def main():
## All Sentinel-2 data subfolders are located within a super folder (make sure data is already unzipped and each sub folder name ends with .SAFE)
path = r'D:\Sentinel\test'
outpath = r'D:\Sentinel\out'
if not os.path.exists(outpath):
os.makedirs(outpath)
for folder in os.listdir(path):
gc.enable()
gc.collect()
print ("Filname: ", path + "\\" + folder + "\\manifest.safe")
##sentinel2 = path + "\\" + folder + "\\MTD_MSIL1C.xml"
sentinel2 = ProductIO.readProduct(path + "\\" + folder + "\\MTD_MSIL1C.xml")
loopstarttime = str(datetime.datetime.now())
print ('Start time: ', loopstarttime)
start_time = time.time()
## Extract mode, product type
modestamp = folder.split("_")[1]
productstamp = folder.split("_")[2]
## Start preprocessing
resample = do_resampling(sentinel2)
biOp = do_biophysical_parameter(resample)
##ndvi = do_vegetation_indices(sentinel2)
print ("outFilename: ", outpath + "\\" + folder.split(".SAFE")[0] + '_biOp' + '.dim')
ProductIO.writeProduct(biOp, outpath + "\\" + folder.split(".SAFE")[0] + '_biOp' + '.dim', "BEAM-DIMAP")
def copy_bands_to_file(src_file_path, dst_file_path, bands=None):
# Get info from source product
src_prod = ProductIO.readProduct(src_file_path)
prod_name = src_prod.getName()
prod_type = src_prod.getProductType()
width = src_prod.getSceneRasterWidth()
height = src_prod.getSceneRasterHeight()
if bands is None:
bands = src_prod.getBandNames()
# Copy geocoding and selected bands from source to destination product
dst_prod = Product(prod_name, prod_type, width, height)
ProductUtils.copyGeoCoding(src_prod.getBandAt(0), dst_prod)
for band in bands:
r = ProductUtils.copyBand(band, src_prod, dst_prod, True)
if r is None:
src_prod.closeIO()
raise RuntimeError(src_file_path + " does not contain band " +
band)
# Write destination product to disk
ext = os.path.splitext(dst_file_path)[1]
if ext == '.dim':
file_type = 'BEAM_DIMAP'
elif ext == '.nc':
file_type = 'NetCDF-CF'
elif ext == '.tif':
file_type = 'GeoTIFF-BigTIFF'
else:
file_type = 'GeoTIFF-BigTIFF'
ProductIO.writeProduct(dst_prod, dst_file_path, file_type)
src_prod.closeIO()
dst_prod.closeIO()
def get_prod_metadata(first_image_location, second_image_location):
prod1 = ProductIO.readProduct(first_image_location)
prod2 = ProductIO.readProduct(second_image_location)
# Get some Metadata
freqMHz = prod1.getMetadataRoot().getElement(
'Abstracted_Metadata').getAttributeDouble('radar_frequency')
wavelengthM = constants.c / (freqMHz * 10**6)
long1 = prod1.getMetadataRoot().getElement(
'Abstracted_Metadata').getAttributeDouble('first_near_long')
long2 = prod2.getMetadataRoot().getElement(
'Abstracted_Metadata').getAttributeDouble('first_near_long')
#lat = prod
prod1.dispose()
prod2.dispose()
return wavelengthM
# import jpy
# Runtime = jpy.get_type('java.lang.Runtime')
# max_memory = Runtime.getRuntime().maxMemory()
# total_memory = Runtime.getRuntime().totalMemory()
# free_memory = Runtime.getRuntime().freeMemory()
# gb = 1e+9
# print('max memory:', max_memory / gb, "GB")
# print('total memory:', total_memory / gb, "GB")
# print('free memory:', free_memory / gb, "GB")
def guardarArchivo():
global flood_mask
#Crear la imagen a partir de la mascara
ProductIO.writeProduct(flood_mask, "C:/CTE_334/Actividad09/final_mask",
'GeoTIFF')
os.path.exists("C:/CTE_334/Actividad09/final_mask.tif")
print("IMAGEN CREADA EXITOSAMENTE A PARTIR DE MASCARA")
def match_up(filename, file, stations):
print('match_up...')
# file = ProductIO.readProduct(filepath+filename)
geo = file.getSceneGeoCoding()
size = file.getSceneRasterSize()
if geo.canGetGeoPos():
for station in stations:
p1 = geo.getPixelPos(geopos(station['Lat'], station['Lon']), None)
if p1.getX() == 'non':
continue
x = p1.getX() - Height / 2
y = p1.getY() - Width / 2
if file.containsPixel(p1):
flist = filename.split('_')
wdir, wspeed = wind_filed_from_ndbc(station['name'], flist[-5])
if wdir == 0 and wspeed == 0:
continue
else:
print(flist[-1].split('.')[0])
print(station)
print(x, y)
print(size.getHeight(), size.getWidth())
if size.getHeight() > 400 and size.getWidth > 400:
subfile = subset(file, int(x), int(y))
if subfile is False:
print('cut exception')
continue
print('write...')
print(filename, station['name'])
try:
ProductIO.writeProduct(
subfile,
'/users/yangchao/GitHub/wind/snap/match_data1/'
+ station['name'] + '_subset_' + wdir + '_' +
wspeed + '_' + station['height'] + '_' +
filename.split('.')[0], 'BEAM-DIMAP')
except Exception:
print('exception')
subfile.dispose()
continue
subfile.dispose()
del subfile
else:
print('copy...')
try:
ProductIO.writeProduct(
file,
'/users/yangchao/GitHub/wind/snap/match_data1/'
+ station['name'] + '_subset_' + wdir + '_' +
wspeed + '_' + station['height'] + '_' +
filename.split('.')[0], 'BEAM-DIMAP')
except Exception:
print('exception')
continue
else:
print(filename + " can't get geo pos")
del size
del geo
file.dispose()
def read_terrain_corrected_product(path_to_dim):
print(path_to_dim)
dataproduct_r = ProductIO.readProduct(path_to_dim)
# will the computed band show up?
for band in dataproduct_r.getBandNames():
print(band)
print('WRITING OUT TO GEOTIFF!!')
sigma0_ortho_bands = ["Sigma0_VH_use_local_inci_angle_from_dem",
"Sigma0_VV_use_local_inci_angle_from_dem"]
convertComputedBandToBand(dataproduct_r.getBand(sigma0_ortho_bands[0]))
convertComputedBandToBand(dataproduct_r.getBand(sigma0_ortho_bands[1]))
HashMap = jpy.get_type('java.util.HashMap')
sub_parameters = HashMap()
sub_parameters.put('bandNames', ",".join(sigma0_ortho_bands)) # Should eventually look at using a local SRTM 1Sec DEM (instead of auto downloading)
subset = GPF.createProduct("Subset", sub_parameters, dataproduct_r)
final_output_name = Path(Path(path_to_dim).parent, "test")
print('writing out final result')
# Get a progressMonitor object
# monitor = self.createProgressMonitor()
# print('WRITING OUT PRODUCT')
ProductIO.writeProduct(subset, str(final_output_name), 'BEAM-DIMAP')
def apply_orbit_file(self,
write_intermediate=False):
parameters = self.HashMap()
dataproduct = GPF.createProduct("Apply-Orbit-File", parameters, self.dataproduct_r)
self.operations_list.append('ApplyOrbit')
SF_filepath = ""
# Get a progressMonitor object
monitor = self.createProgressMonitor()
if write_intermediate:
# Write data product with BEAM-DIM format to given file path
# ProductIO.writeProduct(Product product, String filePath, String formatName)
SF_filepath = Path(self.working_dir, self.name_with_safe + "_ORB")
print('Writing out Orbit File corrected product')
ProductIO.writeProduct(dataproduct, str(SF_filepath), 'BEAM-DIMAP', monitor)
# Set start time and loop counter
finish_time = datetime.datetime.now() # for calculation
elapsed_time = finish_time - self.start_time
print(elapsed_time.strftime("%H:%M:%S"))
if os.path.exists(SF_filepath + ".dim"):
print("Completed applying orbit file:", SF_filepath)
else:
print("Completed applying orbit file: data product saved to in-memory")
self.intermediate_product = dataproduct
def topo_removal(file):
image = ProductIO.readProduct(file)
p = HashMap()
p.put('demName', 'SRTM 1Sec HGT')
result = GPF.createProduct('TopoPhaseRemoval', p, image)
# Write to temporary file
outfile = file.split('.dim')[0] + '_noSRTM.dim'
ProductIO.writeProduct(result, outfile, 'BEAM-DIMAP')
def guardarArchivo():
global flood_mask
#Crear la imagen a partir de la mascara
ProductIO.writeProduct(
flood_mask,
"C:/Users/Usuario/Desktop/Actvidades_CTE_334/Examen_unidad_2/final_mask",
'GeoTIFF')
print("IMAGEN CREADA EXITOSAMENTE A PARTIR DE MASCARA")
def bandMathSnap(input_dim,
output_file,
expression_list,
format_file='float32'):
if debug >= 2:
print(cyan + "bandmathSnap() : " + bold + green +
"Import Dim to SNAP : " + endC + input_dim)
# Info input file
product = ProductIO.readProduct(input_dim)
width = product.getSceneRasterWidth()
height = product.getSceneRasterHeight()
name = product.getName()
description = product.getDescription()
band_names = product.getBandNames()
if debug >= 2:
print(cyan + "bandmathSnap() : " + bold + green +
"Product: %s, %d x %d pixels, %s" %
(name, width, height, description) + endC)
print(cyan + "bandmathSnap() : " + bold + green + "Bands: %s" %
(list(band_names)) + endC)
# Instance de GPF
GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()
# Def operateur SNAP
operator = 'BandMaths'
BandDescriptor = jpy.get_type(
'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor')
targetBands = jpy.array(
'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor',
len(expression_list))
# Get des expressions d'entréées
i = 0
for expression in expression_list:
targetBand = BandDescriptor()
targetBand.name = 'band_' + str(i + 1)
targetBand.type = format_file
targetBand.expression = expression
targetBands[i] = targetBand
i += 1
# Set des parametres
parameters = HashMap()
