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Overview

PhysisLab experiments can be performed with varying levels of equipment depending on which measurement approach you choose. This page outlines all requirements organized by experiment type and methodology.

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Software Requirements

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Python Environment

All camera-based and audio-based experiments require Python with scientific computing libraries:
  • Python: Version 3.7 or higher (3.8+ recommended)
  • Operating System: Windows, macOS, or Linux

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Required Python Libraries

The following libraries are used across different experiments:

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Core Computer Vision

import cv2              # OpenCV for image processing and tracking
import numpy as np      # Numerical computing and arrays

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Scientific Computing & Analysis

import matplotlib.pyplot as plt      # Plotting and visualization
from scipy.optimize import curve_fit # Curve fitting (sinusoidal, parabolic)
from scipy.signal import welch, find_peaks  # Signal processing and spectral analysis

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Audio Processing (Sound-Based Experiments)

import sounddevice as sd  # Real-time audio I/O
import time               # High-precision timing

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GUI Applications (Oscilloscope/Signal Generator)

import tkinter           # GUI framework (or PyQt/customtkinter)
import serial            # Serial communication with ESP32
import websocket         # WebSocket communication (optional)
A complete requirements.txt file will be provided in the installation guide. Install all dependencies with:
pip install opencv-python numpy scipy matplotlib sounddevice pyserial

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Arduino IDE or PlatformIO

For microcontroller-based experiments:
  • Arduino IDE: Version 2.0+ (recommended) or 1.8.19+
  • Alternative: PlatformIO (for advanced users)
  • ESP32 Board Support: Arduino core for ESP32

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Hardware Requirements

Minimum Hardware

For free fall, pendulum, spring-mass, projectile motion, and kinematics experiments using computer vision.

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Essential Components

ComponentSpecificationPurpose
WebcamUSB webcam, 30 fps minimum (60 fps preferred), 720p or higherObject tracking and motion capture
ComputerWindows/Mac/Linux with USB port, 4GB RAM minimumRunning Python scripts and analysis
Colored ObjectsHigh-saturation colors (orange, yellow, pink, green)Easy HSV color detection and tracking
Reference MarkersTriangular or rectangular colored markersHomography calibration for pixel-to-meter conversion
Good LightingUniform lighting, avoid shadows and backlightingConsistent color detection

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Experiment-Specific Hardware

Free Fall
  • Small colored ball (ping pong ball or similar)
  • Measuring tape to calibrate distance
  • Height: 1-2 meters recommended
Pendulum
  • String or fishing line (low mass, non-elastic)
  • Colored pendulum bob (20-100g mass)
  • Fixed pivot point (hook, stand)
  • Measuring tape for length calibration (0.5-1.5m lengths work well)
Spring-Mass System
  • Helical spring (k ≈ 1-10 N/m for visible oscillations)
  • Known mass (50-500g)
  • 3 colored markers forming isosceles triangle for calibration
  • Vertical support frame
Projectile Motion
  • Launcher (manual or mechanical)
  • Projectile with colored marker
  • 4 colored markers forming rectangle for homography
  • Launch angles 20-70° recommended
Kinematics (Camera)
  • Moving colored object
  • Linear track or guided motion (optional)
  • Reference markers for calibration

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Camera Resolution & Frame Rate Guidelines

Higher frame rates are critical for fast-moving experiments:
  • Free fall: 30 fps minimum, 60 fps recommended
  • Projectile motion: 60 fps strongly recommended
  • Pendulum & spring-mass: 30 fps sufficient
  • Slow kinematics: 15-30 fps sufficient
The code automatically measures actual camera FPS: 
# From FreeFallCam.py - automatic FPS detection
fps_from_cap = cap.get(cv2.CAP_PROP_FPS)
num_test_frames = 60
start = time.time()
for i in range(num_test_frames):
    ret, frame = cap.read()
end = time.time()
measured_fps = num_test_frames / (end - start)
real_fps = min(fps_from_cap, measured_fps)

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Option 2: Microcontroller-Based Experiments

ESP32 Setup

For high-precision timing and sensor-based measurements.

