plotastro

Publication-quality matplotlib figures for astronomy journals.

One pip install gives you journal-matched styles for MNRAS, RASTI, A&A, ApJ/ApJL, the Open Journal of Astrophysics, PRD/PRL, JCAP and Nature Astronomy — figures at exactly the right physical size, a colour-blind-friendly palette, and helpers that make the tedious parts (sizing, panel labels, accessibility checks, saving) one-liners.

pip install plotastro

Simplest usage — no new API to learn. Importing plotastro registers the styles with matplotlib itself; after one plt.style.use line you write ordinary matplotlib, and the default figure size is already the journal's column width:

import matplotlib.pyplot as plt
import plotastro                 # just to register the styles

plt.style.use("mnras")           # or "aanda", "apj", "oja", "prd", ...
fig, ax = plt.subplots()         # plain matplotlib from here on

With the helpers (optional, but they make the tedious parts one-liners):

import plotastro as pa

pa.set_style("mnras")
fig, ax = pa.subplots()          # one-column figure, golden-ratio height
ax.plot(x, y, label="model")
ax.set_xlabel("$x$")
ax.legend()
pa.savefig("myplot")             # -> myplot.pdf, ready for \includegraphics

One column	Full width

Full documentation: plotastro.readthedocs.io — or start with the tutorial notebook, which walks through every feature with runnable examples.

Why this exists

Two problems ruin most paper figures:

1. Wrong physical size. If you hand LaTeX a 6-inch figure and it squeezes it into an 84 mm column, every label shrinks by ~50 % and becomes unreadable. The fix: build the figure at its final printed width, then include it with a plain \includegraphics{fig.pdf} — no [width=...].
2. Inaccessible colours. ~5 % of male readers have a colour-vision deficiency, and MNRAS's author guidelines explicitly ask for colour-blind-friendly figures. The default matplotlib cycle is not; the one here is — and pa.check_figure() lets you verify it.

The styles share one visual language — Times-like serif fonts at ~9 pt with ~8 pt tick lettering, inward ticks on all four sides with minors, a subtle grid, frameless legends — and differ only in figure width (plus the sans-serif fonts Nature requires), so your plots stay consistent between papers no matter where you submit.

Supported journals

pa.set_style(...), pa.figsize(...) and plt.style.use(...) accept (aliases in parentheses):

Widths come from each journal's LaTeX class / author guide. For a custom document, put \the\columnwidth or \the\textwidth in your .tex body, compile, read the value off the page, and pass it directly: pa.figsize(width=345.0).

The only hard dependency is matplotlib; plotastro works with both NumPy 1.x and 2.x (CI tests each). Running the examples from a clone? pip install -r requirements-dev.txt.

Tutorial

Figure sizing

pa.figsize("column")                  # one column, golden-ratio height
pa.figsize("full")                    # full text width
pa.figsize("column", fraction=0.5)    # half a column
pa.figsize("column", aspect=1)        # square panel (aspect = height/width)
pa.figsize("column", journal="aanda") # size for a specific journal
pa.figsize(345.0)                     # any width in LaTeX points

pa.subplots() takes the same arguments plus everything plt.subplots accepts, and scales the height with the grid so each panel keeps its aspect:

fig, ax   = pa.subplots()                          # 1 panel, one column
fig, axes = pa.subplots(2, 2, width="full")        # 2x2 grid, full width
fig, axes = pa.subplots(1, 2, width="full", aspect=0.75, sharey=True)

The colour palette

The default cycle has 12 colours, all accessible by name via pa.COLORS (e.g. pa.COLORS["blue"]), or as matplotlib's "C0"…"C11" shorthands:

• C0–C8 are a colour-blind-safe re-ordering of the ColorBrewer Set1 qualitative palette (popularised by Thøger Rivera-Thorsen's CBcycle). Consecutive colours differ in lightness as well as hue, so adjacent lines stay distinguishable under the common deficiencies (deuteranopia, protanopia) and in greyscale print; the notorious red–green pair is pushed far apart in the cycle (green is C2, red is C7), so plots with a handful of lines never rely on it.
• C9–C11 are light companions (from Tableau's Color Blind 10): use them for uncertainty bands, reference curves, or de-emphasised data underneath a saturated line of the same hue.

Matched shades without transparency (better for print and EPS):

ax.plot(x, y, color=pa.COLORS["blue"])
ax.fill_between(x, lo, hi, color=pa.lighten(pa.COLORS["blue"], 0.7))
pa.darken(pa.COLORS["orange"], 0.3)     # the other direction

Three more palettes ship with the package:

• pa.OKABE_ITO — Okabe & Ito (2008), the classic CVD-safe recommendation for categorical colours in science;
• pa.PETROFF10 — Petroff (2021), the CVD-optimised 10-colour cycle used across particle physics;
• pa.PAIRED — light/dark pairs for data/model or before/after comparisons: pa.PAIRED["blue"] → ("#a6cee3", "#1f78b4").

