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231 lines
8.0 KiB
Python
231 lines
8.0 KiB
Python
"""
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***************************************************************************
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PointsDisplacement.py
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---------------------
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Date : July 2013
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Copyright : (C) 2013 by Alexander Bruy
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Email : alexander dot bruy at gmail dot com
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***************************************************************************
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* *
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* This program is free software; you can redistribute it and/or modify *
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* it under the terms of the GNU General Public License as published by *
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* the Free Software Foundation; either version 2 of the License, or *
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* (at your option) any later version. *
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* *
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***************************************************************************
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"""
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__author__ = "Alexander Bruy"
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__date__ = "July 2013"
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__copyright__ = "(C) 2013, Alexander Bruy"
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import math
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from qgis.core import (
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QgsFeatureSink,
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QgsGeometry,
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QgsPointXY,
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QgsSpatialIndex,
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QgsRectangle,
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QgsProcessing,
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QgsProcessingException,
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QgsProcessingParameterFeatureSource,
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QgsProcessingParameterDistance,
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QgsProcessingParameterBoolean,
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QgsProcessingParameterFeatureSink,
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)
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from processing.algs.qgis.QgisAlgorithm import QgisAlgorithm
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class PointsDisplacement(QgisAlgorithm):
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INPUT = "INPUT"
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DISTANCE = "DISTANCE"
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PROXIMITY = "PROXIMITY"
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HORIZONTAL = "HORIZONTAL"
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OUTPUT = "OUTPUT"
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def group(self):
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return self.tr("Vector geometry")
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def groupId(self):
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return "vectorgeometry"
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def __init__(self):
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super().__init__()
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def initAlgorithm(self, config=None):
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self.addParameter(
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QgsProcessingParameterFeatureSource(
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self.INPUT,
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self.tr("Input layer"),
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[QgsProcessing.SourceType.TypeVectorPoint],
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)
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)
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param = QgsProcessingParameterDistance(
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self.PROXIMITY,
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self.tr("Minimum distance to other points"),
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parentParameterName="INPUT",
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minValue=0.00001,
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defaultValue=1.0,
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)
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param.setMetadata({"widget_wrapper": {"decimals": 5}})
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self.addParameter(param)
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param = QgsProcessingParameterDistance(
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self.DISTANCE,
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self.tr("Displacement distance"),
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parentParameterName="INPUT",
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minValue=0.00001,
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defaultValue=1.0,
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)
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param.setMetadata({"widget_wrapper": {"decimals": 5}})
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self.addParameter(param)
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self.addParameter(
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QgsProcessingParameterBoolean(
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self.HORIZONTAL, self.tr("Horizontal distribution for two point case")
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)
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)
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self.addParameter(
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QgsProcessingParameterFeatureSink(
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self.OUTPUT,
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self.tr("Displaced"),
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QgsProcessing.SourceType.TypeVectorPoint,
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)
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)
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def name(self):
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return "pointsdisplacement"
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def displayName(self):
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return self.tr("Points displacement")
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def processAlgorithm(self, parameters, context, feedback):
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source = self.parameterAsSource(parameters, self.INPUT, context)
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if source is None:
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raise QgsProcessingException(
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self.invalidSourceError(parameters, self.INPUT)
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)
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proximity = self.parameterAsDouble(parameters, self.PROXIMITY, context)
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radius = self.parameterAsDouble(parameters, self.DISTANCE, context)
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horizontal = self.parameterAsBoolean(parameters, self.HORIZONTAL, context)
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(sink, dest_id) = self.parameterAsSink(
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parameters,
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self.OUTPUT,
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context,
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source.fields(),
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source.wkbType(),
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source.sourceCrs(),
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)
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if sink is None:
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raise QgsProcessingException(self.invalidSinkError(parameters, self.OUTPUT))
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features = source.getFeatures()
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total = 100.0 / source.featureCount() if source.featureCount() else 0
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def searchRect(p):
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return QgsRectangle(
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p.x() - proximity,
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p.y() - proximity,
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p.x() + proximity,
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p.y() + proximity,
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)
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index = QgsSpatialIndex()
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# NOTE: this is a Python port of QgsPointDistanceRenderer::renderFeature. If refining this algorithm,
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# please port the changes to QgsPointDistanceRenderer::renderFeature also!
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clustered_groups = []
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group_index = {}
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group_locations = {}
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for current, f in enumerate(features):
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if feedback.isCanceled():
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break
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if not f.hasGeometry():
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continue
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point = f.geometry().asPoint()
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other_features_within_radius = index.intersects(searchRect(point))
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if not other_features_within_radius:
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index.addFeature(f)
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group = [f]
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clustered_groups.append(group)
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group_index[f.id()] = len(clustered_groups) - 1
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group_locations[f.id()] = point
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else:
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# find group with closest location to this point (may be more than one within search tolerance)
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min_dist_feature_id = other_features_within_radius[0]
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min_dist = group_locations[min_dist_feature_id].distance(point)
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for i in range(1, len(other_features_within_radius)):
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candidate_id = other_features_within_radius[i]
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new_dist = group_locations[candidate_id].distance(point)
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if new_dist < min_dist:
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min_dist = new_dist
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min_dist_feature_id = candidate_id
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group_index_pos = group_index[min_dist_feature_id]
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group = clustered_groups[group_index_pos]
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# calculate new centroid of group
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old_center = group_locations[min_dist_feature_id]
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group_locations[min_dist_feature_id] = QgsPointXY(
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(old_center.x() * len(group) + point.x()) / (len(group) + 1.0),
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(old_center.y() * len(group) + point.y()) / (len(group) + 1.0),
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)
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# add to a group
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clustered_groups[group_index_pos].append(f)
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group_index[f.id()] = group_index_pos
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feedback.setProgress(int(current * total))
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current = 0
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total = 100.0 / len(clustered_groups) if clustered_groups else 1
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feedback.setProgress(0)
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fullPerimeter = 2 * math.pi
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for group in clustered_groups:
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if feedback.isCanceled():
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break
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count = len(group)
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if count == 1:
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sink.addFeature(group[0], QgsFeatureSink.Flag.FastInsert)
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else:
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angleStep = fullPerimeter / count
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if count == 2 and horizontal:
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currentAngle = math.pi / 2
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else:
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currentAngle = 0
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old_point = group_locations[group[0].id()]
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for f in group:
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if feedback.isCanceled():
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break
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sinusCurrentAngle = math.sin(currentAngle)
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cosinusCurrentAngle = math.cos(currentAngle)
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dx = radius * sinusCurrentAngle
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dy = radius * cosinusCurrentAngle
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# we want to keep any existing m/z values
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point = f.geometry().constGet().clone()
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point.setX(old_point.x() + dx)
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point.setY(old_point.y() + dy)
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f.setGeometry(QgsGeometry(point))
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sink.addFeature(f, QgsFeatureSink.Flag.FastInsert)
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currentAngle += angleStep
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current += 1
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feedback.setProgress(int(current * total))
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sink.finalize()
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return {self.OUTPUT: dest_id}
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