L1B_P.py 35.7 KB
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# -*- coding: utf-8 -*-
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"""
Level 1B Processor:

Detection of global/local geometric displacements.
"""

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import collections
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import os
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import time
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import warnings
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from datetime import datetime, timedelta
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import numpy as np
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from geopandas import GeoDataFrame
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from shapely.geometry import box
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import pytz
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from typing import Union  # noqa F401  # flake8 issue
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from arosics import COREG, DESHIFTER
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from geoarray import GeoArray
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from py_tools_ds.geo.coord_grid import is_coord_grid_equal
from py_tools_ds.geo.coord_calc import corner_coord_to_minmax
from py_tools_ds.geo.coord_trafo import reproject_shapelyGeometry, transform_any_prj
from py_tools_ds.geo.projection import prj_equal, EPSG2WKT, WKT2EPSG
from py_tools_ds.geo.vector.topology import get_overlap_polygon

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from ..options.config import GMS_config as CFG
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from ..model.gms_object import GMS_object
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from .L1A_P import L1A_object
from ..misc import database_tools as DB_T
from ..misc import helper_functions as HLP_F
from ..misc import path_generator as PG
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from ..misc.spatial_index_mediator import SpatialIndexMediator
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from ..misc.definition_dicts import get_GMS_sensorcode, get_outFillZeroSaturated
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__author__ = 'Daniel Scheffler'
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class Scene_finder(object):
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    """Scene_finder class to query the postgreSQL database to find a suitable reference scene for co-registration."""

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    def __init__(self, src_boundsLonLat, src_AcqDate, src_prj, src_footprint_poly, sceneID_excluded=None,
                 min_overlap=20, min_cloudcov=0, max_cloudcov=20, plusminus_days=30, plusminus_years=10):
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        """Initialize Scene_finder.

        :param src_boundsLonLat:
        :param src_AcqDate:
        :param src_prj:
        :param src_footprint_poly:
        :param sceneID_excluded:
        :param min_overlap:         minimum overlap of reference scene in percent
        :param min_cloudcov:        minimum cloud cover of reference scene in percent
        :param max_cloudcov:        maximum cloud cover of reference scene in percent
        :param plusminus_days:      maximum time interval between target and reference scene in days
        :param plusminus_years:     maximum time interval between target and reference scene in years
        """
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        self.boundsLonLat = src_boundsLonLat
        self.src_AcqDate = src_AcqDate
        self.src_prj = src_prj
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        self.src_footprint_poly = src_footprint_poly
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        self.sceneID_excluded = sceneID_excluded
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        self.min_overlap = min_overlap
        self.min_cloudcov = min_cloudcov
        self.max_cloudcov = max_cloudcov
        self.plusminus_days = plusminus_days
        self.plusminus_years = plusminus_years
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        # get temporal constraints
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        def add_years(dt, years): return dt.replace(dt.year + years) \
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            if not (dt.month == 2 and dt.day == 29) else dt.replace(dt.year + years, 3, 1)
        self.timeStart = add_years(self.src_AcqDate, -plusminus_years)
        timeEnd = add_years(self.src_AcqDate, +plusminus_years)
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        timeNow = datetime.utcnow().replace(tzinfo=pytz.UTC)
        self.timeEnd = timeEnd if timeEnd <= timeNow else timeNow
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        self.possib_ref_scenes = None  # set by self.spatial_query()
        self.GDF_ref_scenes = GeoDataFrame()  # set by self.spatial_query()
        self.ref_scene = None
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    def spatial_query(self, timeout=5):
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        """Query the postgreSQL database to find possible reference scenes matching the specified criteria.

