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pep8 --ignore=E111,E128,E201,E202,E203,E211,E221,E222,E225,E226,E227,E231,E241,E261,E265,E272,E302,E303,E501,E701 \
--exclude="ui_*.py,debian/*,python/ext-libs/*" \
.
122 lines
4.5 KiB
Python
122 lines
4.5 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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__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 math
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from qgis.core import QgsFeatureRequest, QgsFeature, QgsDistanceArea
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from processing.core.GeoAlgorithm import GeoAlgorithm
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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, vector
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class NearestNeighbourAnalysis(GeoAlgorithm):
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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 defineCharacteristics(self):
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self.name = 'Nearest neighbour analysis'
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self.group = 'Vector analysis tools'
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self.addParameter(ParameterVector(self.POINTS,
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self.tr('Points'), [ParameterVector.VECTOR_TYPE_POINT]))
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self.addOutput(OutputHTML(self.OUTPUT, self.tr('Result')))
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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 processAlgorithm(self, progress):
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layer = dataobjects.getObjectFromUri(self.getParameterValue(self.POINTS))
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output = self.getOutputValue(self.OUTPUT)
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spatialIndex = vector.spatialindex(layer)
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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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current = 0
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features = vector.features(layer)
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count = len(features)
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total = 100.0 / float(len(features))
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for feat in 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)
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neighbour = layer.getFeatures(request).next()
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sumDist += distance.measureLine(neighbour.geometry().asPoint(),
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feat.geometry().asPoint())
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current += 1
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progress.setPercentage(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: ' + unicode(do))
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data.append('Expected mean distance: ' + unicode(de))
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data.append('Nearest neighbour index: ' + unicode(d))
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data.append('Number of points: ' + unicode(count))
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data.append('Z-Score: ' + unicode(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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f = open(outputFile, 'w')
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for s in algData:
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f.write('<p>' + str(s) + '</p>')
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f.close()
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