QGIS/python/plugins/processing/algs/SaveSelectedFeatures.py

124 lines
6.1 KiB
Python

# -*- coding: utf-8 -*-
"""
***************************************************************************
SaveSelectedFeatures.py
---------------------
Date : August 2012
Copyright : (C) 2012 by Victor Olaya
Email : volayaf at gmail dot com
***************************************************************************
* *
* This program is free software; you can redistribute it and/or modify *
* it under the terms of the GNU General Public License as published by *
* the Free Software Foundation; either version 2 of the License, or *
* (at your option) any later version. *
* *
***************************************************************************
"""
__author__ = 'Victor Olaya'
__date__ = 'August 2012'
__copyright__ = '(C) 2012, Victor Olaya'
# This will get replaced with a git SHA1 when you do a git archive
__revision__ = '$Format:%H$'
from processing.core.GeoAlgorithm import GeoAlgorithm
from processing.outputs.OutputVector import OutputVector
from processing.parameters.ParameterVector import ParameterVector
from qgis.core import *
from PyQt4.QtCore import *
from PyQt4.QtGui import *
from processing.tools import dataobjects, vector
class SaveSelectedFeatures(GeoAlgorithm):
'''This is an example algorithm that takes a vector layer and creates
a new one just with just those features of the input layer that are
selected.
It is meant to be used as an example of how to create your own Processing
algorithms and explain methods and variables used to do it.
An algorithm like this will be available in all Processing elements, and
there is not need for additional work.
All geoprocessingalgorithms should extend the GeoAlgorithm 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.
OUTPUT_LAYER = "OUTPUT_LAYER"
INPUT_LAYER = "INPUT_LAYER"
def defineCharacteristics(self):
'''Here we define the inputs and output of the algorithm, along
with some other properties.
This will give the algorithm its semantics, and allow to use it in the modeler.
As a rule of thumb, do not produce anything not declared here.
It will work fine in the toolbox, but it will not work in the modeler.
If that's what you intend, then set self.showInModeler = False'''
#the name that the user will see in the toolbox
self.name = "Save selected features"
#the branch of the toolbox under which the algorithm will appear
self.group = "Vector general tools"
#we add the input vector layer. It can have any kind of geometry
#It is a mandatory (not optional) one, hence the False argument
self.addParameter(ParameterVector(self.INPUT_LAYER, "Input layer", [ParameterVector.VECTOR_TYPE_ANY], False))
# we add a vector layer as output
self.addOutput(OutputVector(self.OUTPUT_LAYER, "Output layer with selected features"))
def processAlgorithm(self, progress):
'''Here is where the processing itself takes place'''
#the first thing to do is retrieve the values of the parameters
#entered by the user.
#The getParameterValue will return the value with its corresponding type,
#strings in the case of inputs and outputs
inputFilename = self.getParameterValue(self.INPUT_LAYER)
#The output. It will get the value of the destinatation file entered by the user.
#If the user select "Save to temporary file", when we arrive here it will already have an asigned value,
#which will be a temporary file using the first supported file format of the corresponding algorithm provider
output = self.getOutputFromName(self.OUTPUT_LAYER)
#input layers values are always a string with its location.
#That string can be converted into a QGIS object (a QgsVectorLayer in this case))
#using the processing.getObject() method
vectorLayer = dataobjects.getObjectFromUri(inputFilename)
#And now we can process
#First we create the output layer.
#To do so, we call the getVectorWriter method in the Output object.
#That will give us a VectorWriter, that we can later use to add features.
#The destination file has its format selected based on the file extension.
#If the selected format is not supported, the first available format from
#the provider is used, and the corresponding file extension appended
provider = vectorLayer.dataProvider()
writer = output.getVectorWriter( provider.fields(),
provider.geometryType(),
#this is the layer crs. By default all resulting layers are
#assumed to be in the same crs are the inputs, and will be loaded
#with this assumptions when executed from the toolbox.
#The self.crs variable has to be canged in case this is not true,
#or in case there are no input layer from which the output crs can be infered
vectorLayer.crs() )
#Now we take the selected features and add them to the output layer
features = vector.features(vectorLayer)
total = len(features)
i = 0
for i, feat in enumerate(features):
writer.addFeature(feat)
#we use the progress object to communicate with the user
progress.setPercentage(100 * i / float(total))
i += 1
del writer
#There is nothing more to do here. We do not have to open the layer that we have created.
#The processing framework will take care of that, or will handle it if this algorithm is executed within
#a complex model