L1C_P.py 41 KB
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# -*- coding: utf-8 -*-
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"""Level 1C Processor:   Atmospheric correction of TOA-reflectance data."""
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import warnings
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import re
import logging
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import dill
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import traceback
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from typing import List  # noqa F401  # flake8 issue
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import numpy as np
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from geoarray import GeoArray
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from py_tools_ds.geo.map_info import mapinfo2geotransform
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from ..config import GMS_config as CFG
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from . import geoprocessing as GEOP
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from .L1B_P import L1B_object
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from ..model.metadata import get_LayerBandsAssignment
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from ..misc.definition_dicts import get_outFillZeroSaturated, proc_chain, get_mask_classdefinition
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from ..io.input_reader import SRF
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# from .cloud_masking import Cloud_Mask_Creator  # circular dependencies
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from sicor.sicor_ac import ac_gms
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from sicor.sensors import RSImage
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from sicor.Mask import S2Mask
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__author__ = 'Daniel Scheffler'

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class L1C_object(L1B_object):
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    def __init__(self, L1B_obj=None):
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        super(L1C_object, self).__init__()
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        if L1B_obj:
            # populate attributes
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            [setattr(self, key, value) for key, value in L1B_obj.__dict__.items()]
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        # private attributes
        self._VZA_arr = None
        self._VAA_arr = None
        self._SZA_arr = None
        self._SAA_arr = None
        self._RAA_arr = None
        self._lonlat_arr = None

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        self.proc_level = 'L1C'
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    @property
    def lonlat_arr(self):
        """Calculates pixelwise 2D-array with longitude and latitude coordinates.
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        :return:
        """
        if self._lonlat_arr is None:
            self.logger.info('Calculating LonLat array...')
            self._lonlat_arr = \
                GEOP.get_lonlat_coord_array(self.shape_fullArr, self.arr_pos,
                                            mapinfo2geotransform(self.meta_odict['map info']),
                                            self.meta_odict['coordinate system string'],
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                                            meshwidth=10,  # for faster processing
                                            nodata_mask=None,  # dont overwrite areas outside the image with nodata
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                                            outFill=get_outFillZeroSaturated(np.float32)[0])[0]
        return self._lonlat_arr
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    @lonlat_arr.setter
    def lonlat_arr(self, lonlat_arr):
        self._lonlat_arr = lonlat_arr
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    @property
    def VZA_arr(self):
        """Get viewing zenith angle.

        :return:
        """
        if self._VZA_arr is None:
            self.logger.info('Calculating viewing zenith array...')
            if 'ViewingAngle_arrProv' in self.meta_odict and self.meta_odict['ViewingAngle_arrProv']:
                # Sentinel-2
                self._VZA_arr = GEOP.adjust_acquisArrProv_to_shapeFullArr(self.meta_odict['ViewingAngle_arrProv'],
                                                                          self.shape_fullArr,
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                                                                          meshwidth=10,  # for faster processing
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                                                                          subset=None,
                                                                          bandwise=0)
            else:
                self._VZA_arr = GEOP.calc_VZA_array(self.shape_fullArr, self.arr_pos, self.fullSceneCornerPos,
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                                                    float(self.meta_odict['ViewingAngle']),
                                                    float(self.meta_odict['FieldOfView']),
                                                    self.logger,
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                                                    nodata_mask=None,  # dont overwrite areas outside image with nodata
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                                                    outFill=get_outFillZeroSaturated(np.float32)[0],
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                                                    meshwidth=10)  # for faster processing
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        return self._VZA_arr

    @VZA_arr.setter
    def VZA_arr(self, VZA_arr):
        self._VZA_arr = VZA_arr
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    @property
    def VAA_arr(self):
        """Get viewing azimuth angle.
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        :return:
        """
        if self._VAA_arr is None:
            self.logger.info('Calculating viewing azimuth array...')
            if 'IncidenceAngle_arrProv' in self.meta_odict and self.meta_odict['IncidenceAngle_arrProv']:
                # Sentinel-2
                self._VAA_arr = GEOP.adjust_acquisArrProv_to_shapeFullArr(self.meta_odict['IncidenceAngle_arrProv'],
                                                                          self.shape_fullArr,
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                                                                          meshwidth=10,  # for faster processing
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                                                                          subset=None,
                                                                          bandwise=0)
            else:
                # only a mean VAA is available
                if self.VAA_mean is None:
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                    self.VAA_mean = \
                        GEOP.calc_VAA_using_fullSceneCornerLonLat(self.fullSceneCornerLonLat, self.MetaObj.orbitParams)
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                    assert isinstance(self.VAA_mean, float)

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                self._VAA_arr = np.full(self.VZA_arr.shape, self.VAA_mean, np.float32)
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        return self._VAA_arr

    @VAA_arr.setter
    def VAA_arr(self, VAA_arr):
        self._VAA_arr = VAA_arr
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    @property
    def SZA_arr(self):
        """Get solar zenith angle.

