algorithm_platform/evaluation_processing/analysis_report.py

# -*- coding: utf-8 -*-
import numpy as np
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
from bson.decimal128 import Decimal128
import numbers
from common.processing_data_common import str_to_list,generate_unique_colors
from scipy.stats import gaussian_kde
app = Flask('analysis_report——service')


def put_analysis_report_to_html(args, df_clean, df_predict, df_accuracy):
    col_time = args['col_time']
    col_x_env = args['col_x_env']
    col_x_pre = str_to_list(args['col_x_pre'])
    label = args['label']
    label_pre = args['label_pre']
    farmId = args['farmId']
    df_clean = df_clean.applymap(
        lambda x: float(x.to_decimal()) if isinstance(x, Decimal128) else float(x) if isinstance(x,
                                                                                                 numbers.Number) else x).sort_values(
        by=col_time)
    df_predict = df_predict.applymap(
        lambda x: float(x.to_decimal()) if isinstance(x, Decimal128) else float(x) if isinstance(x,
                                                                                                 numbers.Number) else x).sort_values(
        by=col_time)
    df_accuracy = df_accuracy.applymap(
        lambda x: float(x.to_decimal()) if isinstance(x, Decimal128) else float(x) if isinstance(x,
                                                                                                 numbers.Number) else x).sort_values(
        by=col_time)
    total_size = df_clean.shape[0]
    clean_size = total_size
    if 'is_limit' in df_clean.columns:
        df_clean['is_limit'] = df_clean['is_limit'].apply(lambda x: '正常点' if x==0 else '异常点')
        clean_size = df_clean[df_clean['is_limit']=='正常点'].shape[0]
    df_overview = pd.DataFrame(
        {'场站编码':[farmId],
         '数据开始时间': [df_clean[col_time].min()], '数据结束时间': [df_clean[col_time].max()],
         '总天数':[(pd.to_datetime(df_clean[col_time].max())-pd.to_datetime(df_clean[col_time].min())).days],
         '数据总记录数': [total_size],'清洗后记录数':[clean_size],'数据可用率':[clean_size/total_size]})
    overview_html = df_overview.to_html(classes='table table-bordered table-striped', index=False)
    df_clean_after = df_clean[df_clean['is_limit']=='正常点']
    # -------------------- 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 fixed', index=False)

    # -------------------- 实测气象与实际功率散点图--------------------

    fig_scatter = px.scatter(df_clean, x=col_x_env, y=label, color='is_limit')

    # 自定义散点图布局
    fig_scatter.update_layout(
        template='seaborn',  # 使用 seaborn 风格
        plot_bgcolor='rgba(255, 255, 255, 0.8)',  # 背景色（淡白色）
        xaxis=dict(
            showgrid=True,  # 显示网格
            gridcolor='rgba(200, 200, 200, 0.5)',  # 网格线颜色（淡灰色）
            title=col_x_env,  # x 轴标题
            title_font=dict(size=14),  # x 轴标题字体大小
            tickfont=dict(size=12)  # x 轴刻度标签字体大小
        ),
        yaxis=dict(
            showgrid=True,  # 显示网格
            gridcolor='rgba(200, 200, 200, 0.5)',  # 网格线颜色（淡灰色）
            title=label,  # y 轴标题
            title_font=dict(size=14),  # y 轴标题字体大小
            tickfont=dict(size=12)  # y 轴刻度标签字体大小
        ),
        legend=dict(
            x=0.01, y=0.99,  # 图例位置
            bgcolor='rgba(255, 255, 255, 0.7)',  # 图例背景色
            bordercolor='black',  # 图例边框颜色
            borderwidth=1,  # 图例边框宽度
            font=dict(size=12)  # 图例文字大小
        ),
        title=dict(
            text='实际功率与辐照度的散点图',  # 图表标题
            x=0.5,  # 标题居中
            font=dict(size=16)  # 标题字体大小
        ),
    )

    # 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_after.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)

    # -------------------- 6.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）
    y_env = [col_x_env]+ col_x_pre
    fig_line = px.line(df_clean[(df_clean[col_time]>=df_predict[col_time].min())&(df_clean[col_time]<=df_predict[col_time].max())], x=col_time, y=y_env, 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)

