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QGIS/python/plugins/processing/algs/qgis/PointDistance.py
Nyall Dawson 7fa9d4120a [processing] Fix Distance Matrix alg considers distances to 
same points

Add more unit tests

Fixes #17350
2018-04-09 12:48:50 +10:00

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# -*- coding: utf-8 -*-
"""
***************************************************************************
PointDistance.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
from qgis.PyQt.QtGui import QIcon
from qgis.PyQt.QtCore import QVariant
from qgis.core import (QgsFeatureRequest,
QgsField,
QgsFields,
QgsProject,
QgsFeature,
QgsGeometry,
QgsDistanceArea,
QgsFeatureSink,
QgsProcessingParameterFeatureSource,
QgsProcessing,
QgsProcessingParameterEnum,
QgsProcessingParameterField,
QgsProcessingParameterNumber,
QgsProcessingParameterFeatureSink,
QgsSpatialIndex,
QgsWkbTypes)
from processing.algs.qgis.QgisAlgorithm import QgisAlgorithm
pluginPath = os.path.split(os.path.split(os.path.dirname(__file__))[0])[0]
class PointDistance(QgisAlgorithm):
INPUT = 'INPUT'
INPUT_FIELD = 'INPUT_FIELD'
TARGET = 'TARGET'
TARGET_FIELD = 'TARGET_FIELD'
MATRIX_TYPE = 'MATRIX_TYPE'
NEAREST_POINTS = 'NEAREST_POINTS'
OUTPUT = 'OUTPUT'
def icon(self):
return QIcon(os.path.join(pluginPath, 'images', 'ftools', 'matrix.png'))
def group(self):
return self.tr('Vector analysis')
def groupId(self):
return 'vectoranalysis'
def __init__(self):
super().__init__()
def initAlgorithm(self, config=None):
self.mat_types = [self.tr('Linear (N*k x 3) distance matrix'),
self.tr('Standard (N x T) distance matrix'),
self.tr('Summary distance matrix (mean, std. dev., min, max)')]
self.addParameter(QgsProcessingParameterFeatureSource(self.INPUT,
self.tr('Input point layer'),
[QgsProcessing.TypeVectorPoint]))
self.addParameter(QgsProcessingParameterField(self.INPUT_FIELD,
self.tr('Input unique ID field'),
parentLayerParameterName=self.INPUT,
type=QgsProcessingParameterField.Any))
self.addParameter(QgsProcessingParameterFeatureSource(self.TARGET,
self.tr('Target point layer'),
[QgsProcessing.TypeVectorPoint]))
self.addParameter(QgsProcessingParameterField(self.TARGET_FIELD,
self.tr('Target unique ID field'),
parentLayerParameterName=self.TARGET,
type=QgsProcessingParameterField.Any))
self.addParameter(QgsProcessingParameterEnum(self.MATRIX_TYPE,
self.tr('Output matrix type'), options=self.mat_types, defaultValue=0))
self.addParameter(QgsProcessingParameterNumber(self.NEAREST_POINTS,
self.tr('Use only the nearest (k) target points'), type=QgsProcessingParameterNumber.Integer, minValue=0, maxValue=9999, defaultValue=0))
self.addParameter(QgsProcessingParameterFeatureSink(self.OUTPUT, self.tr('Distance matrix'), QgsProcessing.TypeVectorPoint))
def name(self):
return 'distancematrix'
def displayName(self):
return self.tr('Distance matrix')
def processAlgorithm(self, parameters, context, feedback):
source = self.parameterAsSource(parameters, self.INPUT, context)
source_field = self.parameterAsString(parameters, self.INPUT_FIELD, context)
target_source = self.parameterAsSource(parameters, self.TARGET, context)
target_field = self.parameterAsString(parameters, self.TARGET_FIELD, context)
same_source_and_target = parameters[self.INPUT] == parameters[self.TARGET]
matType = self.parameterAsEnum(parameters, self.MATRIX_TYPE, context)
nPoints = self.parameterAsInt(parameters, self.NEAREST_POINTS, context)
if nPoints < 1:
nPoints = target_source.featureCount()
if matType == 0:
# Linear distance matrix
return self.linearMatrix(parameters, context, source, source_field, target_source, target_field, same_source_and_target,
matType, nPoints, feedback)
elif matType == 1:
# Standard distance matrix
return self.regularMatrix(parameters, context, source, source_field, target_source, target_field,
nPoints, feedback)
elif matType == 2:
# Summary distance matrix
return self.linearMatrix(parameters, context, source, source_field, target_source, target_field, same_source_and_target,
matType, nPoints, feedback)
def linearMatrix(self, parameters, context, source, inField, target_source, targetField, same_source_and_target,
matType, nPoints, feedback):
if same_source_and_target:
# need to fetch an extra point from the index, since the closest match will always be the same
# as the input feature
nPoints += 1
inIdx = source.fields().lookupField(inField)
outIdx = target_source.fields().lookupField(targetField)
fields = QgsFields()
input_id_field = source.fields()[inIdx]
