基于python构建的低温胁迫实验

2301_764441332025-10-31 21:34

基于Python和Streamlit的低温胁迫实验追踪评估系统。以下是完整的应用设计方案：

  1. 实时监控​​：温度控制和生理指标实时显示

  2. ​​数据分析​​：多维度数据可视化和统计检验

  3. ​​智能评估​​：基于主成分分析和隶属函数法的耐寒性评价

  4. ​​报告生成​​：自动化报告生成和导出功能

  5. ​​预警系统​​：实时监测和风险预警

系统架构与功能模块

1. 数据管理模块

复制代码 
import pandas as pd import numpy as np import streamlit as st from datetime import datetime import json class ExperimentDataManager: def __init__(self): self.experiment_data = None self.species_info = None def load_template_data(self): """加载实验数据模板""" template = { 'species': ['蓝苹果', '火祭', '丸叶松绿', '筒叶花月'], 'temperatures': [9, 6, 3, 0, -3], 'parameters': ['RWC', 'MDA', 'Chla', 'Chlb', 'TC', 'Car', 'SS', 'Pro'] } return template

2. 实验状态追踪模块

复制代码 
class ExperimentTracker: def __init__(self): self.current_stage = "预处理" self.stages = { "预处理": {"status": "completed", "duration": "5天"}, "低温胁迫": {"status": "in_progress", "current_temp": 3}, "数据采集": {"status": "pending"}, "分析评估": {"status": "pending"} } def update_stage(self, stage_name, status): self.stages[stage_name]["status"] = status

Streamlit应用界面设计

主应用框架

复制代码 
def main(): st.set_page_config( page_title="低温胁迫实验监控系统", page_icon="🌱", layout="wide" ) # 侧边栏导航 st.sidebar.title("🌡️ 实验导航") page = st.sidebar.selectbox( "选择功能模块", ["实验概览", "实时监控", "数据分析", "耐寒性评估", "报告生成"] ) if page == "实验概览": show_overview() elif page == "实时监控": show_real_time_monitoring() elif page == "数据分析": show_data_analysis() elif page == "耐寒性评估": show_cold_resistance_evaluation() elif page == "报告生成": show_report_generation()

1. 实验概览界面

复制代码 
def show_overview(): st.title("🌡️ 低温胁迫实验概览") # 实验基本信息 col1, col2, col3 = st.columns(3) with col1: st.metric("实验品种数", "4", "景天科多肉") with col2: st.metric("温度梯度", "5个", "9℃至-3℃") with col3: st.metric("监测指标", "8项", "生理生化参数") # 实验进度追踪 st.subheader("📊 实验进度") progress_data = { "阶段": ["预处理", "低温胁迫", "数据采集", "分析评估"], "状态": ["已完成", "进行中", "待开始", "待开始"], "进度": [100, 60, 0, 0] } st.dataframe(progress_data) # 品种信息展示 st.subheader("🌿 实验品种信息") species_data = { "品种名称": ["蓝苹果", "火祭", "丸叶松绿", "筒叶花月"], "学名": ["x Sedeveria Blue Elf", "Crassula Campfire", "Sedum lucidum Obesum", "Crassula oblique Gollum"], "当前状态": ["良好", "轻微受损", "优秀", "良好"] } st.dataframe(species_data)

2. 实时监控界面

复制代码 
def show_real_time_monitoring(): st.title("📡 实时实验监控") # 温度控制面板 st.subheader("🌡️ 温度胁迫实时数据") current_temp = st.slider("当前温度设置(℃)", -5, 10, 3) # 实时数据图表 col1, col2 = st.columns(2) with col1: st.line_chart({ '时间': ['0h', '4h', '8h', '12h'], '温度': [9, 6, 3, current_temp], '相对湿度': [45, 45, 45, 45] }) with col2: # 生理指标实时变化 import plotly.graph_objects as go fig = go.Figure() fig.add_trace(go.Scatter(x=[9,6,3,current_temp], y=[0.8,1.2,1.5,2.1], name='MDA含量', line=dict(color='red'))) fig.add_trace(go.Scatter(x=[9,6,3,current_temp], y=[75,78,82,85], name='相对含水量', line=dict(color='blue'))) st.plotly_chart(fig)

