Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "099679b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import dynetworkx as dnx\n",
    "import pandas as pd\n",
    "import multiprocessing as mp\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import powerlaw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8bf94592",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('temp/graphs.pickle','rb') as f:\n",
    "    graphs = pickle.load(f)"
   ]
  },
  {
   "attachments": {
    "motifs.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "062c27d3",
   "metadata": {},
   "source": [
    "![motifs.png](attachment:motifs.png)\n",
    "\n",
    "Figure credit: Paranjape, A., Benson, A.R., Leskovec, J.: Motifs in temporal networks. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 601-610.  (2017) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3ad64cfd",
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# This list all of the edge orders for the motifs in the table above\n",
    "motifs = [[((1,2),(3,2),(1,2)), ((1,2),(3,2),(2,1)), ((1,2),(3,2),(1,3)), ((1,2),(3,2),(3,1)), ((1,2),(3,2),(2,3)), ((1,2),(3,2),(3,2))],\n",
    "          [((1,2),(2,3),(1,2)), ((1,2),(2,3),(2,1)), ((1,2),(2,3),(1,3)), ((1,2),(2,3),(3,1)), ((1,2),(2,3),(2,3)), ((1,2),(2,3),(3,2))],\n",
    "          [((1,2),(3,1),(1,2)), ((1,2),(3,1),(2,1)), ((1,2),(3,1),(1,3)), ((1,2),(3,1),(3,1)), ((1,2),(3,1),(2,3)), ((1,2),(3,1),(3,2))],\n",
    "          [((1,2),(1,3),(1,2)), ((1,2),(1,3),(2,1)), ((1,2),(1,3),(1,3)), ((1,2),(1,3),(3,1)), ((1,2),(1,3),(2,3)), ((1,2),(1,3),(3,2))],\n",
    "          [((1,2),(2,1),(1,2)), ((1,2),(2,1),(2,1)), ((1,2),(2,1),(1,3)), ((1,2),(2,1),(3,1)), ((1,2),(2,1),(2,3)), ((1,2),(2,1),(3,2))],\n",
    "          [((1,2),(1,2),(1,2)), ((1,2),(1,2),(2,1)), ((1,2),(1,2),(1,3)), ((1,2),(1,2),(3,1)), ((1,2),(1,2),(2,3)), ((1,2),(1,2),(3,2))]]\n",
    "\n",
    "mot = [ seq for row in motifs for seq in row]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1879383c",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Calculate motifs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "148a29aa",
   "metadata": {
    "code_folding": [
     3
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "table = list()\n",
    "dict_table = list()\n",
    "\n",
    "for g in graphs:\n",
    "    count_dict = list()\n",
    "    pool = mp.Pool(mp.cpu_count())\n",
    "    results = pool.starmap_async(dnx.count_temporal_motif, [(g, seq, 7*86400, True) for seq in mot]).get()\n",
    "    pool.close()\n",
    "    \n",
    "    count_dict = [results[0:6], results[6:12], results[12:18], results[18:24], results[24:30], results[30:36]]\n",
    "    dict_table.append(count_dict)\n",
    "    \n",
    "    count = list()\n",
    "    for d in results:\n",
    "        count.append(sum(d.values()))\n",
    "    table.append(count)\n",
    "    \n",
    "count_table = pd.DataFrame(table, columns=range(1,37))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "84b62ad5",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>22</td>\n",
       "      <td>315</td>\n",
       "      <td>6423</td>\n",
       "      <td>333</td>\n",
       "      <td>101</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>132</td>\n",
       "      <td>409</td>\n",
       "      <td>161</td>\n",
       "      <td>358</td>\n",
       "      <td>1884</td>\n",
       "      <td>298</td>\n",
       "      <td>947</td>\n",
       "      <td>191</td>\n",
       "      <td>392</td>\n",
       "      <td>6807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9190</td>\n",
       "      <td>291</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>295</td>\n",
       "      <td>6406</td>\n",
       "      <td>336</td>\n",
       "      <td>100</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>433</td>\n",
       "      <td>162</td>\n",
       "      <td>364</td>\n",
       "      <td>1851</td>\n",
       "      <td>297</td>\n",
       "      <td>945</td>\n",
       "      <td>205</td>\n",
       "      <td>392</td>\n",
       "      <td>6786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9139</td>\n",
       "      <td>288</td>\n",
       "      <td>27</td>\n",
       "      <td>24</td>\n",
       "      <td>297</td>\n",
       "      <td>6394</td>\n",
       "      <td>337</td>\n",
       "      <td>101</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>131</td>\n",
       "      <td>425</td>\n",
       "      <td>161</td>\n",
       "      <td>345</td>\n",
       "      <td>1838</td>\n",
       "      <td>296</td>\n",
       "      <td>946</td>\n",
       "      <td>206</td>\n",
       "      <td>392</td>\n",
       "      <td>6801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9196</td>\n",
       "      <td>303</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>280</td>\n",
       "      <td>6396</td>\n",
       "      <td>335</td>\n",
       "      <td>100</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>131</td>\n",
       "      <td>442</td>\n",
