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baltic_AC_forwardNN_TF.py
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baltic_AC_forwardNN_TF.py
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# -*- coding: utf-8 -*-
import collections
import datetime
import json
#from keras.models import load_model
import locale
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
from scipy.optimize import curve_fit, least_squares, minimize
import sys
import time
import glob
import tensorflow as tf
# snappy import
# sys.path.append("C:\\Users\Dagmar\.snap\snap-python")
sys.path.append("F:\Anaconda_envs\py310_keras3\Lib\site-packages\esa_snappy")
import esa_snappy as snp
from esa_snappy import Product
from esa_snappy import ProductData
# from snappy import ProductDataUTC
# from snappy import ProductIO
from esa_snappy import ProductUtils
from esa_snappy import ProgressMonitor
from esa_snappy import FlagCoding
from esa_snappy import jpy
from esa_snappy import GPF
from esa_snappy import HashMap
#from snappy import TimeCoding #org.esa.snap.core.datamodel.TimeCoding
from esa_snappy import PixelPos #org.esa.snap.core.datamodel.PixelPos
#fetchOzone = jpy.get_type('org.esa.s3tbx.c2rcc.ancillary.AncillaryCommons.fetchOzone')
AtmosphericAuxdata = jpy.get_type('eu.esa.opt.c2rcc.ancillary.AtmosphericAuxdata')
AtmosphericAuxdataBuilder = jpy.get_type('eu.esa.opt.c2rcc.ancillary.AtmosphericAuxdataBuilder')
TimeCoding = jpy.get_type('org.esa.snap.core.datamodel.TimeCoding')
AncDownloader = jpy.get_type('eu.esa.opt.c2rcc.ancillary.AncDownloader')
AncillaryCommons = jpy.get_type('eu.esa.opt.c2rcc.ancillary.AncillaryCommons')
AncRepository = jpy.get_type('eu.esa.opt.c2rcc.ancillary.AncRepository')
File = jpy.get_type('java.io.File')
AncDataFormat = jpy.get_type('eu.esa.opt.c2rcc.ancillary.AncDataFormat')
Calendar = jpy.get_type('java.util.Calendar')
# local import
from baltic_corrections import gas_correction, glint_correction, white_caps_correction, Rayleigh_correction, diffuse_transmittance, Rmolgli_correction_Hygeos, vicarious_calibration
import get_bands
from misc import default_ADF, nlinear, scale_minmax
import luts_olci, luts_msi
import lut_hygeos
from auxdata_handling import setAuxData, checkAuxDataAvailablity, getGeoPositionsForS2Product, yearAndDoyAndHourUTC
# Set locale for proper time reading with datetime
# locale.setlocale(locale.LC_ALL, 'en_US.UTF_8')
# Define NNs as global variables
def read_NNs(sensor, NNversion, NNIOPversion):
global nn_forward, nn_backward, nn_backward_iop
global nnForward_IO, nnBackward_IO, nnBackwardIOP_IO
global session
# Define paths of NNs
filePath = glob.glob(os.path.join('NNs',sensor,NNversion,'forward','*.h5'))
if filePath == []:
print("Missing NN in %s format in NNs/%s/%s/forward/"%('h5',sensor,NNversion))
sys.exit(1)
else:
nnForwardFilePath = filePath[0]
filePath = glob.glob(os.path.join('NNs',sensor,NNversion,'backward','*.h5'))
if filePath == []:
print("Missing NN in %s format in NNs/%s/%s/backward/"%('h5',sensor,NNversion))
sys.exit(1)
else:
nnBackwardFilePath = filePath[0]
filePath = glob.glob(os.path.join('NNs',sensor,NNIOPversion,'backward','*.h5'))
if filePath == []:
print("Missing NN in %s format in NNs/%s/%s/backward/"%('h5',sensor,NNIOPversion))
sys.exit(1)
else:
nnBackwardIOPFilePath = filePath[0]
# Open NNs
nn_forward = open_NN(nnForwardFilePath, NNversion)
nn_backward = open_NN(nnBackwardFilePath, NNversion)
nn_backward_iop = open_NN(nnBackwardIOPFilePath, NNIOPversion)
# Define paths of .net files containing the ranges
filePath = glob.glob(os.path.join('NNs',sensor,NNversion,'forward','*.net'))
if filePath == []:
print("Missing NN in %s format in NNs/%s/%s/forward/"%('net',sensor,NNversion))
sys.exit(1)
else:
nnForwardFilePathNet = filePath[0]
filePath = glob.glob(os.path.join('NNs',sensor,NNversion,'backward','*.net'))
if filePath == []:
print("Missing NN in %s format in NNs/%s/%s/backward/"%('net',sensor,NNversion))
sys.exit(1)
else:
nnBackwardFilePathNet = filePath[0]
filePath = glob.glob(os.path.join('NNs',sensor,NNIOPversion,'backward','*.net'))
if filePath == []:
print("Missing NN in %s format in NNs/%s/%s/backward/"%('net',sensor,NNIOPversion))
sys.exit(1)
else:
nnBackwardIOPFilePathNet = filePath[0]
# Define ranges for forward NN and backward NN
nnForward_IO = read_NN_IO_fromFile(nnForwardFilePathNet)
nnBackward_IO = read_NN_IO_fromFile(nnBackwardFilePathNet)
nnBackwardIOP_IO = read_NN_IO_fromFile(nnBackwardIOPFilePathNet)
def open_NN(nnFilePath, NNversion):
global session
# Open session for tf v1.X
TF_version = tf.__version__.split('.')[0]
if TF_version == '1':
if 'session' not in globals() or (session is None):
session = tf.InteractiveSession()
else:
session = None
# Read NNs
nn_object = tf.keras.models.load_model(nnFilePath)
return nn_object
def read_NN_IO_fromFile(nnFilePath):
""" Read input range in the .net file """
file = open(nnFilePath, 'r')