        :return:
        """
        if self._SZA_arr is None:
            self.logger.info('Calculating solar zenith and azimuth arrays...')
            self._SZA_arr, self._SAA_arr = \
                GEOP.calc_SZA_SAA_array(
                    self.shape_fullArr, self.arr_pos,
                    self.meta_odict['AcqDate'],
                    self.meta_odict['AcqTime'],
                    self.fullSceneCornerPos,
                    self.fullSceneCornerLonLat,
                    self.meta_odict['overpass duraction sec'],
                    self.logger,
                    meshwidth=10,
                    nodata_mask=None,  # dont overwrite areas outside the image with nodata
                    outFill=get_outFillZeroSaturated(np.float32)[0],
                    accurracy=CFG.job.SZA_SAA_calculation_accurracy,
                    lonlat_arr=self.lonlat_arr if CFG.job.SZA_SAA_calculation_accurracy == 'fine' else None)
        return self._SZA_arr

    @SZA_arr.setter
    def SZA_arr(self, SZA_arr):
        self._SZA_arr = SZA_arr

    @property
    def SAA_arr(self):
        """Get solar azimuth angle.

        :return:
        """
        if self._SAA_arr is None:
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            # noinspection PyStatementEffect
            self.SZA_arr  # getter also sets self._SAA_arr
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        return self._SAA_arr

    @SAA_arr.setter
    def SAA_arr(self, SAA_arr):
        self._SAA_arr = SAA_arr

    @property
    def RAA_arr(self):
        """Get relative azimuth angle.

        :return:
        """
        if self._RAA_arr is None:
            self.logger.info('Calculating relative azimuth array...')
            self._RAA_arr = GEOP.calc_RAA_array(self.SAA_arr, self.VAA_mean,
                                                nodata_mask=None, outFill=get_outFillZeroSaturated(np.float32)[0])
        return self._RAA_arr

    @RAA_arr.setter
    def RAA_arr(self, RAA_arr):
        self._RAA_arr = RAA_arr
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    def delete_ac_input_arrays(self):
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        self.VZA_arr = None  # not needed anymore
        self.SZA_arr = None  # not needed anymore
        self.SAA_arr = None  # not needed anymore
        self.RAA_arr = None  # not needed anymore
        self.lonlat_arr = None  # not needed anymore
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        # use self.dem deleter
        # would have to be resampled when writing MGRS tiles
        # -> better to directly warp it to the output dims and projection
        del self.dem
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class AtmCorr(object):
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    def __init__(self, *L1C_objs, reporting=False):
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        """Wrapper around atmospheric correction by Andre Hollstein, GFZ Potsdam
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        Creates the input arguments for atmospheric correction from one or multiple L1C_object instance(s) belonging to
        the same scene ID, performs the atmospheric correction and returns the atmospherically corrected L1C object(s).

        :param L1C_objs: one or more instances of L1C_object belonging to the same scene ID
        """
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        # FIXME not yet usable for data < 2012 due to missing ECMWF archive
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        L1C_objs = L1C_objs if isinstance(L1C_objs, tuple) else (L1C_objs,)

        # hidden attributes
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        self._logger = None
        self._GSDs = []
        self._data = {}
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        self._metadata = {}
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        self._nodata = {}
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        self._band_spatial_sampling = {}
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        self._options = {}
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        # assertions
        scene_IDs = [obj.scene_ID for obj in L1C_objs]
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        assert len(list(set(scene_IDs))) == 1, \
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            "Input GMS objects for 'AtmCorr' must all belong to the same scene ID!. Received %s." % scene_IDs
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        self.inObjs = L1C_objs  # type: List[L1C_object]
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        self.reporting = reporting
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        self.ac_input = {}  # set by self.run_atmospheric_correction()
        self.results = None  # direct output of external atmCorr module (set by run_atmospheric_correction)
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        self.proc_info = {}
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        self.outObjs = []  # atmospherically corrected L1C objects
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        # append AtmCorr object to input L1C objects
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        # [setattr(L1C_obj, 'AtmCorr', self) for L1C_obj in self.inObjs] # too big for serialization
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        if not re.search('Sentinel-2', self.inObjs[0].satellite, re.I):
            warnings.warn('Calculation of acquisition geometry arrays is currently only validated for Sentinel-2!')
            # validation possible by comparing S2 angles provided by ESA with own angles

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    @property
    def logger(self):
        if self._logger and self._logger.handlers[:]:
            return self._logger
        else:
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            if len(self.inObjs) == 1:
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                # just use the logger of the inObj
                logger_atmCorr = self.inObjs[0].logger
            else:
                # in case of multiple GMS objects to be processed at once:
                # get the logger of the first inObj
                logger_atmCorr = self.inObjs[0].logger

                # add additional file handlers for the remaining inObj (that belong to the same scene_ID)
                for inObj in self.inObjs[1:]:
                    path_logfile = inObj.pathGen.get_path_logfile()
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                    fileHandler = logging.FileHandler(path_logfile, mode='a')
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                    fileHandler.setFormatter(logger_atmCorr.formatter_fileH)
                    fileHandler.setLevel(logging.DEBUG)

                    logger_atmCorr.addHandler(fileHandler)

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                    inObj.close_GMS_loggers()

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            self._logger = logger_atmCorr
            return self._logger

