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8cad2a6e16
Remove a bunch of manual "max" values for numeric parameters where the maximum just represents a 'large number' and not a real constraint, and let the default parameter max value handling kick in instead. In the case of random selection the max value exceeded the possible range for integers in spin boxes and broke the widget. Fixes #20015
142 lines
5.7 KiB
Python
142 lines
5.7 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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__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 (QgsFeatureSink,
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QgsProcessingException,
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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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QgsProcessingFeatureSource,
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QgsFeatureRequest)
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from collections import defaultdict
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from processing.algs.qgis.QgisAlgorithm import QgisAlgorithm
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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')
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def groupId(self):
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return 'vectorselection'
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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.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))
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self.addParameter(QgsProcessingParameterFeatureSink(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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if source is None:
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raise QgsProcessingException(self.invalidSourceError(parameters, self.INPUT))
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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(QgsFeatureRequest(), QgsProcessingFeatureSource.FlagSkipGeometryValidityChecks)
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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 QgsProcessingException(
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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 QgsProcessingException(
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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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if sink is None:
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raise QgsProcessingException(self.invalidSinkError(parameters, self.OUTPUT))
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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 k, subset in classes.items():
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selValue = value if method != 1 else int(round(value * len(subset), 0))
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if selValue > len(subset):
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selValue = len(subset)
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feedback.reportError(self.tr('Subset "{}" is smaller than requested number of features.'.format(k)))
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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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