3. 数据分析界面

复制代码 
def show_data_analysis(): st.title("📊 实验数据分析") # 数据上传功能 uploaded_file = st.file_uploader("上传实验数据文件", type=['csv', 'xlsx']) if uploaded_file is not None: # 读取并显示数据 if uploaded_file.name.endswith('.csv'): df = pd.read_csv(uploaded_file) else: df = pd.read_excel(uploaded_file) st.subheader("📋 实验数据表") st.dataframe(df) # 多维度数据分析 st.subheader("📈 生理指标变化分析") selected_species = st.selectbox("选择品种", df['品种'].unique()) selected_parameter = st.selectbox("选择生理指标", ['RWC', 'MDA', 'Chla', 'Chlb', 'TC', 'Car', 'SS', 'Pro']) # 绘制趋势图 species_data = df[df['品种'] == selected_species] fig = go.Figure() fig.add_trace(go.Scatter(x=species_data['温度'], y=species_data[selected_parameter], mode='lines+markers', name=selected_species)) st.plotly_chart(fig)

4. 耐寒性评估界面

复制代码 
def show_cold_resistance_evaluation(): st.title("🔬 耐寒性综合评价") st.subheader("主成分分析结果") # PCA分析可视化 pca_results = { '主成分': ['PC1', 'PC2'], '特征值': [4.05, 3.20], '方差贡献率(%)': [50.60, 39.98], '累积贡献率(%)': [50.60, 90.59] } st.dataframe(pca_results) # 隶属函数法评估 st.subheader("隶属函数法耐寒性排序") cold_resistance_data = { '品种': ['丸叶松绿', '蓝苹果', '筒叶花月', '火祭'], '综合评价值(F)': [0.72, 0.58, 0.41, 0.21], '耐寒性排序': [1, 2, 3, 4], '-3℃成活率(%)': [85.5, 76.3, 68.2, 25.5] } st.dataframe(cold_resistance_data) # 可视化排序结果 import plotly.express as px fig = px.bar(cold_resistance_data, x='品种', y='综合评价值(F)', color='综合评价值(F)', title='多肉品种耐寒性综合评价') st.plotly_chart(fig)

5. 报告生成界面

复制代码 
def show_report_generation(): st.title("📄 实验报告生成") # 报告模板选择 report_type = st.selectbox("选择报告类型", ["实验进度报告", "数据分析报告", "最终评估报告"]) # 报告内容编辑 st.subheader("报告内容") experiment_summary = st.text_area("实验摘要", "本研究通过低温胁迫处理，评估4个景天科多肉品种的耐寒性...") key_findings = st.text_area("主要发现", "1. 丸叶松绿表现出最强的耐寒性\n2. 火祭对低温最为敏感...") # 生成报告按钮 if st.button("生成实验报告"): # 创建PDF报告 from reportlab.lib.pagesizes import letter from reportlab.pdfgen import canvas st.success("实验报告生成成功！") st.download_button( label="下载PDF报告", data=generate_pdf_report(experiment_summary, key_findings), file_name=f"低温胁迫实验报告_{datetime.now().strftime('%Y%m%d')}.pdf", mime="application/pdf" )

系统特色功能

1. 智能预警系统

复制代码 
def alert_system(temperature, species_data): """低温胁迫预警系统""" alerts = [] for species, data in species_data.items(): if temperature <= data['min_safe_temp']: alerts.append(f"⚠️ {species} 处于低温危险状态！") if data['mda'] > data['mda_threshold']: alerts.append(f"🔴 {species} 细胞膜受损严重！") return alerts

2. 数据可视化增强

复制代码 
def create_interactive_dashboard(): """创建交互式仪表板""" import plotly.express as px import plotly.graph_objects as go from plotly.subplots import make_subplots # 多子图仪表板 fig = make_subplots( rows=2, cols=2, subplot_titles=('温度变化趋势', '生理指标响应', '耐寒性排序', '成活率统计') ) return fig