    @logger.setter
    def logger(self, logger):
        assert isinstance(logger, logging.Logger) or logger in ['not set', None], \
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            "AtmCorr.logger can not be set to %s." % logger
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        if logger in ['not set', None]:
            self._logger.close()
            self._logger = logger
        else:
            self._logger = logger

    @logger.deleter
    def logger(self):
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        if self._logger not in [None, 'not set']:
            self._logger.close()
            self._logger = None
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        [inObj.close_GMS_loggers() for inObj in self.inObjs]

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    @property
    def GSDs(self):
        """
        Returns a list of spatial samplings within the input GMS objects, e.g. [10,20,60].
        """
        for obj in self.inObjs:
            if obj.arr.xgsd != obj.arr.ygsd:
                warnings.warn("X/Y GSD is not equal for entity ID %s" % obj.entity_ID +
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                              (' (%s)' % obj.subsystem if obj.subsystem else '') +
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                              'Using X-GSD as key for spatial sampling dictionary.')
                self._GSDs.append(obj.arr.xgsd)

        return self._GSDs

    @property
    def data(self):
        """

        :return:
            ___ attribute: data, type:<class 'dict'>
            ______ key:B05, value_type:<class 'numpy.ndarray'>, repr: [[nan nan nan ...,0. [..] 085998540.0803833 ]]
            ______ key:B01, value_type:<class 'numpy.ndarray'>, repr: [[nan nan nan ...,0. [..] 131225590.13208008]]
            ______ key:B06, value_type:<class 'numpy.ndarray'>, repr: [[nan nan nan ...,0. [..] .14965820.13977051]]
            ______ key:B11, value_type:<class 'numpy.ndarray'>, repr: [[nan nan nan ...,0. [..] .11492920.10192871]]
            ______ key:B02, value_type:<class 'numpy.ndarray'>, repr: [[nan nan nan ...,0. [..] 104187010.10308838]]
            ______ key:B10, value_type:<class 'numpy.ndarray'>, repr: [[nan nan nan ...,0. [..] 013099670.01300049]]
            ______ key:B08, value_type:<class 'numpy.ndarray'>, repr: [[nan nan nan ...,0. [..] .16857910.15783691]]
            ______ key:B04, value_type:<class 'numpy.ndarray'>, repr: [[nan nan nan ...,0. [..] 065490720.06228638]]
            ______ key:B03, value_type:<class 'numpy.ndarray'>, repr: [[nan nan nan ...,0. [..] 082702640.08148193]]
            ______ key:B12, value_type:<class 'numpy.ndarray'>, repr: [[nan nan nan ...,0. [..] 068420410.06060791]]
            ______ key:B8A, value_type:<class 'numpy.ndarray'>, repr: [[nan nan nan ...,0. [..] 192138670.17553711]]
            ______ key:B09, value_type:<class 'numpy.ndarray'>, repr: [[nan nan nan ...,0. [..] .09600830.09887695]]
            ______ key:B07, value_type:<class 'numpy.ndarray'>, repr: [[nan nan nan ...,0. [..] 173339840.15600586]]
        """
        if not self._data:
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            data_dict = {}

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            for inObj in self.inObjs:
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                for bandN, bandIdx in inObj.arr.bandnames.items():
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                    if bandN not in data_dict:
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                        # float32! -> conversion to np.float16 will convert -9999 to -10000
                        arr2pass = inObj.arr[:, :, bandIdx].astype(np.float32)
                        arr2pass[arr2pass == inObj.arr.nodata] = np.nan  # set nodata values to np.nan
                        data_dict[bandN] = (arr2pass / inObj.meta_odict['ScaleFactor']).astype(np.float16)
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                    else:
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                        inObj.logger.warning("Band '%s' cannot be included into atmospheric correction because it "
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                                             "exists multiple times." % bandN)
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            # validate: data must have all bands needed for AC
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            full_LBA = get_LayerBandsAssignment(self.inObjs[0].GMS_identifier, return_fullLBA=True)
            all_bNs_AC = ['B%s' % i if len(i) == 2 else 'B0%s' % i for i in full_LBA]
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            if not all([bN in list(data_dict.keys()) for bN in all_bNs_AC]):
                raise RuntimeError('Atmospheric correction did not receive all the needed bands. \n\tExpected: %s;\n\t'
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                                   'Received: %s' % (str(all_bNs_AC), str(list(sorted(data_dict.keys())))))
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            self._data = data_dict

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        return self._data

    @data.setter
    def data(self, data_dict):
        assert isinstance(data_dict, dict), \
            "'data' can only be set to a dictionary with band names as keys and numpy arrays as values."
        self._data = data_dict

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    @property
    def nodata(self):
        """

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

        if not self._nodata:
            for inObj in self.inObjs:
                self._nodata[inObj.arr.xgsd] = ~inObj.arr.mask_nodata[:]

        return self._nodata

    @property
    def tile_name(self):
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        """Returns S2A tile name.
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        NOTE: this is only needed if no DEM is passed to ac_gms
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        :return: e.g.
            '32UMA'
        """

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        return ''  # FIXME
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    @property
    def band_spatial_sampling(self):
        """

