Reclassify raster

reclassify_with_defined_intervals(raster, interval_size, bands=None)

Classify raster with defined intervals.

If bands are not given, all bands are used for classification.

Parameters:

Name	Type	Description	Default
raster	DatasetReader	Raster to be classified.	required
interval_size	int	The number of units in each interval.	required
bands	Optional[Sequence[int]]	Selected bands from multiband raster. Indexing begins from one. Defaults to None.	None

Returns:

Type	Description
ndarray	Raster data classified with defined intervals.
dict	Raster metadata.

Raises:

Type	Description
InvalidRasterBandException	All selected bands are not contained in the input raster.
InvalidParameterValueException	Interval size is less than 1.

Source code in eis_toolkit/raster_processing/reclassify.py

@beartype
def reclassify_with_defined_intervals(  # type: ignore[no-any-unimported]
    raster: rasterio.io.DatasetReader,
    interval_size: int,
    bands: Optional[Sequence[int]] = None,
) -> Tuple[np.ndarray, dict]:
    """Classify raster with defined intervals.

    If bands are not given, all bands are used for classification.

    Args:
        raster: Raster to be classified.
        interval_size: The number of units in each interval.
        bands: Selected bands from multiband raster. Indexing begins from one. Defaults to None.

    Returns:
        Raster data classified with defined intervals.
        Raster metadata.

    Raises:
        InvalidRasterBandException: All selected bands are not contained in the input raster.
        InvalidParameterValueException: Interval size is less than 1.
    """

    if bands is None or len(bands) == 0:
        bands = range(1, raster.count + 1)
    else:
        if not check_raster_bands(raster, bands):
            raise InvalidRasterBandException(f"Input raster does not contain all selected bands: {bands}.")

    if interval_size < 1:
        raise InvalidParameterValueException("Interval size must be 1 or more.")

    out_image = np.empty((len(bands), raster.height, raster.width))
    out_meta = raster.meta.copy()

    for i, band in enumerate(bands):
        band_data = raster.read(band)
        out_image[i] = _reclassify_with_defined_intervals(band_data, interval_size)

    return out_image, out_meta

reclassify_with_equal_intervals(raster, number_of_intervals, bands=None)

Classify raster with equal intervals.

If bands are not given, all bands are used for classification.

Parameters:

Name	Type	Description	Default
raster	DatasetReader	Raster to be classified.	required
number_of_intervals	int	The number of intervals.	required
bands	Optional[Sequence[int]]	Selected bands from multiband raster. Indexing begins from one. Defaults to None.	None

Returns:

Type	Description
ndarray	Raster data classified with equal intervals.
dict	Raster metadata.

Raises:

Type	Description
InvalidRasterBandException	All selected bands are not contained in the input raster.
InvalidParameterValueException	Number of intervals is less than 2.

Source code in eis_toolkit/raster_processing/reclassify.py

@beartype
def reclassify_with_equal_intervals(  # type: ignore[no-any-unimported]
    raster: rasterio.io.DatasetReader,
    number_of_intervals: int,
    bands: Optional[Sequence[int]] = None,
) -> Tuple[np.ndarray, dict]:
    """Classify raster with equal intervals.

    If bands are not given, all bands are used for classification.

    Args:
        raster: Raster to be classified.
        number_of_intervals: The number of intervals.
        bands: Selected bands from multiband raster. Indexing begins from one. Defaults to None.

    Returns:
        Raster data classified with equal intervals.
        Raster metadata.

    Raises:
        InvalidRasterBandException: All selected bands are not contained in the input raster.
        InvalidParameterValueException: Number of intervals is less than 2.
    """

    if bands is None or len(bands) == 0:
        bands = range(1, raster.count + 1)
    else:
        if not check_raster_bands(raster, bands):
            raise InvalidRasterBandException(f"Input raster does not contain all selected bands: {bands}.")

    if number_of_intervals < 2:
        raise InvalidParameterValueException("Number of intervals must be 2 or more.")

    out_image = np.empty((len(bands), raster.height, raster.width))
    out_meta = raster.meta.copy()

    for i, band in enumerate(bands):
        band_data = raster.read(band)
        out_image[i] = _reclassify_with_equal_intervals(band_data, number_of_intervals)

    return out_image, out_meta

reclassify_with_geometrical_intervals(raster, number_of_classes, bands=None)

Classify raster with geometrical intervals.

If bands are not given, all bands are used for classification.

Parameters:

Name	Type	Description	Default
raster	DatasetReader	Raster to be classified.	required
number_of_classes	int	The number of classes. The true number of classes is at most double the amount, depending how symmetrical the input data is.	required
bands	Optional[Sequence[int]]	Selected bands from multiband raster. Indexing begins from one. Defaults to None.	None

Returns:

Type	Description
ndarray	Raster data classified with geometrical intervals.
dict	Raster metadata.

Raises:

Type	Description
InvalidRasterBandException	All selected bands are not contained in the input raster.
InvalidParameterValueException	Number of classes is less than 2.

