLE', 110),\n",
       " ('NOSTE', 110),\n",
       " ('GBGUW', 110),\n",
       " ('FRPVO', 110),\n",
       " ('DKROR', 109),\n",
       " ('GBPOO', 109),\n",
       " ('NOHVG', 108),\n",
       " ('NOKRA', 108),\n",
       " ('NOAHM', 108),\n",
       " ('LVSAL', 108),\n",
       " ('GBHPT', 108),\n",
       " ('IERIN', 106),\n",
       " ('FIPRV', 106),\n",
       " ('SEROV', 106),\n",
       " ('SESDL', 105),\n",
       " ('ITMNP', 104),\n",
       " ('GRYLI', 104),\n",
       " ('GRAST', 104),\n",
       " ('GRDRE', 104),\n",
       " ('NOBGN', 104),\n",
       " ('FRLTR', 104),\n",
       " ('NOODD', 103),\n",
       " ('FRNOU', 103),\n",
       " ('NOSUN', 102),\n",
       " ('IEDFT', 102),\n",
       " ('EEPAS', 101),\n",
       " ('GRSTN', 101),\n",
       " ('GBHTP', 101),\n",
       " ('GBTTL', 100),\n",
       " ('NOETN', 98),\n",
       " ('DEBUM', 98),\n",
       " ('ISGRT', 98),\n",
       " ('ITVVA', 98),\n",
       " ('DKASV', 98),\n",
       " ('ESVIL', 97),\n",
       " ('GRNMA', 96),\n",
       " ('GBSUL', 96),\n",
       " ('GRJSH', 95),\n",
       " ('NOLIT', 94),\n",
       " ('NOAVA', 94),\n",
       " ('SEKOG', 94),\n",
       " ('SEELL', 94),\n",
       " ('ITRRO', 94),\n",
       " ('IELMK', 94),\n",
       " ('HRVIS', 94),\n",
       " ('NOHAL', 93),\n",
       " ('SEOST', 92),\n",
       " ('SEKAF', 92),\n",
       " ('SIPIR', 91),\n",
       " ('GRPSA', 91),\n",
       " ('SEHAN', 91),\n",
       " ('NOGNR', 90),\n",
       " ('NOKPG', 90),\n",
       " ('PLKOL', 90),\n",
       " ('GBPTB', 90),\n",
       " ('NODIR', 88),\n",
       " ('NOTBO', 88),\n",
       " ('GRLRY', 87),\n",
       " ('DEGLU', 87),\n",
       " ('ISNES', 86),\n",
       " ('DKSTR', 86),\n",
       " ('NOHFL', 86),\n",
       " ('NOOSY', 84),\n",
       " ('NOOKF', 84),\n",
       " ('SEOSK', 84),\n",
       " ('NOHAV', 84),\n",
       " ('NOZFF', 84),\n",
       " ('NOHOL', 82),\n",
       " ('SEFAG', 82),\n",
       " ('NOVDS', 82),\n",
       " ('BESVG', 82),\n",
       " ('GRSOU', 81),\n",
       " ('GRAPL', 80),\n",
       " ('FRDPE', 80),\n",
       " ('HRTRO', 80),\n",
       " ('DEWHV', 80),\n",
       " ('GBPSB', 80),\n",
       " ('NOGHV', 80),\n",
       " ('GBFLW', 80),\n",
       " ('IEROE', 80),\n",
       " ('LTBOT', 80),\n",
       " ('ITIMP', 78),\n",
       " ('GBFLH', 78),\n",
       " ('DKHUN', 78),\n",
       " ('GBBAD', 78),\n",
       " ('NOEDF', 76),\n",
       " ('PTVDC', 75),\n",
       " ('SEKXV', 74),\n",
       " ('NOFNY', 74),\n",
       " ('DKSKV', 74),\n",
       " ('NOFOL', 74),\n",
       " ('ITLIC', 74),\n",
       " ('DKSIT', 73),\n",
       " ('ITVSO', 73),\n",
       " ('IEGRN', 73),\n",
       " ('ITFCO', 73),\n",
       " ('GRGMA', 73),\n",