        :return: e.g.
            {'B01': 60.0,
             'B02': 10.0,
             'B03': 10.0,
             'B04': 10.0,
             'B05': 20.0,
             'B06': 20.0,
             'B07': 20.0,
             'B08': 10.0,
             'B09': 60.0,
             'B10': 60.0,
             'B11': 20.0,
             'B12': 20.0,
             'B8A': 20.0}
        """

        if not self._band_spatial_sampling:
            for inObj in self.inObjs:
                for bandN in inObj.arr.bandnames:
                    if bandN not in self._band_spatial_sampling:
                        self._band_spatial_sampling[bandN] = inObj.arr.xgsd
        return self._band_spatial_sampling

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    @property
    def metadata(self):
        """

        :return:
            ___ attribute: metadata, type:<class 'dict'>
            ______ key:spatial_samplings
            _________ key:60.0
            ____________ key:ULY, value_type:<class 'int'>, repr: 4900020
            ____________ key:NCOLS, value_type:<class 'int'>, repr: 1830
            ____________ key:XDIM, value_type:<class 'int'>, repr: 60
            ____________ key:ULX, value_type:<class 'int'>, repr: 600000
            ____________ key:NROWS, value_type:<class 'int'>, repr: 1830
            ____________ key:YDIM, value_type:<class 'int'>, repr: -60
            _________ key:10.0
            ____________ key:ULY, value_type:<class 'int'>, repr: 4900020
            ____________ key:NCOLS, value_type:<class 'int'>, repr: 10980
            ____________ key:XDIM, value_type:<class 'int'>, repr: 10
            ____________ key:ULX, value_type:<class 'int'>, repr: 600000
            ____________ key:NROWS, value_type:<class 'int'>, repr: 10980
            ____________ key:YDIM, value_type:<class 'int'>, repr: -10
            _________ key:20.0
            ____________ key:ULY, value_type:<class 'int'>, repr: 4900020
            ____________ key:NCOLS, value_type:<class 'int'>, repr: 5490
            ____________ key:XDIM, value_type:<class 'int'>, repr: 20
            ____________ key:ULX, value_type:<class 'int'>, repr: 600000
            ____________ key:NROWS, value_type:<class 'int'>, repr: 5490
            ____________ key:YDIM, value_type:<class 'int'>, repr: -20
            ______ key:SENSING_TIME, value_type:<class 'datetime.datetime'>, repr: 2016-03-26 10:34:06.538000+00:00
        """
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        # TODO add SRF object
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        if not self._metadata:
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            del self.logger  # otherwise each input object would have multiple fileHandlers
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            metadata = dict(
                U=self.inObjs[0].meta_odict['EarthSunDist'],
                SENSING_TIME=self.inObjs[0].acq_datetime,
                # SENSING_TIME=datetime.strptime('2015-08-12 10:40:21 +0000', '%Y-%m-%d %H:%M:%S %z'),
                viewing_zenith=self._meta_get_viewing_zenith(),
                viewing_azimuth=self._meta_get_viewing_azimuth(),
                relative_viewing_azimuth=self._meta_get_relative_viewing_azimuth(),
                sun_mean_azimuth=self.inObjs[0].meta_odict['SunAzimuth'],
                sun_mean_zenith=90 - self.inObjs[0].meta_odict['SunElevation'],
                solar_irradiance=self._meta_get_solar_irradiance(),
                aux_data=self._meta_get_aux_data(),
                spatial_samplings=self._meta_get_spatial_samplings()
            )
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            self._metadata = metadata
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        return self._metadata

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    @property
    def options(self):
        """Returns a dictionary containing AC options.
        """
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        # type: -> dict
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        if self._options:
            return self._options
        else:
            self._options = self.inObjs[0].ac_options
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            self._options["AC"]['bands'] = [b for b in self.data.keys() if b in self._options["AC"]['bands']]
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            self._options["report"]["reporting"] = self.reporting
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            return self._options

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    def _meta_get_spatial_samplings(self):
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        """

        :return:
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         {10.0: {'NCOLS': 10980,
           'NROWS': 10980,
           'ULX': 499980.0,
           'ULY': 5800020.0,
           'XDIM': 10.0,
           'YDIM': -10.0},
          20.0: {'NCOLS': 5490,
           'NROWS': 5490,
           'ULX': 499980.0,
           'ULY': 5800020.0,
           'XDIM': 20.0,
           'YDIM': -20.0},
          60.0: {'NCOLS': 1830,
           'NROWS': 1830,
           'ULX': 499980.0,
           'ULY': 5800020.0,
           'XDIM': 60.0,
           'YDIM': -60.0}}
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        """
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        # set corner coordinates and dims
        spatial_samplings = {}
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        for inObj in self.inObjs:

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            # validate GSD
            if inObj.arr.xgsd != inObj.arr.ygsd:
                warnings.warn("X/Y GSD is not equal for entity ID %s" % inObj.entity_ID +
                              (' (%s)' % inObj.subsystem if inObj.subsystem else '') +
                              'Using X-GSD as key for spatial sampling dictionary.')
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            # set spatial information
            spatial_samplings[inObj.arr.xgsd] = dict(
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                ULX=inObj.arr.box.boxMapYX[0][1],
                ULY=inObj.arr.box.boxMapYX[0][0],
                XDIM=inObj.arr.xgsd,
                YDIM=-inObj.arr.ygsd,
                NROWS=inObj.arr.rows,
                NCOLS=inObj.arr.cols)
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        return spatial_samplings

    def _meta_get_solar_irradiance(self):
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        """

