def _band_math(self, product, name, expression):
GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()
HashMap = jpy.get_type('java.util.HashMap')
BandDescriptor = jpy.get_type(
'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor')
targetBand = BandDescriptor()
targetBand.name = name
targetBand.type = 'float32'
targetBand.expression = expression
bands = jpy.array(
'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor', 1)
bands[0] = targetBand
parameters = HashMap()
parameters.put('targetBands', bands)
productMap = HashMap()
if isinstance(product, list):
for ind in range(len(product)):
print('p{}'.format(ind + 1))
productMap.put('p{}'.format(ind + 1), product[ind])
result = GPF.createProduct('BandMaths', parameters, productMap)
else:
result = GPF.createProduct('BandMaths', parameters, product)
return result
def band_maths(self, expression):
"""
Perform a SNAP GPF BandMath operation on the product, excluding non-vegetation and non-soil pixels.
Args:
expression: The band maths expression to execute
"""
print("\tBandMaths, product={}".format(self.product.getName()))
BandDescriptor = jpy.get_type(
'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor')
band = BandDescriptor()
band.name = 'band_maths'
band.type = 'float32'
band.expression = expression
band.noDataValue = np.nan
bands = jpy.array(
'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor', 1)
bands[0] = band
HashMap = jpy.get_type('java.util.HashMap')
parameters = HashMap()
parameters.put('targetBands', bands)
self.product = GPF.createProduct('BandMaths', parameters, self.product)
return self.product
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 extract_values(self, file):
if not exists(self.plots):
mkdir(self.plots)
coords = []
with open(file, 'r') as f:
for line in f:
line = line.replace('\n', '')
coords.append([float(x) for x in line.split(' ')])
GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()
HashMap = jpy.get_type('java.util.HashMap')
Coords = jpy.array('org.esa.snap.pixex.Coordinate', len(coords))
Coord = jpy.get_type('org.esa.snap.pixex.Coordinate')
for ind, coord in enumerate(coords):
c = Coord('Coord{}'.format(ind), coord[0], coord[1], None)
Coords[ind] = c
parameters = HashMap()
parameters.put('exportBands', True)
parameters.put('exportTiePoints', False)
parameters.put('exportMasks', False)
parameters.put('coordinates', Coords)
parameters.put('outputDir', '.')
pre = self._extract_values(parameters, self.product_pre)
post = self._extract_values(parameters, self.product_post)
waves = pre.meta['comments'][-1].replace('\t', ' ')
waves = waves.replace('Wavelength:', '').split(' ')
waves = filter(lambda x: x != '', waves)
waves = [float(x) for x in waves]
waves = filter(lambda x: x != 0, waves)
bands = ['B{}'.format(ind) for ind in range(1, 9)]
bands += ['B8A', 'B9']
bands += ['B{}'.format(ind) for ind in range(11, 13)]
print(pre.colnames)
print(post.colnames)
for ind in range(len(pre)):
f, ax = plt.subplots()
ax.set_xlabel('Wavelength (nm)')
ax.set_ylabel('dl')
radiances = list(pre[bands][ind])
ax.plot(waves, radiances, color='g', label='pre')
ax.scatter(waves, radiances, color='g')
radiances = list(post[bands][ind])
ax.plot(waves, radiances, color='b', label='post')
ax.scatter(waves, radiances, color='b')
lat = pre['Latitude'][ind]
lon = pre['Longitude'][ind]
ax.set_title("{}, {}".format(lat, lon))
plt.legend()
save = "{}/{}.pdf".format(self.plots, pre['Name'][ind])
f.savefig(save, bbox_inches="tight")
def get_band_math_config(band_name, expression):
parameters = HashMap()
band_descriptor = jpy.get_type('org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor')
target_band = band_descriptor()
target_band.name = band_name
target_band.type = 'float32'
target_band.expression = expression
target_bands = jpy.array('org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor', 1)
target_bands[0] = target_band
parameters.put('targetBands', target_bands)
return parameters
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 band_math(src, band_name, expression):
parameters = HashMap()
band_descriptor = jpy.get_type(
'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor')
target_band = band_descriptor()
target_band.name = band_name
target_band.type = 'float32'
target_band.expression = expression
