def processAlgorithm(self, parameters, context, model_feedback):
feedback = QgsProcessingMultiStepFeedback(8, model_feedback)
results = {}
outputs = {}
tracks = self.parameterAsSource(parameters, self.TRACKS, context)
# prepare_tracks
alg_params = {
"TRACKS": parameters[self.TRACKS],
"AXIS": parameters[self.AXIS],
"NAME_FIELD": parameters[self.NAME_FIELD],
"OUTPUT": QgsProcessing.TEMPORARY_OUTPUT,
}
outputs["PrepareTracks"] = processing.run(
"precourlis:prepare_tracks",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
current = outputs["PrepareTracks"]["OUTPUT"]
feedback.setCurrentStep(1)
if feedback.isCanceled():
return {}
# addautoincrementalfield
alg_params = {
"INPUT": current,
"FIELD_NAME": "sec_index",
"START": 1,
"GROUP_FIELDS": [],
"SORT_EXPRESSION": '"abs_long"',
"SORT_ASCENDING": True,
"SORT_NULLS_FIRST": False,
"OUTPUT": QgsProcessing.TEMPORARY_OUTPUT,
}
outputs["AddAutoincrementalField"] = processing.run(
"native:addautoincrementalfield",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
current = outputs["AddAutoincrementalField"]["OUTPUT"]
feedback.setCurrentStep(2)
if feedback.isCanceled():
return {}
# fieldcalculator
alg_params = {
"INPUT": current,
"FIELD_NAME": "sec_id",
"FIELD_TYPE": 0,
"FIELD_LENGTH": 10,
"FIELD_PRECISION": 3,
"NEW_FIELD": False,
"FORMULA": ' "sec_index" ',
"OUTPUT": QgsProcessing.TEMPORARY_OUTPUT,
}
outputs["FieldCalculator"] = processing.run(
"qgis:fieldcalculator",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
current = outputs["FieldCalculator"]["OUTPUT"]
feedback.setCurrentStep(3)
if feedback.isCanceled():
return {}
strict_distance = self.parameterAsBool(
parameters, self.STRICT_DISTANCE, context
)
if strict_distance:
# pointsalonglines
alg_params = {
"INPUT": current,
"DISTANCE": parameters[self.DISTANCE],
"OUTPUT": QgsProcessing.TEMPORARY_OUTPUT,
}
outputs["PointsAlongLines"] = processing.run(
"precourlis:pointsalonglines",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
current = outputs["PointsAlongLines"]["OUTPUT"]
points_order_field = "distance"
else:
# v.to.points
v_to_points = QgsApplication.processingRegistry().createAlgorithmById(
"grass7:v.to.points"
)
alg_params = {
"-i": True,
"-t": False,
"dmax": parameters[self.DISTANCE],
"input": current,
"type": [1],
"use": 1,
"output": v_to_points.parameterDefinition(
"output"
).generateTemporaryDestination(),
}
outputs["Vtopoints"] = processing.run(
"grass7:v.to.points",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
current = outputs["Vtopoints"]["output"]
points_order_field = "fid"
feedback.setCurrentStep(4)
if feedback.isCanceled():
return {}
# assignprojection
alg_params = {
"INPUT": current,
"CRS": tracks.sourceCrs(),
"OUTPUT": QgsProcessing.TEMPORARY_OUTPUT,
}
outputs["AssignProjection"] = processing.run(
"native:assignprojection",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
current = outputs["AssignProjection"]["OUTPUT"]
feedback.setCurrentStep(5)
if feedback.isCanceled():
return {}
# point_to_lines
alg_params = {
"INPUT": current,
"FIRST_SECTION_ABS_LONG": parameters.get(self.FIRST_SECTION_ABS_LONG, None),
"FIRST_AXIS_POINT_ABS_LONG": parameters.get(
self.FIRST_AXIS_POINT_ABS_LONG, None
),
"GROUP_FIELD": "sec_id",
"ORDER_FIELD": points_order_field,
"OUTPUT": QgsProcessing.TEMPORARY_OUTPUT,
}
outputs["PointsToLines"] = processing.run(
"precourlis:points_to_lines",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
current = outputs["PointsToLines"]["OUTPUT"]
feedback.setCurrentStep(6)
if feedback.isCanceled():
return {}
# import_layer_from_dem (edit layer in place with edit buffer)
layer = context.getMapLayer(current)
alg_params = {
"INPUT": layer,
"LAYER_NAME": "zfond",
"DEM": parameters[self.DEM],
"BAND": 1,
}
outputs["ImportLayerFromDem"] = processing.run(
"precourlis:import_layer_from_dem",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
feedback.setCurrentStep(7)
if feedback.isCanceled():
return {}
# orderbyexpression (dump layer with changes from edit buffer)
layer.selectAll()
alg_params = {
"INPUT": layer,
"EXPRESSION": '"sec_id"',
"ASCENDING": True,
"NULLS_FIRST": False,
"OUTPUT": self.parameterAsOutputLayer(parameters, self.OUTPUT, context),
}
outputs["OrderByExpression"] = processing.run(
"native:orderbyexpression",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
current = outputs["OrderByExpression"]["OUTPUT"]
results["OUTPUT"] = current
return results
def getSystematicGridFeaturesWithConstraint(self, featureList, inputLyr, stopScale,
coordinateTransformer, fields, feedback=None, predicate=None):
"""
TODO: Progress
"""
if feedback is not None and feedback.isCanceled():
return
predicate = 'intersects' if predicate is None else predicate
multiStepFeedback = QgsProcessingMultiStepFeedback(2, feedback)
multiStepFeedback.setCurrentStep(0)
multiStepFeedback.pushInfo(self.tr('Creating spatial index'))
spatialIdx, idDict = self.buildSpatialIndexAndIdDict(
inputLyr,
feedback=multiStepFeedback
)
multiStepFeedback.pushInfo(self.tr('Getting candidate start indexes'))
xmin, ymin, xmax, ymax = self.getLyrUnprojectedGeographicBounds(
inputLyr
)
inomenList = self.utmGrid.get_INOM_range_from_BB(
xmin, ymin, xmax, ymax
)
multiStepFeedback.setCurrentStep(1)
multiStepFeedback.pushInfo(self.tr('Building grid'))
gridMultistepFeedback = QgsProcessingMultiStepFeedback(
len(inomenList),
multiStepFeedback
)
constraintDict = {
'spatialIdx': spatialIdx,
'idDict': idDict,
'predicate': predicate
}
sys.setrecursionlimit(10**7)
def compute(x):
self.getSystematicGridFeatures(
featureList,
x,
stopScale,
coordinateTransformer,
fields,
constraintDict=constraintDict,
feedback=gridMultistepFeedback
)
pool = concurrent.futures.ThreadPoolExecutor(os.cpu_count())
futures = []
current_idx = 0
for inomen in inomenList:
# gridMultistepFeedback.setCurrentStep(i)
if gridMultistepFeedback.isCanceled():
break
futures.append(
pool.submit(compute, inomen)
)
for x in concurrent.futures.as_completed(futures):
if gridMultistepFeedback.isCanceled():
break
gridMultistepFeedback.setCurrentStep(current_idx)
current_idx += 1
def processAlgorithm(self, parameters, context, model_feedback):
feedback = QgsProcessingMultiStepFeedback(4, model_feedback)
results = {}
outputs = {}
sections = self.parameterAsSource(parameters, self.SECTIONS, context)
# Retreive first section longitudinal abscissa
request = (QgsFeatureRequest().setFlags(
QgsFeatureRequest.NoGeometry
| QgsFeatureRequest.SubsetOfAttributes).setSubsetOfAttributes(
["abs_long"],
sections.fields()).addOrderBy('"sec_id"', True,
True).setLimit(1))
first_section = next(sections.getFeatures(request))
first_abs_long = first_section.attribute("abs_long")
# Lines to points
alg_params = {
"INPUT":
parameters[self.SECTIONS],
"OUTPUT":
QgsProcessingUtils.generateTempFilename("lines_to_points.shp"),
}
outputs["LinesToPoints"] = processing.run(
"precourlis:lines_to_points",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
current = outputs["LinesToPoints"]["OUTPUT"]
feedback.setCurrentStep(1)
if feedback.isCanceled():
return {}
# Interpolate points
alg_params = {
"SECTIONS":
current,
"AXIS":
parameters[self.AXIS],
"CONSTRAINT_LINES":
parameters.get(self.CONSTRAINT_LINES),
"LONG_STEP":
parameters[self.LONG_STEP],
"LAT_STEP":
parameters[self.LAT_STEP],
"ATTR_CROSS_SECTION":
"sec_id",
"OUTPUT":
QgsProcessingUtils.generateTempFilename("interpolate_points.shp"),
}
outputs["InterpolatePoints"] = processing.run(
"precourlis:interpolate_points",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
current = outputs["InterpolatePoints"]["OUTPUT"]
feedback.setCurrentStep(2)
if feedback.isCanceled():
return {}
# assignprojection
alg_params = {
"INPUT": current,
"CRS": sections.sourceCrs(),
"OUTPUT": QgsProcessing.TEMPORARY_OUTPUT,
}
outputs["AssignProjection"] = processing.run(
"native:assignprojection",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
current = outputs["AssignProjection"]["OUTPUT"]
feedback.setCurrentStep(3)
if feedback.isCanceled():
return {}
output = QgsProcessingOutputLayerDefinition(parameters[self.OUTPUT])
output.destinationName = self.tr("Interpolated")
# Points to lines
alg_params = {
"INPUT": current,
"AXIS": parameters[self.AXIS],
"FIRST_SECTION_ABS_LONG": first_abs_long,
"GROUP_FIELD": "abs_long",
"ORDER_FIELD": "p_id",
"OUTPUT": output,
}
outputs["PointsToLines"] = processing.run(
"precourlis:points_to_lines",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
current = outputs["PointsToLines"]["OUTPUT"]
results["OUTPUT"] = current
return results
def processAlgorithm(self, parameters, context, feedback):
# Params
reporting_mode = self.parameterAsEnum(parameters, self.REPORTING_MODE,
context)
lighting_source, lighting_layer = qgsTreatments.parameterAsSourceLayer(
self, parameters, self.LIGHTING, context, feedback=feedback)
self.fieldname = self.parameterAsString(parameters, FDA.FLUX_FIELD,
context)
surface_source, surface_layer = qgsTreatments.parameterAsSourceLayer(
self, parameters, self.SURFACE, context, feedback=feedback)
roads_source, roads_layer = qgsTreatments.parameterAsSourceLayer(
self, parameters, RE.ROADS, context, feedback=feedback)
# Advanced params
clip_distance = self.parameterAsDouble(parameters, self.CLIP_DISTANCE,
context)
# Outputs
self.output = self.parameterAsOutputLayer(parameters, self.OUTPUT,
context)
out_linear = reporting_mode == 1
# Init steps
nb_steps = 3 if out_linear else 2
mf = QgsProcessingMultiStepFeedback(nb_steps, feedback)
if out_linear:
id_field = 'ID'
if id_field not in roads_layer.fields().names():
raise QgsProcessingException("No 'ID' field in roads layer")
qgsTreatments.fixShapefileFID(surface_layer,
context=context,
feedback=feedback)
# Reporting
reporting_layer = QgsProcessingUtils.generateTempFilename(
'reporting.gpkg')
if reporting_mode == 3: # voronoi
if qgsUtils.isMultipartLayer(lighting_layer):
in_voronoi = QgsProcessingUtils.generateTempFilename(
'lighting_single.gpkg')
qgsTreatments.multiToSingleGeom(lighting_layer,
in_voronoi,
context=context,
feedback=mf)
else:
in_voronoi = lighting_layer
qgsTreatments.applyVoronoi(in_voronoi,
reporting_layer,
context=context,
feedback=mf)
else:
reporting_params = parameters.copy()
reporting_params[RR.ROADS] = roads_layer
reporting_params[RR.BUFFER_EXPR] = RR.DEFAULT_BUFFER_EXPR
reporting_params[RR.NAME_FIELD] = RR.DEFAULT_NAME_FIELD
reporting_params[RR.END_CAP_STYLE] = 1 # Flat buffer cap style
reporting_params[RR.DISSOLVE] = reporting_mode in [2] # Roads
reporting_params[RR.OUTPUT] = reporting_layer
qgsTreatments.applyProcessingAlg('LPT',
RR.ALG_NAME,
reporting_params,
context=context,
feedback=mf)
mf.setCurrentStep(1)
# Light surfacic density
density_params = parameters.copy()
density_params[FDA.LIGHTING] = lighting_layer
density_params[FDA.REPORTING] = reporting_layer
density_params[FDA.CLIP_DISTANCE] = clip_distance
density_params[FDA.SURFACE] = surface_layer
if out_linear:
output_surf = QgsProcessingUtils.generateTempFilename(
'output_surface.gpkg')
density_params[FDA.REPORTING_FIELDS] = [id_field]
density_params[FDA.OUTPUT] = output_surf
else:
density_params[FDA.OUTPUT] = self.output
self.out_id = qgsTreatments.applyProcessingAlg('LPT',
FDA.ALG_NAME,
density_params,
context=context,
feedback=mf)
mf.setCurrentStep(3)
# Join if output linear
if out_linear:
copy_fields = [
FDA.NB_LAMPS, FDA.FLUX_SUM, FDA.SURFACE_AREA, FDA.FLUX_DEN
]
self.out_id = qgsTreatments.joinByAttribute(
roads_layer,
id_field,
output_surf,
id_field,
copy_fields=copy_fields,
out_layer=self.output,
context=context,
feedback=mf)
return {self.OUTPUT: self.output}
def processAlgorithm(self, parameters, context, model_feedback):
# Use a multi-step feedback, so that individual child algorithm progress reports are adjusted for the
# overall progress through the model
feedback = QgsProcessingMultiStepFeedback(2, model_feedback)
results = {}
outputs = {}
# Convert enumerator to zero based index value
valor_classifica = self.parameterAsEnum(
parameters, self.VALOR_CLASSIFICA, context) + 1
valor_natureza_linha = self.parameterAsEnum(
parameters, self.VALOR_NATUREZA_LINHA, context) + 1
# Refactor fields
alg_params = {
'FIELDS_MAPPING': [{
'expression': 'now()',
'length': -1,
'name': 'inicio_objeto',
'precision': -1,
'type': 14
}, {
'expression': str(valor_classifica),
'length': 255,
'name': 'valor_classifica',
'precision': -1,
'type': 10
}, {
'expression': str(valor_natureza_linha),
'length': 255,
'name': 'valor_natureza_linha',
'precision': -1,
'type': 10
}],
'INPUT':
parameters['INPUT'],
'OUTPUT':
QgsProcessing.TEMPORARY_OUTPUT
}
outputs['RefactorFields'] = processing.run('qgis:refactorfields',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
feedback.setCurrentStep(1)
if feedback.isCanceled():
return {}
# Export to PostgreSQL (available connections)
idx = self.parameterAsEnum(parameters, self.POSTGRES_CONNECTION,
context)
postgres_connection = self.postgres_connections_list[idx]
alg_params = {
'ADDFIELDS': True,
'APPEND': True,
'A_SRS': None,
'CLIP': False,
'DATABASE': postgres_connection,
'DIM': 1,
'GEOCOLUMN': 'geometria',
'GT': '',
'GTYPE': 4,
'INDEX': True,
'INPUT': outputs['RefactorFields']['OUTPUT'],
'LAUNDER': True,
'OPTIONS': '',
'OVERWRITE': False,
'PK': '',
'PRECISION': True,
'PRIMARY_KEY': 'identificador',
'PROMOTETOMULTI': True,
'SCHEMA': 'public',
'SEGMENTIZE': '',
'SHAPE_ENCODING': '',
'SIMPLIFY': '',
'SKIPFAILURES': False,
'SPAT': None,
'S_SRS': None,
'TABLE': 'linha_de_quebra',
'T_SRS': None,
'WHERE': ''
}
outputs['ExportToPostgresqlAvailableConnections'] = processing.run(
'gdal:importvectorintopostgisdatabaseavailableconnections',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
return results
def processAlgorithm(self, parameters, context, model_feedback):
feedback = QgsProcessingMultiStepFeedback(1, model_feedback)
results = {}
outputs = {}
#parameters['Out']='/tmp/nasa_1km3857clean_r6s3SE250m_statr4.shp'
parameters['Slope'] = self.parameterAsRasterLayer(parameters, self.INPUT1, context).source()
if parameters['Slope'] is None:
raise QgsProcessingException(self.invalidSourceError(parameters, self.INPUT1))
parameters['Inventory'] = self.parameterAsVectorLayer(parameters, self.INPUT, context).source()
if parameters['Inventory'] is None:
raise QgsProcessingException(self.invalidSourceError(parameters, self.INPUT))
parameters['poly'] = self.parameterAsVectorLayer(parameters, self.EXTENT, context).source()
if parameters['poly'] is None:
raise QgsProcessingException(self.invalidSourceError(parameters, self.EXTENT))
parameters['BufferRadiousInPxl'] = self.parameterAsInt(parameters, self.RADIUS, context)
if parameters['BufferRadiousInPxl'] is None:
raise QgsProcessingException(self.invalidSourceError(parameters, self.RADIUS))
#parameters['out1']=self.parameterAsSink(parameters,self.OUTPUT,context)
#if parameters['out1'] is None:
# raise QgsProcessingException(self.invalidSourceError(parameters, self.OUTPUT))
outFile = self.parameterAsOutputLayer(parameters, self.OUTPUT, context)
parameters['Out'], outputFormat = GdalUtils.ogrConnectionStringAndFormat(outFile, context)
if parameters['Out'] is None:
raise QgsProcessingException(self.invalidSourceError(parameters, self.OUTPUT))
#parameters['Out'] = self.parameterAsSink(
# parameters,
# self.OUTPUT,
# context)
print('importing')
alg_params = {
'INPUT': parameters['poly'],
'INPUT2': parameters['Slope'],
'INPUT3' : parameters['Inventory']
}
raster,ds1,XY=self.importing(alg_params)
outputs['raster'] = raster
outputs['ds1'] = ds1
outputs['XY'] = XY
print('indexing')
alg_params = {
'INPUT': parameters['BufferRadiousInPxl'],
'INPUT3': outputs['raster'],
'INPUT2': outputs['XY'],
'INPUT1': outputs['ds1']
}
XYcoord,attributi=self.indexing(alg_params)
outputs['XYcoord'] = XYcoord
outputs['attributi'] = attributi
print('save')
alg_params = {
'OUTPUT': parameters['Out'],
'INPUT2': outputs['XYcoord'],
'INPUT': outputs['ds1'],
'INPUT3': outputs['attributi']
}
self.saveV(alg_params)
# vlayer = QgsVectorLayer(parameters['Out'], 'vector', "ogr")
# QgsProject.instance().addMapLayer(vlayer)
# # Join attributes by location
# alg_params = {
# 'DISCARD_NONMATCHING': False,
# 'INPUT': parameters['Inventory'],
# 'JOIN': parameters['Out'],
# 'JOIN_FIELDS': [''],
# 'METHOD': 1,
# 'PREDICAT4E': [2],
# 'PREFIX': '',
# 'OUTPUT': parameters['Out1']
# }
# outputs['JoinAttributesByLocation'] = processing.run('native:joinattributesbylocation', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
# results['Out'] = outputs['JoinAttributesByLocation']['OUTPUT']
# vlayer = QgsVectorLayer(parameters['Out1'], 'vector', "ogr")
# QgsProject.instance().addMapLayer(vlayer)
feedback.setCurrentStep(1)
if feedback.isCanceled():
return {}
return results
def _prepare_progress_bar(self, feedback):
"""Prepare number of steps for progress bar."""
