# Scientific Schematics

> Create publication-quality scientific diagrams, flowcharts, and schematics using Python (graphviz, matplotlib, schemdraw, networkx). Specialized in neural network architectures, system diagrams, and flowcharts. Generates SVG/EPS in figures/ folder with automated quality verification.

- Skill: `ttawdtt/scientific-schematics` (Agent Skill, multi-file: 14 files)
- Install (CLI): `npx skillmds@latest add ttawdtt/scientific-schematics`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ttawdtt/scientific-schematics/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: ttawdtt (https://skillmd.com/u/ttawdtt)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/ttawdtt/scientific-schematics

---


# Scientific Schematics and Diagrams

## Overview

Scientific schematics and diagrams transform complex concepts into clear visual representations for publication. Generate neural network architectures, flowcharts, circuit diagrams, biological pathways, and system diagrams using best-in-class Python libraries. **All diagrams are created as SVG/EPS files, stored in the figures/ subfolder, and referenced in papers/posters** - never embedded directly in LaTeX.

## Zero-Shot Diagram Generation Workflow

**Standard workflow for ALL diagrams:**

1. **Analyze requirements** - Identify diagram type and components
2. **Choose optimal library** - Select best tool for the specific diagram type
3. **Generate vector graphic** - Create SVG/EPS with proper spacing and layout
4. **Store in figures/** - Save to `figures/` subfolder with descriptive name
5. **Run quality checks** - Verify no overlaps, good contrast, proper resolution
6. **Reference in document** - Use `\includegraphics{figures/diagram_name.pdf}` in LaTeX

**Key principle:** Generate standalone vector graphics first, then integrate into documents.

## When to Use This Skill

This skill should be used when:
- Creating neural network architecture diagrams (Transformers, CNNs, RNNs, etc.)
- Illustrating system architectures and data flow diagrams
- Drawing methodology flowcharts for study design (CONSORT, PRISMA)
- Visualizing algorithm workflows and processing pipelines
- Creating circuit diagrams and electrical schematics
- Depicting biological pathways and molecular interactions
- Generating network topologies and hierarchical structures
- Illustrating conceptual frameworks and theoretical models
- Designing block diagrams for technical papers

## Best Libraries by Diagram Type

Choose the optimal library for your specific diagram type:

### Neural Network Architectures (Transformers, CNNs, etc.)
**Best library:** `graphviz` via Python's `pygraphviz` or `pydot`
- Excellent automatic layout algorithms
- Clean, professional appearance
- Perfect for layer stacks and connections
- Handles complex cross-connections well

**Alternative:** Custom `matplotlib` with careful positioning
- More control over exact placement
- Better for highly customized designs
- Requires more manual positioning

### Flowcharts and Process Diagrams
**Best library:** `graphviz` with `dot` or `flowchart` layout
- Automatic optimal positioning
- Standard flowchart shapes
- Clean arrow routing
- Minimal overlap issues

**Alternative:** `diagrams` library (for cloud/system architecture style)

### Circuit Diagrams
**Best library:** `schemdraw`
- Purpose-built for electrical circuits
- Extensive component library
- Automatic wire routing
- Professional engineering standard output

### Biological Pathways
**Best library:** `networkx` with custom rendering
- Graph-based pathway representation
- Algorithm-driven layout
- Flexible node/edge styling

### Block Diagrams and System Architecture
**Best library:** `graphviz` or `diagrams`
- Clean hierarchical layouts
- Automatic spacing
- Professional appearance

