Python matplotlib 图表绘制

又见苍岚

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Yiwei Zhang · 2025-08-19 · via 又见苍岚

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from datetime import datetime
import numpy as np
import os
from matplotlib import patheffects
from matplotlib.ticker import FuncFormatter
import matplotlib.font_manager as fm
import pathlib  
import warnings
from io import BytesIO  # 新增用于内存操作
import PIL.Image  # 新增用于图像处理
from loguru import logger
import vvdutils as vvclass ChartPainter:
    def __init__(self, chart, x_axis, y_axis, series, grid, legend, time={}, theme_settings={}, radar_axis={}):
        self.chart = chart
        self.x_axis = x_axis
        self.y_axis = y_axis
        self.series = series
        self.grid = grid
        self.legend = legend
        self.time = time
        self.theme_settings = theme_settings
        self.radar_axis = radar_axis
        # 1. 设置支持中文的字体
        self._load_custom_font()
    def paint(self, show=False):
        """绘制图表"""
        chart_type = self.chart.get('type', 'line').lower()
                if chart_type == 'line':
            return self.line(show)
        elif chart_type == 'bar':
            return self.bar(show)
        elif chart_type == 'combo':
            return self.combo(show)
        elif chart_type == 'stacked_bar':
            return self.stacked_bar(show)
        elif chart_type == 'radar':
            return self.radar(show)
        else:
            raise ValueError(f"Unsupported chart type: {chart_type}")
    def _load_custom_font(self):
        """手动加载自定义字体文件"""
        font_path = str(pathlib.Path(__file__).parent / 'fonts' / 'wqy-microhei.ttc')  # 假设字体文件在lib/fonts目录下
        try:
            # 检查文件是否存在
            if not os.path.exists(font_path):
                warnings.warn(f"字体文件不存在: {font_path}")
                return
            # 获取字体属性
            font_prop = fm.FontProperties(fname=font_path)
            font_name = font_prop.get_name()
                        # 将字体添加到Matplotlib字体管理器
            fm.fontManager.addfont(font_path)
                        # 设置全局字体
            plt.rcParams['font.family'] = font_name
            plt.rcParams['axes.unicode_minus'] = False
            logger.debug(f" @@ 成功加载自定义字体: {font_name}")
                    except Exception as e:
            warnings.warn(f"加载字体失败: {str(e)}")
    def _setup_common_styles(self, ax):
        """通用样式设置（添加中文支持）"""
        # 网格样式
        if self.grid['visible']:
            grid_style = self.grid['style']
            linestyle = '-' if grid_style == '-' else '--' if grid_style == '--' else ':' if grid_style == ':' else '-.'
            ax.grid(True, linestyle=linestyle, alpha=0.3, color=self.theme_settings.get('grid_color', '#493D3D'))
                # 坐标轴标签和标题 - 添加中文字体支持
        ax.set_xlabel(self.x_axis['label'], fontsize=12, 
                     color=self.theme_settings.get('axis_label_color', "#493D3D"), labelpad=10)
        ax.set_ylabel(self.y_axis['label'], fontsize=12, 
                     color=self.theme_settings.get('axis_label_color', "#493D3D"), labelpad=10)
        ax.set_title(self.chart['title'], fontsize=16, 
                    color=self.theme_settings.get('axis_label_color', "#493D3D"), pad=20)
                # 刻度样式 - 确保中文显示
        ax.tick_params(axis='x', colors=self.theme_settings.get('axis_label_color', "#493D3D"))
        ax.tick_params(axis='y', colors=self.theme_settings.get('axis_label_color', "#493D3D"))
                # 边框样式
        for spine in ax.spines.values():
            spine.set_color(self.theme_settings.get('spine_color', 'white'))
            spine.set_linewidth(1.5)
                # 图例样式 - 支持中文
        legend_pos = self.legend['position']
        legend = ax.legend(
            facecolor=self.theme_settings.get('legend_background_color', '#3d3d3d'), 
            edgecolor='none',
            framealpha=0.8,
            loc=legend_pos,
            bbox_to_anchor=(1, 1) if legend_pos == 'upper right' else None,
            ncol=len(self.series) if self.legend['direction'] == 'horizontal' else 1,
            prop={'family': plt.rcParams['font.family']}  # 确保图例使用中文字体
        )
        for text in legend.get_texts():
            text.set_color(self.theme_settings.get('legend_text_color', '#FFFFFF'))
                # 添加时间范围页脚 - 支持中文
        self._add_time_area()
    def _set_figure_size(self):
        """设置图表大小"""
        width, height = self.chart['size']
        temp_width = 16
