RSC-SIM

Radio Science Coexistence Simulator RSC-SIM is a sophisticated Python framework designed for modeling radio astronomy observations, with particular emphasis on simulating the impact of satellite constellations (such as Starlink) on radio telescope observations. The framework provides end-to-end simulation capabilities from trajectory generation to power spectral density analysis, enabling researchers and astronomers to assess and mitigate interference from satellite mega-constellations.

This software grew and evolved out of the RadioMdl codebase, a Julia package created by Dr. Samuel Thé (https://github.com/SJJThe). Modified version of the Julia codes are found at Julia/ directory.

Requirements

• Python: 3.9 or later
• Dependencies: numpy, scipy, pandas, pyarrow, numba, matplotlib, skyfield, pyproj, sgp4, rasterio, ipykernel

Modules (at src/ directory)

• RadioMdl.py: Core constants and utilities
• antenna_pattern.py: Antenna pattern calculations
• astro_mdl.py: Astronomical modeling
• coord_frames.py: Coordinate frame transformations
• obs_mdl.py: Observation modeling
• radio_io.py: Radio I/O operations
• radio_types.py: Radio data types
• sat_mdl.py: Satellite modeling
• env_mdl.py: Environment effects modeling

Tutorial Resources

educational_tutorials/ directory: Recommanded for learning

This directory contains a series of focused, educational tutorials for learning radio astronomy observation modeling with the RSC-SIM framework. Each tutorial builds upon the previous ones, providing a progressive learning experience. Details can be found in the README.md inside the educational_tutorials/.

research_tutorials/ directory

This contains additional learning resources and examples and serves as a reference implementation for the modeling and simulation of radio astronomy observations. It consists of Jupyter Notebooks and their CLI scripts, and also includes data creation scripts for trajectory files of a star and satellite at the research_tutorials/data_creation/ directory. Details can be found at the README.md inside the research_tutorials/ directory.

Installation

Prerequisites

1. Python Installation

Windows:

• Download Python 3.9+ from python.org
• During installation, make sure to check "Add Python to PATH"
• Verify installation: python --version in Command Prompt

macOS:

• Install using Homebrew: brew install [email protected]
• Or download from python.org
• Verify installation: python3 --version in Terminal

Linux (Ubuntu/Debian):

• Install using package manager: sudo apt update && sudo apt install python3.9 python3.9-venv python3.9-pip
• Verify installation: python3.9 --version

Linux (CentOS/RHEL/Fedora):

• Install using package manager: sudo dnf install python3.9 python3.9-pip (Fedora) or sudo yum install python3.9 python3.9-pip (CentOS/RHEL)
• Verify installation: python3.9 --version

2. Visual Studio Code (VS Code) Installation

Windows:

• Download VS Code from code.visualstudio.com
• Run the installer and follow the setup wizard
• Launch VS Code

macOS:

• Download VS Code from code.visualstudio.com
• Drag the downloaded file to Applications folder
• Launch VS Code

Linux:

• Download VS Code from code.visualstudio.com
• Install the .deb package: sudo dpkg -i code_*.deb
• Or use snap: sudo snap install code --classic

3. Jupyter Extension Installation in VS Code for Running a Jupyter Notebook

1. Open VS Code
2. Go to Extensions (Ctrl+Shift+X or Cmd+Shift+X)
3. Search for "Jupyter" extension by Microsoft
4. Click "Install" on the Jupyter extension
5. Restart VS Code if prompted

4. Project Setup in VS Code

1. Open VS Code
2. Go to File → Open Folder
3. Navigate to where you want to clone the repository
4. Open VS Code's integrated terminal (Ctrl+ or Cmd+)
5. Follow the installation steps below

Installation Steps

Note: Run these commands in VS Code's integrated terminal (Ctrl+ or Cmd+)

It's recommended to create a virtual environment before installing the package:

# Clone the repository at a directory
git clone https://github.com/spectrumx/RSC-SIM.git

# Create and activate virtual environment (recommended)
python -m venv rsc-sim-env
# On Windows:
rsc-sim-env\Scripts\activate
# On macOS/Linux:
source rsc-sim-env/bin/activate

# change directory to RSC-SIM and install dependencies (packages)
cd RSC-SIM
pip install -e .

# When you finish working with RSC-SIM, deactivate the virtual environment
deactivate

Running Jupyter Notebooks in VS Code

After installation, you can run the Jupyter notebooks directly in VS Code:

1. Open a .ipynb file from the research_tutorials/ directories
2. VS Code will automatically detect it as a Jupyter notebook
3. Select your Python interpreter (the one from your virtual environment)
4. Run cells using Shift+Enter or the Run button
5. Interactive plots will be displayed inline in VS Code

Dependencies

The package automatically installs the following dependencies:

• numpy>=1.21.0 - Numerical computing
• scipy>=1.5.0 - Scientific computing
• pandas>=1.3.0 - Data manipulation
• pyarrow>=6.0.0 - Data serialization
• numba>=0.56.0 - JIT compilation for performance
• matplotlib>=3.5.0 - Plotting and visualization
• skyfield>=1.40 - Astronomical calculations and ephemeris
• pyproj>=3.0.0 - Geospatial coordinate transformations
• sgp4>=2.0.0 - Satellite orbit propagation
• rasterio>=1.3.0 - Geospatial raster data I/O
• ipykernel>=6.0.0 - Jupyter notebook kernel support

Usage

When working with the source code directly:

import sys
import os

# Add the src directory to the Python path
sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'src'))

# Import modules directly
from radio_types import Antenna, Instrument, Observation
from obs_mdl import model_observed_temp
from astro_mdl import estim_casA_flux, power_to_temperature

# Use functions
result = model_observed_temp(observation, sky_model, constellation)

License

MIT License