        :param timeout:     maximum query duration allowed (seconds)
        """
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        SpIM = SpatialIndexMediator(host=CFG.spatial_index_server_host, port=CFG.spatial_index_server_port,
                                    timeout=timeout, retries=10)
        self.possib_ref_scenes = SpIM.getFullSceneDataForDataset(envelope=self.boundsLonLat,
                                                                 timeStart=self.timeStart,
                                                                 timeEnd=self.timeEnd,
                                                                 minCloudCover=self.min_cloudcov,
                                                                 maxCloudCover=self.max_cloudcov,
                                                                 datasetid=CFG.datasetid_spatial_ref,
                                                                 refDate=self.src_AcqDate,
                                                                 maxDaysDelta=self.plusminus_days)
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        if self.possib_ref_scenes:
            # fill GeoDataFrame with possible ref scene parameters
            GDF = GeoDataFrame(self.possib_ref_scenes, columns=['object'])
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            GDF['sceneid'] = list(GDF['object'].map(lambda scene: scene.sceneid))
            GDF['acquisitiondate'] = list(GDF['object'].map(lambda scene: scene.acquisitiondate))
            GDF['cloudcover'] = list(GDF['object'].map(lambda scene: scene.cloudcover))
            GDF['polyLonLat'] = list(GDF['object'].map(lambda scene: scene.polyLonLat))
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            def LonLat2UTM(polyLL):
                return reproject_shapelyGeometry(polyLL, 4326, self.src_prj)
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            GDF['polyUTM'] = list(GDF['polyLonLat'].map(LonLat2UTM))
            self.GDF_ref_scenes = GDF
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    def _collect_refscene_metadata(self):
        """Collect some reference scene metadata needed for later filtering."""
        GDF = self.GDF_ref_scenes
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        # get overlap parameter
        def get_OL_prms(poly): return get_overlap_polygon(poly, self.src_footprint_poly)

        GDF['overlapParams'] = list(GDF['polyLonLat'].map(get_OL_prms))
        GDF['overlap area'] = list(GDF['overlapParams'].map(lambda OL_prms: OL_prms['overlap area']))
        GDF['overlap percentage'] = list(GDF['overlapParams'].map(lambda OL_prms: OL_prms['overlap percentage']))
        GDF['overlap poly'] = list(GDF['overlapParams'].map(lambda OL_prms: OL_prms['overlap poly']))
        del GDF['overlapParams']

        # get processing level of reference scenes
        procL = GeoDataFrame(
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            DB_T.get_info_from_postgreSQLdb(CFG.conn_database, 'scenes_proc', ['sceneid', 'proc_level'],
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                                            {'sceneid': list(GDF.sceneid)}), columns=['sceneid', 'proc_level'])
        GDF = GDF.merge(procL, on='sceneid', how='left')
        GDF = GDF.where(GDF.notnull(), None)  # replace NaN values with None

        # get path of binary file
        def get_path_binary(GDF_row):
            return PG.path_generator(scene_ID=GDF_row['sceneid'], proc_level=GDF_row['proc_level']) \
                .get_path_imagedata() if GDF_row['proc_level'] else None
        GDF['path_ref'] = GDF.apply(lambda GDF_row: get_path_binary(GDF_row), axis=1)
        GDF['refDs_exists'] = list(GDF['path_ref'].map(lambda p: os.path.exists(p) if p else False))

        # check if a proper entity ID can be gathered from database
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        eID = GeoDataFrame(DB_T.get_info_from_postgreSQLdb(CFG.conn_database, 'scenes', ['id', 'entityid'],
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                                                           {'id': list(GDF.sceneid)}), columns=['sceneid', 'entityid'])
        GDF = GDF.merge(eID, on='sceneid', how='left')
        self.GDF_ref_scenes = GDF.where(GDF.notnull(), None)