        height_size = max(3, temp_width / width * height)
                # 创建新的图形
        fig = plt.figure(figsize=(temp_width, height_size), 
                         facecolor=self.theme_settings.get('facecolor', "#FFFFFF"))
        fig.dpi = width / temp_width
        return fig
    def _figure_to_numpy(self, fig, show=False):
        """将matplotlib图形转换为NumPy数组"""
        # 创建一个内存缓冲区
        buf = BytesIO()
                # 保存图像到缓冲区
        fig.savefig(buf, format='png', dpi=fig.dpi, pad_inches=0.1)
                # 将缓冲区指针重置到开始位置
        buf.seek(0)
                # 使用PIL打开图像并转换为NumPy数组
        img = PIL.Image.open(buf)
        img_array = np.array(img)
                # 关闭缓冲区
        buf.close()
                # 关闭图形释放内存
        plt.close(fig)
                img_array = img_array[:, :, :3]  # 去掉alpha通道
        logger.info(f" @@ 图像定义尺寸： {self.chart['size']}，实际尺寸： {img_array.shape}")
        resized_array = vv.image_resize(img_array, self.chart['size'])
        if show:
            vv.PIS(resized_array)
        return resized_array
    def line(self, show=False):
        """绘制折线图，返回NumPy数组图像"""
        fig = self._set_figure_size()
        ax = fig.add_subplot(111)
        ax.set_facecolor(self.theme_settings.get('axes_facecolor', "#f1f1f1"))
                # 处理日期型x轴
        x = self._process_x_axis()
                for series_data in self.series:
            # 绘制折线
            line = ax.plot(x, series_data['data'], 
                         label=series_data['name'], 
                         color=series_data['color'],
                         linewidth=3,
                         marker='o',
                         markersize=8,
                         markerfacecolor=self.theme_settings.get('marker_facecolor', "#FFFFFF"),
                         markeredgewidth=2)
                        # 添加数据标签
            self._add_data_labels(ax, x, series_data)
                # 设置y轴范围
        if 'range' in self.y_axis:
            ax.set_ylim(self.y_axis['range'][0], self.y_axis['range'][1])
                self._setup_common_styles(ax)
        self._add_glow_effect(ax)
        plt.tight_layout()
                # 返回NumPy数组图像
        return self._figure_to_numpy(fig, show)
    def bar(self, show=False):
        """绘制柱状图，返回NumPy数组图像"""
        fig = self._set_figure_size()
        ax = fig.add_subplot(111)
        ax.set_facecolor(self.theme_settings.get('axes_facecolor', "#f1f1f1"))
                # 处理日期型x轴
        x = self._process_x_axis()
        x_pos = np.arange(len(x))
                # 计算柱宽和位置（分组柱状图）
        bar_width = 0.8 / len(self.series)
        offsets = np.linspace(-0.4 + bar_width/2, 0.4 - bar_width/2, len(self.series))
                for idx, series_data in enumerate(self.series):
            # 绘制柱状图
            bars = ax.bar(x_pos + offsets[idx], series_data['data'], 
                         width=bar_width,
                         label=series_data['name'],
                         color=series_data['color'],
                         edgecolor=self.theme_settings.get('bar_edgecolor', 'white'),
                         linewidth=1,
                         alpha=0.9)
                        # 添加数据标签
            for bar in bars:
                height = bar.get_height()
                ax.text(bar.get_x() + bar.get_width()/2., height,
                       f'{height:.0f}', 
                       ha='center', va='bottom',
                       color=self.theme_settings.get('text_color', '#333333'),
                       fontsize=9)
                # 设置x轴刻度和范围
        ax.set_xticks(x_pos)
        ax.set_xticklabels(x)
        if self.x_axis['type'] == 'date':
            plt.xticks(rotation=45, ha='right')
                # 设置y轴范围
        if 'range' in self.y_axis:
            ax.set_ylim(self.y_axis['range'][0], self.y_axis['range'][1])
        else:
            # 自动调整y轴上限
            max_val = max(max(s['data']) for s in self.series)
            ax.set_ylim(0, max_val * 1.15)
                # 添加千分位分隔符
        ax.yaxis.set_major_formatter(FuncFormatter(lambda x, pos: f'{x:,.0f}'))
                self._setup_common_styles(ax)
        plt.tight_layout()
                # 返回NumPy数组图像
        return self._figure_to_numpy(fig, show)
    def _process_x_axis(self):
        """处理x轴数据（日期/字符串）"""
        if self.x_axis['type'] == 'date':
            return [datetime.strptime(d, '%Y-%m-%d') for d in self.x_axis['categories']]
        return self.x_axis['categories']
    def _add_data_labels(self, ax, x, series_data):
