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
A Python-based application demonstrating various search algorithms, namely Depth-First Search (DFS), Breadth-First Search (BFS), and A* Search (Manhattan Distance Heuristic)

A Python-based application demonstrating various search algorithms, namely Depth-First Search (DFS), Breadth-First Search (BFS), and the A* Search (using the Manhattan Distance Heuristic)

17 Aug 14, 2022
Interactive Web App with Streamlit and Scikit-learn that applies different Classification algorithms to popular datasets

Interactive Web App with Streamlit and Scikit-learn that applies different Classification algorithms to popular datasets Datasets Used: Iris dataset,

Samrat Mitra 2 Nov 18, 2021
Python package for concise, transparent, and accurate predictive modeling

Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use. 📚 docs • 📖 demo notebooks Modern

Chandan Singh 983 Jan 01, 2023
CVXPY is a Python-embedded modeling language for convex optimization problems.

CVXPY The CVXPY documentation is at cvxpy.org. We are building a CVXPY community on Discord. Join the conversation! For issues and long-form discussio

4.3k Jan 08, 2023
fMRIprep Pipeline To Machine Learning

fMRIprep Pipeline To Machine Learning(Demo) 所有配置均在config.py文件下定义 前置环境(lilab) 各个节点均安装docker,并有fmripre的镜像 可以使用conda中的base环境(相应的第三份包之后更新) 1. fmriprep scr

Alien 3 Mar 08, 2022
Price forecasting of SGB and IRFC Bonds and comparing there returns

Project_Bonds Project Title : Price forecasting of SGB and IRFC Bonds and comparing there returns. Introduction of the Project The 2008-09 global fina

Tishya S 1 Oct 28, 2021
Pyomo is an object-oriented algebraic modeling language in Python for structured optimization problems.

Pyomo is a Python-based open-source software package that supports a diverse set of optimization capabilities for formulating and analyzing optimization models. Pyomo can be used to define symbolic p

Pyomo 1.4k Dec 28, 2022
Data Version Control or DVC is an open-source tool for data science and machine learning projects

Continuous Machine Learning project integration with DVC Data Version Control or DVC is an open-source tool for data science and machine learning proj

Azaria Gebremichael 2 Jul 29, 2021
Automatic extraction of relevant features from time series:

tsfresh This repository contains the TSFRESH python package. The abbreviation stands for "Time Series Feature extraction based on scalable hypothesis

Blue Yonder GmbH 7k Jan 06, 2023
This is a curated list of medical data for machine learning

Medical Data for Machine Learning This is a curated list of medical data for machine learning. This list is provided for informational purposes only,

Andrew L. Beam 5.4k Dec 26, 2022
Multiple Linear Regression using the LinearRegression class from sklearn.linear_model library

Multiple-Linear-Regression-master - A python program to implement Multiple Linear Regression using the LinearRegression class from sklearn.linear model library

Kushal Shingote 1 Feb 06, 2022
Uber Open Source 1.6k Dec 31, 2022
QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

152 Jan 02, 2023
A repository of PyBullet utility functions for robotic motion planning, manipulation planning, and task and motion planning

pybullet-planning (previously ss-pybullet) A repository of PyBullet utility functions for robotic motion planning, manipulation planning, and task and

Caelan Garrett 260 Dec 27, 2022
Add built-in support for quaternions to numpy

Quaternions in numpy This Python module adds a quaternion dtype to NumPy. The code was originally based on code by Martin Ling (which he wrote with he

Mike Boyle 531 Dec 28, 2022
PyPOTS - A Python Toolbox for Data Mining on Partially-Observed Time Series

A python toolbox/library for data mining on partially-observed time series, supporting tasks of forecasting/imputation/classification/clustering on incomplete multivariate time series with missing va

Wenjie Du 179 Dec 31, 2022
Learn how to responsibly deliver value with ML.

Made With ML Applied ML · MLOps · Production Join 30K+ developers in learning how to responsibly deliver value with ML. 🔥 Among the top MLOps reposit

Goku Mohandas 32k Dec 30, 2022
Distributed Deep learning with Keras & Spark

Elephas: Distributed Deep Learning with Keras & Spark Elephas is an extension of Keras, which allows you to run distributed deep learning models at sc

Max Pumperla 1.6k Dec 29, 2022
Flask app to predict daily radiation from the time series of Solcast from Islamabad, Pakistan

Solar-radiation-ISB-MLOps - Flask app to predict daily radiation from the time series of Solcast from Islamabad, Pakistan.

Abid Ali Awan 1 Dec 31, 2021