       "      <td>161</td>\n",
       "      <td>356</td>\n",
       "      <td>1862</td>\n",
       "      <td>299</td>\n",
       "      <td>941</td>\n",
       "      <td>204</td>\n",
       "      <td>393</td>\n",
       "      <td>6798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9191</td>\n",
       "      <td>290</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>294</td>\n",
       "      <td>6422</td>\n",
       "      <td>337</td>\n",
       "      <td>100</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>431</td>\n",
       "      <td>161</td>\n",
       "      <td>358</td>\n",
       "      <td>1855</td>\n",
       "      <td>299</td>\n",
       "      <td>942</td>\n",
       "      <td>203</td>\n",
       "      <td>393</td>\n",
       "      <td>6750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9170</td>\n",
       "      <td>283</td>\n",
       "      <td>29</td>\n",
       "      <td>23</td>\n",
       "      <td>302</td>\n",
       "      <td>6391</td>\n",
       "      <td>333</td>\n",
       "      <td>101</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>131</td>\n",
       "      <td>429</td>\n",
       "      <td>161</td>\n",
       "      <td>356</td>\n",
       "      <td>1869</td>\n",
       "      <td>299</td>\n",
       "      <td>948</td>\n",
       "      <td>202</td>\n",
       "      <td>392</td>\n",
       "      <td>6785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9172</td>\n",
       "      <td>264</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>321</td>\n",
       "      <td>6388</td>\n",
       "      <td>335</td>\n",
       "      <td>100</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>415</td>\n",
       "      <td>162</td>\n",
       "      <td>357</td>\n",
       "      <td>1871</td>\n",
       "      <td>299</td>\n",
       "      <td>943</td>\n",
       "      <td>205</td>\n",
       "      <td>393</td>\n",
       "      <td>6845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9269</td>\n",
       "      <td>307</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>278</td>\n",
       "      <td>6429</td>\n",
       "      <td>336</td>\n",
       "      <td>100</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>129</td>\n",
       "      <td>447</td>\n",
       "      <td>162</td>\n",
       "      <td>354</td>\n",
       "      <td>1860</td>\n",
       "      <td>296</td>\n",
       "      <td>944</td>\n",
       "      <td>200</td>\n",
       "      <td>393</td>\n",
       "      <td>6686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9262</td>\n",
       "      <td>295</td>\n",
       "      <td>28</td>\n",
       "      <td>24</td>\n",
       "      <td>286</td>\n",
       "      <td>6390</td>\n",
       "      <td>334</td>\n",
       "      <td>101</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>131</td>\n",
       "      <td>416</td>\n",
       "      <td>162</td>\n",
       "      <td>351</td>\n",
       "      <td>1865</td>\n",
       "      <td>293</td>\n",
       "      <td>946</td>\n",
       "      <td>204</td>\n",
       "      <td>393</td>\n",
       "      <td>6711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9156</td>\n",
       "      <td>298</td>\n",
       "      <td>27</td>\n",
       "      <td>24</td>\n",
       "      <td>285</td>\n",
       "      <td>6312</td>\n",
       "      <td>336</td>\n",
       "      <td>101</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>131</td>\n",
       "      <td>432</td>\n",
       "      <td>162</td>\n",
       "      <td>355</td>\n",
       "      <td>1875</td>\n",
       "      <td>294</td>\n",
       "      <td>947</td>\n",
       "      <td>197</td>\n",
       "      <td>392</td>\n",
       "      <td>6876</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     1    2   3   4    5     6    7    8   9   10  ...   27   28   29   30  \\\n",
       "0  9133  270  30  22  315  6423  333  101  23   2  ...  132  409  161  358   \n",
       "1  9190  291  30  22  295  6406  336  100  20   2  ...  130  433  162  364   \n",
       "2  9139  288  27  24  297  6394  337  101  19   2  ...  131  425  161  345   \n",
       "3  9196  303  30  21  280  6396  335  100  19   2  ...  131  442  161  356   \n",
       "4  9191  290  28  23  294  6422  337  100  22   2  ...  130  431  161  358   \n",
       "5  9170  283  29  23  302  6391  333  101  20   2  ...  131  429  161  356   \n",
       "6  9172  264  30  22  321  6388  335  100  20   2  ...  130  415  162  357   \n",
       "7  9269  307  30  22  278  6429  336  100  23   2  ...  129  447  162  354   \n",
       "8  9262  295  28  24  286  6390  334  101  23   2  ...  131  416  162  351   \n",
       "9  9156  298  27  24  285  6312  336  101  20   2  ...  131  432  162  355   \n",
       "\n",
       "     31   32   33   34   35    36  \n",
       "0  1884  298  947  191  392  6807  \n",
       "1  1851  297  945  205  392  6786  \n",
       "2  1838  296  946  206  392  6801  \n",
       "3  1862  299  941  204  393  6798  \n",
