ESTR 2018 Group Project: Random Walk

This is our ESTR 2018 group project's code, which mainly focuses on achieving particle random movement visualization.

Intro

Random Walk means a particle moves in a random direction along the axes 1 unit each time. We call the scenario that a particle moves on a line which is mentioned in the lecture1 as 1-dimension case. What's more, we try to consider more complex scenario and this program is attempted to show how 2-D or 3-D random walk works.

In this project, let X be a random variable which represents the distance from the original point after n steps. We have successfully calculated the laws for the discrete motion of this particle in the one-dimensional case and envisaged the possibility of a one-dimensional, continuously moving particle. Even though the lack of advanced mathematics knowledge makes the continuous 2-D random walk hard to explain generally, we still give the solution to general situation of 2-D discrete random walk.

You can find the code we used for this project in this repository, including visualizations of the discrete motion of the particle in 1, 2, and 3 dimensions, visualizations of the random directional movement of the particle in two dimensions, and fitting the results of our calculations.

Dependency

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1. python >= 3.7.

2. Numpy. It can be installed using pip as follows:

   pip install numpy

3. matplotlib. It can be installed using pip as follows:

   pip install matplotlib

Quick Start

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1. You can achieve visualisation by running the following code:

   python {dimensional}d.py

   where {dimensional} represent the dimensional you want to show.

2. You can achieve uniform visualisation by running the following code:

   python uniform_2d.py

3. You can achieve data Fitting by running the following code:

   python {task}.py

   where {task} represent the task you want to show.