mayaastrolib 0.3.1

Python library for Traditional and Vedic Astrology (fork of flatangle/flatlib)

mayaastrolib

A modern, typed, unified Western + Vedic astrology engine for Python 3.10+.

mayaastrolib is a Python library for Traditional (Hellenistic/Western) and Vedic (Jyotisha) astrology, computed on the Swiss Ephemeris. It is a thoroughly modernised, heavily extended fork of flatangle/flatlib (MIT, unmaintained in practice since 2024).

Original copyright © João Ventura, MIT licensed. Fork modifications © Rangan C., 2026. See docs/FORK-RATIONALE.md for why this fork exists.

from mayaastrolib import const
from mayaastrolib.chart import Chart
from mayaastrolib.datetime import Datetime
from mayaastrolib.geopos import GeoPos

date = Datetime('2015/03/13', '17:00', '+00:00')
pos = GeoPos('38n32', '8w54')
chart = Chart(date, pos)

sun = chart.get(const.SUN)
print(sun)
# <Sun Pisces +22:47:25 +00:59:51>

---

What this fork adds over flatlib

flatlib is an excellent traditional-astrology core, but it stopped at Python-2-era packaging, had no type hints, ~34% test coverage, and no sidereal/Vedic support. mayaastrolib keeps the calculation core's correctness and rebuilds everything around it:

🪔 A complete Vedic (Jyotisha) subsystem — new, ~3,600 LOC, 12 modules

flatlib is tropical-only. mayaastrolib adds a full sidereal engine behind one switch (Chart(zodiac=const.ZODIAC_SIDEREAL, ayanamsa=const.AYANAMSA_LAHIRI)):

Module	What it computes
vedic.ayanamsa	Lahiri, Krishnamurti (KP), Raman, Fagan-Bradley
vedic.nakshatras	27 nakshatras, lords, padas, tarabala
vedic.divisional	all 16 BPHS divisional charts (D1–D60 / Shodashavarga)
vedic.dasha	Vimshottari Mahadasha / Antardasha / Pratyantardasha
vedic.ashtakavarga	BAV/SAV (337-bindu system), prastara, trikona/ekadhipatya shodhana, kakshya
vedic.yogas	Pancha Mahapurusha, Raja, Dhana, Vipareeta, Neecha-Bhanga, Gaja-Kesari, lesser yogas + strength scoring
vedic.sadesati	Sade Sati phases + small-panoti (ashtama/kantaka shani)
vedic.upagrahas	Sun-derived upagrahas + Gulika/Mandi
vedic.tajika (+_bala, _aspects)	annual charts (Varshaphala), Mudda dasha, Sahams, Harsha/Panchavargiya Bala, Ithasala/Isharafa/Nakta
vedic.kp	Krishnamurti Paddhati — 249-row sub-lord table, sub-sub-lord, horary, Ruling Planets

⚙️ Modern engineering — the whole codebase brought to current standards

• Python 3.10+ baseline (3.12 target); all Python-2 compatibility code removed.
• Type hints rolling out across the public API (geopos, datetime done; core classes in progress).
• Modern packaging — single pyproject.toml (PEP 621); setup.py, requirements.txt, and README.rst deleted.
• ruff format + lint (clean across 121 files) and mypy in CI.
• CI — GitHub Actions matrix on Python 3.10 / 3.11 / 3.12.

🧪 Real correctness guarantees — coverage 34% → 94%

• 553 tests (+87 subtests), all passing, 94% coverage (80% CI floor).
• Golden tests anchor planet positions against Skyfield — an independent ephemeris — at ±2 arc-minutes for reference charts (Einstein, Kahlo, Amundsen), plus astronomical-invariant suites (houses sum to 360°, cusps ordered, etc.).

✨ API ergonomics & correctness fixes

• Chart.houseOf(), Chart.objectsInHouse(), Object.house, House.objects (object↔house links).
• Datetime.from_pydatetime() / .now() / .to_pydatetime().
• Immutable Object.with_longitude(), Chart.profected(), zodiac-aware Chart.solarReturn().
• GeoPos now validates latitude/longitude ranges instead of silently producing a wrong chart.
• Thread-safe sidereal calculation (lock-guarded Swiss-Ephemeris global state).
• Eclipse-keyword bug fixed for pyswisseph 2.x; fixed-star magnitude lookups cached (144× speedup).

See CHANGELOG.md for the full task-by-task history.

---

Development & provenance

Most of the fork's commits — the entire Vedic subsystem, the modernisation, and the test suite — were authored through Claude Code (Anthropic's agentic CLI), with minimal line-by-line human review of individual diffs.

That makes the test discipline the load-bearing part of this project, by design — correctness is verified, not taken on trust:

• Structural tests pin every public function's contract (553 tests, 94% coverage).
• Golden tests anchor the astronomical output against Skyfield — a completely independent ephemeris — at ±2 arc-minutes for known reference charts, so a wrong calculation fails CI rather than shipping silently.
• The library is exercised in real applications (the mayaastro.com lunar site and its local demo), which surfaces integration issues that unit tests don't.

In other words: the code is AI-generated, but the behaviour is held to an independent, reproducible, real-world standard. Classical-technique simplifications (e.g. yoga strength using accidental dignity rather than full Shadbala) are documented honestly in CHANGELOG.md and docs/PROJECT-LOG.md rather than hidden.

---

Documentation

Start here:

• docs/FAQ.md — what the library is, what it computes, what it does not do, threading, accuracy, licensing.
• docs/HOW-IT-WORKS.md — the calculation pipeline, package layout, and gotchas.
• docs/BIRTH-CHART-PRIMER.md — how a birth chart is calculated and what the houses/planets traditionally mean.
• docs/FORK-RATIONALE.md — why this fork exists.
• docs/KNOWN-BUGS.md — tracked bugs and their fixes.

The original flatlib API documentation is largely still applicable — substitute flatlib with mayaastrolib in import paths.

Installation

mayaastrolib requires Python 3.10 or later. Install from source:

git clone https://github.com/ranganc007/mayaastrolib.git
cd mayaastrolib
pip install -e .

The only runtime dependency is pyswisseph (Swiss Ephemeris). A PyPI release will follow once the public API stabilises.

Migrating from flatlib

mayaastrolib ships a compatibility shim: existing import flatlib and from flatlib.x import Y calls keep working but emit a DeprecationWarning. Update imports to mayaastrolib at your convenience; the shim is removed in version 1.0.

Development

git clone https://github.com/ranganc007/mayaastrolib.git
cd mayaastrolib
pip install -e ".[dev]"
pytest
ruff check . && ruff format --check . && mypy mayaastrolib/

Licensing

mayaastrolib is MIT-licensed (preserving flatlib's original copyright chain). It depends on pyswisseph (LGPL) and the Swiss Ephemeris data, which is GPL / commercial dual-licensed — if you ship mayaastrolib in a closed-source product you must comply with the Swiss Ephemeris GPL terms or hold a commercial Swiss Ephemeris licence. See LICENSING.md.