        """添加数据标签"""
        for i, (xi, yi) in enumerate(zip(x, series_data['data'])):
            ax.text(xi, yi+2, f'{yi:.0f}', 
                   color=self.theme_settings.get('text_color', "#200606"), 
                   ha='center', 
                   va='bottom',
                   fontsize=9,
                   bbox=dict(facecolor=series_data['color'], alpha=0.7, 
                            edgecolor='none', boxstyle='round,pad=0.2'))
    def _add_glow_effect(self, ax):
        """添加发光效果"""
        for line in ax.lines:
            line.set_path_effects([
                patheffects.Stroke(linewidth=5, foreground=line.get_color(), alpha=0.5),
                patheffects.Normal()
            ])
    def _add_time_area(self):
        # 添加时间范围页脚 - 支持中文
        if 'start_time' in self.time:
            time_text = f"数据范围: {self.time['start_time']} 至 {self.time['end_time']}"
            plt.figtext(0.002 * len(time_text), 0.02, time_text, 
                       ha='center', 
                       fontsize=5, 
                       color=self.theme_settings.get('text_color', '#493D3D'),
                       bbox=dict(facecolor=self.theme_settings.get('footer_background', '#3d3d3d'), 
                               alpha=0.1, edgecolor='none'))
    def combo(self, show=False):
        """绘制组合图表（柱状图+折线图），返回NumPy数组图像"""
        fig = self._set_figure_size()
        ax = fig.add_subplot(111)
        ax.set_facecolor(self.theme_settings.get('axes_facecolor', "#f1f1f1"))
                # 处理日期型x轴
        x = self._process_x_axis()
        x_pos = np.arange(len(x))
                # 分离柱状图和折线图系列
        bar_series = [s for s in self.series if s['type'] == 'bar']
        line_series = [s for s in self.series if s['type'] == 'line']
                # 绘制柱状图部分
        if bar_series:
            bar_width = 0.8 / len(bar_series)
            offsets = np.linspace(-0.4 + bar_width/2, 0.4 - bar_width/2, len(bar_series))
                        for idx, series_data in enumerate(bar_series):
                bars = ax.bar(x_pos + offsets[idx], series_data['data'], 
                            width=bar_width,
                            label=series_data['name'],
                            color=series_data['color'],
                            edgecolor=self.theme_settings.get('bar_edgecolor', 'white'),
                            linewidth=1,
                            alpha=series_data.get('alpha', 0.9))
                                # 添加数据标签
                for bar in bars:
                    height = bar.get_height()
                    ax.text(bar.get_x() + bar.get_width()/2., height,
                        f'{height:.0f}', 
                        ha='center', va='bottom',
                        color=self.theme_settings.get('text_color', '#333333'),
                        fontsize=9)
                # 绘制折线图部分（使用次坐标轴）
        if line_series:
            ax2 = ax.twinx()
            for series_data in line_series:
                line = ax2.plot(x_pos, series_data['data'], 
                            label=series_data['name'], 
                            color=series_data['color'],
                            linewidth=3,
                            marker='o',
                            markersize=8,
                            markerfacecolor=self.theme_settings.get('marker_facecolor', "#FFFFFF"),
                            markeredgewidth=2)
                                # 添加数据标签
                for i, (xi, yi) in enumerate(zip(x_pos, series_data['data'])):
                    ax2.text(xi, yi, f'{yi:.1f}', 
                            color=self.theme_settings.get('text_color', "#200606"), 
                            ha='center', 
                            va='bottom' if yi < max(series_data['data'])*0.8 else 'top',
                            fontsize=9,
                            bbox=dict(facecolor=series_data['color'], alpha=0.7, 
                                    edgecolor='none', boxstyle='round,pad=0.2'))
                        # 设置右侧y轴样式
            ax2.spines['right'].set_color(self.theme_settings.get('axis_label_color', "#493D3D"))
            ax2.tick_params(axis='y', colors=self.theme_settings.get('axis_label_color', "#493D3D"))
            if line_series[0].get('y_axis_label'):
                ax2.set_ylabel(line_series[0]['y_axis_label'], 
                            color=self.theme_settings.get('axis_label_color', "#493D3D"))
                # 设置x轴刻度和范围 - 优化日期标签显示
