马一丁
775 lines
26 KiB
Python
775 lines
26 KiB
Python
"""
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图表到SVG转换器 - 将Chart.js数据转换为矢量SVG图形
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支持的图表类型:
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- line: 折线图
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- bar: 柱状图
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- pie: 饼图
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- doughnut: 圆环图
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- radar: 雷达图
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- polarArea: 极地区域图
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- scatter: 散点图
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"""
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from __future__ import annotations
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import base64
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import io
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import re
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from typing import Any, Dict, List, Optional, Tuple
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from loguru import logger
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try:
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import matplotlib
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matplotlib.use('Agg') # 使用非GUI后端
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import matplotlib.pyplot as plt
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import matplotlib.font_manager as fm
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from matplotlib.patches import Wedge
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import numpy as np
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MATPLOTLIB_AVAILABLE = True
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except ImportError:
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MATPLOTLIB_AVAILABLE = False
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logger.warning("Matplotlib未安装，PDF图表矢量渲染功能将不可用")
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# 可选依赖：scipy用于曲线平滑
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try:
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from scipy.interpolate import make_interp_spline
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SCIPY_AVAILABLE = True
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except ImportError:
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SCIPY_AVAILABLE = False
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logger.info("Scipy未安装，折线图将不支持曲线平滑功能（不影响基本渲染）")
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class ChartToSVGConverter:
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"""
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将Chart.js图表数据转换为SVG矢量图形
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"""
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# 默认颜色调色板（与Chart.js默认颜色接近）
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DEFAULT_COLORS = [
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'#FF6384', '#36A2EB', '#FFCE56', '#4BC0C0',
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'#9966FF', '#FF9F40', '#FF6384', '#C9CBCF'
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]
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# CSS变量到颜色的映射表（支持常见的Chart.js主题变量）
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CSS_VAR_COLOR_MAP = {
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'var(--color-accent)': '#007AFF', # 蓝色（强调色）
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'var(--re-accent-color)': '#007AFF', # 蓝色
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'var(--color-kpi-down)': '#DC3545', # 红色（下降/危险）
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'var(--re-danger-color)': '#DC3545', # 红色（危险）
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'var(--color-warning)': '#FFC107', # 黄色（警告）
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'var(--re-warning-color)': '#FFC107', # 黄色
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'var(--color-success)': '#28A745', # 绿色（成功）
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'var(--re-success-color)': '#28A745', # 绿色
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'var(--color-primary)': '#007BFF', # 主色
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'var(--color-secondary)': '#6C757D', # 次要色
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}
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def __init__(self, font_path: Optional[str] = None):
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"""
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初始化转换器
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参数:
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font_path: 中文字体路径（可选）
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"""
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if not MATPLOTLIB_AVAILABLE:
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raise RuntimeError("Matplotlib未安装，请运行: pip install matplotlib")
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self.font_path = font_path
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self._setup_chinese_font()
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def _setup_chinese_font(self):
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"""配置中文字体"""
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if self.font_path:
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try:
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# 添加自定义字体
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fm.fontManager.addfont(self.font_path)
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# 设置默认字体
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font_prop = fm.FontProperties(fname=self.font_path)
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plt.rcParams['font.family'] = font_prop.get_name()
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plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
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logger.info(f"已加载中文字体: {self.font_path}")
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except Exception as e:
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logger.warning(f"加载中文字体失败: {e}，将使用系统默认字体")
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else:
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# 尝试使用系统中文字体
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try:
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plt.rcParams['font.sans-serif'] = ['SimHei', 'Arial Unicode MS', 'DejaVu Sans']
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plt.rcParams['axes.unicode_minus'] = False
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except Exception as e:
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logger.warning(f"配置中文字体失败: {e}")
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def convert_widget_to_svg(
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self,
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widget_data: Dict[str, Any],
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width: int = 800,
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height: int = 500,
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dpi: int = 100
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) -> Optional[str]:
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"""
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将widget数据转换为SVG字符串
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参数:
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widget_data: widget块数据（包含widgetType、data、props）
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width: 图表宽度（像素）
