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286 lines
13 KiB
Python
286 lines
13 KiB
Python
# -*- coding: utf-8 -*-
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"""
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***************************************************************************
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PointDistance.py
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---------------------
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Date : August 2012
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Copyright : (C) 2012 by Victor Olaya
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Email : volayaf 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__ = 'Victor Olaya'
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__date__ = 'August 2012'
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__copyright__ = '(C) 2012, Victor Olaya'
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import os
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import math
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from qgis.PyQt.QtGui import QIcon
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from qgis.PyQt.QtCore import QVariant
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from qgis.core import (QgsApplication,
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QgsFeatureRequest,
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QgsField,
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QgsFields,
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QgsProject,
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QgsFeature,
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QgsGeometry,
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QgsDistanceArea,
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QgsFeatureSink,
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QgsProcessingParameterFeatureSource,
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QgsProcessing,
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QgsProcessingException,
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QgsProcessingParameterEnum,
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QgsProcessingParameterField,
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QgsProcessingParameterNumber,
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QgsProcessingParameterFeatureSink,
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QgsSpatialIndex,
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QgsWkbTypes)
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from processing.algs.qgis.QgisAlgorithm import QgisAlgorithm
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pluginPath = os.path.split(os.path.split(os.path.dirname(__file__))[0])[0]
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class PointDistance(QgisAlgorithm):
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INPUT = 'INPUT'
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INPUT_FIELD = 'INPUT_FIELD'
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TARGET = 'TARGET'
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TARGET_FIELD = 'TARGET_FIELD'
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MATRIX_TYPE = 'MATRIX_TYPE'
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NEAREST_POINTS = 'NEAREST_POINTS'
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OUTPUT = 'OUTPUT'
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def icon(self):
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return QgsApplication.getThemeIcon("/algorithms/mAlgorithmDistanceMatrix.svg")
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def svgIconPath(self):
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return QgsApplication.iconPath("/algorithms/mAlgorithmDistanceMatrix.svg")
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def group(self):
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return self.tr('Vector analysis')
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def groupId(self):
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return 'vectoranalysis'
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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.mat_types = [self.tr('Linear (N*k x 3) distance matrix'),
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self.tr('Standard (N x T) distance matrix'),
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self.tr('Summary distance matrix (mean, std. dev., min, max)')]
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self.addParameter(QgsProcessingParameterFeatureSource(self.INPUT,
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self.tr('Input point layer'),
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[QgsProcessing.TypeVectorPoint]))
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self.addParameter(QgsProcessingParameterField(self.INPUT_FIELD,
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self.tr('Input unique ID field'),
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parentLayerParameterName=self.INPUT,
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type=QgsProcessingParameterField.Any))
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self.addParameter(QgsProcessingParameterFeatureSource(self.TARGET,
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self.tr('Target point layer'),
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[QgsProcessing.TypeVectorPoint]))
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self.addParameter(QgsProcessingParameterField(self.TARGET_FIELD,
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self.tr('Target unique ID field'),
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parentLayerParameterName=self.TARGET,
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type=QgsProcessingParameterField.Any))
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self.addParameter(QgsProcessingParameterEnum(self.MATRIX_TYPE,
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self.tr('Output matrix type'), options=self.mat_types, defaultValue=0))
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self.addParameter(QgsProcessingParameterNumber(self.NEAREST_POINTS,
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self.tr('Use only the nearest (k) target points'), type=QgsProcessingParameterNumber.Integer, minValue=0, defaultValue=0))
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self.addParameter(QgsProcessingParameterFeatureSink(self.OUTPUT, self.tr('Distance matrix'), QgsProcessing.TypeVectorPoint))
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def name(self):
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return 'distancematrix'
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def displayName(self):
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return self.tr('Distance matrix')
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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(self.invalidSourceError(parameters, self.INPUT))
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if QgsWkbTypes.isMultiType(source.wkbType()):
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raise QgsProcessingException(self.tr('Input point layer is a MultiPoint layer - first convert to single points before using this algorithm.'))
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source_field = self.parameterAsString(parameters, self.INPUT_FIELD, context)
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target_source = self.parameterAsSource(parameters, self.TARGET, context)
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if target_source is None:
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raise QgsProcessingException(self.invalidSourceError(parameters, self.TARGET))
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if QgsWkbTypes.isMultiType(target_source.wkbType()):
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raise QgsProcessingException(self.tr('Target point layer is a MultiPoint layer - first convert to single points before using this algorithm.'))