self.multi_feedback = QgsProcessingMultiStepFeedback(
self.step_count, feedback)
self.run_next_step = QgisStepManager(self.multi_feedback)
def processAlgorithm(self, parameters, context, model_feedback):
# Use a multi-step feedback, so that individual child algorithm progress reports are adjusted for the
# overall progress through the model
feedback = QgsProcessingMultiStepFeedback(24, model_feedback)
results = {}
outputs = {}
# Gitter (Nächster Nachbar)
alg_params = {
'ANGLE': 0,
'DATA_TYPE': 5,
'INPUT': parameters['depth'],
'NODATA': 0,
'OPTIONS': '',
'RADIUS_1': 0,
'RADIUS_2': 0,
'Z_FIELD': parameters['timeaffterspillinminutes'],
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['GitterNchsterNachbar'] = processing.run('gdal:gridnearestneighbor', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(1)
if feedback.isCanceled():
return {}
# Gitter (Nächster Nachbar)
alg_params = {
'ANGLE': 0,
'DATA_TYPE': 5,
'INPUT': parameters['velocityy'],
'NODATA': 0,
'OPTIONS': '',
'RADIUS_1': 0,
'RADIUS_2': 0,
'Z_FIELD': parameters['timeaffterspillinminutes'],
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['GitterNchsterNachbar'] = processing.run('gdal:gridnearestneighbor', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(2)
if feedback.isCanceled():
return {}
# Zufällige Auswahl
alg_params = {
'INPUT': parameters['percedingspillstage'],
'METHOD': 1,
'NUMBER': QgsExpression('100- floor( (5.91 + 0.045 *@temperatureinc )*ln(@timemin) - ((5.91 + 0.045 * @temperatureinc )*ln(@timemin-1)))').evaluate()
}
outputs['ZuflligeAuswahl'] = processing.run('qgis:randomselection', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(3)
if feedback.isCanceled():
return {}
# Gewählte Objekte exportieren
alg_params = {
'INPUT': outputs['ZuflligeAuswahl']['OUTPUT'],
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['GewhlteObjekteExportieren'] = processing.run('native:saveselectedfeatures', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(4)
if feedback.isCanceled():
return {}
# Gitter (Nächster Nachbar)
alg_params = {
'ANGLE': 0,
'DATA_TYPE': 5,
'INPUT': parameters['velocityx'],
'NODATA': 0,
'OPTIONS': '',
'RADIUS_1': 0,
'RADIUS_2': 0,
'Z_FIELD': parameters['timeaffterspillinminutes'],
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['GitterNchsterNachbar'] = processing.run('gdal:gridnearestneighbor', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(5)
if feedback.isCanceled():
return {}
# Minimale begrenzende Geometrie
alg_params = {
'FIELD': None,
'INPUT': outputs['GewhlteObjekteExportieren']['OUTPUT'],
'TYPE': 3,
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['MinimaleBegrenzendeGeometrie'] = processing.run('qgis:minimumboundinggeometry', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(6)
if feedback.isCanceled():
return {}
# Felder überarbeiten
alg_params = {
'FIELDS_MAPPING': [{'expression': '"id"', 'length': 10, 'name': 'id', 'precision': 0, 'type': 4}],
'INPUT': outputs['GewhlteObjekteExportieren']['OUTPUT'],
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['FelderBerarbeiten'] = processing.run('qgis:refactorfields', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(7)
if feedback.isCanceled():
return {}
# "Leerwert" füllen
alg_params = {
'BAND': 1,
'DISTANCE': 10,
'INPUT': outputs['GitterNchsterNachbar']['OUTPUT'],
'ITERATIONS': 0,
'MASK_LAYER': None,
'NO_MASK': False,
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['LeerwertFllen'] = processing.run('gdal:fillnodata', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(8)
if feedback.isCanceled():
return {}
# "Leerwert" füllen
alg_params = {
'BAND': 1,
'DISTANCE': 10,
'INPUT': outputs['GitterNchsterNachbar']['OUTPUT'],
'ITERATIONS': 0,
'MASK_LAYER': None,
'NO_MASK': False,
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['LeerwertFllen'] = processing.run('gdal:fillnodata', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(9)
if feedback.isCanceled():
return {}
# Zentroide
alg_params = {
'ALL_PARTS': False,
'INPUT': outputs['MinimaleBegrenzendeGeometrie']['OUTPUT'],
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['Zentroide'] = processing.run('native:centroids', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(10)
if feedback.isCanceled():
return {}
# Add raster values to features
alg_params = {
'GRIDS': [outputs['LeerwertFllen']['OUTPUT'],outputs['LeerwertFllen']['OUTPUT'],outputs['GitterNchsterNachbar']['OUTPUT']],
'RESAMPLING': 0,
'SHAPES': parameters['perimeter'],
'RESULT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['AddRasterValuesToFeatures'] = processing.run('saga:addrastervaluestofeatures', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(11)
if feedback.isCanceled():
return {}
# Attribute nach Position zusammenfügen
alg_params = {
'DISCARD_NONMATCHING': False,
'INPUT': outputs['FelderBerarbeiten']['OUTPUT'],
'JOIN': outputs['AddRasterValuesToFeatures']['RESULT'],
'JOIN_FIELDS': None,
'METHOD': 1,
'PREDICATE': 0,
'PREFIX': '',
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['AttributeNachPositionZusammenfgen'] = processing.run('qgis:joinattributesbylocation', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(12)
if feedback.isCanceled():
return {}
# Verschieben
alg_params = {
'DELTA_M': 0,
'DELTA_X': QgsProperty.fromExpression('CASE
WHEN "OUTPUT_2" > 0 THEN
(sqrt((pi()*1.21^2*(((@volumeofthespillinlitres/1000)^2*9.81*0.1793*((1/60)* @timemin )^1.5)/0.000001139^0.5)^(1/3))/pi()))/60 * sin(azimuth( make_point( @Zentroide_OUTPUT_maxx , @Zentroide_OUTPUT_maxy ), $geometry))*60* @timespan
ELSE 0
END'),
'DELTA_Y': QgsProperty.fromExpression('CASE
WHEN "OUTPUT_2" > 0 THEN
(sqrt((pi()*1.21^2*(((@volumeofthespillinlitres/1000)^2*9.81*0.1793*((1/60)* @timemin )^1.5)/0.000001139^0.5)^(1/3))/pi()))/60 * cos(azimuth( make_point( @Zentroide_OUTPUT_maxx , @Zentroide_OUTPUT_maxy ), $geometry))*60* @timespan
ELSE 0
END'),
'DELTA_Z': 0,
'INPUT': outputs['AttributeNachPositionZusammenfgen']['OUTPUT'],
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['Verschieben'] = processing.run('native:translategeometry', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(13)
if feedback.isCanceled():
return {}
# Felder überarbeiten
alg_params = {
'FIELDS_MAPPING': [{'expression': '"id"', 'length': 10, 'name': 'id', 'precision': 0, 'type': 4}],
'INPUT': outputs['Verschieben']['OUTPUT'],
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['FelderBerarbeiten'] = processing.run('qgis:refactorfields', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(14)
if feedback.isCanceled():
return {}
# Attribute nach Position zusammenfügen
alg_params = {
'DISCARD_NONMATCHING': False,
'INPUT': outputs['FelderBerarbeiten']['OUTPUT'],
'JOIN': outputs['AddRasterValuesToFeatures']['RESULT'],
'JOIN_FIELDS': None,
'METHOD': 0,
'PREDICATE': 0,
'PREFIX': '',
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['AttributeNachPositionZusammenfgen'] = processing.run('qgis:joinattributesbylocation', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(15)
if feedback.isCanceled():
return {}
# Verschieben
alg_params = {
'DELTA_M': 0,
'DELTA_X': QgsProperty.fromExpression('CASE
WHEN "OUTPUT_2" > 0.001
THEN "OUTPUT" * 60 * @timespan
ELSE 0
END'),
'DELTA_Y': QgsProperty.fromExpression('CASE
WHEN "OUTPUT_2" > 0.001
THEN "OUTPUT_1" * 60 * @timespan
ELSE 0
END'),
'DELTA_Z': 0,
'INPUT': outputs['AttributeNachPositionZusammenfgen']['OUTPUT'],
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['Verschieben'] = processing.run('native:translategeometry', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(16)
if feedback.isCanceled():
return {}
# Minimale begrenzende Geometrie
alg_params = {
'FIELD': None,
'INPUT': outputs['Verschieben']['OUTPUT'],
'TYPE': 3,
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['MinimaleBegrenzendeGeometrie'] = processing.run('qgis:minimumboundinggeometry', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(17)
if feedback.isCanceled():
return {}
# Difference
alg_params = {
'A': outputs['MinimaleBegrenzendeGeometrie']['OUTPUT'],
'B': parameters['breaklines'],
'SPLIT': True,
'RESULT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['Difference'] = processing.run('saga:difference', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(18)
if feedback.isCanceled():
return {}
# Nach Position extrahieren
alg_params = {
'INPUT': outputs['Verschieben']['OUTPUT'],
'INTERSECT': parameters['breaklines'],
'PREDICATE': 2,
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['NachPositionExtrahieren'] = processing.run('native:extractbylocation', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(19)
if feedback.isCanceled():
return {}
# Attribute nach Position zusammenfügen
alg_params = {
'DISCARD_NONMATCHING': True,
'INPUT': outputs['Verschieben']['OUTPUT'],
'JOIN': parameters['breaklines'],
'JOIN_FIELDS': None,
'METHOD': 1,
'PREDICATE': 5,
'PREFIX': ''
}
outputs['AttributeNachPositionZusammenfgen'] = processing.run('qgis:joinattributesbylocation', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(20)
if feedback.isCanceled():
return {}
# Zufällige Punkte in Polygonen
alg_params = {
'EXPRESSION': outputs['AttributeNachPositionZusammenfgen']['JOINED_COUNT'],
'INPUT': outputs['Difference']['RESULT'],
'MIN_DISTANCE': 0,
'STRATEGY': 0,
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['ZuflligePunkteInPolygonen'] = processing.run('qgis:randompointsinsidepolygons', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(21)
if feedback.isCanceled():
return {}
# Duplikate nach Attribut löschen
alg_params = {
'FIELDS': parameters['id'],
'INPUT': outputs['ZuflligePunkteInPolygonen']['OUTPUT'],
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['DuplikateNachAttributLschen'] = processing.run('native:removeduplicatesbyattribute', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(22)
if feedback.isCanceled():
return {}
# Felder überarbeiten
alg_params = {
'FIELDS_MAPPING': [{'expression': '"id"', 'length': 10, 'name': 'id', 'precision': 0, 'type': 4}],
'INPUT': outputs['DuplikateNachAttributLschen']['OUTPUT'],
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['FelderBerarbeiten'] = processing.run('qgis:refactorfields', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(23)
if feedback.isCanceled():
return {}
# Vektorlayer zusammenführen
alg_params = {
'CRS': None,
'LAYERS': [outputs['NachPositionExtrahieren']['OUTPUT'],outputs['FelderBerarbeiten']['OUTPUT']],
'OUTPUT': parameters['OutputSecondStage']
}
outputs['VektorlayerZusammenfhren'] = processing.run('native:mergevectorlayers', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
results['OutputSecondStage'] = outputs['VektorlayerZusammenfhren']['OUTPUT']
return results
def processAlgorithm(self, params, context, feedback):
steps = 0
totalStpes = 3
# fieldPopulation = params['FIELD_POPULATION']
fieldActivities = str(params['FIELD_ACTIVITIES'])
feedback = QgsProcessingMultiStepFeedback(totalStpes, feedback)
steps = steps + 1
feedback.setCurrentStep(steps)
if not OPTIONAL_GRID_INPUT: params['CELL_SIZE'] = P_CELL_SIZE
grid, isStudyArea = buildStudyArea(params['CELL_SIZE'],
params['BLOCKS'],
params['STUDY_AREA_GRID'], context,
feedback)
gridNeto = grid
tempOutput = self.CURRENT_PATH + '/zaux.shp'
# print(QgsProcessing.TEMPORARY_OUTPUT)
steps = steps + 1
feedback.setCurrentStep(steps)
activitiesGrid = joinAttrByLocation(params['TERTIARYUSES_ACTIVITIES'],
gridNeto['OUTPUT'], ['id_grid'],
[INTERSECTA],
UNDISCARD_NONMATCHING, context,
feedback, tempOutput)
# steps = steps+1
# feedback.setCurrentStep(steps)
# rep = calculateField(gridNeto['OUTPUT'], 'id_ter', '$id', context,
# feedback, type=1)
activitiesLayer = QgsVectorLayer(tempOutput, "activitiesGrid", "ogr")
# activitiesLayer = convertTempOuputToObject(activitiesGrid)
# layer = self.parameterAsVectorLayer(params, activitiesLayer, context)
layer = activitiesLayer
# layer = activitiesGrid
features = layer.getFeatures()
# fields = layer.dataProvider().fields()
field_names = [field.name() for field in layer.fields()]
# print(field_names)
# print(len(features))
df = pd.DataFrame(features, columns=field_names)
# df["id_grid"]= df["id_grid"].astype(int)
aggregation = {fieldActivities: {'amount_class': 'count'}}
grouped = df.groupby(['id_grid', fieldActivities]).agg(aggregation)
grouped.columns = grouped.columns.droplevel(level=0)
aggregation = {
fieldActivities: {
'total_class': 'count' # conteo de todos los puntos
# 'total_class':'nunique' # conteo de los puntos no repetidos
}
}
grouped2 = df.groupby(['id_grid']).agg(aggregation)
grouped2.columns = grouped2.columns.droplevel(level=0)
res = grouped.join(grouped2).reset_index()
print(res['amount_class'])
print(res)
uniqueActivities = pd.unique(df[fieldActivities])
totalActivities = len(uniqueActivities)
res["total_study"] = totalActivities
# cross = pd.crosstab(df['id'], df[fieldActivities])
res["proporcion"] = ((res['amount_class'] / res['total_class']) *
np.log(res['amount_class'] / res['total_class']))
aggregation = {'proporcion': {'shannon': 'sum'}}
res = res.groupby(['id_grid']).agg(aggregation)
res.columns = res.columns.droplevel(level=0)
res['shannon'] = res['shannon'] * -1
outputCsv = self.CURRENT_PATH + '/sett_shannon.csv'
feedback.pushConsoleInfo(str(('Settings shannon en ' + outputCsv)))
# res.to_csv(outputCsv, sep = ";", encoding='utf-8')
res.to_csv(outputCsv)
print(res)
exitCsv = os.path.exists(outputCsv)
if (exitCsv):
print("El archivo CSV existe")
else:
print("No se encuentra CSV")
CSV = QgsVectorLayer(outputCsv, "csv", "ogr")
featuresCSV = CSV.getFeatures()
# fields = layer.dataProvider().fields()
field_names = [field.name() for field in CSV.fields()]
print(field_names)
steps = steps + 1
feedback.setCurrentStep(steps)
formulaDummy = 'to_string(id_grid)'
gridDummy = calculateField(gridNeto['OUTPUT'], 'griid', formulaDummy,
context, feedback,
QgsProcessing.TEMPORARY_OUTPUT, 2)
steps = steps + 1
feedback.setCurrentStep(steps)
gridShannon = joinByAttr2(gridDummy['OUTPUT'], 'griid', outputCsv,
'id_grid', 'shannon', UNDISCARD_NONMATCHING,
'', 1, context, feedback)
steps = steps + 1
feedback.setCurrentStep(steps)
formulaDummy = 'coalesce(shannon * 1, "")'
result = calculateField(gridShannon['OUTPUT'], NAMES_INDEX['IA11'][0],
formulaDummy, context, feedback,
params['OUTPUT'])
# gridShannon = joinByAttr(r'/Users/terra/llactalab/data/SHAPES_PARA_INDICADORES/SIS-OUTPUTS/ia11.shp', 'id_grid',
# '/Users/terra/llactalab/data/SHAPES_PARA_INDICADORES/SIS-OUTPUTS/sett_shannon.csv', 'id_grid',
# ['shannon'],
# UNDISCARD_NONMATCHING,
# '',
# 1,
# context,
# feedback, params['OUTPUT'])
# res.iloc[1:, [4]] = res.iloc[1:, [2]] / res.iloc[1:, [3]]
# print(totalActivities)
# print(grouped)
# print(grouped2)
# print(un)
# print(cross)
# print(df[fieldActivities])
return result
def processAlgorithm(self, parameters, context, model_feedback):
# variables propres à Processing
feedback = QgsProcessingMultiStepFeedback(2, model_feedback)
results = {}
# entrées
profils = self.parameterAsVectorLayer(parameters, 'profils', context)
mnt = self.parameterAsRasterLayer(parameters, 'mnt', context)
# sorties
output = self.parameterAsOutputLayer(parameters, 'OUTPUT', context)
# paramètres
echantillons_nb = parameters[
'echantillons_nb'] # nombre d'échantillons
profils_pt = [] # géométries des points
profils_sp = [] # valeur des points
# les deux listes sont à deux dimension : chaque ligne correspond à un profil
# traitement
# discrétisation des points sur chaque profil
for profil_f in profils.getFeatures():
profil_g = profil_f.geometry()
freq = profil_g.length() / (echantillons_nb - 1)
echantillons_g = [