target_bands = jpy.array(
'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor', 1)
target_bands[0] = target_band
parameters.put('targetBands', target_bands)
return GPF.createProduct("BandMaths", parameters, src)
def sigma_naught(product):
targetBands = jpy.array(
'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor', 1)
targetBand1 = BandDescriptor()
targetBand1.name = 'sigma_int'
targetBand1.type = 'Int32'
targetBand1.expression = 'round(log10(Sigma0_VV)*1000)'
targetBands[0] = targetBand1
parameters = HashMap()
parameters.put('targetBands', targetBands)
result = GPF.createProduct('BandMaths', parameters, product)
return (result)
def convertDim2Tiff(input_dim,
output_file,
name_file,
format_file='float32',
type_file='GeoTIFF'):
if debug >= 2:
print(cyan + "convertDim2Tiff() : " + bold + green +
"Import Dim to SNAP : " + endC + input_dim)
# Info input file
product = ProductIO.readProduct(input_dim)
band = product.getBand(name_file)
width = product.getSceneRasterWidth()
height = product.getSceneRasterHeight()
# Instance de GPF
GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()
# Def operateur SNAP
operator = 'BandMaths'
BandDescriptor = jpy.get_type(
'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor')
targetBands = jpy.array(
'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor', 1)
# Get des expressions d'entréées
targetBand = BandDescriptor()
targetBand.name = 'band_0'
targetBand.type = format_file
targetBand.expression = name_file
targetBands[0] = targetBand
# Set des parametres
parameters = HashMap()
parameters.put('targetBands', targetBands)
result = GPF.createProduct(operator, parameters, product)
ProductIO.writeProduct(result, output_file, type_file)
if debug >= 2:
print(cyan + "convertDim2Tiff() : " + bold + green +
"Writing Done : " + endC + str(output_file))
return
def compute_vegeation_index(product, index):
index = ''.join(index)
GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()
HashMap = jpy.get_type('java.util.HashMap')
BandDescriptor = jpy.get_type(
'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor')
targetBand = BandDescriptor()
targetBand.name = index
targetBand.type = 'float32'
targetBand.expression = indices_expr[index]
targetBands = jpy.array(
'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor', 1)
targetBands[0] = targetBand
parameters = HashMap()
parameters.put('targetBands', targetBands)
print("Start to compute:" + indices_expr[index])
result = GPF.createProduct('BandMaths', parameters, product)
print('Expression computed: ' + indices_expr[index])
print result.getBand(index)
return result.getBand(index)
def apply_threshold_to_product(self):
self.temporary_data_binary = []
snappy.GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()
BandDescriptor = jpy.get_type('org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor')
targetBand = BandDescriptor()
targetBand.name = 'band1'
targetBand.type = 'float32'
targetBand.expression = 'Sigma0_VV_db < -16.5 '
targetBands = jpy.array('org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor', 1)
targetBands[0] = targetBand
for product in self.temporary_data_list:
parameters = snappy.HashMap()
parameters.put('name', 'Sigma0_VV_db_Threshold')
parameters.put('targetBands', targetBands)
binary = snappy.GPF.createProduct('BandMaths', parameters, product)
self.temporary_data_binary.append(binary)
self.temporary_data_list = self.temporary_data_binary
return self.temporary_data_binary
def BandMath(stack):
product = ProductIO.readProduct(stack)
band_names = product.getBandNames()
print(list(band_names))
band1 = input("Enter Band 1:")
band2 = input("Enter Band 2 which will be subtracted:")
GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()
## Band Math
targetBands = jpy.array(
'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor', 2)
#print('{}_minus_{}'.format(bandlist[x], bandlist[y]))
targetBand1 = BandDescriptor()
targetBand1.name = '{}_minus_{}'.format(band1, band2)
targetBand1.type = 'float32'
#targetBand1.expression = '(({} - {})<(-2))? 255 : 0'.format(band1, band2)
targetBand1.expression = '(({} - {})<(-2))? 255 : 0'.format(band1, band2)
parameters = HashMap()
parameters.put('targetBands', targetBands)
result = GPF.createProduct('BandMaths', parameters, product)
print("Writing...")
