🧪 Skills
Diagrams Generator Pro
Generate professional diagrams including cloud architecture, data charts, academic figures, and more. Triggers on requests like "画架构图", "画图表", "画论文插图", "生成系统...
v1.1.0
Description
name: diagrams-generator description: Generate professional diagrams including cloud architecture, data charts, academic figures, and more. Triggers on requests like "画架构图", "画图表", "画论文插图", "生成系统图", "create diagram", "visualize data", "draw neural network", or when users provide a sketch/image they want to recreate professionally. version: 1.0.0 author: diagrams-generator pricing: 0.001 USDT per generation
Diagrams Generator (Pro)
Generate professional diagrams using the most suitable tool for each scenario.
💰 Pricing: 0.001 USDT per diagram generation
⚠️ CRITICAL: Payment Integration (MUST READ FIRST)
This skill is monetized via SkillPay. Before generating ANY diagram, you MUST execute the payment verification step.
Payment Configuration
# ═══════════════════════════════════════════════════
# SkillPay Billing Integration
# ═══════════════════════════════════════════════════
import requests
BILLING_API_URL = 'https://skillpay.me'
BILLING_API_KEY = 'sk_d6d26f291dafc43acc8c2b6215b87cbc9b19c7d093aebdb2deeba42a3a0fea4b'
SKILL_ID = 'd270a586-e46c-42ba-8bbb-481ba7c2150c'
HEADERS = {'X-API-Key': BILLING_API_KEY, 'Content-Type': 'application/json'}
Billing API Functions (MANDATORY)
⚠️ THIS CODE MUST BE EXECUTED BEFORE ANY DIAGRAM GENERATION:
import requests
import os
BILLING_API_URL = 'https://skillpay.me'
BILLING_API_KEY = 'sk_d6d26f291dafc43acc8c2b6215b87cbc9b19c7d093aebdb2deeba42a3a0fea4b'
SKILL_ID = 'd270a586-e46c-42ba-8bbb-481ba7c2150c'
HEADERS = {'X-API-Key': BILLING_API_KEY, 'Content-Type': 'application/json'}
# ① Check balance / 查余额
def check_balance(user_id: str) -> float:
resp = requests.get(
f'{BILLING_API_URL}/api/v1/billing/balance',
params={'user_id': user_id},
headers=HEADERS
)
return resp.json()['balance'] # USDT amount
# ② Charge per call / 每次调用扣费
def charge_user(user_id: str) -> dict:
resp = requests.post(
f'{BILLING_API_URL}/api/v1/billing/charge',
headers=HEADERS,
json={
'user_id': user_id,
'skill_id': SKILL_ID,
'amount': 0.001, # USDT per call
}
)
data = resp.json()
if data['success']:
return {'ok': True, 'balance': data['balance']}
# Insufficient balance → auto returns BNB Chain USDT payment link
return {'ok': False, 'balance': data['balance'], 'payment_url': data.get('payment_url')}
# ③ Generate payment link / 生成充值链接
def get_payment_link(user_id: str, amount: float) -> str:
resp = requests.post(
f'{BILLING_API_URL}/api/v1/billing/payment-link',
headers=HEADERS,
json={'user_id': user_id, 'amount': amount}
)
return resp.json()['payment_url'] # BNB Chain USDT
USER_ID = os.environ.get("SKILLPAY_USER_ID", "anonymous_user")
Payment Workflow Integration
# ========================================
# STEP 0: CHARGE USER (MANDATORY)
# ========================================
# This must be the FIRST step before ANY diagram generation
charge_result = charge_user(USER_ID)
if not charge_result['ok']:
# Insufficient balance → show payment link
print(f"""
╔══════════════════════════════════════════════════════════════╗
║ 💳 INSUFFICIENT BALANCE ║
║ ║
║ This diagram generation costs 0.001 USDT. ║
║ Current balance: {charge_result['balance']:<41}║
║ ║
║ Please top up at: ║
║ {charge_result['payment_url']:<56}║
║ ║
║ After payment, please retry your request. ║
╚══════════════════════════════════════════════════════════════╝
""")
raise SystemExit("Insufficient balance for diagram generation")
