Metadata-Version: 2.1
Name: forecast_clarify
Version: 0.1.0a0
Summary: Local sea surface temperature weekly forecasts based on local statistics (seasonal cycle, week-to-week persistence) and coarse-resolution dynamical forecasts
Home-page: https://github.com/olewu/forecast_clarify
Author: Climate Futures, Clarify
Author-email: owul@norceresearch.no, odd.gunnar@clarify.io
License: MIT
Classifier: Development Status :: 5 - Production/Stable
Classifier: Topic :: Scientific/Engineering
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE.txt

forecast_clarify
==============================
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Local water temperature (3m) weekly forecasts based on local statistics (seasonal cycle, week-to-week persistence) from NorKyst800 (2006 - 2022).

To install, initialize an environment with python (tested with 3.10.0 and 3.10.4 but expect other version of python3 to work).

Install the minimal requirements using
```
pip install -r requirements_minimal.txt
```
then install the package functionality from the project root folder (where this README is located) using
```
pip install -e .
```

This should enable you to run `/notebooks/010_test_load.ipynb`. The notebook will access functionality in `/forecast_clarify/clarify_persistence_package.py`, which in turn needs `/forecast_clarify/main.py` and `/forecast_clarify/config.py` as well as the model parameter files saved in `/data/processed/`.


--------

<p><small>Project based on the <a target="_blank" href="https://github.com/jbusecke/cookiecutter-science-project">cookiecutter science project template</a>.</small></p>
