def main(npy_in,tif_out,tif_like):
    
    arr_in = np.load(npy_in)
    locs = arr_in[:,:2].astype(np.int)

    ## "un-flatten" to array shape of basin raster
    # get correct shape and coordinates
    with rs.drivers():
        with rs.open(tif_like) as src:
            profile = src.profile
            w,h = (src.width, src.height)
            aff = src.affine
            ndv = src.get_nodatavals()
            x,y = [i.astype(np.int) for i in (~aff * (locs[:,0],locs[:,1]))]

    # construct 3d array
    arr_out = np.ones((w,h,arr_in.shape[1]-2),dtype=np.float32)
    arr_out[:] = ndv
    arr_out[x,y,:] = arr_in[:,2:]
    arr_out = np.swapaxes(arr_out,0,2)

    # update output profile to get correct # of bands and compression mechanism
    profile.update(
        count = arr_out.shape[0],
        compress = 'DEFLATE')


    ## output Tiff file
    with rs.drivers():
        with rs.open(tif_out, 'w', **profile) as dst:
            dst.write(arr_out)

def test_drivers():
    with rasterio.drivers() as m:
        assert driver_count() > 0
        assert type(m) == DriverManager
        
        n = rasterio.drivers()
        assert driver_count() > 0
        assert type(n) == DummyManager

def test_drivers():
    with rasterio.drivers() as m:
        assert driver_count() > 0
        assert type(m) == GDALEnv
        
        n = rasterio.drivers()
        assert driver_count() > 0
        assert type(n) == GDALEnv

 def _src(self):
     """ An optionally memoized generator on time series datasets
     """
     if self.keep_open:
         if not hasattr(self, '_src_open'):
             with rasterio.drivers():
                 self._src_open = [rasterio.open(f, 'r') for
                                   f in self.df['filename']]
         for _src in self._src_open:
             yield _src
     else:
         with rasterio.drivers():
             for f in self.df['filename']:
                 yield rasterio.open(f, 'r')

def test_mask_crop(runner, tmpdir, basic_feature, pixelated_image):
    """
    In order to test --crop option, we need to use a transform more similar to
    a normal raster, with a negative y pixel size.
    """

    image = pixelated_image
    outfilename = str(tmpdir.join("pixelated_image.tif"))
    kwargs = {
        "crs": {"init": "epsg:4326"},
        "transform": Affine(1, 0, 0, 0, -1, 0),
        "count": 1,
        "dtype": rasterio.uint8,
        "driver": "GTiff",
        "width": image.shape[1],
        "height": image.shape[0],
        "nodata": 255,
    }
    with rasterio.drivers():
        with rasterio.open(outfilename, "w", **kwargs) as out:
            out.write_band(1, image)

    output = str(tmpdir.join("test.tif"))

    truth = numpy.zeros((4, 3))
    truth[1:3, 0:2] = 1

    result = runner.invoke(
        features.mask, [outfilename, output, "--crop", "--geojson-mask", "-"], input=json.dumps(basic_feature)
    )

    assert result.exit_code == 0
    assert os.path.exists(output)
    with rasterio.open(output) as out:
        assert numpy.array_equal(truth, out.read(1, masked=True).filled(0))

    def write_band(self, output_band, output_file, image_data):
        # colormaps will overwrite our transparency masks so we will manually
        # create three RGB bands

        self.output("Applying ColorMap", normal=True, arrow=True)
        self.cmap[0] = (0, 0, 0, 255)

        v_manual_colormap = numpy.vectorize(self.manual_colormap, otypes=[numpy.uint8])
        rgb_bands = []
        for i in range(3):
            rgb_bands.append(v_manual_colormap(output_band, i))

        with rasterio.drivers(GDAL_TIFF_INTERNAL_MASK=True):
            with rasterio.open(output_file, 'w', driver='GTiff',
                               width=image_data['shape'][1],
                               height=image_data['shape'][0],
                               count=3,
                               dtype=numpy.uint8,
                               nodata=0,
                               photometric='RGB',
                               transform=image_data['dst_transform'],
                               crs=self.dst_crs) as output:

                for i in range(3):
                    output.write_band(i+1, rgb_bands[i])

            self.output("Writing to file", normal=True, color='green', indent=1)
        return output_file

def test_rasterize_supported_dtype(basic_geometry):
    """ Supported data types should return valid results """