ProductIO.writeProduct(result, '{}_minus_{}.dim'.format(band1, band2),
'BEAM-DIMAP')
print('{}_minus_{}.dim Done.'.format(band1, band2))
GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()
HashMap = jpy.get_type('java.util.HashMap')
BandDescriptor = jpy.get_type('org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor')
targetBand1 = BandDescriptor()
targetBand1.name = 'band_1'
targetBand1.type = 'float32'
targetBand1.expression = '(radiance_10 - radiance_7) / (radiance_10 + radiance_7)'
targetBand2 = BandDescriptor()
targetBand2.name = 'band_2'
targetBand2.type = 'float32'
targetBand2.expression = '(radiance_9 - radiance_6) / (radiance_9 + radiance_6)'
targetBands = jpy.array('org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor', 2)
targetBands[0] = targetBand1
targetBands[1] = targetBand2
parameters = HashMap()
parameters.put('targetBands', targetBands)
result = GPF.createProduct('BandMaths', parameters, product)
print("Writing...")
ProductIO.writeProduct(result, 'snappy_bmaths_output.dim', 'BEAM-DIMAP')
print("Done.")
GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()
BandDescriptor = jpy.get_type(
'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor')
targetBand1 = BandDescriptor()
targetBand1.name = 'band_1'
targetBand1.type = 'float32'
targetBand1.expression = '(radiance_10 - radiance_7) / (radiance_10 + radiance_7)'
targetBand2 = BandDescriptor()
targetBand2.name = 'band_2'
targetBand2.type = 'float32'
targetBand2.expression = '(radiance_9 - radiance_6) / (radiance_9 + radiance_6)'
targetBands = jpy.array(
'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor', 2)
targetBands[0] = targetBand1
targetBands[1] = targetBand2
parameters = HashMap()
parameters.put('targetBands', targetBands)
result = GPF.createProduct('BandMaths', parameters, product)
print("Writing...")
ProductIO.writeProduct(result, 'snappy_bmaths_output.dim', 'BEAM-DIMAP')
print("Done.")
"""
Please note: the next major version of snappy/jpy will be more pythonic in the sense that implicit data type
def mosaic(self,
src_data,
trg_file_path,
band_dict,
para_dict,
trg_file_format='GeoTIFF'):
'''
--------------------------------------------------------------------------------------------------------------------------------
Function: Creates a mosaic out of a set of source products.
--------------------------------------------------------------------------------------------------------------------------------
Parameter: - src_data: An object ('Product') of the S2-dataset
- trg_file_path: A string containing the path to the output S-2 dataset and it's name.
- para_dict:
- combine=<string> Specifies the way how conditions are combined.
Value must be one of 'OR', 'AND'.
DEFAULT value is 'OR'.
- crs=<string> The CRS of the target product, represented as WKT or authority code.
DEFAULT value is 'EPSG:4326'.
- northBound=<double> The northern latitude.
Valid interval is [-90,90].
DEFAULT value is '75.0'.
- eastBound=<double> The eastern longitude.
Valid interval is [-180,180].
DEFAULT value is '30.0'.
- southBound=<double> The southern latitude.
Valid interval is [-90,90].
DEFAULT value is '35.0'.
- westBound=<double> The western longitude.
Valid interval is [-180,180].
DEFAULT value is '-15.0'.
- elevationModelName=<string> The name of the elevation model for the orthorectification.
- orthorectify=<boolean> Whether the source product should be orthorectified.
DEFAULT value is 'false'.
- pixelSizeX=<double> Size of a pixel in X-direction in map units.
DEFAULT value is '0.05'.
- pixelSizeY=<double> Size of a pixel in Y-direction in map units.
DEFAULT value is '0.05'.
- resampling=<string> The method used for resampling.
Value must be one of 'Nearest', 'Bilinear', 'Bicubic'.
DEFAULT value is 'Nearest'.
