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baltic_AC_simple.py
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baltic_AC_simple.py
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import matplotlib.pyplot as plt
# from netCDF4 import Dataset
import numpy as np
# from scipy.ndimage.filters import uniform_filter
from keras.models import load_model
import pandas as pd
import snappy as snp
import os
import time
import json
import sys
from snappy import Product
from snappy import ProductData
from snappy import ProductIO
from snappy import ProductUtils
from snappy import ProgressMonitor
from snappy import FlagCoding
from snappy import jpy
def get_band_or_tiePointGrid(product, name, dtype='float32', reshape=True):
##
# This function reads a band or tie-points, identified by its name <name>, from SNAP product <product>
# The fuction returns a numpy array of shape (height, width)
##
height = product.getSceneRasterHeight()
width = product.getSceneRasterWidth()
# print(height, width)
var = np.zeros(width * height, dtype=dtype)
if name in list(product.getBandNames()):
product.getBand(name).readPixels(0, 0, width, height, var)
elif name in list(product.getTiePointGridNames()):
product.getTiePointGrid(name).readPixels(0, 0, width, height, var)
else:
raise Exception('{}: neither a band nor a tie point grid'.format(name))
if reshape:
var = var.reshape((height, width))
return var
def read_NN_metadata(nnpath):
##
# read the metadata:
# nnpath = 'D:\WORK\IdePix\\NN_training_S2\I13x11x9x6x4x3xO1_sqrt_Radical2TrainingSelection_Relu_NoScaler\\'
meta_fnames = os.listdir(nnpath)
meta_fn = [fn for fn in meta_fnames if 'Metadata_' in fn]
with open(nnpath + meta_fn[0], "r") as f:
d = f.read()
training_meta = json.loads(d)
f.close()
model_fn = [fn for fn in meta_fnames if 'MetadataModel_' in fn]
with open(nnpath + model_fn[0], "r") as f:
d = f.read()
model_meta = json.loads(d)
f.close()
return training_meta, model_meta
def radianceToReflectance_Reader(product, sensor = '', print_info=False):
if print_info:
height = product.getSceneRasterHeight()
width = product.getSceneRasterWidth()
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)))
if sensor == 'OLCI':
rad = Level1_Reader(product, sensor, band_group='radiance')
solar_flux = Level1_Reader(product, sensor, band_group='solar_flux')
SZA = get_band_or_tiePointGrid(product, 'SZA', reshape=False)
refl = np.zeros(rad.shape)
for j in range(rad.shape[1]):
refl[:, j] = rad[:, j] * np.pi / (solar_flux[:, j] * np.cos(SZA*np.pi/180.))
elif sensor == 'S2':
refl = Level1_Reader(product, sensor)
return refl
def Level1_Reader(product, sensor, band_group='radiance'):
input_label = []
if sensor == 'S2':
input_label = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B10', 'B11', 'B12']
elif sensor == 'OLCI':
if band_group == 'radiance':
input_label = ["Oa01_radiance", "Oa02_radiance", "Oa03_radiance", "Oa04_radiance", "Oa05_radiance",
"Oa06_radiance", "Oa07_radiance", "Oa08_radiance", "Oa09_radiance", "Oa10_radiance",
"Oa11_radiance", "Oa12_radiance", "Oa13_radiance", "Oa14_radiance", "Oa15_radiance",
"Oa16_radiance", "Oa17_radiance", "Oa18_radiance", "Oa19_radiance", "Oa20_radiance", "Oa21_radiance"]
elif band_group == 'solar_flux':
input_label = ["solar_flux_band_" + str(i+1) for i in range(21)]
