# from __future__ import annotations
import json
import io
import random, pickle
from collections import namedtuple
from os import remove
from os.path import basename, join
from pathlib import Path
from typing import Iterable
from warnings import warn

try:
    import scipy.stats
except Exception as error:
    warn(str(error))

from qgis.PyQt.QtGui import QColor

from _classic.hubdc.progressbar import ProgressBar
from _classic.hubdc.core import *
import _classic.hubdc.applier
from _classic.hubdc.applier import ApplierOutputRaster
from _classic.hubflow.report import *
from _classic.hubflow.errors import *
import _classic.hubflow.signals


class ApplierOptions(dict):

    def __init__(self, grid=None, progressBar=None, progressCallback=None, emitFileCreated=None):
        dict.__init__(self, tuple(
            (k, v) for k, v in locals().items() if v is not None and not k.startswith('_') and k != 'self'))


class Applier(_classic.hubdc.applier.Applier):
    def __init__(self, defaultGrid=None, **kwargs):
        controls = kwargs.get('controls', ApplierControls())
        _classic.hubdc.applier.Applier.__init__(self, controls=controls)

        grid = kwargs.get('grid', defaultGrid)
        if isinstance(grid, Raster):
            grid = grid.grid()

        self.controls.setProgressBar(kwargs.get('progressBar', None))
        self.controls.setProgressCallback(kwargs.get('progressCallback', None))

        self.controls.setGrid(grid)
        self.controls.setEmitFileCreated(kwargs.get('emitFileCreated', True))

        self.kwargs = kwargs

    def apply(self, operatorType=None, description=None, *ufuncArgs, **ufuncKwargs):
        results = _classic.hubdc.applier.Applier.apply(self, operatorType=operatorType, description=description,
            overwrite=self.kwargs.get('overwrite', True), *ufuncArgs, **ufuncKwargs)
        for raster in self.outputRaster.flatRasters():
            if self.controls.emitFileCreated():
                _classic.hubflow.signals.sigFileCreated.emit(raster.filename())
        return results

    def setOutputRaster(self, name, filename):
        driver = self.kwargs.get(name + 'Driver', None)
        creationOptions = self.kwargs.get(name + 'Options', None)
        raster = _classic.hubdc.applier.ApplierOutputRaster(filename=filename, driver=driver, creationOptions=creationOptions)
        self.outputRaster.setRaster(key=name, value=raster)

    def setFlowRaster(self, name, raster):
        if isinstance(raster, Raster):
            self.inputRaster.setRaster(key=name,
                value=_classic.hubdc.applier.ApplierInputRaster(filename=raster.filename()))
        # elif isinstance(raster, RasterStack):
        #    rasterStack = raster
        #    group = _classic.hubdc.applier.ApplierInputRasterGroup()
        #    self.inputRaster.setGroup(key=name, value=group)
        #    for i, raster in enumerate(rasterStack.rasters()):
        #        group.setRaster(key=str(i), value=_classic.hubdc.applier.ApplierInputRaster(filename=raster.filename()))
        else:
            raise errors.TypeError(raster)

    def setFlowMask(self, name, mask):
        if mask is None or mask.filename() is None:
            pass
        elif isinstance(mask, Mask):
            self.setFlowRaster(name=name, raster=mask)
        elif isinstance(mask, Raster):
            self.setFlowRaster(name=name, raster=mask.asMask())
        elif isinstance(mask, (Vector, VectorClassification)):
            self.setFlowVector(name=name, vector=mask)
        else:
            raise errors.TypeError(mask)

    def setFlowMasks(self, masks):
        name = 'mask'
        if masks is None:
            return
        if isinstance(masks, FlowObject):
            masks = [masks]

        for i, mask in enumerate(masks):
            self.setFlowMask(name + str(i), mask=mask)

    def setFlowClassification(self, name, classification):
        if classification is None or classification.filename() is None:
            pass
        elif isinstance(classification, (Classification, Fraction)):
            self.setFlowRaster(name=name, raster=classification)
        elif isinstance(classification, VectorClassification):
            self.setFlowVector(name=name, vector=classification)
        else:
            raise errors.TypeError(classification)

    def setFlowRegression(self, name, regression):
        if regression is None or regression.filename() is None:
            pass
        elif isinstance(regression, Regression):
            self.setFlowRaster(name=name, raster=regression)
        elif isinstance(regression, VectorRegression):
            self.setFlowVector(name=name, vector=regression)
        else:
            raise errors.TypeError(regression)

    def setFlowFraction(self, name, fraction):
        if fraction is None or fraction.filename() is None:
            pass
        elif isinstance(fraction, Fraction):
            self.setFlowRaster(name=name, raster=fraction)
        elif isinstance(fraction, (Classification, VectorClassification)):
            self.setFlowClassification(name=name, classification=fraction)
        else:
            raise errors.TypeError(fraction)

    def setFlowVector(self, name, vector):
        if isinstance(vector, (Vector, VectorClassification)):
            self.inputVector.setVector(key=name, value=_classic.hubdc.applier.ApplierInputVector(filename=vector.filename(),
                layerNameOrIndex=vector.layer()))
        else:
            raise errors.TypeError(vector)

    def setFlowInput(self, name, input):
        if isinstance(input, Raster):
            self.setFlowRaster(name=name, raster=input)
        elif isinstance(input, Vector):
            self.setFlowVector(name=name, vector=input)
        else:
            raise errors.TypeError(input)


class ApplierOperator(_classic.hubdc.applier.ApplierOperator):
    def flowRasterArray(self, name, raster, indices=None, overlap=0):

        if indices is not None:
            assert isinstance(indices, list)

        if isinstance(raster, Regression):
            array = self.flowRegressionArray(name=name, regression=raster, overlap=overlap)
            if indices is not None:
                array = array[indices]
        elif isinstance(raster, Classification):
            array = self.flowClassificationArray(name=name, classification=raster, overlap=overlap)
        elif isinstance(input, Fraction):
            array = self.flowFractionArray(name=name, fraction=raster, overlap=overlap)
            if indices is not None:
                array = array[indices]
        elif isinstance(raster, Mask):
            array = self.flowMaskArray(name=name, mask=raster, overlap=overlap)
            if indices is not None:
                array = array[indices]
        elif isinstance(raster, Raster):
            raster = self.inputRaster.raster(key=name)
            array = raster.array(indices=indices, overlap=overlap)
        else:
            raise errors.TypeError(raster)

        if indices is not None:
            assert len(array) == len(indices)

        return array

    def flowVectorArray(self, name, vector, overlap=0):
        if isinstance(input, VectorClassification):
            array = self.flowClassificationArray(name=name, classification=vector, overlap=overlap)
        elif isinstance(vector, Vector):
            array = self.inputVector.vector(key=name).array(initValue=vector.initValue(), burnValue=vector.burnValue(),
                burnAttribute=vector.burnAttribute(),
                allTouched=vector.allTouched(),
                filterSQL=vector.filterSQL(),
                overlap=overlap, dtype=vector.dtype())
        else:
            raise errors.TypeError(vector)
        return array

    def flowMaskArray(self, name, mask, aggregateFunction=None, overlap=0):

        if aggregateFunction is None:
            aggregateFunction = lambda a: np.all(a, axis=0, keepdims=True)

        if mask is None or mask.filename() is None:
            array = self.full(value=True, bands=1, dtype=bool, overlap=overlap)
        elif isinstance(mask, (Mask, Raster)):

            if not isinstance(mask, Mask):
                noDataValues = mask.noDataValues(default=np.nan)
                mask = mask.asMask(noDataValues=noDataValues)

            # get mask for each band
            maskArrays = list()
            if mask.indices() is None:
                indices = range(mask.dataset().zsize())
            else:
                indices = mask.indices()

            for index in indices:
                fractionArray = 1. - self.inputRaster.raster(key=name).fractionArray(categories=mask.noDataValues(),
                    overlap=overlap,
                    index=index)

                maskArray = fractionArray > min(0.9999, mask.minOverallCoverage())
                if mask.invert():
                    np.logical_not(maskArray, out=maskArray)

                maskArrays.append(maskArray[0])

            # aggregate to single band mask
            array = aggregateFunction(maskArrays)

        elif isinstance(mask, (Classification, VectorClassification, Fraction)):
            array = self.flowClassificationArray(name=name, classification=mask, overlap=overlap) != 0

        elif isinstance(mask, Vector):
            array = self.inputVector.vector(key=name).array(overlap=overlap, allTouched=mask.allTouched(),
                filterSQL=mask.filterSQL(), dtype=np.uint8) != 0
            if isinstance(mask, VectorMask) and mask.invert():
                np.logical_not(array, out=array)

        else:
            raise errors.TypeError(mask)

        return array

    def flowMasksArray(self, masks, aggregateFunction=None, overlap=0):
        name = 'mask'
        array = self.full(value=True, bands=1, dtype=bool, overlap=overlap)
        if masks is None:
            return array

        if isinstance(masks, FlowObject):
            masks = [masks]

        for i, mask in enumerate(masks):
            array *= self.flowMaskArray(name=name + str(i), mask=mask, aggregateFunction=aggregateFunction,
                overlap=overlap)
        return array

    def flowClassificationArray(self, name, classification, overlap=0):
        if classification is None or classification.filename() is None:
            return np.array([])
        elif isinstance(classification, Classification):
            array = self.inputRaster.raster(key=name).array(overlap=overlap)
        elif isinstance(classification, (VectorClassification, Fraction)):
            fractionArray = self.flowFractionArray(name=name, fraction=classification, overlap=overlap)
            invalid = np.all(fractionArray == -1, axis=0, keepdims=True)
            array = np.uint8(np.argmax(fractionArray, axis=0)[None] + 1)
            array[invalid] = 0
        else:
            raise errors.TypeError(classification)

        return array

    def flowRegressionArray(self, name, regression, overlap=0):
        if regression is None or regression.filename() is None:
            array = np.array([])
        elif isinstance(regression, Regression):
            raster = self.inputRaster.raster(key=name)
            array = raster.array(overlap=overlap, resampleAlg=gdal.GRA_Average,
                noDataValue=regression.noDataValue())

            invalid = self.full(value=np.False_, dtype=bool)
            for i, noDataValue in enumerate(regression.noDataValues()):
                overallCoverageArray = 1. - raster.fractionArray(categories=[noDataValue], overlap=overlap, index=0)
                invalid += overallCoverageArray <= min(0.9999, regression.minOverallCoverage())

            for i, noDataValue in enumerate(regression.noDataValues()):
                array[i, invalid[0]] = noDataValue

        elif isinstance(regression, VectorRegression):

            # check if current block contains data
            spatialFilter = self.subgrid().extent().geometry()
            values = regression.uniqueValues(attribute=regression.regressionAttribute(),
                spatialFilter=spatialFilter)

            if len(values) > 0:
                # rasterize with all touched (may contain unwanted pixels at the geometry edges)
                array = self.inputVector.vector(key=name).array(initValue=regression.noDataValue(),
                    burnAttribute=regression.regressionAttribute(),
                    allTouched=True,
                    dtype=regression.dtype())

                # calculate mask fractions
                coverageFractionArray = self.inputVector.vector(key=name).coverageFractionArray(
                    oversampling=regression.oversampling(),
                    overlap=overlap)

                # mask out unwanted pixels
                invalid = self.maskFromFractionArray(fractionArray=coverageFractionArray,
                    minOverallCoverage=regression.minOverallCoverage(),
                    minDominantCoverage=0.,
                    invert=True)
                array[:, invalid] = regression.noDataValue()

            else:
                array = self.full(value=regression.noDataValue(), bands=1, dtype=regression.dtype(), overlap=overlap)

        else:
            raise errors.TypeError(regression)
        return array

    def flowFractionArray(self, name, fraction, overlap=0):
        if fraction is None or fraction.filename() is None:
            array = np.array([])
        elif isinstance(fraction, Fraction):
            array = self.inputRaster.raster(key=name).array(overlap=overlap, resampleAlg=gdal.GRA_Average)
            invalid = self.maskFromFractionArray(fractionArray=array,
                minOverallCoverage=fraction.minOverallCoverage(),
                minDominantCoverage=fraction.minDominantCoverage(),
                invert=True)
            array[:, invalid] = -1
        elif isinstance(fraction, Classification):
            categories = range(1, fraction.classDefinition().classes() + 1)
            array = self.inputRaster.raster(key=name).fractionArray(categories=categories, overlap=overlap)
            invalid = self.maskFromFractionArray(fractionArray=array,
                minOverallCoverage=fraction.minOverallCoverage(),
                minDominantCoverage=fraction.minDominantCoverage(),
                invert=True)
            array[:, invalid] = -1
        elif isinstance(fraction, VectorClassification):

            # get all categories for the current block
            spatialFilter = self.subgrid().extent().geometry()
            categories = fraction.uniqueValues(attribute=fraction.classAttribute(),
                spatialFilter=spatialFilter)
            categories = [v for v in categories if v is not None]

            if len(categories) > 0:
                array = self.full(value=0, bands=fraction.classDefinition().classes(), dtype=np.float32,
                    overlap=overlap)

                fractionArray = self.inputVector.vector(key=name).fractionArray(categories=categories,
                    categoryAttribute=fraction.classAttribute(),
                    oversampling=fraction.oversampling(),
                    overlap=overlap)

                invalid = self.maskFromFractionArray(fractionArray=fractionArray,
                    minOverallCoverage=fraction.minOverallCoverage(),
                    minDominantCoverage=fraction.minDominantCoverage(),
                    invert=True)

                for category, categoryFractionArray in zip(categories, fractionArray):
                    id = int(category)
                    array[id - 1] = categoryFractionArray

                array[:, invalid] = -1

            else:
                array = self.full(value=-1, bands=fraction.classDefinition().classes(), dtype=np.float32,
                    overlap=overlap)

        else:
            raise errors.TypeError(fraction)
        return array

    def flowInputArray(self, name, input, overlap=0):
        if isinstance(input, Vector):
            array = self.flowVectorArray(name=name, vector=input, overlap=overlap)
        elif isinstance(input, Raster):
            array = self.flowRasterArray(name=name, raster=input, overlap=overlap)
        else:
            raise errors.TypeError(input)
        return array

    def flowInputZSize(self, name, input):
        if isinstance(input, Vector):
            shape = 1
        elif isinstance(input, Raster):
            shape = input.dataset().zsize()
        else:
            raise errors.TypeError(input)
        return shape

    def flowInputDType(self, name, input):
        if isinstance(input, Vector):
            dtype = input.dtype()
        elif isinstance(input, Raster):
            dtype = input.dataset().dtype()
        else:
            raise errors.TypeError(input)
        return dtype

    def setFlowMetadataClassDefinition(self, name, classDefinition):
        MetadataEditor.setClassDefinition(rasterDataset=self.outputRaster.raster(key=name),
            classDefinition=classDefinition)

    def setFlowMetadataFractionDefinition(self, name, classDefinition):
        return MetadataEditor.setFractionDefinition(rasterDataset=self.outputRaster.raster(key=name),
            classDefinition=classDefinition)

    def setFlowMetadataRegressionDefinition(self, name, noDataValues, outputNames):
        return MetadataEditor.setRegressionDefinition(rasterDataset=self.outputRaster.raster(key=name),
            noDataValues=noDataValues, outputNames=outputNames)

    def setFlowMetadataBandNames(self, name, bandNames):
        return MetadataEditor.setBandNames(rasterDataset=self.outputRaster.raster(key=name),
            bandNames=bandNames)

    def setFlowMetadataNoDataValues(self, name, noDataValues):
        self.outputRaster.raster(key=name).setNoDataValues(values=noDataValues)

    def setFlowMetadataSensorDefinition(self, name, sensor):
        assert isinstance(sensor, SensorDefinition)
        raster = self.outputRaster.raster(key=name)
        centers = [wd.center() for wd in sensor.wavebandDefinitions()]
        raster.setMetadataItem(key='wavelength', value=centers, domain='ENVI')
        fwhms = [wd.fwhm() for wd in sensor.wavebandDefinitions()]
        if not all([fwhm is None for fwhm in fwhms]):
            raster.setMetadataItem(key='fwhm', value=fwhms, domain='ENVI')
        raster.setMetadataItem(key='wavelength units', value='nanometers', domain='ENVI')

    def maskFromBandArray(self, array, noDataValue=None, noDataValueSource=None, index=None):
        assert array.ndim == 3
        assert len(array) == 1
        if noDataValue is None:
            assert noDataValueSource is not None
            assert index is not None
            noDataValue = self.inputRaster.raster(key=noDataValueSource).noDataValues()[index]

        if noDataValue is not None:
            mask = array != noDataValue
        else:
            mask = np.full_like(array, fill_value=np.True_, dtype=bool)
        return mask

    def maskFromArray(self, array, noDataValues=None, defaultNoDataValue=None, noDataValueSource=None,
            aggregateFunction=None):

        assert array.ndim == 3

        if aggregateFunction is None:
            aggregateFunction = lambda a: np.all(a, axis=0, keepdims=True)

        if noDataValues is None:
            assert noDataValueSource is not None
            raster = self.inputRaster.raster(key=noDataValueSource)
            noDataValues = raster.noDataValues(default=None)

        assert len(array) == len(noDataValues)

        mask = np.full_like(array, fill_value=np.True_, dtype=bool)

        for i, band in enumerate(array):
            if noDataValues[i] is not None:
                mask[i] = band != noDataValues[i]

        mask = aggregateFunction(mask)
        return mask

    def maskFromFractionArray(self, fractionArray, minOverallCoverage, minDominantCoverage, invert=False):
        overallCoverageArray = np.sum(fractionArray, axis=0)
        winnerCoverageArray = np.max(fractionArray, axis=0)
        maskArray = np.logical_and(overallCoverageArray > min(0.9999, minOverallCoverage),
                                   winnerCoverageArray > min(0.9999, minDominantCoverage))
        if invert:
            return np.logical_not(maskArray)
        else:
            return maskArray


class ApplierControls(_classic.hubdc.applier.ApplierControls):

    def setEmitFileCreated(self, bool):
        self._emitFileCreated = bool

    def emitFileCreated(self):
        return self._emitFileCreated


class FlowObject(object):
    '''Base class for all workflow type.'''

    def __repr__(self):
        kwargs = list()
        for key, value in self.__getstate__().items():
            if isinstance(value, str):
                valueRepr = value
            elif isinstance(value, np.ndarray):
                valueRepr = 'array[{}]'.format(', '.join([str(n) for n in value.shape]))
            else:
                valueRepr = repr(value)
            kwarg = '{}={}'.format(key, valueRepr)
            kwargs.append(kwarg)

        return '{}({})'.format(type(self).__name__, ', '.join(kwargs))

    def __getstate__(self):
        return {}

    def __setstate__(self, state):
        self.__init__(**state)

    def pickle(self, filename, progressBar=None, emit=True):
        '''
        Pickles itself into the given file.

        :param filename: path to the output file
        :type filename: str
        :param progressBar: reports pickling action
        :type progressBar: hubdc.progressbar.ProgressBar
        :rtype: FlowObject
        '''
        self._initPickle()
        if not exists(dirname(filename)):
            makedirs(dirname(filename))
        with open(filename, 'wb') as f:
            pickle.dump(obj=self, file=f, protocol=1)

        if emit:
            _classic.hubflow.signals.sigFileCreated.emit(filename)

        if progressBar is not None:
            assert isinstance(progressBar, ProgressBar)
            progressBar.setText('{}.pickle(filename={})'.format(self.__class__.__name__, filename))
            progressBar.setText(repr(self))

        return self

    @classmethod
    def unpickle(cls, filename, raiseError=True):
        '''
        Unpickle FlowObject from the given file.

        :param filename: path to input file.
        :type filename: str
        :param raiseError: If set to ``True`` and unpickling is not successful, an exception is raised.
                           If set to ``False``, ``None`` is returned.
        :type raiseError: bool
        :rtype: Union[FlowObject, None]
        :raises: Union[_classic.hubflow.errors.FlowObjectTypeError, _classic.hubflow.errors.FlowObjectPickleFileError]
        '''

        try:
            with open(filename, 'rb') as f:
                obj = pickle.load(file=f)
        except:
            if raiseError:
                raise FlowObjectPickleFileError('not a valid pickle file: ' + str(filename))
            else:
                return None

        if not isinstance(obj, cls):
            if raiseError:
                raise FlowObjectTypeError('wrong type ({t1}), expected type: {t2}'.format(t1=obj.__class__.__name__,
                    t2=cls.__name__))
            else:
                return None
        assert isinstance(obj, cls)
        return obj

    def _initPickle(self):
        pass


class MapCollection(FlowObject):
    '''Class for managing a collection of :class:`~Map` 's.'''

    def __init__(self, maps):
        '''Create an instance by a given list of maps.'''
        self._maps = maps

    def __getstate__(self):
        return OrderedDict([('maps', self.maps())])

    def _initPickle(self):
        for map in self.maps():
            map._initPickle()

    def maps(self):
        '''Return the list of maps'''
        return self._maps

    def extractAsArray(self, masks, grid=None, onTheFlyResampling=False, **kwargs):
        '''
        Returns a list of arrays, one for each map in the collection.
        Each array holds the extracted profiles for all pixels, where all maps inside ``masks`` evaluate to ``True``.

        :param masks: List of maps that are evaluated as masks.
        :type masks: List[Map]
        :param grid: If set to ``None``, all pixel grids in the collection and in ``masks`` must match.
                     If set to a valid Grid and ``onTheFlyResampling=True``, all maps and masks are resampled.
        :type grid: hubdc.core.Grid
        :param onTheFlyResampling: If set to ``True``, all maps and masks are resampled into the given ``grid``.
        :type onTheFlyResampling: bool
        :param kwargs: passed to :class:`_classic.hubflow.core.Applier`
        :return: list of 2d arrays of size (bands, profiles)
        :rtype: List[numpy.ndarray]


        :example:

        >>> raster = Raster.fromArray(array=[[[1, 2], [3, 4]],[[1, 2], [3, 4]]], filename='/vsimem/raster.bsq')
        >>> raster.array()
        array([[[1, 2],
                [3, 4]],
        <BLANKLINE>
               [[1, 2],
                [3, 4]]])
        >>> mask = Mask.fromArray(array=[[[1, 0], [0, 1]]], filename='/vsimem/mask.bsq')
        >>> mask.array()
        array([[[1, 0],
                [0, 1]]], dtype=uint8)
        >>> mapCollection = MapCollection(maps=[raster])
        >>> mapCollection.extractAsArray(masks=[mask])
        [array([[1, 4],
               [1, 4]])]
        '''

        assert isinstance(masks, list)
        if grid is None:
            grid = self.maps()[0].grid()
        assert isinstance(grid, Grid)
        equalGrids = True
        for map in self.maps() + masks:
            if isinstance(map, Raster):

                if map.dataset().shape()[1:] != grid.shape():
                    equalGrids &= map.grid().equal(other=grid, tol=1e-8)

        if equalGrids:

            def getRasterDataset(map):
                if isinstance(map, Raster):
                    rasterDataset = map.dataset()
                elif isinstance(map, Vector):
                    gdalType = gdal_array.NumericTypeCodeToGDALTypeCode(map.dtype())
                    rasterDataset = map.dataset().rasterize(grid=grid,
                        gdalType=gdalType,
                        initValue=map.initValue(),
                        burnValue=map.burnValue(),
                        burnAttribute=map.burnAttribute(),
                        allTouched=map.allTouched(),
                        filterSQL=map.filterSQL(),
                        noDataValue=map.noDataValue())
                elif map is None:
                    rasterDataset = None
                else:
                    raise errors.TypeError(map)

                return rasterDataset

            # calculate overall mask
            marray = np.full(shape=grid.shape(), fill_value=np.True_)
            for map in masks:
                rasterDataset = getRasterDataset(map=map)
                if rasterDataset is None:
                    continue
                for band in rasterDataset.bands():
                    if isinstance(map, (Mask, Vector, Classification)):
                        noDataValue = band.noDataValue(default=0)
                    else:
                        noDataValue = band.noDataValue()
                    if noDataValue is not None:
                        marray *= band.readAsArray() != noDataValue

            nothingToExtract = not marray.any()

            # extract values for all masked pixels
            arrays = list()
            for map in self.maps():
                if nothingToExtract:
                    zsize = map.dataset().zsize()
                    profiles = np.empty(shape=(zsize, 0), dtype=np.uint8)
                else:
                    rasterDataset = getRasterDataset(map=map)
                    profiles = numpy.array([band.readAsArray()[marray] for band in rasterDataset.bands()])
                arrays.append(profiles)

        else:
            if not onTheFlyResampling is True:
                filenames = set([str(map.filename()) for map in self.maps() + masks if map is not None])
                raise Exception(
                    'Grids do not match and on the fly resampling is turned off.\nFilenames: {}'.format(filenames))
            arrays = extractPixels(inputs=self.maps(), masks=masks, grid=grid, **kwargs)
        return arrays

    def extractAsRaster(self, filenames, masks, grid=None, onTheFlyResampling=False, **kwargs):
        '''
        Returns the result of :meth:`~MapCollection.extractAsArray` as a list of
        :class:`Map` objects.

        :param filenames: list of output paths, one for each map inside the collection
        :type filenames: List[str]
        :rtype: List[Map]


        All other parameters are passed to :meth:`~MapCollection.extractAsArray`.

        :example:

        Same example as in :meth:`~MapCollection.extractAsArray`.

        >>> raster = Raster.fromArray(array=[[[1, 2], [3, 4]],[[1, 2], [3, 4]]], filename='/vsimem/raster.bsq')
        >>> raster.array()
        array([[[1, 2],
                [3, 4]],
        <BLANKLINE>
               [[1, 2],
                [3, 4]]])
        >>> mask = Mask.fromArray(array=[[[1, 0], [0, 1]]], filename='/vsimem/mask.bsq')
        >>> mask.array()
        array([[[1, 0],
                [0, 1]]], dtype=uint8)
        >>> mapCollection = MapCollection(maps=[raster])
        >>> extractedRaster = mapCollection.extractAsRaster(filenames=['/vsimem/rasterExtracted.bsq'], masks=[mask])
        >>> extractedRaster[0].array()
        array([[[1],
                [4]],
        <BLANKLINE>
               [[1],
                [4]]])

        '''
        assert isinstance(filenames, list)
        assert len(filenames) == len(self.maps())
        arrays = self.extractAsArray(grid=grid, masks=masks, onTheFlyResampling=onTheFlyResampling, **kwargs)
        rasters = list()

        for filename, array, map in zip(filenames, arrays, self.maps()):
            # from single line 3d array ...
            array = np.atleast_3d(array)

            # ... to multiple line 3d array (to avoid very long lines)
            from math import ceil
            bands = array.shape[0]
            lines = min(3600, array.shape[1])
            samples = ceil(array.shape[1] / float(lines))
            array2 = np.full(shape=(bands, lines * samples, 1), fill_value=map.noDataValue(default=np.nan),
                dtype=map.dtype())
            array2[:, :array.shape[1]] = array[:]
            array2 = np.reshape(array2, (bands, lines, samples))

            rasterDataset = RasterDataset.fromArray(array=array2,
                filename=filename,
                driver=RasterDriver.fromFilename(filename=filename))

            if isinstance(map, (Classification, VectorClassification)):
                MetadataEditor.setClassDefinition(rasterDataset=rasterDataset,
                    classDefinition=map.classDefinition())
            elif isinstance(map, Fraction):
                MetadataEditor.setFractionDefinition(rasterDataset=rasterDataset,
                    classDefinition=map.classDefinition())
            elif isinstance(map, Regression):
                MetadataEditor.setRegressionDefinition(rasterDataset=rasterDataset,
                    noDataValues=map.noDataValues(),
                    outputNames=map.outputNames())
            elif isinstance(map, Vector):
                pass
            elif isinstance(map, Raster):
                bandCharacteristics = MetadataEditor.bandCharacteristics(rasterDataset=map.dataset())
                MetadataEditor.setBandCharacteristics(rasterDataset=rasterDataset, **bandCharacteristics)
                rasterDataset.setNoDataValues(values=map.noDataValues())
            else:
                raise errors.TypeError(map)

            rasterDataset.flushCache()
            if isinstance(map, Raster):
                raster = type(map).fromRasterDataset(rasterDataset=rasterDataset)
            else:
                raster = Raster.fromRasterDataset(rasterDataset=rasterDataset)
            rasters.append(raster)
        return rasters


class Map(FlowObject):
    '''
    Base class for all spatial workflow types.
    For example :class:`~Raster` and :class:`~Vector`.'''


class Raster(Map):
    '''Class for managing raster maps like :class:`~Mask`, :class:`~Classification`,
    :class:`~Regression` and :class:`~Fraction`.'''

    def __init__(self, filename: str, eAccess=gdal.GA_ReadOnly):
        '''Create instance from the raster located at the given ``filename``.'''
        assert isinstance(filename, (str, Path))
        self._filename = filename
        self._rasterDataset = None
        self._eAccess = eAccess

    def __getstate__(self):
        return OrderedDict([('filename', self.filename())])

    def _initPickle(self):
        self._rasterDataset = None
        return self

    def filename(self):
        '''Return the filename.'''
        return self._filename

    def dataset(self):
        '''Return the :class:`hubdc.core.RasterDataset` object.'''
        if self._rasterDataset is None:
            self._rasterDataset = openRasterDataset(self.filename(), eAccess=self._eAccess)
        assert isinstance(self._rasterDataset, RasterDataset)
        return self._rasterDataset

    @classmethod
    def fromRasterDataset(cls, rasterDataset, **kwargs):
        '''
        Create instance from given ``rasterDataset``.

        :param rasterDataset: existing hubdc.core.RasterDataset
        :type rasterDataset: hubdc.core.RasterDataset
        :param kwargs: passed to class constructor
        :rtype: Raster

        :example:

        >>> rasterDataset = RasterDataset.fromArray(array=[[[1,2,3]]], filename='/vsimem/raster.bsq', driver=EnviBsqDriver())
        >>> rasterDataset # doctest: +ELLIPSIS
        RasterDataset(gdalDataset=<osgeo.gdal.Dataset; proxy of <Swig Object of type 'GDALDatasetShadow *' at 0x...> >)
        >>> Raster.fromRasterDataset(rasterDataset=rasterDataset)
        Raster(filename=/vsimem/raster.bsq)
        '''

        assert isinstance(rasterDataset, RasterDataset)

        raster = cls(rasterDataset.filename(), **kwargs)
        raster._rasterDataset = rasterDataset
        return raster

    @classmethod
    def fromVector(cls, filename, vector, grid, noDataValue=None, **kwargs):
        '''
        Create instance from given ``vector`` by rasterizing it into the given ``grid``.

        :param filename: output path
        :type filename: str
        :param vector: input vector
        :type vector: Vector
        :param grid: output pixel grid
        :type grid: hubdc.core.Grid
        :param noDataValue: output no data value
        :type noDataValue: float
        :param kwargs: passed to :class:`_classic.hubflow.core.Applier`
        :rtype: _classic.hubflow.core.Raster

        :example:

        >>> import tempfile
        >>> vector = Vector.fromPoints(points=[(-1, -1), (1, 1)], filename=join(tempfile.gettempdir(), 'vector.shp'), projection=Projection.wgs84())
        >>> grid = Grid(extent=Extent(xmin=-1.5, xmax=1.5, ymin=-1.5, ymax=1.5), resolution=1, projection=Projection.wgs84())
        >>> raster = Raster.fromVector(filename='/vsimem/raster.bsq', vector=vector, grid=grid)
        >>> print(raster.array())
        [[[ 0.  0.  1.]
          [ 0.  0.  0.]
          [ 1.  0.  0.]]]

