scientific-visualization

1# Scientific Visualization
2 
3## Overview
4 
5Scientific visualization transforms data into clear, accurate figures for publication. Create journal-ready plots with multi-panel layouts, error bars, significance markers, and colorblind-safe palettes. Export as PDF/EPS/TIFF using matplotlib, seaborn, and plotly for manuscripts.
6 
7## When to Use This Skill
8 
9This skill should be used when:
10- Creating plots or visualizations for scientific manuscripts
11- Preparing figures for journal submission (Nature, Science, Cell, PLOS, etc.)
12- Ensuring figures are colorblind-friendly and accessible
13- Making multi-panel figures with consistent styling
14- Exporting figures at correct resolution and format
15- Following specific publication guidelines
16- Improving existing figures to meet publication standards
17- Creating figures that need to work in both color and grayscale
18 
19## Quick Start Guide
20 
21### Basic Publication-Quality Figure
22 
23```python
24import matplotlib.pyplot as plt
25import numpy as np
26 
27# Apply publication style (from scripts/style_presets.py)
28from style_presets import apply_publication_style
29apply_publication_style('default')
30 
31# Create figure with appropriate size (single column = 3.5 inches)
32fig, ax = plt.subplots(figsize=(3.5, 2.5))
33 
34# Plot data
35x = np.linspace(0, 10, 100)
36ax.plot(x, np.sin(x), label='sin(x)')
37ax.plot(x, np.cos(x), label='cos(x)')
38 
39# Proper labeling with units
40ax.set_xlabel('Time (seconds)')
41ax.set_ylabel('Amplitude (mV)')
42ax.legend(frameon=False)
43 
44# Remove unnecessary spines
45…