mirror of
https://github.com/qgis/QGIS.git
synced 2025-03-07 00:02:15 -05:00
1e13d733c2
initAlgorithm() method This allows 2 benefits: - algorithms can be subclassed and have subclasses add additional parameters/outputs to the algorithm. With the previous approach of declaring parameters/outputs in the constructor, it's not possible to call virtual methods to add additional parameters/ outputs (since you can't call virtual methods from a constructor). - initAlgorithm takes a variant map argument, allowing the algorithm to dynamically adjust its declared parameters and outputs according to this configuration map. This potentially allows model algorithms which can be configured to have variable numbers of parameters and outputs at run time. E.g. a "router" algorithm which directs features to one of any number of output sinks depending on some user configured criteria.
143 lines
5.4 KiB
Python
143 lines
5.4 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, QgsFeature, QgsDistanceArea, QgsProcessingUtils
|
|
|
|
from processing.algs.qgis.QgisAlgorithm import QgisAlgorithm
|
|
from processing.core.parameters import ParameterVector
|
|
from processing.core.outputs import OutputHTML
|
|
from processing.core.outputs import OutputNumber
|
|
from processing.tools import dataobjects
|
|
|
|
pluginPath = os.path.split(os.path.split(os.path.dirname(__file__))[0])[0]
|
|
|
|
|
|
class NearestNeighbourAnalysis(QgisAlgorithm):
|
|
|
|
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 icon(self):
|
|
return QIcon(os.path.join(pluginPath, 'images', 'ftools', 'neighbour.png'))
|
|
|
|
def group(self):
|
|
return self.tr('Vector analysis tools')
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
def initAlgorithm(self, config=None):
|
|
self.addParameter(ParameterVector(self.POINTS,
|
|
self.tr('Points'), [dataobjects.TYPE_VECTOR_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 name(self):
|
|
return 'nearestneighbouranalysis'
|
|
|
|
def displayName(self):
|
|
return self.tr('Nearest neighbour analysis')
|
|
|
|
def processAlgorithm(self, parameters, context, feedback):
|
|
layer = QgsProcessingUtils.mapLayerFromString(self.getParameterValue(self.POINTS), context)
|
|
output = self.getOutputValue(self.OUTPUT)
|
|
|
|
spatialIndex = QgsProcessingUtils.createSpatialIndex(layer, context)
|
|
|
|
neighbour = QgsFeature()
|
|
distance = QgsDistanceArea()
|
|
|
|
sumDist = 0.00
|
|
A = layer.extent()
|
|
A = float(A.width() * A.height())
|
|
|
|
features = QgsProcessingUtils.getFeatures(layer, context)
|
|
count = QgsProcessingUtils.featureCount(layer, context)
|
|
total = 100.0 / count if count else 1
|
|
for current, feat in enumerate(features):
|
|
neighbourID = spatialIndex.nearestNeighbor(
|
|
feat.geometry().asPoint(), 2)[1]
|
|
request = QgsFeatureRequest().setFilterFid(neighbourID).setSubsetOfAttributes([])
|
|
neighbour = next(layer.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)
|
|
|
|
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, 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):
|
|
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>')
|