## Zero-Shot Examples for Common Diagram Types

### Example 1: Transformer Architecture (Neural Network)

Creating a Transformer encoder-decoder diagram like in "Attention Is All You Need":

```python
import graphviz
from pathlib import Path

def create_transformer_diagram(output_dir='figures'):
    """
    Create a Transformer architecture diagram.
    Zero-shot generation with automatic layout.
    """
    Path(output_dir).mkdir(exist_ok=True)
    
    # Create directed graph with TB (top-to-bottom) layout
    dot = graphviz.Digraph(
        'transformer',
        format='pdf',
        graph_attr={
            'rankdir': 'BT',  # Bottom to top (like the original paper)
            'splines': 'ortho',  # Orthogonal edges
            'nodesep': '0.5',
            'ranksep': '0.8',
            'bgcolor': 'white',
            'dpi': '300'
        },
        node_attr={
            'shape': 'box',
            'style': 'rounded,filled',
            'fillcolor': 'lightgray',
            'fontname': 'Arial',
            'fontsize': '11',
            'width': '2.5',
            'height': '0.5'
        },
        edge_attr={
            'color': 'black',
            'penwidth': '1.5'
        }
    )
    
    # ENCODER STACK (left side)
    with dot.subgraph(name='cluster_encoder') as enc:
        enc.attr(label='Encoder', fontsize='14', fontname='Arial-Bold')
        enc.attr(style='rounded', color='blue', penwidth='2')
        
        # Encoder layers (bottom to top)
        enc.node('enc_input_emb', 'Input Embedding', fillcolor='#E8F4F8')
        enc.node('enc_pos', 'Positional Encoding', fillcolor='#E8F4F8')
        enc.node('enc_mha', 'Multi-Head\nAttention', fillcolor='#B3D9E6')
        enc.node('enc_an1', 'Add & Norm', fillcolor='#CCE5FF')
        enc.node('enc_ff', 'Feed Forward', fillcolor='#B3D9E6')
        enc.node('enc_an2', 'Add & Norm', fillcolor='#CCE5FF')
        
        # Encoder flow
        enc.edge('enc_input_emb', 'enc_pos')
        enc.edge('enc_pos', 'enc_mha')
        enc.edge('enc_mha', 'enc_an1')
        enc.edge('enc_an1', 'enc_ff')
        enc.edge('enc_ff', 'enc_an2')
    
    # DECODER STACK (right side)
    with dot.subgraph(name='cluster_decoder') as dec:
        dec.attr(label='Decoder', fontsize='14', fontname='Arial-Bold')
        dec.attr(style='rounded', color='red', penwidth='2')
        
        # Decoder layers (bottom to top)
        dec.node('dec_output_emb', 'Output Embedding', fillcolor='#FFE8E8')
        dec.node('dec_pos', 'Positional Encoding', fillcolor='#FFE8E8')
        dec.node('dec_mmha', 'Masked Self-\nAttention', fillcolor='#FFB3B3')
        dec.node('dec_an1', 'Add & Norm', fillcolor='#FFCCCC')
        dec.node('dec_cross', 'Cross-Attention', fillcolor='#FFB3B3')
        dec.node('dec_an2', 'Add & Norm', fillcolor='#FFCCCC')
        dec.node('dec_ff', 'Feed Forward', fillcolor='#FFB3B3')
        dec.node('dec_an3', 'Add & Norm', fillcolor='#FFCCCC')
        dec.node('dec_linear', 'Linear & Softmax', fillcolor='#FF9999')
        dec.node('dec_output', 'Output\nProbabilities', fillcolor='#FFE8E8')
        
        # Decoder flow
        dec.edge('dec_output_emb', 'dec_pos')
        dec.edge('dec_pos', 'dec_mmha')
        dec.edge('dec_mmha', 'dec_an1')
        dec.edge('dec_an1', 'dec_cross')
        dec.edge('dec_cross', 'dec_an2')
        dec.edge('dec_an2', 'dec_ff')
        dec.edge('dec_ff', 'dec_an3')
        dec.edge('dec_an3', 'dec_linear')
        dec.edge('dec_linear', 'dec_output')
    
    # Cross-attention connection (encoder to decoder)
    dot.edge('enc_an2', 'dec_cross', 
             style='dashed', 
             color='purple', 
             label='  context  ',
             fontsize='9')
    
    # Input and output labels
    dot.node('input_seq', 'Input Sequence', 
             shape='ellipse', fillcolor='lightgreen')
    dot.node('target_seq', 'Target Sequence', 
             shape='ellipse', fillcolor='lightgreen')
    
    dot.edge('input_seq', 'enc_input_emb')
    dot.edge('target_seq', 'dec_output_emb')
    
    # Render to files
    output_path = f'{output_dir}/transformer_architecture'
    dot.render(output_path, cleanup=True)
    
    # Also save as SVG and EPS
    dot.format = 'svg'
    dot.render(output_path, cleanup=True)
    dot.format = 'eps'
    dot.render(output_path, cleanup=True)
    
    print(f"✓ Transformer diagram created:")
    print(f"  - {output_path}.pdf")
    print(f"  - {output_path}.svg")
    print(f"  - {output_path}.eps")
    
    return f"{output_path}.pdf"

# Usage
if __name__ == '__main__':
    diagram_path = create_transformer_diagram('figures')
    
    # Run quality checks
    from quality_checker import run_quality_checks
    run_quality_checks(diagram_path.replace('.pdf', '.png'))
```

**LaTeX integration:**
```latex
\begin{figure}[htbp]
\centering
\includegraphics[width=0.9\textwidth]{figures/transformer_architecture.pdf}
\caption{Transformer encoder-decoder architecture showing multi-head attention,
         feed-forward layers, and cross-attention mechanism.}
\label{fig:transformer}
\end{figure}
```

### Example 2: Simple Flowchart (CONSORT-style)

```python
import graphviz
from pathlib import Path

def create_consort_flowchart(output_dir='figures'):
    """Create a CONSORT participant flow diagram."""
    Path(output_dir).mkdir(exist_ok=True)
    
    dot = graphviz.Digraph(
        'consort',
        format='pdf',
        graph_attr={
            'rankdir': 'TB',
            'splines': 'ortho',
            'nodesep': '0.6',
            'ranksep': '0.8',
            'bgcolor': 'white'
        },
        node_attr={
            'shape': 'box',
            'style': 'rounded,filled',
            'fillcolor': '#E8F4F8',
            'fontname': 'Arial',
            'fontsize': '10',
            'width': '3',
            'height': '0.6'
        }
    )
    
    # Enrollment
    dot.node('assessed', 'Assessed for eligibility\n(n=500)')
    dot.node('excluded', 'Excluded (n=150)\n• Age < 18: n=80\n• Declined: n=50\n• Other: n=20')
    dot.node('randomized', 'Randomized\n(n=350)')
    
    # Allocation
    dot.node('treatment', 'Allocated to treatment\n(n=175)', fillcolor='#C8E6C9')
    dot.node('control', 'Allocated to control\n(n=175)', fillcolor='#FFECB3')
    
    # Follow-up
    dot.node('treat_lost', 'Lost to follow-up (n=15)', fillcolor='#FFCDD2')
    dot.node('ctrl_lost', 'Lost to follow-up (n=10)', fillcolor='#FFCDD2')
    
    # Analysis
    dot.node('treat_analyzed', 'Analyzed (n=160)', fillcolor='#C8E6C9')
    dot.node('ctrl_analyzed', 'Analyzed (n=165)', fillcolor='#FFECB3')
    
    # Connect nodes
    dot.edge('assessed', 'excluded')
    dot.edge('assessed', 'randomized')
    dot.edge('randomized', 'treatment')
    dot.edge('randomized', 'control')
    dot.edge('treatment', 'treat_lost')
    dot.edge('treatment', 'treat_analyzed')
    dot.edge('control', 'ctrl_lost')
    dot.edge('control', 'ctrl_analyzed')
    
    # Render
    output_path = f'{output_dir}/consort_flowchart'
    dot.render(output_path, cleanup=True)
    
    print(f"✓ CONSORT flowchart created: {output_path}.pdf")
    return f"{output_path}.pdf"
```

