718 lines
26 KiB
Python
718 lines
26 KiB
Python
import requests
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import json
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from datetime import datetime, timedelta
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import time
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import pandas as pd
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# 变量定义
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login_url = "http://10.200.32.39/jingbo-api/api/server/login"
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search_url = "http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryByItemNos"
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queryDataListItemNos_url = "http://10.200.32.39/jingbo-api//api/warehouse/dwDataItem/queryDataListItemNos"
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login_push_url = "http://10.200.32.39/jingbo-api/api/server/login"
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upload_url = "http://10.200.32.39/jingbo-api/api/dw/dataValue/pushDataValueList"
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login_data = {
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"data": {
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"account": "api_dev",
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"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
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"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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"terminal": "API"
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},
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"funcModule": "API",
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"funcOperation": "获取token"
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}
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login_push_data = {
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"data": {
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"account": "api_dev",
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"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
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"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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"terminal": "API"
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},
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"funcModule": "API",
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"funcOperation": "获取token"
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}
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read_file_path_name = "沥青数据项.xlsx"
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one_cols = []
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two_cols = []
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import seaborn as sn
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import random
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import time
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from plotly import __version__
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from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
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from sklearn import preprocessing
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from pandas import Series,DataFrame
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import matplotlib.pyplot as plt
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import sklearn.datasets as datasets
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#导入机器学习算法模型
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from sklearn.linear_model import Lasso
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from xgboost import XGBRegressor
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import statsmodels.api as sm
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# from keras.preprocessing.sequence import TimeseriesGenerator
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from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
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import plotly.express as px
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import plotly.graph_objects as go
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import xgboost as xgb
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from xgboost import plot_importance, plot_tree
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from sklearn.metrics import mean_absolute_error
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from statsmodels.tools.eval_measures import mse,rmse
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from sklearn.model_selection import GridSearchCV
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from xgboost import XGBRegressor
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import warnings
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import pickle
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from sklearn.metrics import mean_squared_error
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#切割训练数据和样本数据
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from sklearn.model_selection import train_test_split
