Hamradio_copilot

Developed on the base of idea by Rudy, N2WQ - TNX

See live demo for DXCC S5 (&around): https://azure.s53m.com/copilot/

DX Cluster Data Analysis Scripts: Readme

This repository contains two Python scripts for working with DX Cluster data:

• connect.py - Connects to a DX Cluster server or PSK Reporter feed, collects spotted callsigns, and stores them in an SQLite database.
• analyseSNR.py - Analyzes SNR data from the SQLite database and generates an HTML report.

connect.py

This script connects to a DX Cluster server via telnet, retrieves spotted callsign information, and stores it in a local SQLite database (callsigns.db). It filters the data based on the specified ITU Zone and enriches it with additional information like band and CQZone.

Added option is to connect to PSK Reporter feed via MQTT instead of telnet connectiomn to RBN. See http://mqtt.pskreporter.info.

Features:

• Connects to the data feed.
• Parses received data to extract callsign, frequency, mode, SNR, and spotter information using a regular expression.
• Uses a cache to avoid redundant lookups for callsign details (CQZone and ITU Zone).
• Calculates the band category based on the frequency.
• Handles reconnection attempts with exponential backoff in case of connection issues.
• Stores callsign information in an SQLite database with timestamps and spotter information.

Running the script:

python connect.py -c telnet -l <your_callsign> -i <itu_zone> --address <dx_cluster_host> --port <dx_cluster_port> -d

For telnet:

• -c telnet: Use telnet type conection
• -l <your_callsign>: Specify your callsign for login.
• -i <itu_zone>: Specify the ITU Zone to track (integer value).
• --address <dx_cluster_host> (optional): Override the default DX Cluster host (telnet.reversebeacon.net).
• --port <dx_cluster_port> (optional): Override the default DX Cluster port (7001).
• -d (optional): Enable debug output.

python connect.py -c mqtt -r <comma-separated cty codes> -d

For MQTT:

• -c mqtt: Use mqtt type conection
• -r country_code: Specify the receiver country codes (ADIF codes) to track, comma-separated. e.g. -r 499,497,503,504,206,239
• -d (optional): Enable debug output.

Country code list available at https://www.adif.org/304/ADIF_304.htm#Country_Codes

Dependencies:

• Python 3
• sqlite3 library
• re library
• logging library
• argparse library (optional, for command-line arguments)
• paho-mqtt library

Note:

• The script expects a file named cty.plist to be present in the same directory. This file contains callsign information including ITU Zone data.

Latest cty.plist available at: https://www.country-files.com (.plist is in XML format)

analyseSNR.py

This script analyzes SNR data stored in the SQLite database (callsigns.db) generated by connect.py. It generates an HTML report summarizing the data with visualizations and statistics.

Features:

• Connects to the SQLite database and retrieves callsign data.
• Filters data based on a specified time range.
• Calculates statistics for SNR data per band and ITU Zone.
• Creates a heatmap visualization of SNR distribution.
• Generates an HTML report with the analysis results and visualizations.
• Optionally fetches and integrates solar data from an external source (requires internet access).

Running the script:

python analyseSNR.py [-f <frequency>] [-l <lower_threshold>] [-u <upper_threshold>] [-r <time_range>] [-o <output_folder>] [--use-s3] [-d] [--include-solar-data]

• -f <frequency> (optional): Specify the frequency of data collection in minutes (default: 1 minute).
• -l <lower_threshold> (optional): Define the lower threshold for data count visualization (empty square, default: 5).
• -u <upper_threshold> (optional): Define the upper threshold for data count visualization (filled square, default: 10).
• -r <time_range> (optional): Specify the number of hours of data to analyze from the current time (default: 0.25 hours).
• -o <output_folder> (optional): Specify the folder to save the generated index.html report (default: "local_html").
• --use-s3 (optional): Enable uploading the generated report to an S3 bucket (requires configuration).
• -d (optional): Enable debug output.
• --include-solar-data (optional): Include fetching and presenting solar data in the report.

Dependencies:

• Python 3
• pandas library
• argparse library
• boto3 library (optional, for S3 uploads)
• requests library (optional, for external data fetching)
• xml.etree.ElementTree library (optional, for XML parsing)
• os library
• html library
• sqlite3 library
• numpy library (for numeric operations)
• matplotlib.colors library (for custom colormap)

Note:

• None at the moment :)