       " ('GRNAF', 72),\n",
       " ('NOSUR', 72),\n",
       " ('NOAND', 72),\n",
       " ('NOHRI', 72),\n",
       " ('NOFFB', 72),\n",
       " ('FIPAR', 72),\n",
       " ('GRPTL', 71),\n",
       " ('NOZNV', 71),\n",
       " ('GBAYR', 70),\n",
       " ('GRITA', 70),\n",
       " ('NOHOR', 70),\n",
       " ('DKHOR', 70),\n",
       " ('ISHUS', 70),\n",
       " ('SESSR', 70),\n",
       " ('DKKON', 69),\n",
       " ('ITTRI', 68),\n",
       " ('SESAE', 68),\n",
       " ('GRKLM', 67),\n",
       " ('NOEYD', 67),\n",
       " ('GBSFW', 66),\n",
       " ('GRRET', 66),\n",
       " ('DKNVD', 66),\n",
       " ('NOSAT', 66),\n",
       " ('NOZFO', 66),\n",
       " ('SERUV', 66),\n",
       " ('HRRAS', 65),\n",
       " ('NOARD', 65),\n",
       " ('ROMAG', 65),\n",
       " ('SELDK', 65),\n",
       " ('FIJOU', 65),\n",
       " ('ITTMI', 65),\n",
       " ('GRADL', 64),\n",
       " ('NOHEL', 64),\n",
       " ('NOSAG', 64),\n",
       " ('GBNSH', 64),\n",
       " ('NOHSV', 64),\n",
       " ('IERSM', 64),\n",
       " ('HRSGD', 63),\n",
       " ('GRSYM', 62),\n",
       " ('HRBAK', 62),\n",
       " ('DEFLF', 62),\n",
       " ('DEREN', 62),\n",
       " ('SEHOG', 62),\n",
       " ('GRTHP', 61),\n",
       " ('ITOTO', 61),\n",
       " ('NOASV', 60),\n",
       " ('NOUTH', 60),\n",
       " ('NOKJF', 60),\n",
       " ('FIJOE', 60),\n",
       " ('EELHT', 59),\n",
       " ('FIKUO', 59),\n",
       " ('FIMHQ', 58),\n",
       " ('NOSLH', 58),\n",
       " ('ESPAL', 57),\n",
       " ('NOZRD', 57),\n",
       " ('ITMFR', 57),\n",
       " ('GBROY', 56),\n",
       " ('NOTSX', 56),\n",
       " ('HRPUY', 56),\n",
       " ('NOULS', 56),\n",
       " ('ISVES', 56),\n",
       " ('NOHSY', 56),\n",
       " ('NOVEY', 55),\n",
       " ('PLWLA', 55),\n",
       " ('NOSRP', 55),\n",
       " ('NOMEK', 55),\n",
       " ('GRKVA', 54),\n",
       " ('DKRNN', 54),\n",
       " ('GRPME', 54),\n",
       " ('PLDAR', 54),\n",
       " ('FIKIM', 54),\n",
       " ('SEBEA', 54),\n",
       " ('FIKTQ', 54),\n",
       " ('FRBOL', 54),\n",
       " ('NOSRV', 53),\n",
       " ('NOSTS', 52),\n",
       " ('ESPUS', 52),\n",
       " ('NOHVI', 52),\n",
       " ('DKROD', 52),\n",
       " ('DKNAK', 52),\n",
       " ('ISHAF', 52),\n",
       " ('ITCGC', 52),\n",
       " ('GBFIS', 52),\n",
       " ('ISTHH', 51),\n",
       " ('FITOK', 51),\n",
       " ('SELJU', 51),\n",
       " ('NOZFM', 50),\n",
       " ('NOKOP', 50),\n",
       " ('NOELO', 50),\n",
       " ('NOVST', 50),\n",
       " ('DKKBY', 50),\n",
       " ('FISII', 50),\n",
       " ('ITTMT', 50),\n",
       " ('GRZTH', 49),\n",
       " ('NOARE', 49),\n",
       " ('GBFOY', 49),\n",
       " ('NOHYR', 49),\n",