    with rasterio.drivers():
        supported_types = (
            ('int16', -32768),
            ('int32', -2147483648),
            ('uint8', 255),
            ('uint16', 65535),
            ('uint32', 4294967295),
            ('float32', 1.434532),
            ('float64', -98332.133422114)
        )

        for dtype, default_value in supported_types:
            truth = numpy.zeros(DEFAULT_SHAPE, dtype=dtype)
            truth[2:4, 2:4] = default_value

            result = rasterize(
                [basic_geometry],
                out_shape=DEFAULT_SHAPE,
                default_value=default_value,
                dtype=dtype
            )
            assert numpy.array_equal(result, truth)
            assert numpy.dtype(result.dtype) == numpy.dtype(truth.dtype)

            result = rasterize(
                [(basic_geometry, default_value)],
                out_shape=DEFAULT_SHAPE
            )
            if numpy.dtype(dtype).kind == 'f':
                assert numpy.allclose(result, truth)
            else:
                assert numpy.array_equal(result, truth)

def test_shapes(basic_image):
    """ Test creation of shapes from pixel values """

    with rasterio.drivers():
        results = list(shapes(basic_image))

        assert len(results) == 2

        shape, value = results[0]
        assert shape == {
            'coordinates': [
                [(2, 2), (2, 5), (5, 5), (5, 2), (2, 2)]
            ],
            'type': 'Polygon'
        }
        assert value == 1

        shape, value = results[1]
        assert shape == {
            'coordinates': [
                [(0, 0), (0, 10), (10, 10), (10, 0), (0, 0)],
                [(2, 2), (5, 2), (5, 5), (2, 5), (2, 2)]
            ],
            'type': 'Polygon'
        }
        assert value == 0

def shapes(image, mask=None, connectivity=4, transform=IDENTITY):
    """Yields a (shape, image_value) pair for each feature in the image.
    
    The shapes are GeoJSON-like dicts and the image values are ints.
    
    Features are found using a connected-component labeling algorithm.

    The image must be of unsigned 8-bit integer (rasterio.byte or
    numpy.uint8) data type. If a mask is provided, pixels for which the
    mask is `False` will be excluded from feature generation.
    """
    if np.dtype(image.dtype) != np.dtype(rasterio.ubyte):
        raise ValueError("Image must be dtype uint8/ubyte")

    if mask is not None and np.dtype(mask.dtype) != np.dtype(rasterio.bool_):
        raise ValueError("Mask must be dtype rasterio.bool_")

    if connectivity not in (4, 8):
        raise ValueError("Connectivity Option must be 4 or 8")

    transform = guard_transform(transform)

    with rasterio.drivers():
        for s, v in _shapes(image, mask, connectivity, transform.to_gdal()):
            yield s, v

def test_data_dir_2(tmpdir):
    kwargs = {
        "crs": {'init': 'epsg:4326'},
        "transform": (-114, 0.2, 0, 46, 0, -0.1),
        "count": 1,
        "dtype": rasterio.uint8,
        "driver": "GTiff",
        "width": 10,
        "height": 10
        # these files have undefined nodata.
    }

    with rasterio.drivers():

        with rasterio.open(str(tmpdir.join('b.tif')), 'w', **kwargs) as dst:
            data = numpy.zeros((10, 10), dtype=rasterio.uint8)
            data[0:6, 0:6] = 255
            dst.write_band(1, data)

        with rasterio.open(str(tmpdir.join('a.tif')), 'w', **kwargs) as dst:
            data = numpy.zeros((10, 10), dtype=rasterio.uint8)
            data[4:8, 4:8] = 254
            dst.write_band(1, data)

    return tmpdir

def test_sieve_mask():
    """Test proper behavior of mask image, if passed int sieve"""

    with rasterio.drivers():
        shape = (20, 20)
        image = numpy.zeros(shape, dtype=rasterio.ubyte)
        image[5:15, 5:15] = 1
        image[1:3, 1:3] = 2

        # Blank mask has no effect, only areas smaller than size will be
        # removed
        mask = numpy.ones(shape, dtype=rasterio.bool_)
        sieved_image = ftrz.sieve(image, 100, mask=mask)
        truth = numpy.zeros_like(image)
        truth[5:15, 5:15] = 1
        assert numpy.array_equal(sieved_image, truth)

