https://github.com/horheynm/montecarlovisualizer_deploy

https://github.com/horheynm/montecarlovisualizer_deploy

Last synced: 6 days ago
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• Host: GitHub
• URL: https://github.com/horheynm/montecarlovisualizer_deploy
• Owner: horheynm
• Created: 2021-05-15T16:29:43.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2021-05-16T11:15:25.000Z (over 3 years ago)
• Last Synced: 2024-10-29T14:40:31.842Z (about 2 months ago)
• Language: JavaScript
• Size: 14.6 MB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Monte Carlo Simulation Visualizer - Approximating Pi

 

 





##  Website Link

### Deployed using Heroku

## Repository notes

### This repo is for deployment. Check out this repo for detailed commits during development, detailing each step by step addition of components per commit

## How to run in your local computer

1. Download this repo

2. (Optional) Create a virtual environment and activate it. For Mac users follow here  

3. In your virtual/local environment `cd` into the downloaded folder, and execute `pip install -r requirements.txt`

4. Execute `python manage.py runserver`  

5. Go to a browser and enter the url `http://127.0.0.1:8000/` 

6. Monte Carlo Visualizer homepage should render

## Tech Stack

### Frontend

* React

* Ploly.js

* Axios

### Backend

* Django

* Django REST framework 

# Approximating Pi

 

## Description

Monte Carlo simulation is a method used to approximate numerical values. One example is approximating π.

Sample random number from [-1,1] from a uniform distribution and check if it lies inside/outside the unit circle. Approximate π using the area of the circle and the square from [-1,1]

## Components

### Input

* Select the number of iteration (points) dynamically

* Select the frame rate dynamically (iterations per frame)

### Output

* Visualize the randomly generated points inside the figure per frame

* Visualize the approximation of pi per frame