gms_preprocessing.algorithms package

Submodules

gms_preprocessing.algorithms.L1A_P module

class gms_preprocessing.algorithms.L1A_P.L1A_object(image_type='', satellite='', sensor='', subsystem='', sensormode='', acq_datetime=None, entity_ID='', scene_ID=- 9999, filename='', dataset_ID=- 9999, proc_status='', **kwargs)

Bases: gms_preprocessing.model.gms_object.GMS_object

Features input reader and raster-/metadata homogenization.

:param : instance of gms_object.GMS_object or None

ASTER_HDF_to_rasObj(path_archive, path_output=None)

add_rasterInfo_to_MetaObj(custom_rasObj=None)

Add the attributes ‘rows’,’cols’,’bands’,’map_info’,’projection’ and ‘physUnit’ to self.MetaObj

archive_to_rasObj(path_archive, path_output=None, subset=None)

calc_TOARadRefTemp(subset=None)

Convert DN, Rad or TOA_Ref data to TOA Reflectance, to Radiance or to Surface Temperature (depending on CFG.target_radunit_optical and target_radunit_thermal). The function can be executed by a L1A_object representing a full scene or a tile. To process a file from disk in tiles, provide an item of self.tile_pos as the ‘subset’ argument.

calc_center_AcqTime()

Calculate scene acquisition time if not given in provider metadata.

calc_corner_positions()

Get true corner positions in the form [UL, UR, LL, LR] as [(ULrow,ULcol),(URrow,URcol),…],[(ULLon,ULLat),(URLon,URLat),..]

calc_mean_VAA()

Calculates mean viewing azimuth angle using sensor flight line derived from full scene corner coordinates.

calc_orbit_overpassParams()

Calculate orbit parameters.

import_metadata()

Reads metainformation of the given file from the given ASCII metafile. Works for: RapidEye (metadata.xml),SPOT(metadata.dim),LANDSAT(mtl.txt),ASTER(downloaded coremetadata), ALOS(summary.txt & Leader file)

Returns

import_rasterdata()

reference_data(out_CS='UTM')

Perform georeferencing of self.arr or the corresponding data on disk respectively. Method is skipped if self.GeoAlign_ok and self.GeoTransProj_ok evaluate to ‘True’. All attributes connected with the georeference of self.arr are automatically updated.

update_spec_vals_according_to_dtype(dtype=None)

Update self.MetaObj.spec_vals.

Parameters

dtype – <str> or <numpy data type> The data type to be used for updating. If not specified the data type of self.arr is used.

validate_GeoTransProj_GeoAlign()

gms_preprocessing.algorithms.L1B_P module

Level 1B Processor:

Detection of global/local geometric displacements.

class gms_preprocessing.algorithms.L1B_P.L1B_object(L1A_obj=None)

Bases: gms_preprocessing.algorithms.L1A_P.L1A_object

:param : instance of gms_object.GMS_object or None

compute_global_shifts()

correct_spatial_shifts(cliptoextent=True, clipextent=None, clipextent_prj=None, v=False)

Corrects the spatial shifts calculated by self.compute_global_shifts().

Parameters

• cliptoextent (bool) – whether to clip the output to the given extent

• clipextent (Optional[list]) – list of XY-coordinate tuples giving the target extent (if not given and cliptoextent is True, the ‘trueDataCornerLonLat’ attribute of the GMS object is used

• clipextent_prj (Optional[any]) – WKT projection string or EPSG code of the projection for the coordinates in clipextent

• v (bool) –

Return type

None

Returns

get_opt_bands4matching(target_cwlPos_nm=550)

Automatically determines the optimal bands used für fourier shift theorem matching

Parameters

target_cwlPos_nm – the desired wavelength used for matching

get_spatial_reference_scene()

property spatRef_available

class gms_preprocessing.algorithms.L1B_P.Scene_finder(src_boundsLonLat, src_AcqDate, src_prj, src_footprint_poly, sceneID_excluded=None, min_overlap=20, min_cloudcov=0, max_cloudcov=20, plusminus_days=30, plusminus_years=10, logger=None)

Bases: object

Scene_finder class to query the postgreSQL database to find a suitable reference scene for co-registration.