       "4  1855  299  942  203  393  6750  \n",
       "5  1869  299  948  202  392  6785  \n",
       "6  1871  299  943  205  393  6845  \n",
       "7  1860  296  944  200  393  6686  \n",
       "8  1865  293  946  204  393  6711  \n",
       "9  1875  294  947  197  392  6876  \n",
       "\n",
       "[10 rows x 36 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_table # 10 graphs x 36 motif types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f079ad7d",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "with open('temp/count_result.pickle','wb') as f:\n",
    "    pickle.dump(count_table, f)\n",
    "    \n",
    "with open('temp/dictionary_result.pickle','wb') as f:\n",
    "    pickle.dump(dict_table, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2213fae8",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Average count for each motif types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f9ec11d2",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "with open('temp/count_result.pickle','rb') as f:\n",
    "    count_table = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "81b6cd5b",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "with open('temp/dictionary_result.pickle','rb') as f:\n",
    "    dict_table = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2eb461a2",
   "metadata": {
    "code_folding": [
     0
    ],
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-20-f71efb25a1ca>:11: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(row)\n",
      "<ipython-input-20-f71efb25a1ca>:12: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(column)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "###\n",
    "a = count_table.mean(axis=0)\n",
    "noise_count = [list(a[0:6]), list(a[6:12]), list(a[12:18]), list(a[18:24]), list(a[24:30]), list(a[30:36])]\n",
    "\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "ax = fig.add_subplot(1,2,1)\n",
    "ax.imshow(noise_count, cmap='Blues')\n",
    "\n",
    "column = range(7)\n",
    "row = range(7)\n",
    "ax.set_xticklabels(row)\n",
    "ax.set_yticklabels(column)\n",
    "\n",
    "for i in range(len(noise_count)):\n",
    "    for j in range(len(noise_count[i])):\n",
    "        if noise_count[i][j] >= 100000:\n",
    "            label_text = str(np.round(noise_count[i][j]/10**6,1)) + 'M'\n",
    "        elif noise_count[i][j] >= 100:\n",
    "            label_text = str(np.round(noise_count[i][j]/10**3,1)) + 'K'\n",
    "        else:\n",
    "            label_text = str(int(np.round(noise_count[i][j], 0)))\n",
    "\n",
    "        if noise_count[i][j] < np.average(noise_count) *2.1:\n",
    "            text = ax.text(j, i, label_text,\n",
    "                           ha=\"center\", va=\"center\", color='k')\n",
    "        else:\n",
    "            text = ax.text(j, i, label_text,\n",
    "               ha=\"center\", va=\"center\", color='w')\n",
    "\n",
    "fig.savefig('figures/average_count.svg', format='svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a5a6978",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Motif roles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8213e384",
   "metadata": {
    "code_folding": [
     0,
     14,
     25
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def count_role(count_dict, row_i, col_i ,role_i):\n",
    "    countries = list()\n",
    "    count = list()\n",
    "\n",
    "    for country in graphs[0].nodes():\n",
    "        c = 0\n",
    "        for key, co in count_dict[row_i][col_i].items():\n",
    "            if country in key[role_i]:\n",
    "                c = c + co\n",
    "        countries.append(country)\n",
    "        count.append(c)\n",
    "\n",
    "    return pd.Series(count, countries)\n",
    "\n",
    "def get_role_average(dict_table, row_i, col_i, role_i):\n",
    "    series_list = list()\n",
    "    for d in dict_table:\n",
    "        series_list.append(count_role(d, row_i, col_i, role_i))\n",
    "    \n",
    "    a = pd.concat(series_list, axis=1)\n",
    "    mean = pd.Series(a.mean(axis=1), name='mean')\n",
    "    std = pd.Series(a.std(axis=1), name='std')\n",
    "    \n",
    "    return pd.concat([mean, std], axis=1)\n",
    "\n",
    "def plot_role(dict_table, row_is, col_is, role_i1s, role_i2s, role_i3s=None):\n",
    "    colors = ['#7fc3c0', '#cfb845', '#141414'] \n",
    "    ### create plots ###\n",
    "    fig = plt.figure(figsize=(16,10/3))\n",
    "    width = 0.2\n",
    "    motif_string = []\n",
    "    for i in range(len(row_is)):\n",
    "        motif_string.append('M{}{}'.format(row_is[i]+1, col_is[i]+1))\n",
    "\n",
    "    if not role_i3s is None:\n",
    "        ax = fig.add_subplot(1,3,1)\n",
    "        ax2 = fig.add_subplot(1,3,2)\n",
    "        ax3 = fig.add_subplot(1,3,3)\n",
    "\n",
    "        ax.tick_params(axis='x', labelsize=15 )\n",
    "        ax2.tick_params(axis='x', labelsize=15 )\n",
    "        ax3.tick_params(axis='x', labelsize=15 )\n",
    "    else:\n",
    "        ax = fig.add_subplot(1,2,1)\n",