parameters.put('targetBands', targetBands)
# Get snappy Operators
result = GPF.createProduct(operator, parameters, product)
ProductIO.writeProduct(result, output_file, 'BEAM-DIMAP')
if debug >= 2:
print(cyan + "bandmathSnap() : " + bold + green + "Writing Done : " +
endC + str(output_file))
return result
def classify(prepros_folder):
for file in os.listdir(prepros_folder):
### Folder, date & timestamp
classification = rootpath + "\\classification"
scene_name = str(prepros_folder.split("\\")[X])[:32] # [X] element of filepath, change this to match scene name. [:32] first caracters of element
print("scene_name:", scene_name)
output_folder = os.path.join(classification, scene_name)
print("output_folder:", output_folder)
polarization = str(file)[46:48] # polarization from filename
if not os.path.exists(output_folder):
os.mkdir(output_folder)
GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()
HashMap = snappy.jpy.get_type('java.util.HashMap')
gc.enable()
sentinel_1 = ProductIO.readProduct(os.path.join(prepros_folder, file))
res = [20, 50] # parameters for CFAR
for r in res:
resolution = r
gd_window = resolution * 12.0
bg_window = resolution * 37.0
# AdaptiveThresholding (Constant False Alarm Rate CFAR)
parameters = HashMap()
parameters.put('targetWindowSizeInMeter', resolution)
parameters.put('guardWindowSizeInMeter', gd_window)
parameters.put('backgroundWindowSizeInMeter', bg_window)
parameters.put('pfa', 6.0)
target_1 = GPF.createProduct("AdaptiveThresholding", parameters, sentinel_1)
parameters = None
# Subset (extracting classification band)
parameters = HashMap()
parameters.put('bandNames', 'Sigma0_' + polarization + '_ship_bit_msk')
outfile = output_folder + "\\" + scene_name + "_" + polarization + "_cfar_" + str(resolution) + "m"
target_2 = GPF.createProduct("Subset", parameters, target_1)
ProductIO.writeProduct(target_2, outfile, 'GeoTIFF-BigTIFF', pm)
# Classification to vector
ds = gdal.Open(str(outfile + ".tif"))
rasterband = ds.GetRasterBand(1)
dst_layername = outfile + "_Iceberg_outline"
drv = ogr.GetDriverByName("GeoJSON")
dst_ds = drv.CreateDataSource(dst_layername + ".geojson")
dest_srs = ogr.osr.SpatialReference()
dest_srs.ImportFromEPSG(4326)
dst_layer = dst_ds.CreateLayer(dst_layername, dest_srs)
gdal.Polygonize(rasterband, rasterband, dst_layer, -1, [], callback=None) # (input, mask, dest_layer,,,)
def write(self, file_path):
"""
Write the processed product to a file with a file type and close the product.
"""
print("\tWrite, file_path={}".format(file_path))
ProductIO.writeProduct(self.product, file_path, 'GeoTIFF')
self.product.closeIO()
self.product = None
return self.product
def speckle_filter(self,
filter="Gamma Map",
estimateENL=True,
filterSize=11,
numLooksStr="1",
targetWindowStr="3x3",
windowSize="11x11",
write_intermediate=False):
print("-------------")
# Apply SPECKLE FILTER
print("Applying speckle-filter:", self.name)
# Define object parameterisation for filtering
parameters = self.HashMap()
parameters.put('filter', filter)
parameters.put('estimateENL', estimateENL)
parameters.put('filterSizeX', filterSize)
parameters.put('filterSizeY', filterSize)
parameters.put('numLooksStr', numLooksStr)
parameters.put('sourceBands', ",".join(self.srcbands))
parameters.put('targetWindowStr', targetWindowStr)
parameters.put('windowSize', windowSize)
print("Speckle filtering parameters:", parameters)
# Create speckle filter data product using 'Speckle-Filter' operator/parameters and raw data product as source product
# Speckle noise reduction can be applied either by spatial filtering or multilook processing
# Filtered product contains 4 real bands: 2 amplitude and intensity bands for each polarizations
# createProduct(String operatorName, Map<String,Object> parameters, Map<String,Product> sourceProducts)
dataproduct = GPF.createProduct("Speckle-Filter", parameters, self.dataproduct_r)
self.operations_list.append('SpeckleFilter')
SF_filepath = ""
# Get a progressMonitor object
monitor = self.createProgressMonitor()
if write_intermediate:
# Write data product with BEAM-DIM format to given file path
# ProductIO.writeProduct(Product product, String filePath, String formatName)
SF_filepath = Path(self.working_dir, self.name_with_safe + "_SF")
print('Writing out SpeckleFilter Product')
ProductIO.writeProduct(dataproduct, str(SF_filepath), 'BEAM-DIMAP', monitor)
finish_time = datetime.datetime.now() # for calculation
elapsed_time = finish_time - self.start_time
print(elapsed_time.strftime("%H:%M:%S"))
if os.path.exists(SF_filepath + ".dim"):
print("Completed speckle-filtering:", SF_filepath)
else:
print("Completed speckle-filtering: data product saved to in-memory")
self.intermediate_product = dataproduct
def getGeoTiffImage(sarDownloadFilePath,
geopandasDataFilePath,
geoDataIndex,
dstPath=None):
'''
Get GeoTiff image from a .SAFE folder extracted after download
:type sarDownloadFilePath: string or list of string
:type geopandasDataFilePath: string
:type geoDataIndex: int
:type dstPath: string
:param sarDownloadFilePath: directory (or list of directory) of .SAFE folder(s)
:param geopandasDataFilePath: directory of geopandas dataframe file
:param geoDataIndex: geoDataIndex of data needed to retrieve in geopandas Dataframe
:param dstPath: directory of destination file, must have '.tif' extension
:return: None
:example: sarHelpers.getGeoTiffImage(sarDownloadFilePath='S1A_IW_GRDH_1SDV_20170221T225238_20170221T225303_015388_019405_9C41.SAFE',
geopandasDataFilePath='mekongReservoirs',
geoDataIndex=0,
dstPath='geotiff/1.tif')
'''
from snappy import jpy
from snappy import ProductIO
from snappy import GPF
from snappy import HashMap
s1meta = "manifest.safe"
s1product = "%s/%s" % (sarDownloadFilePath, s1meta)
reader = ProductIO.getProductReader("SENTINEL-1")
product = reader.readProductNodes(s1product, None)
parameters = HashMap()
borderRectInGeoCoor = Utils.getGeoDataBorder(geopandasDataFilePath,
geoDataIndex)
subset = Utils.subset(product, borderRectInGeoCoor)
calibrate = Utils.calibrate(subset)
terrain = Utils.terrainCorrection(calibrate)
terrainDB = GPF.createProduct("LinearToFromdB", parameters, terrain)
speckle = Utils.speckleFilter(terrainDB)
if dstPath is None:
dstPath = sarImgPath[:-4] + '.tif'
ProductIO.writeProduct(speckle, dstPath, 'GeoTiff')
product.dispose()
subset.dispose()
calibrate.dispose()
terrain.dispose()
speckle.dispose()
del product, subset, calibrate, terrain, terrainDB, speckle
return dstPath
def snaphu_unwrapping(product, target_Product_File, outFolder, filename):
parameters = HashMap()
parameters.put('targetProductFile',
target_Product_File) # from SNAPHU_export
parameters.put('outputFolder', outFolder)
parameters.put('copyOutputAndDelete', 'Snaphu-unwrapping-after.vm')
parameters.put('copyFilesTemplate', 'Snaphu-unwrapping-before.vm')
product = GPF.createProduct('snaphu-unwrapping', parameters, product)
ProductIO.writeProduct(product, filename + '.dim', 'BEAM-DIMAP')
print('Phase unwrapping performed successfully …')
def collocate_all(input_imgs, write_product):
if len(input_imgs) < 2:
raise ValueError('Error, len(input_imgs)={}'.format(len(input_imgs)))
col = collocate(input_imgs[0],input_imgs[1])
for i in range(2,len(input_imgs)):
col = collocate(col,input_imgs[i])
if write_product:
collocation = "collocation_all.tif"
ProductIO.writeProduct(col, collocation, 'GeoTiff')
return col
def do_ifg(path):
"""
Takes filepath of TDX xml file
Uses snappy to create interferogram
Add in parameters to p if required (see gpt Interferogram -h for details)
"""
target = ifg_file(path)
image = ProductIO.readProduct(path)
p = HashMap()
image = GPF.createProduct('Interferogram',p,image)
ProductIO.writeProduct(image,target,'BEAM-DIMAP')
def createP(function, inputProduct, writeout=False, **kwargs):
# pythonic version of GPF.createProduct()
# function is string of SNAP operator
# inputProduct is SNAP product object
# kwargs contains any parameters to pass to the operator
p = HashMap()
for arg in kwargs:
p.put(arg, kwargs.get(arg))
result = GPF.createProduct(function, p, inputProduct)
if writeout != False:
ProductIO.writeProduct(result, writeout, 'BEAM-DIMAP')
else:
return result
def write_out_result(self, format='BEAM-DIMAP'):
if format == 'BEAM-DIMAP':
# Write data product with BEAM-DIM format to given file path
# ProductIO.writeProduct(Product product, String filePath, String formatName)
SF_filepath = Path(self.working_dir, self.name_with_safe + "_FINAL")
print('WRITING OUT PRODUCT')
ProductIO.writeProduct(self.intermediate_product, str(SF_filepath), 'BEAM-DIMAP')
finish_time = datetime.datetime.now()# for calculation
elapsed_time = finish_time - self.start_time
print(elapsed_time.strftime("%H:%M:%S"))
elif format == 'GEOTIFF':
print('WRITING OUT TO GEOTIFF!!')