Initialize Scene_finder.

Parameters

• src_boundsLonLat (list) –

• src_AcqDate (datetime) –

• src_prj (str) –

• src_footprint_poly (Polygon) –

• sceneID_excluded (int) –

• min_overlap (int) – minimum overlap of reference scene in percent

• min_cloudcov (int) – minimum cloud cover of reference scene in percent

• max_cloudcov (int) – maximum cloud cover of reference scene in percent

• plusminus_days (int) – maximum time interval between target and reference scene in days

• plusminus_years (int) – maximum time interval between target and reference scene in years

choose_ref_scene()

Choose reference scene with minimum cloud cover and maximum overlap.

spatial_query(timeout=5)

Query the postgreSQL database to find possible reference scenes matching the specified criteria.

Parameters

timeout – maximum query duration allowed (seconds)

class gms_preprocessing.algorithms.L1B_P.ref_Scene(GDF_record)

Bases: object

gms_preprocessing.algorithms.L1C_P module

Level 1C Processor: Atmospheric correction of TOA-reflectance data.

class gms_preprocessing.algorithms.L1C_P.AtmCorr(*L1C_objs, reporting=False)

Bases: object

Wrapper around atmospheric correction by Andre Hollstein, GFZ Potsdam

Creates the input arguments for atmospheric correction from one or multiple L1C_object instance(s) belonging to the same scene ID, performs the atmospheric correction and returns the atmospherically corrected L1C object(s).

Parameters

L1C_objs – one or more instances of L1C_object belonging to the same scene ID

property GSDs

Returns a list of spatial samplings within the input GMS objects, e.g. [10,20,60].

property band_spatial_sampling

Returns

e.g. {‘B01’: 60.0,

’B02’: 10.0, ‘B03’: 10.0, ‘B04’: 10.0, ‘B05’: 20.0, ‘B06’: 20.0, ‘B07’: 20.0, ‘B08’: 10.0, ‘B09’: 60.0, ‘B10’: 60.0, ‘B11’: 20.0, ‘B12’: 20.0, ‘B8A’: 20.0}

property data

Returns

___ attribute: data, type:<class ‘dict’> ______ key:B05, value_type:<class ‘numpy.ndarray’>, repr: [[nan nan nan …,0. [..] 085998540.0803833 ]] ______ key:B01, value_type:<class ‘numpy.ndarray’>, repr: [[nan nan nan …,0. [..] 131225590.13208008]] ______ key:B06, value_type:<class ‘numpy.ndarray’>, repr: [[nan nan nan …,0. [..] .14965820.13977051]] ______ key:B11, value_type:<class ‘numpy.ndarray’>, repr: [[nan nan nan …,0. [..] .11492920.10192871]] ______ key:B02, value_type:<class ‘numpy.ndarray’>, repr: [[nan nan nan …,0. [..] 104187010.10308838]] ______ key:B10, value_type:<class ‘numpy.ndarray’>, repr: [[nan nan nan …,0. [..] 013099670.01300049]] ______ key:B08, value_type:<class ‘numpy.ndarray’>, repr: [[nan nan nan …,0. [..] .16857910.15783691]] ______ key:B04, value_type:<class ‘numpy.ndarray’>, repr: [[nan nan nan …,0. [..] 065490720.06228638]] ______ key:B03, value_type:<class ‘numpy.ndarray’>, repr: [[nan nan nan …,0. [..] 082702640.08148193]] ______ key:B12, value_type:<class ‘numpy.ndarray’>, repr: [[nan nan nan …,0. [..] 068420410.06060791]] ______ key:B8A, value_type:<class ‘numpy.ndarray’>, repr: [[nan nan nan …,0. [..] 192138670.17553711]] ______ key:B09, value_type:<class ‘numpy.ndarray’>, repr: [[nan nan nan …,0. [..] .09600830.09887695]] ______ key:B07, value_type:<class ‘numpy.ndarray’>, repr: [[nan nan nan …,0. [..] 173339840.15600586]]