    "        ax2 = fig.add_subplot(1,2,2)\n",
    "\n",
    "        ax.tick_params(axis='x', labelsize=15 )\n",
    "        ax2.tick_params(axis='x', labelsize=15 )\n",
    "\n",
    "    ax.tick_params(axis='y', labelsize=12 )\n",
    "\n",
    "    ax.set_title('Role 1', fontsize = 17)\n",
    "    ax2.set_title('Role 2', fontsize= 17)\n",
    "    if not role_i3s is None:\n",
    "        ax3.set_title('Role 3', fontsize=17)\n",
    "\n",
    "    ### get values ###\n",
    "    agg1 = list()\n",
    "    agg2 = list()\n",
    "    agg3 = list()\n",
    "    for i in range(len(row_is)):\n",
    "        row_i = row_is[i] \n",
    "        col_i = col_is[i]\n",
    "        role_i1 = role_i1s[i]\n",
    "        role_i2 = role_i2s[i]\n",
    "        if not role_i3s is None:\n",
    "            role_i3 = role_i3s[i]\n",
    "\n",
    "        # get values\n",
    "        role_1 = get_role_average(dict_table, row_i, col_i, role_i1)\n",
    "        role_2 = get_role_average(dict_table, row_i, col_i, role_i2)\n",
    "        agg1.append(role_1['mean'])\n",
    "        agg2.append(role_2['mean'])\n",
    "\n",
    "        if not role_i3s is None:\n",
    "            role_3 = get_role_average(dict_table, row_i, col_i, role_i3)\n",
    "            agg3.append(role_3['mean'])\n",
    "\n",
    "    agg1 = pd.concat(agg1, axis=1)\n",
    "    agg1['mean_average'] = agg1.mean(axis=1)\n",
    "    agg1 = agg1.sort_values(by='mean_average', ascending=False)\n",
    "\n",
    "    agg2 = pd.concat(agg2, axis=1)\n",
    "    agg2['mean_average'] = agg2.mean(axis=1)\n",
    "    agg2 = agg2.sort_values(by='mean_average', ascending=False)\n",
    "\n",
    "    if not role_i3s is None:\n",
    "        agg3 = pd.concat(agg3, axis=1)\n",
    "        agg3['mean_average'] = agg3.mean(axis=1)\n",
    "        agg3 = agg3.sort_values(by='mean_average', ascending=False)\n",
    "\n",
    "    ### plot ###\n",
    "    for i in range(len(row_is)):\n",
    "        x = np.arange(5)\n",
    "        if len(row_is) != 1:\n",
    "            ax.set_xticks(x+0.2)\n",
    "            ax.set_xticklabels(agg1.iloc[:5,i].index)\n",
    "            ax.bar(x + (width * i), agg1.iloc[:5,i], width, \n",
    "                   label='M{}{}'.format(row_is[i]+1, col_is[i]+1), color=colors[i])\n",
    "\n",
    "            ax2.set_xticks(x+0.2)\n",
    "            ax2.set_xticklabels(agg2.iloc[:5,i].index)    \n",
    "            ax2.bar(x + (width * i), agg2.iloc[:5,i], width, color=colors[i])\n",
    "\n",
    "            if not role_i3s is None:\n",
    "                ax3.set_xticks(x+0.2)\n",
    "                ax3.set_xticklabels(agg3.iloc[:5,i].index)    \n",
    "                ax3.bar(x + (width * i), agg3.iloc[:5,i], width, color=colors[i])\n",
    "\n",
    "        else: # This is for plotting single motif type\n",
    "            ax.bar(agg1.iloc[:5,i].index, agg1.iloc[:5,i], label='M{}{}'.format(row_is[i]+1, col_is[i]+1),\n",
    "                   color=colors[i])\n",
    "\n",
    "            ax2.bar(agg2.iloc[:5,i].index, agg2.iloc[:5,i], color=colors[i])\n",
    "\n",
    "    ax.legend()\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "070974d9",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x240 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Motifs M11, M16, M66\n",
    "fig = plot_role(dict_table, [0,0,5], [0,5,5], [0,0,0], [1,1,1], [2,2,4]);\n",
    "fig.savefig('figures/M11_M16_M66.svg', format='svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0f5fed88",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x240 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Motifs M41, M43, M63\n",
    "fig = plot_role(dict_table, [3,3,5], [0,2,2], [0,0,0], [1,1,1], [3,3,5])\n",
    "fig.savefig('figures/M41_M43_M63.svg', format='svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "35d39955",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x240 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Motifs M61\n",
    "fig = plot_role(dict_table, [5], [0], [0], [1])\n",
    "fig.savefig('figures/M61.svg', format='svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cc8ee0e",
   "metadata": {},
   "source": [
    "# Motifs by year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b7c35720",
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# calculate motifs with delta=7 for each year\n",
    "def get_count_frame(begin, end):\n",
    "\n",
    "    table = list()\n",
    "\n",
    "    for g in graphs:\n",
    "        edges = g.edges(begin=begin, end=end)\n",
    "        Gtmp = dnx.ImpulseDiGraph()\n",
    "        for edge in edges:\n",
    "            Gtmp.add_edge(edge[0], edge[1], edge[2])\n",
    "        \n",
    "        count_dict = list()\n",
    "        pool = mp.Pool(mp.cpu_count())\n",
    "        results = pool.starmap_async(dnx.count_temporal_motif, [(Gtmp, seq, 7*86400, True) for seq in mot]).get()\n",
    "        pool.close()\n",
    "\n",
    "        count = list()\n",
    "        for d in results:\n",