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height: 图表高度（像素）
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dpi: DPI设置
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返回:
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str: SVG字符串，失败返回None
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"""
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try:
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# 提取图表类型
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widget_type = widget_data.get('widgetType', '')
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if not widget_type or not widget_type.startswith('chart.js'):
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logger.warning(f"不支持的widget类型: {widget_type}")
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return None
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# 从widgetType中提取图表类型，例如 "chart.js/line" -> "line"
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chart_type = widget_type.split('/')[-1] if '/' in widget_type else 'bar'
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# 也检查props中的type
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props = widget_data.get('props', {})
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if props.get('type'):
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chart_type = props['type']
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# 提取数据
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data = widget_data.get('data', {})
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if not data:
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logger.warning("图表数据为空")
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return None
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# 根据图表类型调用相应的渲染方法
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render_method = getattr(self, f'_render_{chart_type}', None)
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if not render_method:
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logger.warning(f"不支持的图表类型: {chart_type}")
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return None
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# 创建图表并转换为SVG
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return render_method(data, props, width, height, dpi)
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except Exception as e:
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logger.error(f"转换图表为SVG失败: {e}", exc_info=True)
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return None
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def _create_figure(
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self,
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width: int,
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height: int,
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dpi: int,
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title: Optional[str] = None
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) -> Tuple[Any, Any]:
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"""
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创建matplotlib图表
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返回:
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tuple: (fig, ax)
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"""
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fig, ax = plt.subplots(figsize=(width/dpi, height/dpi), dpi=dpi)
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if title:
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ax.set_title(title, fontsize=14, fontweight='bold', pad=20)
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return fig, ax
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def _parse_color(self, color: Any) -> str:
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"""
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解析颜色值，将CSS格式转换为matplotlib支持的格式
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参数:
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color: 颜色值（可能是CSS格式如rgba()或十六进制或CSS变量）
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返回:
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str: matplotlib支持的颜色格式
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"""
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if not isinstance(color, str):
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return str(color)
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color = color.strip()
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# 【增强】处理CSS变量，例如 var(--color-accent)
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# 使用预定义的颜色映射表替代CSS变量，确保不同变量有不同的颜色
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if color.startswith('var('):
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# 尝试从映射表中查找对应的颜色
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mapped_color = self.CSS_VAR_COLOR_MAP.get(color)
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if mapped_color:
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return mapped_color
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# 如果映射表中没有，尝试从变量名推断颜色类型
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if 'accent' in color or 'primary' in color:
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return '#007AFF' # 蓝色
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elif 'danger' in color or 'down' in color or 'error' in color:
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return '#DC3545' # 红色
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elif 'warning' in color:
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return '#FFC107' # 黄色
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elif 'success' in color or 'up' in color:
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return '#28A745' # 绿色
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# 默认返回蓝色
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return '#36A2EB'
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# 处理rgba(r, g, b, a)格式
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rgba_pattern = r'rgba\((\d+),\s*(\d+),\s*(\d+),\s*([\d.]+)\)'
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match = re.match(rgba_pattern, color)
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if match:
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r, g, b, a = match.groups()
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# 转换为matplotlib格式 (r/255, g/255, b/255, a)
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return (int(r)/255, int(g)/255, int(b)/255, float(a))
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# 处理rgb(r, g, b)格式
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rgb_pattern = r'rgb\((\d+),\s*(\d+),\s*(\d+)\)'
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match = re.match(rgb_pattern, color)
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if match:
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r, g, b = match.groups()
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# 转换为matplotlib格式 (r/255, g/255, b/255)
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return (int(r)/255, int(g)/255, int(b)/255)
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# 其他格式（十六进制、颜色名等）直接返回
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return color
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def _get_colors(self, datasets: List[Dict[str, Any]]) -> List[str]:
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"""
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获取图表颜色
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优先使用dataset中定义的颜色，否则使用默认调色板
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"""