# Initialise and read all bands contained in input_label into variable X
# X is re-organised to serve as valid input to the neural net. Each row contains one spectrum.
band = get_band_or_tiePointGrid(product, input_label[0])
X = np.zeros((band.shape[0] * band.shape[1], len(input_label)))
print(X.shape)
X[:, 0] = band.reshape((X.shape[0],))
for i, bn in enumerate(input_label[1:]):
#print(bn)
band = get_band_or_tiePointGrid(product, bn)
X[:, i + 1] = band.reshape((X.shape[0],))
return X
def angle_Reader(product, sensor):
if sensor == 'OLCI':
band = get_band_or_tiePointGrid(product, 'OAA')
oaa = np.zeros((band.shape[0] * band.shape[1]))
oaa = band.reshape((oaa.shape[0],))
band = get_band_or_tiePointGrid(product, 'OZA')
oza = np.zeros((band.shape[0] * band.shape[1]))
oza = band.reshape((oza.shape[0],))
band = get_band_or_tiePointGrid(product, 'SZA')
sza = np.zeros((band.shape[0] * band.shape[1]))
sza = band.reshape((sza.shape[0],))
band = get_band_or_tiePointGrid(product, 'SAA')
saa = np.zeros((band.shape[0] * band.shape[1]))
saa = band.reshape((saa.shape[0],))
return oaa, oza, sza, saa
def calculate_diff_azim(oaa, saa):
###
# azimuth difference as input to the NN is defined in a range between 0° and 180°
x = np.array(oaa - saa)
ID = np.array( x < 0)
if np.sum(ID)>0.:
x[ID] = 360. + x[ID]
ID = np.array(x > 180.)
if np.sum(ID)>0.:
x[ID] = x[ID] -180.
return x
def check_valid_pixel_expression_L1(product, sensor):
if sensor == 'OLCI':
height = product.getSceneRasterHeight()
width = product.getSceneRasterWidth()
quality_flags = np.zeros(width * height, dtype='uint32')
product.getBand('quality_flags').readPixels(0, 0, width, height, quality_flags)
#quality_flags = quality_flags.reshape((height, width))
# Masks OLCI L1
## flags: 31=land 30=coastline 29=fresh_inland_water 28=tidal_region 27=bright 26=straylight_risk 25=invalid
## 24=cosmetic 23=duplicated 22=sun-glint_risk 21=dubious 20->00=saturated@Oa01->saturated@Oa21
invalid_mask = np.bitwise_and(quality_flags, 2 ** 25) == 2 ** 25
land_mask = (np.bitwise_and(quality_flags, 2 ** 31) == 2 ** 31) | \
(np.bitwise_and(quality_flags, 2 ** 30) == 2 ** 30)
#(np.bitwise_and(quality_flags, 2 ** 29) == 2 ** 29)
bright_mask = np.bitwise_and(quality_flags, 2 ** 27) == 2 ** 27
invalid_mask = np.logical_or(invalid_mask , np.logical_or( land_mask , bright_mask))
valid_pixel_flag = np.logical_not(invalid_mask)
elif sensor == 'S2':
#todo: set valid pixel expression L1C S2
height = product.getSceneRasterHeight()
width = product.getSceneRasterWidth()
valid_pixel_flag = np.ones(width * height, dtype='uint32')
return valid_pixel_flag
def apply_forwardNN_IOP_to_rhow_keras(X, sensor):
start_time = time.time()
###
# read keras NN + metadata
NN_path = '...' # full path to NN file.
metaNN_path = '...' # folder with metadata files from training
model = load_model(NN_path)
training_meta, model_meta = read_NN_metadata(metaNN_path)
X_trans = np.copy(X)
###
# transformation of input data
transform_method = training_meta['transformation_method']
if transform_method == 'sqrt':
X_trans = np.sqrt(X_trans)
elif transform_method == 'log':
X_trans = np.log10(X_trans)
###
if model_meta['scaling']:
scaler_path = os.listdir(metaNN_path)
scaler_path = [sp for sp in scaler_path if 'scaling' in sp][0]
print(scaler_path)
scaler = pd.read_csv(metaNN_path + '/' + scaler_path, header=0, sep="\t", index_col=0)
for i in range(X.shape[1]):
X_trans[:, i] = (X_trans[:, i] - scaler['mean'].loc[i]) / scaler['var'].loc[i]
