matplotlib

Name: matplotlib
Author: ai4protein

244stars35forks

How to install matplotlib?

ai4protein/venusfactory2/matplotlib

$npx -y skills add ai4protein/VenusFactory2 --skill matplotlib

Installs into the current project.

Summary

Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for int

Files · 1

View on GitHub

SKILL.md

1	# Matplotlib
2
3	## Overview
4
5	Matplotlib is Python's foundational visualization library for creating static, animated, and interactive plots. This skill provides guidance on using matplotlib effectively, covering both the pyplot interface (MATLAB-style) and the object-oriented API (Figure/Axes), along with best practices for creating publication-quality visualizations.
6
7	## When to Use This Skill
8
9	This skill should be used when:
10	- Creating any type of plot or chart (line, scatter, bar, histogram, heatmap, contour, etc.)
11	- Generating scientific or statistical visualizations
12	- Customizing plot appearance (colors, styles, labels, legends)
13	- Creating multi-panel figures with subplots
14	- Exporting visualizations to various formats (PNG, PDF, SVG, etc.)
15	- Building interactive plots or animations
16	- Working with 3D visualizations
17	- Integrating plots into Jupyter notebooks or GUI applications
18
19	## Core Concepts
20
21	### The Matplotlib Hierarchy
22
23	Matplotlib uses a hierarchical structure of objects:
24
25	1. Figure - The top-level container for all plot elements
26	2. Axes - The actual plotting area where data is displayed (one Figure can contain multiple Axes)
27	3. Artist - Everything visible on the figure (lines, text, ticks, etc.)
28	4. Axis - The number line objects (x-axis, y-axis) that handle ticks and labels
29
30	### Two Interfaces
31
32	1. pyplot Interface (Implicit, MATLAB-style)
33	```python
34	import matplotlib.pyplot as plt
35
36	plt.plot([1, 2, 3, 4])
37	plt.ylabel('some numbers')
38	plt.show()
39	```
40	- Convenient for quick, simple plots
41	- Maintains state automatically
42	- Good for interactive work and simple scripts
43
44	2. Object-Oriented Interface (Explicit)
45	```python
46	import matplotlib.pyplot as plt
47
48	fig, ax = plt.subplots()
49	ax.plot([1, 2, 3, 4])
50	ax.set_ylabel('some numbers')
51	plt.show()
52	```
53	- Recommended for most use cases
54	- More explicit control over figure and axes
55	- Better for complex figures with multiple subplots
56	- Easier to maintain and debug
57
58	## Common Workflows
59
60	### 1. Basic Plot Creation
61
62	Single plot workflow:
63	```python
64	import matplotlib.pyplot as plt
65	import numpy as np