        '''
        applier = Applier(defaultGrid=grid, **kwargs)
        applier.setFlowVector('vector', vector=vector)
        applier.setOutputRaster('raster', filename=filename)
        applier.apply(operatorType=_RasterFromVector, vector=vector, noDataValue=noDataValue)
        return Raster(filename=filename)

    @staticmethod
    def fromEnviSpectralLibrary(filename, library):
        '''
        Create instance from given ``library``.

        :param filename: output path
        :type filename: str
        :param library:
        :type library: EnviSpectralLibrary`
        :rtype: Raster

        :example:

        >>> from tests import enmapboxtestdata
        >>> speclib = EnviSpectralLibrary(filename=enmapboxtestdata.speclib)
        >>> raster = Raster.fromEnviSpectralLibrary(filename='/vsimem/raster.bsq', library=speclib)
        >>> raster.shape()
        (177, 75, 1)

        '''
        assert isinstance(library, EnviSpectralLibrary)
        rasterDataset = library.raster().dataset().translate(filename=filename,
            driver=RasterDriver.fromFilename(filename=filename))
        rasterDataset.copyMetadata(other=library.raster().dataset())
        return Raster.fromRasterDataset(rasterDataset=rasterDataset)

    @staticmethod
    def fromAsdTxt(filename, asdFilenames):
        '''
        Create instance from given list of ``asdFilenames`` in text format.

        :param filename: output path
        :type filename: str
        :param asdFilenames:
        :type asdFilenames: list`
        :rtype: Raster

        :example:

        >>> from tests import enmapboxtestdata
        >>> speclib = EnviSpectralLibrary(filename=enmapboxtestdata.library)
        >>> raster = Raster.fromAsdTxt(filename='/vsimem/raster.bsq', library=speclib)
        >>> raster.array().shape
        (177, 75, 1)

        '''

        assert len(asdFilenames) > 0

        def readProfile(asdFilename):
            with open(asdFilename) as f:
                text = f.readlines()
            _, name = text[0].split(';')
            wavelength, profile = np.array([line.split(';') for line in text[1:]], dtype=np.float32).T
            print(profile)
            return wavelength, profile, name.strip()

        wavelength = list(readProfile(asdFilename=asdFilenames[0])[0])
        profiles = list()
        names = list()
        for w, p, name in [readProfile(asdFilename=fn) for fn in asdFilenames]:
            profiles.append(p)
            # print(p)
            names.append(name)

        array = np.atleast_3d(np.array(profiles).T)
        raster = Raster.fromArray(array=array, filename=filename, noDataValues=-1)
        MetadataEditor.setBandCharacteristics(rasterDataset=raster.dataset(), wavelength=wavelength,
            wavelengthUnits='nanometers')
        raster.dataset().setMetadataItem(key='spectra names', value=names, domain='ENVI')
        return raster

    @classmethod
    def fromArray(cls, array, filename, grid=None, noDataValues=None, descriptions=None, **kwargs):
        '''
        Create instance from given ``array``.

        :param array:
        :type array: Union[numpy.ndarray, list]
        :param filename: output path
        :type filename: str
        :param grid: output grid
        :type grid: hubdc.core.Grid
        :param noDataValues: list of band no data values
        :type noDataValues: List[float]
        :param descriptions: list of band descriptions (i.e. band names)
        :type descriptions: List[str]
        :param kwargs: passed to constructor (e.g. Raster, Classification, Regression, ...)
        :rtype: Raster

        :example:

        >>> raster = Raster.fromArray(array=np.zeros(shape=[177, 100, 100]), filename='/vsimem/raster.bsq')
        >>> raster.shape()
        (177, 100, 100)
        >>> raster.grid() # default grid uses WGS84 projection and millisecond (1/3600 degree) resolution
        Grid(extent=Extent(xmin=0.0, xmax=0.027777777777777776, ymin=0.0, ymax=0.027777777777777776), resolution=Resolution(x=0.0002777777777777778, y=0.0002777777777777778), projection=Projection(wkt=GEOGCS["WGS84", DATUM["WGS_1984", SPHEROID["WGS84",6378137,298.257223563, AUTHORITY["EPSG","7030"]], AUTHORITY["EPSG","6326"]], PRIMEM["Greenwich",0, AUTHORITY["EPSG","8901"]], UNIT["degree",0.0174532925199433, AUTHORITY["EPSG","9122"]], AUTHORITY["EPSG","4326"]])

        '''

        if isinstance(array, list):
            array = np.array(array)
        assert isinstance(array, np.ndarray)
        assert array.ndim == 3
        rasterDataset = RasterDataset.fromArray(array=array, grid=grid, filename=filename,
            driver=RasterDriver.fromFilename(filename=filename))
        rasterDataset.setNoDataValues(values=noDataValues)
        if descriptions is not None:
            assert len(descriptions) == rasterDataset.zsize()
            for rasterBand, description in zip(rasterDataset.bands(), descriptions):
                rasterBand.setDescription(value=description)

        return cls.fromRasterDataset(rasterDataset=rasterDataset, **kwargs)

    def grid(self):
        '''Return grid.'''
        return self.dataset().grid()

    def noDataValue(self, default=None, required=False):
        '''Return no value value.'''
        return self.dataset().noDataValue(default=default)

    def noDataValues(self, default=None):
        '''Return bands no value values.'''
        return self.dataset().noDataValues(default=default)

    def descriptions(self):
        '''Return band descriptions.'''
        return [band.description() for band in self.dataset().bands()]

    def array(self, **kwargs):
        '''Return raster data as 3d array of shape = (zsize, ysize, xsize).
        Additional ``kwargs`` are passed to Raster.dataset().array.'''
        return self.dataset().array(**kwargs)

    def dtype(self):
        '''Return numpy data type'''
        return self.dataset().dtype()

    def uniqueValues(self, index):
        '''
        Return list of unique values for band at given ``index``.

        :example:

        >>> raster = Raster.fromArray(array=[[[1, 1, 1, 5, 2]]], filename='/vsimem/raster.bsq')
        >>> raster.uniqueValues(index=0)
        [1, 2, 5]

        '''

        values = list(np.unique(self.dataset().band(index=index).readAsArray()))
        return values

    def convolve(self, filename, kernel, **kwargs):
        '''
        Perform convolution of itself with the given ``kernel`` and return the result raster,
        where an 1D kernel is applied along the z dimension,
        an 2D kernel is applied spatially (i.e. y/x dimensions),
        and an 3D kernel is applied directly to the 3D z-y-x data cube.

        :param filename: output path
        :type filename: str
        :param kernel:
        :type kernel: astropy.convolution.kernels.Kernel
        :param kwargs: passed to :class:`_classic.hubflow.core.Applier`
        :rtype: Raster

        :example:

        >>> array = np.zeros(shape=[1, 5, 5])
        >>> array[0, 2, 2] = 1
        >>> raster = Raster.fromArray(array=array, filename='/vsimem/raster.bsq')
        >>> raster.array()
        array([[[ 0.,  0.,  0.,  0.,  0.],
                [ 0.,  0.,  0.,  0.,  0.],
                [ 0.,  0.,  1.,  0.,  0.],
                [ 0.,  0.,  0.,  0.,  0.],
                [ 0.,  0.,  0.,  0.,  0.]]])

        >>> from astropy.convolution.kernels import Kernel2D
        >>> kernel = Kernel2D(array=np.ones(shape=[3, 3]))
        >>> kernel.array
        array([[ 1.,  1.,  1.],
               [ 1.,  1.,  1.],
               [ 1.,  1.,  1.]])
        >>> result = raster.convolve(filename='/vsimem/result.bsq', kernel=kernel)
        >>> result.array()
        array([[[ 0.,  0.,  0.,  0.,  0.],
                [ 0.,  1.,  1.,  1.,  0.],
                [ 0.,  1.,  1.,  1.,  0.],
                [ 0.,  1.,  1.,  1.,  0.],
                [ 0.,  0.,  0.,  0.,  0.]]], dtype=float32)
        '''
        from astropy.convolution import Kernel
        assert isinstance(kernel, Kernel)
        assert kernel.dimension <= 3
        applier = Applier(defaultGrid=self, **kwargs)
        applier.setFlowRaster('inraster', raster=self)
        applier.setOutputRaster('outraster', filename=filename)
        applier.apply(operatorType=_RasterConvolve, raster=self, kernel=kernel)
        return Raster(filename=filename)

    def applySpatial(self, filename, function, kwds=None, **kwargs):
        '''
        Apply given ``function`` to each band of itself and return the result raster.

        :param filename:
        :type filename: str
        :param function: user defined function that takes one argument ``array``
        :type function: function
        :param kwargs: passed to :class:`_classic.hubflow.core.Applier`
        :rtype: Raster

        :example:

        >>> raster = Raster.fromArray(array=[[[1, 2, 3]]], filename='/vsimem/raster.bsq')
        >>> raster.array()
        array([[[1, 2, 3]]])
        >>> def square(array): return array**2
        >>> result = raster.applySpatial(filename='/vsimem/result.bsq', function=square)
        >>> result.array()
        array([[[1, 4, 9]]])

        '''
        applier = Applier(defaultGrid=self, **kwargs)
        applier.controls.setBlockFullSize()
        applier.setFlowRaster('inraster', raster=self)
        applier.setOutputRaster('outraster', filename=filename)
        applier.apply(operatorType=_RasterApplySpatial, raster=self, function=function, kwds=kwds)
        return Raster(filename=filename)

    def calculate(self, filename, function, **kwargs):
        '''
        Apply given ``function`` to itself and return the result raster.

        :param filename:
        :type filename: str
        :param function: user defined function that takes one argument ``array``
        :type function: function
        :param kwargs: passed to :class:`_classic.hubflow.core.Applier`
        :rtype: Raster

        :example:

        >>> raster = Raster.fromArray(array=[[[1, 2, 3]]], filename='/vsimem/raster.bsq')
        >>> result = raster.calculate(filename='/vsimem/result.bsq', function=lambda a: a**2)
        >>> result.array()
        array([[[1, 4, 9]]])

        '''
        applier = Applier(defaultGrid=self, **kwargs)
        applier.setFlowRaster('inraster', raster=self)
        applier.setOutputRaster('outraster', filename=filename)
        applier.apply(operatorType=_RasterCalculate, raster=self, function=function)
        return Raster(filename=filename)

    def resample(self, filename, grid, resampleAlg=gdal.GRA_NearestNeighbour, **kwargs):
        '''
        Return itself resampled into the given ``grid``.

        :param filename: output path
        :type filename: str
        :param grid:
        :type grid: hubdc.core.Grid
        :param resampleAlg: GDAL resampling algorithm
        :type resampleAlg: int
        :param kwargs: passed to :class:`_classic.hubflow.core.Applier`
        :rtype: Raster

        :example:

        >>> raster = Raster.fromArray(array=[[[1, 2, 3]]], filename='/vsimem/raster.bsq')
        >>> raster.array()
        array([[[1, 2, 3]]])
        >>> grid = Grid(extent=raster.grid().extent(),
        ...             resolution=raster.grid().resolution() / (2, 1))
        >>> result = raster.resample(filename='/vsimem/result.bsq', grid=grid)
        >>> result.array()
        array([[[1, 1, 2, 2, 3, 3]]])
        '''

        applier = Applier(defaultGrid=grid, **kwargs)
        applier.setFlowRaster('inraster', raster=self)
        applier.setOutputRaster('outraster', filename=filename)
        applier.apply(operatorType=_RasterResample, raster=self, resampleAlg=resampleAlg)
        return Raster(filename=filename)

    def subsetWavebands(self, filename, wavelength, invert=False, **kwargs):
        '''
        Return band subset that is closest to the given wavelength (in nanometers).

        :param filename: output path
        :param wavelength: list of wavelength
        :param invert: wether to invert the selection
        :param kwargs: passed to gdal.Translate
        :return: Raster
        '''
        wl = np.array(self.metadataWavelength())
        indices = list()
        for w in wavelength:
            indices.append(np.argmin(np.abs(wl - w)))

        return self.subsetBands(filename=filename, indices=indices, invert=invert, **kwargs)

    def subsetBands(self, filename, indices, invert=False, **kwargs):
        '''
        Return the band subset given by ``indices``.

        :param filename: output path
        :type filename: str
        :param indices:
        :type indices: list
        :param invert: wether to invert the indices list (i.e. dropping bands instead of selecting)
        :type invert: bool
        :param kwargs: passed to gdal.Translate
        :type kwargs: dict

        :rtype: Raster

        :example:

        TODO

        >>> raster = Raster.fromArray(array=[[[1]], [[2]], [[3]]], filename='/vsimem/raster.bsq')
        >>> raster.array()
        array([[[1, 2, 3]]])
        '''

        # prepare bandList for gdal.Translate
        bandList = list()
        zsize = self.dataset().zsize()
        for index in indices:
            index = int(index)
            if index < 0:
                index = zsize + index
            if index < 0 or index >= zsize:
                raise errors.IndexError(index=index, min=0, max=zsize - 1)
            bandList.append(index + 1)
        if invert:
            bandList = [i + 1 for i in range(zsize) if i + 1 not in bandList]

        # subset raster
        rasterDataset = self.dataset().translate(filename=filename, driver=RasterDriver.fromFilename(filename),
            bandList=bandList)

        # copy metadata and especially subset some band related items in the ENVI domain
        indices = [b - 1 for b in bandList]
        meta = self.dataset().metadataDict()
        envi = meta.get('ENVI', dict())
        envi.pop('bands', None)

        if 'band names' in envi:
            MetadataEditor.setBandNames(rasterDataset, [envi['band names'][i] for i in indices])
        for key in ['band names', 'wavelength', 'fwhm']:
            if key in envi:
                envi[key] = [envi[key][i] for i in indices]
        rasterDataset.setMetadataDict(meta)

        noDataValues = self.noDataValues()
        rasterDataset.setNoDataValues(values=[noDataValues[i] for i in indices])

        raster = type(self).fromRasterDataset(rasterDataset)
        return raster

    def asMask(self, noDataValues=None, minOverallCoverage=0.5, indices=None, invert=False):
        '''
        Return itself as a :class:`~_classic.hubflow.core.Mask`.
        
        :param noDataValues: list of band-wise no data values
        :type noDataValues: List[Union[None, float]]
        :param minOverallCoverage: threshold that defines, in case of on-the-fly average-resampling, which pixel will be evaluated as True
        :type minOverallCoverage: float
        :param indices: if set, a band subset mask for the given ``indices`` is created
        :type indices: int
        :param invert: whether to invert the mask
        :type invert: int
        :rtype: Mask

        :example:

        >>> raster = Raster.fromArray(array=[[[-1, 0, 5, 3, 0]]], filename='/vsimem/raster.bsq',
        ...                           noDataValues=[-1])
        >>> raster.array()
        array([[[-1,  0,  5,  3,  0]]])
        >>> raster.asMask().array()
        array([[[0, 1, 1, 1, 1]]], dtype=uint8)
        '''

        return Mask(filename=self.filename(), noDataValues=noDataValues, minOverallCoverage=minOverallCoverage,
            indices=indices, invert=invert)

    def statistics(self, bandIndices=None, mask=None,
            calcPercentiles=False, calcHistogram=False, calcMean=False, calcStd=False,
            percentiles=list(), histogramRanges=None, histogramBins=None,
            **kwargs):
        '''
        Return a list of BandStatistic named tuples:

        ============   ====================================================================
        key            value/description
        ============   ====================================================================
        index          band index
        nvalid         number of valid pixel (not equal to noDataValue and not masked)
        ninvalid       number of invalid pixel (equal to noDataValue or masked)
        min            smallest value
        max            largest value
        percentiles+   list of (rank, value) tuples for given percentiles
        std+           standard deviation
        mean+          mean
        histo+         Histogram(hist, bin_edges) tuple with histogram counts and bin edges
        ============   ====================================================================

        +set corresponding calcPercentiles/Histogram/Mean/Std keyword to True


        :param bandIndices: calculate statistics only for given ``bandIndices``
        :type bandIndices: Union[None, None, None]
        :param mask:
        :type mask: Union[None, None, None]
        :param calcPercentiles: if set True, band percentiles are calculated; see ``percentiles`` keyword
        :type calcPercentiles: bool
        :param calcHistogram: if set True, band histograms are calculated; see ``histogramRanges`` and ``histogramBins`` keywords
        :type calcHistogram: bool
        :param calcMean: if set True, band mean values are calculated
        :type calcMean: bool
        :param calcStd: if set True, band standard deviations are calculated
        :type calcStd: bool
        :param percentiles: values between 0 (i.e. min value) and 100 (i.e. max value), 50 is the median
        :type percentiles: List[float]
        :param histogramRanges: list of ranges, one for each band; ranges are passed to ``numpy.histogram``; None ranges are set to (min, max)
        :type histogramRanges: List[numpy.histogram ranges]
        :param histogramBins: list of bins, one for each band; bins are passed to ``numpy.histogram``; None bins are set to 256
        :type histogramBins: List[numpy.histogram bins]
        :param kwargs: passed to :class:`_classic.hubflow.core.Applier`
        :rtype: List[BandStatistics(index, nvalid, ninvalid, min, max, percentiles, std, mean, histo)]

        :Example:

        >>> # create raster with no data values
        >>> raster = Raster.fromArray(array=[[[1, np.nan, 3], [0, 2, np.inf], [1, 0, 3]]], filename='/vsimem/raster.bsq', noDataValues=[0])
        >>> # calculate basic statistics
        >>> statistics = raster.statistics()
        >>> print(statistics[0])
        BandStatistics(index=0, nvalid=5, ninvalid=4, min=1.0, max=3.0, percentiles=None, std=None, mean=None, histo=None)
        >>> # calculate histograms
        >>> statistics = raster.statistics(calcHistogram=True, histogramRanges=[(1, 4)], histogramBins=[3])
        >>> print(statistics[0].histo)
        Histogram(hist=array([2, 1, 2], dtype=int64), bin_edges=array([ 1.,  2.,  3.,  4.]))
        >>> # calculate percentiles (min, median, max)
        >>> statistics = raster.statistics(calcPercentiles=True, percentiles=[0, 50, 100])
        >>> print(statistics[0].percentiles)
        [Percentile(rank=0, value=1.0), Percentile(rank=50, value=2.0), Percentile(rank=100, value=3.0)]
        '''

        applier = Applier(defaultGrid=self, **kwargs)
        applier.controls.setBlockFullSize()
        applier.setFlowRaster('raster', raster=self)
        applier.setFlowMask('mask', mask=mask)
        return applier.apply(operatorType=_RasterStatistics, raster=self, bandIndices=bandIndices, mask=mask,
            calcPercentiles=calcPercentiles, calcMean=calcMean, calcStd=calcStd,
            calcHistogram=calcHistogram, percentiles=percentiles, histogramRanges=histogramRanges,
            histogramBins=histogramBins)

    def scatterMatrix(self, raster2, bandIndex1, bandIndex2, range1, range2, bins=256, mask=None, stratification=None,
            **kwargs):
        '''
        Return scatter matrix between itself's band given by ``bandIndex1`` and ``raster2``'s band given by ``bandIndex2``
        stored as a named tuple ``ScatterMatrix(H, xedges, yedges)``. Where ``H`` is the 2d count matrix for the
        binning given by ``xedges`` and ``yedges`` lists. If a ``stratication`` is defined, ``H`` will be a list of
        2d count matrices, one for each strata.

        :param raster2:
        :type raster2: Raster
        :param bandIndex1: first band index
        :type bandIndex1: int
        :param bandIndex2: second band index
        :type bandIndex2: int
        :param range1: first band range as (min, max) tuple
        :type range1: Tuple[float, float]
        :param range2: second band range as (min, max) tuple
        :type range2: Tuple[float, float]
        :param bins: passed to ``np.histogram2d``
        :param mask: map that is evaluated as a mask
        :type mask: Map
        :param stratification: classification that stratifies the calculation into different classes
        :type stratification: Classification
        :param kwargs: passed to :class:`_classic.hubflow.core.Applier`
        :rtype: ScatterMatrix(H, xedges, yedges)

        :Example:

        >>> # create two single band raster
        >>> raster1 = Raster.fromArray(array=[[[1, 2, 3]]], filename='/vsimem/raster1.bsq')
        >>> raster2 = Raster.fromArray(array=[[[10, 20, 30]]], filename='/vsimem/raster2.bsq')
        >>> # calculate scatter matrix between both raster bands
        >>> scatterMatrix = raster1.scatterMatrix(raster2=raster2, bandIndex1=0, bandIndex2=0, range1=[1, 4], range2=[10, 40], bins=3)
        >>> scatterMatrix.H
        array([[1, 0, 0],
               [0, 1, 0],
               [0, 0, 1]], dtype=uint64)
        >>> scatterMatrix.xedges
        array([ 1.,  2.,  3.,  4.])
        >>> scatterMatrix.yedges
        array([ 10.,  20.,  30.,  40.])
        '''

        applier = Applier(defaultGrid=self, **kwargs)
        applier.setFlowRaster('raster1', raster=self)
        applier.setFlowRaster('raster2', raster=raster2)
        applier.setFlowMask('mask', mask=mask)
        applier.setFlowClassification('stratification', classification=stratification)

        _, xedges, yedges = np.histogram2d(x=[0], y=[0], bins=bins, range=[range1, range2])
        bins = [xedges, yedges]
        results = applier.apply(operatorType=_RasterScatterMatrix, raster1=self, raster2=raster2,
            bandIndex1=bandIndex1, bandIndex2=bandIndex2, bins=bins, mask=mask,
            stratification=stratification)
        H = np.sum(np.stack(results), axis=0, dtype=np.uint64)
        ScatterMatrix = namedtuple('ScatterMatrix', ['H', 'xedges', 'yedges'])

        return ScatterMatrix(H, xedges, yedges)

    def applyMask(self, filename, mask, noDataValue=None, **kwargs):
        '''
        Applies a ``mask`` to itself and returns the result.
        All pixels where the mask evaluates to False, are set to the no data value.
        If the no data value is nut defined, 0 is used.

        :param filename: output path
        :type filename: str
        :param mask: a map that is evaluated as a mask
        :type mask: Map
        :param noDataValue: set no data value if undefined (default is to use 0)
        :type noDataValue: float
        :param kwargs: passed to :class:`_classic.hubflow.core.Applier`
        :rtype: Raster

        :example:

        >>> raster = Raster.fromArray(array=[[[1, 2, 3]]], filename='/vsimem/raster.bsq', noDataValues=[-1])
        >>> mask = Mask.fromArray(array=[[[0, 0, 1]]], filename='/vsimem/mask.bsq')
        >>> result = raster.applyMask(filename='/vsimem/result.bsq', mask=mask)
        >>> result.array()
        array([[[-1, -1,  3]]])

        '''
        if noDataValue is None:
            noDataValue = 0
        applier = Applier(defaultGrid=self, **kwargs)
        applier.setFlowRaster('raster', raster=self)
        applier.setFlowMask('mask', mask=mask)
        applier.setOutputRaster('maskedRaster', filename=filename)
        applier.apply(operatorType=_RasterApplyMask, raster=self, mask=mask, noDataValue=noDataValue)
        return type(self)(filename=filename)

    def metadataWavelength(self):
        '''
        Return list of band center wavelengths in nanometers.

        :example:

        >>> from tests import enmapboxtestdata
        >>> Raster(filename=enmapboxtestdata.enmap).metadataWavelength() # doctest: +ELLIPSIS
        [460.0, 465.0, 470.0, ..., 2393.0, 2401.0, 2409.0]
        '''

        wavelength = self.dataset().metadataItem(key='wavelength', domain='ENVI', dtype=float, required=True)
        unit = self.dataset().metadataItem(key='wavelength units', domain='ENVI', required=True)
        assert unit.lower() in ['nanometers', 'nm', 'micrometers', 'um']
        wavelength = [float(v) for v in wavelength]
        if unit.lower() in ['micrometers', 'um']:
            wavelength = [v * 1000 for v in wavelength]

        return wavelength

    #    def metadataDict(self):
    #        '''Return metadata dictionary.'''
    #        return self.dataset().metadataDict()

    def metadataFWHM(self, required=False):
        '''
        Return list of band full width at half maximums in nanometers. If not defined, list entries are ``None``.

        :example:

        >>> from tests import enmapboxtestdata
        >>> Raster(filename=enmapboxtestdata.enmap).metadataFWHM() # doctest: +ELLIPSIS
        [5.8, 5.8, 5.8, ..., 9.1, 9.1, 9.1]
        '''

        fwhm = self.dataset().metadataItem(key='fwhm', domain='ENVI', dtype=float, required=required)
        if fwhm is None:
            fwhm = [None] * self.dataset().zsize()
        else:
            unit = self.dataset().metadataItem(key='wavelength units', domain='ENVI', required=True)
            assert unit.lower() in ['nanometers', 'nm', 'micrometers', 'um']
            fwhm = [float(v) for v in fwhm]
            if unit.lower() in ['micrometers', 'um']:
                fwhm = [v * 1000 for v in fwhm]

        return fwhm

    def sensorDefinition(self):
        '''
        Return :class:`~_classic.hubflow.core.SenserDefinition` created from center wavelength and FWHM.

        :example:

        >>> SensorDefinition.predefinedSensorNames()
        ['modis', 'moms', 'mss', 'npp_viirs', 'pleiades1a', 'pleiades1b', 'quickbird', 'rapideye', 'rasat', 'seawifs', 'sentinel2', 'spot', 'spot6', 'tm', 'worldview1', 'worldview2', 'worldview3']
        >>> SensorDefinition.fromPredefined('sentinel2') # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
        SensorDefinition(wavebandDefinitions=[WavebandDefinition(center=443.0, fwhm=None, responses=[...], name=Sentinel-2 - Band B1), ...,
                                              WavebandDefinition(center=2196.5, fwhm=None, responses=[...], name=Sentinel-2 - Band B12)])
        '''
        return SensorDefinition._fromFWHM(centers=self.metadataWavelength(), fwhms=self.metadataFWHM())

    def close(self):
        '''See RasterDataset.show.'''
        self.dataset().close()

    def show(self):
        '''See RasterDataset.show.'''
        self.dataset().show()

    def saveAs(self, filename, driver=None, copyMetadata=True, copyCategories=True):
        '''Save copy of self at given ``filename``. Format will be derived from filename extension if not explicitely specified by ``driver`` keyword.'''

        if driver is None:
            driver = RasterDriver.fromFilename(filename=filename)
        rasterDataset = self.dataset().translate(filename=filename, driver=driver)

        if copyCategories:
            rasterDataset.copyCategories(other=self.dataset())

        # have to re-open the dataset after setting categories (prevent a GDAL bug)
        rasterDataset = rasterDataset.reopen(eAccess=gdal.GA_Update)

        if copyMetadata:
            rasterDataset.copyMetadata(other=self.dataset())

        raster = Raster.fromRasterDataset(rasterDataset)
        return raster

    def extractRegions(self, vector, oversampling=1, tmpFilenameFid=None):
        assert isinstance(vector, Vector)

        oversampledGrid = self.grid().atResolution(resolution=self.grid().resolution() / oversampling)
        fidDataset = vector.dataset().rasterizeFid(grid=oversampledGrid,
            filename=tmpFilenameFid,
            driver=RasterDriver.fromFilename(tmpFilenameFid))
        fid = fidDataset.readAsArray()

        for feature in vector.dataset().features():
            geometry = feature.geometry().reproject(projection=self.dataset().projection())
            grid = Grid(extent=Extent.fromGeometry(geometry=geometry),
                resolution=self.dataset().grid().resolution())
            grid = grid.anchor(point=self.dataset().grid().extent().upperLeft())
            grid = grid.pixelBuffer(buffer=1)
            array = self.dataset().readAsArray(grid=grid)
            mask = vector.dataset().rasterizeFid()
            exit(0)
        profiles = attributes = None

        return profiles, attributes


class _RasterResample(ApplierOperator):
    def ufunc(self, raster, resampleAlg):
        inraster = self.inputRaster.raster(key='inraster')
        outraster = self.outputRaster.raster(key='outraster')

        array = inraster.array(resampleAlg=resampleAlg)
        metadataDict = inraster.metadataDict()
        noDataValues = inraster.noDataValues()

        outraster.setArray(array=array)
        outraster.setMetadataDict(metadataDict=metadataDict)
        outraster.setNoDataValues(values=noDataValues)


class _RasterConvolve(ApplierOperator):
    def ufunc(self, raster, kernel):
        from astropy.convolution import convolve, CustomKernel

        if kernel.dimension == 3:
            pass
        elif kernel.dimension == 2:
            kernel = CustomKernel(array=kernel.array[None])
        elif kernel.dimension == 1:
            kernel = CustomKernel(array=kernel.array.reshape(-1, 1, 1))

        inraster = self.inputRaster.raster(key='inraster')
        outraster = self.outputRaster.raster(key='outraster')
        zsize, ysize, xsize = kernel.shape
        overlap = int((max(ysize, xsize) + 1) / 2.)
        array = np.float32(inraster.array(overlap=overlap))
        noDataValues = self.inputRaster.raster(key='inraster').noDataValues()
        for band, noDataValue in zip(array, noDataValues):
            if noDataValue is not None:
                band[band == noDataValue] = np.nan
        outarray = convolve(array, kernel,
            fill_value=np.nan, nan_treatment='fill',
            normalize_kernel=False)
        outraster.setArray(array=outarray, overlap=overlap)
        outraster.setMetadataDict(metadataDict=inraster.metadataDict())
        outraster.setNoDataValue(value=np.nan)


class _RasterApplySpatial(ApplierOperator):
    def ufunc(self, raster, function, kwds):
        if kwds is None:
            kwds = dict()
        inraster = self.inputRaster.raster(key='inraster')
        outraster = self.outputRaster.raster(key='outraster')
        outraster.setZsize(zsize=inraster.dataset().zsize())
        for index in range(inraster.dataset().zsize()):
            array = inraster.array(indices=[index])[0]
            outarray = function(array, **kwds)
            outraster.band(index=index).setArray(array=outarray)


class _RasterCalculate(ApplierOperator):
    def ufunc(self, raster, function):
        inraster = self.inputRaster.raster(key='inraster')
        outraster = self.outputRaster.raster(key='outraster')
        array = inraster.array()
        outarray = function(array)
        outraster.setArray(array=outarray)


class _RasterStatistics(ApplierOperator):
    def ufunc(self, raster, bandIndices, mask, calcPercentiles, calcHistogram, calcMean, calcStd,
            percentiles, histogramRanges, histogramBins):

        maskValid = self.flowMaskArray('mask', mask=mask)

        if bandIndices is None:
            bandIndices = range(self.inputRaster.raster('raster').dataset().zsize())

        result = list()

        BandStatistics = namedtuple('BandStatistics', ['index', 'nvalid', 'ninvalid', 'min', 'max', 'percentiles',
                                                       'std', 'mean', 'histo'])
        Histogram = namedtuple('Histogram', ['hist', 'bin_edges'])
        Percentile = namedtuple('Percentile', ['rank', 'value'])

        for i, index in enumerate(bandIndices):
            self.progressBar.setPercentage((float(i) + 1) / len(bandIndices) * 100)
            band = self.flowRasterArray('raster', raster=raster, indices=[index]).astype(dtype=np.float64)
            finiteValid = np.isfinite(band)
            valid = self.maskFromBandArray(array=band, noDataValueSource='raster', index=index)
            valid *= maskValid
            valid *= finiteValid
            values = band[valid]  # may still contain NaN
            bandResult = dict()
            bandResult['index'] = index
            bandResult['nvalid'] = np.sum(valid)
            bandResult['ninvalid'] = np.product(band.shape) - bandResult['nvalid']
            bandResult['min'] = np.min(values)
            bandResult['max'] = np.max(values)

            if calcPercentiles:
                qs = percentiles
                ps = np.percentile(values, q=percentiles)
                bandResult['percentiles'] = [Percentile(rank=rank, value=value) for rank, value in zip(qs, ps)]
            else:
                bandResult['percentiles'] = None

            if calcStd:
                bandResult['std'] = np.nanstd(values)
            else:
                bandResult['std'] = None

            if calcMean:
                bandResult['mean'] = np.nanmean(values)
            else:
                bandResult['mean'] = None

            if calcHistogram:
                if histogramRanges is None:
                    range_ = [bandResult['min'], bandResult['max']]
                else:
                    assert len(histogramRanges) == len(bandIndices)
                    range_ = histogramRanges[index]
                if histogramBins is None:
                    bins = 256
                else:
                    assert len(histogramBins) == len(bandIndices)
                    bins = histogramBins[index]

                hist, bin_edges = np.histogram(values, bins=bins, range=range_)
                bandResult['histo'] = Histogram(hist=hist, bin_edges=bin_edges)
            else:
                bandResult['histo'] = None

            result.append(BandStatistics(**bandResult))
        return result

    @staticmethod
    def aggregate(blockResults, grid, *args, **kwargs):
        return blockResults[0]


class _RasterFromVector(ApplierOperator):
    def ufunc(self, vector, noDataValue):
        array = self.flowVectorArray('vector', vector=vector)
        self.outputRaster.raster(key='raster').setArray(array=array)
        self.setFlowMetadataNoDataValues('raster', noDataValues=[noDataValue])


class _RasterScatterMatrix(ApplierOperator):
    def ufunc(self, raster1, raster2, bandIndex1, bandIndex2, bins, mask, stratification):

        band1 = self.flowRasterArray('raster1', raster=raster1, indices=[bandIndex1])
        band2 = self.flowRasterArray('raster2', raster=raster2, indices=[bandIndex2])
        strata = self.flowClassificationArray('stratification', classification=stratification)

        valid = self.maskFromBandArray(array=band1, noDataValueSource='raster1', index=bandIndex1)
        valid *= self.maskFromBandArray(array=band2, noDataValueSource='raster2', index=bandIndex2)
        valid *= self.flowMaskArray('mask', mask=mask)

        x = band1[valid]
        y = band2[valid]

        if strata.size == 0:
            H = np.histogram2d(x=x, y=y, bins=bins)[0]
        else:
            s = strata[valid]
            HList = list()
            for i in range(1, stratification.classDefinition().classes() + 1):
                v = s == i
                Hi = np.histogram2d(x=x[v], y=y[v], bins=bins)[0]
                HList.append(np.array(Hi))
            H = np.stack(HList)

        return H


class _RasterApplyMask(ApplierOperator):
    def ufunc(self, raster, mask, noDataValue):
        noDataValue = self.inputRaster.raster(key='raster').noDataValue(default=noDataValue)
        array = self.flowRasterArray('raster', raster=raster)
        marray = self.flowMaskArray('mask', mask=mask)
        tobefilled = np.logical_not(marray[0])
        array[:, tobefilled] = noDataValue
        inraster = self.inputRaster.raster(key='raster')
        outraster = self.outputRaster.raster(key='maskedRaster')
        outraster.setArray(array=array)
        outraster.setMetadataDict(metadataDict=inraster.metadataDict())
        outraster.setNoDataValue(value=noDataValue)
        outraster.setCategoryColors(colors=inraster.categoryColors())
        outraster.setCategoryNames(names=inraster.categoryNames())


class WavebandDefinition(FlowObject):
    '''Class for managing waveband definitions.'''

    def __init__(self, center, fwhm=None, responses=None, name=None):
        '''
        Create an instance.

