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        "author": "Nathan Longbotham",
        "author_email": "nlongbotham@digitalglobe.com",
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        "description": ".. image:: https://badge.fury.io/py/geoio.svg\n    :target: https://badge.fury.io/py/geoio\n\nIntroduction\n============\n\nGeoio provides facilities to easily interact with geospatial\ndata. The interactions that are supported include data retrieval, spectral\nprocessing, metadata handling, shapefile intersection/extraction, and retrieval\nof statistical information. Specific attention has been paid to accessing\nDigitalGlobe data and metadata, but the same facilities in this module can be\nused to access non-DigitalGlobe data or to build custom processing and\nmetadata handling for other satellite platforms.\n\nInstallation\n============\n\n.. code:: python\n\n    pip install geoio\n\nDependencies will be handled at install if possible.  GDAL is not cleanly\ninstallable via pip so should be handled separately (conda, yum, apt-get, etc.).\nThe run dependencies are:  ``gdal, xmltodict, pytz, tzwhere, ephem, numpy, tinytools``.\nAdditionally, ``dgsamples`` is required for testing and ``matplotlib`` must be\navailable for the plotting functions to work.\n\nNote for MAC users: if pip fails for ephem, try installing it directly with conda within\nthe conda virtual environment, i.e.::\n\n   conda install ephem\n\n\nImports\n=======\n\n.. code:: python\n\n    import geoio\n\nimports the main classes ``GeoImage`` and ``DGImage`` to the module root.\n\nThe ``GeoImage`` class is a relatively thin wrapper around gdal that provides a\npythonic interface for accessing an arbitrary geospatial image format\n(generally those supported by gdal plus the DigitalGlobe .TIL format).\nOperations supported include reading, writing, chipping, reprojecting, and meta\ndata access.  The class methods are populated with reasonable defaults and\nobject interfaces, making image operations less painful so that you can get on\nwith the important stuff!\n\nThe ``DGImage`` class inherits all the capabilites of ``GeoImage`` and adds\nDigitalGlobe meta data handling, spectral processing, and band alias data\nretrieval.  Therefore, it requires that the input image be a valid DigitalGlobe\nimage.  This is currently either a .TIL file with the associated meta data files\n(.IMD and/or .XML) present in the image directory or a .TIF files with an\nidentially named .IMD or .XML file.  The metadata is read into an\n``OrderedBunch`` object (inherited from the tinytools package) attached to the\ninstantiated object.\n\nQuick Start\n===========\n\nThe geoio classes are best used interactively from within ipython where the\nrelevant pretty print methods can be triggered.  Meta data information will be\nreutrned regardless of the interpreter, but the readability is currently\nmuch better in ipython.\n\nThe dgsamples repo is used below.  However, all the operations below can be\nrun on local data by replacing the dgsamples call with a string to the image\nlocation.  From exmaples, instead of typing ```dgsamples.wv2_longmont_1k.ms```,\na local files at ```/path/to/imgfile.TIF``` can be used.\n\nUsing the GeoImage object:\n\n.. code:: python\n\n    import dgsamples\n\n    # Instantiate an image object\n    img = geoio.GeoImage(dgsamples.bayou_chip.extract_test)  # a TIF file\n\n    # Print useful information about the object\n    img.files\n    img.meta\n\n    # Get numpy array\n    data = img.get_data()\n\n    # Process data and write to new image\n    newdata = data*2\n    img.write_img_like_this('/path/to/newfile.TIF',newdata)\n\nUsing the DGImage object:\n\n.. code:: python\n\n    import dgsamples\n\n    # Instantiate an image object\n    img = geoio.DGImage(dgsamples.wv2_longmont_1k.ms)    # a TIL file\n    # Can also be used directly with a DigitalGlobe TIF file if an XML and/or IMD\n    # is available with same name as the TIF file.\n\n    # Print useful information about the object\n    img.files\n    img.meta\n\n    # Print the full IMD OrderedBunch object\n    img.meta_dg.IMD  # tab completeable through the OrderedBunch\n\n    # Return an ImgArr (a numpy array with band meta data handling)\n    data = img.get_data()\n\n    # Convert an ImgArr to a pure numpy array\n    npdata = np.asarray(data)\n\n    # Return a pure numpy array\n    data = img.get_data(meta=False)\n\n    # Get specific bands using aliases - see geoio.constants.DG_BAND_ALIASES for\n    # additional aliases.\n    data = img.get_data(bands='VIS')\n\n    # Get specific bands using band aliases\n    data = img.get_data(bands=['C','Y'])\n\n    # Get image data and convert to TOA reflectance\n    data = img.get_data(stype='toa')\n\nPlotting\n========\n\nPlotting with the ``geoio.plotting`` functions:\n\n.. code:: python\n\n    import dgsamples\n\n    # Instantiate an image object\n    img = geoio.DGImage(dgsamples.wv2_longmont_1k.ms)  # a TIF file\n\n    # Plot the RGB image\n    geoio.plotting.imshow(img.get_data(bands='RGB'))\n\n    # Plot the near-infrared false color image\n    geoio.plotting.imshow(img.get_data(bands=['N1','G','B']))\n\n    # Plotting a histogram of the image bands\n    geoio.plotting.hist(img.get_data())\n\n    # Plotting a histogram of specific bands\n    geoio.plotting.hist(img.get_data(bands='VIS'))\n\nSpatial Resampling\n==================\n\nThe geoio module has upsampling and downsmapling code that allows the user\nto easily resample two images to the same grid for easy multi-image proceesing.\n\n.. code:: python\n\n    import dgsamples\n\n    # Import wv3 images\n    ms = geoio.DGImage(dgsamples.wv3_longmont_1k.ms)\n    swir = geoio.DGImage(dgsamples.wv3_longmont_1k.swir)\n\n    # Upsample the swir image\n    swir.upsample_like_that(ms,method='nearest')  # default method is bilinear\n\n    # Downsample the ms image\n    ms.downsample_like_that(swir)  # default method is aggregation\n\n    # Or let geoio figure it out\n    ms.resample_like_that(swir)\n    swir.resample_like_that(ms)\n\nIterators\n=========\n\nThe geoio module also provides several iterators to allow easy access to\nyield based portions of a raster file.\n\n.. code:: python\n\n    import dgsamples\n    ms = geoio.DGImage(dgsamples.wv2_longmont_1k.ms)\n\n    # iterate through vector geometries\n    v = dgsamples.wv2_longmont_1k_vectors.poly_geojson_latlon\n    [x for x in ms.iter_vector(vector=v,bands='RGB',mask=True)]\n\n    # random windows from the image\n    [x.shape for x in ms.iter_window_random(win_size=[10,10], no_chips=20)]\n\n    # iterate through image with evenly spaced windows based on requested stride\n    [x.shape for x in ms.iter_window(win_size=[10,10], stride=[100,100])\n\n\n",
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