- trg_file_format: A string containing the Export Format Name. e.g. 'BEAM-DIMAP', 'GeoTIFF', 'NetCDF'
DEFAULT value is 'BEAM-DIMAP'
--------------------------------------------------------------------------------------------------------------------------------
How to run this code:
from snappy import ProductIO
from snappy_mosaic import mosaicOp
src_path_1 = 'C:/S2/S2A_USER_MTD_SAFL2A_..._20151129T112140/S2A_USER_MTD_SAFL2A_..._20151129T112140.xml'
src_path_2 = 'C:/S2/S2A_USER_MTD_SAFL2A_..._20151129T112141/S2A_USER_MTD_SAFL2A_..._20151129T112141.xml'
trg_path = 'C:/S2/S2A_USER_MTD_SAFL2A_..._20151129T112140_mos/'
bands = {'B2': 'B2'}
para = {'northBound': 37.4, 'eastBound': 76.8, 'southBound': 36.3, 'westBound': 75.6, \
'pixelSizeX': 0.001, 'pixelSizeY': 0.001}
products = []
products.append(ProductIO.readProduct(src_path_1))
products.append(ProductIO.readProduct(src_path_2))
mosaicOp().mosaic(products, trg_path, bands, para)
--------------------------------------------------------------------------------------------------------------------------------
'''
func_name = 'MOSAIC'
func_code = 'Mosaic'
parameters = self.HashMap()
# Set variables.
print('{}: Set variables'.format(func_name))
variables = jpy.array('org.esa.snap.core.gpf.common.MosaicOp$Variable',
len(band_dict))
for (i, band_name) in enumerate(band_dict):
variable_i = self.Variables()
variable_i.setName(band_name)
variable_i.setExpression(band_dict[band_name])
variables[i] = variable_i
parameters.put('variables', variables)
# Set parameters.
print('{}: Set parameters'.format(func_name))
BB = 0
PS = 0
for para_name in para_dict:
if para_name in ('pixelSizeX', 'pixelSizeY'):
PS = PS + 1
if para_name in ('northBound', 'eastBound', 'southBound',
'westBound'):
BB = BB + 1
parameters.put(para_name, para_dict[para_name])
# Set Bounding Box and Pixelsize if none was defined.
if BB == 4 and PS == 2:
print(
'{}: INFO, Bounding Box Coordinates and Pixelsizes defined through User-Input'
.format(func_name))
elif BB == 0:
print(
'{}: INFO, No Bounding Box Coordinates defined through User-Input'
.format(func_name))
print('{}: Set Parameter Bounding Box'.format(func_name))
bboxE, bboxW, bboxS, bboxN, pxSzNS, pxSzEW = ([] for i in range(6))
for data in src_data:
(b, y_EW, x_NS) = basicOp().BoundingBox(data)
bboxE.append(b[0])
bboxW.append(b[1])
bboxS.append(b[2])
bboxN.append(b[3])
pxSzNS.append(x_NS)
pxSzEW.append(y_EW)
bbox = [min(bboxE), max(bboxW), min(bboxS), max(bboxN)]
parameters.put('eastBound', bbox[1])
parameters.put('westBound', bbox[0])
parameters.put('southBound', bbox[2])
parameters.put('northBound', bbox[3])
if PS != 2:
print('{}: Set Parameter Pixelsize'.format(func_name))
pxSzEW_avg_m, pxSzNS_avg_m = basicOp().deg2m(
bbox, pxSzEW, pxSzNS)
pxSzEW_avg_int_m = int(round(pxSzEW_avg_m / 5.0, 0) * 5.0)
pxSzNS_avg_int_m = int(round(pxSzNS_avg_m / 5.0, 0) * 5.0)
pxSzEW_avg_deg, pxSzNS_avg_deg = basicOp().m2deg(
bbox, pxSzEW_avg_int_m, pxSzNS_avg_int_m)
print('{}: pixelSizeY: {}m'.format(func_name,
pxSzEW_avg_int_m))
print('{}: pixelSizeX: {}m'.format(func_name,
pxSzNS_avg_int_m))
parameters.put('pixelSizeY', pxSzEW_avg_deg)
parameters.put('pixelSizeX', pxSzNS_avg_deg)
elif 1 <= BB < 4:
BB_num = ('One' if BB == 1 else 'Two'
if BB == 2 else 'Tree' if BB == 3 else BB.__str__())
BB_nam = ('Coordinate' if BB == 1 else 'Coordinates')
print('{}: {} Bounding Box {} defined by Input'.format(
func_name, BB_num, BB_nam))
else:
print(
'{}: ERROR, too many Bounding Box input coordinates ({} instead of 4) or missing Pixelsize.'
