""" 图表到SVG转换器 - 将Chart.js数据转换为矢量SVG图形 支持的图表类型: - line: 折线图 - bar: 柱状图 - pie: 饼图 - doughnut: 圆环图 - radar: 雷达图 - polarArea: 极地区域图 - scatter: 散点图 """ from __future__ import annotations import base64 import io import re from typing import Any, Dict, List, Optional, Tuple from loguru import logger try: import matplotlib matplotlib.use('Agg') # 使用非GUI后端 import matplotlib.pyplot as plt import matplotlib.font_manager as fm from matplotlib.patches import Wedge import numpy as np MATPLOTLIB_AVAILABLE = True except ImportError: MATPLOTLIB_AVAILABLE = False logger.warning("Matplotlib未安装，PDF图表矢量渲染功能将不可用") class ChartToSVGConverter: """ 将Chart.js图表数据转换为SVG矢量图形 """ # 默认颜色调色板（与Chart.js默认颜色接近） DEFAULT_COLORS = [ '#FF6384', '#36A2EB', '#FFCE56', '#4BC0C0', '#9966FF', '#FF9F40', '#FF6384', '#C9CBCF' ] def __init__(self, font_path: Optional[str] = None): """ 初始化转换器 参数: font_path: 中文字体路径（可选） """ if not MATPLOTLIB_AVAILABLE: raise RuntimeError("Matplotlib未安装，请运行: pip install matplotlib") self.font_path = font_path self._setup_chinese_font() def _setup_chinese_font(self): """配置中文字体""" if self.font_path: try: # 添加自定义字体 fm.fontManager.addfont(self.font_path) # 设置默认字体 font_prop = fm.FontProperties(fname=self.font_path) plt.rcParams['font.family'] = font_prop.get_name() plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题 logger.info(f"已加载中文字体: {self.font_path}") except Exception as e: logger.warning(f"加载中文字体失败: {e}，将使用系统默认字体") else: # 尝试使用系统中文字体 try: plt.rcParams['font.sans-serif'] = ['SimHei', 'Arial Unicode MS', 'DejaVu Sans'] plt.rcParams['axes.unicode_minus'] = False except Exception as e: logger.warning(f"配置中文字体失败: {e}") def convert_widget_to_svg( self, widget_data: Dict[str, Any], width: int = 800, height: int = 500, dpi: int = 100 ) -> Optional[str]: """ 将widget数据转换为SVG字符串 参数: widget_data: widget块数据（包含widgetType、data、props） width: 图表宽度（像素） height: 图表高度（像素） dpi: DPI设置 返回: str: SVG字符串，失败返回None """ try: # 提取图表类型 widget_type = widget_data.get('widgetType', '') if not widget_type or not widget_type.startswith('chart.js'): logger.warning(f"不支持的widget类型: {widget_type}") return None # 从widgetType中提取图表类型，例如 "chart.js/line" -> "line" chart_type = widget_type.split('/')[-1] if '/' in widget_type else 'bar' # 也检查props中的type props = widget_data.get('props', {}) if props.get('type'): chart_type = props['type'] # 提取数据 data = widget_data.get('data', {}) if not data: logger.warning("图表数据为空") return None # 根据图表类型调用相应的渲染方法 render_method = getattr(self, f'_render_{chart_type}', None) if not render_method: logger.warning(f"不支持的图表类型: {chart_type}") return None # 创建图表并转换为SVG return render_method(data, props, width, height, dpi) except Exception as e: logger.error(f"转换图表为SVG失败: {e}", exc_info=True) return None def _create_figure( self, width: int, height: int, dpi: int, title: Optional[str] = None ) -> Tuple[Any, Any]: """ 创建matplotlib图表 返回: tuple: (fig, ax) """ fig, ax = plt.subplots(figsize=(width/dpi, height/dpi), dpi=dpi) if title: ax.set_title(title, fontsize=14, fontweight='bold', pad=20) return fig, ax def _parse_color(self, color: Any) -> str: """ 解析颜色值，将CSS格式转换为matplotlib支持的格式 参数: color: 颜色值（可能是CSS格式如rgba()或十六进制） 返回: str: matplotlib支持的颜色格式 """ if not isinstance(color, str): return str(color) color = color.strip() # 处理rgba(r, g, b, a)格式 rgba_pattern = r'rgba\((\d+),\s*(\d+),\s*(\d+),\s*([\d.]+)\)' match = re.match(rgba_pattern, color) if match: r, g, b, a = match.groups() # 转换为matplotlib格式 (r/255, g/255, b/255, a) return (int(r)/255, int(g)/255, int(b)/255, float(a)) # 处理rgb(r, g, b)格式 rgb_pattern = r'rgb\((\d+),\s*(\d+),\s*(\d+)\)' match = re.match(rgb_pattern, color) if match: r, g, b = match.groups() # 转换为matplotlib格式 (r/255, g/255, b/255) return (int(r)/255, int(g)/255, int(b)/255) # 其他格式（十六进制、颜色名等）直接返回 return color def _get_colors(self, datasets: List[Dict[str, Any]]) -> List[str]: """ 获取图表颜色 优先使用dataset中定义的颜色，否则使用默认调色板 """ colors = [] for i, dataset in enumerate(datasets): # 尝试获取各种可能的颜色字段 color = ( dataset.get('backgroundColor') or dataset.get('borderColor') or dataset.get('color') or