        # Only areas within the overlap of the mask and values will be kept
        mask = numpy.ones(shape, dtype=rasterio.bool_)
        mask[7:10, 7:10] = False
        sieved_image = ftrz.sieve(image, 100, mask=mask)
        truth = numpy.zeros_like(image)
        truth[7:10, 7:10] = 1
        assert numpy.array_equal(sieved_image, truth)

        # mask of other type than rasterio.bool_ should fail
        mask = numpy.zeros(shape, dtype=rasterio.uint8)
        with pytest.raises(ValueError):
            ftrz.sieve(image, 100, mask=mask)

def test_sieve():
    """Test sieving a 10x10 feature from an ndarray."""

    image = numpy.zeros((20, 20), dtype=rasterio.ubyte)
    image[5:15, 5:15] = 1

    # An attempt to sieve out features smaller than 100 should not change the
    # image.
    with rasterio.drivers():
        sieved_image = ftrz.sieve(image, 100)
        assert numpy.array_equal(sieved_image, image)

    # Setting the size to 100 should leave us an empty, False image.
    with rasterio.drivers():
        sieved_image = ftrz.sieve(image, 101)
        assert not sieved_image.any()

def visualize_labels(geo_tiff, nlcd):
    """Make a picture with color-coded labels as pixels

    :param geo_tiff: path to GeoTiff file
    :param nlcd: path to NLCD .img file
    """
    print "Getting labels..."
    labels = get_labels_tif(geo_tiff, nlcd)
    with rasterio.drivers():
        with rasterio.open(geo_tiff) as src:
            width = src.width
            height = src.height
    rgb = np.zeros((height, width, 3))

    print "Getting colors for labels..."
    for col in range(0, width):
        for row in range(0, height):
            label = labels[row + col*height]
            r, g, b = const.RGB_LABELS[label]
            rgb[(row, col, 0)] = r
            rgb[(row, col, 1)] = g
            rgb[(row, col, 2)] = b

    # show image
    print "Showing image now..."
    plt.imshow(rgb)
    plt.show()

def get_labels_lat_lon(coords, nlcd):
    """Gets the labels corresponding to coordinates

    1. Transforms (latitude, longitude) to the coordinate reference system
       used in the NLCD data set (Alber Conical Equal Area (ACEA))
    2. Transforms ACEA coordinates to pixels in the raster data set
    3. Queries the labels for those pixels
       None if one of the indeces of the pixel is out-of-bounds

    :param coords: list of (latitude, longitude) tuples
    :param main_folder: path to folder where the data folder is found
    :return: list: list containing the labels corresponding to each coordinate
                   None for coordinates not in the NLCD data set
    """
    labels = []
    # transform lat, lon to Albers Conical Equal Area (ACEA)
    acea = pyproj.Proj(ACEA_PROJ4)
    acea_coords = [(acea(lon, lat)) for lat, lon in coords]
    # open NLCD raster data set
    with rasterio.drivers():
        with rasterio.open(nlcd) as src:
            # linear transformation between ACEA coordinates and pixels
            rev = ~src.affine
            # transform ACEA to pixel coordinates
            pixels = [tuple(int(round(i)) for i in rev*coord) for coord in acea_coords]
            for col, row in pixels:
                if col < 0 or col >= src.width or row < 0 or row >= src.height:
                    labels.append(None)
                else:
                    window = ((row, row+1), (col, col+1))
                    labels.append(src.read(1, window=window)[0,0])
    return labels

def test_rasterize_invalid_out_dtype(basic_geometry):
    """ A non-supported data type for out should raise an exception """

    out = numpy.zeros(DEFAULT_SHAPE, dtype=numpy.int64)
    with rasterio.drivers():
        with pytest.raises(ValueError):
            rasterize([basic_geometry], out=out)

def test_sieve_large(basic_image):
    """
    Setting the size larger than size of feature should leave us an empty image.
    """

    with rasterio.drivers():
        assert not numpy.any(sieve(basic_image, basic_image.sum() + 1))

 def getFileGeoDict(cls,filename):
     """Get the spatial extent, resolution, and shape of grid inside ESRI grid file.
     :param filename:
        File name of ESRI grid file.
     :returns:
        - GeoDict specifying spatial extent, resolution, and shape of grid inside ESRI grid file.
        - xvar array specifying X coordinates of data columns
        - yvar array specifying Y coordinates of data rows
     :raises DataSetException:
       When the file contains a grid with more than one band.
     """
     geodict = {}
     with rasterio.drivers():
         with rasterio.open(filename) as src:
             aff = src.affine
             geodict['xdim'] = aff.a
             geodict['ydim'] = -1*aff.e
             geodict['xmin'] = aff.xoff + geodict['xdim']/2.0
             geodict['ymax'] = aff.yoff - geodict['ydim']/2.0
                             
