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{
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"cells": [
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{
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"cell_type": "code",
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2023-02-16 16:58:02 +08:00
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"execution_count": 23,
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2023-02-13 17:52:58 +08:00
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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2023-02-16 16:58:02 +08:00
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"C:\\Users\\25759\\AppData\\Local\\Temp\\ipykernel_20892\\4260360161.py:31: SettingWithCopyWarning: \n",
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2023-02-13 18:18:36 +08:00
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"A value is trying to be set on a copy of a slice from a DataFrame.\n",
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"Try using .loc[row_indexer,col_indexer] = value instead\n",
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"\n",
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"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
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" Firm_attr['Product_Code'] = firm_product\n"
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]
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},
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{
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"data": {
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2023-02-16 16:58:02 +08:00
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"image/png": "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"text/plain": [
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"<Figure size 3600x3600 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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2023-02-13 17:52:58 +08:00
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}
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],
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"source": [
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"import pandas as pd\n",
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2023-02-13 18:18:36 +08:00
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"import networkx as nx\n",
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"import matplotlib.pyplot as plt\n",
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2023-02-16 16:58:02 +08:00
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"import random\n",
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"\n",
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"\n",
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"seed = 0\n",
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"random.seed(seed)\n",
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"plt.rcParams['font.sans-serif'] = 'SimHei'\n",
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2023-02-13 17:52:58 +08:00
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"\n",
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2023-02-16 16:11:54 +08:00
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"# init graph bom\n",
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"BomNodes = pd.read_csv('BomNodes.csv', index_col=0)\n",
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"BomNodes.set_index('Code', inplace=True)\n",
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"BomCateNet = pd.read_csv('BomCateNet.csv', index_col=0)\n",
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"BomCateNet.fillna(0, inplace=True)\n",
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"\n",
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"G_bom = nx.from_pandas_adjacency(BomCateNet, create_using=nx.MultiDiGraph())\n",
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"\n",
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"bom_labels_dict = {}\n",
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"for code in G_bom.nodes:\n",
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" bom_labels_dict[code] = BomNodes.loc[code].to_dict()\n",
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"nx.set_node_attributes(G_bom, bom_labels_dict)\n",
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"\n",
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"# init graph firm\n",
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"Firm = pd.read_csv(\"Firm_amended.csv\")\n",
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"Firm.fillna(0, inplace=True)\n",
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"Firm_attr = Firm[[\"Code\",\"Name\",\"Type_Region\"]]\n",
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"firm_product = []\n",
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"for _, row in Firm.loc[:,'1':].iterrows():\n",
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" firm_product.append(row[row==1].index.to_list())\n",
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"Firm_attr['Product_Code'] = firm_product\n",
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"Firm_attr.set_index('Code')\n",
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"\n",
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2023-02-16 16:11:54 +08:00
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"G_Firm =nx.MultiDiGraph()\n",
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"G_Firm.add_nodes_from(Firm[\"Code\"])\n",
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"\n",
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2023-02-16 16:11:54 +08:00
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"firm_labels_dict = {}\n",
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"for code in G_Firm.nodes:\n",
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" firm_labels_dict[code] = Firm_attr.loc[code].to_dict()\n",
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"nx.set_node_attributes(G_Firm, firm_labels_dict)\n",
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2023-02-13 18:18:36 +08:00
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"\n",
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2023-02-16 16:11:54 +08:00
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"# add edge to G_firm according to G_bom\n",
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"product_codes = nx.get_node_attributes(G_Firm, 'Product_Code')\n",
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"for node in nx.nodes(G_Firm):\n",
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" # print(product_codes[node])\n",
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" for product_code in product_codes[node]:\n",
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" # print(product_code)\n",
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2023-02-16 16:58:02 +08:00
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" for neighbor_product_code in list(G_bom.neighbors(product_code)):\n",
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" neighbor_firms = Firm.index[Firm[neighbor_product_code]==1].to_list()\n",
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" list_added_edges = [(node,neighbor_firm) for neighbor_firm in neighbor_firms]\n",
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" # 目前是任选一个链接\n",
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" if list_added_edges:\n",
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|
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" list_added_edges = [random.choice(list_added_edges)]\n",
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" G_Firm.add_edges_from(list_added_edges)\n",
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2023-02-16 16:11:54 +08:00
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"\n",
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"# print graph\n",
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"pos = nx.nx_agraph.graphviz_layout(G_Firm, prog=\"twopi\", args=\"\")\n",
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"node_labels = nx.get_node_attributes(G_Firm, 'Name')\n",
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2023-02-13 18:18:36 +08:00
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"plt.figure(figsize=(12, 12), dpi=300)\n",
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2023-02-16 16:11:54 +08:00
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"nx.draw_networkx_nodes(G_Firm, pos)\n",
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"nx.draw_networkx_edges(G_Firm, pos)\n",
|
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"nx.draw_networkx_labels(G_Firm, pos, labels = node_labels, font_size=6)\n",
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2023-02-13 18:18:36 +08:00
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"plt.show()"
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]
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},
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2023-02-16 16:11:54 +08:00
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"NodeView(('1', '1.1', '1.1.1', '1.1.2', '1.1.3', '1.2', '1.2.1', '1.2.2', '1.2.3', '1.3', '1.3.1', '1.3.1.1', '1.3.1.2', '1.3.1.3', '1.3.1.4', '1.3.1.5', '1.3.1.6', '1.3.1.7', '1.3.2', '1.3.2.1', '1.3.3', '1.3.3.1', '1.3.3.2', '1.3.3.3', '1.3.3.4', '1.3.3.5', '1.3.3.6', '1.3.3.7', '1.3.4', '1.3.4.1', '1.3.4.2', '1.3.4.3', '1.3.5', '1.3.5.1', '1.4', '1.4.1', '1.4.1.1', '1.4.1.2', '1.4.1.3', '1.4.1.4', '1.4.1.5', '1.4.2', '1.4.2.1', '1.4.2.2', '1.4.2.3', '1.4.2.4', '1.4.2.5', '1.4.2.6', '1.4.2.7', '1.4.3', '1.4.3.1', '1.4.3.2', '1.4.3.3', '1.4.3.4', '1.4.3.5', '1.4.3.6', '1.4.4', '1.4.4.1', '1.4.4.2', '1.4.4.3', '1.4.4.4', '1.4.4.5', '1.4.5', '1.4.5.1', '1.4.5.2', '1.4.5.3', '1.4.5.4', '1.4.5.5', '1.4.5.6', '1.4.5.7', '1.4.5.8', '1.4.5.9', '2', '2.1', '2.1.1', '2.1.1.1', '2.1.1.2', '2.1.1.3', '2.1.1.4', '2.1.1.5', '2.1.2', '2.1.2.1', '2.1.2.2', '2.1.2.3', '2.1.2.4', '2.1.3', '2.1.3.1', '2.1.3.2', '2.1.3.3', '2.1.3.4', '2.1.3.5', '2.1.3.6', '2.1.3.7', '2.1.4', '2.1.4.1', '2.1.4.1.1', '2.1.4.1.2', '2.1.4.1.3', '2.1.4.1.4', '2.1.4.2', '2.1.4.2.1', '2.1.4.2.2', '2.2', '2.3', '2.3.1', '2.3.2', '2.3.3'))"
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]
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},
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"execution_count": 21,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"G_bom.nodes()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['1.1', '1.2', '1.3', '1.4', '2']"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"list(G_bom.neighbors('1'))"
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]
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},
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2023-02-13 17:52:58 +08:00
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "bcdafc093860683ffb58d6956591562b7f8ed5d58147d17d71a5d4d6605a08df"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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