沥青定量调试通过
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"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_6640\\43143909.py:66: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
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"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_1412\\43143909.py:66: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
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" df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)\n",
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" df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)\n",
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"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_6640\\43143909.py:70: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
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"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_1412\\43143909.py:70: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
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" df = df.fillna(method='ffill')\n"
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" df = df.fillna(method='ffill')\n"
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"text": [
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"text": [
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" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 \\\n",
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" 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 \\\n",
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"1399 2025-04-06 3600.0 37.3095 1.0 -1051.74 6000.0 107382.182661 \n",
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"1399 2025-04-06 3600.0 37.3095 1.0 -1051.74 6000.0 107382.182661 \n",
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"1400 2025-04-07 3500.0 36.784 1.0 478.28 6000.0 107382.182661 \n",
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"1400 2025-04-07 3500.0 36.784 1.0 478.28 6000.0 107945.963078 \n",
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"\n",
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"\n",
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" 下游客户价格预期 即期成本 订单结构 计划产量 京博产量 \n",
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" 下游客户价格预期 即期成本 订单结构 计划产量 京博产量 \n",
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"1399 3630.0 3136.0033 1.0 4982.8366 5715.5175 \n",
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"1399 3630.0 3136.0033 1.0 4982.8366 5715.5175 \n",
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"现在的 2972.5098 <class 'float'>\n",
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"现在的 2972.5098 <class 'float'>\n",
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" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 \\\n",
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" index 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 \\\n",
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"0 1399 2025-04-06 3600 37.3095 1 -1051.74 6000 107382.182661 \n",
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"0 1399 2025-04-06 3600 37.3095 1 -1051.74 6000 107382.182661 \n",
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"1 1400 2025-04-07 3500 36.784 1 478.28 6000 107382.182661 \n",
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"1 1400 2025-04-07 3500 36.784 1 478.28 6000 107945.963078 \n",
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"\n",
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"\n",
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" 下游客户价格预期 即期成本 订单结构 计划产量 京博产量 \n",
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" 下游客户价格预期 即期成本 订单结构 计划产量 京博产量 \n",
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"0 3630 3136.0033 1 4982.8366 5715.5175 \n",
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"0 3630 3136.0033 1 4982.8366 5715.5175 \n",
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"import json\n",
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"import json\n",
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"import xlrd\n",
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"import xlrd\n",
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"import xlwt\n",
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"import xlwt\n",
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"import datetime\n",
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"from datetime import datetime, timedelta\n",
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"import time\n",
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"import time\n",
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"import pandas as pd\n",
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"import pandas as pd\n",
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"\n",
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"\n",
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" \"funcOperation\": \"获取token\"\n",
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" \"funcOperation\": \"获取token\"\n",
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"}\n",
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"}\n",
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"\n",
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"\n",
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"read_file_path_name = \"沥青数据项.xls\"\n",
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"read_file_path_name = \"沥青数据项.xlsx\"\n",
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"one_cols = []\n",
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"one_cols = []\n",
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"two_cols = []\n",
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"two_cols = []\n",
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"\n",
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"\n",
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"from sklearn.linear_model import Lasso\n",
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"from sklearn.linear_model import Lasso\n",
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"from xgboost import XGBRegressor\n",
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"from xgboost import XGBRegressor\n",
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"\n",
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"\n",
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"import datetime\n",
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"import statsmodels.api as sm\n",
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"import statsmodels.api as sm\n",
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"# from keras.preprocessing.sequence import TimeseriesGenerator\n",
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"# from keras.preprocessing.sequence import TimeseriesGenerator\n",
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"from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