self.DEFAULT_COLORS[i % len(self.DEFAULT_COLORS)] ) # 如果是颜色数组，取第一个 if isinstance(color, list): color = color[0] if color else self.DEFAULT_COLORS[i % len(self.DEFAULT_COLORS)] # 解析颜色格式 color = self._parse_color(color) colors.append(color) return colors def _figure_to_svg(self, fig: Any) -> str: """ 将matplotlib图表转换为SVG字符串 """ svg_buffer = io.BytesIO() fig.savefig(svg_buffer, format='svg', bbox_inches='tight', transparent=False, facecolor='white') plt.close(fig) svg_buffer.seek(0) svg_string = svg_buffer.getvalue().decode('utf-8') return svg_string def _render_line( self, data: Dict[str, Any], props: Dict[str, Any], width: int, height: int, dpi: int ) -> Optional[str]: """渲染折线图""" try: labels = data.get('labels', []) datasets = data.get('datasets', []) if not labels or not datasets: return None title = props.get('title') fig, ax = self._create_figure(width, height, dpi, title) colors = self._get_colors(datasets) # 绘制每个数据系列 for i, dataset in enumerate(datasets): dataset_data = dataset.get('data', []) label = dataset.get('label', f'系列{i+1}') color = colors[i] # 绘制折线 ax.plot( range(len(labels)), dataset_data, marker='o', label=label, color=color, linewidth=2, markersize=6 ) # 设置x轴标签 ax.set_xticks(range(len(labels))) ax.set_xticklabels(labels, rotation=45, ha='right') # 显示图例 if len(datasets) > 1: ax.legend(loc='best', framealpha=0.9) # 网格 ax.grid(True, alpha=0.3, linestyle='--') return self._figure_to_svg(fig) except Exception as e: logger.error(f"渲染折线图失败: {e}") return None def _render_bar( self, data: Dict[str, Any], props: Dict[str, Any], width: int, height: int, dpi: int ) -> Optional[str]: """渲染柱状图""" try: labels = data.get('labels', []) datasets = data.get('datasets', []) if not labels or not datasets: return None title = props.get('title') fig, ax = self._create_figure(width, height, dpi, title) colors = self._get_colors(datasets) # 计算柱子位置 x = np.arange(len(labels)) width_bar = 0.8 / len(datasets) if len(datasets) > 1 else 0.6 # 绘制每个数据系列 for i, dataset in enumerate(datasets): dataset_data = dataset.get('data', []) label = dataset.get('label', f'系列{i+1}') color = colors[i] offset = (i - len(datasets)/2 + 0.5) * width_bar ax.bar( x + offset, dataset_data, width_bar, label=label, color=color, alpha=0.8, edgecolor='white', linewidth=0.5 ) # 设置x轴标签 ax.set_xticks(x) ax.set_xticklabels(labels, rotation=45, ha='right') # 显示图例 if len(datasets) > 1: ax.legend(loc='best', framealpha=0.9) # 网格 ax.grid(True, alpha=0.3, linestyle='--', axis='y') return self._figure_to_svg(fig) except Exception as e: logger.error(f"渲染柱状图失败: {e}") return None def _render_pie( self, data: Dict[str, Any], props: Dict[str, Any], width: int, height: int, dpi: int ) -> Optional[str]: """渲染饼图""" try: labels = data.get('labels', []) datasets = data.get('datasets', []) if not labels or not datasets: return None # 饼图只使用第一个数据集 dataset = datasets[0] dataset_data = dataset.get('data', []) title = props.get('title') fig, ax = self._create_figure(width, height, dpi, title) # 获取颜色 colors = dataset.get('backgroundColor', self.DEFAULT_COLORS[:len(labels)]) if not isinstance(colors, list): colors = self.DEFAULT_COLORS[:len(labels)] # 绘制饼图 wedges, texts, autotexts = ax.pie( dataset_data, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90, textprops={'fontsize': 10} ) # 设置百分比文字为白色 for autotext in autotexts: autotext.set_color('white') autotext.set_fontweight('bold') ax.axis('equal') # 保持圆形 return self._figure_to_svg(fig) except Exception as e: logger.error(f"渲染饼图失败: {e}") return None def _render_doughnut( self, data: Dict[str, Any], props: Dict[str, Any], width: int, height: int, dpi: int ) -> Optional[str]: """渲染圆环图""" try: labels = data.get('labels', []) datasets = data.get('datasets', []) if not labels or not datasets: return None # 圆环图只使用第一个数据集 dataset = datasets[0] dataset_data = dataset.get('data', []) title = props.get('title') fig, ax = self._create_figure(width, height, dpi, title) # 获取颜色 colors = dataset.get('backgroundColor', self.DEFAULT_COLORS[:len(labels)]) if not isinstance(colors, list): colors = self.DEFAULT_COLORS[:len(labels)] # 绘制圆环图（通过设置wedgeprops实现中空效果） wedges, texts, autotexts = ax.pie( dataset_data, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90, wedgeprops=dict(width=0.5, edgecolor='white'), textprops={'fontsize': 10} ) # 设置百分比文字 for autotext in autotexts: autotext.set_color('white') autotext.set_fontweight('bold') ax.axis('equal') return self._figure_to_svg(fig) except Exception as e: logger.error(f"渲染圆环图失败: {e}") return None def _render_radar( self, data: Dict[str, Any], props: Dict[str, Any], width: int, height: int, dpi: int ) -> Optional[str]: """渲染雷达图""" try: labels = data.get('labels', []) datasets = data.get('datasets', []) if not labels or not datasets: return None title = props.get('title') fig = plt.figure(figsize=(width/dpi, height/dpi), dpi=dpi) # 创建极坐标子图 ax = fig.add_subplot(111, projection='polar') if title: ax.set_title(title, fontsize=14, fontweight='bold', pad=20) colors = self._get_colors(datasets) # 计算角度 angles = np.linspace(0, 2 * np.pi, len(labels), endpoint=False).tolist() angles += angles[:1] # 闭合图形 # 绘制每个数据系列 for i, dataset in enumerate(datasets): dataset_data = dataset.get('data', []) label = dataset.get('label', f'系列{i+1}') color = colors[i] # 闭合数据 values = dataset_data + dataset_data[:1] # 绘制雷达图 ax.plot(angles, values, 'o-', linewidth=2, label=label, color=color) ax.fill(angles, values, alpha=0.25, color=color) # 设置标签 ax.set_xticks(angles[:-1]) ax.set_xticklabels(labels) # 显示图例 if len(datasets) > 1: ax.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1)) return self._figure_to_svg(fig) except Exception as e: logger.error(f"渲染雷达图失败: {e}") return None def _render_scatter( self, data: Dict[str, Any], props: Dict[str, Any], width: int, height: int, dpi: int ) -> Optional[str]: """渲染散点图""" try: datasets = data.get('datasets', []) if not datasets: return None title = props.get('title') fig, ax = self._create_figure(width, height, dpi, title) colors = self._get_colors(datasets) # 绘制每个数据系列 for i, dataset in enumerate(datasets): dataset_data = dataset.get('data', []) label = dataset.get('label', f'系列{i+1}') color = colors[i] # 提取x和y坐标 if dataset_data and isinstance(dataset_data[0], dict): x_values = [point.get('x', 0) for point in dataset_data] y_values = [point.get('y', 0) for point in dataset_data] else: # 如果不是{x,y}格式，使用索引作为x x_values = range(len(dataset_data)) y_values = dataset_data ax.scatter( x_values, y_values, label=label, color=color, s=50, alpha=0.6, edgecolors='white', linewidth=0.5 ) # 显示图例 if len(datasets) > 1: ax.legend(loc='best', framealpha=0.9) # 网格 ax.grid(True, alpha=0.3, linestyle='--') return self._figure_to_svg(fig) except Exception as e: logger.error(f"渲染散点图失败: {e}") return None def _render_polarArea( self, data: Dict[str, Any], props: Dict[str, Any], width: int, height: int, dpi: int ) -> Optional[str]: """渲染极地区域图""" try: labels = data.get('labels', []) datasets = data.get('datasets', []) if not labels or not datasets: return None # 只使用第一个数据集 dataset = datasets[0] dataset_data = dataset.get('data', []) title = props.get('title') fig = plt.figure(figsize=(width/dpi, height/dpi), dpi=dpi) ax = fig.add_subplot(111, projection='polar') if title: ax.set_title(title, fontsize=14, fontweight='bold', pad=20) # 获取颜色 colors = dataset.get('backgroundColor', self.DEFAULT_COLORS[:len(labels)]) if not isinstance(colors, list): colors = self.DEFAULT_COLORS[:len(labels)] # 计算角度 theta = np.linspace(0, 2 * np.pi, len(labels), endpoint=False) width_bar = 2 * np.pi / len(labels) # 绘制极地区域图 bars = ax.bar( theta, dataset_data, width=width_bar, bottom=0.0, color=colors, alpha=0.7, edgecolor='white', linewidth=1 ) # 设置标签 ax.set_xticks(theta) ax.set_xticklabels(labels) return self._figure_to_svg(fig) except Exception as e: logger.error(f"渲染极地区域图失败: {e}") return None def create_chart_converter(font_path: Optional[str] = None) -> ChartToSVGConverter: """ 创建图表转换器实例 参数: font_path: 中文字体路径（可选） 返回: ChartToSVGConverter: 转换器实例 """ return ChartToSVGConverter(font_path=font_path) __all__ = ["ChartToSVGConverter", "create_chart_converter"]