sayonlab 0.1.0

A Scientific Research Productivity Framework for Python.

SayonLab

A Scientific Research Productivity Framework

Reducing the engineering overhead of scientific research — one automated workflow at a time.

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Introduction

Scientific research involves a large amount of repetitive engineering work that has nothing to do with the actual research question: writing boilerplate plotting code, manually formatting figures for publication, re-implementing the same statistical checks across projects, and hand- assembling reports. This work is necessary, but it consumes time that could otherwise go toward the science itself.

SayonLab is an open-source framework aimed at reducing that overhead. It is built as a collection of focused subsystems — plotting, statistics, dataset handling, reporting, and others over time — unified behind a single, consistent Python API.

Current status: SayonLab is in early, active development. As of v0.1.0, one subsystem — sayonlab.plots — is implemented, tested, and usable. The rest of the framework described below is roadmap, not shipped functionality, and is labeled accordingly throughout this document.

Why SayonLab?

SayonLab does not aim to replace NumPy, pandas, Matplotlib, SciPy, or scikit-learn. Those libraries are stable, well-understood, and deeply embedded in the scientific Python ecosystem for good reason.

What SayonLab aims to do is sit on top of them and automate the repetitive decisions researchers make every time they use them — sensible defaults, consistent styling, common validation, and output formats suited to publication — so that using them well requires less repeated code.

A traditional plotting workflow might look like:

Load data → plt.subplots() → manually set fonts/DPI/grid → plot →
manually check for empty/mismatched data → fill_between for CI →
plt.tight_layout() → create output directory manually → savefig for
each format → remember to plt.close()

The equivalent SayonLab workflow:

sl.plot.line(x, y, ci_lower=lo, ci_upper=hi, save_path="fig1", formats=["png", "pdf"])

Validation, styling, layout, directory creation, multi-format export, and figure cleanup are handled internally. Matplotlib still does the actual rendering — SayonLab automates the decisions around it.

Key Features

Available in v0.1

• Publication-ready line, scatter, and bar plots with a single function call
• Optional confidence-interval bands (line) and error bars (bar)
• Automatic plot-type inference via sl.plot.auto()
• Colorblind-safe default palette (Okabe-Ito)
• Scoped styling — never mutates global Matplotlib state
• Multi-format export (PNG, PDF, SVG) with automatic directory creation
• Per-format provenance metadata embedded in exported figures
• Memory-safe figure handling (no leaked figures, even on error)
• Informative, specific validation errors instead of raw Matplotlib tracebacks

Planned for future versions

These are roadmap items, not implemented features. See Roadmap for status.

• Statistical analysis utilities (stats)
• Dataset inspection and validation utilities (datasets)
• Automated report generation (reports)
• Experiment tracking and reproducibility metadata
• Additional plot types (histograms, box/violin plots, ML evaluation curves)
• Optional R-backend interoperability for bioinformatics-specific packages

Current Modules

Module	Status	Description
sayonlab.plots	✅ Implemented	Publication-ready plotting: line, scatter, bar, auto. Fully tested (27 passing tests).
sayonlab.stats	🔮 Planned	Statistical summaries and hypothesis testing utilities. Not yet implemented.
sayonlab.datasets	🔮 Planned	Dataset loading, validation, and inspection utilities. Not yet implemented.
sayonlab.reports	🔮 Planned	Automated report generation (Markdown/HTML/PDF). Not yet implemented.

Only sayonlab.plots currently exists in the repository. The modules above marked "Planned" are described here so the intended scope of the project is visible, not because their files currently exist — see the Roadmap for how these are sequenced.

Installation

SayonLab is not yet published to PyPI. For now, install directly from source:

git clone https://github.com/sayonmitra/SayonLab.git
cd SayonLab
pip install -e .

This installs SayonLab in editable mode, so changes to the source are picked up immediately without reinstalling.

Future (once released):

pip install sayonlab   # not yet available

Quick Start

import sayonlab as sl

x = [1, 2, 3, 4, 5]
y = [2, 4, 5, 7, 8]

sl.plot.line(
    x, y,
    title="Example Trend",
    xlabel="Time",
    ylabel="Value",
    save_path="trend.png",
)

That's it — the figure is styled, laid out, and saved automatically. See examples/plotting_quickstart.py for a complete walkthrough of line, scatter, bar, and auto.