       " ('SEKAA', 49),\n",
       " ('ITGAL', 48),\n",
       " ('NOEDE', 48),\n",
       " ('NOSYK', 48),\n",
       " ('FRCFR', 48),\n",
       " ('SEVVK', 48),\n",
       " ('GRRIO', 47),\n",
       " ('GRLIN', 47),\n",
       " ('ESALD', 47),\n",
       " ('NOBJU', 47),\n",
       " ('GRPAS', 46),\n",
       " ('ESVLG', 46),\n",
       " ('NOLRD', 46),\n",
       " ('NOMOV', 46),\n",
       " ('NOHJL', 46),\n",
       " ('NOSTY', 46),\n",
       " ('IEDDK', 46),\n",
       " ('GBHDD', 46),\n",
       " ('ISRFJ', 46),\n",
       " ('ESCNO', 46),\n",
       " ('FRDON', 46),\n",
       " ('GBNCL', 45),\n",
       " ('GRPVK', 45),\n",
       " ('NOSVB', 45),\n",
       " ('NOAAV', 44),\n",
       " ('NOZAF', 44),\n",
       " ('NOMAH', 44),\n",
       " ('ESCEE', 44),\n",
       " ('DEVIW', 44),\n",
       " ('DKHBO', 44),\n",
       " ('GBGSA', 44),\n",
       " ('GBNEA', 44),\n",
       " ('IENRR', 44),\n",
       " ('DEWYK', 44),\n",
       " ('SESKN', 44),\n",
       " ('NOLKN', 44),\n",
       " ('BEGNK', 44),\n",
       " ('GRSYS', 43),\n",
       " ('NOFLA', 42),\n",
       " ('EEHLT', 42),\n",
       " ('HRLSZ', 42),\n",
       " ('GRKTS', 42),\n",
       " ('NOMEL', 42),\n",
       " ('ITMOL', 42),\n",
       " ('GBLLD', 42),\n",
       " ('SESKE', 42),\n",
       " ('ITPRW', 41),\n",
       " ('FRCEQ', 40),\n",
       " ('GRHYD', 40),\n",
       " ('NOGUD', 40),\n",
       " ('NOFAN', 40),\n",
       " ('ISFAS', 40),\n",
       " ('ITTOA', 40),\n",
       " ('ESACA', 40),\n",
       " ('FRTON', 40),\n",
       " ('SESOR', 40),\n",
       " ('NOOYB', 40),\n",
       " ('FIMER', 40),\n",
       " ('SEHBV', 40),\n",
       " ('GRKAR', 40),\n",
       " ('NOMSD', 40),\n",
       " ('GBGLW', 40),\n",
       " ('FIVRK', 40),\n",
       " ('DEWOL', 39),\n",
       " ('FIFAR', 39),\n",
       " ('SEYST', 39),\n",
       " ('GRSPE', 38),\n",
       " ('GRMON', 38),\n",
       " ('FISVL', 38),\n",
       " ('SEVBY', 38),\n",
       " ('NOBLL', 38),\n",
       " ('BEWLB', 38),\n",
       " ('ISTHO', 38),\n",
       " ('IESNR', 38),\n",
       " ('DEAGE', 38),\n",
       " ('ISSAU', 38),\n",
       " ('IEPAW', 38),\n",
       " ('BEHEX', 38),\n",
       " ('GRANI', 36),\n",
       " ('ITSML', 36),\n",
       " ('GRELT', 36),\n",
       " ('NOLND', 36),\n",
       " ('NORSH', 36),\n",
       " ('GRMYL', 36),\n",
       " ('SESTV', 36),\n",
       " ('FIFOR', 36),\n",
       " ('NOSJH', 36),\n",
       " ('SEVAJ', 36),\n",
       " ('GRMYR', 35),\n",
       " ('ESZFR', 35),\n",
       " ('NOMYO', 35),\n",
       " ('NOBNN', 34),\n",
       " ('NOSLO', 34),\n",
       " ('NOHRS', 34),\n",
       " ('ISBIL', 34),\n",
       " ('FRMTX', 34),\n",
       " ('SEMUN', 34),\n",
       " ('GRSAI', 33),\n",