sigma0_ortho_bands = ["Sigma0_VH_use_local_inci_angle_from_dem",
"Sigma0_VV_use_local_inci_angle_from_dem"]
print(self.intermediate_product)
print(self.intermediate_product.getBandNames())
for band in self.intermediate_product.getBandNames():
print(band)
self.convertComputedBandToBand(self.intermediate_product.getBand(sigma0_ortho_bands[0]))
self.convertComputedBandToBand(self.intermediate_product.getBand(sigma0_ortho_bands[1]))
sub_parameters = self.HashMap()
sub_parameters.put('bandNames', ",".join(sigma0_ortho_bands)) # Should eventually look at using a local SRTM 1Sec DEM (instead of auto downloading)
subset = GPF.createProduct("Subset", sub_parameters, self.intermediate_product)
print(self.name_with_safe)
final_output_name = Path(self.working_dir, self.name_with_safe + "_".join(self.operations_list))
print('writing out final result')
# Get a progressMonitor object
monitor = self.createProgressMonitor()
print('WRITING OUT PRODUCT')
ProductIO.writeProduct(self.intermediate_product, str(final_output_name), 'GeoTIFF')
finish_time = datetime.datetime.now() # for calculation
elapsed_time = finish_time - self.start_time
print(elapsed_time.strftime("%H:%M:%S"))
def BandMathList(stack):
product = ProductIO.readProduct(stack)
band_names = product.getBandNames()
GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()
BandDescriptor = jpy.get_type(
'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor')
bandlist = list(band_names)
#targetBands = list()
x = 0
y = 1
bandlength = len(bandlist)
runs = (bandlength - 1)
targetBands = jpy.array(
'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor', runs)
for i in bandlist:
while y <= runs:
#print('{}_minus_{}'.format(bandlist[x], bandlist[y]))
#targetBand1 = '{}_minus_{}'.format(bandlist[x], bandlist[y])
targetBand1 = BandDescriptor()
targetBand1.name = '{}_minus_{}'.format(bandlist[x], bandlist[y])
targetBand1.type = 'float32'
targetBand1.expression = '(({} - {})<(-2))? 255 : 0'.format(
bandlist[x], bandlist[y])
print("Writing Band {} : {}_minus_{}".format(
x, bandlist[x], bandlist[y]))
#targetBands.append(targetBand1)
targetBands[x] = targetBand1
x = x + 1
y = y + 1
"""targetBand1 = BandDescriptor()
targetBand1.name = 'first_{}_minus_last_{}'.format(bandlist[0], bandlist[bandlength])
targetBand1.type = 'float32'
targetBand1.expression = '(({} - {})<(-2))? 255 : 0'.format(bandlist[x], bandlist[y])
print("Writing Band first_{}_minus_last_{}".format(bandlist[0], bandlist[bandlength])
targetBands[bandlength] = targetBand1"""
parameters = HashMap()
parameters.put('targetBands', targetBands)
result = GPF.createProduct('BandMaths', parameters, product)
print("Writing...")
ProductIO.writeProduct(result, 'BandMaths.dim', 'BEAM-DIMAP')
print("BandMaths.dim Done.")
ProductIO.writeProduct(result, 'BandMaths.tif', "GeoTIFF-BigTIFF")
print("BandMaths.tif Done.")
def range_doppler_to_sigma0(self, resolution=10.0, suffix="", window_size=11, write_intermediate=False):
# APPLY Range Doppler terrain correction and ortho
print('Applying applying R.Doppler terrain corr. and ortho...')
rd_parameters = self.HashMap()
rd_parameters.put('demName', "SRTM 1Sec HGT") # Should eventually look at using a local SRTM 1Sec DEM (instead of auto downloading)
rd_parameters.put('imgResamplingMethod', "BILINEAR_INTERPOLATION")
rd_parameters.put('pixelSpacingInMeter', resolution)
rd_parameters.put('sourceBands', ",".join(self.srcbands))
# rd_parameters.put('mapProjection', projection) # Defined above, should be auto set to best UTM zone
# APPLY RADIOMETRIC NORMALIZATION to SIGMA0 (not clear if necessary) only do if sigma0 NOT done above
rd_parameters.put('applyRadiometricNormalization', True)
rd_parameters.put('incidenceAngleForSigma0', "Use local incidence angle from DEM")
rd_parameters.put('saveSigmaNought', True)
rd_parameters.put('saveLocalIncidenceAngle', True)
rd_corrected = GPF.createProduct("Terrain-Correction", rd_parameters, self.intermediate_product)
self.operations_list.append("RangeDopplerOrthoConversionToSigma0")
print(self.name_with_safe)
final_output_name = self.name_with_safe + "_{}_{}x{}_{}m".format(suffix, window_size, window_size, resolution)
print('writing out final result')
# Get a progressMonitor object
monitor = self.createProgressMonitor()
SF_filepath = ""
if write_intermediate:
# Write data product with BEAM-DIM format to given file path
# ProductIO.writeProduct(Product product, String filePath, String formatName)
SF_filepath = Path(self.working_dir, self.name_with_safe + "_ORTHO")
print('Writing out Terrain-Correction Product')
ProductIO.writeProduct(rd_corrected, str(SF_filepath), 'BEAM-DIMAP', monitor)
finish_time = datetime.datetime.now() # for calculation
elapsed_time = finish_time - self.start_time
print(elapsed_time.strftime("%H:%M:%S"))
if os.path.exists(SF_filepath + ".dim"):
print("Completed speckle-filtering:", SF_filepath)
else:
print("Completed speckle-filtering: data product saved to in-memory")
self.intermediate_product = rd_corrected
def nbr(self):
if hasattr(self, 'path_post'):
name = 'NBR'
exprss = '(B8 - B12) / (B8 + B12)'
product_pre = self._band_math(self.product_pre, name, exprss)
product_post = self._band_math(self.product_post, name, exprss)
print(list(product_pre.getBandNames()))
print(list(product_post.getBandNames()))
name = 'difNBR'
exprss = '$p1.NBR - $p2.NBR'.format(product_pre, product_post)
products = [product_pre, product_post]
product = self._band_math(products, name, exprss)
out = "/".join([self.path, 'NBR.tif'])
ProductIO.writeProduct(product, out, "GeoTIFF-BigTIFF")
def write_snappy_product(file_path, bands, product_name, geo_coding):
try:
(height, width) = bands[0]['band_data'].shape
except AttributeError:
raise RuntimeError(bands[0]['band_name'] + "contains no data.")
product = Product(product_name, product_name, width, height)
product.setSceneGeoCoding(geo_coding)
# Ensure that output is saved in BEAM-DIMAP format,
# otherwise writeHeader does not work.
file_path = os.path.splitext(file_path)[0] + '.dim'
