def __init__(self, graph: Graph, matrix: Matrix) -> None:
if not np.array_equal(matrix.index, graph.centroids):
raise ValueError("Matrix and graph do not have compatible sets of centroids.")
self.graph = graph
self.matrix = matrix
self.pce = 1
self.mode = graph.mode
self.class_flow: np.array
self.results = AssignmentResults()
self.results.prepare(self.graph, self.matrix)
self.results.reset()
self._aon_results = AssignmentResults()
self._aon_results.prepare(self.graph, self.matrix)
def test_set_cores(self):
a = AssignmentResults()
a.set_cores(10)
with self.assertRaises(ValueError):
a.set_cores(1.3)
a.set_cores(-2)
self.assertEqual(a.cores, max(1, mp.cpu_count() - 2))
def test_set_cores(self):
a = AssignmentResults()
a.set_cores(10)
with self.assertRaises(ValueError):
a.set_cores(1.3)
with self.assertRaises(ValueError):
a.set_cores(-2)
def test_execute(self):
# Loads and prepares the graph
res1 = AssignmentResults()
res1.prepare(self.g, self.matrix)
assig1 = allOrNothing(self.matrix, self.g, res1)
assig1.execute()
res2 = AssignmentResults()
res2.prepare(self.g, self.matrix2)
assig2 = allOrNothing(self.matrix2, self.g, res2)
assig2.execute()
load1 = res1.get_load_results()
load2 = res2.get_load_results()
self.assertEqual(list(load1.matrix_tot * 2), list(load2.matrix_tot),
"Something wrong with the AoN")
def __init__(self, graph: Graph, matrix: AequilibraeMatrix) -> None:
"""
Instantiates the class
Args:
graph (:obj:`Graph`): Class/mode-specific graph
matrix (:obj:`AequilibraeMatrix`): Class/mode-specific matrix. Supports multiple user classes
"""
if not np.array_equal(matrix.index, graph.centroids):
raise ValueError("Matrix and graph do not have compatible sets of centroids.")
self.graph = graph
self.matrix = matrix
self.pce = 1
self.mode = graph.mode
self.class_flow: np.array
self.results = AssignmentResults()
self.results.prepare(self.graph, self.matrix)
self.results.reset()
self._aon_results = AssignmentResults()
self._aon_results.prepare(self.graph, self.matrix)
def __init__(self, name: str, graph: Graph,
matrix: AequilibraeMatrix) -> None:
"""
Instantiates the class
Args:
name (:obj:`str`): UNIQUE class name.
graph (:obj:`Graph`): Class/mode-specific graph
matrix (:obj:`AequilibraeMatrix`): Class/mode-specific matrix. Supports multiple user classes
"""
if not np.array_equal(matrix.index, graph.centroids):
raise ValueError(
"Matrix and graph do not have compatible sets of centroids.")
if matrix.matrix_view.dtype != graph.default_types('float'):
raise TypeError(
"Matrix's computational view need to be of type np.float64")
self.graph = graph
self.matrix = matrix
self.pce = 1.0
self.vot = 1.0
self.mode = graph.mode
self.class_flow: np.array
self.results = AssignmentResults()
self.results.prepare(self.graph, self.matrix)
self.fixed_cost = np.zeros(graph.graph.shape[0],
graph.default_types('float'))
self.fixed_cost_field = ''
self.fc_multiplier = 1.0
self.results.reset()
self._aon_results = AssignmentResults()
self._aon_results.prepare(self.graph, self.matrix)
self.__id__ = name
class TrafficClass():
"""Traffic class for equilibrium traffic assignment
::
from aequilibrae.paths import TrafficClass
tc = TrafficClass(graph, demand_matrix)
tc.set_pce(1.3)
"""
def __init__(self, graph: Graph, matrix: AequilibraeMatrix) -> None:
"""
Instantiates the class
Args:
graph (:obj:`Graph`): Class/mode-specific graph
matrix (:obj:`AequilibraeMatrix`): Class/mode-specific matrix. Supports multiple user classes
"""
if not np.array_equal(matrix.index, graph.centroids):
raise ValueError(
"Matrix and graph do not have compatible sets of centroids.")
self.graph = graph
self.matrix = matrix
self.pce = 1
self.mode = graph.mode
self.class_flow: np.array
self.results = AssignmentResults()
self.results.prepare(self.graph, self.matrix)
self.results.reset()
self._aon_results = AssignmentResults()
self._aon_results.prepare(self.graph, self.matrix)
self._id = uuid4().hex
def set_pce(self, pce: Union[float, int]) -> None:
"""Sets Passenger Car equivalent
Args:
pce (:obj:`Union[float, int]`): PCE. Defaults to 1 if not set
"""
if not isinstance(pce, (float, int)):
raise ValueError('PCE needs to be either integer or float ')
self.pce = pce
class TrafficClass():
def __init__(self, graph: Graph, matrix: Matrix) -> None:
if not np.array_equal(matrix.index, graph.centroids):
raise ValueError("Matrix and graph do not have compatible sets of centroids.")
self.graph = graph
self.matrix = matrix
self.pce = 1
self.mode = graph.mode
self.class_flow: np.array
self.results = AssignmentResults()
self.results.prepare(self.graph, self.matrix)
self.results.reset()
self._aon_results = AssignmentResults()
self._aon_results.prepare(self.graph, self.matrix)
def set_pce(self, pce: Union[float, int]) -> None:
if not isinstance(pce, (float, int)):
raise ValueError('PCE needs to be either integer or float ')
self.pce = pce
def test_skimming_on_assignment(self):
res = AssignmentResults()
res.prepare(self.g, self.matrix)
self.g.set_skimming([])
self.g.set_blocked_centroid_flows(True)
assig = allOrNothing(self.matrix, self.g, res)
assig.execute()
if res.skims.distance.sum() > 0:
self.fail(
"skimming for nothing during assignment returned something different than zero"
)
res.prepare(self.g, self.matrix)
assig = allOrNothing(self.matrix, self.g, res)
assig.execute()
def __init__(self, assig_spec, algorithm) -> None:
WorkerThread.__init__(self, None)
self.algorithm = algorithm
self.rgap_target = assig_spec.rgap_target
self.max_iter = assig_spec.max_iter
self.cores = assig_spec.cores
self.assig = assig_spec # type: TrafficAssignment
if None in [
assig_spec.classes,
assig_spec.vdf,
assig_spec.capacity_field,
assig_spec.time_field,
assig_spec.vdf_parameters,
]:
all_par = 'Traffic classes, VDF, VDF_parameters, capacity field & time_field'
raise Exception(
"Parameter missing. Setting the algorithm is the last thing to do "
f"when assigning. Check if you have all of these: {all_par}")
self.traffic_classes = assig_spec.classes # type: List[TrafficClass]
self.num_classes = len(assig_spec.classes)