Source code in eis_toolkit/raster_processing/reclassify.py

@beartype
def reclassify_with_geometrical_intervals(  # type: ignore[no-any-unimported]
    raster: rasterio.io.DatasetReader, number_of_classes: int, bands: Optional[Sequence[int]] = None
) -> Tuple[np.ndarray, dict]:
    """Classify raster with geometrical intervals.

    If bands are not given, all bands are used for classification.

    Args:
        raster: Raster to be classified.
        number_of_classes: The number of classes. The true number of classes is at most double the amount,
            depending how symmetrical the input data is.
        bands: Selected bands from multiband raster. Indexing begins from one. Defaults to None.

    Returns:
        Raster data classified with geometrical intervals.
        Raster metadata.

    Raises:
        InvalidRasterBandException: All selected bands are not contained in the input raster.
        InvalidParameterValueException: Number of classes is less than 2.
    """

    if bands is None or len(bands) == 0:
        bands = range(1, raster.count + 1)
    else:
        if not check_raster_bands(raster, bands):
            raise InvalidRasterBandException(f"Input raster does not contain all selected bands: {bands}.")

    if number_of_classes < 2:
        raise InvalidParameterValueException("Number of classes must be 2 or more.")

    out_image = np.empty((len(bands), raster.height, raster.width))
    out_meta = raster.meta.copy()
    nodata_value = raster.nodata

    for i, band in enumerate(bands):
        band_data = raster.read(band)
        out_image[i] = _reclassify_with_geometrical_intervals(band_data, number_of_classes, nodata_value)

    return out_image, out_meta

reclassify_with_manual_breaks(raster, breaks, bands=None)

Classify raster with manual breaks.

If bands are not given, all bands are used for classification.

Parameters:

Name	Type	Description	Default
raster	DatasetReader	Raster to be classified.	required
breaks	Sequence[int]	List of break values for the classification.	required
bands	Optional[Sequence[int]]	Selected bands from multiband raster. Indexing begins from one. Defaults to None.	None

Returns:

Type	Description
ndarray	Raster data classified with manual breaks.
dict	Raster metadata.

Raises:

Type	Description
InvalidRasterBandException	All selected bands are not contained in the input raster.

Source code in eis_toolkit/raster_processing/reclassify.py

@beartype
def reclassify_with_manual_breaks(  # type: ignore[no-any-unimported]
    raster: rasterio.io.DatasetReader,
    breaks: Sequence[int],
    bands: Optional[Sequence[int]] = None,
) -> Tuple[np.ndarray, dict]:
    """Classify raster with manual breaks.

    If bands are not given, all bands are used for classification.

    Args:
        raster: Raster to be classified.
        breaks: List of break values for the classification.
        bands: Selected bands from multiband raster. Indexing begins from one. Defaults to None.

    Returns:
        Raster data classified with manual breaks.
        Raster metadata.

    Raises:
        InvalidRasterBandException: All selected bands are not contained in the input raster.
    """
    # Add check for input breaks at some point?

    if bands is None or len(bands) == 0:
        bands = range(1, raster.count + 1)
    else:
        if not check_raster_bands(raster, bands):
            raise InvalidRasterBandException(f"Input raster does not contain all selected bands: {bands}.")

    out_image = np.empty((len(bands), raster.height, raster.width))
    out_meta = raster.meta.copy()

    for i, band in enumerate(bands):
        band_data = raster.read(band)
        out_image[i] = _reclassify_with_manual_breaks(band_data, breaks)

    return out_image, out_meta

reclassify_with_natural_breaks(raster, number_of_classes, bands=None)

Classify raster with natural breaks (Jenks Caspall).

If bands are not given, all bands are used for classification.

Parameters:

Name	Type	Description	Default
raster	DatasetReader	Raster to be classified.	required
number_of_classes	int	The number of classes.	required
bands	Optional[Sequence[int]]	Selected bands from multiband raster. Indexing begins from one. Defaults to None.	None

Returns:

Type	Description
ndarray	Raster data classified with natural breaks (Jenks Caspall).
dict	Raster metadata.

Raises:

Type	Description
InvalidRasterBandException	All selected bands are not contained in the input raster.
InvalidParameterValueException	Number of classes is less than 2.

Source code in eis_toolkit/raster_processing/reclassify.py

@beartype
def reclassify_with_natural_breaks(  # type: ignore[no-any-unimported]
    raster: rasterio.io.DatasetReader,
    number_of_classes: int,
    bands: Optional[Sequence[int]] = None,
) -> Tuple[np.ndarray, dict]:
    """Classify raster with natural breaks (Jenks Caspall).

    If bands are not given, all bands are used for classification.

    Args:
        raster: Raster to be classified.
        number_of_classes: The number of classes.
        bands: Selected bands from multiband raster. Indexing begins from one. Defaults to None.

    Returns:
        Raster data classified with natural breaks (Jenks Caspall).
        Raster metadata.