Why Researchers May Like SayonLab

• Publication-ready by default — 300 DPI, colorblind-safe palette, and clean typography without configuring anything.
• Sensible defaults, not rigid ones — every default can be overridden via a PlotStyle object when a figure needs to differ.
• Automation, not obscurity — SayonLab automates repetitive steps (directory creation, multi-format export, layout) rather than hiding what Matplotlib is doing; the output is still a normal Matplotlib figure underneath.
• Reproducibility-minded — exported figures carry embedded provenance metadata (creator, creation date) by default.
• A clean, small public API — four functions (line, scatter, bar, auto) cover the v0.1 surface. Nothing to memorize beyond that.

Architecture Overview

sayonlab/
├── __init__.py          # exposes sl.plot (stable public API)
└── plots/                # the only implemented subsystem in v0.1
    ├── __init__.py       # public surface: line, scatter, bar, auto, PlotStyle, ...
    ├── validation.py      # shared input validation
    ├── style.py            # scoped publication styling, colorblind-safe palette
    ├── base.py              # figure lifecycle: creation, layout, memory-safe cleanup
    ├── export.py            # multi-format save, metadata, directory creation
    └── core.py               # public functions: line(), scatter(), bar(), auto()

Each file in plots/ has exactly one responsibility, and lower layers never depend on higher ones — see sayonlab/plots/ARCHITECTURE.md (local reference, not tracked in version control) for the full internal design rationale.

Roadmap

• ✅ v0.1.0 — Plotting subsystem (sayonlab.plots): line, scatter, bar, auto-inference, publication styling, multi-format export, full test coverage.
• 🚧 v0.2 — Statistics subsystem (sayonlab.stats): summary statistics, common hypothesis tests, effect sizes.
• 🔮 v0.3 — Dataset utilities (sayonlab.datasets): validation, corruption/duplicate detection, class distribution reporting.
• 🔮 v0.4 — Reporting (sayonlab.reports): automated Markdown/HTML report generation from plots and statistics.
• 🔮 v0.5+ — Experiment tracking and reproducibility metadata; additional plot types (ML evaluation curves, statistical plots); exploration of optional R-backend interoperability for bioinformatics workflows.

Version numbers above are indicative of sequencing, not committed dates.

Documentation

A dedicated documentation site is planned but does not yet exist. For now, this README and the docstrings within each module (NumPy-style, complete with parameters/returns/examples) are the primary reference. Every public function in sayonlab.plots has a full docstring accessible via help(sl.plot.line).

Examples

See examples/plotting_quickstart.py for a runnable demonstration of all four plotting functions, including a confidence-interval band, error bars, and automatic plot-type inference.

python examples/plotting_quickstart.py

Testing

pip install pytest
pytest tests/ -v

The sayonlab.plots test suite currently includes 27 tests covering validation, confidence intervals, error bars, plot-type inference, export correctness, and memory-safe figure handling.

Contributing

SayonLab is not yet accepting external contributions in a structured way — CONTRIBUTING.md and issue/PR templates will be added once the project reaches a stage where outside contributions make sense. If you're interested in the project's direction in the meantime, opening a GitHub issue to discuss is welcome.

License

SayonLab is licensed under the Apache License 2.0.

Citation

If you use SayonLab in your research, please cite it using the metadata in CITATION.cff. A DOI via Zenodo will be added once a public release is tagged.

Acknowledgements

SayonLab is built on top of, and would not be possible without, the scientific Python ecosystem — in particular NumPy, Matplotlib, and Pillow.

Contact

Created and maintained by Sayon Mitra. Email: [e-mail] For questions or discussion, please open a GitHub issue.

Project Status

SayonLab is under active, early-stage development. The plotting subsystem (sayonlab.plots) is stable and tested; all other functionality described in this document is planned but not yet implemented. Expect the public API to evolve as new subsystems are added.

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Built by Sayon Mitra