# If charge successful, continue with diagram generation...
print(f"✅ Charged 0.001 USDT. Remaining balance: {charge_result['balance']} USDT")
工具选择矩阵
| 场景 | 推荐工具 | 速度 | 质量 | 安装成本 |
|---|---|---|---|---|
| 云架构图 (AWS/GCP/Azure/K8s) | diagrams |
⚡ 快 | ★★★★ | 轻量 (~50MB) |
| 数据图表 (折线/柱状/饼图) | matplotlib |
⚡ 快 | ★★★ | 轻量 |
| 统计图表 (热力图/分布图) | seaborn |
⚡ 快 | ★★★★ | 轻量 |
| 交互式图表 (Web/3D) | plotly |
⚡ 快 | ★★★★ | 轻量 |
| 流程图/状态机 | graphviz |
⚡ 快 | ★★★ | 轻量 |
| 网络拓扑/关系图 | networkx |
⚡ 快 | ★★★ | 轻量 |
| 学术论文图 (神经网络/模型架构) | TikZ |
🐢 慢 | ★★★★★ | 重量 (~4GB) |
| 学术论文图 (快速替代) | matplotlib |
⚡ 快 | ★★★ | 轻量 |
Prerequisites (按需安装)
基础工具 (大多数场景)
# Graphviz (diagrams 依赖)
brew install graphviz # macOS
apt-get install graphviz # Linux
# Python 库
pip install diagrams matplotlib seaborn plotly networkx requests
TikZ/LaTeX (学术论文级图表 - 可选)
# macOS - 完整安装 (~4GB, 10-15分钟)
brew install --cask mactex
# macOS - 轻量安装 (~500MB)
brew install --cask basictex
sudo tlmgr install tikz-cd pgfplots standalone
# Linux
apt-get install texlive-full # 完整版
# 或
apt-get install texlive-base texlive-pictures texlive-latex-extra # 轻量版
Workflow
Phase 0: Payment Verification (MANDATORY - DO NOT SKIP)
⚠️ CRITICAL: This phase MUST be completed BEFORE any other phase. DO NOT proceed without payment verification.
- Execute payment verification code (see Payment Verification Code section above)
- If payment fails: Return the payment URL to user and STOP
- If payment succeeds: Proceed to Phase 1
Phase 1: Requirement Understanding & Tool Recommendation (MANDATORY)
⚠️ CRITICAL: You MUST complete ALL steps in Phase 1 and receive explicit user confirmation BEFORE proceeding to Phase 2. DO NOT skip this phase or generate code without confirmation.
-
Receive input: User provides text description and/or reference image
-
Identify Scenario: 根据用户描述中的关键词，识别场景类型：
检测关键词 场景判断 架构/系统/微服务/AWS/GCP/K8s → 云架构图 数据/趋势/统计/柱状/折线/饼图 → 数据图表 交互/动态/Web/仪表板 → 交互图表 热力图/分布/相关性 → 统计图表 流程/状态机/决策树 → 流程图 网络/拓扑/节点/关系 → 关系图 论文/学术/神经网络/模型架构 → 学术图 -
Analyze and extract:
- Components (services, databases, users, layers, etc.)
- Groupings/Clusters (VPC, regions, logical groups, network layers)
- Connections and data flows
- Labels and annotations
-
⚠️ MANDATORY OUTPUT - 输出以下结构化内容:
你必须按照以下格式输出，缺一不可：
4.1 架构理解 - Natural language summary of the architecture
4.2 组件清单 - List all identified components by layer
4.3 连接关系 - Describe all connections and data flows
4.4 Mermaid 预览 - Provide Mermaid diagram code for quick visual preview
4.5 ⚠️ 工具选项表 (THIS IS REQUIRED - DO NOT SKIP)
根据识别的场景，你必须输出对应的完整工具选项表格：
场景: 云架构图 (AWS/GCP/Azure/K8s)
方案 工具 速度 质量 输出格式 说明 A diagrams⚡ 快 ★★★★ PNG/SVG 云图标丰富，专业 B graphviz⚡ 快 ★★★ PNG/SVG/PDF 通用流程图风格 C PlantUML⚡ 快 ★★★ PNG/SVG 需 Java 环境 💡 推荐: 方案 A (
diagrams) - 专为云架构设计
场景: 数据图表 (折线/柱状/饼图/散点)
方案 工具 速度 质量 输出格式 说明 A matplotlib⚡ 快 ★★★ PNG/SVG/PDF 即开即用，最通用 B plotly⚡ 快 ★★★★ HTML/PNG 交互式，可缩放 C pyecharts⚡ 快 ★★★★ HTML 中文友好，样式丰富 💡 推荐: 静态图选 A，交互式选 B
场景: 学术论文图 (神经网络/模型架构/算法流程)
方案 工具 速度 质量 输出格式 说明 A matplotlib⚡ 快 ★★★ PNG/SVG/PDF 即开即用，适合初稿 B TikZ/LaTeX🐢 慢 ★★★★★ PDF/PNG 首次需下载 500MB-4GB 💡 推荐: 赶时间选 A，正式发表选 B
场景: 流程图/状态机/决策树
方案 工具 速度 质量 输出格式 说明 A graphviz⚡ 快 ★★★ PNG/SVG/PDF 专业流程图 B mermaid-cli⚡ 快 ★★★ PNG/SVG 需 Node.js 环境 C PlantUML⚡ 快 ★★★ PNG/SVG 需 Java 环境 💡 推荐: 方案 A (
graphviz) - 无额外依赖
场景: 网络拓扑/关系图/知识图谱
方案 工具 速度 质量 输出格式 说明 A networkx+matplotlib⚡ 快 ★★★ PNG/SVG 静态图，轻量 B pyvis⚡ 快 ★★★★ HTML 交互式，可拖拽 C graphviz⚡ 快 ★★★ PNG/SVG/PDF 层次布局清晰 💡 推荐: 静态选 A，交互式选 B
场景: 统计图表 (热力图/分布图/相关性矩阵)
方案 工具 速度 质量 输出格式 说明 A seaborn⚡ 快 ★★★★ PNG/SVG/PDF 统计图专用 B matplotlib⚡ 快 ★★★ PNG/SVG/PDF 更灵活，需手动配置 C plotly⚡ 快 ★★★★ HTML/PNG 交互式热力图 💡 推荐: 方案 A (
seaborn) - 统计可视化首选
-