        :param center: center wavelength location
        :type center: float
        :param fwhm: full width at half maximum
        :type fwhm: float
        :param responses: list of response function (wavelength, value) tuples
        :type responses: List[(float, float)]
        :param name: waveband name
        :type name: str
        '''

        if responses is not None:
            responses = [(float(x), float(y)) for x, y in responses]

        self._center = float(center)
        if fwhm is not None:
            fwhm = float(fwhm)
        self._fwhm = fwhm
        self._responses = responses
        self._name = name

    def __getstate__(self):
        return OrderedDict([('center', self.center()),
                            ('fwhm', self.fwhm()),
                            ('responses', self.responses()),
                            ('name', self.name())])

    @staticmethod
    def fromFWHM(center, fwhm, sigmaLimits=3):
        '''
        Create an instance from given ``center`` and ``fwhm``.
        The waveband response function is modeled inside the range: ``center`` +/- sigma * ``sigmaLimits``,
        where sigma is given by ``fwhm / 2.3548``.

        :example:

        >>> WavebandDefinition.fromFWHM(center=500, fwhm=10) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
        WavebandDefinition(center=500.0, fwhm=10.0, responses=[(487.0, 0.009227241211564235), (488.0, 0.01845426465118729), (489.0, 0.03491721729455092), (490.0, 0.06250295020961404), (491.0, 0.10584721091054979), (492.0, 0.1695806637893581), (493.0, 0.2570344015689991), (494.0, 0.3685735673688072), (495.0, 0.5000059003147861), (496.0, 0.6417177952459099), (497.0, 0.7791678897157294), (498.0, 0.8950267608170881), (499.0, 0.9726554065273144), (500.0, 1.0), (501.0, 0.9726554065273144), (502.0, 0.8950267608170881), (503.0, 0.7791678897157294), (504.0, 0.6417177952459099), (505.0, 0.5000059003147861), (506.0, 0.3685735673688072), (507.0, 0.2570344015689991), (508.0, 0.1695806637893581), (509.0, 0.10584721091054979), (510.0, 0.06250295020961404), (511.0, 0.03491721729455092)], name=None)
        '''
        center = float(center)
        if fwhm is not None:
            fwhm = float(fwhm)
            sigma = fwhm / 2.3548
            wl = np.array(range(int(center - sigmaLimits * sigma), int(center + sigmaLimits * sigma)))
            responses = list(zip(wl, np.exp(-(wl - center) ** 2 / (2 * sigma ** 2))))
        else:
            responses = None
        return WavebandDefinition(center=center, fwhm=fwhm, responses=responses)

    def center(self):
        '''
        Return center wavelength location.

        >>> WavebandDefinition(center=560).center()
        560.0
        '''
        return self._center

    def fwhm(self):
        '''
        Return full width at half maximum.
        
        >>> WavebandDefinition(center=560, fwhm=10).fwhm()
        10.0
        '''
        return self._fwhm

    def responses(self):
        '''
        Return response function as list of (wavelength, response) tuples.

        :example:

        >>> sentinelBlue = SensorDefinition.fromPredefined(name='sentinel2').wavebandDefinition(index=1)
        >>> sentinelBlue.responses()
        [(454.0, 0.02028969), (455.0, 0.06381729), (456.0, 0.14181057), (457.0, 0.27989078), (458.0, 0.53566604), (459.0, 0.75764752), (460.0, 0.81162521), (461.0, 0.81796823), (462.0, 0.82713398), (463.0, 0.8391982), (464.0, 0.85271397), (465.0, 0.85564352), (466.0, 0.85505457), (467.0, 0.86079216), (468.0, 0.86901422), (469.0, 0.8732093), (470.0, 0.8746579), (471.0, 0.87890232), (472.0, 0.88401742), (473.0, 0.88568426), (474.0, 0.8864462), (475.0, 0.89132953), (476.0, 0.89810187), (477.0, 0.89921862), (478.0, 0.89728783), (479.0, 0.899455), (480.0, 0.90808729), (481.0, 0.91663575), (482.0, 0.92044598), (483.0, 0.92225061), (484.0, 0.9262647), (485.0, 0.93060572), (486.0, 0.93187505), (487.0, 0.93234856), (488.0, 0.93660786), (489.0, 0.94359652), (490.0, 0.94689153), (491.0, 0.94277939), (492.0, 0.93912406), (493.0, 0.9435992), (494.0, 0.95384075), (495.0, 0.96115588), (496.0, 0.96098811), (497.0, 0.96023166), (498.0, 0.96653039), (499.0, 0.97646982), (500.0, 0.98081022), (501.0, 0.97624561), (502.0, 0.97399225), (503.0, 0.97796507), (504.0, 0.98398942), (505.0, 0.98579982), (506.0, 0.98173313), (507.0, 0.97932703), (508.0, 0.98329935), (509.0, 0.98777523), (510.0, 0.98546073), (511.0, 0.97952735), (512.0, 0.97936162), (513.0, 0.98807291), (514.0, 0.99619133), (515.0, 0.99330779), (516.0, 0.98572054), (517.0, 0.9860457), (518.0, 0.99517659), (519.0, 1.0), (520.0, 0.99782113), (521.0, 0.93955431), (522.0, 0.70830999), (523.0, 0.42396802), (524.0, 0.24124566), (525.0, 0.13881543), (526.0, 0.07368388), (527.0, 0.03404689), (528.0, 0.01505348)]
        '''
        return self._responses

    def name(self):
        '''
        Return waveband name.

        :example:

        >>> for wavebandDefinition in SensorDefinition.fromPredefined(name='sentinel2').wavebandDefinitions():
        ...     print(wavebandDefinition.name())
        Sentinel-2 - Band B1
        Sentinel-2 - Band B2
        Sentinel-2 - Band B3
        Sentinel-2 - Band B4
        Sentinel-2 - Band B5
        Sentinel-2 - Band B6
        Sentinel-2 - Band B7
        Sentinel-2 - Band B8
        Sentinel-2 - Band B8A
        Sentinel-2 - Band B9
        Sentinel-2 - Band B10
        Sentinel-2 - Band B11
        Sentinel-2 - Band B12
        '''
        return self._name

    def resamplingWeights(self, sensor):
        '''
        Return resampling weights for the center wavelength of the given :class:`~hubflow.core.SensorDefinition`.

        :example:

        Calculate weights for resampling EnMAP sensor into Sentinel-2 band 3.

        >>> from tests import enmapboxtestdata
        >>> enmapSensor = Raster(filename=enmapboxtestdata.enmap).sensorDefinition()
        >>> enmapSensor # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
        SensorDefinition(wavebandDefinitions=[WavebandDefinition(center=460.0, fwhm=5.8, responses=[(452.0, 0.0051192261189367235), ..., (466.0, 0.051454981460462346)], name=None),
                                              ...,
                                              WavebandDefinition(center=2409.0, fwhm=9.1, responses=[(2397.0, 0.008056878623001433), ..., (2419.0, 0.035151930528992195)], name=None)])
        >>> sentinel2Band4 = SensorDefinition.fromPredefined(name='sentinel2').wavebandDefinition(index=2)
        >>> sentinel2Band4 # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
        WavebandDefinition(center=560.0, fwhm=None, responses=[(538.0, 0.01591234), ..., (582.0, 0.01477064)], name=Sentinel-2 - Band B3)
        >>> weights = sentinel2Band4.resamplingWeights(sensor=enmapSensor)
        >>> centers = [wd.center() for wd in enmapSensor.wavebandDefinitions()]
        >>> list(zip(centers, weights)) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
        [(460.0, 0.0), ..., (533.0, 0.0), (538.0, 0.01591234), (543.0, 0.6156192), (549.0, 0.99344666), (554.0, 0.98899243), (559.0, 0.99746124), (565.0, 0.98366361), (570.0, 0.99787368), (575.0, 0.95940618), (581.0, 0.03900649), (587.0, 0.0), ..., (2409.0, 0.0)]
        '''

        assert isinstance(sensor, SensorDefinition)
        weights = list()
        x = np.array([x for x, y in self.responses()])
        for wd in sensor.wavebandDefinitions():
            if wd.center() > x[-1] or wd.center() < x[0]:
                weight = 0.
            else:
                weight = self.responses()[np.abs(np.subtract(x, wd.center())).argmin()][1]
            weights.append(weight)
        return weights

    def plot(self, plotWidget=None, yscale=1., **kwargs):
        '''
        Return response function plot.

        :param plotWidget: if None, a new plot widget is created, otherwise, the given ``plotWidget`` is used
        :type plotWidget: pyqtgraph.graphicsWindows.PlotWindow
        :param yscale: scale factor for y values
        :type yscale: float
        :param kwargs: passed to ``pyqtgraph.graphicsWindows.PlotWindow.plot``
        :rtype: pyqtgraph.graphicsWindows.PlotWindow

        :example:

        >>> plotWidget = SensorDefinition.fromPredefined(name='sentinel2').wavebandDefinition(index=2).plot()

        .. image:: plots/wavebandDefinition_plot.png
        '''
        import pyqtgraph as pg
        if plotWidget is None:
            plotWidget = pg.plot(title='Response Curve')
        x, y = zip(*self.responses())
        plotWidget.plot(x=np.array(x), y=np.array(y) * yscale, **kwargs)
        return plotWidget


class SensorDefinition(FlowObject):
    '''Class for managing sensor definitions.'''

    RESAMPLE_RESPONSE = 'response'
    RESAMPLE_LINEAR = 'linear'
    RESAMPLE_OPTIONS = [RESAMPLE_LINEAR, RESAMPLE_RESPONSE]

    def __init__(self, wavebandDefinitions):
        '''Create an instance by given list of ::class:`~WavebandDefinition`'s.'''
        self._wavebandDefinitions = wavebandDefinitions

    def __getstate__(self):
        return OrderedDict([('wavebandDefinitions', list(self.wavebandDefinitions()))])

    @staticmethod
    def fromPredefined(name):
        '''
        Create an instance for a predefined sensor (e.g. ``name='sentinel2'``).
        See :meth:`~SensorDefinition.predefinedSensorNames` for a full list of predifined sensors.
        Sensor response filter functions (.sli files) are stored here `hubflow/sensors`.

        :example:

        >>> SensorDefinition.fromPredefined(name='sentinel2') # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
        SensorDefinition(wavebandDefinitions=[WavebandDefinition(center=443.0, fwhm=None, responses=[...], name=Sentinel-2 - Band B1),
                                              ...,
                                              WavebandDefinition(center=2196.5, fwhm=None, responses=[...], name=Sentinel-2 - Band B12)])
        '''

        import _classic.hubflow.sensors
        assert isinstance(name, str)
        filename = join(hubflow.sensors.__path__[0], name + '.sli')
        assert exists(filename)
        library = EnviSpectralLibrary(filename=filename)
        return SensorDefinition.fromEnviSpectralLibrary(library=library, isResponseFunction=True)

    @staticmethod
    def predefinedSensorNames():
        '''
        Return list of predefined sensor names.

        :example:

        >>> SensorDefinition.predefinedSensorNames()
        ['modis', 'moms', 'mss', 'npp_viirs', 'pleiades1a', 'pleiades1b', 'quickbird', 'rapideye', 'rasat', 'seawifs', 'sentinel2', 'spot', 'spot6', 'tm', 'worldview1', 'worldview2', 'worldview3']
        '''
        import _classic.hubflow.sensors
        names = [basename(f)[:-4] for f in os.listdir(_classic.hubflow.sensors.__path__[0]) if f.endswith('.sli')]
        return names

    @classmethod
    def fromEnviSpectralLibrary(cls, library, isResponseFunction):
        '''
        Create instance from :class:`EnviSpectralLibrary`.

        :param library:
        :type library: EnviSpectralLibrary
        :param isResponseFunction: If True, ``library`` is interpreted as sensor response function.
                                   If False, center wavelength and FWHM information is used.
        :type isResponseFunction: bool
        :rtype: SensorDefinition

        :example:

        Case 1 - Library contains spectra with wavelength and FWHM information (i.e. set ``isResponseFunction=False``)

        >>> from tests import enmapboxtestdata
        >>> library = EnviSpectralLibrary(filename=enmapboxtestdata.speclib)
        >>> SensorDefinition.fromEnviSpectralLibrary(library=library, isResponseFunction=False) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
        SensorDefinition(wavebandDefinitions=[WavebandDefinition(center=460.0, fwhm=5.8, responses=[...], name=None),
                                              ...,
                                              WavebandDefinition(center=2409.0, fwhm=9.1, responses=[...], name=None)])

        Case 2 - Library contains response function (i.e. set ``isResponseFunction=True``)

        >>> import _classic.hubflow.sensors, os.path
        >>> library = EnviSpectralLibrary(filename = os.path.join(hubflow.sensors.__path__[0], 'sentinel2.sli'))
        >>> SensorDefinition.fromEnviSpectralLibrary(library=library, isResponseFunction=True) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
        SensorDefinition(wavebandDefinitions=[WavebandDefinition(center=443.0, fwhm=None, responses=[...], name=Sentinel-2 - Band B1),
                                              ...,
                                              WavebandDefinition(center=2196.5, fwhm=None, responses=[...], name=Sentinel-2 - Band B12)])
        '''
        assert isinstance(library, EnviSpectralLibrary)

        names = library.raster().dataset().metadataItem(key='spectra names', domain='ENVI')
        wavelengths = np.float32(library.raster().metadataWavelength())

        if isResponseFunction:
            responsess = [list(zip(wavelengths, y)) for y in library.raster().dataset().readAsArray().T[0]]
            sensor = cls._fromResponseFunctions(centers=None, names=names, responsess=responsess)
        else:
            fwhm = library.raster().metadataFWHM(required=True)
            sensor = cls._fromFWHM(centers=wavelengths, fwhms=fwhm)

        return sensor

    @staticmethod
    def _fromResponseFunctions(centers, names, responsess):
        '''
        Create instance from given lists of response function given by ``responsess``.

        :param centers: list of center wavelengths
        :type centers: List[float]
        :param names: list of waveband names
        :type names: List[str]
        :param responsess: list of response functions
        :type responsess: List[List[(float, float)]]
        :rtype: SensorDefinition

        '''
        if names is None:
            names = [None] * len(responsess)
        if centers is None:
            centers = [None] * len(responsess)

        assert len(centers) == len(names)
        assert isinstance(responsess, list)

        wavebandDefinitions = list()
        for responses, name, center in zip(responsess, names, centers):
            x, y = list(zip(*responses))
            valid = np.array(y) > 0.01
            if center is None:
                center = np.array(x)[valid][[0, -1]].mean()
            responsesValid = list(np.array(responses)[valid])
            wavebandDefinitions.append(WavebandDefinition(center=center,
                fwhm=None,
                responses=responsesValid,
                name=name))

        return SensorDefinition(wavebandDefinitions=wavebandDefinitions)

    @staticmethod
    def _fromFWHM(centers, fwhms):
        '''
        Create instance from lists of center wavelengths and FWHMs.

        :param centers: list of center wavelengths
        :type centers: List[float]
        :param fwhms: list if FWHMs
        :type fwhms: List[float]
        :rtype: SensorDefinition
        '''

        assert len(centers) == len(fwhms)
        wavebandDefinitions = [WavebandDefinition.fromFWHM(center=center, fwhm=fwhm)
                               for center, fwhm in zip(centers, fwhms)]
        return SensorDefinition(wavebandDefinitions=wavebandDefinitions)

    @staticmethod
    def fromRaster(raster):
        '''Forwards :meth:`Raster.sensorDefinition`.'''
        assert isinstance(raster, Raster)
        return raster.sensorDefinition()

    def wavebandDefinition(self, index):
        '''Return :class:`WavebandDefinition` for band given by ``index``.'''
        wavebandDefinition = self._wavebandDefinitions[index]
        assert isinstance(wavebandDefinition, WavebandDefinition)
        return wavebandDefinition

    def wavebandDefinitions(self):
        '''Return iterator over all :class:`WavebandDefinition`'s.'''
        for index in range(self.wavebandCount()):
            yield self.wavebandDefinition(index=index)

    def wavebandCount(self):
        '''Return number of wavebands.'''
        return len(self._wavebandDefinitions)

    def plot(self, plotWidget=None, yscale=1., **kwargs):
        '''
        Return sensor definition plot.

        :param plotWidget: if None, a new plot widget is created, otherwise, the given ``plotWidget`` is used
        :type plotWidget: pyqtgraph.graphicsWindows.PlotWindow
        :param yscale: scale factor for y values
        :type yscale: float
        :param kwargs: passed to ``pyqtgraph.graphicsWindows.PlotWindow.plot``
        :rtype: pyqtgraph.graphicsWindows.PlotWindow

        :example:

        >>> plotWidget = SensorDefinition.fromPredefined(name='sentinel2').plot()

        .. image:: plots/sensorDefinition_plot.png

        '''

        import pyqtgraph as pg
        if plotWidget is None:
            plotWidget = pg.plot()

        for wavebandDefinition in self.wavebandDefinitions():
            wavebandDefinition.plot(plotWidget=plotWidget, yscale=yscale, **kwargs)

        return plotWidget

    def resampleRaster(self, filename, raster, minResponse=None, resampleAlg=RESAMPLE_LINEAR, **kwargs):
        '''
        Resample the given spectral ``raster``.

        :param filename: output path
        :type filename: str
        :param raster: spectral raster
        :type raster: hubflow.core.Raster
        :param minResponse: limits the wavelength region of the response filter function to wavelength with responses higher than ``minResponse``;
                            higher values speed up computation; 0.5 corresponds to the full width at half maximum region;
                            values greater 0.5 may lead to very inaccurate results
        :type minResponse: float
        :param resampleAlg: available resampling algorithms are linear interpolation between neighbouring wavelength
                            and response function filtering
        :type resampleAlg: enum(SensorDefinition.RESAMPLE_LINEAR, SensorDefinition.RESAMPLE_RESPONSE)
        :param kwargs: passed to hubflow.core.Applier
        :rtype: Union[hubflow.core.Raster, None]

        :example:

        >>> from tests import enmapboxtestdata
        >>> sentinel2Sensor = SensorDefinition.fromPredefined(name='sentinel2')
        >>> enmapRaster = Raster(filename=enmapboxtestdata.enmap)
        >>> resampled = sentinel2Sensor.resampleRaster(filename='/vsimem/resampledLinear.bsq', raster=enmapRaster)
        >>> pixel = Pixel(x=0, y=0)
        >>> plotWidget = enmapRaster.plotZProfile(pixel=pixel, spectral=True, xscale=1000) # draw original enmap profile
        >>> plotWidget = resampled.plotZProfile(pixel=pixel, spectral=True, plotWidget=plotWidget) # draw resampled profile on top

        .. image:: plots/sensorDefinition_resampleRaster.png
        '''
        assert isinstance(raster, Raster)
        if resampleAlg is None:
            resampleAlg = self.RESAMPLE_LINEAR
        assert resampleAlg in self.RESAMPLE_OPTIONS
        sourceSensor = raster.sensorDefinition()
        applier = Applier(defaultGrid=raster, **kwargs)
        applier.setFlowRaster('raster', raster=raster)
        applier.setOutputRaster('outraster', filename=filename)
        applier.apply(operatorType=_SensorDefinitionResampleRaster, raster=raster,
            targetSensor=self, sourceSensor=sourceSensor, minResponse=minResponse, resampleAlg=resampleAlg)
        return Raster(filename=filename)

    def resampleProfiles(self, array, wavelength, wavelengthUnits, minResponse=None, resampleAlg=None, **kwargs):
        '''
        Resample a list of profiles given as a 2d ``array`` of size (profiles, bands).

        Implementation: the ``array``, together with the ``wavelength`` and ``wavelengthUnits`` metadata,
        is turned into a spectral raster, which is resampled using :class:~hubflow.core.SensorDefinition.resampleRaster``.

        :param array: list of profiles or 2d array of size (profiles, bands)
        :type array: Union[list, numpy.ndarray]
        :param wavelength: list of center wavelength of size (bands, )
        :type wavelength: List[float]
        :param wavelengthUnits: wavelength unit 'nanometers' | 'micrometers'
        :type wavelengthUnits: str
        :param minResponse: passed to :meth:`~hubflow.core.SensorDefinition.resampleRaster`
        :param resampleAlg: passed to :meth:`~hubflow.core.SensorDefinition.resampleRaster`
        :param kwargs: passed to :class:`~hubflow.core.SensorDefinition.resampleRaster`
        :rtype: numpy.ndarray

        :example:

        >>> from tests import enmapboxtestdata        >>> import pyqtgraph as pg
        >>> sentinel2Sensor = SensorDefinition.fromPredefined(name='sentinel2')
        >>> enmapRaster = Raster(filename=enmapboxtestdata.enmap)
        >>> enmapArray = enmapRaster.array().reshape((enmapRaster.shape()[0], -1)).T
        >>> resampled = sentinel2Sensor.resampleProfiles(array=enmapArray, wavelength=enmapRaster.metadataWavelength(), wavelengthUnits='nanometers')
        >>> index = 0 # select single profile
        >>> plotWidget = pg.plot(x=enmapRaster.metadataWavelength(), y=enmapArray[index]) # draw original enmap profile
        >>> plotWidget = plotWidget.plot(x=[wd.center() for wd in sentinel2Sensor.wavebandDefinitions()], y=resampled[index]) # draw resampled profile on top

        .. image:: plots/sensorDefinition_resampleProfiles.png
        '''
        array = np.array(array)
        assert array.ndim == 2
        samples, bands = array.shape
        assert len(wavelength) == bands

        rasterDataset = RasterDataset.fromArray(array=np.atleast_3d(array.T),
            filename='/vsimem/SensorDefinitionResampleInProfiles.bsq',
            driver=EnviDriver())
        MetadataEditor.setBandCharacteristics(rasterDataset=rasterDataset,
            wavelength=wavelength,
            wavelengthUnits=wavelengthUnits)
        rasterDataset.flushCache()
        raster = Raster.fromRasterDataset(rasterDataset=rasterDataset)
        outraster = self.resampleRaster(filename='/vsimem/SensorDefinitionResampleOutProfiles.bsq',
            raster=raster, minResponse=minResponse, resampleAlg=resampleAlg, **kwargs)
        outarray = outraster.dataset().readAsArray().T[0]
        return outarray


class _SensorDefinitionResampleRaster(ApplierOperator):
    def ufunc(self, raster, targetSensor, sourceSensor, minResponse, resampleAlg):
        assert isinstance(targetSensor, SensorDefinition)
        assert isinstance(sourceSensor, SensorDefinition)
        self.targetSensor = targetSensor
        self.sourceSensor = sourceSensor
        self.raster = raster

        if minResponse is None:
            minResponse = 0.001
        self.minResponse = minResponse

        self.marray = self.flowMaskArray(name='raster', mask=raster.asMask(indices=[0]))

        # resample
        if resampleAlg == SensorDefinition.RESAMPLE_LINEAR:
            outarray = self.resampleWithLinearInterpolation()
        elif resampleAlg == SensorDefinition.RESAMPLE_RESPONSE:
            outarray = self.resampleWithResponseFunction()

        # mask
        if raster.dataset().noDataValue() is not None:
            outarray[:, np.logical_not(self.marray[0])] = raster.dataset().noDataValue()

        # write and set metadata
        outraster = self.outputRaster.raster(key='outraster')
        outraster.setArray(array=outarray)
        self.setFlowMetadataSensorDefinition(name='outraster', sensor=targetSensor)
        outraster.setNoDataValue(value=raster.dataset().noDataValue())

    def resampleWithResponseFunction(self):
        outarray = self.full(value=0, bands=self.targetSensor.wavebandCount(), dtype=np.float32)
        for outindex, wavebandDefinition in enumerate(self.targetSensor.wavebandDefinitions()):
            assert isinstance(wavebandDefinition, WavebandDefinition)
            weights = wavebandDefinition.resamplingWeights(sensor=self.sourceSensor)
            weightsSum = 0.

            for inindex, weight in enumerate(weights):
                if weight > self.minResponse:
                    weightsSum += weight
                    invalues = self.flowRasterArray(name='raster',
                        raster=self.raster,
                        indices=[inindex])[self.marray]
                    outarray[outindex][self.marray[0]] += weight * invalues
            if weightsSum == 0:  # if no source bands are inside the responsive region of the target band
                import warnings
                warnings.warn(Warning(
                    'target waveband ({}, from {} nm to {} nm) is outside the responsive region'.format(
                        wavebandDefinition.name(), wavebandDefinition.responses()[0][0],
                        wavebandDefinition.responses()[-1][0])))
                outarray[outindex] = np.nan
            else:
                outarray[outindex][self.marray[0]] /= weightsSum

        return outarray

    def resampleWithLinearInterpolation(self):
        outarray = self.full(value=0, bands=self.targetSensor.wavebandCount(), dtype=np.float32)
        incenters = [wd.center() for wd in self.sourceSensor.wavebandDefinitions()]
        outcenters = [wd.center() for wd in self.targetSensor.wavebandDefinitions()]

        for outindex, outcenter in enumerate(outcenters):
            if outcenter <= incenters[0]:
                indexA = indexB = 0
                wA = wB = 0.5
            elif outcenter >= incenters[-1]:
                indexA = indexB = len(incenters) - 1
                wA = wB = 0.5
            else:
                for inindex, incenter in enumerate(incenters):
                    if incenter > outcenter:
                        indexA = inindex - 1
                        indexB = inindex
                        distanceA = np.abs(incenters[indexA] - outcenter)
                        distanceB = np.abs(incenters[indexB] - outcenter)
                        wA = 1. - distanceA / (distanceA + distanceB)
                        wB = 1. - wA
                        break

            inarrayA, inarrayB = self.flowRasterArray(name='raster', raster=self.raster, indices=[indexA, indexB])
            outarray[outindex][self.marray[0]] = inarrayA[self.marray[0]] * wA + inarrayB[self.marray[0]] * wB
        return outarray


class EnviSpectralLibrary(FlowObject):
    '''Class for managing ENVI Spectral Library files.'''

    def __init__(self, filename):
        '''Create an instance for given ``filename``.'''
        self._filename = filename

    def __getstate__(self):
        return OrderedDict([('filename', self.filename())])

    def filename(self):
        '''Return the filename.'''
        return self._filename

    def raster(self, transpose=True):
        '''
        Return a :class:`~hubflow.core.Raster` pointing to the library's binary file.

        If ``transpose=False`` the binary raster is a single band, where profiles are arranged along the y axis and wavebands along the x axis.
        If ``transpose=True`` profiles are still arranged along the y axis, but wavebands are transpose into the z axis,
        which matches the natural order of spectral raster files, and enables the direct application of various
        raster processing algorithms to library profiles.