###
# Application of the NN to the data.
prediction = model.predict(X_trans)
print(len(prediction.shape))
print("model load, transform, predict: %s seconds " % round(time.time() - start_time, 2))
return prediction
def apply_forwardNN_IOP_to_rhow_example():
# NN inputs (10): SZA, VZA, diffAzi, T, S, log_apig, log_adet, log a_gelb, log_bpart, log_bwit
# NN output (12 bands): log_rw at lambda = 400, 412, 443, 489, 510, 560, 620, 665, 674, 681, 709, 754
nnFilePath = "forwardNN_c2rcc/olci/olci_20161012/iop_rw/17x97x47_464.3.net"
NNffbpAlphaTabFast = jpy.get_type('org.esa.snap.core.nn.NNffbpAlphaTabFast')
nnfile = open(nnFilePath, 'r')
nnCode = nnfile.read()
nn_iop_rw = NNffbpAlphaTabFast(nnCode)
###
# IMPORTANT: initialise input as np.zeros in the correct length!!!
# SZA, VZA, diffAzimuthA, T, S, log_apig, log_adet, log a_gelb, log_bpart, log_bwit
a = np.zeros(10)
a[0] = 51.091805
a[1] = 8.41573
a[2] = 155.42921 - 106.33723
a[3] = 15.
a[4] = 35.
a[5] = -4.3414865
a[6] = -4.956355
a[7] = -3.7658699
a[8] = -1.8608053
a[9] = -2.6944041
log_rw_nn2 = np.array(nn_iop_rw.calc(a), dtype=np.float32) # returns always the same numbers!!
print(np.exp(log_rw_nn2))
test_rhow_fromTOA = np.array((0.009959362, 0.01134242, 0.014025537, 0.01487543, 0.010059207, 0.005442682, 0.0010344568, 5.945379E-4, 5.734044E-4,
5.45633E-4, 2.7334824E-4, 7.638413E-5))
lam = np.array((400, 412, 443, 489, 510, 560, 620, 665, 674, 681, 709, 754))
plt.plot(lam, test_rhow_fromTOA, 'c-')
plt.plot(lam, np.exp(log_rw_nn2), 'c--')
plt.show()
### Example 1 c2rcc 2016/2017
# Name X Y Lon Lat log_adet log_agelb log_apig log_bpart log_bwit rhow_1 rhow_2 rhow_3 rhow_4 rhow_5 rhow_6 rhow_7 rhow_8 rhow_9 rhow_10 rhow_11 rhow_12 OAA OZA SAA SZA
# pin_1 437.5 235.5 3.825742 56.397962 -4.956355 -3.7658699 -4.3414865 -1.8608053 -2.6944041 0.009959362 0.01134242 0.014025537 0.01487543 0.010059207 0.005442682 0.0010344568 5.945379E-4 5.734044E-4 5.45633E-4 2.7334824E-4 7.638413E-5 106.33723 8.41573 155.42921 51.091805
# pin_2 315.5 189.5 3.369955 56.597397 -5.088239 -4.04009 -4.5042415 -1.5932024 -3.473691 0.013867372 0.015864057 0.019084657 0.019100675 0.01233714 0.006331922 0.0011564749 6.558736E-4 6.272484E-4 5.924552E-4 2.9530638E-4 8.18309E-5 105.94441 10.97436 154.92598 51.3791
# pin_3 261.5 291.5 3.013032 56.374021 -3.9385555 -3.2776499 -3.4899228 -1.7674016 -0.70727175 0.0077605355 0.008688468 0.010997476 0.014358465 0.012292771 0.009077219 0.0021156792 0.0012308386 0.0011759225 0.0011434247 6.446244E-4 1.8506091E-4 105.67807 12.095442 154.42883 51.258068
# pin_4 581.5 90.5 4.624798 56.662986 -5.0377655 -3.6873133 -4.332324 -2.0587044 -2.844635 0.008768552 0.009983219 0.012415685 0.013173553 0.008856366 0.004725398 9.098557E-4 5.228566E-4 5.064532E-4 4.8198106E-4 2.3771799E-4 6.621309E-5 106.967384 5.360547 156.48013 51.16074
### Example 2 c2r 201904
#Name X Y Lon Lat iop_adet iop_agelb iop_apig iop_bpart iop_bwit rhow_1 rhow_2 rhow_3 rhow_4 rhow_5 rhow_6 rhow_7 rhow_8 rhow_9 rhow_11 rhow_12 OAA OZA SAA SZA