    "            count.append(sum(d.values()))\n",
    "        table.append(count)\n",
    "\n",
    "    count_table = pd.DataFrame(table, columns=range(1,37))    \n",
    "    a = count_table.mean(axis=0)\n",
    "    count = [list(a[0:6]), list(a[6:12]), list(a[12:18]), list(a[18:24]), list(a[24:30]), list(a[30:36])]\n",
    "        \n",
    "    return count\n",
    "\n",
    "# get motif count frames for each year\n",
    "frames = list()\n",
    "count_dict_list = list()\n",
    "for year in range(1992, 2015):\n",
    "    begin = datetime.datetime(year, 1, 1).timestamp()\n",
    "    end = datetime.datetime(year + 1, 1, 1).timestamp()\n",
    "    \n",
    "    c = get_count_frame(begin, end)\n",
    "    \n",
    "    frames.append((c, year))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ee0ec96f",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('temp/frames.pickle','wb') as f:\n",
    "    pickle.dump(frames, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "54050359",
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### motifs by year distribution\n",
    "years = list()\n",
    "total = list()\n",
    "\n",
    "with open('temp/frames.pickle','rb') as f:\n",
    "     frames = pickle.load(f)\n",
    "\n",
    "for frame, year in frames:\n",
    "    total.append(np.sum(frame))\n",
    "    years.append(year)\n",
    "\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "\n",
    "# Motifs frequency\n",
    "ax = fig.add_subplot(211)\n",
    "ax.plot(years, total, label='Motifs')\n",
    "\n",
    "ax.set_ylabel('Motifs Frequency', color='b')\n",
    "ax.set_ylim(bottom=0)\n",
    "ax.set_xlabel('Year')\n",
    "\n",
    "MIDIP = pd.read_csv('Incident_Level_5_01/MIDIP_5.01.csv')\n",
    "a = MIDIP['styear'].value_counts().sort_index()\n",
    "\n",
    "# Incidents frequency\n",
    "ax1 = ax.twinx()\n",
    "ax1.set_ylabel('Incidents Frequency', color='red')\n",
    "ax1.plot(a.index,a, label='Incidents', color='r')\n",
    "ax1.set_xticks(range(1992,2015))\n",
    "ax1.set_ylim(bottom=0)\n",
    "ax1.set_xticklabels( [ str(year)[2:] for year in range(1992,2015)] );\n",
    "\n",
    "fig.savefig('figures/motif_and_incidents_by_years.svg', format='svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff19e0bb",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 1999 and 2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a4918491",
   "metadata": {
    "code_folding": [
     0
    ],
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%capture --no-display\n",
    "### year 1999 and 2000 ###\n",
    "with open('temp/frames.pickle','rb') as f:\n",
    "     frames = pickle.load(f)\n",
    "\n",
    "# 1999\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "ax = fig.add_subplot(121)\n",
    "count, year = frames[7]\n",
    "ax.clear()\n",
    "ax.imshow(count, cmap='Blues')\n",
    "\n",
    "column = range(7)\n",
    "row = range(7)\n",
    "ax.set_xticklabels(row)\n",
    "ax.set_yticklabels(column)\n",
    "\n",
    "for i in range(len(count)):\n",
    "    for j in range(len(count[i])):\n",
    "        if count[i][j] >= 100000:\n",
    "            label_text = str(np.round(count[i][j]/10**6,1)) + 'M'\n",
    "        elif count[i][j] >= 100:\n",
    "            label_text = str(np.round(count[i][j]/10**3,1)) + 'K'\n",
    "        else:\n",
    "            label_text = str(int(np.round(count[i][j],0)))\n",
    "        \n",
    "        if count[i][j] < np.average(count):\n",
    "            text = ax.text(j, i, label_text, ha=\"center\", va=\"center\", color='k')\n",
    "        else:\n",
    "            text = ax.text(j, i, label_text, ha=\"center\", va=\"center\", color='w')\n",
    "\n",
    "ax.set_title(\"YEAR = {}\".format(year))\n",
    "\n",
    "# 2000\n",
    "ax1 = fig.add_subplot(122)\n",
    "count, year = frames[8]\n",
    "ax1.clear()\n",
    "ax1.imshow(count, cmap='Blues')\n",
    "\n",
    "column = range(7)\n",
    "row = range(7)\n",
    "ax1.set_xticklabels(row)\n",
    "ax1.set_yticklabels(column)\n",
    "\n",
    "for i in range(len(count)):\n",
    "    for j in range(len(count[i])):\n",
    "        if count[i][j] >= 100000:\n",
    "            label_text = str(np.round(count[i][j]/10**6,1)) + 'M'\n",
    "        elif count[i][j] >= 100:\n",
    "            label_text = str(np.round(count[i][j]/10**3,1)) + 'K'\n",
    "        else:\n",
    "            label_text = str(int(np.round(count[i][j],0)))\n",
    "\n",
    "        if count[i][j] < np.average(count)*2:\n",
    "            text = ax1.text(j, i, label_text, ha=\"center\", va=\"center\", color='k')\n",
    "        else:\n",
    "            text = ax1.text(j, i, label_text, ha=\"center\", va=\"center\", color='w')\n",
    "\n",
    "ax1.set_title(\"YEAR = {}\".format(year));\n",
    "\n",
    "fig.savefig('figures/1999_and_2000.svg', format='svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e25b7bd6",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Motif and degree distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dcaf126b",