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#用于模型评分
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from sklearn.metrics import r2_score
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le = preprocessing.LabelEncoder()
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# print(__version__) # requires version >= 1.9.0
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import cufflinks as cf
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cf.go_offline()
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random.seed(100)
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# 数据获取
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def get_head_auth():
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login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5))
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text = json.loads(login_res.text)
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if text["status"]:
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token = text["data"]["accessToken"]
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return token
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else:
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print("获取认证失败")
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return None
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def get_data_value(token, dataItemNoList,date=''):
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search_data = {
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"data": {
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"date": getNow(date)[0],
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"dataItemNoList": dataItemNoList
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},
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"funcModule": "数据项",
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"funcOperation": "查询"
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}
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headers = {"Authorization": token}
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search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))
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search_value = json.loads(search_res.text)["data"]
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if search_value:
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return search_value
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else:
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print("今天没有新数据")
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return None
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# xls文件处理
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def getNow(date='', offset=0):
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"""生成指定日期的两种格式字符串
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Args:
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date: 支持多种输入类型：
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- datetime对象
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- 字符串格式（支持'%Y-%m-%d'和'%Y%m%d'）
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- 空字符串表示当前日期
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offset: 日期偏移天数
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Returns:
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tuple: (紧凑日期字符串, 标准日期字符串)
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"""
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# 日期解析逻辑
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from datetime import datetime,timedelta
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if isinstance(date, datetime):
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now = date
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else:
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now = datetime.now()
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if date:
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# 尝试多种日期格式解析
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for fmt in ('%Y-%m-%d', '%Y%m%d', '%Y/%m/%d'):
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try:
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now = datetime.strptime(str(date), fmt)
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break
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except ValueError:
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continue
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else:
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raise ValueError(f"无法解析的日期格式: {date}")
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# 应用日期偏移
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now = now - timedelta(days=offset)
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# 统一格式化输出
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date_str = now.strftime("%Y-%m-%d")
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compact_date = date_str.replace("-", "")
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return compact_date, date_str
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def get_head_push_auth():
