Metadata-Version: 2.1
Name: LMIPy
Version: 0.5.0
Summary: Pythonic interface to various backend ecosystems related geospatial data.
Home-page: https://github.com/Vizzuality/LMIPy
Author: Vizzuality
Author-email: benjamin.laken@vizzuality.com
License: MIT
Description: # LMIPy
        ## The Vizzuality Ecosystem Python Interface
        
        [![Build Status](https://travis-ci.org/Vizzuality/LMIPy.svg?branch=master)](https://travis-ci.org/Vizzuality/LMIPy) [![codecov](https://codecov.io/gh/Vizzuality/LMIPy/branch/master/graph/badge.svg)](https://codecov.io/gh/Vizzuality/LMIPy) [![PyPI](https://img.shields.io/pypi/v/LMIPy.svg?style=flat)](https://pypi.org/project/LMIPy/) ![](https://img.shields.io/pypi/pyversions/LMIPy.svg?style=flat)  ![](https://img.shields.io/pypi/wheel/LMIPy.svg?style=flat) [![Documentation Status](https://readthedocs.org/projects/lmipy/badge/?version=latest)](https://lmipy.readthedocs.io/en/latest/?badge=latest) [![License](https://img.shields.io/badge/License-MIT-brightgreen.svg)](https://github.com/Vizzuality/LMIPy/blob/master/LICENSE)
        
        LMIPy is a Python library with hooks to Jupyter, backed by the [Skydipper API](https://github.com/Skydipper).
        It provides many functions related to adding, analysing and working with open geospatial datasets.
        
        ## Read the Docs
        
        [Read the docs pages](https://lmipy.readthedocs.io/en/latest/).
        
        ## Installation
        
        `pip install LMIPy`
        
        ## Use
        
        
        ```
        $ python
        >>> import LMIPy
        ```
        
        Create a Dataset object based on an existing ID on default (RW) server.
        ```
        >>> ds = Dataset('044f4af8-be72-4999-b7dd-13434fc4a394')
        >>> print(ds)
        Dataset 044f4af8-be72-4999-b7dd-13434fc4a394
        ```
        
        Create a Layer object based on an existing ID on default (RW) server.
        ```
        >>> ly = Layer(id_hash='dc6f6dd2-0718-4e41-81d2-109866bb9edd')
        >>> print(ly)
        Layer dc6f6dd2-0718-4e41-81d2-109866bb9edd
        ```
        
        Create a Table object based on an existing ID.
        ```
        >>> table = Table('fbf159d7-a462-4af3-8228-43ee3e3391e7')
        # return the head of the table as a geopandas dataframe
        >>> df = table.head(5)
        # return a query of the table as a geopandas dataframe
        >>> result = table.query(sql='SELECT count(*) as my_count FROM data WHERE year > 1991 and year < 1995' )
        ```
        
        Obtain a collection of objects using a search term.
        ```
        >>> col = Collection(search='tree',object_type=['dataset'], app=['gfw'],limit=5)
        >>> print(col)
        [Dataset 70e2549c-d722-44a6-a8d7-4a385d78565e, Dataset 897ecc76-2308-4c51-aeb3-495de0bdca79, Dataset 89755b9f-df05-4e22-a9bc-05217c8eafc8, Dataset 83f8365b-f40b-4b91-87d6-829425093da1, Dataset 044f4af8-be72-4999-b7dd-13434fc4a394]
        ```
        Check the docs for more info!
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