   "metadata": {
    "code_folding": [],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "%%capture --no-display   \n",
    "### Get degree distribution fit\n",
    "l = list()\n",
    "for G in graphs:\n",
    "    country_ed = dict()\n",
    "    for edge in G.edges():\n",
    "        if country_ed.get(edge[0]) is None:\n",
    "            country_ed[edge[0]] = 0\n",
    "\n",
    "        if country_ed.get(edge[1]) is None:\n",
    "            country_ed[edge[1]] = 0\n",
    "\n",
    "        country_ed[edge[0]] += 1\n",
    "        country_ed[edge[1]] += 1\n",
    "\n",
    "    name = list()\n",
    "    co = list()\n",
    "    for n, c in country_ed.items():\n",
    "        name.append(n)\n",
    "        co.append(c)\n",
    "\n",
    "    country_edge_count = pd.Series(co, name)\n",
    "    l.append(country_edge_count)\n",
    "\n",
    "table = pd.concat(l, axis=1)\n",
    "country_edge_count = table.mean(axis=1)\n",
    "\n",
    "degree_fit = powerlaw.Fit(country_edge_count, xmin=(1,10), discrete=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "62b5f93e",
   "metadata": {
    "code_folding": [
     0
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def get_country_count(count_dict):\n",
    "    country_count = dict()\n",
    "\n",
    "    for row_i in range(len(motifs)):\n",
    "        for col_i in range(len(motifs[row_i])):\n",
    "            # calculate for each country\n",
    "            for country in graphs[0].nodes():\n",
    "                c = 0\n",
    "                for key, co in count_dict[row_i][col_i].items():\n",
    "                    if country in key:\n",
    "                        c = c + co\n",
    "\n",
    "                if country_count.get(country) is None:\n",
    "                    country_count[country] = np.zeros((6,6))\n",
    "\n",
    "                country_count[country][row_i][col_i] = c\n",
    "    return country_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1f78c336",
   "metadata": {
    "code_folding": [],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "%%capture --no-display   \n",
    "### Get motif distribution fit\n",
    "l = list()\n",
    "for count_dict in dict_table:\n",
    "    country_count = get_country_count(count_dict)\n",
    "    n = list()\n",
    "    c = list()\n",
    "    for name, ar in country_count.items():\n",
    "        n.append(name)\n",
    "        c.append(sum(sum(ar)))\n",
    "\n",
    "    country_motif_count = pd.Series(c,n)\n",
    "    l.append(country_motif_count)\n",
    "    \n",
    "table = pd.concat(l, axis=1)\n",
    "country_motif_count = table.mean(axis=1)\n",
    "    \n",
    "data = np.array(country_motif_count[country_motif_count>0], dtype=int)\n",
    "motif_fit = powerlaw.Fit(data, xmin=(1,10), discrete=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0a6b63fa",
   "metadata": {
    "code_folding": [],
    "hidden": true,
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%capture --no-display\n",
    "distributions = ['stretched_exponential', 'lognormal', 'truncated_power_law', 'exponential']\n",
    "fig = plt.figure(figsize=(15,5))\n",
    "ax = fig.add_subplot(121)\n",
    "\n",
    "ax.set_title('Degree distribution', fontsize=15);\n",
    "\n",
    "powerlaw.plot_pdf(country_edge_count, label='data', linewidth=3, ax=ax)\n",
    "\n",
    "degree_fit.power_law.plot_pdf(color='b', linestyle='--', ax=ax, label='power law')\n",
    "# degree_fit.lognormal.plot_pdf(color='r', linestyle=':', ax=ax, label='lognormal')\n",
    "# degree_fit.truncated_power_law.plot_pdf(color='g', linestyle=':', ax=ax, label='truncated power law')\n",
    "degree_fit.stretched_exponential.plot_pdf(color='k', linestyle='--', ax=ax, label='stretched exponential')\n",
    "degree_fit.exponential.plot_pdf(color='r', linestyle='--', ax=ax, label='exponential')\n",
    "ax.set_ylim(bottom=10**-5)\n",
    "ax.legend();\n",
    "\n",
    "##\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.set_title('Motif distribution', fontsize=15);\n",
    "\n",
    "powerlaw.plot_pdf(data, label='data', linewidth=3, ax=ax2)\n",
    "\n",
    "motif_fit.power_law.plot_pdf(color='b', linestyle='--', ax=ax2, label='power law')\n",
    "# motif_fit.lognormal.plot_pdf(color='r', linestyle=':', ax=ax2, label='lognormal')\n",
    "# motif_fit.truncated_power_law.plot_pdf(color='g', linestyle=':', ax=ax2, label='truncated power law')\n",
    "motif_fit.stretched_exponential.plot_pdf(color='k', linestyle='--', ax=ax2, label='stretched exponential');\n",
    "motif_fit.exponential.plot_pdf(color='r', linestyle='--', ax=ax2, label='exponential');\n",
    "\n",
    "ax2.set_ylim(bottom=10**-7)\n",
    "\n",
    "fig.savefig('figures/degree_and_motif_distributions.svg', format='svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "872275bf",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Likelihood ratio test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce1a0694",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Negative value for log-likelihood indicates evidence against power law. Otherwise, log-likelihood will be positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ce6d35a2",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Power law vs stretched exponential\n",