QgsGeometry().fromPointXY(profil_g.asMultiPolyline()[0][0])
]
for i in range(1, echantillons_nb - 1):
echantillons_g.append(profil_g.interpolate(freq * i))
echantillons_g.append(QgsGeometry().fromPointXY(
profil_g.asMultiPolyline()[0][-1]))
# note pour ci-dessus : l'interpolation des premiers et derniers points sur une ligne peut parfois planter, on les place à la main pour lever le risque
profils_pt.append(echantillons_g)
elevations = []
for echantillon_g in echantillons_g:
elevation = mnt.dataProvider().sample(echantillon_g.asPoint(),
1)[0]
elevations.append(elevation)
profils_sp.append(elevations)
# interpolation linéaire : calcul des valeurs des points entre le premier et le dernier profil de point à point
steps = []
nb_profils = len(profils_sp)
for i in range(0, echantillons_nb):
# on récupère la valeur du point sur le premier et le dernier profil
v1 = profils_sp[0][i]
v2 = profils_sp[-1][i]
# on détermine le pas de l'interpolation
steps.append((v2 - v1) / nb_profils)
for i in range(1, nb_profils - 1):
for j in range(0, echantillons_nb):
profils_sp[i][j] = profils_sp[i - 1][j] + steps[j]
feedback.setCurrentStep(1)
if feedback.isCanceled():
return {}
# écriture des données en sortie
fields = QgsFields()
fields.append(QgsField("elevation", QVariant.Double))
writer = QgsVectorFileWriter(output, "System", fields,
QgsWkbTypes.Point,
QgsCoordinateReferenceSystem(2154),
"ESRI Shapefile")
for i in range(0, nb_profils):
for j in range(0, echantillons_nb):
f = QgsFeature()
f.setGeometry(profils_pt[i][j])
f.setAttributes([profils_sp[i][j]])
writer.addFeature(f)
feedback.setCurrentStep(2)
results['OUTPUT'] = output
return results
def processAlgorithm(self, parameters, context, model_feedback):
"""
Process the algorithm
:param parameters: parameters of the process
:param context: context of the process
:param model_feedback: feedback instance for the process
:return:
"""
# Use a multi-step feedback, so that individual child algorithm progress reports are adjusted for the
# overall progress through the model
self.xml_path = parameters["XMLPATH"]
self.gpkg_path = parameters["GPKGPATH"]
if not self.xml_path.lower().endswith(".xml"):
feedback = QgsProcessingMultiStepFeedback(0, model_feedback)
feedback.reportError(
"XML Workspace Definition is not an XML file!", True)
return {}
if not self.gpkg_path.lower().endswith(".gpkg"):
feedback = QgsProcessingMultiStepFeedback(0, model_feedback)
feedback.reportError("GeoPackage is not an GPKG file!", True)
return {}
self.pg_conn_name = parameters["DBNAME"]
self.pg_schema = parameters["SCHEMA"]
self.pg_drop_before = parameters["DROPIFEXISTS"]
dataset_list = self.getDatasets()
feedback = QgsProcessingMultiStepFeedback(2 + len(dataset_list),
model_feedback)
step = 0
self.create_pk_metadata_table(context, feedback)
step = 1
for dataset in dataset_list:
step += 1
definition = self.getDatasetDef(dataset)
if definition is not None:
try:
in_layer = self.get_gpkg_vector_layer(definition[0])
if in_layer is not None:
feedback.pushInfo("Feature Class: " + definition[0])
try:
alg_params = {
'DATABASE': self.pg_conn_name,
'SQL': definition[1]
}
feedback.pushInfo(
" processing (A) => qgis:postgisexecutesql")
processing.run('qgis:postgisexecutesql',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
except Exception as e1:
feedback.reportError(
"Error creating table definition: \n" +
definition[1] + ": " + str(e1), False)
break
try:
# Esporta in PostgreSQL (connessioni disponibili)
alg_params = {
'ADDFIELDS': False,
'APPEND': False,
'A_SRS': None,
'CLIP': False,
'DATABASE': self.pg_conn_name,
'DIM': 0,
'GEOCOLUMN': 'geom',
'GT': '',
'GTYPE': definition[4],
'INDEX': False,
'INPUT':
self.get_gpkg_vector_layer(definition[0]),
'LAUNDER': False,
'OPTIONS': '',
'OVERWRITE': True,
'PK': '',
'PRECISION': True,
'PRIMARY_KEY': '',
'PROMOTETOMULTI': True,
'SCHEMA': self.pg_schema,
'SEGMENTIZE': '',
'SHAPE_ENCODING': '',
'SIMPLIFY': '',
'SKIPFAILURES': False,
'SPAT': None,
'S_SRS': None,
'TABLE': definition[0].lower() + '_tmp',
'T_SRS': None,
'WHERE': ''
}
feedback.pushInfo(
" processing (B) => qgis:importvectorintopostgisdatabaseavailableconnections"
)
processing.run(
'gdal:importvectorintopostgisdatabaseavailableconnections',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
except Exception as e2:
feedback.reportError(
"Error importing data: \n" + definition[0] +
": " + str(e2), False)
break
try:
#Copy from TMP to FINAL table
sql_copy = "INSERT INTO %s.%s(%s) SELECT %s FROM %s.%s_tmp" % (
self.pg_schema, definition[0], definition[2],
definition[3], self.pg_schema,
definition[0]) + ";"
sql_drop = "DROP TABLE %s.%s_tmp" % (
self.pg_schema, definition[0]) + ";"
alg_params = {
'DATABASE': self.pg_conn_name,
'SQL': sql_copy + sql_drop
}
feedback.pushInfo(
" processing (C) => qgis:postgisexecutesql")
processing.run('qgis:postgisexecutesql',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
except Exception as e3:
feedback.reportError(
"Error moving data: \n" + sql_copy + sql_drop +
": " + str(e3), False)
break
except Exception as e:
feedback.reportError(
"Error importing domain " + definition[1] + ": " +
str(e), False)
feedback.setCurrentStep(step)
results = {}
outputs = {}
return results
def processAlgorithm(self, parameters, context, feedback):
"""
Here is where the processing itself takes place.
"""
#get the network handler
layerHandler = LayerHandler()
networkHandler = NetworkHandler()
algRunner = AlgRunner()
self.nodeTypeNameDict = networkHandler.nodeTypeDict
# get network layer
networkLayer = self.parameterAsLayer(parameters, self.NETWORK_LAYER, context)
if networkLayer is None:
raise QgsProcessingException(self.invalidSourceError(parameters, self.NETWORK_LAYER))
attributeBlackList = self.parameterAsFields(parameters, self.ATTRIBUTE_BLACK_LIST, context)
ignoreVirtual = self.parameterAsBool(parameters, self.IGNORE_VIRTUAL_FIELDS, context)
ignorePK = self.parameterAsBool(parameters, self.IGNORE_PK_FIELDS, context)
# get network node layer
(nodeSink, dest_id) = self.parameterAsSink(parameters, self.NETWORK_NODES,
context, self.getFields(), QgsWkbTypes.MultiPoint, networkLayer.sourceCrs())
#prepairs flag sink for raising errors
self.prepareFlagSink(parameters, networkLayer, QgsWkbTypes.MultiPoint, context)
waterBodyClasses = self.parameterAsLayer(parameters, self.WATER_BODY_LAYERS, context)
waterBodyClasses = waterBodyClasses if waterBodyClasses is not None else []
# get water sink layer
waterSinkLayer = self.parameterAsLayer(parameters, self.SINK_LAYER, context)
# get spillway layer
spillwayLayer = self.parameterAsLayer(parameters, self.SPILLWAY_LAYER, context)
# get frame layer
frameLayer = self.parameterAsLayer(parameters, self.REF_LAYER, context)
currStep = 0
if frameLayer is None:
raise QgsProcessingException(self.invalidSourceError(parameters, self.REF_LAYER))
multiStepFeedback = QgsProcessingMultiStepFeedback(3, feedback)
multiStepFeedback.setCurrentStep(currStep)
multiStepFeedback.pushInfo(self.tr('Preparing bounds...'))
frame = layerHandler.getFrameOutterBounds(frameLayer, algRunner, context, feedback=multiStepFeedback)
currStep += 1
# get search radius
searchRadius = self.parameterAsDouble(parameters, self.SEARCH_RADIUS, context)
# get ditch layer
ditchLayer = self.parameterAsLayer(parameters, self.DITCH_LAYER, context)
#new step
multiStepFeedback.setCurrentStep(currStep)
multiStepFeedback.pushInfo(self.tr('Performing node identification...'))
self.nodeDict = networkHandler.identifyAllNodes(networkLayer=networkLayer, feedback=multiStepFeedback) #zoado, mudar lógica
multiStepFeedback.pushInfo(self.tr('{node_count} node(s) identificated...').format(node_count=len(self.nodeDict)))
currStep += 1
#new step
multiStepFeedback.setCurrentStep(currStep)
multiStepFeedback.pushInfo(self.tr('Performing node classification...'))
networkHandler.nodeDict = self.nodeDict
self.nodeTypeDict, nodeFlagDict = networkHandler.classifyAllNodes(
networkLayer=networkLayer,
frameLyrContourList=frame,
waterBodiesLayers=waterBodyClasses,
searchRadius=searchRadius,
waterSinkLayer=waterSinkLayer,
spillwayLayer=spillwayLayer,
feedback=multiStepFeedback,
attributeBlackList=attributeBlackList,
excludePrimaryKeys=ignorePK,
ignoreVirtualFields=ignoreVirtual,
ditchLayer=ditchLayer
)
currStep += 1
#new step
multiStepFeedback.setCurrentStep(currStep)
multiStepFeedback.pushInfo(self.tr('Writing nodes...'))
self.fillNodeSink(
nodeSink=nodeSink,
networkLineLayerName=networkLayer.name(),
nodeFlagDict=nodeFlagDict,
feedback=multiStepFeedback)
return {self.NETWORK_NODES : dest_id, self.FLAGS : self.flag_id}
def processAlgorithm(self, params, context, feedback):
steps = 0
totalStpes = 6
fieldPopulation = params['FIELD_POPULATION']
fieldHousing = params['FIELD_HOUSING']
DISCARD = True
UNDISCARD = False
feedback = QgsProcessingMultiStepFeedback(totalStpes, feedback)
steps = steps + 1
feedback.setCurrentStep(steps)
blocksWithId = calculateField(params['BLOCKS'],
'id_block',
'$id',
context,
feedback,
type=1)
steps = steps + 1
feedback.setCurrentStep(steps)
pointsJoined = joinAttrByNear(params['POINTS'], blocksWithId['OUTPUT'],
[], UNDISCARD, 'blk_', 5, 1, context,
feedback)
steps = steps + 1
feedback.setCurrentStep(steps)
statistics = statisticsByCategories(pointsJoined['OUTPUT'],
['blk_id_block'], None, context,
feedback)
steps = steps + 1
feedback.setCurrentStep(steps)
pointsJoinedStast = joinByAttr(pointsJoined['OUTPUT'], 'blk_id_block',
statistics['OUTPUT'], 'blk_id_block',
'count', DISCARD, 'st_', context,
feedback)
steps = steps + 1
feedback.setCurrentStep(steps)
formulaPopulationPerPoint = 'blk_' + fieldPopulation + ' / st_count'
populationPerPoint = calculateField(pointsJoinedStast['OUTPUT'],
'population',
formulaPopulationPerPoint, context,
feedback)
steps = steps + 1
feedback.setCurrentStep(steps)
formulaHousingPerPoint = 'blk_' + fieldHousing + ' / st_count'
housingPerPoint = calculateField(populationPerPoint['OUTPUT'],
'housing', formulaHousingPerPoint,
context, feedback)
gridPopulation = joinByLocation(params['STUDY_AREA_GRID'],
housingPerPoint['OUTPUT'],
'population;housing', [CONTIENE],
[SUM], UNDISCARD_NONMATCHING, context,
feedback, params['OUTPUT'])
return gridPopulation
def processAlgorithm(self, params, context, feedback):
steps = 0
totalStpes = 13
fieldPopulation = params['FIELD_POPULATION']
feedback = QgsProcessingMultiStepFeedback(totalStpes, feedback)
blocks = calculateArea(params['BLOCKS'], 'area_bloc', context,
feedback)
steps = steps + 1
feedback.setCurrentStep(steps)
if not OPTIONAL_GRID_INPUT: params['CELL_SIZE'] = P_CELL_SIZE
grid, isStudyArea = buildStudyArea(params['CELL_SIZE'],
params['BLOCKS'],
params['STUDY_AREA_GRID'], context,
feedback)
gridNeto = grid
steps = steps + 1
feedback.setCurrentStep(steps)
segments = intersection(blocks['OUTPUT'], gridNeto['OUTPUT'],
'area_bloc;' + fieldPopulation,
'id_grid;area_grid', context, feedback)
steps = steps + 1
feedback.setCurrentStep(steps)
segmentsArea = calculateArea(segments['OUTPUT'], 'area_seg', context,
feedback)
steps = steps + 1
feedback.setCurrentStep(steps)
formulaPopulationSegments = '(area_seg/area_bloc) * ' + fieldPopulation
populationForSegments = calculateField(segmentsArea['OUTPUT'],
'pop_seg',
formulaPopulationSegments,
context, feedback)
# Haciendo el buffer inverso aseguramos que los segmentos
# quden dentro de la malla
steps = steps + 1
feedback.setCurrentStep(steps)
segmentsFixed = makeSureInside(populationForSegments['OUTPUT'],
context, feedback)
steps = steps + 1
feedback.setCurrentStep(steps)
gridNetoAndSegments = joinByLocation(gridNeto['OUTPUT'],
segmentsFixed['OUTPUT'],
'pop_seg', [CONTIENE], [SUM],
UNDISCARD_NONMATCHING, context,
feedback)
# CALCULAR AREA VERDE
steps = steps + 1
feedback.setCurrentStep(steps)
greenPerBlocks = intersection(params['GREEN'], gridNeto['OUTPUT'], [],
[], context, feedback)
steps = steps + 1
feedback.setCurrentStep(steps)
greenArea = calculateArea(greenPerBlocks['OUTPUT'], 'area_green',
context, feedback)
steps = steps + 1
feedback.setCurrentStep(steps)
greenAreaFixed = makeSureInside(greenArea['OUTPUT'], context, feedback)
steps = steps + 1
feedback.setCurrentStep(steps)
greenAreaAndPopulation = joinByLocation(gridNetoAndSegments['OUTPUT'],
greenAreaFixed['OUTPUT'],
'area_green', [CONTIENE],
[SUM], UNDISCARD_NONMATCHING,
context, feedback)
steps = steps + 1
feedback.setCurrentStep(steps)
# formulaSurfacePerHab = 'coalesce(area_green_sum/' + fieldPopulation + '_sum, 0)'
formulaSurfacePerHab = 'CASE WHEN pop_seg_sum = 0 THEN "" ' + \
'ELSE coalesce(area_green_sum/pop_seg_sum, 0) END'
print(formulaSurfacePerHab)
surfacePerHab = calculateField(greenAreaAndPopulation['OUTPUT'],
NAMES_INDEX['IB05'][0],
formulaSurfacePerHab, context, feedback,
params['OUTPUT'])
return surfacePerHab
def processAlgorithm(self, parameters, context, feedback):
# Parameters
input_source, input_layer = qgsTreatments.parameterAsSourceLayer(self,
parameters,self.INPUT,context,feedback=feedback)
modes = self.parameterAsEnums(parameters,self.MODE,context)
input_raster = self.parameterAsRasterLayer(parameters,self.INPUT_RASTER,context)
band = self.parameterAsInt(parameters,self.BAND,context)
pop_field = self.parameterAsString(parameters,self.POPULATION_FIELD,context)
# surf_field = self.parameterAsString(parameters,self.SURFACE_FIELD,context)
# output = self.parameterAsOutputLayer(parameters,self.OUTPUT,context)
# Init
input_crs = input_source.sourceCrs().authid()
raster_crs = input_raster.dataProvider().crs().authid()
pop_mode, surf_mode = 0 in modes, 1 in modes
nb_feats = input_layer.featureCount()
multi_feedback = QgsProcessingMultiStepFeedback(nb_feats * 2,feedback)
# CRS
if (input_crs != raster_crs):
raster_reprojected = QgsProcessingUtils.generateTempFilename('raster_reproj.tif')
qgsTreatments.applyWarpReproject(input_raster,raster_reprojected,
dst_crs=input_crs,context=context,feedback=multi_feedback)
input_raster = qgsUtils.loadRasterLayer(raster_reprojected)
multi_feedback.setCurrentStep(1)
pixel_size = input_raster.rasterUnitsPerPixelX() * input_raster.rasterUnitsPerPixelY()
# Zonal stats
prefix = 'rad_'
zonal_stats = QgsProcessingUtils.generateTempFilename('zonal_stats.gpkg')
qgsTreatments.rasterZonalStats(input_layer,input_raster,zonal_stats,
prefix=prefix,band=band,context=context,feedback=multi_feedback)
multi_feedback.setCurrentStep(nb_feats)
# Fields
stats_layer = qgsUtils.loadVectorLayer(zonal_stats)
stats_fields = stats_layer.fields()
stats_fieldnames = stats_fields.names()
if pop_mode and pop_field not in stats_fieldnames:
raise QgsProcessingException("No population field '" + str(pop_field) + "' in input layer")
# if surf_field_mode and surf_field not in stats_fieldnames:
# raise QgsProcessingException("No area field '" + str(surf_field) + "' in input layer")
rad_pop_field = QgsField(self.RAD_POP_FIELDNAME, QVariant.Double)
rad_surf_field = QgsField(self.RAD_SURF_FIELDNAME, QVariant.Double)
out_fields = QgsFields(stats_fields)
out_fields.append(rad_pop_field)
out_fields.append(rad_surf_field)
(sink, self.dest_id) = self.parameterAsSink(parameters, self.OUTPUT,
context, out_fields, input_source.wkbType(), input_source.sourceCrs())
# Division
stats_layer = qgsUtils.loadVectorLayer(zonal_stats)
for current, feat in enumerate(stats_layer.getFeatures()):
new_feat = QgsFeature(out_fields)
new_feat.setGeometry(feat.geometry())
for field in stats_layer.fields().names():
if field != 'fid':
new_feat[field] = feat[field]
rad_sum = float(feat[prefix + 'sum'])
rad_mean = float(feat[prefix + 'mean'])
if (pop_mode and feat[pop_field]):