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login_res = requests.post(url=login_push_url, json=login_push_data, timeout=(3, 5))
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text = json.loads(login_res.text)
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if text["status"]:
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token = text["data"]["accessToken"]
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return token
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else:
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print("获取认证失败")
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return None
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def upload_data_to_system(token_push,date):
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data = {
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"funcModule": "数据表信息列表",
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"funcOperation": "新增",
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"data": [
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{"dataItemNo": "C01100036|Forecast_Price|ACN",
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"dataDate": getNow(date)[0],
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"dataStatus": "add",
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"dataValue": forecast_price()
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}
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]
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}
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headers = {"Authorization": token_push}
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res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))
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print(res.text)
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def forecast_price():
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df_test = pd.read_excel('沥青数据项.xlsx')
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df_test.drop([0],inplace=True)
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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',
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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',
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'布伦特上周收盘价','布伦特昨日收盘价','布伦特收盘价','上期所沥青主力合约','隆重资讯沥青日开工率','隆重资讯沥青月库存',
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'隆重资讯沥青月产量','隆重资讯沥青表观消费量','隆重资讯社会库存率','厂区库容','京博提货量','即期成本','异地库库存',
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'京博签单量','京博库存量','京博产量','加权平均成交价']] = df_test[['汽油执行价','柴油执行价','齐鲁石化销量',
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'齐鲁石化产量','齐鲁石化成交价','齐鲁石化库存','科力达销量',
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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',
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'布伦特上周收盘价','布伦特昨日收盘价','布伦特收盘价','上期所沥青主力合约','隆重资讯沥青日开工率','隆重资讯沥青月库存',
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'隆重资讯沥青月产量','隆重资讯沥青表观消费量','隆重资讯社会库存率','厂区库容','京博提货量','即期成本','异地库库存',
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'京博签单量','京博库存量','京博产量','加权平均成交价']].astype('float')
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# df_test['日期']=pd.to_datetime(df_test['日期'], format='%d/%m/%Y',infer_datetime_format=True)
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df_test['日期']=pd.to_datetime(df_test['日期'], format='%Y-%m-%d',infer_datetime_format=True)
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#查看每个特征缺失值数量
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MisVal_Check=df_test.isnull().sum().sort_values(ascending=False)
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#去掉缺失值百分比>0.4的特征，去掉这些特征后的新表格命名为df_test_1
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df_MisVal_Check = pd.DataFrame(MisVal_Check,)#
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df_MisVal_Check_1=df_MisVal_Check.reset_index()
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df_MisVal_Check_1.columns=['Variable_Name','Missing_Number']
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df_MisVal_Check_1['Missing_Number']=df_MisVal_Check_1['Missing_Number']/len(df_test)
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df_test_1=df_test.drop(df_MisVal_Check_1[df_MisVal_Check_1['Missing_Number']>0.4].Variable_Name,axis = 1)
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#将缺失值补为前一个或者后一个数值
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df_test_1=df_test_1.fillna(df_test.ffill())
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df_test_1=df_test_1.fillna(df_test_1.bfill())
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# 选择用于模型训练的列名称
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col_for_training = df_test_1.columns
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import joblib
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Best_model_DalyLGPrice = joblib.load("日度价格预测_最佳模型.pkl")
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# 最新的一天为最后一行的数据
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df_test_1_Day = df_test_1.tail(1)
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# 移除不需要的列
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df_test_1_Day.index = df_test_1_Day["日期"]
