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        "description": "# Intake-stac\n\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/pangeo-data/intake-stac/master?filepath=examples?urlpath=lab)\n[![Build Status](https://travis-ci.org/pangeo-data/intake-stac.svg?branch=master)](https://travis-ci.org/pangeo-data/intake-stac)\n[![PyPI version](https://badge.fury.io/py/intake-stac.svg)](https://badge.fury.io/py/intake-stac)\n[![Documentation Status](https://readthedocs.org/projects/intake-stac/badge/?version=latest)](https://intake-stac.readthedocs.io/en/latest/?badge=latest)\n[![codecov](https://codecov.io/gh/pangeo-data/intake-stac/branch/master/graph/badge.svg)](https://codecov.io/gh/pangeo-data/intake-stac)\n\nThis is an [intake](https://intake.readthedocs.io/en/latest) data source for [SpatioTemporal Asset Catalogs (STAC)](https://stacspec.org/). The STAC specification provides a common metadata specification, API, and catalog format to describe geospatial assets, so they can more easily indexed and discovered. A 'spatiotemporal asset' is any file that represents information about the earth captured in a certain space and time.\n\nTwo examples of STAC catalogs are:\n\n- https://planet.stac.cloud/?t=catalogs\n- https://landsat-stac.s3.amazonaws.com/catalog.json\n\n[Radiant Earth](https://radiant.earth) keeps track of a more complete listing of STAC implementations [here](https://github.com/radiantearth/stac-spec/blob/master/implementations.md).\n\nThis project provides an opinionated way for users to load datasets from these catalogs into the scientific Python ecosystem. Currently it uses the intake-xarray plugin and supports several file formats including GeoTIFF, netCDF, GRIB, and OpenDAP.\n\n\n## Installation\n\nintake-stac has a few [requirements](requirements.txt), such as the Intake library. Intake-stac can be installed from Pip or the source repository. \n\n```bash\n$ pip install intake-stac\n```\n\nFrom source repository:\n\n```bash\n$ git clone https://github.com/pangeo-data/intake-stac.git\n$ cd intake-stac\n$ pip install .\n```\n\nor\n\n```\n$ pip install git+https://github.com/pangeo-data/intake-stac\n```\n\nThe [examples/](examples/) directory contains some example Jupyter Notebooks that can be used to test the functionality.\n\n#### Versions\nTo install a specific versions of intake-stac, specify the version in the install command\n\n```bash\npip install intake-stac==0.1.0\n```\n\nThe table below shows the corresponding versions between intake-stac and STAC:\n\n| sat-stac | STAC  |\n| -------- | ----  |\n| 0.1.x    | 0.6.x |\n\n\n## Usage\n\nThe package can be imported using\n```python\nfrom intake_stac import StacCatalog, StacCollection, StacItem\n```\n\n### Loading a catalog\n\nYou can load data from a STAC catalog by providing the URL to valid STAC catalog entry:\n```python\ncatalog = StacCatalog('https://storage.googleapis.com/pdd-stac/disasters/catalog.json', 'planet-disaster-data')\nlist(catalog)\n```\n\nYou can also point to STAC Collections or Items. Each constructor returns a Intake Catalog with the top level corresponding to the STAC object used for initialization.\n\n```python\nstac_cat = StacCatalog('https://landsat-stac.s3.amazonaws.com/catalog.json', 'landsat-stac')\ncollection_cat = StacCollection('https://landsat-stac.s3.amazonaws.com/landsat-8-l1/catalog.json', 'landsat-8')\nitems_cat = StacItem('https://landsat-stac.s3.amazonaws.com/landsat-8-l1/111/111/2018-11-30/LC81111112018334LGN00.json', 'LC81111112018334LGN00')\n```\n\nIntake-Stac uses [sat-stac](https://github.com/sat-utils/sat-stac) to parse STAC objects. You can also pass `satstac` objects (e.g. `satstac.Collection`) directly to the Intake-Stac constructors: \n\n```python\nimport satstac\ncol = satstac.Collection.open('https://landsat-stac.s3.amazonaws.com/landsat-8-l1/catalog.json')\ncollection_cat = StacCollection(col, 'landsat-8')\n```\n\n### Using the catalog\n\nOnce you have a catalog, you can display its entries by iterating through its contents:\n\n```python\nfor entry_id, entry in catalog.items():\n    display(entry)\n```\n\nIf the catalog has too many entries to comfortably print all at once,\nyou can narrow it by searching for a term (e.g. 'thumbnail'):\n```python\nfor entry_id, entry in catalog.search('thumbnail').items():\n  display(entry)\n```\n\n### Loading a dataset\nOnce you have identified a dataset, you can load it into a `xarray.DataArray` using `to_dask()`:\n\n```python\nda = entry.to_dask()\n```\n\n## Development\n\nThe `master` branch contains the last versioned release, and the `development` branch contains the latest version of the code. New Pull Requests should be made to the `development` branch. For additional [contributing guidelines](docs/contributing.rst) see the documentation.\n\n### Running the tests\n\nTo run the tests some additional packages need to be installed from the `requirements-dev.txt` file.\n\n```\n$ pip install -r requirements-dev.txt\n$ pytest -v -s --cov intake-stac --cov-report term-missing\n```\n\n\n## About\n[intake-stac](https://github.com/pangeo-data/intake-stac) was created as part of the [Pangeo](http://pangeo.io) initiative.  See the initial [design document](https://hackmd.io/cyJZkjV5TCWTJg1mUAoEVA).\n\n\n",
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