mirror of
https://github.com/qgis/QGIS.git
synced 2025-11-04 00:04:25 -05:00
156 lines
6.0 KiB
Python
156 lines
6.0 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. *
|
|
* *
|
|
***************************************************************************
|
|
"""
|
|
from builtins import next
|
|
from builtins import str
|
|
|
|
__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 qgis.PyQt.QtGui import QIcon
|
|
|
|
from qgis.core import (QgsFeatureRequest,
|
|
QgsDistanceArea,
|
|
QgsProject,
|
|
QgsProcessing,
|
|
QgsProcessingParameterFeatureSource,
|
|
QgsProcessingParameterFileDestination,
|
|
QgsProcessingOutputHtml,
|
|
QgsProcessingOutputNumber,
|
|
QgsSpatialIndex)
|
|
|
|
from processing.algs.qgis.QgisAlgorithm import QgisAlgorithm
|
|
|
|
pluginPath = os.path.split(os.path.split(os.path.dirname(__file__))[0])[0]
|
|
|
|
|
|
class NearestNeighbourAnalysis(QgisAlgorithm):
|
|
|
|
INPUT = 'INPUT'
|
|
OUTPUT_HTML_FILE = 'OUTPUT_HTML_FILE'
|
|
OBSERVED_MD = 'OBSERVED_MD'
|
|
EXPECTED_MD = 'EXPECTED_MD'
|
|
NN_INDEX = 'NN_INDEX'
|
|
POINT_COUNT = 'POINT_COUNT'
|
|
Z_SCORE = 'Z_SCORE'
|
|
|
|
def icon(self):
|
|
return QIcon(os.path.join(pluginPath, 'images', 'ftools', 'neighbour.png'))
|
|
|
|
def group(self):
|
|
return self.tr('Vector analysis')
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
def initAlgorithm(self, config=None):
|
|
self.addParameter(QgsProcessingParameterFeatureSource(self.INPUT,
|
|
self.tr('Input layer'), [QgsProcessing.TypeVectorPoint]))
|
|
|
|
self.addParameter(QgsProcessingParameterFileDestination(self.OUTPUT_HTML_FILE, self.tr('Nearest neighbour'), self.tr('HTML files (*.html)'), None, True))
|
|
self.addOutput(QgsProcessingOutputHtml(self.OUTPUT_HTML_FILE, self.tr('Nearest neighbour')))
|
|
|
|
self.addOutput(QgsProcessingOutputNumber(self.OBSERVED_MD,
|
|
self.tr('Observed mean distance')))
|
|
self.addOutput(QgsProcessingOutputNumber(self.EXPECTED_MD,
|
|
self.tr('Expected mean distance')))
|
|
self.addOutput(QgsProcessingOutputNumber(self.NN_INDEX,
|
|
self.tr('Nearest neighbour index')))
|
|
self.addOutput(QgsProcessingOutputNumber(self.POINT_COUNT,
|
|
self.tr('Number of points')))
|
|
self.addOutput(QgsProcessingOutputNumber(self.Z_SCORE, self.tr('Z-Score')))
|
|
|
|
def name(self):
|
|
return 'nearestneighbouranalysis'
|
|
|
|
def displayName(self):
|
|
return self.tr('Nearest neighbour analysis')
|
|
|
|
def processAlgorithm(self, parameters, context, feedback):
|
|
source = self.parameterAsSource(parameters, self.INPUT, context)
|
|
output_file = self.parameterAsFileOutput(parameters, self.OUTPUT_HTML_FILE, context)
|
|
|
|
spatialIndex = QgsSpatialIndex(source, feedback)
|
|
|
|
distance = QgsDistanceArea()
|
|
distance.setSourceCrs(source.sourceCrs())
|
|
distance.setEllipsoid(context.project().ellipsoid())
|
|
|
|
sumDist = 0.00
|
|
A = source.sourceExtent()
|
|
A = float(A.width() * A.height())
|
|
|
|
features = source.getFeatures()
|
|
count = source.featureCount()
|
|
total = 100.0 / count if count else 1
|
|
for current, feat in enumerate(features):
|
|
if feedback.isCanceled():
|
|
break
|
|
|
|
neighbourID = spatialIndex.nearestNeighbor(
|
|
feat.geometry().asPoint(), 2)[1]
|
|
request = QgsFeatureRequest().setFilterFid(neighbourID).setSubsetOfAttributes([])
|
|
neighbour = next(source.getFeatures(request))
|
|
sumDist += distance.measureLine(neighbour.geometry().asPoint(),
|
|
feat.geometry().asPoint())
|
|
|
|
feedback.setProgress(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)
|
|
|
|
results = {}
|
|
results[self.OBSERVED_MD] = do
|
|
results[self.EXPECTED_MD] = de
|
|
results[self.NN_INDEX] = d
|
|
results[self.POINT_COUNT] = count
|
|
results[self.Z_SCORE] = zscore
|
|
|
|
if output_file:
|
|
data = []
|
|
data.append('Observed mean distance: ' + str(do))
|
|
data.append('Expected mean distance: ' + str(de))
|
|
data.append('Nearest neighbour index: ' + str(d))
|
|
data.append('Number of points: ' + str(count))
|
|
data.append('Z-Score: ' + str(zscore))
|
|
self.createHTML(output_file, data)
|
|
results[self.OUTPUT_HTML_FILE] = output_file
|
|
|
|
return results
|
|
|
|
def createHTML(self, outputFile, algData):
|
|
with codecs.open(outputFile, 'w', encoding='utf-8') as f:
|
|
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>' + str(s) + '</p>')
|
|
f.write('</body></html>')
|