self.cap_field = assig_spec.capacity_field
self.time_field = assig_spec.time_field
self.vdf = assig_spec.vdf
self.vdf_parameters = assig_spec.vdf_parameters
self.iter = 0
self.rgap = np.inf
self.stepsize = 1.0
self.conjugate_stepsize = 0.0
self.fw_class_flow = 0
# rgap can be a bit wiggly, specifying how many times we need to be below target rgap is a quick way to
# ensure a better result. We might want to demand that the solution is that many consecutive times below.
self.steps_below_needed_to_terminate = 1
self.steps_below = 0
# if this is one, we do not have a new direction and will get stuck. Make it 1.
self.conjugate_direction_max = 0.99999
# if FW stepsize is zero, we set it to the corresponding MSA stepsize and then need to not make
# the step direction conjugate to the previous direction.
self.do_fw_step = False
self.do_conjugate_step = False
# BFW specific stuff
self.betas = np.array([1.0, 0.0, 0.0])
# Instantiates the arrays that we will use over and over
self.capacity = assig_spec.capacity
self.free_flow_tt = assig_spec.free_flow_tt
self.fw_total_flow = assig_spec.total_flow
self.congested_time = assig_spec.congested_time
self.vdf_der = np.array(assig_spec.congested_time, copy=True)
self.congested_value = np.array(assig_spec.congested_time, copy=True)
self.step_direction = {} # type: Dict[AssignmentResults]
self.previous_step_direction = {} # type: Dict[AssignmentResults]
self.pre_previous_step_direction = {} # type: Dict[AssignmentResults]
for c in self.traffic_classes:
r = AssignmentResults()
r.prepare(c.graph, c.matrix)
self.step_direction[c.mode] = r
if self.algorithm in ['cfw', 'bfw']:
for c in self.traffic_classes:
r = AssignmentResults()
r.prepare(c.graph, c.matrix)
self.previous_step_direction[c.mode] = r
r = AssignmentResults()
r.prepare(c.graph, c.matrix)
self.step_direction[c.mode] = r
r = AssignmentResults()
r.prepare(c.graph, c.matrix)
self.pre_previous_step_direction[c.mode] = r
def doWork(self):
if self.error is None:
# In case we have only one class
unnasigned = 0
classes = self.matrix.matrix_view.shape[2]
layer = get_vector_layer_by_name(self.layer)
idx = layer.dataProvider().fieldNameIndex(self.id_field)
feature_count = layer.featureCount()
self.desire_lines.emit(('job_size_dl', feature_count))
all_centroids = {}
for P, feat in enumerate(layer.getFeatures()):
geom = feat.geometry()
if geom is not None:
point = list(geom.centroid().asPoint())
centroid_id = feat.attributes()[idx]
all_centroids[centroid_id] = point
self.desire_lines.emit(('jobs_done_dl', P))
self.desire_lines.emit(('text_dl', "Loading Layer Features: " + str(P) + "/" + str(feature_count)))
# Creating resulting layer
EPSG_code = int(layer.crs().authid().split(":")[1])
desireline_layer = QgsVectorLayer("LineString?crs=epsg:" + str(EPSG_code), self.dl_type, "memory")
dlpr = desireline_layer.dataProvider()
base_dl_fields = [QgsField("link_id", QVariant.Int),
QgsField("A_Node", QVariant.Int),
QgsField("B_Node", QVariant.Int),
QgsField("direct", QVariant.Int),
QgsField("distance", QVariant.Double)]
if self.dl_type == "DesireLines":
items = []
for i, j in all_centroids.items():
items.append((i, j[0], j[1]))
coords = np.array(items)
coord_index = np.zeros((self.matrix.index[:].max().astype(np.int64) + 1, 2))
coord_index[coords[:, 0].astype(np.int64), 0] = coords[:, 1]
coord_index[coords[:, 0].astype(np.int64), 1] = coords[:, 2]
self.desire_lines.emit(('text_dl', "Manipulating matrix indices"))
zones = self.matrix.index[:].shape[0]
a = np.array(self.matrix.index[:], np.int64)
ij, ji = np.meshgrid(a, a, sparse=False, indexing='ij')
ij = ij.flatten()
ji = ji.flatten()
arrays = [ij, ji]
self.desire_lines.emit(('text_dl', "Collecting all matrices"))
self.desire_lines.emit(('job_size_dl', len(self.matrix.view_names)))
total_mat = np.zeros((zones, zones), np.float64)
for i, mat in enumerate(self.matrix.view_names):
arrays.append(self.matrix.matrix[mat].flatten())
total_mat += self.matrix.matrix[mat]
self.desire_lines.emit(('jobs_done_dl', i + 1))
# Eliminates the cells for which we don't have geography
self.desire_lines.emit(('text_dl', "Filtering zones with no geography available"))
zones_with_no_geography = [x for x in self.matrix.index[:] if x not in all_centroids]
if zones_with_no_geography:
self.desire_lines.emit(('job_size_dl', len(zones_with_no_geography)))
for k, z in enumerate(zones_with_no_geography):
i = self.matrix.matrix_hash[z]
t = np.nansum(total_mat[i, :]) + np.nansum(total_mat[:, i])
unnasigned += t
self.report.append(
'Zone {} does not have a corresponding centroid/zone. Total flow {}'.format(z, t))
total_mat[i, :] = 0
total_mat[:, i] = 0
self.desire_lines.emit(('jobs_done_dl', k + 1))
self.desire_lines.emit(('text_dl', "Filtering down to OD pairs with flows"))
field_names = [x for x in self.matrix.view_names]
nonzero = np.nonzero(total_mat.flatten())
arrays = np.vstack(arrays).transpose()
arrays = arrays[nonzero, :]
arrays = arrays.reshape(arrays.shape[1], arrays.shape[2])
base_types = [(x, np.float64) for x in ['from', 'to']]
base_types = base_types + [(x + '_AB', np.float64) for x in field_names]
dtypes_ab = [(x, np.int64) for x in ['from', 'to']] + [(x + '_AB', float) for x in field_names]
dtypes_ba = [(x, np.int64) for x in ['to', 'from']] + [(x + '_BA', float) for x in field_names]
ab_mat = np.array(arrays[arrays[:, 0] > arrays[:, 1], :])
ba_mat = np.array(arrays[arrays[:, 0] < arrays[:, 1], :])
flows_ab = ab_mat.view(base_types)
flows_ab = flows_ab.reshape(flows_ab.shape[:-1])
flows_ab = flows_ab.astype(dtypes_ab)
flows_ba = ba_mat.view(base_types)
flows_ba = flows_ba.reshape(flows_ba.shape[:-1])
flows_ba = flows_ba.astype(dtypes_ba)
defaults1 = {x + '_AB': 0.0 for x in field_names}
defaults = {x + '_BA': 0.0 for x in field_names}
defaults = {**defaults, **defaults1}
self.desire_lines.emit(('text_dl', "Concatenating AB & BA flows"))
flows = rfn.join_by(['from', 'to'], flows_ab, flows_ba, jointype='outer', defaults=defaults,