Colormaps: the styles default to viridis (perceptually uniform, CVD-safe). Good picks: viridis/magma/cividis for sequential data, RdBu_r or coolwarm for diverging data (red–blue, not red–green). Avoid jet/rainbow. For more astro-friendly maps see cmasher and cmocean.

Checking accessibility yourself

Don't take the palette's word for it — simulate it (Machado et al. 2009 model, no extra dependencies):

pa.check_colors()                 # any palette under deuteranopia/protanopia/greyscale
pa.check_colors(pa.PAIRED)        # works on your own colour lists/dicts too
pa.check_figure(fig)              # simulate a whole rendered figure — the
                                  # final check before submission
pa.simulate_cvd("#e41a1c", "deuteranopia")   # the raw transform

If two lines merge in any panel, add markers or dash patterns (below), or pick colours further apart in the cycle. MNRAS recommends Color Oracle and ColorBrewer for exactly this; now it's built in.

Markers

pa.MARKERS = ["o", "s", "^", "D", "v", "p", "*", "X"] — filled shapes that survive shrinking to 4 pt. Conventions worth knowing:

Useful tricks: markevery=7 thins markers on dense curves; mfc="none" (hollow markers) keeps overlapping datasets readable; ms= and mew= control size and edge width.

Line styles

Beyond matplotlib's "-", "--", ":", "-.", the dict pa.LINESTYLES provides named dash tuples of the form (offset, (on, off, ...)) in points:

ax.plot(x, y, ls=pa.LINESTYLES["long dash"])       # (0, (9, 3))
ax.plot(x, y, ls=(0, (4, 1, 1, 1)))                # or roll your own

Guidelines: keep to ≤ 4 distinct dash patterns per panel (more becomes noise); use solid for data / the headline result and dashes/dots for models and references; MNRAS explicitly warns against triple-dot-dashed lines.

Redundant encoding — the cycler

Colour should never be the only difference between curves. pa.style_cycler advances colour, marker and/or line style in step, so every series is unique in two or three channels at once (and survives greyscale printing):

ax.set_prop_cycle(pa.style_cycler(markers=True))              # one axes
ax.set_prop_cycle(pa.style_cycler(linestyles=True, markers=True))
plt.rc("axes", prop_cycle=pa.style_cycler(markers=True))      # everywhere

Panel labels

Journals want multi-panel figures labelled (a), (b), (c)…:

fig, axes = pa.subplots(2, 2, width="full")
pa.label_panels(axes)                                    # (a) (b) (c) (d)
pa.label_panels(axes, loc="outside", fmt="{}", fontweight="bold")  # Nature style
pa.label_panels(axes, uppercase=True, loc="lower right") # (A) ... bottom-right

LaTeX text rendering

By default the styles use matplotlib mathtext with STIX fonts: Times-compatible maths, zero dependencies. For pixel-perfect agreement with your manuscript (custom macros, real kerning):

pa.set_style("mnras", usetex=True)   # needs latex + dvipng + ghostscript

This loads the newtx Times fonts (matching the MNRAS/A&A house font), or Helvetica for Nature Astronomy. Develop with usetex=False, flip it on for the final version — LaTeX rendering is slow.

Saving figures

The styles bake in submission-friendly defaults: PDF output, 450 dpi for rasterised elements (journals want ≥ 300–400), tight bounding box, and TrueType font embedding (pdf.fonttype: 42, so no Type-3 font rejections).

pa.savefig("figure1")                              # figure1.pdf
pa.savefig("figure1", formats=("pdf", "png"))      # + a PNG for slides/Slack
pa.savefig("figure1", fig=fig, dpi=600)            # extra options pass through

If a journal insists on EPS, note EPS has no transparency — replace alpha= with pa.lighten() shades (a good habit anyway).

Author lists from a CSV

Assembling the author/affiliation block by hand is error-prone on long collaborations. Feed plotastro the author CSV your collaboration already maintains — it works with real-world lists exactly as they are (this is examples/authors_example.csv):

Lastname,Firstname,Authorname,Email,JoinedAsBuilder,Affiliation,ORCID,
Bandi,Behnood,Behnood Bandi, b.bandi@sussex.ac.uk, False,"Astronomy Centre, University of Sussex, Falmer, Brighton BN1 9QH, UK",0000-0001-5838-3903,
Rocher,Antoine,Antoine Rocher,antoine.rocher@epfl.ch,False,"EPFL, \'{E}cole polytechnique f\'{e}d\'{e}rale de Lausanne, Chemin des Maillettes, 51, 1290 Versoix, Switzerland",0000-0003-4349-6424,
Verdier,Aur\'{e}lien,Aur\'{e}lien Verdier,aurelien.verdier@epfl.ch,False,"EPFL, \'{E}cole polytechnique f\'{e}d\'{e}rale de Lausanne, Chemin des Maillettes, 51, 1290 Versoix, Switzerland",,
Richard,Johan,Johan Richard,johan.richard@univ-lyon1.fr,False,"CRAL, Centre de Recherche Astrophysique de Lyon, Universit\'{e} de Lyon, 9 avenue Charles Andr\'{e}, 69230 Saint-Genis-Laval, France",0000-0001-5492-1049,
Loveday,Jon ,Jon Loveday, j.loveday@sussex.ac.uk, False,"Astronomy Centre, University of Sussex, Falmer, Brighton BN1 9QH, UK",0000-0001-5290-8940,
Brown,Michael,Michael Brown,michael.brown@monash.edu,False,"Monash, School of Physics and Astronomy, Monash University, Wellington Road, Clayton, VIC 3800, Australia",0000-0002-1207-9137,

