def executeIterating(alg, parameters, paramToIter, context, feedback):
# Generate all single-feature layers
parameter_definition = alg.parameterDefinition(paramToIter)
if not parameter_definition:
return False
iter_source = QgsProcessingParameters.parameterAsSource(
parameter_definition, parameters, context)
sink_list = []
if iter_source.featureCount() == 0:
return False
total = 100.0 / iter_source.featureCount()
for current, feat in enumerate(iter_source.getFeatures()):
if feedback.isCanceled():
return False
sink, sink_id = QgsProcessingUtils.createFeatureSink(
'memory:', context, iter_source.fields(), iter_source.wkbType(),
iter_source.sourceCrs())
sink_list.append(sink_id)
sink.addFeature(feat, QgsFeatureSink.FastInsert)
del sink
feedback.setProgress(int(current * total))
# store output values to use them later as basenames for all outputs
outputs = {}
for out in alg.destinationParameterDefinitions():
if out.name() in parameters:
outputs[out.name()] = parameters[out.name()]
# now run all the algorithms
for i, f in enumerate(sink_list):
if feedback.isCanceled():
return False
parameters[paramToIter] = f
for out in alg.destinationParameterDefinitions():
if out.name() not in outputs:
continue
o = outputs[out.name()]
parameters[
out.name()] = QgsProcessingUtils.generateIteratingDestination(
o, i, context)
feedback.setProgressText(
QCoreApplication.translate('AlgorithmExecutor',
'Executing iteration {0}/{1}…').format(
i, len(sink_list)))
feedback.setProgress(i * 100 / len(sink_list))
ret, results = execute(alg, parameters, context, feedback)
if not ret:
return False
handleAlgorithmResults(alg, context, feedback, False)
return True
def executeIterating(alg, parameters, paramToIter, context, feedback):
# Generate all single-feature layers
parameter_definition = alg.parameterDefinition(paramToIter)
if not parameter_definition:
return False
iter_source = QgsProcessingParameters.parameterAsSource(parameter_definition, parameters, context)
sink_list = []
if iter_source.featureCount() == 0:
return False
total = 100.0 / iter_source.featureCount()
for current, feat in enumerate(iter_source.getFeatures()):
if feedback.isCanceled():
return False
sink, sink_id = QgsProcessingUtils.createFeatureSink('memory:', context, iter_source.fields(), iter_source.wkbType(), iter_source.sourceCrs())
sink_list.append(sink_id)
sink.addFeature(feat, QgsFeatureSink.FastInsert)
del sink
feedback.setProgress(int(current * total))
# store output values to use them later as basenames for all outputs
outputs = {}
for out in alg.destinationParameterDefinitions():
if out.name() in parameters:
outputs[out.name()] = parameters[out.name()]
# now run all the algorithms
for i, f in enumerate(sink_list):
if feedback.isCanceled():
return False
parameters[paramToIter] = f
for out in alg.destinationParameterDefinitions():
if out.name() not in outputs:
continue
o = outputs[out.name()]
parameters[out.name()] = QgsProcessingUtils.generateIteratingDestination(o, i, context)
feedback.setProgressText(QCoreApplication.translate('AlgorithmExecutor', 'Executing iteration {0}/{1}…').format(i + 1, len(sink_list)))
feedback.setProgress(i * 100 / len(sink_list))
ret, results = execute(alg, parameters, context, feedback)
if not ret:
return False
handleAlgorithmResults(alg, context, feedback, False)
return True