usemask=True, asrecarray=True)
flows = flows.filled()
flows_ab = 0
flows_ba = 0
for f in flows.dtype.names[2:]:
base_dl_fields.extend([QgsField(f, QVariant.Double)])
dlpr.addAttributes(base_dl_fields)
desireline_layer.updateFields()
self.desire_lines.emit(('text_dl', "Creating Desire Lines"))
self.desire_lines.emit(('job_size_dl', flows.shape[0]))
all_features = []
for i, rec in enumerate(flows):
a_node = rec[0]
b_node = rec[1]
a_point = QgsPointXY(*all_centroids[a_node])
b_point = QgsPointXY(*all_centroids[b_node])
dist = QgsGeometry().fromPointXY(a_point).distance(
QgsGeometry().fromPointXY(b_point))
feature = QgsFeature()
feature.setGeometry(QgsGeometry.fromPolylineXY([a_point, b_point]))
attrs = [i + 1, int(a_node), int(b_node), 0, dist]
attrs.extend([float(x) for x in list(rec)[2:]])
feature.setAttributes(attrs)
all_features.append(feature)
self.desire_lines.emit(('jobs_done_dl', i))
if unnasigned > 0:
self.report.append('Total non assigned flows (not counting intrazonals):' + str(unnasigned))
if flows.shape[0] > 1:
a = dlpr.addFeatures(all_features)
self.result_layer = desireline_layer
else:
self.report.append('Nothing to show')
elif self.dl_type == "DelaunayLines":
for f in self.matrix.view_names:
base_dl_fields.extend([QgsField(f + '_ab', QVariant.Double),
QgsField(f + '_ba', QVariant.Double),
QgsField(f + '_tot', QVariant.Double)])
dlpr.addAttributes(base_dl_fields)
desireline_layer.updateFields()
self.desire_lines.emit(('text_dl', "Building Delaunay dataset"))
points = []
node_id_in_delaunay_results = {}
i = 0
self.desire_lines.emit(('job_size_dl', len(all_centroids)))
for k, v in all_centroids.items():
self.desire_lines.emit(('jobs_done_dl', i))
points.append(v)
node_id_in_delaunay_results[i] = k
i += 1
self.desire_lines.emit(('text_dl', "Computing Delaunay Triangles"))
tri = Delaunay(np.array(points))
# We process all the triangles to only get each edge once
self.desire_lines.emit(('text_dl', "Building Delaunay Network: Collecting Edges"))
edges = []
if self.python_version == 32:
all_edges = tri.vertices
else:
all_edges = tri.simplices
self.desire_lines.emit(('job_size_dl', len(all_edges)))
for j, triangle in enumerate(all_edges):
self.desire_lines.emit(('jobs_done_dl', j))
links = list(itertools.combinations(triangle, 2))
for i in links:
edges.append([min(i[0], i[1]), max(i[0], i[1])])
self.desire_lines.emit(('text_dl', "Building Delaunay Network: Getting unique edges"))
edges = OrderedDict((str(x), x) for x in edges).values()
# Writing Delaunay layer
self.desire_lines.emit(('text_dl', "Building Delaunay Network: Assembling Layer"))
desireline_link_id = 1
data = []
dl_ids_on_links = {}
self.desire_lines.emit(('job_size_dl', len(edges)))
for j, edge in enumerate(edges):
self.desire_lines.emit(('jobs_done_dl', j))
a_node = node_id_in_delaunay_results[edge[0]]
a_point = all_centroids[a_node]
a_point = QgsPointXY(a_point[0], a_point[1])
b_node = node_id_in_delaunay_results[edge[1]]
b_point = all_centroids[b_node]
b_point = QgsPointXY(b_point[0], b_point[1])
dist = QgsGeometry().fromPointXY(a_point).distance(QgsGeometry().fromPointXY(b_point))
line = []
line.append(desireline_link_id)
line.append(a_node)
line.append(b_node)
line.append(dist)
line.append(dist)
line.append(0)
data.append(line)
dl_ids_on_links[desireline_link_id] = [a_node, b_node, 0, dist]
desireline_link_id += 1
self.desire_lines.emit(('text_dl', "Building graph"))
network = np.asarray(data)
del data
# types for the network
self.graph = Graph()
itype = self.graph.default_types('int')
ftype = self.graph.default_types('float')
all_types = [itype, itype, itype, ftype, ftype, np.int8]
all_titles = ['link_id', 'a_node', 'b_node', 'distance_ab', 'distance_ba', 'direction']
dt = [(t, d) for t, d in zip(all_titles, all_types)]
self.graph.network = np.zeros(network.shape[0], dtype=dt)
for k, t in enumerate(dt):
self.graph.network[t[0]] = network[:, k].astype(t[1])
del network
self.graph.type_loaded = 'NETWORK'
self.graph.status = 'OK'
self.graph.network_ok = True
self.graph.prepare_graph(self.matrix.index.astype(np.int64))
self.graph.set_graph(cost_field='distance', skim_fields=False, block_centroid_flows=False)
self.results = AssignmentResults()
self.results.prepare(self.graph, self.matrix)
self.desire_lines.emit(('text_dl', "Assigning demand"))
self.desire_lines.emit(('job_size_dl', self.matrix.index.shape[0]))
assigner = allOrNothing(self.matrix, self.graph, self.results)
assigner.execute()
self.report = assigner.report
print(self.results.link_loads)
self.desire_lines.emit(('text_dl', "Collecting results"))
self.desire_lines.emit(('text_dl', "Building resulting layer"))
features = []
max_edges = len(edges)
self.desire_lines.emit(('job_size_dl', max_edges))
link_loads = self.results.save_to_disk()
for i, link_id in enumerate(link_loads.index):
self.desire_lines.emit(('jobs_done_dl', i))
a_node, b_node, direct, dist = dl_ids_on_links[link_id]
attr = [int(link_id), a_node, b_node, direct, dist]
a_point = all_centroids[a_node]
a_point = QgsPointXY(a_point[0], a_point[1])
b_point = all_centroids[b_node]
b_point = QgsPointXY(b_point[0], b_point[1])
feature = QgsFeature()
feature.setGeometry(QgsGeometry.fromPolylineXY([a_point, b_point]))
for c in self.matrix.view_names:
attr.extend([float(link_loads.data[c + '_ab'][i]),
float(link_loads.data[c + '_ba'][i]),
float(link_loads.data[c + '_tot'][i])])
feature.setAttributes(attr)
features.append(feature)
a = dlpr.addFeatures(features)
self.result_layer = desireline_layer
self.desire_lines.emit(('finished_desire_lines_procedure', 0))
class TrafficClass():
"""Traffic class for equilibrium traffic assignment
::
from aequilibrae.paths import TrafficClass
tc = TrafficClass(graph, demand_matrix)
tc.set_pce(1.3)
"""
def __init__(self, name: str, graph: Graph,
matrix: AequilibraeMatrix) -> None:
"""
Instantiates the class
Args:
name (:obj:`str`): UNIQUE class name.
