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Since it's safe to evaluate parameters in background threads now, it's usually going to be ok to evaluate everything in the processAlgorithm step. This keeps the algorithm code as simple as possible, and will make porting faster. Note that the prepare/postProcess virtual methods still exist and can be used when an algorithm MUST do setup/cleanup work in the main thread.
132 lines
5.4 KiB
Python
132 lines
5.4 KiB
Python
# -*- coding: utf-8 -*-
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"""
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***************************************************************************
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RandomSelectionWithinSubsets.py
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---------------------
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Date : August 2012
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Copyright : (C) 2012 by Victor Olaya
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Email : volayaf at gmail dot com
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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 range
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__author__ = 'Victor Olaya'
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__date__ = 'August 2012'
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__copyright__ = '(C) 2012, Victor Olaya'
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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 random
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from qgis.core import (QgsApplication,
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QgsFeatureSink,
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QgsProcessingUtils,
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QgsProcessingParameterFeatureSource,
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QgsProcessingParameterEnum,
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QgsProcessingParameterField,
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QgsProcessingParameterNumber,
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QgsProcessingParameterFeatureSink,
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QgsProcessingOutputVectorLayer)
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from collections import defaultdict
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from processing.algs.qgis.QgisAlgorithm import QgisAlgorithm
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from processing.core.GeoAlgorithmExecutionException import GeoAlgorithmExecutionException
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class RandomExtractWithinSubsets(QgisAlgorithm):
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INPUT = 'INPUT'
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METHOD = 'METHOD'
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NUMBER = 'NUMBER'
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FIELD = 'FIELD'
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OUTPUT = 'OUTPUT'
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def group(self):
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return self.tr('Vector selection tools')
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def __init__(self):
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super().__init__()
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self.methods = [self.tr('Number of selected features'),
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self.tr('Percentage of selected features')]
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self.addParameter(QgsProcessingParameterFeatureSource(self.INPUT,
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self.tr('Input layer')))
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self.addParameter(QgsProcessingParameterField(self.FIELD,
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self.tr('ID field'), None, self.INPUT))
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self.addParameter(QgsProcessingParameterEnum(self.METHOD,
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self.tr('Method'), self.methods, False, 0))
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self.addParameter(QgsProcessingParameterNumber(self.NUMBER,
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self.tr('Number/percentage of selected features'), QgsProcessingParameterNumber.Integer,
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10, False, 0.0, 999999999999.0))
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self.addParameter(QgsProcessingParameterFeatureSink(self.OUTPUT, self.tr('Extracted (random stratified)')))
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self.addOutput(QgsProcessingOutputVectorLayer(self.OUTPUT, self.tr('Extracted (random stratified)')))
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def name(self):
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return 'randomextractwithinsubsets'
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def displayName(self):
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return self.tr('Random extract within subsets')
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def processAlgorithm(self, parameters, context, feedback):
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source = self.parameterAsSource(parameters, self.INPUT, context)
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method = self.parameterAsEnum(parameters, self.METHOD, context)
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field = self.parameterAsString(parameters, self.FIELD, context)
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index = source.fields().lookupField(field)
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features = source.getFeatures()
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featureCount = source.featureCount()
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unique = source.uniqueValues(index)
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value = self.parameterAsInt(parameters, self.NUMBER, context)
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if method == 0:
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if value > featureCount:
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raise GeoAlgorithmExecutionException(
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self.tr('Selected number is greater that feature count. '
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'Choose lesser value and try again.'))
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else:
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if value > 100:
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raise GeoAlgorithmExecutionException(
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self.tr("Percentage can't be greater than 100. Set "
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"correct value and try again."))
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value = value / 100.0
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(sink, dest_id) = self.parameterAsSink(parameters, self.OUTPUT, context,
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source.fields(), source.wkbType(), source.sourceCrs())
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selran = []
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total = 100.0 / (featureCount * len(unique)) if featureCount else 1
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classes = defaultdict(list)
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for i, feature in enumerate(features):
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if feedback.isCanceled():
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break
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attrs = feature.attributes()
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classes[attrs[index]].append(feature)
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feedback.setProgress(int(i * total))
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for subset in classes.values():
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selValue = value if method != 1 else int(round(value * len(subset), 0))
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selran.extend(random.sample(subset, selValue))
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total = 100.0 / featureCount if featureCount else 1
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for (i, feat) in enumerate(selran):
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if feedback.isCanceled():
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break
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sink.addFeature(feat, QgsFeatureSink.FastInsert)
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feedback.setProgress(int(i * total))
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return {self.OUTPUT: dest_id}
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