### Example 3: CNN Architecture

```python
def create_cnn_architecture(output_dir='figures'):
    """Create a CNN architecture diagram."""
    dot = graphviz.Digraph(
        'cnn',
        format='pdf',
        graph_attr={'rankdir': 'LR', 'bgcolor': 'white'}
    )
    
    # Define layers
    layers = [
        ('input', 'Input\n32×32×3', '#FFE8E8'),
        ('conv1', 'Conv 3×3\n32 filters', '#B3D9E6'),
        ('pool1', 'MaxPool\n2×2', '#FFE5B3'),
        ('conv2', 'Conv 3×3\n64 filters', '#B3D9E6'),
        ('pool2', 'MaxPool\n2×2', '#FFE5B3'),
        ('flatten', 'Flatten', '#D4E8D4'),
        ('fc1', 'FC 128', '#C8B3E6'),
        ('fc2', 'FC 10', '#C8B3E6'),
        ('softmax', 'Softmax', '#FFC8C8')
    ]
    
    # Create nodes
    for node_id, label, color in layers:
        dot.node(node_id, label, 
                shape='box', style='rounded,filled', 
                fillcolor=color, fontname='Arial')
    
    # Connect layers
    for i in range(len(layers) - 1):
        dot.edge(layers[i][0], layers[i+1][0])
    
    output_path = f'{output_dir}/cnn_architecture'
    dot.render(output_path, cleanup=True)
    
    print(f"✓ CNN diagram created: {output_path}.pdf")
    return f"{output_path}.pdf"
```

## Core Capabilities

### 1. Diagram Types Supported

**Neural Network Architectures**
- Transformer encoder-decoder models
- Convolutional Neural Networks (CNNs)
- Recurrent networks (LSTM, GRU)
- Attention mechanisms and variants
- Custom deep learning architectures

**Methodology Flowcharts**
- CONSORT participant flow diagrams
- PRISMA systematic review flows
- Data processing pipelines
- Algorithm workflows
- Subject enrollment flows

**Circuit Diagrams**
- Analog and digital electronic circuits
- Signal processing block diagrams
- Control system diagrams

**Biological Diagrams**
- Signaling pathways
- Metabolic pathway diagrams
- Gene regulatory networks
- Protein interaction networks

**System Architecture Diagrams**
- Software architecture and components
- Data flow diagrams
- Network topology diagrams
- Hierarchical organization charts

## Required Libraries and Installation

### Primary Library: Graphviz (Recommended for 90% of diagrams)

Graphviz is the best tool for most scientific diagrams due to automatic layout, clean rendering, and zero-overlap guarantee.

**Installation:**
```bash
# Install Graphviz binary (required)
# macOS
brew install graphviz

# Ubuntu/Debian
sudo apt-get install graphviz

# Install Python bindings
pip install graphviz
```

**Why Graphviz is optimal:**
- ✓ Automatic optimal layout (no manual positioning needed)
- ✓ Zero overlaps guaranteed by layout algorithms
- ✓ Professional appearance out of the box
- ✓ Supports complex hierarchies and cross-connections
- ✓ Native SVG, PDF, EPS output
- ✓ Minimal code for maximum quality

### Specialized Libraries

**Schemdraw** - Circuit diagrams only
```bash
pip install schemdraw
```

**NetworkX** - Complex network analysis + visualization
```bash
pip install networkx matplotlib
```

**Matplotlib** - Custom manual diagrams (when you need exact control)
```bash
pip install matplotlib
```

## Quick Start Guide for Zero-Shot Diagram Creation

Follow this systematic approach for any diagram type:

### Step 1: Identify Diagram Structure

Ask yourself:
- **Is it a hierarchy?** → Use `rankdir='TB'` or `'BT'` (top-to-bottom or bottom-to-top)
- **Is it a sequence?** → Use `rankdir='LR'` (left-to-right)
- **Does it have parallel branches?** → Use subgraphs/clusters
- **Does it have cross-connections?** → Graphviz handles this automatically