        ax.set_xticks(x_pos)
        if self.x_axis['type'] == 'date':
            # 智能日期格式化
            if len(x) > 10:  # 如果数据点多，使用简写月份
                ax.set_xticklabels([d.strftime('%b %d') for d in x])
            else:
                ax.set_xticklabels([d.strftime('%b %d, %Y') for d in x])
                        # 自动调整标签旋转角度和间隔
            if len(x) > 8:
                plt.xticks(rotation=30, ha='right')
                # 稀疏显示标签（每隔n个显示一个）
                ax.set_xticks(ax.get_xticks()[::max(1, len(x)//8)])
        else:
            ax.set_xticklabels(x)
            if len(x) > 10:
                plt.xticks(rotation=45, ha='right')
                # 设置主y轴范围
        if 'range' in self.y_axis:
            ax.set_ylim(self.y_axis['range'][0], self.y_axis['range'][1])
        elif bar_series:
            max_val = max(max(s['data']) for s in bar_series)
            ax.set_ylim(0, max_val * 1.15)
                # 添加千分位分隔符
        ax.yaxis.set_major_formatter(FuncFormatter(lambda x, pos: f'{x:,.0f}'))
                # 应用通用样式
        self._setup_common_styles(ax)
                # 特殊处理组合图图例
        if line_series:
            lines, labels = ax.get_legend_handles_labels()
            lines2, labels2 = ax2.get_legend_handles_labels()
            ax.legend_.remove()  # 移除自动生成的图例
            ax.legend(lines + lines2, labels + labels2, 
                    facecolor=self.theme_settings.get('legend_background_color', '#3d3d3d'),
                    edgecolor='none',
                    framealpha=0.8,
                    loc=self.legend['position'])
                plt.tight_layout()
                # 返回NumPy数组图像
        return self._figure_to_numpy(fig, show)
    def stacked_bar(self, show=False):
        """绘制分组堆叠柱状图，返回NumPy数组图像"""
        fig = self._set_figure_size()
        ax = fig.add_subplot(111)
        ax.set_facecolor(self.theme_settings.get('axes_facecolor', "#f1f1f1"))
                # 处理日期型x轴
        x_categories = self._process_x_axis()
        num_categories = len(x_categories)
        # 检查堆叠组并排序以确保一致性
        stack_groups = {}
        for series in self.series:
            if 'stack' in series:
                if series['stack'] not in stack_groups:
                    stack_groups[series['stack']] = []
                stack_groups[series['stack']].append(series)
        # 如果没有堆叠组，则所有系列堆叠在一起
        if not stack_groups:
            stack_groups['default_stack'] = self.series
                # 获取所有堆叠组名称并排序
        stack_names = sorted(stack_groups.keys())
        num_stacks = len(stack_names)
                # 设置柱状图位置和宽度
        total_width = 0.8  # 每组柱状图的总宽度
        bar_width = total_width / num_stacks
        bar_positions = np.arange(num_categories)
                # 颜色映射 - 为每个堆叠组分配颜色
        stack_colors = {}
        for stack_name in stack_names:
            # 使用组内第一个系列的颜色作为整个堆叠组的颜色
            stack_colors[stack_name] = stack_groups[stack_name][0]['color']
                # 绘制分组堆叠柱状图
        for stack_idx, stack_name in enumerate(stack_names):
            # 计算当前堆叠组的位置偏移
            offset = (stack_idx - (num_stacks - 1) / 2) * bar_width
                        # 初始化底部位置
            bottom = np.zeros(num_categories)
                        # 绘制当前堆叠组内的所有系列
            for series_data in stack_groups[stack_name]:
                # 计算当前柱状图位置
                positions = bar_positions + offset
                                # 绘制堆叠柱状图
                bars = ax.bar(positions, series_data['data'], 
                            width=bar_width, 
                            bottom=bottom,
                            label=f"{stack_name}: {series_data['name']}",
                            color=series_data['color'],
                            edgecolor=self.theme_settings.get('bar_edgecolor', 'white'),
                            linewidth=1,
                            alpha=series_data.get('alpha', 0.9))
                                # 添加数据标签
                for i, bar in enumerate(bars):
                    height = series_data['data'][i]
                    if height > 0:  # 只显示有高度的标签
                        y_pos = bottom[i] + height/2
                        ax.text(bar.get_x() + bar.get_width()/2., 
                            y_pos,
                            f'{height:.0f}', 
                            ha='center', va='center',
                            color=self.theme_settings.get('text_color', '#333333'),
                            fontsize=9)
                # 更新底部位置