graph (:obj:`Graph`): Class/mode-specific graph
matrix (:obj:`AequilibraeMatrix`): Class/mode-specific matrix. Supports multiple user classes
"""
if not np.array_equal(matrix.index, graph.centroids):
raise ValueError(
"Matrix and graph do not have compatible sets of centroids.")
if matrix.matrix_view.dtype != graph.default_types('float'):
raise TypeError(
"Matrix's computational view need to be of type np.float64")
self.graph = graph
self.matrix = matrix
self.pce = 1.0
self.vot = 1.0
self.mode = graph.mode
self.class_flow: np.array
self.results = AssignmentResults()
self.results.prepare(self.graph, self.matrix)
self.fixed_cost = np.zeros(graph.graph.shape[0],
graph.default_types('float'))
self.fixed_cost_field = ''
self.fc_multiplier = 1.0
self.results.reset()
self._aon_results = AssignmentResults()
self._aon_results.prepare(self.graph, self.matrix)
self.__id__ = name
def set_pce(self, pce: Union[float, int]) -> None:
"""Sets Passenger Car equivalent
Args:
pce (:obj:`Union[float, int]`): PCE. Defaults to 1 if not set
"""
if not isinstance(pce, (float, int)):
raise ValueError('PCE needs to be either integer or float ')
self.pce = pce
def set_fixed_cost(self, field_name: str, multiplier=1):
"""Sets value of time
Args:
field_name (:obj:`str`): Name of the graph field with fixed costs for this class
multiplier (:obj:`Union[float, int]`): Multiplier for the fixed cost. Defaults to 1 if not set
"""
self.fc_multiplier = float(multiplier)
if field_name not in self.graph.graph.columns:
raise ValueError('Field does not exist in the graph')
self.fixed_cost_field = field_name
if np.any(np.isnan(self.graph.graph[field_name].values)):
logger.warning(
f'Cost field {field_name} has NaN values. Converted to zero')
if self.graph.graph[field_name].min() < 0:
msg = f'Cost field {field_name} has negative values. That is not allowed'
logger.error(msg)
raise ValueError(msg)
def set_vot(self, value_of_time: float) -> None:
"""Sets value of time
Args:
value_of_time (:obj:`Union[float, int]`): Value of time. Defaults to 1 if not set
"""
self.vot = float(value_of_time)
def __setattr__(self, key, value):
if key not in [
'graph', 'matrix', 'pce', 'mode', 'class_flow', 'results',
'_aon_results', '__id__', 'vot', 'fixed_cost', 'fc_multiplier',
'fixed_cost_field'
]:
raise KeyError('Traffic Class does not have that element')
self.__dict__[key] = value
def doWork(self):
if self.error is None:
layer = get_vector_layer_by_name(self.layer)
idx = layer.fieldNameIndex(self.id_field)
matrix = self.matrix
featcount = layer.featureCount()
self.emit(SIGNAL("ProgressMaxValue(PyQt_PyObject)"),
(0, featcount))
P = 0
points = []
point_ids = []
for feat in layer.getFeatures():
P += 1
self.emit(SIGNAL("ProgressValue(PyQt_PyObject)"), (0, int(P)))
self.emit(SIGNAL("ProgressText (PyQt_PyObject)"),
(0, "Loading Layer Features: " + str(P) + "/" +
str(featcount)))
geom = feat.geometry()
if geom is not None:
point = list(geom.centroid().asPoint())
points.append(point)
point_ids.append(feat.attributes()[idx])
points = np.array(points)
self.emit(SIGNAL("ProgressValue(PyQt_PyObject)"), (0, featcount))
self.emit(
SIGNAL("ProgressText (PyQt_PyObject)"),
(0, "Preparing consistency check Matrix Vs. Zoning layer"))
vector1 = np.nonzero(np.sum(matrix, axis=0))[0]
vector2 = np.nonzero(np.sum(matrix, axis=1))[0]
nonzero = np.hstack((vector1, vector2))
self.emit(SIGNAL("ProgressValue(PyQt_PyObject)"),
(0, nonzero.shape[0]))
for i, zone in enumerate(nonzero):
if zone not in point_ids:
self.error = 'Zone ' + str(
zone) + ' with positive flow not in zoning file'
break
self.emit(SIGNAL("ProgressMaxValue(PyQt_PyObject)"),
(0, i + 1))
self.emit(SIGNAL("ProgressValue(PyQt_PyObject)"),
(0, nonzero.shape[0]))
if self.error is None:
#Creating resulting layer
EPSG_code = int(layer.crs().authid().split(":")[1])
desireline_layer = QgsVectorLayer(
"LineString?crs=epsg:" + str(EPSG_code), self.dl_type,
"memory")
dlpr = desireline_layer.dataProvider()
dlpr.addAttributes([
QgsField("link_id", QVariant.Int),
QgsField("A_Node", QVariant.Int),
QgsField("B_Node", QVariant.Int),
QgsField("direct", QVariant.Int),
QgsField("length", QVariant.Double),
QgsField("AB_FLOW", QVariant.Double),
QgsField("BA_FLOW", QVariant.Double),
QgsField("TOT_FLOW", QVariant.Double)
])
desireline_layer.updateFields()
if self.dl_type == "DesireLines":
self.emit(SIGNAL("ProgressText (PyQt_PyObject)"),
(0, "Creating Desire Lines"))
self.emit(SIGNAL("ProgressMaxValue(PyQt_PyObject)"),
(0, self.matrix.shape[0] * self.matrix.shape[1] / 2))
#We create the dictionary with point information
all_points = {}
point_ids = np.array(point_ids).astype(np.int)
for i in range(point_ids.shape[0]):
all_points[point_ids[i]] = points[i]
# We are assuming that the matrix is square here. Maybe we could add more general code layer
desireline_link_id = 1
q = 0
all_features = []
for i in range(self.matrix.shape[0]):
for j in xrange(i + 1, self.matrix.shape[1]):
q += 1
self.emit(SIGNAL("ProgressValue(PyQt_PyObject)"),
(0, q))
if self.matrix[i, j] + self.matrix[j, i] > 0:
a_node = i
a_point = QgsPoint(all_points[a_node][0],
all_points[a_node][1])