### Step 2: Set Up Base Template

Start with this template and customize:

```python
import graphviz
from pathlib import Path

def create_diagram(output_dir='figures', diagram_name='my_diagram'):
    """Universal diagram creation template."""
    Path(output_dir).mkdir(exist_ok=True, parents=True)
    
    dot = graphviz.Digraph(
        name=diagram_name,
        format='pdf',
        graph_attr={
            'rankdir': 'TB',      # TB, BT, LR, or RL
            'splines': 'ortho',   # ortho (straight) or curved
            'nodesep': '0.6',     # horizontal spacing
            'ranksep': '0.8',     # vertical spacing
            'bgcolor': 'white',
            'dpi': '300'
        },
        node_attr={
            'shape': 'box',       # box, ellipse, diamond, etc.
            'style': 'rounded,filled',
            'fillcolor': 'lightgray',
            'fontname': 'Arial',
            'fontsize': '11',
            'margin': '0.2',
            'width': '2',         # minimum width
            'height': '0.5'       # minimum height
        },
        edge_attr={
            'color': 'black',
            'penwidth': '1.5',
            'arrowsize': '0.8'
        }
    )
    
    # Add your nodes and edges here
    dot.node('node1', 'Label 1')
    dot.node('node2', 'Label 2')
    dot.edge('node1', 'node2')
    
    # Render to multiple formats
    output_path = f'{output_dir}/{diagram_name}'
    dot.render(output_path, cleanup=True)  # PDF
    dot.format = 'svg'
    dot.render(output_path, cleanup=True)  # SVG
    dot.format = 'eps'
    dot.render(output_path, cleanup=True)  # EPS
    
    print(f"✓ Diagram saved: {output_path}.{{pdf,svg,eps}}")
    return f"{output_path}.pdf"
```

### Step 3: Add Nodes with Clear Labels

**Best practices:**
- Use descriptive node IDs: `'encoder_layer1'` not `'n1'`
- Use `\n` for multi-line labels
- Use fill colors to group related components
- Keep labels concise (3-5 words max per line)

```python
# Good node definitions
dot.node('input_layer', 'Input Layer\n(512 dims)', fillcolor='#E8F4F8')
dot.node('attention', 'Multi-Head\nAttention', fillcolor='#B3D9E6')
dot.node('output', 'Output', fillcolor='#C8E6C9')
```

### Step 4: Connect Nodes with Edges

**Edge types:**
```python
# Standard arrow
dot.edge('node1', 'node2')

# Dashed line (for information flow)
dot.edge('encoder', 'decoder', style='dashed')

# Bidirectional
dot.edge('node1', 'node2', dir='both')

# With label
dot.edge('layer1', 'layer2', label='  ReLU  ')

# Different color
dot.edge('input', 'output', color='red', penwidth='2')
```

### Step 5: Use Subgraphs for Grouping

**For parallel structures (like Encoder/Decoder):**
```python
# Encoder cluster
with dot.subgraph(name='cluster_encoder') as enc:
    enc.attr(label='Encoder', style='rounded', color='blue')
    enc.node('enc1', 'Encoder Layer 1')
    enc.node('enc2', 'Encoder Layer 2')
    enc.edge('enc1', 'enc2')

# Decoder cluster
with dot.subgraph(name='cluster_decoder') as dec:
    dec.attr(label='Decoder', style='rounded', color='red')
    dec.node('dec1', 'Decoder Layer 1')
    dec.node('dec2', 'Decoder Layer 2')
    dec.edge('dec1', 'dec2')

# Cross-connection between clusters
dot.edge('enc2', 'dec1', style='dashed', color='purple')
```

### Step 6: Render and Verify

```python
# Always render to PDF (for LaTeX) and SVG (for web/slides)
output_path = f'{output_dir}/{diagram_name}'

# PDF for papers
dot.format = 'pdf'
dot.render(output_path, cleanup=True)

# SVG for posters/slides
dot.format = 'svg'
dot.render(output_path, cleanup=True)

# EPS for some journals
dot.format = 'eps'
dot.render(output_path, cleanup=True)
```

## Common Graphviz Attributes Quick Reference

### Graph Attributes (overall layout)
```python
graph_attr={
    'rankdir': 'TB',        # Direction: TB, BT, LR, RL
    'splines': 'ortho',     # Edge style: ortho, curved, line, polyline
    'nodesep': '0.5',       # Space between nodes (inches)
    'ranksep': '0.8',       # Space between ranks (inches)
    'bgcolor': 'white',     # Background color
    'dpi': '300',           # Resolution for raster output
    'compound': 'true',     # Allow edges between clusters
    'concentrate': 'true'   # Merge multiple edges
}
```

### Node Attributes (boxes/shapes)
```python
node_attr={
    'shape': 'box',         # box, ellipse, circle, diamond, plaintext
    'style': 'rounded,filled',  # rounded, filled, dashed, bold
    'fillcolor': '#E8F4F8', # Fill color (hex or name)
    'color': 'black',       # Border color
    'penwidth': '1.5',      # Border width
    'fontname': 'Arial',    # Font family
    'fontsize': '11',       # Font size (points)
    'fontcolor': 'black',   # Text color
    'width': '2',           # Minimum width (inches)
    'height': '0.5',        # Minimum height (inches)
    'margin': '0.2'         # Internal padding
}
```

### Edge Attributes (arrows/connections)
```python
edge_attr={
    'color': 'black',       # Line color
    'penwidth': '1.5',      # Line width
    'style': 'solid',       # solid, dashed, dotted, bold
    'arrowsize': '1.0',     # Arrow head size
    'dir': 'forward',       # forward, back, both, none
    'arrowhead': 'normal'   # normal, vee, diamond, dot, none
}
```

## Colorblind-Safe Palettes

Use these color sets to ensure accessibility:

### Okabe-Ito Palette (8 colors)
```python
OKABE_ITO = {
    'orange': '#E69F00',
    'sky_blue': '#56B4E9',
    'green': '#009E73',
    'yellow': '#F0E442',
    'blue': '#0072B2',
    'vermillion': '#D55E00',
    'purple': '#CC79A7',
    'black': '#000000'
}
```