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df_test_1_Day = df_test_1_Day.drop(["日期"], axis= 1)
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df_test_1_Day=df_test_1_Day.drop('京博指导价',axis=1)
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df_test_1_Day=df_test_1_Day.dropna()
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# df_test_1_Day
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#预测今日价格，显示至小数点后两位
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Ypredict_Today=Best_model_DalyLGPrice.predict(df_test_1_Day)
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df_test_1_Day['日度预测价格']=Ypredict_Today
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print(df_test_1_Day['日度预测价格'])
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a = df_test_1_Day['日度预测价格']
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a = a[0]
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a = float(a)
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a = round(a,2)
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return a
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def optimize_Model():
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from sklearn.model_selection import train_test_split
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from sklearn.impute import SimpleImputer
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from sklearn.preprocessing import OrdinalEncoder
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from sklearn.feature_selection import SelectFromModel
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from sklearn.metrics import mean_squared_error, r2_score
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pd.set_option('display.max_rows',40)
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pd.set_option('display.max_columns',40)
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df_test = pd.read_excel('沥青数据项.xlsx')
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df_test.drop([0],inplace=True)
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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',
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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',
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'布伦特上周收盘价','布伦特昨日收盘价','布伦特收盘价','上期所沥青主力合约','隆重资讯沥青日开工率','隆重资讯沥青月库存',
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'隆重资讯沥青月产量','隆重资讯沥青表观消费量','隆重资讯社会库存率','厂区库容','京博提货量','即期成本','异地库库存',
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'京博签单量','京博库存量','京博产量','加权平均成交价']] = 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',
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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',
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'布伦特上周收盘价','布伦特昨日收盘价','布伦特收盘价','上期所沥青主力合约','隆重资讯沥青日开工率','隆重资讯沥青月库存',
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'隆重资讯沥青月产量','隆重资讯沥青表观消费量','隆重资讯社会库存率','厂区库容','京博提货量','即期成本','异地库库存',
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'京博签单量','京博库存量','京博产量','加权平均成交价']].astype('float')
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# df_test = pd.read_csv('定价模型数据收集20190901-20230615.csv',encoding = 'gbk',engine = 'python')
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# df_test['日期']=pd.to_datetime(df_test['日期'], format='%m/%d/%Y',infer_datetime_format=True)
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df_test['日期']=pd.to_datetime(df_test['日期'], format='%Y-%m-%d',infer_datetime_format=True)
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# df_test.tail(3)
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MisVal_Check=df_test.isnull().sum().sort_values(ascending=False)
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#去掉缺失值百分比>0.4的特征，去掉这些特征后的新表格命名为df_test_1
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df_MisVal_Check = pd.DataFrame(MisVal_Check,)#
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df_MisVal_Check_1=df_MisVal_Check.reset_index()
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df_MisVal_Check_1.columns=['Variable_Name','Missing_Number']
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df_MisVal_Check_1['Missing_Number']=df_MisVal_Check_1['Missing_Number']/len(df_test)
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df_test_1=df_test.drop(df_MisVal_Check_1[df_MisVal_Check_1['Missing_Number']>0.4].Variable_Name,axis = 1)
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#将缺失值补为前一个或者后一个数值
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df_test_1=df_test_1.fillna(df_test.ffill())
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df_test_1=df_test_1.fillna(df_test_1.bfill())
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df_test_1["日期"] = pd.to_datetime(df_test_1["日期"])
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df_test_1.index = df_test_1["日期"]
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df_test_1 = df_test_1.drop(["日期"], axis= 1)
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dataset1=df_test_1.drop('京博指导价',axis=1)#.astype(float)
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y=df_test_1['京博指导价']
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x=dataset1