    # -------------------- 5.实测气象与预测气象偏差密度曲线 --------------------
    # 创建 Plotly 图形对象
    fig_density = go.Figure()
    colors = generate_unique_colors(len(col_x_pre))
    for col in zip(col_x_pre,colors):
        df_clean[f"{col[0]}_deviation"] = df_clean[col[0]] - df_clean[col_x_env]
        data = df_clean[f"{col[0]}_deviation"].dropna()  # 确保没有 NaN 值
        kde = gaussian_kde(data)
        x_vals = np.linspace(data.min(), data.max(), 1000)
        y_vals = kde(x_vals)

        # 添加曲线
        fig_density.add_trace(go.Scatter(
            x=x_vals,
            y=y_vals,
            mode='lines',
            fill='tozeroy',
            line=dict(color=col[1]),  # 循环使用颜色
            name=f'Density {col[0]}'  # 图例名称
        ))
    # 生成预测与实测辐照度偏差的密度曲线图
    # 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）
    # 创建一个图表对象
    fig = go.Figure()

    # 获取所有的模型
    models = df_predict['model'].unique()
    # 添加实际功率曲线
    fig.add_trace(go.Scatter(
        x=df_predict[col_time],
        y=df_predict[label],
        mode='lines+markers',
        name='实际功率',  # 实际功率
        line=dict(dash='dot', width=2),  # 虚线
        marker=dict(symbol='cross'),
    ))
    # 为每个模型添加预测值和实际功率的曲线
    for model in models:
        # 筛选该模型的数据
        model_data = df_predict[df_predict['model'] == model]

        # 添加预测值曲线
        fig.add_trace(go.Scatter(
            x=model_data[col_time],
            y=model_data[label_pre],
            mode='lines+markers',
            name=f'{model} 预测值',  # 预测值
            marker=dict(symbol='circle'),
            line=dict(width=2)
        ))

    # 设置图表的标题和标签
    fig.update_layout(
        template='seaborn',  # 使用 seaborn 模板
        title=dict(
            # text=f"{label_pre} 与 {label} 对比",  # 标题
            x=0.5, font=dict(size=20, color='darkblue')  # 标题居中并设置字体大小和颜色
        ),
        plot_bgcolor='rgba(255, 255, 255, 0.8)',  # 背景色
        xaxis=dict(
            showgrid=True,
            gridcolor='rgba(200, 200, 200, 0.5)',  # 网格线颜色
            title='时间',  # 时间轴标题
            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)',
            title='功率'  # y轴标题
        ),
        legend=dict(
            x=0.01,
            y=0.99,
            bgcolor='rgba(255, 255, 255, 0.7)',  # 背景透明
            bordercolor='black',
            borderwidth=1,
            font=dict(size=12)  # 字体大小
        ),
        hovermode='x unified',  # 鼠标悬停时显示统一的提示框
        hoverlabel=dict(
            bgcolor='white',
            font_size=14,
            font_family="Rockwell",  # 设置字体样式
            bordercolor='black'
        ),
        margin=dict(l=50, r=50, t=50, b=50)  # 调整边距，避免标题或标签被遮挡
    )
    # 将折线图保存为 HTML 片段
    power_html = pio.to_html(fig, full_html=False)

    # -------------------- 准确率表展示--------------------
    acc_html = df_accuracy.sort_values(by=col_time).to_html(classes='table table-bordered table-striped', index=False)
    # -------------------- 准确率汇总展示--------------------
    # 指定需要转换的列
    cols_to_convert = ['MAE', 'accuracy', 'RMSE', 'deviationElectricity', 'deviationAssessment']
    for col in cols_to_convert:
        if col in df_accuracy.columns:
            df_accuracy[col] = df_accuracy[col].apply(
                lambda x: float(x.to_decimal()) if isinstance(x, Decimal128) else float(x) if isinstance(x,
                                                                                                         numbers.Number) else np.nan)