b_node = j
b_point = QgsPoint(all_points[b_node][0],
all_points[b_node][1])
dist = QgsGeometry().fromPoint(a_point).distance(
QgsGeometry().fromPoint(b_point))
feature = QgsFeature()
feature.setGeometry(
QgsGeometry.fromPolyline([a_point, b_point]))
feature.setAttributes([
desireline_link_id, a_node, b_node, 0, dist,
float(self.matrix[i, j]),
float(self.matrix[j, i]),
float(self.matrix[i, j] + self.matrix[j, i])
])
all_features.append(feature)
desireline_link_id += 1
a = dlpr.addFeatures(all_features)
self.result_layer = desireline_layer
elif self.dl_type == "DelaunayLines":
self.emit(SIGNAL("ProgressText (PyQt_PyObject)"),
(0, "Computing Delaunay Triangles"))
tri = Delaunay(points)
#We process all the triangles to only get each edge once
self.emit(SIGNAL("ProgressText (PyQt_PyObject)"),
(0, "Building Delaunay Network: Collecting Edges"))
edges = []
for triangle in tri.simplices:
links = list(itertools.combinations(triangle, 2))
for i in links:
l = [min(i[0], i[1]), max(i[0], i[1])]
if l not in edges:
edges.append(l)
#Writing Delaunay layer
self.emit(SIGNAL("ProgressText (PyQt_PyObject)"),
(0, "Building Delaunay Network: Assembling Layer"))
desireline_link_id = 1
data = []
for edge in edges:
a_node = edge[0]
a_point = QgsPoint(points[a_node][0], points[a_node][1])
b_node = edge[1]
b_point = QgsPoint(points[b_node][0], points[b_node][1])
dist = QgsGeometry().fromPoint(a_point).distance(
QgsGeometry().fromPoint(b_point))
line = []
line.append(desireline_link_id)
line.append(point_ids[a_node])
line.append(point_ids[b_node])
line.append(dist)
line.append(dist)
line.append(0)
data.append(line)
desireline_link_id += 1
self.emit(SIGNAL("ProgressText (PyQt_PyObject)"),
(0, "Building graph"))
network = np.asarray(data)
del data
#types for the network
all_types = [
np.int64, np.int64, np.int64, np.float64, np.float64,
np.int64
]
all_titles = [
'link_id', 'a_node', 'b_node', 'length_ab', 'length_ba',
'direction'
]
dt = [(t, d) for t, d in zip(all_titles, all_types)]
self.graph = Graph()
self.graph.network = np.zeros(network.shape[0], dtype=dt)
for k, t in enumerate(dt):
self.graph.network[t[0]] = network[:, k].astype(t[1])
del network
# Here we transform the network to go from node 1 to N
max_node = max(np.max(self.graph.network['a_node']),
np.max(self.graph.network['b_node']))
max_node = max(max_node, self.matrix.shape[0],
self.matrix.shape[1]) + 1
self.hash = np.zeros(max_node, np.int)
# Checks if any zone from the matrix is not present in the areas/node layer
t1 = np.sum(self.matrix, axis=0)
t2 = np.sum(self.matrix, axis=1)
if t1.shape[0] > t2.shape[0]:
t2.resize(t1.shape)
elif t2.shape[0] > t1.shape[0]:
t1.resize(t2.shape)
totals = t1 + t2
all_nodes = np.bincount(self.graph.network['a_node'])
for i in range(totals.shape[0]):
if totals[i]:
if not all_nodes[i]:
qgis.utils.iface.messageBar().pushMessage(
"Matrix has demand for zones that do not exist "
"in the zones/nodes provided. Demand for those"
"ones were ignored. e.g. " + str(i),
'',
level=3)
break
h = 1
for i in range(self.graph.network.shape[0]):
a_node = self.graph.network['a_node'][i]
if self.hash[a_node] == 0:
self.hash[a_node] = h
h += 1
b_node = self.graph.network['b_node'][i]
if self.hash[b_node] == 0:
self.hash[b_node] = h
h += 1
self.graph.network['a_node'][i] = self.hash[a_node]
self.graph.network['b_node'][i] = self.hash[b_node]
# End of network transformation
#Now we transform the matrix appropriately
self.matrix = reblocks_matrix(self.matrix, self.hash, h)
self.graph.type_loaded = 'NETWORK'
self.graph.status = 'OK'
self.graph.network_ok = True
self.graph.prepare_graph()
self.graph.set_graph(h - 1,
cost_field='length',
block_centroid_flows=False)
self.results = AssignmentResults()
self.results.prepare(self.graph)
self.results.set_cores(1)
# Do the assignment
all_or_nothing(self.matrix, self.graph, self.results)
f = self.results.link_loads[:, 0]
link_loads = np.zeros((f.shape[0] + 1, 2))
for i in range(f.shape[0] - 1):
direction = self.graph.graph['direction'][i]
link_id = self.graph.graph['link_id'][i]
flow = f[i]
if direction == 1:
link_loads[link_id, 0] = flow
else:
link_loads[link_id, 1] = flow
desireline_link_id = 1
for edge in edges:
a_node = edge[0]
a_point = QgsPoint(points[a_node][0], points[a_node][1])
b_node = edge[1]
b_point = QgsPoint(points[b_node][0], points[b_node][1])
dist = QgsGeometry().fromPoint(a_point).distance(
QgsGeometry().fromPoint(b_point))
feature = QgsFeature()
feature.setGeometry(
QgsGeometry.fromPolyline([a_point, b_point]))
feature.setAttributes([
desireline_link_id, point_ids[a_node],
point_ids[b_node], 0, dist,
float(link_loads[desireline_link_id, 0]),
float(link_loads[desireline_link_id, 1]),
float(link_loads[desireline_link_id, 0] +
link_loads[desireline_link_id, 1])
])
a = dlpr.addFeatures([feature])
desireline_link_id += 1
self.result_layer = desireline_layer
self.emit(SIGNAL("finished_threaded_procedure( PyQt_PyObject )"), True)
def doWork(self):
if self.error is None:
# In case we have only one class
unnasigned = 0
classes = self.matrix.matrix_view.shape[2]
layer = get_vector_layer_by_name(self.layer)
