plot-styling

Name: plot-styling
Author: neuromechanist

37stars7forks

How to install plot-styling?

neuromechanist/research-skills/plot-styling

$npx -y skills add neuromechanist/research-skills --skill plot-styling

Installs into the current project.

Summary

This skill should be used when the user asks to "plot data", "make a plot for a paper", "make a publication plot", "create a journal-quality plot", "matplotlib for a figure", "use seaborn", "use plotnine", "use ggplot in Python", "improve plot quality", "style my plot", "clean up

Files · 1

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SKILL.md

1	# Plot Styling
2
3	Choose the right Python plotting library for the chart type, then apply journal-quality defaults so the panel that comes out of `savefig` is ready for the `[[scientific-figure]]` composer — no manual cleanup, no font-size firefighting, no chart junk.
4
5	## Two questions, two minutes
6
7	1. What chart type am I drawing? That answer picks the library (`references/library-decision-tree.md`).
8	2. Which journal am I targeting? That answer picks the style sheet and font minimum (`references/sciplots-recipes.md`).
9
10	Run those choices through the export conventions below and the output SVG should pass `figure-qa`'s plot-script branch and feed `scientific-figure` without scale-down surprises.
11
12	## Library decision tree (summary)
13
14	\| Chart type \| Library \| Why \|
15	\|---\|---\|---\|
16	\| Line / scatter / bar with custom layout \| matplotlib \| Most control; the lingua franca. Pair with SciencePlots styles to fix defaults. \|
17	\| Statistical (box, violin, regression, faceted) \| seaborn \| Better defaults than matplotlib; less code for the common cases. Built on top of matplotlib so the SciencePlots style still applies. \|
18	\| Grammar-of-graphics / R-style faceting \| plotnine \| Same `geom_*` API as ggplot2 without the rpy2 bridge. \|
19	\| Interactive HTML supplement (deck / dashboard) \| plotly \| Use plotly for the interactive companion; ship a matplotlib version for print. \|
20	\| 3D / volumetric / mesh \| PyVista (when interactive matters) or matplotlib 3d (for static print) \| matplotlib 3d is acceptable for simple panels; PyVista when the figure is the interaction. \|
21	\| Already in matplotlib, output looks ugly \| stay matplotlib + SciencePlots \| `plt.style.use(['science', 'nature'])` fixes 80% of "ugly defaults" complaints without rewriting. \|
22
23	Full decision tree with concrete code snippets per branch: `references/library-decision-tree.md`.
24
25	## Journal-quality defaults (matplotlib / seaborn)
26
27	The cleanest path is to install [SciencePlots](https://github.com/garrettj403/SciencePlots) and apply its style sheet at the top of every plot script. The relevant style names:
28
29	- `science` — base style (sans-serif, no chart junk, tight margins)
30	- `nature` — Nature column dimensions and font sizing
31	- `ieee` — IEEE narrow-column with grayscale-safe palette
32	- `vibrant` / `bright` / `high-contrast` — colorblind-safe palettes from Paul Tol
33	- `notebook` — slightly larger fonts for screen reading (avoid for paper)
34
35	```python
36	import matplotlib.pyplot as plt
37	import scienceplots # noqa: F401 (registers the styles)
38
39	plt.style.use(["science", "nature", "no-latex"])
40	```
41
42	`no-latex` is non-optional unless you have system LaTeX installed AND want path-rendered text (in which case `validate_fonts.py` cannot inspect font sizes — see the "no-latex" note in `references/sciplots-recipes.md`).
43
44	For a Nature panel at 1-column width with three lines:
45
46	```python
47	import numpy as np
48	import matplotlib.pyplot as plt
49	import scienceplots # noqa: F401
50
51	plt.style.use(["science", "nature", "no-latex"])
52
53	fig, ax = plt.subplots(figsize=(3.5, 2.5)) # 89 mm x ~63 mm
54	t = np.linspace(0, 1, 200)
55	for k, label in enumerate(("control", "drug A", "drug B")):
56	ax.plot(t, np.sin(2 * np.pi * (k + 1) * t), label=label, linewidth=1.0)
57	ax.set_xlabel("time (s)")
58	ax.set_ylabel("amplitude (a.u.)")
59	ax.legend(frameon=False, loc="upper right")
60	fig.savefig("panel_a.svg", bbox_inches="tight", transparent=True)
61	```
62
63	That single style declaration sets sans-serif fonts at journal-appropriate sizes (Nature 5–7 pt range), removes the right and top spines, applies tight margin defaults, and picks a colorblind-safe palette. See `references/sciplots-recipes.md` for IEEE, Science, and APS variants.
64
65	## Export conventions
66
67	Every plot panel that will be composed by `[[scientific-figure]]` should:
68
69	1. Save as SVG. `savefig("panel.svg", ...)`. SVG preserves text as `<text>` so `validate_fonts.py` can inspect every label.
70	2. Use `transparent=True`. The composer expects panels with transparent backgrounds so they composite cleanly onto the figure canvas. `figure-qa`'s plot-script branch flags `transparent=False` and missing-`transparent` as issues.
71	3. **Use `bbox_inches=

