# -*- coding: utf-8 -*- """ *************************************************************************** PointsDisplacement.py --------------------- Date : July 2013 Copyright : (C) 2013 by Alexander Bruy Email : alexander dot bruy at gmail dot com *************************************************************************** * * * This program is free software; you can redistribute it and/or modify * * it under the terms of the GNU General Public License as published by * * the Free Software Foundation; either version 2 of the License, or * * (at your option) any later version. * * * *************************************************************************** """ __author__ = 'Alexander Bruy' __date__ = 'July 2013' __copyright__ = '(C) 2013, Alexander Bruy' import math from qgis.core import (QgsFeatureSink, QgsGeometry, QgsPointXY, QgsSpatialIndex, QgsRectangle, QgsProcessing, QgsProcessingException, QgsProcessingParameterFeatureSource, QgsProcessingParameterDistance, QgsProcessingParameterBoolean, QgsProcessingParameterFeatureSink) from processing.algs.qgis.QgisAlgorithm import QgisAlgorithm class PointsDisplacement(QgisAlgorithm): INPUT = 'INPUT' DISTANCE = 'DISTANCE' PROXIMITY = 'PROXIMITY' HORIZONTAL = 'HORIZONTAL' OUTPUT = 'OUTPUT' def group(self): return self.tr('Vector geometry') def groupId(self): return 'vectorgeometry' def __init__(self): super().__init__() def initAlgorithm(self, config=None): self.addParameter(QgsProcessingParameterFeatureSource(self.INPUT, self.tr('Input layer'), [QgsProcessing.TypeVectorPoint])) self.addParameter(QgsProcessingParameterDistance(self.PROXIMITY, self.tr('Minimum distance to other points'), parentParameterName='INPUT', minValue=0.00001, defaultValue=1.0)) self.addParameter(QgsProcessingParameterDistance(self.DISTANCE, self.tr('Displacement distance'), parentParameterName='INPUT', minValue=0.00001, defaultValue=1.0)) self.addParameter(QgsProcessingParameterBoolean(self.HORIZONTAL, self.tr('Horizontal distribution for two point case'))) self.addParameter(QgsProcessingParameterFeatureSink(self.OUTPUT, self.tr('Displaced'), QgsProcessing.TypeVectorPoint)) def name(self): return 'pointsdisplacement' def displayName(self): return self.tr('Points displacement') def processAlgorithm(self, parameters, context, feedback): source = self.parameterAsSource(parameters, self.INPUT, context) if source is None: raise QgsProcessingException(self.invalidSourceError(parameters, self.INPUT)) proximity = self.parameterAsDouble(parameters, self.PROXIMITY, context) radius = self.parameterAsDouble(parameters, self.DISTANCE, context) horizontal = self.parameterAsBoolean(parameters, self.HORIZONTAL, context) (sink, dest_id) = self.parameterAsSink(parameters, self.OUTPUT, context, source.fields(), source.wkbType(), source.sourceCrs()) if sink is None: raise QgsProcessingException(self.invalidSinkError(parameters, self.OUTPUT)) features = source.getFeatures() total = 100.0 / source.featureCount() if source.featureCount() else 0 def searchRect(p): return QgsRectangle(p.x() - proximity, p.y() - proximity, p.x() + proximity, p.y() + proximity) index = QgsSpatialIndex() # NOTE: this is a Python port of QgsPointDistanceRenderer::renderFeature. If refining this algorithm, # please port the changes to QgsPointDistanceRenderer::renderFeature also! clustered_groups = [] group_index = {} group_locations = {} for current, f in enumerate(features): if feedback.isCanceled(): break if not f.hasGeometry(): continue point = f.geometry().asPoint() other_features_within_radius = index.intersects(searchRect(point)) if not other_features_within_radius: index.addFeature(f) group = [f] clustered_groups.append(group) group_index[f.id()] = len(clustered_groups) - 1 group_locations[f.id()] = point else: # find group with closest location to this point (may be more than one within search tolerance) min_dist_feature_id = other_features_within_radius[0] min_dist = group_locations[min_dist_feature_id].distance(point) for i in range(1, len(other_features_within_radius)): candidate_id = other_features_within_radius[i] new_dist = group_locations[candidate_id].distance(point) if new_dist < min_dist: min_dist = new_dist min_dist_feature_id = candidate_id group_index_pos = group_index[min_dist_feature_id] group = clustered_groups[group_index_pos] # calculate new centroid of group old_center = group_locations[min_dist_feature_id] group_locations[min_dist_feature_id] = QgsPointXY((old_center.x() * len(group) + point.x()) / (len(group) + 1.0), (old_center.y() * len(group) + point.y()) / (len(group) + 1.0)) # add to a group clustered_groups[group_index_pos].append(f) group_index[f.id()] = group_index_pos feedback.setProgress(int(current * total)) current = 0 total = 100.0 / len(clustered_groups) if clustered_groups else 1 feedback.setProgress(0) fullPerimeter = 2 * math.pi for group in clustered_groups: if feedback.isCanceled(): break count = len(group) if count == 1: sink.addFeature(group[0], QgsFeatureSink.FastInsert) else: angleStep = fullPerimeter / count if count == 2 and horizontal: currentAngle = math.pi / 2 else: currentAngle = 0 old_point = group_locations[group[0].id()] for f in group: if feedback.isCanceled(): break sinusCurrentAngle = math.sin(currentAngle) cosinusCurrentAngle = math.cos(currentAngle) dx = radius * sinusCurrentAngle dy = radius * cosinusCurrentAngle # we want to keep any existing m/z values point = f.geometry().constGet().clone() point.setX(old_point.x() + dx) point.setY(old_point.y() + dy) f.setGeometry(QgsGeometry(point)) sink.addFeature(f, QgsFeatureSink.FastInsert) currentAngle += angleStep current += 1 feedback.setProgress(int(current * total)) return {self.OUTPUT: dest_id}