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    "info": {
        "author": "Vincent Sarago",
        "author_email": "vincent@developmentseed.org",
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        "classifiers": [
            "Intended Audience :: Information Technology",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: GIS"
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        "description": "# Rio-tiler\n\nRasterio plugin to read mercator tiles from Cloud Optimized GeoTIFF dataset.\n\n[![Packaging status](https://badge.fury.io/py/rio-tiler.svg)](https://badge.fury.io/py/rio-tiler)\n[![CircleCI](https://circleci.com/gh/cogeotiff/rio-tiler.svg?style=svg)](https://circleci.com/gh/cogeotiff/rio-tiler)\n[![codecov](https://codecov.io/gh/cogeotiff/rio-tiler/branch/master/graph/badge.svg?token=zuHupC20cG)](https://codecov.io/gh/cogeotiff/rio-tiler)\n\nAdditional support is provided for the following satellite missions hosted on **AWS Public Dataset**:\n\n- [Sentinel2](https://registry.opendata.aws/sentinel-2/) (**requester-pays**) (please read [this](https://github.com/cogeotiff/rio-tiler#Partial-reading-on-Cloud-hosted-dataset))\n- [Sentinel1](https://registry.opendata.aws/sentinel-1/) (**requester-pays**)\n- [Landsat8](https://aws.amazon.com/fr/public-datasets/landsat)\n- [CBERS](https://registry.opendata.aws/cbers/)  (**requester-pays**)\n\nRio-tiler supports Python 2.7 and 3.3-3.7.\n\n\n## Install\n\nYou can install rio-tiler using pip\n\n```bash\n$ pip install -U pip\n$ pip install rio-tiler\n```\n\nor install from source:\n\n```bash\n$ git clone https://github.com/cogeotiff/rio-tiler.git\n$ cd rio-tiler\n$ pip install -U pip\n$ pip install -e .\n```\n\n## Overview\n\nCreate tiles using one of these rio_tiler modules: `main`, `sentinel2`, `sentinel1`, `landsat8`, `cbers`.\n\nThe `main` module can create mercator tiles from any raster source supported by Rasterio (i.e. local files, http, s3, gcs etc.). The mission specific modules make it easier to extract tiles from AWS S3 buckets (i.e. only a scene ID is required); They can also be used to return metadata.\n\nEach tilling modules have a method to return image metadata (e.g bounds).\n\n## Usage\n\nRead a tile from a file over the internet\n\n```python\nfrom rio_tiler import main\n\ntile, mask = main.tile(\n  'http://oin-hotosm.s3.amazonaws.com/5a95f32c2553e6000ce5ad2e/0/10edab38-1bdd-4c06-b83d-6e10ac532b7d.tif',\n  691559,\n  956905,\n  21,\n  tilesize=256\n)\nprint(tile.shape)\n> (3, 256, 256)\n\nprint(mask.shape)\n> (256, 256)\n```\n\nCreate image from tile\n\n```python\nfrom rio_tiler.utils import array_to_image\n\nbuffer = array_to_img(tile, mask=mask) # this returns a buffer (PNG by default)\n```\n\nUse creation options to match `mapnik` default\n\n```python\nfrom rio_tiler.utils import array_to_image\nfrom rio_tiler.profiles import img_profiles\n\noptions = img_profiles[\"webp\"]\nbuffer = array_to_img(tile, mask=mask, img_format=\"webp\", **options)\n```\n\nWrite image to file\n\n```python\nwith open(\"my.png\", \"wb\") as f:\n  f.write(buffer)\n```\n\nGet a Sentinel2 tile and its nodata mask.\n\n```python\nfrom rio_tiler import sentinel2\n\ntile, mask = sentinel2.tile('S2A_tile_20170729_19UDP_0', 77, 89, 8)\nprint(tile.shape)\n> (3, 256, 256)\n```\n\nGet bounds for a Landsat scene (WGS84).\n\n```python\nfrom rio_tiler import landsat8\n\nlandsat8.bounds('LC08_L1TP_016037_20170813_20170814_01_RT')\n> {'bounds': [-81.30836, 32.10539, -78.82045, 34.22818],\n>  'sceneid': 'LC08_L1TP_016037_20170813_20170814_01_RT'}\n```\n\nGet metadata of a Landsat scene (i.e. percentiles (pc) min/max values, histograms, and bounds in WGS84) .