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Luca Lombardo 2 years ago
parent 699238c656
commit 2edeb02703
1 changed files with 788 additions and 2 deletions
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790
testing.ipynb
Unescape Escape View File

@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 1,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -28,6 +28,792 @@
"import sys\n", "import sys\n",
"warnings.filterwarnings(\"ignore\")" "warnings.filterwarnings(\"ignore\")"
] ]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
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" <th>Graph</th>\n",
" <th>Number of Nodes</th>\n",
" <th>Number of Edges</th>\n",
" <th>Average Degree</th>\n",
" <th>Average Clustering Coefficient</th>\n",
" <th>log N</th>\n",
" <th>Average Shortest Path Length</th>\n",
" <th>betweenness centrality</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Brightkite Checkins Graph</td>\n",
" <td>6493</td>\n",
" <td>292973</td>\n",
" <td>90.242723</td>\n",
" <td>0.713999</td>\n",
" <td>8.778480</td>\n",
" <td>3.013369</td>\n",
" <td>0.000534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Gowalla Checkins Graph</td>\n",
" <td>3073</td>\n",
" <td>62790</td>\n",
" <td>40.865604</td>\n",
" <td>0.548372</td>\n",
" <td>8.030410</td>\n",
" <td>3.508031</td>\n",
" <td>0.001277</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Foursquare Checkins Graph</td>\n",
" <td>2324</td>\n",
" <td>246702</td>\n",
" <td>212.30809</td>\n",
" <td>0.65273</td>\n",
" <td>7.751045</td>\n",
" <td>2.186112</td>\n",
" <td>0.000938</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Brightkite Friendship Graph</td>\n",
" <td>5420</td>\n",
" <td>14690</td>\n",
" <td>5.420664</td>\n",
" <td>0.218571</td>\n",
" <td>8.597851</td>\n",
" <td>5.231807</td>\n",
" <td>0.000664</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>(Filtered) Gowalla Friendship Graph</td>\n",
" <td>2294</td>\n",
" <td>5548</td>\n",
" <td>4.836966</td>\n",
" <td>0.234293</td>\n",
" <td>7.738052</td>\n",
" <td>5.396488</td>\n",
" <td>0.001331</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Foursquare Friendship Graph</td>\n",
" <td>1397</td>\n",
" <td>5323</td>\n",
" <td>7.620616</td>\n",
" <td>0.183485</td>\n",
" <td>7.242082</td>\n",
" <td>6.45841</td>\n",
" <td>0.001531</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" Graph Number of Nodes Number of Edges \\\n",
"0 Brightkite Checkins Graph 6493 292973 \n",
"1 Gowalla Checkins Graph 3073 62790 \n",
"2 Foursquare Checkins Graph 2324 246702 \n",
"3 Brightkite Friendship Graph 5420 14690 \n",
"4 (Filtered) Gowalla Friendship Graph 2294 5548 \n",
"5 Foursquare Friendship Graph 1397 5323 \n",
"\n",
" Average Degree Average Clustering Coefficient log N \\\n",
"0 90.242723 0.713999 8.778480 \n",
"1 40.865604 0.548372 8.030410 \n",
"2 212.30809 0.65273 7.751045 \n",
"3 5.420664 0.218571 8.597851 \n",
"4 4.836966 0.234293 7.738052 \n",
"5 7.620616 0.183485 7.242082 \n",
"\n",
" Average Shortest Path Length betweenness centrality \n",
"0 3.013369 0.000534 \n",
"1 3.508031 0.001277 \n",
"2 2.186112 0.000938 \n",
"3 5.231807 0.000664 \n",
"4 5.396488 0.001331 \n",
"5 6.45841 0.001531 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analysis_results = pd.read_pickle('analysis_results.pkl')\n",
"analysis_results"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
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" <th>Average Clustering Coefficient</th>\n",
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" <th>2</th>\n",
" <td>Foursquare Checkins Graph</td>\n",
" <td>2324</td>\n",
" <td>246702</td>\n",
" <td>212.30809</td>\n",
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" <td>2.186112</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>Brightkite Friendship Graph</td>\n",
" <td>5420</td>\n",
