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Like the main Join Attributes by Location algorithm, this algorithm takes two layers and combines the attributes based on a spatial criteria. However this algorithm calculates summaries for the attributes for all matching features, e.g. calculating the mean/min/max/etc. The list of fields to summaries, and the summaries to calculate for those, can be selected.
296 lines
12 KiB
Python
Executable File
296 lines
12 KiB
Python
Executable File
# -*- 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 (QgsProcessingParameterFeatureSource,
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QgsStatisticalSummary,
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QgsDateTimeStatisticalSummary,
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QgsStringStatisticalSummary,
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QgsFeatureRequest,
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QgsProcessingParameterField,
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QgsProcessingParameterFeatureSink,
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QgsFields,
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QgsField,
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QgsWkbTypes,
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QgsCoordinateReferenceSystem,
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QgsFeature,
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QgsFeatureSink,
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QgsProcessing,
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NULL)
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from qgis.PyQt.QtCore import QVariant
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from processing.algs.qgis.QgisAlgorithm import QgisAlgorithm
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from collections import defaultdict
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class StatisticsByCategories(QgisAlgorithm):
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INPUT = 'INPUT'
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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 analysis')
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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(QgsProcessingParameterFeatureSource(self.INPUT,
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self.tr('Input vector layer'),
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types=[QgsProcessing.TypeVector]))
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self.addParameter(QgsProcessingParameterField(self.VALUES_FIELD_NAME,
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self.tr(
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'Field to calculate statistics on (if empty, only count is calculated)'),
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parentLayerParameterName=self.INPUT, optional=True))
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self.addParameter(QgsProcessingParameterField(self.CATEGORIES_FIELD_NAME,
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self.tr('Field(s) with categories'),
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parentLayerParameterName=self.INPUT,
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type=QgsProcessingParameterField.Any, allowMultiple=True))
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self.addParameter(QgsProcessingParameterFeatureSink(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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source = self.parameterAsSource(parameters, self.INPUT, context)
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value_field_name = self.parameterAsString(parameters, self.VALUES_FIELD_NAME, context)
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category_field_names = self.parameterAsFields(parameters, self.CATEGORIES_FIELD_NAME, context)
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value_field_index = source.fields().lookupField(value_field_name)
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if value_field_index >= 0:
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value_field = source.fields().at(value_field_index)
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else:
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value_field = None
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category_field_indexes = [source.fields().lookupField(n) for n in category_field_names]
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# generate output fields
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fields = QgsFields()
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for c in category_field_indexes:
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fields.append(source.fields().at(c))
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def addField(name):
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"""
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Adds a field to the output, keeping the same data type as the value_field
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"""
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field = QgsField(value_field)
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field.setName(name)
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fields.append(field)
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if value_field is None:
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field_type = 'none'
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fields.append(QgsField('count', QVariant.Int))
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elif value_field.isNumeric():
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field_type = 'numeric'
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fields.append(QgsField('count', QVariant.Int))
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fields.append(QgsField('unique', QVariant.Int))
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fields.append(QgsField('min', QVariant.Double))
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fields.append(QgsField('max', QVariant.Double))
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fields.append(QgsField('range', QVariant.Double))
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fields.append(QgsField('sum', QVariant.Double))
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fields.append(QgsField('mean', QVariant.Double))
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fields.append(QgsField('median', QVariant.Double))
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fields.append(QgsField('stddev', QVariant.Double))
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fields.append(QgsField('minority', QVariant.Double))
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fields.append(QgsField('majority', QVariant.Double))
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fields.append(QgsField('q1', QVariant.Double))
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fields.append(QgsField('q3', QVariant.Double))
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fields.append(QgsField('iqr', QVariant.Double))
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elif value_field.type() in (QVariant.Date, QVariant.Time, QVariant.DateTime):
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field_type = 'datetime'
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fields.append(QgsField('count', QVariant.Int))
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fields.append(QgsField('unique', QVariant.Int))
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fields.append(QgsField('empty', QVariant.Int))
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fields.append(QgsField('filled', QVariant.Int))
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# keep same data type for these fields
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addField('min')
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addField('max')
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else:
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field_type = 'string'
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fields.append(QgsField('count', QVariant.Int))
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fields.append(QgsField('unique', QVariant.Int))
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fields.append(QgsField('empty', QVariant.Int))
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fields.append(QgsField('filled', QVariant.Int))
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# keep same data type for these fields
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addField('min')
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addField('max')
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fields.append(QgsField('min_length', QVariant.Int))
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fields.append(QgsField('max_length', QVariant.Int))
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fields.append(QgsField('mean_length', QVariant.Double))
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request = QgsFeatureRequest().setFlags(QgsFeatureRequest.NoGeometry)
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if value_field is not None:
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attrs = [value_field_index]
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else:
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attrs = []
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attrs.extend(category_field_indexes)
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request.setSubsetOfAttributes(attrs)