        :return:
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        {'B01': 1913.57,
         'B02': 1941.63,
         'B03': 1822.61,
         'B04': 1512.79,
         'B05': 1425.56,
         'B06': 1288.32,
         'B07': 1163.19,
         'B08': 1036.39,
         'B09': 813.04,
         'B10': 367.15,
         'B11': 245.59,
         'B12': 85.25,
         'B8A': 955.19}
        """

        solar_irradiance = {}

        for inObj in self.inObjs:
            for bandN, bandIdx in inObj.arr.bandnames.items():
                if bandN not in solar_irradiance:
                    solar_irradiance[bandN] = inObj.meta_odict['SolIrradiance'][bandIdx]
        return solar_irradiance

    def _meta_get_viewing_zenith(self):
        """

        :return: {B10:ndarray(dtype=float16),[...],B09:ndarray(dtype=float16)}
        """

        viewing_zenith = {}

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        for inObj in self.inObjs:  # type: L1C_object
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            for bandN, bandIdx in inObj.arr.bandnames.items():
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                if bandN not in viewing_zenith:
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                    arr2pass = inObj.VZA_arr[:, :, bandIdx] if inObj.VZA_arr.ndim == 3 else inObj.VZA_arr
                    viewing_zenith[bandN] = arr2pass.astype(np.float16)
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                    # viewing_zenith[bandN] = inObj.VZA_arr[:, :, bandIdx] if inObj.VZA_arr.ndim==3 else inObj.VZA_arr
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        return viewing_zenith

    def _meta_get_viewing_azimuth(self):
        """

        :return: {B10:ndarray(dtype=float16),[...],B09:ndarray(dtype=float16)}
        """

        viewing_azimuth = {}

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        for inObj in self.inObjs:  # type: L1C_object
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            for bandN, bandIdx in inObj.arr.bandnames.items():
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                if bandN not in viewing_azimuth:
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                    arr2pass = inObj.VAA_arr[:, :, bandIdx] if inObj.VAA_arr.ndim == 3 else inObj.VAA_arr
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                    viewing_azimuth[bandN] = arr2pass.astype(np.float16)
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                    # viewing_azimuth[bandN] = inObj.VAA_arr[:, :, bandIdx] if inObj.VAA_arr.ndim==3 else inObj.VAA_arr
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        return viewing_azimuth

    def _meta_get_relative_viewing_azimuth(self):
        """

        :return: {B10:ndarray(dtype=float16),[...],B09:ndarray(dtype=float16)}
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        """

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        relative_viewing_azimuth = {}

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        for inObj in self.inObjs:  # type: L1C_object
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            for bandN, bandIdx in inObj.arr.bandnames.items():
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                if bandN not in relative_viewing_azimuth:
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                    arr2pass = inObj.RAA_arr[:, :, bandIdx] if inObj.RAA_arr.ndim == 3 else inObj.RAA_arr
                    relative_viewing_azimuth[bandN] = arr2pass.astype(np.float16)
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                    # relative_viewing_azimuth[bandN] = \
                    #     inObj.RAA_arr[:, :, bandIdx] if inObj.RAA_arr.ndim==3 else inObj.RAA_arr
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        return relative_viewing_azimuth
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    def _meta_get_aux_data(self):
        """

        :return:  {lons:ndarray(dtype=float16),,lats:ndarray(dtype=float16)}
        """

        aux_data = dict(
            # set lons and lats (a 2D array for all bands is enough (different band resolutions dont matter))
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            lons=self.inObjs[0].lonlat_arr[::10, ::10, 0].astype(np.float16),  # 2D array of lon values: 0° - 360°
            lats=self.inObjs[0].lonlat_arr[::10, ::10, 1].astype(np.float16)  # 2D array of lat values: -90° - 90°
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            # FIXME correct to reduce resolution here by factor 10?
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        )

        return aux_data

    def _get_dem(self):
        """Get a DEM to be used in atmospheric correction.

        :return: <np.ndarray> 2D array (with 20m resolution in case of Sentinel-2)
        """
        # determine which input GMS object is used to generate DEM
        if re.search('Sentinel-2', self.inObjs[0].satellite):
            # in case of Sentinel-2 the 20m DEM must be passed
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            inObj4dem = [obj for obj in self.inObjs if obj.arr.xgsd == 20]
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            if not inObj4dem:
                self.logger.warning('Sentinel-2 20m subsystem could not be found. DEM passed to '
                                    'atmospheric correction might have wrong resolution.')
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            inObj4dem = inObj4dem[0]
        else:
            inObj4dem = self.inObjs[0]

