matplotlabs 0.2.0

Matplotlib styles for science

matplotlabs

Matplotlib style sheets and colormaps for scientific publications.

matplotlabs gives you publication-ready defaults for figures — clean sans-serif fonts, trimmed spines, constrained layout, and carefully designed color palettes — so you can focus on the science instead of the formatting.

Installation

pip install matplotlabs

For development:

pip install matplotlabs[dev]

Quick start

import matplotlabs as mpll  # registers styles, colormaps, and named colors on import
import matplotlib.pyplot as plt

plt.style.use("mpll")

fig, ax = plt.subplots()
ax.plot([0, 1, 2], [0, 1, 4])
ax.set_xlabel("x")
ax.set_ylabel("y")
fig.savefig("figure.pdf")

That's it. The mpll style applies publication-ready defaults — sans-serif fonts (Arial/Helvetica), 3.5 in single-column width, 7 pt font size, no top/right spines, inward ticks, constrained layout, and PDF output.

Styles

Styles are composable — stack them in any order with plt.style.use([...]).

Style	Description	Usage
mpll	Default scientific style. Sans-serif (Arial/Helvetica), 3.5 in width, 7 pt fonts, no top/right spines, inward ticks, 8-color qualitative cycle, constrained layout, PDF save.	plt.style.use("mpll")
qualitative	Explicit 8-hue qualitative color cycle at mid-saturation. Same as the default cycle — use when you want to apply the palette without the full base style.	plt.style.use("qualitative")
latex	LaTeX text rendering modifier. Enables text.usetex with Helvetica via helvet + sfmath. Requires a LaTeX installation.	plt.style.use(["mpll", "latex"])

Composition examples

# Default style with LaTeX rendering
plt.style.use(["mpll", "latex"])

# Just the default style
plt.style.use("mpll")

# Only the color cycle, nothing else
plt.style.use("qualitative")

Named colors

All colors use the mpll: prefix (similar to tab:blue) and work anywhere matplotlib accepts a color string. Each hue family has 6 shades numbered 1 (lightest) to 6 (darkest). The unadorned name maps to shade 4.

Family	mpll:{name} (shade 4)	Shades 1-6
lightone	#A5A083	warm cream to dark khaki
grey	#6E788D	light grey to dark slate
red	#C5373D	light pink to dark red
blue	#006EAE	light blue to dark navy
yellow	#CA9B23	light yellow to dark amber
olive	#96A00A	light lime to dark olive
green	#429130	light green to dark green
teal	#0096A0	light teal to dark teal
purple	#A1478E	light lavender to dark purple
orange	#E26600	light peach to dark orange
skin	#8D6651	light beige to dark brown

Usage

import matplotlabs as mpll
import matplotlib.pyplot as plt

plt.style.use("mpll")

fig, ax = plt.subplots()
ax.plot([0, 1, 2], [0, 1, 4], color="mpll:red")       # shade 4 (default)
ax.plot([0, 1, 2], [4, 1, 0], color="mpll:blue1")      # lightest blue
ax.axhline(2, color="mpll:grey6")                       # darkest grey

Programmatic access

mpll.colors["red"]        # ['#F6CECA', '#E9A0A5', '#DC6464', '#C5373D', '#9B241C', '#730C0D']
mpll.colors["lightone"]   # ['#F6F2EE', '#E0DCCA', '#C5C1A5', '#A5A083', '#888364', '#5E5948']

Colormaps

All colormaps use the mpll- prefix and are registered automatically on import. Each colormap also has a reversed variant with the _r suffix.

Sequential

Colormap	Colors
mpll-red	light pink to dark red
mpll-blue	light blue to dark navy
mpll-teal	light teal to dark teal
mpll-green	light green to dark green
mpll-yellow	light yellow to dark amber
mpll-orange	light peach to dark orange
mpll-purple	light lavender to dark purple
mpll-grey	light grey to dark slate
mpll-olive	light lime to dark olive
mpll-lightone	warm cream to dark khaki
mpll-skin	light beige to dark brown

Diverging

Colormap	Description
mpll-red-blue	Red ← white → blue
mpll-orange-teal	Orange ← white → teal
mpll-purple-green	Purple ← white → green

Usage

import matplotlabs as mpll
import matplotlib.pyplot as plt
import numpy as np

data = np.random.randn(10, 10)

fig, ax = plt.subplots()
im = ax.imshow(data, cmap="mpll-red-blue")
fig.colorbar(im, ax=ax)

# Reversed variant
im2 = ax.imshow(data, cmap="mpll-red-blue_r")

Utilities

annotate_heatmap

Overlay formatted numbers on each cell of a heatmap with automatic text color contrast (dark text on light cells, light text on dark cells).

import matplotlabs as mpll
import matplotlib.pyplot as plt
import numpy as np

plt.style.use("mpll")

data = np.array([[1.0, -0.5, 0.3], [-0.8, 0.001, 2500]])

fig, ax = plt.subplots()
im = ax.imshow(data, cmap="mpll-red-blue")
mpll.annotate_heatmap(im, data)
fig.colorbar(im, ax=ax)
fig.savefig("heatmap.pdf")

Parameters:

• im — The AxesImage returned by ax.imshow().
• data — The 2-D numpy array used to create the image.
• fmt — Explicit format string (e.g. "{:.2f}"). When None, each cell auto-selects between fixed-point, scientific notation, or integer formatting.
• sci_threshold — Magnitude threshold for scientific notation (default 1e3).
• fontsize — Annotation font size. Defaults to rcParams["font.size"] - 1.
• textcolors — Tuple of (low_color, high_color) for contrast. Default ("black", "white").
• threshold — Colormap midpoint for switching text color. Defaults to (vmin + vmax) / 2.

Default style (mpll)

The mpll style sets these key rcParams:

Parameter	Value
Font family	Sans-serif (Arial, Helvetica)
Font size	7 pt
Figure size	3.5 x 2.5 in
Figure DPI	450
Save DPI	300
Save format	PDF
Spines	Bottom + left only
Ticks	Major only, inward, no minor
Layout	Constrained layout
Legend	No frame, 6 pt
Grid	Off
Marker size	3
Color cycle	8 hues at mid-saturation

Development

# Clone and install in editable mode
git clone https://github.com/lvvittor/matplotlabs.git
cd matplotlabs
pip install -e ".[dev]"

# Run tests
pytest

# Lint and format
ruff check .
ruff format .

# Generate example figures
python examples/plot.py

License

MIT