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October 5, 2018 19:20
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# -*- coding: utf-8 -*- | |
from PyQt5.QtCore import QCoreApplication | |
from qgis.core import (QgsProcessing, | |
QgsFeatureSink, | |
QgsProcessingException, | |
QgsProcessingAlgorithm, | |
QgsProcessingParameterFeatureSource, | |
QgsProcessingParameterFeatureSink, | |
QgsProcessingParameterVectorLayer, | |
QgsProcessingParameterField, | |
QgsCoordinateReferenceSystem, | |
QgsProcessingParameterVectorDestination) | |
import processing | |
class Campo(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' | |
FIELD = 'FIELD' | |
BUFFER = 'BUFFER' | |
def tr(self, string): | |
return QCoreApplication.translate('Processing', string) | |
def createInstance(self): | |
return Campo() | |
def name(self): | |
return 'campo' | |
def displayName(self): | |
return self.tr('Campo') | |
def group(self): | |
return self.tr('Example scripts') | |
def groupId(self): | |
return 'examplescripts' | |
def initAlgorithm(self, config=None): | |
# 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] | |
) | |
) | |
self.addParameter( | |
QgsProcessingParameterField( | |
self.FIELD, | |
self.tr("Campo"), | |
'STRANIERI', | |
self.INPUT | |
) | |
) | |
self.addParameter( | |
QgsProcessingParameterVectorDestination( | |
self.BUFFER, | |
self.tr('Buffer nativo') | |
) | |
) | |
def processAlgorithm(self, parameters, context, feedback): | |
""" | |
Here is where the processing itself takes place. | |
""" | |
def dummy(alg, context, feedback): | |
pass | |
# 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.parameterAsVectorLayer( | |
parameters, | |
self.INPUT, | |
context | |
) | |
# If source was not found, throw an exception to indicate that the algorithm | |
# encountered a fatal error. The exception text can be any string, but in this | |
# case we use the pre-built invalidSourceError method to return a standard | |
# helper text for when a source cannot be evaluated | |
if source is None: | |
raise QgsProcessingException(self.invalidSourceError(parameters, self.INPUT)) | |
campo = self.parameterAsFields( | |
parameters, | |
self.FIELD, | |
context | |
)[0] | |
buffer_output = self.parameterAsOutputLayer( | |
parameters, | |
self.BUFFER, | |
context | |
) | |
# If sink was not created, throw an exception to indicate that the algorithm | |
# encountered a fatal error. The exception text can be any string, but in this | |
# case we use the pre-built invalidSinkError method to return a standard | |
# helper text for when a sink cannot be evaluated | |
# 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() | |
# To run another Processing algorithm as part of this algorithm, you can use | |
# processing.run(...). Make sure you pass the current context and feedback | |
# to processing.run to ensure that all temporary layer outputs are available | |
# to the executed algorithm, and that the executed algorithm can send feedback | |
# reports to the user (and correctly handle cancelation and progress reports!) | |
for i in features: | |
buffered_layer = processing.run("native:buffer", | |
{ | |
'INPUT': source, | |
'DISTANCE': i[campo], | |
'SEGMENTS': 5, | |
'END_CAP_STYLE': 0, | |
'JOIN_STYLE': 0, | |
'MITER_LIMIT': 2, | |
'DISSOLVE': False, | |
'OUTPUT': buffer_output | |
}, context=context, feedback=feedback, onFinish=dummy)['OUTPUT'] | |
feedback.pushDebugInfo(str(i["STRANIERI"])) | |
reprojected = processing.run("native:reprojectlayer", | |
{ | |
'INPUT': buffered_layer, | |
'TARGET_CRS': QgsCoordinateReferenceSystem('EPSG:4326'), | |
'OUTPUT':'memory:' | |
}, context=context, feedback=feedback)['OUTPUT'] | |
# 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.BUFFER: reprojected} |
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