        :param transpose: whether or not to transpose the profiles from [1, profiles, bands] to [bands, profiles, 1] order.
        :type transpose: bool
        :rtype: hubflow.core.Raster

        :example:

        >>> import enmapboxtestdata
        >>> # as single column multiband raster
        >>> EnviSpectralLibrary(filename=enmapboxtestdata.speclib).raster().shape()
        (177, 75, 1)
        >>> # as single band raster (original ENVI format)
        >>> EnviSpectralLibrary(filename=enmapboxtestdata.speclib).raster(transpose=False).shape()
        (1, 75, 177)
        '''

        filename = r'/vsimem/{filename}{transpose}.vrt'.format(filename=self.filename(),
            transpose='.transposed' if transpose else '')
        try:
            raster = Raster(filename=filename)
            raster.dataset()  # try opening
        except (RuntimeError, errors.InvalidGDALDatasetError):
            metadata = ENVI.readHeader(filenameHeader=ENVI.findHeader(filenameBinary=self.filename()))
            gdalType = ENVI.gdalType(enviType=int(metadata['data type']))
            bytes = ENVI.typeSize(enviType=int(metadata['data type']))
            byteOrder = ['LSB', 'MSB'][int(metadata['byte order'])]

            profiles = int(metadata['lines'])
            bands = int(metadata['samples'])
            options = 'subclass=VRTRawRasterBand\n' \
                      'SourceFilename={SourceFilename}\n' \
                      'ByteOrder={ByteOrder}\n'

            if transpose:
                rasterDataset = RasterDriver(name='VRT').create(grid=PseudoGrid(size=RasterSize(x=1, y=profiles)),
                    bands=0, gdalType=gdalType, filename=filename)

                options += 'ImageOffset={ImageOffset}\n' \
                           'PixelOffset={PixelOffset}\n' \
                           'LineOffset={LineOffset}'

                for band in range(bands):
                    rasterDataset.gdalDataset().AddBand(datatype=gdalType,
                        options=options.format(SourceFilename=self.filename(),
                            ByteOrder=byteOrder,
                            ImageOffset=band * bytes,
                            PixelOffset=bands * bytes,
                            LineOffset=bands * bytes).split('\n'))

            else:
                rasterDataset = RasterDriver(name='VRT').create(grid=PseudoGrid(size=RasterSize(x=bands, y=profiles)),
                    bands=0, gdalType=gdalType, filename=filename)

                rasterDataset.gdalDataset().AddBand(datatype=gdalType,
                    options=options.format(SourceFilename=self.filename(),
                        ByteOrder=byteOrder).split('\n'))

            for key in ['file compression', 'band names']:
                metadata.pop(key=key, default=None)

            rasterDataset.setMetadataDomain(metadataDomain=metadata, domain='ENVI')

            rasterDataset.flushCache()
            rasterDataset.close()
            raster = Raster(filename=filename)

        return raster

    @staticmethod
    def fromRaster(filename, raster, spectraNames=None):
        '''
        Create instance from given ``raster``.

        :param filename: output path
        :type filename:
        :param raster: input raster
        :type raster: hubflow.core.Raster
        :return:
        :rtype: EnviSpectralLibrary

        :example:

        >>> import enmapboxtestdata, tempfile, os
        >>> raster = EnviSpectralLibrary(filename=enmapboxtestdata.speclib).raster()
        >>> library = EnviSpectralLibrary.fromRaster(filename='/vsimem/speclib.sli', raster=raster)
        >>> library
        EnviSpectralLibrary(filename=/vsimem/speclib.sli)
        '''
        assert isinstance(raster, Raster)
        bands = raster.dataset().zsize()
        array = raster.dataset().readAsArray().reshape(bands, -1).T[None]
        profiles = array.shape[1]
        rasterDataset = RasterDataset.fromArray(array=array, grid=PseudoGrid.fromArray(array=array),
            filename=filename, driver=EnviDriver())
        metadata = raster.dataset().metadataDomain(domain='ENVI')
        for key in ['file compression']:
            metadata.pop(key, None)
        if spectraNames is None:
            spectraNames = ['profile {}'.format(i + 1) for i in range(profiles)]
        assert len(spectraNames) == profiles
        metadata['spectra names'] = spectraNames
        rasterDataset.setMetadataDomain(metadataDomain=metadata, domain='ENVI')
        rasterDataset.band(0).setDescription('Spectral Library')
        rasterDataset.flushCache()
        rasterDataset.close()

        # fix header

        # - delete PAM header
        try:
            remove(filename + '.aux.xml')
        except:
            pass

        # - rewrite ENVI header
        try:
            filenameHeader = ENVI.findHeader(filenameBinary=filename)
            metadata = ENVI.readHeader(filenameHeader=filenameHeader)
            metadata['file type'] = 'ENVI Spectral Library'
            for key in ['file compression', 'coordinate system string', 'map info', 'projection_info', 'x start',
                        'y start']:
                metadata.pop(key, None)
            ENVI.writeHeader(filenameHeader=filenameHeader, metadata=metadata)
        except:
            pass
        return EnviSpectralLibrary(filename=filename)

    @classmethod
    def fromSample(cls, sample, filename):

        assert isinstance(sample, Sample)

        if isinstance(sample, ClassificationSample):
            filenames = ['/vsimem/{}/raster.bsq', '/vsimem/{}/classification.bsq', ]
            raster, classification = sample.extractAsRaster(filenames=filenames)
            cls.fromRaster(filename=filename, raster=raster)
            labels = classification.array().flatten()
            # names = np.full_like(labels, fill_value=classification.classDefinition().noDataName(), dtype=np.str)
            classNames = [classification.classDefinition().name(label) for label in labels]
            spectraNames = ['profile {}'.format(i + 1) for i in range(len(labels))]

            table = list()
            table.append(('names', spectraNames))
            table.append(('id', classNames))
            definitions = dict()
            definitions['id'] = AttributeDefinitionEditor.makeClassDefinitionDict(
                classDefinition=sample.classification().classDefinition())
            ENVI.writeAttributeTable(filename=filename, table=table)
            AttributeDefinitionEditor.writeToJson(filename=ENVI.findHeader(filename).replace('.hdr', '.json'),
                definitions=definitions)

        else:
            raise errors.TypeError(sample)

        for f in filenames:
            gdal.Unlink(f)

        return EnviSpectralLibrary(filename=filename)

    def attributeTable(self, delimiter=','):
        '''Return attribute table as dictionary.'''

        result = OrderedDict()
        filenameCsv = '{}.csv'.format(splitext(self.filename())[0])
        if filenameCsv is not None:
            array = np.genfromtxt(filenameCsv, delimiter=delimiter, dtype=np.str)
            keys = list(array[0])
            values = array[1:].T
            for i, (key, value) in enumerate(zip(keys, values)):
                value = [str(v) for v in value]
                result[key] = value
        return result

    def attributeDefinitions(self):
        '''Return attribute definitions as dictionary.'''

        filenameJson = '{}.json'.format(splitext(self.filename())[0])
        if filenameJson is not None:
            definitions = AttributeDefinitionEditor.readFromJson(filename=filenameJson)
        else:
            definitions = dict()

        return definitions

    def attributeClassDefinition(self, attribute):
        '''Return ClassDefinition for given attribute.'''
        return AttributeDefinitionEditor.makeClassDefinition(definitions=self.attributeDefinitions(),
            attribute=attribute)

    def attributeNames(self):
        '''
        Return attribute names.

        :example:

        >>> import enmapboxtestdata
        >>> EnviSpectralLibrary(filename=enmapboxtestdata.library).attributeNames()
        ['level 1', 'level 2', 'spectra names']
        '''
        return list(self.attributeTable().keys())

    def profiles(self):
        '''
        Return the number of profiles.

        :example:

        >>> import enmapboxtestdata
        >>> EnviSpectralLibrary(filename=enmapboxtestdata.speclib).profiles()
        75
        '''
        return self.raster().dataset().ysize()


# class RasterStack(FlowObject):
#     '''
#     Class for managing virtual raster stacks that can be used inside an :class:`~hubflow.core.Applier`.
#
#     :example:
#
#     Stack two rasters virtually and read on-the-fly stacked data inside Applier.
#
#     >>> raster = RasterStack(rasters=[Raster.fromArray(array=[[[1, 1], [1, 1]]], filename='/vsimem/raster1.bsq'),
#     ...                               Raster.fromArray(array=[[[2, 2], [2, 2]]], filename='/vsimem/raster2.bsq')])
#     >>>
#     >>> applier = Applier(progressBar=SilentProgressBar())
#     >>> applier.setFlowRaster(name='stack', raster=raster)
#     >>>
#     >>> class MyOperator(ApplierOperator):
#     ...     def ufunc(self, raster):
#     ...         array = self.flowRasterArray(name='stack', raster=raster)
#     ...         return array
#     >>>
#     >>> applier.apply(operatorType=MyOperator, raster=raster) # doctest: +NORMALIZE_WHITESPACE
#     [array([[[1, 1],
#              [1, 1]],
#     <BLANKLINE>
#             [[2, 2],
#              [2, 2]]])]
#     '''
#
#     def __init__(self, rasters):
#         self._rasters = rasters
#
#     def __getstate__(self):
#         return OrderedDict([('rasters', list(self.rasters()))])
#
#     def raster(self, index):
#         '''Return raster at given ``index``'''
#         assert isinstance(self._rasters[index], Raster)
#         return self._rasters[index]
#
#     def rasters(self):
#         '''Return iterator over all raster.'''
#         for i in range(len(self._rasters)):
#             yield self.raster(i)


class Mask(Raster):
    def __init__(self, filename, noDataValues=None, minOverallCoverage=0.5, indices=None, invert=False):
        '''
        Class for managing raster mask maps.

        :param filename: input path
        :type filename: str
        :param noDataValues: if not None, it overwrites the band no data values given by the raster.
        :type noDataValues: List[floats] or None
        :param minOverallCoverage: in case of resampling, values between 0 (False) and 1 (True) can ocure,
                                   minOverallCoverage is the threshold at which those "soft" mask values are categoriest into True and False
        :type minOverallCoverage: float
        :param indices: if not None, only the bands corresponding to the given indices are used as mask
        :type indices: List[int]
        :param invert: whether to invert the mask
        :type invert: bool

        :examples:

        If a raster is interpreted as a mask, all pixel that contain a no data value in at leased one band are interpreted as False.

        >>> raster = Raster.fromArray(array=[[[-99, 0], [1, 2]], [[-99, 0], [1, 0]]], noDataValues=[-99, -99], filename='/vsimem/raster.bsq')
        >>> raster.array()
        array([[[-99,   0],
                [  1,   2]],
        <BLANKLINE>
               [[-99,   0],
                [  1,   0]]])

        >>> Mask(filename=raster.filename()).array()
        array([[[0, 1],
                [1, 1]]], dtype=uint8)

        Masks can be inverted.
        >>> Mask(filename=raster.filename(), invert=True).array()
        array([[[1, 0],
                [0, 0]]], dtype=uint8)

        If no data values are not defined, it defaults to zero in all bands.

        >>> rasterWithoutNoData = Raster.fromArray(array=[[[-99, 0], [1, 2]], [[-99, 0], [1, 0]]], filename='/vsimem/rasterWithoutNoData.bsq')
        >>> Mask(filename=rasterWithoutNoData.filename()).array()
        array([[[1, 0],
                [1, 0]]], dtype=uint8)

        Missing no data values can be passed to the mask constructor.

        >>> Mask(filename=rasterWithoutNoData.filename(), noDataValues=[-99, -99]).array()
        array([[[0, 1],
                [1, 1]]], dtype=uint8)

        Pick a single band as mask from a multiband raster.

        >>> Mask(filename=rasterWithoutNoData.filename(), indices=[0]).array()
        array([[[1, 0],
                [1, 1]]], dtype=uint8)
        >>> Mask(filename=rasterWithoutNoData.filename(), indices=[1]).array()
        array([[[1, 0],
                [1, 0]]], dtype=uint8)

        '''
        Raster.__init__(self, filename)
        if noDataValues is None:
            noDataValues = self.dataset().noDataValues(default=0)
        assert len(noDataValues) == self.dataset().zsize()
        self._noDataValues = noDataValues
        self._minOverallCoverage = float(minOverallCoverage)
        self._indices = indices
        self._invert = invert

    def noDataValues(self):
        '''Return band no data values.'''
        assert isinstance(self._noDataValues, list)
        return self._noDataValues

    def minOverallCoverage(self):
        '''Return minimal overall coverage threshold.'''
        return self._minOverallCoverage

    def indices(self):
        '''Return band subset indices.'''
        return self._indices

    def invert(self):
        '''Whether to invert the mask.'''
        return self._invert

    @staticmethod
    def fromVector(filename, vector, grid, **kwargs):
        '''Create a mask from a vector.

        :param filename: output path
        :type filename:
        :param vector: input vector
        :type vector: hubflow.core.Vector
        :param grid:
        :type grid: hubdc.core.Grid
        :param kwargs:
        :type kwargs:
        :return:
        :rtype: hubflow.core.Mask

        :example:

        >>> import enmapboxtestdata
        >>> vector = Vector(filename=enmapboxtestdata.landcover, initValue=0)
        >>> grid = Raster(filename=enmapboxtestdata.enmap).grid()
        >>> mask = Mask.fromVector(filename='/vsimem/mask.bsq', vector=vector, grid=grid)
        >>> plotWidget = mask.plotSinglebandGrey()

        .. image:: plots/mask_fromVector.png
        '''
        return Raster.fromVector(filename=filename, vector=vector, grid=grid, **kwargs).asMask()

    @staticmethod
    def fromRaster(filename, raster, initValue=False, true=(), false=(),
            invert=False, aggregateFunction=None, **kwargs):
        '''
        Returns a mask created from a raster map, where given lists of ``true`` and ``false`` values and value ranges
        are used to define True and False regions.

        :param filename: output path
        :type filename:
        :param raster: input raster
        :type raster: hubflow.core.Raster
        :param initValue: initial fill value, default is False
        :type initValue: bool
        :param true: list of forground numbers and ranges
        :type true: List[number or range]
        :param false: list of forground numbers and ranges
        :type false: List[number or range]
        :param invert: whether to invert the mask
        :type invert: bool
        :param aggregateFunction: aggregation function (e.g. numpy.all or numpy.any) to reduce multiband rasters to a single band mask;
                                  the default is to not reduce and returning a multiband mask
        :type aggregateFunction: func
        :param kwargs: passed to hubflow.core.Applier
        :type kwargs:
        :return: hubflow.core.Mask
        :rtype:

        :example:

        >>> raster = Raster.fromArray(array=[[[-99, 1, 2, 3, 4, 5]]], filename='/vsimem/raster.bsq')
        >>> raster.array()
        array([[[-99,   1,   2,   3,   4,   5]]])
        >>> # values 1, 2, 3 are True
        >>> Mask.fromRaster(raster=raster, true=[1, 2, 3], filename='/vsimem/mask.bsq').array()
        array([[[0, 1, 1, 1, 0, 0]]], dtype=uint8)
        >>> # value range 1 to 4 is True
        >>> Mask.fromRaster(raster=raster, true=[range(1, 4)], filename='/vsimem/mask.bsq').array()
        array([[[0, 1, 1, 1, 1, 0]]], dtype=uint8)
        >>> # all values are True, but -99
        >>> Mask.fromRaster(raster=raster, initValue=True, false=[-99], filename='/vsimem/mask.bsq').array()
        array([[[0, 1, 1, 1, 1, 1]]], dtype=uint8)

        Different aggregations over multiple bands

        >>> raster = Raster.fromArray(array=[[[0, 0, 1, 1]], [[0, 1, 0, 1]]], filename='/vsimem/raster.bsq')
        >>> raster.array()
        array([[[0, 0, 1, 1]],
        <BLANKLINE>
               [[0, 1, 0, 1]]])
        >>> # no aggregation
        >>> Mask.fromRaster(raster=raster, true=[1], filename='/vsimem/mask.bsq').readAsArray()
        array([[[0, 0, 1, 1]],
        <BLANKLINE>
               [[0, 1, 0, 1]]], dtype=uint8)
        >>> # True if all pixel profile values are True
        >>> def aggregate(array): return np.all(array, axis=0)
        >>> Mask.fromRaster(raster=raster, true=[1], aggregateFunction=aggregate, filename='/vsimem/mask.bsq').readAsArray()
        array([[[0, 0, 0, 1]]], dtype=uint8)

        >>> # True if any pixel profile values are True
        >>> def aggregate(array): return np.any(array, axis=0)
        >>> Mask.fromRaster(raster=raster, true=[1], aggregateFunction=aggregate, filename='/vsimem/mask.bsq').readAsArray()
        array([[[0, 1, 1, 1]]], dtype=uint8)

        '''
        assert isinstance(raster, Raster)
        applier = Applier(defaultGrid=raster, **kwargs)
        applier.setFlowRaster('raster', raster=raster)
        applier.setOutputRaster('mask', filename=filename)
        applier.apply(operatorType=_MaskFromRaster, raster=raster, initValue=initValue,
            true=true, false=false, invert=invert,
            aggregateFunction=aggregateFunction)
        return Mask(filename=filename)

    def resample(self, filename, grid, **kwargs):
        '''
        Returns a resampled mask of itself into the given ``grid``.

        :param filename: output path
        :type filename: str
        :param grid: output grid
        :type grid: hubdc.core.Grid
        :param kwargs: passed to hubflow.core.Applier
        :type kwargs:
        :return:
        :rtype: hubflow.core.Mask

        :example:

        >>> mask = Mask.fromArray(array=[[[0, 1]]], filename='/vsimem/mask.bsq')
        >>> grid = Grid(extent=mask.grid().extent(), resolution=mask.grid().resolution().zoom(factor=(2, 1)))
        >>> mask.resample(grid=grid, filename='/vsimem/resampled.bsq').array()
        array([[[0, 0, 1, 1]]], dtype=uint8)
        '''
        applier = Applier(defaultGrid=grid, **kwargs)
        applier.setFlowMask('inmask', mask=self)
        applier.setOutputRaster('outmask', filename=filename)
        applier.apply(operatorType=_MaskResample, mask=self)
        return Mask(filename=filename, minOverallCoverage=self.minOverallCoverage())


class _MaskResample(ApplierOperator):
    def ufunc(self, mask):
        array = self.flowMaskArray('inmask', mask=mask)
        self.outputRaster.raster(key='outmask').setArray(array=array)


class _MaskFromRaster(ApplierOperator):
    def ufunc(self, raster, initValue, true, false, invert,
            aggregateFunction=None):
        array = self.flowRasterArray('raster', raster=raster)
        marray = np.full_like(array, fill_value=initValue, dtype=bool)

        for value in true:
            if isinstance(value, (int, float)):
                marray[array == value] = True
            elif isinstance(value, range):
                assert value.step == 1
                marray[(array >= value.start) * (array <= value.stop)] = True

        for value in false:
            if isinstance(value, (int, float)):
                marray[array == value] = False
            elif isinstance(value, range):
                assert value.step == 1
                marray[(array >= value.start) * (array <= value.stop)] = False

        if aggregateFunction is not None:
            marray = aggregateFunction(marray)
            assert (marray.ndim == 3 and len(marray) == 1) or marray.ndim == 2

        if invert:
            marray = np.logical_not(marray)

        self.outputRaster.raster(key='mask').setArray(array=marray)


class Vector(Map):
    '''Class for managing vector maps. See also :class:`~VectorMask`, :class:`~VectorClassification`'''

    def __init__(self, filename, layer=0, initValue=0, burnValue=1, burnAttribute=None, allTouched=False,
            filterSQL=None, dtype=np.float32, noDataValue=None):

        '''
        Create an instance from the vector located at ``filename``.

        :param filename: input path
        :type filename: str
        :param layer: layer index or name
        :type layer: int or str
        :param initValue: rasterization option - values to pre-initialize the output band
        :type initValue: number
        :param burnValue: rasterization option - fixed value to burn into output band for all objects; excusive with burnAttribute.
        :type burnValue: number
        :param burnAttribute: rasterization option - identifies an attribute field on the features to be used for a burn-in value; exclusive with burnValue
        :type burnAttribute: str
        :param allTouched: rasterization option - whether to enable the ALL_TOUCHED rasterization option so that all pixels touched by lines or polygons will be updated, not just those on the line render path, or whose center point is within the polygon
        :type allTouched: bool
        :param filterSQL: rasterization option - SQL filter statement to apply to the source dataset
        :type filterSQL: str
        :param dtype: rasterization option - output numpy data type
        :type dtype: type
        :param noDataValue: rasterization option - no data value
        :type noDataValue: None or float
        '''
        self._filename = filename
        self._layer = layer
        self._initValue = initValue
        self._burnValue = burnValue
        self._burnAttribute = burnAttribute
        self._allTouched = allTouched
        self._filterSQL = filterSQL
        self._dtype = dtype
        self._noDataValue = noDataValue
        self._vectorDataset = None

    def __getstate__(self):
        return OrderedDict([('filename', self.filename()),
                            ('layer', self.layer()),
                            ('initValue', self.initValue()),
                            ('burnValue', self.burnValue()),
                            ('burnAttribute', self.burnAttribute()),
                            ('allTouched', self.allTouched()),
                            ('filterSQL', self.filterSQL()),
                            ('dtype', self.dtype()),
                            ('noDataValue', self.noDataValue())])

    def filename(self):
        '''Return filename.'''
        return self._filename

    def layer(self):
        '''Return layer name or index'''
        return self._layer

    def initValue(self):
        '''Return rasterization initialization value.'''
        return self._initValue

    def burnValue(self):
        '''Return rasterization burn value.'''
        return self._burnValue

    def burnAttribute(self):
        '''Return rasterization burn attribute.'''
        return self._burnAttribute

    def allTouched(self):
        '''Return rasterization all touched option.'''
        return self._allTouched

    def filterSQL(self):
        '''Return rasterization SQL filter statement.'''
        return self._filterSQL

    def dtype(self):
        '''Return rasterization data type.'''
        return self._dtype

    def noDataValue(self, default=None):
        '''Return rasterization no data value.'''
        if self._noDataValue is None:
            return default
        return self._noDataValue

    def dataset(self):
        '''Return hubdc.core.VectorDataset object.'''
        if self._vectorDataset is None:
            self._vectorDataset = openVectorDataset(self.filename(), layerNameOrIndex=self.layer())
        assert isinstance(self._vectorDataset, VectorDataset)
        return self._vectorDataset

    @classmethod
    def fromVectorDataset(cls, vectorDataset, **kwargs):
        '''
        Create instance from ``vectorDataset``. Additional ``kwargs`` are passed to the contructor.

        :example:

        >>> import enmapboxtestdata
        >>> vectorDataset = Vector(filename=enmapboxtestdata.landcover).dataset()
        >>> Vector.fromVectorDataset(vectorDataset=vectorDataset) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
        Vector(filename=...LandCov_BerlinUrbanGradient.shp, layer=0, initValue=0, burnValue=1, burnAttribute=None, allTouched=False, filterSQL=None, dtype=<class 'numpy.float32'>, noDataValue=None)
        '''
        assert isinstance(vectorDataset, VectorDataset)
        vector = cls(vectorDataset.filename(), **kwargs)
        vector._vectorDataset = vectorDataset
        return vector

    @classmethod
    def fromPoints(cls, filename, points, attributes=None):
        '''
        Create instance from given points. Projection of first point is used.

        :example:

        >>> vector = Vector.fromPoints(points=[(-1, -1), (1, 1)], filename=join(tempfile.gettempdir(), 'vector.shp'))
        >>> grid = Grid(extent=Extent(xmin=-1.5, xmax=1.5, ymin=-1.5, ymax=1.5), resolution=1, projection=Projection.wgs84())
        >>> raster = Raster.fromVector(filename='/vsimem/raster.bsq', vector=vector, grid=grid)
        >>> raster.array()
        array([[[ 0.,  0.,  1.],
                [ 0.,  0.,  0.],
                [ 1.,  0.,  0.]]], dtype=float32)
        '''

        driver = VectorDriver.fromFilename(filename=filename)
        vectorDataset = VectorDataset.fromPoints(points=points, attributes=attributes, filename=filename, driver=driver)
        return Vector.fromVectorDataset(vectorDataset=vectorDataset)

    @classmethod
    def fromRandomPointsFromMask(cls, filename, mask, n, **kwargs):
        '''
        Draw random locations from raster mask and return as point vector.

        :param filename: output path
        :type filename: str
        :param mask: input mask
        :type mask: hubflow.core.Mask
        :param n: number of points
        :type n: int
        :param kwargs: passed to hubflow.core.Applier
        :type kwargs:
        :return:
        :rtype: hubflow.core.Vector

        :example:

        Create a mask, ...

        >>> import enmapboxtestdata
        >>> grid = Raster(filename=enmapboxtestdata.enmap).grid()
        >>> mask = Mask.fromVector(filename='/vsimem/mask.bsq',
        ...                        vector=Vector(filename=enmapboxtestdata.landcover), grid=grid)
        >>> mask.plotSinglebandGrey()

        .. image:: plots/mask_fromVector.png

        ... draw 10 random locations, ...

        >>> points = Vector.fromRandomPointsFromMask(mask=mask, n=10, filename=join(tempfile.gettempdir(), 'vector.shp'))

        ... and rasterize the result into the grid of the mask.

        >>> Mask.fromVector(filename='/vsimem/mask.bsq', vector=points, grid=grid).plotSinglebandGrey()

        .. image:: plots/vector_fromRandomPointsFromMask.png

        '''
        applier = Applier(**kwargs)
        applier.setFlowMask('mask', mask=mask)
        applier.apply(operatorType=_VectorFromRandomPointsFromMask, mask=mask, n=n, filename=filename)
        return Vector(filename=filename)

    @classmethod
    def fromRandomPointsFromClassification(cls, filename, classification, n, **kwargs):
        '''
        Draw stratified random locations from raster classification and return as point vector.

        :param filename: output path
        :type filename: str
        :param classification: input classification used as stratification
        :type classification:
        :param n: list of number of points, one for each class
        :type n: List[int]
        :param kwargs: passed to hubflow.core.Applier
        :type kwargs:
        :return:
        :rtype: hubflow.core.Vector

        :example:

        Create classification from landcover polygons, ...

        >>> import enmapboxtestdata
        >>> grid = Raster(filename=enmapboxtestdata.enmap).grid()
        >>> vectorClassification = VectorClassification(filename=enmapboxtestdata.landcover,
        ...                                             classAttribute=enmapboxtestdata.landcoverAttributes.Level_2_ID,
        ...                                             classDefinition=ClassDefinition(colors=enmapboxtestdata.landcoverClassDefinition.level2.lookup),
        ...                                             oversampling=5)
        >>> classification = Classification.fromClassification(filename='/vsimem/classification.bsq', classification=vectorClassification, grid=grid)
        >>> classification.plotCategoryBand()

        .. image:: plots/classification_fromClassification.png

        ... draw 10 random locations from each class, ...

        >>> points = Vector.fromRandomPointsFromClassification(classification=classification, n=[10]*6, filename=join(tempfile.gettempdir(), 'vector.shp'))

        ... apply those points as mask to the original classification

        >>> labels = classification.applyMask(filename='/vsimem/labels.bsq', mask=points)
        >>> labels.plotCategoryBand()

        .. image:: plots/classification_applyMask.png

        '''
        applier = Applier(**kwargs)
        applier.setFlowClassification('classification', classification=classification)
        applier.apply(operatorType=_VectorFromRandomPointsFromClassification, classification=classification, n=n,
            filename=filename)
        return Vector(filename=filename)

    def metadataItem(self, key, domain='', dtype=str, required=False, default=None):
        '''Returns the value (casted to a specific ``dtype``) of a metadata item.'''
        return self.dataset().metadataItem(key=key, domain=domain, dtype=dtype, required=required, default=default)

    def metadataDict(self):
        '''Return the metadata dictionary for all domains.'''
        return self.dataset().metadataDict()

    def uniqueValues(self, attribute, spatialFilter=None):
        '''
        Return unique values for given attribute.

        :param attribute:
        :type attribute: str
        :param spatialFilter: optional spatial filter
        :type spatialFilter: hubdc.core.Geometry
        :return:
        :rtype: List

        :example:

        >>> import enmapboxtestdata
        >>> vector = Vector(filename=enmapboxtestdata.landcover)
        >>> vector.uniqueValues(attribute=enmapboxtestdata.landcoverAttributes.Level_2)
        ['Low vegetation', 'Other', 'Pavement', 'Roof', 'Soil', 'Tree']

        >>> spatialFilter = SpatialExtent(xmin=384000, xmax=384800,
        ...                               ymin=5818000, ymax=5819000,
        ...                               projection=vector.projection()).geometry()
        >>> spatialFilter # doctest: +ELLIPSIS
        SpatialGeometry(wkt='POLYGON ((384000 5819000 0,384800 5819000 0,384800 5818000 0,384000 5818000 0,384000 5819000 0))', projection=Projection(wkt=PROJCS["WGS_1984_UTM_Zone_33N", ..., AUTHORITY["EPSG","32633"]]))
        >>> vector.uniqueValues(attribute=enmapboxtestdata.landcoverAttributes.Level_2,
        ...                     spatialFilter=spatialFilter)
        ['Low vegetation', 'Pavement', 'Roof', 'Tree']
        '''

        vector = openVectorDataset(filename=self.filename(), layerNameOrIndex=self.layer())
        layer = vector.ogrLayer()

        if attribute not in vector.fieldNames():
            raise errors.UnknownAttributeTableField(name=attribute)

        layer.SetAttributeFilter(self.filterSQL())
        values = OrderedDict()
        if spatialFilter is not None:
            assert isinstance(spatialFilter, Geometry)
            spatialFilterReprojected = spatialFilter.reproject(projection=vector.projection())
            layer.SetSpatialFilter(spatialFilterReprojected.ogrGeometry())
        for feature in layer:
            values[feature.GetField(attribute)] = None
        return list(values.keys())

    def extent(self):
        '''
        Returns the spatial extent.

        :example:

        >>> import enmapboxtestdata
        >>> Vector(filename=enmapboxtestdata.landcover).extent() # doctest: +ELLIPSIS
        SpatialExtent(xmin=383918.24389999924, xmax=384883.2196000004, ymin=5815685.854300001, ymax=5818407.0616999995, projection=Projection(wkt=PROJCS["WGS_1984_UTM_Zone_33N", GEOGCS["GCS_WGS_1984", DATUM["WGS_1984", SPHEROID["WGS_84",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295], AUTHORITY["EPSG","4326"]], ..., AUTHORITY["EPSG","32633"]]))
        '''
        return self.dataset().extent()

    def projection(self):
        '''
        Returns the projection.

        :example:

        >>> import enmapboxtestdata
        >>> Vector(filename=enmapboxtestdata.landcover).projection() # doctest: +ELLIPSIS
        Projection(wkt=PROJCS["WGS_1984_UTM_Zone_33N", GEOGCS["GCS_WGS_1984", DATUM["WGS_1984", SPHEROID["WGS_84",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295], AUTHORITY["EPSG","4326"]], PROJECTION["Transverse_Mercator"], PARAMETER["latitude_of_origin",0], PARAMETER["central_meridian",15], PARAMETER["scale_factor",0.9996], PARAMETER["false_easting",500000], PARAMETER["false_northing",0], UNIT["Meter",1], AUTHORITY["EPSG","32633"]])
        '''
        return self.dataset().projection()

    def grid(self, resolution):
        '''
        Returns the grid for the given ``resolution``.