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train = x
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target = y
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#切割数据样本集合测试集
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X_train,x_test,y_train,y_true = train_test_split(train,target,test_size=0.2,random_state=0)
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from sklearn.linear_model import Lasso
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from xgboost import XGBRegressor
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import statsmodels.api as sm
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# from keras.preprocessing.sequence import TimeseriesGenerator
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from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
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import plotly.express as px
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import plotly.graph_objects as go
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import xgboost as xgb
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from xgboost import plot_importance, plot_tree
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from sklearn.metrics import mean_absolute_error
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from statsmodels.tools.eval_measures import mse,rmse
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from sklearn.model_selection import GridSearchCV
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from xgboost import XGBRegressor
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import warnings
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import pickle
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from sklearn.metrics import mean_squared_error
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#切割训练数据和样本数据
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from sklearn.model_selection import train_test_split
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#用于模型评分
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from sklearn.metrics import r2_score
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#模型缩写
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Lasso = Lasso(random_state=0)
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XGBR = XGBRegressor(random_state=0)
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Lasso.fit(X_train,y_train)
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XGBR.fit(X_train,y_train)
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y_pre_Lasso = Lasso.predict(x_test)
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y_pre_XGBR = XGBR.predict(x_test)
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#计算Lasso、XGBR、RandomForestR、AdaBoostR、GradientBoostingR、BaggingRegressor各模型的R²
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Lasso_score = r2_score(y_true,y_pre_Lasso)
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XGBR_score=r2_score(y_true,y_pre_XGBR)
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#计算Lasso、XGBR的MSE和RMSE
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Lasso_MSE=mean_squared_error(y_true, y_pre_Lasso)
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XGBR_MSE=mean_squared_error(y_true, y_pre_XGBR)
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Lasso_RMSE=np.sqrt(Lasso_MSE)
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XGBR_RMSE=np.sqrt(XGBR_MSE)
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model_results = pd.DataFrame([['Lasso', Lasso_RMSE, Lasso_score],
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['XgBoost', XGBR_RMSE, XGBR_score]],
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columns = ['模型(Model)','均方根误差(RMSE)', 'R^2 score'])
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model_results1=model_results.set_index('模型(Model)')
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def plot_feature_importance(importance,names,model_type):
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feature_importance = np.array(importance)
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feature_names = np.array(names)
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data={'feature_names':feature_names,'feature_importance':feature_importance}
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fi_df = pd.DataFrame(data)
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fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)
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plt.figure(figsize=(10,8))
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sn.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names'])
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plt.title(model_type + " "+'FEATURE IMPORTANCE')
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plt.xlabel('FEATURE IMPORTANCE')
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plt.ylabel('FEATURE NAMES')
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from pylab import mpl
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mpl.rcParams['font.sans-serif'] = ['SimHei']
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from xgboost import XGBRegressor
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from sklearn.model_selection import GridSearchCV
