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Algorithms and other processing code should use this method (instead of dataobjects.getLayerFromString) to retrieve layers from a string, as it considers the processing context and allows resolving strings to temporarily stored layers. This permits processing models to function correctly when intermediate results are stored as memory layers. Subsequent model algorithms can then access these temporary layers as inputs. All temporary layers will be removed when the context object is destroyed after the model algorithm is run.
142 lines
5.2 KiB
Python
142 lines
5.2 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 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 QgsFeature, QgsProcessingUtils
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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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pluginPath = os.path.split(os.path.split(os.path.dirname(__file__))[0])[0]
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class RandomSelectionWithinSubsets(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 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 tools')
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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 defineCharacteristics(self):
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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('Selection stratified'), True))
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def processAlgorithm(self, context, feedback):
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filename = self.getParameterValue(self.INPUT)
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layer = dataobjects.QgsProcessingUtils.mapLayerFromString(filename, context)
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field = self.getParameterValue(self.FIELD)
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method = self.getParameterValue(self.METHOD)
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layer.removeSelection()
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index = layer.fields().lookupField(field)
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unique = QgsProcessingUtils.uniqueValues(layer, index, context)
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featureCount = layer.featureCount()
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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 a "
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"different value and try again."))
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value = value / 100.0
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selran = []
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inFeat = QgsFeature()
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current = 0
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total = 100.0 / (featureCount * len(unique))
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if not len(unique) == featureCount:
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for i in unique:
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features = QgsProcessingUtils.getFeatures(layer, context)
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FIDs = []
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for inFeat in features:
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attrs = inFeat.attributes()
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if attrs[index] == i:
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FIDs.append(inFeat.id())
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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(FIDs), 0))
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else:
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selValue = value
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if selValue >= len(FIDs):
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selFeat = FIDs
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else:
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selFeat = random.sample(FIDs, selValue)
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selran.extend(selFeat)
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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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self.setOutputValue(self.OUTPUT, filename)
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