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ionis-jupyter

Binder PyPI License: MIT

Jupyter notebooks and helper library for HF propagation research using IONIS datasets.

What's Inside

175+ million propagation signatures spanning 18 years (2008-2026), ready for analysis:

Dataset Signatures Source SNR Type
WSPR 93.6M 10.8B WSPR spots Measured (-30 to +20 dB)
RBN 67.3M 2.2B CW/RTTY spots Real measured (8-29 dB)
Contest 5.7M 234M SSB/RTTY QSOs Anchored (+10/0 dB)
PSKR Growing PSK Reporter MQTT Measured (-34 to +38 dB)
DXpedition 260K 3.9M rare-grid paths Real measured

Plus solar indices (1932-2026), DSCOVR solar wind, and IRI-2020 ionospheric atlas.

Quick Start

Option 1: Binder (No Install)

Click the Binder badge above to launch notebooks in the cloud — nothing to install.

Option 2: Local Install

# Install packages
pip install ionis-jupyter ionis-mcp

# Download datasets (choose your bundle)
ionis-download --bundle minimal      # ~430 MB: contest + grids + solar
ionis-download --bundle recommended  # ~1.1 GB: + pskr + dscovr
ionis-download --bundle full         # ~15 GB: all 9 datasets

# Launch Jupyter
jupyter lab

Notebooks

Notebook Description Audience
01-getting-started Load data, basic queries Everyone
02-band-openings Hour×month heatmaps per band Contesters, DXers
03-solar-correlation SFI effect on propagation Researchers
04-path-analysis TX→RX specific path deep dive Path planners
05-greyline-terminator Day/night boundary propagation Low-band DXers
06-tid-detection SNR variance for TID research HamSCI, ionospheric science
07-cross-source-comparison WSPR vs RBN vs Contest vs PSKR Data scientists
08-distance-vs-snr Signal decay with distance Antenna analysts
09-seasonal-patterns Summer vs winter, solar cycle Long-term researchers
10-ionis-model-api Query live IONIS predictions API users

Helper Library

The ionis_jupyter package provides utilities for common tasks:

from ionis_jupyter import (
    # Data loading
    load_dataset, list_datasets,

    # Grid calculations
    grid_to_latlon, grid_distance, grid_bearing,

    # Solar geometry
    solar_elevation, is_dark, classify_path,

    # Plotting
    plot_band_heatmap, plot_solar_correlation,
    plot_path_profile, plot_distance_snr,
)

# Load WSPR signatures
wspr = load_dataset("wspr")
print(f"Loaded {len(wspr):,} signatures")

# Filter to 20m band
wspr_20m = wspr[wspr["band"] == 107]

# Plot hour×month heatmap
plot_band_heatmap(wspr_20m, band=107)

Data Directory

By default, datasets are stored in ~/.ionis-mcp/data/. Override with:

export IONIS_DATA_DIR=/path/to/your/data

Or download directly:

# Specific datasets
ionis-download --datasets wspr,rbn,solar

# To custom directory
ionis-download --bundle minimal --data-dir /my/data/path

For Researchers

This repository is designed for:

  • PhD students studying ionospheric propagation
  • HamSCI researchers analyzing TIDs, eclipse effects, greyline
  • Data scientists needing labeled propagation data for ML
  • Contesters analyzing historical band conditions
  • Amateur radio operators exploring propagation patterns

Citation

If you use these datasets in research, please cite:

IONIS-AI Propagation Datasets.
Greg Beam, KI7MT. IONIS-AI Project.
https://ionis-ai.com

Data sources: WSPRNet, Reverse Beacon Network, PSK Reporter, CQ Contests.

Links

License

MIT — see LICENSE.

Datasets are CC BY 4.0 (see per-directory LICENSE.md files on SourceForge).

Metadata

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