#pin_1 476.5 231.5 3.993265 56.381533 0.027343486 0.021865448 0.02313981 0.16197646 0.11456131 0.0096926335 0.009946768 0.009698673 0.009246618 0.0071290717 0.0045980685 0.0011407222 6.9232716E-4 6.9392053E-4 6.8216515E-4 3.2724423E-4 9.311986E-5 106.47686 7.592013 155.63219 51.038136
#pin_2 791.5 144.5 5.430028 56.375004 0.06849463 0.049048793 0.042024814 0.737513 0.65764487 0.014872396 0.015654514 0.016917245 0.019503482 0.017883737 0.014397462 0.0044253394 0.002696726 0.0026146006 0.0025478457 0.0013499877 3.8966344E-4 107.70104 0.86169 157.40138 50.720493
#pin_3 1087.5 140.5 6.659461 56.159049 0.047663715 0.052653775 0.08010797 0.55395997 0.42619917 0.009425671 0.009672539 0.009948069 0.011273121 0.010529963 0.008871017 0.0027428195 0.0016406277 0.0016266676 0.001634283 8.882042E-4 2.5643408E-4 -71.3413 5.483421 158.88246 50.267815
#pin_4 893.5 153.5 5.840092 56.27681 0.07499708 0.08046007 0.080823615 1.7166388 1.4494008 0.022707473 0.023912517 0.026493134 0.03272301 0.03230792 0.029830737 0.010091868 0.005850822 0.0055907085 0.005481569 0.0029984459 8.490639E-4 -72.05109 1.3295213 157.88736 50.542614
def load_example_data( exampleNo = 1):
lam = np.array((400, 412, 443, 489, 510, 560, 620, 665, 674, 681, 709, 754))
if exampleNo == 2: # c2r 201904
iop = np.zeros((2, 5))
# log_apig, log_adet, log a_gelb, log_bpart, log_bwit
iop[0, :] = (0.02313981, 0.027343486, 0.021865448, 0.16197646, 0.11456131)
iop[1, :] = (0.042024814, 0.06849463, 0.049048793, 0.737513, 0.65764487)
iop = np.log(iop)
sza = np.array((51.038136, 50.720493))
vza = np.array((7.592013, 0.86169))
diff_azim = np.array((155.63219 - 106.47686 , 157.40138 - 107.70104))
test_rhow_fromTOA = np.zeros((2, 12))
test_rhow_fromTOA[0, :] = (0.0096926335, 0.009946768, 0.009698673, 0.009246618, 0.0071290717, 0.0045980685,
0.0011407222, 6.9232716E-4, 6.9392053E-4, 6.8216515E-4, 3.2724423E-4, 9.311986E-5)
test_rhow_fromTOA[1, :] = (0.014872396, 0.015654514, 0.016917245, 0.019503482, 0.017883737, 0.014397462,
0.0044253394, 0.002696726, 0.0026146006, 0.0025478457, 0.0013499877, 3.8966344E-4)
if exampleNo == 1: # c2rcc 2016
iop = np.zeros((2, 5))
# log_apig, log_adet, log a_gelb, log_bpart, log_bwit
iop[0, :] = (-4.3414865, -4.956355, - 3.7658699, - 1.8608053, - 2.6944041)
iop[1, :] = (-4.5042415, -5.088239, -4.04009, -1.5932024, -3.473691)
sza = np.array((51.091805, 51.3791))
vza = np.array((8.41573, 10.97436))
diff_azim = np.array((155.42921 - 106.33723, 154.92598 - 105.94441))
test_rhow_fromTOA = np.zeros((2, 12))
test_rhow_fromTOA[0, :] = (