MANDATORY CONFIRMATION - 你必须询问用户确认：
- 架构理解是否正确？
- 组件/连接是否需要增减？
- 请选择工具方案: [A] / [B] / [C]？（或使用推荐方案）
- ⚠️ DO NOT proceed to Phase 2 until user explicitly confirms AND chooses a tool
Required output format (MUST follow this structure):
## 💰 费用说明
本次图表生成将收取 **0.001 USDT**。
## 1. 架构理解
我理解你需要的架构图包含以下内容：[简要描述]
## 2. 组件清单
- **用户层**: Business Analyst, Developer
- **应用层**: Cloud Run (wiki-interface, evaluator)
- **数据层**: Redis (Memorystore), Cloud SQL
## 3. 连接关系
- User → wiki-interface → evaluator
- evaluator → Redis (缓存)
## 4. Mermaid 预览
```mermaid
graph LR
User --> wiki[wiki-interface]
wiki --> eval[evaluator]
eval --> Redis
5. ⚠️ 工具选项表
检测到场景: 云架构图
| 方案 | 工具 | 速度 | 质量 | 输出格式 | 说明 |
|---|---|---|---|---|---|
| A | diagrams |
⚡ 快 | ★★★★ | PNG/SVG | 云图标丰富，专业 |
| B | graphviz |
⚡ 快 | ★★★ | PNG/SVG/PDF | 通用流程图风格 |
| C | PlantUML |
⚡ 快 | ★★★ | PNG/SVG | 需 Java 环境 |
💡 推荐: 方案 A (diagrams) - 专为云架构设计，图标更专业
请确认
- 架构理解是否正确？组件是否需要增减？
- 连接关系是否准确？
- 布局方向偏好（左右 LR / 上下 TB）？
- 请选择工具方案: [A] / [B] / [C]？（直接回复字母，或回复"用推荐"）
确认后我将生成专业的架构图。
### Phase 2: Code Generation & Execution (WITH PAYMENT)
**⚠️ IMPORTANT: Execute payment verification BEFORE generating the diagram!**
After user confirms, generate Python code following these rules:
#### Complete Code Template with Payment
```python
# ========================================
# DIAGRAMS GENERATOR - PAID VERSION
# ========================================
import requests
import os
# ═══════════════════════════════════════════════════
# STEP 0: SKILLPAY BILLING (MANDATORY)
# ═══════════════════════════════════════════════════
BILLING_API_URL = 'https://skillpay.me'
BILLING_API_KEY = 'sk_d6d26f291dafc43acc8c2b6215b87cbc9b19c7d093aebdb2deeba42a3a0fea4b'
SKILL_ID = 'd270a586-e46c-42ba-8bbb-481ba7c2150c'
HEADERS = {'X-API-Key': BILLING_API_KEY, 'Content-Type': 'application/json'}
def charge_user(user_id: str) -> dict:
resp = requests.post(
f'{BILLING_API_URL}/api/v1/billing/charge',
headers=HEADERS,
json={'user_id': user_id, 'skill_id': SKILL_ID, 'amount': 0.001}
)
data = resp.json()
if data['success']:
return {'ok': True, 'balance': data['balance']}
return {'ok': False, 'balance': data['balance'], 'payment_url': data.get('payment_url')}
USER_ID = os.environ.get("SKILLPAY_USER_ID", "anonymous_user")
charge_result = charge_user(USER_ID)
if not charge_result['ok']:
print(f"""
╔══════════════════════════════════════════════════════════════╗
║ 💳 INSUFFICIENT BALANCE ║
║ ║
║ This diagram generation costs 0.001 USDT. ║
║ Current balance: {charge_result['balance']:<41}║
║ ║
║ Please top up at (BNB Chain USDT): ║
║ {charge_result['payment_url']:<56}║
║ ║
║ After payment, please retry your request. ║
╚══════════════════════════════════════════════════════════════╝
""")
raise SystemExit("Insufficient balance for diagram generation")
print(f"✅ Charged 0.001 USDT. Remaining balance: {charge_result['balance']} USDT")
# ========================================
# STEP 1: DIAGRAM GENERATION
# ========================================
from diagrams import Diagram, Cluster, Edge
# Import nodes based on cloud provider
# from diagrams.gcp.compute import Run, ComputeEngine
# from diagrams.aws.compute import EC2, Lambda
# from diagrams.k8s.compute import Pod, Deployment
output_dir = "./pic/{diagram_name}"
os.makedirs(output_dir, exist_ok=True)
with Diagram(
"{Diagram Title}",
filename=f"{output_dir}/{diagram_name}",
outformat=["png", "svg"],
show=False,
direction="LR"
):
# Define clusters and nodes
with Cluster("Cluster Name"):
node1 = ServiceType("Label")
# Define connections
node1 >> Edge(label="description") >> node2
print(f"✅ Diagram generated successfully!")
