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99cfb8faf3
Instead we output an empty layer - this may be critical for multi-step models where there is validly no features present in a source layer
141 lines
5.4 KiB
Python
141 lines
5.4 KiB
Python
# -*- coding: utf-8 -*-
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"""
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***************************************************************************
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NearestNeighbourAnalysis.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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from builtins import next
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from builtins import str
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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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# This will get replaced with a git SHA1 when you do a git archive
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__revision__ = '$Format:%H$'
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import os
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import math
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import codecs
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from qgis.PyQt.QtGui import QIcon
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from qgis.core import QgsFeatureRequest, QgsFeature, QgsDistanceArea, QgsProcessingUtils
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from processing.algs.qgis.QgisAlgorithm import QgisAlgorithm
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from processing.core.parameters import ParameterVector
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from processing.core.outputs import OutputHTML
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from processing.core.outputs import OutputNumber
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from processing.tools import dataobjects
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pluginPath = os.path.split(os.path.split(os.path.dirname(__file__))[0])[0]
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class NearestNeighbourAnalysis(QgisAlgorithm):
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POINTS = 'POINTS'
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OUTPUT = 'OUTPUT'
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OBSERVED_MD = 'OBSERVED_MD'
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EXPECTED_MD = 'EXPECTED_MD'
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NN_INDEX = 'NN_INDEX'
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POINT_COUNT = 'POINT_COUNT'
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Z_SCORE = 'Z_SCORE'
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def icon(self):
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return QIcon(os.path.join(pluginPath, 'images', 'ftools', 'neighbour.png'))
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def group(self):
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return self.tr('Vector analysis tools')
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def __init__(self):
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super().__init__()
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self.addParameter(ParameterVector(self.POINTS,
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self.tr('Points'), [dataobjects.TYPE_VECTOR_POINT]))
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self.addOutput(OutputHTML(self.OUTPUT, self.tr('Nearest neighbour')))
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self.addOutput(OutputNumber(self.OBSERVED_MD,
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self.tr('Observed mean distance')))
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self.addOutput(OutputNumber(self.EXPECTED_MD,
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self.tr('Expected mean distance')))
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self.addOutput(OutputNumber(self.NN_INDEX,
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self.tr('Nearest neighbour index')))
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self.addOutput(OutputNumber(self.POINT_COUNT,
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self.tr('Number of points')))
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self.addOutput(OutputNumber(self.Z_SCORE, self.tr('Z-Score')))
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def name(self):
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return 'nearestneighbouranalysis'
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def displayName(self):
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return self.tr('Nearest neighbour analysis')
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def processAlgorithm(self, parameters, context, feedback):
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layer = QgsProcessingUtils.mapLayerFromString(self.getParameterValue(self.POINTS), context)
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output = self.getOutputValue(self.OUTPUT)
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spatialIndex = QgsProcessingUtils.createSpatialIndex(layer, context)
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neighbour = QgsFeature()
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distance = QgsDistanceArea()
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sumDist = 0.00
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A = layer.extent()
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A = float(A.width() * A.height())
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features = QgsProcessingUtils.getFeatures(layer, context)
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count = QgsProcessingUtils.featureCount(layer, context)
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total = 100.0 / count if count else 1
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for current, feat in enumerate(features):
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neighbourID = spatialIndex.nearestNeighbor(
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feat.geometry().asPoint(), 2)[1]
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request = QgsFeatureRequest().setFilterFid(neighbourID).setSubsetOfAttributes([])
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neighbour = next(layer.getFeatures(request))
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sumDist += distance.measureLine(neighbour.geometry().asPoint(),
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feat.geometry().asPoint())
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feedback.setProgress(int(current * total))
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do = float(sumDist) / count
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de = float(0.5 / math.sqrt(count / A))
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d = float(do / de)
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SE = float(0.26136 / math.sqrt(count ** 2 / A))
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zscore = float((do - de) / SE)
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data = []
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data.append('Observed mean distance: ' + str(do))
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data.append('Expected mean distance: ' + str(de))
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data.append('Nearest neighbour index: ' + str(d))
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data.append('Number of points: ' + str(count))
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data.append('Z-Score: ' + str(zscore))
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self.createHTML(output, data)
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self.setOutputValue(self.OBSERVED_MD, float(data[0].split(': ')[1]))
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self.setOutputValue(self.EXPECTED_MD, float(data[1].split(': ')[1]))
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self.setOutputValue(self.NN_INDEX, float(data[2].split(': ')[1]))
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self.setOutputValue(self.POINT_COUNT, float(data[3].split(': ')[1]))
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self.setOutputValue(self.Z_SCORE, float(data[4].split(': ')[1]))
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def createHTML(self, outputFile, algData):
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with codecs.open(outputFile, 'w', encoding='utf-8') as f:
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f.write('<html><head>')
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f.write('<meta http-equiv="Content-Type" content="text/html; \
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charset=utf-8" /></head><body>')
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for s in algData:
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f.write('<p>' + str(s) + '</p>')
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f.write('</body></html>')
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