scientific-visualization

Name: scientific-visualization
Author: LeonChaoX

698stars61forks

How to install scientific-visualization?

leonchaox/qinyan-academic-skills/scientific-visualization

$npx -y skills add LeonChaoX/qinyan-academic-skills --skill scientific-visualization

Installs into the current project.

Summary

Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly wi

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SKILL.md

1	# Scientific Visualization
2
3	## Overview
4
5	Scientific 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
9	This 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
24	import matplotlib.pyplot as plt
25	import numpy as np
26
27	# Apply publication style (from scripts/style_presets.py)
28	from style_presets import apply_publication_style
29	apply_publication_style('default')
30
31	# Create figure with appropriate size (single column = 3.5 inches)
32	fig, ax = plt.subplots(figsize=(3.5, 2.5))
33
34	# Plot data
35	x = np.linspace(0, 10, 100)
36	ax.plot(x, np.sin(x), label='sin(x)')
37	ax.plot(x, np.cos(x), label='cos(x)')
38
39	# Proper labeling with units
40	ax.set_xlabel('Time (seconds)')
41	ax.set_ylabel('Amplitude (mV)')
42	ax.legend(frameon=False)
43
44	# Remove unnecessary spines
45	ax.spines['top'].set_visible(False)
46	ax.spines['right'].set_visible(False)
47
48	# Save in publication formats (from scripts/figure_export.py)
49	from figure_export import save_publication_figure
50	save_publication_figure(fig, 'figure1', formats=['pdf', 'png'], dpi=300)
51	```
52
53	### Using Pre-configured Styles
54
55	Apply journal-specific styles using the matplotlib style files in `assets/`:
56
57	```python
58	import matplotlib.pyplot as plt
59
60	# Option 1: Use style file directly
61	plt.style.use('assets/nature.mplstyle')
62
63	# Option 2: Use style_presets.py helper
64	from style_presets import configure_for_journal
65	configure_for_journal('nature', figure_width='single')
66
67	# Now create figures - they'll automatically match Nature specifications
68	fig, ax = plt.subplots()
69	# ... your plotting code ...
70	```
71
72	### Quick Start with Seaborn
73
74	For statistical plots, use seaborn with publication styling:
75
76	```python
77	import seaborn as sns
78	import matplotlib.pyplot as plt
79	from style_presets import apply_publication_style
80
81	# Apply publication style
82	apply_publication_style('default')
83	sns.set_theme(style='ticks', context='paper', font_scale=1.1)
84	sns.set_palette('colorblind')
85
86	# Create statistical comparison figure
87	fig, ax = plt.subplots(figsize=(3.5, 3))
88	sns.boxplot(data=df, x='treatment', y='response',
89	order=['Control', 'Low', 'High'], palette='Set2', ax=ax)
90	sns.stripplot(data=df, x='treatment', y='response',
91	order=['Control', 'Low', 'High'],
92	color='black', alpha=0.3, size=3, ax=ax)
93	ax.set_ylabel('Response (μM)')
94	sns.despine()
95
96	# Save figure
97	from figure_export import save_publication_figure
98	save_publication_figure(fig, 'treatment_comparison', formats=['pdf', 'png'], dpi=300)
99	```
100
101	## Core Principles and Best Practices
102
103	### 1. Resolution and File Format
104
105	Critical requirements (detailed in `references/publication_guidelines.md`):
106	- Raster images (photos, microscopy): 300-600 DPI
107	- Line art (graphs, plots): 600-1200 DPI or vector format
108	- Vector formats (preferred): PDF, EPS, SVG
109	- Raster formats: TIFF, PNG (never JPEG for scientific data)
110
111	Implementation:
112	```python
113	# Use the figure_export.py script for correct settings
114	from figure_export import save_publication_figure
115
116	# Saves in multiple formats with proper DPI
117	save_publication_figure(fig, 'myfigure', formats=['pdf', 'png'], dpi=300)
118
119	# Or save for specific journal requirements
120	from figure_export import save_for_journal
121	save_for_journal(fig, 'figure1', journal='nature', figure_type='combination')
122	```
123
124	### 2. Color Selection - Colorblind Accessibility
125
126	Always use colorblind-friendly palettes (detailed in `references/color_palettes.md`):
127
128	Recommended: Okabe-Ito palette (distinguishable by all types of color blindness):
129	```python
130	# Option 1: Use assets/color_palettes.py
131	from color_palettes import OKABE_ITO_LIST, apply_palette
132	apply_palette('okabe_ito')
133
134	# Option 2: Manual specification
135	okabe_ito = ['#E69F00', '#56B4E9', '#009E73', '#F0E442',
136	'#0072B2', '#D55E00', '#CC79A7', '#000000']
137	plt.rcParams['axes.p