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"from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
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" ]\n",
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" ]\n",
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" }\n",
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" }\n",
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" headers = {\"Authorization\": token_push}\n",
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" headers = {\"Authorization\": token_push}\n",
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" # res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))\n",
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" res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))\n",
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" # print(res.text)\n",
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" print(res.text)\n",
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"\n",
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" \n",
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"# def upload_data_to_system(token):\n",
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"# data = {\n",
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"# \"funcModule\": \"数据表信息列表\",\n",
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"# \"funcOperation\": \"新增\",\n",
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"# \"data\": [\n",
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"# {\"dataItemNo\": \"C01100036|Forecast_ Price|ACN\",\n",
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"# \"dataDate\": '20230706',\n",
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"# \"dataStatus\": \"add\",\n",
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"# \"dataValue\": 3780.0\n",
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"# }\n",
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"\n",
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"# ]\n",
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"# }\n",
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"# headers = {\"Authorization\": token}\n",
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"# res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))\n",
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"# print(res.text)\n",
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"\n",
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"\n",
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"def forecast_price():\n",
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"def forecast_price():\n",
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" df_test = pd.read_excel('沥青数据项.xls',sheet_name='数据项历史数据')\n",
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" df_test = pd.read_excel('沥青数据项.xlsx',sheet_name='数据项历史数据')\n",
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" df_test.drop([0],inplace=True)\n",
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" df_test.drop([0],inplace=True)\n",
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" df_test[['汽油执行价','柴油执行价','齐鲁石化销量','齐鲁石化产量','齐鲁石化成交价','齐鲁石化库存','科力达销量',\n",
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" df_test[['汽油执行价','柴油执行价','齐鲁石化销量','齐鲁石化产量','齐鲁石化成交价','齐鲁石化库存','科力达销量',\n",
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" '科力达产量','科力达成交价','科力达库存','弘润销量','弘润产量','弘润成交价','弘润库存','市场成交价','京博指导价',\n",
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" '科力达产量','科力达成交价','科力达库存','弘润销量','弘润产量','弘润成交价','弘润库存','市场成交价','京博指导价',\n",
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"\n",
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"\n",
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" pd.set_option('display.max_rows',40) \n",
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" pd.set_option('display.max_rows',40) \n",
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" pd.set_option('display.max_columns',40) \n",
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" pd.set_option('display.max_columns',40) \n",
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" df_test = pd.read_excel('沥青数据项.xls',sheet_name='数据项历史数据')\n",
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" df_test = pd.read_excel('沥青数据项.xlsx',sheet_name='数据项历史数据')\n",
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" df_test.drop([0],inplace=True)\n",
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" df_test.drop([0],inplace=True)\n",
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" df_test[['汽油执行价','柴油执行价','齐鲁石化销量','齐鲁石化产量','齐鲁石化成交价','齐鲁石化库存','科力达销量',\n",
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" df_test[['汽油执行价','柴油执行价','齐鲁石化销量','齐鲁石化产量','齐鲁石化成交价','齐鲁石化库存','科力达销量',\n",
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" '科力达产量','科力达成交价','科力达库存','弘润销量','弘润产量','弘润成交价','弘润库存','市场成交价','京博指导价',\n",
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" '科力达产量','科力达成交价','科力达库存','弘润销量','弘润产量','弘润成交价','弘润库存','市场成交价','京博指导价',\n",
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" from sklearn.linear_model import Lasso\n",
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" from sklearn.linear_model import Lasso\n",
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" from xgboost import XGBRegressor\n",
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" from xgboost import XGBRegressor\n",
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"\n",
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"\n",
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" from datetime import datetime\n",
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" import statsmodels.api as sm\n",
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" import statsmodels.api as sm\n",
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" # from keras.preprocessing.sequence import TimeseriesGenerator\n",
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" # from keras.preprocessing.sequence import TimeseriesGenerator\n",
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" from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
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" from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
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" save_xls(append_rows)\n",
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" save_xls(append_rows)\n",
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"\n",
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"\n",
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" # 获取当月的数据写入到指定文件\n",
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" # 获取当月的数据写入到指定文件\n",
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" queryDataListItemNos(token)\n",
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" # optimize_Model()\n",
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" # optimize_Model()\n",
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" # upload_data_to_system(token_push,date)\n",