print(f"📁 Output files:")
print(f" - {output_dir}/{diagram_name}.png")
print(f" - {output_dir}/{diagram_name}.svg")
Output Configuration
with Diagram(
"Diagram Name",
filename="./pic/{subfolder}/{name}", # Output to pic subdirectory
outformat=["png", "svg"], # Both formats
show=False, # Don't auto-open
direction="LR" # Left-to-right by default
):
Directory Setup
Before execution, ensure output directory exists:
import os
os.makedirs("./pic/{subfolder}", exist_ok=True)
Phase 3: Save Code & Result Feedback
After execution:
- Save the Python source code to
./pic/{name}/{name}.py(same folder as generated images) - Report generated file paths:
./pic/{name}/{name}.py(source code)./pic/{name}/{name}.png./pic/{name}/{name}.svg
- If errors occur, analyze and fix automatically
- Log transaction ID for payment tracking
Node Import Reference
See references/diagrams-api.md for complete node import paths.
Quick Reference - Common Providers
| Provider | Import Pattern | Example |
|---|---|---|
| GCP | diagrams.gcp.{category} |
from diagrams.gcp.compute import Run |
| AWS | diagrams.aws.{category} |
from diagrams.aws.compute import EC2 |
| Azure | diagrams.azure.{category} |
from diagrams.azure.compute import VM |
| K8s | diagrams.k8s.{category} |
from diagrams.k8s.compute import Pod |
| Generic | diagrams.generic.{category} |
from diagrams.generic.compute import Rack |
| On-Premise | diagrams.onprem.{category} |
from diagrams.onprem.database import PostgreSQL |
Common Categories
compute: EC2, Run, Pod, VMdatabase: RDS, SQL, PostgreSQL, Redisnetwork: ELB, VPC, DNS, CDNstorage: S3, GCS, PersistentDiskanalytics: BigQuery, Dataflowml: SageMaker, VertexAIsecurity: IAM, KMS, WAFclient: User, Users, Client
扩展能力：多种 Python 绑图库支持
除了 diagrams 库用于架构图，本 Skill 支持根据用户需求动态选择合适的 Python 绘图库：
| 场景 | 推荐库 | 安装命令 | 适用场景 |
|---|---|---|---|
| 云架构图 | diagrams |
pip install diagrams |
AWS/GCP/Azure/K8s 架构可视化 |
| 数据可视化 | matplotlib |
pip install matplotlib |
折线图、柱状图、散点图、饼图等 |
| 统计图表 | seaborn |
pip install seaborn |
高级统计图、热力图、分布图 |
| 交互式图表 | plotly |
pip install plotly |
可交互的 Web 图表、3D 图 |
| 流程图/思维导图 | graphviz |
pip install graphviz |
流程图、状态机、决策树 |
| 网络拓扑图 | networkx + matplotlib |
pip install networkx |
网络关系图、图论可视化 |
| 甘特图/时序图 | plotly / matplotlib |
- | 项目管理、时间线 |
动态选择策略
在 Phase 1 需求理解阶段，根据用户描述判断最适合的绘图库：
- "架构图"、"系统图"、"云服务" → 使用
diagrams - "数据图表"、"趋势图"、"统计" → 使用
matplotlib/seaborn - "交互式"、"Web展示"、"可缩放" → 使用
plotly - "流程图"、"状态图"、"决策树" → 使用
graphviz - "关系图"、"网络拓扑"、"节点连接" → 使用
networkx
示例：matplotlib 数据可视化 (带支付)
import requests
import os
import matplotlib.pyplot as plt
# Payment verification (same as above)
# ... verify_payment code ...
output_dir = "./pic/data-chart"
os.makedirs(output_dir, exist_ok=True)
# 数据
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May']
values = [100, 150, 200, 180, 250]
plt.figure(figsize=(10, 6))
plt.bar(months, values, color='steelblue')
plt.title('Monthly Sales')
plt.xlabel('Month')
plt.ylabel('Sales')
plt.savefig(f"{output_dir}/sales-chart.png", dpi=150, bbox_inches='tight')
plt.savefig(f"{output_dir}/sales-chart.svg", bbox_inches='tight')
plt.close()
示例：plotly 交互式图表 (带支付)
