MCP Sheet Parser

""" SVG图表渲染器：将Excel图表转换为高质量SVG格式。 """ import math from typing import Any import xml.etree.ElementTree as ET from xml.dom import minidom from src.utils.color_utils import DEFAULT_CHART_COLORS, normalize_color, ensure_distinct_colors class SVGChartRenderer: """Excel图表数据到SVG的渲染器。""" # 默认图表尺寸常量 DEFAULT_WIDTH = 800 DEFAULT_HEIGHT = 500 def __init__(self, width: int = DEFAULT_WIDTH, height: int = DEFAULT_HEIGHT, show_axes: bool = False): """ 初始化SVG渲染器。 参数： width: SVG宽度（像素） height: SVG高度（像素） show_axes: 是否显示坐标轴和网格线 """ # Excel默认图表大小15x7.5cm self.width = width self.height = height self.show_axes = show_axes # 如果不显示坐标轴，减少边距，整体往下移动 if show_axes: self.margin = {'top': 70, 'right': 80, 'bottom': 60, 'left': 80} # 增加top边距 else: self.margin = {'top': 40, 'right': 20, 'bottom': 20, 'left': 20} # 简洁模式，增加top边距 self.plot_width = width - self.margin['left'] - self.margin['right'] self.plot_height = height - self.margin['top'] - self.margin['bottom'] def render_chart_to_svg(self, chart_data: dict[str, Any]) -> str: """ 渲染图表数据为SVG字符串。 参数： chart_data 图表数据字典 返回： SVG字符串 """ # 使用原始Excel尺寸如果可用 if 'size' in chart_data: size_info = chart_data['size'] if 'width_px' in size_info and 'height_px' in size_info: self.width = max(200, size_info['width_px']) # 最小宽度200px self.height = max(150, size_info['height_px']) # 最小高度150px # 重新计算内边距和绘图区域 self.plot_width = self.width - self.margin['left'] - self.margin['right'] self.plot_height = self.height - self.margin['top'] - self.margin['bottom'] chart_type = chart_data.get('type', 'bar') if chart_type == 'bar': return self._render_bar_chart(chart_data) elif chart_type == 'line': return self._render_line_chart(chart_data) elif chart_type == 'pie': return self._render_pie_chart(chart_data) elif chart_type == 'area': return self._render_area_chart(chart_data) elif chart_type == 'image': return self._render_image_chart(chart_data) else: return self._render_fallback_chart(chart_data) def _render_legend_if_needed(self, svg: ET.Element, chart_data: dict[str, Any], series_list: list[dict], colors: list[str]) -> None: """渲染图例。""" show_legend = ( chart_data.get('show_legend', False) or chart_data.get('legend', {}).get('show', False) or chart_data.get('legend', {}).get('enabled', False) ) if show_legend: legend_style = chart_data.get('legend_style') # 如果有图例条目信息，使用条目信息；否则使用系列信息 legend_entries = chart_data.get('legend', {}).get('entries', []) if legend_entries: # 使用图例条目信息创建图例 legend_series = [] for entry in legend_entries: if not entry.get('delete', False): legend_series.append({ 'name': entry.get('text', f"Series {entry.get('index', 0) + 1}") }) self._draw_legend(svg, legend_series, colors, legend_style=legend_style) else: # 使用系列信息创建图例 self._draw_legend(svg, series_list, colors, legend_style=legend_style) def _render_common_chart_elements(self, svg: ET.Element, chart_data: dict[str, Any], series_list: list[dict], colors: list[str]) -> None: """渲染通用图表元素（图例和注释）。""" self._render_legend_if_needed(svg, chart_data, series_list, colors) self._render_annotations(svg, chart_data) def _should_show_data_labels(self, chart_data: dict[str, Any], series_data: dict[str, Any] | None = None) -> bool: """判断是否显示数据标签。""" # 检查图表级别的数据标签设置 if chart_data.get('show_data_labels', False): return True # 检查系列级别的数据标签设置 if series_data and series_data.get('show_data_labels', False): return True # 检查嵌套的数据标签配置 data_labels_config = chart_data.get('data_labels', {}) if isinstance(data_labels_config, dict) and data_labels_config.get('show', False): return True return False def _create_data_label_element(self, svg: ET.Element, x: float, y: float, text: str, font_size: str = '10px', fill: str = 'white', text_anchor: str = 'middle') -> None: """创建数据标签元素。""" text_elem = ET.SubElement(svg, 'text', { 'x': str(x), 'y': str(y), 'text-anchor': text_anchor, 'alignment-baseline': 'middle', 'class': 'axis-label', 'fill': fill, 'font-size': font_size }) text_elem.text = text def _handle_chart_error(self, chart_type: str, error: Exception) -> str: """处理渲染错误，返回错误占位符。""" svg = self._create_svg_root(f"Chart Error: {chart_type}") # 绘制错误占位符 ET.SubElement(svg, 'rect', { 'x': str(self.margin['left']), 'y': str(self.margin['top']), 'width': str(self.plot_width), 'height': str(self.plot_height), 'fill': '#ffebee', 'stroke': '#f44336', 'stroke-dasharray': '5,5' }) text = ET.SubElement(svg, 'text', { 'x': str(self.width // 2), 'y': str(self.height // 2), 'text-anchor': 'middle', 'class': 'axis-label', 'fill': '#d32f2f' }) text.text = f"Error rendering {chart_type}: {str(error)}" return self._format_svg(svg) def _create_svg_root(self, title: str = "", title_style: dict[str, Any] | None = None) -> ET.Element: """ 创建SVG根元素。 参数：title 图表标题，title_style 标题样式 """ svg = ET.Element('svg', { 'width': f'{self.width}px', 'height': f'{self.height}px', 'viewBox': f'0 0 {self.width} {self.height}', 'xmlns': 'http://www.w3.org/2000/svg', 'class': 'excel-chart-svg' }) # 添加样式定义 style = ET.SubElement(svg, 'style') style.text = self._get_chart_css() # 添加标题 if title: title_attrs = { 'x': str(self.width // 2), 'y': '35', # 往下移动10px 'text-anchor': 'middle', 'class': 'chart-title' } # 合并外部传入的样式 if title_style: style_str = "" if 'font_family' in title_style and title_style['font_family']: style_str += f"font-family: '{title_style['font_family']}';" if 'font_size' in title_style and title_style['font_size']: style_str += f"font-size: {title_style['font_size']}px;" if 'color' in title_style and title_style['color']: style_str += f"fill: {title_style['color']};" if 'bold' in title_style and title_style['bold']: style_str += "font-weight: bold;" if style_str: title_attrs['style'] = style_str title_elem = ET.SubElement(svg, 'text', title_attrs) title_elem.text = title return svg def _get_series_colors(self, chart_data: dict[str, Any]) -> list[str]: """ 获取系列颜色，优先使用Excel原色。 """ if 'colors' in chart_data and chart_data['colors']: return chart_data['colors'] colors = [] for series in chart_data.get('series', []): if 'color' in series and series['color']: colors.append(series['color']) if colors: return colors return DEFAULT_CHART_COLORS def _get_chart_css(self) -> str: """获取图表CSS样式。""" return """ .excel-chart-svg { font-family: 'Microsoft YaHei', 'SimHei', 'PingFang SC', 'Hiragino Sans GB', 'Source Han Sans SC', 'Noto Sans CJK SC', 'Segoe UI', Arial, sans-serif; } .chart-title { font-size: 18px; /* 基于搜索结果：Excel默认18pt */ font-weight: bold; fill: #333; } .axis-title { font-size: 12px; fill: #666; } .axis-label { font-size: 11px; fill: #666; } .grid-line { stroke: #e0e0e0; stroke-width: 0.5; } .axis-line { stroke: #666; stroke-width: 1; } .bar-rect { fill-opacity: 0.8; stroke-width: 1; stroke: #fff; } .bar-rect:hover { fill-opacity: 1; } .line-path { fill: none; stroke-width: 2; } .line-point { r: 3; fill-opacity: 0.8; } .line-point:hover { r: 4; } .area-path { fill-opacity: 0.3; stroke-width: 2; } .area-point { r: 3; fill-opacity: 0.8; } .area-point:hover { r: 4; } .pie-slice { stroke: #fff; stroke-width: 1; } .pie-slice:hover { stroke-width: 2; } .legend-item { font-size: 11px; fill: #666; } """ def _render_bar_chart(self, chart_data: dict[str, Any]) -> str: """渲染柱状图。""" title_style = chart_data.get('title_style') svg = self._create_svg_root(chart_data.get('title', ''), title_style=title_style) series_list = chart_data.get('series', []) # 智能判断是否应该去重X轴标签 should_deduplicate = self._should_deduplicate_x_labels(series_list) # 保存当前系列，供其他方法使用 self.current_series = series_list # 确保颜色列表与系列列表匹配 colors = self._get_series_colors(chart_data) if len(colors) < len(series_list): colors.extend(DEFAULT_CHART_COLORS * (len(series_list) - len(colors))) if not series_list: return self._format_svg(svg) all_x_labels = [] all_y_values = [] for series in series_list: x_data = series.get('x_data', []) y_data = series.get('y_data', []) all_x_labels.extend(x_data) all_y_values.extend(y_data) if not all_x_labels or not all_y_values: return self._format_svg(svg) # 根据判断结果决定X轴标签策略 if should_deduplicate: # 分组柱状图：去重X轴标签 unique_x_labels = list(dict.fromkeys(all_x_labels)) display_x_labels = unique_x_labels else: # 连续柱状图：保留所有X轴标签 unique_x_labels = list(dict.fromkeys(all_x_labels)) # 仍需要用于计算范围 display_x_labels = all_x_labels # 修复：支持负数值，Y轴范围应该包含所有数据 y_min = min(all_y_values) if all_y_values else 0 # 确保y_max在传递给float()之前绝不为None y_val = chart_data.get('y_axis_max') if y_val is None or not isinstance(y_val, (int, float)): y_val = max(all_y_values) if all_y_values else 1 # 确保y_min和y_max是数值类型，并处理可能的转换错误 try: y_min = float(y_min) y_max = float(y_val) except (ValueError, TypeError): y_min = 0.0 y_max = 1.0 # 如果所有值都是正数，从0开始；如果有负数，包含负数范围 if y_min > 0: y_min = 0 # 正数图表从0开始更直观 y_range = y_max - y_min if y_max != y_min else 1 # 绘制X轴标签（保持Excel样式） self._draw_x_axis_labels(svg, display_x_labels) # 如果有负数，绘制零线 if y_min < 0 and y_range > 0: zero_line_y = self.margin['top'] + self.plot_height - ((-y_min) / y_range) * self.plot_height zero_line = ET.SubElement(svg, 'line', { 'x1': str(self.margin['left']), 'y1': str(zero_line_y), 'x2': str(self.margin['left'] + self.plot_width), 'y2': str(zero_line_y), 'stroke': '#666', 'stroke-width': '1', 'class': 'zero-line' }) colors = self._get_series_colors(chart_data) # 根据标签策略决定柱子渲染方式 if should_deduplicate: # 分组柱状图：每个标签下有多个系列的柱子 num_groups = len(unique_x_labels) num_series = len(series_list) group_width = self.plot_width / num_groups bar_width = group_width / num_series * 0.8 # 每个系列的柱子宽度 else: # 连续柱状图：每个数据点都是独立的柱子 total_bars = len(all_x_labels) bar_group_width = self.plot_width / total_bars bar_width = bar_group_width * 0.5 bar_index = 0 # 全局柱子索引（用于连续模式） for series_idx, series in enumerate(series_list): x_data = series.get('x_data', []) y_data = series.get('y_data', []) # 收集数据标签位置信息 x_positions = [] y_positions = [] for i, (x_label, y_value) in enumerate(zip(x_data, y_data)): color = normalize_color(series.get('color') or colors[series_idx % len(colors)]) # 根据标签策略计算柱子位置 if should_deduplicate: # 分组柱状图：每个标签下有多个系列的柱子 group_index = unique_x_labels.index(x_label) if x_label in unique_x_labels else i group_center = self.margin['left'] + (group_index + 0.5) * group_width bar_offset = (series_idx - (num_series - 1) / 2) * bar_width bar_center_x = group_center + bar_offset bar_x = bar_center_x - bar_width / 2 else: # 连续柱状图：每个数据点都是独立的柱子 bar_center_x = self.margin['left'] + (bar_index + 0.5) * bar_group_width bar_x = bar_center_x - bar_width / 2 # 计算柱子高度和位置，支持负数 if y_range > 0: # 计算零线位置（基准线） zero_line_y = self.margin['top'] + self.plot_height - ((-y_min) / y_range) * self.plot_height # 计算柱子的顶部和底部位置 value_y = self.margin['top'] + self.plot_height - ((y_value - y_min) / y_range) * self.plot_height if y_value >= 0: # 正数：从零线向上 bar_y = value_y bar_height = zero_line_y - value_y else: # 负数：从零线向下 bar_y = zero_line_y bar_height = value_y - zero_line_y # 确保柱子至少有最小高度可见 bar_height = max(2, abs(bar_height)) else: bar_height = 5 bar_y = self.margin['top'] + self.plot_height - bar_height # 绘制柱子 rect = ET.SubElement(svg, 'rect', { 'x': str(bar_x), 'y': str(bar_y), 'width': str(bar_width), 'height': str(bar_height), 'fill': color, 'class': 'bar-rect' }) # 收集位置信息用于数据标签 x_positions.append(bar_center_x) y_positions.append(bar_y) # 使用新的通用方法显示数据标签，支持正负数 if self._should_show_data_labels(chart_data, series) and bar_height > 8: # 根据正负值调整标签位置 if y_value >= 0: # 正数：标签在柱子顶部上方 label_y = bar_y - 5 else: # 负数：标签在柱子底部下方 label_y = bar_y + bar_height + 15 self._create_data_label_element( svg, bar_x + bar_width / 2, label_y, str(int(y_value)) ) # 只在连续模式下更新bar_index if not should_deduplicate: bar_index += 1 # 渲染该系列的数据标签 if x_positions and y_positions: self._render_data_labels(svg, series, x_positions, y_positions, colors) # 渲染图例和注释 