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" # upload_data_to_system(token_push,date)\n",
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" # data_list.append(three_cols)\n",
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" # data_list.append(three_cols)\n",
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"\n",
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"\n",
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"def start_1():\n",
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"def start_1():\n",
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" '''更新数据'''\n",
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" '''更新数据'''\n",
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" print(\"更新当天数据\")\n",
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" read_xls_data()\n",
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" read_xls_data()\n",
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"\n",
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"\n",
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" token = get_head_auth()\n",
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" token = get_head_auth()\n",
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" append_rows.append(dataItemNo_dataValue[value])\n",
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" append_rows.append(dataItemNo_dataValue[value])\n",
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" else:\n",
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" else:\n",
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" append_rows.append(\"\")\n",
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" append_rows.append(\"\")\n",
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" print(\"当天数据为：\",append_rows)\n",
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" save_xls_1(append_rows)\n",
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" save_xls_1(append_rows)\n",
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"\n",
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"\n",
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" \n",
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" \n",
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"def save_xls_1(append_rows):\n",
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"def save_xls_1(append_rows):\n",
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"\n",
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"\n",
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" # 打开xls文件\n",
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" # 打开xls文件\n",
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" workbook = xlrd.open_workbook('沥青数据项.xls')\n",
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" workbook = xlrd.open_workbook('沥青数据项.xlsx')\n",
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"\n",
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"\n",
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" # 获取所有sheet的个数\n",
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" # 获取所有sheet的个数\n",
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" sheet_count = len(workbook.sheet_names())\n",
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" sheet_count = len(workbook.sheet_names())\n",
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" new_sheet.write(row_count, col, append_rows[col])\n",
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" new_sheet.write(row_count, col, append_rows[col])\n",
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"\n",
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"\n",
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" # 保存新的xls文件\n",
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" # 保存新的xls文件\n",
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" new_workbook.save(\"沥青数据项.xls\") \n",
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" new_workbook.save(\"沥青数据项.xlsx\") \n",
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"\n",
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"\n",
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" \n",
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" \n",
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" \n",
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" \n",
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" new_sheet.write(row_count, col, append_rows[col])\n",
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" new_sheet.write(row_count, col, append_rows[col])\n",
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"\n",
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"\n",
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" # 保存新的xls文件\n",
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" # 保存新的xls文件\n",
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" new_workbook.save(\"沥青数据项.xls\")\n",
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" new_workbook.save(\"沥青数据项.xlsx\")\n",
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"\n",
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"\n",
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"\n",
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"\n",
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" # 保存新的xls文件\n",
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" # 保存新的xls文件\n",
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" new_workbook.save(\"沥青数据项.xls\")\n",
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" new_workbook.save(\"沥青数据项.xlsx\")\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"def queryDataListItemNos(token=None):\n",
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"def queryDataListItemNos(token=None):\n",
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" df = pd.read_excel('沥青数据项.xls')\n",
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" df = pd.read_excel('沥青数据项.xlsx')\n",
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" dataItemNoList = df.iloc[0].tolist()[1:]\n",
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" dataItemNoList = df.iloc[0].tolist()[1:]\n",
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" \n",
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" \n",
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" if token is None:\n",
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" if token is None:\n",
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" save_queryDataListItemNos_xls(data_df,dataItemNoList)\n",
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" save_queryDataListItemNos_xls(data_df,dataItemNoList)\n",
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" print('当月数据更新完成')\n",
|
" print('当月数据更新完成')\n",
|
||||||
"\n",
|
"\n",
|
||||||
|
"def main(start_date=None,token=None,token_push=None):\n",
|
||||||
|
" if start_date is None:\n",
|
||||||
|
" start_date = datetime.now()\n",
|
||||||
|
" if token is None:\n",
|
||||||
|
" token = get_head_auth()\n",