.format(func_name, BB))
sys.exit(1)
# Create product.
print('{}: Create product...'.format(func_name))
trg_data = GPF.createProduct(func_code, parameters, src_data)
# Save product
# <supported-format>: BEAM-DIMAP, GeoTIFF, NetCDF, ...
#
# ------------------------------------------------------------------------------------------------------------------------------
# ATTENTION, in case of:
# RuntimeError: java.lang.OutOfMemoryError: Java heap space
# ------------------------------------------------------------------------------------------------------------------------------
# SOLUTION 1:
# (http://forum.step.esa.int/t/snappy-error-productio-writeproduct/1102)
#
# 1. CHANGE <snappy>/jpyconfig.py:
# jvm_maxmem = None ----> jvm_maxmem = '6G'
# Increase RAM
#
# 2. CHANGE <snappy>/snappy.ini:
# # java_max_mem: 4G ----> java_max_mem: 6G
# Remove '#' and increase RAM
# ------------------------------------------------------------------------------------------------------------------------------
# SOLUTION 2:
# If you swapped Latitude/Longitude in POLYGON(...) there is also a out-of-memory-error
#
print('{}: Save as {} ...'.format(func_name, trg_file_path))
ProductIO.writeProduct(trg_data, trg_file_path, trg_file_format)
print('{}: Done.'.format(func_name))
HashMap = jpy.get_type('java.util.HashMap')
BandDescriptor = jpy.get_type(
'org.esa.snap.gpf.operators.standard.BandMathsOp$BandDescriptor')
targetBand1 = BandDescriptor()
targetBand1.name = 'band_1'
targetBand1.type = 'float32'
targetBand1.expression = '(radiance_10 - radiance_7) / (radiance_10 + radiance_7)'
targetBand2 = BandDescriptor()
targetBand2.name = 'band_2'
targetBand2.type = 'float32'
targetBand2.expression = '(radiance_9 - radiance_6) / (radiance_9 + radiance_6)'
targetBands = jpy.array(
'org.esa.snap.gpf.operators.standard.BandMathsOp$BandDescriptor', 2)
targetBands[0] = targetBand1
targetBands[1] = targetBand2
parameters = HashMap()
parameters.put('targetBands', targetBands)
result = GPF.createProduct('BandMaths', parameters, product)
print("Writing...")
ProductIO.writeProduct(result, 'snappy_bmaths_output.dim', 'BEAM-DIMAP')
print("Done.")
"""
Please note: the next major version of snappy/jpy will be more pythonic in the sense that implicit data type
width = Product.getSceneRasterWidth()
os.chdir(TempFolder)
os.system(snaphu_dir + ' -f snaphu.conf ' + phasen + ' ' + str(width))
print("Phase unwrapping performed successfully ..............................")
#%%
# Snaphu import
bandList = os.listdir(TempFolder)
for item in bandList:
if item.endswith('.hdr') and item[0:8] == 'UnwPhase':
upha = item
print(upha)
#working_dir='D:\\PhD Info\\InSAR\\Examples\\SNAPPY_Ecuador_Galapagos'
#os.chdir (working_dir + '\\' + Fname)
#
Files = jpy.array('org.esa.snap.core.datamodel.Product', 2)
#Files[0] = ProductIO.readProduct(os.path.join(r'D:\PhD Info\InSAR\Examples\SNAPPY_Ecuador_Galapagos','subset_0_of_InSAR_pipeline_II.dim'))
Files[0] = read(os.path.join(Fpath, Fname + '.dim')) # reads .dim
Files[1] = ProductIO.readProduct(glob.glob(TempFolder + '\\' + upha)[0])
HashMap = jpy.get_type("java.util.HashMap")
params = HashMap()
Product = GPF.createProduct("SnaphuImport", params, Files)
os.chdir(Fpath)
ProductIO.writeProduct(Product, Fname + "_ifg_ml_fit_unwph.dim", "BEAM-DIMAP")
print("Snaphu import performed successfully .................................")
