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BasicStatistics.py
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BasicStatistics.py
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# -*- coding: utf-8 -*-
"""
***************************************************************************
BasicStatistics.py
---------------------
Date : November 2016
Copyright : (C) 2016 by Nyall Dawson
Email : nyall dot dawson at gmail dot com
***************************************************************************
* *
* This program is free software; you can redistribute it and/or modify *
* it under the terms of the GNU General Public License as published by *
* the Free Software Foundation; either version 2 of the License, or *
* (at your option) any later version. *
* *
***************************************************************************
"""
__author__ = 'Nyall Dawson'
__date__ = 'November 2016'
__copyright__ = '(C) 2016, Nyall Dawson'
# This will get replaced with a git SHA1 when you do a git archive
__revision__ = '$Format:%H$'
import os
import codecs
from qgis.PyQt.QtCore import QVariant
from qgis.PyQt.QtGui import QIcon
from qgis.core import (QgsStatisticalSummary,
QgsFeatureSink,
QgsStringStatisticalSummary,
QgsDateTimeStatisticalSummary,
QgsFeatureRequest,
QgsProcessingUtils,
QgsProcessingParameterFeatureSource,
QgsProcessingParameterField,
QgsProcessingParameterFileDestination,
QgsProcessingOutputHtml,
QgsProcessingOutputNumber)
from processing.algs.qgis.QgisAlgorithm import QgisAlgorithm
pluginPath = os.path.split(os.path.split(os.path.dirname(__file__))[0])[0]
class BasicStatisticsForField(QgisAlgorithm):
INPUT_LAYER = 'INPUT_LAYER'
FIELD_NAME = 'FIELD_NAME'
OUTPUT_HTML_FILE = 'OUTPUT_HTML_FILE'
MIN = 'MIN'
MAX = 'MAX'
COUNT = 'COUNT'
UNIQUE = 'UNIQUE'
EMPTY = 'EMPTY'
FILLED = 'FILLED'
MIN_LENGTH = 'MIN_LENGTH'
MAX_LENGTH = 'MAX_LENGTH'
MEAN_LENGTH = 'MEAN_LENGTH'
CV = 'CV'
SUM = 'SUM'
MEAN = 'MEAN'
STD_DEV = 'STD_DEV'
RANGE = 'RANGE'
MEDIAN = 'MEDIAN'
MINORITY = 'MINORITY'
MAJORITY = 'MAJORITY'
FIRSTQUARTILE = 'FIRSTQUARTILE'
THIRDQUARTILE = 'THIRDQUARTILE'
IQR = 'IQR'
def icon(self):
return QIcon(os.path.join(pluginPath, 'images', 'ftools', 'basic_statistics.png'))
def tags(self):
return self.tr('stats,statistics,date,time,datetime,string,number,text,table,layer,maximum,minimum,mean,average,standard,deviation,'
'count,distinct,unique,variance,median,quartile,range,majority,minority').split(',')
def group(self):
return self.tr('Vector table tools')
def __init__(self):
super().__init__()
self.addParameter(QgsProcessingParameterFeatureSource(self.INPUT_LAYER,
self.tr('Input layer')))
self.addParameter(QgsProcessingParameterField(self.FIELD_NAME,
self.tr('Field to calculate statistics on'),
None, self.INPUT_LAYER, QgsProcessingParameterField.Any))
self.addParameter(QgsProcessingParameterFileDestination(self.OUTPUT_HTML_FILE, self.tr('Statistics'), self.tr('HTML files (*.html)'), None, True))
self.addOutput(QgsProcessingOutputHtml(self.OUTPUT_HTML_FILE, self.tr('Statistics')))
self.addOutput(QgsProcessingOutputNumber(self.COUNT, self.tr('Count')))
self.addOutput(QgsProcessingOutputNumber(self.UNIQUE, self.tr('Number of unique values')))
self.addOutput(QgsProcessingOutputNumber(self.EMPTY, self.tr('Number of empty (null) values')))
self.addOutput(QgsProcessingOutputNumber(self.FILLED, self.tr('Number of non-empty values')))
self.addOutput(QgsProcessingOutputNumber(self.MIN, self.tr('Minimum value')))