property logger

property metadata

Returns

___ attribute: metadata, type:<class ‘dict’> ______ key:spatial_samplings _________ key:60.0 ____________ key:ULY, value_type:<class ‘int’>, repr: 4900020 ____________ key:NCOLS, value_type:<class ‘int’>, repr: 1830 ____________ key:XDIM, value_type:<class ‘int’>, repr: 60 ____________ key:ULX, value_type:<class ‘int’>, repr: 600000 ____________ key:NROWS, value_type:<class ‘int’>, repr: 1830 ____________ key:YDIM, value_type:<class ‘int’>, repr: -60 _________ key:10.0 ____________ key:ULY, value_type:<class ‘int’>, repr: 4900020 ____________ key:NCOLS, value_type:<class ‘int’>, repr: 10980 ____________ key:XDIM, value_type:<class ‘int’>, repr: 10 ____________ key:ULX, value_type:<class ‘int’>, repr: 600000 ____________ key:NROWS, value_type:<class ‘int’>, repr: 10980 ____________ key:YDIM, value_type:<class ‘int’>, repr: -10 _________ key:20.0 ____________ key:ULY, value_type:<class ‘int’>, repr: 4900020 ____________ key:NCOLS, value_type:<class ‘int’>, repr: 5490 ____________ key:XDIM, value_type:<class ‘int’>, repr: 20 ____________ key:ULX, value_type:<class ‘int’>, repr: 600000 ____________ key:NROWS, value_type:<class ‘int’>, repr: 5490 ____________ key:YDIM, value_type:<class ‘int’>, repr: -20 ______ key:SENSING_TIME, value_type:<class ‘datetime.datetime’>, repr: 2016-03-26 10:34:06.538000+00:00

property nodata

Returns

___ attribute: nodata, type:<class ‘dict’> ______ key:60.0, value_type:<class ‘numpy.ndarray’>, repr: [[ TrueTrueTrue …, [..] False False False]] ______ key:10.0, value_type:<class ‘numpy.ndarray’>, repr: [[ TrueTrueTrue …, [..] False False False]] ______ key:20.0, value_type:<class ‘numpy.ndarray’>, repr: [[ TrueTrueTrue …, [..] False False False]]

property options

Returns a dictionary containing AC options.

Return type

dict

run_atmospheric_correction(dump_ac_input=False)

Collects all input data for atmospheric correction, runs the AC and returns the corrected L1C objects containing surface reflectance.

Parameters

dump_ac_input (bool) – allows to dump the inputs of AC to the scene’s processing folder in case AC fails

Return type

list

Returns

list of L1C_object instances containing atmospherically corrected data

property tile_name

Returns S2A tile name. NOTE: this is only needed if no DEM is passed to ac_gms

Returns

e.g. ‘32UMA’

class gms_preprocessing.algorithms.L1C_P.L1C_object(L1B_obj=None)

Bases: gms_preprocessing.algorithms.L1B_P.L1B_object

:param : instance of gms_object.GMS_object or None

property RAA_arr

Get relative azimuth angle.

Returns

property SAA_arr

Get solar azimuth angle.

Returns

property SZA_arr

Get solar zenith angle.

Returns

property VAA_arr

Get viewing azimuth angle.

Returns

property VZA_arr

Get viewing zenith angle.

Returns

delete_ac_input_arrays()

Delete AC input arrays if they are not needed anymore.

property lonlat_arr

Calculates pixelwise 2D-array with longitude and latitude coordinates.

