2D fluid simulation implementation of Jos Stam paper on real-time fuild dynamics, including some suggested extensions.

Overview

Fluid Simulation

image

Usage

  1. Download this repo and store it in your computer.
  2. Open a terminal and go to the root directory of this folder.
  3. Make sure you have installed the needed dependencies by typing:
$ pip install numpy
$ pip install matplotlib
$ pip install ffmpeg

Note: Go to Install FFmpeg on Windows section if you haven't installed FFmpeg software locally before. It must be added to PATH so that videos can be saved.

  1. Type to run:
$ python fluid.py -i config.json

Where the config.json file is the input file inside the same folder as main.py file.

The Development Log file is also located in the root directory of this repository, where all the logic and structure of the programming done is explained.

Input

The config.json file is the input file you must provide as a command parameter. The structure of the file must be the following:

  1. color: string that contains any of the available options in colors.py.

  2. frames: integer that determines the frame duration of the video.

  3. sources: an array of dictionaries. Each dictionary in the array represents an emitter, which is a source of density and velocity. There cannot be emitters of just velocity or just density, because it would not make sense. Emitters must contain:

    • position: x and y integers, which are the top left position.
    • size: integer that defines an NxN square emitter.
    • density: integer that represents the amount of density of the emitter.
    • velocity:
      • x and y float/integer numbers that represent the velocity direction of the emitter.
      • behaviour: string that contains any of the available options in behaviours.py.
      • factor: float integer/float number that will act as a parameter depending on the behaviour chosen.
  4. objects: an array of dictionaries. Each dictionary in the array represents an object, where each of the objects must contain:

    • position: x and y integers, which are the top left position.
    • size: height and width integers, which will be the shape of a height x width rectangular object.
    • density: integer that represents the amount of density of the object. An object is indeed having a constant amount of density that will not be modified by the liquid, since it's a solid, but you need to determine the density or 'color' the object will have visually.

The folder evidences contains a series of example JSON files and their output videos, with both simple and complex examples of the output.

Features

  • Color Scheme

Inside the config.json file, change the color property and write the color scheme you want from the list below.

image

For example, by having 'hot' as the color property in the json file, you get the following:

image

  • Sources Placement

Inside the config.json file, you can specify the characteristics of an emitter you want to place. An emitter is a source of density and certain velocity.

image

  • Objects Placement

Inside the config.json file, you can specify the position and shape of a solid object inside the fluid.

image

  • Velocity Behaviours

Inside the config.json file, change the behaviour property inside velocity and write the behaviour of the velocity of said emitter you wish for. Supported options are:

  1. zigzag vertical,

image

  1. zigzag horizontal, that works the same as the above but horizontally.

  2. vortex,

image

  1. noise,

image

  1. fourier (left), which is a bit like a zigzag (right) but noisier.

image

  1. motor

image

Install FFmpeg on Windows

Apart from the pip installation of ffmpeg, you need to install ffmpeg for your machine OS (in my case, Windows 10) by going to either of the following links:

  • ffmpeg.org

    • Click on the Windows icon.
    • Click on gyan dev option.
  • gyan.dev

    • Go to the Git section and click on the first link.
    • Extract the folder from the zip.
    • Cut and paste the folder in your C: disk.
    • Add C:\FFmpeg\bin to PATH by typing in a terminal with admin rights:
     $ setx /m PATH "C:\FFmpeg\bin;%PATH%"
    
    • Open another terminal and test the installation by typing:
     $ ffmpeg -version
    

Handy Links

Owner
Mariana Ávalos Arce
I like code and math. I like football too. [Software & Computer Graphics]
Mariana Ávalos Arce
🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

Real-time water systems lab 416 Jan 06, 2023
Titanic Traveller Survivability Prediction

The aim of the mini project is predict whether or not a passenger survived based on attributes such as their age, sex, passenger class, where they embarked and more.

John Phillip 0 Jan 20, 2022
DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning.

DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning. DirectML provides GPU acceleration for common machine learning tasks across a broad range of supported ha

Microsoft 1.1k Jan 04, 2023
This is a Cricket Score Predictor that predicts the first innings score of a T20 Cricket match using Machine Learning

This is a Cricket Score Predictor that predicts the first innings score of a T20 Cricket match using Machine Learning. It is a Web Application.

Developer Junaid 3 Aug 04, 2022
Time-series momentum for momentum investing strategy

Time-series-momentum Time-series momentum strategy. You can use the data_analysis.py file to find out the best trigger and window for a given asset an

Victor Caldeira 3 Jun 18, 2022
A model to predict steering torque fully end-to-end

torque_model The torque model is a spiritual successor to op-smart-torque, which was a project to train a neural network to control a car's steering f

Shane Smiskol 4 Jun 03, 2022
Predicting Baseball Metric Clusters: Clustering Application in Python Using scikit-learn

Clustering Clustering Application in Python Using scikit-learn This repository contains the prediction of baseball metric clusters using MLB Statcast

Tom Weichle 2 Apr 18, 2022
database for artificial intelligence/machine learning data

AIDB v0.0.1 database for artificial intelligence/machine learning data Overview aidb is a database designed for large dataset for machine learning pro

Aarush Gupta 1 Oct 24, 2021
A simple machine learning package to cluster keywords in higher-level groups.

Simple Keyword Clusterer A simple machine learning package to cluster keywords in higher-level groups. Example: "Senior Frontend Engineer" -- "Fronte

Andrea D'Agostino 10 Dec 18, 2022
scikit-fem is a lightweight Python 3.7+ library for performing finite element assembly.

scikit-fem is a lightweight Python 3.7+ library for performing finite element assembly. Its main purpose is the transformation of bilinear forms into sparse matrices and linear forms into vectors.

Tom Gustafsson 297 Dec 13, 2022
A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

pmdarima Pmdarima (originally pyramid-arima, for the anagram of 'py' + 'arima') is a statistical library designed to fill the void in Python's time se

alkaline-ml 1.3k Jan 06, 2023
Cool Python features for machine learning that I used to be too afraid to use. Will be updated as I have more time / learn more.

python-is-cool A gentle guide to the Python features that I didn't know existed or was too afraid to use. This will be updated as I learn more and bec

Chip Huyen 3.3k Jan 05, 2023
A machine learning project that predicts the price of used cars in the UK

Car Price Prediction Image Credit: AA Cars Project Overview Scraped 3000 used cars data from AA Cars website using Python and BeautifulSoup. Cleaned t

Victor Umunna 7 Oct 13, 2022
A Time Series Library for Apache Spark

Flint: A Time Series Library for Apache Spark The ability to analyze time series data at scale is critical for the success of finance and IoT applicat

Two Sigma 970 Jan 04, 2023
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

CatBoost 6.9k Jan 05, 2023
Napari sklearn decomposition

napari-sklearn-decomposition A simple plugin to use with napari This napari plug

1 Sep 01, 2022
Simple linear model implementations from scratch.

Hand Crafted Models Simple linear model implementations from scratch. Table of contents Overview Project Structure Getting started Citing this project

Jonathan Sadighian 2 Sep 13, 2021
Distributed Evolutionary Algorithms in Python

DEAP DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data stru

Distributed Evolutionary Algorithms in Python 4.9k Jan 05, 2023
This machine-learning algorithm takes in data from the last 60 days and tries to predict tomorrow's price of any crypto you ask it.

Crypto-Currency-Predictor This machine-learning algorithm takes in data from the last 60 days and tries to predict tomorrow's price of any crypto you

Hazim Arafa 6 Dec 04, 2022
BentoML is a flexible, high-performance framework for serving, managing, and deploying machine learning models.

Model Serving Made Easy BentoML is a flexible, high-performance framework for serving, managing, and deploying machine learning models. Supports multi

BentoML 4.4k Jan 04, 2023