### Light Backgrounds (for filled nodes)
```python
LIGHT_FILLS = {
    'blue': '#E8F4F8',
    'green': '#E8F5E9',
    'orange': '#FFF3E0',
    'purple': '#F3E5F5',
    'red': '#FFEBEE',
    'yellow': '#FFFDE7',
    'gray': '#F5F5F5'
}
```

### 4. Publication Standards

All diagrams follow scientific publication best practices:

**Vector Format Output**
- PDF for LaTeX integration (preferred)
- SVG for web and presentations
- EPS for legacy publishing systems
- High-resolution PNG as fallback (300+ DPI)

**Colorblind-Friendly Design**
- Okabe-Ito palette for categorical elements
- Perceptually uniform colormaps for continuous data
- Redundant encoding (shapes + colors)
- Grayscale compatibility verification

**Typography Standards**
- Sans-serif fonts (Arial, Helvetica) for consistency
- Minimum 7-8 pt text at final print size
- Clear, readable labels with units
- Consistent notation throughout

**Accessibility**
- High contrast between elements
- Adequate line weights (0.5-1 pt minimum)
- Clear visual hierarchy
- Descriptive captions and alt text

For comprehensive publication guidelines, see `references/best_practices.md`.

## Quick Start Examples

### Example 1: Simple Flowchart in TikZ

```latex
\documentclass{article}
\usepackage{tikz}
\usetikzlibrary{shapes.geometric, arrows.meta}

% Load colorblind-safe colors
\input{tikz_styles.tex}

\begin{document}

\begin{figure}[h]
\centering
\begin{tikzpicture}[
    node distance=2cm,
    process/.style={rectangle, rounded corners, draw=black, thick, 
                    fill=okabe-blue!20, minimum width=3cm, minimum height=1cm},
    decision/.style={diamond, draw=black, thick, fill=okabe-orange!20, 
                     minimum width=2cm, aspect=2},
    arrow/.style={-Stealth, thick}
]

% Nodes
\node (start) [process] {Screen Participants\\(n=500)};
\node (exclude) [process, below of=start] {Exclude (n=150)\\Age $<$ 18 years};
\node (randomize) [process, below of=exclude] {Randomize (n=350)};
\node (treatment) [process, below left=1.5cm and 2cm of randomize] 
                  {Treatment Group\\(n=175)};
\node (control) [process, below right=1.5cm and 2cm of randomize] 
                {Control Group\\(n=175)};
\node (analyze) [process, below=3cm of randomize] {Analyze Data};

% Arrows
\draw [arrow] (start) -- (exclude);
\draw [arrow] (exclude) -- (randomize);
\draw [arrow] (randomize) -| (treatment);
\draw [arrow] (randomize) -| (control);
\draw [arrow] (treatment) |- (analyze);
\draw [arrow] (control) |- (analyze);

\end{tikzpicture}
\caption{Study participant flow diagram following CONSORT guidelines.}
\label{fig:consort}
\end{figure}

\end{document}
```

### Example 2: Circuit Diagram with Schemdraw

```python
import schemdraw
import schemdraw.elements as elm

# Create drawing with colorblind-safe colors
d = schemdraw.Drawing()

# Voltage source
d += elm.SourceV().label('$V_s$')

# Resistors in series
d += elm.Resistor().right().label('$R_1$\n1kΩ')
d += elm.Resistor().label('$R_2$\n2kΩ')

# Capacitor
d += elm.Capacitor().down().label('$C_1$\n10µF')

# Close the circuit
d += elm.Line().left().tox(d.elements[0].start)

# Add ground
d += elm.Ground()

# Save as vector graphics
d.save('circuit_diagram.svg')
d.save('circuit_diagram.pdf')
```

### Example 3: Biological Pathway with Python

```python
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.patches import FancyBboxPatch, FancyArrowPatch

# Okabe-Ito colorblind-safe palette
colors = {
    'protein': '#56B4E9',    # Blue
    'gene': '#009E73',       # Green
    'process': '#F0E442',    # Yellow
    'inhibition': '#D55E00'  # Orange
}

fig, ax = plt.subplots(figsize=(8, 6))

# Define proteins as rounded rectangles
proteins = [
    ('Receptor', 1, 5),
    ('Kinase A', 3, 5),
    ('Kinase B', 5, 5),
    ('TF', 7, 5),
    ('Gene', 7, 3)
]

for name, x, y in proteins:
    color = colors['gene'] if name == 'Gene' else colors['protein']
    box = FancyBboxPatch((x-0.4, y-0.3), 0.8, 0.6, 
                         boxstyle="round,pad=0.1", 
                         facecolor=color, edgecolor='black', linewidth=2)
    ax.add_patch(box)
    ax.text(x, y, name, ha='center', va='center', fontsize=10, fontweight='bold')

# Add activation arrows
arrows = [
    (1.5, 5, 2.5, 5, 'black'),   # Receptor -> Kinase A
    (3.5, 5, 4.5, 5, 'black'),   # Kinase A -> Kinase B
    (5.5, 5, 6.5, 5, 'black'),   # Kinase B -> TF
    (7, 4.7, 7, 3.6, 'black')    # TF -> Gene
]

for x1, y1, x2, y2, color in arrows:
    arrow = FancyArrowPatch((x1, y1), (x2, y2),
                           arrowstyle='->', mutation_scale=20, 
                           linewidth=2, color=color)
    ax.add_patch(arrow)

# Configure axes
ax.set_xlim(0, 8.5)
ax.set_ylim(2, 6)
ax.set_aspect('equal')
ax.axis('off')

plt.tight_layout()
plt.savefig('signaling_pathway.pdf', bbox_inches='tight', dpi=300)
plt.savefig('signaling_pathway.png', bbox_inches='tight', dpi=300)
```