# Bands have to be created before header is written
# but header has to be written before band data is written.
for b in bands:
band = product.addBand(b['band_name'], ProductData.TYPE_FLOAT32)
if 'description' in b.keys():
band.setDescription(b['description'])
if 'unit' in b.keys():
band.setUnit(b['unit'])
product.setProductWriter(ProductIO.getProductWriter('BEAM-DIMAP'))
product.writeHeader(String(file_path))
for b in bands:
band = product.getBand(b['band_name'])
band.writePixels(0, 0, width, height,
b['band_data'].astype(np.float32))
product.closeIO()
def resample(DIR, band, resolution):
"""
resamples a band of a SENTINEL product to a given target resolution
:param DIR: base directory of Sentinel2 directory tree
:param band: band name (e.g. B4)
:param resolution: target resolution in meter (e.g 10)
:return: resampled band
"""
from snappy import GPF
GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()
HashMap = jpy.get_type('java.util.HashMap')
BandDescriptor = jpy.get_type(
'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor')
parameters = HashMap()
parameters.put('targetResolution', resolution)
parameters.put('upsampling', 'Bicubic')
parameters.put('downsampling', 'Mean')
parameters.put('flagDownsampling', 'FlagMedianAnd')
parameters.put('resampleOnPyramidLevels', True)
product = ProductIO.readProduct(DIR)
product = GPF.createProduct('Resample', parameters, product)
rsp_band = product.getBand(band)
return rsp_band
def get_bands_in_np(filepath):
prod = ProductIO.readProduct(filepath)
# Get some Metadata
width = prod.getSceneRasterWidth()
height = prod.getSceneRasterHeight()
# Extract bands and tie points from the product
bandnames = list(prod.getBandNames())
tpnames = list(prod.getTiePointGridNames())
bandStack = list(prod.getBands())
tiepointStack = list(prod.getTiePointGrids())
prod.dispose()
img = np.zeros((len(bandnames), width, height), dtype=np.float32)
try:
i = 0
for currentBand in bandStack:
img[i, :, :] = currentBand.readPixels(0, 0, width, height,
img[i, :, :])
i += 1
except:
for currentBand in bandStack:
for y in range(height):
# print("processing line ", y, " of ", height)
currentBand.readPixels(0, y, width, 1, img)
return img, bandnames
def __init__(self, pre, post=None):
self.plots = "PixEx"
self.path = "/".join(pre['name'].split('/')[:-2])
self.path_pre = pre['name']
self.product_pre = ProductIO.readProduct(self.path_pre)
self.producttype_pre = pre['producttype']
if post:
if pre['tile_id'] == post['tile_id']:
self.tile_id = pre['tile_id']
else:
raise
self.path_post = post['name']
self.product_post = ProductIO.readProduct(self.path_post)
self.producttype_post = post['producttype']
else:
self.tile_id = pre['tile_id']
def readDim(input_dim):
if debug >= 2:
print(cyan + "readDim() : " + bold + green + "Import Dim to SNAP : " + endC + str(input_dim ))
product = ProductIO.readProduct(input_dim)
band_names_list = list(product.getBandNames())
return product, band_names_list
def get_ProductFile( p_path ):
files = []
if(os.path.isfile(p_path)):
print("Checking if the file is readable by snap:\n %s ..." % p_path )
files.append(p_path)
else:
print("Finding snap product file at directory:\n %s ..." % p_path )
files.extend( [os.path.join(p_path, fa) for fa in os.listdir(p_path)
if os.path.isfile( os.path.join(p_path, fa) )] )
p_file_aux = None
for fi in files:
p = None
try:
# Reads data product. The method does not automatically read band data
p = ProductIO.readProduct(fi)
except:
pass
if p:
p_type = p.getProductType()
if p.getProductType() in AUX_DATA_FILES:
p_file_aux = fi
else:
print(' ------------------------------------------------')
print(' Type: ' + p.getProductType() )
print(' Mission/Format: ' + ', '.join( p.getProductReader().getReaderPlugIn().getFormatNames() ))
#print(' Band Names: ' + ', '.join( p.getBandNames() ) )
#print(' Raster Sizes: heigth=%d, Width=%d' % (p.getSceneRasterHeight(), p.getSceneRasterWidth()) )
print(' ------------------------------------------------')
return fi
return p_file_aux
def process_mosaic(files, fileName):
products = [ProductIO.readProduct(i) for i in files]
mosaic = do_sar_mosaic(products)
return save_product(mosaic, fileName, 'GeoTiff')
def extract_feature_array_from_product(
file_path: str) -> (np.ndarray, int, int, int, list):
"""
Extract bands from an ESA data product
:param file_path: path to a ESA SNAP dim file
:return: (numpy array of shape (bands, pixels), image width, image height, number of pixels)
"""
print('Extracting feature array from product', file_path)
util.start_timer()
from snappy import ProductIO
p = ProductIO.readProduct(file_path)
bands = [p.getBand(x) for x in p.getBandNames()]
if len(bands) == 0:
raise Exception("No bands found in product")
image_width = bands[0].getRasterWidth()
image_height = bands[0].getRasterHeight()
number_of_pixels = image_width * image_height
feature_array = np.array([
band.readPixels(0, 0, image_width, image_height,
np.zeros(number_of_pixels, np.float32))
for band in bands
])
band_names = [band.getName() for band in bands]
util.end_timer_print_duration()
return feature_array, image_width, image_height, number_of_pixels, band_names
def write_images(file):
# Disable JAI native MediaLib extensions
System = jpy.get_type('java.lang.System')
System.setProperty('com.sun.media.jai.disableMediaLib', 'true')
NUM_BANDS = 15
product = ProductIO.readProduct(file)
band_arrays = []
w = product.getSceneRasterWidth()
h = product.getSceneRasterHeight()
for i in range(0, NUM_BANDS):
band = product.getBand("radiance_%d" % (i + 1))
band_data = np.zeros(w * h, dtype=np.float32)
band.readPixels(0, 0, w, h, band_data)
band_arrays.append(band_data)
result = []
value_min = -1
value_max = 1
for i in range(0, NUM_BANDS):
for j in range(i + 1, NUM_BANDS):
a1 = band_arrays[i]
a2 = band_arrays[j]
array = (a1 - a2) / (a1 + a2)
array = np.ma.masked_invalid(array)
array = array.clip(value_min, value_max)
array -= value_min
array *= 1.0 / (value_max - value_min)
array.shape = (h, w)
array = cm.jet(array, bytes=True)
image = Image.fromarray(array, 'RGBA')
name = 'radiance_%d_to_%d.png' % (i + 1, j + 1)
with io.FileIO(name, 'w') as fp:
print('Writing ' + name)
image.save(fp, format='PNG')
result.append(name)
return result
import sys
import numpy
from snappy import String
from snappy import Product
from snappy import ProductData
from snappy import ProductIO
from snappy import ProductUtils
if len(sys.argv) != 2:
print("usage: %s <file>" % sys.argv[0]);
sys.exit(1)
print("Reading...")
product = ProductIO.readProduct(sys.argv[1])
width = product.getSceneRasterWidth()
height = product.getSceneRasterHeight()
name = product.getName()
desc = product.getDescription()
band_names = product.getBandNames()
print("Product: %s, %d x %d pixels, %s" % (name, width, height, desc))
print("Bands: %s" % (band_names))
b7 = product.getBand('radiance_7')
b10 = product.getBand('radiance_10')
ndviProduct = Product('NDVI', 'NDVI', width, height)
ndviBand = ndviProduct.addBand('ndvi', ProductData.TYPE_FLOAT32)
ndviBand.setNoDataValue(numpy.nan)
ndviBand.setNoDataValueUsed(True)
jpy = snappy.jpy
# More Java type definitions required for image generation
Color = jpy.get_type('java.awt.Color')
ColorPoint = jpy.get_type('org.esa.snap.core.datamodel.ColorPaletteDef$Point')
ColorPaletteDef = jpy.get_type('org.esa.snap.core.datamodel.ColorPaletteDef')
ImageInfo = jpy.get_type('org.esa.snap.core.datamodel.ImageInfo')
ImageManager = jpy.get_type('org.esa.snap.core.image.ImageManager')
JAI = jpy.get_type('javax.media.jai.JAI')
# Disable JAI native MediaLib extensions
System = jpy.get_type('java.lang.System')
System.setProperty('com.sun.media.jai.disableMediaLib', 'true')
def write_image(band, points, filename, format):
cpd = ColorPaletteDef(points)
ii = ImageInfo(cpd)
band.setImageInfo(ii)
im = ImageManager.getInstance().createColoredBandImage([band], band.getImageInfo(), 0)
JAI.create("filestore", im, filename, format)
product = ProductIO.readProduct(file)
band = product.getBand('radiance_13')
# The colour palette assigned to pixel values 0, 50, 100 in the band's geophysical units
points = [ColorPoint(0.0, Color.YELLOW),
ColorPoint(50.0, Color.RED),
ColorPoint(100.0, Color.BLUE)]
write_image(band, points, 'snappy_write_image.png', 'PNG')
import sys
import numpy
from snappy import String
from snappy import Product
from snappy import ProductData
from snappy import ProductIO
from snappy import ProductUtils
if len(sys.argv) != 2:
print("usage: %s <file>" % sys.argv[0]);
sys.exit(1)
print("Reading...")
sourceProduct = ProductIO.readProduct(sys.argv[1])
b1 = sourceProduct.getBand('reflec_5')
b2 = sourceProduct.getBand('reflec_7')
b3 = sourceProduct.getBand('reflec_9')
w1 = b1.getSpectralWavelength()
w2 = b2.getSpectralWavelength()
w3 = b3.getSpectralWavelength()
a = (w2 - w1) / (w3 - w1)
k = 1.03
width = sourceProduct.getSceneRasterWidth()
height = sourceProduct.getSceneRasterHeight()
targetProduct = Product('FLH_Product', 'FLH_Type', width, height)
targetBand = targetProduct.addBand('FLH', ProductData.TYPE_FLOAT32)
ProductUtils.copyGeoCoding(sourceProduct, targetProduct)
targetProduct.setProductWriter(ProductIO.getProductWriter('GeoTIFF'))
import snappy
from snappy import ProductIO
SubsetOp = snappy.jpy.get_type('org.esa.snap.gpf.operators.standard.SubsetOp')
WKTReader = snappy.jpy.get_type('com.vividsolutions.jts.io.WKTReader')
if len(sys.argv) != 3:
print("usage: %s <file> <geometry-wkt>" % sys.argv[0])
print(" %s ./TEST.N1 \"POLYGON((15.786082 45.30223, 11.798364 46.118263, 10.878688 43.61961, 14.722727"
"42.85818, 15.786082 45.30223))\"" % sys.argv[0])
sys.exit(1)
file = sys.argv[1]
wkt = sys.argv[2]
geom = WKTReader().read(wkt)
print("Reading...")
product = ProductIO.readProduct(file)
op = SubsetOp()
op.setSourceProduct(product)
op.setGeoRegion(geom)
sub_product = op.getTargetProduct()
print("Writing...")
