Skip to content

Tracking and object-based analysis of clouds

License

Notifications You must be signed in to change notification settings

gewitterblitz/tobac

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

tobac - Tracking and Object-based Analysis of Clouds

Documentation StatusBuild Status

What is it?

tobac is a Python package for identifiying, tracking and analysing of clouds in different types of gridded datasets, i.e. 3D model output from cloud resolving model simulations or 2D data of satellite observations.

Documentation

Individual features are indentified as either maxima or minima in a two dimensional time varying field. The volume/area associated with the identified objects can be determined based on a time-varying 2D or 3D field and a threshold value. The in thre tracking step, the identified objects are linked into consistent trajectories representing the cloud over its lifecycle

Installation

Tobac is written in Python 3, it will not work in a Python 2 installation.

Required packages: trackpy scipy numpy iris scikit-learn cartopy pandas pytables

If you are using anaconda, the following command should make sure all dependencies are met and up to date:

conda install -c conda-forge trackpy scipy numpy iris scikit-learn cartopy pandas pytables 

You can directly install the package directly from github with pip and either of the two following commands:

pip install --upgrade git+ssh://git@github.com/climate-processes/tobac.git
pip install --upgrade git+https://github.com/climate-processes/tobac.git

You can also clone the package with any of the two following commands

git clone git@github.com:climate-processes/tobac.git
git clone https://github.com/climate-processes/tobac.git

and install the package from the locally cloned version:

pip install tobac/

About

Tracking and object-based analysis of clouds

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Python 100.0%