kshana 0.24.0

Open hybrid quantum/classical PNT performance simulator

क्षण — Sanskrit for the precise instant, the smallest measure of time.
Open, reproducible PNT-resilience simulation with published quantum-sensor performance models.

Kshana is an open, reproducible PNT-resilience simulator with quantum-sensor performance models — positioning, navigation, and timing. This package is a thin PyO3 (abi3) wrapper over the same Rust engine: you pass a scenario TOML string in and get a reproducible JSON result back. It quantifies, in hard numbers, what quantum clocks, quantum inertial sensors, and optical time-transfer buy a navigation system over classical PNT. Every result is reproducible from scenario + seed + engine version, and every sensor parameter is traceable to a published source.

Validated, not asserted. 666/666 AIAA SGP4 vectors to 4.12 mm · Cowell force model 0.08 m vs Orekit 12.2 · Galileo 0.61 m / Swarm-A 0.10 m vs real ESA precise ephemerides · GCRS→ITRS bit-for-bit vs SOFA/ERFA · ML metrics exact vs scikit-learn · 51 of 102 capabilities validated against independent external oracles; 47 honestly labelled Modelled, 4 partner-owned.

Validated against external oracles — every row CI-gated

Each row is checked against an independent external oracle (real dataset, independent reference implementation, or published reference vectors) and re-checked in CI.

	Capability	Result	External oracle
✅	SGP4/SDP4 propagation	666/666 vectors, worst 4.12 mm	AIAA 2006-6753 (Vallado) + independent sgp4 crate
✅	Numerical Cowell force model	0.08 m / 24 h, 275 epochs	Orekit 12.2 DormandPrince853 (CS GROUP)
✅	Orbit fit vs precise ephemeris	Galileo 0.61 m · Swarm-A 0.10 m	ESA/ESOC SP3 precise orbits
✅	GCRS→ITRS frame chain	bit-for-bit vs SOFA; ≤ 0.86 m vs SPICE	ERFA/SOFA + ANISE (pure-Rust SPICE)
✅	Allan deviations	reproduce reference deviations	NIST SP 1065 + Stable32 on a real Cs clock
✅	GNSS DOP · ML detector metrics	to 1e-6 · to 1e-9	gnss_lib_py · scikit-learn

Install

pip install kshana

Wheels are built for Linux, macOS, and Windows on each release tag. To build from source instead, use maturin: pip install maturin && maturin develop --features python.

Usage

import json, kshana

result = json.loads(kshana.run(open("clock-holdover.toml").read()))
print(result["quantum"]["fom"]["integrity"])

# JSON, SVG chart, and a one-line summary at once:
result_json, chart_svg, summary = kshana.run_full(open("orbit-gnss-challenged.toml").read())
print(kshana.version(), summary)

Beyond run / run_full / version, the module exposes run_typed (a structured result object), validate_toml (lint → list of error strings), list_kinds / scenario_kinds (the dispatchable kinds), and error_kind (the KshanaError tag for a rejected scenario) — see docs/PYTHON_API.md.

Every figure of merit is labelled validated or modelled; optical-clock figures are space goals on ground hardware (no strontium optical clock has flown). Maturity is not uniform across domains — Earth PNT is real-data validated; deep-space / Mars navigation is simulation-validated; real-mission deep-space OD is on the roadmap.

Learn more

• Full README & validation matrix → https://github.com/AshfordeOU/kshana
• Live playground (runs in your browser as WebAssembly) → https://ashforde.org
• Python API → docs/PYTHON_API.md
• Capabilities → docs/CAPABILITY.md
• Validation & provenance → docs/VALIDATION.md · docs/PROVENANCE.md

Licence

Free and open source under the GNU AGPL-3.0-only. A commercial licence is available from Ashforde OÜ for proprietary/closed integration — see LICENSING.md. Professionally developed and maintained by Ashforde OÜ; commercial support, integration, and proprietary extensions available.