def processAlgorithm(self, parameters, context, feedback): layer = QgsProcessingUtils.mapLayerFromString(self.getParameterValue(self.INPUT_LAYER), context) valuesFieldName = self.getParameterValue(self.VALUES_FIELD_NAME) categoriesFieldName = self.getParameterValue(self.CATEGORIES_FIELD_NAME) output = self.getOutputFromName(self.OUTPUT) valuesField = layer.fields().lookupField(valuesFieldName) categoriesField = layer.fields().lookupField(categoriesFieldName) features = QgsProcessingUtils.getFeatures(layer, context) total = 100.0 / QgsProcessingUtils.featureCount(layer, context) values = {} for current, feat in enumerate(features): feedback.setProgress(int(current * total)) attrs = feat.attributes() try: value = float(attrs[valuesField]) cat = str(attrs[categoriesField]) if cat not in values: values[cat] = [] values[cat].append(value) except: pass fields = ['category', 'min', 'max', 'mean', 'stddev', 'sum', 'count'] writer = output.getTableWriter(fields) stat = QgsStatisticalSummary(QgsStatisticalSummary.Min | QgsStatisticalSummary.Max | QgsStatisticalSummary.Mean | QgsStatisticalSummary.StDevSample | QgsStatisticalSummary.Sum | QgsStatisticalSummary.Count) for (cat, v) in list(values.items()): stat.calculate(v) record = [cat, stat.min(), stat.max(), stat.mean(), stat.sampleStDev(), stat.sum(), stat.count()] writer.addRecord(record)
def processAlgorithm(self, progress): layer = dataobjects.getObjectFromUri(self.getParameterValue(self.INPUT_LAYER)) valuesFieldName = self.getParameterValue(self.VALUES_FIELD_NAME) categoriesFieldName = self.getParameterValue(self.CATEGORIES_FIELD_NAME) output = self.getOutputFromName(self.OUTPUT) valuesField = layer.fieldNameIndex(valuesFieldName) categoriesField = layer.fieldNameIndex(categoriesFieldName) features = vector.features(layer) total = 100.0 / len(features) if len(features) > 0 else 1 values = {} for current, feat in enumerate(features): progress.setPercentage(int(current * total)) attrs = feat.attributes() try: value = float(attrs[valuesField]) cat = unicode(attrs[categoriesField]) if cat not in values: values[cat] = [] values[cat].append(value) except: pass fields = ['category', 'min', 'max', 'mean', 'stddev', 'sum', 'count'] writer = output.getTableWriter(fields) stat = QgsStatisticalSummary(QgsStatisticalSummary.Min | QgsStatisticalSummary.Max | QgsStatisticalSummary.Mean | QgsStatisticalSummary.StDevSample | QgsStatisticalSummary.Sum | QgsStatisticalSummary.Count) for (cat, v) in values.items(): stat.calculate(v) record = [cat, stat.min(), stat.max(), stat.mean(), stat.sampleStDev(), stat.sum(), stat.count()] writer.addRecord(record)
def processAlgorithm(self, progress): layer = dataobjects.getObjectFromUri(self.getParameterValue(self.INPUT_LAYER)) valuesFieldName = self.getParameterValue(self.VALUES_FIELD_NAME) categoriesFieldName = self.getParameterValue(self.CATEGORIES_FIELD_NAME) output = self.getOutputFromName(self.OUTPUT) valuesField = layer.fieldNameIndex(valuesFieldName) categoriesField = layer.fieldNameIndex(categoriesFieldName) features = vector.features(layer) total = 100.0 / len(features) values = {} for current, feat in enumerate(features): progress.setPercentage(int(current * total)) attrs = feat.attributes() try: value = float(attrs[valuesField]) cat = unicode(attrs[categoriesField]) if cat not in values: values[cat] = [] values[cat].append(value) except: pass fields = ['category', 'min', 'max', 'mean', 'stddev', 'sum', 'count'] writer = output.getTableWriter(fields) stat = QgsStatisticalSummary(QgsStatisticalSummary.Min | QgsStatisticalSummary.Max | QgsStatisticalSummary.Mean | QgsStatisticalSummary.StDevSample | QgsStatisticalSummary.Sum | QgsStatisticalSummary.Count) for (cat, v) in values.items(): stat.calculate(v) record = [cat, stat.min(), stat.max(), stat.mean(), stat.sampleStDev(), stat.sum(), stat.count()] writer.addRecord(record)