ProductIO.writeProduct(sub_product, "snappy_subset_output.dim", "BEAM-DIMAP")
print("Done.")
print("usage: %s <inputProduct> <wkt>" % sys.argv[0])
sys.exit(1)
input = sys.argv[1]
inputFileName = input[input.rfind('/')+1:]
wellKnownText = sys.argv[2]
try:
geometry = WKTReader().read(wellKnownText)
except:
geometry = None
print('Failed to convert WKT into geometry')
sys.exit(2)
product = ProductIO.readProduct(input)
wktFeatureType = PlainFeatureFactory.createDefaultFeatureType(DefaultGeographicCRS.WGS84)
featureBuilder = SimpleFeatureBuilder(wktFeatureType)
wktFeature = featureBuilder.buildFeature('shape')
wktFeature.setDefaultGeometry(geometry)
newCollection = ListFeatureCollection(wktFeatureType)
newCollection.add(wktFeature)
productFeatures = FeatureUtils.clipFeatureCollectionToProductBounds(newCollection, product, None, ProgressMonitor.NULL)
node = VectorDataNode('shape', wktFeatureType)
product.getVectorDataGroup().add(node)
vdGroup = product.getVectorDataGroup()
import sys
import numpy
from snappy import Product, ProductData, ProductIO, ProductUtils
if len(sys.argv) < 2:
print 'Product file requires'
sys.exit(1)
# input product & dimensions
input_product = ProductIO.readProduct(sys.argv[1])
width = input_product.getSceneRasterWidth()
height = input_product.getSceneRasterHeight()
product_name = input_product.getName()
# input product red & nir bands
red_band = input_product.getBand('B4')
nir_band = input_product.getBand('B8')
# output product (ndvi) & new band
output_product = Product('NDVI', 'NDVI', width, height)
ProductUtils.copyGeoCoding(input_product, output_product)
output_band = output_product.addBand('ndvi', ProductData.TYPE_FLOAT32)
# output writer
output_product_writer = ProductIO.getProductWriter('BEAM-DIMAP')
output_product.setProductWriter(output_product_writer)
output_product.writeHeader(product_name + '.ndvi.dim')
# compute & save ndvi line by line
red_row = numpy.zeros(width, dtype=numpy.float32)
def test_end_to_end(self):
# PRODPATH = "C:\\Users\\carsten\\Dropbox\\Carsten\\SWProjects\\Rayleigh-Correction\\testdata\\"
PRODPATH = "D:\\Dropbox\\Carsten\\SWProjects\\Rayleigh-Correction\\testdata\\"
# validate here
numerr=0
# read output
# SENSOR = 'MERIS'
SENSOR = 'OLCI'
main([SENSOR])
if (SENSOR=='MERIS'):
REF_FILE =OUT_FILE = PRODPATH+'Reftestprodukt1_MER_RR_20050713.dim'
TEST_FILE=OUT_FILE = PRODPATH+'Testprodukt1_MER_RR_20050713.dim'
NBANDS=15
if (SENSOR=='OLCI'):
REF_FILE = PRODPATH+'Reftestproduct3_S3A_OL_1_EFR____20160509T103945.dim'
TEST_FILE = PRODPATH+'Testproduct3_OL_1_EFR____20160509T103945.dim'
NBANDS=21
print("Opening reference product ...")
refproduct = ProductIO.readProduct(REF_FILE)
width = refproduct.getSceneRasterWidth()
height = refproduct.getSceneRasterHeight()
print("Opening test product ...")
testproduct = ProductIO.readProduct(TEST_FILE)
widthtest = testproduct.getSceneRasterWidth()
heighttest = testproduct.getSceneRasterHeight()
# compare
print("Start comparing ...")
try:
self.assertEqual(width, widthtest)
print(" widths agree: ",width)
except:
print(" widths error: ref=",width," test=",widthtest)
numerr+=1
try:
self.assertEqual(height, heighttest)
print(" height agree: ",height)
except:
print(" height error: ref=",height," test=",heighttest)
numerr+=1
refvalues=np.zeros((width,height),dtype=np.float32)
testvalues=np.zeros((width,height),dtype=np.float32)
bandname="rBRR_"
for i in range(1,NBANDS+1):
refsource = refproduct.getBand(bandname+str(i))
refvalues = refsource.readPixels(0, 0, width, height, refvalues)
testsource = testproduct.getBand(bandname+str(i))
testvalues = testsource.readPixels(0, 0, width, height, testvalues)
try:
result=np.allclose(refvalues,testvalues, rtol=0.0, atol=1e-6, equal_nan=True)
self.assertEqual(result,True)
print(" ",bandname+str(i)," agrees")
except:
print(" ",bandname+str(i)," disagrees")
numerr+=1
print("toal number of tests failed =", numerr)
def main(args=sys.argv[1:]):
if len(args) != 1:
print("usage: raycorr-processor <SENSOR>")
sys.exit(1)
SENSOR = args[0]
# SENSOR = 'OLCI'
# SENSOR = 'MERIS'
# PRODPATH = "C:\\Users\\carsten\\Dropbox\\Carsten\\SWProjects\\Rayleigh-Correction\\testdata\\"
# AUXPATH = "C:\\Users\\carsten\\Dropbox\\Carsten\\Tagesordner\\20160104\\Rayleigh-Correction-Processor\\"
# O3PATH="C:\\Users\\carsten\\Dropbox\\Carsten\\SWProjects\\Rayleigh-Correction\\raycorr\\"
PRODPATH = "D:\\Dropbox\\Carsten\\SWProjects\\Rayleigh-Correction\\testdata\\"
# AUXPATH = "D:\\Dropbox\\Carsten\\Tagesordner\\20160104\\Rayleigh-Correction-Processor\\"
O3PATH="D:\\Dropbox\\Carsten\\SWProjects\\Rayleigh-Correction\\raycorr\\"
DEMFactory = jpy.get_type('org.esa.snap.dem.dataio.DEMFactory')
Resampling = jpy.get_type('org.esa.snap.core.dataop.resamp.Resampling')
GeoPos = jpy.get_type('org.esa.snap.core.datamodel.GeoPos')
if (SENSOR=='MERIS'):
IN_FILE = PRODPATH+"subset_1_of_MER_RR__1PTACR20050713_094325_000002592039_00022_17611_0000.dim"
OUT_FILE = PRODPATH+'Testprodukt1_MER_RR_20050713.dim'
else:
if (SENSOR=='OLCI'):
IN_FILE = PRODPATH+'subset_3_of_S3A_OL_1_EFR____20160509T103945_20160509T104245_20160509T124907_0180_004_051_1979_SVL_O_NR_001.dim'
OUT_FILE = PRODPATH+'Testproduct3_OL_1_EFR____20160509T103945.dim'
else:
print("Sensor ",SENSOR," not supported - exit")
return
file = IN_FILE
# AUX_FILE = AUXPATH+'ADF\\MER_ATP_AXVACR20091026_144725_20021224_121445_20200101_000000'
# adf = ADF(AUX_FILE)
# ray_coeff_matrix = adf.ray_coeff_matrix
# rayADF = readRayADF(AUX_FILE)
# new_aux = OrderedDict()
# new_aux['tau_ray'] = rayADF['tR']
# new_aux['theta'] = rayADF['theta']
# new_aux['ray_albedo_lut'] = rayADF['rayAlbLUT']
# new_aux['ray_coeff_matrix'] = ray_coeff_matrix
# with open('raycorr_auxdata.json', 'w') as fp:
# json.dumps(new_aux, fp, cls=JSONNumpyEncoder, indent=2)
# fp.close()
with open('../test/raycorr_auxdata.json', 'r') as fp:
obj = json.load(fp, object_hook=json_as_numpy)
# json_str = json.dumps(new_aux, cls=JSONNumpyEncoder, indent=2)
# print(json_str)
# obj = json.loads(json_str, object_hook=json_as_numpy)
# rayADF = new_aux
rayADF = obj
ray_coeff_matrix=rayADF['ray_coeff_matrix']
print("Reading...")