\n\n```python\nfrom rio_tiler import landsat8\n\nlandsat8.metadata('LC08_L1TP_016037_20170813_20170814_01_RT', pmin=5, pmax=95)\n{\n  'sceneid': 'LC08_L1TP_016037_20170813_20170814_01_RT',\n  'bounds': {\n    'value': (-81.30844102941015, 32.105321365706104,  -78.82036599673634, 34.22863519772504),\n    'crs': '+init=EPSG:4326'\n  },\n  'statistics': {\n    '1': {\n      'pc': [1251.297607421875, 5142.0126953125],\n      'min': -1114.7020263671875,\n      'max': 11930.634765625,\n      'std': 1346.6463388957156,\n      'histogram': [\n        [1716, 257951, 174296, 36184, 20828, 11783, 6862, 2941, 635, 99],\n        [-1114.7020263671875, 189.83164978027344, 1494.3653564453125, 2798.89892578125, 4103.4326171875, 5407.96630859375, 6712.5, 8017.03369140625, 9321.5673828125, 10626.1015625, 11930.634765625]\n      ]\n    },\n    ...\n    ...\n    '11': {\n      'pc': [278.3393859863281, 293.4466247558594],\n      'min': 147.27650451660156,\n      'max': 297.4621276855469,\n      'std': 7.660112832018338,\n      'histogram': [\n        [207, 201, 204, 271, 350, 944, 1268, 2383, 43085, 453084],\n        [147.27650451660156, 162.29507446289062, 177.31362915039062, 192.33218383789062, 207.3507537841797, 222.36932373046875, 237.38787841796875, 252.40643310546875, 267.42498779296875, 282.4435729980469, 297.4621276855469]\n      ]\n    }\n  }\n}\n```\n\nThe primary purpose for calculating minimum and maximum values of an image is to rescale pixel values from their original range (e.g. 0 to 65,535) to the range used by computer screens (i.e. 0 and 255) through a linear transformation.\nThis will make images look good on display.\n\n## Requester-pays Buckets \n\nOn AWS, `sentinel2`, `sentinel1`, and `cbers` dataset are stored in a `requester-pays` bucket, meaning the cost of GET, LIST requests will be charged to the users. For rio-tiler to work with those buckets, you'll need to set `AWS_REQUEST_PAYER=\"requester\"` in your environement.\n\n## Partial reading on Cloud hosted dataset\n\nRio-tiler perform partial reading on local or distant dataset, which is why it will perform best on Cloud Optimized GeoTIFF (COG).\nIt's important to note that **Sentinel-2 scenes hosted on AWS are not in Cloud Optimized format but in JPEG2000**.\nWhen performing partial reading of JPEG2000 dataset GDAL (rasterio backend library) will need to make a lot of **GET requests** and transfer a lot of data.\n\nRef: [Do you really want people using your data](https://medium.com/@_VincentS_/do-you-really-want-people-using-your-data-ec94cd94dc3f) blog post.\n\n## Contribution & Development\n\nIssues and pull requests are more than welcome.\n\n**dev install**\n\n```bash\n$ git clone https://github.com/cogeotiff/rio-tiler.git\n$ cd rio-tiler\n$ pip install -e .[dev]\n```\n\n**Python3.6 only**\n\nThis repo is set to use `pre-commit` to run *flake8*, *pydocstring* and *black* (\"uncompromising Python code formatter\") when commiting new code.\n\n```bash\n$ pre-commit install\n```\n\n## License\n\nSee [LICENSE.txt](https://github.com/cogeotiff/rio-tiler/blob/master/LICENSE.txt)\n\n## Authors\n\nThe rio-tiler project was begun at Mapbox and has been transferred in January 2019.\n\nSee [AUTHORS.txt](https://github.com/cogeotiff/rio-tiler/blob/master/AUTHORS.txt) for a listing of individual contributors.\n\n## Changes\n\nSee [CHANGES.txt](https://github.com/cogeotiff/rio-tiler/blob/master/CHANGES.txt).\n\n\n## Create an AWS Lambda package\n\nThe easiest way to make sure the package will work on AWS is to use docker\n\n```dockerfile\nFROM lambci/lambda:build-python3.6\n\nENV LANG=en_US.UTF-8 LC_ALL=en_US.UTF-8 CFLAGS=\"--std=c99\"\n\nRUN pip3 install rio-tiler --no-binary numpy -t /tmp/python -U\n\nRUN cd /tmp/python && zip -r9q /tmp/package.zip *\n```\n\nRef: https://github.com/vincentsarago/simple-rio-lambda\n\n\n## Plugins\n- [rio-tiler-mosaic](https://github.com/cogeotiff/rio-tiler-mosaic)\n- [rio-tiler-mvt](https://github.com/cogeotiff/rio-tiler-mvt)\n\n## Implementations\n- [remotepixel-tiler](https://github.com/RemotePixel/remotepixel-tiler)\n- [CosmiQ/solaris](https://github.com/CosmiQ/solaris)\n- [lambda-tiler](https://github.com/vincentsarago/lambda-tiler)\n- [cogeo-mosaic](https://github.com/developmentseed/cogeo-mosaic)",
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