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colors = []
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for i, dataset in enumerate(datasets):
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# 尝试获取各种可能的颜色字段
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color = (
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dataset.get('backgroundColor') or
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dataset.get('borderColor') or
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dataset.get('color') or
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self.DEFAULT_COLORS[i % len(self.DEFAULT_COLORS)]
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)
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# 如果是颜色数组，取第一个
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if isinstance(color, list):
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color = color[0] if color else self.DEFAULT_COLORS[i % len(self.DEFAULT_COLORS)]
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# 解析颜色格式
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color = self._parse_color(color)
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colors.append(color)
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return colors
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def _figure_to_svg(self, fig: Any) -> str:
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"""
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将matplotlib图表转换为SVG字符串
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"""
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svg_buffer = io.BytesIO()
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fig.savefig(svg_buffer, format='svg', bbox_inches='tight', transparent=False, facecolor='white')
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plt.close(fig)
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svg_buffer.seek(0)
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svg_string = svg_buffer.getvalue().decode('utf-8')
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return svg_string
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def _render_line(
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self,
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data: Dict[str, Any],
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props: Dict[str, Any],
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width: int,
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height: int,
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dpi: int
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) -> Optional[str]:
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"""
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渲染折线图（增强版）
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支持特性：
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- 双y轴（yAxisID: 'y' 和 'y1'）
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- 填充区域（fill: true）
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- 透明度（backgroundColor中的alpha通道）
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- 线条样式（tension曲线平滑）
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"""
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try:
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labels = data.get('labels', [])
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datasets = data.get('datasets', [])
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if not labels or not datasets:
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return None
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# 检查是否有双y轴
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has_dual_axis = any(
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dataset.get('yAxisID') == 'y1' for dataset in datasets
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)
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title = props.get('title')
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options = props.get('options', {})
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# 创建图表，如果有双y轴则创建双y轴布局
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if has_dual_axis:
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fig, ax1 = plt.subplots(figsize=(width/dpi, height/dpi), dpi=dpi)
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ax2 = ax1.twinx() # 创建共享x轴的第二个y轴
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else:
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fig, ax1 = self._create_figure(width, height, dpi, title)
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ax2 = None
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if title and has_dual_axis:
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ax1.set_title(title, fontsize=14, fontweight='bold', pad=20)
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colors = self._get_colors(datasets)
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# 分别收集两个y轴的数据系列
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y1_lines = []
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y2_lines = []
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# 绘制每个数据系列
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for i, dataset in enumerate(datasets):
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dataset_data = dataset.get('data', [])
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label = dataset.get('label', f'系列{i+1}')
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color = colors[i]
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# 获取配置
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y_axis_id = dataset.get('yAxisID', 'y')
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fill = dataset.get('fill', False)
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tension = dataset.get('tension', 0) # 0表示直线，0.4表示平滑曲线
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border_color = self._parse_color(dataset.get('borderColor', color))
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background_color = self._parse_color(dataset.get('backgroundColor', color))
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# 选择对应的坐标轴
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ax = ax2 if (y_axis_id == 'y1' and ax2 is not None) else ax1
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# 绘制折线
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x_data = range(len(labels))
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# 根据tension值决定是否平滑
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if tension > 0 and SCIPY_AVAILABLE:
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# 使用样条插值平滑曲线（需要scipy）
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if len(dataset_data) >= 4: # 至少需要4个点才能平滑
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try:
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x_smooth = np.linspace(0, len(labels)-1, len(labels)*3)
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spl = make_interp_spline(x_data, dataset_data, k=min(3, len(dataset_data)-1))
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y_smooth = spl(x_smooth)
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line, = ax.plot(x_smooth, y_smooth, label=label, color=border_color, linewidth=2)
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# 如果需要填充
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if fill:
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ax.fill_between(x_smooth, y_smooth, alpha=0.3, color=background_color)
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except:
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# 如果平滑失败，使用普通折线
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line, = ax.plot(x_data, dataset_data, marker='o', label=label,