    # 确定存在的列
    agg_dict = {}
    rename_cols = ['model']
    if 'MAE' in df_accuracy.columns:
        agg_dict['MAE'] = np.nanmean
        rename_cols.append('MAE平均值')
    if 'accuracy' in df_accuracy.columns:
        agg_dict['accuracy'] = np.nanmean
        rename_cols.append('准确率平均值')
    if 'RMSE' in df_accuracy.columns:
        agg_dict['RMSE'] = np.nanmean
        rename_cols.append('RMSE平均值')
    if 'deviationElectricity' in df_accuracy.columns:
        agg_dict['deviationElectricity'] = [np.nanmean, np.nansum]
        rename_cols.append('考核电量平均值')
        rename_cols.append('考核总电量')
    if 'deviationAssessment' in df_accuracy.columns:
        agg_dict['deviationAssessment'] = [np.nanmean, np.nansum]
        rename_cols.append('考核分数平均值')
        rename_cols.append('考核总分数')
    # 进行分组聚合，如果有需要聚合的列
    summary_df = df_accuracy.groupby('model').agg(agg_dict).reset_index()
    summary_df.columns = rename_cols
    summary_html = summary_df.to_html(classes='table table-bordered table-striped', index=False)
    # -------------------- 生成完整 HTML 页面 --------------------

    html_content = f"""
    <!DOCTYPE html>
    <html lang="en">
    <head>
        <meta charset="UTF-8">
        <meta name="viewport" content="width=device-width, initial-scale=1.0">
        <title>Data Analysis Report</title>
        <!-- 引入 Bootstrap CSS -->
        <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/css/bootstrap.min.css" rel="stylesheet">
        <style>
         justify-between;{{
                display: flex;
                justify-content: space-between;
           }}
            body {{
                background-color: #f4f4f9;
                font-family: Arial, sans-serif;
                padding: 20px;
            }}
            .container {{
                background-color: #fff;
                padding: 20px;
                border-radius: 10px;
                box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
                margin-bottom: 30px;
            }}
           h1 {{
                text-align: center;
                color: #333;
                margin-bottom: 20px;
            }}
            .plot-container {{
                margin: 20px 0;
                max-height: 500px;  /* 限制高度 */
                overflow-y: auto;   /* 显示垂直滚动条 */
            }}
            .table-container {{
                margin-top: 30px;
                overflow-x: auto;   /* 水平滚动条 */
                max-width: 100%;     /* 限制宽度 */
                white-space: nowrap; /* 防止内容换行 */
                max-height: 500px;  /* 限制高度 */
                overflow-y: auto;   /* 显示垂直滚动条 */
            }}
             .fixed-table thead tr > th:first-child,
             .fixed-table tbody tr > td:first-child {{
             position: sticky;
             left: 0;
             z-index: 1;
        
             }}
            .fixed-table-header thead tr > th {{
                position: sticky;
                top: 0;
                z-index: 2;
            }}
            table {{
                width: 100%;
                font-size: 12px;  /* 设置字体大小为12px */
            }}
            th, td {{
                text-align: center;  /* 表头和单元格文字居中 */
            }}
        }}
        
        </style>
    </head>
    <body>
        <div class="container">
            <h1>分析报告</h1>
            <!-- Pandas DataFrame 表格 -->
            <div class="table-container">
                <h2>1. 数据总览</h2>
                {overview_html}
            </div>
            <!-- Pandas DataFrame 表格 -->
             <h2>2. 数据描述</h2>
            <div class="table-container fixed-table">
                {describe_html}
            </div>
            <div class="plot-container">
                <h2>3. 实测气象与实际功率散点图</h2>
                {scatter_html}
            </div>
            <div class="plot-container">
                <h2>4. 相关性分析</h2>
                {corr_html}
            </div>
            <div class="plot-container">
                <h2>5. 预测气象与实测气象偏差曲线</h2>
                {density_html}
            </div>
             <div class="plot-container">
                <h2>6. 实测气象与预测气象曲线趋势</h2>
                {env_pre_html}
            </div>
            <div class="plot-container">
                <h2>7. 预测功率与实际功率曲线对比</h2>
                {power_html}
            </div>
            <!-- Pandas DataFrame 表格 -->
            <div style="display:flex; justify-content: space-between;">
                <h2>8. 准确率对比</h2>
                <span>
                    <a href="/formula.xlsx">公式</a>
                </span>
            </div>
            <div class="table-container fixed-table-header"> 
                {acc_html}
            </div>
            <!-- Pandas DataFrame 表格 -->
            <div class="table-container">
                <h2>9. 准确率汇总对比</h2>
                {summary_html}
            </div>
        </div>
    </body>
    </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_predict, df_accuracy = get_df_list_from_mongo(args)[0], get_df_list_from_mongo(args)[1], \
        get_df_list_from_mongo(args)[2]
        path = put_analysis_report_to_html(args, df_clean, df_predict, 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!")