Classes

Raster Maps

Raster

• hubflow.core.Raster:

  • applyMask() applySpatial() array() asMask() convolve() dataset() dtype() filename() fromArray() fromRasterDataset() fromVector() grid() metadataFWHM() metadataWavelength() noDataValue() noDataValues() resample() scatterMatrix() sensorDefinition() statistics() subsetBands() uniqueValues()

class hubflow.core.Raster(filename, eAccess=<sphinx.ext.autodoc.importer._MockObject object>)

Bases: hubflow.core.Map

Class for managing raster maps like Mask, Classification, Regression and Fraction.

applyMask(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.

Parameters:

• filename (str) – output path
• mask (Map) – a map that is evaluated as a mask
• noDataValue (float) – set no data value if undefined (default is to use 0)
• kwargs – passed to hubflow.core.Applier

Return type:

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]]])

applySpatial(filename, function, **kwargs)

Apply given function to each band of itself and return the result raster.

Parameters:

• filename (str) –
• function (function) – user defined function that takes one argument array
• kwargs – passed to hubflow.core.Applier

Return type:

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]]])

array(**kwargs)

Return raster data as 3d array of shape = (zsize, ysize, xsize). Additional kwargs are passed to Raster.dataset().array.

asMask(noDataValues=None, minOverallCoverage=0.5, indices=None, invert=False)

Return itself as a Mask.

Parameters:

• noDataValues (List[Union[None, float]]) – list of band-wise no data values
• minOverallCoverage (float) – threshold that defines, in case of on-the-fly average-resampling, which pixel will be evaluated as True
• indices (int) – if set, a band subset mask for the given indices is created
• invert (int) – whether to invert the mask

Return type:

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)

close()

See RasterDataset.show.

convolve(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.

Parameters:

• filename (str) – output path
• kernel (astropy.convolution.kernels.Kernel) –
• kwargs – passed to hubflow.core.Applier

Return type:

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)

dataset()

Return the hubdc.core.RasterDataset object.

dtype()

Return numpy data type

filename()

Return the filename.

classmethod fromArray(array, filename, grid=None, noDataValues=None, descriptions=None, **kwargs)

Create instance from given array.

Parameters:

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

Return type:

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"]])

static fromEnviSpectralLibrary(filename, library)

Create instance from given library.

Parameters:

• filename (str) – output path
• library (EnviSpectralLibrary`) –

Return type:

Raster

Example:

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

classmethod fromRasterDataset(rasterDataset, **kwargs)

Create instance from given rasterDataset.

Parameters:

• rasterDataset (hubdc.core.RasterDataset) – existing hubdc.core.RasterDataset
• kwargs – passed to class constructor

Return type:

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)

classmethod fromVector(filename, vector, grid, noDataValue=None, **kwargs)

Create instance from given vector by rasterizing it into the given grid.

Parameters:

• filename (str) – output path
• vector (Vector) – input vector
• grid (hubdc.core.Grid) – output pixel grid
• noDataValue (float) – output no data value
• kwargs – passed to hubflow.core.Applier

Return type:

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.]]]

grid()

Return grid.

metadataFWHM(required=False)

Return list of band full width at half maximums in nanometers. If not defined, list entries are None.

Example:

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

metadataWavelength()

Return list of band center wavelengths in nanometers.

Example:

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

noDataValue(default=None, required=False)

Return no value value.

noDataValues(default=None)

Return bands no value values.

resample(filename, grid, resampleAlg=<sphinx.ext.autodoc.importer._MockObject object>, **kwargs)

Return itself resampled into the given grid.

Parameters:

• filename (str) – output path
• grid (hubdc.core.Grid) –
• resampleAlg (int) – GDAL resampling algorithm
• kwargs – passed to hubflow.core.Applier

Return type:

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]]])

saveAs(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.

scatterMatrix(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.

