ODE-Designer

[!IMPORTANT] This repository has been moved to the Federal University of São João Del-Rei's organization, here: https://github.com/ufsj-dcomp/ode-designer-rs

Introduction

Below are the description of the software, how to install it, and a brief introduction on how to use it.

About the software

The names of the software developers are in the CONTRIBUTORS file.

ODE-Designer was developed in the context of a FAPEMIG research project by postgraduate students and undergraduate students in Computer Science at the Federal University of São João Del-Rei ([UFSJ](https: //ufsj.edu.br)).

The main objective of ODE-Designer is to facilitate the creation, implementation and simulation of Ordinary Differential Equation (ODE) models. The software can be applied to research and teaching-learning of Computational Modeling.

Through a visually intuitive graphical user interface (GUI), ODE-Designer allows users to conceptualize and ``draw'' mathematical models, while the software automatically generates the requisite implementation, simulation, and result visualization code. A distinguished feature of the software is the model's visual representation. It is designed to be intuitive and easy to use.

Current functionalities

Node-based editor

Plotting

Python code export and pdf export

# imports of scipy and numpy omitted

def initial_values() -> np.ndarray:
    Var_0 = 1.0
    return np.array((Var_0,))

def constants() -> list:
    Const = 2.0
    return [Const]

def variable_names() -> list[str]:
    return ["Var"]

def system(t: np.float64, y: np.ndarray, *constants) -> np.ndarray:
    Var, = y
    Const, = constants

    dVar_dt = Var*Const 

    return np.array([dVar_dt])

# Rest of the code used to simulate and plot to PDF omitted

Extensibility via Python code

Given the following Python code:

import math

@node
def sine(x):
    return math.sin(x)

@node(format="$1 ^ $2")
def power(x, y):
    return x ** y

By importing it in the Manage Extensions menu, you can use the defined nodes as if they were native, as in the image below.

The code can be used to simulate just like native nodes.

How to install

It is recommended to use the executables available in Releases on GitHub. This link can be used to always redirect to the latest version.

Linux

The Linux distribution uses AppImages, which requires a runtime (fuse2) installable on all Linux-based distributions. It is commonly included by default by some distributions, or is already installed by another program.

Windows

The Windows distribution consists of a ZIP that can be extracted and its contents executed.

Compilation

Using Docker

To compile and use the software and its full potential, you can use the provided Docker image. The dependencies are only docker itself and the docker-buildx plugin.

$ docker buildx build -t ode-designer-appimage-builder .
$ docker run -v ./container-target:/ode-designer/target/ ode-designer-appimage-builder

After this execution, the AppImage should be available in .container-target/appimage/ode-designer-rs.AppImage. This AppImage, as well as those found in Releases, have Python and the scipy and matplotlib dependencies for code generation, interactive simulation and PDF export.

Manually

To compile and run the software outside of the AppImage, the Rust toolchain (nightly version) is required, as well as Python >= 3.11, and the dependencies listed in requirements.txt. Fulfilling these requirements, simply compile like any Rust project, running

$ cargo run

Or

$ cargo build --release
$ ./target/release/ode-designer-rs

If anything goes wrong, try

$ git submodule update --init --recursive
$ git submodule update --recursive --remote

Then, recompile and run.