lines = [line.rstrip('\n') for line in file]
file.close()
Ninput = 0
Noutput = 0
for line in lines:
if 'the net has' in line:
if 'input' in line:
Ninput = int(line.split(' ')[3])
if 'output' in line:
Noutput = int(line.split(' ')[3])
inputVariables = []
inputRange = np.zeros((Ninput, 2))
outputRange = np.zeros((Noutput, 2))
bands = [] # Either input or output bands
iIN = 0
iOUT = 0
scaling = False
for line in lines:
if line.startswith('input'):
inputVariables.append(line.split('is ')[1].split(' ')[0])
t = line.split('[')[1][:-1]
t = t.split(',')
inputRange[iIN, 0] = float(t[0])
inputRange[iIN, 1] = float(t[1])
if 'log_rw_' in line:
bands.append(int(line.split('log_rw_')[1].split(' ')[0]))
iIN += 1
if line.startswith('output'):
t = line.split('[')[1][:-1]
outputRange[iOUT, 0] = float(t.split(',')[0])
outputRange[iOUT, 1] = float(t.split(',')[1])
if 'log_rw_' in line:
bands.append(int(line.split('log_rw_')[1].split(' ')[0]))
iOUT += 1
if line.startswith('scaling'):
scaling = line.split('is ')[1].split(' ')[0].lower() == 'true'
nnIO = {
'inputVariables': inputVariables,
'inputRange': inputRange,
'outputRange': outputRange,
'bands': bands,
'scaling': scaling
}
return collections.namedtuple('NN_IO', nnIO.keys())(*nnIO.values())
def get_band_or_tiePointGrid(product, name, dtype='float32', reshape=True, subset=None):
##
# 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)
##
if subset is None:
height = product.getSceneRasterHeight()
width = product.getSceneRasterWidth()
sline, eline, scol, ecol = 0, height-1, 0, width -1
else:
sline,eline,scol,ecol = subset
height = eline - sline + 1
width = ecol - scol + 1
var = np.zeros(width * height, dtype=dtype)
if name in list(product.getBandNames()):
product.getBand(name).readPixels(scol, sline, width, height, var)
elif name in list(product.getTiePointGridNames()):
var.shape = (height, width)
for i,iglob in enumerate(range(sline,eline+1)):
for j,jglob in enumerate(range(scol,ecol+1)):
var[i, j] = product.getTiePointGrid(name).getPixelDouble(jglob, iglob)
var.shape = (height*width)
else:
raise Exception('{}: neither a band nor a tie point grid'.format(name))
if reshape:
var.shape = (height, width)
return var
def Level1_Reader(product, sensor, band_group='radiance', reshape=True, subset=None):
input_label = []
if sensor == 'S2MSI':
input_label = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B10', 'B11', 'B12']
elif sensor == 'OLCI':
if band_group == 'radiance':
input_label = ["Oa%02d_radiance"%(i+1) for i in range(21)]
elif band_group == 'solar_flux':
input_label = ["solar_flux_band_%d"%(i+1) for i in range(21)]
elif band_group == 'lambda0':
input_label = ["lambda0_band_%d"%(i+1) for i in range(21)]
# Initialise and read all bands contained in input_label (pixel x band)
if subset is None:
height = product.getSceneRasterHeight()
width = product.getSceneRasterWidth()
else:
sline,eline,scol,ecol = subset
height = eline - sline + 1
width = ecol - scol + 1
var = np.zeros((width * height, len(input_label)))
for i, bn in enumerate(input_label):
var[:,i] = get_band_or_tiePointGrid(product, bn, reshape=reshape, subset=subset)
return var
def get_yday(product,reshape=True, subset=None):
# Get product size
height_full = product.getSceneRasterHeight()
width_full = product.getSceneRasterWidth()
if subset is None:
height = height_full
width = width_full
else:
sline,eline,scol,ecol = subset
height = eline - sline + 1
width = ecol - scol + 1
## time handling for match-up files
if str(product.getFileLocation()).endswith('.txt') or str(product.getFileLocation()).endswith('.csv'):
if not subset is None:
print('Error: subset option not compatible with match-up file')
sys.exit(1)
yday = np.zeros((height, width))
for y in range(height):
for x in range(width):
a = product.getSceneTimeCoding().getMJD(PixelPos(x + 0.5, y + 0.5))
year, yday[y, x], hour = yearAndDoyAndHourUTC(a)
else:
## time handling for a scene
# Get yday of each row (full scene)
dstart = datetime.datetime.strptime(str(product.getStartTime()),'%d-%b-%Y %H:%M:%S.%f')
dstop = datetime.datetime.strptime(str(product.getEndTime()),'%d-%b-%Y %H:%M:%S.%f')
dstart = np.datetime64(dstart)
dstop = np.datetime64(dstop)
dpix = dstart + (dstop-dstart)/float(height_full-1.)*np.arange(height_full)
yday = [k.timetuple().tm_yday for k in dpix.astype(datetime.datetime)]
# Limit to subset
if subset:
yday = yday[sline:eline+1]
# Apply to all columns
yday = np.array(yday*width).reshape(width,height).transpose()
if not reshape:
yday = np.ravel(yday)
return yday
def radianceToReflectance_Reader(product, sensor = '', print_info=False, subset=None):
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',reshape=False, subset=subset)
solar_flux = Level1_Reader(product, sensor, band_group='solar_flux',reshape=False, subset=subset)
SZA = get_band_or_tiePointGrid(product, 'SZA', reshape=False, subset=subset)
refl = rad * np.pi / (solar_flux * np.cos(SZA.reshape(rad.shape[0],1)*np.pi/180.))
