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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.
275 lines
12 KiB
Python
275 lines
12 KiB
Python
# -*- coding: utf-8 -*-
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"""
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***************************************************************************
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BasicStatistics.py
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---------------------
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Date : November 2016
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Copyright : (C) 2016 by Nyall Dawson
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Email : nyall dot dawson 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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__author__ = 'Nyall Dawson'
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__date__ = 'November 2016'
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__copyright__ = '(C) 2016, Nyall Dawson'
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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 codecs
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from qgis.PyQt.QtCore import QVariant
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from qgis.PyQt.QtGui import QIcon
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from qgis.core import (QgsStatisticalSummary,
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QgsFeatureSink,
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QgsStringStatisticalSummary,
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QgsDateTimeStatisticalSummary,
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QgsFeatureRequest,
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QgsProcessingUtils,
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QgsProcessingParameterFeatureSource,
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QgsProcessingParameterField,
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QgsProcessingParameterFileDestination,
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QgsProcessingOutputHtml,
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QgsProcessingOutputNumber)
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from processing.algs.qgis.QgisAlgorithm import QgisAlgorithm
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pluginPath = os.path.split(os.path.split(os.path.dirname(__file__))[0])[0]
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class BasicStatisticsForField(QgisAlgorithm):
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INPUT_LAYER = 'INPUT_LAYER'
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FIELD_NAME = 'FIELD_NAME'
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OUTPUT_HTML_FILE = 'OUTPUT_HTML_FILE'
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MIN = 'MIN'
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MAX = 'MAX'
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COUNT = 'COUNT'
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UNIQUE = 'UNIQUE'
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EMPTY = 'EMPTY'
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FILLED = 'FILLED'
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MIN_LENGTH = 'MIN_LENGTH'
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MAX_LENGTH = 'MAX_LENGTH'
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MEAN_LENGTH = 'MEAN_LENGTH'
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CV = 'CV'
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SUM = 'SUM'
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MEAN = 'MEAN'
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STD_DEV = 'STD_DEV'
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RANGE = 'RANGE'
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MEDIAN = 'MEDIAN'
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MINORITY = 'MINORITY'
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MAJORITY = 'MAJORITY'
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FIRSTQUARTILE = 'FIRSTQUARTILE'
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THIRDQUARTILE = 'THIRDQUARTILE'
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IQR = 'IQR'
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def icon(self):
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return QIcon(os.path.join(pluginPath, 'images', 'ftools', 'basic_statistics.png'))
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def tags(self):
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return self.tr('stats,statistics,date,time,datetime,string,number,text,table,layer,maximum,minimum,mean,average,standard,deviation,'
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'count,distinct,unique,variance,median,quartile,range,majority,minority').split(',')
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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(QgsProcessingParameterFeatureSource(self.INPUT_LAYER,
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self.tr('Input layer')))
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self.addParameter(QgsProcessingParameterField(self.FIELD_NAME,
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self.tr('Field to calculate statistics on'),
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None, self.INPUT_LAYER, QgsProcessingParameterField.Any))
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self.addParameter(QgsProcessingParameterFileDestination(self.OUTPUT_HTML_FILE, self.tr('Statistics'), self.tr('HTML files (*.html)'), None, True))
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self.addOutput(QgsProcessingOutputHtml(self.OUTPUT_HTML_FILE, self.tr('Statistics')))
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self.addOutput(QgsProcessingOutputNumber(self.COUNT, self.tr('Count')))
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self.addOutput(QgsProcessingOutputNumber(self.UNIQUE, self.tr('Number of unique values')))
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self.addOutput(QgsProcessingOutputNumber(self.EMPTY, self.tr('Number of empty (null) values')))
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self.addOutput(QgsProcessingOutputNumber(self.FILLED, self.tr('Number of non-empty values')))
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self.addOutput(QgsProcessingOutputNumber(self.MIN, self.tr('Minimum value')))
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self.addOutput(QgsProcessingOutputNumber(self.MAX, self.tr('Maximum value')))
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self.addOutput(QgsProcessingOutputNumber(self.MIN_LENGTH, self.tr('Minimum length')))
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self.addOutput(QgsProcessingOutputNumber(self.MAX_LENGTH, self.tr('Maximum length')))
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self.addOutput(QgsProcessingOutputNumber(self.MEAN_LENGTH, self.tr('Mean length')))
