chart-generator

name	chart-generator
description	Data visualization chart generator. Use when user needs to create charts from data for reports, presentations, or documents. Supports bar, line, pie, scatter, radar charts with PNG/SVG output. 数据可视化、图表生成、数据报告。
version	1.0.2
license	MIT-0
metadata	{"openclaw":{"emoji":"📊","requires":{"bins":["python3"]}}}
dependencies	pip install matplotlib pandas openpyxl python-docx pillow

Chart Generator

Professional data visualization chart generator for reports, presentations, and documents.

Features

• 📊 Multiple Chart Types: Bar, line, pie, scatter, radar, area, stacked
• 📁 Multiple Data Sources: CSV, Excel, JSON, manual, web, document extraction
• 🎨 Professional Styling: Clean, publication-ready charts with custom options
• 📐 Flexible Output: PNG, SVG, PDF, Word, Excel, Markdown, HTML
• 🔗 Embed Support: Direct embedding into documents
• 🌍 Multi-Language: Chinese, English, Japanese, Korean (no encoding issues)
• ✅ Cross-Platform: Windows, macOS, Linux

Supported Chart Types

Type	Use Case	Best For
Bar Chart	Compare values	Sales, rankings
Line Chart	Show trends	Time series, growth
Pie Chart	Show proportions	Market share, composition
Scatter Plot	Show correlation	Data relationships
Radar Chart	Multi-dimension	Performance comparison
Area Chart	Cumulative values	Stacked data
Stacked Bar	Composition	Multi-category breakdown

Trigger Conditions

• "帮我画图" / "Create a chart"
• "生成柱状图" / "Generate bar chart"
• "数据可视化" / "Data visualization"
• "做一个趋势图" / "Make a trend chart"
• "图表分析" / "Chart analysis"
• "chart-generator"

---

Step 1: Understand Requirements

请提供以下信息：

图表类型：（柱状图/折线图/饼图/散点图/雷达图）
数据来源：（手动输入/CSV/Excel/JSON）
数据内容：
标题：
X轴标签：
Y轴标签：
输出格式：（PNG/SVG）
颜色要求：（默认/自定义）

---

Step 2: Generate Chart

Python Script Template

python3 << 'PYEOF'
import os
import matplotlib.pyplot as plt
import matplotlib
import pandas as pd
import numpy as np
from matplotlib import font_manager

# 设置中文字体
plt.rcParams['font.sans-serif'] = ['Noto Sans SC', 'SimHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False

class ChartGenerator:
    def __init__(self):
        self.fig = None
        self.ax = None
        
    def create_bar_chart(self, labels, values, title='', 
                         xlabel='', ylabel='', 
                         color='#3182ce', output_path=None):
        """Create bar chart"""
        self.fig, self.ax = plt.subplots(figsize=(10, 6))
        
        bars = self.ax.bar(labels, values, color=color, edgecolor='white', linewidth=0.5)
        
        # Add value labels on bars
        for bar in bars:
            height = bar.get_height()
            self.ax.text(bar.get_x() + bar.get_width()/, height,
                        ,
                        ha=, va=, fontsize=)
        
        .ax.set_title(title, fontsize=, fontweight=, pad=)
        .ax.set_xlabel(xlabel, fontsize=)
        .ax.set_ylabel(ylabel, fontsize=)
        
        
        .ax.spines[].set_visible()
        .ax.spines[].set_visible()
        .ax.grid(axis=, alpha=)
        
        plt.tight_layout()
        
         output_path:
            .fig.savefig(output_path, dpi=, bbox_inches=)
            plt.close()
             output_path
        
         .fig
    
     ():
        
        .fig, .ax = plt.subplots(figsize=(, ))
        
         colors  :
            colors = [, , , , ]
        
         i, y_data  (y_data_list):
            color = colors[i % (colors)]
            label = labels[i]  labels  i < (labels)  
            .ax.plot(x_data, y_data, marker=, linewidth=, 
                        color=color, label=label, markersize=)
        