    def _filter_excluded_sceneID(self):
        """Filter reference scene with the same scene ID like the target scene."""
        GDF = self.GDF_ref_scenes
        if not GDF.empty:
            self.GDF_ref_scenes = GDF.loc[GDF['sceneid'] != self.sceneID_excluded]
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    def _filter_by_overlap(self):
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        """Filter all scenes with less spatial overlap than self.min_overlap."""
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        GDF = self.GDF_ref_scenes
        if not GDF.empty:
            self.GDF_ref_scenes = GDF.loc[GDF['overlap percentage'] >= self.min_overlap]
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    def _filter_by_proc_status(self):
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        """Filter all scenes that have not been processed before according to proc. status (at least L1A is needed)."""
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        GDF = self.GDF_ref_scenes
        if not GDF.empty:
            self.GDF_ref_scenes = GDF[GDF['proc_level'].notnull()]
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    def _filter_by_dataset_existance(self):
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        """Filter all scenes where no processed data can be found on fileserver."""
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        GDF = self.GDF_ref_scenes
        if not GDF.empty:
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            self.GDF_ref_scenes = GDF[GDF['refDs_exists']]
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    def _filter_by_entity_ID_availability(self):
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        """Filter all scenes where no proper entity ID can be found in the database (database errors)."""
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        GDF = self.GDF_ref_scenes
        if not GDF.empty:
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            self.GDF_ref_scenes = GDF[GDF['entityid'].notnull()]
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    def _filter_by_projection(self):
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        """Filter all scenes that have a different projection than the target image."""
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        GDF = self.GDF_ref_scenes[self.GDF_ref_scenes.refDs_exists]
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        if not GDF.empty:
            # compare projections of target and reference image
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            GDF['prj_equal'] = \
                list(GDF['path_ref'].map(lambda path_ref: prj_equal(self.src_prj, GeoArray(path_ref).prj)))
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            self.GDF_ref_scenes = GDF[GDF['prj_equal']]
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    def choose_ref_scene(self):
        """Choose reference scene with minimum cloud cover and maximum overlap."""
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        if self.possib_ref_scenes:
            # First, collect some relavant reference scene metadata
            self._collect_refscene_metadata()
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            # Filter possible scenes by running all filter functions
            self._filter_excluded_sceneID()
            self._filter_by_overlap()
            self._filter_by_proc_status()
            self._filter_by_dataset_existance()
            self._filter_by_entity_ID_availability()
            self._filter_by_projection()
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        # Choose the reference scene out of the filtered DataFrame
        if not self.GDF_ref_scenes.empty:
            GDF = self.GDF_ref_scenes
            GDF = GDF[GDF['cloudcover'] == GDF['cloudcover'].min()]
            GDF = GDF[GDF['overlap percentage'] == GDF['overlap percentage'].max()]
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            if not GDF.empty:
                GDF_res = GDF.iloc[0]
                return ref_Scene(GDF_res)
        else:
            return None
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class ref_Scene:
    def __init__(self, GDF_record):
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        self.scene_ID = int(GDF_record['sceneid'])
        self.entity_ID = GDF_record['entityid']
        self.AcqDate = GDF_record['acquisitiondate']
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        self.cloudcover = GDF_record['cloudcover']
        self.polyLonLat = GDF_record['polyLonLat']
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        self.polyUTM = GDF_record['polyUTM']
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        self.proc_level = GDF_record['proc_level']
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        self.filePath = GDF_record['path_ref']
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class L1B_object(L1A_object):
    def __init__(self, L1A_obj=None):

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        super(L1B_object, self).__init__()
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        # set defaults
        self._spatRef_available = None
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        self.spatRef_scene = None  # set by self.get_spatial_reference_scene()
        self.deshift_results = collections.OrderedDict()
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        if L1A_obj:
            # populate attributes
            [setattr(self, key, value) for key, value in L1A_obj.__dict__.items()]

        self.proc_level = 'L1B'
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        self.proc_status = 'initialized'
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    @property
    def spatRef_available(self):
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        if self._spatRef_available is not None:
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            return self._spatRef_available
        else:
            self.get_spatial_reference_scene()
            return self._spatRef_available

    @spatRef_available.setter
    def spatRef_available(self, spatRef_available):
        self._spatRef_available = spatRef_available

    def get_spatial_reference_scene(self):
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        boundsLonLat = corner_coord_to_minmax(self.trueDataCornerLonLat)
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        footprint_poly = HLP_F.CornerLonLat_to_shapelyPoly(self.trueDataCornerLonLat)
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        RSF = Scene_finder(boundsLonLat, self.acq_datetime, self.meta_odict['coordinate system string'],
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                           footprint_poly, self.scene_ID,
                           min_overlap=CFG.spatial_ref_min_overlap,
                           min_cloudcov=CFG.spatial_ref_min_cloudcov,
                           max_cloudcov=CFG.spatial_ref_max_cloudcov,
                           plusminus_days=CFG.spatial_ref_plusminus_days,
                           plusminus_years=CFG.spatial_ref_plusminus_years)
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        # run spatial query
        self.logger.info('Querying database in order to find a suitable reference scene for co-registration.')
        RSF.spatial_query(timeout=5)
        if RSF.possib_ref_scenes:
            self.logger.info('Query result: %s reference scenes with matching metadata.' % len(RSF.possib_ref_scenes))

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            # try to get a spatial reference scene by applying some filter criteria
            self.spatRef_scene = RSF.choose_ref_scene()  # type: Union[ref_Scene, None]
            if self.spatRef_scene:
                self.spatRef_available = True
                self.logger.info('Found a suitable reference image for coregistration: scene ID %s (entity ID %s).'
                                 % (self.spatRef_scene.scene_ID, self.spatRef_scene.entity_ID))
            else:
                self.spatRef_available = False
                self.logger.warning('No scene fulfills all requirements to serve as spatial reference for scene %s '
                                    '(entity ID %s). Coregistration impossible.' % (self.scene_ID, self.entity_ID))

