clouddrift 0.46.0

Accelerating the use of Lagrangian data for atmospheric, oceanic, and climate sciences

clouddrift

📦 Distributions

👥 Social

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📚 Binders and examples

• : MOSAiC get started (🧊 sea ice trajectories)

• : GDP get started (🌊 ocean drifter trajectories)

• : HURDAT2 get started (🌀 cyclone/hurricane trajectories from 1852 - 2022)

• HYCOM-OceanTrack: A repository with notebook examples using clouddrift with a very large , analysis-ready cloud-optimized, Lagrangian dataset hosted in the cloud: HYCOM OceanTrack: Integrated HYCOM Eulerian Fields and Lagrangian Trajectories Dataset.

clouddrift is a Python package that accelerates the use of Lagrangian data for atmospheric, oceanic, and climate sciences. It is funded by NSF EarthCube through the EarthCube Capabilities Grant No. 2126413.

Read the documentation.

Using clouddrift

Start by reading the documentation.

Example Jupyter notebooks that showcase the library, as well as scripts to process various Lagrangian datasets, can be found in gdp-get-started, mosaic-get-started, hurdat2-get-started, or a demo for the EarthCube community workshop 2023.

Contributing and scope

We welcome and invite contributions from the community in any shape or form! Please visit our Contributing Guide to get Started 😃

The scope of clouddrift includes:

• Working with contiguous ragged-array data; for example, see the clouddrift.ragged module.
• Common scientific analysis of Lagrangian data, oceanographic or otherwise; for example, see the clouddrift.kinematics, clouddrift.signal, and clouddrift.wavelet modules.
• Processing existing Lagrangian datasets into a common data structure and format; for example, see the clouddrift.adapters.mosaic module.
• Making cloud-optimized ragged-array datasets easily accessible; for example, see the clouddrift.datasets module.

If you have an idea that does not fit into the scope of clouddrift but you think it should, please open an issue to discuss it.

Getting started

Install clouddrift

You can install the latest release of clouddrift using pip or conda.

Latest official release:

pip:

In your virtual environment, type:

pip install clouddrift

To install optional dependencies needed by the clouddrift.plotting module, type:

pip install clouddrift[plotting]

Conda:

First add conda-forge to your channels in your Conda configuration (~/.condarc):

conda config --add channels conda-forge
conda config --set channel_priority strict

then install clouddrift:

conda install clouddrift

To install optional dependencies needed by the clouddrift.plotting module, type:

conda install matplotlib cartopy

Development branch:

If you need the latest development version, you can install it directly from this GitHub repository.

pip:

In your existing virtual environment, you can use pip as follows.

1. Get the code:

git clone https://github.com/cloud-drift/clouddrift
cd clouddrift/

2. Install dependencies and local version of clouddrift:

pip install .

Conda:

Using conda, you can proceed as follows.

1. Get the code:

git clone https://github.com/cloud-drift/clouddrift
cd clouddrift/

2. Create an environment as specified in the yml file with the required library dependencies:

conda env create -f environment.yml # creates a new env with the dependencies

2a. Make sure you created the environment by activating it:

conda activate clouddrift

3. Finally, install the local version of clouddrift:

pip install .

Installing clouddrift on unsupported platforms

One or more dependencies of clouddrift may not have pre-built wheels for platforms like IBM Power9 or Raspberry Pi. If you are using pip to install clouddrift and are getting errors during the installation step, try installing clouddrift using Conda. If you still have issues installing clouddrift, you may need to install system dependencies first. Please let us know by opening an issue and we will do our best to help you.

Found an issue or need help?

Please create a new issue here and provide as much detail as possible about your problem or question.