## Production Workflow (From Concept to Publication)

Follow this systematic workflow for all diagrams:

### Phase 1: Analysis (2 minutes)

1. **Identify diagram type** - What are you visualizing?
   - Neural network architecture? → Use graphviz
   - Flowchart (CONSORT, PRISMA)? → Use graphviz
   - Circuit diagram? → Use schemdraw
   - Complex network? → Use networkx + graphviz

2. **Determine layout direction**
   - Vertical flow (top-to-bottom)? → `rankdir='TB'`
   - Bottom-up (like Transformer)? → `rankdir='BT'`  
   - Left-to-right sequence? → `rankdir='LR'`
   - Right-to-left? → `rankdir='RL'`

3. **Identify groupings**
   - Parallel structures (encoder/decoder)? → Use clusters/subgraphs
   - Sequential only? → Simple node chain
   - Cross-connections? → Graphviz handles automatically

### Phase 2: Implementation (10-15 minutes)

**Standard procedure for 95% of diagrams:**

```python
import graphviz
from pathlib import Path

# 1. Set up output directory
output_dir = 'figures'
Path(output_dir).mkdir(exist_ok=True, parents=True)

# 2. Create diagram with base template
dot = graphviz.Digraph(
    'my_diagram',
    format='pdf',
    graph_attr={
        'rankdir': 'TB',      # Adjust based on Phase 1
        'splines': 'ortho',   # Clean orthogonal edges
        'nodesep': '0.6',     # Good default spacing
        'ranksep': '0.8',
        'bgcolor': 'white',
        'dpi': '300'
    },
    node_attr={
        'shape': 'box',
        'style': 'rounded,filled',
        'fillcolor': 'lightgray',
        'fontname': 'Arial',
        'fontsize': '11'
    },
    edge_attr={'color': 'black', 'penwidth': '1.5'}
)

# 3. Add nodes (with descriptive IDs and clear labels)
dot.node('input', 'Input Layer', fillcolor='#E8F4F8')
dot.node('hidden', 'Hidden Layer', fillcolor='#B3D9E6')
dot.node('output', 'Output Layer', fillcolor='#C8E6C9')

# 4. Add edges
dot.edge('input', 'hidden')
dot.edge('hidden', 'output')

# 5. Render to figures/ folder
output_path = f'{output_dir}/my_diagram'
dot.render(output_path, cleanup=True)  # Creates PDF
dot.format = 'svg'
dot.render(output_path, cleanup=True)  # Creates SVG
dot.format = 'eps'
dot.render(output_path, cleanup=True)  # Creates EPS

print(f"✓ Saved to: {output_path}.{{pdf,svg,eps}}")
```

### Phase 3: Quality Verification (5 minutes)

**Automatic checks:**
```python
# Convert PDF to PNG for quality checking
from pdf2image import convert_from_path

pages = convert_from_path(f'{output_path}.pdf', dpi=300)
pages[0].save(f'{output_path}.png')

# Run quality checks
from quality_checker import run_quality_checks
report = run_quality_checks(f'{output_path}.png')

if report['overall_status'] != 'PASS':
    print("⚠️ Issues detected - review quality_reports/")
    # Adjust spacing: increase nodesep or ranksep
    # Adjust colors: check accessibility report
else:
    print("✓ Quality checks passed!")
```

**Manual verification:**
1. Open PDF in viewer - check for overlaps
2. Verify text is readable (zoom to 100%)
3. Check alignment and spacing looks professional
4. Ensure colors are distinguishable

### Phase 4: LaTeX Integration (2 minutes)

**In your LaTeX document:**

```latex
% In preamble
\usepackage{graphicx}

% In document
\begin{figure}[htbp]
\centering
\includegraphics[width=0.8\textwidth]{figures/my_diagram.pdf}
\caption{Clear, descriptive caption explaining all components and abbreviations.
         Define any non-standard notation used in the diagram.}
\label{fig:my_diagram}
\end{figure}

% Reference in text
As shown in Figure~\ref{fig:my_diagram}, the architecture consists of...
```

**For posters (beamer):**
```latex
\begin{frame}{Architecture}
\begin{center}
\includegraphics[width=0.9\textwidth]{figures/my_diagram.pdf}
\end{center}
\end{frame}
```

### Phase 5: Version Control (1 minute)

**Always commit:**
1. Python source code (`create_my_diagram.py`)
2. Generated outputs (`figures/my_diagram.{pdf,svg,eps}`)
3. Quality reports (`my_diagram_quality_reports/`)

```bash
git add create_my_diagram.py
git add figures/my_diagram.*
git add my_diagram_quality_reports/
git commit -m "Add architecture diagram with quality verification"
```

## Troubleshooting Common Issues

### Graphviz-Specific Problems

**Problem:** Nodes overlap or are too close
```python
# Solution: Increase spacing
graph_attr={
    'nodesep': '1.0',   # Increase from default 0.6
    'ranksep': '1.2'    # Increase from default 0.8
}
```

**Problem:** Edges cross in confusing ways
```python
# Solution 1: Use orthogonal splines
graph_attr={'splines': 'ortho'}

# Solution 2: Adjust rank direction
graph_attr={'rankdir': 'LR'}  # Try different directions
```