input_id_field.setName('InputID')
fields.append(input_id_field)
if matType == 0:
target_id_field = target_source.fields()[outIdx]
target_id_field.setName('TargetID')
fields.append(target_id_field)
fields.append(QgsField('Distance', QVariant.Double))
else:
fields.append(QgsField('MEAN', QVariant.Double))
fields.append(QgsField('STDDEV', QVariant.Double))
fields.append(QgsField('MIN', QVariant.Double))
fields.append(QgsField('MAX', QVariant.Double))
out_wkb = QgsWkbTypes.multiType(source.wkbType()) if matType == 0 else source.wkbType()
(sink, dest_id) = self.parameterAsSink(parameters, self.OUTPUT, context,
fields, out_wkb, source.sourceCrs())
index = QgsSpatialIndex(target_source.getFeatures(QgsFeatureRequest().setSubsetOfAttributes([]).setDestinationCrs(source.sourceCrs(), context.transformContext())), feedback)
distArea = QgsDistanceArea()
distArea.setSourceCrs(source.sourceCrs(), context.transformContext())
distArea.setEllipsoid(context.project().ellipsoid())
features = source.getFeatures(QgsFeatureRequest().setSubsetOfAttributes([inIdx]))
total = 100.0 / source.featureCount() if source.featureCount() else 0
for current, inFeat in enumerate(features):
if feedback.isCanceled():
break
inGeom = inFeat.geometry()
inID = str(inFeat.attributes()[inIdx])
featList = index.nearestNeighbor(inGeom.asPoint(), nPoints)
distList = []
vari = 0.0
request = QgsFeatureRequest().setFilterFids(featList).setSubsetOfAttributes([outIdx]).setDestinationCrs(source.sourceCrs(), context.transformContext())
for outFeat in target_source.getFeatures(request):
if feedback.isCanceled():
break
if same_source_and_target and inFeat.id() == outFeat.id():
continue
outID = outFeat.attributes()[outIdx]
outGeom = outFeat.geometry()
dist = distArea.measureLine(inGeom.asPoint(),
outGeom.asPoint())
if matType == 0:
out_feature = QgsFeature()
out_geom = QgsGeometry.unaryUnion([inFeat.geometry(), outFeat.geometry()])
out_feature.setGeometry(out_geom)
out_feature.setAttributes([inID, outID, dist])
sink.addFeature(out_feature, QgsFeatureSink.FastInsert)
else:
distList.append(float(dist))
if matType != 0:
mean = sum(distList) / len(distList)
for i in distList:
vari += (i - mean) * (i - mean)
vari = math.sqrt(vari / len(distList))
out_feature = QgsFeature()
out_feature.setGeometry(inFeat.geometry())
out_feature.setAttributes([inID, mean, vari, min(distList), max(distList)])
sink.addFeature(out_feature, QgsFeatureSink.FastInsert)
feedback.setProgress(int(current * total))
return {self.OUTPUT: dest_id}
def regularMatrix(self, parameters, context, source, inField, target_source, targetField,
nPoints, feedback):
distArea = QgsDistanceArea()
distArea.setSourceCrs(source.sourceCrs(), context.transformContext())
distArea.setEllipsoid(context.project().ellipsoid())
inIdx = source.fields().lookupField(inField)
targetIdx = target_source.fields().lookupField(targetField)
index = QgsSpatialIndex(target_source.getFeatures(QgsFeatureRequest().setSubsetOfAttributes([]).setDestinationCrs(source.sourceCrs(), context.transformContext())), feedback)
first = True
sink = None
dest_id = None
features = source.getFeatures(QgsFeatureRequest().setSubsetOfAttributes([inIdx]))
total = 100.0 / source.featureCount() if source.featureCount() else 0
for current, inFeat in enumerate(features):
if feedback.isCanceled():
break
inGeom = inFeat.geometry()
if first:
featList = index.nearestNeighbor(inGeom.asPoint(), nPoints)
first = False
fields = QgsFields()
input_id_field = source.fields()[inIdx]
input_id_field.setName('ID')
fields.append(input_id_field)
for f in target_source.getFeatures(QgsFeatureRequest().setFilterFids(featList).setSubsetOfAttributes([targetIdx]).setDestinationCrs(source.sourceCrs(), context.transformContext())):
fields.append(QgsField(str(f[targetField]), QVariant.Double))
(sink, dest_id) = self.parameterAsSink(parameters, self.OUTPUT, context,
fields, source.wkbType(), source.sourceCrs())
data = [inFeat[inField]]
for target in target_source.getFeatures(QgsFeatureRequest().setSubsetOfAttributes([]).setFilterFids(featList).setDestinationCrs(source.sourceCrs(), context.transformContext())):
if feedback.isCanceled():
break
outGeom = target.geometry()
dist = distArea.measureLine(inGeom.asPoint(),
outGeom.asPoint())
data.append(dist)
out_feature = QgsFeature()
out_feature.setGeometry(inGeom)
out_feature.setAttributes(data)
sink.addFeature(out_feature, QgsFeatureSink.FastInsert)
feedback.setProgress(int(current * total))
return {self.OUTPUT: dest_id}
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