|
||||||
|
" if token_push is None:\n",
|
||||||
|
" token_push = get_head_push_auth()\n",
|
||||||
|
" date = start_date.strftime('%Y%m%d')\n",
|
||||||
|
" print(date)\n",
|
||||||
|
" # 更新当月数据\n",
|
||||||
|
" queryDataListItemNos(token)\n",
|
||||||
|
" # 更新当日数据\n",
|
||||||
|
" start_3(start_date,token,token_push)\n",
|
||||||
|
" # 训练模型\n",
|
||||||
|
" optimize_Model()\n",
|
||||||
|
" # 预测&上传预测结果\n",
|
||||||
|
" upload_data_to_system(token_push,start_date)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
"\n",
|
"\n",
|
||||||
"if __name__ == \"__main__\":\n",
|
"if __name__ == \"__main__\":\n",
|
||||||
" pass\n",
|
" print(\"运行中ing...\")\n",
|
||||||
" # 需要单独运行放开\n",
|
|
||||||
"\n",
|
|
||||||
"# start()\n",
|
|
||||||
"\n",
|
|
||||||
" # 每天定时12点运行\n",
|
" # 每天定时12点运行\n",
|
||||||
" # while True:\n",
|
" while True:\n",
|
||||||
" # # 获取当前时间\n",
|
" # 获取当前时间\n",
|
||||||
" # current_time = time.strftime(\"%H:%M:%S\", time.localtime())\n",
|
" current_time = time.strftime(\"%H:%M:%S\", time.localtime())\n",
|
||||||
" # current_time_1 = time.strftime(\"%H:%M:%S\", time.localtime())\n",
|
|
||||||
"\n",
|
"\n",
|
||||||
"\n",
|
" # 判断当前时间是否为执行任务的时间点\n",
|
||||||
" \n",
|
|
||||||
"\n",
|
|
||||||
" # # 判断当前时间是否为执行任务的时间点\n",
|
|
||||||
" # if current_time == \"12:00:00\":\n",
|
" # if current_time == \"12:00:00\":\n",
|
||||||
" # print(\"执行定时任务\")\n",
|
" if current_time == \"15:36:00\":\n",
|
||||||
"\n",
|
" print(\"执行定时任务\")\n",
|
||||||
" # 这个函数，获取当天数据，\n",
|
" main()\n",
|
||||||
" # 预测训练，\n",
|
" time.sleep(1)\n",
|
||||||
" # 预测结果上传\n",
|
" elif current_time == \"15:40:00\":\n",
|
||||||
"\n",
|
" start_1()\n",
|
||||||
" # start()\n",
|
" time.sleep(1)\n",
|
||||||
"\n",
|
|
||||||
" # # 休眠1秒钟，避免过多占用CPU资源\n",
|
|
||||||
" # time.sleep(1)\n",
|
|
||||||
" \n",
|
|
||||||
" # elif current_time_1 == \"20:00:00\":\n",
|
|
||||||
" # print(\"更新数据\")\n",
|
|
||||||
"\n",
|
|
||||||
" # 这个函数，获取当天数据，保存\n",
|
|
||||||
" # start_1()\n",
|
|
||||||
" # time.sleep(1)\n",
|
|
||||||
"\n",
|
"\n",
|
||||||
"\n",
|
"\n",
|
||||||
"# # 检测数据准确性, 需要检测放开\n",
|
"# # 检测数据准确性, 需要检测放开\n",
|
||||||
@ -941,40 +929,24 @@
|
|||||||
"name": "stdout",
|
"name": "stdout",
|
||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"20250401\n"
|
"更新当天数据\n",
|
||||||
]
|
"当天数据为： ['2025-04-08', '', '', 0.05, 0.25, 3680.0, 0.7, 0.0, 0.0, 3450.0, 7.9, 0.15, 0.2, 3400.0, 10.27, '', 3450.0, 65.88, 64.36, '', '', '', '', '', '', '', '', '', 3096.5238, '', '', '', '', '']\n"
|
||||||
},
|
|
||||||
{
|
|
||||||
"ename": "PermissionError",
|
|
||||||
"evalue": "[Errno 13] Permission denied: '沥青数据项.xls'",
|
|
||||||
"output_type": "error",
|
|
||||||
"traceback": [
|
|
||||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
|
||||||
"\u001b[1;31mPermissionError\u001b[0m Traceback (most recent call last)",
|
|
||||||
"Cell \u001b[1;32mIn[12], line 11\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m start_date \u001b[38;5;241m<\u001b[39m end_date:\n\u001b[0;32m 10\u001b[0m \u001b[38;5;28mprint\u001b[39m(start_date\u001b[38;5;241m.\u001b[39mstrftime(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mm\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m---> 11\u001b[0m start_3(start_date,token,token_push)\n\u001b[0;32m 12\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m 13\u001b[0m start_2(start_date,token)\n",
|
|
||||||
"Cell \u001b[1;32mIn[11], line 553\u001b[0m, in \u001b[0;36mstart_3\u001b[1;34m(date, token, token_push)\u001b[0m\n\u001b[0;32m 551\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 552\u001b[0m append_rows\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 553\u001b[0m save_xls(append_rows)\n\u001b[0;32m 555\u001b[0m \u001b[38;5;66;03m# 获取当月的数据写入到指定文件\u001b[39;00m\n\u001b[0;32m 556\u001b[0m queryDataListItemNos(token)\n",
|
|
||||||
"Cell \u001b[1;32mIn[11], line 731\u001b[0m, in \u001b[0;36msave_xls\u001b[1;34m(append_rows)\u001b[0m\n\u001b[0;32m 728\u001b[0m new_sheet\u001b[38;5;241m.\u001b[39mwrite(row_count, col, append_rows[col])\n\u001b[0;32m 730\u001b[0m \u001b[38;5;66;03m# 保存新的xls文件\u001b[39;00m\n\u001b[1;32m--> 731\u001b[0m new_workbook\u001b[38;5;241m.\u001b[39msave(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m沥青数据项.xls\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
|
|
||||||
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\xlwt\\Workbook.py:710\u001b[0m, in \u001b[0;36mWorkbook.save\u001b[1;34m(self, filename_or_stream)\u001b[0m\n\u001b[0;32m 707\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CompoundDoc\n\u001b[0;32m 709\u001b[0m doc \u001b[38;5;241m=\u001b[39m CompoundDoc\u001b[38;5;241m.\u001b[39mXlsDoc()\n\u001b[1;32m--> 710\u001b[0m doc\u001b[38;5;241m.\u001b[39msave(filename_or_stream, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_biff_data())\n",
|
|
||||||
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\xlwt\\CompoundDoc.py:262\u001b[0m, in \u001b[0;36mXlsDoc.save\u001b[1;34m(self, file_name_or_filelike_obj, stream)\u001b[0m\n\u001b[0;32m 260\u001b[0m we_own_it \u001b[38;5;241m=\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(f, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwrite\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 261\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m we_own_it:\n\u001b[1;32m--> 262\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(file_name_or_filelike_obj, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mw+b\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 263\u001b[0m f\u001b[38;5;241m.\u001b[39mwrite(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mheader)\n\u001b[0;32m 264\u001b[0m f\u001b[38;5;241m.\u001b[39mwrite(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpacked_MSAT_1st)\n",
|
|
||||||
"\u001b[1;31mPermissionError\u001b[0m: [Errno 13] Permission denied: '沥青数据项.xls'"
|
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"source": [
|
"source": [
|
||||||
"# from datetime import datetime, timedelta\n",
|
"# start_date = datetime(2025, 4, 8)\n",
|
||||||
"\n",
|
"# end_date = datetime(2025, 4, 9)\n",
|
||||||
"# start_date = datetime(2025, 4, 1)\n",
|
|
||||||
"# end_date = datetime(2025, 4, 2)\n",
|
|
||||||
"# token = get_head_auth()\n",
|
"# token = get_head_auth()\n",
|
||||||
"\n",
|
"\n",
|
||||||
"# token_push = get_head_push_auth()\n",
|
"# token_push = get_head_push_auth()\n",
|
||||||
"\n",
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
"# while start_date < end_date:\n",
|
"# while start_date < end_date:\n",
|
||||||
"# print(start_date.strftime('%Y%m%d'))\n",
|
" # main(start_date,token,token_push)\n",
|
||||||
"# start_3(start_date,token,token_push)\n",
|
"# start_date += timedelta(days=1)\n"
|
||||||
"# time.sleep(1)\n",
|
|
||||||
"# start_2(start_date,token)\n",
|
|
||||||
"# start_date += timedelta(days=1)"
|
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
|
|||||||
BIN
aisenzhecode/沥青/沥青数据项 - 副本.xls
Normal file
BIN
aisenzhecode/沥青/沥青数据项 - 副本.xls
Normal file
Binary file not shown.
