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1e13d733c2
initAlgorithm() method This allows 2 benefits: - algorithms can be subclassed and have subclasses add additional parameters/outputs to the algorithm. With the previous approach of declaring parameters/outputs in the constructor, it's not possible to call virtual methods to add additional parameters/ outputs (since you can't call virtual methods from a constructor). - initAlgorithm takes a variant map argument, allowing the algorithm to dynamically adjust its declared parameters and outputs according to this configuration map. This potentially allows model algorithms which can be configured to have variable numbers of parameters and outputs at run time. E.g. a "router" algorithm which directs features to one of any number of output sinks depending on some user configured criteria.
104 lines
4.4 KiB
Python
104 lines
4.4 KiB
Python
# -*- coding: utf-8 -*-
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"""
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***************************************************************************
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StatisticsByCategories.py
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---------------------
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Date : September 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 str
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__author__ = 'Victor Olaya'
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__date__ = 'September 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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from qgis.core import (QgsApplication,
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QgsFeatureSink,
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QgsStatisticalSummary,
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QgsProcessingUtils)
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from processing.core.outputs import OutputTable
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from processing.algs.qgis.QgisAlgorithm import QgisAlgorithm
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from processing.core.parameters import ParameterVector
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from processing.core.parameters import ParameterTableField
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class StatisticsByCategories(QgisAlgorithm):
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INPUT_LAYER = 'INPUT_LAYER'
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VALUES_FIELD_NAME = 'VALUES_FIELD_NAME'
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CATEGORIES_FIELD_NAME = 'CATEGORIES_FIELD_NAME'
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OUTPUT = 'OUTPUT'
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def group(self):
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return self.tr('Vector table tools')
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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.addParameter(ParameterVector(self.INPUT_LAYER,
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self.tr('Input vector layer')))
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self.addParameter(ParameterTableField(self.VALUES_FIELD_NAME,
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self.tr('Field to calculate statistics on'),
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self.INPUT_LAYER, ParameterTableField.DATA_TYPE_NUMBER))
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self.addParameter(ParameterTableField(self.CATEGORIES_FIELD_NAME,
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self.tr('Field with categories'),
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self.INPUT_LAYER, ParameterTableField.DATA_TYPE_ANY))
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self.addOutput(OutputTable(self.OUTPUT, self.tr('Statistics by category')))
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def name(self):
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return 'statisticsbycategories'
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def displayName(self):
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return self.tr('Statistics by categories')
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def processAlgorithm(self, parameters, context, feedback):
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layer = QgsProcessingUtils.mapLayerFromString(self.getParameterValue(self.INPUT_LAYER), context)
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valuesFieldName = self.getParameterValue(self.VALUES_FIELD_NAME)
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categoriesFieldName = self.getParameterValue(self.CATEGORIES_FIELD_NAME)
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output = self.getOutputFromName(self.OUTPUT)
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valuesField = layer.fields().lookupField(valuesFieldName)
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categoriesField = layer.fields().lookupField(categoriesFieldName)
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features = QgsProcessingUtils.getFeatures(layer, context)
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total = 100.0 / layer.featureCount() if layer.featureCount() else 0
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values = {}
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for current, feat in enumerate(features):
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feedback.setProgress(int(current * total))
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attrs = feat.attributes()
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try:
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value = float(attrs[valuesField])
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cat = str(attrs[categoriesField])
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if cat not in values:
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values[cat] = []
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values[cat].append(value)
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except:
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pass
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fields = ['category', 'min', 'max', 'mean', 'stddev', 'sum', 'count']
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writer = output.getTableWriter(fields)
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stat = QgsStatisticalSummary(QgsStatisticalSummary.Min | QgsStatisticalSummary.Max |
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QgsStatisticalSummary.Mean | QgsStatisticalSummary.StDevSample |
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QgsStatisticalSummary.Sum | QgsStatisticalSummary.Count)
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for (cat, v) in list(values.items()):
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stat.calculate(v)
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record = [cat, stat.min(), stat.max(), stat.mean(), stat.sampleStDev(), stat.sum(), stat.count()]
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writer.addRecord(record)
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