self.addOutput(QgsProcessingOutputNumber(self.MAX, self.tr('Maximum value')))
self.addOutput(QgsProcessingOutputNumber(self.MIN_LENGTH, self.tr('Minimum length')))
self.addOutput(QgsProcessingOutputNumber(self.MAX_LENGTH, self.tr('Maximum length')))
self.addOutput(QgsProcessingOutputNumber(self.MEAN_LENGTH, self.tr('Mean length')))
self.addOutput(QgsProcessingOutputNumber(self.CV, self.tr('Coefficient of Variation')))
self.addOutput(QgsProcessingOutputNumber(self.SUM, self.tr('Sum')))
self.addOutput(QgsProcessingOutputNumber(self.MEAN, self.tr('Mean value')))
self.addOutput(QgsProcessingOutputNumber(self.STD_DEV, self.tr('Standard deviation')))
self.addOutput(QgsProcessingOutputNumber(self.RANGE, self.tr('Range')))
self.addOutput(QgsProcessingOutputNumber(self.MEDIAN, self.tr('Median')))
self.addOutput(QgsProcessingOutputNumber(self.MINORITY, self.tr('Minority (rarest occurring value)')))
self.addOutput(QgsProcessingOutputNumber(self.MAJORITY, self.tr('Majority (most frequently occurring value)')))
self.addOutput(QgsProcessingOutputNumber(self.FIRSTQUARTILE, self.tr('First quartile')))
self.addOutput(QgsProcessingOutputNumber(self.THIRDQUARTILE, self.tr('Third quartile')))
self.addOutput(QgsProcessingOutputNumber(self.IQR, self.tr('Interquartile Range (IQR)')))
def name(self):
return 'basicstatisticsforfields'
def displayName(self):
return self.tr('Basic statistics for fields')
def processAlgorithm(self, parameters, context, feedback):
source = self.parameterAsSource(parameters, self.INPUT_LAYER, context)
field_name = self.parameterAsString(parameters, self.FIELD_NAME, context)
field = source.fields().at(source.fields().lookupField(field_name))
output_file = self.parameterAsFileOutput(parameters, self.OUTPUT_HTML_FILE, context)
request = QgsFeatureRequest().setFlags(QgsFeatureRequest.NoGeometry).setSubsetOfAttributes([field_name], source.fields())
features = source.getFeatures(request)
count = source.featureCount()
data = []
data.append(self.tr('Analyzed field: {}').format(field_name))
results = {}
if field.isNumeric():
d, results = self.calcNumericStats(features, feedback, field, count)
data.extend(d)
elif field.type() in (QVariant.Date, QVariant.Time, QVariant.DateTime):
d, results = self.calcDateTimeStats(features, feedback, field, count)
data.extend(d)
else:
d, results = self.calcStringStats(features, feedback, field, count)
data.extend(d)
if output_file:
self.createHTML(output_file, data)
results[self.OUTPUT_HTML_FILE] = output_file
return results
def calcNumericStats(self, features, feedback, field, count):
total = 100.0 / count if count else 0
stat = QgsStatisticalSummary()
for current, ft in enumerate(features):
if feedback.isCanceled():
break
stat.addVariant(ft[field.name()])
feedback.setProgress(int(current * total))
stat.finalize()
cv = stat.stDev() / stat.mean() if stat.mean() != 0 else 0
results = {self.COUNT: stat.count(),
self.UNIQUE: stat.variety(),
self.EMPTY: stat.countMissing(),
self.FILLED: count - stat.countMissing(),
self.MIN: stat.min(),
self.MAX: stat.max(),
self.RANGE: stat.range(),
self.SUM: stat.sum(),
self.MEAN: stat.mean(),
self.MEDIAN: stat.median(),
self.STD_DEV: stat.stDev(),
self.CV: cv,
self.MINORITY: stat.minority(),
self.MAJORITY: stat.majority(),
self.FIRSTQUARTILE: stat.firstQuartile(),
self.THIRDQUARTILE: stat.thirdQuartile(),
self.IQR: stat.interQuartileRange()}
data = []
data.append(self.tr('Count: {}').format(stat.count()))
data.append(self.tr('Unique values: {}').format(stat.variety()))