Parameters:

• raster2 (Raster) –
• bandIndex1 (int) – first band index
• bandIndex2 (int) – second band index
• range1 (Tuple[float, float]) – first band range as (min, max) tuple
• range2 (Tuple[float, float]) – second band range as (min, max) tuple
• bins – passed to np.histogram2d
• mask (Map) – map that is evaluated as a mask
• stratification (Classification) – classification that stratifies the calculation into different classes
• kwargs – passed to hubflow.core.Applier

Return type:

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.])

sensorDefinition()

Return 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)])

show()

See RasterDataset.show.

statistics(bandIndices=None, mask=None, calcPercentiles=False, calcHistogram=False, calcMean=False, calcStd=False, percentiles=[], 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

Parameters:

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

Return type:

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)]

subsetBands(filename, indices, invert=False, **kwargs)

Return the band subset given by indices.

Parameters:

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

Return type:

Raster

Example:

TODO

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

uniqueValues(index)

Return 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)
array([1, 2, 5])

Mask

• hubflow.core.Mask:

  • indices() invert() minOverallCoverage() noDataValues() resample()

class hubflow.core.Mask(filename, noDataValues=None, minOverallCoverage=0.5, indices=None, invert=False)

Bases: hubflow.core.Raster

static 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.

Parameters:

• filename – output path
• raster (hubflow.core.Raster) – input raster
• initValue (bool) – initial fill value, default is False
• true (List[number or range]) – list of forground numbers and ranges
• false (List[number or range]) – list of forground numbers and ranges
• invert (bool) – whether to invert the mask
• aggregateFunction (func) – 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
• kwargs – passed to hubflow.core.Applier

Returns:

hubflow.core.Mask

Return type:

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)

static fromVector(filename, vector, grid, **kwargs)

Create a mask from a vector.

Parameters:

• filename – output path
• vector (hubflow.core.Vector) – input vector
• grid (hubdc.core.Grid) –
• kwargs –

Returns:

Return type:

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()

indices()

Return band subset indices.

invert()

Whether to invert the mask.

minOverallCoverage()

Return minimal overall coverage threshold.

noDataValues()

Return band no data values.

resample(filename, grid, **kwargs)

Returns a resampled mask of itself into the given grid.

Parameters:

• filename (str) – output path
• grid (hubdc.core.Grid) – output grid
• kwargs – passed to hubflow.core.Applier

Returns:

Return type:

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)

Classification

• hubflow.core.Classification:

  • asMask() classDefinition() dtype() fromArray() fromClassification() fromFraction() fromRasterAndFunction() minDominantCoverage() minOverallCoverage() noDataValues() reclassify() resample() statistics()

class hubflow.core.Classification(filename, classDefinition=None, minOverallCoverage=0.5, minDominantCoverage=0.5, eAccess=<sphinx.ext.autodoc.importer._MockObject object>)

Bases: hubflow.core.Raster

Class for managing classifications.

asMask(minOverallCoverage=0.5, invert=False)

Return itself as a mask.

classDefinition()

Return class definition.

dtype()

Return the smalles data type that is suitable for the number of classes.

classmethod fromArray(array, filename, classDefinition=None, grid=None, **kwargs)

Create instance from given array.

Parameters:

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

Returns:

Return type:

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)

classmethod fromClassification(filename, classification, grid=None, masks=None, **kwargs)

Create instance from classification-like raster.

Parameters:

• filename (str) – output path
• classification (Union[Classification, VectorClassification, Fraction]) – classification-like raster
• grid (hubdc.core.Grid) –
• masks (Mask) –
• kwargs – passed to Applier

Returns:

Return type:

hubflow.core.Classification

static 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.

Parameters:

• filename – output path
• library (EnviSpectralLibrary) –
• attribute (str) – attribute defined in the corresponding csv file
• classDefinition (ClassDefinition) –

Returns:

Return type:

Classification

Example:

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

classmethod fromFraction(filename, fraction, grid=None, masks=None, **kwargs)

Forwarded to fromClassification().

classmethod fromRasterAndFunction(filename, raster, ufunc, classDefinition=None, **kwargs)

Create instance from raster by applying a user-function to it.

