丙烯py添加当月数据维护功能
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aisenzhecode/聚合级丙烯/丙烯基础数据收集表.xlsx
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aisenzhecode/聚合级丙烯/丙烯基础数据收集表.xlsx
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@ -1,3 +1,31 @@
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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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from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
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import statsmodels.api as sm
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import datetime
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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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@ -7,6 +35,7 @@ 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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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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@ -37,67 +66,24 @@ read_file_path_name = "丙烯基础数据收集表.xls"
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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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# 导入机器学习算法模型
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# 切割训练数据和样本数据
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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 datetime
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import statsmodels.api as sm
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from 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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# 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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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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@ -121,7 +107,8 @@ def get_data_value(token, dataItemNoList):
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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_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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@ -166,7 +153,8 @@ def get_cur_time():
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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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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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@ -176,7 +164,6 @@ def get_head_push_auth():
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return None
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def upload_data_to_system(token_push):
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data = {
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"funcModule": "数据表信息列表",
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@ -185,17 +172,18 @@ def upload_data_to_system(token_push):
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{"dataItemNo": "C01100007|Forecast_Price|ACN",
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"dataDate": get_cur_time()[0],
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"dataStatus": "add",
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# "dataValue": 7100
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# "dataValue": 7100
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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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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 upload_data_to_system(token):
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# data = {
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# "funcModule": "数据表信息列表",
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@ -213,92 +201,95 @@ def upload_data_to_system(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_csv('定价模型数据收集0212.csv')
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df_test = pd.read_excel('丙烯基础数据收集表.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 = pd.read_excel('丙烯基础数据收集表.xlsx')
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df_test.drop([0], inplace=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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# 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 = 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_丙烯最佳模型.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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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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# 转换数据类型
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df_test_1_Day = df_test_1_Day.astype(float)
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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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# 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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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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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.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('丙烯基础数据收集表.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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#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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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('丙烯基础数据收集表.xlsx')
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df_test.drop([0], inplace=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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# 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'] / \
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len(df_test)
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df_test_1 = df_test.drop(
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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
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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 = 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.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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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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# 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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from datetime import datetime
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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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@ -306,7 +297,7 @@ def optimize_Model():
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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 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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@ -314,63 +305,67 @@ def optimize_Model():
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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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# 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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# 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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dataset1 = df_test_1.drop('Price', axis=1) # .astype(float)
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y=df_test_1['Price']
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y = df_test_1['Price']
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x=dataset1
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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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# 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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# 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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# 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、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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# 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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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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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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# 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,'feature_importance':feature_importance}
|
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data = {'feature_names': feature_names,
|
||||
'feature_importance': feature_importance}
|
||||
fi_df = pd.DataFrame(data)
|
||||
|
||||
fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)
|
||||
fi_df.sort_values(by=['feature_importance'],
|
||||
ascending=False, inplace=True)
|
||||
|
||||
plt.figure(figsize=(10,8))
|
||||
plt.figure(figsize=(10, 8))
|
||||
sn.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names'])
|
||||
|
||||
plt.title(model_type + " "+'FEATURE IMPORTANCE')
|
||||
@ -378,55 +373,145 @@ def optimize_Model():
|
||||
plt.ylabel('FEATURE NAMES')
|
||||
from pylab import mpl
|
||||
mpl.rcParams['font.sans-serif'] = ['SimHei']
|
||||
## Xgboost 模型参数优化-初步
|
||||
#参考: https://juejin.im/post/6844903661013827598
|
||||
#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）
|
||||
# Xgboost 模型参数优化-初步
|
||||
# 参考: https://juejin.im/post/6844903661013827598
|
||||
# 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）
|
||||
|
||||
from xgboost import XGBRegressor
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
|
||||
estimator = XGBRegressor(random_state=0,
|
||||
nthread=4,
|
||||
seed=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
|
||||
'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,
|
||||
# n_jobs = 10,
|
||||
cv=3,
|
||||
verbose=True
|
||||
)
|
||||
|
||||
grid_search_XGB.fit(X_train, y_train)
|
||||
#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
|
||||
# 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
|
||||
|
||||
best_parameters = grid_search_XGB.best_estimator_.get_params()
|
||||
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)
|
||||
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)')
|
||||
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=False)
|
||||
import pickle
|
||||
|
||||
Pkl_Filename = "日度价格预测_丙烯最佳模型.pkl"
|
||||
Pkl_Filename = "日度价格预测_丙烯最佳模型.pkl"
|
||||
|
||||
with open(Pkl_Filename, 'wb') as file:
|
||||
pickle.dump(grid_search_XGB, file)
|
||||
with open(Pkl_Filename, 'wb') as file:
|
||||
pickle.dump(grid_search_XGB, file)
|
||||
|
||||
|
||||
|
||||
def queryDataListItemNos(token=None):
|
||||
df = pd.read_excel('丙烯基础数据收集表.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')
|
||||
# dateStart = '20241026'
|
||||
|
||||
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_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('丙烯基础数据收集表.xlsx')
|
||||
|
||||
# 创建新工作簿
|
||||
new_workbook = load_workbook('丙烯基础数据收集表.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("丙烯基础数据收集表.xlsx")
|
||||
|
||||
|
||||
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 read_xls_data():
|
||||
global one_cols, two_cols
|
||||
@ -456,8 +541,6 @@ def read_xls_data():
|
||||
# workbook.close()
|
||||
|
||||
|
||||
|
||||
|
||||
def start():
|
||||
read_xls_data()
|
||||
|
||||
@ -480,8 +563,9 @@ def start():
|
||||
print(data_value)
|
||||
dataItemNo_dataValue[data_value["dataItemNo"]] = ""
|
||||
else:
|
||||
dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
|
||||
|
||||
dataItemNo_dataValue[data_value["dataItemNo"]
|
||||
] = data_value["dataValue"]
|
||||
|
||||
for value in one_cols[1:]:
|
||||
if value in dataItemNo_dataValue:
|
||||
append_rows.append(dataItemNo_dataValue[value])
|
||||
@ -500,7 +584,6 @@ def start_1():
|
||||
token = get_head_auth()
|
||||
if not token:
|
||||
return
|
||||
|
||||
|
||||
datas = get_data_value(token, one_cols[1:])
|
||||
# if not datas:
|
||||
@ -514,7 +597,8 @@ def start_1():
|
||||
print(data_value)
|
||||
dataItemNo_dataValue[data_value["dataItemNo"]] = ""
|
||||
else:
|
||||
dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
|
||||
dataItemNo_dataValue[data_value["dataItemNo"]
|
||||
] = data_value["dataValue"]
|
||||
|
||||
for value in one_cols[1:]:
|
||||
if value in dataItemNo_dataValue:
|
||||
@ -523,10 +607,10 @@ def start_1():
|
||||
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
|
||||
@ -568,11 +652,9 @@ def save_xls_1(append_rows):
|
||||
new_sheet.write(row_count, col, append_rows[col])
|
||||
|
||||
# 保存新的xls文件
|
||||
new_workbook.save("丙烯基础数据收集表.xls")
|
||||
new_workbook.save("丙烯基础数据收集表.xls")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def check_data(dataItemNo):
|
||||
token = get_head_auth()
|
||||
if not token:
|
||||
@ -628,5 +710,8 @@ def save_xls(append_rows):
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
start()
|
||||
|
||||
# start()
|
||||
queryDataListItemNos()
|
||||
optimize_Model()
|
||||
forecast_price()
|
||||
upload_data_to_system(token_push=get_head_push_auth())
|
||||
|
||||
Binary file not shown.
Loading…
Reference in New Issue
Block a user