pyplotutil 2.1.0

Python utilities for academic plotting

pyplotutil: plotting utility for academic publications

pyplotutil is a wrapper library of polars and matplotlib, which allows you to handle time series data easily and create graphs tolerable to scientific publications.

Features

• Load tabular data from CSV, Parquet, JSON/NDJSON, or Excel files, buffers, or polars frames with column-attribute access (data.time, data.voltage)
• Group rows by a tag column (TaggedData, load_tagged_data) or manage whole directories of data files (Dataset)
• Plot multiple time series and mean-with-error graphs (standard deviation, standard error, variance, range, confidence interval)
• Plot directly from data objects: one line per file for a Dataset, one labeled line per tag for TaggedData
• Apply publication-ready matplotlib styles (SciencePlots): science, ieee, nature, notebook
• Annotate plotted lines with boxed labels and arrows (annotate_with_arrow) and add direction arrowheads along trajectories (add_direction_arrows)
• Highlight time spans from boolean masks (shade_spans) and configure axes cosmetics in one call (setup_axes)
• Save figures to multiple formats in one call with sanitized filenames
• Event logging helpers with file and console output

Installation

Requires Python 3.11+.

pip install git+https://github.com/hrshtst/pyplotutil.git

Interactive plotting with plt.show() needs a GUI backend, which is provided by the optional gui extra:

pip install "pyplotutil[gui] @ git+https://github.com/hrshtst/pyplotutil.git"

Quickstart

import matplotlib.pyplot as plt

from pyplotutil import Data, Dataset, apply_style, plot_mean_err, save_figure

# Publication-ready style.
apply_style("science", no_latex=True)

# Load a single CSV file; columns are accessible as attributes.
data = Data("experiment.csv")
fig, ax = plt.subplots()
ax.plot(data.t, data.position)

# Load every CSV file in a directory and plot mean with standard error.
dataset = Dataset("results/")
plot_mean_err(ax, dataset, "position", "se", capsize=2, label="mean")

# Write figure.png and figure.pdf in one call.
save_figure(fig, "output", "figure", ["png", "pdf"])

Group rows by a tag column:

from pyplotutil import TaggedData

tagged = TaggedData("trials.csv", tag_column="trial")
for tag, data in tagged:
    print(tag, data.param("gain"))

Development

This project uses uv and nox:

uv sync                 # set up the development environment
uv run pytest           # run the test suite
uv run mypy             # type check
uv run ruff check       # lint
nox                     # lint + type check + tests on Python 3.11-3.14

License

MIT