Parameters:

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

Returns:

Return type:

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)

minDominantCoverage()

Return minimal dominant class coverage threshold.

minOverallCoverage()

Return minimal overall coverage threshold.

noDataValues()

Returns always [0].

reclassify(filename, classDefinition, mapping, **kwargs)

Reclassify classes by given mapping new classDefinition.

Parameters:

• filename (str) – output path
• classDefinition (ClassDefinition) –
• mapping (dict) –
• kwargs – passed to Applier

Returns:

Return type:

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)

resample(filename, grid, **kwargs)

Resample itself into the gives grid.

Parameters:

• filename (str) – output path
• grid (hubdc.core.Grid) –
• kwargs – passed to Applier

Returns:

Return type:

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)

setClassDefinition(classDefinition)

statistics(mask=None, **kwargs)

Returns list of class counts.

Parameters:

• mask (Mask) –
• kwargs – passed to Applier

Returns:

Return type:

list

Example:

>>> Classification.fromArray(array=[[[1, 1, 2, 3, 3, 3]]], filename='/vsimem/classification.bsq').statistics()
[2, 1, 3]

Fraction

• hubflow.core.Fraction:

  • asClassColorRGBRaster() classDefinition() fromClassification() minDominantCoverage() minOverallCoverage() noDataValues() resample() subsetClasses() subsetClassesByName()

class hubflow.core.Fraction(filename, classDefinition=None, minOverallCoverage=0.0, minDominantCoverage=0.0)

Bases: hubflow.core.Regression

Class for managing fraction maps.

asClassColorRGBRaster(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.

Parameters:

• filename – input path
• kwargs – passed to Applier

Returns:

Return type:

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

classDefinition()

Return the class definition.

classmethod fromClassification(filename, classification, **kwargs)

Create instance from given classification. A simple binarization in fractions of 0 and 1 is performed.

Parameters:

• filename – output path
• classification (Classification) – input classification
• kwargs – passed to Applier

Returns:

Fraction

Return type:

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)

minDominantCoverage()

Return the minimal dominant class coverage threshold.

minOverallCoverage()

Return the minimal overall coverage threshold.

noDataValues()

Returns no data values.

resample(filename, grid, **kwargs)

Resample itself into the given grid using gdal.GRA_Average resampling.

Parameters:

• filename – output path
• grid (hubdc.core.Grid) –
• kwargs – passed to Applier

Returns:

Return type:

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. ]]])

subsetClasses(filename, labels, **kwargs)

Subset itself by given class labels.

Parameters:

• filename – input path
• labels – list of labels to be subsetted
• kwargs – passed to Applier

Returns:

Return type:

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]]])

subsetClassesByName(filename, names, **kwargs)

Subset itself by given class names.

Parameters:

• filename – input path
• names – list of class names to be subsetted
• kwargs – passed to Applier

Returns:

Return type:

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]]])

Regression

• hubflow.core.Regression:

  • asMask() minOverallCoverage() noDataValues() outputNames() outputs() resample()

class hubflow.core.Regression(filename, noDataValues=None, outputNames=None, minOverallCoverage=0.5)

Bases: hubflow.core.Raster

Class for managing regression maps.

asMask(minOverallCoverage=None, noDataValues=None)

Creates a mask instance from itself. Optionally, the minimal overall coverage can be changed.

minOverallCoverage()

Return minimal overall coverage threshold.

noDataValues(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]

outputNames()

Return output names.

Example:

>>> import enmapboxtestdata
>>> Regression(filename=enmapboxtestdata.landcoverfractions).outputNames()
['Roof', 'Pavement', 'Low vegetation', 'Tree', 'Soil', 'Other']

outputs()

Return number of outputs (i.e. number of bands).