self._render_common_chart_elements(svg, chart_data, series_list, colors) return self._format_svg(svg) def _render_line_chart(self, chart_data: dict[str, Any]) -> str: """渲染折线图。""" title_style = chart_data.get('title_style') svg = self._create_svg_root(chart_data.get('title', ''), title_style=title_style) series_list = chart_data.get('series', []) self.current_series = series_list if not series_list: return self._format_svg(svg) # 获取所有数据点 all_x_labels = [] all_y_values = [] for series in series_list: x_data = series.get('x_data', []) y_data = series.get('y_data', []) all_x_labels.extend(x_data) all_y_values.extend(y_data) if not all_x_labels or not all_y_values: return self._format_svg(svg) unique_x_labels = list(dict.fromkeys(all_x_labels)) y_min = min(all_y_values) if all_y_values else 0 y_max = max(all_y_values) if all_y_values else 1 y_range = y_max - y_min if y_max != y_min else 1 # 绘制坐标轴 self._draw_axes(svg, unique_x_labels, y_min, y_max) # 绘制数据系列 colors = self._get_series_colors(chart_data) for series_idx, series in enumerate(series_list): color = normalize_color(colors[series_idx % len(colors)]) x_data = series.get('x_data', []) y_data = series.get('y_data', []) if len(x_data) < 2 or len(y_data) < 2: continue # 创建路径点 points = [] for x_label, y_value in zip(x_data, y_data): if x_label in unique_x_labels: x_pos = unique_x_labels.index(x_label) if len(unique_x_labels) == 1: x_coord = self.margin['left'] + self.plot_width / 2 else: x_coord = self.margin['left'] + x_pos * (self.plot_width / (len(unique_x_labels) - 1)) y_coord = self.margin['top'] + self.plot_height - (y_value - y_min) / y_range * self.plot_height points.append((x_coord, y_coord)) if len(points) >= 2: # 创建路径 path_data = f"M {points[0][0]} {points[0][1]}" for x, y in points[1:]: path_data += f" L {x} {y}" path = ET.SubElement(svg, 'path', { 'd': path_data, 'stroke': color, 'class': 'line-path' }) # 添加数据点 for x, y in points: circle = ET.SubElement(svg, 'circle', { 'cx': str(x), 'cy': str(y), 'r': '3', # 添加半径属性 'fill': color, 'class': 'line-point' }) # 渲染图例和注释 self._render_common_chart_elements(svg, chart_data, series_list, colors) return self._format_svg(svg) def _render_pie_chart(self, chart_data: dict[str, Any]) -> str: """渲染饼图。""" # 设置饼图标志，供图例位置计算使用 self._is_pie_chart = True # 饼图标题：优先使用图表标题，如果没有则使用系列名称 title = chart_data.get('title', '') if not title or title.startswith('Chart '): series_list = chart_data.get('series', []) if series_list and series_list[0].get('name'): title = series_list[0]['name'] # 饼图需要特殊的标题位置处理，先创建不带标题的SVG svg = self._create_svg_root("") series_list = chart_data.get('series', []) if not series_list or not series_list[0].get('y_data'): return self._format_svg(svg) series = series_list[0] # 饼图通常只有一个系列 labels = series.get('x_data', []) values = series.get('y_data', []) if not values: return self._format_svg(svg) # 计算饼图参数 total = sum(values) if total == 0: return self._format_svg(svg) # 为图例预留空间，饼图偏左放置 legend_width = 100 # 图例区域宽度 pie_area_width = self.width - legend_width center_x = pie_area_width // 2 # 调整垂直位置，考虑标题空间，避免过于偏上 title_height = 40 if chart_data.get('title') else 20 center_y = title_height + (self.height - title_height) // 2 radius = min(pie_area_width, self.height - title_height) // 2 - 30 # 优先使用从Excel提取的颜色 colors = [] if series.get('colors'): # 饼图的每个片段可能有不同颜色 colors = series['colors'] elif 'colors' in chart_data and chart_data['colors']: colors = chart_data['colors'] else: colors = DEFAULT_CHART_COLORS + ['#A5A5A5', '#70E000'] # 扩展颜色用于多系列饼图 # 确保有足够的不重复颜色用于所有片段 colors = ensure_distinct_colors(colors, len(values)) current_angle = 0 for i, (label, value) in enumerate(zip(labels, values)): angle = (value / total) * 360 color = normalize_color(colors[i % len(colors)]) # 计算扇形路径 start_angle_rad = current_angle * 3.14159 / 180 end_angle_rad = (current_angle + angle) * 3.14159 / 180 start_x = center_x + radius * math.cos(start_angle_rad) start_y = center_y + radius * math.sin(start_angle_rad) end_x = center_x + radius * math.cos(end_angle_rad) end_y = center_y + radius * math.sin(end_angle_rad) large_arc = 1 if angle > 180 else 0 path_data = f"M {center_x} {center_y} L {start_x} {start_y} A {radius} {radius} 0 {large_arc} 1 {end_x} {end_y} Z" path = ET.SubElement(svg, 'path', { 'd': path_data, 'fill': color, 'class': 'pie-slice' }) # 添加数据标签（如果启用） series_data = series_list[0] if series_list else {} data_labels = series_data.get('data_labels', {}) if data_labels.get('enabled', True): # 饼图默认显示标签 mid_angle = (current_angle + angle / 2) * 3.14159 / 180 label_x = center_x + (radius + 20) * math.cos(mid_angle) label_y = center_y + (radius + 20) * math.sin(mid_angle) # 构建标签文本 label_text = "" if data_labels.get('show_category_name', True): # 饼图默认显示分类名 label_text = label if data_labels.get('show_value', False): if label_text: label_text += f": {value}" else: label_text = str(value) if data_labels.get('show_percent', True): # 饼图默认显示百分比 percentage = (value / total) * 100 if label_text: label_text += f" ({percentage:.1f}%)" else: label_text = f"{percentage:.1f}%" # 如果没有设置任何显示选项，默认显示标签和百分比 if not label_text: percentage = (value / total) * 100 label_text = f"{label} ({percentage:.1f}%)" text = ET.SubElement(svg, 'text', { 'x': str(label_x), 'y': str(label_y), 'text-anchor': 'middle', 'class': 'axis-label' }) text.text = label_text current_angle += angle # 渲染图例和注释 - 饼图使用分类标签作为图例 pie_legend_series = [] for i, label in enumerate(labels): pie_legend_series.append({'name': label}) # 使用分类标签创建图例，而不是系列名称 show_legend = chart_data.get('legend', {}).get('enabled', True) if show_legend: legend_style = chart_data.get('legend_style') self._draw_legend(svg, pie_legend_series, colors, legend_style=legend_style) # 添加饼图标题（在饼图上方） if title: title_y = 20 # 标题位置在饼图上方 title_attrs = { 'x': str(self.width // 2), 'y': str(title_y), 'text-anchor': 'middle', 'class': 'chart-title' } title_elem = ET.SubElement(svg, 'text', title_attrs) title_elem.text = title # 渲染注释 self._render_annotations(svg, chart_data) # 清除饼图标志 self._is_pie_chart = False return self._format_svg(svg) def _render_area_chart(self, chart_data: dict[str, Any]) -> str: """渲染面积图。""" svg = self._create_svg_root(chart_data.get('title', '')) series_list = chart_data.get('series', []) if not series_list: return self._format_svg(svg) # 获取所有数据点 all_x_labels = [] all_y_values = [] for series in series_list: x_data = series.get('x_data', []) y_data = series.get('y_data', []) all_x_labels.extend(x_data) all_y_values.extend(y_data) if not all_x_labels or not all_y_values: return self._format_svg(svg) unique_x_labels = list(dict.fromkeys(all_x_labels)) y_min = min(all_y_values) if all_y_values else 0 y_max = max(all_y_values) if all_y_values else 1 y_range = y_max - y_min if y_max != y_min else 1 # 绘制坐标轴 self._draw_axes(svg, unique_x_labels, y_min, y_max) # 绘制数据系列 colors = self._get_series_colors(chart_data) baseline_y = self.margin['top'] + self.plot_height # 基线位置 for series_idx, series in enumerate(series_list): color = normalize_color(colors[series_idx % len(colors)]) x_data = series.get('x_data', []) y_data = series.get('y_data', []) if len(x_data) < 2 or len(y_data) < 2: continue # 创建面积路径点 points = [] for x_label, y_value in zip(x_data, y_data): if x_label in unique_x_labels: x_pos = unique_x_labels.index(x_label) if len(unique_x_labels) == 1: x_coord = self.margin['left'] + self.plot_width / 2 else: x_coord = self.margin['left'] + x_pos * (self.plot_width / (len(unique_x_labels) - 1)) y_coord = self.margin['top'] + self.plot_height - (y_value - y_min) / y_range * self.plot_height points.append((x_coord, y_coord)) if len(points) >= 2: # 创建面积路径（从基线开始，到数据点，再回到基线） path_data = f"M {points[0][0]} {baseline_y}" # 从基线开始 path_data += f" L {points[0][0]} {points[0][1]}" # 到第一个数据点 # 连接所有数据点 for x, y in points[1:]: path_data += f" L {x} {y}" # 