pyfortracc 1.0.0rc0

A Python package for track and forecasting.

FortraCC - Python Framework for Tracking and Forecasting Clusters

Overview

FortraCC is a Python package designed to identify, track and analyze hydrological phenomena with various data formats.
Using time-varying 2D input frames along with user-specified parameters, pyFortraCC is able to detect objects (clusters) and associate their displacement in time.

Algorithm Workflow

The algorithm is divided into three main modules and form the Tracking Workflow.

1. Feature detection: Focuses on identifying individual clusters detection from individual frame of data and extraction of features and statistics.
2. Spatial Operations: Involves spatial operations (overlap, union, difference, etc) between objects (clusters) from consecutive time steps (t-1 and t).
3. Trajectory Linking: Link objects of consecutive time steps based on the spatial association.

Documentation

For a more detailed information of FortraCC package please read the user guide available click here.

Installation

Download the package from github or clone the repository using the command:

git clone https://github.com/fortracc-project/pyfortracc/

To install the FortraCC package. It is highly advisable to use virtual environments (Anaconda3, Miniconda, Mamba, or etc) to install dependencies. And you can do this in different ways:

Create environment using conda and install from environment.yml file:

cd pyfortracc
conda env create -f environment.yml
conda activate pyfortracc

Create virtual environment and install package:

cd pyfortracc
python3 -m venv venv
source venv/bin/activate
pip3 install .

Install only requirements from local file:

cd pyfortracc
pip3 install -r requirements.txt

And it is also possible to install directly from the python package repositories:

pip3 install pyfortracc

Example Gallery

The development of this framework is constantly evolving, and several application examples can be seen in our example gallery.

- 01 - Introducing Example

- 02 - Algorithm Workflow with Radar Example

- 03 - Track Infrared Dataset

- 04 - Track High Resolution Global Precipitation Dataset

- 05 Track Deforestation Dataset

Support and Contact

For support, email [e-mail], [e-mail]