**Problem:** Labels are cut off or too small
```python
# Solution: Adjust node size and font
node_attr={
    'fontsize': '12',      # Increase from 11
    'margin': '0.3',       # More internal padding
    'width': '2.5',        # Wider boxes
    'height': '0.6'        # Taller boxes
}
```

**Problem:** Clusters/subgraphs not appearing
```python
# Solution: Cluster names MUST start with 'cluster_'
with dot.subgraph(name='cluster_encoder') as enc:  # ✓ Correct
    enc.attr(label='Encoder')

with dot.subgraph(name='encoder') as enc:  # ✗ Won't show as cluster
    enc.attr(label='Encoder')
```

**Problem:** Cross-cluster edges not working
```python
# Solution: Enable compound edges
dot.attr(compound='true')

# Then use lhead/ltail for cluster connections
dot.edge('node1', 'node2', lhead='cluster_decoder')
```

**Problem:** Graphviz not found error
```bash
# Solution: Install graphviz binary (not just Python package)
# macOS
brew install graphviz

# Ubuntu
sudo apt-get install graphviz

# Then install Python bindings
pip install graphviz
```

### Visual Quality Issues

**Problem:** Colors not colorblind-safe
```python
# Solution: Use Okabe-Ito palette
COLORS = {
    'blue': '#56B4E9',
    'green': '#009E73',
    'orange': '#E69F00',
    'purple': '#CC79A7'
}
dot.node('n1', 'Node', fillcolor=COLORS['blue'])
```

**Problem:** Text too small when printed
```python
# Solution: Increase font size and DPI
node_attr={'fontsize': '12'}  # Minimum 11-12 for print
graph_attr={'dpi': '300'}     # Publication quality
```

**Problem:** PDF too large
```python
# Solution 1: Use simpler edge routing
graph_attr={'splines': 'line'}  # Simpler than 'ortho'

# Solution 2: Reduce DPI for drafts
graph_attr={'dpi': '150'}  # For drafts only
```

### Workflow Issues

**Problem:** Need to regenerate diagram after changes
```python
# Solution: Make diagram generation a function
def create_diagram(params):
    # ... diagram code ...
    return output_path

# Easy to regenerate with different parameters
create_diagram({'nodesep': '0.8', 'ranksep': '1.0'})
```

**Problem:** Diagram doesn't match paper figures style
```python
# Solution: Create a reusable style configuration
PAPER_STYLE = {
    'graph_attr': {
        'rankdir': 'TB',
        'bgcolor': 'white',
        'dpi': '300'
    },
    'node_attr': {
        'fontname': 'Arial',
        'fontsize': '11',
        'style': 'rounded,filled',
        'fillcolor': '#E8F4F8'
    },
    'edge_attr': {
        'color': 'black',
        'penwidth': '1.5'
    }
}

# Use for all diagrams
dot = graphviz.Digraph(**PAPER_STYLE)
```

## Visual Verification and Quality Control

All diagrams undergo automated visual quality checks to prevent overlaps, ensure readability, and verify accessibility. This multi-stage verification process uses computer vision techniques to detect common issues.

### Stage 1: Overlap Detection

Automatically detect overlapping elements that reduce clarity:

```python
import numpy as np
from PIL import Image
import json
from pathlib import Path

def detect_overlaps(image_path, threshold=0.95):
    """
    Detect potential overlapping regions in a diagram.
    
    Args:
        image_path: Path to the rendered diagram (PNG/PDF)
        threshold: Similarity threshold for detecting overlaps (0-1)
        
    Returns:
        dict: Overlap report with locations and severity
    """
    # Load image
    img = Image.open(image_path).convert('RGB')
    img_array = np.array(img)
    
    # Detect dense regions (potential overlaps)
    gray = np.mean(img_array, axis=2)
    
    # Edge detection to find boundaries
    from scipy.ndimage import sobel
    edges_x = sobel(gray, axis=0)
    edges_y = sobel(gray, axis=1)
    edge_magnitude = np.hypot(edges_x, edges_y)
    
    # Find regions with high edge density (overlaps)
    from scipy.ndimage import label, find_objects
    binary_edges = edge_magnitude > np.percentile(edge_magnitude, 85)
    labeled_regions, num_features = label(binary_edges)
    
    overlaps = []
    slices = find_objects(labeled_regions)
    
    for i, slice_obj in enumerate(slices):
        if slice_obj is not None:
            region = edge_magnitude[slice_obj]
            density = np.mean(region)
            
            # High density suggests potential overlap
            if density > threshold * np.max(edge_magnitude):
                y_center = (slice_obj[0].start + slice_obj[0].stop) // 2
                x_center = (slice_obj[1].start + slice_obj[1].stop) // 2
                
                overlaps.append({
                    'region_id': i + 1,
                    'position': (x_center, y_center),
                    'density': float(density),
                    'severity': 'high' if density > 0.98 * np.max(edge_magnitude) else 'medium'
                })
    
    report = {
        'image': str(image_path),
        'overlaps_detected': len(overlaps),
        'overlap_regions': overlaps,
        'status': 'PASS' if len(overlaps) == 0 else 'WARNING'
    }
    
    return report

def save_overlap_report(report, output_path='overlap_report.json'):
    """Save overlap detection report to JSON."""
    with open(output_path, 'w') as f:
        json.dump(report, indent=2, fp=f)
    
    print(f"Overlap Report: {report['status']}")
    print(f"  - Overlaps detected: {report['overlaps_detected']}")
    if report['overlap_regions']:
        print("  - Regions requiring review:")
        for region in report['overlap_regions']:
            print(f"    * Region {region['region_id']}: "
                  f"Position {region['position']}, Severity: {region['severity']}")
```

