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        "author": "Nat Wilson",
        "author_email": "natw@fortyninemaps.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: GIS"
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        "description": "# picoraster\n\nSmall Python library for processing large raster images.\n\nCurrently a work in progress.\n\n## Example usage\n\n```python\nsource = AWSLandsat8Source(\"LC08_L1TP_139045_20170304_20170316_01_T1\", band=\"8\")\n\n# Lazily create a band and build a description of processing steps\nband = Band(source) \\\n    .and_then(Resize(extents)) \\\n    .and_then(HistogramAdjust()) \\\n    .and_then(Reproject(crs))\n\n# Forces computation\narray = band.render_to_array()\n\nband.render_to_file(\"output.tif\")\n```\n\n## Installation\n\nInstalling GDAL is the most challenging part. Installing directly from PyPI is historically unlikely to work.\n\nFirst, install numpy:\n`pip install numpy`\n\nThen, choose one of the following:\n\n- install with a system package manager\n    - Ubuntu: `sudo apt install libgdal-dev`\n    - MacOS: `brew install gdal`\n- install from conda-forge: `conda install -c conda-forge gdal`\n- [compile manually](http://trac.osgeo.org/gdal/wiki/BuildHints)\n\nAfterwards, the correct Python bindings can be installed with\n```bash\npip install GDAL==$(gdal-config --version) --global-option=build_ext --global-option=\"-I/usr/include/gdal\" \n```\n\nFinally,\n`pip install picoraster`\n\n## Running tests\n\n`python -m src.tests`\n\n",
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