def processAlgorithm(self, parameters, context, feedback):
feedback.pushInfo(
self.tr(
"[QNEAT3Algorithm] This is a QNEAT3 Algorithm: '{}'".format(
self.displayName())))
network = self.parameterAsSource(parameters, self.INPUT,
context) #QgsProcessingFeatureSource
startPoints = self.parameterAsSource(
parameters, self.START_POINTS,
context) #QgsProcessingFeatureSource
id_field = self.parameterAsString(parameters, self.ID_FIELD,
context) #str
max_dist = self.parameterAsDouble(parameters, self.MAX_DIST,
context) #float
cell_size = self.parameterAsInt(parameters, self.CELL_SIZE,
context) #int
strategy = self.parameterAsEnum(parameters, self.STRATEGY,
context) #int
directionFieldName = self.parameterAsString(
parameters, self.DIRECTION_FIELD,
context) #str (empty if no field given)
forwardValue = self.parameterAsString(parameters, self.VALUE_FORWARD,
context) #str
backwardValue = self.parameterAsString(parameters, self.VALUE_BACKWARD,
context) #str
bothValue = self.parameterAsString(parameters, self.VALUE_BOTH,
context) #str
defaultDirection = self.parameterAsEnum(parameters,
self.DEFAULT_DIRECTION,
context) #int
speedFieldName = self.parameterAsString(parameters, self.SPEED_FIELD,
context) #str
defaultSpeed = self.parameterAsDouble(parameters, self.DEFAULT_SPEED,
context) #float
tolerance = self.parameterAsDouble(parameters, self.TOLERANCE,
context) #float
output_path = self.parameterAsOutputLayer(parameters, self.OUTPUT,
context)
analysisCrs = context.project().crs()
input_coordinates = getListOfPoints(startPoints)
feedback.pushInfo("[QNEAT3Algorithm] Building Graph...")
feedback.setProgress(10)
net = Qneat3Network(network, input_coordinates, strategy,
directionFieldName, forwardValue, backwardValue,
bothValue, defaultDirection, analysisCrs,
speedFieldName, defaultSpeed, tolerance, feedback)
feedback.setProgress(40)
list_apoints = [
Qneat3AnalysisPoint("from", feature, id_field, net,
net.list_tiedPoints[i], feedback) for i,
feature in enumerate(getFeaturesFromQgsIterable(startPoints))
]
feedback.pushInfo("[QNEAT3Algorithm] Calculating Iso-Pointcloud...")
iso_pointcloud = net.calcIsoPoints(list_apoints, max_dist)
feedback.setProgress(70)
uri = "Point?crs={}&field=vertex_id:int(254)&field=cost:double(254,7)&field=origin_point_id:string(254)&index=yes".format(
analysisCrs.authid())
iso_pointcloud_layer = QgsVectorLayer(uri, "iso_pointcloud_layer",
"memory")
iso_pointcloud_provider = iso_pointcloud_layer.dataProvider()
iso_pointcloud_provider.addFeatures(iso_pointcloud,
QgsFeatureSink.FastInsert)
feedback.pushInfo(
"[QNEAT3Algorithm] Calculating Iso-Interpolation-Raster using QGIS TIN-Interpolator..."
)
net.calcIsoTinInterpolation(iso_pointcloud_layer, cell_size,
output_path)
feedback.setProgress(99)
feedback.pushInfo("[QNEAT3Algorithm] Ending Algorithm")
feedback.setProgress(100)
results = {}
results[self.OUTPUT] = output_path
return results
def processAlgorithm(self, parameters, context, feedback):
feedback.pushInfo(
self.tr(
"[QNEAT3Algorithm] This is a QNEAT3 Algorithm: '{}'".format(
self.displayName())))
network = self.parameterAsSource(parameters, self.INPUT,
context) #QgsProcessingFeatureSource
points = self.parameterAsSource(parameters, self.POINTS,
context) #QgsProcessingFeatureSource
id_field = self.parameterAsString(parameters, self.ID_FIELD,
context) #str
strategy = self.parameterAsEnum(parameters, self.STRATEGY,
context) #int
directionFieldName = self.parameterAsString(
parameters, self.DIRECTION_FIELD,
context) #str (empty if no field given)
forwardValue = self.parameterAsString(parameters, self.VALUE_FORWARD,
context) #str
backwardValue = self.parameterAsString(parameters, self.VALUE_BACKWARD,
context) #str
bothValue = self.parameterAsString(parameters, self.VALUE_BOTH,
context) #str
defaultDirection = self.parameterAsEnum(parameters,
self.DEFAULT_DIRECTION,
context) #int
speedFieldName = self.parameterAsString(parameters, self.SPEED_FIELD,
context) #str
defaultSpeed = self.parameterAsDouble(parameters, self.DEFAULT_SPEED,
context) #float
tolerance = self.parameterAsDouble(parameters, self.TOLERANCE,
context) #float
analysisCrs = network.sourceCrs()
if analysisCrs.isGeographic():
raise QgsProcessingException(
'QNEAT3 algorithms are designed to work with projected coordinate systems. Please use a projected coordinate system (eg. UTM zones) instead of geographic coordinate systems (eg. WGS84)!'
)
if analysisCrs != points.sourceCrs():
raise QgsProcessingException(
'QNEAT3 algorithms require that all inputs to be the same projected coordinate reference system (including project coordinate system).'
