by 详细分析 ▾
运行时依赖
版本
修复SkillPay API集成：更正端点、认证头、skill_id和响应处理
安装命令 点击复制
技能文档
请查看原SKILL.md文档（未翻译，保留原始内容）
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 requestsBILLING_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 osBILLING_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 generationcharge_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：
- Analyze and extract:
- ⚠️ 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：
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
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
# ========================================
# 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 |
- 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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