部署与运行说明

环境要求

复制代码 
# requirements.txt streamlit>=1.28.0 pandas>=2.0.0 plotly>=5.15.0 openpyxl>=3.0.0 reportlab>=4.0.0 scikit-learn>=1.3.0

运行方式

复制代码 
streamlit run cold_stress_monitor.py

完整代码

复制代码 
import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
from datetime import datetime, timedelta
import io
import base64
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
import seaborn as sns
import sys
import os


# 增强中文字体设置函数
def set_chinese_font():
    """设置中文字体，解决中文显示问题"""
    try:
        # 设置matplotlib中文字体
        plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans', 'Arial Unicode MS', 'SimSun']
        plt.rcParams['axes.unicode_minus'] = False

        # 设置seaborn中文字体
        sns.set(font=['SimHei', 'Microsoft YaHei', 'DejaVu Sans'])
        sns.set_style("whitegrid", {'font.sans-serif': ['SimHei', 'Microsoft YaHei']})

        return True
    except Exception as e:
        print(f"字体设置警告: {e}")
        # 备用方案
        plt.rcParams['font.sans-serif'] = ['DejaVu Sans']
        plt.rcParams['axes.unicode_minus'] = False
        return False


# 初始化中文字体
font_setting_success = set_chinese_font()

# 设置页面配置
st.set_page_config(
    page_title="低温胁迫实验监控系统",
    page_icon="🌡️",
    layout="wide",
    initial_sidebar_state="expanded"
)

# 添加自定义CSS改善中文显示
st.markdown("""
<style>
    /* 全局中文字体设置 */
    .css-1d391kg, .css-1lcbmhc, .css-1outwn7 {
        font-family: 'Microsoft YaHei', 'SimHei', 'PingFang SC', 'Helvetica Neue', Arial, sans-serif !important;
    }

    .main .block-container {
        font-family: 'Microsoft YaHei', 'SimHei', 'PingFang SC', 'Helvetica Neue', Arial, sans-serif !important;
    }

    /* 表格和文本内容 */
    .stDataFrame, .stMarkdown, .stText {
        font-family: 'Microsoft YaHei', 'SimHei', 'PingFang SC', 'Helvetica Neue', Arial, sans-serif !important;
    }

    /* 侧边栏 */
    .css-1d391kg {
        font-family: 'Microsoft YaHei', 'SimHei', 'PingFang SC', 'Helvetica Neue', Arial, sans-serif !important;
    }

    /* 确保所有文本元素使用中文字体 */
    * {
        font-family: 'Microsoft YaHei', 'SimHei', 'PingFang SC', 'Helvetica Neue', Arial, sans-serif !important;
    }
</style>
""", unsafe_allow_html=True)


# 实验数据管理类
class ColdStressExperiment:
    def __init__(self):
        self.species_data = {
            '蓝苹果': {'scientific_name': 'x Sedeveria Blue Elf', 'cold_resistance': 0.58},
            '火祭': {'scientific_name': 'Crassula Campfire', 'cold_resistance': 0.21},
            '丸叶松绿': {'scientific_name': 'Sedum lucidum Obesum', 'cold_resistance': 0.72},
            '筒叶花月': {'scientific_name': 'Crassula oblique Gollum', 'cold_resistance': 0.41}
        }

        self.temperature_gradient = [9, 6, 3, 0, -3]
        self.physiological_params = ['RWC', 'MDA', 'Chla', 'Chlb', 'TC', 'Car', 'SS', 'Pro']

        # 模拟实验数据
        self.experiment_data = self._generate_sample_data()

    def _generate_sample_data(self):
        """生成模拟实验数据"""
        data = []
        np.random.seed(42)  # 保证结果可重现

        for species in self.species_data.keys():
            for temp in self.temperature_gradient:
                # 基于已知的耐寒性生成合理的数据趋势
                cold_resistance = self.species_data[species]['cold_resistance']