### Stage 2: Contrast and Accessibility Verification

Ensure diagrams meet accessibility standards for colorblind readers:

```python
def verify_accessibility(image_path):
    """
    Verify diagram meets accessibility standards.
    
    Checks:
    - Sufficient contrast ratios
    - Grayscale readability
    - Text size adequacy
    """
    from PIL import ImageFilter, ImageStat
    
    img = Image.open(image_path).convert('RGB')
    
    # Test 1: Grayscale conversion
    grayscale = img.convert('L')
    gray_stat = ImageStat.Stat(grayscale)
    
    # Calculate contrast (std dev of grayscale)
    contrast = gray_stat.stddev[0]
    min_contrast = 30  # Minimum standard deviation for good contrast
    
    # Test 2: Color distribution
    rgb_array = np.array(img)
    unique_colors = len(np.unique(rgb_array.reshape(-1, 3), axis=0))
    
    # Test 3: Simulate common color blindness (deuteranopia)
    def simulate_colorblind(img_array):
        # Simplified deuteranopia simulation
        colorblind = img_array.copy().astype(float)
        colorblind[:, :, 0] = 0.625 * img_array[:, :, 0] + 0.375 * img_array[:, :, 1]
        colorblind[:, :, 1] = 0.7 * img_array[:, :, 1] + 0.3 * img_array[:, :, 0]
        return colorblind.astype(np.uint8)
    
    colorblind_img = simulate_colorblind(np.array(img))
    cb_image = Image.fromarray(colorblind_img)
    cb_gray = cb_image.convert('L')
    cb_stat = ImageStat.Stat(cb_gray)
    cb_contrast = cb_stat.stddev[0]
    
    report = {
        'image': str(image_path),
        'checks': {
            'grayscale_contrast': {
                'value': contrast,
                'threshold': min_contrast,
                'status': 'PASS' if contrast >= min_contrast else 'FAIL'
            },
            'colorblind_contrast': {
                'value': cb_contrast,
                'threshold': min_contrast * 0.8,
                'status': 'PASS' if cb_contrast >= min_contrast * 0.8 else 'FAIL'
            },
            'color_diversity': {
                'unique_colors': unique_colors,
                'status': 'INFO'
            }
        },
        'overall_status': 'PASS' if (contrast >= min_contrast and 
                                     cb_contrast >= min_contrast * 0.8) else 'FAIL'
    }
    
    return report

def save_accessibility_report(report, output_path='accessibility_report.json'):
    """Save accessibility report to JSON."""
    with open(output_path, 'w') as f:
        json.dump(report, indent=2, fp=f)
    
    print(f"Accessibility Report: {report['overall_status']}")
    for check_name, check_data in report['checks'].items():
        print(f"  - {check_name}: {check_data['status']}")
        if 'value' in check_data and 'threshold' in check_data:
            print(f"    Value: {check_data['value']:.2f}, Threshold: {check_data['threshold']:.2f}")
```

### Stage 3: Text Size and Resolution Validation

Verify text is readable at publication size:

```python
def validate_resolution(image_path, target_dpi=300, min_text_size_pt=7):
    """
    Validate image resolution and estimated text size.
    
    Args:
        image_path: Path to diagram image
        target_dpi: Target DPI for publication (default 300)
        min_text_size_pt: Minimum acceptable text size in points
    """
    from PIL import Image
    import pytesseract
    
    img = Image.open(image_path)
    
    # Check DPI
    dpi = img.info.get('dpi', (72, 72))
    actual_dpi = dpi[0] if isinstance(dpi, tuple) else dpi
    
    # Estimate text size (simplified - assumes text detection)
    # For production, use OCR or PDF text extraction
    width, height = img.size
    dpi_ratio = actual_dpi / 72  # Convert to screen pixels
    
    # Calculate physical size in inches
    width_inches = width / actual_dpi if actual_dpi > 0 else width / 72
    height_inches = height / actual_dpi if actual_dpi > 0 else height / 72
    
    report = {
        'image': str(image_path),
        'resolution': {
            'dpi': actual_dpi,
            'target_dpi': target_dpi,
            'status': 'PASS' if actual_dpi >= target_dpi else 'WARNING'
        },
        'dimensions': {
            'pixels': {'width': width, 'height': height},
            'inches': {'width': round(width_inches, 2), 
                      'height': round(height_inches, 2)}
        },
        'recommendations': []
    }
    
    if actual_dpi < target_dpi:
        report['recommendations'].append(
            f"Increase resolution to {target_dpi} DPI for publication quality"
        )
    
    if width_inches > 7:
        report['recommendations'].append(
            "Image width exceeds typical single-column width (7 inches)"
        )
    
    return report
```

### Stage 4: Comprehensive Quality Check Pipeline

Run all verification stages in sequence:

```python
def run_quality_checks(image_path, output_dir='quality_reports'):
    """
    Run comprehensive quality checks on diagram.
    
    Args:
        image_path: Path to diagram to verify
        output_dir: Directory to save reports
        
    Returns:
        dict: Comprehensive quality report
    """
    import os
    from datetime import datetime
    
    Path(output_dir).mkdir(exist_ok=True)
    
    print(f"Running quality checks on: {image_path}")
    print("=" * 60)
    
    # Stage 1: Overlap Detection
    print("\n[Stage 1/4] Detecting overlaps...")
    overlap_repo

…(truncated)