BIN
aisenzhecode/沥青/沥青数据项.xlsx
Normal file
BIN
aisenzhecode/沥青/沥青数据项.xlsx
Normal file
Binary file not shown.
@ -11,30 +11,23 @@
|
|||||||
" <script type=\"text/javascript\">\n",
|
" <script type=\"text/javascript\">\n",
|
||||||
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
|
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
|
||||||
" if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
|
" if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
|
||||||
|
" if (typeof require !== 'undefined') {\n",
|
||||||
|
" require.undef(\"plotly\");\n",
|
||||||
|
" requirejs.config({\n",
|
||||||
|
" paths: {\n",
|
||||||
|
" 'plotly': ['https://cdn.plot.ly/plotly-2.12.1.min']\n",
|
||||||
|
" }\n",
|
||||||
|
" });\n",
|
||||||
|
" require(['plotly'], function(Plotly) {\n",
|
||||||
|
" window._Plotly = Plotly;\n",
|
||||||
|
" });\n",
|
||||||
|
" }\n",
|
||||||
" </script>\n",
|
" </script>\n",
|
||||||
" <script type=\"module\">import \"https://cdn.plot.ly/plotly-3.0.1.min\"</script>\n",
|
|
||||||
" "
|
" "
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"output_type": "display_data"
|
"output_type": "display_data"
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0MzEwNTAxMCwianRpIjoiN2QwMjE1N2Q0NzIwNDZjMjk3NTE4ODMzNjA3NzY3OTQifQ.F2QXnRxR_YUmQ8w2GIWkvjy0xLMTZg93rJ-wHkuyIzw\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "stderr",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"C:\\Users\\h5218\\AppData\\Local\\Temp\\ipykernel_31728\\3153608672.py:885: DeprecationWarning:\n",
|
|
||||||
"\n",
|
|
||||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
|
||||||
"\n"
|
|
||||||
]
|
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"source": [
|
"source": [
|
||||||
@ -555,7 +548,7 @@
|
|||||||
" print('更新数据前')\n",
|
" print('更新数据前')\n",
|
||||||
" print(df.tail(1))\n",
|
" print(df.tail(1))\n",
|
||||||
" # 检查日期是否已存在于数据中\n",
|
" # 检查日期是否已存在于数据中\n",
|
||||||
" if previous_date not in df['日期'].values:\n",
|
" if previous_date not in df['Date'].values:\n",
|
||||||
" # 将新的数据添加到DataFrame中\n",
|
" # 将新的数据添加到DataFrame中\n",
|
||||||
" new_row = pd.DataFrame([append_rows], columns=df.columns.tolist())\n",
|
" new_row = pd.DataFrame([append_rows], columns=df.columns.tolist())\n",
|
||||||
" df = pd.concat([df, new_row], ignore_index=True)\n",
|
" df = pd.concat([df, new_row], ignore_index=True)\n",
|
||||||
@ -563,7 +556,7 @@
|
|||||||
" # 更新现有数据\n",
|
" # 更新现有数据\n",
|
||||||
" print('日期存在，即将更新')\n",
|
" print('日期存在，即将更新')\n",
|
||||||
" print('新数据',append_rows[1:])\n",
|
" print('新数据',append_rows[1:])\n",
|
||||||
" df.loc[df['日期'] == previous_date, df.columns.tolist()[1:]] = append_rows[1:]\n",
|
" df.loc[df['Date'] == previous_date, df.columns.tolist()[1:]] = append_rows[1:]\n",
|
||||||
"\n",
|
"\n",
|
||||||
" print('更新数据后')\n",
|
" print('更新数据后')\n",
|
||||||
" print(df.tail(1))\n",
|
" print(df.tail(1))\n",
|
||||||
@ -635,6 +628,11 @@
|
|||||||
" else:\n",
|
" else:\n",
|
||||||
" append_rows.append(\"\")\n",
|
" append_rows.append(\"\")\n",
|
||||||
" save_xls(append_rows)\n",
|
" save_xls(append_rows)\n",
|
||||||
|
" \n",
|
||||||
|
" \n",
|
||||||
|
" # 获取当月的数据写入到指定文件\n",
|
||||||
|
" # queryDataListItemNos(token=token)\n",
|
||||||
|
" \n",
|
||||||
" # optimize_Model()\n",
|
" # optimize_Model()\n",
|
||||||
" # upload_data_to_system(token_push,cur_time)\n",
|
" # upload_data_to_system(token_push,cur_time)\n",
|
||||||
" \n",
|
" \n",
|
||||||
@ -795,8 +793,14 @@
|
|||||||
"\n",
|
"\n",
|
||||||
"def save_xls(append_rows):\n",
|
"def save_xls(append_rows):\n",
|
||||||
"\n",
|
"\n",
|
||||||
" # 打开xls文件\n",
|
" # 修改文件打开方式\n",
|
||||||
|
" try:\n",
|
||||||
|
" # 尝试用xlrd打开旧版xls文件\n",
|
||||||
" workbook = xlrd.open_workbook('液化气数据.xls')\n",
|
" workbook = xlrd.open_workbook('液化气数据.xls')\n",
|
||||||
|
" except:\n",
|
||||||
|
" # 使用openpyxl打开新版xlsx文件\n",
|
||||||
|