             shp = src.shape
             if len(shp) > 2:
                 raise DataSetException('Cannot support grids with more than one band')
             geodict['nrows'] = src.height
             geodict['ncols'] = src.width
             geodict['xmax'] = geodict['xmin'] + (geodict['ncols']-1)*geodict['xdim']
             geodict['ymin'] = geodict['ymax'] - (geodict['nrows']-1)*geodict['ydim']
     xvar = np.arange(geodict['xmin'],geodict['xmax']+geodict['xdim'],geodict['xdim'])
     yvar = np.arange(geodict['ymin'],geodict['ymax']+geodict['ydim'],geodict['ydim'])
     return (geodict,xvar,yvar)

def main(infile, outfile, with_threads=False):
    
    with rasterio.drivers():

        # Open the source dataset.
        with rasterio.open(infile) as src:

            # Create a destination dataset based on source params. The
            # destination will be tiled, and we'll "process" the tiles
            # concurrently.

            meta = src.meta
            del meta['transform']
            meta.update(affine=src.affine)
            meta.update(blockxsize=256, blockysize=256, tiled='yes')
            with rasterio.open(outfile, 'w', **meta) as dst:

                loop = asyncio.get_event_loop()
                
                # With the exception of the ``yield from`` statement,
                # process_window() looks like callback-free synchronous
                # code. With a coroutine, we can keep the read, compute,
                # and write statements close together for
                # maintainability. As in the concurrent-cpu-bound.py
                # example, all of the speedup is provided by
                # distributing raster computation across multiple
                # threads. The difference here is that we're submitting
                # jobs to the thread pool asynchronously.

                @asyncio.coroutine
                def process_window(window):
                    
                    # Read a window of data.
                    data = src.read(window=window)
                    
                    # We run the raster computation in a separate thread
                    # and pause until the computation finishes, letting
                    # other coroutines advance.
                    #
                    # The _example.compute function modifies no Python
                    # objects and releases the GIL. It can execute
                    # concurrently.
                    result = numpy.zeros(data.shape, dtype=data.dtype)
                    if with_threads:
                        yield from loop.run_in_executor(
                                            None, compute, data, result)
                    else:
                        compute(data, result)
                    
                    # Write the result.
                    for i, arr in enumerate(result, 1):
                        dst.write_band(i, arr, window=window)

                # Queue up the loop's tasks.
                tasks = [asyncio.Task(process_window(window)) 
                         for ij, window in dst.block_windows(1)]
                
                # Wait for all the tasks to finish, and close.
                loop.run_until_complete(asyncio.wait(tasks))
                loop.close()

def pixelated_image_file(tmpdir, pixelated_image):
    """
    A basic raster file with a 10x10 array for testing sieve functions.
    Contains data from pixelated_image.

    Returns
    -------

    string
        Filename of test raster file
    """

    from affine import Affine
    import rasterio

    image = pixelated_image

    outfilename = str(tmpdir.join('pixelated_image.tif'))
    kwargs = {
        "crs": {'init': 'epsg:4326'},
        "transform": Affine.identity(),
        "count": 1,
        "dtype": rasterio.uint8,
        "driver": "GTiff",
        "width": image.shape[1],
        "height": image.shape[0],
        "nodata": 255
    }
    with rasterio.drivers():
        with rasterio.open(outfilename, 'w', **kwargs) as out:
            out.write_band(1, image)

    return outfilename

def test_reproject_dst_nodata_default():
    """
    If nodata is not provided, destination will be filled with 0
    instead of nodata
    """

    params = default_reproject_params()

    with rasterio.drivers():
        source = numpy.ones((params.width, params.height), dtype=numpy.uint8)
        out = numpy.zeros((params.dst_width, params.dst_height),
                          dtype=source.dtype)
        out.fill(120)  # Fill with arbitrary value

        reproject(
            source,
            out,
            src_transform=params.src_transform,
            src_crs=params.src_crs,
            dst_transform=params.dst_transform,
            dst_crs=params.dst_crs
        )

        assert (out == 1).sum() == 4461
        assert (out == 0).sum() == (params.dst_width *
                                    params.dst_height - 4461)