def processAlgorithm(self, parameters, context, feedback): source = self.parameterAsSource(parameters, self.INPUT, context) value_field_name = self.parameterAsString(parameters, self.VALUES_FIELD_NAME, context) category_field_name = self.parameterAsString(parameters, self.CATEGORIES_FIELD_NAME, context) value_field_index = source.fields().lookupField(value_field_name) category_field_index = source.fields().lookupField(category_field_name) features = source.getFeatures(QgsFeatureRequest().setFlags(QgsFeatureRequest.NoGeometry)) total = 100.0 / source.featureCount() if source.featureCount() else 0 values = {} for current, feat in enumerate(features): if feedback.isCanceled(): break feedback.setProgress(int(current * total)) attrs = feat.attributes() try: value = float(attrs[value_field_index]) cat = attrs[category_field_index] if cat not in values: values[cat] = [] values[cat].append(value) except: pass fields = QgsFields() fields.append(source.fields().at(category_field_index)) fields.append(QgsField('min', QVariant.Double)) fields.append(QgsField('max', QVariant.Double)) fields.append(QgsField('mean', QVariant.Double)) fields.append(QgsField('stddev', QVariant.Double)) fields.append(QgsField('sum', QVariant.Double)) fields.append(QgsField('count', QVariant.Int)) (sink, dest_id) = self.parameterAsSink(parameters, self.OUTPUT, context, fields, QgsWkbTypes.NoGeometry, QgsCoordinateReferenceSystem()) stat = QgsStatisticalSummary(QgsStatisticalSummary.Min | QgsStatisticalSummary.Max | QgsStatisticalSummary.Mean | QgsStatisticalSummary.StDevSample | QgsStatisticalSummary.Sum | QgsStatisticalSummary.Count) for (cat, v) in list(values.items()): stat.calculate(v) f = QgsFeature() f.setAttributes([cat, stat.min(), stat.max(), stat.mean(), stat.sampleStDev(), stat.sum(), stat.count()]) sink.addFeature(f, QgsFeatureSink.FastInsert) return {self.OUTPUT: dest_id}
def processAlgorithm(self, parameters, context, feedback): source = self.parameterAsSource(parameters, self.INPUT, context) value_field_name = self.parameterAsString(parameters, self.VALUES_FIELD_NAME, context) category_field_name = self.parameterAsString(parameters, self.CATEGORIES_FIELD_NAME, context) value_field_index = source.fields().lookupField(value_field_name) category_field_index = source.fields().lookupField(category_field_name) features = source.getFeatures(QgsFeatureRequest().setFlags(QgsFeatureRequest.NoGeometry)) total = 100.0 / source.featureCount() if source.featureCount() else 0 values = {} for current, feat in enumerate(features): if feedback.isCanceled(): break feedback.setProgress(int(current * total)) attrs = feat.attributes() try: value = float(attrs[value_field_index]) cat = attrs[category_field_index] if cat not in values: values[cat] = [] values[cat].append(value) except: pass fields = QgsFields() fields.append(source.fields().at(category_field_index)) fields.append(QgsField('min', QVariant.Double)) fields.append(QgsField('max', QVariant.Double)) fields.append(QgsField('mean', QVariant.Double)) fields.append(QgsField('stddev', QVariant.Double)) fields.append(QgsField('sum', QVariant.Double)) fields.append(QgsField('count', QVariant.Int)) (sink, dest_id) = self.parameterAsSink(parameters, self.OUTPUT, context, fields, QgsWkbTypes.NoGeometry, QgsCoordinateReferenceSystem()) stat = QgsStatisticalSummary(QgsStatisticalSummary.Min | QgsStatisticalSummary.Max | QgsStatisticalSummary.Mean | QgsStatisticalSummary.StDevSample | QgsStatisticalSummary.Sum | QgsStatisticalSummary.Count) for (cat, v) in list(values.items()): stat.calculate(v) f = QgsFeature() f.setAttributes([cat, stat.min(), stat.max(), stat.mean(), stat.sampleStDev(), stat.sum(), stat.count()]) sink.addFeature(f, QgsFeatureSink.FastInsert) return {self.OUTPUT: dest_id}