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5339d62715
Include descriptive text with the specified parameter value in error, and always check that sources were loaded to avoid raw Python exceptions when they are not
137 lines
5.4 KiB
Python
137 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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__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, 999999999999.0))
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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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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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