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Optimize the Method of Automatically Repairing Charts in PDF

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马一丁
2025-11-21 05:51:51 +08:00
parent 373b85a3f8
commit 452640f8c5
4 changed files with 303 additions and 57 deletions
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ReportEngine/renderers/chart_to_svg.py
+116 -28
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@@ -16,6 +16,7 @@ from __future__ import annotations
import base64
import io
import re
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
from loguru import logger
@@ -23,6 +24,7 @@ try:
import matplotlib
matplotlib.use('Agg') # 使用非GUI后端
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.font_manager as fm
from matplotlib.patches import Wedge, Rectangle
import numpy as np
@@ -70,6 +72,15 @@ class ChartToSVGConverter:
'var(--color-secondary)': '#95A5A6', # 浅灰色
}
# 支持解析 rgba(var(--color-primary-rgb), 0.5) 这类格式的兜底映射
CSS_VAR_RGB_MAP = {
'color-primary-rgb': (52, 152, 219),
'color-tone-up-rgb': (80, 200, 120),
'color-tone-down-rgb': (232, 93, 117),
'color-accent-positive-rgb': (80, 200, 120),
'color-accent-neutral-rgb': (149, 165, 166),
}
def __init__(self, font_path: Optional[str] = None):
"""
初始化转换器
@@ -192,6 +203,25 @@ class ChartToSVGConverter:
color = color.strip()
# 处理 rgba(var(--color-primary-rgb), 0.5) / rgb(var(--color-primary-rgb))
var_rgba_pattern = r'rgba?\(var\(--([\w-]+)\)\s*(?:,\s*([\d.]+))?\)'
match = re.match(var_rgba_pattern, color)
if match:
var_name, alpha_str = match.groups()
rgb_tuple = self.CSS_VAR_RGB_MAP.get(var_name)
# 兼容缺少 -rgb 后缀的写法
if not rgb_tuple:
if var_name.endswith('-rgb'):
rgb_tuple = self.CSS_VAR_RGB_MAP.get(var_name[:-4])
else:
rgb_tuple = self.CSS_VAR_RGB_MAP.get(f"{var_name}-rgb")
if rgb_tuple:
r, g, b = rgb_tuple
alpha = float(alpha_str) if alpha_str is not None else 1.0
return (r / 255, g / 255, b / 255, alpha)
# 【增强】处理CSS变量，例如 var(--color-accent)
# 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
if color.startswith('var('):
@@ -288,10 +318,17 @@ class ChartToSVGConverter:
- 线条样式（tension曲线平滑）
"""
try:
labels = data.get('labels', [])
datasets = data.get('datasets', [])
labels = data.get('labels') or []
datasets = data.get('datasets') or []
if not labels or not datasets:
has_object_points = any(
isinstance(ds, dict)
and isinstance(ds.get('data'), list)
and any(isinstance(pt, dict) and ('x' in pt or 'y' in pt) for pt in ds.get('data'))
for ds in datasets
)
if (not datasets) or ((not labels) and not has_object_points):
return None
# 收集所有唯一的yAxisID
@@ -312,6 +349,7 @@ class ChartToSVGConverter:
title = props.get('title')
options = props.get('options', {})
scales = options.get('scales', {})
x_tick_labels = list(labels) if isinstance(labels, list) else []
# 创建图表和多个y轴
fig, ax1 = plt.subplots(figsize=(width/dpi, height/dpi), dpi=dpi)
@@ -376,41 +414,90 @@ class ChartToSVGConverter:
# 选择对应的坐标轴
ax = axes.get(y_axis_id, ax1)
# 绘制折线
x_data = range(len(labels))
is_object_data = isinstance(dataset_data, list) and any(
isinstance(point, dict) and ('x' in point or 'y' in point)
for point in dataset_data
)
