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}
