24QString QgsRandomExtractAlgorithm::name()
const
26 return QStringLiteral(
"randomextract" );
29QString QgsRandomExtractAlgorithm::displayName()
const
31 return QObject::tr(
"Random extract" );
34QStringList QgsRandomExtractAlgorithm::tags()
const
36 return QObject::tr(
"extract,filter,random,number,percentage" ).split(
',' );
39QString QgsRandomExtractAlgorithm::group()
const
41 return QObject::tr(
"Vector selection" );
44QString QgsRandomExtractAlgorithm::groupId()
const
46 return QStringLiteral(
"vectorselection" );
49QString 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." );
62QgsRandomExtractAlgorithm *QgsRandomExtractAlgorithm::createInstance()
const
64 return new QgsRandomExtractAlgorithm();
67void QgsRandomExtractAlgorithm::initAlgorithm(
const QVariantMap & )
71 addParameter(
new QgsProcessingParameterEnum( QStringLiteral(
"METHOD" ), QObject::tr(
"Method" ), QStringList() << QObject::tr(
"Number of features" ) << QObject::tr(
"Percentage of features" ),
false, 0 ) );
80 std::unique_ptr< QgsProcessingFeatureSource > source( parameterAsSource( parameters, QStringLiteral(
"INPUT" ), context ) );
85 std::unique_ptr< QgsFeatureSink > sink( parameterAsSink( parameters, QStringLiteral(
"OUTPUT" ), context, dest, source->fields(),
90 const int method = parameterAsEnum( parameters, QStringLiteral(
"METHOD" ), context );
91 int number = parameterAsInt( parameters, QStringLiteral(
"NUMBER" ), context );
92 const long count = source->featureCount();
98 throw QgsProcessingException( QObject::tr(
"Selected number is greater than feature count. Choose a lower value and try again." ) );
104 throw QgsProcessingException( QObject::tr(
"Percentage can't be greater than 100. Choose a lower value and try again." ) );
106 number =
static_cast< int >( std::ceil( number * count / 100 ) );
113 std::vector< QgsFeatureId > allFeats;
114 allFeats.reserve( count );
116 feedback->
pushInfo( QObject::tr(
"Building list of all features..." ) );
120 return QVariantMap();
121 allFeats.push_back( f.
id() );
123 feedback->
pushInfo( QObject::tr(
"Done." ) );
126 std::random_device randomDevice;
127 std::mt19937 mersenneTwister( randomDevice() );
128 std::uniform_int_distribution<size_t> fidsDistribution;
132 size_t actualFeatureCount = allFeats.size();
133 size_t shuffledFeatureCount = number;
134 bool invertSelection =
static_cast< size_t>( number ) > actualFeatureCount / 2;
135 if ( invertSelection )
136 shuffledFeatureCount = actualFeatureCount - number;
138 size_t nb = actualFeatureCount;
141 feedback->
pushInfo( QObject::tr(
"Randomly select %1 features" ).arg( number ) );
142 auto cursor = allFeats.begin();
143 using difference_type = std::vector<QgsFeatureId>::difference_type;
144 while ( shuffledFeatureCount-- )
147 return QVariantMap();
150 fidsDistribution.param( std::uniform_int_distribution<size_t>::param_type( 0, nb - 1 ) );
152 std::swap( *cursor, *( cursor +
static_cast<difference_type
>( fidsDistribution( mersenneTwister ) ) ) );
162 if ( invertSelection )
163 for (
auto it = cursor; it != allFeats.end(); ++it )
164 selected.insert( *it );
166 for (
auto it = allFeats.begin(); it != cursor; ++it )
167 selected.insert( *it );
169 feedback->
pushInfo( QObject::tr(
"Adding selected features" ) );
174 return QVariantMap();
181 outputs.insert( QStringLiteral(
"OUTPUT" ), dest );
@ Vector
Tables (i.e. vector layers with or without geometry). When used for a sink this indicates the sink ha...
@ NoGeometry
Geometry is not required. It may still be returned if e.g. required for a filter condition.
@ RegeneratesPrimaryKey
Algorithm always drops any existing primary keys or FID values and regenerates them in outputs.
QFlags< ProcessingAlgorithmDocumentationFlag > ProcessingAlgorithmDocumentationFlags
Flags describing algorithm behavior for documentation purposes.
@ SkipGeometryValidityChecks
Invalid geometry checks should always be skipped. This flag can be useful for algorithms which always...
Wrapper for iterator of features from vector data provider or vector layer.
bool nextFeature(QgsFeature &f)
Fetch next feature and stores in f, returns true on success.
This class wraps a request for features to a vector layer (or directly its vector data provider).
QgsFeatureRequest & setFlags(Qgis::FeatureRequestFlags flags)
Sets flags that affect how features will be fetched.
QgsFeatureRequest & setFilterFids(const QgsFeatureIds &fids)
Sets the feature IDs that should be fetched.
QgsFeatureRequest & setNoAttributes()
Set that no attributes will be fetched.
@ 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
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.
Base class for providing feedback from a processing algorithm.
virtual void pushInfo(const QString &info)
Pushes a general informational message from the 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.
QSet< QgsFeatureId > QgsFeatureIds