import requests
import os
import plotly.express as px
# Payment verification (same as above)
# ... verify_payment code ...
output_dir = "./pic/interactive-chart"
os.makedirs(output_dir, exist_ok=True)
df = px.data.gapminder().query("year == 2007")
fig = px.scatter(df, x="gdpPercap", y="lifeExp", size="pop", color="continent",
hover_name="country", log_x=True, title="GDP vs Life Expectancy")
fig.write_html(f"{output_dir}/chart.html")
fig.write_image(f"{output_dir}/chart.png")
Design Best Practices
- Use Clusters for logical grouping (VPC, Region, Service Group)
- Direction: Use
LR(left-right) for wide diagrams,TB(top-bottom) for tall ones - Edge labels: Add
Edge(label="HTTP")for connection descriptions - Consistent naming: Use clear, descriptive labels
- Color coding: Leverage built-in provider colors for visual distinction
Example: GCP Architecture (with Payment)
import requests
import os
# ========================================
# SKILLPAY BILLING
# ========================================
BILLING_API_URL = 'https://skillpay.me'
BILLING_API_KEY = 'sk_d6d26f291dafc43acc8c2b6215b87cbc9b19c7d093aebdb2deeba42a3a0fea4b'
SKILL_ID = 'd270a586-e46c-42ba-8bbb-481ba7c2150c'
HEADERS = {'X-API-Key': BILLING_API_KEY, 'Content-Type': 'application/json'}
def charge_user(user_id: str) -> dict:
resp = requests.post(
f'{BILLING_API_URL}/api/v1/billing/charge',
headers=HEADERS,
json={'user_id': user_id, 'skill_id': SKILL_ID, 'amount': 0.001}
)
data = resp.json()
if data['success']:
return {'ok': True, 'balance': data['balance']}
return {'ok': False, 'balance': data['balance'], 'payment_url': data.get('payment_url')}
USER_ID = os.environ.get("SKILLPAY_USER_ID", "anonymous_user")
charge_result = charge_user(USER_ID)
if not charge_result['ok']:
raise SystemExit(f"💳 Insufficient balance. Top up at: {charge_result['payment_url']}")
# ========================================
# DIAGRAM GENERATION
# ========================================
from diagrams import Diagram, Cluster, Edge
from diagrams.gcp.compute import Run
from diagrams.gcp.database import Memorystore, SQL
from diagrams.gcp.network import LoadBalancing
from diagrams.onprem.client import Users
output_dir = "./pic/gcp-architecture"
os.makedirs(output_dir, exist_ok=True)
with Diagram(
"GCP Web Service",
filename=f"{output_dir}/gcp-architecture",
outformat=["png", "svg"],
show=False,
direction="LR"
):
users = Users("Users")
with Cluster("Google Cloud Platform"):
lb = LoadBalancing("Load Balancer")
with Cluster("Application Layer"):
api = Run("API Service")
worker = Run("Worker Service")
with Cluster("Data Layer"):
cache = Memorystore("Redis Cache")
db = SQL("Cloud SQL")
users >> lb >> api
api >> cache
api >> worker >> db
print("✅ Diagram generated successfully!")
TikZ 学术论文图 (Academic-Grade Figures)
TikZ 是 LaTeX 生态中的专业绘图工具，广泛用于顶级学术论文。适合绘制神经网络架构、模型结构、算法流程等。
检查 LaTeX 环境
# 检查是否已安装
pdflatex --version
# 如未安装，参考 Prerequisites 部分
TikZ 代码模板 - 神经网络
% neural_network.tex
\documentclass[tikz,border=10pt]{standalone}
\usepackage{tikz}
\usetikzlibrary{positioning,arrows.meta,shapes.geometric,fit,backgrounds}
\begin{document}
\begin{tikzpicture}[
node distance=1.5cm,
layer/.style={rectangle, draw, minimum width=2cm, minimum height=0.8cm, align=center},
arrow/.style={-{Stealth[scale=1.2]}, thick}
]
% 输入层
\node[layer, fill=blue!20] (input) {Input\\$x \in \mathbb{R}^{784}$};
% 隐藏层
\node[layer, fill=green!20, right=of input] (hidden1) {Hidden Layer\\ReLU, 256};
\node[layer, fill=green!20, right=of hidden1] (hidden2) {Hidden Layer\\ReLU, 128};
% 输出层
\node[layer, fill=red!20, right=of hidden2] (output) {Output\\Softmax, 10};
% 连接箭头