)
feedback.pushInfo("[QNEAT3Algorithm] Building Graph...")
net = Qneat3Network(network, points, strategy, directionFieldName,
forwardValue, backwardValue, bothValue,
defaultDirection, analysisCrs, speedFieldName,
defaultSpeed, tolerance, feedback)
list_analysis_points = [
Qneat3AnalysisPoint("point", feature, id_field, net,
net.list_tiedPoints[i], feedback)
for i, feature in enumerate(
getFeaturesFromQgsIterable(net.input_points))
]
feat = QgsFeature()
fields = QgsFields()
output_id_field_data_type = getFieldDatatype(points, id_field)
fields.append(
QgsField('InputID', output_id_field_data_type, '', 254, 0))
fields.append(
QgsField('TargetID', output_id_field_data_type, '', 254, 0))
fields.append(QgsField('entry_cost', QVariant.Double, '', 20, 7))
fields.append(QgsField('network_cost', QVariant.Double, '', 20, 7))
fields.append(QgsField('exit_cost', QVariant.Double, '', 20, 7))
fields.append(QgsField('total_cost', QVariant.Double, '', 20, 7))
feat.setFields(fields)
(sink, dest_id) = self.parameterAsSink(parameters, self.OUTPUT,
context, fields,
QgsWkbTypes.LineString,
network.sourceCrs())
total_workload = float(pow(len(list_analysis_points), 2))
feedback.pushInfo(
"[QNEAT3Algorithm] Expecting total workload of {} iterations".
format(int(total_workload)))
current_workstep_number = 0
for start_point in list_analysis_points:
#optimize in case of undirected (not necessary to call calcDijkstra as it has already been calculated - can be replaced by reading from list)
dijkstra_query = net.calcDijkstra(start_point.network_vertex_id, 0)
for query_point in list_analysis_points:
if (current_workstep_number % 1000) == 0:
feedback.pushInfo(
"[QNEAT3Algorithm] {} OD-pairs processed...".format(
current_workstep_number))
if query_point.point_id == start_point.point_id:
feat['InputID'] = start_point.point_id
feat['TargetID'] = query_point.point_id
feat['entry_cost'] = 0.0
feat['network_cost'] = 0.0
feat['exit_cost'] = 0.0
feat['total_cost'] = 0.0
sink.addFeature(feat, QgsFeatureSink.FastInsert)
elif dijkstra_query[0][query_point.network_vertex_id] == -1:
feat['InputID'] = start_point.point_id
feat['TargetID'] = query_point.point_id
#do not populate cost field so that it defaults to null
sink.addFeature(feat, QgsFeatureSink.FastInsert)
else:
network_cost = dijkstra_query[1][
query_point.network_vertex_id]
feat.setGeometry(
QgsGeometry.fromPolylineXY(
[start_point.point_geom, query_point.point_geom]))
feat['InputID'] = start_point.point_id
feat['TargetID'] = query_point.point_id
feat['entry_cost'] = start_point.entry_cost
feat['network_cost'] = network_cost
feat['exit_cost'] = query_point.entry_cost
feat[
'total_cost'] = network_cost + start_point.entry_cost + query_point.entry_cost
sink.addFeature(feat, QgsFeatureSink.FastInsert)
current_workstep_number = current_workstep_number + 1
feedback.setProgress(
(current_workstep_number / total_workload) * 100)
feedback.pushInfo(
"[QNEAT3Algorithm] Total number of OD-pairs processed: {}".format(
current_workstep_number))
feedback.pushInfo("[QNEAT3Algorithm] Ending Algorithm")
results = {}
results[self.OUTPUT] = dest_id
return results
def processAlgorithm(self, parameters, context, feedback):
feedback.pushInfo(
self.tr(
"[QNEAT3Algorithm] This is a QNEAT3 Algorithm: '{}'".format(
self.displayName())))
network = self.parameterAsSource(parameters, self.INPUT,
context) #QgsProcessingFeatureSource
startPoints = self.parameterAsSource(
parameters, self.START_POINTS,
context) #QgsProcessingFeatureSource
id_field = self.parameterAsString(parameters, self.ID_FIELD,
context) #str
max_dist = self.parameterAsDouble(parameters, self.MAX_DIST,
context) #float
strategy = self.parameterAsEnum(parameters, self.STRATEGY,
context) #int
entry_cost_calc_method = self.parameterAsEnum(
parameters, self.ENTRY_COST_CALCULATION_METHOD, context) #int
directionFieldName = self.parameterAsString(
parameters, self.DIRECTION_FIELD,
context) #str (empty if no field given)
forwardValue = self.parameterAsString(parameters, self.VALUE_FORWARD,
context) #str
backwardValue = self.parameterAsString(parameters, self.VALUE_BACKWARD,
context) #str
bothValue = self.parameterAsString(parameters, self.VALUE_BOTH,
context) #str
defaultDirection = self.parameterAsEnum(parameters,
self.DEFAULT_DIRECTION,
context) #int
speedFieldName = self.parameterAsString(parameters, self.SPEED_FIELD,
context) #str
defaultSpeed = self.parameterAsDouble(parameters, self.DEFAULT_SPEED,
context) #float
tolerance = self.parameterAsDouble(parameters, self.TOLERANCE,
context) #float
analysisCrs = network.sourceCrs()
input_coordinates = getListOfPoints(startPoints)
feedback.pushInfo("[QNEAT3Algorithm] Building Graph...")
feedback.setProgress(10)
net = Qneat3Network(network, input_coordinates, strategy,
directionFieldName, forwardValue, backwardValue,
bothValue, defaultDirection, analysisCrs,
speedFieldName, defaultSpeed, tolerance, feedback)
feedback.setProgress(40)
list_apoints = [
Qneat3AnalysisPoint("from", feature, id_field, net,
net.list_tiedPoints[i], entry_cost_calc_method,
feedback) for i, feature in enumerate(
getFeaturesFromQgsIterable(startPoints))
]
fields = QgsFields()
fields.append(QgsField('vertex_id', QVariant.Int, '', 254, 0))
fields.append(QgsField('cost', QVariant.Double, '', 254, 7))
fields.append(
QgsField('origin_point_id',
getFieldDatatype(startPoints, id_field)))
(sink, dest_id) = self.parameterAsSink(parameters, self.OUTPUT,
context, fields,
QgsWkbTypes.Point,
network.sourceCrs())
feedback.pushInfo("[QNEAT3Algorithm] Calculating Iso-Pointcloud...")