                # 模拟生理指标数据
                row = {
                    '品种': species,
                    '温度': temp,
                    'RWC': 75 + (25 - cold_resistance * 15) * (1 - (temp + 3) / 12),
                    'MDA': 0.5 + (2.5 - cold_resistance * 2) * (1 - (temp + 3) / 12),
                    'Chla': 1.2 + cold_resistance * 0.5 * (1 - abs(temp - 3) / 12),
                    'Chlb': 0.4 + cold_resistance * 0.3 * (1 - abs(temp - 3) / 12),
                    'TC': 1.6 + cold_resistance * 0.8 * (1 - abs(temp - 3) / 12),
                    'Car': 0.3 + cold_resistance * 0.2 * (1 - abs(temp - 3) / 12),
                    'SS': 15 + cold_resistance * 20 * (1 - (temp + 3) / 12),
                    'Pro': 15 + cold_resistance * 60 * (1 - (temp + 3) / 12),
                    '成活率': max(20, 100 - (1 - cold_resistance) * 80 * (1 - (temp + 3) / 12))
                }
                # 添加一些随机噪声
                for param in self.physiological_params:
                    row[param] += np.random.normal(0, row[param] * 0.05)

                data.append(row)

        return pd.DataFrame(data)

    def calculate_cold_resistance_score(self, species):
        """计算耐寒性综合评分"""
        species_data = self.experiment_data[self.experiment_data['品种'] == species]

        # 使用主成分分析思路计算综合评分
        positive_params = ['RWC', 'Chla', 'Chlb', 'TC', 'Car', 'SS', 'Pro', '成活率']
        negative_params = ['MDA']

        # 标准化处理
        normalized_scores = []

        for param in positive_params:
            min_val = self.experiment_data[param].min()
            max_val = self.experiment_data[param].max()
            param_score = (species_data[param].mean() - min_val) / (max_val - min_val)
            normalized_scores.append(param_score)

        for param in negative_params:
            min_val = self.experiment_data[param].min()
            max_val = self.experiment_data[param].max()
            param_score = 1 - (species_data[param].mean() - min_val) / (max_val - min_val)
            normalized_scores.append(param_score)

        # 加权平均
        weights = [0.1, 0.1, 0.08, 0.08, 0.08, 0.15, 0.15, 0.2, 0.06]
        final_score = sum(score * weight for score, weight in zip(normalized_scores, weights))

        return final_score


# 初始化实验数据
@st.cache_resource
def load_experiment_data():
    return ColdStressExperiment()


experiment = load_experiment_data()

# 侧边栏导航
st.sidebar.title("🌡️ 低温胁迫实验系统")
st.sidebar.markdown("---")

page = st.sidebar.selectbox(
    "导航菜单",
    ["🏠 实验总览", "📊 实时监控", "🔬 数据分析", "📈 耐寒评估", "📋 报告生成"]
)

st.sidebar.markdown("---")
st.sidebar.info("""
**实验信息**
- 品种数: 4个景天科多肉
- 温度梯度: 5个水平(9℃至-3℃)
- 监测指标: 8项生理参数
""")

# 显示字体设置状态
if not font_setting_success:
    st.sidebar.warning("⚠️ 中文字体设置遇到问题，使用备用方案")

# 主页面内容
if page == "🏠 实验总览":
    st.title("🌡️ 低温胁迫实验总览面板")

    # 关键指标展示
    col1, col2, col3, col4 = st.columns(4)

    with col1:
        st.metric("实验品种数", "4", "景天科多肉")
    with col2:
        st.metric("温度梯度", "5个", "9℃至-3℃")
    with col3:
        st.metric("监测指标", "8项", "生理生化参数")
    with col4:
        st.metric("实验进度", "75%", "+15%")

    st.markdown("---")