Example:

>>> import enmapboxtestdata
>>> Regression(filename=enmapboxtestdata.landcoverfractions).outputs()
6

resample(filename, grid, **kwargs)

Resample itself into the given grid using gdal.GRA_Average resampling.

Parameters:

• filename – output filename
• grid – hubdc.core.Grid
• kwargs – passed to Applier

Returns:

Regression

Return type:

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. ]]])

Vector Maps

Vector

• hubflow.core.Vector:

  • allTouched() burnAttribute() burnValue() dataset() dtype() extent() filename() filterSQL() fromPoints() fromRandomPointsFromClassification() fromRandomPointsFromMask() fromVectorDataset() grid() initValue() layer() metadataDict() metadataItem() noDataValue() projection() uniqueValues()

class hubflow.core.Vector(filename, layer=0, initValue=0, burnValue=1, burnAttribute=None, allTouched=False, filterSQL=None, dtype=<sphinx.ext.autodoc.importer._MockObject object>, noDataValue=None)

Bases: hubflow.core.Map

Class for managing vector maps. See also VectorMask, VectorClassification

allTouched()

Return rasterization all touched option.

burnAttribute()

Return rasterization burn attribute.

burnValue()

Return rasterization burn value.

dataset()

Return hubdc.core.VectorDataset object.

dtype()

Return rasterization data type.

extent()

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"]]))

filename()

Return filename.

filterSQL()

Return rasterization SQL filter statement.

classmethod fromPoints(filename, points)

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)

classmethod fromRandomPointsFromClassification(filename, classification, n, **kwargs)

Draw stratified random locations from raster classification and return as point vector.

Parameters:

• filename (str) – output path
• classification – input classification used as stratification
• n (List[int]) – list of number of points, one for each class
• kwargs – passed to hubflow.core.Applier

Returns:

Return type:

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()

… 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()

classmethod fromRandomPointsFromMask(filename, mask, n, **kwargs)

Draw random locations from raster mask and return as point vector.

Parameters:

• filename (str) – output path
• mask (hubflow.core.Mask) – input mask
• n (int) – number of points
• kwargs – passed to hubflow.core.Applier

Returns:

Return type:

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()

… 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()

classmethod fromVectorDataset(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)

grid(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"]])

initValue()

Return rasterization initialization value.

layer()

Return layer name or index

metadataDict()

Return the metadata dictionary for all domains.

metadataItem(key, domain='', dtype=<class 'str'>, required=False, default=None)

Returns the value (casted to a specific dtype) of a metadata item.

noDataValue(default=None)

Return rasterization no data value.

projection()

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"]])

uniqueValues(attribute, spatialFilter=None)

Return unique values for given attribute.

Parameters:

• attribute (str) –
• spatialFilter (hubdc.core.Geometry) – optional spatial filter

Returns:

Return type:

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']

VectorMask

• hubflow.core.VectorMask:

  • invert() kwargs()

class hubflow.core.VectorMask(filename, invert=False, **kwargs)

Bases: hubflow.core.Vector

Class for managing vector masks.

invert()

Returns whether to invert the mask.

kwargs()

Returns additional keyword arguments.

VectorClassification

• hubflow.core.VectorClassification:

  • classAttribute() classDefinition() minDominantCoverage() minOverallCoverage() oversampling()

class hubflow.core.VectorClassification(filename, classAttribute, classDefinition=None, layer=0, minOverallCoverage=0.5, minDominantCoverage=0.5, dtype=<sphinx.ext.autodoc.importer._MockObject object>, oversampling=1)

Bases: hubflow.core.Vector

Class for manaing vector classifications.

classAttribute()

Returns the class attribute.

classDefinition()

Returns the class definition.

minDominantCoverage()

Returns the minimal dominant class coverage threshold.

minOverallCoverage()

Returns the minimal overall coverage threshold.

oversampling()

Returns the oversampling factor.