iso_pointcloud = net.calcIsoPoints(list_apoints, max_dist)
feedback.setProgress(90)
sink.addFeatures(iso_pointcloud, QgsFeatureSink.FastInsert)
feedback.pushInfo("[QNEAT3Algorithm] Ending Algorithm")
feedback.setProgress(100)
results = {}
results[self.OUTPUT] = dest_id
return results
def processAlgorithm(self, parameters, context, feedback):
feedback.pushInfo(self.tr("[QNEAT3Algorithm] This is a QNEAT3 Algorithm: '{}'".format(self.displayName())))
network = self.parameterAsSource(parameters, self.INPUT, context) #QgsProcessingFeatureSource
startPoints = self.parameterAsSource(parameters, self.START_POINTS, context) #QgsProcessingFeatureSource
id_field = self.parameterAsString(parameters, self.ID_FIELD, context) #str
interval = self.parameterAsDouble(parameters, self.INTERVAL, context)#float
max_dist = self.parameterAsDouble(parameters, self.MAX_DIST, context)#float
cell_size = self.parameterAsInt(parameters, self.CELL_SIZE, context)#int
strategy = self.parameterAsEnum(parameters, self.STRATEGY, context) #int
directionFieldName = self.parameterAsString(parameters, self.DIRECTION_FIELD, context) #str (empty if no field given)
forwardValue = self.parameterAsString(parameters, self.VALUE_FORWARD, context) #str
backwardValue = self.parameterAsString(parameters, self.VALUE_BACKWARD, context) #str
bothValue = self.parameterAsString(parameters, self.VALUE_BOTH, context) #str
defaultDirection = self.parameterAsEnum(parameters, self.DEFAULT_DIRECTION, context) #int
speedFieldName = self.parameterAsString(parameters, self.SPEED_FIELD, context) #str
defaultSpeed = self.parameterAsDouble(parameters, self.DEFAULT_SPEED, context) #float
tolerance = self.parameterAsDouble(parameters, self.TOLERANCE, context) #float
output_path = self.parameterAsOutputLayer(parameters, self.OUTPUT_INTERPOLATION, context) #string
analysisCrs = network.sourceCrs()
input_coordinates = getListOfPoints(startPoints)
if analysisCrs.isGeographic():
raise QgsProcessingException('QNEAT3 algorithms are designed to work with projected coordinate systems. Please use a projected coordinate system (eg. UTM zones) instead of geographic coordinate systems (eg. WGS84)!')
if analysisCrs != startPoints.sourceCrs():
raise QgsProcessingException('QNEAT3 algorithms require that all inputs to be the same projected coordinate reference system (including project coordinate system).')
feedback.pushInfo("[QNEAT3Algorithm] Building Graph...")
feedback.setProgress(10)
net = Qneat3Network(network, input_coordinates, strategy, directionFieldName, forwardValue, backwardValue, bothValue, defaultDirection, analysisCrs, speedFieldName, defaultSpeed, tolerance, feedback)
feedback.setProgress(40)
list_apoints = [Qneat3AnalysisPoint("from", feature, id_field, net, net.list_tiedPoints[i], feedback) for i, feature in enumerate(getFeaturesFromQgsIterable(startPoints))]
feedback.pushInfo("[QNEAT3Algorithm] Calculating Iso-Pointcloud...")
iso_pointcloud = net.calcIsoPoints(list_apoints, max_dist+(max_dist*0.1))
feedback.setProgress(50)
uri = "Point?crs={}&field=vertex_id:int(254)&field=cost:double(254,7)&field=origin_point_id:string(254)&index=yes".format(analysisCrs.authid())
iso_pointcloud_layer = QgsVectorLayer(uri, "iso_pointcloud_layer", "memory")
iso_pointcloud_provider = iso_pointcloud_layer.dataProvider()
iso_pointcloud_provider.addFeatures(iso_pointcloud, QgsFeatureSink.FastInsert)
feedback.pushInfo("[QNEAT3Algorithm] Calculating Iso-Interpolation-Raster using QGIS TIN-Interpolator...")
net.calcIsoTinInterpolation(iso_pointcloud_layer, cell_size, output_path)
feedback.setProgress(70)
fields = QgsFields()
fields.append(QgsField('id', QVariant.Int, '', 254, 0))
fields.append(QgsField('cost_level', QVariant.Double, '', 20, 7))
(sink, dest_id) = self.parameterAsSink(parameters, self.OUTPUT_CONTOURS, context, fields, QgsWkbTypes.LineString, network.sourceCrs())
feedback.pushInfo("[QNEAT3Algorithm] Calculating Iso-Contours using numpy and matplotlib...")