0.009959362, 0.01134242, 0.014025537, 0.01487543, 0.010059207, 0.005442682, 0.0010344568, 5.945379E-4,
5.734044E-4, 5.45633E-4, 2.7334824E-4, 7.638413E-5)
test_rhow_fromTOA[1, :] = (
0.013867372, 0.015864057, 0.019084657, 0.019100675, 0.01233714, 0.006331922, 0.0011564749, 6.558736E-4,
6.272484E-4, 5.924552E-4, 2.9530638E-4, 8.18309E-5)
return iop, sza, vza, diff_azim, test_rhow_fromTOA, lam
def apply_forwardNN_IOP_to_rhow_arrayExample(sensor, exampleNo=1):
if exampleNo == 1:
iop, sza, vza, diff_azim, test_rhow_fromTOA, lam = load_example_data(1)
rhow_mod = apply_forwardNN_IOP_to_rhow(iop, sza, vza, diff_azim, sensor)
label = 'c2rcc_2016'
if exampleNo == 2:
iop, sza, vza, diff_azim, test_rhow_fromTOA, lam = load_example_data(2)
rhow_mod = apply_forwardNN_IOP_to_rhow(iop, sza, vza, diff_azim, sensor, nn='new')
label = 'c2rcc_2019'
print('rhow_mod')
print(rhow_mod)
print()
mycol = np.array(('c', 'r'))
for i in range(rhow_mod.shape[0]):
plt.plot(lam, rhow_mod[i, :], '--', color=mycol[i], label=label)
#plt.plot(lam, rhow_mod_new[i, :], '-.', color=mycol[i], label='nn_2019')
plt.plot(lam, test_rhow_fromTOA[i, :], '-', color=mycol[i], label='c2rcc_rhow_from_TOA')
plt.legend()
plt.show()
def apply_forwardNN_IOP_to_rhow(iop, sun_zenith, view_zenith, diff_azimuth, sensor, T=15, S=35, nn=''):
# iop : pixels x (log_apig, log_adet, log a_gelb, log_bpart, log_bwit)
# sun_zenith, view_zenith, diff_azimuth (0-180°) : pixels
# T, S: currently constant.
# valid ranges can be found at the beginning of the .net-file.
# NN inputs (10): SZA, VZA, diffAzi, T, S, log_apig, log_adet, log a_gelb, log_bpart, log_bwit
# NN output (12 bands): log_rw at lambda = 400, 412, 443, 489, 510, 560, 620, 665, 674, 681, 709, 754
# return: should probably be an array of shape (pixels, wavelengths) ??
nBands = None
if sensor == 'OLCI':
nBands=12
output = np.zeros((iop.shape[0], nBands))
nnFilePath = "forwardNN_c2rcc/olci/olci_20161012/iop_rw/17x97x47_464.3.net"
#if nn == 'new':
# nnFilePath = "forwardNN_c2rcc/olci/olci_20190414/iop_rw/55x55x55_40.3.net"
NNffbpAlphaTabFast = jpy.get_type('org.esa.snap.core.nn.NNffbpAlphaTabFast')
nnfile = open(nnFilePath, 'r')
nnCode = nnfile.read()
nn_iop_rw = NNffbpAlphaTabFast(nnCode)
for i in range(iop.shape[0]):
###
# IMPORTANT: initialise input as np.zeros in the correct length!!! otherwise, the function will not work!
# SZA, VZA, diffAzimuthA, T, S, log_apig, log_adet, log a_gelb, log_bpart, log_bwit
inputNN = np.zeros(10)
inputNN[0] = sun_zenith[i]
inputNN[1] = view_zenith[i]
inputNN[2] = diff_azimuth[i]
inputNN[3] = T
inputNN[4] = S
for j in range(iop.shape[1]): # log_apig, log_adet, log a_gelb, log_bpart, log_bwit
inputNN[j+5] = iop[i, j]
log_rw_nn2 = np.array(nn_iop_rw.calc(inputNN), dtype=np.float32) # returns always the same numbers!!