        .ax.set_title(title, fontsize=, fontweight=, pad=)
        .ax.set_xlabel(xlabel, fontsize=)
        .ax.set_ylabel(ylabel, fontsize=)
        
         labels:
            .ax.legend(loc=, framealpha=)
        
        .ax.spines[].set_visible()
        .ax.spines[].set_visible()
        .ax.grid(alpha=)
        
        plt.tight_layout()
        
         output_path:
            .fig.savefig(output_path, dpi=, bbox_inches=)
            plt.close()
             output_path
        
         .fig
    
     ():
        
        .fig, .ax = plt.subplots(figsize=(, ))
        
         colors  :
            colors = [, , , , ,
                     , , ]
        
        wedges, texts, autotexts = .ax.pie(
            values, labels=labels, colors=colors[:(values)],
            autopct=, startangle=,
            textprops={: }
        )
        
         autotext  autotexts:
            autotext.set_color()
            autotext.set_fontweight()
        
        .ax.set_title(title, fontsize=, fontweight=, pad=)
        
        plt.tight_layout()
        
         output_path:
            .fig.savefig(output_path, dpi=, bbox_inches=)
            plt.close()
             output_path
        
         .fig
    
     ():
        
        .fig, .ax = plt.subplots(figsize=(, ))
        
        .ax.scatter(x_data, y_data, c=color, alpha=, s=)
        
        
        z = np.polyfit(x_data, y_data, )
        p = np.poly1d(z)
        .ax.plot(x_data, p(x_data), , color=, alpha=, label=)
        
        .ax.set_title(title, fontsize=, fontweight=, pad=)
        .ax.set_xlabel(xlabel, fontsize=)
        .ax.set_ylabel(ylabel, fontsize=)
        .ax.legend()
        
        .ax.spines[].set_visible()
        .ax.spines[].set_visible()
        .ax.grid(alpha=)
        
        plt.tight_layout()
        
         output_path:
            .fig.savefig(output_path, dpi=, bbox_inches=)
            plt.close()
             output_path
        
         .fig
    
     ():
        
        .fig, .ax = plt.subplots(figsize=(, ))
        
        x = np.arange((labels))
        width =  / (data_dict)
        
        colors = [, , , , ]
        
         i, (name, values)  (data_dict.items()):
            offset = (i - (data_dict)/ + ) * width
            bars = .ax.bar(x + offset, values, width, label=name,
                             color=colors[i % (colors)], edgecolor=)
        
        .ax.set_title(title, fontsize=, fontweight=, pad=)
        .ax.set_xlabel(xlabel, fontsize=)
        .ax.set_ylabel(ylabel, fontsize=)
        .ax.set_xticks(x)
        .ax.set_xticklabels(labels)
        .ax.legend()
        
        .ax.spines[].set_visible()
        .ax.spines[].set_visible()
        .ax.grid(axis=, alpha=)
        
        plt.tight_layout()
        
         output_path:
            .fig.savefig(output_path, dpi=, bbox_inches=)
            plt.close()
             output_path
        
         .fig
    
     ():
        
        df = pd.read_csv(csv_path)
        
         x_col  :
            x_col = df.columns[]
         y_cols  :
            y_cols = [col  col  df.columns  col != x_col]
        
         {
            : df[x_col].tolist(),
            : {col: df[col].tolist()  col  y_cols},
            : df
        }
    
     ():
        
        df = pd.read_excel(excel_path, sheet_name=sheet_name)
        
         x_col  :
            x_col = df.columns[]
         y_cols  :
            y_cols = [col  col  df.columns  col != x_col]
        
         {
            : df[x_col].tolist(),
            : {col: df[col].tolist()  col  y_cols},
            : df
        }
    
     ():
        
         json
         (json_path, , encoding=)  f:
            data = json.load(f)
         data
    
     ():
        
         glob
        
        all_data = []
         file_path  glob.glob(os.path.join(dir_path, file_pattern)):
             file_path.endswith():
                df = pd.read_csv(file_path)
             file_path.endswith():
                df = pd.read_excel(file_path)
            :
                
            df[] = os.path.basename(file_path)
            all_data.append(df)
        
         all_data:
             pd.concat(all_data, ignore_index=)
         pd.DataFrame()
    