        :example:

        >>> import enmapboxtestdata
        >>> Vector(filename=enmapboxtestdata.landcover).grid(resolution=30) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
        Grid(extent=Extent(xmin=383918.24389999924, xmax=384878.24389999924,
                           ymin=5815685.854300001, ymax=5818415.854300001),
             resolution=Resolution(x=30.0, y=30.0),
             projection=Projection(wkt=PROJCS["WGS_1984_UTM_Zone_33N", GEOGCS["GCS_WGS_1984", DATUM["WGS_1984", SPHEROID["WGS_84",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295], AUTHORITY["EPSG","4326"]], ..., AUTHORITY["EPSG","32633"]])
        '''
        return Grid(extent=self.extent(), resolution=resolution)

    def extractPixel(self, raster, filenameRaster, filenameRegression, noDataValues=None):

        assert isinstance(raster, Raster)

        rasterValues, vectorValues, fids = self.dataset().extractPixel(rasterDataset=raster.dataset())

        rasterArray = np.atleast_3d(rasterValues)
        array = np.array(list(vectorValues.values()))
        array = np.atleast_3d(array)

        raster = Raster.fromArray(array=rasterArray, filename=filenameRaster)

        if noDataValues is None:
            try:
                noDataValues = np.iinfo(array.dtype).min
            except ValueError:
                noDataValues = np.finfo(array.dtype).min

        regression = Regression.fromArray(array=array, filename=filenameRegression,
            noDataValues=noDataValues,
            descriptions=list(vectorValues.keys()))
        return raster, regression


class _VectorFromRandomPointsFromMask(ApplierOperator):
    def ufunc(self, mask, n, filename):
        array = self.flowMaskArray('mask', mask=mask)
        xmap = self.subgrid().xMapCoordinatesArray()[array[0]]
        ymap = self.subgrid().yMapCoordinatesArray()[array[0]]
        return xmap, ymap

    @staticmethod
    def aggregate(blockResults, grid, mask, n, filename):
        assert isinstance(grid, Grid)
        xmap = np.concatenate([result[0] for result in blockResults])
        ymap = np.concatenate([result[1] for result in blockResults])
        indicis = np.arange(0, len(xmap), 1)
        indicis = np.random.choice(indicis, size=n, replace=False)
        xmap, ymap = xmap[indicis], ymap[indicis]

        # Create the output vector
        driver = VectorDriver.fromFilename(filename=filename)
        driver.prepareCreation(filename)
        ds = driver.ogrDriver().CreateDataSource(filename)
        srs = grid.projection().osrSpatialReference()
        layer = ds.CreateLayer('random_points', srs, ogr.wkbPoint)

        for x, y in zip(xmap, ymap):
            point = ogr.Geometry(ogr.wkbPoint)
            point.AddPoint(x, y)
            feature = ogr.Feature(layer.GetLayerDefn())
            feature.SetGeometry(point)
            layer.CreateFeature(feature)

        layer = None
        ds = None


class _VectorFromRandomPointsFromClassification(ApplierOperator):
    def ufunc(self, classification, n, filename):
        array = self.flowClassificationArray('classification', classification=classification)
        valid = array != 0
        xmap = self.subgrid().xMapCoordinatesArray()[valid[0]]
        ymap = self.subgrid().yMapCoordinatesArray()[valid[0]]
        id = array[valid]
        return xmap, ymap, id

    @staticmethod
    def aggregate(blockResults, grid, classification, n, filename):
        assert isinstance(grid, Grid)
        assert len(n) == classification.classDefinition().classes(), 'n must be a list with length of number of classes'
        xmap = np.concatenate([result[0] for result in blockResults])
        ymap = np.concatenate([result[1] for result in blockResults])
        id = np.concatenate([result[2] for result in blockResults])
        indicis = np.arange(0, len(xmap), 1)
        indicisList = list()
        for i, ni in enumerate(n):
            indicisi = indicis[id == i + 1]
            ni = min(ni, len(indicisi))
            indicisi = np.random.choice(indicisi, size=ni, replace=False)
            indicisList.append(indicisi)

        indicis = np.concatenate(indicisList)
        xmap, ymap = xmap[indicis], ymap[indicis]

        # Create the output vector
        driver = VectorDriver.fromFilename(filename=filename)
        driver.prepareCreation(filename)

        ds = driver.ogrDriver().CreateDataSource(filename)
        if ds is None:
            raise Exception('OGR data source creation failed: {}'.format(filename))
        srs = grid.projection().osrSpatialReference()
        layer = ds.CreateLayer('random_points', srs, ogr.wkbPoint)

        for x, y in zip(xmap, ymap):
            point = ogr.Geometry(ogr.wkbPoint)
            point.AddPoint(x, y)
            feature = ogr.Feature(layer.GetLayerDefn())
            feature.SetGeometry(point)
            layer.CreateFeature(feature)

        layer = None
        ds = None


class VectorMask(Vector):
    '''Class for managing vector masks.'''

    def __init__(self, filename, invert=False, **kwargs):
        '''
        Create new instace.

        :param filename: input path
        :type filename: str
        :param invert: whether to invert the mask
        :type invert: bool
        :param kwargs: passed to Vector
        :type kwargs: Dict
        '''
        Vector.__init__(self, filename=filename, **kwargs)
        self._kwargs = kwargs
        self._invert = invert

    def __getstate__(self):
        return OrderedDict([('filename', self.filename()),
                            ('invert', self.invert())]
                           + list(self.kwargs().items()))

    def invert(self):
        'Returns whether to invert the mask.'
        return self._invert

    def kwargs(self):
        'Returns additional keyword arguments.'
        return self._kwargs


class VectorClassification(Vector):
    '''Class for managing vector classifications.'''

    def __init__(self, filename, classAttribute, classDefinition=None, layer=0, minOverallCoverage=0.5,
            minDominantCoverage=0.5, dtype=np.uint8, oversampling=1):
        '''
        Creates a new instance.

        :param filename: input path
        :type filename: str
        :param classDefinition: if not provided, the namber of classes is assumed to be the largest value in the class attribute
                                with generic class names and random class colors
        :type classDefinition: hubflow.core.ClassDefinition
        :param classAttribute: numeric attribute with class ids from 1 to number of classes
        :type classAttribute: str
        :param layer: input layer name or index
        :type layer: int or str
        :param minOverallCoverage: in case of resampling, pixels that are not fully covered can ocure,
                                   minOverallCoverage is the threshold under which those pixels are rejected (i.e. set to unclassified)
        :type minOverallCoverage: float
        :param minDominantCoverage: in case of resampling, pixels that are not fully covered by a single class can ocure,
                                   minDominantCoverage is the threshold under which "not pure enought" pixels are rejected (i.e. set to unclassified)
        :type minDominantCoverage: float
        :param dtype: numpy datatype
        :type dtype: type
        :param oversampling: factor by which the target resolution is oversampled before aggregated back
        :type oversampling: int

        :example:

        >>> import enmapboxtestdata
        >>> classDefinition = ClassDefinition(names=['Roof', 'Pavement', 'Low vegetation', 'Tree', 'Soil', 'Other'],
        ...                                   colors=[(230, 0, 0),   (156, 156, 156),   (152, 230, 0),   (38, 115, 0),   (168, 112, 0), (245, 245, 122)])
        >>> vectorClassification = VectorClassification(filename=enmapboxtestdata.landcover, classAttribute='Level_2_ID', classDefinition=classDefinition)
        >>> vectorClassification # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
        VectorClassification(filename=...LandCov_BerlinUrbanGradient.shp, classDefinition=ClassDefinition(classes=6, names=['Roof', 'Pavement', 'Low vegetation', 'Tree', 'Soil', 'Other'], colors=['#e60000', '#9c9c9c', '#98e600', '#267300', '#a87000', '#f5f57a']), classAttribute=Level_2_ID, minOverallCoverage=0.5, minDominantCoverage=0.5, oversampling=1)

        '''

        Vector.__init__(self, filename=filename, layer=layer, burnAttribute=classAttribute, dtype=dtype)

        fieldNames = self.dataset().fieldNames()
        if classAttribute not in fieldNames:
            raise errors.UnknownAttributeTableField(name=classAttribute)

        type = self.dataset().fieldTypeNames()[fieldNames.index(classAttribute)]

        if type.startswith('Integer'):
            pass
        else:
            raise errors.TypeError(obj=type)

        # if not defined, try to get definition from json
        if classDefinition is None:

            filenameJson = '{}.json'.format(splitext(self.filename())[0])
            if exists(filenameJson):
                definitions = AttributeDefinitionEditor.readFromJson(filename=filenameJson)
                if classAttribute in definitions:
                    classDefinition = AttributeDefinitionEditor.makeClassDefinition(definitions=definitions,
                        attribute=classAttribute)

        # if still not defined, get definition from unique values
        if classDefinition is None:
            values = self.uniqueValues(attribute=classAttribute)
            values = [v for v in values if v is not None]
            classDefinition = ClassDefinition(classes=max(values))

        assert isinstance(classDefinition, ClassDefinition)
        self._classDefinition = classDefinition
        self._minOverallCoverage = float(minOverallCoverage)
        self._minDominantCoverage = float(minDominantCoverage)
        self._oversampling = oversampling

    def __getstate__(self):
        return OrderedDict([('filename', self.filename()),
                            ('classDefinition', self.classDefinition()),
                            ('classAttribute', self.classAttribute()),
                            ('minOverallCoverage', self.minOverallCoverage()),
                            ('minDominantCoverage', self.minDominantCoverage()),
                            ('oversampling', self.oversampling())])

    def classDefinition(self):
        '''Returns the class definition.'''
        assert isinstance(self._classDefinition, ClassDefinition)
        return self._classDefinition

    def classAttribute(self):
        '''Returns the class attribute.'''
        return self.burnAttribute()

    def minOverallCoverage(self):
        '''Returns the minimal overall coverage threshold.'''
        return self._minOverallCoverage

    def minDominantCoverage(self):
        '''Returns the minimal dominant class coverage threshold.'''
        return self._minDominantCoverage

    def oversampling(self):
        '''Returns the oversampling factor.'''
        return self._oversampling


class VectorRegression(Vector):
    '''Class for managing (single target) vector regressions.'''

    def __init__(self, filename, regressionAttribute, layer=0, noDataValue=None, minOverallCoverage=0.5,
            dtype=np.float32, oversampling=1):

        if noDataValue is None:
            try:
                noDataValue = float(np.finfo(dtype).min)
            except ValueError:
                noDataValue = int(np.iinfo(dtype).min)

        Vector.__init__(self, filename=filename, layer=layer, initValue=noDataValue, burnAttribute=regressionAttribute,
            dtype=dtype, noDataValue=noDataValue)

        fieldNames = self.dataset().fieldNames()
        if regressionAttribute not in fieldNames:
            raise errors.UnknownAttributeTableField(name=regressionAttribute)

        type = self.dataset().fieldTypeNames()[fieldNames.index(regressionAttribute)]

        if type.startswith('Integer'):
            pass
        elif type.startswith('Real'):
            pass
        else:
            raise errors.TypeError(obj=type)

        self._minOverallCoverage = float(minOverallCoverage)
        self._oversampling = oversampling

    def __getstate__(self):
        return OrderedDict([('filename', self.filename()),
                            ('regressionAttribute', self.regressionAttribute()),
                            ('layer', self.layer()),
                            ('noDataValue', self.noDataValue()),
                            ('minOverallCoverage', self.minOverallCoverage()),
                            ('dtype', self.dtype()),
                            ('oversampling', self.oversampling())])

    def regressionAttribute(self):
        '''Returns the regression attribute.'''
        return self.burnAttribute()

    def minOverallCoverage(self):
        '''Returns the minimal overall coverage threshold.'''
        return self._minOverallCoverage

    def oversampling(self):
        '''Returns the oversampling factor.'''
        return self._oversampling


class Color(FlowObject):

    def __init__(self, *args):
        '''Create a new instance by interpreting ``args`` as a color.'''

        if len(args) == 1:
            obj = args[0]
        else:
            obj = args

        qColor = self._parseQColor(obj)
        assert isinstance(qColor, QColor)
        self._qColor = qColor
        self._args = args

    def __setstate__(self, state):
        self.__init__(*state)

    def __getstate__(self):
        return self._args

    def __repr__(self):
        return 'Color({})'.format(', '.join([repr(arg) for arg in self._args]))

    def qColor(self):
        '''Return QColor object.'''
        assert isinstance(self._qColor, QColor)
        return self._qColor

    @staticmethod
    def _parseQColor(obj):

        if isinstance(obj, Color):
            qColor = obj.qColor()
        elif isinstance(obj, QColor):
            qColor = QColor(obj)
        elif isinstance(obj, str):
            qColor = QColor(obj)
        elif isinstance(obj, Iterable) and (len(obj) == 3 or len(obj) == 4):
            qColor = QColor(*obj)
        else:
            raise errors.ObjectParserError(obj=obj, type=QColor)

        assert isinstance(qColor, QColor)
        return qColor

    def name(self):
        return self._qColor.name()

    def red(self):
        return self._qColor.red()

    def green(self):
        return self._qColor.green()

    def blue(self):
        return self._qColor.blue()

    def colorNames(self):
        return self._qColor.colorNames()

    def rgb(self):
        return self.red(), self.green(), self.blue()


class AttributeDefinitionEditor(object):

    @staticmethod
    def readFromJson(filename):
        '''Read from json file.'''
        with open(filename) as f:
            definitions = json.load(f)

        for attribute, valueDict in definitions.items():
            for key in valueDict:
                if key not in ["categories", "no data value", "description"]:
                    raise errors.HubDcError('Unknown key "{}" in json file: {}'.format(key, filename))
        return definitions

    @staticmethod
    def writeToJson(filename, definitions):
        '''Write to json file.'''

        assert isinstance(definitions, dict)

        for attribute, valueDict in definitions.items():
            for key in valueDict:
                if key not in ["categories", "no data value", "description"]:
                    raise errors.HubDcError('Unknown key "{}" in attribute definition'.format(key, filename))

        with open(filename, 'w') as f:
            json.dump(definitions, f)

    @classmethod
    def makeClassDefinition(cls, definitions, attribute):

        if attribute not in definitions:
            raise Exception('Unspecified attribute: {}'.format(attribute))

        if "categories" not in definitions[attribute]:
            raise Exception('Categories are not specified for attribute: {}'.format(attribute))

        # init names and colors with defaults
        classes = definitions[attribute]["categories"][-1][0]  # last category id defines the number of classes
        names = ['class {}'.format(i) for i in range(classes + 1)]
        names[0] = 'unclassified'
        colors = [[random.randint(1, 255), random.randint(1, 255), random.randint(1, 255)] for _ in range(classes + 1)]
        colors[0] = 'black'
        # overwrite default with specified values
        for id, name, color in definitions[attribute]["categories"]:
            names[id] = name
            colors[id] = color

        classDefinition = ClassDefinition(names=names[1:], colors=colors[1:])
        classDefinition.setNoDataNameAndColor(name=names[0], color=colors[0])
        return classDefinition

    @classmethod
    def makeRegressionDefinition(cls, definitions, attribute):
        raise NotImplementedError()

    @staticmethod
    def makeClassDefinitionDict(classDefinition):

        assert isinstance(classDefinition, ClassDefinition)

        definition = dict()
        definition["categories"] = list()
        definition["categories"].append([0, classDefinition.noDataName(), classDefinition.noDataColor().name()])
        for i in range(classDefinition.classes()):
            definition["categories"].append([i + 1, classDefinition.name(i + 1), classDefinition.color(i + 1).name()])
        definition["no data value"] = 0
        definition["description"] = "Classification"
        return definition


class ClassDefinition(FlowObject):
    '''Class for managing class definitions.'''

    def __init__(self, classes=None, names=None, colors=None, ids=None):
        '''
        Create new instance.

        :param classes: number of classes; if not provided, it is derived from length of names or colors
        :type classes: int
        :param names: class names; if not provided, generic names are used
        :type names: List[str]
        :param colors: class colors as (r, g, b) tripel or '#000000' strings; if not provided, random colors are used;
        :type colors: List

        :example:

        >>> ClassDefinition(names=['a', 'b', 'c'], colors=['red', 'green', 'blue'])
        ClassDefinition(classes=3, names=['a', 'b', 'c'], colors=[Color('red'), Color('green'), Color('blue')])
        '''

        if ids is not None:
            if classes is None:
                classes = max(ids)
            assert isinstance(classes, int)
            if names is not None:
                assert len(ids) == len(names)
                names = [names[ids.index(i)] if i in ids else 'Unclassified' for i in range(1, classes+1)]
            if colors is not None:
                assert len(ids) == len(colors)
                colors = [colors[ids.index(i)] if i in ids else Color('#000000') for i in range(1, classes+1)]

        if classes is not None:
            pass
        elif names is not None:
            classes = len(names)
        elif colors is not None:
            classes = len(colors)
        else:
            raise errors.HubDcError('can not create class definition, insufficient inputs')

        if names is None:
            names = ['class {}'.format(i + 1) for i in range(classes)]
        if colors is None:  # create random colors
            colors = [random.randint(1, 255) for i in range(classes * 3)]
        if len(colors) == classes * 3:  # format as tripels
            colors = [colors[i * 3: i * 3 + 3] for i in range(classes)]

        assert len(names) == classes
        assert len(colors) == classes

        self._classes = int(classes)
        self._names = [str(name) for name in names]
        self._colors = [Color(color) for color in colors]
        self.setNoDataNameAndColor()

    def __getstate__(self):
        return OrderedDict([('classes', self.classes()),
                            ('names', self.names()),
                            ('colors', self.colors())])

    def classes(self):
        '''Return number of classes.'''
        return self._classes

    def names(self):
        '''Return class names.'''
        assert isinstance(self._names, list)
        return self._names

    def colors(self):
        '''Return class colors.'''
        assert isinstance(self._colors, list)
        return self._colors

    def labels(self):
        '''Return class labels.'''
        return list(range(1, self.classes() + 1))

    def setNoDataNameAndColor(self, name='Unclassified', color='black'):
        '''Set no data name and color.'''
        self._noDataName = name
        self._noDataColor = Color(color)

    def noDataName(self):
        '''Return no data name.'''
        assert isinstance(self._noDataName, str)
        return self._noDataName

    def noDataColor(self):
        '''Return no data color.'''
        assert isinstance(self._noDataColor, Color)
        return self._noDataColor

    @staticmethod
    def fromArray(array):
        '''
        Create instance by deriving the number of classes from the maximum value of the array.

        :example:

        >>> ClassDefinition.fromArray(array=[[[1, 2, 3]]]) # doctest: +ELLIPSIS
        ClassDefinition(classes=3, names=['class 1', 'class 2', 'class 3'], colors=[...])
        '''
        return ClassDefinition(classes=int(np.max(array)))

    @staticmethod
    def fromRaster(raster):
        '''Create instance by trying to 1) use :meth:`~hubflow.core.ClassDefinition.fromENVIClassification`,
         2) use :meth:`~hubflow.core.ClassDefinition.fromGDALMeta` and finally 3) derive number of classes from raster band maximum value.'''

        try:
            classDefinition = ClassDefinition.fromENVIClassification(raster=raster)
        except errors.MissingMetadataItemError:
            try:
                classDefinition = ClassDefinition.fromGDALMeta(raster=raster)
            except:
                # statistics = Raster(filename=raster.filename()).statistics()
                classes = int(Raster(filename=raster.filename()).dataset().band(0).readAsArray().max())
                classDefinition = ClassDefinition(classes=classes)
        return classDefinition

    @staticmethod
    def fromQml(filename, delimiter=';'):
        '''Create instance from QGIS QML file.'''

        names = list()
        colors = list()
        with open(filename) as file:

            # get categories

            while True:
                line = file.readline().strip()
                if line == '</categories>':
                    break
                else:
                    if '<category label=' not in line:
                        continue
                    else:
                        names.append(line.split('"')[1])

            # get colors

            while True:
                line = file.readline().strip()
                if line == '</symbols>':
                    break
                else:
                    if 'k="color"' not in line:
                        continue
                    else:
                        for s in line.split('"'):
                            if ',' in s:
                                colors.append(Color(*eval(s)))

            return ClassDefinition(names=names, colors=colors)

    @staticmethod
    def fromENVIClassification(raster):
        '''Create instance by deriving metadata information for `classes`, `class names` and `class lookup` from the ENVI domain.'''

        assert isinstance(raster, Raster)
        ds = raster.dataset()
        classes = ds.metadataItem(key='classes', domain='ENVI', dtype=int, required=True)
        names = ds.metadataItem(key='class names', domain='ENVI', required=True)
        lookup = ds.metadataItem(key='class lookup', domain='ENVI', dtype=int, required=True)
        return ClassDefinition(classes=classes - 1, names=names[1:], colors=lookup[3:])

    @staticmethod
    def fromENVIFraction(raster):
        '''Create instance by deriving metadata information for `band names` and `band lookup` from the ENVI domain.'''

        assert isinstance(raster, Raster)
        ds = raster.dataset()
        names = ds.metadataItem(key='band names', domain='ENVI')
        lookup = ds.metadataItem(key='band lookup', domain='ENVI', dtype=int)
        return ClassDefinition(names=names, colors=lookup)

    @staticmethod
    def fromGDALMeta(raster, index=0, skipZeroClass=True):
        '''Create instance by deriving category names and color table from GDAL raster dataset.'''

        assert isinstance(raster, Raster)
        ds = raster.dataset()
        names = ds.gdalDataset().GetRasterBand(index + 1).GetCategoryNames()
        classes = len(names)
        colors = [ds.gdalDataset().GetRasterBand(index + 1).GetColorTable().GetColorEntry(i)[:3] for i in
                  range(classes)]
        if skipZeroClass:
            classes = classes - 1
            names = names[1:]
            colors = colors[1:]
        return ClassDefinition(classes=classes, names=names, colors=colors)

    def dtype(self):
        '''
        Return the smalles unsigned integer data type suitable for the number of classes.

        :example:

        >>> ClassDefinition(classes=10).dtype()
        <class 'numpy.uint8'>
        >>> ClassDefinition(classes=1000).dtype()
        <class 'numpy.uint16'>
        '''
        for dtype in [np.uint8, np.uint16, np.uint32, np.uint64]:
            if self.classes() == dtype(self.classes()):
                return dtype

    def equal(self, other, compareColors=True):
        '''Return whether self is equal to another instance.'''

        assert isinstance(other, ClassDefinition)
        equal = self.classes() == other.classes()
        equal &= all([a == b for a, b in zip(self.names(), other.names())])
        if compareColors:
            for color1, color2 in zip(self.colors(), other.colors()):
                equal &= color1.red() == color2.red()
                equal &= color1.green() == color2.green()
                equal &= color1.blue() == color2.blue()

        return equal

    def color(self, label):
        '''Return color for given label.'''
        return self.colors()[label - 1]

    def colorByName(self, name):
        '''Return color for given name.'''
        return self.color(label=self.names().index((name)) + 1)

    def name(self, label):
        '''Return name for giben label.'''
        return self.names()[label - 1]

    def labelByName(self, name):
        '''Return label for given name.'''
        return self.names().index(name) + 1

    def colorsFlatRGB(self):
        '''
        Return colors as flat list of r, g, b values.

        :example:

        >>> ClassDefinition(colors=['red', 'blue']).colorsFlatRGB()
        [255, 0, 0, 0, 0, 255]
        '''
        values = list()
        for color in self.colors():
            values.append(color.red())
            values.append(color.green())
            values.append(color.blue())
        return values


class Classification(Raster):
    '''Class for managing classifications.'''

    def __init__(self, filename, classDefinition=None, minOverallCoverage=0.5, minDominantCoverage=0.5,
            eAccess=gdal.GA_ReadOnly):
        '''
        Create an instance.

        :param filename: input path
        :type filename: str
        :param classDefinition: if not provided, it is derived from raster metadata
        :type classDefinition: ClassDefinition
        :param minOverallCoverage: in case of resampling, pixels that are not fully covered can ocure,
                                   minOverallCoverage is the threshold under which those pixels are rejected (i.e. set to unclassified)
        :type minOverallCoverage: float
        :param minDominantCoverage: in case of resampling, pixels that are not fully covered by a single class can ocure,
                                   minDominantCoverage is the threshold under which "not pure enought" pixels are rejected (i.e. set to unclassified)
        :type minDominantCoverage: float

        '''

        Raster.__init__(self, filename=filename, eAccess=eAccess)
        self._classDefinition = classDefinition
        self._minOverallCoverage = minOverallCoverage
        self._minDominantCoverage = minDominantCoverage

    def __getstate__(self):
        return OrderedDict([('filename', self.filename()),
                            ('classDefinition', self.classDefinition()),
                            ('minOverallCoverage', self.minOverallCoverage()),
                            ('minDominantCoverage', self.minDominantCoverage())])

    def classDefinition(self):
        '''Return class definition.'''

        if self._classDefinition is None:
            self._classDefinition = ClassDefinition.fromRaster(raster=self)

        assert isinstance(self._classDefinition, ClassDefinition)
        return self._classDefinition

    def setClassDefinition(self, classDefinition):
        assert isinstance(classDefinition, ClassDefinition)
        names = [classDefinition.noDataName()] + classDefinition.names()
        colors = [classDefinition.noDataColor()] + classDefinition.colors()
        self.dataset().band(0).setCategoryNames(names=names)
        self.dataset().band(0).setCategoryColors(colors=[c.rgb() for c in colors])
        self._classDefinition = classDefinition

    def minOverallCoverage(self):
        '''Return minimal overall coverage threshold.'''
        assert isinstance(self._minOverallCoverage, float)
        return self._minOverallCoverage

    def minDominantCoverage(self):
        '''Return minimal dominant class coverage threshold.'''
        assert isinstance(self._minDominantCoverage, float)
        return self._minDominantCoverage

    def noDataValues(self):
        '''Returns always [0].'''
        return [0]

    def dtype(self):
        '''Return the smalles data type that is suitable for the number of classes.'''
        return self.classDefinition().dtype()

    def asMask(self, minOverallCoverage=0.5, invert=False):
        '''Return itself as a mask.'''
        return Raster.asMask(self, minOverallCoverage=minOverallCoverage, invert=invert)

    @classmethod
    def fromArray(cls, array, filename, classDefinition=None, grid=None, **kwargs) -> 'Classification':
        '''
        Create instance from given ``array``.

        :param array: input array of shape (1, lines, sample)
        :type array: numpy.ndarray
        :param filename: output path
        :type filename: str
        :param classDefinition:
        :type classDefinition: hubflow.core.ClassDefinition
        :param grid:
        :type grid: hubdc.core.Grid
        :param kwargs: additional kwargs are passed to Classification contructor
        :return:
        :rtype: hubflow.core.Classification

        :exemple:

        >>> Classification.fromArray(array=[[[0, 1],[1,2]]],
        ...                          filename='/vsimem/classification.bsq',
        ...                          classDefinition=ClassDefinition(colors=['red', 'blue']))
        Classification(filename=/vsimem/classification.bsq, classDefinition=ClassDefinition(classes=2, names=['class 1', 'class 2'], colors=[Color(255, 0, 0), Color(0, 0, 255)]), minOverallCoverage=0.5, minDominantCoverage=0.5)
        '''

        if not isinstance(array, np.ndarray):
            array = np.array(array, dtype=np.uint8)
        if classDefinition is None:
            classDefinition = ClassDefinition.fromArray(array)
        raster = Raster.fromArray(array=array, filename=filename, grid=grid, noDataValues=[0])
        MetadataEditor.setClassDefinition(rasterDataset=raster.dataset(), classDefinition=classDefinition)
        # raster.dataset().close() # need to close to flush
        # return Classification(filename=filename)
        # Classification(minOverallCoverage=0.5, minDominantCoverage=0.5, eAccess=gdal.GA_ReadOnly)
        return Classification.fromRasterDataset(rasterDataset=raster.dataset(), **kwargs)

    @classmethod
    def fromClassification(cls, filename, classification, grid=None, masks=None, **kwargs):
        '''
        Create instance from classification-like raster.

        :param filename: output path
        :type filename: str
        :param classification: classification-like raster
        :type classification: Union[Classification, VectorClassification, Fraction]
        :param grid:
        :type grid: hubdc.core.Grid
        :param masks:
        :type masks: Mask
        :param kwargs: passed to Applier
        :type kwargs:
        :return:
        :rtype: hubflow.core.Classification
        '''
        applier = Applier(defaultGrid=grid, **kwargs)
        applier.setFlowClassification('classification', classification=classification)
        applier.setOutputRaster('classification', filename=filename)
        applier.setFlowMasks(masks=masks)
        applier.apply(operatorType=_ClassificationFromClassification, classification=classification, masks=masks)
        return Classification(filename=filename)

    @classmethod
    def fromVector(cls, filename, vector, grid, masks=None, **kwargs):
        assert isinstance(vector, VectorClassification)
        classification = cls.fromClassification(filename=filename, classification=vector, grid=grid, masks=masks,
            **kwargs)
        assert isinstance(classification, Classification)
        return classification

    @classmethod
    def fromFraction(cls, filename, fraction, grid=None, masks=None, **kwargs):
        '''Forwarded to :meth:`~hubflow.core.Classification.fromClassification`.'''
        return cls.fromClassification(filename=filename, classification=fraction, grid=grid, masks=masks, **kwargs)

    @classmethod
    def fromRasterAndFunction(cls, filename, raster, ufunc, classDefinition=None, **kwargs):
        '''
        Create instance from raster by applying a user-function to it.

        :param filename: output path
        :type filename: str
        :param raster: input raster
        :type raster: Raster
        :param ufunc: user-function (taking two arguments: array, metadataDict) to be applied to the raster data (see example below)
        :type ufunc: function
        :param classDefinition:
        :type classDefinition: ClassDefinition
        :param kwargs: passed to Applier
        :type kwargs:
        :return:
        :rtype: Classification

        :example:

        >>> raster = Raster.fromArray(array=[[[1,2,3,4,5]]], filename='/vsimem/raster.bsq')
        >>> def ufunc(array, metadataDict):
        ...     result = np.zeros_like(array) # init result with zeros
        ...     result[array < 3] = 1 # map all values < 3 to class 1
        ...     result[array > 3] = 2 # map all values > 3 to class 2
        ...     return result
        >>> classification = Classification.fromRasterAndFunction(raster=raster, ufunc=ufunc, filename='/vsimem/classification.bsq')
        >>> classification.array()
        array([[[1, 1, 0, 2, 2]]], dtype=uint8)
        '''

        applier = Applier(defaultGrid=raster, **kwargs)
        applier.setFlowRaster('raster', raster=raster)
        applier.setOutputRaster('classification', filename=filename)
        applier.apply(operatorType=_ClassificationFromRasterAndFunction, raster=raster, ufunc=ufunc,
            classDefinition=classDefinition)
        return Classification(filename=filename)

    @staticmethod
    def fromEnviSpectralLibrary(filename, library, attribute, classDefinition=None):
        '''
        Create instance from library attribute. If the ClassDefinition is not defined, it is taken from an accompanied JSON file.

        :param filename: output path
        :type filename:
        :param library:
        :type library: EnviSpectralLibrary
        :param attribute: attribute defined in the corresponding csv file
        :type attribute: str
        :param classDefinition:
        :type classDefinition: ClassDefinition
        :return:
        :rtype: Classification

        :example:

        >>> import enmapboxtestdata
        >>> library = EnviSpectralLibrary(filename=enmapboxtestdata.library)
        >>> Classification.fromEnviSpectralLibrary(filename='/vsimem/classification.bsq', library=library, attribute='level_1')

        '''
        assert isinstance(library, EnviSpectralLibrary)

        table = library.attributeTable()

        if attribute not in table:
            raise errors.UnknownAttributeTableField(name=attribute)

        if classDefinition is None:
            classDefinition = library.attributeClassDefinition(attribute=attribute)

        labels = np.array(table[attribute])

        # convert names to ids

        array = np.zeros(shape=len(labels))
        for i, name in enumerate(classDefinition.names()):
            array[labels == name] = str(i + 1)

        # take over IDs
        valid = np.logical_and(labels >= '1', labels <= str(classDefinition.classes()))
        array[valid] = labels[valid]

        # sort profiles

        ordered = OrderedDict()
        spectraNames = next(iter(table.values()))
        if not len(spectraNames) == library.raster().dataset().ysize():
            raise errors.HubDcError(
                'number of spectra in .hdr file and .csv file not matching: {}'.format(library.filename()))

        for name in library.raster().dataset().metadataItem(key='spectra names', domain='ENVI', required=True):
            if name in ordered:  # check for duplicated
                raise HubFlowError('detected spectra name duplicates, check for name: {}'.format(name))
            ordered[name] = array[spectraNames.index(name)]

        # write to dataset
        bands, lines, samples = library.raster().dataset().shape()
        array = np.reshape(list(ordered.values()), newshape=(1, lines, samples)).astype(dtype=classDefinition.dtype())

        rasterDataset = RasterDataset.fromArray(array=array, filename=filename,
            driver=RasterDriver.fromFilename(filename=filename))
        MetadataEditor.setClassDefinition(rasterDataset=rasterDataset, classDefinition=classDefinition)
        rasterDataset.setNoDataValues(values=[0])
        rasterDataset.flushCache().close()
        return Classification(filename=filename)

    def reclassify(self, filename, classDefinition, mapping, **kwargs):
        '''
        Reclassify classes by given ``mapping`` new ``classDefinition``.

