# -*- coding: utf-8 -*- from plotly.subplots import make_subplots from flask import Flask,request import time import random import logging import traceback import os from common.database_dml import get_df_list_from_mongo,insert_data_into_mongo import plotly.express as px import plotly.graph_objects as go import pandas as pd import plotly.io as pio app = Flask('analysis_report——service') def put_analysis_report_to_html(args,df_clean,df_accuracy): col_time = args['col_time'] col_x_env = args['col_x_env'] col_x_pre = args['col_x_pre'] label = args['label'] label_pre = args['label_pre'] farmId = args['farmId'] df_overview = pd.DataFrame( {'数据开始时间': [df_clean[col_time].min()], '数据结束时间': [df_clean[col_time].max()], '数据总记录数': [df_clean.shape[0]]}) overview_html = df_overview.to_html(classes='table table-bordered table-striped', index=False) # -------------------- 数据描述 -------------------- describe_html = df_clean.describe().reset_index().rename(columns={'index': '统计量'}).to_html( classes='table table-bordered table-striped', index=False) # -------------------- 实测气象与实际功率散点图-------------------- # 生成实际功率与辐照度的散点图 fig_scatter = px.scatter(df_clean, x=col_x_env, y=label) # 自定义散点图布局 fig_scatter.update_layout( template='seaborn', plot_bgcolor='rgba(255, 255, 255, 0.8)', # 背景色 xaxis=dict( showgrid=True, gridcolor='rgba(200, 200, 200, 0.5)', title=col_x_env ), yaxis=dict( showgrid=True, gridcolor='rgba(200, 200, 200, 0.5)', title=label ), legend=dict(x=0.01, y=0.99, bgcolor='rgba(255, 255, 255, 0.7)', bordercolor='black', borderwidth=1) ) # 将散点图保存为 HTML 片段 scatter_html = pio.to_html(fig_scatter, full_html=False) # -------------------- 生成相关性热力图 -------------------- # 计算相关矩阵 correlation_matrix = df_clean.corr() # 生成热力图，带数值标签和新配色 fig_heatmap = go.Figure(data=go.Heatmap( z=correlation_matrix.values, x=correlation_matrix.columns, y=correlation_matrix.columns, colorscale='RdBu', # 使用红蓝配色：正相关为蓝色，负相关为红色 text=correlation_matrix.round(2).astype(str), # 将相关性值保留两位小数并转换为字符串 texttemplate="%{text}", # 显示数值标签 colorbar=dict(title='Correlation'), zmin=-1, zmax=1 # 设置颜色映射的范围 )) # 自定义热力图布局 fig_heatmap.update_layout( # title='Correlation Matrix Heatmap', xaxis=dict(tickangle=45), yaxis=dict(autorange='reversed'), template='seaborn' ) # 将热力图保存为 HTML 片段 corr_html = pio.to_html(fig_heatmap, full_html=False) # -------------------- 实测气象与预测气象趋势曲线 -------------------- # 生成折线图（以 C_GLOBALR 和 NWP预测总辐射 为例） fig_line = px.line(df_clean, x=col_time, y=[col_x_env, col_x_pre], markers=True) # 自定义趋势图布局 fig_line.update_layout( template='seaborn', # title=dict(text=f"{col_x_env}与{col_x_pre}趋势曲线", # x=0.5, font=dict(size=24, color='darkblue')), plot_bgcolor='rgba(255, 255, 255, 0.8)', # 改为白色背景 xaxis=dict( showgrid=True, gridcolor='rgba(200, 200, 200, 0.5)', # 网格线颜色 rangeslider=dict(visible=True), # 显示滚动条 rangeselector=dict(visible=True) # 显示预设的时间范围选择器 ), yaxis=dict(showgrid=True, gridcolor='rgba(200, 200, 200, 0.5)'), legend=dict(x=0.01, y=0.99, bgcolor='rgba(255, 255, 255, 0.7)', bordercolor='black', borderwidth=1) ) # 将折线图保存为 HTML 片段 env_pre_html = pio.to_html(fig_line, full_html=False) # -------------------- 实测气象与预测气象偏差密度曲线 -------------------- df_clean['deviation'] = df_clean[col_x_pre] - df_clean[col_x_env] # 生成预测与实测辐照度偏差的密度曲线图 # 生成偏差的密度图 fig_density = px.histogram(df_clean, x='deviation', nbins=30, marginal='rug', opacity=0.75, histnorm='density') # 自定义密度曲线图布局 fig_density.update_layout( template='seaborn', # # title=dict(text=f"{col_x_pre}与{col_x_env}偏差密度曲线", # x=0.5, font=dict(size=24, color='darkred')), plot_bgcolor='rgba(255, 255, 255, 0.8)', xaxis=dict( showgrid=True, gridcolor='rgba(200, 200, 200, 0.5)', title='偏差' ), yaxis=dict( showgrid=True, gridcolor='rgba(200, 200, 200, 0.5)', title='Density' ), legend=dict(x=0.01, y=0.99, bgcolor='rgba(255, 255, 255, 0.7)', bordercolor='black', borderwidth=1) ) # 将密度曲线图保存为 HTML 片段 density_html = pio.to_html(fig_density, full_html=False) # -------------------- 预测功率与实际功率曲线 -------------------- # 生成折线图（以 C_GLOBALR 和 NWP预测总辐射 为例） fig_line = px.line(df_clean, x='dateTime', y=[label, label_pre], markers=True) # 自定义趋势图布局 fig_line.update_layout( template='seaborn', # title=dict(text=f"{label_pre}与{label}曲线", # x=0.5, font=dict(size=24, color='darkblue')), plot_bgcolor='rgba(255, 255, 255, 0.8)', # 改为白色背景 xaxis=dict( showgrid=True, gridcolor='rgba(200, 200, 200, 0.5)', # 网格线颜色 rangeslider=dict(visible=True), # 显示滚动条 rangeselector=dict(visible=True) # 显示预设的时间范围选择器 ), yaxis=dict(showgrid=True, gridcolor='rgba(200, 200, 200, 0.5)'), legend=dict(x=0.01, y=0.99, bgcolor='rgba(255, 255, 255, 0.7)', bordercolor='black', borderwidth=1) ) # 将折线图保存为 HTML 片段 power_html = pio.to_html(fig_line, full_html=False) # -------------------- 准确率表展示-------------------- acc_html = df_accuracy.to_html(classes='table table-bordered table-striped', index=False) # -------------------- 生成完整 HTML 页面 -------------------- html_content = f""" Data Analysis Report