Returns

gms_preprocessing.algorithms.L2A_P module

Level 2A Processor: Spatial homogenization

class gms_preprocessing.algorithms.L2A_P.L2A_object(L1C_obj=None)

Bases: gms_preprocessing.algorithms.L1C_P.L1C_object

:param : instance of gms_object.GMS_object or None

gms_preprocessing.algorithms.L2B_P module

Level 2B Processor: Spectral homogenization

class gms_preprocessing.algorithms.L2B_P.L2B_object(L2A_obj=None)

Bases: gms_preprocessing.algorithms.L2A_P.L2A_object

:param : instance of gms_object.GMS_object or None

spectral_homogenization()

Apply spectral homogenization, i.e., prediction of the spectral bands of the target sensor.

gms_preprocessing.algorithms.L2C_P module

Level 2C Processor: Quality layers

class gms_preprocessing.algorithms.L2C_P.AccuracyCube(GMS_obj)

Bases: geoarray.baseclasses.GeoArray

Get an instance of GeoArray.

Parameters

• path_or_array – a numpy.ndarray or a valid file path

• geotransform – GDAL geotransform of the given array or file on disk

• projection – projection of the given array or file on disk as WKT string (only needed if GeoArray is instanced with an array)

• bandnames – names of the bands within the input array, e.g. [‘mask_1bit’, ‘mask_clouds’], (default: [‘B1’, ‘B2’, ‘B3’, …])

• nodata – nodata value

• progress – show progress bars (default: True)

• q – quiet mode (default: False)

generate_array()

get_bandnames()

property layers

Return type

OrderedDict

class gms_preprocessing.algorithms.L2C_P.L2C_object(L2B_obj=None)

Bases: gms_preprocessing.algorithms.L2B_P.L2B_object

:param : instance of gms_object.GMS_object or None

gms_preprocessing.algorithms.cloud_masking module

class gms_preprocessing.algorithms.cloud_masking.Cloud_Mask_Creator(GMS_object, algorithm, target_res=None, tempdir_root=None)

Bases: object

A class for creating cloud masks.

Parameters

• GMS_object – instance or subclass instance of model.gms_object.GMS_object

• algorithm – ‘FMASK’ or ‘Classical Bayesian

• target_res – target resolution of the computed cloud mask (if not given, the appropriate resolution needed for atmospheric correction is chosen)

calc_cloud_mask()

validate_cloud_mask()

class gms_preprocessing.algorithms.cloud_masking.FMASK_Runner_Landsat(path_providerArchive, satellite, TOARef=None, opticalDNs=None, thermalDNs=None, tempdir_root=None)

Bases: gms_preprocessing.algorithms.cloud_masking._FMASK_Runner

FMASK wrapper class for Landsat 4-8.

Parameters

• path_providerArchive – file path of the provider .tar.gz archive

• satellite – name of the satellite: ‘Landsat-4’, ‘Landsat-5’, ‘Landsat-7’, ‘Landsat-8’

• TOARef – file path or GeoArray instance of top-of-atmosphere reflectance data scaled from 0 to 10000 (optional -> generated from archive if not given)

• opticalDNs – file path or GeoArray instance of optical data (digital numbers)

• thermalDNs – file path or GeoArray instance of thermal data (digital numbers)

• tempdir_root – directory to write intermediate data (auto-determined if not given)

property TOARef

property angles_stack

calc_cloudMask(path_out=None, fmt=None)

Calculate the cloud mask!

Parameters

• path_out (Optional[str]) – output path (if not given, a GeoArray instance is returned)

• fmt (Optional[str]) – output GDAL driver code, e.g. ‘ENVI’ (only needed if path_out is given)

Return type

any

Returns

a GeoArray instance of the computed cloud mask

clean()

property metaFile

property optical_stack

property saturationmask

property thermal_stack

class gms_preprocessing.algorithms.cloud_masking.FMASK_Runner_Sentinel2(path_providerArchive, satellite, scene_ID=None, granule_ID='', target_res=20, TOARef=None, extract_archive=False, tempdir_root=None)

Bases: gms_preprocessing.algorithms.cloud_masking._FMASK_Runner

FMASK wrapper class for Sentinel-2.

