784 lines
24 KiB
Python
784 lines
24 KiB
Python
from statsmodels.tools.eval_measures import mse, rmse
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from pandas import Series, DataFrame
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import cufflinks as cf
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from sklearn.metrics import r2_score
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import mean_squared_error
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import pickle
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import warnings
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from sklearn.model_selection import GridSearchCV
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from sklearn.metrics import mean_absolute_error
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from xgboost import plot_importance, plot_tree
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import xgboost as xgb
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import plotly.graph_objects as go
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import plotly.express as px
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import statsmodels.api as sm
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from xgboost import XGBRegressor
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from sklearn.linear_model import Lasso
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import sklearn.datasets as datasets
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from sklearn import preprocessing
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from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
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from plotly import __version__
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import random
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import seaborn as sn
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import requests
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import json
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import xlrd
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import xlwt
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from datetime import datetime, timedelta
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import time
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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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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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queryDataListItemNos_url = "http://10.200.32.39/jingbo-api//api/warehouse/dwDataItem/queryDataListItemNos"
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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 = "纯苯数据项.xls"
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one_cols = []
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two_cols = []
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# 导入机器学习算法模型
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try:
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from keras.preprocessing.sequence import TimeseriesGenerator
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except:
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from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
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# 切割训练数据和样本数据
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# 用于模型评分
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le = preprocessing.LabelEncoder()
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# print(__version__) # requires version >= 1.9.0
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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(
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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 search_value
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# xls文件处理
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def write_xls(data, date):
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# 创建一个Workbook对象
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workbook = xlwt.Workbook()
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# 创建一个Sheet对象，可指定名称
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sheet = workbook.load('Sheet1')
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# 写入数据行
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for row_index, row_data in enumerate(data):
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for col_index, cell_data in enumerate(row_data):
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sheet.write(row_index, col_index, cell_data)
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# 保存Workbook到文件
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workbook.save(get_cur_time(date)[0] + '.xls')
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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_cur_time(date=''):
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if date == '':
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now = datetime.now()
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# 如果是字符串，尝试解析日期
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elif isinstance(date, str):
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now = datetime.strptime(date, '%Y-%m-%d')
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else:
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now = date
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year = now.year
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month = now.month
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day = now.day
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if month < 10:
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month = "0" + str(month)
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if day < 10:
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day = "0" + str(day)
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cur_time = str(year) + str(month) + str(day)
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cur_time2 = str(year) + "-" + str(month) + "-" + str(day)
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return cur_time, cur_time2
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def get_head_push_auth():
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login_res = requests.post(
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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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datavalue = forecast_price()
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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": "C01100047|FORECAST_PRICE",