data.append(self.tr('NULL (missing) values: {}').format(stat.countMissing()))
data.append(self.tr('Minimum value: {}').format(stat.min()))
data.append(self.tr('Maximum value: {}').format(stat.max()))
data.append(self.tr('Range: {}').format(stat.range()))
data.append(self.tr('Sum: {}').format(stat.sum()))
data.append(self.tr('Mean value: {}').format(stat.mean()))
data.append(self.tr('Median value: {}').format(stat.median()))
data.append(self.tr('Standard deviation: {}').format(stat.stDev()))
data.append(self.tr('Coefficient of Variation: {}').format(cv))
data.append(self.tr('Minority (rarest occurring value): {}').format(stat.minority()))
data.append(self.tr('Majority (most frequently occurring value): {}').format(stat.majority()))
data.append(self.tr('First quartile: {}').format(stat.firstQuartile()))
data.append(self.tr('Third quartile: {}').format(stat.thirdQuartile()))
data.append(self.tr('Interquartile Range (IQR): {}').format(stat.interQuartileRange()))
return data, results
def calcStringStats(self, features, feedback, field, count):
total = 100.0 / count if count else 1
stat = QgsStringStatisticalSummary()
for current, ft in enumerate(features):
if feedback.isCanceled():
break
stat.addValue(ft[field.name()])
feedback.setProgress(int(current * total))
stat.finalize()
results = {self.COUNT: stat.count(),
self.UNIQUE: stat.countDistinct(),
self.EMPTY: stat.countMissing(),
self.FILLED: stat.count() - stat.countMissing(),
self.MIN: stat.min(),
self.MAX: stat.max(),
self.MIN_LENGTH: stat.minLength(),
self.MAX_LENGTH: stat.maxLength(),
self.MEAN_LENGTH: stat.meanLength()}
data = []
data.append(self.tr('Count: {}').format(count))
data.append(self.tr('Unique values: {}').format(stat.countDistinct()))
data.append(self.tr('NULL (missing) values: {}').format(stat.countMissing()))
data.append(self.tr('Minimum value: {}').format(stat.min()))
data.append(self.tr('Maximum value: {}').format(stat.max()))
data.append(self.tr('Minimum length: {}').format(stat.minLength()))
data.append(self.tr('Maximum length: {}').format(stat.maxLength()))
data.append(self.tr('Mean length: {}').format(stat.meanLength()))
return data, results
def calcDateTimeStats(self, features, feedback, field, count):
total = 100.0 / count if count else 1
stat = QgsDateTimeStatisticalSummary()
for current, ft in enumerate(features):
if feedback.isCanceled():
break
stat.addValue(ft[field.name()])
feedback.setProgress(int(current * total))
stat.finalize()
results = {self.COUNT: stat.count(),
self.UNIQUE: stat.countDistinct(),
self.EMPTY: stat.countMissing(),
self.FILLED: stat.count() - stat.countMissing(),
self.MIN: stat.statistic(QgsDateTimeStatisticalSummary.Min),
self.MAX: stat.statistic(QgsDateTimeStatisticalSummary.Max)}
data = []
data.append(self.tr('Count: {}').format(count))
data.append(self.tr('Unique values: {}').format(stat.countDistinct()))
data.append(self.tr('NULL (missing) values: {}').format(stat.countMissing()))
data.append(self.tr('Minimum value: {}').format(field.displayString(stat.statistic(QgsDateTimeStatisticalSummary.Min))))
data.append(self.tr('Maximum value: {}').format(field.displayString(stat.statistic(QgsDateTimeStatisticalSummary.Max))))
return data, results
def createHTML(self, outputFile, algData):
with codecs.open(outputFile, 'w', encoding='utf-8') as f:
f.write('<html><head>\n')
f.write('<meta http-equiv="Content-Type" content="text/html; \
charset=utf-8" /></head><body>\n')
for s in algData:
f.write('<p>' + str(s) + '</p>\n')
f.write('</body></html>\n')