208 lines
5.8 KiB
Plaintext
208 lines
5.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "f74ce078-f7e8-4733-a14c-14d8815a3626",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:57:35.618124Z",
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"start_time": "2025-04-09T14:57:34.837095Z"
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}
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},
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"outputs": [],
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"source": [
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"import tushare as ts\n",
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"ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
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"pro = ts.pro_api()"
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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": 2,
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"id": "44dd8d87-e60b-49e5-aed9-efaa7f92d4fe",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:57:38.089531Z",
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"start_time": "2025-04-09T14:57:35.854308Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" ts_code trade_date\n",
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"0 801001.SI 20250221\n",
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"1 801002.SI 20250221\n",
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"2 801003.SI 20250221\n",
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"3 801005.SI 20250221\n",
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"4 801010.SI 20250221\n",
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".. ... ...\n",
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"434 859811.SI 20250506\n",
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"435 859821.SI 20250506\n",
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"436 859822.SI 20250506\n",
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"437 859852.SI 20250506\n",
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"438 859951.SI 20250506\n",
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"\n",
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"[1065465 rows x 2 columns]\n",
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"20250506\n",
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"start_date: 20250507\n"
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]
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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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"import time\n",
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"\n",
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"h5_filename = '../../../data/sw_daily.h5'\n",
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"key = '/sw_daily'\n",
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"max_date = None\n",
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"with pd.HDFStore(h5_filename, mode='r') as store:\n",
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" df = store[key][['ts_code', 'trade_date']]\n",
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" print(df)\n",
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" max_date = df['trade_date'].max()\n",
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"\n",
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"print(max_date)\n",
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"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250620')\n",
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"trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n",
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"trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
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"start_date = min(trade_dates)\n",
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"print(f'start_date: {start_date}')"
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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": 3,
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"id": "747acc47-0884-4f76-90fb-276f6494e31d",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:57:40.754159Z",
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"start_time": "2025-04-09T14:57:38.104541Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"任务 20250619 完成\n",
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"任务 20250620 完成\n",
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"任务 20250618 完成\n",
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"任务 20250617 完成\n",
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"任务 20250616 完成\n",
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"任务 20250613 完成\n",
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"任务 20250612 完成\n",
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"任务 20250611 完成\n",
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"任务 20250610 完成\n",
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"任务 20250609 完成\n",
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"任务 20250606 完成\n",
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"任务 20250605 完成\n",
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"任务 20250604 完成\n",
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"任务 20250603 完成\n",
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"任务 20250530 完成\n",
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"任务 20250528 完成\n",
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"任务 20250529 完成\n",
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"任务 20250526 完成\n",
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"任务 20250527 完成\n",
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"任务 20250523 完成\n",
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"任务 20250522 完成\n",
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"任务 20250521 完成\n",
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"任务 20250520 完成\n",
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"任务 20250519 完成\n",
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"任务 20250516 完成\n",
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"任务 20250514 完成\n",
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"任务 20250515 完成\n",
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"任务 20250513 完成\n",
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"任务 20250512 完成\n",
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"任务 20250509 完成\n",
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"任务 20250508 完成\n",
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"任务 20250507 完成\n"
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]
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}
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],
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"source": [
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"from concurrent.futures import ThreadPoolExecutor, as_completed\n",
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"\n",
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"all_daily_data = []\n",
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"\n",
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"# API 调用计数和时间控制变量\n",
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"api_call_count = 0\n",
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"batch_start_time = time.time()\n",
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"\n",
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"\n",
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"def get_data(trade_date):\n",
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" time.sleep(0.1)\n",
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" data = pro.sw_daily(trade_date=trade_date)\n",
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" if data is not None and not data.empty:\n",
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" return data\n",
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"\n",
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"\n",
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"with ThreadPoolExecutor(max_workers=2) as executor:\n",
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" future_to_date = {executor.submit(get_data, td): td for td in trade_dates}\n",
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"\n",
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" for future in as_completed(future_to_date):\n",
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" trade_date = future_to_date[future] # 获取对应的交易日期\n",
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" try:\n",
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" result = future.result() # 获取任务执行的结果\n",
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" all_daily_data.append(result)\n",
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" print(f\"任务 {trade_date} 完成\")\n",
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" except Exception as e:\n",
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" print(f\"获取 {trade_date} 数据时出错: {e}\")\n",
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"\n"
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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": 4,
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"id": "c6765638-481f-40d8-a259-2e7b25362618",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:57:40.994975Z",
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"start_time": "2025-04-09T14:57:40.773783Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"所有每日基础数据获取并保存完毕！\n"
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]
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}
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],
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"source": [
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"all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n",
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"\n",
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"# 将所有数据合并为一个 DataFrame\n",
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"\n",
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"# 将数据保存为 HDF5 文件（table 格式）\n",
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"all_daily_data_df.to_hdf(h5_filename, key=key, mode='a', format='table', append=True, data_columns=True)\n",
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"\n",
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"print(\"所有每日基础数据获取并保存完毕！\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "new_trader",
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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": "3.11.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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