rfi-fileparser 0.6.1

Python tools to download JSON files from rfi.stanford.edu.

GNSS Interference Monitoring Data Download Tool

This repository explains a Python package <rfi_fileparser> to download JSON files from GNSS Interference Monitoring Website: http://rfi.stanford.edu/. Users can specify a start date, end date, and data type (jamming, spoofing, or dashboard).

Installation

Prerequisites

• Python 3.8+ installed

Setup

(TestPyPI)

Open a terminal (or Anaconda prompt if using that) and run:

pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple rfi-fileparser==0.6.0

That way, dependencies come from real PyPI, and your package from TestPyPI.

(PyPI: ignore this, this will be used after making repository public)

pip install rfi-fileparser

Usage

To download JSON files:

from rfi_fileparser import downloader

downloader.download_files("2025/03/23", "2025/03/26", "dashboard")
downloader.download_files("2025/03/23", "2025/03/26", "jamming")
downloader.download_files("2025/03/23", "2025/03/26", "spoofing")

• You can ignore the warnings which is caused by bypassing https protection from our website to download the files.
• A week of all types of data would take few minutes to download, please be patient!
• Once code is done running, files can be found in folder "downloaded_json_files/" under your current path.

To visualize Heatmaps:

from rfi_fileparser import plot_daily_heatmap, plot_hourly_heatmap

plot_daily_heatmap("downloaded_json_files", "2025/03/25")
plot_hourly_heatmap("downloaded_json_files", "2025/03/25")

To visualize events:

from rfi_fileparser import plot_jamming, plot_spoofing

plot_jamming("downloaded_json_files", "2025/03/25")
plot_spoofing("downloaded_json_files", "2025/03/25")

Data Structure

.downloaded_json_files/
├── dashboard
│   ├──general.json
│   └── 2025
│       └── 03
│           └── statistics.json
├── jamming
│   └── 2025
│       └── 03
│           └── 23
│               ├── events.json
│               ├── heatmap.json
│               ├── 0000
│               │   └── heatmap.json
│               ├── 0100
│               │   └── heatmap.json
│               └── ...
└── spoofing
    └── 2025
        └── 03
            └── 23
                ├── beforeAndDuringSpoofing.json
                ├── duringAndAfterSpoofing.json
                ├── events.json
                └── heatmap.json

Data Information

1. jamming/2025/03/23/heatmap.json

→ Daily heatmap data.

2. jamming/2025/03/23/0000/heatmap.json

→ Hourly heatmap data.

• h3Index: Unique index for each hexagonal cell (H3 system). Use it to get hexgaon boundaries in Python:

  from h3 import h3
  from shapely.geometry import Polygon
  
  def h3_to_polygon(h3_index):
      boundary = h3.h3_to_geo_boundary(h3_index, geo_json=True)
      return Polygon(boundary)
  

  (Functions for this are included in the GitHub repository.)

• lowQualityCount and totalAircraftCount: Number of low NIC / total aircraft seen within that hexagonal cell.

3. jamming/2025/03/23/event.json

→ All jamming events detected on that day.

• latitude and longitude: Centroid location of each jamming event.
• startTime and endTime: Start and end times of each jamming event.

4. spoofing/2025/03/23/beforeAndDuringSpoofing.json

→ For each spoofing event, shows the last known normal position and the first spoofed position observed for each affected flight.

• beforeSpoofing: Last known normal position
• spoofedInto: First spoofed position

Example:

{
  "event_1": [
    {
      "beforeSpoofing": {
        "lat": 53.1131,
        "lon": 49.9999,
        "alt": 9098.28,
        "nic": 7,
        "time": 1740858262.297
      },
      "spoofedInto": {
        "lat": 53.102,
        "lon": 49.9545,
        "alt": 9098.28,
        "nic": 0,
        "time": 1740859918.787
      },
    },
    ...
  ],
  ...
}

5. spoofing/2025/03/23/duringAndAfterSpoofing.json

→ For each spoofing event, shows the last spoofed position and the first normal position after recovery.

• spoofedInto: Last spoofed position
• afterRecovering: First normal position after spoofing

Example:

{
  "event_1": [
    {
      "spoofedInto": {
        "lat": 53.101,
        "lon": 49.9843,
        "alt": 9098.28,
        "nic": 0,
        "time": 1740867858.763
      },
      "afterRecovering": {
        "lat": 51.7174,
        "lon": 55.0444,
        "alt": 9098.28,
        "nic": 0,
        "time": 1740868558.257
      }
    },
    ...
  ],
  ...
}

6. spoofing/2025/03/23/event.json

→ All spoofing events detected on that day.

• latitude and longitude: Centroid location of each spoofing event.
• startTime and endTime: Start and end times of each spoofing event.

7. spoofing/2025/03/23/heatmap.json

→ Daily heatmap of spoofing-affected region.

• h3Index: Unique index for each hexagonal cell (H3 system). (See usage example above under jamming/2025/03/23/0000/heatmap.json.)

• spoofedFlightCount: Number of affected aircraft within each cell, based on interpolated true paths.

• seenAircraftCount: Number of unaffected aircraft in the same cell, based on ADS-B data.

Example:

{
  "event_1": [
    {
      "h3Index": "841f533ffffffff",
      "spoofedFlightCount": 2,
      "seenAircraftCount": 26
    },
    {
      "h3Index": "841f53bffffffff",
      "spoofedFlightCount": 3,
      "seenAircraftCount": 19
    },
    ...
  ],
  ...
}