      "Degree fit log-likelihood: -16.680861011759667, p-value: 0.00017936160352498217\n",
      "Motif fit log-likelihood: -19.320593767162308, p-value: 5.461141002200181e-05\n"
     ]
    }
   ],
   "source": [
    "a = degree_fit.distribution_compare('power_law', 'stretched_exponential')\n",
    "b = motif_fit.distribution_compare('power_law', 'stretched_exponential')\n",
    "\n",
    "print('Power law vs stretched exponential')\n",
    "print('Degree fit log-likelihood: {}, p-value: {}'.format(a[0],a[1]))\n",
    "print('Motif fit log-likelihood: {}, p-value: {}'.format(b[0],b[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f234a513",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Power law vs lognormal\n",
      "Degree fit log-likelihood: -15.403660025066971, p-value: 0.0006219271626425249\n",
      "Motif fit log-likelihood: -17.98482997181584, p-value: 0.00014614629485960444\n"
     ]
    }
   ],
   "source": [
    "a = degree_fit.distribution_compare('power_law', 'lognormal')\n",
    "b = motif_fit.distribution_compare('power_law', 'lognormal')\n",
    "\n",
    "print('Power law vs lognormal')\n",
    "print('Degree fit log-likelihood: {}, p-value: {}'.format(a[0],a[1]))\n",
    "print('Motif fit log-likelihood: {}, p-value: {}'.format(b[0],b[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "879bcf6a",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Power law vs truncated power law\n",
      "Degree fit log-likelihood: -17.294608631473867, p-value: 4.071554093876273e-09\n",
      "Motif fit log-likelihood: -19.283779678203025, p-value: 5.289076954184679e-10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Assuming nested distributions\n",
      "Assuming nested distributions\n"
     ]
    }
   ],
   "source": [
    "a = degree_fit.distribution_compare('power_law', 'truncated_power_law')\n",
    "b = motif_fit.distribution_compare('power_law', 'truncated_power_law')\n",
    "\n",
    "print('Power law vs truncated power law')\n",
    "print('Degree fit log-likelihood: {}, p-value: {}'.format(a[0],a[1]))\n",
    "print('Motif fit log-likelihood: {}, p-value: {}'.format(b[0],b[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d9a87253",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Power law vs exponential\n",
      "Degree fit log-likelihood: 37.687482234743385, p-value: 0.012621605434712106\n",
      "Motif fit log-likelihood: 49.72504449551463, p-value: 0.022686075385139535\n"
     ]
    }
   ],
   "source": [
    "a = degree_fit.distribution_compare('power_law', 'exponential')\n",
    "b = motif_fit.distribution_compare('power_law', 'exponential')\n",
    "\n",
    "print('Power law vs exponential')\n",
    "print('Degree fit log-likelihood: {}, p-value: {}'.format(a[0],a[1]))\n",
    "print('Motif fit log-likelihood: {}, p-value: {}'.format(b[0],b[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17d00207",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Tail heaviness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "53563d85",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degree fit power law alpha: 1.435642235219196\n",
      "Motif fit power law alpha: 1.2883760401315638\n"
     ]
    }
   ],
   "source": [
    "print('Degree fit power law alpha: {}'.format(degree_fit.power_law.alpha))\n",
    "print('Motif fit power law alpha: {}'.format(motif_fit.power_law.alpha))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "036b25be",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degree fit stretched exponential beta: 0.3703816594931685\n",
      "Motif fit stretched exponential beta: 0.3475873586129612\n"
     ]
    }
   ],
   "source": [
    "print('Degree fit stretched exponential beta: {}'.format(degree_fit.stretched_exponential.beta))\n",
    "print('Motif fit stretched exponential beta: {}'.format(motif_fit.stretched_exponential.beta))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "36e54c25",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degree fit lognormal sigma: 1.9781192381154558\n",
      "Motif fit lognormal sigma: 2.378885819447281\n"
     ]
    }
   ],
   "source": [
    "print('Degree fit lognormal sigma: {}'.format(degree_fit.lognormal.sigma))\n",
    "print('Motif fit lognormal sigma: {}'.format(motif_fit.lognormal.sigma))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "876ccbf8",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degree fit truncated power law alpha: 1.0631698397282987\n",
      "Motif fit truncated power law alpha: 1.000085490237818\n"
     ]
    }
   ],
   "source": [
    "print('Degree fit truncated power law alpha: {}'.format(degree_fit.truncated_power_law.alpha))\n",