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estimator = XGBRegressor(random_state=0,
|
||
nthread=4,
|
||
seed=0
|
||
)
|
||
parameters = {
|
||
'max_depth': range (2, 11, 2), # 树的最大深度
|
||
'n_estimators': range (50, 101, 10), # 迭代次数
|
||
'learning_rate': [0.01, 0.03, 0.1, 0.3, 0.5, 1]
|
||
}
|
||
|
||
grid_search_XGB = GridSearchCV(
|
||
estimator=estimator,
|
||
param_grid=parameters,
|
||
# n_jobs = 10,
|
||
cv = 3,
|
||
verbose=True
|
||
)
|
||
|
||
grid_search_XGB.fit(X_train, y_train)
|
||
print("Best score: %0.3f" % grid_search_XGB.best_score_)
|
||
print("Best parameters set:")
|
||
best_parameters = grid_search_XGB.best_estimator_.get_params()
|
||
for param_name in sorted(parameters.keys()):
|
||
print("\t%s: %r" % (param_name, best_parameters[param_name]))
|
||
y_pred = grid_search_XGB.predict(x_test)
|
||
|
||
op_XGBR_score = r2_score(y_true,y_pred)
|
||
op_XGBR_MSE= mean_squared_error(y_true, y_pred)
|
||
op_XGBR_RMSE= np.sqrt(op_XGBR_MSE)
|
||
|
||
model_results2 = pd.DataFrame([['Optimized_Xgboost', op_XGBR_RMSE, op_XGBR_score]],
|
||
columns = ['模型(Model)', '均方根误差(RMSE)', 'R^2 score'])
|
||
model_results2=model_results2.set_index('模型(Model)')
|
||
|
||
# results = model_results1.append(model_results2, ignore_index = False)
|
||
results = pd.concat([model_results1,model_results2],ignore_index=True)
|
||
results
|
||
import pickle
|
||
|
||
Pkl_Filename = "日度价格预测_最佳模型.pkl"
|
||
|
||
with open(Pkl_Filename, 'wb') as file:
|
||
pickle.dump(grid_search_XGB, file)
|
||
|
||
def read_xls_data():
|
||
"""获取特征项ID"""
|
||
global one_cols, two_cols
|
||
# 使用pandas读取Excel文件
|
||
df = pd.read_excel(read_file_path_name, header=None) # 不自动识别列名
|
||
# 获取第二行数据（索引为1）
|
||
one_cols = df.iloc[1].tolist()[1:]
|
||
print(f'获取到的数据项ID{one_cols}')
|
||
|
||
def get_queryDataListItemNos_value(token, url, dataItemNoList, dateStart, dateEnd):
|
||
|
||
search_data = {
|
||
"funcModule": "数据项",
|
||
"funcOperation": "查询",
|
||
"data": {
|
||
"dateStart": dateStart,
|
||
"dateEnd": dateEnd,
|
||
"dataItemNoList": dataItemNoList # 数据项编码,代表 brent最低价和最高价
|
||
}
|
||
}
|
||
|
||
headers = {"Authorization": token}
|
||
search_res = requests.post(url=url, headers=headers, json=search_data, timeout=(3, 5))
|
||
search_value = json.loads(search_res.text)["data"]
|
||
if search_value:
|
||
return search_value
|
||
else:
|
||
return None
|
||
|
||
|
||
def save_queryDataListItemNos_xls(data_df,dataItemNoList):
|
||
from datetime import datetime,timedelta
|
||
current_year_month = datetime.now().strftime('%Y-%m')
|
||
grouped = data_df.groupby("dataDate")
|
||
|
||
# 使用openpyxl打开xlsx文件
|
||
from openpyxl import load_workbook
|
||
workbook = load_workbook('沥青数据项.xlsx')
|
||
|
||
# 创建新工作簿
|
||
new_workbook = load_workbook('沥青数据项.xlsx')
|
||
|
||
for sheetname in workbook.sheetnames:
|
||
sheet = workbook[sheetname]
|
||
new_sheet = new_workbook[sheetname]
|
||
|
||
current_year_month_row = 0
|
||
# 查找当前月份数据起始行
|
||
for row_idx, row in enumerate(sheet.iter_rows(values_only=True), 1):
|
||
if str(row[0]).startswith(current_year_month):
|
||
current_year_month_row += 1
|
||
|
||
# 追加新数据
|
||
if sheetname == workbook.sheetnames[0]:
|
||
start_row = sheet.max_row - current_year_month_row + 1
|
||
for row_idx, (date, group) in enumerate(grouped, start=start_row):
|
||
new_sheet.cell(row=row_idx, column=1, value=date)
|
||
for j, dataItemNo in enumerate(dataItemNoList, start=2):
|
||
if group[group["dataItemNo"] == dataItemNo]["dataValue"].values:
|
||
new_sheet.cell(row=row_idx, column=j,
|
||
value=group[group["dataItemNo"] == dataItemNo]["dataValue"].values[0])
|
||
|
||
# 保存修改后的xlsx文件
|
||
new_workbook.save("沥青数据项.xlsx")
|
||
|
||
|
||
# def save_queryDataListItemNos_xls(data_df,dataItemNoList):
|
||
# from datetime import datetime, timedelta
|
||
# current_year_month = datetime.now().strftime('%Y-%m')
|
||
# grouped = data_df.groupby("dataDate")
|
||
|
||
# # 打开xls文件
|
||
# workbook = xlrd.open_workbook('沥青数据项.xlsx')
|
||
|
||
# # 获取所有sheet的个数
|
||
# sheet_count = len(workbook.sheet_names())
|
||
|
||
# # 获取所有sheet的名称
|
||
# sheet_names = workbook.sheet_names()
|
||
|
||
# new_workbook = xlwt.Workbook()
|
||
# for i in range(sheet_count):
|
||
# # 获取当前sheet
|
||
# sheet = workbook.sheet_by_index(i)
|
||
|
||
# # 获取sheet的行数和列数
|
||
# row_count = sheet.nrows
|
||
# col_count = sheet.ncols
|
||
# # 获取原有数据
|
||
# data = []
|
||
# for row in range(row_count):
|
||
# row_data = []
|
||
# for col in range(col_count):
|
||
# row_data.append(sheet.cell_value(row, col))
|
||
# data.append(row_data)
|
||
|
||
# # 创建xlwt的Workbook对象
|
||
# # 创建sheet
|
||
# new_sheet = new_workbook.add_sheet(sheet_names[i])
|
||
|
||
|
||
# current_year_month_row = 0
|
||
# # 将原有的数据写入新的sheet
|
||
# for row in range(row_count):
|
||
# for col in range(col_count):
|
||
# col0 = data[row][0]
|
||
# # print("col0",col0[:7])
|
||
# if col0[:7] == current_year_month:
|
||
# current_year_month_row += 1
|
||
# break
|
||
# new_sheet.write(row, col, data[row][col])
|
||
|
||
|
||
# # print("current_year_month_row",current_year_month_row)
|
||
# if i == 0:
|
||
# rowFlag = 0
|
||
# # 查看每组数据
|
||
# for date, group in grouped:
|
||
