How to use the mapchete.io.raster.prepare_array function in mapchete

"""Convert masked array data into a proper array."""
    # input is ma.masked_array
    data = ma.empty((1, 1, 1))
    # output ndarray
    output = prepare_array(data, masked=False)
    assert isinstance(output, np.ndarray)
    assert not isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1)
    # output masked array
    output = prepare_array(data)
    assert isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1)
    # input is ma.masked_array with full mask
    data = ma.masked_array(data=np.ones((1, 1, 1)), mask=np.ones((1, 1, 1)))
    # output ndarray
    output = prepare_array(data, masked=False)
    assert isinstance(output, np.ndarray)
    assert not isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1)
    # output masked array
    output = prepare_array(data)
    assert isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1)

def test_prepare_array_errors():
    """Convert ndarray data into a proper array."""
    # input is iterable
    data = [None]
    try:
        prepare_array(data)
        raise Exception()
    except ValueError:
        pass
    # input is not array
    data = 5
    try:
        prepare_array(data)
        raise Exception()
    except ValueError:
        pass

def test_prepare_array_errors():
    """Convert ndarray data into a proper array."""
    # input is iterable
    data = [None]
    try:
        prepare_array(data)
        raise Exception()
    except ValueError:
        pass
    # input is not array
    data = 5
    try:
        prepare_array(data)
        raise Exception()
    except ValueError:
        pass

assert isinstance(output, np.ndarray)
    assert not isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1)
    # output masked array
    output = prepare_array(data)
    assert isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1)
    # input is ma.masked_array with full mask
    data = ma.masked_array(data=np.ones((1, 1, 1)), mask=np.ones((1, 1, 1)))
    # output ndarray
    output = prepare_array(data, masked=False)
    assert isinstance(output, np.ndarray)
    assert not isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1)
    # output masked array
    output = prepare_array(data)
    assert isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1)

def test_prepare_array_maskedarrays():
    """Convert masked array data into a proper array."""
    # input is ma.masked_array
    data = ma.empty((1, 1, 1))
    # output ndarray
    output = prepare_array(data, masked=False)
    assert isinstance(output, np.ndarray)
    assert not isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1)
    # output masked array
    output = prepare_array(data)
    assert isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1)
    # input is ma.masked_array with full mask
    data = ma.masked_array(data=np.ones((1, 1, 1)), mask=np.ones((1, 1, 1)))
    # output ndarray
    output = prepare_array(data, masked=False)
    assert isinstance(output, np.ndarray)
    assert not isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1)
    # output masked array
    output = prepare_array(data)
    assert isinstance(output, ma.masked_array)
    assert output.shape == (1, 1, 1)

def _extract(self, in_tile=None, in_data=None, out_tile=None):
        """Extract data from tile."""
        if self.config.output.METADATA["data_type"] == "raster":
            return raster.extract_from_array(
                in_raster=raster.prepare_array(
                    in_data, nodata=self.config.output.nodata,
                    dtype=self.config.output.output_params["dtype"]
                ),
                in_affine=in_tile.affine,
                out_tile=out_tile
            )
        elif self.config.output.METADATA["data_type"] == "vector":
            return [
                feature
                for feature in in_data
                if shape(feature["geometry"]).intersects(out_tile.bbox)
            ]

def _prepare_array(self, data):
        data = prepare_array(-(data - 255), dtype="uint8", masked=False, nodata=0)[0]
        zeros = np.zeros(data.shape)
        if self.old_band_num:
            data = np.stack([zeros, zeros, zeros, data])
        else:
            data = np.stack([zeros, data])
        return prepare_array(data, dtype="uint8", masked=True, nodata=255)

def extract_subset(self, input_data_tiles=None, out_tile=None):
        """
        Extract subset from multiple tiles.
        input_data_tiles : list of (``Tile``, process data) tuples
        out_tile : ``Tile``
        Returns
        -------
        NumPy array or list of features.
        """
        if self.METADATA["data_type"] == "raster":
            mosaic = create_mosaic(input_data_tiles)
            return extract_from_array(
                in_raster=prepare_array(
                    mosaic.data,
                    nodata=self.output_params["nodata"],
                    dtype=self.output_params["dtype"]
                ),
                in_affine=mosaic.affine,
                out_tile=out_tile
            )
        elif self.METADATA["data_type"] == "vector":
            return [
                feature for feature in list(
                    chain.from_iterable([features for _, features in input_data_tiles])
                )
                if shape(feature["geometry"]).intersects(out_tile.bbox)
            ]

def _prepare_array(self, data):
        data = prepare_array(-(data - 255), dtype="uint8", masked=False, nodata=0)[0]
        zeros = np.zeros(data.shape)
        if self.old_band_num:
            data = np.stack([zeros, zeros, zeros, data])
        else:
            data = np.stack([zeros, data])
        return prepare_array(data, dtype="uint8", masked=True, nodata=255)

def _prepare_array_for_png(self, data):
        data = prepare_array(data, dtype=np.uint8)
        # Create 3D NumPy array with alpha channel.
        if len(data) == 1:
            rgba = np.stack((
                data[0], data[0], data[0],
                np.where(
                    data[0].data == self.output_params["nodata"], 0, 255)
                .astype("uint8")
            ))
        elif len(data) == 2:
            rgba = np.stack((data[0], data[0], data[0], data[1]))
        elif len(data) == 3:
            rgba = np.stack((
                data[0], data[1], data[2], np.where(
                    data[0].data == self.output_params["nodata"], 0, 255
                ).astype("uint8", copy=False)
            ))