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        else:
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            self.logger.warning('Spatial query returned no matches. Coregistration impossible.')
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            self.spatRef_available = False
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    def _get_reference_image_params_pgSQL(self):
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        # TODO implement earlier version of this function as a backup for SpatialIndexMediator
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        """postgreSQL query: get IDs of overlapping scenes and select most suitable scene_ID
            (with respect to DGM, cloud cover"""
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        warnings.warn('_get_reference_image_params_pgSQL is deprecated an will not work anymore.', DeprecationWarning)

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        # vorab-check anhand wolkenmaske, welche region von im2shift überhaupt für shift-corr tauglich ist
        # -> diese region als argument in postgresql abfrage
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        # scene_ID            = 14536400 # LE71510322000093SGS00 im2shift
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        # set query conditions
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        min_overlap = 20  # %
        max_cloudcov = 20  # %
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        plusminus_days = 30
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        AcqDate = self.im2shift_objDict['acquisition_date']
        date_minmax = [AcqDate - timedelta(days=plusminus_days), AcqDate + timedelta(days=plusminus_days)]
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        dataset_cond = 'datasetid=%s' % CFG.datasetid_spatial_ref
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        cloudcov_cond = 'cloudcover < %s' % max_cloudcov
        # FIXME cloudcover noch nicht für alle scenes im proc_level METADATA verfügbar
        dayrange_cond = "(EXTRACT(MONTH FROM scenes.acquisitiondate), EXTRACT(DAY FROM scenes.acquisitiondate)) " \
                        "BETWEEN (%s, %s) AND (%s, %s)" \
                        % (date_minmax[0].month, date_minmax[0].day, date_minmax[1].month, date_minmax[1].day)
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        # TODO weitere Kriterien einbauen!

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        def query_scenes(condlist):
            return DB_T.get_overlapping_scenes_from_postgreSQLdb(
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                CFG.conn_database,
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                table='scenes',
                tgt_corners_lonlat=self.trueDataCornerLonLat,
                conditions=condlist,
                add_cmds='ORDER BY scenes.cloudcover ASC',
                timeout=30000)
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        conds_descImportance = [dataset_cond, cloudcov_cond, dayrange_cond]

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        self.logger.info('Querying database in order to find a suitable reference scene for co-registration.')
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        count, filt_overlap_scenes = 0, []
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        while not filt_overlap_scenes:
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            if count == 0:
                # search within already processed scenes
                # das ist nur Ergebnis aus scenes_proc
                # -> dort liegt nur eine referenz, wenn die szene schon bei CFG.job-Beginn in Datensatzliste drin war
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                res = DB_T.get_overlapping_scenes_from_postgreSQLdb(
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                    CFG.conn_database,
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                    tgt_corners_lonlat=self.trueDataCornerLonLat,
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                    conditions=['datasetid=%s' % CFG.datasetid_spatial_ref],
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                    add_cmds='ORDER BY scenes.cloudcover ASC',
                    timeout=25000)
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                filt_overlap_scenes = self._sceneIDList_to_filt_overlap_scenes([i[0] for i in res[:50]], 20.)
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            else:
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                # search within complete scenes table using less filter criteria with each run
                # TODO: Daniels Index einbauen, sonst  bei wachsender Tabellengröße evtl. irgendwann zu langsam
                res = query_scenes(conds_descImportance)
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                filt_overlap_scenes = self._sceneIDList_to_filt_overlap_scenes([i[0] for i in res[:50]], min_overlap)
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                if len(conds_descImportance) > 1:  # FIXME anderer Referenzsensor?
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                    del conds_descImportance[-1]
                else:  # reduce min_overlap to 10 percent if there are overlapping scenes
                    if res:
                        min_overlap = 10
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                        filt_overlap_scenes = \
                            self._sceneIDList_to_filt_overlap_scenes([i[0] for i in res[:50]], min_overlap)
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                    # raise warnings if no match found
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                    if not filt_overlap_scenes:
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                        if not res:
                            warnings.warn('No reference scene found for %s (scene ID %s). Coregistration of this scene '
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                                          'failed.' % (self.baseN, self.scene_ID))
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                        else:
                            warnings.warn('Reference scenes for %s (scene ID %s) have been found but none has more '
                                          'than %s percent overlap. Coregistration of this scene failed.'
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                                          % (self.baseN, self.scene_ID, min_overlap))
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                        break
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            count += 1
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        if filt_overlap_scenes:
            ref_available = False
            for count, sc in enumerate(filt_overlap_scenes):
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                if count == 2:  # FIXME Abbuch schon bei 3. Szene?
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                    warnings.warn('No reference scene for %s (scene ID %s) available. '
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                                  'Coregistration of this scene failed.' % (self.baseN, self.scene_ID))
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                    break

