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ede452be85
Algorithms are now passed a QgsProcessingFeedback object instead of the loosely defined progress parameter.
131 lines
5.1 KiB
Python
131 lines
5.1 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 processing.core.GeoAlgorithm import GeoAlgorithm
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from processing.core.GeoAlgorithmExecutionException import GeoAlgorithmExecutionException
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from processing.core.parameters import ParameterSelection
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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 ParameterTableField
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from processing.core.outputs import OutputVector
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from processing.tools import dataobjects, vector
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class RandomExtractWithinSubsets(GeoAlgorithm):
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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 defineCharacteristics(self):
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self.name, self.i18n_name = self.trAlgorithm('Random extract within subsets')
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self.group, self.i18n_group = self.trAlgorithm('Vector selection tools')
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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(ParameterVector(self.INPUT,
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self.tr('Input layer')))
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self.addParameter(ParameterTableField(self.FIELD,
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self.tr('ID field'), self.INPUT))
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self.addParameter(ParameterSelection(self.METHOD,
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self.tr('Method'), self.methods, 0))
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self.addParameter(ParameterNumber(self.NUMBER,
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self.tr('Number/percentage of selected features'), 1, None, 10))
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self.addOutput(OutputVector(self.OUTPUT, self.tr('Extracted (random stratified)')))
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def processAlgorithm(self, feedback):
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filename = self.getParameterValue(self.INPUT)
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layer = dataobjects.getObjectFromUri(filename)
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field = self.getParameterValue(self.FIELD)
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method = self.getParameterValue(self.METHOD)
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index = layer.fields().lookupField(field)
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features = vector.features(layer)
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featureCount = len(features)
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unique = vector.getUniqueValues(layer, index)
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value = int(self.getParameterValue(self.NUMBER))
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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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writer = self.getOutputFromName(self.OUTPUT).getVectorWriter(
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layer.fields().toList(), layer.wkbType(), layer.crs())
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selran = []
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current = 0
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total = 100.0 / (featureCount * len(unique))
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features = vector.features(layer)
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if not len(unique) == featureCount:
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for classValue in unique:
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classFeatures = []
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for i, feature in enumerate(features):
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attrs = feature.attributes()
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if attrs[index] == classValue:
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classFeatures.append(i)
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current += 1
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feedback.setProgress(int(current * total))
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if method == 1:
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selValue = int(round(value * len(classFeatures), 0))
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else:
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selValue = value
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if selValue >= len(classFeatures):
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selFeat = classFeatures
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else:
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selFeat = random.sample(classFeatures, selValue)
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selran.extend(selFeat)
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else:
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selran = list(range(featureCount))
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features = vector.features(layer)
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total = 100.0 / len(features)
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for (i, feat) in enumerate(features):
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if i in selran:
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writer.addFeature(feat)
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feedback.setProgress(int(i * total))
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del writer
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