QGIS API Documentation 3.29.0-Master (19d7edcfed)
qgsclassificationjenks.cpp
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1/***************************************************************************
2 qgsclassificationjenks.h
3 ---------------------
4 begin : September 2019
5 copyright : (C) 2019 by Denis Rouzaud
7 ***************************************************************************
8 * *
9 * This program is free software; you can redistribute it and/or modify *
10 * it under the terms of the GNU General Public License as published by *
11 * the Free Software Foundation; either version 2 of the License, or *
12 * (at your option) any later version. *
13 * *
14 ***************************************************************************/
15
16#include <limits>
18#include "qgsapplication.h"
19
20#if QT_VERSION >= QT_VERSION_CHECK(5, 15, 0)
21#include <QRandomGenerator>
22#endif
23
26{
27
28}
29
31{
32 return QObject::tr( "Natural Breaks (Jenks)" );
33}
34
36{
37 return QStringLiteral( "Jenks" );
38}
39
41{
43 copyBase( c );
44 return c;
45}
46
48{
49 return QgsApplication::getThemeIcon( "classification_methods/mClassificationNaturalBreak.svg" );
50}
51
52
53QList<double> QgsClassificationJenks::calculateBreaks( double &minimum, double &maximum,
54 const QList<double> &values, int nclasses )
55{
56 // Jenks Optimal (Natural Breaks) algorithm
57 // Based on the Jenks algorithm from the 'classInt' package available for
58 // the R statistical prgramming language, and from Python code from here:
59 // http://danieljlewis.org/2010/06/07/jenks-natural-breaks-algorithm-in-python/
60 // and is based on a JAVA and Fortran code available here:
61 // https://stat.ethz.ch/pipermail/r-sig-geo/2006-March/000811.html
62
63 // Returns class breaks such that classes are internally homogeneous while
64 // assuring heterogeneity among classes.
65
66 if ( values.isEmpty() )
67 return QList<double>();
68
69 if ( nclasses <= 1 )
70 {
71 return QList<double>() << maximum;
72 }
73
74 if ( nclasses >= values.size() )
75 {
76 return values;
77 }
78
79 QVector<double> sample;
80 QVector<double> sorted;
81
82 // if we have lots of values, we need to take a random sample
83 if ( values.size() > mMaximumSize )
84 {
85 // for now, sample at least maximumSize values or a 10% sample, whichever
86 // is larger. This will produce a more representative sample for very large
87 // layers, but could end up being computationally intensive...
88
89 sample.resize( std::max( mMaximumSize, static_cast<int>( values.size() ) / 10 ) );
90
91 QgsDebugMsgLevel( QStringLiteral( "natural breaks (jenks) sample size: %1" ).arg( sample.size() ), 2 );
92 QgsDebugMsgLevel( QStringLiteral( "values:%1" ).arg( values.size() ), 2 );
93
94 sample[ 0 ] = minimum;
95 sample[ 1 ] = maximum;
96
97 sorted = values.toVector();
98 std::sort( sorted.begin(), sorted.end() );
99
100 int j = -1;
101
102 // loop through all values in initial array
103 // skip the first value as it is a minimum one
104 // skip the last value as that one is a maximum one
105 // and those are already in the sample as items 0 and 1
106 for ( int i = 1; i < sorted.size() - 2; i++ )
107 {
108 if ( ( i * ( mMaximumSize - 2 ) / ( sorted.size() - 2 ) ) > j )
109 {
110 j++;
111 sample[ j + 2 ] = sorted[ i ];
112 }
113 }
114 }
115 else
116 {
117 sample = values.toVector();
118 }
119
120 const int n = sample.size();
121
122 // sort the sample values
123 std::sort( sample.begin(), sample.end() );
124
125 QVector< QVector<int> > matrixOne( n + 1 );
126 QVector< QVector<double> > matrixTwo( n + 1 );
127
128 for ( int i = 0; i <= n; i++ )
129 {
130 matrixOne[i].resize( nclasses + 1 );
131 matrixTwo[i].resize( nclasses + 1 );
132 }
133
134 for ( int i = 1; i <= nclasses; i++ )
135 {
136 matrixOne[0][i] = 1;
137 matrixOne[1][i] = 1;
138 matrixTwo[0][i] = 0.0;
139 for ( int j = 2; j <= n; j++ )
140 {
141 matrixTwo[j][i] = std::numeric_limits<double>::max();
142 }
143 }
144
145 for ( int l = 2; l <= n; l++ )
146 {
147 double s1 = 0.0;
148 double s2 = 0.0;
149 int w = 0;
150
151 double v = 0.0;
152
153 for ( int m = 1; m <= l; m++ )
154 {
155 const int i3 = l - m + 1;
156
157 const double val = sample[ i3 - 1 ];
158
159 s2 += val * val;
160 s1 += val;
161 w++;
162
163 v = s2 - ( s1 * s1 ) / static_cast< double >( w );
164 const int i4 = i3 - 1;
165 if ( i4 != 0 )
166 {
167 for ( int j = 2; j <= nclasses; j++ )
168 {
169 if ( matrixTwo[l][j] >= v + matrixTwo[i4][j - 1] )
170 {
171 matrixOne[l][j] = i4;
172 matrixTwo[l][j] = v + matrixTwo[i4][j - 1];
173 }
174 }
175 }
176 }
177 matrixOne[l][1] = 1;
178 matrixTwo[l][1] = v;
179 }
180
181 QVector<double> breaks( nclasses );
182 breaks[nclasses - 1] = sample[n - 1];
183
184 for ( int j = nclasses, k = n; j >= 2; j-- )
185 {
186 const int id = matrixOne[k][j] - 1;
187 breaks[j - 2] = sample[id];
188 k = matrixOne[k][j] - 1;
189 }
190
191 return breaks.toList();
192}
static QIcon getThemeIcon(const QString &name, const QColor &fillColor=QColor(), const QColor &strokeColor=QColor())
Helper to get a theme icon.
QgsClassificationJenks is an implementation of QgsClassificationMethod for natural breaks based on Je...
QgsClassificationMethod * clone() const override
Returns a clone of the method.
QIcon icon() const override
The icon of the method.
QString name() const override
The readable and translate name of the method.
QString id() const override
The id of the method as saved in the project, must be unique in registry.
QgsClassificationMethod is an abstract class for implementations of classification methods.
void copyBase(QgsClassificationMethod *c) const
Copy the parameters (shall be used in clone implementation)
As part of the API refactoring and improvements which landed in the Processing API was substantially reworked from the x version This was done in order to allow much of the underlying Processing framework to be ported into c
#define QgsDebugMsgLevel(str, level)
Definition: qgslogger.h:39