                # start download of scene data not available and start L1A processing
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                def dl_cmd(scene_ID): print('%s %s %s' % (
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                    CFG.java_commands['keyword'].strip(),  # FIXME CFG.java_commands is deprecated
                    CFG.java_commands["value_download"].strip(), scene_ID))
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                path = PG.path_generator(scene_ID=sc['scene_ID']).get_path_imagedata()
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                if not os.path.exists(path):
                    # add scene 2 download to scenes_jobs.missing_scenes

                    # print JAVA download command
                    t_dl_start = time.time()
                    dl_cmd(sc['scene_ID'])

                    # check if scene is downloading
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                    download_start_timeout = 5  # seconds
                    # set timout for external processing
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                    # -> DEPRECATED BECAUSE CREATION OF EXTERNAL CFG WITHIN CFG IS NOT ALLOWED
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                    processing_timeout = 5  # seconds # FIXME increase timeout if processing is really started
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                    proc_level = None
                    while True:
                        proc_level_chk = DB_T.get_info_from_postgreSQLdb(
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                            CFG.conn_database, 'scenes', ['proc_level'], {'id': sc['scene_ID']})[0][0]
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                        if proc_level != proc_level_chk:
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                            print('Reference scene %s, current processing level: %s' % (sc['scene_ID'], proc_level_chk))
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                        proc_level = proc_level_chk
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                        if proc_level_chk in ['SCHEDULED', 'METADATA'] and \
                           time.time() - t_dl_start >= download_start_timeout:
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                            warnings.warn('Download of reference scene %s (entity ID %s) timed out. '
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                                          'Coregistration of this scene failed.' % (self.baseN, self.scene_ID))
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                            break
                        if proc_level_chk == 'L1A':
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                            ref_available = True
                            break
                        elif proc_level_chk == 'DOWNLOADED' and time.time() - t_dl_start >= processing_timeout:
                            # proc_level='DOWNLOADED' but scene has not been processed
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                            warnings.warn('L1A processing of reference scene %s (entity ID %s) timed out. '
                                          'Coregistration of this scene failed.' % (self.baseN, self.scene_ID))
                            break
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                            # DB_T.set_info_in_postgreSQLdb(CFG.conn_database,'scenes',
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                            #                             {'proc_level':'METADATA'},{'id':sc['scene_ID']})
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                        time.sleep(5)
                else:
                    ref_available = True

                if not ref_available:
                    continue
                else:
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                    self.path_imref = path
                    self.imref_scene_ID = sc['scene_ID']
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                    self.imref_footprint_poly = sc['scene poly']
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                    self.overlap_poly = sc['overlap poly']
                    self.overlap_percentage = sc['overlap percentage']
                    self.overlap_area = sc['overlap area']

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                    query_res = DB_T.get_info_from_postgreSQLdb(CFG.conn_database, 'scenes', ['entityid'],
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                                                                {'id': self.imref_scene_ID}, records2fetch=1)
                    assert query_res != [], 'No entity-ID found for scene number %s' % self.imref_scene_ID
                    self.imref_entity_ID = query_res[0][0]  # [('LC81510322013152LGN00',)]
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                    break
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        self.logger.close()
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    def _sceneIDList_to_filt_overlap_scenes(self, sceneIDList, min_overlap):
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        """find reference scenes that cover at least 20% of the scene with the given ID
        ONLY FIRST 50 scenes are considered"""

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        # t0 = time.time()
        dict_sceneID_poly = [{'scene_ID': ID, 'scene poly': HLP_F.scene_ID_to_shapelyPolygon(ID)}
                             for ID in sceneIDList]  # always returns LonLot polygons
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        # get overlap polygons and their parameters. result: [{overlap poly, overlap percentage, overlap area}]
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        for dic in dict_sceneID_poly:  # input: dicts {scene_ID, ref_poly}
            dict_overlap_poly_params = get_overlap_polygon(dic['scene poly'], self.arr.footprint_poly)
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            dic.update(dict_overlap_poly_params)  # adds {overlap poly, overlap percentage, overlap area}
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        # print('polygon creation time', time.time()-t0)
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        # filter those scene_IDs out where overlap percentage is below 20%
        if min_overlap:
            filt_overlap_scenes = [scene for scene in dict_sceneID_poly if scene['overlap percentage'] >= min_overlap]
        else:
            filt_overlap_scenes = dict_sceneID_poly

        return filt_overlap_scenes
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    def get_opt_bands4matching(self, target_cwlPos_nm=550, v=False):
        """Automatically determines the optimal bands used für fourier shift theorem matching