                bottom += np.array(series_data['data'])
                # 设置x轴刻度和范围
        ax.set_xticks(bar_positions)
                # 设置x轴标签
        if self.x_axis['type'] == 'date':
            # 智能日期格式化
            if num_categories > 10:  # 如果数据点多，使用简写月份
                ax.set_xticklabels([d.strftime('%b %d') for d in x_categories])
            else:
                ax.set_xticklabels([d.strftime('%b %d, %Y') for d in x_categories])
                        # 自动调整标签旋转角度
            plt.xticks(rotation=45, ha='right')
        else:
            ax.set_xticklabels(x_categories)
            if num_categories > 10:
                plt.xticks(rotation=45, ha='right')
                # 设置y轴范围
        max_val = max([sum(series['data']) for stack in stack_groups.values() for series in stack]) * 1.1
        if 'range' in self.y_axis:
            ax.set_ylim(self.y_axis['range'][0], self.y_axis['range'][1])
        else:
            ax.set_ylim(0, max_val)
                # 添加千分位分隔符
        ax.yaxis.set_major_formatter(FuncFormatter(lambda x, pos: f'{x:,.0f}'))
                # 添加分组图例
        stack_legend_handles = []
        for stack_name in stack_names:
            # 创建图例条目
            handle = plt.Rectangle((0,0), 1, 1, 
                                color=stack_colors[stack_name],
                                alpha=0.7,
                                label=stack_name)
            stack_legend_handles.append(handle)
                # 添加堆叠组图例
        if stack_legend_handles:
            # 获取原始图例位置
            legend_pos = self.legend['position']
                        # 添加堆叠组图例
            stack_legend = ax.legend(
                handles=stack_legend_handles,
                title="堆叠组",
                facecolor=self.theme_settings.get('legend_background_color', '#3d3d3d'), 
                edgecolor='none',
                framealpha=0.8,
                loc='upper left',
                bbox_to_anchor=(1.05, 1) if legend_pos == 'upper right' else (0, 1)
            )
                # 应用通用样式
        self._setup_common_styles(ax)
        plt.tight_layout()
                # 返回NumPy数组图像
        return self._figure_to_numpy(fig, show)
    def radar(self, show=False):
        """绘制雷达图，返回NumPy数组图像"""
        fig = self._set_figure_size()
                # 获取雷达图维度
        dimensions = self.radar_axis.get('dimensions', [])
        if not dimensions:
            raise ValueError("Radar chart requires 'radar_axis.dimensions' in chart config")
        # 计算角度
        angles = np.linspace(0, 2*np.pi, len(dimensions), endpoint=False).tolist()
        angles += angles[:1]  # 闭合图形
        # 创建极坐标子图
        ax = fig.add_subplot(111, polar=True)
        # 绘制每个系列
        for series_data in self.series:
            values = series_data['data']
            values += values[:1]  # 闭合图形
            # 绘制雷达图线条
            ax.plot(angles, values, 
                   linewidth=2,
                   linestyle='solid',
                   label=series_data['name'],
                   color=series_data['color'])
            # 填充颜色
            ax.fill(angles, values, 
                   color=series_data['color'], 
                   alpha=0.2)
            # 添加数据标签
            for angle, value, dim in zip(angles[:-1], series_data['data'], dimensions):
                ax.text(angle, value*1.05, f'{value:.1f}',
                       color=series_data['color'],
                       ha='center', va='center',
                       fontsize=9,
                       bbox=dict(facecolor='white', alpha=0.7, 
                                edgecolor='none', boxstyle='round,pad=0.2'))
                # 设置极坐标轴
        ax.set_theta_offset(np.pi / 2)  # 0度在顶部
        ax.set_theta_direction(-1)  # 顺时针方向
        # 设置维度标签
        ax.set_xticks(angles[:-1])
        ax.set_xticklabels(dimensions, 
                          color=self.theme_settings.get('axis_label_color', "#493D3D"))
        # 设置径向轴
        max_margin = 1.2  # 边距
        if 'max_value' in self.radar_axis:
            radar_max_value = self.radar_axis['max_value']
        else: 
            max_val = max(max(s['data']) for s in self.series) if self.series else 1
            radar_max_value = max_val * max_margin
        ax.set_rlabel_position(30)  # 径向标签位置
        ax.set_yticks(np.linspace(0, radar_max_value, 5))
        ax.set_yticklabels([f"{tick:.1f}" for tick in np.linspace(0,radar_max_value, 5)],
                         color=self.theme_settings.get('axis_label_color', "#493D3D"))