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color=border_color, linewidth=2, markersize=6)
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if fill:
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ax.fill_between(x_data, dataset_data, alpha=0.3, color=background_color)
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else:
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line, = ax.plot(x_data, dataset_data, marker='o', label=label,
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color=border_color, linewidth=2, markersize=6)
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if fill:
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ax.fill_between(x_data, dataset_data, alpha=0.3, color=background_color)
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else:
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# 直线连接（tension=0或scipy不可用）
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line, = ax.plot(x_data, dataset_data, marker='o', label=label,
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color=border_color, linewidth=2, markersize=6)
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# 如果需要填充
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if fill:
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ax.fill_between(x_data, dataset_data, alpha=0.3, color=background_color)
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# 记录哪个轴有哪些线
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if ax == ax2:
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y2_lines.append(line)
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else:
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y1_lines.append(line)
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# 设置x轴标签
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ax1.set_xticks(range(len(labels)))
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ax1.set_xticklabels(labels, rotation=45, ha='right')
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# 设置y轴标签和标题
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if has_dual_axis and ax2:
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# 从options中获取y轴配置
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scales = options.get('scales', {})
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y_config = scales.get('y', {})
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y1_config = scales.get('y1', {})
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# 设置左侧y轴
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y_title = y_config.get('title', {}).get('text', '')
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if y_title:
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ax1.set_ylabel(y_title, fontsize=11)
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# 设置右侧y轴
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y1_title = y1_config.get('title', {}).get('text', '')
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if y1_title:
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ax2.set_ylabel(y1_title, fontsize=11)
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# 设置网格（只在主轴显示）
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ax1.grid(True, alpha=0.3, linestyle='--')
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ax2.grid(False) # 右侧y轴不显示网格
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# 合并图例（显示所有数据系列）
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lines = y1_lines + y2_lines
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labels_list = [line.get_label() for line in lines]
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ax1.legend(lines, labels_list, loc='best', framealpha=0.9)
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else:
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# 单y轴的情况
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if len(datasets) > 1:
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ax1.legend(loc='best', framealpha=0.9)
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ax1.grid(True, alpha=0.3, linestyle='--')
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return self._figure_to_svg(fig)
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except Exception as e:
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logger.error(f"渲染折线图失败: {e}", exc_info=True)
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return None
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def _render_bar(
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self,
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data: Dict[str, Any],
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props: Dict[str, Any],
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width: int,
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height: int,
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dpi: int
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) -> Optional[str]:
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"""渲染柱状图"""
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try:
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labels = data.get('labels', [])
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datasets = data.get('datasets', [])
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if not labels or not datasets:
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return None
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title = props.get('title')
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fig, ax = self._create_figure(width, height, dpi, title)
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colors = self._get_colors(datasets)
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# 计算柱子位置
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x = np.arange(len(labels))
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width_bar = 0.8 / len(datasets) if len(datasets) > 1 else 0.6
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# 绘制每个数据系列
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for i, dataset in enumerate(datasets):
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dataset_data = dataset.get('data', [])
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label = dataset.get('label', f'系列{i+1}')
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color = colors[i]
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offset = (i - len(datasets)/2 + 0.5) * width_bar
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ax.bar(
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x + offset,
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dataset_data,
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width_bar,
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label=label,
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color=color,
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alpha=0.8,
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edgecolor='white',
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linewidth=0.5
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)
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# 设置x轴标签
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ax.set_xticks(x)
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ax.set_xticklabels(labels, rotation=45, ha='right')
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|
||
# 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)]
|
||
|
||
# 【修复】解析每个颜色，将CSS格式转换为matplotlib格式
|
||
colors = [self._parse_color(c) for c in colors]
|
||
|
||
# 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)]
|
||
|
||
# 【修复】解析每个颜色，将CSS格式转换为matplotlib格式
|
||
colors = [self._parse_color(c) for c in colors]
|
||
|
||
# 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"]
|