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features = source.getFeatures(request)
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total = 50.0 / source.featureCount() if source.featureCount() else 0
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if field_type == 'none':
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values = defaultdict(lambda: 0)
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else:
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values = defaultdict(list)
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for current, feat in enumerate(features):
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if feedback.isCanceled():
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break
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feedback.setProgress(int(current * total))
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attrs = feat.attributes()
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cat = tuple([attrs[c] for c in category_field_indexes])
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if field_type == 'none':
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values[cat] += 1
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continue
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if field_type == 'numeric':
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if attrs[value_field_index] == NULL:
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continue
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else:
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value = float(attrs[value_field_index])
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elif field_type == 'string':
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if attrs[value_field_index] == NULL:
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value = ''
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else:
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value = str(attrs[value_field_index])
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elif attrs[value_field_index] == NULL:
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value = NULL
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else:
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value = attrs[value_field_index]
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values[cat].append(value)
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(sink, dest_id) = self.parameterAsSink(parameters, self.OUTPUT, context,
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fields, QgsWkbTypes.NoGeometry, QgsCoordinateReferenceSystem())
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if field_type == 'none':
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self.saveCounts(values, sink, feedback)
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elif field_type == 'numeric':
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self.calcNumericStats(values, sink, feedback)
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elif field_type == 'datetime':
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self.calcDateTimeStats(values, sink, feedback)
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else:
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self.calcStringStats(values, sink, feedback)
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return {self.OUTPUT: dest_id}
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def saveCounts(self, values, sink, feedback):
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total = 50.0 / len(values) if values else 0
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current = 0
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for cat, v in values.items():
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if feedback.isCanceled():
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break
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feedback.setProgress(int(current * total) + 50)
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f = QgsFeature()
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f.setAttributes(list(cat) + [v])
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sink.addFeature(f, QgsFeatureSink.FastInsert)
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current += 1
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def calcNumericStats(self, values, sink, feedback):
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stat = QgsStatisticalSummary()
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total = 50.0 / len(values) if values else 0
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current = 0
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for cat, v in values.items():
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if feedback.isCanceled():
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break
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feedback.setProgress(int(current * total) + 50)
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stat.calculate(v)
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f = QgsFeature()
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f.setAttributes(list(cat) + [stat.count(),
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stat.variety(),
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stat.min(),
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stat.max(),
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stat.range(),
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stat.sum(),
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stat.mean(),
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stat.median(),
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stat.stDev(),
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stat.minority(),
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stat.majority(),
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stat.firstQuartile(),
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stat.thirdQuartile(),
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stat.interQuartileRange()])
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sink.addFeature(f, QgsFeatureSink.FastInsert)
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current += 1
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def calcDateTimeStats(self, values, sink, feedback):
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stat = QgsDateTimeStatisticalSummary()
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total = 50.0 / len(values) if values else 0
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current = 0
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for cat, v in values.items():
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if feedback.isCanceled():
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break
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feedback.setProgress(int(current * total) + 50)
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stat.calculate(v)
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f = QgsFeature()
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f.setAttributes(list(cat) + [stat.count(),
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stat.countDistinct(),
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stat.countMissing(),
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stat.count() - stat.countMissing(),
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stat.statistic(QgsDateTimeStatisticalSummary.Min),
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stat.statistic(QgsDateTimeStatisticalSummary.Max)
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])
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sink.addFeature(f, QgsFeatureSink.FastInsert)
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current += 1
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def calcStringStats(self, values, sink, feedback):
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stat = QgsStringStatisticalSummary()
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total = 50.0 / len(values) if values else 0
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current = 0
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for cat, v in values.items():
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if feedback.isCanceled():
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break
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feedback.setProgress(int(current * total) + 50)
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stat.calculate(v)
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f = QgsFeature()
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f.setAttributes(list(cat) + [stat.count(),
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stat.countDistinct(),
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stat.countMissing(),
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stat.count() - stat.countMissing(),
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stat.min(),
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stat.max(),
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stat.minLength(),
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stat.maxLength(),
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stat.meanLength()
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])
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sink.addFeature(f, QgsFeatureSink.FastInsert)
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current += 1
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