V2.0 五大核心增强: 锚点定位/原生图表/插件架构/WebSocket/LLM智能

ppt_manager/scripts/employment_table.py

@@ -0,0 +1,62 @@
+import pandas as pd
+import numpy as np
+from pathlib import Path
+from loguru import logger
+import matplotlib.pyplot as plt
+
+def generate(output_dir):
+    logger.info("开始生成就业数据表")
+    
+    categories = ['城镇调查失业率', '青年失业率', '制造业PMI', '服务业PMI']
+    current = [5.1, 15.3, 49.8, 53.2]
+    previous = [5.2, 15.6, 49.5, 53.0]
+    change = [c - p for c, p in zip(current, previous)]
+    
+    data = pd.DataFrame({
+        '指标': categories,
+        '当前值': current,
+        '上期值': previous,
+        '变动': change
+    })
+    
+    fig, ax = plt.subplots(figsize=(10, 6))
+    ax.axis('tight')
+    ax.axis('off')
+    
+    table_data = []
+    for _, row in data.iterrows():
+        change_color = '#4CAF50' if row['变动'] >= 0 else '#F44336'
+        table_data.append([
+            row['指标'],
+            f"{row['当前值']:.1f}",
+            f"{row['上期值']:.1f}",
+            f"{row['变动']:+.1f}"
+        ])
+    
+    table = ax.table(
+        cellText=table_data,
+        colLabels=['指标', '当前值', '上期值', '变动'],
+        cellLoc='center',
+        loc='center',
+        colColours=['#4A90E2'] * 4
+    )
+    
+    table.auto_set_font_size(False)
+    table.set_fontsize(12)
+    table.scale(1, 2)
+    
+    for key, cell in table.get_celld().items():
+        if key[0] > 0 and key[1] == 3:
+            cell.set_text_props(color='green' if '+' in cell.get_text().get_text() else 'red')
+    
+    ax.set_title('就业与PMI数据摘要', fontsize=16, fontweight='bold', pad=20)
+    
+    output_path = Path(output_dir) / 'employment_table.png'
+    plt.savefig(str(output_path), dpi=150, bbox_inches='tight')
+    plt.close()
+    
+    logger.info(f"就业数据表已生成: {output_path}")
+    return str(output_path)
+
+if __name__ == "__main__":
+    generate(Path.cwd())

ppt_manager/scripts/gdp_chart.py

@@ -0,0 +1,32 @@
+import pandas as pd
+import numpy as np
+from pathlib import Path
+from loguru import logger
+
+def generate(output_dir):
+    logger.info("开始生成GDP趋势图表")
+    
+    quarters = ['2024Q1', '2024Q2', '2024Q3', '2024Q4', '2025Q1', '2025Q2']
+    gdp_growth = [5.2, 4.8, 5.0, 5.3, np.random.uniform(4.5, 5.5), np.random.uniform(4.5, 5.5)]
+    
+    data = pd.DataFrame({
+        '季度': quarters,
+        'GDP增长率(%)': [round(x, 2) for x in gdp_growth]
+    })
+    data = data.set_index('季度')
+    
+    from dynamic_generator import DynamicContentGenerator
+    generator = DynamicContentGenerator()
+    output_path = generator.generate_chart_matplotlib(
+        data,
+        title='中国GDP增长趋势',
+        x_label='季度',
+        y_label='增长率 (%)',
+        filename='gdp_chart.png',
+        kind='line'
+    )
+    
+    return output_path
+
+if __name__ == "__main__":
+    generate(Path.cwd())

ppt_manager/scripts/inflation_chart.py

@@ -0,0 +1,34 @@
+import pandas as pd
+import numpy as np
+from pathlib import Path
+from loguru import logger
+
+def generate(output_dir):
+    logger.info("开始生成CPI/PPI通胀图表")
+    
+    months = ['1月', '2月', '3月', '4月', '5月', '6月']
+    cpi = [0.7, 0.8, 0.9, 1.0, np.random.uniform(0.8, 1.2), np.random.uniform(0.8, 1.2)]
+    ppi = [-2.5, -2.3, -2.0, -1.8, np.random.uniform(-2.5, -1.5), np.random.uniform(-2.5, -1.5)]
+    
+    data = pd.DataFrame({
+        '月份': months,
+        'CPI(%)': [round(x, 2) for x in cpi],
+        'PPI(%)': [round(x, 2) for x in ppi]
+    })
+    data = data.set_index('月份')
+    
+    from dynamic_generator import DynamicContentGenerator
+    generator = DynamicContentGenerator()
+    output_path = generator.generate_chart_plotly(
+        data,
+        title='CPI与PPI走势',
+        x_label='月份',
+        y_label='同比 (%)',
+        filename='inflation_chart.png',
+        kind='line'
+    )
+    
+    return output_path
+
+if __name__ == "__main__":
+    generate(Path.cwd())