分析报告

1. 数据总览

{overview_html}

2. 数据描述

{describe_html}

3. 数据清洗后实测气象与实际功率散点图

{scatter_html}

4. 相关性分析

{corr_html}

5. 实测气象与预测气象曲线趋势

{env_pre_html}

6. 预测气象与实测气象偏差曲线

{density_html}

7. 预测功率与实际功率曲线对比

{power_html}

8. 准确率对比

{acc_html}
""" filename = f"{farmId}_{int(time.time() * 1000)}_{random.randint(1000, 9999)}.html" # 保存为 HTML directory = '/usr/share/nginx/html' if not os.path.exists(directory): os.makedirs(directory) file_path = os.path.join(directory, filename) path = f"http://ds3:10010/{filename}" # 将 HTML 内容写入文件 with open(file_path, "w", encoding="utf-8") as f: f.write(html_content) print("HTML report generated successfully!") return path @app.route('/analysis_report', methods=['POST']) def analysis_report(): start_time = time.time() result = {} success = 0 path = "" print("Program starts execution!") try: args = request.values.to_dict() print('args',args) logger.info(args) #获取数据 df_clean, df_accuracy = get_df_list_from_mongo(args)[0], get_df_list_from_mongo(args)[1] path = put_analysis_report_to_html(args, df_clean, df_accuracy) success = 1 except Exception as e: my_exception = traceback.format_exc() my_exception.replace("\n","\t") result['msg'] = my_exception end_time = time.time() result['success'] = success result['args'] = args result['start_time'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(start_time)) result['end_time'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(end_time)) result['file_path'] = path print("Program execution ends!") return result if __name__=="__main__": print("Program starts execution!") logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') logger = logging.getLogger("analysis_report log") from waitress import serve serve(app, host="0.0.0.0", port=10092) print("server start!")