    Raises:
        InvalidRasterBandException: All selected bands are not contained in the input raster.
        InvalidParameterValueException: Number of classes is less than 2.
    """

    if bands is None or len(bands) == 0:
        bands = range(1, raster.count + 1)
    else:
        if not check_raster_bands(raster, bands):
            raise InvalidRasterBandException(f"Input raster does not contain all selected bands: {bands}.")

    if number_of_classes < 2:
        raise InvalidParameterValueException("Number of classes must be 2 or more.")

    out_image = np.empty((len(bands), raster.height, raster.width))
    out_meta = raster.meta.copy()

    for i, band in enumerate(bands):
        band_data = raster.read(band)
        out_image[i] = _reclassify_with_natural_breaks(band_data, number_of_classes)

    return out_image, out_meta

reclassify_with_quantiles(raster, number_of_quantiles, bands=None)

Classify raster with quantiles.

If bands are not given, all bands are used for classification.

Parameters:

Name	Type	Description	Default
raster	DatasetReader	Raster to be classified.	required
number_of_quantiles	int	The number of quantiles.	required
bands	Optional[Sequence[int]]	Selected bands from multiband raster. Indexing begins from one. Defaults to None.	None

Returns:

Type	Description
ndarray	Raster data classified with quantiles.
dict	Raster metadata.

Raises:

Type	Description
InvalidRasterBandException	All selected bands are not contained in the input raster.
InvalidParameterValueException	Number of quantiles is less than 2.

Source code in eis_toolkit/raster_processing/reclassify.py

@beartype
def reclassify_with_quantiles(  # type: ignore[no-any-unimported]
    raster: rasterio.io.DatasetReader,
    number_of_quantiles: int,
    bands: Optional[Sequence[int]] = None,
) -> Tuple[np.ndarray, dict]:
    """Classify raster with quantiles.

    If bands are not given, all bands are used for classification.

    Args:
        raster: Raster to be classified.
        number_of_quantiles: The number of quantiles.
        bands: Selected bands from multiband raster. Indexing begins from one. Defaults to None.

    Returns:
        Raster data classified with quantiles.
        Raster metadata.

    Raises:
        InvalidRasterBandException: All selected bands are not contained in the input raster.
        InvalidParameterValueException: Number of quantiles is less than 2.
    """

    if bands is None or len(bands) == 0:
        bands = range(1, raster.count + 1)
    else:
        if not check_raster_bands(raster, bands):
            raise InvalidRasterBandException(f"Input raster does not contain all selected bands: {bands}.")

    if number_of_quantiles < 2:
        raise InvalidParameterValueException("Number of quantiles must be 2 or more.")

    out_image = np.empty((len(bands), raster.height, raster.width))
    out_meta = raster.meta.copy()

    for i, band in enumerate(bands):
        band_data = raster.read(band)
        out_image[i] = _reclassify_with_quantiles(band_data, number_of_quantiles)

    return out_image, out_meta

reclassify_with_standard_deviation(raster, number_of_intervals, bands=None)

Classify raster with standard deviation.

If bands are not given, all bands are used for classification.

Parameters:

Name	Type	Description	Default
raster	DatasetReader	Raster to be classified.	required
number_of_intervals	int	The number of intervals.	required
bands	Optional[Sequence[int]]	Selected bands from multiband raster. Indexing begins from one. Defaults to None.	None

Returns:

Type	Description
ndarray	Raster data classified with standard deviation.
dict	Raster metadata.

Raises:

Type	Description
InvalidRasterBandException	All selected bands are not contained in the input raster.
InvalidParameterValueException	Number of intervals is less than 2.

Source code in eis_toolkit/raster_processing/reclassify.py

@beartype
def reclassify_with_standard_deviation(  # type: ignore[no-any-unimported]
    raster: rasterio.io.DatasetReader,
    number_of_intervals: int,
    bands: Optional[Sequence[int]] = None,
) -> Tuple[np.ndarray, dict]:
    """Classify raster with standard deviation.

    If bands are not given, all bands are used for classification.

    Args:
        raster: Raster to be classified.
        number_of_intervals: The number of intervals.
        bands: Selected bands from multiband raster. Indexing begins from one. Defaults to None.

    Returns:
        Raster data classified with standard deviation.
        Raster metadata.

    Raises:
        InvalidRasterBandException: All selected bands are not contained in the input raster.
        InvalidParameterValueException: Number of intervals is less than 2.
    """

    if bands is None or len(bands) == 0:
        bands = range(1, raster.count + 1)
    else:
        if not check_raster_bands(raster, bands):
            raise InvalidRasterBandException(f"Input raster does not contain all selected bands: {bands}.")

    if number_of_intervals < 2:
        raise InvalidParameterValueException("Number of intervals must be 2 or more.")

    out_image = np.empty((len(bands), raster.height, raster.width))
    out_meta = raster.meta.copy()

    for i, band in enumerate(bands):
        band_data = raster.read(band)
        out_image[i] = _reclassify_with_standard_deviation(band_data, number_of_intervals)

    return out_image, out_meta