product = ProductIO.readProduct(file)
width = product.getSceneRasterWidth()
height = product.getSceneRasterHeight()
name = product.getName()
description = product.getDescription()
band_names = product.getBandNames()
print("Sensor: %s" % SENSOR)
print("Product: %s, %s" % (name, description))
print("Raster size: %d x %d pixels" % (width, height))
print("Start time: " + str(product.getStartTime()))
print("End time: " + str(product.getEndTime()))
print("Bands: %s" % (list(band_names)))
raycorProduct = Product('RayCorr', 'RayCorr', width, height)
writer = ProductIO.getProductWriter('BEAM-DIMAP')
raycorProduct.setProductWriter(writer)
if (SENSOR == 'MERIS'):
nbands = product.getNumBands() - 2 # the last 2 bands are l1flags and detector index; we don't need them
band_name = ["radiance_1"]
for i in range(1,nbands):
band_name += ["radiance_" + str(i+1)]
if (SENSOR == 'OLCI'):
nbands = 21
band_name = ["Oa01_radiance"]
sf_name = ["solar_flux_band_1"]
for i in range(1,nbands):
if (i < 9):
band_name += ["Oa0" + str(i + 1) + "_radiance"]
sf_name += ["solar_flux_band_" + str(i + 1)]
else:
band_name += ["Oa" + str(i + 1) + "_radiance"]
sf_name += ["solar_flux_band_" + str(i + 1)]
# Create TOA reflectance and Rayleig optical thickness bands
for i in range(nbands):
# bsource = product.getBandAt(i)
bsource = product.getBand(band_name[i])
btoa_name = "rtoa_" + str(i + 1)
toareflBand = raycorProduct.addBand(btoa_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, toareflBand)
btaur_name = "taur_" + str(i + 1)
taurBand = raycorProduct.addBand(btaur_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, taurBand)
brhor_name = "rRay_" + str(i + 1)
rhorBand = raycorProduct.addBand(brhor_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rhorBand)
# Fourier Terms, during debugging only
brhorF1_name = "rRayF1_" + str(i + 1)
rhorF1Band = raycorProduct.addBand(brhorF1_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rhorF1Band)
brhorF2_name = "rRayF2_" + str(i + 1)
rhorF2Band = raycorProduct.addBand(brhorF2_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rhorF2Band)
brhorF3_name = "rRayF3_" + str(i + 1)
rhorF3Band = raycorProduct.addBand(brhorF3_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rhorF3Band)
rayTransS_name = "transSRay_" + str(i + 1)
rayTransSBand = raycorProduct.addBand(rayTransS_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rayTransSBand)
rayTransV_name = "transVRay_" + str(i + 1)
rayTransVBand = raycorProduct.addBand(rayTransV_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rayTransVBand)
sARay_name = "sARay_" + str(i + 1)
sARayBand = raycorProduct.addBand(sARay_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, sARayBand)
rtoaR_name = "rtoaRay_" + str(i + 1)
rtoaRBand = raycorProduct.addBand(rtoaR_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rtoaRBand)
rBRR_name = "rBRR_" + str(i + 1)
rBRRBand = raycorProduct.addBand(rBRR_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rBRRBand)
spf_name = "sphericalAlbedoFactor_" + str(i + 1)
spfBand = raycorProduct.addBand(spf_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, spfBand)
# simple Rayleigh reflectance (Roland's formular)
rRaySimple_name = "RayleighSimple_" + str(i + 1)
rRaySimpleBand = raycorProduct.addBand(rRaySimple_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rRaySimpleBand)
# gaseous absorption corrected TOA reflectances
rho_ng_name = "rtoa_ng_" + str(i + 1)
rho_ngBand = raycorProduct.addBand(rho_ng_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rho_ngBand)
# simple Rayleigh optical thickness, for debugging
taurS_name = "taurS_" + str(i + 1)
taurSBand = raycorProduct.addBand(taurS_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, taurSBand)
raycorProduct.setAutoGrouping(
'rtoa:taur:rRay:rRayF1:rRayF2:rRayF3:transSRay:transVRay:sARay:rtoaRay:rBRR:sphericalAlbedoFactor:RayleighSimple:rtoa_ng:taurS')
airmassBand = raycorProduct.addBand('airmass', ProductData.TYPE_FLOAT32)
azidiffBand = raycorProduct.addBand('azidiff', ProductData.TYPE_FLOAT32)
altBand = raycorProduct.addBand('altitude', ProductData.TYPE_FLOAT32)
# Create flag coding
raycorFlagsBand = raycorProduct.addBand('raycor_flags', ProductData.TYPE_UINT8)
raycorFlagCoding = FlagCoding('raycor_flags')
raycorFlagCoding.addFlag("testflag_1", 1, "Flag 1 for Rayleigh Correction")
raycorFlagCoding.addFlag("testflag_2", 2, "Flag 2 for Rayleigh Correction")
group = raycorProduct.getFlagCodingGroup()
group.add(raycorFlagCoding)
raycorFlagsBand.setSampleCoding(raycorFlagCoding)
# add geocoding and create the product on disk (meta data, empty bands)
ProductUtils.copyGeoCoding(product, raycorProduct) #geocoding is copied when tie point grids are copied,
ProductUtils.copyTiePointGrids(product, raycorProduct)
raycorProduct.writeHeader(OUT_FILE)
# Calculate and write toa reflectances and Rayleigh optical thickness
# ===================================================================
# some stuff needed to get the altitude from an external DEM; can be omitted if altitude is used from the product
# resamplingMethod = 'NEAREST_NEIGHBOUR' # Resampling.NEAREST_NEIGHBOUR.getName()
resamplingMethod = Resampling.NEAREST_NEIGHBOUR.getName()
demName = 'GETASSE30' # alternative 'SRTM 3Sec'
dem = DEMFactory.createElevationModel(demName, resamplingMethod)
# constants
AVO = 6.0221367E+23 # Avogadro's number
m_a_zero = 28.9595 # Mean molecular weight of dry ait (zero CO2)
g0_45 = 980.616 # Acceleration of gravity (sea level and 458 latitude)
Ns = 2.5469E19 # Molecular density of gas in molecules / cm3
# constants describing the state of the atmosphere and which we don't know; better values may be used if known
CO2 = 3.E-4 # CO2 concentration at pixel; typical values are 300 to 360 ppm
C_CO2 = CO2 * 100 # CO2 concentration in ppm
m_a = 15.0556 * CO2 + m_a_zero # mean molecular weight of dry air as function of actual CO2
# other constants
PA = 0.9587256 # Rayleigh Phase function, molecular asymetry factor 1
PB = 1. - PA # Rayleigh Phase function, molecular asymetry factor 2
tpoly = rayADF['tau_ray'] # Polynomial coefficients for Rayleigh transmittance
h2o_cor_poly = np.array(
[0.3832989, 1.6527957, -1.5635101, 0.5311913]) # Polynomial coefficients for WV transmission @ 709nm
# absorb_ozon = np.array([0.0, 0.0002174, 0.0034448, 0.0205669, 0.0400134, 0.105446, 0.1081787, 0.0501634, 0.0410249, \
# 0.0349671, 0.0187495, 0.0086322, 0.0, 0.0, 0.0, 0.0084989, 0.0018944, 0.0012369, 0.0, 0.0, 0.0000488]) # OLCI
# absorb_ozon = np.array([0.0002174, 0.0034448, 0.0205669, 0.0400134, 0.105446, 0.1081787, 0.0501634, \
# 0.0349671, 0.0187495, 0.0086322, 0.0, 0.0084989, 0.0018944, 0.0012369, 0.0]) # MERIS
O3_FILE = O3PATH+'ozone-highres.txt'
ozoneO = O3(O3_FILE)
absorb_ozon = ozoneO.convolveInstrument(SENSOR)
# arrays which are needed to store some stuff
E0 = np.zeros(width, dtype=np.float32)
radiance = np.zeros(width, dtype=np.float32)
reflectance = np.zeros((nbands, width), dtype=np.float32)
taur = np.zeros((nbands, width), dtype=np.float32)
sigma = np.zeros(nbands, dtype=np.float32)
airmass = np.zeros(width, dtype=np.float32)
azidiff = np.zeros(width, dtype=np.float32)
PR = np.zeros(3, dtype=np.float32) # Fourier coefficients of the Rayleigh Phase function
rho_Rf = np.zeros(3, dtype=np.float32) # Fourier terms of the Rayleigh primary scattering reflectance
rho_Rm = np.zeros((3, nbands, width),
dtype=np.float32) # Fourier terms of the Rayleigh scattering reflectance, corrected for multiple scattering
rho_R = np.zeros((nbands, width), dtype=np.float32) # first approximation of Rayleigh reflectance
rho_toaR = np.zeros((nbands, width), dtype=np.float32) # toa reflectance corrected for Rayleigh scattering
rho_BRR = np.zeros((nbands, width),
dtype=np.float32) # top of aerosol reflectance, which is equal to bottom of Rayleigh reflectance
sphericalFactor = np.zeros((nbands, width),
dtype=np.float32) # spherical Albedo Correction Factor (for testing only, can be integrated into the equation later)
rRaySimple = np.zeros((nbands, width),
dtype=np.float32) # simple Rayleigh reflectance formular, after Roland (for testing only)
rho_ng = np.zeros((nbands, width),
dtype=np.float32) # toa reflectance corrected for gaseous absorption (rho_ng = "rho no gas")
X2 = np.zeros(width, dtype=np.float32) # temporary variable used for WV correction algorithm for gaseous absorption
trans709 = np.zeros(width,
dtype=np.float32) # WV transmission at 709nm, used for WV correction algorithm for gaseous absorption
taurS = np.zeros((nbands, width), dtype=np.float32) # simple Rayleigh optical thickness, for debugging only
if (SENSOR == 'MERIS'):
dem_alt = 'dem_alt'
atm_press = 'atm_press'
ozone = 'ozone'
latitude = 'latitude'
longitude = 'longitude'
sun_zenith = 'sun_zenith'
view_zenith = 'view_zenith'
sun_azimuth = 'sun_azimuth'
view_azimuth = 'view_azimuth'
# water vapour correction:
# MERIS band 9 @ 709nm to be corrected; WV absorption 900nm = band 15, WV reference 885nm= band 14
b709 = 8 # the band to be corrected
bWVRef = 13 # the reference reflectance outside WV absorption band
bWV = 14 # the reflectance within the WV absorption band
if (SENSOR == 'OLCI'):
dem_alt = 'N/A'
atm_press = 'sea_level_pressure'
ozone = 'total_ozone'
latitude = 'TP_latitude'
longitude = 'TP_longitude'
sun_zenith = 'SZA'
view_zenith = 'OZA'
sun_azimuth = 'SAA'
view_azimuth = 'OAA'
# water vapour correction:
# OLCI band 11 @ 709nm, WV absorption 900nm = band 19, WV reference 885nm = band 18
b709 = 11 # the band to be corrected
bWVRef=17 # the reference reflectance outside WV absorption band
bWV=18 # the reference reflectance outside WV absorption band
if (SENSOR == 'MERIS'): # check if this is required at all!