" from openpyxl import load_workbook\n",
|
||||||
|
" workbook = load_workbook('液化气数据.xls')\n",
|
||||||
"\n",
|
"\n",
|
||||||
" # 获取所有sheet的个数\n",
|
" # 获取所有sheet的个数\n",
|
||||||
" sheet_count = len(workbook.sheet_names())\n",
|
" sheet_count = len(workbook.sheet_names())\n",
|
||||||
@ -922,31 +926,34 @@
|
|||||||
" dataItemNo = dataItemNoList[j]\n",
|
" dataItemNo = dataItemNoList[j]\n",
|
||||||
"\n",
|
"\n",
|
||||||
" # for dataItemNo in dataItemNoList:\n",
|
" # for dataItemNo in dataItemNoList:\n",
|
||||||
" if group[group[\"dataItemNo\"] == dataItemNo][\"dataValue\"].values and (not str(group[group[\"dataItemNo\"] == dataItemNo][\"dataValue\"].values[0]) == 'nan'):\n",
|
" if group[group[\"dataItemNo\"] == dataItemNo][\"dataValue\"].values:\n",
|
||||||
" # if group[group[\"dataItemNo\"] == dataItemNo][\"dataValue\"].values:\n",
|
|
||||||
"\n",
|
"\n",
|
||||||
" new_sheet.write(row_count + rowFlag - current_year_month_row, j + 1, group[group[\"dataItemNo\"] == dataItemNo][\"dataValue\"].values[0])\n",
|
" new_sheet.write(row_count + rowFlag - current_year_month_row, j + 1, group[group[\"dataItemNo\"] == dataItemNo][\"dataValue\"].values[0])\n",
|
||||||
"\n",
|
"\n",
|
||||||
" rowFlag += 1\n",
|
" rowFlag += 1\n",
|
||||||
"\n",
|
"\n",
|
||||||
"\n",
|
|
||||||
" # 保存新的xls文件\n",
|
" # 保存新的xls文件\n",
|
||||||
" new_workbook.save(\"液化气数据.xls\")\n",
|
" new_workbook.save(\"液化气数据.xls\")\n",
|
||||||
"\n",
|
"\n",
|
||||||
"\n",
|
"\n",
|
||||||
"\n",
|
"\n",
|
||||||
"\n",
|
"\n",
|
||||||
"def queryDataListItemNos():\n",
|
"def queryDataListItemNos(date=None,token=None):\n",
|
||||||
" df = pd.read_excel('液化气数据.xls')\n",
|
" df = pd.read_excel('液化气数据.xls')\n",
|
||||||
" dataItemNoList = df.iloc[0].tolist()[1:]\n",
|
" dataItemNoList = df.iloc[0].tolist()[1:]\n",
|
||||||
"\n",
|
" \n",
|
||||||
|
" if token is None:\n",
|
||||||
" token = get_head_auth()\n",
|
" token = get_head_auth()\n",
|
||||||
|
"\n",
|
||||||
" if not token:\n",
|
" if not token:\n",
|
||||||
|
" print('token获取失败')\n",
|
||||||
" return\n",
|
" return\n",
|
||||||
"\n",
|
"\n",
|
||||||
" # 获取当前日期\n",
|
" # 获取当前日期\n",
|
||||||
|
" if date is None:\n",
|
||||||
" current_date = datetime.now()\n",
|
" current_date = datetime.now()\n",
|
||||||
"\n",
|
" else:\n",
|
||||||
|
" current_date = date\n",
|
||||||
" # 获取当月1日\n",
|
" # 获取当月1日\n",
|
||||||
" first_day_of_month = current_date.replace(day=1)\n",
|
" first_day_of_month = current_date.replace(day=1)\n",
|
||||||
"\n",
|
"\n",
|
||||||
@ -961,7 +968,10 @@
|
|||||||
"\n",
|
"\n",
|
||||||
" data_df[\"dataDate\"] = pd.to_datetime(data_df[\"dataDate\"])\n",
|
" data_df[\"dataDate\"] = pd.to_datetime(data_df[\"dataDate\"])\n",
|
||||||
" data_df[\"dataDate\"] = data_df[\"dataDate\"].dt.strftime('%Y-%m-%d')\n",
|
" data_df[\"dataDate\"] = data_df[\"dataDate\"].dt.strftime('%Y-%m-%d')\n",
|
||||||
|
" print(data_df)\n",
|
||||||
" save_queryDataListItemNos_xls(data_df,dataItemNoList)\n",
|
" save_queryDataListItemNos_xls(data_df,dataItemNoList)\n",
|
||||||
|
" print('当月数据更新完成')\n",
|
||||||
|
"\n",
|
||||||
"\n",
|
"\n",
|
||||||
"\n",
|
"\n",
|
||||||
"\n",
|
"\n",
|
||||||
@ -974,7 +984,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": null,
|
"execution_count": 61,
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"scrolled": true
|
"scrolled": true
|
||||||
},
|
},
|
||||||
@ -997,14 +1007,12 @@
|
|||||||
"# # print(current_time_1)\n",
|
"# # print(current_time_1)\n",
|
||||||
"\n",
|
"\n",
|
||||||
"\n",
|
"\n",
|
||||||
" # 获取当月的数据写入到指定文件\n",
|
|
||||||
" # queryDataListItemNos()\n",
|
|
||||||
"\n",
|
"\n",
|
||||||
"\n",
|
"\n",
|
||||||
"# # 判断当前时间是否为执行任务的时间点\n",
|
"# # 判断当前时间是否为执行任务的时间点\n",
|
||||||
"# if current_time == \"09:15:00\":\n",
|
"# if current_time == \"09:15:00\":\n",
|
||||||
"# print(\"执行定时任务\")\n",
|
"# print(\"执行定时任务\")\n",
|
||||||
"# start()\n",
|