    # 实验进度追踪
    st.subheader("📅 实验进度追踪")

    progress_data = pd.DataFrame({
        '阶段': ['实验设计', '材料准备', '预处理', '低温胁迫', '数据采集', '分析评估'],
        '状态': ['已完成', '已完成', '已完成', '进行中', '待开始', '待开始'],
        '开始日期': ['2024-01-01', '2024-01-10', '2024-01-15', '2024-01-20', '2024-01-25', '2024-02-01'],
        '结束日期': ['2024-01-05', '2024-01-12', '2024-01-19', '2024-01-24', '2024-01-30', '2024-02-05'],
        '进度': [100, 100, 100, 75, 0, 0]
    })

    # 进度条可视化
    for _, stage in progress_data.iterrows():
        st.write(f"**{stage['阶段']}** ({stage['开始日期']} 至 {stage['结束日期']})")
        st.progress(stage['进度'] / 100)
        st.write(f"状态: {stage['状态']}")
        st.markdown("---")

    # 品种基本信息
    st.subheader("🌿 实验品种信息")

    species_info = []
    for species, info in experiment.species_data.items():
        species_info.append({
            '品种名称': species,
            '学名': info['scientific_name'],
            '耐寒性评分': f"{experiment.calculate_cold_resistance_score(species):.3f}",
            '当前状态': '良好' if experiment.calculate_cold_resistance_score(species) > 0.5 else '需关注'
        })

    species_df = pd.DataFrame(species_info)
    st.dataframe(species_df, use_container_width=True)

elif page == "📊 实时监控":
    st.title("📡 实验实时监控面板")

    col1, col2 = st.columns([2, 1])

    with col1:
        # 温度控制面板
        st.subheader("🌡️ 温度胁迫控制")
        current_temp = st.slider("设置当前温度(℃)", -5, 10, 3, 1)
        duration = st.slider("处理持续时间(小时)", 1, 24, 12)

        # 温湿度监控
        st.subheader("📈 环境参数实时监测")

        # 模拟实时数据
        time_points = list(range(0, 25, 3))
        temperatures = [9, 7, 5, 3, current_temp, current_temp, current_temp, current_temp, current_temp]
        humidities = [45] * 9

        monitor_df = pd.DataFrame({
            '时间点(小时)': time_points,
            '温度(℃)': temperatures,
            '相对湿度(%)': humidities
        })

        st.line_chart(monitor_df.set_index('时间点(小时)'))

    with col2:
        st.subheader("🔔 系统状态")

        # 设备状态
        st.success("✅ 气候培养箱: 正常运行")
        st.success("✅ 数据采集系统: 正常")
        st.warning("⚠️ 摄像头: 部分遮挡")

        # 预警信息
        if current_temp <= 0:
            st.error(f"🚨 低温预警: 当前温度{current_temp}℃已低于冰点")
        if current_temp <= -3:
            st.error("🚨 极端低温预警: 植物可能受到严重伤害")

        st.subheader("📋 实验日志")
        log_entries = [
            "08:00 - 实验开始，温度设置为9℃",
            "12:00 - 降温至6℃，植物状态正常",
            "16:00 - 降温至3℃，开始数据采集",
            "20:00 - 维持当前温度处理"
        ]

        for entry in log_entries:
            st.write(f"• {entry}")

elif page == "🔬 数据分析":
    st.title("📊 实验数据分析中心")

    tab1, tab2, tab3 = st.tabs(["📈 指标趋势分析", "📊 多维度对比", "🔍 统计分析"])

    with tab1:
        st.subheader("生理指标变化趋势分析")

        col1, col2 = st.columns(2)

        with col1:
            selected_species = st.selectbox("选择品种", experiment.species_data.keys())
            selected_param = st.selectbox("选择生理指标", experiment.physiological_params)

        with col2:
            show_all_species = st.checkbox("显示所有品种对比")
            show_trend_line = st.checkbox("显示趋势线", value=True)

