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backporting #6678 to release-3_0

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ghtmtt authored and nyalldawson committed Apr 16, 2018
1 parent c2ece6c commit 20c2d10533fe071d40a7c269f6f017021e596db2
Showing with 145 additions and 0 deletions.
  1. +145 −0 python/plugins/processing/script/ScriptTemplate.py
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# -*- coding: utf-8 -*-

from PyQt5.QtCore import QCoreApplication
from qgis.core import (QgsProcessing,
QgsFeatureSink,
QgsProcessingAlgorithm,
QgsProcessingParameterFeatureSource,
QgsProcessingParameterFeatureSink)


class ExampleProcessingAlgorithm(QgsProcessingAlgorithm):
"""
This is an example algorithm that takes a vector layer and
creates a new identical one.
It is meant to be used as an example of how to create your own
algorithms and explain methods and variables used to do it. An
algorithm like this will be available in all elements, and there
is not need for additional work.
All Processing algorithms should extend the QgsProcessingAlgorithm
class.
"""

# Constants used to refer to parameters and outputs. They will be
# used when calling the algorithm from another algorithm, or when
# calling from the QGIS console.

INPUT = 'INPUT'
OUTPUT = 'OUTPUT'

def tr(self, string):
"""
Returns a translatable string with the self.tr() function.
"""
return QCoreApplication.translate('Processing', string)

def createInstance(self):
return ExampleProcessingAlgorithm()

def name(self):
"""
Returns the algorithm name, used for identifying the algorithm. This
string should be fixed for the algorithm, and must not be localised.
The name should be unique within each provider. Names should contain
lowercase alphanumeric characters only and no spaces or other
formatting characters.
"""
return 'duplicatevectorlayer'

def displayName(self):
"""
Returns the translated algorithm name, which should be used for any
user-visible display of the algorithm name.
"""
return self.tr('Duplicate a vector layer')

def group(self):
"""
Returns the name of the group this algorithm belongs to. This string
should be localised.
"""
return self.tr('Example scripts')

def groupId(self):
"""
Returns the unique ID of the group this algorithm belongs to. This
string should be fixed for the algorithm, and must not be localised.
The group id should be unique within each provider. Group id should
contain lowercase alphanumeric characters only and no spaces or other
formatting characters.
"""
return 'examplescripts'

def initAlgorithm(self, config=None):
"""
Here we define the inputs and output of the algorithm, along
with some other properties.
"""

# We add the input vector features source. It can have any kind of
# geometry.
self.addParameter(
QgsProcessingParameterFeatureSource(
self.INPUT,
self.tr('Input layer'),
[QgsProcessing.TypeVectorAnyGeometry]
)
)

# We add a feature sink in which to store our processed features (this
# usually takes the form of a newly created vector layer when the
# algorithm is run in QGIS).
self.addParameter(
QgsProcessingParameterFeatureSink(
self.OUTPUT,
self.tr('Output layer')
)
)

def processAlgorithm(self, parameters, context, feedback):
"""
Here is where the processing itself takes place.
"""

# Retrieve the feature source and sink. The 'dest_id' variable is used
# to uniquely identify the feature sink, and must be included in the
# dictionary returned by the processAlgorithm function.
source = self.parameterAsSource(
parameters,
self.INPUT,
context
)
(sink, dest_id) = self.parameterAsSink(
parameters,
self.OUTPUT,
context,
source.fields(),
source.wkbType(),
source.sourceCrs()
)

# Compute the number of steps to display within the progress bar and
# get features from source
total = 100.0 / source.featureCount() if source.featureCount() else 0
features = source.getFeatures()

for current, feature in enumerate(features):
# Stop the algorithm if cancel button has been clicked
if feedback.isCanceled():
break

# Add a feature in the sink
sink.addFeature(feature, QgsFeatureSink.FastInsert)

# Update the progress bar
feedback.setProgress(int(current * total))

# Return the results of the algorithm. In this case our only result is
# the feature sink which contains the processed features, but some
# algorithms may return multiple feature sinks, calculated numeric
# statistics, etc. These should all be included in the returned
# dictionary, with keys matching the feature corresponding parameter
# or output names.
return {self.OUTPUT: dest_id}

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