24 QString QgsRandomExtractAlgorithm::name()
const
26 return QStringLiteral(
"randomextract" );
29 QString QgsRandomExtractAlgorithm::displayName()
const
31 return QObject::tr(
"Random extract" );
34 QStringList QgsRandomExtractAlgorithm::tags()
const
36 return QObject::tr(
"extract,filter,random,number,percentage" ).split(
',' );
39 QString QgsRandomExtractAlgorithm::group()
const
41 return QObject::tr(
"Vector selection" );
44 QString QgsRandomExtractAlgorithm::groupId()
const
46 return QStringLiteral(
"vectorselection" );
49 QString QgsRandomExtractAlgorithm::shortHelpString()
const
51 return QObject::tr(
"This algorithm takes a vector layer and generates a new one that contains only a subset "
52 "of the features in the input layer.\n\n"
53 "The subset is defined randomly, using a percentage or count value to define the total number "
54 "of features in the subset." );
57 QgsRandomExtractAlgorithm *QgsRandomExtractAlgorithm::createInstance()
const
59 return new QgsRandomExtractAlgorithm();
62 void QgsRandomExtractAlgorithm::initAlgorithm(
const QVariantMap & )
66 addParameter(
new QgsProcessingParameterEnum( QStringLiteral(
"METHOD" ), QObject::tr(
"Method" ), QStringList() << QObject::tr(
"Number of features" ) << QObject::tr(
"Percentage of features" ),
false, 0 ) );
75 std::unique_ptr< QgsProcessingFeatureSource > source( parameterAsSource( parameters, QStringLiteral(
"INPUT" ), context ) );
80 std::unique_ptr< QgsFeatureSink > sink( parameterAsSink( parameters, QStringLiteral(
"OUTPUT" ), context, dest, source->fields(),
85 const int method = parameterAsEnum( parameters, QStringLiteral(
"METHOD" ), context );
86 int number = parameterAsInt( parameters, QStringLiteral(
"NUMBER" ), context );
88 const long count = source->featureCount();
94 throw QgsProcessingException( QObject::tr(
"Selected number is greater than feature count. Choose a lower value and try again." ) );
100 throw QgsProcessingException( QObject::tr(
"Percentage can't be greater than 100. Choose a lower value and try again." ) );
102 number =
static_cast< int >( std::ceil( number * count / 100 ) );
106 std::random_device randomDevice;
107 const std::mt19937 mersenneTwister( randomDevice() );
108 const std::uniform_int_distribution<int> fidsDistribution( 0, count );
110 QVector< QgsFeatureId > fids( number );
111 std::generate( fids.begin(), fids.end(), bind( fidsDistribution, mersenneTwister ) );
113 QHash< QgsFeatureId, int > idsCount;
124 const QgsFeatureIds ids = qgis::listToSet( idsCount.keys() );
135 const int count = idsCount.value( f.
id() );
136 for (
int i = 0; i < count; ++i )
144 outputs.insert( QStringLiteral(
"OUTPUT" ), dest );
Wrapper for iterator of features from vector data provider or vector layer.
bool nextFeature(QgsFeature &f)
This class wraps a request for features to a vector layer (or directly its vector data provider).
@ FastInsert
Use faster inserts, at the cost of updating the passed features to reflect changes made at the provid...
@ RegeneratePrimaryKey
This flag indicates, that a primary key field cannot be guaranteed to be unique and the sink should i...
The feature class encapsulates a single feature including its unique ID, geometry and a list of field...
bool isCanceled() const SIP_HOLDGIL
Tells whether the operation has been canceled already.
Contains information about the context in which a processing algorithm is executed.
Custom exception class for processing related exceptions.
@ FlagSkipGeometryValidityChecks
Invalid geometry checks should always be skipped. This flag can be useful for algorithms which always...
Base class for providing feedback from a processing algorithm.
An enum based parameter for processing algorithms, allowing for selection from predefined values.
A feature sink output for processing algorithms.
An input feature source (such as vector layers) parameter for processing algorithms.
A numeric parameter for processing algorithms.
@ TypeVector
Tables (i.e. vector layers with or without geometry). When used for a sink this indicates the sink ha...
QSet< QgsFeatureId > QgsFeatureIds
qint64 QgsFeatureId
64 bit feature ids negative numbers are used for uncommitted/newly added features