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156 lines
6.0 KiB
Python
156 lines
6.0 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,
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QgsDistanceArea,
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QgsProject,
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QgsProcessing,
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QgsProcessingParameterFeatureSource,
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QgsProcessingParameterFileDestination,
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QgsProcessingOutputHtml,
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QgsProcessingOutputNumber,
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QgsSpatialIndex)
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from processing.algs.qgis.QgisAlgorithm import QgisAlgorithm
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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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INPUT = 'INPUT'
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OUTPUT_HTML_FILE = 'OUTPUT_HTML_FILE'
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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')
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def __init__(self):
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super().__init__()
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def initAlgorithm(self, config=None):
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self.addParameter(QgsProcessingParameterFeatureSource(self.INPUT,
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self.tr('Input layer'), [QgsProcessing.TypeVectorPoint]))
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self.addParameter(QgsProcessingParameterFileDestination(self.OUTPUT_HTML_FILE, self.tr('Nearest neighbour'), self.tr('HTML files (*.html)'), None, True))
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self.addOutput(QgsProcessingOutputHtml(self.OUTPUT_HTML_FILE, self.tr('Nearest neighbour')))
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self.addOutput(QgsProcessingOutputNumber(self.OBSERVED_MD,
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self.tr('Observed mean distance')))
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self.addOutput(QgsProcessingOutputNumber(self.EXPECTED_MD,
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self.tr('Expected mean distance')))
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self.addOutput(QgsProcessingOutputNumber(self.NN_INDEX,
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self.tr('Nearest neighbour index')))
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self.addOutput(QgsProcessingOutputNumber(self.POINT_COUNT,
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self.tr('Number of points')))
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self.addOutput(QgsProcessingOutputNumber(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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source = self.parameterAsSource(parameters, self.INPUT, context)
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output_file = self.parameterAsFileOutput(parameters, self.OUTPUT_HTML_FILE, context)
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spatialIndex = QgsSpatialIndex(source, feedback)
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distance = QgsDistanceArea()
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distance.setSourceCrs(source.sourceCrs())
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distance.setEllipsoid(context.project().ellipsoid())
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sumDist = 0.00
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A = source.sourceExtent()
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A = float(A.width() * A.height())
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features = source.getFeatures()
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count = source.featureCount()
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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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if feedback.isCanceled():
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break
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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(source.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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results = {}
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results[self.OBSERVED_MD] = do
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results[self.EXPECTED_MD] = de
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results[self.NN_INDEX] = d
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results[self.POINT_COUNT] = count
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results[self.Z_SCORE] = zscore
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if output_file:
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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_file, data)
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results[self.OUTPUT_HTML_FILE] = output_file
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return results
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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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