Prelabs are essential activities designed to prepare you for the lab activities. They are due before the lab (see Canvas for the due dates), and are graded on completion. However, correctness is essential for being able to complete the labs correctly. It is recommended to corroborate with peers and/or come to office hours to ensure your prelabs are done correctly and you are properly prepared for lab.
Prelabs are not busywork. They are the cognitive preparation that allows you to focus on experimental challenges during lab rather than simultaneously trying to understand theory, operate unfamiliar equipment, and troubleshoot circuits.
Manage cognitive load: Labs involve equipment operation, troubleshooting, data collection, and analysis. If you are also trying to derive transfer functions or understand complex impedance during lab, you cannot focus on the experimental skills. Prelabs offload the conceptual work so lab time can focus on doing science.
Make predictions: Physics learning research shows that making predictions before experiments—and then confronting discrepancies—is central to learning. Prelabs are not about “getting the right answer”; they are about having an expectation that can be tested. If your measurements match your predictions, you gain confidence in your understanding. If they differ, you have something interesting to investigate.
Build ready-to-use tools: When you arrive at lab with a working Python function and an LTspice simulation, you have predictions that can immediately be compared to measurements. You can update your functions with measured component values and get new predictions in seconds.
For each prelab, aim to:
Before beginning the prelabs, ensure you are comfortable with these foundational skills:
Complex numbers: Labs 3–6 involve transfer functions with magnitude and phase. If expressions like \(T = 1/(1 + j\omega RC)\) look unfamiliar, review the Complex Numbers resource. Work through Practice Problems 1 and 2 at minimum.
LTspice simulation: Every prelab includes simulation work. The Lab 1 prelab walks you through your first LTspice simulation. If you have never used circuit simulation software, watch the tutorial videos linked in Lab 1 before attempting the simulation questions.
These resources are not “extra”—they are prerequisites for effective prelab work.
Before arriving at lab, ask yourself:
Conceptual: Can I explain, in words, what the circuit does and why? (e.g., “This low-pass filter attenuates high-frequency signals because the capacitor impedance decreases at high frequency”)
Quantitative: Do I have numerical predictions for the key measurements? (e.g., “The cutoff frequency should be around 16 kHz given my component values”)
Computational: Can I update my Python functions with measured component values and immediately get new predictions?
Simulated: Does my LTspice simulation match my analytical prediction? If not, do I understand why?
Procedural: Do I know what I will build and what I will measure in lab?
If you cannot answer “yes” to all five, you are not ready. Come to office hours or collaborate with peers.
You will be asked to perform calculations in the prelab using predetermined or given values. For example, you may be asked to calculate the parallel resistance of 2 resistors
\[\frac{1}{R_\text{total}} = \frac{1}{R_1}+\frac{1}{R_2}\]
using \(50\ \Omega\) and \(100\ \Omega\). When you get to lab and grab those resistors, you will measure them and they could be something like \(50.5\ \Omega\) and \(98.4\ \Omega\). It will save you time to write your calculations as functions (in Python, Mathematica, etc.) so that you can re-call the function with updated input values.
For example (in Python) instead of
r = 1. / (1. / 50. + 1. / 100.)You should
def parallel_r(r1, r2):
return 1. / (1. / r1 + 1. / r2)
print(parallel_r(50., 100.))This way you can re-call the function later in the lab with the updated values without copy-pasting a bunch of code and editing it (this is very bad practice).
Many of the functions you build could be used several times
throughout the course. The parallel_r() function
above is a great example of one you may use repeatedly.
A decent way to make it easy to access all your functions is
to make a .py script in the directory you will be
working in. You can call it whatever you want, but for sake of
example, here we will call it jlab.py.
In jlab.py you can write all your scripts, and
then in your Jupyter notebooks, you can import and access your
functions like this
import jlab as jl
print(jl.parallel_r(50.5, 98.4))Throughout the course, you will develop functions for:
Keep your jlab.py organized and commented—it
becomes a valuable reference.
Treating prelabs as homework to “get done”: If you complete your prelab without understanding what you calculated, you will be lost in lab. Check: Can you explain, without looking at your notes, what your transfer function means physically?
Copy-pasting code without understanding: The point of writing functions is not just efficiency—it is building tools you understand. If you copy a function from a peer, make sure you can explain what each line does.
Skipping the simulation because the math is “right”: LTspice is a second model. Comparing your analytical prediction to your simulation is a check on both. Discrepancies are learning opportunities.
Ignoring units and orders of magnitude: A transfer function of 0.7 at 1 kHz should make sense given your component values. If your Python function returns 0.0007 or 7000, something is wrong.
Waiting until the night before: Prelabs often reveal gaps in understanding. Give yourself time to get help if needed.
If you make a directory called images in your
working directory and place all your images in there, you can
include them in a Markdown cell with:
For hand-drawn circuit diagrams or derivations, scan or photograph them clearly and include them the same way.
You can export Jupyter notebooks to PDF directly through VS Code. Here’s more info on setting up.
If you’re having trouble exporting to PDF, you can export to HTML and then convert the HTML to PDF with an online tool like Cloud Convert.