        :param target_cwlPos_nm:   the desired wavelength used for matching
        :param v:                  verbose mode
        """
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        # get GMS_object for reference scene
        path_gmsFile = PG.path_generator(scene_ID=self.spatRef_scene.scene_ID).get_path_gmsfile()
        ref_obj = GMS_object().from_disk((path_gmsFile, ['cube', None]))

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        # get spectral characteristics
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        ref_cwl, shift_cwl = [[float(i) for i in GMS_obj.meta_odict['wavelength']] for GMS_obj in [ref_obj, self]]
        ref_fwhm, shift_fwhm = [[float(i) for i in GMS_obj.meta_odict['bandwidths']] for GMS_obj in [ref_obj, self]]
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        # exclude cirrus/oxygen band of Landsat-8/Sentinel-2
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        shift_bbl, ref_bbl = [False] * len(shift_cwl), [False] * len(ref_cwl)  # bad band lists
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        for GMS_obj, s_r, bbl in zip([self, ref_obj], ['shift', 'ref'], [shift_bbl, ref_bbl]):
            GMS_obj.GMS_identifier['logger'] = None  # set a dummy value in order to avoid Exception
            sensorcode = get_GMS_sensorcode(GMS_obj.GMS_identifier)
            if sensorcode in ['LDCM', 'S2A', 'S2B'] and '9' in GMS_obj.LayerBandsAssignment:
                bbl[GMS_obj.LayerBandsAssignment.index('9')] = True
            if sensorcode in ['S2A', 'S2B'] and '10' in GMS_obj.LayerBandsAssignment:
                bbl[GMS_obj.LayerBandsAssignment.index('10')] = True
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        # cwl_overlap = (max(min(shift_cwl),min(ref_cwl)),  min(max(shift_cwl),max(ref_cwl))) # -> (min wvl, max wvl)
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        # find matching band of reference image for each band of image to be shifted
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        match_dic = collections.OrderedDict()
        for idx, cwl, fwhm in zip(range(len(shift_cwl)), shift_cwl, shift_fwhm):
            if shift_bbl[idx]:
                continue  # skip cwl if it is declared as bad band above
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            def is_inside(r_cwl, s_cwl, s_fwhm): return s_cwl - s_fwhm / 2 < r_cwl < s_cwl + s_fwhm / 2

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            matching_r_cwls = [r_cwl for i, r_cwl in enumerate(ref_cwl) if
                               is_inside(r_cwl, cwl, fwhm) and not ref_bbl[i]]
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            if matching_r_cwls:
                match_dic[cwl] = matching_r_cwls[0] if len(matching_r_cwls) else \
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                    matching_r_cwls[np.abs(np.array(matching_r_cwls) - cwl).argmin()]
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        # set bands4 match based on the above results
        poss_cwls = [cwl for cwl in shift_cwl if cwl in match_dic]
        if poss_cwls:
            if not target_cwlPos_nm:
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                shift_band4match = shift_cwl.index(poss_cwls[0]) + 1  # first possible shift band
                ref_band4match = ref_cwl.index(match_dic[poss_cwls[0]]) + 1  # matching reference band
            else:  # target_cwlPos is given
                closestWvl_to_target = poss_cwls[np.abs(np.array(poss_cwls) - target_cwlPos_nm).argmin()]
                shift_band4match = shift_cwl.index(closestWvl_to_target) + 1  # the shift band closest to target
                ref_band4match = ref_cwl.index(match_dic[closestWvl_to_target]) + 1  # matching ref
        else:  # all reference bands are outside of shift-cwl +- fwhm/2
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            warnings.warn('Optimal bands for matching could not be automatically determined. Choosing first band of'
                          'each image.')
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            shift_band4match = 1
            ref_band4match = 1
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        if v:
            print('Shift band for matching:     %s (%snm)' % (shift_band4match, shift_cwl[shift_band4match - 1]))
            print('Reference band for matching: %s (%snm)' % (ref_band4match, ref_cwl[ref_band4match - 1]))
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        return ref_band4match, shift_band4match

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    def compute_global_shifts(self):
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        spatIdxSrv_status = os.environ['GMS_SPAT_IDX_SRV_STATUS'] if 'GMS_SPAT_IDX_SRV_STATUS' in os.environ else True

        if spatIdxSrv_status == 'unavailable':
            self.logger.warning('Coregistration skipped due to unavailable Spatial Index Mediator Server!"')