contour_featurelist = net.calcIsoContours(max_dist, interval, output_path)
feedback.setProgress(90)
sink.addFeatures(contour_featurelist, QgsFeatureSink.FastInsert)
feedback.pushInfo("[QNEAT3Algorithm] Ending Algorithm")
feedback.setProgress(100)
results = {}
results[self.OUTPUT_INTERPOLATION] = output_path
results[self.OUTPUT_CONTOURS] = dest_id
return results
def processAlgorithm(self, parameters, context, feedback):
feedback.pushInfo(self.tr('This is a QNEAT Algorithm'))
network = self.parameterAsSource(parameters, self.INPUT, context) #QgsProcessingFeatureSource
points = self.parameterAsSource(parameters, self.POINTS, context) #QgsProcessingFeatureSource
id_field = self.parameterAsString(parameters, self.ID_FIELD, context) #str
strategy = self.parameterAsEnum(parameters, self.STRATEGY, context) #int
directionFieldName = self.parameterAsString(parameters, self.DIRECTION_FIELD, context) #str (empty if no field given)
forwardValue = self.parameterAsString(parameters, self.VALUE_FORWARD, context) #str
backwardValue = self.parameterAsString(parameters, self.VALUE_BACKWARD, context) #str
bothValue = self.parameterAsString(parameters, self.VALUE_BOTH, context) #str
defaultDirection = self.parameterAsEnum(parameters, self.DEFAULT_DIRECTION, context) #int
speedFieldName = self.parameterAsString(parameters, self.SPEED_FIELD, context) #str
defaultSpeed = self.parameterAsDouble(parameters, self.DEFAULT_SPEED, context) #float
tolerance = self.parameterAsDouble(parameters, self.TOLERANCE, context) #float
analysisCrs = context.project().crs()
net = Qneat3Network(network, points, strategy, directionFieldName, forwardValue, backwardValue, bothValue, defaultDirection, analysisCrs, speedFieldName, defaultSpeed, tolerance, feedback)
list_analysis_points = [Qneat3AnalysisPoint("point", feature, id_field, net.network, net.list_tiedPoints[i]) for i, feature in enumerate(getFeaturesFromQgsIterable(net.input_points))]
feat = QgsFeature()
fields = QgsFields()
output_id_field_data_type = getFieldDatatype(points, id_field)
fields.append(QgsField('origin_id', output_id_field_data_type, '', 254, 0))
fields.append(QgsField('destination_id', output_id_field_data_type, '', 254, 0))
fields.append(QgsField('network_cost', QVariant.Double, '', 20, 7))
feat.setFields(fields)
(sink, dest_id) = self.parameterAsSink(parameters, self.OUTPUT, context,
fields, QgsWkbTypes.NoGeometry, network.sourceCrs())
total_workload = float(pow(len(list_analysis_points),2))
feedback.pushInfo("Expecting total workload of {} iterations".format(int(total_workload)))
current_workstep_number = 0
for start_point in list_analysis_points:
#optimize in case of undirected (not necessary to call calcDijkstra as it has already been calculated - can be replaced by reading from list)
dijkstra_query = net.calcDijkstra(start_point.network_vertex_id, 0)
for query_point in list_analysis_points:
if (current_workstep_number%1000)==0:
feedback.pushInfo("{} OD-pairs processed...".format(current_workstep_number))
if query_point.point_id == start_point.point_id:
feat['origin_id'] = start_point.point_id
feat['destination_id'] = query_point.point_id
feat['network_cost'] = 0.0
sink.addFeature(feat, QgsFeatureSink.FastInsert)
elif dijkstra_query[0][query_point.network_vertex_id] == -1:
feat['origin_id'] = start_point.point_id
feat['destination_id'] = query_point.point_id
#do not populate cost field so that it defaults to null
sink.addFeature(feat, QgsFeatureSink.FastInsert)
else:
entry_cost = start_point.calcEntryCost(strategy)+query_point.calcEntryCost(strategy)
total_cost = dijkstra_query[1][query_point.network_vertex_id]+entry_cost
feat['origin_id'] = start_point.point_id
feat['destination_id'] = query_point.point_id
feat['network_cost'] = total_cost
sink.addFeature(feat, QgsFeatureSink.FastInsert)
current_workstep_number=current_workstep_number+1
feedback.setProgress(current_workstep_number/total_workload)
feedback.pushInfo("Total number of OD-pairs processed: {}".format(current_workstep_number))
feedback.pushInfo("Initialization Done")
feedback.pushInfo("Ending Algorithm")
results = {}
results[self.OUTPUT] = dest_id
return results
def processAlgorithm(self, parameters, context, feedback):
feedback.pushInfo(
self.tr(
"[QNEAT3Algorithm] This is a QNEAT3 Algorithm: '{}'".format(
self.displayName())))
network = self.parameterAsSource(parameters, self.INPUT,
context) #QgsProcessingFeatureSource
from_points = self.parameterAsSource(
parameters, self.FROM_POINT_LAYER,
context) #QgsProcessingFeatureSource
from_id_field = self.parameterAsString(parameters, self.FROM_ID_FIELD,
context) #str
to_points = self.parameterAsSource(parameters, self.TO_POINT_LAYER,
context)
to_id_field = self.parameterAsString(parameters, self.TO_ID_FIELD,
context)
strategy = self.parameterAsEnum(parameters, self.STRATEGY,
context) #int
entry_cost_calc_method = self.parameterAsEnum(
parameters, self.ENTRY_COST_CALCULATION_METHOD, context) #int
directionFieldName = self.parameterAsString(
parameters, self.DIRECTION_FIELD,
context) #str (empty if no field given)
forwardValue = self.parameterAsString(parameters, self.VALUE_FORWARD,
context) #str
backwardValue = self.parameterAsString(parameters, self.VALUE_BACKWARD,
context) #str
bothValue = self.parameterAsString(parameters, self.VALUE_BOTH,
context) #str
defaultDirection = self.parameterAsEnum(parameters,
self.DEFAULT_DIRECTION,
context) #int
speedFieldName = self.parameterAsString(parameters, self.SPEED_FIELD,
context) #str
defaultSpeed = self.parameterAsDouble(parameters, self.DEFAULT_SPEED,
context) #float
tolerance = self.parameterAsDouble(parameters, self.TOLERANCE,
context) #float
analysisCrs = network.sourceCrs()
#Points of both layers have to be merged into one layer --> then tied to the Qneat3Network
#get point list of from layer
from_coord_list = getListOfPoints(from_points)
from_coord_list_length = len(from_coord_list)
to_coord_list = getListOfPoints(to_points)
merged_coords = from_coord_list + to_coord_list
feedback.pushInfo("[QNEAT3Algorithm] Building Graph...")