Parameters

• path_providerArchive – file path of the provider .zip archive

• satellite – name of the satellite: ‘Sentinel-2A’ or ‘Sentinel-2B’

• scene_ID – the GeoMultiSens scene ID of the given scene (needed if granule_ID is not given)

• granule_ID – the Sentinel-2 granule ID

• target_res – target spatial resolution of the cloud mask (default: 20m)

• TOARef – file path or GeoArray instance of top-of-atmosphere reflectance data scaled from 0 to 10000 (optional -> read from archive if not given)

• extract_archive – whether to extract the archive to disk or to read from the archive directly (default: False); NOTE: There is no remarkable speed difference.

• tempdir_root – directory to write intermediate data (auto-determined if not given)

property TOARef

property angles_stack

calc_cloudMask(path_out=None, fmt=None)

Calculate the cloud mask!

Parameters

• path_out (Optional[str]) – output path (if not given, a GeoArray instance is returned)

• fmt (Optional[str]) – output GDAL driver code, e.g. ‘ENVI’ (only needed if path_out is given)

Return type

any

Returns

a GeoArray instance of the computed cloud mask

clean()

property granule_ID

Gets the Sentinel-2 granule ID from the database using the scene ID in case the granule ID has not been given.

property metaFile

gms_preprocessing.algorithms.geoprocessing module

GEOPROCESSING OBJECT “GIS operations to manipulate spatial data. Takes an input dataset, performs an operation on that dataset and returns the result of the operation as an output dataset.

gms_preprocessing.algorithms.geoprocessing.DN2DegreesCelsius_fastforward(ndarray, offsets, gains, K1, K2, emissivity=0.95, inFill=None, inZero=None, inSaturated=None)

Convert thermal DNs to temperatures in degrees Celsius by calculating TOARadiance and applying the inverse of the Planck function.

Parameters

• ndarray – <np.ndarray> array of DNs to be converted into Degrees Celsius

• offsets –

• gains –

• K1 –

• K2 –

• emissivity –

• inFill –

• inZero –

• inSaturated –

gms_preprocessing.algorithms.geoprocessing.DN2Rad(ndarray, offsets, gains, inFill=None, inZero=None, inSaturated=None, cutNeg=True)

Convert DN to Radiance [W * m-2 * sr-1 * micrometer-1].

NOTE: InputGains and Offsets should be in [W * m-2 * sr-1 * micrometer-1]!!!

Parameters

• ndarray (ndarray) – <np.ndarray> array of DNs to be converted into radiance

• offsets (list) – [W * m-2 * sr-1 * micrometer-1]: list that includes the offsets of the individual rasterbands [offset_band1, offset_band2, … ,offset_bandn] or optional input as number if Dataset has only 1 Band

• gains (list) – [W * m-2 * sr-1 * micrometer-1]: list that includes the gains of the individual rasterbands [gain_band1, gain_band2, … , gain_bandn] or optional input as number if Dataset has only 1 Band

• inFill (Optional[int]) – pixelvalues allocated to background/dummy/fill pixels

• inZero (Optional[int]) – pixelvalues allocated to zero radiance

• inSaturated (Optional[int]) – pixelvalues allocated to saturated pixels

• cutNeg (bool) – cutNegvalues -> all negative values set to 0

Return type

ndarray

gms_preprocessing.algorithms.geoprocessing.DN2TOARef(ndarray, offsets, gains, irradiances, zenith, earthSunDist, inFill=None, inZero=None, inSaturated=None, cutNeg=True)

Converts DN data to TOA Reflectance.

