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
Winning solution for the Galaxy Challenge on Kaggle

Winning solution for the Galaxy Challenge on Kaggle

Sander Dieleman 483 Jan 02, 2023
High performance implementation of Extreme Learning Machines (fast randomized neural networks).

High Performance toolbox for Extreme Learning Machines. Extreme learning machines (ELM) are a particular kind of Artificial Neural Networks, which sol

Anton Akusok 174 Dec 07, 2022
Machine Learning Model to predict the payment date of an invoice when it gets created in the system.

Payment-Date-Prediction Machine Learning Model to predict the payment date of an invoice when it gets created in the system.

15 Sep 09, 2022
Retrieve annotated intron sequences and classify them as minor (U12-type) or major (U2-type)

(intron I nterrogator and C lassifier) intronIC is a program that can be used to classify intron sequences as minor (U12-type) or major (U2-type), usi

Graham Larue 4 Jul 26, 2022
learn python in 100 days, a simple step could be follow from beginner to master of every aspect of python programming and project also include side project which you can use as demo project for your personal portfolio

learn python in 100 days, a simple step could be follow from beginner to master of every aspect of python programming and project also include side project which you can use as demo project for your

BDFD 6 Nov 05, 2022
A visual dataflow programming language for sklearn

Persimmon What is it? Persimmon is a visual dataflow language for creating sklearn pipelines. It represents functions as blocks, inputs and outputs ar

Álvaro Bermejo 194 Jan 04, 2023
About Solve CTF offline disconnection problem - based on python3's small crawler

About Solve CTF offline disconnection problem - based on python3's small crawler, support keyword search and local map bed establishment, currently support Jianshu, xianzhi,anquanke,freebuf,seebug

天河 32 Oct 25, 2022
SmartSim makes it easier to use common Machine Learning (ML) libraries like PyTorch and TensorFlow

SmartSim makes it easier to use common Machine Learning (ML) libraries like PyTorch and TensorFlow, in High Performance Computing (HPC) simulations and workloads.

A simple and lightweight genetic algorithm for optimization of any machine learning model

geneticml This package contains a simple and lightweight genetic algorithm for optimization of any machine learning model. Installation Use pip to ins

Allan Barcelos 8 Aug 10, 2022
An easier way to build neural search on the cloud

Jina is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the effici

Jina AI 17k Jan 01, 2023
This is the material used in my free Persian course: Machine Learning with Python

This is the material used in my free Persian course: Machine Learning with Python

Yara Mohamadi 4 Aug 07, 2022
ClearML - Auto-Magical Suite of tools to streamline your ML workflow. Experiment Manager, MLOps and Data-Management

ClearML - Auto-Magical Suite of tools to streamline your ML workflow Experiment Manager, MLOps and Data-Management ClearML Formerly known as Allegro T

ClearML 4k Jan 09, 2023
Confidence intervals for scikit-learn forest algorithms

forest-confidence-interval: Confidence intervals for Forest algorithms Forest algorithms are powerful ensemble methods for classification and regressi

272 Dec 01, 2022
A toolkit for making real world machine learning and data analysis applications in C++

dlib C++ library Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real worl

Davis E. King 11.6k Jan 02, 2023
Python module for machine learning time series:

seglearn Seglearn is a python package for machine learning time series or sequences. It provides an integrated pipeline for segmentation, feature extr

David Burns 536 Dec 29, 2022
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
Tools for Optuna, MLflow and the integration of both.

HPOflow - Sphinx DOC Tools for Optuna, MLflow and the integration of both. Detailed documentation with examples can be found here: Sphinx DOC Table of

Telekom Open Source Software 17 Nov 20, 2022
Can a machine learning project be implemented to estimate the salaries of baseball players whose salary information and career statistics for 1986 are shared?

END TO END MACHINE LEARNING PROJECT ON HITTERS DATASET Can a machine learning project be implemented to estimate the salaries of baseball players whos

Pinar Oner 7 Dec 18, 2021
Predicting India’s COVID-19 Third Wave with LSTM

Predicting India’s COVID-19 Third Wave with LSTM Complete project of predicting new COVID-19 cases in the next 90 days with LSTM India is seeing a ste

Samrat Dutta 4 Jan 27, 2022
Apache (Py)Spark type annotations (stub files).

PySpark Stubs A collection of the Apache Spark stub files. These files were generated by stubgen and manually edited to include accurate type hints. T

Maciej 114 Nov 22, 2022