python-aaronia

Python bindings for sdr-aaronia-rs — stream IQ samples from Aaronia SPECTRAN V6 devices (via an RTSA-Suite PRO HTTP server block or the native SDK) or play back recorded .rtsa files, straight into NumPy or Apache Arrow.

  • PyPI package: python-aaronia · importable module: aaronia
  • Wheels: abi3, CPython ≥ 3.9, one wheel per OS/arch (plus an sdist for everything else — building from source needs a Rust toolchain)
  • License: GPL-3.0-or-later

Install

pip install python-aaronia

Or from a checkout (requires Rust + maturin):

cd python-aaronia
maturin develop --release

Quickstart

import aaronia

cfg = aaronia.AaroniaConfig()
cfg.http_base_url = "http://localhost:54664"  # RTSA-Suite HTTP server block
cfg.center_freq = 2.44e9                      # Hz
cfg.sample_rate = 15.36e6                     # Hz
cfg.format = "F32"                            # wire format: F32, F16, or I16

src = aaronia.AaroniaSource()
src.start_streaming(cfg)

samples = src.read_samples_numpy(65536)       # numpy complex64 array
batch = src.read_samples_arrow(65536)         # pyarrow FixedSizeListArray of [re, im]

src.set_center_frequency(2.41e9)              # live retune, no teardown

print(src.cumulative_drops(), src.take_overrun(), src.last_timestamp_ns())
src.stop_streaming()

File playback: set cfg.file_path = "capture.rtsa" instead of http_base_url.

Configuration (AaroniaConfig)

Every field is readable and writable.

Field Meaning
http_base_url RTSA-Suite HTTP server URL; pins the HTTP backend
file_path Path to a recorded .rtsa file; pins the file backend
device_serial Device selection for the native-SDK backend
center_freq Center frequency, Hz
sample_rate IQ sample rate, Hz (the Aaronia "span")
reference_level Reference level, dBm
format HTTP wire format: "F32", "F16", or "I16" (true low-bandwidth mode)
receiver_channel "Rx1" (default), "Rx2", or "Rx1And2" (native SDK, full V6)

Unknown format/receiver_channel strings raise ValueError instead of silently defaulting.

Semantics worth knowing

  • One copy per read. Samples are copied once out of the Rust receive buffer into a NumPy/Arrow-owned buffer — safe to hold indefinitely. (Not "zero-copy"; one copy is the honest count.)
  • Blocking calls release the GIL. Other Python threads keep running; KeyboardInterrupt is delivered between calls. Reads block until count samples arrive or an internal 30 s timeout raises AaroniaTimeoutError.
  • Typed exceptions. AaroniaConnectionError (unreachable endpoint), AaroniaTimeoutError, AaroniaHardwareError (device/SDK errors), ValueError (invalid configuration) — mapped from the Rust error enum, with the full cause chain in the message.
  • Dual-channel (receiver_channel = "Rx1And2", read_samples_dual_numpy(count) → two time-aligned arrays) requires the native-SDK backend: Windows/Linux with the Aaronia SDK installed, and a full (two-input) V6. Hardware-unverified — the development device is a single-channel V6 ECO.

Source methods

Method Purpose
start_streaming(cfg) / stop_streaming() Session lifecycle
read_samples_numpy(count) NumPy complex64 array
read_samples_arrow(count) PyArrow FixedSizeListArray of [re, im] float32 pairs
read_samples_dual_numpy(count) (rx1, rx2) NumPy arrays (dual-channel captures)
set_center_frequency(hz) / set_sample_rate(hz) / set_reference_level(dbm) Live retuning
cumulative_drops() Total server-reported dropped samples
take_overrun() True once per detected receive-side overrun
last_timestamp_ns() Epoch-ns timestamp of the last received block (HTTP backend; 0 otherwise)

Project links

Data verified by PyPI on Aug 11, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (0.11.2)

1 maintainer

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Avatar for ibentley from gravatar.com ibentley

GitHub Statistics

Data verified by PyPI on Aug 11, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

GNU General Public License v3 or later (GPLv3+) (GPL-3.0-or-later)

Requires

Python >=3.9

Classifiers

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