# Phase To Displacement
#Product = read(os.path.join(r'D:\PhD Info\InSAR\Examples\SNAPPY_Ecuador_Galapagos\exp_subset','subset_0_of_InSAR_pipeline_II_ifg_ml_fit_unwph.dim')) # reads .dim
params = HashMap()
Product = GPF.createProduct("PhaseToDisplacement", params, Product)
os.chdir(Fpath)
ProductIO.writeProduct(Product, Fname + '_ifg_ml_fit_unwph_disp.dim',
snappy.GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()
HashMap = jpy.get_type('java.util.HashMap')
Coord = jpy.get_type('org.esa.snap.pixex.Coordinate')
snow_dataset=pd.read_csv("/home/simsek/workspace/EDUCATION/Thesis/Scripts/turku_SnowCover_insitu.csv", names=["year","month","day","lat","lon","sd"])
nosnow_dataset=pd.read_csv("/home/simsek/workspace/EDUCATION/Thesis/Scripts/turku_NoSnow_insitu.csv", names=["year","month","day","lat","lon","sd"])
snow_coords=np.asarray(nosnow_dataset[["lat","lon"]].drop_duplicates())
n=np.asarray(nosnow_dataset[["lat","lon"]].drop_duplicates()).shape[0]
## Parameters for resampling
resampling_parameters = HashMap()
resampling_parameters.put('targetResolution', 60)
## Parameters for Pixel extraction
insitu_coordinates = jpy.array('org.esa.snap.pixex.Coordinate',n)
for i in range(n):
insitu_coordinates[i] = Coord('station'+str(i), snow_coords[i,0], snow_coords[i,1], None)
#insitu_coordinates[0] = Coord('station0', 61.00, 24.5, None)
# insitu_coordinates[1] = Coord('station1', 60.82, 23.5, None)
# insitu_coordinates[2] = Coord('station2', 60.45, 23.65, None)
# insitu_coordinates[3] = Coord('station3', 60.65, 23.81, None)
# insitu_coordinates[4] = Coord('station4', 60.52, 24.65, None)
# insitu_coordinates[5] = Coord('station5', 61.12, 24.33, None)
# insitu_coordinates[6] = Coord('station6', 60.37, 23.12, None)
print(insitu_coordinates[8])
pixex_parameters = HashMap()
pixex_parameters.put('PexportBands', 1)
pixex_parameters.put('PexportExpressionResult', 0)
pixex_parameters.put('PexportMasks', 0)
GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()
HashMap = jpy.get_type('java.util.HashMap')
BandDescriptor = jpy.get_type('org.esa.snap.gpf.operators.standard.BandMathsOp$BandDescriptor')
targetBand1 = BandDescriptor()
targetBand1.name = 'band_1'
targetBand1.type = 'float32'
targetBand1.expression = '(radiance_10 - radiance_7) / (radiance_10 + radiance_7)'
targetBand2 = BandDescriptor()
targetBand2.name = 'band_2'
targetBand2.type = 'float32'
targetBand2.expression = '(radiance_9 - radiance_6) / (radiance_9 + radiance_6)'
targetBands = jpy.array('org.esa.snap.gpf.operators.standard.BandMathsOp$BandDescriptor', 2)
targetBands[0] = targetBand1
targetBands[1] = targetBand2
parameters = HashMap()
parameters.put('targetBands', targetBands)
result = GPF.createProduct('BandMaths', parameters, product)
print("Writing...")
ProductIO.writeProduct(result, 'snappy_bmaths_output.dim', 'BEAM-DIMAP')
print("Done.")