Parameters

• ndarray (ndarray) – <np.ndarray> array of DNs to be converted into TOA reflectance

• offsets (list) – list: of offsets of each rasterband [W * m-2 * sr-1 * micrometer-1] [offset_band1, offset_band2, … ,offset_bandn] or optional as number if Dataset has only 1 Band

• gains (list) – list: of gains of each rasterband [W * m-2 * sr-1 * micrometer-1] [gain_band1, gain_band2, … ,gain_bandn] or optional as number if Dataset has only 1 Band

• irradiances (list) – list: of irradiance of each band [W * m-2 * micrometer-1] [irradiance_band1, irradiance_band2, … ,irradiance_bandn]

• zenith (float) – number: sun zenith angle

• earthSunDist (float) – earth-sun- distance for a certain day

• inFill (Optional[int]) – number: pixelvalues allocated to background/dummy/fill pixels

• inZero (Optional[int]) – number: pixelvalues allocated to zero radiance

• inSaturated (Optional[int]) – number: pixelvalues allocated to saturated pixles

• cutNeg (bool) – bool: if true. all negative values turned to zero. default: True

Return type

ndarray

Returns

Int16 TOA_Reflectance in [0-10000]

class gms_preprocessing.algorithms.geoprocessing.GEOPROCESSING(geodata, logger, workspace=None, subset=None, v=None)

Bases: object

Layerstacking(layers_pathlist, path_output=None)

add_GeoTransform_Projection_using_MetaData(CornerTieP_LonLat, CS_EPSG=None, CS_TYPE=None, CS_DATUM=None, CS_UTM_ZONE=None, gResolution=None, shape_fullArr=None)

Method assumes that north is up. Map rotations are not respected.

Parameters

• CornerTieP_LonLat –

• CS_EPSG –

• CS_TYPE –

• CS_DATUM –

• CS_UTM_ZONE –

• gResolution –

• shape_fullArr –

calc_mask_data_nodata(array=None, custom_nodataVal=- 9999)

Berechnet den Bildbereich, der von allen Kanälen abgedeckt wird.

Parameters

• array – input numpy array to be used for mask calculation (otherwise read from disk)

• custom_nodataVal –

gdalinfo()

get properties of the Inputdatasets via gdalinfo

(die Infos die er hier ausschreibt in subrocess.Popen können einer variable als Text übergeben werden. Aus diesem Text kann ich dann die Infos als Attribute ausschreiben. als ersatz für die jetzige Attributerzeugung Muss die Attributerzeugung wirklich sein? kann ich nicht alle irgendwie über das GDAL-objekt abfragen! bis jetzt hab ich aber nur die Infos die oben stehen als Abfrage ermöglichen können

georeference_by_TieP_or_inherent_GCPs(use_inherent_GCPs=False, TieP=None, dst_EPSG_code=None, dst_CS='UTM', dst_CS_datum='WGS84', mode='GDAL', use_workspace=True, inFill=None)

Warp image to new Projection.

Parameters

• use_inherent_GCPs –

• TieP – Corner Tie Points - always LonLat.

• dst_EPSG_code – EPSG-Code defines LonLat or UTM coordinates.

• dst_CS –

• dst_CS_datum –

• mode –

• use_workspace –

• inFill –

get_corner_coordinates(targetProj)

Returns corner coordinates of the entire GEOP object in lon/lat or UTM.

ATTENTION: coordinates represent PIXEL CORNERS:

UL=UL-coordinate of (0,0) UR=UR-coordinate of (0,self.cols-1) => lonlat_arr always contains UL-coordinate of each pixel

Parameters

targetProj – ‘LonLat’ or ‘UTM’

get_projection_type()

pixelToWorldCoord_using_geotransform_and_projection(Pixel_row_col, targetProj)

subset_kwargs_to_cols_rows_bands_colStart_rowStart_bandStart()

tondarray(direction=1, startpix=None, extent=None, UL=None, LR=None, v=0)

Convert gdalobject to 3dimensional ndarray stack ([x,y,z]).