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self.addOutput(QgsProcessingOutputNumber(self.CV, self.tr('Coefficient of Variation')))
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self.addOutput(QgsProcessingOutputNumber(self.SUM, self.tr('Sum')))
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self.addOutput(QgsProcessingOutputNumber(self.MEAN, self.tr('Mean value')))
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self.addOutput(QgsProcessingOutputNumber(self.STD_DEV, self.tr('Standard deviation')))
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self.addOutput(QgsProcessingOutputNumber(self.RANGE, self.tr('Range')))
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self.addOutput(QgsProcessingOutputNumber(self.MEDIAN, self.tr('Median')))
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self.addOutput(QgsProcessingOutputNumber(self.MINORITY, self.tr('Minority (rarest occurring value)')))
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self.addOutput(QgsProcessingOutputNumber(self.MAJORITY, self.tr('Majority (most frequently occurring value)')))
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self.addOutput(QgsProcessingOutputNumber(self.FIRSTQUARTILE, self.tr('First quartile')))
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self.addOutput(QgsProcessingOutputNumber(self.THIRDQUARTILE, self.tr('Third quartile')))
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self.addOutput(QgsProcessingOutputNumber(self.IQR, self.tr('Interquartile Range (IQR)')))
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def name(self):
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return 'basicstatisticsforfields'
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def displayName(self):
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return self.tr('Basic statistics for fields')
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def processAlgorithm(self, parameters, context, feedback):
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source = self.parameterAsSource(parameters, self.INPUT_LAYER, context)
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field_name = self.parameterAsString(parameters, self.FIELD_NAME, context)
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field = source.fields().at(source.fields().lookupField(field_name))
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output_file = self.parameterAsFileOutput(parameters, self.OUTPUT_HTML_FILE, context)
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request = QgsFeatureRequest().setFlags(QgsFeatureRequest.NoGeometry).setSubsetOfAttributes([field_name], source.fields())
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features = source.getFeatures(request)
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count = source.featureCount()
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data = []
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data.append(self.tr('Analyzed field: {}').format(field_name))
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results = {}
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if field.isNumeric():
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d, results = self.calcNumericStats(features, feedback, field, count)
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data.extend(d)
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elif field.type() in (QVariant.Date, QVariant.Time, QVariant.DateTime):
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d, results = self.calcDateTimeStats(features, feedback, field, count)
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data.extend(d)
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else:
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d, results = self.calcStringStats(features, feedback, field, count)
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data.extend(d)
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if output_file:
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self.createHTML(output_file, data)
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results[self.OUTPUT_HTML_FILE] = output_file
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return results
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def calcNumericStats(self, features, feedback, field, count):
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total = 100.0 / count if count else 0
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stat = QgsStatisticalSummary()
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for current, ft in enumerate(features):
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if feedback.isCanceled():
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break
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stat.addVariant(ft[field.name()])
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feedback.setProgress(int(current * total))
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stat.finalize()
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cv = stat.stDev() / stat.mean() if stat.mean() != 0 else 0
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results = {self.COUNT: stat.count(),
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self.UNIQUE: stat.variety(),
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self.EMPTY: stat.countMissing(),
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self.FILLED: count - stat.countMissing(),
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self.MIN: stat.min(),
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self.MAX: stat.max(),
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self.RANGE: stat.range(),
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self.SUM: stat.sum(),
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self.MEAN: stat.mean(),
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self.MEDIAN: stat.median(),
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self.STD_DEV: stat.stDev(),
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self.CV: cv,
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self.MINORITY: stat.minority(),
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self.MAJORITY: stat.majority(),
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self.FIRSTQUARTILE: stat.firstQuartile(),
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self.THIRDQUARTILE: stat.thirdQuartile(),
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self.IQR: stat.interQuartileRange()}
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data = []
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data.append(self.tr('Count: {}').format(stat.count()))
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data.append(self.tr('Unique values: {}').format(stat.variety()))
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data.append(self.tr('NULL (missing) values: {}').format(stat.countMissing()))
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data.append(self.tr('Minimum value: {}').format(stat.min()))
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data.append(self.tr('Maximum value: {}').format(stat.max()))