66
67	# Create figure and axes (OO interface - RECOMMENDED)
68	fig, ax = plt.subplots(figsize=(10, 6))
69
70	# Generate and plot data
71	x = np.linspace(0, 2*np.pi, 100)
72	ax.plot(x, np.sin(x), label='sin(x)')
73	ax.plot(x, np.cos(x), label='cos(x)')
74
75	# Customize
76	ax.set_xlabel('x')
77	ax.set_ylabel('y')
78	ax.set_title('Trigonometric Functions')
79	ax.legend()
80	ax.grid(True, alpha=0.3)
81
82	# Save and/or display
83	plt.savefig('plot.png', dpi=300, bbox_inches='tight')
84	plt.show()
85	```
86
87	### 2. Multiple Subplots
88
89	Creating subplot layouts:
90	```python
91	# Method 1: Regular grid
92	fig, axes = plt.subplots(2, 2, figsize=(12, 10))
93	axes[0, 0].plot(x, y1)
94	axes[0, 1].scatter(x, y2)
95	axes[1, 0].bar(categories, values)
96	axes[1, 1].hist(data, bins=30)
97
98	# Method 2: Mosaic layout (more flexible)
99	fig, axes = plt.subplot_mosaic([['left', 'right_top'],
100	['left', 'right_bottom']],
101	figsize=(10, 8))
102	axes['left'].plot(x, y)
103	axes['right_top'].scatter(x, y)
104	axes['right_bottom'].hist(data)
105
106	# Method 3: GridSpec (maximum control)
107	from matplotlib.gridspec import GridSpec
108	fig = plt.figure(figsize=(12, 8))
109	gs = GridSpec(3, 3, figure=fig)
110	ax1 = fig.add_subplot(gs[0, :]) # Top row, all columns
111	ax2 = fig.add_subplot(gs[1:, 0]) # Bottom two rows, first column
112	ax3 = fig.add_subplot(gs[1:, 1:]) # Bottom two rows, last two columns
113	```
114
115	### 3. Plot Types and Use Cases
116
117	Line plots - Time series, continuous data, trends
118	```python
119	ax.plot(x, y, linewidth=2, linestyle='--', marker='o', color='blue')
120	```
121
122	Scatter plots - Relationships between variables, correlations
123	```python
124	ax.scatter(x, y, s=sizes, c=colors, alpha=0.6, cmap='viridis')
125	```
126
127	Bar charts - Categorical comparisons
128	```python
129	ax.bar(categories, values, color='steelblue', edgecolor='black')
130	# For horizontal bars:
131	ax.barh(categories, values)
132	```
133
134	Histograms - Distributions
135	```python
136	ax.hist(data, bins=30, edgecolor='black', alpha=0.7)
137	```
138
139	Heatmaps - Matrix data, correlations
140	```python
141	im = ax.imshow(matrix, cmap='coolwarm', aspect='auto')
142	plt.colorbar(im, ax=ax)
143	```
144
145	Contour plots - 3D data on 2D plane
146	```python
147	contour = ax.contour(X, Y, Z, levels=10)
148	ax.clabel(contour, inline=True, fontsize=8)
149	```
150
151	Box plots - Statistical distributions
152	```python
153	ax.boxplot([data1, data2, data3], labels=['A', 'B', 'C'])
154	```
155
156	Violin plots - Distribution