elif sensor == 'S2MSI':
refl = Level1_Reader(product, sensor,reshape=False, subset=subset)
return refl
def angle_Reader(product, sensor, subset=None):
if sensor == 'OLCI':
oaa = get_band_or_tiePointGrid(product, 'OAA', reshape=False, subset=subset)
oza = get_band_or_tiePointGrid(product, 'OZA', reshape=False, subset=subset)
saa = get_band_or_tiePointGrid(product, 'SAA', reshape=False, subset=subset)
sza = get_band_or_tiePointGrid(product, 'SZA', reshape=False, subset=subset)
elif sensor == 'S2MSI':
oaa = get_band_or_tiePointGrid(product, 'view_azimuth_mean', reshape=False, subset=subset)
oza = get_band_or_tiePointGrid(product, 'view_zenith_mean', reshape=False, subset=subset)
saa = get_band_or_tiePointGrid(product, 'sun_azimuth', reshape=False, subset=subset)
sza = get_band_or_tiePointGrid(product, 'sun_zenith', reshape=False, subset=subset)
return oaa, oza, saa, sza
def calculate_diff_azim(oaa, saa):
###
# MERIS/OLCI ground segment definition
raa = np.degrees(np.arccos(np.cos(np.radians(oaa - saa))))
###
# azimuth difference as input to the NN is defined in a range between 0° and 180°
## from c2rcc JAVA implementation
nn_raa = np.abs(180 + oaa - saa)
ID = np.array(nn_raa > 180)
if np.sum(ID) > 0:
nn_raa = 360 - nn_raa
return raa, nn_raa
def run_IdePix_processor(product, sensor):
### todo: check, if L1 flags are given as band 'quality_flags'
# (in CalValus extracts this single band representation of the flags might be missing.)
# calculate single band 'quality_flags' if necessary.
# invoke IdePix.
# define valid pixel expression.
idepixParameters = HashMap()
if product.getProductType() == 'CSV':
idepixParameters.put("computeCloudBuffer", 'false')
idepixParameters.put("computeCloudShadow", 'false')
else:
idepixParameters.put("computeCloudBuffer", 'true')
idepixParameters.put("cloudBufferWidth", '2')
idepixProducts = HashMap()
# idepixProducts.put("l1bProduct", product) # SNAP v6 ?
idepixProducts.put("sourceProduct", product)
idepix_product = None
if sensor == 'OLCI':
#idepix_product = GPF.createProduct("Idepix.Sentinel3.Olci", idepixParameters, product) # SNAP v6
idepix_product = GPF.createProduct("Idepix.Olci", idepixParameters, idepixProducts) # SNAP v7
elif sensor == 'S2MSI':
idepixParameters.put("computeCloudBufferForCloudAmbiguous", 'true')
idepix_product = GPF.createProduct("Idepix.S2", idepixParameters, idepixProducts) # SNAP v7
return idepix_product
def check_valid_pixel_expression_Idepix(product, sensor, subset=None):
valid_pixel_flag = None
if subset is None:
height = product.getSceneRasterHeight()
width = product.getSceneRasterWidth()
sline = 0
scol = 0
else:
sline,eline,scol,ecol = subset
height = eline - sline + 1
width = ecol - scol + 1
# quality_flags = np.zeros(width * height, dtype='uint32')
quality_flags = np.zeros(width * height, dtype='uint64')
product.getBand('pixel_classif_flags').readPixels(scol, sline, width, height, quality_flags)
if sensor == 'OLCI':
# Masks OLCI Idepix
invalid_mask = np.bitwise_and(quality_flags, 2 ** 0) == 2 ** 0
cloud_mask = np.bitwise_and(quality_flags, 2 ** 1) == 2 ** 1
cloudbuffer_mask = np.bitwise_and(quality_flags, 2 ** 4) == 2 ** 4
snowice_mask = np.bitwise_and(quality_flags, 2 ** 6) == 2 ** 6
invalid_mask = np.logical_or(np.logical_or(invalid_mask, snowice_mask), np.logical_or(cloud_mask, cloudbuffer_mask))
valid_pixel_flag = np.logical_not(invalid_mask)
if sensor == 'S2MSI':
# Masks S2MSI Idepix
invalid_mask = np.bitwise_and(quality_flags, 2 ** 0) == 2 ** 0
cloud_mask = np.bitwise_and(quality_flags, 2 ** 1) == 2 ** 1
cloudbuffer_mask = np.bitwise_and(quality_flags, 2 ** 4) == 2 ** 4
snowice_mask = np.bitwise_and(quality_flags, 2 ** 6) == 2 ** 6
invalid_mask = np.logical_or(np.logical_or(invalid_mask, snowice_mask),
np.logical_or(cloud_mask, cloudbuffer_mask))
valid_pixel_flag = np.logical_not(invalid_mask)
return valid_pixel_flag
def check_valid_pixel_expression_L1(product, sensor, subset=None):
valid_pixel_flag = None
if subset is None:
height = product.getSceneRasterHeight()
width = product.getSceneRasterWidth()
sline = 0
scol = 0
else:
sline,eline,scol,ecol = subset
height = eline - sline + 1
width = ecol - scol + 1
if sensor == 'OLCI':
# quality_flags = np.zeros(width * height, dtype='uint32')
quality_flags = np.zeros(width * height, dtype='uint64')
# match-up extractions from Calvalus do not have a 'quality_flags' band
if product.getBand('quality_flags') is None:
invalid_mask = np.zeros(width * height, dtype='uint32')
product.getBand('quality_flags.invalid').readPixels(scol, sline, width, height, invalid_mask)
land_mask = np.zeros(width * height, dtype='uint32')
product.getBand('quality_flags.land').readPixels(scol, sline, width, height, land_mask)
coastline_mask = np.zeros(width * height, dtype='uint32')
product.getBand('quality_flags.coastline').readPixels(scol, sline, width, height, coastline_mask)
inland_mask = np.zeros(width * height, dtype='uint32')
product.getBand('quality_flags.fresh_inland_water').readPixels(scol, sline, width, height, inland_mask)
land_mask = coastline_mask | (land_mask & ~inland_mask)
bright_mask = np.zeros(width * height, dtype='uint32')
product.getBand('quality_flags.bright').readPixels(scol, sline, width, height, bright_mask)
invalid_mask = np.logical_or(invalid_mask, np.logical_or(land_mask, bright_mask))
valid_pixel_flag = np.logical_not(invalid_mask)
else:
product.getBand('quality_flags').readPixels(scol, sline, width, height, quality_flags)
# 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
## todo: could also include simple bright NIR test: rho_toa(865nm) < 0.2
invalid_mask = np.bitwise_and(quality_flags, 2 ** 25) == 2 ** 25
land_mask = np.bitwise_and(quality_flags, 2 ** 31) == 2 ** 31
coastline_mask = np.bitwise_and(quality_flags, 2 ** 30) == 2 ** 30
inland_mask = np.bitwise_and(quality_flags, 2 ** 29) == 2 ** 29
land_mask = coastline_mask | (land_mask & ~inland_mask)
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 == 'S2MSI':
#TODO: set valid pixel expression L1C S2
valid_pixel_flag = np.ones(width * height, dtype='uint32')
b8 = get_band_or_tiePointGrid(product, 'B8', reshape=False, subset=subset)
valid_pixel_flag = np.logical_and(np.array(b8 > 0), np.array(b8 < 0.1))
return valid_pixel_flag
def apply_forwardNN_TF(log_iop, sun_zenith, view_zenith, diff_azimuth, valid):
"""
Apply the forwardNN: IOP to rhow
input: numpy array log_iop, shape = (Npixels x log_iops= (log_apig, log_adet, log a_gelb, log_bpart, log_bwit)),
np.array sza, shape = (Npixels,)
np.array oza, shape = (Npixels,)
np.array raa, shape = (Npixels,); range: 0-180
returns: np.array rhow, shape = (Npixels, wavelengths)
"""
# Initialise output
nBands = len(iband_forwardNN)
rhow = np.zeros((log_iop.shape[0], nBands)) + np.NaN
# Initialise input
nInput = 3 + 5 # angle + IOPs
if 'temperature' in nnForward_IO.inputVariables:
nInput +=1
if 'salinity' in nnForward_IO.inputVariables:
nInput += 1
NN_input = np.zeros((log_iop.shape[0], nInput)) -1. # CARE -1 used by default for IOP when niop < 5
# Prepare the NN
if 'cos_' in nnForward_IO.inputVariables[0]:
NN_input[:,0] = np.cos(sun_zenith * np.pi / 180.)
NN_input[:,1] = np.cos(view_zenith * np.pi / 180.)
NN_input[:,2] = np.cos(diff_azimuth * np.pi / 180.)
else:
NN_input[:, 0] = sun_zenith
NN_input[:, 1] = view_zenith
NN_input[:, 2] = diff_azimuth
i_input = 3
if 'temperature' in nnForward_IO.inputVariables:
NN_input[:,i_input] = 15. # default temperature
i_input += 1
if 'salinity' in nnForward_IO.inputVariables:
NN_input[:,i_input] = 35. # default salinity
i_input += 1
for i in range(log_iop.shape[1]):
NN_input[:,i_input+i] = log_iop[:,i]
# Scale inputs if necessary, first constraining in the range
if nnForward_IO.scaling:
NN_input[:,i_input:] = check_and_constrain_iop(NN_input[:,i_input:], nnForward_IO)
NN_input = scale_minmax(NN_input, nnForward_IO.inputRange)
# Limit to valid pixels
NN_input = np.array(NN_input[valid, :], dtype='float32')
# Launch NN
if session is not None:
log_rhow = nn_forward(NN_input).eval(session=session)
else:
log_rhow = nn_forward(NN_input)
# Scale outputs if necessary
if nnForward_IO.scaling:
log_rhow = scale_minmax(np.array(log_rhow), nnForward_IO.outputRange, reverse=True)
# Get NN output
for i in range(nBands):
rhow[valid,i] = np.exp(log_rhow[:,i])
return rhow
def apply_backwardNN_TF(rhow, sun_zenith, view_zenith, diff_azimuth, valid, NN_IO, NNIOP=False):
"""
Apply the backwardNN: rhow to IOP
input: numpy array rhow, shape = (Npixels x rhow = (log_rw_band1, log_rw_band2_.... )),
np.array sza, shape = (Npixels,)
np.array oza, shape = (Npixels,)
np.array raa, shape = (Npixels,); range: 0-180
returns: np.array log_iop, shape = (Npixels x log_iop= (log_apig, log_adet, log a_gelb, log_bpart, log_bwit))
T, S: currently constant #TODO take ECMWF temperature at sea surface?
