# -*- 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'
# This will get replaced with a git SHA1 when you do a git archive
__revision__ = '$Format:%H$'
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.parameterAsBool(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}