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" <td>0.000664</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>(Filtered) Gowalla Friendship Graph</td>\n",
" <td>2294</td>\n",
" <td>5548</td>\n",
" <td>4.836966</td>\n",
" <td>0.234293</td>\n",
" <td>7.738052</td>\n",
" <td>5.396488</td>\n",
" <td>0.001331</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Foursquare Friendship Graph</td>\n",
" <td>1397</td>\n",
" <td>5323</td>\n",
" <td>7.620616</td>\n",
" <td>0.183485</td>\n",
" <td>7.242082</td>\n",
" <td>6.45841</td>\n",
" <td>0.001531</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Graph Number of Nodes Number of Edges \\\n",
"0 Brightkite Checkins Graph 6493 292973 \n",
"1 Gowalla Checkins Graph 3073 62790 \n",
"2 Foursquare Checkins Graph 2324 246702 \n",
"3 Brightkite Friendship Graph 5420 14690 \n",
"4 (Filtered) Gowalla Friendship Graph 2294 5548 \n",
"5 Foursquare Friendship Graph 1397 5323 \n",
"\n",
" Average Degree Average Clustering Coefficient log N \\\n",
"0 90.242723 0.713999 8.778480 \n",
"1 40.865604 0.548372 8.030410 \n",
"2 212.30809 0.65273 7.751045 \n",
"3 5.420664 0.218571 8.597851 \n",
"4 4.836966 0.234293 7.738052 \n",
"5 7.620616 0.183485 7.242082 \n",
"\n",
" Average Shortest Path Length betweenness centrality omega-coefficient \n",
"0 3.013369 0.000534 NaN \n",
"1 3.508031 0.001277 NaN \n",
"2 2.186112 0.000938 NaN \n",
"3 5.231807 0.000664 NaN \n",
"4 5.396488 0.001331 NaN \n",
"5 6.45841 0.001531 NaN "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analysis_results['omega-coefficient'] = np.nan\n",
"analysis_results"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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" <th>Number of Nodes</th>\n",
" <th>Number of Edges</th>\n",
" <th>Average Degree</th>\n",
" <th>Average Clustering Coefficient</th>\n",
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" <td>292973</td>\n",
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" <td>Gowalla Checkins Graph</td>\n",
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" <td>Foursquare Checkins Graph</td>\n",
" <td>2324</td>\n",
" <td>246702</td>\n",
" <td>212.30809</td>\n",
" <td>0.65273</td>\n",
" <td>7.751045</td>\n",
" <td>2.186112</td>\n",
" <td>0.000938</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Brightkite Friendship Graph</td>\n",
" <td>5420</td>\n",
" <td>14690</td>\n",
" <td>5.420664</td>\n",
" <td>0.218571</td>\n",
" <td>8.597851</td>\n",
" <td>5.231807</td>\n",
" <td>0.000664</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>(Filtered) Gowalla Friendship Graph</td>\n",
" <td>2294</td>\n",
" <td>5548</td>\n",
" <td>4.836966</td>\n",
" <td>0.234293</td>\n",
" <td>7.738052</td>\n",
" <td>5.396488</td>\n",
" <td>0.001331</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Foursquare Friendship Graph</td>\n",
" <td>1397</td>\n",
" <td>5323</td>\n",
" <td>7.620616</td>\n",
" <td>0.183485</td>\n",
" <td>7.242082</td>\n",
" <td>6.45841</td>\n",
" <td>0.001531</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Graph Number of Nodes Number of Edges \\\n",
"0 Brightkite Checkins Graph 6493 292973 \n",
"1 Gowalla Checkins Graph 3073 62790 \n",
"2 Foursquare Checkins Graph 2324 246702 \n",
"3 Brightkite Friendship Graph 5420 14690 \n",
"4 (Filtered) Gowalla Friendship Graph 2294 5548 \n",
"5 Foursquare Friendship Graph 1397 5323 \n",
"\n",
" Average Degree Average Clustering Coefficient log N \\\n",
"0 90.242723 0.713999 8.778480 \n",
"1 40.865604 0.548372 8.030410 \n",
"2 212.30809 0.65273 7.751045 \n",
"3 5.420664 0.218571 8.597851 \n",
"4 4.836966 0.234293 7.738052 \n",
"5 7.620616 0.183485 7.242082 \n",
"\n",
" Average Shortest Path Length betweenness centrality omega-coefficient \n",
"0 3.013369 0.000534 NaN \n",
"1 3.508031 0.001277 NaN \n",
"2 2.186112 0.000938 NaN \n",
"3 5.231807 0.000664 NaN \n",
"4 5.396488 0.001331 NaN \n",
"5 6.45841 0.001531 NaN "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# rename (Filtered) Gowalla Friendship Graph in Gowalla Friendship Graph\n",