        # 绘制趋势图
        if show_all_species:
            fig = px.line(experiment.experiment_data, x='温度', y=selected_param,
                          color='品种', title=f'{selected_param}随温度变化趋势')
            fig.update_layout(
                font=dict(size=12, family="Microsoft YaHei, SimHei, sans-serif"),
                title_font=dict(size=14, family="Microsoft YaHei, SimHei, sans-serif")
            )
        else:
            species_data = experiment.experiment_data[experiment.experiment_data['品种'] == selected_species]
            fig = px.line(species_data, x='温度', y=selected_param,
                          title=f'{selected_species} - {selected_param}随温度变化趋势')
            fig.update_layout(
                font=dict(size=12, family="Microsoft YaHei, SimHei, sans-serif"),
                title_font=dict(size=14, family="Microsoft YaHei, SimHei, sans-serif")
            )

            if show_trend_line:
                z = np.polyfit(species_data['温度'], species_data[selected_param], 2)
                p = np.poly1d(z)
                fig.add_scatter(x=species_data['温度'], y=p(species_data['温度']),
                                mode='lines', name='趋势线', line=dict(dash='dash'))

        st.plotly_chart(fig, use_container_width=True)

        # 指标解释
        param_descriptions = {
            'RWC': '相对含水量 - 反映植物保水能力',
            'MDA': '丙二醛含量 - 指示细胞膜受损程度',
            'Chla': '叶绿素a - 光合作用关键色素',
            'Chlb': '叶绿素b - 辅助光合色素',
            'TC': '总叶绿素 - 光合能力指标',
            'Car': '类胡萝卜素 - 光保护色素',
            'SS': '可溶性糖 - 渗透调节物质',
            'Pro': '脯氨酸 - 重要的渗透调节剂'
        }

        st.info(f"**{selected_param}指标说明**: {param_descriptions.get(selected_param, '')}")

    with tab2:
        st.subheader("多品种指标对比分析")

        selected_temp = st.selectbox("选择温度条件", experiment.temperature_gradient)
        temp_data = experiment.experiment_data[experiment.experiment_data['温度'] == selected_temp]

        # 多指标雷达图
        st.subheader("生理指标雷达图对比")

        params_for_radar = ['RWC', 'MDA', 'SS', 'Pro', '成活率']
        fig = go.Figure()

        for species in experiment.species_data.keys():
            species_data = temp_data[temp_data['品种'] == species]
            values = [species_data[param].values[0] for param in params_for_radar]
            max_vals = [temp_data[param].max() for param in params_for_radar]
            normalized_values = [v / max_v for v, max_v in zip(values, max_vals)]

            fig.add_trace(go.Scatterpolar(
                r=normalized_values + [normalized_values[0]],
                theta=params_for_radar + [params_for_radar[0]],
                fill='toself',
                name=species
            ))

        fig.update_layout(
            polar=dict(radialaxis=dict(visible=True, range=[0, 1])),
            showlegend=True,
            title=f"{selected_temp}℃条件下各品种生理指标对比",
            font=dict(size=12, family="Microsoft YaHei, SimHei, sans-serif"),
            title_font=dict(size=14, family="Microsoft YaHei, SimHei, sans-serif")
        )

        st.plotly_chart(fig, use_container_width=True)

    with tab3:
        st.subheader("统计分析结果")

        # 相关性分析
        st.write("**生理指标相关性矩阵**")

        analysis_temp = st.selectbox("选择分析温度", experiment.temperature_gradient, key="analysis_temp")
        analysis_data = experiment.experiment_data[experiment.experiment_data['温度'] == analysis_temp]

        # 计算相关性矩阵
        corr_matrix = analysis_data[experiment.physiological_params + ['成活率']].corr()

        # 创建热图
        fig = px.imshow(corr_matrix,
                        text_auto=True,
                        aspect="auto",
                        color_continuous_scale='RdBu_r',
                        title=f"{analysis_temp}℃条件下生理指标相关性热图")
        fig.update_layout(
            font=dict(size=10, family="Microsoft YaHei, SimHei, sans-serif"),
            title_font=dict(size=12, family="Microsoft YaHei, SimHei, sans-serif")
        )
        st.plotly_chart(fig, use_container_width=True)

        # 统计摘要
        st.write("**描述性统计摘要**")
        st.dataframe(analysis_data.describe(), use_container_width=True)

elif page == "📈 耐寒评估":
    st.title("🔬 耐寒性综合评价系统")

    tab1, tab2, tab3 = st.tabs(["📋 主成分分析", "📊 隶属函数评估", "🏆 综合排序"])

    with tab1:
        st.subheader("主成分分析结果")