"""
# Initialise output
log_iop = np.zeros((rhow.shape[0], 5))
log_iop[:] = NN_IO.outputRange[:,0] # set default values, for invalid NN inversion (rhow<0)
# Initialise input
rhow_pos = np.all(rhow>0, axis=1) # limit to rhow >0
valid2 = valid & rhow_pos
nvalid = np.count_nonzero(valid2)
nInput = 3 + rhow.shape[1] # angle + log_rw
if 'temperature' in NN_IO.inputVariables:
nInput +=1
if 'salinity' in NN_IO.inputVariables:
nInput += 1
NN_input = np.zeros((nvalid, nInput), dtype='float32')
# Prepare the NN
if 'cos_' in NN_IO.inputVariables[0]:
NN_input[:,0] = np.cos(sun_zenith[valid2] * np.pi / 180.)
NN_input[:,1] = np.cos(view_zenith[valid2] * np.pi / 180.)
NN_input[:,2] = np.cos(diff_azimuth[valid2] * np.pi / 180.)
else:
NN_input[:, 0] = sun_zenith[valid2]
NN_input[:, 1] = view_zenith[valid2]
NN_input[:, 2] = diff_azimuth[valid2]
i_input = 3
if 'temperature' in NN_IO.inputVariables:
NN_input[:,i_input] = 15. # default temperature
i_input += 1
if 'salinity' in NN_IO.inputVariables:
NN_input[:,i_input] = 35. # default salinity
i_input += 1
NN_input[:,i_input:] = np.log(rhow[valid2,:])
# Scale inputs if necessary, , first constraining log_rw in the range
if NN_IO.scaling:
NN_input[:,i_input:] = check_and_constrain_rw(NN_input[:,i_input:], NN_IO)
NN_input = scale_minmax(NN_input, NN_IO.inputRange)
# Launch the NN
if NNIOP:
if session is not None:
log_iop[valid2,:] = nn_backward_iop(NN_input).eval(session=session)
else:
log_iop[valid2,:] = nn_backward_iop(NN_input)
else:
if session is not None:
log_iop[valid2,:] = nn_backward(NN_input).eval(session=session)
else:
log_iop[valid2,:] = nn_backward(NN_input)
# Scale outputs if necessary
if NN_IO.scaling:
log_iop[valid2,:] = scale_minmax(log_iop[valid2,:], NN_IO.outputRange, reverse=True)
return log_iop
def write_BalticP_AC_Product(product, baltic__product_path, sensor, spectral_dict, scalar_dict=None,
copyOriginalProduct=False, outputProductFormat="BEAM-DIMAP", addname='',
add_Idepix_Flags=False, idepixProduct=None, add_L2Flags=False, L2FlagArray=None,
add_Geometry=False, subset=None):
# Initialise the output product
File = jpy.get_type('java.io.File')
width = product.getSceneRasterWidth()
height = product.getSceneRasterHeight()
bandShape = (height, width)
if subset is None:
height_subset = height
width_subset = width
sline, eline, scol, ecol = 0, height-1, 0, width -1
else:
sline,eline,scol,ecol = subset
height_subset = eline - sline + 1
width_subset = ecol - scol + 1
bandShape_subset = (height_subset, width_subset)
dirname = os.path.dirname(baltic__product_path)
outname, ext = os.path.splitext(os.path.basename(baltic__product_path))
if outputProductFormat == "BEAM-DIMAP":
baltic__product_path = os.path.join(dirname, outname + addname +'.dim')
elif outputProductFormat == 'CSV':
baltic__product_path = os.path.join(dirname, outname + addname +'.csv')
balticPACProduct = Product('balticPAC', 'balticPAC', width, height)
balticPACProduct.setFileLocation(File(baltic__product_path))
# Copy geocoding
ProductUtils.copyGeoCoding(product, balticPACProduct)
# replacement by Tonio below does not work, geocoding is on the full image, not subset
"""# PixelSubsetRegion = jpy.get_type('org.esa.snap.core.subset.PixelSubsetRegion')
ProductSubsetDef = jpy.get_type('org.esa.snap.core.dataio.ProductSubsetDef')
# subset_region = PixelSubsetRegion(scol, sline, ecol, eline)
subset_def = ProductSubsetDef()
subset_def.setRegion(scol, sline, width, height)
product.transferGeoCodingTo(balticPACProduct, subset_def)"""
# writer = ProductIO.getProductWriter(outputProductFormat)
# writer.writeProductNodes(balticPACProduct, baltic__product_path)
# Define total number of bands (TOA)
if (sensor == 'OLCI'):
nbands = 21
elif (sensor == 'S2MSI'):
nbands = 13
for key in spectral_dict.keys():
data = spectral_dict[key].get('data')
if not data is None:
nbands_key = data.shape[-1]
if outputProductFormat == 'BEAM-DIMAP':
if sensor == 'OLCI':
bsources = [product.getBand("Oa%02d_radiance" % (i + 1)) for i in range(nbands)]
elif sensor == 'S2MSI':
bsources = [product.getBand("B%d" % (i + 1)) for i in range(8)]
bsources.append(product.getBand('B8A'))
[bsources.append(product.getBand("B%d" % (i + 9))) for i in range(4)]
sourceData = np.ndarray(bandShape,dtype='float32') + np.nan # create unique instance to avoid MemoryError
for i in range(nbands_key):
brtoa_name = key + "_" + str(i + 1)
# print(brtoa_name)
band = balticPACProduct.addBand(brtoa_name, ProductData.TYPE_FLOAT32)
if outputProductFormat == 'BEAM-DIMAP':
ProductUtils.copySpectralBandProperties(bsources[i], band)
band.setNoDataValue(np.nan)
band.setNoDataValueUsed(True)
sourceData[sline:eline+1,scol:ecol+1] = data[:, i].reshape(bandShape_subset).astype('float32')
band.setRasterData(ProductData.createInstance(sourceData))
# Create empty bands for scalar fields
if not scalar_dict is None:
sourceData = np.ndarray(bandShape,dtype='float32') + np.nan # create unique instance to avoid MemoryError
for key in scalar_dict.keys():
singleBand = balticPACProduct.addBand(key, ProductData.TYPE_FLOAT32)
singleBand.setNoDataValue(np.nan)
singleBand.setNoDataValueUsed(True)
data = scalar_dict[key].get('data')
if not data is None:
sourceData[sline:eline+1,scol:ecol+1] = data.reshape(bandShape_subset).astype('float32')
singleBand.setRasterData(ProductData.createInstance(sourceData))
if copyOriginalProduct:
originalBands = product.getBandNames()
balticBands = balticPACProduct.getBandNames()
for bb in balticBands:
originalBands = [ob for ob in originalBands if ob != bb]
for ob in originalBands:
singleBand = balticPACProduct.addBand(ob, ProductData.TYPE_FLOAT32)
singleBand.setNoDataValue(np.nan)
singleBand.setNoDataValueUsed(True)
data = get_band_or_tiePointGrid(product,ob)
sourceData = data.reshape(bandShape).astype('float32')
singleBand.setRasterData(ProductData.createInstance(sourceData))
if add_Idepix_Flags:
flagBand = balticPACProduct.addBand('pixel_classif_flags', ProductData.TYPE_INT32)
flagBand.setDescription('Idepix flag information')
flagBand.setNoDataValue(np.nan)
flagBand.setNoDataValueUsed(True)
data = get_band_or_tiePointGrid(idepixProduct, 'pixel_classif_flags')
sourceData = np.array(data, dtype='int32').reshape(bandShape)
flagBand.setRasterData(ProductData.createInstance(sourceData))
idepixFlagCoding = FlagCoding('pixel_classif_flags')
flagNames = list(idepixProduct.getAllFlagNames())
IDflags = 'pixel_classif_flags'
flagNames = [fn[(len(IDflags)+1):] for fn in flagNames if IDflags in fn]
for i, fn in enumerate(flagNames):
idepixFlagCoding.addFlag(fn, 2**i, fn)
balticPACProduct.getFlagCodingGroup().add(idepixFlagCoding)
flagBand.setSampleCoding(idepixFlagCoding)
if add_L2Flags:
flagBand = balticPACProduct.addBand('baltic_L2_flags', ProductData.TYPE_INT32)
flagBand.setDescription('L2 flag information for the baltic+ AC')
flagBand.setNoDataValue(np.nan)
flagBand.setNoDataValueUsed(True)
sourceData = np.array(L2FlagArray, dtype='int32').reshape(bandShape)
flagBand.setRasterData(ProductData.createInstance(sourceData))
L2FlagCoding = FlagCoding('baltic_L2_flags')
flagNames = ['OOR_NN_IOP', 'OOR_NN_normalisation', 'NELDER_MEAD_FAIL']
flagDescription = ['input IOPs to forwardNN out of range. at least one IOP has been constrained.',
'input rho_w to NormalisationNN out of range. at least one rho_w has been constrained.',
'Nelder-Mead Optimisation failed.']
#IDflags = 'baltic_L2_flags'
#flagNames = [fn[(len(IDflags) + 1):] for fn in flagNames if IDflags in fn]
i = 0
for fn, dscr in zip(flagNames, flagDescription):
L2FlagCoding.addFlag(fn, 2 ** i, dscr)
i += 1
balticPACProduct.getFlagCodingGroup().add(L2FlagCoding)
flagBand.setSampleCoding(L2FlagCoding)
if add_Geometry and not copyOriginalProduct:
oaa, oza, saa, sza = angle_Reader(product, sensor, subset=subset)
if sensor == 'OLCI':
geomNames = ['OAA', 'OZA', 'SAA', 'SZA']
elif sensor == 'S2MSI':
geomNames = ['view_azimuth_mean', 'view_zenith_mean', 'sun_azimuth', 'sun_zenith']
dataList = [oaa, oza, saa, sza]
sourceData = np.ndarray(bandShape,dtype='float32') + np.nan # create unique instance to avoid MemoryError
for gn, data in zip(geomNames, dataList):
singleBand = balticPACProduct.addBand(gn, ProductData.TYPE_FLOAT32)
singleBand.setNoDataValue(np.nan)
singleBand.setNoDataValueUsed(True)
sourceData[sline:eline+1,scol:ecol+1] = data.reshape(bandShape_subset)
singleBand.setRasterData(ProductData.createInstance(sourceData))
if outputProductFormat == 'BEAM-DIMAP':
# Set auto grouping
autoGroupingString = ':'.join(spectral_dict.keys())
balticPACProduct.setAutoGrouping(autoGroupingString)
GPF.writeProduct(balticPACProduct, File(baltic__product_path), outputProductFormat, False, ProgressMonitor.NULL)
balticPACProduct.closeIO()
def polymer_matrix(bands_sat,bands,valid,rho_g,rho_r,sza,oza,wavelength):
"""
Compute matrices associated to the polynomial modelling of the atmosphere (POLYMER)
Care: direct matrice (forward model) is for 'bands_sat', while inverse (backward model) is limited to 'bands'
"""
# Define matrix of polynomial modelling (c0 T0 + c1 lambda^-1 + c2 rho_R)
ncoef = 3
nband = len(bands)
nband_sat = len(bands_sat)
npix = rho_g.shape[0]
Aatm = np.zeros((npix, nband_sat, ncoef), dtype='float32') # Care, defined for bands_sat
Aatm_inv = np.zeros((npix, ncoef, nband), dtype='float32') # Care, defined for only bands
# Indices of bands_corr in all bands
iband = np.searchsorted(bands_sat, bands)
# Compute T0
taum = 0.00877*((wavelength/1000.)**(-4.05))
air_mass = 1./np.cos(np.radians(sza))+1./np.cos(np.radians(oza))
rho_g0 = 0.02
factor = (1-0.5*np.exp(-rho_g/rho_g0))*air_mass
T0 = np.exp(-taum*factor[:,None])
Aatm[:,:,0] = T0
Aatm[:,:,1] = (wavelength/1000.)**-1.