# new_sheet.write(row_count + rowFlag - current_year_month_row, 0, date)
|
||
# for j in range(len(dataItemNoList)):
|
||
# dataItemNo = dataItemNoList[j]
|
||
|
||
# # for dataItemNo in dataItemNoList:
|
||
# if group[group["dataItemNo"] == dataItemNo]["dataValue"].values:
|
||
|
||
# new_sheet.write(row_count + rowFlag - current_year_month_row, j + 1, group[group["dataItemNo"] == dataItemNo]["dataValue"].values[0])
|
||
|
||
# rowFlag += 1
|
||
|
||
|
||
# # 保存新的xls文件
|
||
# new_workbook.save("沥青数据项.xlsx")
|
||
|
||
def queryDataListItemNos(token=None):
|
||
df = pd.read_excel('沥青数据项.xlsx')
|
||
dataItemNoList = df.iloc[0].tolist()[1:]
|
||
|
||
if token is None:
|
||
token = get_head_auth()
|
||
|
||
if not token:
|
||
print('token获取失败')
|
||
return
|
||
|
||
# 获取当前日期
|
||
from datetime import datetime, timedelta
|
||
current_date = datetime.now()
|
||
|
||
# 获取当月1日
|
||
first_day_of_month = current_date.replace(day=1)
|
||
|
||
# 格式化为 YYYYMMDD 格式
|
||
dateEnd = current_date.strftime('%Y%m%d')
|
||
dateStart = first_day_of_month.strftime('%Y%m%d')
|
||
|
||
search_value = get_queryDataListItemNos_value(token, queryDataListItemNos_url, dataItemNoList, dateStart, dateEnd)
|
||
|
||
|
||
data_df = pd.DataFrame(search_value)
|
||
|
||
data_df["dataDate"] = pd.to_datetime(data_df["dataDate"])
|
||
data_df["dataDate"] = data_df["dataDate"].dt.strftime('%Y-%m-%d')
|
||
save_queryDataListItemNos_xls(data_df,dataItemNoList)
|
||
print('当月数据更新完成')
|
||
|
||
|
||
def save_xls_1(append_rows):
|
||
|
||
# 打开xls文件
|
||
workbook = xlrd.open_workbook('沥青数据项.xlsx')
|
||
|
||
# 获取所有sheet的个数
|
||
sheet_count = len(workbook.sheet_names())
|
||
|
||
# 获取所有sheet的名称
|
||
sheet_names = workbook.sheet_names()
|
||
|
||
new_workbook = xlwt.Workbook()
|
||
for i in range(sheet_count):
|
||
# 获取当前sheet
|
||
sheet = workbook.sheet_by_index(i)
|
||
|
||
# 获取sheet的行数和列数
|
||
row_count = sheet.nrows - 1
|
||
col_count = sheet.ncols
|
||
# 获取原有数据
|
||
data = []
|
||
for row in range(row_count):
|
||
row_data = []
|
||
for col in range(col_count):
|
||
row_data.append(sheet.cell_value(row, col))
|
||
data.append(row_data)
|
||
# 创建xlwt的Workbook对象
|
||
# 创建sheet
|
||
new_sheet = new_workbook.add_sheet(sheet_names[i])
|
||
|
||
# 将原有的数据写入新的sheet
|
||
for row in range(row_count):
|
||
for col in range(col_count):
|
||
new_sheet.write(row, col, data[row][col])
|
||
|
||
if i == 0:
|
||
# 在新的sheet中添加数据
|
||
for col in range(col_count):
|
||
new_sheet.write(row_count, col, append_rows[col])
|
||
|
||
# 保存新的xls文件
|
||
new_workbook.save("沥青数据项.xlsx")
|
||
|
||
|
||
|
||
def start(date=''):
|
||
"""获取当日数据"""
|
||
read_xls_data()
|
||
token = get_head_auth()
|
||
if not token:
|
||
return
|
||
|
||
cur_time,cur_time2 = getNow(date)
|
||
print(f"获取{cur_time}数据")
|
||
datas = get_data_value(token, one_cols,date=cur_time)
|
||
print(len(datas))
|
||
print(datas)
|
||
if not datas:
|
||
return
|
||
|
||
append_rows = [cur_time2]
|
||
dataItemNo_dataValue = {}
|
||
for data_value in datas:
|
||
if "dataValue" not in data_value:
|
||
print(data_value)
|
||
dataItemNo_dataValue[data_value["dataItemNo"]] = ""
|
||
else:
|
||
dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
|
||
|
||
for value in one_cols:
|
||
if value in dataItemNo_dataValue:
|
||
append_rows.append(dataItemNo_dataValue[value])
|
||
else:
|
||
append_rows.append("")
|
||
|
||
print('添加的行：',len(append_rows),append_rows)
|
||
save_xls_2(append_rows)
|
||
|
||
|
||
def save_xls_2(append_rows):
|
||
"""保存或更新数据到Excel文件
|
||
参数:
|
||
append_rows (list): 需要追加/更新的数据行，格式为[日期, 数据项1, 数据项2,...]
|
||
"""
|
||
# try:
|
||
# 读取现有数据（假设第一行为列名）
|
||
df = pd.read_excel('沥青数据项.xlsx', sheet_name=0)
|
||
print('文件中的数据列数：',len(df.columns),df.columns)
|
||
# 转换append_rows为DataFrame
|
||
if len(append_rows) != len(df.columns):
|
||
# 去除第二个元素 ，不知道什么原因多一个空数据
|
||
append_rows.pop(1)
|
||
append_rows = pd.DataFrame([append_rows],columns=df.columns)
|
||
# 创建新数据行
|
||
new_date = append_rows['日期'].values[0]
|
||
|
||
dates = df['日期'].to_list()
|
||
# 判断日期是否存在
|
||
if new_date in dates:
|
||
# 找到日期所在行的索引
|
||
date_mask = df['日期'] == new_date
|
||
# 存在则更新数据
|
||
df.loc[date_mask] = append_rows.values
|
||
print(f"更新 {new_date} 数据")
|
||
else:
|
||
# 不存在则追加数据
|
||
df = pd.concat([df, append_rows], ignore_index=True)
|
||
print(df.head())
|
||
print(df.tail())
|
||
print(f"插入 {new_date} 新数据")
|
||
|
||
# 保存更新后的数据
|
||
df.to_excel('沥青数据项.xlsx', index=False, engine='openpyxl')
|
||
|
||
# except FileNotFoundError:
|
||
# # 如果文件不存在则创建新文件
|
||
# pd.DataFrame([append_rows]).to_excel('沥青数据项.xlsx', index=False, engine='openpyxl')
|
||
# except Exception as e:
|
||
# print(f"保存数据时发生错误: {str(e)}")
|
||
|
||
|
||
def main(start_date=None,token=None,token_push=None):
|
||
from datetime import datetime, timedelta
|
||
if start_date is None:
|
||
start_date = datetime.now()
|
||
if token is None:
|
||
token = get_head_auth()
|
||
if token_push is None:
|
||
token_push = get_head_push_auth()
|
||
date = start_date.strftime('%Y%m%d')
|
||
print(date)
|
||
# start(date)
|
||
# 更新当月数据
|
||
queryDataListItemNos(token)
|
||
# 训练模型
|
||
optimize_Model()
|
||
# # 预测&上传预测结果
|
||
upload_data_to_system(token_push,start_date)
|
||
|
||
if __name__ == "__main__":
|
||
print("运行中ing...")
|
||
main() |