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"dataDate": getNow(date)[0],
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"dataStatus": "add",
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"dataValue": datavalue
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}
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]
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}
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print(data)
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headers = {"Authorization": token_push}
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res = requests.post(url=upload_url, headers=headers,
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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_csv('定价模型数据收集0212.csv')
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df_test = pd.read_excel('纯苯数据项.xls', sheet_name='Sheet1')
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df_test.drop([0], inplace=True)
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# df_test['Date']=pd.to_datetime(df_test['Date'], format='%m/%d/%Y',infer_datetime_format=True)
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df_test['Date'] = pd.to_datetime(
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df_test['Date'], format=r'%Y-%m-%d', infer_datetime_format=True)
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df_test_1 = df_test
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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["Date"]
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df_test_1_Day = df_test_1_Day.drop(["Date"], axis=1)
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df_test_1_Day = df_test_1_Day.drop('Price', axis=1)
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df_test_1_Day = df_test_1_Day.dropna()
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for col in df_test_1_Day.columns:
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df_test_1_Day[col] = pd.to_numeric(df_test_1_Day[col], errors='coerce')
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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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import pandas as pd
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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('纯苯数据项.xls')
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df_test.drop([0], inplace=True)
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# df_test['Date']=pd.to_datetime(df_test['Date'], format='%m/%d/%Y',infer_datetime_format=True)
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df_test['Date'] = pd.to_datetime(
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df_test['Date'], format='%Y-%m-%d', infer_datetime_format=True)
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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
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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["Date"] = pd.to_datetime(df_test_1["Date"])
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df_test_1.index = df_test_1["Date"]
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df_test_1 = df_test_1.drop(["Date"], axis=1)
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df_test_1 = df_test_1.astype('float')
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import numpy as np
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import pandas as pd
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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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try:
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from keras.preprocessing.sequence import TimeseriesGenerator
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except:
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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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dataset1 = df_test_1.drop('Price', axis=1) # .astype(float)
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y = df_test_1['Price']
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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(
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train, target, test_size=0.2, random_state=0)
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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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# 训练模型
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Lasso.fit(X_train, y_train)
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XGBR.fit(X_train, y_train)
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# 模型拟合
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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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# 将不同模型的不同误差值整合成一个表格
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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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# 将模型名称(Model)列设置为索引
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model_results1 = model_results.set_index('模型(Model)')
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model_results1
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# 定义plot_feature_importance函数，该函数用于计算特征重要性。此部分代码无需调整
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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,
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'feature_importance': feature_importance}
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fi_df = pd.DataFrame(data)
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fi_df.sort_values(by=['feature_importance'],
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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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# Xgboost 模型参数优化-初步
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# 参考: https://juejin.im/post/6844903661013827598
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# 每次调参时，备选参数数值以同数量级的1、3、10设置即可（比如设置1、3、10，或0.1、0.3、1.0，或0.01,0.03,0.10即可）
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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,
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nthread=4,
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seed=0
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)
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parameters = {
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'max_depth': range(2, 11, 2), # 树的最大深度
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'n_estimators': range(50, 101, 10), # 迭代次数