# 根据tension值决定是否平滑
if tension > 0 and SCIPY_AVAILABLE:
# 使用样条插值平滑曲线（需要scipy）
if len(dataset_data) >= 4: # 至少需要4个点才能平滑
if is_object_data:
x_data = []
y_data = []
annotations = []
for idx, point in enumerate(dataset_data):
if not isinstance(point, dict):
continue
label_text = str(point.get('x', f"点{idx + 1}"))
if len(x_tick_labels) < len(dataset_data):
x_tick_labels.append(label_text)
x_data.append(len(x_data))
y_val = point.get('y', 0)
try:
x_smooth = np.linspace(0, len(labels)-1, len(labels)*3)
spl = make_interp_spline(x_data, dataset_data, k=min(3, len(dataset_data)-1))
y_smooth = spl(x_smooth)
line, = ax.plot(x_smooth, y_smooth, label=label, color=border_color, linewidth=2)
y_val = float(y_val)
except (TypeError, ValueError):
y_val = 0
y_data.append(y_val)
annotations.append(point.get('event'))
# 如果需要填充（使用极低透明度避免遮挡）
if fill:
ax.fill_between(x_smooth, y_smooth, alpha=0.08, color=background_color)
except:
# 如果平滑失败，使用普通折线
if not x_data:
continue
line, = ax.plot(x_data, y_data, marker='o', label=label,
color=border_color, linewidth=2, markersize=6)
if fill:
ax.fill_between(x_data, y_data, alpha=0.08, color=background_color)
for pos, y_val, text in zip(x_data, y_data, annotations):
if text:
ax.annotate(
text,
(pos, y_val),
textcoords='offset points',
xytext=(0, 8),
ha='center',
fontsize=8,
rotation=20
)
else:
# 绘制折线
x_data = range(len(labels))
# 根据tension值决定是否平滑
if tension > 0 and SCIPY_AVAILABLE:
# 使用样条插值平滑曲线（需要scipy）
if len(dataset_data) >= 4: # 至少需要4个点才能平滑
try:
x_smooth = np.linspace(0, len(labels)-1, len(labels)*3)
spl = make_interp_spline(x_data, dataset_data, k=min(3, len(dataset_data)-1))
y_smooth = spl(x_smooth)
line, = ax.plot(x_smooth, y_smooth, label=label, color=border_color, linewidth=2)
# 如果需要填充（使用极低透明度避免遮挡）
if fill:
ax.fill_between(x_smooth, y_smooth, alpha=0.08, color=background_color)
except:
# 如果平滑失败，使用普通折线
line, = ax.plot(x_data, dataset_data, marker='o', label=label,
color=border_color, linewidth=2, markersize=6)
if fill:
ax.fill_between(x_data, dataset_data, alpha=0.08, color=background_color)
else:
line, = ax.plot(x_data, dataset_data, marker='o', label=label,
color=border_color, linewidth=2, markersize=6)
if fill:
ax.fill_between(x_data, dataset_data, alpha=0.08, color=background_color)
else:
# 直线连接（tension=0或scipy不可用）
line, = ax.plot(x_data, dataset_data, marker='o', label=label,
color=border_color, linewidth=2, markersize=6)
# 如果需要填充（使用极低透明度避免遮挡）
if fill:
ax.fill_between(x_data, dataset_data, alpha=0.08, color=background_color)
else:
# 直线连接（tension=0或scipy不可用）
line, = ax.plot(x_data, dataset_data, marker='o', label=label,
color=border_color, linewidth=2, markersize=6)
# 如果需要填充（使用极低透明度避免遮挡）
if fill:
ax.fill_between(x_data, dataset_data, alpha=0.08, color=background_color)
# 记录这条线属于哪个轴
axis_lines[y_axis_id].append(line)
@@ -430,8 +517,9 @@ class ChartToSVGConverter:
legend_labels.append(label)
# 设置x轴标签
ax1.set_xticks(range(len(labels)))
ax1.set_xticklabels(labels, rotation=45, ha='right')
if x_tick_labels:
ax1.set_xticks(range(len(x_tick_labels)))
ax1.set_xticklabels(x_tick_labels, rotation=45, ha='right')
# 设置y轴标签和标题
for y_axis_id, ax in axes.items():