A simple Python library for stochastic graphical ecological models

Overview

What is Viridicle?

Viridicle is a library for simulating stochastic graphical ecological models. It implements the continuous time models described in Section 6 of Durrett and Levin 1994. The library is written in C for speed, but has a Python API for ease of use. It supports well-mixed systems, multi-dimensional periodic lattices, and arbitrary graphs, with up to 256 species, and is substantially faster than comparable software written in Python alone.

Viridicle models an ecological system as a graph, where each vertex denotes a discrete site - an island, an oasis, a point on a Petri dish, etc. Each site is small enough that we can model its state by assigning it a single number specifying which species is occupying it. Traditionally, 0 denotes an unoccupied site, but neither the mathematics nor the code require this.

The system changes over time as a continuous-time Markov chain, where each directed edge in the system is a Poisson point process. That is, at any given moment, for every edge in the system, the time until the edge's vertices change to state is exponentially distributed with some rate that depends on the state of the system. The model is implemented using the Gillespie algorithm. For further details, consult the documentation.

Installation

To install Viridicle from the PyPi repo, run:

pip install viridicle

If you want to compile from source yourself, first make sure that you have gcc installed, along with Python, including its development headers. Then, run:

git clone https://github.com/TheoremEngine/viridicle.git
cd viridicle
python3 setup.py install
Owner
Theorem Engine
Theorem Engine
PyTorch implementation of Self-supervised Contrastive Regularization for DG (SelfReg)

SelfReg PyTorch official implementation of Self-supervised Contrastive Regularization for Domain Generalization (SelfReg, https://arxiv.org/abs/2104.0

64 Dec 16, 2022
Python Single Object Tracking Evaluation

pysot-toolkit The purpose of this repo is to provide evaluation API of Current Single Object Tracking Dataset, including VOT2016 VOT2018 VOT2018-LT OT

348 Dec 22, 2022
PyTorch implementation for OCT-GAN Neural ODE-based Conditional Tabular GANs (WWW 2021)

OCT-GAN: Neural ODE-based Conditional Tabular GANs (OCT-GAN) Code for reproducing the experiments in the paper: Jayoung Kim*, Jinsung Jeon*, Jaehoon L

BigDyL 7 Dec 27, 2022
The official code of "SCROLLS: Standardized CompaRison Over Long Language Sequences".

SCROLLS This repository contains the official code of the paper: "SCROLLS: Standardized CompaRison Over Long Language Sequences". Links Official Websi

TAU NLP Group 39 Dec 23, 2022
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Apache MXNet (incubating) for Deep Learning Master Docs License Apache MXNet (incubating) is a deep learning framework designed for both efficiency an

ROCm Software Platform 29 Nov 16, 2022
MaskTrackRCNN for video instance segmentation based on mmdetection

MaskTrackRCNN for video instance segmentation Introduction This repo serves as the official code release of the MaskTrackRCNN model for video instance

411 Jan 05, 2023
Callable PyTrees and filtered JIT/grad transformations => neural networks in JAX.

Equinox Callable PyTrees and filtered JIT/grad transformations = neural networks in JAX Equinox brings more power to your model building in JAX. Repr

Patrick Kidger 909 Dec 30, 2022
Source code for Transformer-based Multi-task Learning for Disaster Tweet Categorisation (UCD's participation in TREC-IS 2020A, 2020B and 2021A).

Source code for "UCD participation in TREC-IS 2020A, 2020B and 2021A". *** update at: 2021/05/25 This repo so far relates to the following work: Trans

Congcong Wang 4 Oct 19, 2021
PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO

Self-Supervised Vision Transformers with DINO PyTorch implementation and pretrained models for DINO. For details, see Emerging Properties in Self-Supe

Facebook Research 4.2k Jan 03, 2023
Use your Philips Hue lights as Racing Flags. Works with Assetto Corsa, Assetto Corsa Competizione and iRacing.

phue-racing-flags Use your Philips Hue lights as Racing Flags. Explore the docs » Report Bug · Request Feature Table of Contents About The Project Bui

50 Sep 03, 2022
BaseCls BaseCls 是一个基于 MegEngine 的预训练模型库,帮助大家挑选或训练出更适合自己科研或者业务的模型结构

BaseCls BaseCls 是一个基于 MegEngine 的预训练模型库,帮助大家挑选或训练出更适合自己科研或者业务的模型结构。 文档地址:https://basecls.readthedocs.io 安装 安装环境 BaseCls 需要 Python = 3.6。 BaseCls 依赖 M

MEGVII Research 28 Dec 23, 2022
Testbed of AI Systems Quality Management

qunomon Description A testbed for testing and managing AI system qualities. Demo Sorry. Not deployment public server at alpha version. Requirement Ins

AIST AIRC 15 Nov 27, 2021
(AAAI 2021) Progressive One-shot Human Parsing

End-to-end One-shot Human Parsing This is the official repository for our two papers: Progressive One-shot Human Parsing (AAAI 2021) End-to-end One-sh

54 Dec 30, 2022
Fast image augmentation library and easy to use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about library: https://www.mdpi.com/2078-2489/11/2/125

Albumentations Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to inc

11.4k Jan 09, 2023
Model Zoo for MindSpore

Welcome to the Model Zoo for MindSpore In order to facilitate developers to enjoy the benefits of MindSpore framework, we will continue to add typical

MindSpore 226 Jan 07, 2023
TumorInsight is a Brain Tumor Detection and Classification model built using RESNET50 architecture.

A Brain Tumor Detection and Classification Model built using RESNET50 architecture. The model is also deployed as a web application using Flask framework.

Pranav Khurana 0 Aug 17, 2021
New approach to benchmark VQA models

VQA Benchmarking This repository contains the web application & the python interface to evaluate VQA models. Documentation Please see the documentatio

4 Jul 25, 2022
Efficiently computes derivatives of numpy code.

Note: Autograd is still being maintained but is no longer actively developed. The main developers (Dougal Maclaurin, David Duvenaud, Matt Johnson, and

Formerly: Harvard Intelligent Probabilistic Systems Group -- Now at Princeton 6.1k Jan 08, 2023
The official implementation of the research paper "DAG Amendment for Inverse Control of Parametric Shapes"

DAG Amendment for Inverse Control of Parametric Shapes This repository is the official Blender implementation of the paper "DAG Amendment for Inverse

Elie Michel 157 Dec 26, 2022
Language Models for the legal domain in Spanish done @ BSC-TEMU within the "Plan de las Tecnologías del Lenguaje" (Plan-TL).

Spanish legal domain Language Model ⚖️ This repository contains the page for two main resources for the Spanish legal domain: A RoBERTa model: https:/

Plan de Tecnologías del Lenguaje - Gobierno de España 12 Nov 14, 2022