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        elif CFG.skip_coreg:
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            self.logger.warning('Coregistration skipped according to user configuration.')
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        elif self.coreg_needed and self.spatRef_available:
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            geoArr_ref = GeoArray(self.spatRef_scene.filePath)
            geoArr_shift = GeoArray(self.arr)
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            r_b4match, s_b4match = self.get_opt_bands4matching(target_cwlPos_nm=CFG.coreg_band_wavelength_for_matching,
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                                                               v=False)
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            coreg_kwargs = dict(
                r_b4match=r_b4match,
                s_b4match=s_b4match,
                align_grids=True,  # FIXME not needed here
                match_gsd=True,  # FIXME not needed here
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                max_shift=CFG.coreg_max_shift_allowed,
Daniel Scheffler's avatar
Fix.    
Daniel Scheffler committed
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                ws=CFG.coreg_window_size,
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                data_corners_ref=[[x, y] for x, y in self.spatRef_scene.polyUTM.convex_hull.exterior.coords],
                data_corners_tgt=[transform_any_prj(EPSG2WKT(4326), self.meta_odict['coordinate system string'], x, y)
                                  for x, y in HLP_F.reorder_CornerLonLat(self.trueDataCornerLonLat)],
                nodata=(get_outFillZeroSaturated(geoArr_ref.dtype)[0],
                        get_outFillZeroSaturated(geoArr_shift.dtype)[0]),
                ignore_errors=True,
                v=False,
                q=True
            )
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            COREG_obj = COREG(geoArr_ref, geoArr_shift, **coreg_kwargs)
            COREG_obj.calculate_spatial_shifts()

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            self.coreg_info.update(
                COREG_obj.coreg_info)  # no clipping to trueCornerLonLat until here -> only shift correction
            self.coreg_info.update({'reference scene ID': self.spatRef_scene.scene_ID})
            self.coreg_info.update({'reference entity ID': self.spatRef_scene.entity_ID})
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            self.coreg_info.update({'shift_reliability': COREG_obj.shift_reliability})
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            if COREG_obj.success:
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                self.coreg_info['success'] = True
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                self.logger.info("Calculated map shifts (X,Y): %s / %s"
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                                 % (COREG_obj.x_shift_map,
                                    COREG_obj.y_shift_map))  # FIXME direkt in calculate_spatial_shifts loggen
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            else:
                # TODO add database entry with error hint
                [self.logger.error('ERROR during coregistration of scene %s (entity ID %s):\n%s'
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                                   % (self.scene_ID, self.entity_ID, err)) for err in COREG_obj.tracked_errors]
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        else:
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            if self.coreg_needed:
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                self.logger.warning('Coregistration skipped because no suitable reference scene is available or '
                                    'spatial query failed.')
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            else:
                self.logger.info('Coregistration of scene %s (entity ID %s) skipped because target dataset ID equals '
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                                 'reference dataset ID.' % (self.scene_ID, self.entity_ID))

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    def correct_spatial_shifts(self, cliptoextent=True, clipextent=None, clipextent_prj=None, v=False):
        # type: (bool, list, any, bool) -> None
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        """Corrects the spatial shifts calculated by self.compute_global_shifts().
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        :param cliptoextent:    whether to clip the output to the given extent
        :param clipextent:      list of XY-coordinate tuples giving the target extent (if not given and cliptoextent is
                                True, the 'trueDataCornerLonLat' attribute of the GMS object is used
        :param clipextent_prj:  WKT projection string or EPSG code of the projection for the coordinates in clipextent
        :param v:
        :return:
        """

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        # cliptoextent is automatically True if an extent is given
        cliptoextent = cliptoextent if not clipextent else True
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        if cliptoextent or self.resamp_needed or (self.coreg_needed and self.coreg_info['success']):

