ECE 313H Linear Systems and Signals Honors – Fall 2026 (#18595)

Python

We’ll use Python for simulation and analysis in this course. While MATLAB has long been the default tool in signal processing, the growth of open-source libraries (and machine learning in particular) has made Python a practical, popular alternative for research and teaching.

Installation: uv + Python 3.14

We will use uv to manage Python and the Python packages for this course. The course uses Python 3.14.

You do not need to install Python separately, create a virtual environment, or activate an environment yourself. uv will download and manage Python as needed and will manage the project environment automatically.

Each student should create a folder named ece313hfa26 on their computer. This folder will be the uv project for the course and is a good place to keep your notebooks, Python scripts, and other computational work.

Detailed setup instructions for Windows, macOS, and Linux are given below.

Jupyter Notebook (*.ipynb)

Jupyter Notebook lets you combine executable code, text, math, and figures in one document in the browser—great for demos, homework, and sharing results. Behind the scenes, Jupyter uses the IPython kernel, which adds useful features like tab-completion, rich history, and inline help.

A short, interactive introduction used for this class: - HTML version: https://users.ece.utexas.edu/~jtamir/files/python_tutorial.html
- Notebook file: https://users.ece.utexas.edu/~jtamir/files/python_tutorial.ipynb

Visual Studio Code (VS Code)

In this class we recommend using VS Code with the Python and Jupyter extensions. This setup lets you run .ipynb files directly, with features:
- Inline execution, just like JupyterLab
- Integrated plots and outputs rendered inside the editor
- Variable explorer and data viewer, inspect arrays, DataFrames, and variables in a spreadsheet-like UI
- Full debugging support, set breakpoints, step through code
- Version control integration, Git, GitHub, etc.
- Rich editing tools, autocomplete, formatting, etc.
- Multi-file projects: work with .py scripts, notebooks, and .md documentation all in one place

NumPy

NumPy is the core array library in Python. It provides: - An efficient N-dimensional array object
- Broadcasting and vectorized operations
- Tools to interface with C/C++/Fortran
- Linear algebra, FFTs, random sampling, and more

SciPy

SciPy adds a large collection of algorithms and toolboxes (optimization, signal processing, statistics, etc.) on top of NumPy.

Matplotlib

Matplotlib is a cross-platform plotting library for creating publication-quality 2D figures.

Resources

Setup: Python and Jupyter Notebooks in VS Code

The following steps will guide you through setting up the course Python project and running Jupyter notebooks in VS Code. If you have any questions, feel free to contact jiachenwang@utexas.edu.

S1. Install VS Code

Download and install Visual Studio Code: https://code.visualstudio.com/

S2. Install VS Code extensions

Open VS Code, press Ctrl+Shift+X (Windows/Linux) or Cmd+Shift+X (macOS) to open the Extensions sidebar, then search for and install:

If prompted, restart VS Code.

S3. Install uv

S3.1. Windows

We recommend using native Windows for this course. You do not need to install WSL.

Open PowerShell and run:

winget install --id=astral-sh.uv -e

If winget is unavailable, use the official uv PowerShell installer instead:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Close and reopen PowerShell, then verify the installation:

uv --version

S3.2. macOS and Linux

Open Terminal and run the official installer:

curl -LsSf https://astral.sh/uv/install.sh | sh

Close and reopen Terminal (or follow the installer’s instructions for updating your shell), then verify the installation:

uv --version

S4. Create the course folder and uv project

First navigate in your terminal to the parent folder where you want to keep your coursework, such as Documents. Then run:

mkdir ece313hfa26
cd ece313hfa26

Initialize this folder as a minimal uv project using Python 3.14:

uv init --bare --python 3.14 --pin-python

Add the scientific Python packages used in the course:

uv add numpy scipy matplotlib
uv add --dev ipykernel

uv will download Python 3.14 if needed, resolve the package dependencies, and create/manage the project environment automatically. Do not run python -m venv, uv venv, or environment activation commands.

You should now see files such as pyproject.toml, .python-version, and uv.lock in the ece313hfa26 folder. You may also see a .venv directory after the first package installation. This directory is created and managed automatically by uv; you do not need to activate or modify it.

If you need another Python package later in the semester, add it from inside the ece313hfa26 folder. For example:

uv add scikit-image

S5. Open the project in VS Code

From inside the ece313hfa26 folder, you can launch VS Code with:

code .

You can also open VS Code normally and choose File -> Open Folder, then select the ece313hfa26 folder.

For .py files, set the Python interpreter by pressing Ctrl+Shift+P (Windows/Linux) or Cmd+Shift+P (macOS), selecting Python: Select Interpreter, and choosing the Python environment associated with the ece313hfa26 project.

S6. Create or open a Jupyter notebook

To create a notebook, use File -> New File -> Jupyter Notebook, or open an existing .ipynb file.

In the notebook editor, click Select Kernel in the top-right corner, then choose Python Environments and select the environment associated with ece313hfa26. It will typically correspond to:

Again, you do not need to activate this environment; uv manages it for the project.

S7. Verify your setup by running the course tutorial

Download the course Python tutorial notebook:

Save python_tutorial.ipynb inside your ece313hfa26 folder, then open it in VS Code.

  1. In the notebook editor, click Select Kernel in the top-right corner.
  2. Choose Python Environments and select the environment associated with your ece313hfa26 project, as described in S6.
  3. Click Run All at the top of the notebook, or run the cells one at a time with Shift+Enter.
  4. Check that the notebook runs without import or package errors and that its numerical results and plots appear normally.

If the tutorial runs successfully, your Python, uv, Jupyter, and scientific-package setup is working correctly.

If you need to diagnose a setup problem, create a new code cell and run:

import sys
import numpy as np
import scipy
import matplotlib
import matplotlib.pyplot as plt

print(f"Python:     {sys.version.split()[0]}")
print(f"NumPy:      {np.__version__}")
print(f"SciPy:      {scipy.__version__}")
print(f"Matplotlib: {matplotlib.__version__}")

The Python version should begin with 3.14.

S8. Running Python from the terminal

When running Python code from the terminal, use uv run from inside the course folder. For example:

uv run python

or, for a script named homework.py:

uv run python homework.py

Using uv run ensures that the command runs with the Python version and packages associated with the course project.

Generated by jon-doc