       " ('DELEE', 33),\n",
       " ('NOSVI', 33),\n",
       " ('NOHIT', 33),\n",
       " ('DKRMO', 32),\n",
       " ('NOSKV', 32),\n",
       " ('NOHFF', 32),\n",
       " ('NOFLY', 32),\n",
       " ('NOTAV', 32),\n",
       " ('DKSTP', 32),\n",
       " ('GRATK', 32),\n",
       " ('HRZAD', 32),\n",
       " ('IEWIC', 32),\n",
       " ('SELUG', 32),\n",
       " ('IECOB', 31),\n",
       " ('IECBR', 31),\n",
       " ('NOSAF', 30),\n",
       " ('NOVLL', 30),\n",
       " ('NOSSS', 30),\n",
       " ('ISVPN', 30),\n",
       " ('DKASH', 30),\n",
       " ('SEHON', 30),\n",
       " ('SEKSD', 30),\n",
       " ('DKDAS', 30),\n",
       " ('SESTK', 30),\n",
       " ('FIPOH', 30),\n",
       " ('NOZHF', 30),\n",
       " ('DKENS', 30),\n",
       " ('DKGED', 30),\n",
       " ('GRFIS', 29),\n",
       " ('NOHOT', 29),\n",
       " ('GRKRT', 29),\n",
       " ('NOELL', 29),\n",
       " ('PTPRM', 28),\n",
       " ('SIIZO', 28),\n",
       " ('NOSDN', 28),\n",
       " ('NOBKS', 28),\n",
       " ('NOHUS', 28),\n",
       " ('NOLOD', 28),\n",
       " ('BEBON', 28),\n",
       " ('ISHFN', 28),\n",
       " ('NOSBA', 27),\n",
       " ('NOVIS', 26),\n",
       " ('DKORE', 26),\n",
       " ('DKHVS', 26),\n",
       " ('NORMV', 26),\n",
       " ('ROTCE', 26),\n",
       " ('NLHAN', 26),\n",
       " ('DKVOR', 26),\n",
       " ('ISESK', 26),\n",
       " ('ESLES', 26),\n",
       " ('NOLOV', 25),\n",
       " ('GRMHI', 24),\n",
       " ('NONHS', 24),\n",
       " ('NOVSN', 24),\n",
       " ('NOFFD', 24),\n",
       " ('NOGRV', 24),\n",
       " ('NOLIL', 24),\n",
       " ('NOHES', 24),\n",
       " ('DKNYF', 24),\n",
       " ('DKFAK', 24),\n",
       " ('DEGRD', 24),\n",
       " ('GRMXK', 24),\n",
       " ('IETQY', 24),\n",
       " ('FRCLY', 24),\n",
       " ('DEHGL', 24),\n",
       " ('GBBAW', 24),\n",
       " ('IEWID', 24),\n",
       " ('HROMI', 24),\n",
       " ('NOSTJ', 23),\n",
       " ('GRPLG', 23),\n",
       " ('GBCOR', 23),\n",
       " ('NOFNN', 22),\n",
       " ('NOGTD', 22),\n",
       " ('NOUME', 22),\n",
       " ('NOESP', 22),\n",
       " ('NOTOM', 22),\n",
       " ('NOSOS', 22),\n",
       " ('SEOSB', 22),\n",
       " ('GBSIL', 22),\n",
       " ('ISNJA', 22),\n",
       " ('ISSTR', 22),\n",
       " ('SEHND', 22),\n",
       " ('GBBRW', 22),\n",
       " ('NOZNN', 22),\n",
       " ('FRROS', 22),\n",
       " ('CYVTE', 22),\n",
       " ('NOOLD', 22),\n",
       " ('SEKHN', 22),\n",
       " ('GRPYL', 21),\n",
       " ('NOKVG', 21),\n",
       " ('GRPAX', 20),\n",
       " ('SEKLI', 20),\n",
       " ('GRNIS', 20),\n",
       " ('NOBYS', 20),\n",
       " ('NOHOK', 20),\n",
       " ('NOLYF', 20),\n",
       " ('NOZXA', 20),\n",