" # start()\n",
|
||||||
"\n",
|
"\n",
|
||||||
"# # 休眠1秒钟，避免过多占用CPU资源\n",
|
"# # 休眠1秒钟，避免过多占用CPU资源\n",
|
||||||
"# time.sleep(1)\n",
|
"# time.sleep(1)\n",
|
||||||
@ -1025,32 +1033,45 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 2,
|
"execution_count": 62,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"ename": "NameError",
|
"name": "stdout",
|
||||||
"evalue": "name 'datetime' is not defined",
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0NDEyODI3NywianRpIjoiMjk0MTk4NTU0MWIxNGY2NmJjMTgyY2Y5ZDY1MWVjYzUifQ.2uFevYbViecixxMPgJNCvWO_q2hjzJM35_FavDmEXnI\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"ename": "ValueError",
|
||||||
|
"evalue": "Unknown engine: xlwt",
|
||||||
"output_type": "error",
|
"output_type": "error",
|
||||||
"traceback": [
|
"traceback": [
|
||||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||||
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
|
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
|
||||||
"Cell \u001b[1;32mIn[2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m start_date \u001b[38;5;241m=\u001b[39m datetime(\u001b[38;5;241m2025\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m 2\u001b[0m end_date \u001b[38;5;241m=\u001b[39m datetime(\u001b[38;5;241m2025\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m12\u001b[39m)\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m start_date \u001b[38;5;241m<\u001b[39m end_date:\n",
|
"Cell \u001b[1;32mIn[62], line 8\u001b[0m\n\u001b[0;32m 6\u001b[0m date \u001b[38;5;241m=\u001b[39m start_date\u001b[38;5;241m.\u001b[39mstrftime(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mm\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 7\u001b[0m date2 \u001b[38;5;241m=\u001b[39m start_date\u001b[38;5;241m.\u001b[39mstrftime(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mY-\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mm-\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m----> 8\u001b[0m updateYesterdayExcelData(date\u001b[38;5;241m=\u001b[39mdate2,token\u001b[38;5;241m=\u001b[39mtoken)\n\u001b[0;32m 9\u001b[0m \u001b[38;5;66;03m# queryDataListItemNos(date=start_date,token=token)\u001b[39;00m\n\u001b[0;32m 10\u001b[0m start(date)\n",
|
||||||
"\u001b[1;31mNameError\u001b[0m: name 'datetime' is not defined"
|
"Cell \u001b[1;32mIn[60], line 471\u001b[0m, in \u001b[0;36mupdateYesterdayExcelData\u001b[1;34m(date, token)\u001b[0m\n\u001b[0;32m 469\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mupdateYesterdayExcelData\u001b[39m(date\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m, token\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m 470\u001b[0m \u001b[38;5;66;03m# 使用pandas读取Excel文件\u001b[39;00m\n\u001b[1;32m--> 471\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_excel(read_file_path_name, engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mxlwt\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 473\u001b[0m \u001b[38;5;66;03m# 获取第二行的数据作为列名\u001b[39;00m\n\u001b[0;32m 474\u001b[0m one_cols \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m0\u001b[39m,:]\u001b[38;5;241m.\u001b[39mtolist()\n",
|
||||||
|
"File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\excel\\_base.py:504\u001b[0m, in \u001b[0;36mread_excel\u001b[1;34m(io, sheet_name, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, date_format, thousands, decimal, comment, skipfooter, storage_options, dtype_backend, engine_kwargs)\u001b[0m\n\u001b[0;32m 502\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(io, ExcelFile):\n\u001b[0;32m 503\u001b[0m should_close \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 504\u001b[0m io \u001b[38;5;241m=\u001b[39m ExcelFile(\n\u001b[0;32m 505\u001b[0m io,\n\u001b[0;32m 506\u001b[0m storage_options\u001b[38;5;241m=\u001b[39mstorage_options,\n\u001b[0;32m 507\u001b[0m engine\u001b[38;5;241m=\u001b[39mengine,\n\u001b[0;32m 508\u001b[0m engine_kwargs\u001b[38;5;241m=\u001b[39mengine_kwargs,\n\u001b[0;32m 509\u001b[0m )\n\u001b[0;32m 510\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m engine \u001b[38;5;129;01mand\u001b[39;00m engine \u001b[38;5;241m!