new_feat[self.RAD_POP_FIELDNAME] = (rad_sum / feat[pop_field]) * 1000
if surf_mode:
new_feat[self.RAD_SURF_FIELDNAME] = (rad_mean / pixel_size) * 1000000
sink.addFeature(new_feat, QgsFeatureSink.FastInsert)
self.pop_mode, self.surf_mode = pop_mode, surf_mode
multi_feedback.setCurrentStep(current + nb_feats)
return { self.OUTPUT : self.dest_id }
def processAlgorithm(self, parameters, context, model_feedback):
# Use a multi-step feedback, so that individual child algorithm progress reports are adjusted for the
# overall progress through the model
feedback = QgsProcessingMultiStepFeedback(5, model_feedback)
results = {}
outputs = {}
# Split lines by maximum length
alg_params = {
'INPUT': parameters['Shapedecaminhos'],
'LENGTH': parameters['Comprimentodossegmentos'],
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['SplitLinesByMaximumLength'] = processing.run('native:splitlinesbylength', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(1)
if feedback.isCanceled():
return {}
# Drape (set Z value from raster)
alg_params = {
'BAND': 1,
'INPUT': outputs['SplitLinesByMaximumLength']['OUTPUT'],
'NODATA': -9999,
'RASTER': parameters['MDT'],
'SCALE': 1,
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['DrapeSetZValueFromRaster'] = processing.run('native:setzfromraster', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(2)
if feedback.isCanceled():
return {}
# Extract Z values
alg_params = {
'COLUMN_PREFIX': 'z_',
'INPUT': outputs['DrapeSetZValueFromRaster']['OUTPUT'],
'SUMMARIES': [0,1],
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['ExtractZValues'] = processing.run('native:extractzvalues', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(3)
if feedback.isCanceled():
return {}
# Field calculator1
alg_params = {
'FIELD_LENGTH': 10,
'FIELD_NAME': 'Length',
'FIELD_PRECISION': 2,
'FIELD_TYPE': 0,
'FORMULA': ' $length ',
'INPUT': outputs['ExtractZValues']['OUTPUT'],
'OUTPUT': QgsProcessing.TEMPORARY_OUTPUT
}
outputs['FieldCalculator1'] = processing.run('qgis:fieldcalculator', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
feedback.setCurrentStep(4)
if feedback.isCanceled():
return {}
# Field calculator2
alg_params = {
'FIELD_LENGTH': 10,
'FIELD_NAME': 'Slope_%',
'FIELD_PRECISION': 3,
'FIELD_TYPE': 0,
'FORMULA': '(abs(\"z_first\" - \"z_last\") / \"length\" ) *100',
'INPUT': outputs['FieldCalculator1']['OUTPUT'],
'OUTPUT': parameters['CalculatedRoadSlopes']
}
outputs['FieldCalculator2'] = processing.run('qgis:fieldcalculator', alg_params, context=context, feedback=feedback, is_child_algorithm=True)
results['CalculatedRoadSlopes'] = outputs['FieldCalculator2']['OUTPUT']
return results
def processAlgorithm(self, params, context, feedback):
steps = 0
totalStpes = 12
fieldPopulation = params['FIELD_POPULATION']
# fieldHab = params['NUMBER_HABITANTS']
feedback = QgsProcessingMultiStepFeedback(totalStpes, feedback)
blocks = calculateArea(params['BLOCKS'], 'area_bloc', context,
feedback)
steps = steps+1
feedback.setCurrentStep(steps)
if not OPTIONAL_GRID_INPUT: params['CELL_SIZE'] = P_CELL_SIZE
grid, isStudyArea = buildStudyArea(params['CELL_SIZE'], params['BLOCKS'],
params['STUDY_AREA_GRID'],
context, feedback)
gridNeto = grid
steps = steps+1
feedback.setCurrentStep(steps)
segments = intersection(blocks['OUTPUT'], gridNeto['OUTPUT'],
'area_bloc;' + fieldPopulation,
'id_grid;area_grid',
context, feedback)
steps = steps+1
feedback.setCurrentStep(steps)
segmentsArea = calculateArea(segments['OUTPUT'],
'area_seg',
context, feedback)
steps = steps+1
feedback.setCurrentStep(steps)
formulaPopulationSegments = '(area_seg/area_bloc) * ' + fieldPopulation
housingForSegments = calculateField(segmentsArea['OUTPUT'], 'hou_seg',
formulaPopulationSegments,
context,
feedback)
# Haciendo el buffer inverso aseguramos que los segmentos
# quden dentro de la malla
steps = steps+1
feedback.setCurrentStep(steps)
segments = makeSureInside(housingForSegments['OUTPUT'],
context,
feedback)
steps = steps+1
feedback.setCurrentStep(steps)
gridNetoAndSegments = joinByLocation(gridNeto['OUTPUT'],
segments['OUTPUT'],
'hou_seg',
[CONTIENE], [SUM],
UNDISCARD_NONMATCHING,
context,
feedback)
# Calcular cuantos robos hay en cada grid
steps = steps+1
feedback.setCurrentStep(steps)
thef = calculateField(params['THEFTS'], 'idx', '$id', context,
feedback, type=1)
steps = steps+1
feedback.setCurrentStep(steps)
gridNetoAndSegments = joinByLocation(gridNetoAndSegments['OUTPUT'],
thef['OUTPUT'],
['idx'],
[CONTIENE], [COUNT],
UNDISCARD_NONMATCHING,
context,
feedback)
# steps = steps+1
# feedback.setCurrentStep(steps)
# formulaThefPerHab = 'idx_count/' + str(fieldHab)
# thefPerHab = calculateField(gridNetoAndSegments['OUTPUT'],
# NAMES_INDEX['ID09'][0],
# formulaThefPerHab,
# context,
# feedback, params['OUTPUT'])
steps = steps+1
feedback.setCurrentStep(steps)
formulaThefPerHab = 'coalesce((coalesce(idx_count, 0)/sum(idx_count)) * 100, "")'
thefPerHab = calculateField(gridNetoAndSegments['OUTPUT'],
NAMES_INDEX['ID09'][0],
formulaThefPerHab,
context,
feedback, params['OUTPUT'])
return thefPerHab
class QualityAssuranceWorkflow(QObject):
"""
Works as a multi-model runner. Understands all models' parameters as an
output vector layer.
"""
workflowFinished = pyqtSignal()
haltedOnFlags = pyqtSignal(DsgToolsProcessingModel)
modelStarted = pyqtSignal(DsgToolsProcessingModel)
modelFinished = pyqtSignal(DsgToolsProcessingModel)
modelFinishedWithFlags = pyqtSignal(DsgToolsProcessingModel)
modelFailed = pyqtSignal(DsgToolsProcessingModel)
def __init__(self, parameters, feedback=None):
"""
Class constructor. Materializes an workflow set of parameters.
:param parameters: (dict) map of workflow attributes.
:param feedback: (QgsProcessingFeedback) task progress tracking QGIS
object.
"""
super(QualityAssuranceWorkflow, self).__init__()
msg = self.validateParameters(parameters)
if msg:
raise Exception(
self.tr("Invalid workflow parameter:\n{msg}").format(msg=msg))
self._param = parameters
self._modelOrderMap = dict()
self.output = dict()
self.feedback = feedback or QgsProcessingFeedback()
def validateParameters(self, parameters):
"""
Validates a set of parameters for a valid Workflow.
:param parameters: (dict) map of workflow attributes to be validated.
:return: (str) invalidation reason.
"""
if "displayName" not in parameters or not parameters["displayName"]:
# this is not a mandatory item, but it defaults to a value
parameters["displayName"] = self.tr("DSGTools Validation Workflow")
if "models" not in parameters or not parameters["models"]:
return self.tr(
"Workflow seems to have no models associated with it.")
for modelName, modelParam in parameters["models"].items():
model = DsgToolsProcessingModel(modelParam, modelName)
if not model.isValid():
return self.tr("Model {model} is invalid: '{reason}'.").format(
model=modelName,
reason=model.validateParameters(modelParam))
# if "flagLayer" not in parameters or not parameters["flagLayer"]:
# self.tr("No flag layer was provided.")
# if "historyLayer" not in parameters or not parameters["historyLayer"]:
# self.tr("No history layer was provided.")
return ""
def metadata(self):
"""
A map to Workflow's metadata.
:return: (dict) metadata.
"""
return self._param["metadata"] if "metadata" in self._param else dict()
def author(self):
"""
Retrieves Workflow's author, if available.
:return: (str) Workflow's author.
"""
meta = self.metadata()
return meta["author"] if "author" in meta else ""
def version(self):
"""
Retrieves Workflow's version, if available.
:return: (str) Workflow's version.
"""
meta = self.metadata()
return meta["version"] if "version" in meta else ""
def lastModified(self):
"""
Retrieves Workflow's last modification "timestamp", if available.
:return: (str) Workflow's last modification time and date.
"""
meta = self.metadata()
return meta["lastModified"] if "lastModified" in meta else ""
def metadataText(self):
"""
Retrieves Workflow's metadata string.
:return: (str) Workflow's metadata string.
"""
if not self.metadata():
return ""
return self.tr(
"Workflow {name} v{version} ({lastModified}) by {author}.").format(
name=self.displayName(), **self.metadata())
def displayName(self):
"""
Friendly name for the workflow.
:return: (str) display name.
"""
return self._param["displayName"] if \
"displayName" in self._param else ""
def name(self):
"""
Proxy method for displayName.
:return: (str) display name.
"""
return self.displayName()
def models(self):
"""
Model parameters defined to run in this workflow.
:return: (dict) models maps to valid and invalid models.
"""
models = {"valid": dict(), "invalid": dict()}
self._multiStepFeedback = QgsProcessingMultiStepFeedback(
len(self._param["models"]), self.feedback)
self._multiStepFeedback.setCurrentStep(0)
for modelName, modelParam in self._param["models"].items():
model = DsgToolsProcessingModel(modelParam,
modelName,
feedback=self._multiStepFeedback)
if not model.isValid():
models["invalid"][modelName] = model.validateParameters(
modelParam)
else:
models["valid"][modelName] = model
return models
def validModels(self):
"""
Returns all valid models from workflow parameters.
:return: (dict) models maps to valid and invalid models.
"""
models = dict()
self._multiStepFeedback = QgsProcessingMultiStepFeedback(
len(self._param["models"]), self.feedback)
self._multiStepFeedback.setCurrentStep(0)
for idx, (modelName,
modelParam) in enumerate(self._param["models"].items()):
model = DsgToolsProcessingModel(modelParam,
modelName,
feedback=self._multiStepFeedback)
if model.isValid():
models[modelName] = model
self._modelOrderMap[modelName] = idx
return models
def invalidModels(self):
"""
Returns all valid models from workflow parameters.
:return: (dict) models maps invalid models to their invalidation reason.
"""
models = dict()
self._multiStepFeedback = QgsProcessingMultiStepFeedback(
len(self._param["models"]), self.feedback)
self._multiStepFeedback.setCurrentStep(0)
for modelName, modelParam in self._param["models"].items():
model = DsgToolsProcessingModel(modelParam,
modelName,
feedback=self._multiStepFeedback)
if not model.isValid():
models[modelName] = model.validateParameters(modelParam)
return models
def hasInvalidModel(self):
"""
Checks if any of the nested models is invalid.
:return: (bool) if there are invalid models.
"""
models = dict()
for modelName, modelParam in self._param["models"].items():
model = DsgToolsProcessingModel(modelParam, modelName)
if not model.isValid():
return True
return False
# def flagLayer(self):
# """
# Layer to work as a sink to flag output for all models.
# :return: (QgsVectorLayer) flag layer.
# """
# return self._param["flagLayer"] if "flagLayer" in self._param else ""
# def historyLayer(self):
# """
# A table (a layer with no geometry) to store execution history.
# :return: (QgsVectorLayer) flag layer.
# """
# return self._param["flagLayer"] if "flagLayer" in self._param else ""
def export(self, filepath):
"""
Dumps workflow's parameters as a JSON file.
:param filepath: (str) path to JSON file.
:return: (bool) operation success.
"""
with open(filepath, "w", encoding="utf-8") as fp:
fp.write(json.dumps(self._param, indent=4))
return os.path.exists(filepath)
def asDict(self):
"""
Dumps model parameters as a JSON file.
:param filepath: (str) path to JSON file.
:return: (dict) DSGTools processing model definitions.
"""
return dict(self._param)
def finished(self):
"""
Executes all post-processing actions.
"""
# Add default post-processing actions here!
self.workflowFinished.emit()
def runOnMainThread(self):
"""
If, for some reason, Workflow should not be run from secondary threads,
this method provides a 'static' execution alternative.
:return: (dict) a map to each model's output.
"""
self.output = dict()
for model in self.validModels().values():
start = time()
mName = model.name()
self.output[mName] = dict()
try:
self.output[mName]["result"] = {
k.split(":", 2)[-1] : v \
for k, v in model.runModel(model.feedback).items()
}
self.output[mName]["status"] = True
except:
self.output[mName]["result"] = None
self.output[mName]["status"] = False
self.output[mName]["executionTime"] = time() - start
self.finished()
return self.output
def setupModelTask(self, model):
"""
Sets model to run on QGIS task manager.
"""
QgsApplication.taskManager().addTask(model)
def hold(self):
"""
Puts current active tasks/models on hold.
"""
if not hasattr(self, "_executionOrder"):
return
for m in self._executionOrder.values():
if m.status() == m.Running:
m.hold()
def unhold(self):
"""
Puts current paused tasks/models back to active status.
"""
if not hasattr(self, "_executionOrder"):
return
for m in self._executionOrder.values():
if m.status() == m.OnHold:
m.unhold()
def currentModel(self):
"""
Retrieves the model currently running, if any.
:return: (DsgToolsProcessingModel) current active model.
"""
if not hasattr(self, "_executionOrder"):
return None
for m in self._executionOrder.values():
if m.status() == m.Running:
return m
return None
def raiseFlagWarning(self, model):
"""
Advises connected objects that flags were raised even though workflow
:param model: (DsgToolsProcessingModel) model to have its flags checked.
"""
if model.hasFlags():
self.modelFinishedWithFlags.emit(model)
else:
self.modelFinished.emit(model)
def raiseFlagError(self, model):
"""
It stops the workflow execution if flags are identified.
:param model: (DsgToolsProcessingModel) model to have its flags checked.
"""
if model.hasFlags():
self.feedback.cancel()
self.haltedOnFlags.emit(model)
else:
self.modelFinished.emit(model)
return self.feedback.isCanceled()
def handleFlags(self, model):
"""
Handles Workflow behaviour for a model's flag output.
:param model: (DsgToolsProcessingModel) model to have its output handled.
"""
onFlagsMethod = {
"warn": partial(self.raiseFlagWarning, model),
"halt": partial(self.raiseFlagError, model),
"ignore": partial(self.modelFinished.emit, model)
}[model.onFlagsRaised()]()
def run(self, firstModelName=None, cooldown=None):
"""
Executes all models in secondary threads.
:param firstModelName: (str) first model's name to be executed.
:param cooldown: (float) time to wait till next model is started.
"""
self._executionOrder = {
idx: model
for idx, model in enumerate(self.validModels().values())
}
modelCount = len(self._executionOrder)
if self.hasInvalidModel() or modelCount == 0:
return None
def modelCompleted(model, step):
self.output[model.name()] = model.output
self._multiStepFeedback.setCurrentStep(step)
self.handleFlags(model)
if firstModelName is not None:
for idx, model in self._executionOrder.items():
if model.name() == firstModelName:
initialIdx = idx
break
else:
# name was not found
return None
else:
initialIdx = 0
self.output = dict()
for idx, currentModel in self._executionOrder.items():
if idx < initialIdx:
continue
# all models MUST pass through this postprocessing method
currentModel.taskCompleted.connect(
partial(modelCompleted, currentModel, idx + 1))
currentModel.begun.connect(
partial(self.modelStarted.emit, currentModel))
currentModel.taskTerminated.connect(
partial(self.modelFailed.emit, currentModel))
if idx != modelCount - 1:
self._executionOrder[idx + 1].addSubTask(
currentModel,
subTaskDependency=currentModel.ParentDependsOnSubTask)
else:
# last model indicates workflow finish
currentModel.taskCompleted.connect(self.finished)
# last model will trigger every dependent model till the first added to
# the task manager
self.setupModelTask(currentModel)
def lastModelName(self):
"""
Gets the last model prepared to execute but has either failed or not
run.
:return: (str) first model's name not to run.