Samples

Sample

• hubflow.core.Sample:

  • extractAsArray() extractAsRaster() grid() mask() masks() raster()

class hubflow.core.Sample(raster, mask=None, grid=None)

Bases: hubflow.core.MapCollection

Class for managing unsupervised samples.

extractAsArray(grid=None, masks=None, onTheFlyResampling=False, **kwargs)

Extract profiles from raster as array

Parameters:

• grid (Grid) – optional grid for on-the-fly resampling
• masks (List[Map]) – list of masks instead of self.masks()
• onTheFlyResampling (bool) – whether to allow on-the-fly resampling
• kwargs – passed to Applier

Returns:

Return type:

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]])

extractAsRaster(filenames, grid=None, masks=None, onTheFlyResampling=False, **kwargs)

Performes extractAsArray() and stores the result as raster.

grid()

Return grid.

mask()

Return mask.

masks()

Return maps concidered as masks during sampling (i.e. both raster and mask)

raster()

Return raster.

ClassificationSample

• hubflow.core.ClassificationSample:

  • classification() masks() synthMix()

class hubflow.core.ClassificationSample(raster, classification, mask=None, grid=None)

Bases: hubflow.core.Sample

Class for managing classification samples.

classification()

masks()

Return maps concidered as masks during sampling (i.e. both raster and mask)

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

FractionSample

• hubflow.core.FractionSample:

  • fraction()

class hubflow.core.FractionSample(raster, fraction, mask=None, grid=None)

Bases: hubflow.core.RegressionSample

fraction()

RegressionSample

• hubflow.core.RegressionSample:

  • masks() regression()

class hubflow.core.RegressionSample(raster, regression, mask=None, grid=None)

Bases: hubflow.core.Sample

masks()

Return maps concidered as masks during sampling (i.e. both raster and mask)

regression()

MapCollection

• hubflow.core.MapCollection:

  • extractAsArray() extractAsRaster() maps()

class hubflow.core.MapCollection(maps)

Bases: hubflow.core.FlowObject

Class for managing a collection of Map ‘s.

extractAsArray(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.

Parameters:

• masks (List[Map]) – List of maps that are evaluated as masks.
• grid (hubdc.core.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.
• onTheFlyResampling (bool) – If set to True, all maps and masks are resampled into the given grid.
• kwargs – passed to hubflow.core.Applier

Returns:

list of 2d arrays of size (bands, profiles)

Return type:

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]])]

extractAsRaster(filenames, masks, grid=None, onTheFlyResampling=False, **kwargs)

Returns the result of extractAsArray() as a list of Map objects.

Parameters:filenames (List[str]) – list of output paths, one for each map inside the collection Return type:List[Map]

All other parameters are passed to extractAsArray().

Example:

Same example as in 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]]])

maps()

Return the list of maps

Estimators

Classifier

• hubflow.core.Classifier:

  • PREDICT_TYPE() SAMPLE_TYPE() crossValidation() fit() predict() predictProbability()

class hubflow.core.Classifier(sklEstimator, sample=None)

Bases: hubflow.core.Estimator

PREDICT_TYPE

alias of Classification

SAMPLE_TYPE

alias of ClassificationSample

crossValidation(sample=None, cv=3, n_jobs=None)

fit(sample)

predict(filename, raster, mask=None, **kwargs)

predictProbability(filename, raster, mask=None, mask2=None, **kwargs)

Regressor

• hubflow.core.Regressor:

  • PREDICT_TYPE() SAMPLE_TYPE() fit() predict()

class hubflow.core.Regressor(sklEstimator, sample=None)

Bases: hubflow.core.Estimator

PREDICT_TYPE

alias of Regression

SAMPLE_TYPE

alias of RegressionSample

fit(sample)

predict(filename, raster, mask=None, **kwargs)

Clusterer

• hubflow.core.Clusterer:

  • PREDICT_TYPE() SAMPLE_TYPE() classDefinition() fit() predict() transform()

class hubflow.core.Clusterer(sklEstimator, sample=None, classDefinition=None)

Bases: hubflow.core.Estimator

PREDICT_TYPE

alias of Classification

SAMPLE_TYPE

alias of Sample

classDefinition()

fit(sample)

predict(filename, raster, mask=None, **kwargs)

transform(filename, raster, inverse=False, mask=None, mask2=None, **kwargs)

Transformer

• hubflow.core.Transformer:

  • PREDICT_TYPE() SAMPLE_TYPE() fit() inverseTransform() transform()

class hubflow.core.Transformer(sklEstimator, sample=None)

Bases: hubflow.core.Estimator

PREDICT_TYPE

alias of Raster

SAMPLE_TYPE

alias of Sample

fit(sample)

inverseTransform(filename, raster, mask=None, mask2=None, **kwargs)

transform(filename, raster, inverse=False, mask=None, mask2=None, **kwargs)

Accuracy Assessment

ClassificationPerformance

• hubflow.core.ClassificationPerformance:

  • report()

class hubflow.core.ClassificationPerformance(yP, yT, classDefinitionP, classDefinitionT, classProportions=None, N=0)

Bases: hubflow.core.FlowObject

static fromRaster(prediction, reference, mask=None, **kwargs)

report()

RegressionPerformance

• hubflow.core.RegressionPerformance:

  • fromRaster() report()

class hubflow.core.RegressionPerformance(yT, yP, outputNamesT, outputNamesP)

Bases: hubflow.core.FlowObject

classmethod fromRaster(prediction, reference, mask=None, **kwargs)

report()

FractionPerformance

• hubflow.core.FractionPerformance:

  • fromRaster() report()

class hubflow.core.FractionPerformance(yP, yT, classDefinitionP, classDefinitionT)

Bases: hubflow.core.FlowObject

Class for performing ROC curve analysis.

classmethod fromRaster(prediction, reference, mask=None, **kwargs)

Parameters:

• prediction (Fraction) –
• reference (Classification) –
• mask (Mask) –
• kwargs – passed to Applier

Returns:

Return type:

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)

report()

Returns report. :return: :rtype: hubflow.report.Report

ClusteringPerformance

• hubflow.core.ClusteringPerformance:

  • report()

class hubflow.core.ClusteringPerformance(yT, yP)

Bases: hubflow.core.FlowObject

static fromRaster(prediction, reference, mask=None, **kwargs)

report()

Miscellaneous

ClassDefinition

• hubflow.core.ClassDefinition:

  • classes() color() colorByName() colors() colorsFlatRGB() dtype() equal() labelByName() labels() name() names() noDataColor() noDataName() setNoDataNameAndColor()

class hubflow.core.ClassDefinition(classes=None, names=None, colors=None)

Bases: hubflow.core.FlowObject

Class for managing class definitions.

classes()

Return number of classes.

color(label)

Return color for given label.

colorByName(name)

Return color for given name.

colors()

Return class colors.

colorsFlatRGB()

Return colors as flat list of r, g, b values.

Example:

>>> ClassDefinition(colors=['red', 'blue']).colorsFlatRGB()
[255, 0, 0, 0, 0, 255]

dtype()

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'>

equal(other, compareColors=True)

Return whether self is equal to another instance.

static 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=[...])

static fromENVIClassification(raster)

Create instance by deriving metadata information for classes, class names and class lookup from the ENVI domain.

static fromENVIFraction(raster)

Create instance by deriving metadata information for band names and band lookup from the ENVI domain.

static fromGDALMeta(raster, index=0, skipZeroClass=True)

Create instance by deriving category names and color table from GDAL raster dataset.

static fromQml(filename, delimiter=';')

Create instance from QGIS QML file.

static fromRaster(raster)

Create instance by trying to 1) use fromENVIClassification(), 2) use fromGDALMeta() and finally 3) derive number of classes from raster band maximum value.

labelByName(name)

Return label for given name.

labels()

Return class labels.

name(label)

Return name for giben label.

names()

Return class names.

noDataColor()

Return no data color.

noDataName()

Return no data name.

setNoDataNameAndColor(name='Unclassified', color='black')

Set no data name and color.

