Network Processing and Visualization

Project Overview

A simplified class to process touchstone files (.snp) representing frequency responses. This project targets the processing and visualization of frequency responses, unlike some more powerful tools, like ADS Python and scikit-rf.

This class is designed to perform simple processes such as computing mixed-mode S-parameters, de-embedding using T-matrix inversion, etc. The main feature of this project is its ability to execute with an Excel file acting as a control panel to allow users to automate the process.

Directory Structure

network/ init.py network.py utils.py plotting.py main.py

Features

• Compute mixed-mode S-parameters for differential pairs
• De-embed networks using T-matrix inversion
• Cascade networks
• Plot S-parameters and mixed-mode S-parameters
• Convert to and from skrf.Network objects

Installation

1. Clone the repository:

   git clone https://github.com/your_username/your_repository.git
   

2. Navigate to the project directory:

   cd your_repository
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

Usage

1. Create a Network object by providing frequency, S-parameters, and characteristic impedance data:

   from network import Network
   import numpy as np
   
   frequency = np.linspace(1e9, 10e9, 101)
   s_parameters = np.random.rand(101, 4, 4) + 1j * np.random.rand(101, 4, 4)
   z0 = np.ones((101, 4)) * (50 + 1j * 5)
   
   network = Network(frequency, s_parameters, z0, name="Example Network")

2. Plot S-parameters:

   import matplotlib.pyplot as plt
   
   fig, ax = plt.subplots(figsize=(10, 6))
   network.plot_s_parameters(fig=fig, ax=ax)

3. Compute and plot mixed-mode S-parameters:

   fig, ax = plt.subplots(figsize=(10, 6))
   network.plot_mixed_mode_s_parameters(differential_pairs=[(0, 1), (2, 3)], fig=fig, ax=ax)

4. Cascade networks:

   network1 = Network(frequency, s_parameters, z0, name="Network 1")
   network2 = Network(frequency, s_parameters, z0, name="Network 2")
   
   cascaded_network = network1.cascade_with(network2)

5. Convert to and from skrf.Network:

   import skrf as rf
   
   skrft_network = rf.Network(f=frequency, s=s_parameters, z0=z0, name="skrf Network")
   network_from_skrft = Network.from_skrft(skrft_network)
   skrft_network_converted_back = network_from_skrft.to_skrft()

License

This project is licensed under the MIT License - see the LICENSE file for details.