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            # get target bounds # TODO implement boxObj call instead here
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            if not clipextent:
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                trueDataCornerUTM = [transform_any_prj(EPSG2WKT(4326), self.MetaObj.projection, x, y)
                                     for x, y in self.trueDataCornerLonLat]
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                xmin, xmax, ymin, ymax = corner_coord_to_minmax(trueDataCornerUTM)
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                mapBounds = box(xmin, ymin, xmax, ymax).bounds
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            else:
                assert clipextent_prj, \
                    "'clipextent_prj' must be given together with 'clipextent'. Received only 'clipextent'."
                clipextent_UTM = clipextent if prj_equal(self.MetaObj.projection, clipextent_prj) else \
                    [transform_any_prj(clipextent_prj, self.MetaObj.projection, x, y) for x, y in clipextent]
                xmin, xmax, ymin, ymax = corner_coord_to_minmax(clipextent_UTM)
                mapBounds = box(xmin, ymin, xmax, ymax).bounds

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            # correct shifts and clip to extent
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            # ensure self.masks exists (does not exist in case of inmem_serialization mode because
            # then self.fill_from_disk() is skipped)
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            if not hasattr(self, 'masks') or self.masks is None:
                self.build_combined_masks_array()  # creates self.masks and self.masks_meta

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            # exclude self.mask_nodata, self.mask_clouds from warping
            del self.mask_nodata, self.mask_clouds

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            attributes2deshift = [attrname for attrname in
                                  ['arr', 'masks', 'dem', 'ac_errors', 'mask_clouds_confidence']
                                  if getattr(self, '_%s' % attrname) is not None]
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            for attrname in attributes2deshift:
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                geoArr = getattr(self, attrname)
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                # do some logging
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                if self.coreg_needed:
                    if self.coreg_info['success']:
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                        self.logger.info("Correcting spatial shifts for attribute '%s'..." % attrname)
                    elif cliptoextent and is_coord_grid_equal(
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                         geoArr.gt, CFG.spatial_ref_gridx, CFG.spatial_ref_gridy):
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                        self.logger.info("Attribute '%s' has only been clipped to it's extent because no valid "
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                                         "shifts have been detected and the pixel grid equals the target grid."
                                         % attrname)
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                    elif self.resamp_needed:
                        self.logger.info("Resampling attribute '%s' to target grid..." % attrname)
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                elif self.resamp_needed:
                    self.logger.info("Resampling attribute '%s' to target grid..." % attrname)

                # correct shifts
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                DS = DESHIFTER(geoArr, self.coreg_info,
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                               target_xyGrid=[CFG.spatial_ref_gridx, CFG.spatial_ref_gridy],
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                               cliptoextent=cliptoextent,
                               clipextent=mapBounds,
                               align_grids=True,
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                               resamp_alg='nearest' if attrname == 'masks' else CFG.spatial_resamp_alg,
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                               CPUs=None if CFG.allow_subMultiprocessing else 1,
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                               progress=True if v else False,
                               q=True,
                               v=v)
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                DS.correct_shifts()

                # update coreg_info
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                if attrname == 'arr':
                    self.coreg_info['is shifted'] = DS.is_shifted
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                    self.coreg_info['is resampled'] = DS.is_resampled
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                # update geoinformations and array shape related attributes
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                self.logger.info("Updating geoinformations of '%s' attribute..." % attrname)
                if attrname == 'arr':
                    self.meta_odict['map info'] = DS.updated_map_info
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                    self.meta_odict['coordinate system string'] = EPSG2WKT(WKT2EPSG(DS.updated_projection))
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                    self.shape_fullArr = DS.arr_shifted.shape
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                    self.meta_odict['lines'], self.meta_odict['samples'] = DS.arr_shifted.shape[:2]
                else:
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                    self.masks_meta['map info'] = DS.updated_map_info
                    self.masks_meta['coordinate system string'] = EPSG2WKT(WKT2EPSG(DS.updated_projection))
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                    self.masks_meta['lines'], self.masks_meta['samples'] = DS.arr_shifted.shape[:2]

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                    # NOTE: mask_nodata and mask_clouds are updated later by L2A_map mapper function (module pipeline)

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                # update the GeoArray instance without loosing its inherent metadata (nodata, ...)
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                geoArr.arr, geoArr.gt, geoArr.prj = \
                    DS.GeoArray_shifted.arr, DS.GeoArray_shifted.gt, DS.GeoArray_shifted.prj
                # setattr(self,attrname, DS.GeoArray_shifted) # NOTE: don't set array earlier because setter will also
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                #                                            # update arr.gt/.prj/.nodata from meta_odict

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            self.resamp_needed = False
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            self.coreg_needed = False
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            # recreate self.masks_nodata and self.mask_clouds from self.masks
            self.mask_nodata = self.mask_nodata
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            self.mask_clouds = self.mask_clouds
            # FIXME move functionality of self.masks only to output writer and remove self.masks completely