Aatm[:,:,2] = rho_r
# Compute pseudo inverse: A* = ((A'.A)^(-1)).A' /!\ limited to bands
Aatm_corr = Aatm[:,iband,:]
Aatm_inv[valid] = np.linalg.pinv(Aatm_corr[valid])
return Aatm, Aatm_inv
def check_and_constrain_iop(log_iop, NN_IO):
for i, var in enumerate(['log_apig', 'log_adet', 'log_agelb', 'log_bpart', 'log_bwit']):
j = NN_IO.inputVariables.index(var)
mi = NN_IO.inputRange[j,0]
ma = NN_IO.inputRange[j,1]
log_iop[:, i][log_iop[:, i] < mi] = mi
log_iop[:, i][log_iop[:, i] > ma] = ma
return log_iop
def check_and_constrain_rw(log_rw, NN_IO):
for i, band in enumerate(NN_IO.bands):
var = 'log_rw_%d'%band
j = NN_IO.inputVariables.index(var)
mi = NN_IO.inputRange[j,0]
ma = NN_IO.inputRange[j,1]
log_rw[:, i][log_rw[:, i] < mi] = mi
log_rw[:, i][log_rw[:, i] > ma] = ma
return log_rw
def baltic_AC(scene_path='', filename='', outpath='', sensor='', platform='', subset=None, addName = '', outputSpectral=None,
outputScalar=None, correction='HYGEOS', copyOriginalProduct=False, outputProductFormat="BEAM-DIMAP",
atmosphericAuxDataPath = None, niop=5, add_Idepix_Flags=True, add_L2Flags=False, add_c2rccIOPs=False,
runAC=True, NNversion='v1_baltic+', NNIOPversion='c2rcc_20171221'):
"""
Main function to run the Baltic+ AC based on forward NN
correction: 'HYGEOS' or 'IPF' for Rayleigh+glint correction
"""
# Read the NNs
read_NNs(sensor, NNversion, NNIOPversion)
# Get sensor & NN bands
global bands_sat, bands_corr, bands_chi2, bands_forwardNN, bands_backwardNN, bands_abs
global iband_corr, iband_chi2, iband_forwardNN, iband_backwardNN, iband_backwardNNIOP, iband_abs
bands_sat, bands_corr, bands_chi2, bands_abs = get_bands.main(sensor)
nbands = len(bands_sat)
# Get band of NNs
bands_forwardNN = np.array(nnForward_IO.bands)
bands_backwardNN = np.array(nnBackward_IO.bands)
bands_backwardNNIOP = np.array(nnBackwardIOP_IO.bands)
# Check NNs bands
for NN, band_set in zip(['forward_NN','backward_NN','backward_NNIOP'], [bands_forwardNN, bands_backwardNN, bands_backwardNNIOP]):
if not set(band_set).issubset(set(bands_sat)):
print("Error: bands of %s does not match sensor band:"%NN, band_set)
sys.exit(1)
# Check bands_corr and band_chi2 are provided by forward NN. If not, take intersection
bands_AC = {
'bands_corr': bands_corr,
'bands_chi2': bands_chi2
}
for band_set, bands_value in zip(bands_AC, bands_AC.values()):
if not set(bands_value).issubset(set(bands_forwardNN)):
print("WARNING: %s is not a subset of bands_forwardNN:"%band_set)
# Searching intersection
bands_value = list(set(bands_value) & set(bands_forwardNN))
if len(bands_value) == 0:
print(" Intersection empty between %s and bands_forwardNN - Aborted"%band_set)
sys.exit(1)
else:
bands_value.sort()
bands_AC[band_set] = bands_value
print(" %s limited to "%band_set, bands_AC[band_set])
bands_corr = bands_AC['bands_corr']
bands_chi2 = bands_AC['bands_chi2']
# Identify index of bands
iband_corr = np.searchsorted(bands_sat, bands_corr)
iband_chi2 = np.searchsorted(bands_sat, bands_chi2)
iband_forwardNN = np.searchsorted(bands_sat, bands_forwardNN)
iband_backwardNN = np.searchsorted(bands_sat, bands_backwardNN)
iband_backwardNNIOP = np.searchsorted(bands_sat, bands_backwardNNIOP)
iband_abs = np.searchsorted(bands_sat, bands_abs)
# Initialising a product for Reading with snappy
product = snp.ProductIO.readProduct(os.path.join(scene_path, filename))
if sensor=='OLCI' and product.getProductType()=='CSV':
product.setProductType('OL_1_')
# Resampling S2MSI to 60m
if sensor == "S2MSI" and product.isMultiSize():
print( "Resample MSI data")
parameters = HashMap()
parameters.put('resolution', '60')
parameters.put('upsampling', 'Bicubic')
parameters.put('downsampling', 'Mean') #
parameters.put('flagDownsampling', 'FlagOr') # 'First', 'FlagAnd'
product = GPF.createProduct('S2Resampling', parameters, product)
# Get scene size
if subset is None:
height = product.getSceneRasterHeight()
width = product.getSceneRasterWidth()
else:
sline,eline,scol,ecol = subset
height = eline - sline + 1
width = ecol - scol + 1
npix = width*height
# Read L1B product and convert Radiance to reflectance
rho_toa = radianceToReflectance_Reader(product, sensor=sensor, subset=subset)
# Read geometry and compute relative azimuth angle
oaa, oza, saa, sza = angle_Reader(product, sensor, subset=subset)
raa, nn_raa = calculate_diff_azim(oaa, saa)
# Classify pixels with Level-1 flags
valid = check_valid_pixel_expression_L1(product, sensor, subset=subset)
# Check valid geometry (necessary for S2MSI)
valid[np.isnan(oza)] = False
valid[np.isnan(sza)] = False
valid[np.isnan(raa)] = False
print("%d valid pixels on %d"%(np.sum(valid), len(valid)))
# Apply Idepix
if add_Idepix_Flags:
print("Launch Idepix")
if product.getBand('quality_flags') is None: #Idepix needs a band of this name to run. L1-flags are evaluated st a different step, so values can be zero here.