        :param filename: output path
        :type filename: str
        :param classDefinition:
        :type classDefinition: ClassDefinition
        :param mapping:
        :type mapping: dict
        :param kwargs: passed to Applier
        :type kwargs:
        :return:
        :rtype: Classification

        :example:

        >>> classification = Classification.fromArray(array=[[[1,2,3,4]]], filename='/vsimem/classification.bsq')
        >>> reclassified = classification.reclassify(filename='/vsimem/reclassified.bsq',
        ...                                          classDefinition=ClassDefinition(classes=2),
        ...                                          mapping={1: 0, 2: 1, 3: 1, 4: 2})
        >>> reclassified.array()
        array([[[0, 1, 1, 2]]], dtype=uint8)
        '''

        assert isinstance(classDefinition, ClassDefinition)
        assert isinstance(mapping, dict)
        applier = Applier(defaultGrid=self, **kwargs)
        applier.setFlowClassification('inclassification', classification=self)
        applier.setOutputRaster('outclassification', filename=filename)
        applier.apply(operatorType=_ClassificationReclassify, classification=self, classDefinition=classDefinition,
            mapping=mapping)
        classification = Classification(filename=filename)

        ### workaround to fix issue #410 ###
        classification.dataset().gdalDataset().GetFileList()
        classification.dataset().gdalDataset().GetRasterBand(1)
        ####################################


        return classification


    def resample(self, filename, grid, **kwargs):
        '''
        Resample itself into the gives ``grid``.

        :param filename: output path
        :type filename: str
        :param grid:
        :type grid: hubdc.core.Grid
        :param kwargs: passed to Applier
        :type kwargs:
        :return:
        :rtype: Classification

        :example:

        Resample into a grid with 2x finer resolution

        >>> classification = Classification.fromArray(array=[[[1,2,3,4]]], filename='/vsimem/classification.bsq')
        >>> classification.array()
        array([[[1, 2, 3, 4]]], dtype=uint8)
        >>> grid = classification.grid()
        >>> grid2 = grid.atResolution(resolution=grid.resolution()/2)
        >>> resampled = classification.resample(filename='/vsimem/resampled.bsq', grid=grid2)
        >>> resampled.array()
        array([[[1, 1, 2, 2, 3, 3, 4, 4],
                [1, 1, 2, 2, 3, 3, 4, 4]]], dtype=uint8)
        '''
        applier = Applier(defaultGrid=grid, **kwargs)
        applier.setFlowClassification('inclassification', classification=self)
        applier.setOutputRaster('outclassification', filename=filename)
        applier.apply(operatorType=_ClassificationResample, classification=self)
        return Classification(filename=filename)

    def statistics(self, mask=None, **kwargs):
        '''
        Returns list of class counts.

        :param mask:
        :type mask: Mask
        :param kwargs: passed to Applier
        :type kwargs:
        :return:
        :rtype: list

        :example:

        >>> Classification.fromArray(array=[[[1, 1, 2, 3, 3, 3]]], filename='/vsimem/classification.bsq').statistics()
        [2, 1, 3]
        '''
        applier = Applier(defaultGrid=self, **kwargs)
        applier.setFlowClassification('classification', classification=self)
        applier.setFlowMask('mask', mask=mask)
        return list(applier.apply(operatorType=_ClassificationStatistics, classification=self, mask=mask))


class _ClassificationStatistics(ApplierOperator):
    def ufunc(self, classification, mask):
        array = self.flowClassificationArray('classification', classification=classification)
        array[np.logical_not(self.flowMaskArray('mask', mask=mask))] = 0
        hist, bin_edges = np.histogram(array,
            bins=classification.classDefinition().classes(),
            range=[1, classification.classDefinition().classes() + 1, ])
        return hist

    @staticmethod
    def aggregate(blockResults, grid, *args, **kwargs):
        return np.sum(blockResults, axis=0)


class _ClassificationReclassify(ApplierOperator):
    def ufunc(self, classification, classDefinition, mapping):
        inclassification = self.flowClassificationArray('inclassification', classification=classification)
        outclassification = self.full(value=0, bands=1, dtype=np.uint16)
        for inclass, outclass in mapping.items():
            if inclass in classification.classDefinition().names():
                inclass = classification.classDefinition().names().index(inclass) + 1
            if outclass in classDefinition.names():
                outclass = classDefinition.names().index(outclass) + 1
            outclassification[inclassification == inclass] = outclass
        self.outputRaster.raster(key='outclassification').setArray(array=outclassification)
        self.setFlowMetadataClassDefinition(name='outclassification', classDefinition=classDefinition)


class _ClassificationResample(ApplierOperator):
    def ufunc(self, classification):
        array = self.flowClassificationArray('inclassification', classification=classification)
        self.outputRaster.raster(key='outclassification').setArray(array=array)
        self.setFlowMetadataClassDefinition(name='outclassification', classDefinition=classification.classDefinition())


class _ClassificationFromClassification(ApplierOperator):
    def ufunc(self, classification, masks):
        array = self.flowClassificationArray('classification', classification=classification)
        marray = self.flowMasksArray(masks=masks)
        array[marray == 0] = 0
        self.outputRaster.raster(key='classification').setArray(array=array)
        self.setFlowMetadataClassDefinition(name='classification', classDefinition=classification.classDefinition())


class _ClassificationFromRasterAndFunction(ApplierOperator):
    def ufunc(self, raster, ufunc, classDefinition):
        array = self.flowRasterArray('raster', raster=raster)
        metadataDict = self.inputRaster.raster(key='raster').metadataDict()
        classificationArray = ufunc(array, metadataDict)
        self.outputRaster.raster(key='classification').setArray(array=classificationArray)
        if classDefinition is not None:
            self.setFlowMetadataClassDefinition(name='classification', classDefinition=classDefinition)


# class RegressionDefinition(FlowObject):
#    '''Class for managing regression definitions.'''

#    def __init__(self, targets=None, names=None, noDataValues=None, colors=None):
#        raise NotImplementedError()


class Regression(Raster):
    '''Class for managing regression maps.'''

    def __init__(self, filename, noDataValues=None, outputNames=None, minOverallCoverage=0.5):
        '''
        Create instance.

        :param filename: output path
        :type filename:
        :param noDataValues: provide list of band no data values if not specified in the metadata
        :type noDataValues: list
        :param outputNames: provide list of output names if not specified in the metadata (key='band names', domain='ENVI)
        :type outputNames: list
        :param minOverallCoverage: in case of resampling, pixels that are not fully covered can ocure,
                                   minOverallCoverage is the threshold under which those pixels are rejected (i.e. set to no data value)
        :type minOverallCoverage:
        '''
        Raster.__init__(self, filename)
        self._noDataValues = noDataValues
        self._outputNames = outputNames
        self._minOverallCoverage = float(minOverallCoverage)

    def __getstate__(self):
        return OrderedDict([('filename', self.filename()),
                            ('noDataValues', self.noDataValues()),
                            ('outputNames', self.outputNames()),
                            ('minOverallCoverage', self.minOverallCoverage())])

    def noDataValues(self, default=None, required=True):
        '''
        Return no data values.

        :example:

        >>> import enmapboxtestdata
        >>> Regression(filename=enmapboxtestdata.landcoverfractions).noDataValues()
        [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0]
        '''
        if self._noDataValues is None:
            self._noDataValues = self.dataset().noDataValues(default=default, required=required)
        assert isinstance(self._noDataValues, list)
        return self._noDataValues

    def outputs(self):
        '''
        Return number of outputs (i.e. number of bands).

        :example:

        >>> import enmapboxtestdata
        >>> Regression(filename=enmapboxtestdata.landcoverfractions).outputs()
        6
        '''
        return self.dataset().zsize()

    def outputNames(self):
        '''
        Return output names.

        :example:

        >>> import enmapboxtestdata
        >>> Regression(filename=enmapboxtestdata.landcoverfractions).outputNames()
        ['Roof', 'Pavement', 'Low vegetation', 'Tree', 'Soil', 'Other']
        '''
        if self._outputNames is None:
            self._outputNames = [band.description() for band in self.dataset().bands()]
        assert isinstance(self._outputNames, list)
        return self._outputNames

    def minOverallCoverage(self):
        '''Return minimal overall coverage threshold.'''
        assert isinstance(self._minOverallCoverage, float)
        return self._minOverallCoverage

    def asMask(self, minOverallCoverage=None, noDataValues=None):
        '''Creates a mask instance from itself. Optionally, the minimal overall coverage can be changed.'''
        if minOverallCoverage is None:
            minOverallCoverage = self.minOverallCoverage()
        if noDataValues is None:
            noDataValues = self.noDataValues()
        return Raster.asMask(self, noDataValues=noDataValues, minOverallCoverage=minOverallCoverage)

    def resample(self, filename, grid, **kwargs):
        '''
        Resample itself into the given grid using gdal.GRA_Average resampling.

        :param filename: output filename
        :type filename:
        :param grid: hubdc.core.Grid
        :type grid:
        :param kwargs: passed to Applier
        :type kwargs:
        :return: Regression
        :rtype:

        :example:

        Resample into a grid that is 1.5x as fine.

        >>> regression = Regression.fromArray([[[0., 0.5, 1.]]], noDataValues=[-1], filename='/vsimem/regression.bsq')
        >>> regression.array()
        array([[[ 0. ,  0.5,  1. ]]])
        >>> grid = regression.grid()
        >>> grid2 = grid.atResolution(resolution=grid.resolution() / 2)
        >>> resampled = regression.resample(grid=grid2, filename='/vsimem/resampled.bsq')
        >>> resampled.array()
        array([[[ 0. ,  0. ,  0.5,  0.5,  1. ,  1. ],
                [ 0. ,  0. ,  0.5,  0.5,  1. ,  1. ]]])

        '''

        applier = Applier(defaultGrid=grid, **kwargs)
        applier.setFlowRegression('inregression', regression=self)
        applier.setOutputRaster('outregression', filename=filename)
        applier.apply(operatorType=_RegressionResample, regression=self)
        return Regression(filename=filename)

    @classmethod
    def fromVectorRegression(cls, filename, vectorRegression, grid=None, **kwargs):
        '''
        Create instance from regression-like raster.

        :param filename: output path
        :type filename: str
        :param vectorRegression: vector regression
        :type vectorRegression: VectorRegression
        :param grid:
        :type grid: hubdc.core.Grid
        :param masks:
        :type masks: Mask
        :param kwargs: passed to Applier
        :type kwargs:
        :return:
        :rtype: hubflow.core.Regression
        '''
        applier = Applier(defaultGrid=grid, **kwargs)
        applier.setFlowRegression('vectorRegression', regression=vectorRegression)
        applier.setOutputRaster('regression', filename=filename)
        applier.apply(operatorType=_RegressionFromVectorRegression, vectorRegression=vectorRegression)
        return Regression(filename=filename)

    def ordinationFeilhauerEtAl2014(self, filename, filenameVector, n_components, noDataValue=None):
        # see https://dx.doi.org/10.1111/avsc.12115

        from sklearn.neighbors import DistanceMetric
        from sklearn.manifold import MDS
        from sklearn.decomposition import PCA

        if noDataValue is None:
            noDataValue = float(np.finfo(np.float32).min)

        # NOTE: we assume self to be a raster of extracted values (i.e. a pseudo image), without noData Pixel!!!!!!!!!!!!!!!!
        # todo: make it tile-based (memory efficient) and aware of noDataValues

        array = self.array().T[0]

        arraySpecies = np.array([a for a, name in zip(array.T, self.outputNames()) if not name.startswith('I_')]).T
        arrayIndicators = np.array([a for a, name in zip(array.T, self.outputNames()) if name.startswith('I_')]).T
        indicatorNames = [name for name in self.outputNames() if name.startswith('I_')]

        ## bray curtis
        dist = DistanceMetric.get_metric('braycurtis')
        arr_bc = dist.pairwise(arraySpecies)
        arr_bc_d = 1 - arr_bc

        ## explained variance
        cumulativeExplainedVariance = list()
        for i in [1, 2, 3, 4]:
            mds = MDS(n_components=i, metric=False, dissimilarity='precomputed')
            mds_arr = mds.fit_transform(arr_bc_d)
            euclidean = DistanceMetric.get_metric('euclidean').pairwise(mds_arr)
            r2 = scipy.stats.pearsonr(arr_bc_d.flatten(), euclidean.flatten())[0] ** 2
            cumulativeExplainedVariance.append(r2)

        ## MDS
        mds = MDS(n_components=n_components, metric=False, dissimilarity='precomputed')
        mds_arr = mds.fit_transform(arr_bc_d)
        stress = mds.stress_

        ## rotate with PCA
        pca = PCA(n_components=n_components)
        pca_arr = pca.fit_transform(mds_arr)

        # create output
        out_array = np.atleast_3d(pca_arr.T).astype(np.float32)

        regression = Regression.fromArray(array=out_array, grid=self.grid(), noDataValues=noDataValue,
            filename=filename,
            descriptions=['NMDS component {}'.format(i + 1) for i in range(n_components)])

        x = self.grid().xMapCoordinates()[0]  # we assume a PseudoImage!!!!!!!!!!!!!
        projection = self.grid().projection()
        points = [Point(x=x, y=y, projection=projection) for y in self.grid().yMapCoordinates()]
        attributes = OrderedDict()
        for a, name in zip(pca_arr.T, regression.outputNames()):
            attributes[name.replace(' ', '.')] = a
        for a, name in zip(arrayIndicators.T, indicatorNames):
            attributes[name] = a

        vector = Vector.fromPoints(points=points, attributes=attributes, filename=filenameVector)

        return regression, vector, cumulativeExplainedVariance, stress


class _RegressionResample(ApplierOperator):
    def ufunc(self, regression):
        array = self.flowRegressionArray('inregression', regression=regression)
        self.outputRaster.raster(key='outregression').setArray(array=array)
        self.setFlowMetadataRegressionDefinition(name='outregression', noDataValues=regression.noDataValues(),
            outputNames=regression.outputNames())


class _RegressionFromVectorRegression(ApplierOperator):
    def ufunc(self, vectorRegression):
        assert isinstance(vectorRegression, VectorRegression)
        array = self.flowRegressionArray('vectorRegression', regression=vectorRegression)
        self.outputRaster.raster(key='regression').setArray(array=array)
        self.setFlowMetadataRegressionDefinition(name='regression', noDataValues=[vectorRegression.noDataValue()],
            outputNames=[vectorRegression.regressionAttribute()])


class Fraction(Regression):
    '''Class for managing fraction maps.'''

    def __init__(self, filename, classDefinition=None, minOverallCoverage=0., minDominantCoverage=0.):
        '''
        Create a new instance.

        :param filename: input path
        :type filename:
        :param classDefinition: provide class definition if not specified inside the metadata
        :type classDefinition: ClassDefinition
        :param minOverallCoverage: minOverallCoverage is the threshold under which edge pixels are rejected (i.e. set to no data value)
        :type minOverallCoverage: float
        :param minDominantCoverage: minDominantCoverage is the threshold under which "not pure enought" pixels are rejected (i.e. set to no data value)
        :type minDominantCoverage: float
        '''
        Raster.__init__(self, filename=filename)
        if classDefinition is None:
            classDefinition = ClassDefinition.fromENVIFraction(raster=self)
        self._classDefinition = classDefinition
        Regression.__init__(self, filename=filename, noDataValues=self.noDataValues(),
            outputNames=classDefinition.names())
        self._minOverallCoverage = minOverallCoverage
        self._minDominantCoverage = minDominantCoverage

    def __getstate__(self):
        return OrderedDict([('filename', self.filename()),
                            ('classDefinition', self.classDefinition()),
                            ('minDominantCoverage', self.minDominantCoverage()),
                            ('minOverallCoverage', self.minOverallCoverage())])

    def classDefinition(self):
        '''Return the class definition.'''
        assert isinstance(self._classDefinition, ClassDefinition)
        return self._classDefinition

    def minOverallCoverage(self):
        '''Return the minimal overall coverage threshold.'''
        return self._minOverallCoverage

    def minDominantCoverage(self):
        '''Return the minimal dominant class coverage threshold.'''
        return self._minDominantCoverage

    def noDataValues(self, default=None):
        '''Returns no data values.'''
        return [-1.] * self.classDefinition().classes()

    @classmethod
    def fromClassification(cls, filename, classification, **kwargs):
        '''
        Create instance from given classification. A simple binarization in fractions of 0 and 1 is performed.

        :param filename: output path
        :type filename:
        :param classification: input classification
        :type classification: Classification
        :param kwargs: passed to Applier
        :type kwargs:
        :return: Fraction
        :rtype:

        :example:

        >>> classification = Classification.fromArray(array=[[[1, 2, 3]]], filename='/vsimem/classification.bsq')
        >>> classification.array()
        array([[[1, 2, 3]]], dtype=uint8)
        >>> fraction = Fraction.fromClassification(classification=classification, filename='/vsimem/fraction.bsq')
        >>> fraction.array()
        array([[[ 1.,  0.,  0.]],
        <BLANKLINE>
               [[ 0.,  1.,  0.]],
        <BLANKLINE>
               [[ 0.,  0.,  1.]]], dtype=float32)
        '''

        applier = Applier(defaultGrid=classification, **kwargs)
        applier.setFlowClassification('classification', classification=classification)
        applier.setOutputRaster('fraction', filename=filename)
        applier.apply(operatorType=_FractionFromClassification, classification=classification)
        return Fraction(filename=filename)

    # def fromEnviSpectralLibrary(filename, library, attributes):
    #     '''
    #     Create instance from library attributes.
    #
    #     :param filename: output path
    #     :type filename:
    #     :param library:
    #     :type library: EnviSpectralLibrary
    #     :param attributes: target attributes defined in the corresponding csv file
    #     :type attribute: List[str]
    #     :return:
    #     :rtype: Fraction
    #
    #     :example:
    #
    #     >>> import enmapboxtestdata
    #     >>> library = EnviSpectralLibrary(filename=enmapboxtestdata.speclib2)
    #     >>> Fraction.fromEnviSpectralLibrary(filename='/vsimem/regression.bsq', library=library,
    #     ...                                  attributes=['Roof', 'Pavement', 'Low vegetation', 'Tree', 'Soil', 'Other']) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
    #     Fraction(filename=/vsimem/regression.bsq,
    #              classDefinition=ClassDefinition(classes=6,
    #                                              names=['Roof', 'Pavement', 'Low vegetation', 'Tree', 'Soil', 'Other'],
    #                                              colors=[Color(230, 0, 0), Color(156, 156, 156), Color(152, 230, 0), Color(38, 115, 0), Color(168, 112, 0), Color(245, 245, 122)]))
    #     '''
    #
    #     assert isinstance(library, EnviSpectralLibrary)
    #     regression = Regression.fromEnviSpectralLibrary(filename=filename, library=library, attributes=attributes)
    #     colors = list()
    #     for attribute in attributes:
    #         colors.append(Color(*[int(v) for v in
    #                               library.raster().dataset().metadataItem(key=attribute, domain='REGR_LOOKUP',
    #                                                                       required=True)]))
    #     classDefinition = ClassDefinition(names=attributes, colors=colors)
    #     MetadataEditor.setFractionDefinition(rasterDataset=regression.dataset(), classDefinition=classDefinition)
    #     rasterDataset = regression.dataset().flushCache()
    #     return Fraction(filename=filename)

    def resample(self, filename, grid, **kwargs):
        '''
        Resample itself into the given grid using gdal.GRA_Average resampling.

        :param filename: output path
        :type filename:
        :param grid:
        :type grid: hubdc.core.Grid
        :param kwargs: passed to Applier
        :type kwargs:
        :return:
        :rtype: Fraction

        :example:

        Resample into grid that is 2x as fine.

        >>> fraction = Fraction.fromArray(array=[[[0., 0.5, 1.]],
        ...                                     [[1., 0.5, 1.]]],
        ...                               filename='/vsimem/fraction.bsq')
        >>> fraction.array()
        array([[[ 0. ,  0.5,  1. ]],
        <BLANKLINE>
               [[ 1. ,  0.5,  1. ]]])
        >>> grid = fraction.grid()
        >>> grid2 = grid.atResolution(grid.resolution()/(2, 1)) # change only resolution in x dimension
        >>> resampled = fraction.resample(grid=grid2, filename='/vsimem/resampled.bsq')
        >>> resampled.array()
        array([[[ 0. ,  0. ,  0.5,  0.5,  1. ,  1. ]],
        <BLANKLINE>
               [[ 1. ,  1. ,  0.5,  0.5,  1. ,  1. ]]])

        '''
        applier = Applier(defaultGrid=grid, **kwargs)
        applier.setFlowFraction('infraction', fraction=self)
        applier.setOutputRaster('outfraction', filename=filename)
        applier.apply(operatorType=_FractionResample, fraction=self)
        return Fraction(filename=filename)

    def subsetClasses(self, filename, labels, **kwargs):
        '''
        Subset itself by given class labels.

        :param filename: input path
        :type filename: 
        :param labels: list of labels to be subsetted
        :type labels: 
        :param kwargs: passed to Applier
        :type kwargs: 
        :return: 
        :rtype: Fraction

        :example:

        Subset 2 classes fom a fraction map with 3 classes.

        >>> fraction = Fraction.fromArray(array=[[[0.0, 0.3]],
        ...                                      [[0.2, 0.5]],
        ...                                      [[0.8, 0.2]]],
        ...                               filename='/vsimem/fraction.bsq')
        >>> fraction.array()
        array([[[0. , 0.3]],
        <BLANKLINE>
               [[0.2, 0.5]],
        <BLANKLINE>
               [[0.8, 0.2]]])
        >>> subsetted = fraction.subsetClasses(labels=[1, 3], filename='/vsimem/subsetted.bsq')
        >>> subsetted.array()
        array([[[0. , 0.3]],
        <BLANKLINE>
               [[0.8, 0.2]]])
        '''
        indices = [label - 1 for label in labels]
        applier = Applier(defaultGrid=self, **kwargs)
        applier.setFlowRaster('fraction', raster=self)
        applier.setOutputRaster('fractionSubset', filename=filename)
        applier.apply(operatorType=_FractionSubsetClasses, indices=indices, fraction=self)
        return Fraction(filename=filename)

    def subsetClassesByName(self, filename, names, **kwargs):
        '''
        Subset itself by given class names.

        :param filename: input path
        :type filename:
        :param names: list of class names to be subsetted
        :type names:
        :param kwargs: passed to Applier
        :type kwargs:
        :return:
        :rtype: Fraction

        :example:

        Subset 2 classes fom a fraction map with 3 classes.

        >>> fraction = Fraction.fromArray(array=[[[0.0, 0.3]],
        ...                                      [[0.2, 0.5]],
        ...                                      [[0.8, 0.2]]],
        ...                               classDefinition=ClassDefinition(names=['a', 'b', 'c']),
        ...                               filename='/vsimem/fraction.bsq')
        >>> fraction.classDefinition().names()
        ['a', 'b', 'c']
        >>> fraction.array()
        array([[[0. , 0.3]],
        <BLANKLINE>
               [[0.2, 0.5]],
        <BLANKLINE>
               [[0.8, 0.2]]])
        >>> subsetted = fraction.subsetClassesByName(names=['a', 'c'], filename='/vsimem/subsetted.bsq')
        >>> subsetted.classDefinition().names()
        ['a', 'c']
        >>> subsetted.array()
        array([[[0. , 0.3]],
        <BLANKLINE>
               [[0.8, 0.2]]])
        '''
        labels = [self.classDefinition().names().index(name) + 1 for name in names]
        return self.subsetClasses(filename=filename, labels=labels, **kwargs)

    def asClassColorRGBRaster(self, filename, **kwargs):
        '''
        Create RGB image, where the pixel color is the average of the original class colors,
        weighted by the pixel fractions. Regions with purer pixels (i.e. fraction of a specific class is near 1),
        appear in the original class colors, and regions with mixed pixels appear in mixed class colors.

        :param filename: input path
        :type filename:
        :param kwargs: passed to Applier
        :type kwargs:
        :return:
        :rtype:

        >>> import enmapboxtestdata
        >>> fraction = Fraction(filename=enmapboxtestdata.landcoverfractions)
        >>> rgb = fraction.asClassColorRGBRaster(filename='/vsimem/rgb.bsq')
        >>> rgb.plotMultibandColor()

        .. image:: plots/fracion_asClassColorRGBRaster.png

        '''

        applier = Applier(defaultGrid=self, **kwargs)
        applier.setFlowRaster('fraction', raster=self)
        applier.setOutputRaster('raster', filename=filename)
        applier.apply(operatorType=_FractionAsClassColorRGBRaster, fraction=self)
        return Raster(filename=filename)


class _FractionAsClassColorRGBRaster(ApplierOperator):
    def ufunc(self, fraction):
        assert isinstance(fraction, Fraction)
        colors = fraction.classDefinition().colors()
        array = self.flowRasterArray('fraction', raster=fraction)
        rgb = self.full(value=0, bands=3, dtype=np.float32)
        for id, (band, color) in enumerate(zip(array, colors), start=1):
            colorRGB = (color.red(), color.green(), color.blue())
            rgb += band * np.reshape(colorRGB, newshape=(3, 1, 1))
        np.uint8(np.clip(rgb, a_min=0, a_max=255, out=rgb))
        mask = np.any(rgb != 0, axis=0)
        np.clip(rgb, a_min=1, a_max=255, out=rgb)
        rgb *= mask
        self.outputRaster.raster(key='raster').setArray(array=np.uint8(rgb))


class _FractionFromClassification(ApplierOperator):
    def ufunc(self, classification):
        array = self.flowFractionArray('classification', fraction=classification)
        self.outputRaster.raster(key='fraction').setArray(array=array)
        self.setFlowMetadataFractionDefinition(name='fraction',
            classDefinition=classification.classDefinition())


class _FractionResample(ApplierOperator):
    def ufunc(self, fraction):
        array = self.flowFractionArray('infraction', fraction=fraction)
        self.outputRaster.raster(key='outfraction').setArray(array=array)
        self.setFlowMetadataFractionDefinition(name='outfraction', classDefinition=fraction.classDefinition())


class _FractionSubsetClasses(ApplierOperator):
    def ufunc(self, indices, fraction):
        classes = len(indices)
        colors = [fraction.classDefinition().color(label=index + 1) for index in indices]
        names = [fraction.classDefinition().name(label=index + 1) for index in indices]
        classDefinition = ClassDefinition(classes=classes, names=names, colors=colors)
        fractionSubset = self.inputRaster.raster(key='fraction').array(indices=indices)
        self.outputRaster.raster(key='fractionSubset').setArray(array=fractionSubset)
        self.setFlowMetadataFractionDefinition(name='fractionSubset', classDefinition=classDefinition)


class FractionPerformance(FlowObject):
    '''Class for performing ROC curve analysis.'''

    def __init__(self, yP, yT, classDefinitionP, classDefinitionT):
        '''
        Create an instance and perform calculations.

        :param yP: predicted probabilities/fractions
        :type yP: ndarray[samples, classes]
        :param yT: reference class labels
        :type yT: ndarray[samples, 1]
        :param classDefinitionP: class definition for prediction
        :type classDefinitionP: ClassDefinition
        :param classDefinitionT: class definition for reference
        :type classDefinitionT: ClassDefinition

        See example given at :meth:`~FractionPerformance.fromRaster`.
        '''
        import sklearn.metrics
        assert isinstance(classDefinitionP, ClassDefinition)
        assert isinstance(classDefinitionT, ClassDefinition)
        assert classDefinitionT.classes() == classDefinitionP.classes()
        assert isinstance(yP, np.ndarray)
        assert isinstance(yT, np.ndarray)
        assert yT.shape[1] == yP.shape[1]
        assert len(yT) == 1
        assert len(yP) == classDefinitionP.classes()

        self.classDefinitionT = classDefinitionT
        self.classDefinitionP = classDefinitionP

        self.yP = yP.T
        self.yT = yT[0]
        self.n = yP.shape[1]
        self.log_loss = sklearn.metrics.log_loss(y_true=self.yT, y_pred=self.yP)
        self.roc_curves = dict()
        self.roc_auc_scores = dict()

        for i in range(1, self.classDefinitionT.classes() + 1):
            self.roc_curves[i] = sklearn.metrics.roc_curve(y_true=self.yT, y_score=self.yP[:, i - 1], pos_label=i,
                drop_intermediate=True)
            self.roc_auc_scores[i] = sklearn.metrics.roc_auc_score(y_true=self.yT == i, y_score=self.yP[:, i - 1])

    def __getstate__(self):
        return OrderedDict([('yP', self.yP.T),
                            ('yT', self.yT[None]),
                            ('classDefinitionP', self.classDefinitionP),
                            ('classDefinitionT', self.classDefinitionT)])

    @classmethod
    def fromRaster(self, prediction, reference, mask=None, **kwargs):
        '''

        :param prediction:
        :type prediction: Fraction
        :param reference:
        :type reference: Classification
        :param mask:
        :type mask: Mask
        :param kwargs: passed to Applier
        :type kwargs:
        :return:
        :rtype: FractionPerformance

        :example:

        >>> import enmapboxtestdata
        >>> performance = FractionPerformance.fromRaster(prediction=Fraction(filename=enmapboxtestdata.landcoverfractions),
        ...                                              reference=Classification(filename=enmapboxtestdata.landcoverclassification))
        >>> performance.log_loss
        0.4840965149878993
        >>> performance.roc_auc_scores
        {1: 0.9640992638757171, 2: 0.8868830628381189, 3: 0.9586349099478203, 4: 0.9102916036557301, 5: 0.9998604910714286, 6: 0.9966195132099022}
        >>> performance.report().saveHTML(filename=join(tempfile.gettempdir(), 'report.html'), open=True)

        .. image:: plots/fractionPerformance.png

        '''

        assert isinstance(prediction, Fraction)
        assert isinstance(reference, Classification)

        yP, yT = MapCollection(maps=[prediction, reference]).extractAsArray(masks=[prediction, reference, mask],
            **kwargs)

        return FractionPerformance(yP=yP, yT=yT, classDefinitionP=prediction.classDefinition(),
            classDefinitionT=reference.classDefinition())

    def report(self):
        '''
        Returns report.
        :return:
        :rtype: hubflow.report.Report
        '''
        classes = self.classDefinitionT.classes()
        names = self.classDefinitionT.names()
        report = Report('ROC Curve and AUC Performance')
        report.append(ReportHeading('Performance Measures'))
        colHeaders = [['', 'AUC'], ['n', 'Log loss'] + names]
        colSpans = [[2, classes], [1] * (classes + 2)]
        roc_auc_scores_rounded = [round(elem, 3) for elem in self.roc_auc_scores.values()]
        data = [[str(self.n), numpy.round(self.log_loss, 2)] + roc_auc_scores_rounded]
        report.append(ReportTable(data, '', colHeaders=colHeaders, colSpans=colSpans))

        report.append(ReportHeading('Receiver Operating Characteristic (ROC) Curves'))

        fig, ax = plt.subplots(facecolor='white', figsize=(9, 6))
        for i in range(len(self.roc_curves)):
            # rgb = [v / 255. for v in self.classDefinitionP.color[i]]
            plt.plot(self.roc_curves[i + 1][0], self.roc_curves[i + 1][1]
                , label=names[i])  # , color=rgb) # problem: plots do not show the correct RGB colors
        ax.set_xlabel('False Positive Rate')
        ax.set_ylabel('True Positive Rate')
        plt.plot([0, 1], [0, 1], 'k--')
        plt.legend(loc="lower right")
        fig.tight_layout()
        report.append(ReportPlot(fig, 'ROC Curves'))

        report.append(ReportHeading('Scikit-Learn Documentation'))
        report.append(ReportHyperlink('http://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics',
            'ROC User Guide'))
        report.append(ReportHyperlink('http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html',
            'ROC Curve'))
        report.append(ReportHyperlink(
            'http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score',
            'AUC score'))
        report.append(ReportHyperlink('http://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html',
            'Log Loss Metric'))

        return report


class Sample(MapCollection):
    '''Class for managing unsupervised samples.'''

    def __init__(self, raster, mask=None, grid=None):
        '''
        Create new instance from given raster (and mask).