"analysis_results.loc[analysis_results['Graph'] == 'Filtered Gowalla Friendship Graph', 'Graph'] = 'Gowalla Friendship Graph'\n",
"analysis_results"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Graph</th>\n",
" <th>Number of Nodes</th>\n",
" <th>Number of Edges</th>\n",
" <th>Average Degree</th>\n",
" <th>Average Clustering Coefficient</th>\n",
" <th>log N</th>\n",
" <th>Average Shortest Path Length</th>\n",
" <th>betweenness centrality</th>\n",
" <th>omega-coefficient</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Brightkite Checkins Graph</td>\n",
" <td>6493</td>\n",
" <td>292973</td>\n",
" <td>90.242723</td>\n",
" <td>0.713999</td>\n",
" <td>8.778480</td>\n",
" <td>3.013369</td>\n",
" <td>0.000534</td>\n",
" <td>-0.180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Gowalla Checkins Graph</td>\n",
" <td>3073</td>\n",
" <td>62790</td>\n",
" <td>40.865604</td>\n",
" <td>0.548372</td>\n",
" <td>8.030410</td>\n",
" <td>3.508031</td>\n",
" <td>0.001277</td>\n",
" <td>-0.240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Foursquare Checkins Graph</td>\n",
" <td>2324</td>\n",
" <td>246702</td>\n",
" <td>212.30809</td>\n",
" <td>0.65273</td>\n",
" <td>7.751045</td>\n",
" <td>2.186112</td>\n",
" <td>0.000938</td>\n",
" <td>-0.056</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Brightkite Friendship Graph</td>\n",
" <td>5420</td>\n",
" <td>14690</td>\n",
" <td>5.420664</td>\n",
" <td>0.218571</td>\n",
" <td>8.597851</td>\n",
" <td>5.231807</td>\n",
" <td>0.000664</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>(Filtered) Gowalla Friendship Graph</td>\n",
" <td>2294</td>\n",
" <td>5548</td>\n",
" <td>4.836966</td>\n",
" <td>0.234293</td>\n",
" <td>7.738052</td>\n",
" <td>5.396488</td>\n",
" <td>0.001331</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Foursquare Friendship Graph</td>\n",
" <td>1397</td>\n",
" <td>5323</td>\n",
" <td>7.620616</td>\n",
" <td>0.183485</td>\n",
" <td>7.242082</td>\n",
" <td>6.45841</td>\n",
" <td>0.001531</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Graph Number of Nodes Number of Edges \\\n",
"0 Brightkite Checkins Graph 6493 292973 \n",
"1 Gowalla Checkins Graph 3073 62790 \n",
"2 Foursquare Checkins Graph 2324 246702 \n",
"3 Brightkite Friendship Graph 5420 14690 \n",
"4 (Filtered) Gowalla Friendship Graph 2294 5548 \n",
"5 Foursquare Friendship Graph 1397 5323 \n",
"\n",
" Average Degree Average Clustering Coefficient log N \\\n",
"0 90.242723 0.713999 8.778480 \n",
"1 40.865604 0.548372 8.030410 \n",
"2 212.30809 0.65273 7.751045 \n",
"3 5.420664 0.218571 8.597851 \n",
"4 4.836966 0.234293 7.738052 \n",
"5 7.620616 0.183485 7.242082 \n",
"\n",
" Average Shortest Path Length betweenness centrality omega-coefficient \n",
"0 3.013369 0.000534 -0.180 \n",
"1 3.508031 0.001277 -0.240 \n",
"2 2.186112 0.000938 -0.056 \n",
"3 5.231807 0.000664 NaN \n",
"4 5.396488 0.001331 NaN \n",
"5 6.45841 0.001531 NaN "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Foursquare Checkins Graph : -0.056\n",
"# Gowalla Checkins Graph : -0.24\n",
"# Brightkite Checkins Graph : -0.18\n",
"\n",
"# add omega-coefficient to the respective graphs\n",
"analysis_results.loc[analysis_results['Graph'] == 'Foursquare Checkins Graph', 'omega-coefficient'] = -0.056\n",
"analysis_results.loc[analysis_results['Graph'] == 'Gowalla Checkins Graph', 'omega-coefficient'] = -0.24\n",
"analysis_results.loc[analysis_results['Graph'] == 'Brightkite Checkins Graph', 'omega-coefficient'] = -0.18\n",
"analysis_results"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"# rename (Filtered) Gowalla Friendship Graph in Gowalla Friendship Graph\n",
"analysis_results.loc[analysis_results['Graph'] == '(Filtered) Gowalla Friendship Graph', 'Graph'] = 'Gowalla Friendship Graph'"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"# FourSquare Friendship Graph : -0.17\n",