Parameters

• direction –

  1: shape: [bands, rows, cols]

  -> same as theres read envi structure ([band1],[band2],..[bandn])

  2: shape: [rows, bands, cols] 3: shape: [rows, cols, bands]

  -> Structure used in PIL and IDL for example ([pixband1,pixband2,..,pixbandn],[pixband1,pixband2,..,pixbandn],…)

• startpix – [x,y] pixels->UL:pixelcoordinates for subset generation

• extent – [cols, rows] -> number of pixels in x and y-direction

• UL – [x,y] map coordinates -> UL map coordinates for subset generation

• LR – [x,y] map coordinates -> LR map coordinates for subset generation

• v –

update_dataset_related_attributes()

gms_preprocessing.algorithms.geoprocessing.TOARad2Kelvin_fastforward(ndarray, K1, K2, emissivity=0.95, inFill=None, inZero=None, inSaturated=None)

Convert top-of-atmosphere radiances of thermal bands to temperatures in Kelvin by applying the inverse of the Planck function.

Parameters

• ndarray (ndarray) – <np.ndarray> array of TOA radiance values to be converted into Kelvin

• K1 (list) –

• K2 (list) –

• emissivity (float) –

• inFill (Optional[int]) –

• inZero (Optional[int]) –

• inSaturated (Optional[int]) –

Return type

ndarray

gms_preprocessing.algorithms.geoprocessing.adjust_acquisArrProv_to_shapeFullArr(arrProv, shapeFullArr, meshwidth=1, subset=None, bandwise=False)

gms_preprocessing.algorithms.geoprocessing.calc_AcqTime_array(shape_fullArr, arr_pos, AcqDate, CenterAcqTime, fullSceneCornerPos, overpassDurationSec, meshwidth=1)

gms_preprocessing.algorithms.geoprocessing.calc_RAA_array(SAA_array, VAA_array, nodata_mask=None, outFill=None)

Calculate relative azimuth angle between solar azimuth and viewing azimuth in degrees.

Parameters

• SAA_array –

• VAA_array –

• nodata_mask –

• outFill – the value to be used to fill areas outside the actual image bounds

Returns

gms_preprocessing.algorithms.geoprocessing.calc_SZA_SAA(date, lon, lat)

Calculates solar zenith and azimuth angle using pyephem.

Parameters

• date –

• lon –

• lat –

gms_preprocessing.algorithms.geoprocessing.calc_SZA_SAA_array(shape_fullArr, arr_pos, AcqDate, CenterAcqTime, fullSceneCornerPos, fullSceneCornerLonLat, overpassDurationSec, logger, meshwidth=1, nodata_mask=None, outFill=None, accurracy='coarse', lonlat_arr=None)

Calculates solar zenith and azimuth angles for each pixel in the dataset using pyorbital.

Parameters

• shape_fullArr –

• arr_pos –

• AcqDate –

• CenterAcqTime –

• fullSceneCornerPos –

• fullSceneCornerLonLat – UL, UR, LL, LR

• overpassDurationSec –

• logger –

• meshwidth – <int> defines the density of the mesh used for generating the output (1: full resolution; 10: one point each 10 pixels)

• nodata_mask – <numpy array>, used for declaring nodata values in the output SZA/SAA array

• outFill – the value that is assigned to nodata area in the output SZA/SAA array

• accurracy –

  ‘fine’ or ‘coarse’ - ‘fine’ : pixelwise acquisition time is used to calculate SZA/SAA

  • requires lonlat_arr to be specified

  • ’coarse: SZA/SAA is calculated for image corners then interpolated by solving

    an equation system with 4 variables for each image corner: SZA/SAA = a + b*col + c*row + d*col*row.

• lonlat_arr –

gms_preprocessing.algorithms.geoprocessing.calc_VAA_using_fullSceneCornerLonLat(fullSceneCornerLonLat, orbit_params)

Calculates the Viewing azimuth angle (defined as 90 degrees from the flight line), e.g. if flight line is 8 degrees from North -> VAA will be 98 degrees.

Parameters

• fullSceneCornerLonLat (list) – UL, UR, LL, LR

• orbit_params (list) – list of [altitude, inclination, period] => inclination is used as fallback

Return type

float

gms_preprocessing.algorithms.geoprocessing.calc_VZA_array(shape_fullArr, arr_pos, fullSceneCornerPos, viewing_angle, FOV, logger, meshwidth=1, nodata_mask=None, outFill=None)

Calculate viewing zenith angles for each pixel in the dataset.