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6 picks

Type

matplotlib

244stars35forks

View on GitHub

How to install matplotlib?

ai4protein/venusfactory2/matplotlib

$npx -y skills add ai4protein/VenusFactory2 --skill matplotlib

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Matplotlib
2
3	## Overview
4
5	Matplotlib is Python's foundational visualization library for creating static, animated, and interactive plots. This skill provides guidance on using matplotlib effectively, covering both the pyplot interface (MATLAB-style) and the object-oriented API (Figure/Axes), along with best practices for creating publication-quality visualizations.
6
7	## When to Use This Skill
8
9	This skill should be used when:
10	- Creating any type of plot or chart (line, scatter, bar, histogram, heatmap, contour, etc.)
11	- Generating scientific or statistical visualizations
12	- Customizing plot appearance (colors, styles, labels, legends)
13	- Creating multi-panel figures with subplots
14	- Exporting visualizations to various formats (PNG, PDF, SVG, etc.)
15	- Building interactive plots or animations
16	- Working with 3D visualizations
17	- Integrating plots into Jupyter notebooks or GUI applications
18
19	## Core Concepts
20
21	### The Matplotlib Hierarchy
22
23	Matplotlib uses a hierarchical structure of objects:
24
25	1. Figure - The top-level container for all plot elements
26	2. Axes - The actual plotting area where data is displayed (one Figure can contain multiple Axes)
27	3. Artist - Everything visible on the figure (lines, text, ticks, etc.)
28	4. Axis - The number line objects (x-axis, y-axis) that handle ticks and labels
29
30	### Two Interfaces
31
32	1. pyplot Interface (Implicit, MATLAB-style)
33	```python
34	import matplotlib.pyplot as plt
35
36	plt.plot([1, 2, 3, 4])
37	plt.ylabel('some numbers')
38	plt.show()
39	```
40	- Convenient for quick, simple plots
41	- Maintains state automatically
42	- Good for interactive work and simple scripts
43
44	2. Object-Oriented Interface (Explicit)
45	```python
46	import matplotlib.pyplot as plt
47
48	fig, ax = plt.subplots()
49	ax.plot([1, 2, 3, 4])
50	ax.set_ylabel('some numbers')
51	plt.show()
52	```
53	- Recommended for most use cases
54	- More explicit control over figure and axes
55	- Better for complex figures with multiple subplots
56	- Easier to maintain and debug
57
58	## Common Workflows
59
60	### 1. Basic Plot Creation
61
62	Single plot workflow:
63	```python
64	import matplotlib.pyplot as plt
65	import numpy as np
66
67	# Create figure and axes (OO interface - RECOMMENDED)
68	fig, ax = plt.subplots(figsize=(10, 6))
69
70	# Generate and plot data
71	x = np.linspace(0, 2*np.pi, 100)
72	ax.plot(x, np.sin(x), label='sin(x)')
73	ax.plot(x, np.cos(x), label='cos(x)')
74
75	# Customize
76	ax.set_xlabel('x')
77	ax.set_ylabel('y')
78	ax.set_title('Trigonometric Functions')
79	ax.legend()
80	ax.grid(True, alpha=0.3)
81
82	# Save and/or display
83	plt.savefig('plot.png', dpi=300, bbox_inches='tight')
84	plt.show()
85	```
86
87	### 2. Multiple Subplots
88
89	Creating subplot layouts:
90	```python
91	# Method 1: Regular grid
92	fig, axes = plt.subplots(2, 2, figsize=(12, 10))
93	axes[0, 0].plot(x, y1)
94	axes[0, 1].scatter(x, y2)
95	axes[1, 0].bar(categories, values)
96	axes[1, 1].hist(data, bins=30)
97
98	# Method 2: Mosaic layout (more flexible)
99	fig, axes = plt.subplot_mosaic([['left', 'right_top'],
100	['left', 'right_bottom']],
101	figsize=(10, 8))
102	axes['left'].plot(x, y)
103	axes['right_top'].scatter(x, y)
104	axes['right_bottom'].hist(data)
105
106	# Method 3: GridSpec (maximum control)
107	from matplotlib.gridspec import GridSpec
108	fig = plt.figure(figsize=(12, 8))
109	gs = GridSpec(3, 3, figure=fig)
110	ax1 = fig.add_subplot(gs[0, :]) # Top row, all columns
111	ax2 = fig.add_subplot(gs[1:, 0]) # Bottom two rows, first column
112	ax3 = fig.add_subplot(gs[1:, 1:]) # Bottom two rows, last two columns
113	```
114
115	### 3. Plot Types and Use Cases
116
117	Line plots - Time series, continuous data, trends
118	```python
119	ax.plot(x, y, linewidth=2, linestyle='--', marker='o', color='blue')
120	```
121
122	Scatter plots - Relationships between variables, correlations
123	```python
124	ax.scatter(x, y, s=sizes, c=colors, alpha=0.6, cmap='viridis')
125	```
126
127	Bar charts - Categorical comparisons
128	```python
129	ax.bar(categories, values, color='steelblue', edgecolor='black')
130	# For horizontal bars:
131	ax.barh(categories, values)
132	```
133
134	Histograms - Distributions
135	```python
136	ax.hist(data, bins=30, edgecolor='black', alpha=0.7)
137	```
138
139	Heatmaps - Matrix data, correlations
140	```python
141	im = ax.imshow(matrix, cmap='coolwarm', aspect='auto')
142	plt.colorbar(im, ax=ax)
143	```
144
145	Contour plots - 3D data on 2D plane
146	```python
147	contour = ax.contour(X, Y, Z, levels=10)
148	ax.clabel(contour, inline=True, fontsize=8)
149	```
150
151	Box plots - Statistical distributions
152	```python
153	ax.boxplot([data1, data2, data3], labels=['A', 'B', 'C'])
154	```
155
156	Violin plots - Distribution

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6 picks

Type