net = Qneat3Network(network, merged_coords, strategy,
directionFieldName, forwardValue, backwardValue,
bothValue, defaultDirection, analysisCrs,
speedFieldName, defaultSpeed, tolerance, feedback)
#read the merged point-list seperately for the two layers --> index at the first element of the second layer begins at len(firstLayer) and gets added the index of the current point of layer b.
list_from_apoints = [
Qneat3AnalysisPoint("from", feature, from_id_field, net,
net.list_tiedPoints[i], entry_cost_calc_method,
feedback) for i, feature in enumerate(
getFeaturesFromQgsIterable(from_points))
]
list_to_apoints = [
Qneat3AnalysisPoint(
"to", feature, to_id_field, net,
net.list_tiedPoints[from_coord_list_length + i],
entry_cost_calc_method, feedback)
for i, feature in enumerate(getFeaturesFromQgsIterable(to_points))
]
feat = QgsFeature()
fields = QgsFields()
output_id_field_data_type = getFieldDatatype(from_points,
from_id_field)
fields.append(
QgsField('origin_id', output_id_field_data_type, '', 254, 0))
fields.append(
QgsField('destination_id', output_id_field_data_type, '', 254, 0))
fields.append(QgsField('entry_cost', QVariant.Double, '', 20, 7))
fields.append(QgsField('network_cost', QVariant.Double, '', 20, 7))
fields.append(QgsField('exit_cost', QVariant.Double, '', 20, 7))
fields.append(QgsField('total_cost', QVariant.Double, '', 20, 7))
feat.setFields(fields)
(sink, dest_id) = self.parameterAsSink(parameters, self.OUTPUT,
context, fields,
QgsWkbTypes.LineString,
network.sourceCrs())
total_workload = float(len(from_coord_list) * len(to_coord_list))
feedback.pushInfo(
"[QNEAT3Algorithm] Expecting total workload of {} iterations".
format(int(total_workload)))
current_workstep_number = 0
for start_point in list_from_apoints:
#optimize in case of undirected (not necessary to call calcDijkstra as it has already been calculated - can be replaced by reading from list)
dijkstra_query = net.calcDijkstra(start_point.network_vertex_id, 0)
for query_point in list_to_apoints:
if (current_workstep_number % 1000) == 0:
feedback.pushInfo(
"[QNEAT3Algorithm] {} OD-pairs processed...".format(
current_workstep_number))
if dijkstra_query[0][query_point.network_vertex_id] == -1:
feat['origin_id'] = start_point.point_id
feat['destination_id'] = query_point.point_id
feat['entry_cost'] = None
feat['network_cost'] = None
feat['exit_cost'] = None
feat['total_cost'] = None
sink.addFeature(feat, QgsFeatureSink.FastInsert)
else:
entry_cost = start_point.entry_cost
network_cost = dijkstra_query[1][
query_point.network_vertex_id]
exit_cost = query_point.entry_cost
total_cost = network_cost + entry_cost + exit_cost
feat.setGeometry(
QgsGeometry.fromPolylineXY(
[start_point.point_geom, query_point.point_geom]))
feat['origin_id'] = start_point.point_id
feat['destination_id'] = query_point.point_id
feat['entry_cost'] = entry_cost
feat['network_cost'] = network_cost
feat['exit_cost'] = exit_cost
feat['total_cost'] = total_cost
sink.addFeature(feat, QgsFeatureSink.FastInsert)
current_workstep_number = current_workstep_number + 1
feedback.setProgress(
(current_workstep_number / total_workload) * 100)
feedback.pushInfo(
"[QNEAT3Algorithm] Total number of OD-pairs processed: {}".format(
current_workstep_number))
feedback.pushInfo("[QNEAT3Algorithm] Ending Algorithm")
results = {}
results[self.OUTPUT] = dest_id
return results
def processAlgorithm(self, parameters, context, feedback):
feedback.pushInfo(self.tr('This is a QNEAT Algorithm'))
network = self.parameterAsSource(parameters, self.INPUT, context) #QgsProcessingFeatureSource
points = self.parameterAsSource(parameters, self.POINTS, context) #QgsProcessingFeatureSource
id_field = self.parameterAsString(parameters, self.ID_FIELD, context) #str
strategy = self.parameterAsEnum(parameters, self.STRATEGY, context) #int
directionFieldName = self.parameterAsString(parameters, self.DIRECTION_FIELD, context) #str (empty if no field given)
forwardValue = self.parameterAsString(parameters, self.VALUE_FORWARD, context) #str
backwardValue = self.parameterAsString(parameters, self.VALUE_BACKWARD, context) #str
bothValue = self.parameterAsString(parameters, self.VALUE_BOTH, context) #str
defaultDirection = self.parameterAsEnum(parameters, self.DEFAULT_DIRECTION, context) #int
speedFieldName = self.parameterAsString(parameters, self.SPEED_FIELD, context) #str
defaultSpeed = self.parameterAsDouble(parameters, self.DEFAULT_SPEED, context) #float
tolerance = self.parameterAsDouble(parameters, self.TOLERANCE, context) #float
output_path = self.parameterAsFileOutput(parameters, self.OUTPUT, context) #str (filepath)
feedback.pushInfo(pluginPath)
analysisCrs = context.project().crs()
net = Qneat3Network(network, points, strategy, directionFieldName, forwardValue, backwardValue, bothValue, defaultDirection, analysisCrs, speedFieldName, defaultSpeed, tolerance, feedback)