"""
if not hasattr(self, "_executionOrder"):
return None
modelCount = len(self._executionOrder)
for idx, model in self._executionOrder.items():
modelName = self._executionOrder[idx].displayName()
if modelName not in self.output or \
self.output[modelName]["finishStatus"] != "finished":
return modelName
else:
return None
def processAlgorithm(self, parameters, context, feedback):
steps = 0
totalStpes = 17
outputs = {}
results = {}
feedback = QgsProcessingMultiStepFeedback(totalStpes, feedback)
isValid = lambda x: False if x is None or x is '' else True
isBlocks = isValid(parameters['BLOCKS'])
isFieldPopulation = isValid(parameters['FIELD_POPULATION'])
isFieldHousing = isValid(parameters['FIELD_HOUSING'])
isStudyArea = isValid(parameters['STUDY_AREA_GRID'])
# isIcv = isValid(parameters['ICV'])
# isValue = isValid(parameters['VALUE'])
isCensoVivienda = isValid(parameters['CENSO_VIVIENDA'])
isDpaMan = isValid(parameters['DPA_MAN'])
print("Censo Vivienda")
print(isCensoVivienda)
if isBlocks and isCensoVivienda and isDpaMan:
steps = steps + 1
feedback.setCurrentStep(steps)
if feedback.isCanceled():
return {}
# D02 Viviendas con carencias constructivas
alg_params = {
'BLOCKS': parameters['BLOCKS'],
'CENSO_VIVIENDA': parameters['CENSO_VIVIENDA'],
'DPA_MAN': parameters['DPA_MAN'],
'STUDY_AREA_GRID': parameters['STUDY_AREA_GRID'],
'OUTPUT': parameters['OUTPUT_D02']
}
outputs['D02ViviendasConCarenciasConstructivas'] = processing.run(
'SISURBANO:D02 Viviendas con carencias constructivas',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
results['OUTPUT_D02'] = outputs[
'D02ViviendasConCarenciasConstructivas']['OUTPUT']
isCensoHogar = isValid(parameters['CENSO_HOGAR'])
if isCensoVivienda and isBlocks and isCensoHogar and isDpaMan:
steps = steps + 1
feedback.setCurrentStep(steps)
if feedback.isCanceled():
return {}
# D05 Acceso a internet
alg_params = {
'BLOCKS': parameters['BLOCKS'],
'CENSO_HOGAR': parameters['CENSO_HOGAR'],
'CENSO_VIVIENDA': parameters['CENSO_VIVIENDA'],
'DPA_MAN': parameters['DPA_MAN'],
'STUDY_AREA_GRID': parameters['STUDY_AREA_GRID'],
'OUTPUT': parameters['OUTPUT_D05']
}
outputs['D05AccesoAInternet'] = processing.run(
'SISURBANO:D05 Acceso a internet',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
results['OUTPUT_D05'] = outputs['D05AccesoAInternet']['OUTPUT']
isThefts = isValid(parameters['THEFTS'])
if isBlocks and isFieldPopulation and isThefts:
steps = steps + 1
feedback.setCurrentStep(steps)
if feedback.isCanceled():
return {}
# D09 Número de robos anuales
alg_params = {
'BLOCKS': parameters['BLOCKS'],
'FIELD_POPULATION': parameters['FIELD_POPULATION'],
'STUDY_AREA_GRID': parameters['STUDY_AREA_GRID'],
'THEFTS': parameters['THEFTS'],
'OUTPUT': parameters['OUTPUT_D09']
}
outputs['D09NmeroDeRobosAnuales'] = processing.run(
'SISURBANO:D09 Número de robos anuales',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
results['OUTPUT_D09'] = outputs['D09NmeroDeRobosAnuales']['OUTPUT']
isCensoPoblacion = isValid(parameters['CENSO_POBLACION'])
if isBlocks and isCensoPoblacion and isDpaMan:
steps = steps + 1
feedback.setCurrentStep(steps)
if feedback.isCanceled():
return {}
# D13 Población activa con estudios universitarios
alg_params = {
'BLOCKS': parameters['BLOCKS'],
'CENSO_POBLACION': parameters['CENSO_POBLACION'],
'DPA_MAN': parameters['DPA_MAN'],
'STUDY_AREA_GRID': parameters['STUDY_AREA_GRID'],
'OUTPUT': parameters['OUTPUT_D13']
}
outputs[
'D13PoblacinActivaConEstudiosUniversitarios'] = processing.run(
'SISURBANO:D13 Población activa con estudios universitarios',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
results['OUTPUT_D13'] = outputs[
'D13PoblacinActivaConEstudiosUniversitarios']['OUTPUT']
if isBlocks and isCensoVivienda and isDpaMan:
steps = steps + 1
feedback.setCurrentStep(steps)
if feedback.isCanceled():
return {}
# D01 Viviendas con cobertura total de servicios básicos
alg_params = {
'BLOCKS': parameters['BLOCKS'],
'CENSO_VIVIENDA': parameters['CENSO_VIVIENDA'],
'DPA_MAN': parameters['DPA_MAN'],
'STUDY_AREA_GRID': parameters['STUDY_AREA_GRID'],
'OUTPUT': parameters['OUTPUT_D01']
}
outputs[
'D01ViviendasConCoberturaTotalDeServiciosBsicos'] = processing.run(
'SISURBANO:D01 Viviendas con cobertura total de servicios básicos',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
results['OUTPUT_D01'] = outputs[
'D01ViviendasConCoberturaTotalDeServiciosBsicos']['OUTPUT']
isDistanceOptions = isValid(parameters['DISTANCE_OPTIONS'])
isMarket = isValid(parameters['EQUIPMENT_MARKET'])
isRoads = isValid(parameters['ROADS'])
if isBlocks and isDistanceOptions and isMarket and isRoads:
steps = steps + 1
feedback.setCurrentStep(steps)
if feedback.isCanceled():
return {}
# D08 Cercanía y asequibilidad a alimentos
alg_params = {
'BLOCKS': parameters['BLOCKS'],
'DISTANCE_OPTIONS': parameters['DISTANCE_OPTIONS'],
'EQUIPMENT_MARKET': parameters['EQUIPMENT_MARKET'],
'FIELD_HOUSING': parameters['FIELD_HOUSING'],
'ROADS': parameters['ROADS'],
'STUDY_AREA_GRID': parameters['STUDY_AREA_GRID'],
'OUTPUT': parameters['OUTPUT_D08']
}
outputs['D08CercanaYAsequibilidadAAlimentos'] = processing.run(
'SISURBANO:D08 Cercanía y asequibilidad a alimentos',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
results['OUTPUT_D08'] = outputs[
'D08CercanaYAsequibilidadAAlimentos']['OUTPUT']
isZonasCensales = isValid(parameters['ZONAS_CENSALES'])
isEncuestaTiempo = isValid(parameters['ENCUESTA_TIEMPO'])
isDpaSector = isValid(parameters['DPA_SECTOR'])
if isZonasCensales and isDpaSector and isEncuestaTiempo:
steps = steps + 1
feedback.setCurrentStep(steps)
if feedback.isCanceled():
return {}
# D06 Uso del tiempo
alg_params = {
'BLOCKS': parameters['ZONAS_CENSALES'],
'DPA_SECTOR': parameters['DPA_SECTOR'],
'ENCUESTA': parameters['ENCUESTA_TIEMPO'],
'STUDY_AREA_GRID': parameters['STUDY_AREA_GRID'],
'OUTPUT': parameters['OUTPUT_D06']
}
outputs['D06UsoDelTiempo'] = processing.run(
'SISURBANO:D06 Uso del tiempo',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
results['OUTPUT_D06'] = outputs['D06UsoDelTiempo']['OUTPUT']
isEncuestaSeguridad = isValid(parameters['ENCUESTA_SEGURIDAD'])
if isZonasCensales and isDpaSector and isEncuestaSeguridad:
steps = steps + 1
feedback.setCurrentStep(steps)
if feedback.isCanceled():
return {}
# D15 Percepción de inseguridad
alg_params = {
'BLOCKS': parameters['ZONAS_CENSALES'],
'DPA_SECTOR': parameters['DPA_SECTOR'],
'ENCUESTA': parameters['ENCUESTA_SEGURIDAD'],
'STUDY_AREA_GRID': parameters['STUDY_AREA_GRID'],
'OUTPUT': parameters['OUTPUT_D15']
}
outputs['D15PercepcinDeInseguridad'] = processing.run(
'SISURBANO:D15 Percepción de inseguridad',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
results['OUTPUT_D15'] = outputs['D15PercepcinDeInseguridad'][
'OUTPUT']
if isBlocks and isCensoPoblacion and isDpaMan:
steps = steps + 1
feedback.setCurrentStep(steps)
if feedback.isCanceled():
return {}
# D16 Índice de envejecimiento
alg_params = {
'BLOCKS': parameters['BLOCKS'],
'CENSO_POBLACION': parameters['CENSO_POBLACION'],
'DPA_MAN': parameters['DPA_MAN'],
'STUDY_AREA_GRID': parameters['STUDY_AREA_GRID'],
'OUTPUT': parameters['OUTPUT_D16']
}
outputs['D16NdiceDeEnvejecimiento'] = processing.run(
'SISURBANO:D16 Índice de envejecimiento',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
results['OUTPUT_D16'] = outputs['D16NdiceDeEnvejecimiento'][
'OUTPUT']
isEncuestaDesempleo = isValid(parameters['ENCUESTA_DESEMPLEO'])
if isZonasCensales and isDpaSector and isEncuestaDesempleo:
steps = steps + 1
feedback.setCurrentStep(steps)
if feedback.isCanceled():
return {}
# D11 Tasa de desempleo
alg_params = {
'BLOCKS': parameters['ZONAS_CENSALES'],
'DPA_SECTOR': parameters['DPA_SECTOR'],
'ENCUESTA': parameters['ENCUESTA_DESEMPLEO'],
'STUDY_AREA_GRID': parameters['STUDY_AREA_GRID'],
'OUTPUT': parameters['OUTPUT_D11']
}
outputs['D11TasaDeDesempleo'] = processing.run(
'SISURBANO:D11 Tasa de desempleo',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
results['OUTPUT_D11'] = outputs['D11TasaDeDesempleo']['OUTPUT']
isOpenSpace = isValid(parameters['OPEN_SPACE'])
isSpace2Improvement = isValid(parameters['SPACE2IMPROVEMENT'])
if isOpenSpace and isSpace2Improvement:
steps = steps + 1
feedback.setCurrentStep(steps)
if feedback.isCanceled():
return {}
# D04 Espacios públicos abiertos que necesitan mejoras
alg_params = {
'OPEN_SPACE': parameters['OPEN_SPACE'],
'SPACE2IMPROVEMENT': parameters['SPACE2IMPROVEMENT'],
'STUDY_AREA_GRID': parameters['STUDY_AREA_GRID'],
'OUTPUT': parameters['OUTPUT_D04']
}
outputs[
'D04EspaciosPblicosAbiertosQueNecesitanMejoras'] = processing.run(
'SISURBANO:D04 Espacios públicos abiertos que necesitan mejoras',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
results['OUTPUT_D04'] = outputs[
'D04EspaciosPblicosAbiertosQueNecesitanMejoras']['OUTPUT']
if isBlocks and isCensoPoblacion and isDpaMan:
steps = steps + 1
feedback.setCurrentStep(steps)
if feedback.isCanceled():
return {}
# D12 Mujeres en la fuerza de trabajo remunerado
alg_params = {
'BLOCKS': parameters['BLOCKS'],
'CENSO_POBLACION': parameters['CENSO_POBLACION'],
'DPA_MAN': parameters['DPA_MAN'],
'STUDY_AREA_GRID': parameters['STUDY_AREA_GRID'],
'OUTPUT': parameters['OUTPUT_D12']
}
outputs[
'D12MujeresEnLaFuerzaDeTrabajoRemunerado'] = processing.run(
'SISURBANO:D12 Mujeres en la fuerza de trabajo remunerado',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
results['OUTPUT_D12'] = outputs[
'D12MujeresEnLaFuerzaDeTrabajoRemunerado']['OUTPUT']
isRisk = isValid(parameters['RISK'])
if isBlocks and isFieldHousing and isRisk:
steps = steps + 1
feedback.setCurrentStep(steps)
if feedback.isCanceled():
return {}
# D03 Viviendas emplazadas en zonas de riesgo
alg_params = {
'BLOCKS': parameters['BLOCKS'],
'FIELD_HOUSING': parameters['FIELD_HOUSING'],
'RISK': parameters['RISK'],
'STUDY_AREA_GRID': parameters['STUDY_AREA_GRID'],
'OUTPUT': parameters['OUTPUT_D03']
}
outputs['D03ViviendasEmplazadasEnZonasDeRiesgo'] = processing.run(
'SISURBANO:D03 Viviendas emplazadas en zonas de riesgo',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
results['OUTPUT_D03'] = outputs[
'D03ViviendasEmplazadasEnZonasDeRiesgo']['OUTPUT']
if isBlocks and isCensoHogar and isCensoVivienda and isDpaMan:
steps = steps + 1
feedback.setCurrentStep(steps)
if feedback.isCanceled():
return {}
# D10 Seguridad de tenencia de la vivienda
alg_params = {
'BLOCKS': parameters['BLOCKS'],
'CENSO_HOGAR': parameters['CENSO_HOGAR'],
'CENSO_VIVIENDA': parameters['CENSO_VIVIENDA'],
'DPA_MAN': parameters['DPA_MAN'],
'STUDY_AREA_GRID': parameters['STUDY_AREA_GRID'],
'OUTPUT': parameters['OUTPUT_D10']
}
outputs['D10SeguridadDeTenenciaDeLaVivienda'] = processing.run(
'SISURBANO:D10 Seguridad de tenencia de la vivienda',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
results['OUTPUT_D10'] = outputs[
'D10SeguridadDeTenenciaDeLaVivienda']['OUTPUT']
if isBlocks and isCensoPoblacion and isDpaMan:
steps = steps + 1
feedback.setCurrentStep(steps)
if feedback.isCanceled():
return {}
# D14 Estabilidad de la comunidad
alg_params = {
'BLOCKS': parameters['BLOCKS'],
'CENSO_POBLACION': parameters['CENSO_POBLACION'],
'DPA_MAN': parameters['DPA_MAN'],
'STUDY_AREA_GRID': parameters['STUDY_AREA_GRID'],
'OUTPUT': parameters['OUTPUT_D14']
}
outputs['D14EstabilidadDeLaComunidad'] = processing.run(
'SISURBANO:D14 Estabilidad de la comunidad',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
results['OUTPUT_D14'] = outputs['D14EstabilidadDeLaComunidad'][
'OUTPUT']
if isBlocks and isCensoHogar and isCensoPoblacion and isCensoVivienda and isDpaMan:
steps = steps + 1
feedback.setCurrentStep(steps)
if feedback.isCanceled():
return {}
# D07 Indice de calidad de vida
alg_params = {
'BLOCKS': parameters['BLOCKS'],
'CENSO_HOGAR': parameters['CENSO_HOGAR'],
'CENSO_POBLACION': parameters['CENSO_POBLACION'],
'CENSO_VIVIENDA': parameters['CENSO_VIVIENDA'],
'DPA_MAN': parameters['DPA_MAN'],
'STUDY_AREA_GRID': parameters['STUDY_AREA_GRID'],
'OUTPUT': parameters['OUTPUT_D07']
}
outputs['D07IndiceDeCalidadDeVida'] = processing.run(
'SISURBANO:D07 Indice de calidad de vida',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
results['OUTPUT_D07'] = outputs['D07IndiceDeCalidadDeVida'][
'OUTPUT']
if results['OUTPUT_D07']:
# D17 Segregación espacial
alg_params = {
'ICV': results['OUTPUT_D07'],
'VALUE': 'PQ1',
'OUTPUT': parameters['OUTPUT_D17']
}
outputs['D17SegregacinEspacial'] = processing.run(
'SISURBANO:D17 Segregación espacial',
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True)
results['OUTPUT_D17'] = outputs['D17SegregacinEspacial']['OUTPUT']
return results
def processAlgorithm(self, parameters, context, model_feedback):
# Use a multi-step feedback, so that individual child algorithm progress reports are adjusted for the
# overall progress through the model
feedback = QgsProcessingMultiStepFeedback(13, model_feedback)
results = {}
outputs = {}
input_layer = self.parameterAsVectorLayer(parameters, "inputlayer",
context)
overlay_layer = self.parameterAsVectorLayer(parameters, "overlaylayer",
context)
input_epsg_code = input_layer.crs().authid()
overlay_epsg_code = overlay_layer.crs().authid()
crs_input = QgsCoordinateReferenceSystem(input_epsg_code)
crs_overlay = QgsCoordinateReferenceSystem(overlay_epsg_code)
if crs_input.isGeographic():
feedback.reportError(
"CRS of the Input Layer is Geographic. Results accuracy may get impacted. For most accurate results, both input and overlay layers should be in the same Projected CRS\n"
)
if crs_overlay.isGeographic():
feedback.reportError(
"CRS of the Input Layer is Geographic. Results accuracy may get impacted. For most accurate results, both input and overlay layers should be in the same Projected CRS\n"
)
if input_epsg_code == overlay_epsg_code:
pass
else:
feedback.reportError(
"Input and Overlay Layers are in different CRS. For most accurate results, both input and overlay layers should be in the same Projected CRS\n"
)
# add_ID_field to input layer
alg_params = {
"FIELD_NAME": "input_feat_id",
"GROUP_FIELDS": [""],
"INPUT": parameters["inputlayer"],
"SORT_ASCENDING": True,
"SORT_EXPRESSION": "",
"SORT_NULLS_FIRST": False,
"START": 1,
"OUTPUT": QgsProcessing.TEMPORARY_OUTPUT,
}
outputs["Add_id_field"] = processing.run(
"native:addautoincrementalfield",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
feedback.setCurrentStep(1)
if feedback.isCanceled():
return {}
# add_area_field to input layer
alg_params = {
"FIELD_LENGTH": 0,
"FIELD_NAME": "area_awa",
"FIELD_PRECISION": 0,
"FIELD_TYPE": 0,
"FORMULA": "area($geometry)",
"INPUT": outputs["Add_id_field"]["OUTPUT"],
"OUTPUT": QgsProcessing.TEMPORARY_OUTPUT,
}
outputs["Add_area_field"] = processing.run(
"qgis:fieldcalculator",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
feedback.setCurrentStep(2)
if feedback.isCanceled():
return {}
# dissolve all overlay fields so as not to repeat record in reporting
alg_params = {
"FIELD": [parameters["fieldtoaverage"]] + [
field for field in parameters["additionalfields"]
if field != str(parameters["fieldtoaverage"])
],
"INPUT":
parameters["overlaylayer"],
"OUTPUT":
QgsProcessing.TEMPORARY_OUTPUT,
}
outputs["Dissolve"] = processing.run(
"native:dissolve",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
feedback.setCurrentStep(3)
if feedback.isCanceled():
return {}
# intersection between input and overlay layer
# delete no field in input layer and all fields in overlay layer
# except field to average and additional fields
alg_params = {
"INPUT":
outputs["Add_area_field"]["OUTPUT"],
"INPUT_FIELDS": [""],
"OVERLAY":
outputs["Dissolve"]["OUTPUT"],
"OVERLAY_FIELDS": [str(parameters["fieldtoaverage"])] +
parameters["additionalfields"],
"OVERLAY_FIELDS_PREFIX":
"",
"OUTPUT":