\draw[arrow] (input) -- (hidden1);
\draw[arrow] (hidden1) -- (hidden2);
\draw[arrow] (hidden2) -- (output);
\end{tikzpicture}
\end{document}
TikZ 代码模板 - Transformer 架构 (V5 最佳实践)
% transformer_v5.tex - 完整 Encoder-Decoder 架构
\documentclass[tikz,border=15pt]{standalone}
\usepackage{tikz}
\usepackage{xcolor}
\usepackage{amsmath}
\usetikzlibrary{positioning,arrows.meta,shapes.geometric,fit,backgrounds,calc}
% ===== Material Design 配色 =====
\definecolor{orange1}{HTML}{FFB74D} % Attention
\definecolor{blue1}{HTML}{64B5F6} % FFN
\definecolor{green1}{HTML}{81C784} % Norm
\definecolor{purple1}{HTML}{BA68C8} % Embedding
\definecolor{pink1}{HTML}{F48FB1} % Softmax
\definecolor{yellow1}{HTML}{FFF176} % Positional
\definecolor{gray1}{HTML}{BDBDBD} % Linear
\begin{document}
\begin{tikzpicture}[
% ===== 样式系统：基础样式 + 继承 =====
box/.style={
rectangle, draw=black!70, line width=0.5pt,
minimum width=2.4cm, minimum height=0.7cm,
align=center, font=\footnotesize\sffamily, rounded corners=2pt,
},
attn/.style={box, fill=orange1},
ffn/.style={box, fill=blue1},
norm/.style={box, fill=green1!70, minimum height=0.55cm, font=\scriptsize\sffamily},
emb/.style={box, fill=purple1!60},
posenc/.style={box, fill=yellow1!80, minimum width=2cm, minimum height=0.5cm, font=\scriptsize\sffamily},
lin/.style={box, fill=gray1},
soft/.style={box, fill=pink1},
addcircle/.style={circle, draw=black!70, fill=white, inner sep=0pt, minimum size=14pt, font=\scriptsize},
% ===== 三级箭头系统 =====
arr/.style={-{Stealth[length=5pt, width=4pt]}, line width=0.5pt, black!70},
arrgray/.style={-{Stealth[length=4pt, width=3pt]}, line width=0.4pt, black!40},
arrblue/.style={-{Stealth[length=5pt, width=4pt]}, line width=0.7pt, blue!60},
]
% ===== 间距变量 =====
\def\gap{0.45}
\def\biggap{0.7}
% ==================== ENCODER ====================
\node[font=\small\sffamily] (inputs) at (0, 0) {Inputs};
\node[emb, above=\biggap of inputs] (enc_emb) {Input Embedding};
\node[addcircle, above=\gap of enc_emb] (enc_add) {$+$};
\node[posenc, left=0.6cm of enc_add] (enc_pos) {\scriptsize Positional Encoding};
\node[attn, above=\biggap of enc_add] (enc_mha) {Multi-Head\\Attention};
\node[norm, above=\gap of enc_mha] (enc_n1) {Add \& Norm};
\node[ffn, above=\gap of enc_n1] (enc_ff) {Feed Forward};
\node[norm, above=\gap of enc_ff] (enc_n2) {Add \& Norm};
% Encoder 连线
\draw[arr] (inputs) -- (enc_emb);
\draw[arr] (enc_emb) -- (enc_add);
\draw[arr] (enc_pos) -- (enc_add);
\draw[arr] (enc_add) -- (enc_mha);
\draw[arr] (enc_mha) -- (enc_n1);
\draw[arr] (enc_n1) -- (enc_ff);
\draw[arr] (enc_ff) -- (enc_n2);
% Encoder 残差连接 (微偏移 + 左右交替)
\draw[arrgray] ($(enc_add.north)+(0.05,0)$) -- ++(0,0.25) -| ($(enc_mha.east)+(0.4,0)$) |- (enc_n1.east);
\draw[arrgray] ($(enc_n1.north)+(0.05,0)$) -- ++(0,0.15) -| ($(enc_ff.west)+(-0.4,0)$) |- (enc_n2.west);
% Encoder 框 (背景层 + 淡色)
\begin{scope}[on background layer]
\node[draw=black!40, line width=0.8pt, inner xsep=18pt, inner ysep=10pt, rounded corners=4pt,
fit=(enc_mha)(enc_n1)(enc_ff)(enc_n2)] (enc_box) {};
\end{scope}
\node[font=\scriptsize\sffamily, anchor=west] at ($(enc_box.north east)+(0.1,-0.1)$) {$\times N$};
% ==================== DECODER ====================
\begin{scope}[xshift=5.5cm]
\node[font=\small\sffamily, align=center] (outputs) at (0, 0) {Outputs\\[-3pt]{\tiny(shifted right)}};
\node[emb, above=\biggap of outputs] (dec_emb) {Output Embedding};
\node[addcircle, above=\gap of dec_emb] (dec_add) {$+$};
\node[posenc, right=0.6cm of dec_add] (dec_pos) {\scriptsize Positional Encoding};
\node[attn, above=\biggap of dec_add] (dec_mha1) {Masked\\Multi-Head Attention};
\node[norm, above=\gap of dec_mha1] (dec_n1) {Add \& Norm};