tp_alt = product.getTiePointGrid(dem_alt)
alt = np.zeros(width, dtype=np.float32)
tp_press = product.getTiePointGrid(atm_press)
press0 = np.zeros(width, dtype=np.float32)
tp_ozone = product.getTiePointGrid(ozone)
ozone = np.zeros(width, dtype=np.float32)
tp_latitude = product.getTiePointGrid(latitude)
lat = np.zeros(width, dtype=np.float32)
tp_longitude = product.getTiePointGrid(longitude)
lon = np.zeros(width, dtype=np.float32)
tp_theta_s = product.getTiePointGrid(sun_zenith)
theta_s = np.zeros(width, dtype=np.float32)
tp_theta_v = product.getTiePointGrid(view_zenith)
theta_v = np.zeros(width, dtype=np.float32)
tp_azi_s = product.getTiePointGrid(sun_azimuth)
azi_s = np.zeros(width, dtype=np.float32)
tp_azi_v = product.getTiePointGrid(view_azimuth)
azi_v = np.zeros(width, dtype=np.float32)
# Rayleigh multiple scattering
# - Coefficients LUT
dimTheta = 12
dimThetaS = dimThetaV = dimTheta
gridThetaS = rayADF['theta']
gridThetaV = rayADF['theta']
gridGeometry = [gridThetaS, gridThetaV]
RayScattCoeffA = ray_coeff_matrix[:, :, :, 0]
RayScattCoeffB = ray_coeff_matrix[:, :, :, 1]
RayScattCoeffC = ray_coeff_matrix[:, :, :, 2]
RayScattCoeffD = ray_coeff_matrix[:, :, :, 3]
# - Fourier terms
a = np.zeros(3, dtype=np.float32)
b = np.zeros(3, dtype=np.float32)
c = np.zeros(3, dtype=np.float32)
d = np.zeros(3, dtype=np.float32)
rayMultiCorr = np.zeros(3, dtype=np.float32)
# Rayleigh transmittances and spherical albedo
tR_thetaS = np.zeros((nbands, width), dtype=np.float32) # Rayleigh Transmittance sun - surface
tR_thetaV = np.zeros((nbands, width), dtype=np.float32) # Rayleigh Transmittance surface - sun
dimTaur = 17
taurTab = np.linspace(0.0, 1.0, num=dimTaur)
rayAlb_f = interp1d(taurTab, rayADF['ray_albedo_lut'])
sARay = np.zeros((nbands, width), dtype=np.float32) # Rayleigh spherical albedo
print("Processing ...")
# Calculate the Rayleigh cross section, which depends only on wavelength but not on air pressure
for i in range(nbands):
print("processing Rayleigh cross section of band", i)
# b_source = product.getBandAt(i)
b_source = product.getBand(band_name[i])
lam = b_source.getSpectralWavelength() # wavelength of band i in nm
lam = lam / 1000.0 # wavelength in micrometer
lam2 = lam / 10000.0 # wavelength in cm
F_N2 = 1.034 + 0.000317 / (lam ** 2) # King factor of N2
F_O2 = 1.096 + 0.001385 / (lam ** 2) + 0.0001448 / (lam ** 4) # King factor of O2
F_air = (78.084 * F_N2 + 20.946 * F_O2 + 0.934 * 1 + C_CO2 * 1.15) / (
78.084 + 20.946 + 0.934 + C_CO2) # depolarization ratio or King Factor, (6+3rho)/(6-7rho)
n_ratio = 1 + 0.54 * (CO2 - 0.0003)
n_1_300 = (8060.51 + (2480990. / (132.274 - lam ** (-2))) + (17455.7 / (39.32957 - lam ** (-2)))) / 100000000.0
nCO2 = n_ratio * (1 + n_1_300) # reflective index at CO2
sigma[i] = (24 * math.pi ** 3 * (nCO2 ** 2 - 1) ** 2) / (lam2 ** 4 * Ns ** 2 * (nCO2 ** 2 + 2) ** 2) * F_air
for y in range(height):
print("processing line ", y, " of ", height)
# start radiance to reflectance conversion
theta_s = tp_theta_s.readPixels(0, y, width, 1, theta_s) # sun zenith angle in degree
for i in range(nbands):
b_source = product.getBand(band_name[i])
radiance = b_source.readPixels(0, y, width, 1, radiance)
if (SENSOR == 'MERIS'):
E0.fill(b_source.getSolarFlux())
if (SENSOR == 'OLCI'):
b_source = product.getBand(sf_name[i])
E0 = b_source.readPixels(0, y, width, 1, E0)
reflectance[i] = radiance * math.pi / (E0 * np.cos(np.radians(theta_s)))
b_out = raycorProduct.getBand("rtoa_" + str(i + 1))
b_out.writePixels(0, y, width, 1, reflectance[i])
# radiance to reflectance conversion completed
# this is dummy code to create a flag
flag1 = np.zeros(width, dtype=np.bool_)
flag2 = np.zeros(width, dtype=np.bool_)
raycorFlags = flag1 + 2 * flag2
raycorFlagsBand.writePixels(0, y, width, 1, raycorFlags)
# end flags dummy code
# raycorProduct.closeIO()
# if (0==1):
lat = tp_latitude.readPixels(0, y, width, 1, lat)
lon = tp_longitude.readPixels(0, y, width, 1, lon)
# start Rayleigh optical thickness calculation
# alt = tp_alt.readPixels(0, y, width, 1, alt) # using the tie-point DEM in a MERIS product
# get the altitude from an external DEM
for x in range(width): alt[x] = dem.getElevation(GeoPos(lat[x], lon[x]))
press0 = tp_press.readPixels(0, y, width, 1, press0)
ozone = tp_ozone.readPixels(0, y, width, 1, ozone)
theta_s = tp_theta_s.readPixels(0, y, width, 1, theta_s) # sun zenith angle in degree
theta_v = tp_theta_v.readPixels(0, y, width, 1, theta_v) # view zenith angle in degree
azi_s = tp_azi_s.readPixels(0, y, width, 1, azi_s) # sun azimuth angle in degree
azi_v = tp_azi_v.readPixels(0, y, width, 1, azi_v) # view azimuth angle in degree
# gaseous absorption correction
rho_ng = reflectance # to start: gaseous corrected reflectances equals toa reflectances
# water vapour correction:
# MERIS band 9 @ 709nm to be corrected; WV absorption 900nm = band 15, WV reference 885nm= band 14
# b709 = 8 # the band to be corrected
# bWVRef = 13 # the reference reflectance outside WV absorption band
# bWV = 14 # the reflectance within the WV absorption band
# OLCI band 11 @ 709nm, WV absorption 900nm = band 19, WV reference 885nm = band 18
# b709 = 11 # the band to be corrected
# bWVRef=17 # the reference reflectance outside WV absorption band
# bWV=18 # the reference reflectance outside WV absorption band
for i in range(width):
if (reflectance[(bWV, i)] > 0):
X2[i] = reflectance[(bWV, i)] / reflectance[(bWVRef, i)]
else:
X2[i] = 1
trans709 = h2o_cor_poly[0] + (h2o_cor_poly[1] + (h2o_cor_poly[2] + h2o_cor_poly[3] * X2) * X2) * X2
rho_ng[b709] /= trans709
# ozone correction
model_ozone = 0
for x in range(width):
ts = math.radians(theta_s[x]) # sun zenith angle in radian
cts = math.cos(ts) # cosine of sun zenith angle
sts = math.sin(ts) # sinus of sun zenith angle
tv = math.radians(theta_v[x]) # view zenith angle in radian
ctv = math.cos(tv) # cosine of view zenith angle
stv = math.sin(tv) # sinus of view zenith angle
for i in range(nbands):
trans_ozoned12 = math.exp(-(absorb_ozon[i] * ozone[x] / 1000.0 - model_ozone) / cts)
trans_ozoneu12 = math.exp(-(absorb_ozon[i] * ozone[x] / 1000.0 - model_ozone) / ctv)
trans_ozone12 = trans_ozoned12 * trans_ozoneu12
rho_ng[(i, x)] /= trans_ozone12
# here we can decide if we continue with gaseous corrected reflectances or not
reflectance = rho_ng
# Now calculate the pixel dependent terms (like pressure) and finally the Rayleigh optical thickness
for x in range(width):
# Calculation to get the pressure
z = alt[x] # altitude at pixel in meters, taken from MERIS tie-point grid
z = max(z, 0) # clip to sea level
Psurf0 = press0[x] # pressure at sea level in hPa, taken from MERIS tie-point grid
Psurf = Psurf0 * (
1. - 0.0065 * z / 288.15) ** 5.255 # air pressure at the pixel (i.e. at altitude) in hPa, using the international pressure equation
P = Psurf * 1000. # air pressure at pixel location in dyn / cm2, which is hPa * 1000
# calculation to get the constant of gravity at the pixel altitude, taking the air mass above into account
dphi = math.radians(lat[x]) # latitude in radians
cos2phi = math.cos(2 * dphi)
g0 = g0_45 * (1 - 0.0026373 * cos2phi + 0.0000059 * cos2phi ** 2)
zs = 0.73737 * z + 5517.56 # effective mass-weighted altitude
g = g0 - (0.0003085462 + 0.000000227 * cos2phi) * zs + (0.00000000007254 + 0.0000000000001 * cos2phi) * \
zs ** 2 - (1.517E-17 + 6E-20 * cos2phi) * zs ** 3
# calculations to get the Rayeigh optical thickness
factor = (P * AVO) / (m_a * g)
for i in range(nbands):
taur[(i, x)] = sigma[i] * factor
# Calculate Rayleigh Phase function
ts = math.radians(theta_s[x]) # sun zenith angle in radian
cts = math.cos(ts) # cosine of sun zenith angle
sts = math.sin(ts) # sinus of sun zenith angle
tv = math.radians(theta_v[x]) # view zenith angle in radian
ctv = math.cos(tv) # cosine of view zenith angle
stv = math.sin(tv) # sinus of view zenith angle
airmass[x] = 1 / cts + 1 / ctv # air mass
# Rayleigh Phase function, 3 Fourier terms
PR[0] = 3. * PA / 4. * (1. + cts ** 2 * ctv ** 2 + (sts ** 2 * stv ** 2) / 2.) + PB
PR[1] = -3. * PA / 4. * cts * ctv * sts * stv
PR[2] = 3. * PA / 16. * sts ** 2 * stv ** 2
# Calculate azimuth difference
azs = math.radians(azi_s[x])
azv = math.radians(azi_v[x])
cosdeltaphi = math.cos(azv - azs)
azidiff[x] = math.acos(cosdeltaphi) # azimuth difference in radian
# Fourier components of multiple scattering
for j in [0, 1, 2]:
a[j] = interpn(gridGeometry, RayScattCoeffA[j, :, :], [theta_s[x], theta_v[x]], method='linear',
bounds_error=False, fill_value=None)
b[j] = interpn(gridGeometry, RayScattCoeffB[j, :, :], [theta_s[x], theta_v[x]], method='linear',
bounds_error=False, fill_value=None)
c[j] = interpn(gridGeometry, RayScattCoeffC[j, :, :], [theta_s[x], theta_v[x]], method='linear',
bounds_error=False, fill_value=None)
d[j] = interpn(gridGeometry, RayScattCoeffD[j, :, :], [theta_s[x], theta_v[x]], method='linear',
bounds_error=False, fill_value=None)
for i in range(nbands):
# Fourier series, loop
for j in [0, 1, 2]:
# Rayleigh primary scattering
rho_Rf[j] = (PR[j] / (4.0 * (cts + ctv))) * (1. - math.exp(-airmass[x] * taur[(i, x)]))
# correction for multiple scattering
rayMultiCorr[j] = a[j] + b[j] * taur[(i, x)] + c[j] * taur[(i, x)] ** 2 + d[j] * taur[(i, x)] ** 3
rho_Rm[(j, i, x)] = rho_Rf[j] * rayMultiCorr[j]