idx = layer.fieldNameIndex(self.id_field)
feature_count = layer.featureCount()
self.emit(SIGNAL("desire_lines"), ('job_size_dl', feature_count))
all_centroids = {}
P = 0
for feat in layer.getFeatures():
P += 1
self.emit(SIGNAL("desire_lines"), ('jobs_done_dl', P))
self.emit(SIGNAL("desire_lines"), ('text_dl',"Loading Layer Features: " + str(P) + "/" + str(feature_count)))
geom = feat.geometry()
if geom is not None:
point = list(geom.centroid().asPoint())
centroid_id = feat.attributes()[idx]
all_centroids[centroid_id] = point
#Creating resulting layer
EPSG_code = int(layer.crs().authid().split(":")[1])
desireline_layer = QgsVectorLayer("LineString?crs=epsg:" + str(EPSG_code), self.dl_type, "memory")
dlpr = desireline_layer.dataProvider()
fields = [QgsField("link_id", QVariant.Int),
QgsField("A_Node", QVariant.Int),
QgsField("B_Node", QVariant.Int),
QgsField("direct", QVariant.Int),
QgsField("distance", QVariant.Double)]
for f in self.matrix.view_names:
fields.extend([QgsField(f + '_ab', QVariant.Double),
QgsField(f + '_ba', QVariant.Double),
QgsField(f + '_tot', QVariant.Double)])
dlpr.addAttributes(fields)
desireline_layer.updateFields()
if self.dl_type == "DesireLines":
self.emit(SIGNAL("desire_lines"), ('text_dl',"Creating Desire Lines"))
self.emit(SIGNAL("desire_lines"), ('job_size_dl', self.matrix.zones ** 2 / 2))
desireline_link_id = 1
q = 0
all_features = []
for i in range(self.matrix.zones):
a_node = self.matrix.index[i]
if a_node in all_centroids.keys():
if np.sum(self.matrix.matrix_view[i, :, :]) + np.sum(self.matrix.matrix_view[:, i, :]) > 0:
columns_with_filled_cells = np.nonzero(np.sum(self.matrix.matrix_view[i, :, :], axis=1))
for j in columns_with_filled_cells[0]:
if np.sum(self.matrix.matrix_view[i, j, :]) + np.sum(self.matrix.matrix_view[j, i, :]) > 0:
b_node = self.matrix.index[j]
if a_node in all_centroids.keys() and b_node in all_centroids.keys():
a_point = all_centroids[a_node]
a_point = QgsPoint(a_point[0], a_point[1])
b_point = all_centroids[b_node]
b_point = QgsPoint(b_point[0], b_point[1])
dist = QgsGeometry().fromPoint(a_point).distance(QgsGeometry().fromPoint(b_point))
feature = QgsFeature()
feature.setGeometry(QgsGeometry.fromPolyline([a_point, b_point]))
attrs = [desireline_link_id, int(a_node), int(b_node), 0, dist]
for c in range(classes):
attrs.extend([float(self.matrix.matrix_view[i, j, c]),
float(self.matrix.matrix_view[j, i, c]),
float(self.matrix.matrix_view[i, j, c]) +
float(self.matrix.matrix_view[j, i, c])])
feature.setAttributes(attrs)
all_features.append(feature)
desireline_link_id += 1
else:
tu = (a_node, b_node, np.sum(self.matrix.matrix_view[i, j, :]),
np.sum(self.matrix.matrix_view[j, i, :]))
self.report.append('No centroids available to depict flow between node {0} and node'
'{1}. Total AB flow was equal to {2} and total BA flow was '
'equal to {3}'.format(*tu))
unnasigned += np.sum(self.matrix.matrix_view[i, j, :]) + \
np.sum(self.matrix.matrix_view[j, i, :])
else:
tu = (a_node, np.sum(self.matrix.matrix_view[i, :, :]))
self.report.append('No centroids available to depict flows from node {0} to all the others.'
'Total flow from this zone is equal to {1}'.format(*tu))
unnasigned += np.sum(self.matrix.matrix_view[i, :, :])
q += self.matrix.zones
self.emit(SIGNAL("desire_lines"), ('jobs_done_dl', q))
if unnasigned > 0:
self.report.append('Total non assigned flows (not counting intrazonals):' + str(unnasigned))
if desireline_link_id > 1:
a = dlpr.addFeatures(all_features)
self.result_layer = desireline_layer
else:
self.report.append('Nothing to show')
elif self.dl_type == "DelaunayLines":
self.emit(SIGNAL("desire_lines"), ('text_dl', "Building Delaunay dataset"))
points = []
node_id_in_delaunay_results = {}
i = 0
self.emit(SIGNAL("desire_lines"), ('job_size_dl', len(all_centroids)))
for k, v in all_centroids.iteritems():
self.emit(SIGNAL("desire_lines"), ('jobs_done_dl', i))
points.append(v)
node_id_in_delaunay_results[i] = k
i += 1
self.emit(SIGNAL("desire_lines"), ('text_dl', "Computing Delaunay Triangles"))
tri = Delaunay(np.array(points))
# We process all the triangles to only get each edge once
self.emit(SIGNAL("desire_lines"), ('text_dl', "Building Delaunay Network: Collecting Edges"))
edges = []
if self.python_version == 32:
all_edges = tri.vertices
else:
all_edges = tri.simplices
self.emit(SIGNAL("desire_lines"), ('job_size_dl', len(all_edges)))
for j, triangle in enumerate(all_edges):
self.emit(SIGNAL("desire_lines"), ('jobs_done_dl', j))
links = list(itertools.combinations(triangle, 2))
for i in links:
edges.append([min(i[0],i[1]), max(i[0],i[1])])
self.emit(SIGNAL("desire_lines"), ('text_dl', "Building Delaunay Network: Getting unique edges"))
edges = OrderedDict((str(x), x) for x in edges).values()
# Writing Delaunay layer
self.emit(SIGNAL("desire_lines"), ('text_dl', "Building Delaunay Network: Assembling Layer"))
desireline_link_id = 1
data = []
dl_ids_on_links = {}
self.emit(SIGNAL("desire_lines"), ('job_size_dl', len(edges)))
for j, edge in enumerate(edges):
self.emit(SIGNAL("desire_lines"), ('jobs_done_dl', j))
a_node = node_id_in_delaunay_results[edge[0]]