band = product.addBand('quality_flags', ProductData.TYPE_INT32)
band.setNoDataValue(np.nan)
band.setNoDataValueUsed(True)
sourceData = np.zeros((product.getSceneRasterHeight(), product.getSceneRasterWidth()), dtype='uint32') # Idepix launched on full scene, not subset
band.setRasterData(ProductData.createInstance(sourceData))
idepixProduct = run_IdePix_processor(product, sensor)
validIdepix = check_valid_pixel_expression_Idepix(idepixProduct, sensor, subset=subset)
print('Idepix valid', np.sum(validIdepix))
valid = np.logical_and(valid, validIdepix)
print('total valid', np.sum(valid))
else:
idepixProduct=None
# Read wavelength (per-pixel for OLCI) and geolocation
if sensor == 'OLCI':
# Read per-pixel wavelength
wavelength = Level1_Reader(product, sensor, 'lambda0', reshape=False, subset=subset)
## if wavelength == -1: spectrum is invald!
ID = np.all(wavelength>0., axis=1)
valid = np.logical_and(ID, valid)
print('total valid 2', np.sum(valid))
# Read latitude, longitude
latitude = get_band_or_tiePointGrid(product, 'latitude', reshape=False, subset=subset)
longitude = get_band_or_tiePointGrid(product, 'longitude', reshape=False, subset=subset)
elif sensor == 'S2MSI':
# Duplicate wavelengths for all pixels
wavelength = np.tile(bands_sat,(len(sza),1)) #TODO: integrate band with S2 SRF
# Read latitude, longitude
latitude, longitude = getGeoPositionsForS2Product(product, reshape=False, subset=subset)
# Read day in the year
yday = get_yday(product, reshape=False, subset=subset)
# Read meteo data
print( "Read meteo data")
if sensor == 'OLCI':
pressure = get_band_or_tiePointGrid(product, 'sea_level_pressure', reshape=False, subset=subset)
ozone = get_band_or_tiePointGrid(product, 'total_ozone', reshape=False, subset=subset) / 2.1415e-5 # convert kg/m2 to DU
tcwv = get_band_or_tiePointGrid(product, 'total_columnar_water_vapour', reshape=False, subset=subset)
wind_u = get_band_or_tiePointGrid(product, 'horizontal_wind_vector_1', reshape=False, subset=subset)
wind_v = get_band_or_tiePointGrid(product, 'horizontal_wind_vector_2', reshape=False, subset=subset)
windm = np.sqrt(wind_u*wind_u+wind_v*wind_v)
altitude = get_band_or_tiePointGrid(product, 'altitude', reshape=False, subset=subset)
elif sensor == 'S2MSI':
bandNames = list(product.getBandNames())
## only for match-up data with included meteorology.
if ('pressure' in bandNames) and ('ozone' in bandNames) and ('tcwv' in bandNames)\
and ('wind_u' in bandNames) and ('wind_v' in bandNames):
pressure = get_band_or_tiePointGrid(product, 'pressure', reshape=False, subset=subset)
ozone = get_band_or_tiePointGrid(product, 'ozone', reshape=False, subset=subset)
tcwv = get_band_or_tiePointGrid(product, 'tcwv', reshape=False, subset=subset)
wind_u = get_band_or_tiePointGrid(product, 'wind_u', reshape=False, subset=subset)
wind_v = get_band_or_tiePointGrid(product, 'wind_v', reshape=False, subset=subset)
windm = np.sqrt(wind_v * wind_v + wind_u * wind_u)
altitude = get_band_or_tiePointGrid(product, 'altitude', reshape=False, subset=subset)
else:
if atmosphericAuxDataPath != None:
# Compute aux data (one unique value per scene)
AuxFullFilePath_dict = checkAuxDataAvailablity(atmosphericAuxDataPath, product=product)
AuxDataDict = setAuxData(product, AuxFullFilePath_dict)
# Apply values to the whole image