\node[attn, above=\gap of dec_n1] (dec_mha2) {Multi-Head\\Attention};
\node[norm, above=\gap of dec_mha2] (dec_n2) {Add \& Norm};
\node[ffn, above=\gap of dec_n2] (dec_ff) {Feed Forward};
\node[norm, above=\gap of dec_ff] (dec_n3) {Add \& Norm};
% Decoder 连线
\draw[arr] (outputs) -- (dec_emb);
\draw[arr] (dec_emb) -- (dec_add);
\draw[arr] (dec_pos) -- (dec_add);
\draw[arr] (dec_add) -- (dec_mha1);
\draw[arr] (dec_mha1) -- (dec_n1);
\draw[arr] (dec_n1) -- (dec_mha2);
\draw[arr] (dec_mha2) -- (dec_n2);
\draw[arr] (dec_n2) -- (dec_ff);
\draw[arr] (dec_ff) -- (dec_n3);
% Decoder 残差连接
\draw[arrgray] ($(dec_add.north)+(-0.05,0)$) -- ++(0,0.25) -| ($(dec_mha1.west)+(-0.4,0)$) |- (dec_n1.west);
\draw[arrgray] ($(dec_n1.north)+(-0.05,0)$) -- ++(0,0.15) -| ($(dec_mha2.east)+(0.4,0)$) |- (dec_n2.east);
\draw[arrgray] ($(dec_n2.north)+(-0.05,0)$) -- ++(0,0.15) -| ($(dec_ff.west)+(-0.4,0)$) |- (dec_n3.west);
% Decoder 框
\begin{scope}[on background layer]
\node[draw=black!40, line width=0.8pt, inner xsep=18pt, inner ysep=10pt, rounded corners=4pt,
fit=(dec_mha1)(dec_n1)(dec_mha2)(dec_n2)(dec_ff)(dec_n3)] (dec_box) {};
\end{scope}
\node[font=\scriptsize\sffamily, anchor=west] at ($(dec_box.north east)+(0.1,-0.1)$) {$\times N$};
% Output 层
\node[lin, above=\biggap of dec_n3] (linear) {Linear};
\node[soft, above=\gap of linear] (softmax) {Softmax};
\node[font=\small\sffamily, above=\gap of softmax] (outprob) {Output Probabilities};
\draw[arr] (dec_n3) -- (linear);
\draw[arr] (linear) -- (softmax);
\draw[arr] (softmax) -- (outprob);
\end{scope}
% ==================== Encoder → Decoder (蓝色高亮) ====================
\draw[arrblue] (enc_n2.north) -- ++(0,0.4) -| ++(1.8,0) |- (dec_mha2.west);
% 标签 (灰色淡化)
\node[font=\small\sffamily\bfseries, gray!80, above=0.15cm of enc_box.north] {Encoder};
\node[font=\small\sffamily\bfseries, gray!80, above=0.15cm of dec_box.north] {Decoder};
\end{tikzpicture}
\end{document}
V5 设计要点：
- 样式继承:
attn/.style={box, fill=orange1}避免重复代码 - 三级箭头: 主连接 / 残差 / 跨模块，主次分明
- 间距变量:
\gap/\biggap全局统一 - 残差偏移:
+(0.05,0)微偏移避免重叠，左右交替绕行 - 背景层框:
on background layer+black!40淡色不抢眼
TikZ 编译与输出
# 生成 PDF (矢量图，推荐，直接用于论文)
pdflatex neural_network.tex
TikZ 完整工作流 (带支付)
import subprocess
import os
import requests
# SkillPay Billing
BILLING_API_URL = 'https://skillpay.me'
BILLING_API_KEY = 'sk_d6d26f291dafc43acc8c2b6215b87cbc9b19c7d093aebdb2deeba42a3a0fea4b'
SKILL_ID = 'd270a586-e46c-42ba-8bbb-481ba7c2150c'
HEADERS = {'X-API-Key': BILLING_API_KEY, 'Content-Type': 'application/json'}
def charge_user(user_id: str) -> dict:
resp = requests.post(
f'{BILLING_API_URL}/api/v1/billing/charge',
headers=HEADERS,
json={'user_id': user_id, 'skill_id': SKILL_ID, 'amount': 0.001}
)
data = resp.json()
if data['success']:
return {'ok': True, 'balance': data['balance']}
return {'ok': False, 'balance': data['balance'], 'payment_url': data.get('payment_url')}
USER_ID = os.environ.get("SKILLPAY_USER_ID", "anonymous_user")
charge_result = charge_user(USER_ID)
if not charge_result['ok']:
raise SystemExit(f"💳 Insufficient balance. Top up at: {charge_result['payment_url']}")
def compile_tikz(tex_file, output_dir="./pic/tikz"):
"""编译 TikZ 文件生成 PDF"""
os.makedirs(output_dir, exist_ok=True)
# 编译 LaTeX
result = subprocess.run(
["pdflatex", "-output-directory", output_dir, tex_file],
capture_output=True, text=True
)
if result.returncode != 0:
print(f"LaTeX 编译失败:\n{result.stderr}")
return None
# 获取生成的 PDF 路径
base_name = os.path.splitext(os.path.basename(tex_file))[0]
pdf_path = os.path.join(output_dir, f"{base_name}.pdf")
# 清理临时文件
for ext in [".aux", ".log"]:
tmp_file = os.path.join(output_dir, f"{base_name}{ext}")
if os.path.exists(tmp_file):
os.remove(tmp_file)
return pdf_path
# 使用示例
# compile_tikz("neural_network.tex", "./pic/neural-net")
matplotlib 快速替代方案 (无需 LaTeX，带支付)
当用户选择快速方案时，使用 matplotlib 绘制学术风格图：
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import os
import requests
# SkillPay Billing
BILLING_API_URL = 'https://skillpay.me'
BILLING_API_KEY = 'sk_d6d26f291dafc43acc8c2b6215b87cbc9b19c7d093aebdb2deeba42a3a0fea4b'
SKILL_ID = 'd270a586-e46c-42ba-8bbb-481ba7c2150c'
HEADERS = {'X-API-Key': BILLING_API_KEY, 'Content-Type': 'application/json'}
def charge_user(user_id: str) -> dict:
resp = requests.post(
f'{BILLING_API_URL}/api/v1/billing/charge',
headers=HEADERS,
json={'user_id': user_id, 'skill_id': SKILL_ID, 'amount': 0.001}
)
data = resp.json()
if data['success']:
return {'ok': True, 'balance': data['balance']}
return {'ok': False, 'balance': data['balance'], 'payment_url': data.get('payment_url')}
USER_ID = os.environ.get("SKILLPAY_USER_ID", "anonymous_user")
charge_result = charge_user(USER_ID)
if not charge_result['ok']:
raise SystemExit(f"💳 Insufficient balance. Top up at: {charge_result['payment_url']}")
def draw_neural_network_matplotlib(output_dir="./pic/nn-matplotlib"):
"""使用 matplotlib 绘制神经网络架构图"""
os.makedirs(output_dir, exist_ok=True)
fig, ax = plt.subplots(figsize=(12, 6))
ax.set_xlim(0, 10)
ax.set_ylim(0, 4)
ax.axis('off')
# 定义层的位置和颜色
layers = [
{"x": 1, "label": "Input\n784", "color": "#a8d5e5"},
{"x": 3, "label": "Hidden\n256", "color": "#b8e0b8"},
{"x": 5, "label": "Hidden\n128", "color": "#b8e0b8"},
{"x": 7, "label": "Hidden\n64", "color": "#b8e0b8"},
{"x": 9, "label": "Output\n10", "color": "#f5b8b8"},
]
# 绘制层
for layer in layers:
rect = patches.FancyBboxPatch(
(layer["x"] - 0.5, 1.5), 1, 1.5,
boxstyle="round,pad=0.05,rounding_size=0.1",
facecolor=layer["color"], edgecolor="black", linewidth=1.5
)
ax.add_patch(rect)
ax.text(layer["x"], 2.25, layer["label"], ha='center', va='center', fontsize=10, fontweight='bold')
# 绘制箭头
for i in range(len(layers) - 1):
ax.annotate('', xy=(layers[i+1]["x"] - 0.5, 2.25), xytext=(layers[i]["x"] + 0.5, 2.25),
arrowprops=dict(arrowstyle='->', color='gray', lw=1.5))
plt.title("Neural Network Architecture", fontsize=14, fontweight='bold', pad=20)
plt.tight_layout()
plt.savefig(f"{output_dir}/neural_network.png", dpi=150, bbox_inches='tight', facecolor='white')
plt.savefig(f"{output_dir}/neural_network.svg", bbox_inches='tight', facecolor='white')
plt.close()
return f"{output_dir}/neural_network.png"
# 使用示例
# draw_neural_network_matplotlib()
输出格式指南
| 格式 | 适用场景 | 工具支持 |
|---|---|---|
| PNG | 通用展示、PPT、网页 | 所有工具 |
| SVG | 网页嵌入、可缩放 | matplotlib, diagrams, plotly |
| 论文插图、打印 | TikZ (原生), matplotlib | |
| HTML | 交互式展示 | plotly |
按需转换
# PNG → 其他格式 (使用 Pillow)
from PIL import Image
img = Image.open("diagram.png")
img.save("diagram.jpg", quality=95)
# PDF → PNG (使用 pdf2image)
from pdf2image import convert_from_path
images = convert_from_path("diagram.pdf", dpi=300)
images[0].save("diagram.png", "PNG")
# SVG → PNG (使用 cairosvg)
import cairosvg
cairosvg.svg2png(url="diagram.svg", write_to="diagram.png", scale=2)
TikZ 学术图表美化指南
详见 references/styling-guide.md，涵盖：
| 章节 | 内容 |
|---|---|
| 配色策略 | Material Design 7色方案、透明度微调 |
| 样式系统 | 继承机制、尺寸区分 |
| 字体层级 | \small → \footnotesize → \scriptsize → \tiny |
| 间距系统 | \gap / \biggap 变量化控制 |
| 三级箭头 | 主连接 0.5pt / 残差 0.4pt / 跨模块 0.7pt |
| 残差连接 | 微偏移 + 左右交替绕行 |
| 模块框 | 背景层 + 淡色 + 大圆角 |
| 快速清单 | 生成前 10 项自查 |
核心原则：
- 不使用阴影 (
shadow)，学术图表追求简洁 - 边框用
black!70而非纯黑，模块框用black!40更淡 - 组件圆角 2pt，模块框圆角 4pt，形成层级
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