Python matplotlib 图表绘制

本文记录一个可以绘制折线图、柱状图、堆叠柱状图、雷达图、折线柱状图的一个管理者。

图表绘制

设计思想：

1. 复用输入表结构
2. 复用初始化结构
3. 复用尺寸配置
4. 复用图片输出配置
5. 根据类型自适应生成图表
6. 自定义中文字体支持

核心源码

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 vv


class 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)

工程包

• 测试工程包链接：https://uipv4.zywvvd.com:33030/HexoFiles/code/draw_chart.zip

效果测试

测试数据：

from lib import ChartPainter


theme_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)

• 雷达图

• 分组堆叠柱状图

• 柱状折线图

• 折线图

• 柱状图

文章链接：
https://www.zywvvd.com/notes/coding/python/matplotlib-manager/matplotlib-manager/