        ax.set_ylim(0, radar_max_value)
                # 设置标题和图例
        ax.set_title(self.chart['title'], 
                    pad=20,
                    color=self.theme_settings.get('axis_label_color', "#493D3D"),
                    fontsize=16)
                # 图例样式
        legend = ax.legend(
            facecolor=self.theme_settings.get('legend_background_color', '#3d3d3d'),
            edgecolor='none',
            framealpha=0.8,
            loc=self.legend['position'],
            bbox_to_anchor=(1.1, 1) if self.legend['position'] == 'upper right' else None
        )
        for text in legend.get_texts():
            text.set_color(self.theme_settings.get('legend_text_color', '#FFFFFF'))
                # 添加时间范围页脚 - 支持中文
        self._add_time_area()
        plt.tight_layout()
                # 返回NumPy数组图像
        return self._figure_to_numpy(fig, show)

from lib import ChartPaintertheme_settings = {
        "facecolor": "#F5F7FA",
        "axes_facecolor": "#FFFFFF",
        "text_color": "#333333",
        "axis_label_color": "#4A6FA5",
        "grid_color": "#E1E5EB",
        "legend_background_color": "#FFFFFF",
        "legend_text_color": "#333333",
        "spine_color": "#C1C9D6",
        "bar_edgecolor": "#FFFFFF",
        "footer_background": "#493808"
    }
# 雷达图测试数据
radar_chart_data = {
    "theme_settings": theme_settings,
    "chart": {
        "type": "radar",
        "title": "Product Performance Comparison",
        "size": [4000, 3000],
    },
    "radar_axis": {
            "dimensions": ["中文设计", "Functionality嘟嘟读", "Usability", "Performance", "Support", "Compatibility"],
            "max_value": 15
        },
    "x_axis": {},
    "y_axis": {},
    "series": [
        {
            "name": "Product A嘟嘟读",
            "data": [4.5, 4.2, 4.7, 4.3, 4.1, 14.6],
            "color": "#4E79A7"
        },
        {
            "name": "Product B嘟嘟读",
            "data": [3.8, 4.5, 4.1, 4.6, 3.9, 14.2],
            "color": "#F28E2B"
        },
        {
            "name": "Industry Average",
            "data": [4.0, 4.0, 4.0, 4.0, 4.0, 4.0],
            "color": "#59A14F",
        }
    ],
    "grid": {
        "visible": True
    },
    "legend": {
        "position": "upper right",
        "direction": "vertical"
    },
    "time": {
        "start_time": "2023-01-01",
        "end_time": "2023-12-31"
    }
}
# 使用示例
painter = ChartPainter(**radar_chart_data)
painter.paint(True)
# 堆叠柱状图测试数据
stacked_bar_data = {
    "theme_settings": theme_settings,
    "chart": {
        "type": "stacked_bar",
        "title": "Quarterly Sales by Product Category (2023)嘟嘟读",
        "size": [2000, 500]
    },
    "x_axis": {
        "label": "Quarter",
        "type": "string",
        "categories": ["Q1", "Q2", "Q3", "Q4"]
    },
    "y_axis": {
        "label": "Sales (thousand $)",
        "range": [0, 1000]
    },
    "series": [
        {
            "name": "Electronics",
            "data": [120, 150, 130, 180],
            "color": "#4E79A7",
            "stack": "category"
        },
        {
            "name": "Clothing嘟嘟读",
            "data": [80, 90, 95, 110],
            "color": "#F28E2B",
            "stack": "category"
        },
        {
            "name": "Home Goods嘟嘟读",
            "data": [60, 70, 75, 90],
            "color": "#E15759",
            "stack": "category"
        },
        {
            "name": "Online",
            "data": [150, 180, 170, 220],
            "color": "#76B7B2",
            "stack": "channel"
        },
        {
            "name": "Offline",
            "data": [110, 130, 130, 160],
            "color": "#59A14F",
            "stack": "channel"
        }
    ],
    "grid": {
        "visible": True,
        "style": "--"
    },
    "legend": {
        "position": "upper right",
        "direction": "vertical"
    },
    "time": {
        "start_time": "2023-01-01",
        "end_time": "2023-12-31"
    }
}
# 使用示例
painter = ChartPainter(**stacked_bar_data)
painter.paint(True)
# 日期型堆叠柱状图数据
date_stacked_data = {
    "theme_settings": theme_settings,
    "chart": {
        "type": "stacked_bar",
        "title": "Monthly Website Traffic Sources (2023)",
        "size": [2000, 900],
        "save_path": "traffic_sources.png"
    },
    "x_axis": {
        "label": "Month",
        "type": "date",
        "categories": [
            "2023-01-01", "2023-02-01", "2023-03-01", 
            "2023-04-01", "2023-05-01", "2023-06-01"
        ]