    "print('Motif fit truncated power law alpha: {}'.format(motif_fit.truncated_power_law.alpha))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3a617183",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degree fit exponential lambda: 0.01323272705078125\n",
      "Motif fit exponential lambda: 0.0009524576227523312\n"
     ]
    }
   ],
   "source": [
    "print('Degree fit exponential lambda: {}'.format(degree_fit.exponential.Lambda))\n",
    "print('Motif fit exponential lambda: {}'.format(motif_fit.exponential.Lambda))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72f1c87d",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Different time intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "ed96c3d1",
   "metadata": {
    "code_folding": [
     0,
     2
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "## count motifs at different delta intervals\n",
    "day_delta = [3, 7, 30, 120]\n",
    "def different_delta(graphs):\n",
    "    \n",
    "    count0 = list()\n",
    "    \n",
    "    for k in range(len(day_delta)):\n",
    "        table = list()\n",
    "        for G in graphs:    \n",
    "            count_dict = list()\n",
    "            pool = mp.Pool(mp.cpu_count())\n",
    "            results = pool.starmap_async(dnx.count_temporal_motif, [(G, seq, day_delta[k]*86400) for seq in mot]).get()\n",
    "            pool.close()\n",
    "            count_dict=[results[0:6], results[6:12], results[12:18], results[18:24], results[24:30], results[30:36]]\n",
    "\n",
    "            table.append(results)\n",
    "\n",
    "        count_table = pd.DataFrame(table, columns=range(1,37))\n",
    "        a = count_table.mean()\n",
    "        count = [list(a[0:6]), list(a[6:12]), list(a[12:18]), list(a[18:24]), list(a[24:30]), list(a[30:36])]    \n",
    "\n",
    "        count0.append(count)\n",
    "    \n",
    "    return count0\n",
    "\n",
    "def plot_interval_motifs(count0):\n",
    "    diffcount = list()\n",
    "    diffcount.append(np.array(count0[0]))\n",
    "    diffcount.append(np.array(count0[1])-np.array(count0[0]))\n",
    "    diffcount.append(np.array(count0[2])-np.array(count0[1]))\n",
    "    diffcount.append(np.array(count0[3])-np.array(count0[2]))\n",
    "\n",
    "    fig = plt.figure(figsize=(8, 8))\n",
    "\n",
    "    for k in range(len(diffcount)):\n",
    "        diff = diffcount[k]\n",
    "        ax = fig.add_subplot(2,2,k+1)\n",
    "        ax.imshow(diff, cmap='Blues')\n",
    "\n",
    "        column = range(7)\n",
    "        row = range(7)\n",
    "        ax.set_xticklabels(row)\n",
    "        ax.set_yticklabels(column)\n",
    "\n",
    "        for i in range(len(diff)):\n",
    "            for j in range(len(diff[i])):\n",
    "                if diff[i][j] >= 100000:\n",
    "                    label_text = str(np.round(diff[i][j]/10**6,1)) + 'M'\n",
    "                elif diff[i][j] >= 100:\n",
    "                    label_text = str(np.round(diff[i][j]/10**3,1)) + 'K'\n",
    "                else:\n",
    "                    label_text = str(int(np.round(diff[i][j],0)))\n",
    "                \n",
    "                if diff[i][j] < np.average(diff) *2.3:\n",
    "                    text = ax.text(j, i, label_text, ha=\"center\", va=\"center\", color='k')\n",
    "                else:\n",
    "                    text = ax.text(j, i, label_text, ha=\"center\", va=\"center\", color='w')\n",
    "\n",
    "        if k == 0:\n",
    "            ax.set_title(\"[{}, {}] days\".format(0, day_delta[k]))\n",
    "        else:\n",
    "            plt.setp(ax.get_yticklabels(), visible=False)\n",
    "            ax.set_title(\"({}, {}] days\".format(day_delta[k-1], day_delta[k]))\n",
    "    \n",
    "    fig.savefig('figures/different_delta_count.svg', format='svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ce6e4509",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "count0 = different_delta(graphs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "fd8bd825",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "with open('temp/different_intervals.pickle','wb') as f:\n",
    "    pickle.dump(count0, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2e14b47f",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "with open('temp/different_intervals.pickle','rb') as f:\n",
    "    count0 = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1c9599d9",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-72-2777ad5ea85f>:42: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(row)\n",
      "<ipython-input-72-2777ad5ea85f>:43: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(column)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_interval_motifs(count0)"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "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": "3.9.4"
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
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    "builtin_function_or_method",
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 "nbformat": 4,
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}