Metadata-Version: 2.1
Name: argopy
Version: 0.1.0
Summary: A python library for Argo data beginners and experts
Home-page: https://github.com/euroargodev/argopy
Author: argopy Developers
Author-email: gmaze@ifremer.fr
License: UNKNOWN
Description: # Argo data python library
        
        ``argopy`` is a python library that aims to easy Argo data access, visualisation and manipulation for regular users as well as Argo experts and operators.
        
        Several python packages exist: we are currently in the process of merging and structuring these libraries to have a single powerfull tool.
        
        ## Install
        
        Since this is a library in active development, use direct install from this repo to benefit from the last version:
        
        ```bash
        pip install git+http://github.com/euroargodev/argopy.git
        ```
        
        ## Examples
        
        Examples of data fetching, manipulation and visualisation are available on the [erddap examples repository](https://github.com/euroargodev/erddap_usecase).
        
        You can run and test live example notebooks on Binder (thanks [Pangeo](pangeo.io)) here:
        [![badge](https://img.shields.io/badge/launch-Pangeo%20binder-579ACA.svg?logo=data:image/png;base64,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)](https://binder.pangeo.io/v2/gh/euroargodev/erddap_usecases/master)
        
        
        ## Usage
        
        The primary data model used to manipulate Argo data is [xarray](https://github.com/pydata/xarray).
        
        ### Argo Data Fetcher
        
        API usage:
        ```python
            from argopy import DataFetcher as ArgoDataFetcher
        
            argo_loader = ArgoDataFetcher()
            argo_loader = ArgoDataFetcher(backend='erddap')
            argo_loader = ArgoDataFetcher(cachedir='tmp')
        
            argo_loader.region([-85,-45,10.,20.,0,1000.]).to_xarray()
            argo_loader.region([-85,-45,10.,20.,0,1000.,'2012-01','2014-12']).to_xarray()
        
            argo_loader.profile(6902746, 34).to_xarray()
            argo_loader.profile(6902746, np.arange(12,45)).to_xarray()
            argo_loader.profile(6902746, [1,12]).to_xarray()
        
            argo_loader.float(6902746).to_xarray()
            argo_loader.float([6902746, 6902747, 6902757, 6902766]).to_xarray()
            argo_loader.float([6902746, 6902747, 6902757, 6902766], CYC=1).to_xarray()
        ```
        
        **Devlopment roadmap**:
        
        We aim to provide high level helper methods to load Argo data from:
        - [x] Ifremer erddap
        - [ ] local copy of the GDAC ftp folder (help wanted)
        - [ ] the argovis dataset (help wanted)
        - [ ] any other usefull access point to Argo data ?
        
        At this point data are fetched in memory as [xarray.DataSet](http://xarray.pydata.org/en/stable/data-structures.html#dataset). 
        From there, it is easy to convert it to other formats like a [Pandas dataframe](https://pandas.pydata.org/pandas-docs/stable/getting_started/dsintro.html#dataframe):
        ```python
        ds = argo_loader.profile(6902746, 34).to_xarray()
        df = ds.to_dataframe()
        ```
        
        and to export it to files:
        ```python
        ds = argo_loader.region([-85,-45,10.,20.,0,1000.]).to_xarray()
        ds.to_netcdf('my_selection.nc')
        # or by profiles:
        ds.argo.point2profile().to_netcdf('my_selection.nc')
        ```
        
        ### Argo data manipulation with xarray
        
        The ``argopy`` library provides an xarray accessor named ``argo`` to help manipulate and analyse data.
        
        Example: Convert a collection of points to a collections of profiles:
        ```python
        from argopy import DataFetcher as ArgoDataFetcher
        ds = ArgoDataFetcher(ds='bgc').float(5903248).to_xarray()
        ds = ds.argo.point2profile()
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 3 - Alpha
Description-Content-Type: text/markdown