list_analysis_points = [Qneat3AnalysisPoint("point", feature, id_field, net.network, net.list_tiedPoints[i]) for i, feature in enumerate(getFeaturesFromQgsIterable(net.input_points))]
total_workload = float(pow(len(list_analysis_points),2))
feedback.pushInfo("Expecting total workload of {} iterations".format(int(total_workload)))
with open(output_path, 'w', newline='') as csvfile:
csv_writer = csv.writer(csvfile, delimiter=';',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
#write header
csv_writer.writerow(["origin_id","destination_id","cost"])
current_workstep_number = 0
for start_point in list_analysis_points:
#optimize in case of undirected (not necessary to call calcDijkstra as it has already been calculated - can be replaced by reading from list)
dijkstra_query = net.calcDijkstra(start_point.network_vertex_id, 0)
for query_point in list_analysis_points:
if (current_workstep_number%1000)==0:
feedback.pushInfo("{} OD-pairs processed...".format(current_workstep_number))
if query_point.point_id == start_point.point_id:
csv_writer.writerow([start_point.point_id, query_point.point_id, float(0)])
elif dijkstra_query[0][query_point.network_vertex_id] == -1:
csv_writer.writerow([start_point.point_id, query_point.point_id, None])
else:
entry_cost = start_point.calcEntryCost(strategy)+query_point.calcEntryCost(strategy)
total_cost = dijkstra_query[1][query_point.network_vertex_id]+entry_cost
csv_writer.writerow([start_point.point_id, query_point.point_id, total_cost])
current_workstep_number=current_workstep_number+1
feedback.setProgress(current_workstep_number/total_workload)
feedback.pushInfo("Total number of OD-pairs processed: {}".format(current_workstep_number))
feedback.pushInfo("Initialization Done")
feedback.pushInfo("Ending Algorithm")
results = {self.OUTPUT: output_path}
return results
def processAlgorithm(self, parameters, context, feedback):
feedback.pushInfo(
self.tr(
"[QNEAT3Algorithm] This is a QNEAT3 Algorithm: '{}'".format(
self.displayName())))
network = self.parameterAsSource(parameters, self.INPUT,
context) #QgsProcessingFeatureSource
startPoint = self.parameterAsPoint(parameters,
self.START_POINT, context,
network.sourceCrs()) #QgsPointXY
max_dist = self.parameterAsDouble(parameters, self.MAX_DIST,
context) #float
cell_size = self.parameterAsInt(parameters, self.CELL_SIZE,
context) #int
strategy = self.parameterAsEnum(parameters, self.STRATEGY,
context) #int
interpolation_method = self.parameterasEnum(parameters, self.METHOD,
context) #int
entry_cost_calc_method = self.parameterAsEnum(
parameters, self.ENTRY_COST_CALCULATION_METHOD, context) #int
directionFieldName = self.parameterAsString(
parameters, self.DIRECTION_FIELD,
context) #str (empty if no field given)
forwardValue = self.parameterAsString(parameters, self.VALUE_FORWARD,
context) #str
backwardValue = self.parameterAsString(parameters, self.VALUE_BACKWARD,
context) #str
bothValue = self.parameterAsString(parameters, self.VALUE_BOTH,
context) #str
defaultDirection = self.parameterAsEnum(parameters,
self.DEFAULT_DIRECTION,
context) #int
speedFieldName = self.parameterAsString(parameters, self.SPEED_FIELD,
context) #str
defaultSpeed = self.parameterAsDouble(parameters, self.DEFAULT_SPEED,
context) #float
tolerance = self.parameterAsDouble(parameters, self.TOLERANCE,
context) #float
output_path = self.parameterAsOutputLayer(parameters, self.OUTPUT,
context)
analysisCrs = network.sourceCrs()
input_coordinates = [startPoint]
input_point = getFeatureFromPointParameter(startPoint)
feedback.pushInfo("[QNEAT3Algorithm] Building Graph...")
feedback.setProgress(10)
net = Qneat3Network(network, input_coordinates, strategy,
directionFieldName, forwardValue, backwardValue,
bothValue, defaultDirection, analysisCrs,
speedFieldName, defaultSpeed, tolerance, feedback)
feedback.setProgress(40)
analysis_point = Qneat3AnalysisPoint("point", input_point, "point_id",
net, net.list_tiedPoints[0],
entry_cost_calc_method, feedback)
feedback.pushInfo("[QNEAT3Algorithm] Calculating Iso-Pointcloud...")
iso_pointcloud = net.calcIsoPoints([analysis_point], max_dist)
feedback.setProgress(70)
uri = "Point?crs={}&field=vertex_id:int(254)&field=cost:double(254,7)&field=origin_point_id:string(254)&index=yes".format(
analysisCrs.authid())
iso_pointcloud_layer = QgsVectorLayer(uri, "iso_pointcloud_layer",
"memory")
iso_pointcloud_provider = iso_pointcloud_layer.dataProvider()
iso_pointcloud_provider.addFeatures(iso_pointcloud,
QgsFeatureSink.FastInsert)
feedback.pushInfo(
"[QNEAT3Algorithm] Calculating Iso-Interpolation-Raster using QGIS TIN-Interpolator..."
)
if interpolation_method == 0:
feedback.pushInfo(
"[QNEAT3Algorithm] Calculating Iso-Interpolation-Raster using QGIS TIN-Interpolator..."
