Skip to main content

A package to fully run the comparison between data and model to assess model skill.

Project description

ocean-model-skill-assessor

Build Status Code Coverage License:MIT Documentation Status Code Style Status Conda Version

A package to fully run the comparison between data and model to assess model skill.


Project based on the cookiecutter science project template.

Run Demo without Installation

Click the binder button to open up a demonstration notebook in your browser window

Binder

Installation

Set up fresh environment for this package

First, make sure you have Anaconda or Miniconda installed.

Then, clone this repository:

$ git clone https://github.com/axiom-data-science/ocean-model-skill-assessor.git

In the ocean_model_skill_assessor directory, install a conda environment with convenient packages for working with this package (beyond the requirements):

$ conda env create -f environment.yml

Note that installing the packages is faster if you first install mamba to your base Python and then use mamba in place of conda.

Activate your new Python environment to use it with

$ conda activate ocean-model-skill-assessor

Install into existing Python environment

Install the package plus its requirements from conda-forge with

$ conda install -c conda-forge ocean_model_skill_assessor

Or you can git clone the repository and then pip install it locally into your existing Python environment: For local package install, in the ocean_model_skill_assessor directory:

$ pip install -e .

Extra packages for development

To also develop this package, install additional packages with:

$ conda install --file requirements-dev.txt

To then check code before committing and pushing it to github, locally run

$ pre-commit run --all-files

Run Demo

In your terminal window, activate your Python environment if you are using one, then type jupyter lab in the ocean_model_skill_assessor directory. This will open into your browser window. Navigate to docs/Demo-AK.ipynb or any of the other notebooks and double-click to open. Inside a notebook, push shift-enter to run individual cells, or the play button at the top to run all cells, or select commands under the Run menu.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ocean-model-skill-assessor-0.4.1.tar.gz (1.5 MB view details)

Uploaded Source

Built Distribution

ocean_model_skill_assessor-0.4.1-py3-none-any.whl (21.1 kB view details)

Uploaded Python 3

File details

Details for the file ocean-model-skill-assessor-0.4.1.tar.gz.

File metadata

File hashes

Hashes for ocean-model-skill-assessor-0.4.1.tar.gz
Algorithm Hash digest
SHA256 552e4fab7ce3d6503d880f41e21a1e07df289ad7bc04b9a6b79e25eed037c88e
MD5 7a7c687a78c7cb3cfa41b9fcc8a997fc
BLAKE2b-256 3f32a5cb03cdda24f83759a8cfcbc7d1182cb0bba078798ec4f53ef5e782349a

See more details on using hashes here.

File details

Details for the file ocean_model_skill_assessor-0.4.1-py3-none-any.whl.

File metadata

File hashes

Hashes for ocean_model_skill_assessor-0.4.1-py3-none-any.whl
Algorithm Hash digest
SHA256 1562b8220a79a1710ec467afff249caec0d74b62f11d859871a26cc7c82e40bb
MD5 33416661fed6d2259555dc17f6718125
BLAKE2b-256 4e87427eb39f28e303f28f106e1dc4d1410e18079ff4989ef40b09cf1852e45e

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page