QgsProcessing.TEMPORARY_OUTPUT,
}
outputs["Intersection"] = processing.run(
"native:intersection",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
feedback.setCurrentStep(4)
if feedback.isCanceled():
return {}
# add_Weight
alg_params = {
"FIELD_LENGTH": 0,
"FIELD_NAME": parameters["fieldtoaverage"] + "_area",
"FIELD_PRECISION": 0,
"FIELD_TYPE": 0,
"FORMULA":
' "' + parameters["fieldtoaverage"] + '" * area($geometry)',
"INPUT": outputs["Intersection"]["OUTPUT"],
"OUTPUT": QgsProcessing.TEMPORARY_OUTPUT,
}
outputs["Add_Weight"] = processing.run(
"qgis:fieldcalculator",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
feedback.setCurrentStep(5)
if feedback.isCanceled():
return {}
# area_average
weighted_field = "weighted_" + parameters["fieldtoaverage"]
alg_params = {
"FIELD_LENGTH": 0,
"FIELD_NAME": weighted_field,
"FIELD_PRECISION": 0,
"FIELD_TYPE": 0,
"FORMULA": ' sum("' + parameters["fieldtoaverage"] + "_area"
'","input_feat_id")/"area_awa"',
"INPUT": outputs["Add_Weight"]["OUTPUT"],
"OUTPUT": QgsProcessing.TEMPORARY_OUTPUT,
}
outputs["area_average"] = processing.run(
"qgis:fieldcalculator",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
feedback.setCurrentStep(6)
if feedback.isCanceled():
return {}
# remerge input layer elements
alg_params = {
"FIELD": ["input_feat_id"],
"INPUT": outputs["area_average"]["OUTPUT"],
"OUTPUT": QgsProcessing.TEMPORARY_OUTPUT,
}
outputs["Dissolve2"] = processing.run(
"native:dissolve",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
feedback.setCurrentStep(7)
if feedback.isCanceled():
return {}
input_layer = self.parameterAsVectorLayer(parameters, "inputlayer",
context)
result_name = input_layer.name() + "_" + parameters["fieldtoaverage"]
parameters["result"].destinationName = result_name
# drop field(s) for Result
alg_params = {
"COLUMN":
["input_feat_id", "area_awa"] + [parameters["fieldtoaverage"]] + [
field for field in parameters["additionalfields"]
if field != str(parameters["fieldtoaverage"])
] + [parameters["fieldtoaverage"] + "_area"],
"INPUT":
outputs["Dissolve2"]["OUTPUT"],
"OUTPUT":
parameters["result"],
}
outputs["Drop1"] = processing.run(
"qgis:deletecolumn",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
feedback.setCurrentStep(8)
if feedback.isCanceled():
return {}
results["result"] = outputs["Drop1"]["OUTPUT"]
# Reporting
# Drop field(s) for Report
int_layer = context.takeResultLayer(outputs["area_average"]["OUTPUT"])
all_fields = [f.name() for f in int_layer.fields()]
fields_to_keep = (["input_feat_id", weighted_field] + [
field for field in parameters["additionalfields"]
if field != str(parameters["fieldtoaverage"])
] + [parameters["fieldtoaverage"]] +
[parameters["identifierfieldforreport"]])
fields_to_drop = [f for f in all_fields if f not in fields_to_keep]
alg_params = {
"COLUMN": fields_to_drop,
"INPUT": int_layer,
"OUTPUT": QgsProcessing.TEMPORARY_OUTPUT,
}
outputs["Drop2"] = processing.run(
"qgis:deletecolumn",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
feedback.setCurrentStep(9)
if feedback.isCanceled():
return {}
# update area
alg_params = {
"FIELD_LENGTH": 20,
"FIELD_NAME": "area_crs_units",
"FIELD_PRECISION": 5,
"FIELD_TYPE": 0,
"FORMULA": "round(area($geometry),5)",
"INPUT": outputs["Drop2"]["OUTPUT"],
"OUTPUT": QgsProcessing.TEMPORARY_OUTPUT,
}
outputs["update_area"] = processing.run(
"qgis:fieldcalculator",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
feedback.setCurrentStep(10)
if feedback.isCanceled():
return {}
parameters["reportaslayer"].destinationName = "Report as Layer"
# add area %
alg_params = {
"FIELD_LENGTH": 9,
"FIELD_NAME": "area_prcnt",
"FIELD_PRECISION": 5,
"FIELD_TYPE": 0,
"FORMULA":
' round("area_crs_units" *100/ sum( "area_crs_units" , "input_feat_id" ),5)',
"INPUT": outputs["update_area"]["OUTPUT"],
"OUTPUT": QgsProcessing.TEMPORARY_OUTPUT,
}
outputs["area_prcnt"] = processing.run(
"qgis:fieldcalculator",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
feedback.setCurrentStep(11)
if feedback.isCanceled():
return {}
# Order by expression
alg_params = {
"ASCENDING": True,
"EXPRESSION": ' "input_feat_id" + area_prcnt" ',
"INPUT": outputs["area_prcnt"]["OUTPUT"],
"NULLS_FIRST": False,
"OUTPUT": parameters["reportaslayer"],
}
outputs["OrderByExpression"] = processing.run(
"native:orderbyexpression",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
feedback.setCurrentStep(12)
if feedback.isCanceled():
return {}
results["reportaslayer"] = outputs["OrderByExpression"]["OUTPUT"]
output_file = self.parameterAsFileOutput(parameters, "reportasHTML",
context)
# create HTML report
if output_file:
try:
try:
import pandas as pd
except ImportError:
feedback.pushInfo(
"Python library pandas was not found. Installing pandas to QGIS python ..."
)
import pathlib as pl
import subprocess
qgis_Path = pl.Path(sys.executable)
qgis_python_path = (qgis_Path.parent /
"python3.exe").as_posix()
subprocess.check_call([
qgis_python_path, "-m", "pip", "install", "--user",
"pandas"
])
import pandas as pd
feedback.pushInfo(
"Python library pandas was successfully installed for QGIS python"
)
except:
feedback.reportError(
"Failed to import pandas. Tried installing pandas but failed.\nPlease manually install pandas for the python that comes with your QGIS.",
True,
)
return results
# Drop geometries
alg_params = {
"INPUT": outputs["area_prcnt"]["OUTPUT"],
"OUTPUT": QgsProcessing.TEMPORARY_OUTPUT,
}
outputs["DropGeometries"] = processing.run(
"native:dropgeometries",
alg_params,
context=context,
feedback=feedback,
is_child_algorithm=True,
)
feedback.setCurrentStep(13)
if feedback.isCanceled():
return {}
with tempfile.TemporaryDirectory() as td:
f_name = os.path.join(td, "report_df.csv")
report_layer = context.takeResultLayer(
outputs["DropGeometries"]["OUTPUT"])
QgsVectorFileWriter.writeAsVectorFormat(
report_layer,
f_name,
fileEncoding="utf-8",
driverName="CSV",
)
df = pd.read_csv(f_name)
total_FIDs = df["input_feat_id"].max()
ident_name = parameters["identifierfieldforreport"]
html = ""
df.sort_values(by="area_prcnt", ascending=False, inplace=True)
pd.set_option("display.float_format", "{:.5f}".format)
for i in range(1, total_FIDs + 1):
df_sub = df.loc[df["input_feat_id"] == i]
df_sub.reset_index(inplace=True, drop=True)
avg_value = df_sub.at[0, weighted_field]
if ident_name:
feature_name = df_sub.at[0, ident_name]
df_sub.drop(
columns=["input_feat_id", ident_name, weighted_field],
inplace=True,
)
html += f"<p><b>{i}. {feature_name}</b><br>{weighted_field}: {avg_value}<br>count of distinct intersecting features: {len(df_sub.index)}<br></p>\n"
else:
df_sub.drop(columns=["input_feat_id", weighted_field],
inplace=True)
html += f"<p><b>Feature ID: {i}</b><br>{weighted_field}: {avg_value}<br>count of distinct intersecting features: {len(df_sub.index)}<br></p>\n"
html += f"{df_sub.to_html(bold_rows=False, index=False, na_rep='Null',justify='left')}<br>\n"
with codecs.open(output_file, "w", encoding="utf-8") as f:
f.write("<html><head>\n")
f.write(
'<meta http-equiv="Content-Type" content="text/html; \
charset=utf-8" /></head><body>\n')
f.write(html)
f.write("</body></html>\n")
results["reportasHTML"] = output_file
# log usage
with open(os.path.join(cmd_folder, "usage_counter.log"), "r+") as f:
counter = int(f.readline())
f.seek(0)
f.write(str(counter + 1))
# check if counter is a milestone
if (counter + 1) % 25 == 0:
appeal_file = NamedTemporaryFile("w", suffix=".html", delete=False)
self.createHTML(appeal_file.name, counter + 1)
results["Message"] = appeal_file.name
return results
def processAlgorithm(self, parameters, context, feedback):
self.dest_id = None
# Parameters
# lighting = self.parameterAsVectorLayer(parameters, self.LIGHTING, context)
lighting_source, lighting_layer = qgsTreatments.parameterAsSourceLayer(
self, parameters, self.LIGHTING, context, feedback=feedback)
if not lighting_source:
raise QgsProcessingException("No lighting layer")
fieldname = self.parameterAsString(parameters, self.FLUX_FIELD,
context)
if not fieldname:
raise QgsProcessingException("No field given for light flux")
flux_div_flag = self.parameterAsBool(parameters, self.FLUX_DIV,
context)
reporting, reporting_layer = qgsTreatments.parameterAsSourceLayer(
self, parameters, self.REPORTING, context, feedback=feedback)
if not reporting:
raise QgsProcessingException("No reporting layer")
init_reporting_fields = reporting_layer.fields().names()
surface, surface_layer = qgsTreatments.parameterAsSourceLayer(
self, parameters, self.SURFACE, context, feedback=feedback)
dissolve_flag = self.parameterAsBool(parameters, self.DISSOLVE,
context)
clip_val = self.parameterAsInt(parameters, self.CLIP_DISTANCE, context)
reporting_fields = self.parameterAsFields(parameters,
self.REPORTING_FIELDS,
context)
skip_flag = self.parameterAsBool(parameters, self.SKIP_EMPTY, context)
min_area = self.parameterAsDouble(parameters, self.MIN_AREA, context)
min_lamps = self.parameterAsInt(parameters, self.MIN_NB_LAMPS, context)
# Reprojection if needed
light_crs = lighting_source.sourceCrs().authid()
reporting_crs = reporting.sourceCrs()
# reporting_crs = reporting.dataProvider().sourceCrs()
if reporting_crs.isGeographic():
raise QgsProcessingException(
"Reporting CRS must be a projection (not lat/lon)")
feedback.pushDebugInfo("reporting_crs = " + str(type(reporting_crs)))
feedback.pushDebugInfo("reporting_crs = " + str(reporting_crs))
reporting_crs_id = reporting_crs.authid()
feedback.pushDebugInfo("reporting_crs_id = " +
str(type(reporting_crs_id)))
feedback.pushDebugInfo("reporting_crs_id = " + str(reporting_crs_id))
if light_crs != reporting_crs_id:
lighting_path = QgsProcessingUtils.generateTempFilename(
'light_reproj.gpkg')
qgsTreatments.applyReprojectLayer(lighting_layer,
reporting_crs,
lighting_path,
context=context,
feedback=feedback)
lighting_layer = lighting_path
if surface:
surface_crs = surface.sourceCrs().authid()
if reporting_crs_id != surface_crs:
surface_reproj = QgsProcessingUtils.generateTempFilename(
'surface_reproj.gpkg')
qgsTreatments.applyReprojectLayer(surface_layer,
reporting_crs,
surface_reproj,
context=context,
feedback=feedback)
surface_fixed = QgsProcessingUtils.generateTempFilename(
'surface_fixed.gpkg')
qgsTreatments.fixGeometries(surface_reproj,
surface_fixed,
context=context,
feedback=feedback)
surface_layer = qgsUtils.loadVectorLayer(surface_fixed)
qgsTreatments.createSpatialIndex(surface_layer,
context=context,
feedback=feedback)
# Output fields initialization
nb_lamps_field = QgsField(self.NB_LAMPS, QVariant.Int)
flux_sum_field = QgsField(self.FLUX_SUM, QVariant.Double)
surface_field = QgsField(self.SURFACE_AREA, QVariant.Double)
flux_den_field = QgsField(self.FLUX_DEN, QVariant.Double)
out_fields = QgsFields()
for f in reporting_layer.fields():
if f.name() in reporting_fields:
# feedback.pushDebugInfo("f2 = " + str( f.name()))
out_fields.append(f)
out_fields.append(nb_lamps_field)
out_fields.append(flux_sum_field)
out_fields.append(surface_field)
out_fields.append(flux_den_field)
(sink, self.dest_id) = self.parameterAsSink(parameters, self.OUTPUT,
context, out_fields,
reporting.wkbType(),
reporting.sourceCrs())
# Progess bar step
nb_feats = reporting.featureCount()
total = 100.0 / nb_feats if nb_feats else 0
# Clip according to distance to lighting
if clip_val:
buffered_path = QgsProcessingUtils.generateTempFilename(
'light_buf.gpkg')
buffered = qgsTreatments.applyBufferFromExpr(lighting_layer,
clip_val,
buffered_path,
context=context,
feedback=feedback)
clipped_path = QgsProcessingUtils.generateTempFilename(
'reporting_clip.gpkg')
qgsTreatments.createSpatialIndex(reporting_layer,
context=context,
feedback=feedback)
clipped = qgsTreatments.applyVectorClip(reporting_layer,
buffered_path,
clipped_path,
context=context,
feedback=feedback)
reporting_layer = clipped_path
# Get reporting units count per light
if flux_div_flag:
if 'ID' in init_reporting_fields:
id_field = 'ID'
elif 'fid' in init_reporting_fields:
id_field = 'fid'
else:
raise QgsProcessingException(
"ID field does not exist in reporting layer")
qgsTreatments.createSpatialIndex(lighting_layer,
context=context,
feedback=feedback)
joined_light_path = QgsProcessingUtils.generateTempFilename(
'joined_light.gpkg')
qgsTreatments.joinByLocSummary(lighting_layer,
reporting_layer,
joined_light_path, [id_field],
summaries=[0],
predicates=[0],
context=context,
feedback=feedback)
joined_light_layer = qgsUtils.loadVectorLayer(joined_light_path)
id_cpt_name = id_field + '_count'
def funcDiv(f):
if f[fieldname]:
try:
flux = float(f[fieldname])
nb_units = int(f[id_cpt_name])
return flux / nb_units
except ValueError:
return None
else:
return None
qgsUtils.createOrUpdateField(joined_light_layer, funcDiv,
self.FLUX_DIV_FIELD)
lighting_layer, fieldname = joined_light_layer, self.FLUX_DIV_FIELD
# Join light points summary by reporting unit
joined_path = QgsProcessingUtils.generateTempFilename('joined.gpkg')
# SUM = 5
summaries = [0, 1, 2, 3, 5, 6]
qgsTreatments.createSpatialIndex(reporting_layer,
context=context,
feedback=feedback)
joined = qgsTreatments.joinByLocSummary(reporting_layer,
lighting_layer,
joined_path, [fieldname],
summaries,
predicates=[0],
context=context,
feedback=feedback)
joined_layer = qgsUtils.loadVectorLayer(joined_path)
nb_lamps_fieldname = fieldname + "_count"
flux_field_sum = fieldname + "_sum"
# Set context and feedback
if not context:
context = QgsProcessingContext()
context = context.setInvalidGeometryCheck(
QgsFeatureRequest.GeometryNoCheck)
multi_feedback = QgsProcessingMultiStepFeedback(nb_feats, feedback)
# Iteration on each reporting unit
qgsTreatments.createSpatialIndex(joined_layer,
context=context,
feedback=feedback)
for current, feat in enumerate(joined_layer.getFeatures()):
if feedback.isCanceled():
break
f_geom = feat.geometry()
f_area = f_geom.area()
f_id = feat.id()
nb_lamps = feat[nb_lamps_fieldname]
flux_sum = feat[flux_field_sum]
if skip_flag and flux_sum == 0:
continue
if f_area < min_area:
continue
if nb_lamps < min_lamps:
continue
try:
if surface:
# Clip surface layer to reporting feature boundaries to retrieve intersecting area
nb_steps = 4 if dissolve_flag else 3
mmf = QgsProcessingMultiStepFeedback(
nb_steps, multi_feedback)
joined_layer.selectByIds([f_id])
suffix = "_" + str(f_id) + ".gpkg"
input_feat = QgsProcessingUtils.generateTempFilename(
"selection" + suffix)
qgsTreatments.saveSelectedAttributes(joined_layer,
input_feat,
context=context,
feedback=mmf)
mmf.setCurrentStep(1)
# input_feat = QgsProcessingFeatureSourceDefinition(joined_layer.id(),True)
clipped_path = QgsProcessingUtils.generateTempFilename(
"clipped" + str(f_id) + ".gpkg")
clipped = qgsTreatments.applyVectorClip(surface_layer,
input_feat,
clipped_path,
context=context,
feedback=mmf)
mmf.setCurrentStep(2)
if dissolve_flag:
feat_surface_path = QgsProcessingUtils.generateTempFilename(
"dissolved" + str(f_id) + ".gpkg")
qgsTreatments.dissolveLayer(clipped,
feat_surface_path,
context=context,
feedback=mmf)
mmf.setCurrentStep(3)
else:
feat_surface_path = clipped_path
feat_surface_layer = qgsUtils.loadVectorLayer(
feat_surface_path)
joined_layer.removeSelection()
surface_area = 0
for surface_feat in feat_surface_layer.getFeatures():
surface_geom = surface_feat.geometry()
intersection = f_geom.intersection(surface_geom)
surface_area += intersection.area()
mmf.setCurrentStep(nb_steps)
else:
surface_area = f_area
# Output result feature
new_feat = QgsFeature(out_fields)
new_feat.setGeometry(feat.geometry())
for report_field in reporting_fields:
new_feat[report_field] = feat[report_field]
new_feat[self.NB_LAMPS] = nb_lamps
new_feat[self.FLUX_SUM] = flux_sum
new_feat[self.SURFACE_AREA] = surface_area
new_feat[
self.