)
net.calcIsoTinInterpolation(iso_pointcloud_layer, cell_size,
output_path)
feedback.setProgress(99)
else:
#implement spatial index for lines (closest line, etc...)
spt_idx = QgsSpatialIndex(
iso_pointcloud_layer.getFeatures(QgsFeatureRequest()),
self.feedback)
#prepare numpy coordinate grids
NoData_value = -9999
raster_rectangle = iso_pointcloud_layer.extent()
#top left point
xmin = raster_rectangle.xMinimum()
ymin = raster_rectangle.yMinimum()
xmax = raster_rectangle.xMaximum()
ymax = raster_rectangle.yMaximum()
cols = int((xmax - xmin) / cell_size)
rows = int((ymax - ymin) / cell_size)
output_interpolation_raster = gdal.GetDriverByName('GTiff').Create(
output_path, cols, rows, 1, gdal.GDT_Float64)
output_interpolation_raster.SetGeoTransform(
(xmin, cell_size, 0, ymax, 0, -cell_size))
band = output_interpolation_raster.GetRasterBand(1)
band.SetNoDataValue(NoData_value)
#initialize zero array with 2 dimensions (according to rows and cols)
raster_routingcost_data = zeros(shape=(rows, cols))
#compute raster cell MIDpoints
x_pos = linspace(xmin + (cell_size / 2), xmax - (cell_size / 2),
raster_routingcost_data.shape[1])
y_pos = linspace(ymax - (cell_size / 2), ymin + (cell_size / 2),
raster_routingcost_data.shape[0])
x_grid, y_grid = meshgrid(x_pos, y_pos)
gridpoint_list = array(
list(zip(x_grid.flatten(), y_grid.flatten())))
list_cellpoints_interpolation = [
QgsPointXY(coords[0], coords[1]) for coords in gridpoint_list
]
list_cellpoints_interpolation.insert(0, startPoint)
iso_net = Qneat3Network(network, list_cellpoints_interpolation,
strategy, directionFieldName, forwardValue,
backwardValue, bothValue, defaultDirection,
analysisCrs, speedFieldName, defaultSpeed,
tolerance, feedback)
iso_analysis_start_point = Qneat3AnalysisPoint(
"point", input_point, "point_id", iso_net,
iso_net.list_tiedPoints[0], entry_cost_calc_method, feedback)
#add 1 because of iso_analysis_start_point that is prepended
list_to_apoints = [
Qneat3AnalysisPoint("to", feature, "vertex_id", iso_net,
iso_net.list_tiedPoints[1 + i],
entry_cost_calc_method, feedback)
for i, feature in enumerate(
getFeaturesFromQgsIterable(iso_pointcloud_layer))
]
total_workload = float(len(list_to_apoints))
current_point_index = 0
#raster data indices
dijkstra_query = net.calcDijkstra(
iso_analysis_start_point.network_vertex_id, 0)
i = 0
while i < len(raster_routingcost_data):
j = 0
while j < len(raster_routingcost_data[i]):
if (current_point_index % 1000) == 0:
feedback.pushInfo(
"[QNEAT3Algorithm] {} cells interpolated...".
format(current_point_index))
if dijkstra_query[0][list_to_apoints[current_point_index].
network_vertex_id] == -1:
#write nodata to raster
raster_routingcost_data[i][j] = -9999
else:
network_cost = dijkstra_query[1][list_to_apoints[
current_point_index].network_vertex_id]
raster_routingcost_data[i][
j] = iso_analysis_start_point.entry_cost + network_cost + list_to_apoints[
current_point_index].entry_cost
current_point_index = current_point_index + 1
feedback.setProgress(
(current_point_index / total_workload) * 100)
j = j + 1
i = i + 1
band.WriteArray(raster_routingcost_data)
outRasterSRS = osr.SpatialReference()
outRasterSRS.ImportFromWkt(self.AnalysisCrs.toWkt())
output_interpolation_raster.SetProjection(
outRasterSRS.ExportToWkt())
band.FlushCache()
feedback.pushInfo("[QNEAT3Algorithm] Ending Algorithm")
feedback.setProgress(100)
results = {}
results[self.OUTPUT] = output_path
return results
def processAlgorithm(self, parameters, context, feedback):
feedback.pushInfo(
self.tr(
"[QNEAT3Algorithm] This is a QNEAT3 Algorithm: '{}'".format(
self.displayName())))
network = self.parameterAsSource(parameters, self.INPUT,
context) #QgsProcessingFeatureSource
points = self.parameterAsSource(parameters, self.POINTS,
context) #QgsProcessingFeatureSource
id_field = self.parameterAsString(parameters, self.ID_FIELD,
context) #str
strategy = self.parameterAsEnum(parameters, self.STRATEGY,
context) #int
directionFieldName = self.parameterAsString(
parameters, self.DIRECTION_FIELD,
context) #str (empty if no field given)
forwardValue = self.parameterAsString(parameters, self.VALUE_FORWARD,
context) #str
backwardValue = self.parameterAsString(parameters, self.VALUE_BACKWARD,
context) #str
bothValue = self.parameterAsString(parameters, self.VALUE_BOTH,
context) #str
defaultDirection = self.parameterAsEnum(parameters,
self.DEFAULT_DIRECTION,
context) #int
speedFieldName = self.parameterAsString(parameters, self.SPEED_FIELD,
context) #str
defaultSpeed = self.parameterAsDouble(parameters, self.DEFAULT_SPEED,
context) #float
tolerance = self.parameterAsDouble(parameters, self.TOLERANCE,
context) #float
output_path = self.parameterAsFileOutput(parameters, self.OUTPUT,
context) #str (filepath)
feedback.pushInfo(pluginPath)
analysisCrs = network.sourceCrs()
if analysisCrs.isGeographic():
raise QgsProcessingException(
'QNEAT3 algorithms are designed to work with projected coordinate systems. Please use a projected coordinate system (eg. UTM zones) instead of geographic coordinate systems (eg. WGS84)!'
)
if analysisCrs != points.sourceCrs():
raise QgsProcessingException(
'QNEAT3 algorithms require that all inputs to be the same projected coordinate reference system (including project coordinate system).'