    },
    "y_axis": {
        "label": "Visitors (thousands)"
    },
    "series": [
        {
            "name": "Organic Search",
            "data": [45, 48, 52, 55, 58, 62],
            "color": "#4E79A7",
            "stack": "source"
        },
        {
            "name": "Direct",
            "data": [30, 32, 35, 38, 40, 42],
            "color": "#F28E2B",
            "stack": "source"
        },
        {
            "name": "Social Media",
            "data": [20, 22, 25, 28, 30, 32],
            "color": "#E15759",
            "stack": "source"
        },
        {
            "name": "Referral",
            "data": [15, 18, 20, 22, 25, 28],
            "color": "#76B7B2",
            "stack": "source"
        }
    ],
    "grid": {
        "visible": True,
        "style": ":"
    },
    "legend": {
        "position": "upper left",
        "direction": "vertical"
    },
    "time": {
        "start_time": "2023-01-01",
        "end_time": "2023-06-30"
    }
}
# 使用示例
painter = ChartPainter(**date_stacked_data)
painter.paint(True)
# 组合图表测试数据（销售额柱状图+增长率折线图）
combo_chart_data = {
    "theme_settings": theme_settings,
    "chart": {
        "type": "combo",
        "title": "Monthly Sales & Growth Rate (2023)",
        "size": [1800, 800]
    },
    "x_axis": {
        "label": "Month",
        "type": "string",
        "categories": ["Jan", "Feb", "Mar", "Apr", "May", "Jun", 
                      "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
    },
    "y_axis": {
        "label": "Sales (thousand $)",
        "range": [0, 180]
    },
    "series": [
        {
            "name": "Sales Amount",
            "type": "bar",
            "data": [85, 78, 92, 105, 98, 112, 125, 118, 132, 145, 138, 152],
            "color": "#4E79A7"
        },
        {
            "name": "Growth Rate (%)",
            "type": "line",
            "data": [0, -8.2, 17.9, 14.1, -6.7, 14.3, 11.6, -5.6, 11.9, 9.8, -4.8, 10.1],
            "color": "#E15759",
            "y_axis_label": "Growth Rate %"
        }
    ],
    "grid": {
        "visible": True,
        "style": "--"
    },
    "legend": {
        "position": "upper left",
        "direction": "horizontal"
    },
    "time": {
        "start_time": "2023-01-01",
        "end_time": "2023-12-31"
    }
}
# 使用示例
painter = ChartPainter(**combo_chart_data)
painter.paint(True)
# 日期型组合图表数据（网站流量柱状图+转化率折线图）
date_combo_data = {
    "theme_settings": theme_settings,
    "chart": {
        "type": "combo",
        "title": "Weekly Performance (Q1 2023)",
        "size": [2000, 500],
        "save_path": "weekly_performance.png"
    },
    "x_axis": {
        "label": "Week",
        "type": "date",
        "categories": [
            "2023-01-02", "2023-01-09", "2023-01-16", "2023-01-23", "2023-01-30",
            "2023-02-06", "2023-02-13", "2023-02-20", "2023-02-27",
            "2023-03-06", "2023-03-13", "2023-03-20", "2023-03-27"
        ]
    },
    "y_axis": {
        "label": "Visitors (thousands)",
        "range": [0, 230]
    },
    "series": [
        {
            "name": "Total Visitors",
            "type": "bar",
            "data": [125, 132, 118, 145, 138, 152, 165, 158, 172, 185, 178, 192, 205],
            "color": "#F06595"
        },
        {
            "name": "Total Visitors2",
            "type": "bar",
            "data": [125, 132, 118, 145, 138, 152, 165, 158, 172, 185, 178, 192, 205],
            "color": "#20C97A"
        },
        {
            "name": "Total Visitors3",
            "type": "bar",
            "data": [125, 132, 118, 145, 138, 152, 165, 158, 172, 185, 178, 192, 205],
            "color": "#3120C9"
        },
        {
            "name": "Conversion Rate (%)",
            "type": "line",
            "data": [2.8, 3.1, 2.9, 3.5, 3.2, 3.7, 4.0, 3.8, 4.2, 4.5, 4.3, 4.6, 5.0],
            "color": "#5C7CFA",
        }
    ],
    "grid": {
        "visible": True,
        "style": ":"
    },
    "legend": {
        "position": "upper right",
        "direction": "vertical"
    },
    "time": {
        "start_time": "2023-01-01",
        "end_time": "2023-03-31"
    }
}
# 使用示例
painter = ChartPainter(**date_combo_data)
painter.paint(True)
# 日期型折线图数据
date_bar_data = {
    "theme_settings": theme_settings,
    "chart": {
        "type": "line",
        "title": "Weekly Website Traffic (Jan-Mar 2023)",
        "size": [2000, 900]
    },
    "x_axis": {
        "label": "Week",
        "type": "date",
        "categories": [