a_point = all_centroids[a_node]
a_point = QgsPoint(a_point[0], a_point[1])
b_node = node_id_in_delaunay_results[edge[1]]
b_point = all_centroids[b_node]
b_point = QgsPoint(b_point[0], b_point[1])
dist = QgsGeometry().fromPoint(a_point).distance(QgsGeometry().fromPoint(b_point))
line = []
line.append(desireline_link_id)
line.append(a_node)
line.append(b_node)
line.append(dist)
line.append(dist)
line.append(0)
data.append(line)
dl_ids_on_links[desireline_link_id] = [a_node, b_node, 0, dist]
desireline_link_id += 1
self.emit(SIGNAL("desire_lines"), ('text_dl', "Building graph"))
network = np.asarray(data)
del data
#types for the network
self.graph = Graph()
itype = self.graph.default_types('int')
ftype = self.graph.default_types('float')
all_types = [itype, itype, itype, ftype, ftype, np.int8]
all_titles = ['link_id', 'a_node', 'b_node', 'distance_ab', 'distance_ba', 'direction']
dt = [(t, d) for t, d in zip(all_titles, all_types)]
self.graph.network = np.zeros(network.shape[0], dtype=dt)
for k, t in enumerate(dt):
self.graph.network[t[0]] = network[:, k].astype(t[1])
del network
self.graph.type_loaded = 'NETWORK'
self.graph.status = 'OK'
self.graph.network_ok = True
try:
self.graph.prepare_graph(self.matrix.index.astype(np.int64))
self.graph.set_graph(cost_field='distance', skim_fields=False, block_centroid_flows=False)
self.results = AssignmentResults()
self.results.prepare(self.graph, self.matrix)
self.emit(SIGNAL("desire_lines"), ('text_dl', "Assigning demand"))
self.emit(SIGNAL("desire_lines"), ('job_size_dl', self.matrix.index.shape[0]))
assigner = allOrNothing(self.matrix, self.graph, self.results)
assigner.execute()
self.report = assigner.report
self.emit(SIGNAL("desire_lines"), ('text_dl', "Collecting results"))
link_loads = self.results.save_to_disk()
self.emit(SIGNAL("desire_lines"), ('text_dl', "Building resulting layer"))
features = []
max_edges = len(edges)
self.emit(SIGNAL("desire_lines"), ('job_size_dl', max_edges))
for i, link_id in enumerate(link_loads.index):
self.emit(SIGNAL("desire_lines"), ('jobs_done_dl', i))
a_node, b_node, direct, dist = dl_ids_on_links[link_id]
attr = [int(link_id), a_node, b_node, direct, dist]
a_point = all_centroids[a_node]
a_point = QgsPoint(a_point[0], a_point[1])
b_point = all_centroids[b_node]
b_point = QgsPoint(b_point[0], b_point[1])
feature = QgsFeature()
feature.setGeometry(QgsGeometry.fromPolyline([a_point, b_point]))
for c in self.matrix.view_names:
attr.extend([float(link_loads.data[c + '_ab'][i]),
float(link_loads.data[c + '_ba'][i]),
float(link_loads.data[c + '_tot'][i])])
feature.setAttributes(attr)
features.append(feature)
a = dlpr.addFeatures(features)
self.result_layer = desireline_layer
except ValueError as error:
self.report = [error.message]
self.emit(SIGNAL("desire_lines"), ('finished_desire_lines_procedure', 0))
def doWork(self):
if self.error is None:
layer = get_vector_layer_by_name(self.layer)
idx = layer.fieldNameIndex(self.id_field)
matrix = self.matrix
matrix_nodes = max(self.matrix_hash.values()) + 1
featcount = layer.featureCount()
self.emit(SIGNAL("ProgressMaxValue(PyQt_PyObject)"),
(0, featcount))
all_centroids = {}
P = 0
for feat in layer.getFeatures():
P += 1
self.emit(SIGNAL("ProgressValue(PyQt_PyObject)"), (0, int(P)))
self.emit(SIGNAL("ProgressText (PyQt_PyObject)"),
(0, "Loading Layer Features: " + str(P) + "/" +
str(featcount)))
geom = feat.geometry()
if geom is not None:
point = list(geom.centroid().asPoint())
centroid_id = feat.attributes()[idx]
all_centroids[centroid_id] = point
if centroid_id not in self.matrix_hash.keys():
self.matrix_hash[centroid_id] = matrix_nodes
matrix_nodes += 1
reverse_hash = {v: k for k, v in self.matrix_hash.iteritems()}
#Creating resulting layer
EPSG_code = int(layer.crs().authid().split(":")[1])
desireline_layer = QgsVectorLayer(
"LineString?crs=epsg:" + str(EPSG_code), self.dl_type,
"memory")
dlpr = desireline_layer.dataProvider()
dlpr.addAttributes([
QgsField("link_id", QVariant.Int),
QgsField("A_Node", QVariant.Int),
QgsField("B_Node", QVariant.Int),
QgsField("direct", QVariant.Int),
QgsField("length", QVariant.Double),
QgsField("ab_flow", QVariant.Double),
QgsField("ba_flow", QVariant.Double),
QgsField("tot_flow", QVariant.Double)
])
desireline_layer.updateFields()
if self.dl_type == "DesireLines":
self.emit(SIGNAL("ProgressText (PyQt_PyObject)"),
(0, "Creating Desire Lines"))
self.emit(SIGNAL("ProgressMaxValue(PyQt_PyObject)"),
(0, self.matrix.shape[0] * self.matrix.shape[1] / 2))
desireline_link_id = 1
q = 0
all_features = []
for i in range(self.matrix.shape[0]):
if np.sum(self.matrix[i, :]) > 0:
a_node = reverse_hash[i]
for j in xrange(i + 1, self.matrix.shape[1]):
q += 1
b_node = reverse_hash[j]
self.emit(SIGNAL("ProgressValue(PyQt_PyObject)"),
(0, q))
if self.matrix[i, j] + self.matrix[j, i] > 0:
if a_node in all_centroids.keys(
) and b_node in all_centroids.keys():
a_point = all_centroids[a_node]
a_point = QgsPoint(a_point[0], a_point[1])
b_point = all_centroids[b_node]
b_point = QgsPoint(b_point[0], b_point[1])
dist = QgsGeometry().fromPoint(
a_point).distance(
QgsGeometry().fromPoint(b_point))