FLUX_DEN] = flux_sum / surface_area if surface_area > 0 else None
sink.addFeature(new_feat, QgsFeatureSink.FastInsert)
except Exception as e:
feedback.reportError('Unexpected error : ' + str(e))
raise e
multi_feedback.setCurrentStep(current + 1)
return {self.OUTPUT: self.dest_id}
def convertFromMap(self,
conversionMap=None,
featureConversionMap=None,
feedback=None):
"""
Converts all datasets from a conversion map.
:param conversionMap: (dict) conversion map generated by Datasource Conversion tool.
:param featureConversionMap: (dict) map of features based on given input.
:param feedback: (QgsProcessingMultiStepFeedback) QGIS tool for progress tracking.
:return: (tuple) successfull and failed translations lists.
"""
if conversionMap is None:
conversionMap = self.conversionMap
if feedback is None:
feedback = self.feedback
allInputLayers = dict()
allOutputLayers = dict()
errors = dict()
successfulLayers, failedLayers = None, None
nSteps = len(
self.getAllUniqueInputDb()) + len(self.getAllUniqueOutputDb()) * 4
multiStepFeedback = QgsProcessingMultiStepFeedback(nSteps, feedback)
# start log
conversionSummary = self.getLogHeader()
conversionStep = 1
currentStep = 0
for inputDb, conversionStepMaps in conversionMap.items():
if multiStepFeedback.isCanceled() or self.isCanceled():
break
multiStepFeedback.setCurrentStep(currentStep)
currentStep += 1
# input setup
self.conversionUpdated.emit(
self.tr("\nConversion Step {0} started...\n\n").format(
conversionStep))
self.conversionUpdated.emit(
self.tr("[INPUT] Reading {0}'s layers...\n").format(inputDb))
if inputDb not in allInputLayers:
allInputLayers[inputDb] = self.readInputLayers(
datasourcePath=inputDb, feedback=multiStepFeedback)
inputLayers = allInputLayers[inputDb]
for currentOutput, conversionStepMap in enumerate(
conversionStepMaps):
startTime = time.time()
if multiStepFeedback.isCanceled() or self.isCanceled():
break
# output setup
outputDb = conversionStepMap["outDs"]
if outputDb not in allOutputLayers:
if conversionStepMap["createDs"]:
self.conversionUpdated.emit(
self.tr("[OUTPUT] Creating dataset {0}...\n").
format(outputDb))
outputAbstractDb, error = self.checkAndCreateDataset(
conversionStepMap)
del outputAbstractDb
if error != "":
k = "{0} to {1}".format(inputDb, outputDb)
self.conversionUpdated.emit(
self.tr("Dataset creation error ({0}): '{1}'\n"
).format(outputDb, error))
errors[k] = error
conversionSummary += self.addConversionStepToLog(conversionStep, inputDb, outputDb, \
inputLayers, errors, {}, {}, "{0:.2f} s".format(time.time() - startTime))
conversionStep += 1
continue
self.conversionUpdated.emit(
self.tr("[OUTPUT] Reading {0}'s layers...\n").format(
outputDb))
multiStepFeedback.setCurrentStep(currentStep)
currentStep += 1
allOutputLayers[outputDb] = self.readOutputLayers(
datasourcePath=outputDb, feedback=multiStepFeedback)
outputLayers = allOutputLayers[outputDb]
# now conversion starts
self.conversionUpdated.emit(
self.tr("Preparing {0}'s layers for conversion...").format(
inputDb))
multiStepFeedback.setCurrentStep(currentStep)
currentStep += 1
preparedLayers = self.prepareInputLayers(
inputLayers, conversionStepMap, feedback=multiStepFeedback)
self.conversionUpdated.emit(self.tr("Mapping features..."))
multiStepFeedback.setCurrentStep(currentStep)
currentStep += 1
mappedFeatures = self.mapFeatures(preparedLayers,
outputLayers,
feedback=multiStepFeedback)
self.conversionUpdated.emit(
self.tr("Loading layers to {0}...").format(outputDb))
multiStepFeedback.setCurrentStep(currentStep)
currentStep += 1
successfulLayers, failedLayers = self.loadToOuput(
mappedFeatures, outputLayers, conversionStepMap["conversionMode"],\
feedback=multiStepFeedback
)
# log update
conversionSummary += self.addConversionStepToLog(conversionStep, inputDb, outputDb, inputLayers, \
errors, successfulLayers, failedLayers, "{0:.2f} s".format(time.time() - startTime))
conversionStep += 1
self.conversionFinished.emit()
return {
'creationErrors': errors,
'successfulLayers': successfulLayers,
'failedLayers': failedLayers,
'status': not feedback.isCanceled(),
'log': conversionSummary
}
def processAlgorithm(self, parameters, context, feedback):
# Parameters
reporting_mode = self.parameterAsEnum(parameters, self.REPORTING_MODE,
context)
lighting_source, lighting_layer = qgsTreatments.parameterAsSourceLayer(
self, parameters, self.LIGHTING, context, feedback=feedback)
self.fieldname = self.parameterAsString(parameters, FDA.FLUX_FIELD,
context)
roads_source, roads_layer = qgsTreatments.parameterAsSourceLayer(
self, parameters, RE.ROADS, context, feedback=feedback)
cadastre_source, cadastre_layer = qgsTreatments.parameterAsSourceLayer(
self, parameters, RE.CADASTRE, context, feedback=feedback)
hydro_source, hydro_layer = qgsTreatments.parameterAsSourceLayer(
self, parameters, self.SURFACE_HYDRO, context, feedback=feedback)
extent_source, extent_layer = qgsTreatments.parameterAsSourceLayer(
self, parameters, RE.EXTENT_LAYER, context, feedback=feedback)
clip_distance = self.parameterAsDouble(parameters, self.CLIP_DISTANCE,
context)
dissolve_step = self.parameterAsEnum(parameters, self.DISSOLVE_STEP,
context)
include_layers = self.parameterAsLayerList(parameters,
RE.INCLUDE_LAYERS, context)
diff_layers = self.parameterAsLayerList(parameters, RE.DIFF_LAYERS,
context)
output_surface = self.parameterAsOutputLayer(parameters,
self.OUTPUT_SURFACE,
context)
self.output = self.parameterAsOutputLayer(parameters, self.OUTPUT,
context)
out_linear = reporting_mode == 1
# Init steps
nb_steps = 3 if out_linear else 2
mf = QgsProcessingMultiStepFeedback(nb_steps, feedback)
if out_linear:
id_field = 'ID'
if id_field not in roads_layer.fields().names():
raise QgsProcessingException("No 'ID' field in roads layer")
# Surface
surface_params = parameters.copy()
surface_params[RE.ROADS] = roads_layer
surface_params[RE.CADASTRE] = cadastre_layer
surface_params[RE.EXTENT_LAYER] = extent_layer
surface_params[RE.INCLUDE_LAYERS] = include_layers
surface_params[RE.DIFF_LAYERS] = diff_layers
if hydro_source:
surface_params[RE.DIFF_LAYERS] += [hydro_layer]
surface_params[RE.DISSOLVE] = dissolve_step == 0
surface_params[RE.OUTPUT] = output_surface
surface = qgsTreatments.applyProcessingAlg('LPT',
RE.ALG_NAME,
surface_params,
context=context,
feedback=mf)
mf.setCurrentStep(1)
# Light surfacic density
qgsTreatments.fixShapefileFID(surface, context=context, feedback=mf)
density_params = parameters.copy()
density_params[FDA.LIGHTING] = lighting_layer
# density_params[FDA.REPORTING] = reporting_layer
density_params[FDA.CLIP_DISTANCE] = clip_distance
density_params[FDA.SURFACE] = surface
density_params[RE.ROADS] = roads_layer
density_params[FDA.DISSOLVE] = dissolve_step == 1
density_params[FDA.SKIP_EMPTY] = True
if out_linear:
output_surf = QgsProcessingUtils.generateTempFilename(
'output_surface.gpkg')
density_params[FDA.REPORTING_FIELDS] = [id_field]
density_params[FDA.OUTPUT] = output_surf
else:
density_params[FDA.OUTPUT] = self.output
self.out_id = qgsTreatments.applyProcessingAlg('LPT',
DSFLSurface.ALG_NAME,
density_params,
context=context,
feedback=mf)
mf.setCurrentStep(2)
# Join if output linear
if out_linear:
copy_fields = [
FDA.NB_LAMPS, FDA.FLUX_SUM, FDA.SURFACE_AREA, FDA.FLUX_DEN
]
self.out_id = qgsTreatments.joinByAttribute(
roads_layer,
id_field,
output_surf,
id_field,
copy_fields=copy_fields,
out_layer=self.output,
context=context,
feedback=mf)
mf.setCurrentStep(3)
return {self.OUTPUT: self.output}
def relateDrainagesWithContours(self, drainageLyr, contourLyr, frameLinesLyr, heightFieldName, threshold, topologyRadius, feedback=None):
"""
Checks the conformity between directed drainages and contours.
Drainages must be propperly directed.
:param drainageLyr: QgsVectorLayer (line) with drainage lines.
This must have a primary key field;
:param contourLyr: QgsVectorLayer (line) with contour lines.
This must have a primary key field;
:param frameLinesLyrLyr: QgsVectorLayer (line) with frame lines;
:param heightFieldName: (str) name of the field that stores
contour's height;
:param threshold: (int) equidistance between contour lines;
:param threshold: (float) topology radius;
Process steps:
1- Build spatial indexes;
2- Compute intersections between drainages and contours;
3- Relate intersections grouping by drainages: calculate the
distance between the start point and each intersection, then
order the points by distance. If the height of each point does
not follow this order, flag the intersection.
4- After relating everything,
"""
maxSteps = 4
multiStepFeedback = QgsProcessingMultiStepFeedback(maxSteps, feedback) if feedback is not None else None
currentStep = 0
if multiStepFeedback is not None:
if multiStepFeedback.isCanceled():
return []
multiStepFeedback.setCurrentStep(currentStep)
currentStep += 1
multiStepFeedback.pushInfo(
self.tr('Building contour structures...')
)
contourSpatialIdx, contourIdDict, contourNodeDict, heightsDict = self.buildSpatialIndexAndIdDictRelateNodesAndAttributeGroupDict(
inputLyr=contourLyr,
attributeName=heightFieldName,
feedback=multiStepFeedback
)
if multiStepFeedback is not None:
if multiStepFeedback.isCanceled():
return []
multiStepFeedback.setCurrentStep(currentStep)
currentStep += 1
multiStepFeedback.pushInfo(
self.tr('Validating contour structures. Check 1/4...')
)
invalidDict = self.validateContourRelations(
contourNodeDict,
feedback=multiStepFeedback
)
if invalidDict:
multiStepFeedback.setCurrentStep(maxSteps-1)
return invalidDict
if multiStepFeedback is not None:
if multiStepFeedback.isCanceled():
return []
multiStepFeedback.setCurrentStep(currentStep)
currentStep += 1
multiStepFeedback.pushInfo(
self.tr('Building drainage spatial index...')
)
drainageSpatialIdx, drainageIdDict, drainageNodeDict = self.buildSpatialIndexAndIdDictAndRelateNodes(
inputLyr=drainageLyr,
feedback=multiStepFeedback
)
if multiStepFeedback is not None:
if multiStepFeedback.isCanceled():
return []
multiStepFeedback.setCurrentStep(currentStep)
currentStep += 1
multiStepFeedback.pushInfo(
self.tr('Relating contours with drainages...')
)
intersectionDict = self.buildIntersectionDict(
drainageLyr,
drainageIdDict,
drainageSpatialIdx,
contourIdDict,
contourIdDict
)
def runAlgorithm(self):
alg_parameters = []
feedback = self.createFeedback()
load_layers = self.mainWidget().checkLoadLayersOnCompletion.isChecked()
project = QgsProject.instance() if load_layers else None
for row in range(self.mainWidget().tblParameters.rowCount()):
col = 0
parameters = {}
for param in self.algorithm().parameterDefinitions():
if param.flags() & QgsProcessingParameterDefinition.FlagHidden or param.isDestination():
continue
wrapper = self.mainWidget().wrappers[row][col]
parameters[param.name()] = wrapper.parameterValue()
if not param.checkValueIsAcceptable(wrapper.parameterValue()):
self.messageBar().pushMessage("", self.tr('Wrong or missing parameter value: {0} (row {1})').format(
param.description(), row + 1),
level=Qgis.Warning, duration=5)
return
col += 1
count_visible_outputs = 0
for out in self.algorithm().destinationParameterDefinitions():
if out.flags() & QgsProcessingParameterDefinition.FlagHidden:
continue
count_visible_outputs += 1
widget = self.mainWidget().tblParameters.cellWidget(row, col)
text = widget.getValue()
if out.checkValueIsAcceptable(text):
if isinstance(out, (QgsProcessingParameterRasterDestination,
QgsProcessingParameterVectorDestination,
QgsProcessingParameterFeatureSink)):
# load rasters and sinks on completion
parameters[out.name()] = QgsProcessingOutputLayerDefinition(text, project)
else:
parameters[out.name()] = text
col += 1
else:
self.messageBar().pushMessage("", self.tr('Wrong or missing output value: {0} (row {1})').format(
out.description(), row + 1),
level=Qgis.Warning, duration=5)
return
alg_parameters.append(parameters)
task = QgsScopedProxyProgressTask(self.tr('Batch Processing - {0}').format(self.algorithm().displayName()))
multi_feedback = QgsProcessingMultiStepFeedback(len(alg_parameters), feedback)
feedback.progressChanged.connect(task.setProgress)
with OverrideCursor(Qt.WaitCursor):
self.mainWidget().setEnabled(False)
self.cancelButton().setEnabled(True)
# Make sure the Log tab is visible before executing the algorithm
try:
self.showLog()
self.repaint()
except:
pass
start_time = time.time()
algorithm_results = []
for count, parameters in enumerate(alg_parameters):
if feedback.isCanceled():
break
self.setProgressText(QCoreApplication.translate('BatchAlgorithmDialog', '\nProcessing algorithm {0}/{1}…').format(count + 1, len(alg_parameters)))
self.setInfo(self.tr('<b>Algorithm {0} starting…</b>').format(self.algorithm().displayName()), escapeHtml=False)
multi_feedback.setCurrentStep(count)
parameters = self.algorithm().preprocessParameters(parameters)
feedback.pushInfo(self.tr('Input parameters:'))
feedback.pushCommandInfo(pformat(parameters))
feedback.pushInfo('')
# important - we create a new context for each iteration
# this avoids holding onto resources and layers from earlier iterations,
# and allows batch processing of many more items then is possible
# if we hold on to these layers
context = dataobjects.createContext(feedback)
alg_start_time = time.time()
ret, results = execute(self.algorithm(), parameters, context, multi_feedback)
if ret:
self.setInfo(QCoreApplication.translate('BatchAlgorithmDialog', 'Algorithm {0} correctly executed…').format(self.algorithm().displayName()), escapeHtml=False)
feedback.pushInfo(
self.tr('Execution completed in {0:0.2f} seconds'.format(time.time() - alg_start_time)))
feedback.pushInfo(self.tr('Results:'))
feedback.pushCommandInfo(pformat(results))
feedback.pushInfo('')
algorithm_results.append(results)
else:
break
handleAlgorithmResults(self.algorithm(), context, multi_feedback, False, parameters)
feedback.pushInfo(self.tr('Batch execution completed in {0:0.2f} seconds'.format(time.time() - start_time)))
task = None
self.finish(algorithm_results)
self.cancelButton().setEnabled(False)
def processAlgorithm(self, parameters, context, feedback):
"""
Method that triggers the data processing algorithm.
:param parameters: (dict) mapping from algorithms input's name to its
value.
:param context: (QgsProcessingContext) execution's environmental info.
:param feedback: (QgsProcessingFeedback) QGIS object to keep track of
algorithm's progress/status.
:return: (dict) output mapping for identified flags.
"""
layers, selected, silent, ratio = self.getParameters(
parameters, context, feedback)
if not layers:
raise QgsProcessingException(self.tr("No layers were provided."))
for layer in layers:
if layer.featureCount() > 0:
geomType = next(layer.getFeatures()).geometry().wkbType()
break
else:
raise QgsProcessingException(self.tr("All layers are empty."))
self.prepareFlagSink(parameters, layers[0], geomType, context)
flags = dict()
lh = LayerHandler()
flagCount = 0
# a step for each input + 1 for loading flags into sink
multiStepFeedback = QgsProcessingMultiStepFeedback(
len(layers) + 1, feedback)
multiStepFeedback.setCurrentStep(0)
for step, layer in enumerate(layers):
if multiStepFeedback.isCanceled():
break
# running polygon slivers to purposely raise an exception if an
# empty geometry is found
multiStepFeedback.pushInfo(
self.tr("Checking {0}...").format(layer.name()))
slivers = lh.getPolygonSlivers(layer, ratio, selected, silent,
multiStepFeedback)
if slivers:
# pushWarnign is only avalailable on 3.16.2+
# multiStepFeedback.pushWarning(
multiStepFeedback.pushDebugInfo(
self.tr("{0} slivers were found on {1}!")\
.format(len(slivers), layer.name())
)
flags[layer] = slivers
flagCount += len(slivers)
multiStepFeedback.setCurrentStep(step + 1)
self.tr("Populating flags layer...")
self.flagPolygonSlivers(flags, flagCount, multiStepFeedback)
multiStepFeedback.setCurrentStep(step + 2)
return {self.FLAGS: self.flag_id}
def processAlgorithm(self, parameters, context, feedback):
"""
Here is where the processing itself takes place.
