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5339d62715
Include descriptive text with the specified parameter value in error, and always check that sources were loaded to avoid raw Python exceptions when they are not
159 lines
6.0 KiB
Python
159 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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__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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QgsProcessingException,
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QgsProcessingParameterFeatureSource,
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QgsProcessingParameterFileDestination,
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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 groupId(self):
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return 'vectoranalysis'
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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(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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if source is None:
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raise QgsProcessingException(self.invalidSourceError(parameters, self.INPUT))
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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(), context.transformContext())
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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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