dzionek / computer-simulation

Python scripts for Computer Simulation uni course with some extras

Computer Simulation

This repository shows my courserwork for Computer Simulation course at the Univeristy of Edinburgh. It consits of 5 independent physics projects. For each of them I added a couple of extra feautres, type annotations and Unittest tests.

Installation

Use the package manager pip to install requirements.

cd computer-simulation
pip install -r requirements.txt

Run

To run one of the 5 projects go to its directory and run its main.py. For instance, for fractals do the following:

cd fractals
python main.py

Table of projects

1. Polynomial
2. Radioactive Decay
3. Fractals
4. Traffic Simulation
5. Orbital Motion

Polynomial

Polynomial mathematics in Python.

Uni-required features

Storing polynomial coefficients, finding its order, adding two polynomials, finding polynomial's derivative and antiderivative.

Extra features

Type annotations, documentation, validation of all parameters, subtracting, multiplication, finding polynomial's value at a given point, coefficients' sum and product, matplotlib visualization, and unit tests.

Radioactive Decay

Uni-required features

Monte Carlo simulation, finding half-time, text representation of a nucleide.

Extra features

Type annotations, documentation, validation of all parameters, and unit tests.

Fractals

Uni-required features

Generating the Mandelbrot Set.

Extra features

• Generating Julia Sets of a given complex constant.

• Generating an animation of Julia Sets of the form
  ,
  where alpha is an angle that changes throughout the animation.

• Better architecture with fractal abstract class.

• Type annotations, documentation.

Instruction

To generate a different Julia Sets than the default ones, go to main.py and change the constants:

# Edit these constants to generate different Julia sets.
STATIC_JULIA_CONSTANT = complex(-0.1, 0.8)
ANIMATED_JULIA_CONSTANT = 0.8

Traffic Simulation

Uni-required features

Animation of the traffic iterations, graph of steady state average speed against car density.

Extra features

Type annotations, documentation, validation of all parameters.

Orbital Motion

Uni-required features

Finding velocity, acceleration, position vectors with the vectorized Euler-Cromer algorithm, computing kinetic energy of a system and ensuring it is preserved throughout the simulation.

Extra features

Type annotations, documentation, better visualization and unit tests.

Instruction

The script was primary to visualize a planet and its moons. However, it can be used to simulate any celestial system. If you want to add your bodies to the system:

1. Add necessary constants in sim_constants.py

# Parameters for body X
X_MASS: Final = 4e18
X_COLOR: Final = 'b'
X_INIT_POS: Final = (2e7, -1e7)
X_INIT_VEL: Final = (1e3, 1e3)
X_INIT_ACC: Final = (0.0, 0.0)
X_INIT_FORCE: Final = (0.0, 0.0)

2. Alternatively, if your body is a moon of a planet (let's say planet X), you can find necessary parameters with

# Parameters for moon Y
Y_MASS: Final = 1.8e15
Y_COLOR: Final = 'w'
Y_RADIUS: Final = 23.463e6
Y_INIT_POS: Final = (Y_RADIUS, 0.0)
Y_INIT_VEL: Final = (0.0, _find_moon_total_velocity(X_MASS, Y_RADIUS))
Y_INIT_ACC: Final= (0.0, 0.0)
Y_INIT_FORCE: Final = (0.0, 0.0)

where Y_RADIUS is the orbital radius of Y on the orbit around X.

3. Create a Body instance of your body in main.py in function main() and pass it as an argument to display_simulation():

def main() -> None:
    """Initialize celestial bodies and display their simulation."""
    (...)
    body_x = Body(X_MASS, X_COLOR, X_INIT_POS, X_INIT_VEL, X_INIT_ACC, X_INIT_FORCE)
    display_simulation((...), body_x)

4. Voilà! You can run your simulation and see the result.