        # 模拟PCA结果
        pca_results = pd.DataFrame({
            '主成分': ['PC1', 'PC2', 'PC3', 'PC4'],
            '特征值': [4.05, 3.20, 0.45, 0.30],
            '方差贡献率(%)': [50.60, 39.98, 5.63, 3.79],
            '累积贡献率(%)': [50.60, 90.59, 96.22, 100.00]
        })

        st.dataframe(pca_results, use_container_width=True)

        # 主成分贡献可视化
        fig = px.bar(pca_results, x='主成分', y='方差贡献率(%)',
                     title='主成分方差贡献率', color='方差贡献率(%)')
        fig.update_layout(
            font=dict(size=12, family="Microsoft YaHei, SimHei, sans-serif"),
            title_font=dict(size=14, family="Microsoft YaHei, SimHei, sans-serif")
        )
        st.plotly_chart(fig, use_container_width=True)

        # 因子载荷矩阵
        st.subheader("因子载荷矩阵")
        loading_matrix = pd.DataFrame({
            '生理指标': experiment.physiological_params + ['成活率'],
            'PC1': [0.85, -0.78, 0.82, 0.80, 0.83, 0.75, 0.88, 0.86, 0.90],
            'PC2': [0.45, 0.55, 0.40, 0.42, 0.38, 0.52, 0.35, 0.38, 0.32],
            'PC3': [0.15, 0.20, 0.18, 0.22, 0.20, 0.18, 0.12, 0.15, 0.10]
        })

        st.dataframe(loading_matrix, use_container_width=True)

    with tab2:
        st.subheader("隶属函数法评估")

        # 计算各品种的耐寒性评分
        cold_resistance_scores = []
        for species in experiment.species_data.keys():
            score = experiment.calculate_cold_resistance_score(species)
            survival_rate = experiment.experiment_data[
                (experiment.experiment_data['品种'] == species) &
                (experiment.experiment_data['温度'] == -3)
                ]['成活率'].values[0]

            cold_resistance_scores.append({
                '品种': species,
                '综合评价值(F)': round(score, 3),
                '耐寒性排序': 0,
                '-3℃成活率(%)': round(survival_rate, 1),
                '评价等级': '强' if score > 0.6 else '中等' if score > 0.4 else '弱'
            })

        # 按综合评价值排序
        cold_resistance_df = pd.DataFrame(cold_resistance_scores)
        cold_resistance_df = cold_resistance_df.sort_values('综合评价值(F)', ascending=False)
        cold_resistance_df['耐寒性排序'] = range(1, len(cold_resistance_df) + 1)

        st.dataframe(cold_resistance_df, use_container_width=True)

        # 可视化排序结果
        fig = px.bar(cold_resistance_df, x='品种', y='综合评价值(F)',
                     color='综合评价值(F)',
                     title='多肉品种耐寒性综合评价排序',
                     color_continuous_scale='viridis')
        fig.update_layout(
            font=dict(size=12, family="Microsoft YaHei, SimHei, sans-serif"),
            title_font=dict(size=14, family="Microsoft YaHei, SimHei, sans-serif"),
            xaxis_tickangle=-45
        )
        st.plotly_chart(fig, use_container_width=True)

    with tab3:
        st.subheader("耐寒性综合排名与推荐")

        # 最终排序结果
        st.success("### 🏆 耐寒性综合排名")

        cold_resistance_scores = []
        for species in experiment.species_data.keys():
            score = experiment.calculate_cold_resistance_score(species)
            cold_resistance_scores.append((species, score))

        cold_resistance_scores.sort(key=lambda x: x[1], reverse=True)

        for i, (species, score) in enumerate(cold_resistance_scores, 1):
            medal = "🥇" if i == 1 else "🥈" if i == 2 else "🥉" if i == 3 else f"{i}."
            survival_rate = experiment.experiment_data[
                (experiment.experiment_data['品种'] == species) &
                (experiment.experiment_data['温度'] == -3)
                ]['成活率'].values[0]

            st.write(f"{medal} **{species}** - 评分: {score:.3f} | "
                     f"成活率: {survival_rate:.1f}%")

        st.markdown("---")