=\u001b[39m io\u001b[38;5;241m.\u001b[39mengine:\n\u001b[0;32m 511\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 512\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEngine should not be specified when passing \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 513\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124man ExcelFile - ExcelFile already has the engine set\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 514\u001b[0m )\n",
|
||||||
|
"File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\excel\\_base.py:1531\u001b[0m, in \u001b[0;36mExcelFile.__init__\u001b[1;34m(self, path_or_buffer, engine, storage_options, engine_kwargs)\u001b[0m\n\u001b[0;32m 1528\u001b[0m engine_kwargs \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m 1530\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m engine \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m engine \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engines:\n\u001b[1;32m-> 1531\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnknown engine: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mengine\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 1533\u001b[0m \u001b[38;5;66;03m# First argument can also be bytes, so create a buffer\u001b[39;00m\n\u001b[0;32m 1534\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(path_or_buffer, \u001b[38;5;28mbytes\u001b[39m):\n",
|
||||||
|
"\u001b[1;31mValueError\u001b[0m: Unknown engine: xlwt"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"source": [
|
"source": [
|
||||||
"start_date = datetime(2025, 3, 1)\n",
|
"start_date = datetime(2025, 4, 2)\n",
|
||||||
"end_date = datetime(2025, 3, 12)\n",
|
"end_date = datetime(2025, 4, 3)\n",
|
||||||
|
"token = get_head_auth()\n",
|
||||||
"\n",
|
"\n",
|
||||||
"while start_date < end_date:\n",
|
"while start_date < end_date:\n",
|
||||||
" date = start_date.strftime('%Y%m%d')\n",
|
" date = start_date.strftime('%Y%m%d')\n",
|
||||||
" updateYesterdayExcelData(date)\n",
|
" date2 = start_date.strftime('%Y-%m-%d')\n",
|
||||||
" # start(date)\n",
|
" updateYesterdayExcelData(date=date2,token=token)\n",
|
||||||
|
" # queryDataListItemNos(date=start_date,token=token)\n",
|
||||||
|
" start(date)\n",
|
||||||
" # # time.sleep(1)\n",
|
" # # time.sleep(1)\n",
|
||||||
" # start_1(start_date)\n",
|
" # start_1(start_date)\n",
|
||||||
" # start_date += timedelta(days=1)\n",
|
" start_date += timedelta(days=1)\n",
|
||||||
" time.sleep(5)\n",
|
" time.sleep(5)\n",
|
||||||
"\n",
|
"\n",
|
||||||
"# print(price_list)"
|
"# print(price_list)"
|
||||||
@ -1066,7 +1087,7 @@
|
|||||||
],
|
],
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"kernelspec": {
|
"kernelspec": {
|
||||||
"display_name": "Python 3",
|
"display_name": "base",
|
||||||
"language": "python",
|
"language": "python",
|
||||||
"name": "python3"
|
"name": "python3"
|
||||||
},
|
},
|
||||||
@ -1080,7 +1101,7 @@
|
|||||||
"name": "python",
|
"name": "python",
|
||||||
"nbconvert_exporter": "python",
|
"nbconvert_exporter": "python",
|
||||||
"pygments_lexer": "ipython3",
|
"pygments_lexer": "ipython3",
|
||||||
"version": "3.12.4"
|
"version": "3.11.7"
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
"nbformat": 4,
|
"nbformat": 4,
|
||||||
|
|||||||
Binary file not shown.
@ -2,7 +2,7 @@
|
|||||||
"cells": [
|
"cells": [
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": null,
|
"execution_count": 20,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
@ -828,7 +828,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": null,
|
"execution_count": 21,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user