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        try:
            dem = inObj4dem.dem[:].astype(np.float32)
        except Exception as e:
            dem = None
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            self.logger.warning('A static elevation is assumed during atmospheric correction due to an error during '
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                                'creation of the DEM corresponding to scene %s (entity ID: %s). Error message was: '
                                '\n%s\n' % (self.inObjs[0].scene_ID, self.inObjs[0].entity_ID, repr(e)))
            self.logger.info("Print traceback in case you care:")
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            self.logger.warning(traceback.format_exc())
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        return dem
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    def _get_srf(self):
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        """Returns an instance of SRF in the same structure like sicor.sensors.SRF.SensorSRF
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        """
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        # FIXME calculation of center wavelengths within SRF() used not the GMS algorithm
        # SRF instance must be created for all bands and the previous proc level
        GMS_identifier_fullScene = self.inObjs[0].GMS_identifier
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        GMS_identifier_fullScene['Subsystem'] = ''
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        GMS_identifier_fullScene['proc_level'] = proc_chain[proc_chain.index(self.inObjs[0].proc_level) - 1]

        return SRF(GMS_identifier_fullScene, wvl_unit='nanometers', format_bandnames=True)
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    def _get_mask_clouds(self):
        """Returns an instance of S2Mask in case cloud mask is given by input GMS objects. Otherwise None is returned.

        :return:
        """

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        tgt_res = self.inObjs[0].ac_options['cld_mask']['target_resolution']

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        # check if input GMS objects provide a cloud mask
        avail_cloud_masks = {inObj.GMS_identifier['Subsystem']: inObj.mask_clouds for inObj in self.inObjs}
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        no_avail_CMs = list(set(avail_cloud_masks.values())) == [None]
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        # compute cloud mask if not already provided
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        if no_avail_CMs:
            algorithm = CFG.job.cloud_masking_algorithm[self.inObjs[0].satellite]
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            if algorithm == 'SICOR':
                return None
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            else:
                # FMASK or Classical Bayesian
                try:
                    from .cloud_masking import Cloud_Mask_Creator

                    CMC = Cloud_Mask_Creator(self.inObjs[0], algorithm=algorithm)
                    CMC.calc_cloud_mask()
                    cm_geoarray = CMC.cloud_mask_geoarray
                    cm_array = CMC.cloud_mask_array
                    cm_legend = CMC.cloud_mask_legend
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                except Exception:
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                    self.logger.error('\nAn error occurred during FMASK cloud masking. Error message was: ')
                    self.logger.error(traceback.format_exc())
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                    return None
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        else:
            # check if there is a cloud mask with suitable GSD
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            inObjs2use = [obj for obj in self.inObjs if obj.mask_clouds is not None and obj.mask_clouds.xgsd == tgt_res]
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            if not inObjs2use:
                raise ValueError('Error appending cloud mask to input arguments of atmospheric correction. No input '
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                                 'GMS object provides a cloud mask with spatial resolution of %s.' % tgt_res)
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            inObj2use = inObjs2use[0]

            # get mask (geo)array
            cm_geoarray = inObj2use.mask_clouds
            cm_array = inObj2use.mask_clouds[:]

            # get legend
            cm_legend = get_mask_classdefinition('mask_clouds', inObj2use.satellite)
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            #    {'Clear': 10, 'Thick Clouds': 20, 'Thin Clouds': 30, 'Snow': 40}  # FIXME hardcoded
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            # validate that xGSD equals yGSD
            if cm_geoarray.xgsd != cm_geoarray.ygsd:
                warnings.warn("Cloud mask X/Y GSD is not equal for entity ID %s" % inObj2use.entity_ID +
                              (' (%s)' % inObj2use.subsystem if inObj2use.subsystem else '') +
                              'Using X-GSD as key for cloud mask geocoding.')
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        # get geocoding
        cm_geocoding = self.metadata["spatial_samplings"][tgt_res]

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        # get nodata value
        self.options['cld_mask']['nodata_value_mask'] = cm_geoarray.nodata
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        # append cloud mask to input object with the same spatial resolution if there was no mask before
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        for inObj in self.inObjs:
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            if inObj.arr.xgsd == cm_geoarray.xgsd:
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                inObj.mask_clouds = cm_geoarray
                inObj.build_combined_masks_array()
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                break  # appending it to one inObj is enough
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        return S2Mask(mask_array=cm_array,
                      mask_legend=cm_legend,
                      geo_coding=cm_geocoding)
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    def run_atmospheric_correction(self, dump_ac_input=False):
        # type: (bool) -> list
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        """Collects all input data for atmospheric correction, runs the AC and returns the corrected L1C objects
        containing surface reflectance.

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        :param dump_ac_input:   allows to dump the inputs of AC to the scene's processing folder in case AC fails
        :return:                list of L1C_object instances containing atmospherically corrected data
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        """
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        # collect input args/kwargs for AC
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        self.logger.info('Calculating input data for atmospheric correction...')