        :param raster: raster to sample from
        :type raster: Raster
        :param mask:
        :type mask: map interpreted as mask to restrict valid locations
        :param grid:
        :type grid: hubdc.core.Grid
        '''
        assert isinstance(raster, Raster)
        if grid is None:
            grid = raster.dataset().grid()
        assert isinstance(grid, Grid), repr(grid)
        MapCollection.__init__(self, maps=[raster])
        self._mask = mask
        self._grid = grid

    def __getstate__(self):
        return OrderedDict([('raster', self.raster()),
                            ('mask', self.mask()),
                            ('grid', self.grid())])

    def _initPickle(self):
        MapCollection._initPickle(self)
        if self._mask is not None:
            self._mask._initPickle()

    def raster(self):
        '''Return raster.'''
        raster = self.maps()[0]
        assert isinstance(raster, Raster)
        return raster

    def mask(self):
        '''Return mask.'''
        return self._mask

    def masks(self):
        '''Return maps concidered as masks during sampling (i.e. both raster and mask)'''
        return [self.raster(), self.mask()]

    def grid(self):
        '''Return grid.'''
        assert isinstance(self._grid, Grid)
        return self._grid

    def extractAsArray(self, grid=None, masks=None, onTheFlyResampling=False, **kwargs):
        '''
        Extract profiles from raster as array

        :param grid: optional grid for on-the-fly resampling
        :type grid: Grid
        :param masks: list of masks instead of self.masks()
        :type masks: List[Map]
        :param onTheFlyResampling: whether to allow on-the-fly resampling
        :type onTheFlyResampling: bool
        :param kwargs: passed to Applier
        :type kwargs:
        :return:
        :rtype: Sample

        :example:

        >>> sample = Sample(raster=Raster.fromArray(array=[[[1, 2, 3]],
        ...                                               [[1, 2, 3]]],
        ...                                         filename='/vsimem/fraction.bsq'),
        ...                 mask=Mask.fromArray(array=[[[1, 0, 1]]],
        ...                                     filename='/vsimem/mask.bsq'))
        >>> sample.extractAsArray()[0]
        array([[1, 3],
               [1, 3]])
        '''
        if grid is None:
            grid = self.grid()
        if masks is None:
            masks = self.masks()
        return MapCollection.extractAsArray(self, grid=grid, masks=masks, onTheFlyResampling=onTheFlyResampling,
            **kwargs)

    def extractAsRaster(self, filenames, grid=None, masks=None, onTheFlyResampling=False, **kwargs):
        '''Performes :meth:`~hubflow.core.Sample.extractAsArray` and stores the result as raster.'''

        if grid is None:
            grid = self.grid()
        if masks is None:
            masks = self.masks()
        return MapCollection.extractAsRaster(self, filenames=filenames, grid=grid, masks=masks,
            onTheFlyResampling=onTheFlyResampling, **kwargs)


class ClassificationSample(Sample):
    '''Class for managing classification samples.'''

    def __init__(self, raster, classification, mask=None, grid=None):
        '''
        Create new instance.

        :param raster:
        :type raster: hubflow.core.Raster
        :param classification:
        :type classification: hubflow.core.Classification
        :param mask:
        :type mask: None
        :param grid:
        :type grid: None

        '''
        Sample.__init__(self, raster=raster, mask=mask, grid=grid)
        assert isinstance(classification, Classification)
        self.maps().append(classification)

    def __getstate__(self):
        return OrderedDict([('raster', self.raster()),
                            ('classification', self.classification()),
                            ('mask', self.mask()),
                            ('grid', self.grid())])

    def masks(self):
        return Sample.masks(self) + [self.classification()]

    def classification(self):
        classification = self.maps()[1]
        assert isinstance(classification, Classification)
        return classification

    def synthMix(self, filenameFeatures, filenameFractions, target, mixingComplexities, classProbabilities=None,
            n=10, includeEndmember=False, includeWithinclassMixtures=False, targetRange=(0, 1), **kwargs):

        classDefinition = self.classification().classDefinition()
        if classProbabilities is None:
            classProbabilities = 'proportional'
        if classProbabilities == 'proportional':
            counts = self.classification().statistics()
            classProbabilities = {i + 1: float(count) / sum(counts) for i, count in enumerate(counts)}
        elif classProbabilities == 'equalized':
            classProbabilities = {i + 1: 1. / classDefinition.classes() for i in range(classDefinition.classes())}

        assert isinstance(mixingComplexities, dict)
        assert isinstance(classProbabilities, dict)

        features, labels = self.extractAsArray(**kwargs)
        classes = classDefinition.classes()

        # cache label indices and setup 0%/100% fractions from class labels
        indices = dict()
        zeroOneFractions = np.zeros((classes, features.shape[1]), dtype=np.float32)
        for label in range(1, classes + 1):
            indices[label] = np.where(labels == label)[1]
            zeroOneFractions[label - 1, indices[label]] = 1.

        # create mixtures
        mixtures = list()
        fractions = list()

        classProbabilities2 = {k: v / (1 - classProbabilities[target]) for k, v in classProbabilities.items() if k != target}
        for i in range(n):
            complexity = np.random.choice(list(mixingComplexities.keys()), p=list(mixingComplexities.values()))
            drawnLabels = [target]

            if includeWithinclassMixtures:
                drawnLabels.extend(np.random.choice(list(classProbabilities.keys()), size=complexity - 1, replace=True,
                    p=list(classProbabilities.values())))
            else:
                drawnLabels.extend(np.random.choice(list(classProbabilities2.keys()), size=complexity - 1, replace=False,
                    p=list(classProbabilities2.values())))

            drawnIndices = [np.random.choice(indices[label]) for label in drawnLabels]
            drawnFeatures = features[:, drawnIndices]
            drawnFractions = zeroOneFractions[:, drawnIndices]

            randomWeights = list()
            for i in range(complexity - 1):
                if i == 0:
                    weight = numpy.random.random() * (targetRange[1] - targetRange[0]) + targetRange[0]
                else:
                    weight = numpy.random.random() * (1. - sum(randomWeights))
                randomWeights.append(weight)
            randomWeights.append(1. - sum(randomWeights))

            assert sum(randomWeights) == 1.
            mixtures.append(np.sum(drawnFeatures * randomWeights, axis=1))
            fractions.append(np.sum(drawnFractions * randomWeights, axis=1)[target - 1])

        if includeEndmember:
            mixtures.extend(features.T)
            fractions.extend(np.float32(labels == target)[0])  # 1. for target class, 0. for the rest

        mixtures = np.atleast_3d(np.transpose(mixtures))
        fractions = np.atleast_3d(np.transpose(fractions))

        featuresDataset = RasterDataset.fromArray(array=mixtures, filename=filenameFeatures,
            driver=RasterDriver.fromFilename(filename=filenameFeatures))
        featuresDataset.flushCache().close()
        fractionsDataset = RasterDataset.fromArray(array=fractions, filename=filenameFractions,
            driver=RasterDriver.fromFilename(
                filename=filenameFractions))
        outClassDefinition = ClassDefinition(classes=1,
            names=[classDefinition.name(label=target)],
            colors=[classDefinition.color(label=target)])
        MetadataEditor.setFractionDefinition(rasterDataset=fractionsDataset, classDefinition=outClassDefinition)
        fractionsDataset.flushCache().close()

        return FractionSample(raster=Raster(filename=filenameFeatures),
            fraction=Fraction(filename=filenameFractions))


class RegressionSample(Sample):
    def __init__(self, raster, regression, mask=None, grid=None):
        Sample.__init__(self, raster=raster, mask=mask, grid=grid)
        assert isinstance(regression, Regression)
        self.maps().append(regression)
        self._regression = regression

    def __getstate__(self):
        return OrderedDict([('raster', self.raster()),
                            ('regression', self.regression()),
                            ('mask', self.mask()),
                            ('grid', self.grid())])

    def masks(self):
        return Sample.masks(self) + [self.regression()]

    def regression(self):
        assert isinstance(self._regression, Regression)
        return self._regression

    @staticmethod
    def fromArtmo(filenameRaster, filenameRegression, filenameArtmo, filenameArtmoMeta, scale=None):

        with open(filenameArtmo) as f:
            text = f.readlines()
        with open(filenameArtmoMeta, errors='ignore') as f:
            textMeta = f.readlines()

        data = np.array([l.split(',') for l in text], dtype=np.float32)
        numberOfTargets = np.sum(data[0] == 0)
        wavelength = list(data[0, numberOfTargets:])
        labels = data[1:, 0:numberOfTargets]
        profiles = data[1:, numberOfTargets:]
        if scale is not None:
            profiles *= scale

        outputNames = [l for l in textMeta if
                       l.startswith('Column ')]  # find lines like: Column 1: Cab	min: 3.1575	 max: 97.8976	 count: 20
        outputNames = [l.split(':')[1].replace('min', '').strip() for l in outputNames]  # extract names

        raster = Raster.fromArray(array=np.atleast_3d(profiles.T), filename=filenameRaster,
            noDataValues=-9999)
        raster.dataset().setMetadataItem(key='wavelength', value=wavelength, domain='ENVI')
        raster.dataset().setMetadataItem(key='wavelength units', value='nanometers', domain='ENVI')

        regression = Regression.fromArray(array=np.atleast_3d(labels.T), filename=filenameRegression,
            noDataValues=np.finfo(np.float32).min,
            descriptions=outputNames)

        sample = RegressionSample(raster=raster, regression=regression)
        return sample


class FractionSample(RegressionSample):
    def __init__(self, raster, fraction, mask=None, grid=None):
        assert isinstance(fraction, Fraction)
        RegressionSample.__init__(self, raster=raster, regression=fraction, mask=mask, grid=grid)

    def __getstate__(self):
        return OrderedDict([('raster', self.raster()),
                            ('fraction', self.regression()),
                            ('mask', self.mask()),
                            ('grid', self.grid())])

    def fraction(self):
        return self.regression()


class Estimator(FlowObject):
    SAMPLE_TYPE = Sample
    PREDICT_TYPE = Raster

    def __init__(self, sklEstimator, sample=None):
        self._sklEstimator = sklEstimator
        self._sample = sample
        self._X = None
        self._y = None

    def __getstate__(self):
        return OrderedDict([('sklEstimator', self.sklEstimator()),
                            ('sample', self.sample()),
                            ('X', self.X()),
                            ('y', self.y())])

    def __setstate__(self, state):
        self.__init__(**{'sklEstimator': state['sklEstimator'],
                         'sample': state['sample']})
        self._X = state['X']
        self._y = state['y']

    def _initPickle(self):
        if isinstance(self._sample, Sample):
            self._sample._initPickle()

    def sklEstimator(self):
        return self._sklEstimator

    def sample(self):
        assert isinstance(self._sample, (Sample, type(None)))
        return self._sample

    def X(self):
        assert isinstance(self._X, (np.ndarray, type(None)))
        return self._X

    def y(self):
        assert isinstance(self._y, (np.ndarray, type(None)))
        return self._y

    def _fit(self, sample):
        import sklearn.multioutput
        assert isinstance(sample, self.SAMPLE_TYPE)
        self._sample = sample

        if isinstance(sample, (ClassificationSample, RegressionSample, FractionSample)):
            features, labels = sample.extractAsArray()
            X = np.float64(features.T)
            if labels.shape[0] == 1 and not isinstance(
                    self.sklEstimator(), (
                            sklearn.multioutput.MultiOutputClassifier,
                            sklearn.multioutput.MultiOutputRegressor,
                    )
            ):
                y = labels.ravel()
            else:
                y = labels.T
        elif isinstance(sample, Sample):
            features, = sample.extractAsArray()
            X = np.float64(features.T)
            y = None
        else:
            raise errors.TypeError(obj=sample)

        try:
            self.sklEstimator().fit(X=X, y=y)
        except ValueError as error:
            if error.args[0] == 'y must have at least two dimensions for multi-output regression but has only one.':
                y = np.atleast_2d(y)
            self.sklEstimator().fit(X=X, Y=y)

        self._X = X
        self._y = y

        if isinstance(self, Clusterer):
            yTrain = self.sklEstimator().predict(X=X)
            self._classDefinition = ClassDefinition(classes=max(yTrain) + 1)
        return self

    def _predict(self, filename, raster, mask=None, **kwargs):

        if isinstance(raster, Raster):
            grid = raster.grid()
        else:
            raise errors.TypeError(obj=raster)

        # if number of features in estimator and raster not match:
        # - try to match features by band description
        # - subset matching raster bands for prediction
        doSubsetBands = False
        doSubsetWavebands = False

        if raster.dataset().zsize() == self.sample().raster().dataset().zsize():
            pass
        elif raster.dataset().zsize() > self.sample().raster().dataset().zsize():
            indices = list()
            for i, name in enumerate(self.sample().raster().descriptions()):
                name = name.split('(')[
                    0].strip()  # cut wavelength part if needed, e.g. "Band 1 (400 Nanometers)" to "Band 1"
                for name2 in raster.descriptions():
                    name2 = name2.split('(')[0].strip()
                    if name == name2:
                        indices.append(i)
                        break
            if len(indices) == self.sample().raster().dataset().zsize():
                doSubsetBands = True
                raster = raster.subsetBands(filename='/vsimem/Estimator._predict/raster.bsq', indices=indices)
            else:
                try:  # spectral subsetting
                    raster = raster.subsetWavebands(filename='/vsimem/Estimator._predict/raster.bsq',
                        wavelength=self.sample().raster().metadataWavelength())
                    doSubsetBands = True
                except:
                    pass

        applier = Applier(defaultGrid=grid, **kwargs)
        applier.setFlowRaster('raster', raster=raster)
        applier.setFlowMask('mask', mask=mask)
        applier.setOutputRaster('prediction', filename=filename)
        applier.apply(operatorType=_EstimatorPredict, raster=raster, estimator=self, mask=mask)
        prediction = self.PREDICT_TYPE(filename=filename)

        if doSubsetBands:
            gdal.Unlink(raster.filename())

        assert isinstance(prediction, Raster)
        return prediction

    def _predictProbability(self, filename, raster, mask=None, mask2=None, **kwargs):
        applier = Applier(defaultGrid=raster, **kwargs)
        applier.setFlowRaster('raster', raster=raster)
        applier.setFlowMask('mask', mask=mask)
        applier.setFlowMask('mask2', mask=mask2)
        applier.setOutputRaster('fraction', filename=filename)
        applier.apply(operatorType=_EstimatorPredictProbability, raster=raster, estimator=self, mask=mask, mask2=mask2)
        fraction = Fraction(filename=filename)
        return fraction

    def _transform(self, filename, raster, inverse=False, mask=None, mask2=None, **kwargs):
        applier = Applier(defaultGrid=raster, **kwargs)
        applier.setFlowRaster('raster', raster=raster)
        applier.setFlowMask('mask', mask=mask)
        applier.setFlowMask('mask2', mask=mask2)
        applier.setOutputRaster('transformation', filename=filename)
        applier.apply(operatorType=_EstimatorTransform, estimator=self, raster=raster, mask=mask, mask2=mask2,
            inverse=inverse)
        return Raster(filename=filename)

    def _inverseTransform(self, filename, raster, mask=None, mask2=None, **kwargs):
        return self._transform(filename=filename, raster=raster, inverse=True, mask=None, mask2=None, **kwargs)

    def saveX(self, filename):
        raster = Raster.fromArray(array=np.atleast_3d(self.X().T), filename=filename,
            noDataValues=self.sample().raster().noDataValues(),
            descriptions=self.sample().raster().descriptions())
        raster.dataset().setMetadataDict(self.sample().raster().dataset().metadataDict())
        return raster

    def saveY(self, filename):
        raise NotImplementedError()


class _EstimatorPredict(ApplierOperator):
    def ufunc(self, estimator, raster, mask):
        self.features = self.flowRasterArray('raster', raster=raster)
        etype, dtype, noutputs = self.getInfos(estimator)

        if isinstance(estimator, (Classifier, Clusterer)):
            noDataValues = [0]
        elif isinstance(estimator, Regressor):
            noDataValues = estimator.sample().regression().noDataValues()
        else:
            raise errors.TypeError(obj=estimator)

        prediction = self.full(value=noDataValues, bands=noutputs, dtype=dtype)

        valid = self.maskFromArray(array=self.features, noDataValueSource='raster')
        valid *= self.flowMaskArray('mask', mask=mask)

        if np.any(valid):
            X = np.float64(self.features[:, valid[0]].T)
            y = estimator.sklEstimator().predict(X=X)

            if isinstance(estimator, Clusterer):
                y += 1  # start with id=1, because zero is reserved as no data value

            prediction[:, valid[0]] = y.reshape(X.shape[0], -1).T

        self.outputRaster.raster(key='prediction').setArray(array=prediction)

        if isinstance(estimator, Classifier):
            self.setFlowMetadataClassDefinition('prediction',
                classDefinition=estimator.sample().classification().classDefinition())
        elif isinstance(estimator, Clusterer):
            self.setFlowMetadataClassDefinition('prediction', classDefinition=estimator.classDefinition())
        elif isinstance(estimator, Regressor):
            if isinstance(estimator.sample(), FractionSample):
                self.setFlowMetadataFractionDefinition('prediction',
                    classDefinition=estimator.sample().fraction().classDefinition())
            else:
                self.outputRaster.raster(key='prediction').setNoDataValues(values=noDataValues)
            self.setFlowMetadataBandNames('prediction', bandNames=estimator.sample().regression().outputNames())

    def getInfos(self, estimator):
        etype = estimator.sklEstimator()._estimator_type
        if etype in ['classifier', 'clusterer']:
            noutputs = 1
            dtype = np.uint8
        elif etype == 'regressor':
            X0 = np.atleast_2d(np.float64(self.features[:, 0, 0]))
            y0 = estimator.sklEstimator().predict(X=X0)
            noutputs = max(y0.shape)
            dtype = np.float32
        else:
            raise errors.TypeError(obj=estimator)
        return etype, dtype, noutputs


class _EstimatorPredictProbability(ApplierOperator):
    def ufunc(self, estimator, raster, mask, mask2):
        assert isinstance(estimator, Classifier)
        self.features = self.flowRasterArray('raster', raster=raster)
        noutputs = estimator.sample().classification().classDefinition().classes()
        noDataValue = -1
        prediction = self.full(value=noDataValue, bands=noutputs, dtype=np.float32)

        valid = self.maskFromArray(array=self.features, noDataValueSource='raster')
        valid *= self.flowMaskArray('mask', mask=mask)
        valid *= self.flowMaskArray('mask2', mask=mask2)

        if np.any(valid):
            X = np.float64(self.features[:, valid[0]].T)
            y = estimator.sklEstimator().predict_proba(X=X)
            for ci, yi in zip(estimator.sklEstimator().classes_, y.reshape(X.shape[0], -1).T):
                prediction[ci - 1, valid[0]] = yi
            # fill missing classes with zeroes
            for i in range(noutputs):
                if i + 1 not in estimator.sklEstimator().classes_:
                    prediction[i, valid[0]] = 0

        self.outputRaster.raster(key='fraction').setArray(array=prediction)
        self.setFlowMetadataFractionDefinition('fraction',
            classDefinition=estimator.sample().classification().classDefinition())


class _EstimatorTransform(ApplierOperator):
    def ufunc(self, estimator, raster, mask, mask2, inverse):
        if inverse:
            sklTransform = estimator.sklEstimator().inverse_transform
        else:
            sklTransform = estimator.sklEstimator().transform

        noDataValue = np.finfo(np.float32).min
        features = self.flowRasterArray('raster', raster=raster)

        X0 = np.float64(np.atleast_2d(features[:, 0, 0]))
        _, noutputs = sklTransform(X=X0).shape

        transformation = self.full(value=noDataValue, bands=noutputs, dtype=np.float32)

        valid = self.maskFromArray(array=features, noDataValueSource='raster')
        valid *= self.flowMaskArray('mask', mask=mask)
        valid *= self.flowMaskArray('mask2', mask=mask2)

        X = np.float64(features[:, valid[0]].T)
        y = sklTransform(X=X)
        transformation[:, valid[0]] = np.float32(y.reshape(-1, noutputs).T)

        self.outputRaster.raster(key='transformation').setArray(array=transformation)
        self.outputRaster.raster(key='transformation').setNoDataValue(value=noDataValue)


class Classifier(Estimator):
    SAMPLE_TYPE = ClassificationSample
    PREDICT_TYPE = Classification
    fit = Estimator._fit
    predict = Estimator._predict
    predictProbability = Estimator._predictProbability

    def sample(self):
        assert isinstance(self._sample, (ClassificationSample, type(None)))
        return self._sample

    def crossValidation(self, sample=None, cv=3, n_jobs=None):
        # todo: replace this by self.performanceCrossValidation
        if sample is None:
            sample = self._sample
        assert isinstance(sample, ClassificationSample)

        features, labels = sample.extractAsArray()
        X = np.float64(features.T)
        y = labels.ravel()

        from sklearn.model_selection import cross_val_predict
        yCV = cross_val_predict(estimator=self.sklEstimator(), X=X, y=y, cv=cv, n_jobs=n_jobs)

        return ClassificationPerformance(yP=yCV, yT=y.flatten(),
            classDefinitionP=sample.classification().classDefinition(),
            classDefinitionT=sample.classification().classDefinition())

    def saveY(self, filename):
        classification = Classification.fromArray(array=self.X(), filename=filename)
        MetadataEditor.setClassDefinition(rasterDataset=classification.dataset(),
            classDefinition=self.sample().classification().classDefinition())
        return classification

    def performanceCrossValidation(self, nfold, **kwargs):
        from sklearn.model_selection import cross_val_predict

        yP = cross_val_predict(estimator=self.sklEstimator(), X=self.X(), y=self.y(), cv=nfold, **kwargs)

        classDefinition = self.sample().classification().classDefinition()
        performance = ClassificationPerformanceCrossValidation(nfold=nfold, yT=self.y().T, yP=yP.T,
            classDefinitionP=classDefinition, classDefinitionT=classDefinition)
        return performance

    def performanceTraining(self):

        yP = self.sklEstimator().predict(X=self.X())

        classDefinition = self.sample().classification().classDefinition()
        performance = ClassificationPerformanceTraining(yT=self.y().T, yP=yP.T, classDefinitionP=classDefinition,
            classDefinitionT=classDefinition)
        return performance


class Regressor(Estimator):
    SAMPLE_TYPE = RegressionSample
    PREDICT_TYPE = Regression
    fit = Estimator._fit
    predict = Estimator._predict

    def sample(self):
        assert isinstance(self._sample, (RegressionSample, type(None)))
        return self._sample

    def saveY(self, filename):
        regression = Regression.fromArray(array=np.atleast_3d(self.y().T), filename=filename)
        MetadataEditor.setRegressionDefinition(rasterDataset=regression.dataset(),
            noDataValues=self.sample().regression().noDataValues(),
            outputNames=self.sample().regression().outputNames())
        return regression

    def performanceCrossValidation(self, nfold, **kwargs):
        from sklearn.model_selection import cross_val_predict

        yP = cross_val_predict(estimator=self.sklEstimator(), X=self.X(), y=self.y(), cv=nfold, **kwargs)

        outputNames = self.sample().regression().outputNames()
        yT = np.atleast_2d(self.y().T)
        yP = np.atleast_2d(yP.T)
        performance = RegressionPerformanceCrossValidation(nfold=nfold, yT=yT, yP=yP, outputNamesT=outputNames,
            outputNamesP=outputNames)
        return performance

    def performanceTraining(self):
        yP = self.sklEstimator().predict(X=self.X())

        outputNames = self.sample().regression().outputNames()
        performance = RegressionPerformanceTraining(yT=self.y().T, yP=yP.T, outputNamesT=outputNames,
            outputNamesP=outputNames)
        return performance

    def refitOnFeatureSubset(self, indices, filenameRaster, filenameRegression, invert=False):
        raster = self.saveX(filename='/vsimem/Regressor.refitOnFeatureSubset.raster.bsq')
        rasterSubset = raster.subsetBands(filename=filenameRaster, indices=indices, invert=invert)
        regression = self.saveY(filename=filenameRegression)
        sample = RegressionSample(raster=rasterSubset, regression=regression)
        regressor = Regressor(sklEstimator=self.sklEstimator())
        regressor.fit(sample=sample)
        gdal.Unlink(raster.filename())
        return regressor


class Transformer(Estimator):
    SAMPLE_TYPE = Sample
    PREDICT_TYPE = Raster
    fit = Estimator._fit
    transform = Estimator._transform
    inverseTransform = Estimator._inverseTransform


class Clusterer(Estimator):
    SAMPLE_TYPE = Sample
    PREDICT_TYPE = Classification
    fit = Estimator._fit
    predict = Estimator._predict
    transform = Estimator._transform

    def __init__(self, sklEstimator, sample=None, classDefinition=None):
        Estimator.__init__(self, sklEstimator=sklEstimator, sample=sample)
        self._classDefinition = classDefinition

    def __getstate__(self):
        result = super().__getstate__()
        result['classDefinition'] = self.classDefinition()
        return result

    def classDefinition(self):
        if self._classDefinition is None:
            return ClassDefinition(classes=0)
        assert isinstance(self._classDefinition, ClassDefinition)
        return self._classDefinition


class ClassificationPerformance(FlowObject):
    def __init__(self, yP, yT, classDefinitionP, classDefinitionT, classProportions=None, N=0):
        assert isinstance(yP, np.ndarray) and yP.ndim == 1
        assert isinstance(yT, np.ndarray) and yT.shape == yP.shape
        assert isinstance(classDefinitionP, ClassDefinition)
        assert isinstance(classDefinitionT, ClassDefinition)
        assert classDefinitionT.classes() == classDefinitionP.classes()

        self.classDefinitionT = classDefinitionT
        self.classDefinitionP = classDefinitionP
        self.N = N

        import sklearn.metrics
        self.yP = yP
        self.yT = yT
        self.mij = np.int64(
            sklearn.metrics.confusion_matrix(yT, yP, labels=range(1, classDefinitionT.classes() + 1)).T)
        self.m = np.int64(yP.size)
        self.classProportions = self.Wi = classProportions
        self._assessPerformance()

    def __getstate__(self):
        return OrderedDict([('yP', self.yP),
                            ('yT', self.yT),
                            ('classDefinitionP', self.classDefinitionP),
                            ('classDefinitionT', self.classDefinitionT),
                            ('classProportions', self.classProportions),
                            ('N', self.N)])

    @staticmethod
    def fromRaster(prediction, reference, mask=None, **kwargs):
        assert isinstance(prediction, Classification)
        assert isinstance(reference, Classification)
        stratification = prediction
        classes = stratification.classDefinition().classes()
        histogram = stratification.statistics(calcHistogram=True,
            histogramBins=[classes],
            histogramRanges=[(1, classes + 1)], **kwargs)
        classProportions = [float(count) / sum(histogram) for i, count in enumerate(histogram)]
        N = sum(histogram)

        yP, yT = MapCollection(maps=[prediction, reference]).extractAsArray(masks=[prediction, reference, mask])

        return ClassificationPerformance(yP=yP[0], yT=yT[0],
            classDefinitionP=prediction.classDefinition(),
            classDefinitionT=reference.classDefinition(),
            classProportions=classProportions,
            N=N)

    def _assessPerformance(self):

        old_error_state = np.geterr()
        np.seterr(divide='ignore', invalid='ignore', over='raise', under='raise')

        # get some stats from the confusion matrix mij
        self.mi_ = np.sum(self.mij, axis=0, dtype=np.float64)  # class-wise sum over all prediction
        self.m_j = np.sum(self.mij, axis=1, dtype=np.float64)  # class-wise sum over references
        self.mii = np.diag(self.mij)  # main diagonal -> class-wise correctly classified samples

        # estimate mapped class proportions from the reference sample, if not provided by the user
        if self.Wi is None:
            self.Wi = self.mi_ / self.m  # note that in this case pij is reduced to pij=mij/m

        # pij is the proportion of area estimate
        # pij = Wi*mij/mi_
        self.pij = np.zeros_like(self.mij, dtype=np.float64)
        for i in range(self.classDefinitionT.classes()):
            for j in range(self.classDefinitionT.classes()):
                self.pij[i, j] = self.Wi[i] * self.mij[i, j] / self.mi_[i]

        self.pi_ = np.sum(self.pij, axis=0, dtype=np.float64)
        self.p_j = np.sum(self.pij, axis=1, dtype=np.float64)
        self.pii = np.diag(self.pij)

        # calculate performance measures
        self.ProducerAccuracy = self._fix(self.mii / self.mi_)
        self.UserAccuracy = self._fix(self.mii / self.m_j)

        self.F1Accuracy = self._fix(
            2 * self.UserAccuracy * self.ProducerAccuracy / (self.UserAccuracy + self.ProducerAccuracy))
        self.ConditionalKappaAccuracy = self._fix(
            (self.m * self.mii - self.mi_ * self.m_j) / (self.m * self.mi_ - self.mi_ * self.m_j))
        self.OverallAccuracy = self._fix(self.mii.sum() / float(self.m))
        self.KappaAccuracy = self._fix(
            (self.m * self.mii.sum() - np.sum(self.mi_ * self.m_j)) / (self.m ** 2 - np.sum(self.mi_ * self.m_j)))