ppt_manager/scripts/market_analysis.py

@@ -0,0 +1,66 @@
+import pandas as pd
+import numpy as np
+from loguru import logger
+
+def generate(output_dir):
+    logger.info("开始生成市场分析图")
+    
+    indices = ['上证指数', '深证成指', '创业板指', '沪深300']
+    current = [3150, 10200, 2050, 3800]
+    change_pct = [
+        np.random.uniform(-2, 2),
+        np.random.uniform(-2, 2),
+        np.random.uniform(-2, 2),
+        np.random.uniform(-2, 2)
+    ]
+    
+    data = pd.DataFrame({
+        '指数': indices,
+        '点位': current,
+        '涨跌幅(%)': [round(x, 2) for x in change_pct]
+    })
+    
+    import matplotlib.pyplot as plt
+    plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei']
+    plt.rcParams['axes.unicode_minus'] = False
+    
+    colors = ['green' if x < 0 else 'red' for x in change_pct]
+    
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 7))
+    
+    bars = ax1.bar(data['指数'], data['点位'], color=colors, alpha=0.7)
+    ax1.set_title('主要指数点位', fontsize=14, fontweight='bold')
+    ax1.set_ylabel('点位')
+    ax1.grid(axis='y', linestyle='--', alpha=0.3)
+    
+    for bar in bars:
+        height = bar.get_height()
+        ax1.text(bar.get_x() + bar.get_width()/2., height,
+                 f'{int(height)}', ha='center', va='bottom')
+    
+    bars2 = ax2.bar(data['指数'], data['涨跌幅(%)'], color=colors, alpha=0.7)
+    ax2.set_title('涨跌幅', fontsize=14, fontweight='bold')
+    ax2.set_ylabel('涨跌幅 (%)')
+    ax2.axhline(y=0, color='black', linestyle='-', linewidth=0.5)
+    ax2.grid(axis='y', linestyle='--', alpha=0.3)
+    
+    for bar in bars2:
+        height = bar.get_height()
+        va = 'bottom' if height > 0 else 'top'
+        ax2.text(bar.get_x() + bar.get_width()/2., height,
+                 f'{height:+.2f}%', ha='center', va=va, fontweight='bold')
+    
+    plt.suptitle('A股市场概况', fontsize=18, fontweight='bold')
+    plt.tight_layout()
+    
+    from pathlib import Path
+    output_path = Path(output_dir) / 'market_analysis.png'
+    plt.savefig(str(output_path), dpi=150, bbox_inches='tight')
+    plt.close()
+    
+    logger.info(f"市场分析图已生成: {output_path}")
+    return str(output_path)
+
+if __name__ == "__main__":
+    from pathlib import Path
+    generate(Path.cwd())

ppt_manager/scripts/trade_chart.py

@@ -0,0 +1,34 @@
+import pandas as pd
+import numpy as np
+from loguru import logger
+
+def generate(output_dir):
+    logger.info("开始生成进出口贸易图表")
+    
+    months = ['1月', '2月', '3月', '4月', '5月', '6月']
+    exports = np.random.randint(2800, 3200, 6)
+    imports = np.random.randint(2000, 2400, 6)
+    
+    data = pd.DataFrame({
+        '月份': months,
+        '出口(亿美元)': exports,
+        '进口(亿美元)': imports
+    })
+    data = data.set_index('月份')
+    
+    from dynamic_generator import DynamicContentGenerator
+    generator = DynamicContentGenerator()
+    output_path = generator.generate_chart_matplotlib(
+        data,
+        title='进出口贸易情况',
+        x_label='月份',
+        y_label='金额 (亿美元)',
+        filename='trade_chart.png',
+        kind='bar'
+    )
+    
+    return output_path
+
+if __name__ == "__main__":
+    from pathlib import Path
+    generate(Path.cwd())