     ():
        
         re
        
        
        patterns = [
            ,
            ,
        ]
        
        data = {}
         pattern  patterns:
            matches = re.findall(pattern, text)
               matches:
                 () == :
                    key, value = 
                    :
                        data[key] = (value)
                     ValueError:
                        
        
         data
    
     ():
        
         .fig:
            .fig.savefig(output_path, dpi=dpi, bbox_inches=, 
                           facecolor=, edgecolor=)
             output_path
    
     ():
        
         .fig:
            .fig.savefig(output_path, =, bbox_inches=,
                           facecolor=, edgecolor=)
             output_path
    
     ():
        
         .fig:
            .fig.savefig(output_path, =, bbox_inches=,
                           facecolor=, edgecolor=)
             output_path
    
     ():
        
         io
         base64
        
         .fig:
            buffer = io.BytesIO()
            .fig.savefig(buffer, =, bbox_inches=,
                           facecolor=, edgecolor=)
            buffer.seek()
            img_str = base64.b64encode(buffer.read()).decode()
             
    
     ():
        
        base64_img = .save_to_base64()
        
        md = 
         title:
            md += 
        md += 
         caption:
            md += 
        
         md
    
     ():
        
        base64_img = .save_to_base64()
        
        html = 
         html
    
     ():
        
         docx  Document
         docx.shared  Inches
        
        doc = Document()
        
         title:
            doc.add_heading(title, level=)
        
        
        temp_img = output_path.replace(, )
        .save_to_png(temp_img)
        
        
        doc.add_picture(temp_img, width=Inches())
        
         caption:
            last_para = doc.paragraphs[-]
            last_para.alignment =   
        
        doc.save(output_path)
        
        
         os.path.exists(temp_img):
            os.remove(temp_img)
        
         output_path


generator = ChartGenerator()
output_dir = os.environ.get(, os.getcwd())


labels = [, , , ]
values = [, , , ]
generator.create_bar_chart(
    labels, values,
    title=,
    xlabel=,
    ylabel=,
    output_path=os.path.join(output_dir, )
)


months = [, , , , , ]
product_a = [, , , , , ]
product_b = [, , , , , ]
generator.create_line_chart(
    months, [product_a, product_b],
    labels=[, ],
    title=,
    xlabel=,
    ylabel=,
    output_path=os.path.join(output_dir, )
)


pie_labels = [, , , ]
pie_values = [, , , ]
generator.create_pie_chart(
    pie_labels, pie_values,
    title=,
    output_path=os.path.join(output_dir, )
)

()
PYEOF

---

Data Sources (数据来源)

From CSV

generator = ChartGenerator()
data = generator.load_from_csv('data.csv', x_col='Month', y_cols=['Sales', 'Profit'])

generator.create_line_chart(
    data['x'], 
    [data['y']['Sales'], data['y']['Profit']],
    labels=['Sales', 'Profit'],
    title='Monthly Performance'
)

From Excel

data = generator.load_from_excel('report.xlsx', sheet_name='Sheet1')

Manual Input

labels = ['A', 'B', 'C', 'D']
values = [100, 200, 150, 300]
generator.create_bar_chart(labels, values)

---

Styling Options (样式选项)

Colors

# Single color
color='#3182ce'  # Blue

# Multiple colors
colors=['#3182ce', '#48bb78', '#ed8936', '#e53e3e']

Size

# Default size
figsize=(10, 6)

# Large for presentations
figsize=(16, 9)

# Square for reports
figsize=(8, 8)

---

Security Notes

• ✅ No network calls or external endpoints
• ✅ No credentials or API keys required
• ✅ Local file processing only
• ✅ Open source dependencies (matplotlib, pandas)
• ✅ No data uploaded to external servers

---

Notes

• Uses matplotlib for chart generation
• Supports CSV, Excel, and manual data input
• Output formats: PNG, SVG, PDF
• Chinese font support with Noto Sans SC
• Cross-platform compatible