# -*- coding: utf-8 -*- """ *************************************************************************** NearestNeighbourAnalysis.py --------------------- Date : August 2012 Copyright : (C) 2012 by Victor Olaya Email : volayaf 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__ = 'Victor Olaya' __date__ = 'August 2012' __copyright__ = '(C) 2012, Victor Olaya' import os import math import codecs from qgis.PyQt.QtGui import QIcon from qgis.core import (QgsApplication, QgsFeatureRequest, QgsDistanceArea, QgsProject, QgsProcessing, QgsProcessingException, QgsProcessingParameterFeatureSource, QgsProcessingParameterFileDestination, QgsProcessingOutputNumber, QgsSpatialIndex) from processing.algs.qgis.QgisAlgorithm import QgisAlgorithm pluginPath = os.path.split(os.path.split(os.path.dirname(__file__))[0])[0] class NearestNeighbourAnalysis(QgisAlgorithm): INPUT = 'INPUT' OUTPUT_HTML_FILE = 'OUTPUT_HTML_FILE' OBSERVED_MD = 'OBSERVED_MD' EXPECTED_MD = 'EXPECTED_MD' NN_INDEX = 'NN_INDEX' POINT_COUNT = 'POINT_COUNT' Z_SCORE = 'Z_SCORE' def icon(self): return QgsApplication.getThemeIcon("/algorithms/mAlgorithmNearestNeighbour.svg") def svgIconPath(self): return QgsApplication.iconPath("/algorithms/mAlgorithmNearestNeighbour.svg") def group(self): return self.tr('Vector analysis') def groupId(self): return 'vectoranalysis' def __init__(self): super().__init__() def initAlgorithm(self, config=None): self.addParameter(QgsProcessingParameterFeatureSource(self.INPUT, self.tr('Input layer'), [QgsProcessing.TypeVectorPoint])) self.addParameter(QgsProcessingParameterFileDestination(self.OUTPUT_HTML_FILE, self.tr('Nearest neighbour'), self.tr('HTML files (*.html)'), None, True)) self.addOutput(QgsProcessingOutputNumber(self.OBSERVED_MD, self.tr('Observed mean distance'))) self.addOutput(QgsProcessingOutputNumber(self.EXPECTED_MD, self.tr('Expected mean distance'))) self.addOutput(QgsProcessingOutputNumber(self.NN_INDEX, self.tr('Nearest neighbour index'))) self.addOutput(QgsProcessingOutputNumber(self.POINT_COUNT, self.tr('Number of points'))) self.addOutput(QgsProcessingOutputNumber(self.Z_SCORE, self.tr('Z-Score'))) def name(self): return 'nearestneighbouranalysis' def displayName(self): return self.tr('Nearest neighbour analysis') def processAlgorithm(self, parameters, context, feedback): source = self.parameterAsSource(parameters, self.INPUT, context) if source is None: raise QgsProcessingException(self.invalidSourceError(parameters, self.INPUT)) output_file = self.parameterAsFileOutput(parameters, self.OUTPUT_HTML_FILE, context) spatialIndex = QgsSpatialIndex(source, feedback) distance = QgsDistanceArea() distance.setSourceCrs(source.sourceCrs(), context.transformContext()) distance.setEllipsoid(context.project().ellipsoid()) sumDist = 0.00 A = source.sourceExtent() A = float(A.width() * A.height()) features = source.getFeatures() count = source.featureCount() total = 100.0 / count if count else 1 for current, feat in enumerate(features): if feedback.isCanceled(): break neighbourID = spatialIndex.nearestNeighbor( feat.geometry().asPoint(), 2)[1] request = QgsFeatureRequest().setFilterFid(neighbourID).setSubsetOfAttributes([]) neighbour = next(source.getFeatures(request)) sumDist += distance.measureLine(neighbour.geometry().asPoint(), feat.geometry().asPoint()) feedback.setProgress(int(current * total)) do = float(sumDist) / count de = float(0.5 / math.sqrt(count / A)) d = float(do / de) SE = float(0.26136 / math.sqrt(count ** 2 / A)) zscore = float((do - de) / SE) results = {} results[self.OBSERVED_MD] = do results[self.EXPECTED_MD] = de results[self.NN_INDEX] = d results[self.POINT_COUNT] = count results[self.Z_SCORE] = zscore if output_file: data = [] data.append('Observed mean distance: ' + str(do)) data.append('Expected mean distance: ' + str(de)) data.append('Nearest neighbour index: ' + str(d)) data.append('Number of points: ' + str(count)) data.append('Z-Score: ' + str(zscore)) self.createHTML(output_file, data) results[self.OUTPUT_HTML_FILE] = output_file return results def createHTML(self, outputFile, algData): with codecs.open(outputFile, 'w', encoding='utf-8') as f: f.write('
') f.write('') for s in algData: f.write('' + str(s) + '
') f.write('')