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scientific-visualization

698stars61forks

View on GitHub

How to install scientific-visualization?

leonchaox/qinyan-academic-skills/scientific-visualization

$npx -y skills add LeonChaoX/qinyan-academic-skills --skill scientific-visualization

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Scientific Visualization
2
3	## Overview
4
5	Scientific 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
9	This 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
24	import matplotlib.pyplot as plt
25	import numpy as np
26
27	# Apply publication style (from scripts/style_presets.py)
28	from style_presets import apply_publication_style
29	apply_publication_style('default')
30
31	# Create figure with appropriate size (single column = 3.5 inches)
32	fig, ax = plt.subplots(figsize=(3.5, 2.5))
33
34	# Plot data
35	x = np.linspace(0, 10, 100)
36	ax.plot(x, np.sin(x), label='sin(x)')
37	ax.plot(x, np.cos(x), label='cos(x)')
38
39	# Proper labeling with units
40	ax.set_xlabel('Time (seconds)')
41	ax.set_ylabel('Amplitude (mV)')
42	ax.legend(frameon=False)
43
44	# Remove unnecessary spines
45	ax.spines['top'].set_visible(False)
46	ax.spines['right'].set_visible(False)
47
48	# Save in publication formats (from scripts/figure_export.py)
49	from figure_export import save_publication_figure
50	save_publication_figure(fig, 'figure1', formats=['pdf', 'png'], dpi=300)
51	```
52
53	### Using Pre-configured Styles
54
55	Apply journal-specific styles using the matplotlib style files in `assets/`:
56
57	```python
58	import matplotlib.pyplot as plt
59
60	# Option 1: Use style file directly
61	plt.style.use('assets/nature.mplstyle')
62
63	# Option 2: Use style_presets.py helper
64	from style_presets import configure_for_journal
65	configure_for_journal('nature', figure_width='single')
66
67	# Now create figures - they'll automatically match Nature specifications
68	fig, ax = plt.subplots()
69	# ... your plotting code ...
70	```
71
72	### Quick Start with Seaborn
73
74	For statistical plots, use seaborn with publication styling:
75
76	```python
77	import seaborn as sns
78	import matplotlib.pyplot as plt
79	from style_presets import apply_publication_style
80
81	# Apply publication style
82	apply_publication_style('default')
83	sns.set_theme(style='ticks', context='paper', font_scale=1.1)
84	sns.set_palette('colorblind')
85
86	# Create statistical comparison figure
87	fig, ax = plt.subplots(figsize=(3.5, 3))
88	sns.boxplot(data=df, x='treatment', y='response',
89	order=['Control', 'Low', 'High'], palette='Set2', ax=ax)
90	sns.stripplot(data=df, x='treatment', y='response',
91	order=['Control', 'Low', 'High'],
92	color='black', alpha=0.3, size=3, ax=ax)
93	ax.set_ylabel('Response (μM)')
94	sns.despine()
95
96	# Save figure
97	from figure_export import save_publication_figure
98	save_publication_figure(fig, 'treatment_comparison', formats=['pdf', 'png'], dpi=300)
99	```
100
101	## Core Principles and Best Practices
102
103	### 1. Resolution and File Format
104
105	Critical requirements (detailed in `references/publication_guidelines.md`):
106	- Raster images (photos, microscopy): 300-600 DPI
107	- Line art (graphs, plots): 600-1200 DPI or vector format
108	- Vector formats (preferred): PDF, EPS, SVG
109	- Raster formats: TIFF, PNG (never JPEG for scientific data)
110
111	Implementation:
112	```python
113	# Use the figure_export.py script for correct settings
114	from figure_export import save_publication_figure
115
116	# Saves in multiple formats with proper DPI
117	save_publication_figure(fig, 'myfigure', formats=['pdf', 'png'], dpi=300)
118
119	# Or save for specific journal requirements
120	from figure_export import save_for_journal
121	save_for_journal(fig, 'figure1', journal='nature', figure_type='combination')
122	```
123
124	### 2. Color Selection - Colorblind Accessibility
125
126	Always use colorblind-friendly palettes (detailed in `references/color_palettes.md`):
127
128	Recommended: Okabe-Ito palette (distinguishable by all types of color blindness):
129	```python
130	# Option 1: Use assets/color_palettes.py
131	from color_palettes import OKABE_ITO_LIST, apply_palette
132	apply_palette('okabe_ito')
133
134	# Option 2: Manual specification
135	okabe_ito = ['#E69F00', '#56B4E9', '#009E73', '#F0E442',
136	'#0072B2', '#D55E00', '#CC79A7', '#000000']
137	plt.rcParams['axes.p

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