semvec/pypi-packages huggingface dataset:
pypago 1.0.6
Python Physical Analysis of Gridded Ocean
Keywords: ocean; grid model; transport; sections; budgets; heat; freshwater; ROMS; NEMO; IPSL; CNRM; GFDL

![License](https://img.shields.io/github/license/barriern/PyPago)
[![DOI](https://zenodo.org/badge/154350333.svg)](https://zenodo.org/record/4091652)
[![PyPI](https://img.shields.io/pypi/v/pypago)](https://pypi.org/project/pypago/)
[![Latest Release](https://img.shields.io/github/release/barriern/PyPago)](https://github.com/barriern/PyPago/issues)
[![GitHub Issues](https://img.shields.io/github/issues/barriern/PyPago)](https://github.com/barriern/PyPago/issues)

![PyPago Logo](http://pypago.nicolasbarrier.fr/_images/pago_background.jpg)

# Description

Inter-comparison of model and gridded observations of the ocean is a challenge when they use different grids. 
Interpolation from one grid to another brings eventual errors that may affect significantly large scale budgets of tracers (heat, salt, freshwater). 

The PyPAGO suite of programs offers the possibility to analyze gridded ocean data along physical sections with minimum interpolation. 
For example, this allows to monitor the circulation across an observed array in various model outputs, whatever their spatial resolution or type of discretization (B- or C- grids). 
When defining sections that enclose a specific volume, large scale budgets of tracers can be reconstructed and inter-compared among all kinds of gridded ocean data.

# Documentation

Full documentation is available in [http://pypago.nicolasbarrier.fr/](http://pypago.nicolasbarrier.fr/)
object-cache 0.1.6
This module caches the result of function.
Requires Python: >=3.8,<4.0

=====================
object_cache
=====================

description
============

This module caches the processing result of the function in the storage, and if the cache hits, skips the processing in the function and returns the result. Create ".object_cache" in the current path and cache it.

install
========

.. code-block:: shell

    pip install object-cache

code example
============

.. code-block:: python

    import time

    from object_cache import object_cache


    @object_cache
    def factorial(a):
        result = 1
        for i in range(2, a + 1):
            result *= i

        return result


    for _ in range(5):
        start = time.time()
        factorial(100000)
        print("elapsed time", time.time() - start)

clear cache
============

.. code-block:: shell

    rm -fr .object_cache