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Essential Components

ComponentSpecificationWhere to Buy
ESP32 Dev BoardESP32-WROOM-32 or ESP32-DevKitC, 240MHz dual-coreAmazon, AliExpress, local electronics
USB CableMicro-USB or USB-C (depending on board)Included with most boards
Breadboard830 points minimumElectronics suppliers
Jumper WiresMale-to-male and male-to-female assortmentElectronics suppliers
Power Supply5V via USB (500mA minimum)USB power adapter or computer USB

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Experiment-Specific Sensors

Free Fall Timing Two approaches available:
  • 2x IR Break Beam Sensors: Transmitter + receiver pairs
  • Pins Used: GPIO 18 (start), GPIO 5 (end)
  • Resolution: Microsecond timing precision
  • Range: 5-30 cm beam distance
  • Cost: ~$5-10 per pair
Kinematics (Distance Sensors)
  • Sensor: Adafruit VL53L0X or compatible
  • Range: 5-200 cm accurate measurement
  • Interface: I2C (pins 21=SDA, 22=SCL)
  • Interrupt Pin: GPIO 27
  • Update Rate: ~5 Hz (200ms intervals)
  • Library: Adafruit_VL53L0X
  • Advantages: High accuracy, not affected by object color
  • Cost: ~$15
Both sensor approaches include advanced filtering:
  • Exponential Moving Average (EMA)
  • 2nd-order Butterworth low-pass filter
  • α-β filter for position and velocity estimation

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Arduino Libraries Required

#include <Arduino.h>           // Core Arduino functions
#include <Wire.h>              // I2C communication
#include <Adafruit_VL53L0X.h> // VL53L0X sensor (if using ToF)
#include "driver/dac.h"        // ESP32 DAC (oscilloscope/generator)
Install via Arduino Library Manager:
  • Adafruit VL53L0X (for kinematics with ToF sensor)
  • ESP32 board definitions (via Boards Manager)

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Option 3: Audio-Based Experiments

Sound Detection Setup

For free fall experiments using acoustic impact detection.

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Required Hardware

ComponentSpecificationNotes
ComputerAny with audio inputBuilt-in mic often sufficient
Microphone3.5mm jack or USB micHeadphones with mic work well
Sound SourceBouncing ball or dropping objectShould create clear impact sounds
Quiet EnvironmentLow background noiseCritical for threshold detection

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Audio Setup Parameters

SAMPLE_RATE = 44100      # Standard audio sample rate
BLOCK_SIZE = 8           # Small buffer for low latency
THRESHOLD = 0.65         # RMS threshold for event detection
DEVICE_INDEX = 0         # Audio input device
The system includes:
  • Real-time RMS level monitoring
  • Latency compensation (estimated from block size)
  • Visual feedback during measurement
  • Accept/reject interface for data quality control
Test your microphone sensitivity before experiments:
device_info = sd.query_devices(DEVICE_INDEX)
print(f"Using: {device_info['name']}")
Adjust THRESHOLD based on ambient noise levels.

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Bonus: Oscilloscope & Signal Generator

Lab Instrument Setup

Build a dual-channel oscilloscope and arbitrary waveform generator.

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Hardware Requirements

ComponentPin ConnectionsFunction
ESP32Standard dev boardCore processor
DAC OutputsGPIO 25 (CH1), GPIO 26 (CH2)Signal generation (0-3.3V)
ADC InputsGPIO 34 (CH1), GPIO 35 (CH2)Signal measurement
USB ConnectionVia built-in USBSerial communication at 115200 baud

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Optional: WebSocket Mode

  • WiFi Connection: ESP32 connects to local network
  • Communication: WebSocket protocol for lower latency
  • Advantages: Wireless operation, potentially faster streaming

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Capabilities

Signal Generator:
  • Waveforms: Sine, square, triangle, sawtooth, DC
  • Frequency range: 0.1 Hz - 10 kHz
  • Amplitude control: 0-255 (8-bit DAC)
  • Offset control: 0-255
  • Sample rate: 40 kHz
  • Independent dual channels
Oscilloscope:
  • Dual ADC channels (12-bit)
  • Sample rate: 200 samples/second (configurable)
  • Input range: 0-3.3V
  • Real-time streaming via serial
  • Python GUI with FFT analysis

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Next Steps

Installation Guide

Follow step-by-step instructions to install all required software

Choose Your First Experiment

Start with a simple free fall experiment

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