        # calculate squared standard errors (SSE)

        self.OverallAccuracySSE = 0.
        for i in range(self.classDefinitionT.classes()):
            self.OverallAccuracySSE += self.pij[i, i] * (
                    self.Wi[i] - self.pij[i, i]) / (self.Wi[i] * self.m)

        a1 = self.mii.sum() / self.m
        a2 = (self.mi_ * self.m_j).sum() / self.m ** 2
        a3 = (self.mii * (self.mi_ + self.m_j)).sum() / self.m ** 2
        a4 = 0.
        for i in range(self.classDefinitionT.classes()):
            for j in range(self.classDefinitionT.classes()):
                a4 += self.mij[i, j] * (self.mi_[j] + self.m_j[i]) ** 2
        a4 /= self.m ** 3
        b1 = a1 * (1 - a1) / (1 - a2) ** 2
        b2 = 2 * (1 - a1) * (2 * a1 * a2 - a3) / (1 - a2) ** 3
        b3 = (1 - a1) ** 2 * (a4 - 4 * a2 ** 2) / (1 - a2) ** 4
        self.KappaAccuracySSE = (b1 + b2 + b3) / self.m

        self.ProducerAccuracySSE = np.zeros(self.classDefinitionT.classes(), dtype=np.float64)
        for i in range(self.classDefinitionT.classes()):
            sum = 0.
            for j in range(self.classDefinitionT.classes()):
                if i == j:
                    continue
                sum += self.pij[i, j] * (self.Wi[j] - self.pij[i, j]) / (self.Wi[j] * self.m)
                self.ProducerAccuracySSE[i] = self.pij[i, i] * self.p_j[i] ** (-4) * (
                        self.pij[i, i] * sum + (self.Wi[i] - self.pij[i, i]) * (self.p_j[i] - self.pij[i, i]) ** 2 / (
                        self.Wi[i] * self.m))

        self.UserAccuracySSE = np.zeros(self.classDefinitionT.classes(), dtype=np.float64)
        for i in range(self.classDefinitionT.classes()):
            self.UserAccuracySSE[i] = self.pij[i, i] * (self.Wi[i] - self.pij[i, i]) / (self.Wi[i] ** 2 * self.m)

        self.F1AccuracySSE = self._fix(
            2 * self.UserAccuracySSE * self.ProducerAccuracySSE / (self.UserAccuracySSE + self.ProducerAccuracySSE))

        self.ConditionalKappaAccuracySSE = self.m * (self.mi_ - self.mii) / (self.mi_ * (self.m - self.m_j)) ** 3 * (
                (self.mi_ - self.mii) * (self.mi_ * self.m_j - self.m * self.mii) + self.m * self.mii * (
                self.m - self.mi_ - self.m_j + self.mii))

        self.ClassProportion = self.m_j / self.m
        self.ClassProportionSSE = np.zeros(self.classDefinitionT.classes(), dtype=np.float64)
        for j in range(self.classDefinitionT.classes()):
            for i in range(self.classDefinitionT.classes()):
                self.ClassProportionSSE[j] += self.Wi[i] ** 2 * (
                        (self.mij[i, j] / self.mi_[i]) * (1 - self.mij[i, j] / self.mi_[i])) / (self.mi_[i] - 1)

        np.seterr(**old_error_state)

    def _confidenceIntervall(self, mean, sse, alpha):
        import scipy.stats
        se = np.sqrt(np.clip(sse, 0, np.inf))
        lower = scipy.stats.norm.ppf(alpha / 2.) * se + mean
        upper = scipy.stats.norm.ppf(1 - alpha / 2.) * se + mean
        return lower, upper

    def _fix(self, a, fill=0):
        if isinstance(a, np.ndarray):
            a[np.logical_not(np.isfinite(a))] = fill
        elif not np.isfinite(a):
            a = fill
        return a

    def reportTitle(self):
        return 'Classification Performance'

    def appendReportHeader(self, report):
        assert isinstance(report, Report)
        pass

    def report(self):

        report = Report(title=self.reportTitle())

        self.appendReportHeader(report=report)

        report.append(ReportHeading('Class Overview'))
        colHeaders = None
        rowSpans = [[1, 2], [1, 1, 1]]
        rowHeaders = [['', 'Class Names'], ['Class ID', 'Reference', 'Prediction']]
        data = [np.hstack((range(1, self.classDefinitionT.classes() + 1))), self.classDefinitionT.names(),
                self.classDefinitionP.names()]

        report.append(ReportTable(data, '', colHeaders=colHeaders, rowHeaders=rowHeaders,
            colSpans=None, rowSpans=rowSpans))

        # Confusion Matrix Table
        report.append(ReportHeading('Confusion Matrix'))
        classNumbers = []
        for i in range(self.classDefinitionT.classes()):
            classNumbers.append('(' + str(i + 1) + ')')
        colHeaders = [['Reference Class'], classNumbers + ['Sum']]
        colSpans = [[self.classDefinitionT.classes()], np.ones(self.classDefinitionT.classes() + 1, dtype=int)]
        classNamesColumn = []
        for i in range(self.classDefinitionT.classes()):
            classNamesColumn.append(
                '(' + str(i + 1) + ') ' + self.classDefinitionT.names()[i])
        rowHeaders = [classNamesColumn + ['Sum']]
        data = np.vstack(((np.hstack((self.mij, self.m_j[:, None]))), np.hstack((self.mi_, self.m)))).astype(
            int)

        report.append(ReportTable(data, '', colHeaders=colHeaders, rowHeaders=rowHeaders, colSpans=colSpans))

        # Accuracies Table
        report.append(ReportHeading('Accuracies'))
        colHeaders = [['Measure', 'Estimate [%]', '95 % Confidence Interval [%]']]
        colSpans = [[1, 1, 2]]
        rowHeaders = None
        data = [['Overall Accuracy', np.round(self.OverallAccuracy * 100, 2),
                 np.round(self._confidenceIntervall(self.OverallAccuracy, self.OverallAccuracySSE, 0.05)[0] * 100),
                 round(self._confidenceIntervall(self.OverallAccuracy, self.OverallAccuracySSE, 0.05)[1] * 100, 2)],
                ['Kappa Accuracy', np.round(self.KappaAccuracy * 100, 2),
                 np.round(self._confidenceIntervall(self.KappaAccuracy, self.KappaAccuracySSE, 0.05)[0] * 100, 2),
                 np.round(self._confidenceIntervall(self.KappaAccuracy, self.KappaAccuracySSE, 0.05)[1] * 100, 2)],
                ['Mean F1 Accuracy', np.round(np.mean(self.F1Accuracy) * 100, 2), '-', '-']]
        report.append(ReportTable(data, '', colHeaders, rowHeaders, colSpans, rowSpans))

        # Class-wise Accuracies Table
        report.append(ReportHeading('Class-wise Accuracies'))
        colSpans = [[1, 3, 3, 3], [1, 1, 2, 1, 2, 1, 2]]
        colHeaders = [['', 'User\'s Accuracy [%]', 'Producer\'s Accuracy [%]', 'F1 Accuracy'],
                      ['Map class', 'Estimate', '95 % Interval', 'Estimate', '95% Interval', 'Estimate',
                       '95% Interval']]
        data = [classNamesColumn, np.round(self.UserAccuracy * 100, 2)
            , np.round(self._confidenceIntervall(self.UserAccuracy, self.UserAccuracySSE, 0.05)[0] * 100, 2)
            , np.round(self._confidenceIntervall(self.UserAccuracy, self.UserAccuracySSE, 0.05)[1] * 100, 2)
            , np.round(self.ProducerAccuracy * 100, 2)
            , np.round(self._confidenceIntervall(self.ProducerAccuracy, self.ProducerAccuracySSE, 0.05)[0] * 100, 2)
            , np.round(self._confidenceIntervall(self.ProducerAccuracy, self.ProducerAccuracySSE, 0.05)[1] * 100, 2)
            , np.round(self.F1Accuracy * 100, 2)
            , np.round(self._confidenceIntervall(self.F1Accuracy, self.F1AccuracySSE, 0.05)[0] * 100, 2)
            , np.round(self._confidenceIntervall(self.F1Accuracy, self.F1AccuracySSE, 0.05)[1] * 100, 2)]
        data = [list(x) for x in zip(*data)]
        report.append(ReportTable(data, '', colHeaders=colHeaders, rowHeaders=rowHeaders, colSpans=colSpans))

        # Proportion Matrix Table
        report.append(ReportHeading('Proportion Matrix'))
        colHeaders = [['Reference Class'], classNumbers + ['Sum']]
        colSpans = [[self.classDefinitionT.classes()], np.ones(self.classDefinitionT.classes() + 1, dtype=int)]
        rowHeaders = [classNamesColumn + ['Sum']]
        data = np.vstack(
            ((np.hstack((self.pij, self.p_j[:, None]))), np.hstack((self.pi_, 1))))
        report.append(ReportTable(np.round(data, 4), '', colHeaders=colHeaders, rowHeaders=rowHeaders,
            colSpans=colSpans))

        # Class-wise Area Estimates
        report.append(ReportHeading('Class-wise Proportion and Area Estimates'))
        colSpans = [[1, 3, 3], [1, 1, 2, 1, 2]]
        colHeaders = [['', 'Proportion', 'Area [px]'],
                      ['Map class', 'Estimate', '95 % Interval', 'Estimate', '95 % Interval']]
        data = [classNamesColumn,
                np.round(self.ClassProportion, 4),
                np.round(self._confidenceIntervall(self.ClassProportion, self.ClassProportionSSE, 0.05)[0], 4),
                np.round(self._confidenceIntervall(self.ClassProportion, self.ClassProportionSSE, 0.05)[1], 4),
                np.round(self.ClassProportion * self.N, 1),
                np.round(self._confidenceIntervall(self.ClassProportion, self.ClassProportionSSE, 0.05)[0] * self.N, 1),
                np.round(self._confidenceIntervall(self.ClassProportion, self.ClassProportionSSE, 0.05)[1] * self.N, 1)]
        data = [list(x) for x in zip(*data)]
        report.append(ReportTable(data, '', colHeaders=colHeaders, colSpans=colSpans))

        return report


class ClassificationPerformanceCrossValidation(ClassificationPerformance):

    def __init__(self, nfold, *args, **kwargs):
        ClassificationPerformance.__init__(self, *args, **kwargs)
        self.nfold = nfold

    def reportTitle(self):
        return 'Cross-validated Classifier Performance'

    def appendReportHeader(self, report):
        assert isinstance(report, Report)
        report.append(item=ReportParagraph(text='Number of cross-validation folds: {}'.format(self.nfold)))


class ClassificationPerformanceTraining(ClassificationPerformance):

    def reportTitle(self):
        return 'Classifier Fit/Training Performance'


class RegressionPerformance(FlowObject):
    def __init__(self, yT, yP, outputNamesT, outputNamesP):
        import sklearn.metrics

        assert isinstance(yT, np.ndarray)
        assert isinstance(yP, np.ndarray)
        assert yT.ndim == 2
        assert yP.ndim == 2
        assert yT.shape == yP.shape
        assert len(yT) == len(outputNamesT)
        assert len(yP) == len(outputNamesP)

        self.yP = yP
        self.yT = yT
        self.outputNamesT = outputNamesT
        self.outputNamesP = outputNamesP
        self.residuals = self.yP - self.yT
        self.n = self.yT[0].size

        self.explained_variance_score = [sklearn.metrics.explained_variance_score(self.yT[i], self.yP[i]) for i, _ in
                                         enumerate(outputNamesT)]
        self.mean_absolute_error = [sklearn.metrics.mean_absolute_error(self.yT[i], self.yP[i]) for i, _ in
                                    enumerate(outputNamesT)]
        self.mean_squared_error = [sklearn.metrics.mean_squared_error(self.yT[i], self.yP[i]) for i, _ in
                                   enumerate(outputNamesT)]
        self.ratio_of_performance_to_deviation = np.std(self.yT, axis=1) / np.sqrt(self.mean_squared_error)
        self.median_absolute_error = [sklearn.metrics.median_absolute_error(self.yT[i], self.yP[i]) for i, _ in
                                      enumerate(outputNamesT)]
        self.r2_score = [sklearn.metrics.r2_score(self.yT[i], self.yP[i]) for i, _ in enumerate(outputNamesT)]
        self.mean_error = [np.mean(self.yP[i] - self.yT[i]) for i, _ in enumerate(outputNamesT)]

        import scipy.stats
        self.squared_pearson_correlation_score = [scipy.stats.pearsonr(self.yT[i], self.yP[i])[0] ** 2 for i, _ in
                                                  enumerate(outputNamesT)]

        # f(x) = m*x + n
        self.fitted_line = [np.polyfit(self.yT[i], self.yP[i], 1) for i, _ in enumerate(outputNamesT)]

    def __getstate__(self):
        return OrderedDict([('yP', self.yP),
                            ('yT', self.yT),
                            ('outputNamesT', self.outputNamesT),
                            ('outputNamesP', self.outputNamesP)])

    @classmethod
    def fromRaster(self, prediction, reference, mask=None, **kwargs):
        assert isinstance(prediction, Regression)
        assert isinstance(reference, Regression), reference

        yP, yT = MapCollection(maps=[prediction, reference]).extractAsArray(masks=[prediction, reference, mask])

        return RegressionPerformance(yP=yP, yT=yT, outputNamesP=prediction.outputNames(),
            outputNamesT=reference.outputNames())

    def reportTitle(self):
        return 'Regression Performance'

    def appendReportHeader(self, report):
        assert isinstance(report, Report)
        pass

    def report(self):
        import matplotlib
        # matplotlib.use('QT4Agg')
        from matplotlib import pyplot

        report = Report(title=self.reportTitle())
        self.appendReportHeader(report=report)
        report.append(ReportHeading('Outputs Overview'))
        colHeaders = [['Outputs']]
        colSpans = [[len(self.outputNamesT)]]
        rowHeaders = [['Reference', 'Prediction']]
        data = [self.outputNamesT, self.outputNamesP]

        report.append(ReportTable(data, '', colHeaders=colHeaders, colSpans=colSpans, rowHeaders=rowHeaders))

        report.append(ReportHeading('Metrics'))

        report.append(ReportParagraph('Number of samples: {}'.format(self.n)))

        colHeaders = [['Outputs'], self.outputNamesT]
        colSpans = [[len(self.outputNamesT)], [1] * len(self.outputNamesT)]
        rowHeaders = [[
            'Mean absolute error (MAE)',
            'Root MSE (RMSE)',
            'Ratio of performance to deviation (RPD)',
            'Mean error (ME)',
            'Mean squared error (MSE)',
            'Median absolute error (MedAE)',
            'Squared pearson correlation (r^2)',
            'Explained variance score',
            'Coefficient of determination (R^2)',
        ]]

        data = np.array([
            np.round(np.array(self.mean_absolute_error).astype(float), 4),
            np.round(np.sqrt(np.array(self.mean_squared_error)).astype(float), 4),
            np.round(np.array(self.ratio_of_performance_to_deviation).astype(float), 4),
            np.round(np.array(self.mean_error).astype(float), 4),
            np.round(np.array(self.mean_squared_error).astype(float), 4),
            np.round(np.array(self.median_absolute_error).astype(float), 4),
            np.round(np.array(self.squared_pearson_correlation_score).astype(float), 4),
            np.round(np.array(self.explained_variance_score).astype(float), 4),
            np.round(np.array(self.r2_score).astype(float), 4),
        ])

        report.append(
            ReportTable(data, colHeaders=colHeaders, colSpans=colSpans, rowHeaders=rowHeaders, attribs_align='left'))

        report.append(
            ReportHyperlink(url=r'http://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics',
                text='See Scikit-Learn documentation for details.'))

        report.append(
            ReportHyperlink(url=r'https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html',
                text='See Scipy documentation for details on pearson correlation.'))

        report.append(ReportHeading('Scatter and Residuals Plots'))

        for i, name in enumerate(self.outputNamesT):
            fig, ax = pyplot.subplots(facecolor='white', figsize=(7, 7))
            # prepare 2x2 grid for plotting scatterplot on lower left, and adjacent histograms
            gs = matplotlib.gridspec.GridSpec(2, 2, width_ratios=[3, 1], height_ratios=[1, 3])

            ax0 = pyplot.subplot(gs[0, 0])
            ax0.hist(self.yT[i], bins=100, edgecolor='None', color='navy')
            pyplot.xlim([np.min(self.yT[i]), np.max(self.yT[i])])
            pyplot.tick_params(which='both', direction='out', length=10, pad=10)
            # hide ticks and ticklabels
            ax0.set_xticklabels([])
            ax0.set_ylabel('counts')
            ax0.set_title(name)
            ax0.xaxis.set_ticks_position('bottom')
            ax0.yaxis.set_ticks_position('left')

            # plot only every second tick, starting with the second
            # for label in ax0.get_yticklabels()[1::2]: label.set_visible(False)
            # plot only first and last ticklabel
            # for label in ax0.get_yticklabels()[1:-1]: label.set_visible(False)

            ax1 = pyplot.subplot(gs[1, 1])
            ax1.hist(self.yP[i], orientation='horizontal', bins=100, edgecolor='None', color='navy')
            pyplot.tick_params(which='both', direction='out', length=10, pad=10)
            pyplot.ylim([np.min(self.yT[i]), np.max(self.yT[i])])
            # hide ticks and ticklabels
            ax1.set_yticklabels([])
            ax1.set_xlabel('counts')
            ax1.yaxis.set_ticks_position('left')
            ax1.xaxis.set_ticks_position('bottom')
            # plot only every second tick, starting with the second
            # for label in ax1.get_xticklabels()[1::2]: label.set_visible(False)
            # plot only first and last ticklabel
            # for label in ax1.get_xticklabels()[1:-1]: label.set_visible(False)

            ax2 = pyplot.subplot(gs[1, 0])
            ax2.scatter(self.yT[i], self.yP[i], s=10) # , edgecolor='', color='navy')
            ymin = np.min(self.yT[i])
            ymax = np.max(self.yT[i])
            yspan = ymax - ymin
            ymin -= yspan * 0.01  # give some more space
            ymax += yspan * 0.01

            pyplot.xlim([ymin, ymax])
            pyplot.ylim([ymin, ymax])
            pyplot.tick_params(which='both', direction='out')
            pyplot.xlabel('Observed')
            pyplot.ylabel('Predicted')

            minX = np.min(self.yT[i])
            maxX = np.max(self.yT[i])
            # 1:1 line
            pyplot.plot([minX, maxX], [minX, maxX], 'k-')
            # fitted line
            m, n = self.fitted_line[i]
            if n > 0:
                fittedLineText = 'f(x) = {} * x + {}'.format(round(m, 5), round(n, 5))
            else:
                fittedLineText = 'f(x) = {} * x - {}'.format(round(m, 5), abs(round(n, 5)))

            pyplot.plot([minX, maxX], [m * minX + n, m * maxX + n], 'r--', label=fittedLineText)
            # pyplot.legend(loc='upper left')
            pyplot.legend(bbox_to_anchor=(0.75, -0.15))

            # Colorbar
            # cbaxes = fig.add_axes([0.05, 0.1, 0.05, 0.35])
            # cBar = pyplot.colorbar(sct, ticklocation='left', extend='neither', drawedges=False,cax = cbaxes)
            # cBar.ax.set_ylabel('label')

            fig.tight_layout()
            report.append(ReportPlot(fig))  # , caption=fittedLineText))
            pyplot.close()

            fig, ax = pyplot.subplots(facecolor='white', figsize=(7, 5))
            ax.hist(self.residuals[i], bins=100, edgecolor='None', color='navy')
            ax.set_title(name)
            ax.set_xlabel('Predicted - Observed')
            ax.set_ylabel('Counts')
            fig.tight_layout()
            report.append(ReportPlot(fig))
            pyplot.close()

        return report


class RegressionPerformanceCrossValidation(RegressionPerformance):

    def __init__(self, nfold, *args, **kwargs):
        RegressionPerformance.__init__(self, *args, **kwargs)
        self.nfold = nfold

    def reportTitle(self):
        return 'Cross-validated Regressor Performance'

    def appendReportHeader(self, report):
        assert isinstance(report, Report)
        report.append(item=ReportParagraph(text='Number of cross-validation folds: {}'.format(self.nfold)))


class RegressionPerformanceTraining(RegressionPerformance):

    def reportTitle(self):
        return 'Regressor Fit/Training Performance'


class ClusteringPerformance(FlowObject):
    def __init__(self, yT, yP):
        import sklearn.metrics
        assert isinstance(yP, np.ndarray)
        assert isinstance(yT, np.ndarray)
        assert yT.shape == yP.shape
        assert len(yT) == 1 and len(yP) == 1
        self.yP = yP[0]
        self.yT = yT[0]
        self.n = yT.shape[1]
        self.adjusted_mutual_info_score = sklearn.metrics.cluster.adjusted_mutual_info_score(labels_true=self.yT,
            labels_pred=self.yP)
        self.adjusted_rand_score = sklearn.metrics.cluster.adjusted_rand_score(labels_true=self.yT, labels_pred=self.yP)
        self.completeness_score = sklearn.metrics.cluster.completeness_score(labels_true=self.yT, labels_pred=self.yP)

    def __getstate__(self):
        return OrderedDict([('yP', self.yP[None]),
                            ('yT', self.yT[None])])

    @staticmethod
    def fromRaster(prediction, reference, mask=None, **kwargs):
        assert isinstance(prediction, Classification)
        assert isinstance(reference, Classification)

        yP, yT = MapCollection(maps=[prediction, reference]).extractAsArray(masks=[prediction, reference, mask],
            **kwargs)

        return ClusteringPerformance(yP=yP, yT=yT)

    def report(self):
        report = Report('Clustering Performance')
        report.append(ReportHeading('Performance Measures'))
        report.append(ReportParagraph('n = ' + str(self.n)))
        rowHeaders = [['Adjusted Mutual Information', 'Adjusted Rand Score', 'Completeness Score']]
        data = numpy.transpose([[numpy.round(self.adjusted_mutual_info_score, 3),
                                 numpy.round(self.adjusted_rand_score, 3), numpy.round(self.completeness_score, 3)]])
        report.append(ReportTable(data, '', rowHeaders=rowHeaders))
        report.append(ReportHeading('Scikit-Learn Documentation'))
        report.append(
            ReportHyperlink('http://scikit-learn.org/stable/modules/clustering.html#clustering-performance-evaluation',
                'Clustering Performance Evaluation Overview'))
        report.append(ReportHyperlink(
            'http://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_mutual_info_score.html#sklearn.metrics.adjusted_mutual_info_score',
            'Adjusted Mutual Information'))
        report.append(ReportHyperlink(
            'http://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html#sklearn.metrics.adjusted_rand_score',
            'Adjusted Rand Score'))
        report.append(ReportHyperlink(
            'http://scikit-learn.org/stable/modules/generated/sklearn.metrics.completeness_score.html#sklearn.metrics.completeness_score',
            'Completeness Score'))

        return report


class StringParser():
    class Range():
        def __init__(self, start, end):
            self.start = int(start)
            self.end = int(end)
            self.step = np.sign(end - start)

        def range(self):
            return range(self.start, self.end + self.step, self.step)

    def eval(self, text):
        if text.startswith(r'\eval'):
            return eval(text.replace(r'\eval', ''))
        else:
            raise Exception(r'text must start with "\eval"')

    def range(self, text):
        # try resolve range syntax, e.g. 2-4 as [2,4] or -4--2 as [-4, -2]
        i = text.index('-', 1)
        return self.Range(start=int(text[:i]), end=int(text[i + 1:]))

    def value(self, text):

        # try to evaluate as int or float
        try:
            result = float(text)
            if str(int(text)) == str(result):
                result = int(result)
        except:
            # try to evaluate as range
            try:
                result = self.range(text)
            except:
                result = text
        return result

    def strlist(self, text):
        for c in '''[]{}()'",''':
            text = text.replace(c, ' ')

        if text == '':
            return None
        else:
            return text.split()

    def list(self, text, extendRanges=True):

        # try to evaluate as python expression
        try:
            result = self.eval(text)
            assert isinstance(result, list)
        except:
            strlist = self.strlist(text)
            if strlist is None:
                result = None
            else:
                result = list()
                for strvalue in strlist:
                    value = self.value(strvalue)
                    if isinstance(value, self.Range):
                        if extendRanges:
                            result.extend(value.range())
                        else:
                            result.append((value.start, value.end))
                    else:
                        result.append(value)
        return result


def extractPixels(inputs, masks, grid, **kwargs):
    applier = Applier(defaultGrid=grid, **kwargs)
    for i, input in enumerate(inputs):
        name = 'input' + str(i)
        applier.setFlowInput(name=name, input=input)

    for i, mask in enumerate(masks):
        name = 'mask' + str(i)
        applier.setFlowMask(name=name, mask=mask)

    results = applier.apply(operatorType=_ExtractPixels, inputs=inputs, masks=masks)
    return results


class _ExtractPixels(ApplierOperator):
    def ufunc(self, inputs, masks):

        # calculate overall mask
        marray = self.full(value=True)
        nothingToExtract = False
        for i, mask in enumerate(masks):
            name = 'mask' + str(i)
            imarray = self.flowMaskArray(name=name, mask=mask)
            np.logical_and(marray, imarray, out=marray)
            if not marray.any():
                nothingToExtract = True
                break

        # extract values for all masked pixels
        result = list()
        for i, input in enumerate(inputs):
            name = 'input' + str(i)
            if nothingToExtract:
                zsize = self.flowInputZSize(name=name, input=input)
                dtype = np.uint8
                profiles = np.empty(shape=(zsize, 0), dtype=dtype)
            else:
                array = self.flowInputArray(name=name, input=input)
                profiles = array[:, marray[0]]

            result.append(profiles)
        return result

    @staticmethod
    def aggregate(blockResults, grid, inputs, *args, **kwargs):
        result = list()
        for i, input in enumerate(inputs):
            profilesList = [r[i] for r in blockResults]
            profiles = np.concatenate(profilesList, axis=1)
            result.append(profiles)
        return result


class MetadataEditor(object):

    @staticmethod
    def setClassDefinition(rasterDataset, classDefinition):
        assert isinstance(rasterDataset, (RasterDataset, ApplierOutputRaster))
        assert isinstance(classDefinition, ClassDefinition)

        noDataName = classDefinition.noDataName()
        noDataColor = classDefinition.noDataColor()

        names = [noDataName] + classDefinition.names()
        lookup = list(np.array([c.rgb() for c in [noDataColor] + classDefinition.colors()]).flatten())
        rasterDataset.setNoDataValue(value=0)

        # setup in ENVI domain
        rasterDataset.setMetadataItem(key='classes', value=classDefinition.classes() + 1, domain='ENVI')
        rasterDataset.setMetadataItem(key='class names', value=names, domain='ENVI')
        rasterDataset.setMetadataItem(key='file type', value='ENVI Classification', domain='ENVI')
        rasterDataset.setMetadataItem(key='class lookup', value=lookup, domain='ENVI')

        # setup in GDAL data model
        colors = np.array(lookup).reshape(-1, 3)
        colors = [tuple(color) for color in colors]
        band = rasterDataset.band(0)
        band.setCategoryNames(names=names)
        band.setCategoryColors(colors=colors)
        band.setDescription('Classification')

    @classmethod
    def setFractionDefinition(cls, rasterDataset, classDefinition):
        assert isinstance(rasterDataset, (RasterDataset, ApplierOutputRaster))
        assert isinstance(classDefinition, ClassDefinition)

        lookup = classDefinition.colorsFlatRGB()
        rasterDataset.setMetadataItem(key='band lookup', value=lookup, domain='ENVI')
        cls.setBandNames(rasterDataset=rasterDataset, bandNames=classDefinition.names())
        rasterDataset.setNoDataValue(value=-1)

    @classmethod
    def setRegressionDefinition(cls, rasterDataset, noDataValues, outputNames):
        assert isinstance(rasterDataset, (RasterDataset, ApplierOutputRaster))
        rasterDataset.setNoDataValues(values=noDataValues)
        cls.setBandNames(rasterDataset=rasterDataset, bandNames=outputNames)

    @classmethod
    def setBandNames(cls, rasterDataset, bandNames):
        assert isinstance(rasterDataset, (RasterDataset, ApplierOutputRaster))
        if len(bandNames) != rasterDataset.zsize():
            raise errors.HubDcError('number of bands not matching number of band names')
        rasterDataset.setMetadataItem(key='band names', value=bandNames, domain='ENVI')
        for band, bandName in zip(rasterDataset.bands(), bandNames):
            band.setDescription(value=bandName)

    @classmethod
    def setBandCharacteristics(cls, rasterDataset, bandNames=None, wavelength=None, fwhm=None, wavelengthUnits=None):
        assert isinstance(rasterDataset, (RasterDataset, ApplierOutputRaster))
        if bandNames is not None:
            cls.setBandNames(rasterDataset=rasterDataset, bandNames=bandNames)
        if wavelength is not None:
            rasterDataset.setMetadataItem(key='wavelength', value=wavelength, domain='ENVI')
        if fwhm is not None:
            rasterDataset.setMetadataItem(key='fwhm', value=fwhm, domain='ENVI')
        if wavelengthUnits is not None:
            rasterDataset.setMetadataItem(key='wavelength units', value=wavelengthUnits, domain='ENVI')

    @classmethod
    def bandNames(cls, rasterDataset):
        assert isinstance(rasterDataset, (RasterDataset, ApplierOutputRaster))
        return [band.description() for band in rasterDataset.bands()]

    @classmethod
    def bandCharacteristics(cls, rasterDataset):
        assert isinstance(rasterDataset, (RasterDataset, ApplierOutputRaster))
        return {'bandNames': cls.bandNames(rasterDataset=rasterDataset),
                'wavelength': rasterDataset.metadataItem(key='wavelength', domain='ENVI'),
                'fwhm': rasterDataset.metadataItem(key='fwhm', domain='ENVI'),
                'wavelengthUnits': rasterDataset.metadataItem(key='wavelength units', domain='ENVI')}


class LoggerFlowObject(object):

    def __init__(self, filename):
        self.filename = filename

    def setItems(self, items):
        self.items = items

    def logItems(self):
        with open(self.filename, mode='w') as file:
            for key, value in self.items:
                self.logKeyValue(file=file, key=key, value=value)

    def logKeyValue(self, file, key, value):

        assert isinstance(file, io.TextIOBase)

        file.write(key)
        file.write('\n')

        if isinstance(value, np.ndarray):
            if value.ndim == 1:
                file.write(', '.join(value.astype(str)))
                file.write('\n')
            elif value.ndim == 2:
                for line in value:
                    file.write(', '.join(line.astype(str)))
                    file.write('\n')
            else:
                raise NotImplementedError()
        elif isinstance(value, list):
            file.write(', '.join([str(v) for v in value]))
        else:
            file.write(str(value))
            file.write('\n')

        file.write('\n')

    def setSklEstimatorItems(self, estimator):

        from sklearn.model_selection import GridSearchCV

        from sklearn.linear_model import LinearRegression
        from sklearn.ensemble import RandomForestRegressor
        from sklearn.cross_decomposition import PLSRegression

        from sklearn.ensemble import RandomForestClassifier

        if isinstance(estimator, GridSearchCV):
            estimator = estimator.best_estimator_

        # regressors
        if isinstance(estimator, LinearRegression):
            self.setItems(items=[('coef_', estimator.coef_),
                                 ('intercept_', estimator.intercept_),
                                 ('Also see',
                                  'https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html')])
        elif isinstance(estimator, RandomForestRegressor):
            self.setItems(items=[('feature_importances_', estimator.feature_importances_),
                                 ('oob_score_', getattr(estimator, 'oob_score_', None)),
                                 ('oob_prediction_', getattr(estimator, 'oob_prediction_', None)),
                                 ('Also see',
                                  'https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html')])
        elif isinstance(estimator, PLSRegression):
            self.setItems(items=[('coef_', estimator.coef_),
                                 ('Also see',
                                  'https://scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSRegression.html')])
        # classifiers
        elif isinstance(estimator, RandomForestClassifier):
            self.setItems(items=[('feature_importances_', estimator.feature_importances_),
                                 ('oob_score_', getattr(estimator, 'oob_score_', None)),
                                 ('oob_decision_function_', getattr(estimator, 'oob_decision_function_', None)),
                                 ('Also see',
                                  'https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html')])
        else:
            raise NotImplementedError(type(estimator))

        return self
