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140 lines
5.4 KiB
Python
140 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 os
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import random
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from qgis.PyQt.QtGui import QIcon
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from qgis.core import (QgsFeatureRequest,
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QgsProcessingException,
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QgsProcessingUtils,
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QgsProcessingParameterVectorLayer,
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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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pluginPath = os.path.split(os.path.split(os.path.dirname(__file__))[0])[0]
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class RandomSelectionWithinSubsets(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 icon(self):
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return QIcon(os.path.join(pluginPath, 'images', 'ftools', 'sub_selection.png'))
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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(QgsProcessingParameterVectorLayer(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'),
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QgsProcessingParameterNumber.Integer,
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10, False, 0.0, 999999999999.0))
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self.addOutput(QgsProcessingOutputVectorLayer(self.OUTPUT, self.tr('Selected (stratified random)')))
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def name(self):
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return 'randomselectionwithinsubsets'
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def displayName(self):
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return self.tr('Random selection within subsets')
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def processAlgorithm(self, parameters, context, feedback):
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layer = self.parameterAsVectorLayer(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 = layer.fields().lookupField(field)
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unique = layer.uniqueValues(index)
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featureCount = layer.featureCount()
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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 a "
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"different value and try again."))
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value = value / 100.0
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total = 100.0 / (featureCount * len(unique)) if featureCount else 1
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if not len(unique) == featureCount:
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classes = defaultdict(list)
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features = layer.getFeatures(QgsFeatureRequest().setFlags(QgsFeatureRequest.NoGeometry).setSubsetOfAttributes([index]))
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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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classes[feature.attributes()[index]].append(feature.id())
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feedback.setProgress(int(i * total))
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selran = []
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for subset in classes.values():
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if feedback.isCanceled():
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break
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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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layer.selectByIds(selran)
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else:
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layer.selectByIds(list(range(featureCount))) # FIXME: implies continuous feature ids
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return {self.OUTPUT: parameters[self.INPUT]}
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