SensorDefinition

• hubflow.core.SensorDefinition:

  • fromENVISpectralLibrary() plot() resampleProfiles() resampleRaster() wavebandCount() wavebandDefinition() wavebandDefinitions()

class hubflow.core.SensorDefinition(wavebandDefinitions)

Bases: hubflow.core.FlowObject

Class for managing sensor definitions.

classmethod fromEnviSpectralLibrary(library, isResponseFunction)

Create instance from EnviSpectralLibrary.

Parameters:

• library (EnviSpectralLibrary) –
• isResponseFunction (bool) – If True, library is interpreted as sensor response function. If False, center wavelength and FWHM information is used.

Return type:

SensorDefinition

Example:

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

>>> 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 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)])

static fromPredefined(name)

Create an instance for a predefined sensor (e.g. name='sentinel2'). See 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)])

static fromRaster(raster)

Forwards Raster.sensorDefinition().

plot(plotWidget=None, yscale=1.0, **kwargs)

Return sensor definition plot.

Parameters:

• plotWidget (pyqtgraph.graphicsWindows.PlotWindow) – if None, a new plot widget is created, otherwise, the given plotWidget is used
• yscale (float) – scale factor for y values
• kwargs – passed to pyqtgraph.graphicsWindows.PlotWindow.plot

Return type:

pyqtgraph.graphicsWindows.PlotWindow

Example:

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

static 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']

resampleProfiles(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``.

Parameters:

• array (Union[list, numpy.ndarray]) – list of profiles or 2d array of size (profiles, bands)
• wavelength (List[float]) – list of center wavelength of size (bands, )
• wavelengthUnits (str) – wavelength unit ‘nanometers’ | ‘micrometers’
• minResponse – passed to resampleRaster()
• resampleAlg – passed to resampleRaster()
• kwargs – passed to resampleRaster

Return type:

numpy.ndarray

Example:

>>> import pyqtgraph as pg
>>> import enmapboxtestdata
>>> 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

resampleRaster(filename, raster, minResponse=None, resampleAlg='linear', **kwargs)

Resample the given spectral raster.

Parameters:

• filename (str) – output path
• raster (hubflow.core.Raster) – spectral raster
• minResponse (float) – 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
• resampleAlg (enum(SensorDefinition.RESAMPLE_LINEAR, SensorDefinition.RESAMPLE_RESPONSE)) – available resampling algorithms are linear interpolation between neighbouring wavelength and response function filtering
• kwargs – passed to hubflow.core.Applier

Return type:

Union[hubflow.core.Raster, None]

Example:

>>> 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

wavebandCount()

Return number of wavebands.

wavebandDefinition(index)

Return WavebandDefinition for band given by index.

wavebandDefinitions()

Return iterator over all WavebandDefinition’s.

RESAMPLE_LINEAR = 'linear'

RESAMPLE_OPTIONS = ['linear', 'response']

RESAMPLE_RESPONSE = 'response'

WavebandDefinition

• hubflow.core.WavebandDefinition:

  • center() fwhm() name() plot() resamplingWeights() responses()

class hubflow.core.WavebandDefinition(center, fwhm=None, responses=None, name=None)

Bases: hubflow.core.FlowObject

Class for managing waveband definitions.

center()

Return center wavelength location.

>>> WavebandDefinition(center=560).center()
560.0

static 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)

fwhm()

Return full width at half maximum.

>>> WavebandDefinition(center=560, fwhm=10).fwhm()
10.0

name()

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

plot(plotWidget=None, yscale=1.0, **kwargs)

Return response function plot.

