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274 lines
12 KiB
Python
274 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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QgsProcessingParameterFileOutput,
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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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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(QgsProcessingParameterFileOutput(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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