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
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
Requires-Dist: requests (>=2.2.0)
Requires-Dist: pypng (>=0.0.19)
Requires-Dist: folium (==0.8.3)
Requires-Dist: geopandas (>=0.4.1)
Requires-Dist: geojson (>=2.4.0)
Requires-Dist: tqdm (>=4.21.0)

# LMIPy
## The Vizzuality Ecosystem Python Interface

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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!