)
feedback.pushInfo("[QNEAT3Algorithm] Building Graph...")
net = Qneat3Network(network, points, strategy, directionFieldName,
forwardValue, backwardValue, bothValue,
defaultDirection, analysisCrs, speedFieldName,
defaultSpeed, tolerance, feedback)
list_analysis_points = [
Qneat3AnalysisPoint("point", feature, id_field, net,
net.list_tiedPoints[i], feedback)
for i, feature in enumerate(
getFeaturesFromQgsIterable(net.input_points))
]
total_workload = float(pow(len(list_analysis_points), 2))
feedback.pushInfo(
"[QNEAT3Algorithm] Expecting total workload of {} iterations".
format(int(total_workload)))
with open(output_path, 'w', newline='') as csvfile:
csv_writer = csv.writer(csvfile,
delimiter=';',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
#write header
csv_writer.writerow([
"InputID", "TargetID", "entry_cost", "network_cost",
"exit_cost", "total_cost"
])
current_workstep_number = 0
for start_point in list_analysis_points:
#optimize in case of undirected (not necessary to call calcDijkstra as it has already been calculated - can be replaced by reading from list)
dijkstra_query = net.calcDijkstra(
start_point.network_vertex_id, 0)
for query_point in list_analysis_points:
if (current_workstep_number % 1000) == 0:
feedback.pushInfo(
"[QNEAT3Algorithm] {} OD-pairs processed...".
format(current_workstep_number))
if query_point.point_id == start_point.point_id:
csv_writer.writerow([
start_point.point_id, query_point.point_id,
float(0)
])
elif dijkstra_query[0][
query_point.network_vertex_id] == -1:
csv_writer.writerow(
[start_point.point_id, query_point.point_id, None])
else:
entry_cost = start_point.entry_cost
network_cost = dijkstra_query[1][
query_point.network_vertex_id]
exit_cost = query_point.entry_cost
total_cost = entry_cost + network_cost + exit_cost
csv_writer.writerow([
start_point.point_id, query_point.point_id,
entry_cost, network_cost, exit_cost, total_cost
])
current_workstep_number = current_workstep_number + 1
feedback.setProgress(
(current_workstep_number / total_workload) * 100)
feedback.pushInfo(
"[QNEAT3Algorithm] Total number of OD-pairs processed: {}".
format(current_workstep_number))
feedback.pushInfo("[QNEAT3Algorithm] Ending Algorithm")
results = {self.OUTPUT: output_path}
return results
def processAlgorithm(self, parameters, context, feedback):
feedback.pushInfo(
self.tr(
"[QNEAT3Algorithm] This is a QNEAT3 Algorithm: '{}'".format(
self.displayName())))
network = self.parameterAsSource(parameters, self.INPUT,
context) #QgsProcessingFeatureSource
startPoints = self.parameterAsSource(
parameters, self.START_POINTS,
context) #QgsProcessingFeatureSource
id_field = self.parameterAsString(parameters, self.ID_FIELD,
context) #str
max_dist = self.parameterAsDouble(parameters, self.MAX_DIST,
context) #float
strategy = self.parameterAsEnum(parameters, self.STRATEGY,
context) #int
directionFieldName = self.parameterAsString(
parameters, self.DIRECTION_FIELD,
context) #str (empty if no field given)
forwardValue = self.parameterAsString(parameters, self.VALUE_FORWARD,
context) #str
backwardValue = self.parameterAsString(parameters, self.VALUE_BACKWARD,
context) #str
bothValue = self.parameterAsString(parameters, self.VALUE_BOTH,
context) #str
defaultDirection = self.parameterAsEnum(parameters,
self.DEFAULT_DIRECTION,
context) #int
speedFieldName = self.parameterAsString(parameters, self.SPEED_FIELD,
context) #str
defaultSpeed = self.parameterAsDouble(parameters, self.DEFAULT_SPEED,
context) #float
tolerance = self.parameterAsDouble(parameters, self.TOLERANCE,
context) #float
analysisCrs = network.sourceCrs()
input_coordinates = getListOfPoints(startPoints)
if analysisCrs.isGeographic():
raise QgsProcessingException(
'QNEAT3 algorithms are designed to work with projected coordinate systems. Please use a projected coordinate system (eg. UTM zones) instead of geographic coordinate systems (eg. WGS84)!'
)
if analysisCrs != startPoints.sourceCrs():
raise QgsProcessingException(
'QNEAT3 algorithms require that all inputs to be the same projected coordinate reference system (including project coordinate system).'
)
feedback.pushInfo("[QNEAT3Algorithm] Building Graph...")
feedback.setProgress(10)
net = Qneat3Network(network, input_coordinates, strategy,
directionFieldName, forwardValue, backwardValue,
bothValue, defaultDirection, analysisCrs,
speedFieldName, defaultSpeed, tolerance, feedback)
feedback.setProgress(40)
list_apoints = [
Qneat3AnalysisPoint("from", feature, id_field, net,
net.list_tiedPoints[i], feedback) for i,
feature in enumerate(getFeaturesFromQgsIterable(startPoints))
]
fields = QgsFields()
fields.append(QgsField('vertex_id', QVariant.Int, '', 254, 0))
fields.append(QgsField('cost', QVariant.Double, '', 254, 7))
fields.append(
QgsField('origin_point_id',
getFieldDatatype(startPoints, id_field)))
(sink, dest_id) = self.parameterAsSink(parameters, self.OUTPUT,
context, fields,
QgsWkbTypes.Point,
network.sourceCrs())
feedback.pushInfo("[QNEAT3Algorithm] Calculating Iso-Pointcloud...")
iso_pointcloud = net.calcIsoPoints(list_apoints, max_dist)
feedback.setProgress(90)
sink.addFeatures(iso_pointcloud, QgsFeatureSink.FastInsert)
feedback.pushInfo("[QNEAT3Algorithm] Ending Algorithm")
feedback.setProgress(100)
results = {}
results[self.OUTPUT] = dest_id
return results
