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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.
268 lines
11 KiB
Python
268 lines
11 KiB
Python
# -*- coding: utf-8 -*-
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"""
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***************************************************************************
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SpatialJoin.py
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---------------------
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Date : October 2013
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Copyright : (C) 2013 by Joshua Arnott
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Email : josh at snorfalorpagus dot net
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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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from builtins import str
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from builtins import zip
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from builtins import range
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__author__ = 'Joshua Arnott'
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__date__ = 'October 2013'
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__copyright__ = '(C) 2013, Joshua Arnott'
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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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from qgis.PyQt.QtGui import QIcon
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from qgis.PyQt.QtCore import QVariant
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from qgis.core import QgsFields, QgsField, QgsFeatureSink, QgsFeature, QgsGeometry, NULL, QgsWkbTypes, QgsProcessingUtils
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from processing.algs.qgis.QgisAlgorithm import QgisAlgorithm
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from processing.core.parameters import ParameterVector
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from processing.core.parameters import ParameterNumber
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from processing.core.parameters import ParameterSelection
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from processing.core.parameters import ParameterString
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from processing.core.outputs import OutputVector
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from processing.tools import vector
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pluginPath = os.path.split(os.path.split(os.path.dirname(__file__))[0])[0]
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class SpatialJoin(QgisAlgorithm):
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TARGET = "TARGET"
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JOIN = "JOIN"
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PREDICATE = "PREDICATE"
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PRECISION = 'PRECISION'
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SUMMARY = "SUMMARY"
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STATS = "STATS"
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KEEP = "KEEP"
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OUTPUT = "OUTPUT"
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def icon(self):
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return QIcon(os.path.join(pluginPath, 'images', 'ftools', 'join_location.png'))
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def group(self):
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return self.tr('Vector general tools')
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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.predicates = (
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('intersects', self.tr('intersects')),
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('contains', self.tr('contains')),
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('equals', self.tr('equals')),
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('touches', self.tr('touches')),
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('overlaps', self.tr('overlaps')),
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('within', self.tr('within')),
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('crosses', self.tr('crosses')))
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self.summarys = [
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self.tr('Take attributes of the first located feature'),
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self.tr('Take summary of intersecting features')
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]
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self.keeps = [
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self.tr('Only keep matching records'),
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self.tr('Keep all records (including non-matching target records)')
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]
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self.addParameter(ParameterVector(self.TARGET,
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self.tr('Target vector layer')))
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self.addParameter(ParameterVector(self.JOIN,
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self.tr('Join vector layer')))
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self.addParameter(ParameterSelection(self.PREDICATE,
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self.tr('Geometric predicate'),
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self.predicates,
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multiple=True))
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self.addParameter(ParameterNumber(self.PRECISION,
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self.tr('Precision'),
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0.0, None, 0.0))
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self.addParameter(ParameterSelection(self.SUMMARY,
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self.tr('Attribute summary'), self.summarys))
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self.addParameter(ParameterString(self.STATS,
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self.tr('Statistics for summary (comma separated)'),
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'sum,mean,min,max,median', optional=True))
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self.addParameter(ParameterSelection(self.KEEP,
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self.tr('Joined table'), self.keeps))
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self.addOutput(OutputVector(self.OUTPUT, self.tr('Joined layer')))
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def name(self):
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return 'joinattributesbylocation'
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def displayName(self):
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return self.tr('Join attributes by location')
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def processAlgorithm(self, parameters, context, feedback):
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target = QgsProcessingUtils.mapLayerFromString(self.getParameterValue(self.TARGET), context)
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join = QgsProcessingUtils.mapLayerFromString(self.getParameterValue(self.JOIN), context)
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predicates = self.getParameterValue(self.PREDICATE)
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precision = self.getParameterValue(self.PRECISION)
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summary = self.getParameterValue(self.SUMMARY) == 1
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keep = self.getParameterValue(self.KEEP) == 1
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sumList = self.getParameterValue(self.STATS).lower().split(',')
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targetFields = target.fields()
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joinFields = join.fields()
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fieldList = QgsFields()
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if not summary:
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joinFields = vector.testForUniqueness(targetFields, joinFields)
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seq = list(range(len(targetFields) + len(joinFields)))
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targetFields.extend(joinFields)
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targetFields = dict(list(zip(seq, targetFields)))
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else:
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numFields = {}
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for j in range(len(joinFields)):
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if joinFields[j].type() in [QVariant.Int, QVariant.Double, QVariant.LongLong, QVariant.UInt, QVariant.ULongLong]:
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numFields[j] = []
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for i in sumList:
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field = QgsField(i + str(joinFields[j].name()), QVariant.Double, '', 24, 16)
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fieldList.append(field)
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field = QgsField('count', QVariant.Double, '', 24, 16)
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fieldList.append(field)
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joinFields = vector.testForUniqueness(targetFields, fieldList)
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targetFields.extend(fieldList)
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seq = list(range(len(targetFields)))
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targetFields = dict(list(zip(seq, targetFields)))
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fields = QgsFields()
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for f in list(targetFields.values()):
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fields.append(f)
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writer = self.getOutputFromName(self.OUTPUT).getVectorWriter(fields, target.wkbType(), target.crs(), context)
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outFeat = QgsFeature()
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inFeatB = QgsFeature()
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inGeom = QgsGeometry()
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index = QgsProcessingUtils.createSpatialIndex(join, context)
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mapP2 = dict()
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features = QgsProcessingUtils.getFeatures(join, context)
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for f in features:
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mapP2[f.id()] = QgsFeature(f)
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features = QgsProcessingUtils.getFeatures(target, context)
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total = 100.0 / target.featureCount() if target.featureCount() else 0
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for c, f in enumerate(features):
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atMap1 = f.attributes()
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outFeat.setGeometry(f.geometry())
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inGeom = vector.snapToPrecision(f.geometry(), precision)
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none = True
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joinList = []
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if inGeom.type() == QgsWkbTypes.PointGeometry:
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bbox = inGeom.buffer(10, 2).boundingBox()
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else:
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bbox = inGeom.boundingBox()
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bbox.grow(0.51 * precision)
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joinList = index.intersects(bbox)
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if len(joinList) > 0:
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count = 0
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for i in joinList:
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inFeatB = mapP2[i]
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inGeomB = vector.snapToPrecision(inFeatB.geometry(), precision)
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res = False
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for predicate in predicates:
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res = getattr(inGeom, predicate)(inGeomB)
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if res:
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break
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if res:
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count = count + 1
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none = False
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atMap2 = inFeatB.attributes()
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if not summary:
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atMap = atMap1
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atMap2 = atMap2
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atMap.extend(atMap2)
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atMap = dict(list(zip(seq, atMap)))
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break
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else:
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for j in list(numFields.keys()):
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numFields[j].append(atMap2[j])
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if summary and not none:
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atMap = atMap1
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for j in list(numFields.keys()):
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for k in sumList:
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if k == 'sum':
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atMap.append(sum(self._filterNull(numFields[j])))
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elif k == 'mean':
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try:
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nn_count = sum(1 for _ in self._filterNull(numFields[j]))
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atMap.append(sum(self._filterNull(numFields[j])) / nn_count)
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except ZeroDivisionError:
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atMap.append(NULL)
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elif k == 'min':
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try:
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atMap.append(min(self._filterNull(numFields[j])))
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except ValueError:
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atMap.append(NULL)
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elif k == 'median':
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atMap.append(self._median(numFields[j]))
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else:
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try:
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atMap.append(max(self._filterNull(numFields[j])))
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except ValueError:
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atMap.append(NULL)
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numFields[j] = []
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atMap.append(count)
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atMap = dict(list(zip(seq, atMap)))
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if none:
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outFeat.setAttributes(atMap1)
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else:
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outFeat.setAttributes(list(atMap.values()))
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if keep:
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writer.addFeature(outFeat, QgsFeatureSink.FastInsert)
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else:
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if not none:
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writer.addFeature(outFeat, QgsFeatureSink.FastInsert)
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feedback.setProgress(int(c * total))
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del writer
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def _filterNull(self, values):
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"""Takes an iterator of values and returns a new iterator
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returning the same values but skipping any NULL values"""
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return (v for v in values if v != NULL)
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def _median(self, data):
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count = len(data)
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if count == 1:
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return data[0]
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data.sort()
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median = 0
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if count > 1:
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if (count % 2) == 0:
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median = 0.5 * ((data[count / 2 - 1]) + (data[count / 2]))
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else:
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median = data[(count + 1) / 2 - 1]
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return median
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