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data.append(self.tr('Range: {}').format(stat.range()))
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data.append(self.tr('Sum: {}').format(stat.sum()))
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data.append(self.tr('Mean value: {}').format(stat.mean()))
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data.append(self.tr('Median value: {}').format(stat.median()))
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data.append(self.tr('Standard deviation: {}').format(stat.stDev()))
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data.append(self.tr('Coefficient of Variation: {}').format(cv))
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data.append(self.tr('Minority (rarest occurring value): {}').format(stat.minority()))
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data.append(self.tr('Majority (most frequently occurring value): {}').format(stat.majority()))
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data.append(self.tr('First quartile: {}').format(stat.firstQuartile()))
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data.append(self.tr('Third quartile: {}').format(stat.thirdQuartile()))
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data.append(self.tr('Interquartile Range (IQR): {}').format(stat.interQuartileRange()))
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return data, results
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def calcStringStats(self, features, feedback, field, count):
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total = 100.0 / count if count else 1
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stat = QgsStringStatisticalSummary()
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for current, ft in enumerate(features):
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if feedback.isCanceled():
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break
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stat.addValue(ft[field.name()])
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feedback.setProgress(int(current * total))
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stat.finalize()
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results = {self.COUNT: stat.count(),
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self.UNIQUE: stat.countDistinct(),
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self.EMPTY: stat.countMissing(),
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self.FILLED: stat.count() - stat.countMissing(),
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self.MIN: stat.min(),
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self.MAX: stat.max(),
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self.MIN_LENGTH: stat.minLength(),
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self.MAX_LENGTH: stat.maxLength(),
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self.MEAN_LENGTH: stat.meanLength()}
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data = []
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data.append(self.tr('Count: {}').format(count))
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data.append(self.tr('Unique values: {}').format(stat.countDistinct()))
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data.append(self.tr('NULL (missing) values: {}').format(stat.countMissing()))
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data.append(self.tr('Minimum value: {}').format(stat.min()))
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data.append(self.tr('Maximum value: {}').format(stat.max()))
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data.append(self.tr('Minimum length: {}').format(stat.minLength()))
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data.append(self.tr('Maximum length: {}').format(stat.maxLength()))
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data.append(self.tr('Mean length: {}').format(stat.meanLength()))
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return data, results
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def calcDateTimeStats(self, features, feedback, field, count):
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total = 100.0 / count if count else 1
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stat = QgsDateTimeStatisticalSummary()
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for current, ft in enumerate(features):
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if feedback.isCanceled():
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break
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stat.addValue(ft[field.name()])
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feedback.setProgress(int(current * total))
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stat.finalize()
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results = {self.COUNT: stat.count(),
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self.UNIQUE: stat.countDistinct(),
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self.EMPTY: stat.countMissing(),
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self.FILLED: stat.count() - stat.countMissing(),
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self.MIN: stat.statistic(QgsDateTimeStatisticalSummary.Min),
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self.MAX: stat.statistic(QgsDateTimeStatisticalSummary.Max)}
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data = []
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data.append(self.tr('Count: {}').format(count))
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data.append(self.tr('Unique values: {}').format(stat.countDistinct()))
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data.append(self.tr('NULL (missing) values: {}').format(stat.countMissing()))
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data.append(self.tr('Minimum value: {}').format(field.displayString(stat.statistic(QgsDateTimeStatisticalSummary.Min))))
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data.append(self.tr('Maximum value: {}').format(field.displayString(stat.statistic(QgsDateTimeStatisticalSummary.Max))))
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return data, results
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def createHTML(self, outputFile, algData):
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with codecs.open(outputFile, 'w', encoding='utf-8') as f:
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f.write('<html><head>\n')
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f.write('<meta http-equiv="Content-Type" content="text/html; \
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charset=utf-8" /></head><body>\n')
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for s in algData:
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f.write('<p>' + str(s) + '</p>\n')
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f.write('</body></html>\n')
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