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plot-styling

37stars7forks

View on GitHub

How to install plot-styling?

neuromechanist/research-skills/plot-styling

$npx -y skills add neuromechanist/research-skills --skill plot-styling

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Plot Styling
2
3	Choose the right Python plotting library for the chart type, then apply journal-quality defaults so the panel that comes out of `savefig` is ready for the `[[scientific-figure]]` composer — no manual cleanup, no font-size firefighting, no chart junk.
4
5	## Two questions, two minutes
6
7	1. What chart type am I drawing? That answer picks the library (`references/library-decision-tree.md`).
8	2. Which journal am I targeting? That answer picks the style sheet and font minimum (`references/sciplots-recipes.md`).
9
10	Run those choices through the export conventions below and the output SVG should pass `figure-qa`'s plot-script branch and feed `scientific-figure` without scale-down surprises.
11
12	## Library decision tree (summary)
13
14	\| Chart type \| Library \| Why \|
15	\|---\|---\|---\|
16	\| Line / scatter / bar with custom layout \| matplotlib \| Most control; the lingua franca. Pair with SciencePlots styles to fix defaults. \|
17	\| Statistical (box, violin, regression, faceted) \| seaborn \| Better defaults than matplotlib; less code for the common cases. Built on top of matplotlib so the SciencePlots style still applies. \|
18	\| Grammar-of-graphics / R-style faceting \| plotnine \| Same `geom_*` API as ggplot2 without the rpy2 bridge. \|
19	\| Interactive HTML supplement (deck / dashboard) \| plotly \| Use plotly for the interactive companion; ship a matplotlib version for print. \|
20	\| 3D / volumetric / mesh \| PyVista (when interactive matters) or matplotlib 3d (for static print) \| matplotlib 3d is acceptable for simple panels; PyVista when the figure is the interaction. \|
21	\| Already in matplotlib, output looks ugly \| stay matplotlib + SciencePlots \| `plt.style.use(['science', 'nature'])` fixes 80% of "ugly defaults" complaints without rewriting. \|
22
23	Full decision tree with concrete code snippets per branch: `references/library-decision-tree.md`.
24
25	## Journal-quality defaults (matplotlib / seaborn)
26
27	The cleanest path is to install [SciencePlots](https://github.com/garrettj403/SciencePlots) and apply its style sheet at the top of every plot script. The relevant style names:
28
29	- `science` — base style (sans-serif, no chart junk, tight margins)
30	- `nature` — Nature column dimensions and font sizing
31	- `ieee` — IEEE narrow-column with grayscale-safe palette
32	- `vibrant` / `bright` / `high-contrast` — colorblind-safe palettes from Paul Tol
33	- `notebook` — slightly larger fonts for screen reading (avoid for paper)
34
35	```python
36	import matplotlib.pyplot as plt
37	import scienceplots # noqa: F401 (registers the styles)
38
39	plt.style.use(["science", "nature", "no-latex"])
40	```
41
42	`no-latex` is non-optional unless you have system LaTeX installed AND want path-rendered text (in which case `validate_fonts.py` cannot inspect font sizes — see the "no-latex" note in `references/sciplots-recipes.md`).
43
44	For a Nature panel at 1-column width with three lines:
45
46	```python
47	import numpy as np
48	import matplotlib.pyplot as plt
49	import scienceplots # noqa: F401
50
51	plt.style.use(["science", "nature", "no-latex"])
52
53	fig, ax = plt.subplots(figsize=(3.5, 2.5)) # 89 mm x ~63 mm
54	t = np.linspace(0, 1, 200)
55	for k, label in enumerate(("control", "drug A", "drug B")):
56	ax.plot(t, np.sin(2 * np.pi * (k + 1) * t), label=label, linewidth=1.0)
57	ax.set_xlabel("time (s)")
58	ax.set_ylabel("amplitude (a.u.)")
59	ax.legend(frameon=False, loc="upper right")
60	fig.savefig("panel_a.svg", bbox_inches="tight", transparent=True)
61	```
62
63	That single style declaration sets sans-serif fonts at journal-appropriate sizes (Nature 5–7 pt range), removes the right and top spines, applies tight margin defaults, and picks a colorblind-safe palette. See `references/sciplots-recipes.md` for IEEE, Science, and APS variants.
64
65	## Export conventions
66
67	Every plot panel that will be composed by `[[scientific-figure]]` should:
68
69	1. Save as SVG. `savefig("panel.svg", ...)`. SVG preserves text as `<text>` so `validate_fonts.py` can inspect every label.
70	2. Use `transparent=True`. The composer expects panels with transparent backgrounds so they composite cleanly onto the figure canvas. `figure-qa`'s plot-script branch flags `transparent=False` and missing-`transparent` as issues.
71	3. **Use `bbox_inches=

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Type