How to use pygeoprocessing - 5 common examples

regression = numpy.polyfit(timesteps,
                                   reshaped, deg=deg, w=weights)[0]
        out_block = regression.reshape(blocks[0].shape)
        # Mask out any pixel stacks where there's a nodata value in the stack.
        # Out block is multiplied by 365.25 to convert m/day to m/year trend.
        return numpy.where(numpy.min(stacked_array, axis=2) == 0, 0, out_block*365.25)

    raster_cell_sizes = [pygeoprocessing.get_cell_size_from_uri(r)
                         for r in rasters]
    min_cell_size = min(raster_cell_sizes)
    if not len(set(raster_cell_sizes)) == 1:
        warnings.warn(('Cell sizes of input rasters do not all match. '
                       'Using min pixelsize of %s. Mismatched values: %s') % (
                      min_cell_size, set(raster_cell_sizes)))

    pygeoprocessing.vectorize_datasets(
        dataset_uri_list=rasters,
        dataset_pixel_op=_regression,
        dataset_out_uri=out_filename,
        datatype_out=gdal.GDT_Float32,
        nodata_out=0,
        pixel_size_out=min_cell_size,
        bounding_box_mode='intersection',
        vectorize_op=False,
        datasets_are_pre_aligned=False)

Returns:
            ``numpy.ndarray``, in 2 dimensions.  This will contain the ``m``
            parameter from the fitted line.
        """
        stacked_array = numpy.dstack(blocks)
        new_shape = (stacked_array.shape[0]*stacked_array.shape[1],
                     len(timesteps))
        reshaped = numpy.swapaxes(numpy.reshape(stacked_array, new_shape), 0, 1)
        regression = numpy.polyfit(timesteps,
                                   reshaped, deg=deg, w=weights)[0]
        out_block = regression.reshape(blocks[0].shape)
        # Mask out any pixel stacks where there's a nodata value in the stack.
        # Out block is multiplied by 365.25 to convert m/day to m/year trend.
        return numpy.where(numpy.min(stacked_array, axis=2) == 0, 0, out_block*365.25)

    raster_cell_sizes = [pygeoprocessing.get_cell_size_from_uri(r)
                         for r in rasters]
    min_cell_size = min(raster_cell_sizes)
    if not len(set(raster_cell_sizes)) == 1:
        warnings.warn(('Cell sizes of input rasters do not all match. '
                       'Using min pixelsize of %s. Mismatched values: %s') % (
                      min_cell_size, set(raster_cell_sizes)))

    pygeoprocessing.vectorize_datasets(
        dataset_uri_list=rasters,
        dataset_pixel_op=_regression,
        dataset_out_uri=out_filename,
        datatype_out=gdal.GDT_Float32,
        nodata_out=0,
        pixel_size_out=min_cell_size,
        bounding_box_mode='intersection',
        vectorize_op=False,

Parameters:
            stack_trend (numpy.ndarray): Array of values from the stack trend
                raster.
            pgp_trend (numpy.ndarray): Array of values from the pygeoprocessing
                trend raster.

        Returns:
            ``numpy.ndarray`` of the difference between ``stack_trend`` and
            ``pgp_trend``"""
        valid_mask = ((stack_trend != stack_nodata) & (pgp_trend != pgp_nodata))
        out_array = numpy.empty_like(stack_trend)
        out_array[:] = -9999
        out_array[valid_mask] = stack_trend[valid_mask] - pgp_trend[valid_mask]
        return out_array

    pygeoprocessing.vectorize_datasets(
        dataset_uri_list=[stack_trend_file, pgp_trend_file],
        dataset_pixel_op=_diff,
        dataset_out_uri=diff_file,
        datatype_out=gdal.GDT_Float32,
        nodata_out=-9999,
        pixel_size_out=32.,
        bounding_box_mode='intersection',
        vectorize_op=False,
        datasets_are_pre_aligned=False)

diff_file = stack_trend_file - pgp_trend_file

    Parameters:
        stack_trend_file (string): The path to the trend raster output of
            ``make_stack.py`` (usually named ``stack_trend.tif``).  This
            file must exist on disk.
        pgp_trend_file (string): The path to the trend raster output from
            ``make_regression()``, also in this module.  This file must
            exist on disk.
        diff_file (string): The path to where the difference raster should be
            saved.

    Returns:
        ``None``"""
    stack_nodata = pygeoprocessing.get_nodata_from_uri(stack_trend_file)
    pgp_nodata = pygeoprocessing.get_nodata_from_uri(pgp_trend_file)

    def _diff(stack_trend, pgp_trend):
        """Calculate a diff between two matrices, ignoring nodata.

        Parameters:
            stack_trend (numpy.ndarray): Array of values from the stack trend
                raster.
            pgp_trend (numpy.ndarray): Array of values from the pygeoprocessing
                trend raster.

        Returns:
            ``numpy.ndarray`` of the difference between ``stack_trend`` and
            ``pgp_trend``"""
        valid_mask = ((stack_trend != stack_nodata) & (pgp_trend != pgp_nodata))
        out_array = numpy.empty_like(stack_trend)

diff_file = stack_trend_file - pgp_trend_file

    Parameters:
        stack_trend_file (string): The path to the trend raster output of
            ``make_stack.py`` (usually named ``stack_trend.tif``).  This
            file must exist on disk.
        pgp_trend_file (string): The path to the trend raster output from
            ``make_regression()``, also in this module.  This file must
            exist on disk.
        diff_file (string): The path to where the difference raster should be
            saved.

    Returns:
        ``None``"""
    stack_nodata = pygeoprocessing.get_nodata_from_uri(stack_trend_file)
    pgp_nodata = pygeoprocessing.get_nodata_from_uri(pgp_trend_file)

    def _diff(stack_trend, pgp_trend):
        """Calculate a diff between two matrices, ignoring nodata.

        Parameters:
            stack_trend (numpy.ndarray): Array of values from the stack trend
                raster.
            pgp_trend (numpy.ndarray): Array of values from the pygeoprocessing
                trend raster.

        Returns:
            ``numpy.ndarray`` of the difference between ``stack_trend`` and
            ``pgp_trend``"""
        valid_mask = ((stack_trend != stack_nodata) & (pgp_trend != pgp_nodata))
        out_array = numpy.empty_like(stack_trend)
        out_array[:] = -9999