mirror of
https://github.com/qgis/QGIS.git
synced 2025-03-03 00:02:25 -05:00
133 lines
5.1 KiB
Python
133 lines
5.1 KiB
Python
# -*- 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'
|
|
|
|
# This will get replaced with a git SHA1 when you do a git archive
|
|
|
|
__revision__ = '$Format:%H$'
|
|
|
|
import os
|
|
import math
|
|
import codecs
|
|
|
|
from PyQt4.QtGui import QIcon
|
|
|
|
from qgis.core import QgsFeatureRequest, QgsFeature, QgsDistanceArea
|
|
|
|
from processing.core.GeoAlgorithm import GeoAlgorithm
|
|
from processing.core.parameters import ParameterVector
|
|
from processing.core.outputs import OutputHTML
|
|
from processing.core.outputs import OutputNumber
|
|
from processing.tools import dataobjects, vector
|
|
|
|
pluginPath = os.path.split(os.path.split(os.path.dirname(__file__))[0])[0]
|
|
|
|
|
|
class NearestNeighbourAnalysis(GeoAlgorithm):
|
|
|
|
POINTS = 'POINTS'
|
|
OUTPUT = 'OUTPUT'
|
|
OBSERVED_MD = 'OBSERVED_MD'
|
|
EXPECTED_MD = 'EXPECTED_MD'
|
|
NN_INDEX = 'NN_INDEX'
|
|
POINT_COUNT = 'POINT_COUNT'
|
|
Z_SCORE = 'Z_SCORE'
|
|
|
|
def getIcon(self):
|
|
return QIcon(os.path.join(pluginPath, 'images', 'ftools', 'neighbour.png'))
|
|
|
|
def defineCharacteristics(self):
|
|
self.name, self.i18n_name = self.trAlgorithm('Nearest neighbour analysis')
|
|
self.group, self.i18n_group = self.trAlgorithm('Vector analysis tools')
|
|
|
|
self.addParameter(ParameterVector(self.POINTS,
|
|
self.tr('Points'), [ParameterVector.VECTOR_TYPE_POINT]))
|
|
|
|
self.addOutput(OutputHTML(self.OUTPUT, self.tr('Nearest neighbour')))
|
|
|
|
self.addOutput(OutputNumber(self.OBSERVED_MD,
|
|
self.tr('Observed mean distance')))
|
|
self.addOutput(OutputNumber(self.EXPECTED_MD,
|
|
self.tr('Expected mean distance')))
|
|
self.addOutput(OutputNumber(self.NN_INDEX,
|
|
self.tr('Nearest neighbour index')))
|
|
self.addOutput(OutputNumber(self.POINT_COUNT,
|
|
self.tr('Number of points')))
|
|
self.addOutput(OutputNumber(self.Z_SCORE, self.tr('Z-Score')))
|
|
|
|
def processAlgorithm(self, progress):
|
|
layer = dataobjects.getObjectFromUri(self.getParameterValue(self.POINTS))
|
|
output = self.getOutputValue(self.OUTPUT)
|
|
|
|
spatialIndex = vector.spatialindex(layer)
|
|
|
|
neighbour = QgsFeature()
|
|
distance = QgsDistanceArea()
|
|
|
|
sumDist = 0.00
|
|
A = layer.extent()
|
|
A = float(A.width() * A.height())
|
|
|
|
features = vector.features(layer)
|
|
count = len(features)
|
|
total = 100.0 / count
|
|
for current, feat in enumerate(features):
|
|
neighbourID = spatialIndex.nearestNeighbor(
|
|
feat.geometry().asPoint(), 2)[1]
|
|
request = QgsFeatureRequest().setFilterFid(neighbourID)
|
|
neighbour = layer.getFeatures(request).next()
|
|
sumDist += distance.measureLine(neighbour.geometry().asPoint(),
|
|
feat.geometry().asPoint())
|
|
|
|
progress.setPercentage(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)
|
|
|
|
data = []
|
|
data.append('Observed mean distance: ' + unicode(do))
|
|
data.append('Expected mean distance: ' + unicode(de))
|
|
data.append('Nearest neighbour index: ' + unicode(d))
|
|
data.append('Number of points: ' + unicode(count))
|
|
data.append('Z-Score: ' + unicode(zscore))
|
|
|
|
self.createHTML(output, data)
|
|
|
|
self.setOutputValue(self.OBSERVED_MD, float(data[0].split(': ')[1]))
|
|
self.setOutputValue(self.EXPECTED_MD, float(data[1].split(': ')[1]))
|
|
self.setOutputValue(self.NN_INDEX, float(data[2].split(': ')[1]))
|
|
self.setOutputValue(self.POINT_COUNT, float(data[3].split(': ')[1]))
|
|
self.setOutputValue(self.Z_SCORE, float(data[4].split(': ')[1]))
|
|
|
|
def createHTML(self, outputFile, algData):
|
|
f = codecs.open(outputFile, 'w', encoding='utf-8')
|
|
f.write('<html><head>')
|
|
f.write('<meta http-equiv="Content-Type" content="text/html; \
|
|
charset=utf-8" /></head><body>')
|
|
for s in algData:
|
|
f.write('<p>' + unicode(s) + '</p>')
|
|
f.write('</body></html>')
|
|
f.close()
|