       " ('DKGFH', 20),\n",
       " ('GBKLR', 20),\n",
       " ('DKNYM', 20),\n",
       " ('PTVEL', 20),\n",
       " ('NOLRI', 20),\n",
       " ('IEROS', 20),\n",
       " ('SEKUB', 20),\n",
       " ('NOHRR', 20),\n",
       " ('NOSVA', 19),\n",
       " ('NOTRN', 19),\n",
       " ('GRPHE', 18),\n",
       " ('GRPTR', 18),\n",
       " ('GRIOS', 18),\n",
       " ('NOBTS', 18),\n",
       " ('GRPKK', 18),\n",
       " ('ESROT', 18),\n",
       " ('EERHK', 18),\n",
       " ('GRVTH', 18),\n",
       " ('GRGYT', 18),\n",
       " ('DESAS', 18),\n",
       " ('NOVTS', 18),\n",
       " ('NODIM', 18),\n",
       " ('NORUB', 18),\n",
       " ('NORDB', 18),\n",
       " ('NOOTO', 18),\n",
       " ('GRAEN', 18),\n",
       " ('NOKAH', 18),\n",
       " ('NONSN', 18),\n",
       " ('HRKRK', 18),\n",
       " ('NOMLK', 18),\n",
       " ('NOBUV', 18),\n",
       " ('IEDPP', 18),\n",
       " ('PTCDP', 18),\n",
       " ('SEOTT', 18),\n",
       " ('ITPRJ', 18),\n",
       " ('IEFEN', 18),\n",
       " ('SEBER', 18),\n",
       " ('DKSSV', 18),\n",
       " ('HRSVK', 18),\n",
       " ('CYDHK', 18),\n",
       " ('GBFNT', 18),\n",
       " ('NOLRE', 18),\n",
       " ('NOAUE', 18),\n",
       " ('GRACL', 17),\n",
       " ('GBNEH', 17),\n",
       " ('GRITH', 16),\n",
       " ('GRPIS', 16),\n",
       " ('DKSTN', 16),\n",
       " ('NOSTH', 16),\n",
       " ('NOGPN', 16),\n",
       " ('HRROV', 16),\n",
       " ('NOBOG', 16),\n",
       " ('NOVNN', 16),\n",
       " ('GBPER', 16),\n",
       " ('DKNRS', 16),\n",
       " ('NOVAO', 16),\n",
       " ('NOSKU', 16),\n",
       " ('NOZHI', 16),\n",
       " ('ITGEA', 16),\n",
       " ('GRDOS', 16),\n",
       " ('ITVIA', 15),\n",
       " ('NOBVG', 15),\n",
       " ('GRREV', 15),\n",
       " ('GRANT', 15),\n",
       " ('NOBRV', 15),\n",
       " ('NOHEN', 14),\n",
       " ('GRJSI', 14),\n",
       " ('GRVLM', 14),\n",
       " ('NOFLO', 14),\n",
       " ('NOGIK', 14),\n",
       " ('NOBLV', 14),\n",
       " ('NOALV', 14),\n",
       " ('NOFED', 14),\n",
       " ('NOEDS', 14),\n",
       " ('NOSAD', 14),\n",
       " ('NOSPV', 14),\n",
       " ('DKLHH', 14),\n",
       " ('NOSRA', 14),\n",
       " ('DKVSV', 14),\n",
       " ('GBNHV', 14),\n",
       " ('ITPOZ', 14),\n",
       " ('SEVAN', 14),\n",
       " ('NOVAE', 14),\n",
       " ('GBKEA', 14),\n",
       " ...]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(list(G.degree(weight='weight')), key=lambda x: x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19999089-a1ae-4b08-b9f5-ae333d4ea5b1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "12e05678aec5d01903c565c4746362f9679a97f0739a2bb5a61d0e442f10996c"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}