"""
layerHandler = LayerHandler()
algRunner = AlgRunner()
inputLyr = self.parameterAsVectorLayer(parameters, self.INPUT, context)
onlySelected = self.parameterAsBool(parameters, self.SELECTED, context)
tol = self.parameterAsDouble(parameters, self.TOLERANCE, context)
multiStepFeedback = QgsProcessingMultiStepFeedback(3, feedback)
multiStepFeedback.setCurrentStep(0)
multiStepFeedback.pushInfo(
self.tr('Identifying dangles on {layer}...').format(
layer=inputLyr.name()))
dangleLyr = algRunner.runIdentifyDangles(inputLyr,
tol,
context,
feedback=multiStepFeedback,
onlySelected=onlySelected)
multiStepFeedback.setCurrentStep(1)
layerHandler.filterDangles(dangleLyr, tol, feedback=multiStepFeedback)
multiStepFeedback.setCurrentStep(2)
multiStepFeedback.pushInfo(
self.tr('Snapping layer {layer} to dangles...').format(
layer=inputLyr.name()))
algRunner.runSnapLayerOnLayer(inputLyr,
dangleLyr,
tol,
context,
feedback=multiStepFeedback,
onlySelected=onlySelected,
behavior=0)
return {self.OUTPUT: inputLyr}
def runAlgorithm(self):
alg_parameters = []
feedback = self.createFeedback()
load_layers = self.mainWidget().checkLoadLayersOnCompletion.isChecked()
project = QgsProject.instance() if load_layers else None
for row in range(self.mainWidget().batchRowCount()):
parameters = self.mainWidget().parametersForRow(
row, destinationProject=project, warnOnInvalid=True)
alg_parameters.append(parameters)
task = QgsScopedProxyProgressTask(
self.tr('Batch Processing - {0}').format(
self.algorithm().displayName()))
multi_feedback = QgsProcessingMultiStepFeedback(
len(alg_parameters), feedback)
feedback.progressChanged.connect(task.setProgress)
with OverrideCursor(Qt.WaitCursor):
self.mainWidget().setEnabled(False)
self.cancelButton().setEnabled(True)
# Make sure the Log tab is visible before executing the algorithm
try:
self.showLog()
self.repaint()
except Exception: # FIXME which one?
pass
start_time = time.time()
algorithm_results = []
for count, parameters in enumerate(alg_parameters):
if feedback.isCanceled():
break
self.setProgressText(
QCoreApplication.translate(
'BatchAlgorithmDialog',
'\nProcessing algorithm {0}/{1}…').format(
count + 1, len(alg_parameters)))
self.setInfo(
self.tr('<b>Algorithm {0} starting…</b>').format(
self.algorithm().displayName()),
escapeHtml=False)
multi_feedback.setCurrentStep(count)
parameters = self.algorithm().preprocessParameters(parameters)
feedback.pushInfo(self.tr('Input parameters:'))
feedback.pushCommandInfo(pformat(parameters))
feedback.pushInfo('')
# important - we create a new context for each iteration
# this avoids holding onto resources and layers from earlier iterations,
# and allows batch processing of many more items then is possible
# if we hold on to these layers
context = dataobjects.createContext(feedback)
alg_start_time = time.time()
ret, results = execute(self.algorithm(), parameters, context,
multi_feedback)
if ret:
self.setInfo(QCoreApplication.translate(
'BatchAlgorithmDialog',
'Algorithm {0} correctly executed…').format(
self.algorithm().displayName()),
escapeHtml=False)
feedback.pushInfo(
self.tr(
'Execution completed in {0:0.2f} seconds'.format(
time.time() - alg_start_time)))
feedback.pushInfo(self.tr('Results:'))
feedback.pushCommandInfo(pformat(results))
feedback.pushInfo('')
algorithm_results.append({
'parameters': parameters,
'results': results
})
else:
break
handleAlgorithmResults(self.algorithm(), context,
multi_feedback, False, parameters)
feedback.pushInfo(
self.tr('Batch execution completed in {0:0.2f} seconds'.format(
time.time() - start_time)))
task = None
self.finish(algorithm_results)
self.cancelButton().setEnabled(False)
def processAlgorithm(self, parameters, context, feedback):
"""
Here is where the processing itself takes place.
"""
layerHandler = LayerHandler()
algRunner = AlgRunner()
layerHandler = LayerHandler()
inputLyr = self.parameterAsVectorLayer(
parameters,
self.INPUT,
context
)
onlySelected = self.parameterAsBool(
parameters,
self.SELECTED,
context
)
tol = self.parameterAsDouble(
parameters,
self.TOLERANCE,
context
)
attributeBlackList = self.parameterAsFields(
parameters,
self.ATTRIBUTE_BLACK_LIST,
context
)
ignoreVirtual = self.parameterAsBool(
parameters,
self.IGNORE_VIRTUAL_FIELDS,
context
)
ignorePK = self.parameterAsBool(
parameters,
self.IGNORE_PK_FIELDS,
context
)
layerHandler.mergeLinesOnLayer(
inputLyr,
feedback=feedback,
onlySelected=onlySelected,
ignoreVirtualFields=ignoreVirtual,
attributeBlackList=attributeBlackList,
excludePrimaryKeys=ignorePK
)
#aux layer
multiStepFeedback = QgsProcessingMultiStepFeedback(8, feedback)
multiStepFeedback.setCurrentStep(0)
if onlySelected:
multiStepFeedback.pushInfo(self.tr('Building auxiliar layer...'))
coverage = layerHandler.createAndPopulateUnifiedVectorLayer(
[inputLyr],
geomType=QgsWkbTypes.MultiPolygon,
onlySelected=onlySelected,
feedback=multiStepFeedback
)
else:
coverage = inputLyr
#dangles
multiStepFeedback.setCurrentStep(1)
multiStepFeedback.pushInfo(self.tr('Identifying dangles on {layer}...').format(layer=coverage.name()))
dangleLyr = algRunner.runIdentifyDangles(
inputLayer=coverage,
searchRadius=tol,
context=context,
feedback=multiStepFeedback,
onlySelected=False
)
#filter dangles
multiStepFeedback.setCurrentStep(2)
layerHandler.filterDangles(
dangleLyr,
tol,
feedback=multiStepFeedback
)
#snap layer to dangles
multiStepFeedback.setCurrentStep(3)
multiStepFeedback.pushInfo(self.tr('Snapping layer {layer} to dangles...').format(layer=coverage.name()))
algRunner.runSnapLayerOnLayer(
coverage,
dangleLyr,
tol,
context,
feedback=multiStepFeedback,
onlySelected=False, #this is done due to the aux layer usage
behavior=0
)
#inner layer snap
multiStepFeedback.setCurrentStep(4)
multiStepFeedback.pushInfo(self.tr('Snapping layer {layer} with {layer}...').format(layer=coverage.name()))
algRunner.runSnapLayerOnLayer(
coverage,
coverage,
tol,
context,
feedback=multiStepFeedback,
onlySelected=False, #this is done due to the aux layer usage
behavior=0
)
#clean to break lines
multiStepFeedback.setCurrentStep(5)
multiStepFeedback.pushInfo(self.tr('Running clean on {layer}...').format(layer=coverage.name()))
multiStepFeedback.pushInfo(self.tr('Running clean on unified layer...'))
cleanedCoverage, error = algRunner.runClean(
coverage,
[
algRunner.RMSA,
algRunner.Break,
algRunner.RmDupl,
algRunner.RmDangle
],
context,
returnError=True,
snap=snap,
minArea=minArea,
feedback=multiStepFeedback
)
#remove duplicated features
multiStepFeedback.setCurrentStep(6)
multiStepFeedback.pushInfo(self.tr('Removing duplicated features from {layer}...').format(layer=coverage.name()))
algRunner.runRemoveDuplicatedFeatures(
inputLyr=cleanedCoverage,
context=context,
onlySelected=False,
attributeBlackList=attributeBlackList,
excludePrimaryKeys=excludePrimaryKeys,
ignorePK=ignorePK,
ignoreVirtual=ignoreVirtual
)
#merging lines with same attributes
multiStepFeedback.setCurrentStep(6)
multiStepFeedback.pushInfo(self.tr('Merging lines from {layer} with same attribute set...').format(layer=coverage.name()))
multiStepFeedback.setCurrentStep(7)
multiStepFeedback.pushInfo(self.tr('Updating original layers...'))
layerHandler.updateOriginalLayersFromUnifiedLayer(
[inputLyr],
coverage,
feedback=multiStepFeedback,
onlySelected=onlySelected
)
return {self.INPUTLAYERS : inputLyrList}
def processAlgorithm(self, parameters, context, model_feedback):
# variables propres à Processing
feedback = QgsProcessingMultiStepFeedback(2, model_feedback)
results = {}
# entrées
lignes = self.parameterAsVectorLayer(parameters, 'lignes', context)
rasters = parameters['rasters']
# sorties
output = self.parameterAsOutputLayer(parameters, 'OUTPUT', context)
profils = []
# paramètres
echantillons_nb = parameters['echantillons_nb']
# traitement
if len(rasters) == 0:
feedback.pushInfo(
"⚠ Il est nécessaire de fournir au moins un raster en entrée.\n"
)
return {}
for ligne_f in lignes.getFeatures():
ligne_g = ligne_f.geometry()
freq = ligne_g.length() / (echantillons_nb - 1)
echantillons_g = [
QgsGeometry().fromPointXY(ligne_g.asMultiPolyline()[0][0])
]
for i in range(1, echantillons_nb - 1):
echantillons_g.append(ligne_g.interpolate(freq * i))
echantillons_g.append(QgsGeometry().fromPointXY(
ligne_g.asMultiPolyline()[0][-1]))
feedback.pushInfo(str(echantillons_g))
for raster in rasters:
elevations = []
for echantillon_g in echantillons_g:
elevation = raster.dataProvider().sample(
echantillon_g.asPoint(), 1)[0]
elevations.append(elevation)
profils.append([
echantillons_g,
ligne_f.attributes(),
raster.name(), elevations
])
feedback.setCurrentStep(1)
if feedback.isCanceled():
return {}
# écriture des données en sortie
fields = lignes.fields()
fields.append(QgsField("ordre", QVariant.Int))
fields.append(QgsField("raster", QVariant.String))
fields.append(QgsField("elevation", QVariant.Double))
writer = QgsVectorFileWriter(output, "System", fields,
QgsWkbTypes.Point,
QgsCoordinateReferenceSystem(2154),
"ESRI Shapefile")
for echantillons_g, attributes, raster_name, elevations in profils:
ordre = 0
for echantillon_g, elevation in zip(echantillons_g, elevations):
f = QgsFeature()
f.setGeometry(echantillon_g)
echantillon_att = attributes.copy()
echantillon_att.append(ordre)
echantillon_att.append(raster_name)
echantillon_att.append(elevation)
f.setAttributes(echantillon_att)
feedback.pushInfo(str(f.attributes()))
writer.addFeature(f)
ordre += 1
feedback.setCurrentStep(2)
results['OUTPUT'] = output
return results
def runAlgorithm(self):
alg_parameters = []
feedback = self.createFeedback()
load_layers = self.mainWidget().checkLoadLayersOnCompletion.isChecked()
project = QgsProject.instance() if load_layers else None
for row in range(self.mainWidget().tblParameters.rowCount()):
col = 0
parameters = {}
for param in self.algorithm().parameterDefinitions():
if param.flags() & QgsProcessingParameterDefinition.FlagHidden or param.isDestination():
continue
wrapper = self.mainWidget().wrappers[row][col]
parameters[param.name()] = wrapper.parameterValue()
if not param.checkValueIsAcceptable(wrapper.parameterValue()):
self.messageBar().pushMessage("", self.tr('Wrong or missing parameter value: {0} (row {1})').format(
param.description(), row + 1),
level=Qgis.Warning, duration=5)
return
col += 1
count_visible_outputs = 0
for out in self.algorithm().destinationParameterDefinitions():
if out.flags() & QgsProcessingParameterDefinition.FlagHidden:
continue
count_visible_outputs += 1
widget = self.mainWidget().tblParameters.cellWidget(row, col)
text = widget.getValue()
if out.checkValueIsAcceptable(text):
if isinstance(out, (QgsProcessingParameterRasterDestination,
QgsProcessingParameterVectorDestination,
QgsProcessingParameterFeatureSink)):
# load rasters and sinks on completion
parameters[out.name()] = QgsProcessingOutputLayerDefinition(text, project)
else:
parameters[out.name()] = text
col += 1
else:
self.messageBar().pushMessage("", self.tr('Wrong or missing output value: {0} (row {1})').format(
out.description(), row + 1),
level=Qgis.Warning, duration=5)
return
alg_parameters.append(parameters)
task = QgsScopedProxyProgressTask(self.tr('Batch Processing - {0}').format(self.algorithm().displayName()))
multi_feedback = QgsProcessingMultiStepFeedback(len(alg_parameters), feedback)
feedback.progressChanged.connect(task.setProgress)
with OverrideCursor(Qt.WaitCursor):
self.mainWidget().setEnabled(False)
self.cancelButton().setEnabled(True)
# Make sure the Log tab is visible before executing the algorithm
try:
self.showLog()
self.repaint()
except:
pass
start_time = time.time()
algorithm_results = []
for count, parameters in enumerate(alg_parameters):
if feedback.isCanceled():
break
self.setProgressText(QCoreApplication.translate('BatchAlgorithmDialog', '\nProcessing algorithm {0}/{1}…').format(count + 1, len(alg_parameters)))
self.setInfo(self.tr('<b>Algorithm {0} starting…</b>').format(self.algorithm().displayName()), escapeHtml=False)
multi_feedback.setCurrentStep(count)
parameters = self.algorithm().preprocessParameters(parameters)
feedback.pushInfo(self.tr('Input parameters:'))
feedback.pushCommandInfo(pformat(parameters))
feedback.pushInfo('')
# important - we create a new context for each iteration
# this avoids holding onto resources and layers from earlier iterations,
# and allows batch processing of many more items then is possible
# if we hold on to these layers
context = dataobjects.createContext(feedback)
alg_start_time = time.time()
ret, results = execute(self.algorithm(), parameters, context, multi_feedback)
if ret:
self.setInfo(QCoreApplication.translate('BatchAlgorithmDialog', 'Algorithm {0} correctly executed…').format(self.algorithm().displayName()), escapeHtml=False)
feedback.pushInfo(
self.tr('Execution completed in {0:0.2f} seconds'.format(time.time() - alg_start_time)))
feedback.pushInfo(self.tr('Results:'))
feedback.pushCommandInfo(pformat(results))
feedback.pushInfo('')
algorithm_results.append(results)
else:
break
handleAlgorithmResults(self.algorithm(), context, multi_feedback, False)
feedback.pushInfo(self.tr('Batch execution completed in {0:0.2f} seconds'.format(time.time() - start_time)))
task = None
self.finish(algorithm_results)
self.cancelButton().setEnabled(False)
def runAlgorithm(self):
alg_parameters = []
feedback = self.createFeedback()
load_layers = self.mainWidget().checkLoadLayersOnCompletion.isChecked()
project = QgsProject.instance() if load_layers else None
for row in range(self.mainWidget().batchRowCount()):
parameters = self.mainWidget().parametersForRow(row, destinationProject=project, warnOnInvalid=True)
alg_parameters.append(parameters)
task = QgsScopedProxyProgressTask(self.tr('Batch Processing - {0}').format(self.algorithm().displayName()))
multi_feedback = QgsProcessingMultiStepFeedback(len(alg_parameters), feedback)
feedback.progressChanged.connect(task.setProgress)
with OverrideCursor(Qt.WaitCursor):
self.mainWidget().setEnabled(False)
self.cancelButton().setEnabled(True)
# Make sure the Log tab is visible before executing the algorithm
try:
self.showLog()
self.repaint()
except:
pass
start_time = time.time()
algorithm_results = []
for count, parameters in enumerate(alg_parameters):
if feedback.isCanceled():
break
self.setProgressText(
QCoreApplication.translate('BatchAlgorithmDialog', '\nProcessing algorithm {0}/{1}…').format(
count + 1, len(alg_parameters)))
self.setInfo(self.tr('<b>Algorithm {0} starting…</b>').format(self.algorithm().displayName()),
escapeHtml=False)
multi_feedback.setCurrentStep(count)
parameters = self.algorithm().preprocessParameters(parameters)
feedback.pushInfo(self.tr('Input parameters:'))
feedback.pushCommandInfo(pformat(parameters))
feedback.pushInfo('')
# important - we create a new context for each iteration
# this avoids holding onto resources and layers from earlier iterations,
# and allows batch processing of many more items then is possible
# if we hold on to these layers
context = dataobjects.createContext(feedback)
alg_start_time = time.time()
ret, results = execute(self.algorithm(), parameters, context, multi_feedback)
if ret:
self.setInfo(
QCoreApplication.translate('BatchAlgorithmDialog', 'Algorithm {0} correctly executed…').format(
self.algorithm().displayName()), escapeHtml=False)
feedback.pushInfo(
self.tr('Execution completed in {0:0.2f} seconds'.format(time.time() - alg_start_time)))
feedback.pushInfo(self.tr('Results:'))
feedback.pushCommandInfo(pformat(results))
feedback.pushInfo('')
algorithm_results.append(results)
else:
break
handleAlgorithmResults(self.algorithm(), context, multi_feedback, False, parameters)
feedback.pushInfo(self.tr('Batch execution completed in {0:0.2f} seconds'.format(time.time() - start_time)))
task = None
self.finish(algorithm_results)
self.cancelButton().setEnabled(False)