# rho_Rm[(0, i, x)] = rho_Rf[0]
# rho_Rm[(1, i, x)] = 0.
# rho_Rm[(2, i, x)] = 0.
# Fourier sum to get the Rayleigh Reflectance
rho_R[(i, x)] = rho_Rm[(0, i, x)] + 2.0 * rho_Rm[(1, i, x)] * math.cos(azidiff[x]) + 2. * rho_Rm[
(2, i, x)] * math.cos(2. * azidiff[x])
# complete the Rayleigh correction: see MERIS DPM PDF-p251 or DPM 9-16
# polynomial coefficients tpoly0, tpoly1 and tpoly2 from MERIS LUT
tRs = ((2. / 3. + cts) + (2. / 3. - cts) * math.exp(-taur[(i, x)] / cts)) / (4. / 3. + taur[(i, x)])
tR_thetaS[(i, x)] = tpoly[0] + tpoly[1] * tRs + tpoly[
2] * tRs ** 2 # Rayleigh Transmittance sun - surface
tRv = ((2. / 3. + ctv) + (2. / 3. - ctv) * math.exp(-taur[(i, x)] / ctv)) / (4. / 3. + taur[(i, x)])
tR_thetaV[(i, x)] = tpoly[0] + tpoly[1] * tRv + tpoly[
2] * tRv ** 2 # Rayleigh Transmittance surface - sensor
sARay[(i, x)] = rayAlb_f(taur[(i, x)]) # Rayleigh spherical albedo
rho_toaR[(i, x)] = (reflectance[(i, x)] - rho_R[(i, x)]) / (
tR_thetaS[(i, x)] * tR_thetaV[(i, x)]) # toa reflectance corrected for Rayleigh scattering
sphericalFactor[(i, x)] = 1.0 / (1.0 + sARay[(i, x)] * rho_toaR[
(i, x)]) # factor used in the next equation to account for the spherical albedo
rho_BRR[(i, x)] = rho_toaR[(i, x)] * sphericalFactor[
(i, x)] # top of aerosol reflectance, which is equal to bottom of Rayleigh reflectance
# simple Rayleigh correction
azi_diff_deg = math.fabs(azi_v[x] - azi_s[x])
if (azi_diff_deg > 180.0):
azi_diff_deg = 360.0 - azi_diff_deg
azi_diff_rad = math.radians(azi_diff_deg)
cos_scat_ang = (-ctv * cts) - (stv * sts * math.cos(azi_diff_rad))
phase_rayl_min = 0.75 * (1.0 + cos_scat_ang * cos_scat_ang)
for i in range(nbands):
# b_source = product.getBandAt(i)
b_source = product.getBand(band_name[i])
lam = b_source.getSpectralWavelength()
taurS[(i, x)] = math.exp(-4.637) * math.pow((lam / 1000.0), -4.0679)
pressureAtms = press0[x] * math.exp(-alt[x] / 8000.0)
pressureFactor = taurS[(i, x)] / 1013.0
taurS[(i, x)] = pressureAtms * pressureFactor
rRaySimple[(i, x)] = cts * taurS[(i, x)] * phase_rayl_min / (4 * 3.1415926) * (1 / ctv) * 3.1415926
# Write bands to product
airmassBand.writePixels(0, y, width, 1, airmass)
azidiffBand.writePixels(0, y, width, 1, azidiff)
altBand.writePixels(0, y, width, 1, alt)
for i in range(nbands):
taurBand = raycorProduct.getBand("taur_" + str(i + 1))
taurBand.writePixels(0, y, width, 1, taur[i])
rhorBand = raycorProduct.getBand("rRay_" + str(i + 1))
rhorBand.writePixels(0, y, width, 1, rho_R[i])
rhorF1Band = raycorProduct.getBand("rRayF1_" + str(i + 1))
rhorF1Band.writePixels(0, y, width, 1, rho_Rm[0, i])
rhorF2Band = raycorProduct.getBand("rRayF2_" + str(i + 1))
rhorF2Band.writePixels(0, y, width, 1, rho_Rm[1, i])
rhorF3Band = raycorProduct.getBand("rRayF3_" + str(i + 1))
rhorF3Band.writePixels(0, y, width, 1, rho_Rm[2, i])
rayTransSBand = raycorProduct.getBand("transSRay_" + str(i + 1))
rayTransSBand.writePixels(0, y, width, 1, tR_thetaS[i])
rayTransVBand = raycorProduct.getBand("transVRay_" + str(i + 1))
rayTransVBand.writePixels(0, y, width, 1, tR_thetaV[i])
sARayBand = raycorProduct.getBand("sARay_" + str(i + 1))
sARayBand.writePixels(0, y, width, 1, sARay[i])
rtoaRBand = raycorProduct.getBand("rtoaRay_" + str(i + 1))
rtoaRBand.writePixels(0, y, width, 1, rho_toaR[i])
rBRRBand = raycorProduct.getBand("rBRR_" + str(i + 1))
rBRRBand.writePixels(0, y, width, 1, rho_BRR[i])
spfBand = raycorProduct.getBand("sphericalAlbedoFactor_" + str(i + 1))
spfBand.writePixels(0, y, width, 1, sphericalFactor[i])
rRaySimpleBand = raycorProduct.getBand("RayleighSimple_" + str(i + 1))
rRaySimpleBand.writePixels(0, y, width, 1, rRaySimple[i])
rho_ngBand = raycorProduct.getBand("rtoa_ng_" + str(i + 1))
rho_ngBand.writePixels(0, y, width, 1, rho_ng[i])
taurSBand = raycorProduct.getBand("taurS_" + str(i + 1))
taurSBand.writePixels(0, y, width, 1, taurS[i])
# Rayleigh calculation completed
raycorProduct.closeIO()
print("Done.")
import sys
from snappy import ProductIO
from snappy import GPF
from snappy import jpy
if len(sys.argv) != 2:
print("usage: %s <file>" % sys.argv[0])
sys.exit(1)
file = sys.argv[1]
print("Reading...")
product = ProductIO.readProduct(file)
width = product.getSceneRasterWidth()
height = product.getSceneRasterHeight()
name = product.getName()
description = product.getDescription()
band_names = product.getBandNames()
print("Product: %s, %d x %d pixels, %s" % (name, width, height, description))
print("Bands: %s" % (list(band_names)))
GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()
HashMap = jpy.get_type('java.util.HashMap')
BandDescriptor = jpy.get_type('org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor')
targetBand1 = BandDescriptor()
targetBand1.name = 'band_1'
import numpy
from snappy import Product
from snappy import ProductData
from snappy import ProductIO
from snappy import ProductUtils
from snappy import FlagCoding
if len(sys.argv) != 2:
print("usage: %s <file>" % sys.argv[0])
sys.exit(1)
file = sys.argv[1]
print("Reading...")
product = ProductIO.readProduct(file)
width = product.getSceneRasterWidth()
height = product.getSceneRasterHeight()
name = product.getName()
description = product.getDescription()
band_names = product.getBandNames()
print("Product: %s, %s" % (name, description))
print("Raster size: %d x %d pixels" % (width, height))
print("Start time: " + str(product.getStartTime()))
print("End time: " + str(product.getEndTime()))
print("Bands: %s" % (list(band_names)))
b7 = product.getBand('radiance_7')
b10 = product.getBand('radiance_10')
print 'Product file and band index required'
sys.exit(1)
# check if band index given is correct
if not sys.argv[2] in ['2', '3', '4', '8']:
print 'Incorrect band index'
# get cli arguments
product_file = sys.argv[1]
band_index = sys.argv[2]
band_name = 'B' + band_index
product_name = {
'B2': 'blue',
'B3': 'green',
'B4': 'red',
'B8': 'nir',
}[band_name]
# input product: open and get dimensions & name
input_product = ProductIO.readProduct(product_file)
product_width = input_product.getSceneRasterWidth()
product_height = input_product.getSceneRasterHeight()
product_name = input_product.getName()
# output product: copy selected band & save product
output_product = Product(product_name, product_name, product_width, product_height)
ProductUtils.copyGeoCoding(input_product, output_product)
ProductUtils.copyBand(band_name, input_product, output_product, True)
ProductIO.writeProduct(output_product, product_name + '.band.dim', 'BEAM-DIMAP')
output_product.closeIO()