By solving an equation system with 4 variables for each image corner: VZA = a + b*col + c*row + d*col*row.

Parameters

• shape_fullArr –

• arr_pos –

• fullSceneCornerPos –

• viewing_angle –

• FOV –

• logger –

• meshwidth – <int> defines the density of the mesh used for generating the output (1: full resolution; 10: one point each 10 pixels)

• nodata_mask – <numpy array>, used for declaring nodata values in the output VZA array

• outFill – the value that is assigned to nodata area in the output VZA array

gms_preprocessing.algorithms.geoprocessing.clip_array_using_mapBounds(array, bounds, im_prj, im_gt, fillVal=0)

Parameters

• array –

• bounds –

• im_prj –

• im_gt –

• fillVal –

gms_preprocessing.algorithms.geoprocessing.convertGdalNumpyDataType(dType)

input:

dType: GDALdataType string or numpy dataType

output:

corresponding dataType

gms_preprocessing.algorithms.geoprocessing.get_common_extent(list_extentcoords, alg='outer', return_box=True)

Returns the common extent coordinates of all given coordinate extents. NOTE: this function asserts that all input coordinates belong to the same projection!

Parameters

• list_extentcoords – a list of coordinate sets, e.g. [ [ULxy1,URxy1,LRxy1,LLxy1], ULxy2,URxy2,LRxy2,LLxy2],]

• alg – ‘outer’: return the outer Polygon extent ‘inner’: return the inner Polygon extent

• return_box – whether to return a coordinate box/envelope of the output Polygon

Returns

[ULxy,URxy,LRxy,LLxy]

gms_preprocessing.algorithms.geoprocessing.get_lonlat_coord_array(shape_fullArr, arr_pos, geotransform, projection, meshwidth=1, nodata_mask=None, outFill=None)

Returns numpy array containing longitude pixel coordinates (band 0) and latitude pixel coordinates (band 1).

Parameters

• shape_fullArr –

• arr_pos –

• geotransform –

• projection –

• meshwidth – <int> defines the density of the mesh used for generating the output (1: full resolution; 10: one point each 10 pixels)

• nodata_mask – <numpy array>, used for declaring nodata values in the output lonlat array

• outFill – the value that is assigned to nodata area in the output lonlat array

gms_preprocessing.algorithms.geoprocessing.get_point_bounds(points)

gms_preprocessing.algorithms.geoprocessing.get_subsetProps_from_shapeFullArr_arrPos(shape_fullArr, arr_pos)

Returns array dims with respect to possible subsetting.

gms_preprocessing.algorithms.geoprocessing.get_subsetProps_from_subsetArg(shape_fullArr, subset)

gms_preprocessing.algorithms.geoprocessing.is_granule(trueCornerPos)

Idee: testen, ob es sich um Granule handelt oder um die volle Szene - dazu Winkel der Kanten zu Nord oder Ost berechnen

gms_preprocessing.algorithms.geoprocessing.ndarray2gdal(ndarray, outPath=None, importFile=None, direction=1, GDAL_Type='ENVI', geotransform=None, projection=None, v=0)

Converts a numpy array to a Georasterdataset (default envi bsq).

bis jetzt nur georeferenzierung und projectionszuweisung durch import aus anderem file möglich

Parameters

• ndarray – [bands, rows, cols]

• outPath – Path were to store the outputFile

• importFile – path of a file, where to import the projection and mapinfos

• direction –

• GDAL_Type –

• geotransform – gdal list for geotransformation [originX, pixelWidth, rot1, originY, pixelHeight, rot2]

• projection – a gdal compatible projection string -> if importFile is not availabele geotransform and projection need to be set, otherwise a gdaldata set without coordinate system will be created

• v –

Returns

GDAL data File

gms_preprocessing.algorithms.geoprocessing.zoom_2Darray_to_shapeFullArr(arr2D, shapeFullArr, meshwidth=1, subset=None, method='linear')

Module contents