        # 种植建议
        st.subheader("🌱 种植推广建议")

        for species, score in cold_resistance_scores:
            survival_rate = experiment.experiment_data[
                (experiment.experiment_data['品种'] == species) &
                (experiment.experiment_data['温度'] == -3)
                ]['成活率'].values[0]

            if score > 0.6:
                st.success(f"**{species}**: 耐寒性强，适合在冬季低温地区推广种植，-3℃下成活率达{survival_rate:.1f}%")
            elif score > 0.4:
                st.warning(f"**{species}**: 耐寒性中等，建议在冬季有防护措施的地区种植")
            else:
                st.error(f"**{species}**: 耐寒性较弱，仅适合在温暖地区或温室种植")

elif page == "📋 报告生成":
    st.title("📄 实验报告生成中心")

    col1, col2 = st.columns(2)

    with col1:
        st.subheader("报告配置")

        report_type = st.selectbox("选择报告类型",
                                   ["实验进度报告", "数据分析报告", "耐寒性评估报告", "完整实验报告"])

        include_species = st.multiselect("包含品种",
                                         list(experiment.species_data.keys()),
                                         default=list(experiment.species_data.keys()))

    with col2:
        st.subheader("内容选项")

        include_charts = st.checkbox("包含图表", value=True)
        include_statistics = st.checkbox("包含统计分析", value=True)
        include_recommendations = st.checkbox("包含种植建议", value=True)

    st.markdown("---")

    # 报告内容预览
    st.subheader("报告内容预览")

    # 生成报告摘要
    st.write("### 报告摘要")
    st.write("""
    本报告基于低温胁迫实验数据，对4个景天科多肉品种的耐寒性进行了系统评价。
    通过主成分分析和隶属函数法，综合评估了各品种在低温胁迫下的生理响应和适应能力。
    """)

    # 显示关键结果
    cold_resistance_scores = []
    for species in include_species:
        score = experiment.calculate_cold_resistance_score(species)
        cold_resistance_scores.append((species, score))

    cold_resistance_scores.sort(key=lambda x: x[1], reverse=True)

    st.write("### 关键发现")
    st.write("耐寒性综合评价值排序（从高到低）:")
    for i, (species, score) in enumerate(cold_resistance_scores, 1):
        st.write(f"{i}. **{species}**: {score:.3f}")

    # 简单的报告下载功能
    if st.button("📥 生成报告摘要"):
        # 创建简单的文本报告
        report_content = f"""
低温胁迫实验报告
生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M')}

实验概述:
- 分析品种: {', '.join(include_species)}
- 温度梯度: 9℃至-3℃
- 监测指标: 8项生理参数

耐寒性评估结果:
"""

        for i, (species, score) in enumerate(cold_resistance_scores, 1):
            survival_rate = experiment.experiment_data[
                (experiment.experiment_data['品种'] == species) &
                (experiment.experiment_data['温度'] == -3)
                ]['成活率'].values[0]
            report_content += f"{i}. {species}: 评分{score:.3f}, -3℃成活率{survival_rate:.1f}%\n"

        # 提供下载
        st.download_button(
            label="下载文本报告",
            data=report_content,
            file_name=f"低温胁迫实验报告_{datetime.now().strftime('%Y%m%d')}.txt",
            mime="text/plain"
        )


# 确保中文字体设置在所有情况下都生效
def ensure_chinese_font():
    """确保中文字体设置"""
    try:
        import matplotlib
        matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans', 'Arial Unicode MS']
        matplotlib.rcParams['axes.unicode_minus'] = False
    except:
        pass


if __name__ == "__main__":
    ensure_chinese_font()
    # 显示启动信息
    if not font_setting_success:
        st.toast("中文字体设置使用备用方案", icon="⚠️")
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