Parameters:

• plotWidget (pyqtgraph.graphicsWindows.PlotWindow) – if None, a new plot widget is created, otherwise, the given plotWidget is used
• yscale (float) – scale factor for y values
• kwargs – passed to pyqtgraph.graphicsWindows.PlotWindow.plot

Return type:

pyqtgraph.graphicsWindows.PlotWindow

Example:

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

resamplingWeights(sensor)

Return resampling weights for the center wavelength of the given SensorDefinition.

Example:

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

>>> 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)]

responses()

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)]

MetadataEditor

• hubflow.core.MetadataEditor:

  • bandCharacteristics() bandNames() setBandCharacteristics() setBandNames() setFractionDefinition() setRegressionDefinition()

class hubflow.core.MetadataEditor

Bases: object

classmethod bandCharacteristics(rasterDataset)

classmethod bandNames(rasterDataset)

classmethod setBandCharacteristics(rasterDataset, bandNames=None, wavelength=None, fwhm=None, wavelengthUnits=None)

classmethod setBandNames(rasterDataset, bandNames)

static setClassDefinition(rasterDataset, classDefinition)

classmethod setFractionDefinition(rasterDataset, classDefinition)

classmethod setRegressionDefinition(rasterDataset, noDataValues, outputNames)

Applier

Applier

• hubflow.core.Applier:

  • apply() setFlowClassification() setFlowFraction() setFlowInput() setFlowMask() setFlowMasks() setFlowRaster() setFlowRegression() setFlowVector() setOutputRaster()

class hubflow.core.Applier(defaultGrid=None, **kwargs)

Bases: sphinx.ext.autodoc.importer._MockObject

apply(operatorType=None, description=None, *ufuncArgs, **ufuncKwargs)

setFlowClassification(name, classification)

setFlowFraction(name, fraction)

setFlowInput(name, input)

setFlowMask(name, mask)

setFlowMasks(masks)

setFlowRaster(name, raster)

setFlowRegression(name, regression)

setFlowVector(name, vector)

setOutputRaster(name, filename)

ApplierOperator

• hubflow.core.ApplierOperator:

  • flowClassificationArray() flowFractionArray() flowInputArray() flowInputDType() flowInputZSize() flowMaskArray() flowMasksArray() flowRasterArray() flowRegressionArray() flowVectorArray() maskFromArray() maskFromBandArray() maskFromFractionArray() setFlowMetadataBandNames() setFlowMetadataClassDefinition() setFlowMetadataFractionDefinition() setFlowMetadataNoDataValues() setFlowMetadataRegressionDefinition() setFlowMetadataSensorDefinition()

class hubflow.core.ApplierOperator(*args, **kwargs)

Bases: sphinx.ext.autodoc.importer._MockObject

flowClassificationArray(name, classification, overlap=0)

flowFractionArray(name, fraction, overlap=0)

flowInputArray(name, input, overlap=0)

flowInputDType(name, input)

flowInputZSize(name, input)

flowMaskArray(name, mask, aggregateFunction=None, overlap=0)

flowMasksArray(masks, aggregateFunction=None, overlap=0)

flowRasterArray(name, raster, indices=None, overlap=0)

flowRegressionArray(name, regression, overlap=0)

flowVectorArray(name, vector, overlap=0)

maskFromArray(array, noDataValues=None, defaultNoDataValue=None, noDataValueSource=None, aggregateFunction=None)

maskFromBandArray(array, noDataValue=None, noDataValueSource=None, index=None)

maskFromFractionArray(fractionArray, minOverallCoverage, minDominantCoverage, invert=False)

setFlowMetadataBandNames(name, bandNames)

setFlowMetadataClassDefinition(name, classDefinition)

setFlowMetadataFractionDefinition(name, classDefinition)

setFlowMetadataNoDataValues(name, noDataValues)

setFlowMetadataRegressionDefinition(name, noDataValues, outputNames)

setFlowMetadataSensorDefinition(name, sensor)