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        rs_data = dict(
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            data=self.data,
            metadata=self.metadata,
            nodata=self.nodata,
            band_spatial_sampling=self.band_spatial_sampling,
            tile_name=self.tile_name,
            dem=self._get_dem(),
            srf=self._get_srf(),
            mask_clouds=self._get_mask_clouds()
            # returns an instance of S2Mask or None if cloud mask is not given by input GMS objects
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        )  # NOTE: all keys of this dict are later converted to attributes of RSImage
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        script = False
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        # create an instance of RSImage
        rs_image = RSImage(**rs_data)

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        self.ac_input = dict(
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            rs_image=rs_image,
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            options=self.options,  # type: dict
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            logger=repr(self.logger),  # only a string
            script=script
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        )
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        # path_dump = self.inObjs[0].pathGen.get_path_ac_input_dump()
        # with open(path_dump, 'wb') as outF:
        #     dill.dump(self.ac_input, outF)

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        # run AC
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        self.logger.info('Atmospheric correction started.')
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        try:
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            rs_image.logger = self.logger
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            self.results = ac_gms(rs_image, self.options, logger=self.logger, script=script)
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        except Exception as e:
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            # serialialize AC input
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            if dump_ac_input:
                path_dump = self.inObjs[0].pathGen.get_path_ac_input_dump()
                with open(path_dump, 'wb') as outF:
                    dill.dump(self.ac_input, outF)

                self.logger.error('An error occurred during atmospheric correction. Inputs have been dumped to %s.'
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                                  % path_dump)
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            # delete AC input arrays
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            for inObj in self.inObjs:  # type: L1C_object
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                inObj.delete_ac_input_arrays()

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            self.logger.error('\nAn error occurred during atmospheric correction. BE AWARE THAT THE SCENE %s '
                              '(ENTITY ID %s) HAS NOT BEEN ATMOSPHERICALLY CORRECTED! Error message was: \n%s\n'
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                              % (self.inObjs[0].scene_ID, self.inObjs[0].entity_ID, repr(e)))
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            self.logger.error(traceback.format_exc())
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            # TODO include that in the job summary
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            return list(self.inObjs)

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        # get processing infos
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        self.proc_info = self.ac_input['options']['processing']  # FIXME this is not appended to GMS objects
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        # join results
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        self._join_results_to_inObjs()  # sets self.outObjs
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        # delete input arrays that are not needed anymore
        [inObj.delete_ac_input_arrays() for inObj in self.inObjs]
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        return self.outObjs

    def _join_results_to_inObjs(self):
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        """
        Join results of atmospheric correction to the input GMS objects.
        """
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        self.logger.info('Joining results of atmospheric correction to input GMS objects.')
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        # delete logger
        # -> otherwise logging in inObjs would open a second FileHandler to the same file (which is permitted)
        del self.logger
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        self._join_data_ac()
        self._join_data_errors()
        self._join_mask_clouds()
        self._join_mask_confidence_array()

        # update masks (always do that because masks can also only contain one layer)
        [inObj.build_combined_masks_array() for inObj in self.inObjs]
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        self.outObjs = self.inObjs

    def _join_data_ac(self):
        """
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        Join ATMOSPHERICALLY CORRECTED ARRAY as 3D int8 or int16 BOA reflectance array, scaled to scale factor from
        config.
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        """
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        if self.results.data_ac is not None:
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            for inObj in self.inObjs:
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                assert isinstance(inObj, L1B_object)
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                nodata = self.results.nodata[inObj.arr.xgsd]  # 2D mask with True outside of image coverage
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                ac_bandNs = [bandN for bandN in inObj.arr.bandnames if bandN in self.results.data_ac.keys()]
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                out_LBA = [bN.split('B0')[1] if bN.startswith('B0') else bN.split('B')[1] for bN in ac_bandNs]
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                # update metadata
                inObj.arr_desc = 'BOA_Ref'
                inObj.MetaObj.bands = len(self.results.data_ac)
                inObj.MetaObj.PhysUnit = 'BOA_Reflectance in [0-%d]' % CFG.usecase.scale_factor_BOARef
                inObj.MetaObj.LayerBandsAssignment = out_LBA
                inObj.MetaObj.filter_layerdependent_metadata()
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                inObj.meta_odict = inObj.MetaObj.to_odict()  # actually auto-updated by getter
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                # join SURFACE REFLECTANCE as 3D int16 array, scaled to scale factor from config
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                # FIXME AC output nodata values = 0 -> new nodata areas but mask not updated
                oF_refl, oZ_refl, oS_refl = get_outFillZeroSaturated(inObj.arr.dtype)
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                surf_refl = np.dstack((self.results.data_ac[bandN] for bandN in ac_bandNs))
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                surf_refl *= CFG.usecase.scale_factor_BOARef  # scale using scale factor (output is float16)
                # FIXME really set AC nodata values to GMS outZero?
                surf_refl[nodata] = oZ_refl  # overwrite AC nodata values with GMS outZero
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                # apply the original nodata mask (indicating background values)
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                surf_refl[np.array(inObj.mask_nodata).astype(np.int8) == 0] = oF_refl
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                if self.results.bad_data_value is np.nan:
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                    surf_refl[np.isnan(surf_refl)] = oF_refl
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                else:
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                    surf_refl[
                        surf_refl == self.results.bad_data_value] = oF_refl  # FIXME meaningful to set AC nans to -9999?
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                # overwrite LayerBandsAssignment and use inObj.arr setter to generate a GeoArray
                inObj.LayerBandsAssignment = out_LBA
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                inObj.arr = surf_refl.astype(inObj.arr.dtype)  # -> int16 (also converts NaNs to 0 if needed
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        else:
            self.logger.warning('Atmospheric correction did not return a result for the input array. '
                                'Thus the output keeps NOT atmospherically corrected.')
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    def _join_data_errors(self):
        """
        Join ERRORS ARRAY as 3D int8 or int16 BOA reflectance array, scaled to scale factor from config.
        """