print(np.exp(log_rw_nn2))
output[i, :] = np.exp(log_rw_nn2)
# #// (9.5.4)
# #check if log_IOPs out of range
# mi = nn_rw_iop.getOutmin();
# ma = nn_rw_iop.getOutmax();
# boolean iop_oor_flag = false;
# for (int iv = 0; iv < log_iops_nn1.length; iv++) {
# if (log_iops_nn1[iv] < mi[iv] | log_iops_nn1[iv] > ma[iv]) {
# iop_oor_flag = true;
# }
# }
# flags = BitSetter.setFlag(flags, FLAG_INDEX_IOP_OOR, iop_oor_flag);
#
# #// (9.5.5)
# # check if log_IOPs at limit
# int firstIopMaxFlagIndex = FLAG_INDEX_APIG_AT_MAX;
# for (int i = 0; i < log_iops_nn1.length; i++) {
# final boolean iopAtMax = log_iops_nn1[i] > (ma[i] - log_threshfak_oor);
# flags = BitSetter.setFlag(flags, i + firstIopMaxFlagIndex, iopAtMax);
# }
#
# int firstIopMinFlagIndex = FLAG_INDEX_APIG_AT_MIN;
# for (int i = 0; i < log_iops_nn1.length; i++) {
# final boolean iopAtMin = log_iops_nn1[i] < (mi[i] + log_threshfak_oor);
# flags = BitSetter.setFlag(flags, i + firstIopMinFlagIndex, iopAtMin);
# }
#
#
return output
def write_BalticP_AC_Product(product, baltic__product_path, sensor, data_dict, singleBand_dict=None):
File = jpy.get_type('java.io.File')
width = product.getSceneRasterWidth()
height = product.getSceneRasterHeight()
bandShape = (height, width)
balticPACProduct = Product('balticPAC', 'balticPAC', width, height)
balticPACProduct.setFileLocation(File(baltic__product_path))
ProductUtils.copyGeoCoding(product, balticPACProduct)
ProductUtils.copyTiePointGrids(product, balticPACProduct)
if (sensor == 'OLCI'):
nbands = 21
band_name = ["Oa01_radiance"]
for i in range(1, nbands):
if (i < 9):
band_name += ["Oa0" + str(i + 1) + "_radiance"]
else:
band_name += ["Oa" + str(i + 1) + "_radiance"]
# Create empty bands for rhow, rhown, uncertainties for rhow
for i in range(nbands):
bsource = product.getBand(band_name[i]) # TOA radiance
for key in data_dict.keys():
brtoa_name = key + "_" + str(i + 1)
rtoaBand = balticPACProduct.addBand(brtoa_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rtoaBand)
rtoaBand.setNoDataValue(np.nan)
rtoaBand.setNoDataValueUsed(True)
dataNames = [*data_dict.keys()]
autoGroupingString = dataNames[0]
for key in dataNames[1:]:
autoGroupingString += ':' + key
balticPACProduct.setAutoGrouping(autoGroupingString)
if not singleBand_dict is None:
for key in singleBand_dict.keys():
singleBand = balticPACProduct.addBand(key, ProductData.TYPE_FLOAT32)
singleBand.setNoDataValue(np.nan)
singleBand.setNoDataValueUsed(True)
writer = ProductIO.getProductWriter('BEAM-DIMAP')
balticPACProduct.setProductWriter(writer)
balticPACProduct.writeHeader(baltic__product_path)
writer.writeProductNodes(balticPACProduct, baltic__product_path)
# set datarhow, rhown, uncertainties for rhow
for key in data_dict.keys():
x = data_dict[key].get('data')
if not x is None:
for i in range(nbands):
brtoa_name = key + "_" + str(i + 1)
rtoaBand = balticPACProduct.getBand(brtoa_name)
out = np.array(x[:, i]).reshape(bandShape)
rtoaBand.writeRasterData(0, 0, width, height, snp.ProductData.createInstance(np.float32(out)),
ProgressMonitor.NULL)
if not singleBand_dict is None:
for key in singleBand_dict.keys():
x = singleBand_dict[key].get('data')
if not x is None:
singleBand = balticPACProduct.getBand(key)
out = np.array(x).reshape(bandShape)
singleBand.writeRasterData(0, 0, width, height, snp.ProductData.createInstance(np.float32(out)),
ProgressMonitor.NULL)
# # Create flag coding
# raycorFlagsBand = balticPACProduct.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 = balticPACProduct.getFlagCodingGroup()
# group.add(raycorFlagCoding)
# raycorFlagsBand.setSampleCoding(raycorFlagCoding)
balticPACProduct.closeIO()
def apply_NN_to_scene(scene_path='', filename='', outpath='', sensor=''):
###
# Initialising a product for Reading with snappy
##
product = snp.ProductIO.readProduct(scene_path + filename)