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target_field = self.parameterAsString(parameters, self.TARGET_FIELD, context)
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same_source_and_target = parameters[self.INPUT] == parameters[self.TARGET]
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matType = self.parameterAsEnum(parameters, self.MATRIX_TYPE, context)
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nPoints = self.parameterAsInt(parameters, self.NEAREST_POINTS, context)
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if nPoints < 1:
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nPoints = target_source.featureCount()
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if matType == 0:
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# Linear distance matrix
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return self.linearMatrix(parameters, context, source, source_field, target_source, target_field, same_source_and_target,
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matType, nPoints, feedback)
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elif matType == 1:
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# Standard distance matrix
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return self.regularMatrix(parameters, context, source, source_field, target_source, target_field,
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nPoints, feedback)
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elif matType == 2:
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# Summary distance matrix
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return self.linearMatrix(parameters, context, source, source_field, target_source, target_field, same_source_and_target,
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matType, nPoints, feedback)
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def linearMatrix(self, parameters, context, source, inField, target_source, targetField, same_source_and_target,
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matType, nPoints, feedback):
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if same_source_and_target:
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# need to fetch an extra point from the index, since the closest match will always be the same
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# as the input feature
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nPoints += 1
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inIdx = source.fields().lookupField(inField)
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outIdx = target_source.fields().lookupField(targetField)
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fields = QgsFields()
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input_id_field = source.fields()[inIdx]
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input_id_field.setName('InputID')
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fields.append(input_id_field)
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if matType == 0:
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target_id_field = target_source.fields()[outIdx]
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target_id_field.setName('TargetID')
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fields.append(target_id_field)
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fields.append(QgsField('Distance', QVariant.Double))
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else:
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fields.append(QgsField('MEAN', QVariant.Double))
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fields.append(QgsField('STDDEV', QVariant.Double))
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fields.append(QgsField('MIN', QVariant.Double))
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fields.append(QgsField('MAX', QVariant.Double))
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out_wkb = QgsWkbTypes.multiType(source.wkbType()) if matType == 0 else source.wkbType()
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(sink, dest_id) = self.parameterAsSink(parameters, self.OUTPUT, context,
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fields, out_wkb, source.sourceCrs())
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if sink is None:
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raise QgsProcessingException(self.invalidSinkError(parameters, self.OUTPUT))
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index = QgsSpatialIndex(target_source.getFeatures(QgsFeatureRequest().setSubsetOfAttributes([]).setDestinationCrs(source.sourceCrs(), context.transformContext())), feedback)
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distArea = QgsDistanceArea()
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distArea.setSourceCrs(source.sourceCrs(), context.transformContext())
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distArea.setEllipsoid(context.project().ellipsoid())
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features = source.getFeatures(QgsFeatureRequest().setSubsetOfAttributes([inIdx]))
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total = 100.0 / source.featureCount() if source.featureCount() else 0
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for current, inFeat in enumerate(features):
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if feedback.isCanceled():
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break
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inGeom = inFeat.geometry()
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inID = str(inFeat[inIdx])
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featList = index.nearestNeighbor(inGeom.asPoint(), nPoints)
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distList = []
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vari = 0.0
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request = QgsFeatureRequest().setFilterFids(featList).setSubsetOfAttributes([outIdx]).setDestinationCrs(source.sourceCrs(), context.transformContext())
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for outFeat in target_source.getFeatures(request):
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if feedback.isCanceled():
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break
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if same_source_and_target and inFeat.id() == outFeat.id():
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continue
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outID = outFeat[outIdx]
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outGeom = outFeat.geometry()
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dist = distArea.measureLine(inGeom.asPoint(),
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outGeom.asPoint())
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if matType == 0:
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out_feature = QgsFeature()
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out_geom = QgsGeometry.unaryUnion([inFeat.geometry(), outFeat.geometry()])
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out_feature.setGeometry(out_geom)
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out_feature.setAttributes([inID, outID, dist])
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sink.addFeature(out_feature, QgsFeatureSink.FastInsert)
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else:
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distList.append(float(dist))
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if matType != 0:
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mean = sum(distList) / len(distList)
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for i in distList:
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vari += (i - mean) * (i - mean)
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vari = math.sqrt(vari / len(distList))
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out_feature = QgsFeature()
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out_feature.setGeometry(inFeat.geometry())
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out_feature.setAttributes([inID, mean, vari, min(distList), max(distList)])
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sink.addFeature(out_feature, QgsFeatureSink.FastInsert)
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feedback.setProgress(int(current * total))
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return {self.OUTPUT: dest_id}
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def regularMatrix(self, parameters, context, source, inField, target_source, targetField,
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nPoints, feedback):
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distArea = QgsDistanceArea()
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distArea.setSourceCrs(source.sourceCrs(), context.transformContext())
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distArea.setEllipsoid(context.project().ellipsoid())
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inIdx = source.fields().lookupField(inField)
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targetIdx = target_source.fields().lookupField(targetField)
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index = QgsSpatialIndex(target_source.getFeatures(QgsFeatureRequest().setSubsetOfAttributes([]).setDestinationCrs(source.sourceCrs(), context.transformContext())), feedback)
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first = True
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sink = None
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dest_id = None
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features = source.getFeatures(QgsFeatureRequest().setSubsetOfAttributes([inIdx]))
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total = 100.0 / source.featureCount() if source.featureCount() else 0
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for current, inFeat in enumerate(features):
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if feedback.isCanceled():
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break
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inGeom = inFeat.geometry()
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if first:
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featList = index.nearestNeighbor(inGeom.asPoint(), nPoints)
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first = False
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fields = QgsFields()
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input_id_field = source.fields()[inIdx]
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input_id_field.setName('ID')
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fields.append(input_id_field)
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for f in target_source.getFeatures(QgsFeatureRequest().setFilterFids(featList).setSubsetOfAttributes([targetIdx]).setDestinationCrs(source.sourceCrs(), context.transformContext())):
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fields.append(QgsField(str(f[targetField]), QVariant.Double))
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(sink, dest_id) = self.parameterAsSink(parameters, self.OUTPUT, context,
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fields, source.wkbType(), source.sourceCrs())
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if sink is None:
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raise QgsProcessingException(self.invalidSinkError(parameters, self.OUTPUT))
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data = [inFeat[inField]]
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for target in target_source.getFeatures(QgsFeatureRequest().setSubsetOfAttributes([]).setFilterFids(featList).setDestinationCrs(source.sourceCrs(), context.transformContext())):
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if feedback.isCanceled():
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break
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outGeom = target.geometry()
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dist = distArea.measureLine(inGeom.asPoint(),
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outGeom.asPoint())
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data.append(dist)
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out_feature = QgsFeature()
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out_feature.setGeometry(inGeom)
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out_feature.setAttributes(data)
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sink.addFeature(out_feature, QgsFeatureSink.FastInsert)
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feedback.setProgress(int(current * total))
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return {self.OUTPUT: dest_id}
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