回到基线闭合路径 path_data += f" L {points[-1][0]} {baseline_y} Z" # 绘制填充区域 area_path = ET.SubElement(svg, 'path', { 'd': path_data, 'fill': color, 'fill-opacity': '0.3', 'stroke': color, 'stroke-width': '2', 'class': 'area-path' }) # 添加数据点 for x, y in points: circle = ET.SubElement(svg, 'circle', { 'cx': str(x), 'cy': str(y), 'r': '3', 'fill': color, 'class': 'area-point' }) # 渲染图例和注释 self._render_common_chart_elements(svg, chart_data, series_list, colors) return self._format_svg(svg) def _render_image_chart(self, chart_data: dict[str, Any]) -> str: """渲染图像（支持图片和占位符）。""" # 检查是否有有效的图片数据 - 支持多种数据结构 img_data = None # 尝试从不同的位置获取图片数据 if isinstance(chart_data, dict): # 方式1：直接从chart_data中获取 img_data = chart_data.get('image_data') # 方式2：从嵌套字典中获取（这是xlsx_parser存储的方式） if img_data and isinstance(img_data, dict): # 如果img_data是字典，尝试从中获取真正的图片数据 img_data = img_data.get('image_data') elif not img_data and 'image_data' in chart_data: nested_data = chart_data['image_data'] if isinstance(nested_data, dict): img_data = nested_data.get('image_data') else: img_data = nested_data if img_data and isinstance(img_data, bytes) and len(img_data) > 10: # 有真实图片数据，渲染为HTML img标签而不是SVG import base64 # 检测图片格式 img_format = 'png' # 默认 if img_data.startswith(b'\x89PNG'): img_format = 'png' elif img_data.startswith(b'\xff\xd8\xff'): img_format = 'jpeg' elif img_data.startswith(b'GIF'): img_format = 'gif' # 转换为Base64 img_base64 = base64.b64encode(img_data).decode('utf-8') data_url = f"data:image/{img_format};base64,{img_base64}" # 获取标题 title = chart_data.get('title', 'Excel Image') # 返回HTML img标签 - 正常显示，无背景 return f''' <div class="chart-svg-wrapper" style="background: none; padding: 0; min-height: auto;"> <h3 style="margin: 0 0 10px 0; font-size: 16px; color: #333;">{title}</h3> <img src="{data_url}" alt="{title}" style="max-width: 100%; height: auto; border-radius: 4px; background: transparent;" /> </div> ''' # 没有有效图片数据，渲染占位符SVG svg = self._create_svg_root(chart_data.get('title', 'Image')) # 绘制图像占位符（移除不必要的背景框） placeholder_rect = ET.SubElement(svg, 'rect', { 'x': str(self.margin['left']), 'y': str(self.margin['top']), 'width': str(self.plot_width), 'height': str(self.plot_height), 'fill': 'none', 'stroke': 'none' }) # 添加图像图标（简化的SVG图标） icon_size = min(self.plot_width, self.plot_height) * 0.3 icon_x = self.margin['left'] + (self.plot_width - icon_size) / 2 icon_y = self.margin['top'] + (self.plot_height - icon_size) / 2 # 图像图标框（简化样式） icon_rect = ET.SubElement(svg, 'rect', { 'x': str(icon_x), 'y': str(icon_y), 'width': str(icon_size), 'height': str(icon_size * 0.7), 'fill': 'none', 'stroke': '#9ca3af', 'stroke-width': '1.5', 'rx': '6' }) # 图像图标中的"山"形状（优化颜色） mountain_points = f"{icon_x + icon_size * 0.1},{icon_y + icon_size * 0.5} {icon_x + icon_size * 0.3},{icon_y + icon_size * 0.3} {icon_x + icon_size * 0.5},{icon_y + icon_size * 0.45} {icon_x + icon_size * 0.7},{icon_y + icon_size * 0.25} {icon_x + icon_size * 0.9},{icon_y + icon_size * 0.5}" mountain = ET.SubElement(svg, 'polyline', { 'points': mountain_points, 'fill': 'none', 'stroke': '#9ca3af', 'stroke-width': '1.5', 'stroke-linejoin': 'round' }) # 图像图标中的"太阳"（优化颜色和大小） sun_cx = icon_x + icon_size * 0.75 sun_cy = icon_y + icon_size * 0.2 sun = ET.SubElement(svg, 'circle', { 'cx': str(sun_cx), 'cy': str(sun_cy), 'r': str(icon_size * 0.06), 'fill': '#f59e0b' }) # 添加文本说明（优化颜色） text_y = icon_y + icon_size * 0.85 text = ET.SubElement(svg, 'text', { 'x': str(self.width // 2), 'y': str(text_y), 'text-anchor': 'middle', 'class': 'axis-label', 'fill': '#9ca3af' }) text.text = "图像内容" # 如果有图像数据，显示额外信息（优化颜色） if img_data: info_text = ET.SubElement(svg, 'text', { 'x': str(self.width // 2), 'y': str(text_y + 20), 'text-anchor': 'middle', 'class': 'axis-label', 'fill': '#9ca3af', 'font-size': '10' }) info_text.text = "包含嵌入图像数据" return self._format_svg(svg) def _render_fallback_chart(self, chart_data: dict[str, Any]) -> str: """渲染不支持类型的占位图表。""" svg = self._create_svg_root(chart_data.get('title', 'Unsupported Chart')) # 绘制占位符 rect = ET.SubElement(svg, 'rect', { 'x': str(self.margin['left']), 'y': str(self.margin['top']), 'width': str(self.plot_width), 'height': str(self.plot_height), 'fill': '#f0f0f0', 'stroke': '#ccc', 'stroke-dasharray': '5,5' }) text = ET.SubElement(svg, 'text', { 'x': str(self.width // 2), 'y': str(self.height // 2), 'text-anchor': 'middle', 'class': 'axis-label' }) text.text = f"Chart type '{chart_data.get('type', 'unknown')}' not supported" return self._format_svg(svg) def _draw_x_axis_labels(self, svg: ET.Element, x_labels: list[str]): """绘制X轴标签。""" # 使用传入的唯一标签，而不是重复的标签 unique_labels = x_labels # 计算标签组的宽度（每个标签对应多个系列的柱子） if not unique_labels: return label_group_width = self.plot_width / len(unique_labels) series_count = len(self.current_series) # X轴标签 - 每个标签居中于对应的柱子组 for i, label in enumerate(unique_labels): # 计算标签位置（居中于柱子组） group_center = self.margin['left'] + (i + 0.5) * label_group_width text = ET.SubElement(svg, 'text', { 'x': str(group_center), 'y': str(self.margin['top'] + self.plot_height + 15), 'text-anchor': 'middle', 'class': 'axis-label', 'fill': '#666', # 确保标签可见 'font-size': '10px' # 稍微减小字体，避免重叠 }) text.text = str(label) def _draw_axes(self, svg: ET.Element, x_labels: list[str], y_min: float, y_max: float): """绘制坐标轴、网格线和标签。""" # X轴 x_axis = ET.SubElement(svg, 'line', { 'x1': str(self.margin['left']), 'y1': str(self.margin['top'] + self.plot_height), 'x2': str(self.margin['left'] + self.plot_width), 'y2': str(self.margin['top'] + self.plot_height), 'class': 'axis-line' }) # Y轴 y_axis = ET.SubElement(svg, 'line', { 'x1': str(self.margin['left']), 'y1': str(self.margin['top']), 'x2': str(self.margin['left']), 'y2': str(self.margin['top'] + self.plot_height), 'class': 'axis-line' }) # 复用X轴标签绘制 self._draw_x_axis_labels(svg, x_labels) # Y轴标签 y_range = y_max - y_min if y_max != y_min else 1 tick_count = 5 for i in range(tick_count + 1): y_value = y_min + i * y_range / tick_count y_pos = self.margin['top'] + self.plot_height - i * (self.plot_height / tick_count) # 网格线 grid_line = ET.SubElement(svg, 'line', { 'x1': str(self.margin['left']), 'y1': str(y_pos), 'x2': str(self.margin['left'] + self.plot_width), 'y2': str(y_pos), 'class': 'grid-line' }) # 标签 text = ET.SubElement(svg, 'text', { 'x': str(self.margin['left'] - 10), 'y': str(y_pos), 'text-anchor': 'end', 'alignment-baseline': 'middle', 'class': 'axis-label' }) text.text = f"{y_value:.1f}" def _draw_legend(self, svg: ET.Element, series_list: list[dict], colors: list[str], legend_style: dict[str, Any] | None = None): """ 绘制图例。 参数：svg根元素，series_list系列列表，colors颜色列表，legend_style字体样式 """ # 图例位置计算：根据图表类型调整 if hasattr(self, '_is_pie_chart') and self._is_pie_chart: # 饼图：图例放在右侧 legend_width = 100 # 与饼图布局中的legend_width保持一致 pie_area_width = self.width - legend_width legend_x = pie_area_width + 10 # 饼图区域右侧，留10px间距 legend_y = self.height // 2 - (len(series_list) * 20) // 2 # 垂直居中 else: # 其他图表：图例放在右上角 legend_text_width = 80 legend_x = max(10, self.width - legend_text_width - 20) legend_y = self.margin['top'] # 准备基础样式 base_style = {'class': 'legend-item'} # 合并外部传入的样式 if legend_style: style_str = "" if 'font_family' in legend_style and legend_style['font_family']: style_str += f"font-family: '{legend_style['font_family']}';" if 'font_size' in legend_style and legend_style['font_size']: style_str += f"font-size: {legend_style['font_size']}px;" if 'color' in legend_style and legend_style['color']: style_str += f"fill: {legend_style['color']};" if style_str: base_style['style'] = style_str for i, series in enumerate(series_list): color = normalize_color(colors[i % len(colors)]) series_name = series.get('name', f'Series {i + 1}') # 图例色块 ET.SubElement(svg, 'rect', { 'x': str(legend_x), 'y': str(legend_y + i * 20), 'width': '12', 'height': '12', 'fill': color }) # 图例文字的属性 text_attrs = { 'x': str(legend_x + 16), 'y': str(legend_y + i * 20 + 9), **base_style } text = ET.SubElement(svg, 'text', text_attrs) text.text = series_name def _render_data_labels(self, svg: ET.Element, series_data: dict, x_positions: list, y_positions: list, colors: list | None = None) -> None: """ 渲染数据标签。 """ data_labels = series_data.get('data_labels', {}) if not data_labels.get('enabled', False): return y_data = series_data.get('y_data', []) x_data = series_data.get('x_data', []) for i, (x_pos, y_pos) in enumerate(zip(x_positions, y_positions)): if i >= len(y_data): break label_text = "" # 根据设置决定显示什么内容 if data_labels.get('show_value', False) and i < len(y_data): label_text = str(y_data[i]) if data_labels.get('show_category_name', False) and i < len(x_data): if label_text: label_text += f" ({x_data[i]})" else: label_text = str(x_data[i]) if data_labels.get('show_series_name', False): series_name = series_data.get('name', '') if series_name: if label_text: label_text = f"{series_name}: {label_text}" else: label_text = series_name if data_labels.get('show_percent', False): # 计算百分比 if 'total' in series_data: # 使用预计算的总数（主要用于饼图） total = series_data['total'] else: # 计算当前系列的总数 total = sum(y_data) if y_data else 0 if total > 0 and i < len(y_data): percent = (y_data[i] / total) * 100 if label_text: label_text += f" ({percent:.1f}%)" else: label_text = f"{percent:.1f}%" # 如果没有设置任何显示选项，但数据标签已启用，默认显示值 if not label_text and data_labels.get('enabled', False) and i < len(y_data): # 检查是否有任何显示选项被设置 has_any_show_option = any([ data_labels.get('show_value', False), data_labels.get('show_category_name', False), data_labels.get('show_series_name', False), data_labels.get('show_percent', False) ]) # 如果没有明确的显示选项，默认显示值 if not has_any_show_option: label_text = str(y_data[i]) if label_text: # 调整标签位置，避免与数据点重叠 label_y = y_pos - 5 # 标签显示在数据点上方 text_elem = ET.SubElement(svg, 'text', { 'x': str(x_pos), 'y': str(label_y), 'text-anchor': 'middle', 'font-family': 'Arial, sans-serif', 'font-size': '10', 'fill': '#333' }) text_elem.text = label_text def _render_annotations(self, svg: ET.Element, chart_data: dict) -> None: """ 渲染图表注释（不含标题）。 """ annotations = chart_data.get('annotations', []) if not annotations: return for annotation in annotations: annotation_type = annotation.get('type', '') text = annotation.get('text', '') position = annotation.get('position', '') if not text: continue # 跳过标题类型的注释，因为标题已经在_create_svg_root中处理了 if annotation_type == 'title': continue # 根据注释类型和位置确定坐标 x, y = self._get_annotation_position(annotation_type, position) text_elem = ET.SubElement(svg, 'text', { 'x': str(x), 'y': str(y), 'text-anchor': 'middle', 'font-family': 'Arial, sans-serif', 'font-size': '11', 'font-weight': 'normal', 'fill': '#666' }) text_elem.text = text def _get_annotation_position(self, annotation_type: str, position: str) -> tuple[int, int]: """ 获取注释坐标。 """ if annotation_type == 'title' and position == 'top': return (self.width // 2, 15) elif annotation_type == 'axis_title': if position == 'bottom': return (self.width // 2, self.height - 5) elif position == 'left': return (15, self.height // 2) elif position == 'right': return (self.width - 15, self.height // 2) # 默认位置 return (self.width // 2, self.height // 2) def _format_svg(self, svg: ET.Element) -> str: """格式化SVG为字符串。""" rough_string = ET.tostring(svg, encoding='unicode') reparsed = minidom.parseString(rough_string) return reparsed.toprettyxml(indent=" ")[22:] # 去掉XML声明 def _should_deduplicate_x_labels(self, series_list: list) -> bool: """ 判断是否去重X轴标签。 """ if not series_list or len(series_list) <= 1: return True # 单系列或无系列，默认去重 # 获取第一个系列的X轴数据作为基准 first_x_data = series_list[0].get('x_data', []) # 检查其他系列的X轴数据是否与第一个系列相同 for series in series_list[1:]: x_data = series.get('x_data', []) if x_data != first_x_data: return False # 发现不同的X轴数据，不应该去重 return True # 所有系列的X轴数据都相同，应该去重