###
# Read L1B product and convert Radiance to reflectance (if necessary).
# returns: numpy array with shape(pixels, wavelength).
###
refl = radianceToReflectance_Reader(product, sensor=sensor)
###
# classification of pixels
# returns: boolean array (pixels).
###
valid_L1 = check_valid_pixel_expression_L1(product, sensor)
###
# forwardNN: IOP to rhow
# input: numpy array iop, shape=(Npixels x iops= (log_apig, log_adet, log a_gelb, log_bpart, log_bwit)),
# np.array sza, shape = (Npixels,)
# np.array vza, shape = (Npixels,)
# np.array diff_azim, shape = (Npixels,); range: 0-180
# returns: numpy array, shape=(Npixels, wavelengths)
###
# Examples:
# apply_forwardNN_IOP_to_rhow_example()
apply_forwardNN_IOP_to_rhow_arrayExample(sensor, 1)
apply_forwardNN_IOP_to_rhow_arrayExample(sensor, 2)
###
# iop = np.zeros((2,5))
# # Please keep this order! Necessary for NN application : log_apig, log_adet, log a_gelb, log_bpart, log_bwit
# iop[0,:] = (-4.3414865, -4.956355, - 3.7658699 ,- 1.8608053, - 2.6944041)
# iop[1,:] = ( -4.5042415, -5.088239, -4.04009, -1.5932024, -3.473691)
# sza = np.array((51.091805, 51.3791))
# vza = np.array((8.41573, 10.97436))
# diff_azim = np.array((155.42921-106.33723, 154.92598-105.94441)) # between 0 - 180.
#
# rhow_mod = apply_forwardNN_IOP_to_rhow(iop, sza, vza, diff_azim, sensor)
###
# todo Normalisation
###
###
# Writing a product
# input: data_dict holds the band
###
baltic__product_path = outpath + 'baltic_' + filename
data_dict = {
'rtoa':{
'data': refl
},
'rhow':{
'data': None
},
'rhown': {
'data': None
},
'unc_rhow': {
'data': None
}
}
write_BalticP_AC_Product(product, baltic__product_path, sensor, data_dict)
product.closeProductReader()
def main(args=sys.argv[1:]):
if len(args) != 1:
print("usage: baltic_AC_simple <SENSOR>")
sys.exit(1)
sensor = args[0]
outpath = 'E:\Documents\projects\Baltic+\WP3_AC\\test_data\\results\\'
current_path = os.path.dirname(__file__)
path = current_path + '\\test_data\\'
###
# use SNAP with option S3TBX pixelGeoCoding turned off!
fnames = os.listdir(path)
fnames = [fn for fn in fnames if '.dim' in fn] # OLCI
print(len(fnames))
for fn in fnames[:1]:
print(fn)
apply_NN_to_scene(scene_path=path, filename=fn, outpath=outpath, sensor=sensor)
if __name__ == '__main__':
main()