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        "author": "Ciaran Robb",
        "author_email": "",
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        "classifiers": [
            "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: GIS",
            "Topic :: Utilities"
        ],
        "description": ".. -*- mode: rst -*-\n\n.. |Python35| image:: https://img.shields.io/badge/python-3.5-blue.svg\n\ngeospatial-learn\n============\n\ngeospatial-learn is a Python module for using scikit-learn and xgb models with geo-spatial data, chiefly raster and vector formats. \n\nThe module also contains various fuctionality for manipulating raster and vector data as well as some utilities aimed at processing Sentinel 2 data.\n\nThe aim is to produce convenient, minimal commands for putting together geo-spatial processing chains using machine learning libs. Development will aim to expand the variety of libs/algorithms available for machine learning beyond the current complement.  \n\n\nDependencies\n~~~~~~~~~~~~\n\ngeospatial-learn requires:\n\n- Python 3\n\nUser installation\n~~~~~~~~~~~~~~~~~\n\nAt present the setup.py only installs some of the dependencies. An anaconda package is in the works, but until that is done please do the following. This assumes you have an anaconda installation with a python 3  root OR env.\n\nLinux - based\n~~~~~~~~~~~~~~~~~\n\nLibrary & pypi install\n\nStep 1.\n\n- open a terminal and type:\n\n.. code-block:: bash\n   \n   pip install geospatial-learn\n\nOR\n\n- download the zip from here: \n\n  https://github.com/Ciaran1981/geospatial-learn/raw/master/archive/geospatial-learn-0.1.tar.gz\n\n\n- cd into the folder\n\n- open a terminal and type:\n\n.. code-block:: bash\n    \n   python setup.py install\n\nThis will install the library and packages unavailable on anaconda.\n\nStep 2.\n\nConda is very handy at managing packages, hence the 2 stage install, as some of these are external to python or themselves have multiple depends.\n\nNext, type the following (in the same terminal).\n\n.. code-block:: bash\n\n   chmod +x install_conda_packages.sh\n\n   bash ./install_conda_packages.sh\n\nAll the appropriate anaconda packages will then install\n\nWindows - based\n~~~~~~~~~~~~~~~~~   \n\nCommiserations, you are using Windows (hehe). This seems to work, though I have only tested on 1 machine. \n\nLibrary & pypi install\n\nStep 1.\n\n- open a powershell/anaconda prompt and type:\n\n.. code-block:: bash\n   \n   pip install geospatial-learn\n\nOR\n\n- download the zip from here: \n\n  https://github.com/Ciaran1981/geospatial-learn/raw/master/archive/geospatial-learn-0.1.tar.gz\n\n- cd into the folder\n\n- open a powershell and type:\n\n.. code-block:: bash\n    \n   python setup.py install\n\nThis will install the library and packages unavailable on anaconda.\n\nStep 2.\n\nConda is very handy at managing packages, hence the 2 stage install, as some of these are external to python or themselves have multiple depends.\n\nNext, type the following (in the same terminal).\n\n.. code-block:: bash\n\n   .\\install_conda_packages.bat\n\nIf you run into problems here, such as certain packages unavailable with Python 3.5/6, I suggest creating a conda environment with python 3.4, then following the above procedure. At the time of writing for example (31/08/17), gdal is not available in py3.5+ on windows anaconda and py3.6 on linux platforms.\n\nI have not provided xgboost instructions here, there are some on the native website along with ensuring the lib points to your python environment of choice. \n\n\nQuickstart\n----------\n\nA summary of some functions can be found here:\n\nhttps://github.com/Ciaran1981/geospatial-learn/blob/master/docs/quickstart.rst\n\nThis is currently a work in progress of course! \n\nDocs\n----\n\nDocumentation can be found here:\n\nhttp://geospatial-learn.readthedocs.io/en/latest/\n\nThese are a work in progress!\n\n\nDevelopment\n-----------\n\nNew contributors of all experience levels are welcome\n\nUseful links\n~~~~~~~~~~~~~~~\nHere are some links to the principal libs used in geospatial-learn.\n\nhttps://github.com/scikit-learn/\n\nhttp://xgboost.readthedocs.io/en/latest/\n\nhttp://scikit-learn.org/stable/\n\nhttp://www.gdal.org/\n\nhttp://www.numpy.org/\n\nhttps://www.scipy.org/\n\nhttp://scikit-image.org/\n\nSubmitting a Pull Request\n~~~~~~~~~~~~~~~~~~~~~~~~~\navailable soon\n\nProject History\n---------------\n\nGeospatial-learn was originally written by Dr Ciaran Robb, University of Leicester. The functionality was written as part of various research projects involving Earth observation & geo-spatial data. \n\nGeospatial-learn is currently written and maintained by Ciaran Robb and John Roberts. The module is at a very early stage at present and there is more material wrtten that has yet to be added.     \n\nHelp and Support\n----------------\n\navailable soon\n\nCitation\n~~~~~~~~\n\nIf you use geospatial-learn in a scientific publication, citations would be appreciated \n",
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