"# Gowalla Friendship Graph : -0.25\n",
"# Brightkite Friendship Graph : -0.20\n",
"\n",
"# add omega-coefficient to the respective graphs\n",
"analysis_results.loc[analysis_results['Graph'] == 'Foursquare Friendship Graph', 'omega-coefficient'] = -0.17\n",
"analysis_results.loc[analysis_results['Graph'] == 'Gowalla Friendship Graph', 'omega-coefficient'] = -0.25\n",
"analysis_results.loc[analysis_results['Graph'] == 'Brightkite Friendship Graph', 'omega-coefficient'] = -0.20"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" 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>Graph</th>\n",
" <th>Number of Nodes</th>\n",
" <th>Number of Edges</th>\n",
" <th>Average Degree</th>\n",
" <th>Average Clustering Coefficient</th>\n",
" <th>log N</th>\n",
" <th>Average Shortest Path Length</th>\n",
" <th>betweenness centrality</th>\n",
" <th>omega-coefficient</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Brightkite Checkins Graph</td>\n",
" <td>6493</td>\n",
" <td>292973</td>\n",
" <td>90.242723</td>\n",
" <td>0.713999</td>\n",
" <td>8.778480</td>\n",
" <td>3.013369</td>\n",
" <td>0.000534</td>\n",
" <td>-0.180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Gowalla Checkins Graph</td>\n",
" <td>3073</td>\n",
" <td>62790</td>\n",
" <td>40.865604</td>\n",
" <td>0.548372</td>\n",
" <td>8.030410</td>\n",
" <td>3.508031</td>\n",
" <td>0.001277</td>\n",
" <td>-0.240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Foursquare Checkins Graph</td>\n",
" <td>2324</td>\n",
" <td>246702</td>\n",
" <td>212.30809</td>\n",
" <td>0.65273</td>\n",
" <td>7.751045</td>\n",
" <td>2.186112</td>\n",
" <td>0.000938</td>\n",
" <td>-0.056</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Brightkite Friendship Graph</td>\n",
" <td>5420</td>\n",
" <td>14690</td>\n",
" <td>5.420664</td>\n",
" <td>0.218571</td>\n",
" <td>8.597851</td>\n",
" <td>5.231807</td>\n",
" <td>0.000664</td>\n",
" <td>-0.200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Gowalla Friendship Graph</td>\n",
" <td>2294</td>\n",
" <td>5548</td>\n",
" <td>4.836966</td>\n",
" <td>0.234293</td>\n",
" <td>7.738052</td>\n",
" <td>5.396488</td>\n",
" <td>0.001331</td>\n",
" <td>-0.250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Foursquare Friendship Graph</td>\n",
" <td>1397</td>\n",
" <td>5323</td>\n",
" <td>7.620616</td>\n",
" <td>0.183485</td>\n",
" <td>7.242082</td>\n",
" <td>6.45841</td>\n",
" <td>0.001531</td>\n",
" <td>-0.170</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Graph Number of Nodes Number of Edges Average Degree \\\n",
"0 Brightkite Checkins Graph 6493 292973 90.242723 \n",
"1 Gowalla Checkins Graph 3073 62790 40.865604 \n",
"2 Foursquare Checkins Graph 2324 246702 212.30809 \n",
"3 Brightkite Friendship Graph 5420 14690 5.420664 \n",
"4 Gowalla Friendship Graph 2294 5548 4.836966 \n",
"5 Foursquare Friendship Graph 1397 5323 7.620616 \n",
"\n",
" Average Clustering Coefficient log N Average Shortest Path Length \\\n",
"0 0.713999 8.778480 3.013369 \n",
"1 0.548372 8.030410 3.508031 \n",
"2 0.65273 7.751045 2.186112 \n",
"3 0.218571 8.597851 5.231807 \n",
"4 0.234293 7.738052 5.396488 \n",
"5 0.183485 7.242082 6.45841 \n",
"\n",
" betweenness centrality omega-coefficient \n",
"0 0.000534 -0.180 \n",
"1 0.001277 -0.240 \n",
"2 0.000938 -0.056 \n",
"3 0.000664 -0.200 \n",
"4 0.001331 -0.250 \n",
"5 0.001531 -0.170 "
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analysis_results\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# save the results into a pickle file\n",
"analysis_results.to_pickle('analysis_results.pkl')"
]
} }
], ],
"metadata": { "metadata": {
@ -46,7 +832,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.9" "version": "3.10.9 (main, Dec 19 2022, 17:35:49) [GCC 12.2.0]"
}, },
"orig_nbformat": 4, "orig_nbformat": 4,
"vscode": { "vscode": {

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