Accelerating the use of Lagrangian data for atmospheric, oceanic, and climate sciences
Project description
CloudDrift
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 or explore the Jupyter Notebook Examples.
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
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
Development branch:
If you need the latest development version, you can install it directly from this GitHub repository.
pip:
In your virtual environment, type:
pip install git+https://github.com/cloud-drift/clouddrift
Conda:
conda env create -f environment.yml
with the environment file located in the main repository.
Run the tests
To run the tests, you need to first download the CloudDrift source code from GitHub:
git clone https://github.com/cloud-drift/clouddrift
cd clouddrift/
and create the virtual environment.
With pip:
python3 -m venv .venv
source .venv/bin/activate
pip install .
With Conda:
conda env create -f environment.yml
conda activate clouddrift
Then, run the tests like this:
python -m unittest tests/*.py
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.
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 clouddrift-examples.
Found an issue or need help?
Please create a new issue here and provide as much detail as possible about your problem or question.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for clouddrift-0.19.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9ab126ba154f2bf3d2e6ab7cb9c605c0e717aec1750ab85bcc39cac337d23f91 |
|
MD5 | f1a34e2bcef1eb623b96bb6c340b87df |
|
BLAKE2b-256 | e564d762efd6598ddf2702b23df2240bfd2648b031b7b3ad86a746a921c2c1b6 |