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'learning_rate': [0.01, 0.03, 0.1, 0.3, 0.5, 1]
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}
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grid_search_XGB = GridSearchCV(
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estimator=estimator,
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param_grid=parameters,
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# n_jobs = 10,
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cv=3,
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verbose=True
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)
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grid_search_XGB.fit(X_train, y_train)
|
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# 如果电脑在此步骤报错，可能是因为计算量太大，超过硬件可支持程度，可注释掉“n_jobs=10”一行
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best_parameters = grid_search_XGB.best_estimator_.get_params()
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y_pred = grid_search_XGB.predict(x_test)
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op_XGBR_score = r2_score(y_true, y_pred)
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op_XGBR_MSE = mean_squared_error(y_true, y_pred)
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op_XGBR_RMSE = np.sqrt(op_XGBR_MSE)
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model_results2 = pd.DataFrame([['Optimized_Xgboost', op_XGBR_RMSE, op_XGBR_score]],
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columns=['模型(Model)', '均方根误差(RMSE)', 'R^2 score'])
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model_results2 = model_results2.set_index('模型(Model)')
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# results = model_results1.append(model_results2, ignore_index = False)
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results = pd.concat([model_results1, model_results2], ignore_index=True)
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import pickle
|
||
|
||
Pkl_Filename = "日度价格预测_最佳模型.pkl"
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||
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with open(Pkl_Filename, 'wb') as file:
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pickle.dump(grid_search_XGB, file)
|
||
|
||
|
||
def read_xls_data():
|
||
global one_cols, two_cols
|
||
# 打开 XLS 文件
|
||
workbook = xlrd.open_workbook(read_file_path_name)
|
||
|
||
# 获取所有表格名称
|
||
# sheet_names = workbook.sheet_names()
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||
|
||
# 选择第一个表格
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||
sheet = workbook.sheet_by_index(0)
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||
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||
# 获取行数和列数
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||
num_rows = sheet.nrows
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||
# num_cols = sheet.ncols
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||
# 遍历每一行，获取单元格数据
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||
# for i in range(num_rows):
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||
# row_data = sheet.row_values(i)
|
||
# one_cols.append(row_data)
|
||
# two_cols.append(row_data[1])
|
||
|
||
row_data = sheet.row_values(1)
|
||
print(f'获取到的数据项ID{row_data}')
|
||
one_cols = row_data[1:]
|
||
print(f'获取到的数据项ID{one_cols}')
|
||
|
||
# 关闭 XLS 文件
|
||
# workbook.close()
|
||
|
||
|
||
def start(date=None, token=None, token_push=None):
|
||
read_xls_data()
|
||
if date == None:
|
||
date = getNow()[0]
|
||
if token == None:
|
||
token = get_head_auth()
|
||
token_push = get_head_push_auth()
|
||
|
||
datas = get_data_value(token, one_cols, date)
|
||
if not datas:
|
||
print("今天没有新数据")
|
||
return
|
||
|
||
# data_list = [two_cols, one_cols]
|
||
append_rows = [getNow(date)[1]]
|
||
# append_rows = [getNow()[1]]
|
||
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("")
|
||
save_xls(append_rows)
|
||
|
||
# 获取当月的数据写入到指定文件，如果是补充数据，不需要执行
|
||
queryDataListItemNos(date=date)
|
||
# 模型训练
|
||
optimize_Model()
|
||
# 上传预测数据
|
||
upload_data_to_system(token_push, date)
|
||
# data_list.append(three_cols)
|
||
# write_xls(data_list)
|
||
|
||
|
||
def start_1(date=None):
|
||
read_xls_data()
|
||
if date == None:
|
||
date = getNow(offset=1)[0]
|
||
token = get_head_auth()
|
||
if not token:
|
||
return
|
||
|
||
datas = get_data_value(token, one_cols, date=date)
|
||
# if not datas:
|
||
# return
|
||
|
||
# data_list = [two_cols, one_cols]
|
||
append_rows = [getNow(offset=1)[1]]
|
||
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("")
|
||
save_xls_1(append_rows)
|
||
|
||
# data_list.append(three_cols)
|
||
# write_xls(data_list)
|
||
|
||
|
||
def save_xls_1(append_rows):
|
||
|
||
# 打开xls文件
|
||
workbook = xlrd.open_workbook('纯苯数据项.xls')
|
||
|
||
# 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("纯苯数据项.xls")
|
||
|
||
|
||
def check_data(dataItemNo):
|
||
token = get_head_auth()
|
||
if not token:
|
||
return
|
||
|
||
datas = get_data_value(token, dataItemNo)
|
||
if not datas:
|
||
return
|
||
|
||
|
||
def save_xls(append_rows):
|
||
|
||
# 打开xls文件
|
||
workbook = xlrd.open_workbook('纯苯数据项.xls')
|
||
|
||
# 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])
|
||
|
||
# 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("纯苯数据项.xls")
|
||
|
||
|
||
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")
|
||
|
||
# 打开xls文件
|
||
workbook = xlrd.open_workbook('纯苯数据项.xls')
|
||
|
||
# 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]
|
||
|
||
if group[group["dataItemNo"] == dataItemNo]["dataValue"].values and (not str(group[group["dataItemNo"] == dataItemNo]["dataValue"].values[0]) == 'nan'):
|
||
|
||
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("纯苯数据项.xls")
|
||
|
||
|
||
def queryDataListItemNos(date=None, token=None):
|
||
from datetime import datetime, timedelta
|
||
df = pd.read_excel('纯苯数据项.xls')
|
||
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
|
||
if date is None:
|
||
current_date = datetime.now()
|
||
else:
|
||
current_date = date
|
||
# 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')
|
||
# dateEnd = 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')
|
||
|
||
|
||
if __name__ == "__main__":
|
||
print('运行中ing')
|
||
start()
|
||
# 自定义日期执行预测
|
||
# start_date = datetime(2025, 7, 6)
|
||
# end_date = datetime(2025, 7, 7)
|
||
|
||
# token = get_head_auth()
|
||
# token_push = get_head_push_auth()
|
||
# while start_date < end_date:
|
||
# print(start_date.strftime('%Y%m%d'))
|
||
# start(start_date, token, token_push)
|
||
# time.sleep(2)
|
||
# # start_1(start_date)
|
||
# start_date += timedelta(days=1)
|