feature = QgsFeature()
feature.setGeometry(
QgsGeometry.fromPolyline(
[a_point, b_point]))
feature.setAttributes([
desireline_link_id,
int(a_node),
int(b_node), 0, dist,
float(self.matrix[i, j]),
float(self.matrix[j, i]),
float(self.matrix[i, j] +
self.matrix[j, i])
])
all_features.append(feature)
desireline_link_id += 1
else:
tu = (a_node, b_node, self.matrix[i, j],
self.matrix[j, i])
self.report.append(
'No centroids available to depict flow between node {0} and node {1}. AB flow was equal to {2} and BA flow was equal to {3}'
.format(*tu))
else:
q += self.matrix.shape[1]
self.emit(SIGNAL("ProgressValue(PyQt_PyObject)"),
(0, q))
if desireline_link_id > 1:
a = dlpr.addFeatures(all_features)
self.result_layer = desireline_layer
else:
self.error = 'Nothing to show'
elif self.dl_type == "DelaunayLines":
self.emit(SIGNAL("ProgressText (PyQt_PyObject)"),
(0, "Computing Delaunay Triangles"))
points = []
seccond_relation = {}
i = 0
for k, v in all_centroids.iteritems():
points.append(v)
seccond_relation[i] = k
i += 1
tri = Delaunay(np.array(points))
#We process all the triangles to only get each edge once
self.emit(SIGNAL("ProgressText (PyQt_PyObject)"),
(0, "Building Delaunay Network: Collecting Edges"))
edges = []
if self.python_version == 32:
all_edges = tri.vertices
else:
all_edges = tri.simplices
for triangle in all_edges:
links = list(itertools.combinations(triangle, 2))
for i in links:
l = [min(i[0], i[1]), max(i[0], i[1])]
if l not in edges:
edges.append(l)
#Writing Delaunay layer
self.emit(SIGNAL("ProgressText (PyQt_PyObject)"),
(0, "Building Delaunay Network: Assembling Layer"))
desireline_link_id = 1
data = []
dl_link_ids = {}
for edge in edges:
a_node = seccond_relation[edge[0]]
a_point = all_centroids[a_node]
a_point = QgsPoint(a_point[0], a_point[1])
b_node = seccond_relation[edge[1]]
b_point = all_centroids[b_node]
b_point = QgsPoint(b_point[0], b_point[1])
dist = QgsGeometry().fromPoint(a_point).distance(
QgsGeometry().fromPoint(b_point))
line = []
line.append(desireline_link_id)
line.append(self.matrix_hash[a_node])
line.append(self.matrix_hash[b_node])
line.append(dist)
line.append(dist)
line.append(0)
data.append(line)
if a_node not in dl_link_ids.keys():
dl_link_ids[a_node] = {}
dl_link_ids[a_node][b_node] = desireline_link_id
desireline_link_id += 1
self.emit(SIGNAL("ProgressText (PyQt_PyObject)"),
(0, "Building graph"))
network = np.asarray(data)
del data
#types for the network
all_types = [
np.int64, np.int64, np.int64, np.float64, np.float64,
np.int64
]
all_titles = [
'link_id', 'a_node', 'b_node', 'length_ab', 'length_ba',
'direction'
]
dt = [(t, d) for t, d in zip(all_titles, all_types)]
self.graph = Graph()
self.graph.network = np.zeros(network.shape[0], dtype=dt)
for k, t in enumerate(dt):
self.graph.network[t[0]] = network[:, k].astype(t[1])
del network
self.graph.type_loaded = 'NETWORK'
self.graph.status = 'OK'
self.graph.network_ok = True
self.graph.prepare_graph()
self.graph.set_graph(matrix_nodes,
cost_field='length',
block_centroid_flows=False)
self.results = AssignmentResults()
self.results.prepare(self.graph)
# self.results.set_cores(1)
self.emit(SIGNAL("ProgressText (PyQt_PyObject)"),
(0, "Assigning demand"))
# Do the assignment
#self.all_or_nothing(self.matrix, self.graph, self.results)
self.report = all_or_nothing(self.matrix, self.graph,
self.results)
self.emit(SIGNAL("ProgressText (PyQt_PyObject)"),
(0, "Collecting results"))
f = self.results.link_loads
link_loads = np.zeros((f.shape[0] + 1, 2))
self.emit(SIGNAL("ProgressMaxValue(PyQt_PyObject)"),
(0, f.shape[0] - 1))
for i in range(f.shape[0] - 1):
self.emit(SIGNAL("ProgressValue(PyQt_PyObject)"), (0, i))
direction = self.graph.graph['direction'][i]
link_id = self.graph.graph['link_id'][i]
flow = f[i]
if direction == 1:
link_loads[link_id, 0] = flow
else:
link_loads[link_id, 1] = flow
self.emit(SIGNAL("ProgressText (PyQt_PyObject)"),
(0, "Building resulting layer"))
features = []
max_edges = len(edges)
self.emit(SIGNAL("ProgressMaxValue(PyQt_PyObject)"),
(0, max_edges))
for i, edge in enumerate(edges):
self.emit(SIGNAL("ProgressValue(PyQt_PyObject)"), (0, i))
a_node = seccond_relation[edge[0]]
a_point = all_centroids[a_node]
a_point = QgsPoint(a_point[0], a_point[1])
b_node = seccond_relation[edge[1]]
b_point = all_centroids[b_node]
b_point = QgsPoint(b_point[0], b_point[1])
dist = QgsGeometry().fromPoint(a_point).distance(
QgsGeometry().fromPoint(b_point))
feature = QgsFeature()
feature.setGeometry(
QgsGeometry.fromPolyline([a_point, b_point]))
desireline_link_id = dl_link_ids[a_node][b_node]
feature.setAttributes([
desireline_link_id, a_node, b_node, 0, dist,
float(link_loads[desireline_link_id, 0]),
float(link_loads[desireline_link_id, 1]),
float(link_loads[desireline_link_id, 0] +
link_loads[desireline_link_id, 1])
])
features.append(feature)
a = dlpr.addFeatures(features)
self.result_layer = desireline_layer
self.emit(SIGNAL("finished_threaded_procedure( PyQt_PyObject )"), True)
def test_set_save_path_file(self):
a = AssignmentResults()
# Never save by default
self.assertEqual(a.save_path_file, False)