It recognises Authorname (or name, or Firstname+Lastname), Affiliation/affiliations (several separated by ;, or one row per affiliation — repeated author rows are merged), and optional ORCID and Email; every other column is ignored (JoinedAsBuilder, ...), stray spaces are stripped, and LaTeX already in the file (accents like \'{e}) passes through untouched. Affiliations are numbered in order of first appearance and shared between authors automatically; the first author with an email becomes the corresponding author.

print(pa.authorlist("authors_example.csv", journal="mnras"))

\author[B. Bandi et al.]{
Behnood Bandi,$^{1}$\thanks{E-mail: b.bandi@sussex.ac.uk}
Antoine Rocher,$^{2}$
Aur\'{e}lien Verdier,$^{2}$
Johan Richard,$^{3}$
Jon Loveday$^{1}$
and Michael Brown$^{4}$
\\
% List of institutions
$^{1}$Astronomy Centre, University of Sussex, Falmer, Brighton BN1 9QH, UK\\
$^{2}$EPFL, \'{E}cole polytechnique f\'{e}d\'{e}rale de Lausanne, Chemin des Maillettes, 51, 1290 Versoix, Switzerland\\
$^{3}$CRAL, Centre de Recherche Astrophysique de Lyon, Universit\'{e} de Lyon, 9 avenue Charles Andr\'{e}, 69230 Saint-Genis-Laval, France\\
$^{4}$Monash, School of Physics and Astronomy, Monash University, Wellington Road, Clayton, VIC 3800, Australia
}

The same CSV works for every journal: mnras/rasti, aanda (\inst/\institute), apj/oja (AASTeX \author/\affiliation with ORCIDs), prd (REVTeX), jcap (lettered \affiliation[a]), or generic for a plain numbered block. A command-line tool ships with the package, so co-authors who don't use Python can run it too:

plotastro-authors authors.csv --journal aanda
plotastro-authors authors.csv -j apj -o authors.tex

See examples/authors_example.csv for a complete example.

API summary

Tweaks and FAQ

• Turn the grid off: pa.set_style("mnras", grid=False), or per-axes ax.grid(False).
• Override anything: pa.set_style("mnras", **{"font.size": 10}), or plt.rcParams[...] = ... after set_style.
• "Times New Roman not found" warning: the font list falls back through Times → Nimbus Roman → STIX → DejaVu automatically; install mscorefonts/STIX to silence it, or ignore it.
• Labels getting cut off? They shouldn't be — the styles enable constrained_layout. If you manage layout manually, disable it with plt.rcParams["figure.constrained_layout.use"] = False.
• Astronomical images: use origin="lower" in imshow (or uncomment image.origin: lower in the style file), and ax.grid(False).
• Figures look huge/small on screen: that's just figure.dpi: 150 for display; the saved size is exact.
• Styles without Python helpers: after import plotastro once, plt.style.use("mnras") works in any code; or copy the .mplstyle files from src/plotastro/styles/ into matplotlib.get_configdir()/stylelib/.
• Old API: plotastro.set_size(...) reproduces the original myfigsize.set_size(); the old MNRAS_Style.mplstyle is now plt.style.use("mnras").

Development

git clone <this repo> && cd <repo>
pip install -e ".[dev]"
pytest                              # run the test suite
python tools/generate_styles.py     # regenerate styles/ after editing the template
python examples/make_reference_figures.py   # regenerate README figures

The .mplstyle files are generated from a single template in tools/generate_styles.py — edit that, not the files (CI checks they stay in sync). Releases: bump the version in pyproject.toml and CHANGELOG.md, then push a v* tag — the publish workflow builds and uploads to PyPI (see the one-time trusted-publishing setup notes in that file).

Credits

• Original MNRAS style this grew from: M. Knabenhans' mplstyle_for_MNRAS
• Colour-blind-friendly Set1 ordering: Thøger Rivera-Thorsen; light colours from Tableau Color Blind 10
• Palettes: Okabe & Ito, Petroff (2021), ColorBrewer Paired
• CVD model: Machado, Oliveira & Fernandes (2009), IEEE TVCG 15(6)
• Figure-size approach after Jack Walton's guide
• Journal guidelines: MNRAS · A&A · AAS Journals · OJA · APS · Nature

MIT licensed — see LICENSE.