        if self.results.data_errors is not None:
            for inObj in self.inObjs:
                nodata = self.results.nodata[inObj.arr.xgsd]  # 2D mask with True outside of image coverage
                ac_bandNs = [bandN for bandN in inObj.arr.bandnames if bandN in self.results.data_ac.keys()]

                ac_errors = np.dstack((self.results.data_errors[bandN] for bandN in ac_bandNs))
                ac_errors *= CFG.usecase.scale_factor_errors_ac  # scale using scale factor (output is float16)
                out_dtype = np.int8 if CFG.usecase.scale_factor_errors_ac <= 255 else np.int16
                ac_errors[nodata] = get_outFillZeroSaturated(out_dtype)[0]
                ac_errors = ac_errors.astype(out_dtype)
                inObj.ac_errors = ac_errors  # setter generates a GeoArray with the same bandnames like inObj.arr
                # TODO how to handle nans?
        else:
            self.logger.warning("Atmospheric correction did not provide a 'data_errors' array. Maybe due to "
                                "missing SNR model? GMS_object.ac_errors kept None.")

    def _join_mask_clouds(self):
        """
        Join CLOUD MASK as 2D uint8 array.
        NOTE: mask_clouds has also methods 'export_mask_rgb()', 'export_confidence_to_jpeg2000()', ...
        """

        if self.results.mask_clouds.mask_array is not None:
            mask_clouds_ac = self.results.mask_clouds.mask_array  # uint8 2D array
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            joined = False
            for inObj in self.inObjs:
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                # delete all previous cloud masks
                del inObj.mask_clouds
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                # append mask_clouds only to the input GMS object with the same dimensions
                if inObj.arr.shape[:2] == mask_clouds_ac.shape:
                    inObj.mask_clouds = mask_clouds_ac
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                    inObj.mask_clouds.legend = self.results.mask_clouds.mask_legend  # dict(value=string, string=value))
                    # FIXME legend is not used later
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                    # set cloud mask nodata value
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                    tgt_nodata = get_outFillZeroSaturated(mask_clouds_ac.dtype)[0]
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                    ac_out_nodata = self.ac_input['options']['cld_mask']['nodata_value_mask']
                    if tgt_nodata not in self.results.mask_clouds.mask_legend.keys():
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                        inObj.mask_clouds[inObj.mask_clouds[:] == ac_out_nodata] = tgt_nodata
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                        mask_clouds_nodata = tgt_nodata
                    else:
                        warnings.warn('The cloud mask from AC output already uses the desired nodata value %s for the '
                                      'class %s. Using AC output nodata value %s.'
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                                      % (tgt_nodata, self.results.mask_clouds.mask_legend[tgt_nodata], ac_out_nodata))
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                        mask_clouds_nodata = ac_out_nodata

                    inObj.mask_clouds.nodata = mask_clouds_nodata

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                    joined = True
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            if not joined:
                self.logger.warning('Cloud mask has not been appended to one of the AC inputs because there was no'
                                    'input GMS object with the same dimensions.')

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        else:
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            self.logger.warning("Atmospheric correction did not provide a 'mask_clouds.mask_array' array. "
                                "GMS_object.mask_clouds kept None.")
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    def _join_mask_confidence_array(self):
        """
        Join confidence array for mask_clouds.
        """

        if self.results.mask_clouds.mask_confidence_array is not None:
            cfd_arr = self.results.mask_clouds.mask_confidence_array  # float32 2D array, scaled [0-1, nodata 255]
            cfd_arr[cfd_arr == self.ac_input['options']['cld_mask']['nodata_value_mask']] = -1
            cfd_arr = (cfd_arr * CFG.usecase.scale_factor_BOARef).astype(np.int16)
            cfd_arr[cfd_arr == -CFG.usecase.scale_factor_BOARef] = get_outFillZeroSaturated(cfd_arr.dtype)[0]

            joined = False
            for inObj in self.inObjs:

                # append mask_clouds only to the input GMS object with the same dimensions
                if inObj.arr.shape[:2] == cfd_arr.shape:
                    # set cloud mask confidence array
                    inObj.mask_clouds_confidence = GeoArray(cfd_arr, inObj.arr.gt, inObj.arr.prj,
                                                            nodata=get_outFillZeroSaturated(cfd_arr.dtype)[0])
                    joined = True
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            if not joined:
                self.logger.warning('Cloud mask confidence array has not been appended to one of the AC inputs because '
                                    'there was no input GMS object with the same dimensions.')
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        else:
            self.logger.warning("Atmospheric correction did not provide a 'mask_confidence_array' array for "
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                                "attribute 'mask_clouds. GMS_object.mask_clouds_confidence kept None.")