            "2023-01-02", "2023-01-09", "2023-01-16", "2023-01-23", "2023-01-30",
            "2023-02-06", "2023-02-13", "2023-02-20", "2023-02-27",
            "2023-03-06", "2023-03-13", "2023-03-20", "2023-03-27"
        ]
    },
    "y_axis": {
        "label": "Visitors (thousands)"
    },
    "series": [
        {
            "name": "New Visitors",
            "data": [45, 52, 48, 55, 50, 58, 65, 62, 68, 72, 78, 75, 82],
            "color": "#6C8EBF"
        },
        {
            "name": "Returning Visitors",
            "data": [85, 92, 88, 95, 102, 108, 115, 122, 118, 125, 132, 128, 135],
            "color": "#82B366"
        }
    ],
    "grid": {
        "visible": True,
        "style": ":"
    },
    "legend": {
        "position": "upper left",
        "direction": "vertical"
    },
    "time": {
        "start_time": "2023-01-01",
        "end_time": "2023-03-31"
    }
}
# 使用示例
painter = ChartPainter(**date_bar_data)
painter.paint(True)
# Example usage
data = {
    "theme_settings": theme_settings,
  "chart": {
    "type": "line",
    "title": "Monthly Sales Performance (2023)",
    "size": [2000, 700]
  },
  "x_axis": {
    "label": "Month",
    "type": "string",
    "categories": ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
  },
  "y_axis": {
    "label": "Sales (in thousands)",
    "range": [0, 120]
  },
  "series": [
    {
      "name": "Product A",
      "data": [45, 58, 36, 74, 52, 69, 84, 97, 68, 76, 89, 94],
      "color": "#4E79A7"
    },
    {
      "name": "Product B",
      "data": [28, 33, 45, 57, 62, 78, 65, 72, 85, 91, 78, 105],
      "color": "#F28E2B"
    },
    {
      "name": "Product C",
      "data": [15, 22, 38, 42, 56, 61, 73, 68, 74, 82, 95, 88],
      "color": "#E15759"
    }
  ],
  "grid": {
    "visible": True,
    "style": "--"
  },
  "legend": {
    "position": "upper right",
    "direction": "horizontal"
  },
  "time": {
    "start_time": "2023-01-01 00:00:00",
    "end_time": "2023-12-31 23:59:59"
  }
}
painter = ChartPainter(**data)
painter.paint(True)
# 柱状图测试数据
bar_chart_data = {
    "theme_settings": theme_settings,
    "chart": {
        "type": "bar",
        "title": "Quarterly Revenue Comparison (2022-2023)",
        "size": [1600, 900]
    },
    "x_axis": {
        "label": "Quarter",
        "type": "string",
        "categories": ["Q1", "Q2", "Q3", "Q4"]
    },
    "y_axis": {
        "label": "Revenue (million $)",
        "range": [0, 300]
    },
    "series": [
        {
            "name": "2022",
            "data": [125, 145, 132, 185],
            "color": "#4C72B0"
        },
        {
            "name": "2023",
            "data": [155, 178, 162, 210],
            "color": "#55A868"
        },
        {
            "name": "Target",
            "data": [140, 160, 150, 200],
            "color": "#DD8452"
        }
    ],
    "grid": {
        "visible": True,
        "style": "--"
    },
    "legend": {
        "position": "upper right",
        "direction": "horizontal"
    },
    "time": {
        "start_time": "2022-01-01",
        "end_time": "2023-12-31"
    }
}
# 使用示例
painter = ChartPainter(**bar_chart_data)
painter.paint(True)
# 日期型柱状图数据
date_bar_data = {
    "theme_settings": theme_settings,
    "chart": {
        "type": "bar",
        "title": "Weekly Website Traffic (Jan-Mar 2023)",
        "size": [2000, 900]
    },
    "x_axis": {
        "label": "Week",
        "type": "date",
        "categories": [
            "2023-01-02", "2023-01-09", "2023-01-16", "2023-01-23", "2023-01-30",
            "2023-02-06", "2023-02-13", "2023-02-20", "2023-02-27",
            "2023-03-06", "2023-03-13", "2023-03-20", "2023-03-27"
        ]
    },
    "y_axis": {
        "label": "Visitors (thousands)"
    },
    "series": [
        {
            "name": "New Visitors",
            "data": [45, 52, 48, 55, 50, 58, 65, 62, 68, 72, 78, 75, 82],
            "color": "#6C8EBF"
        },
        {
            "name": "Returning Visitors",
            "data": [85, 92, 88, 95, 102, 108, 115, 122, 118, 125, 132, 128, 135],
            "color": "#82B366"
        }
    ],
    "grid": {
        "visible": True,
        "style": ":"
    },
    "legend": {
        "position": "upper left",
        "direction": "vertical"
    },
    "time": {
        "start_time": "2023-01-01",
        "end_time": "2023-03-31"
    }
}
# 使用示例
painter = ChartPainter(**date_bar_data)
painter.paint(True)

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