class ApplyGravityProcedure(WorkerThread):
def __init__(self, parentThread, impedance, rows, columns, model):
WorkerThread.__init__(self, parentThread)
self.gravity = GravityApplication(rows, columns, impedance, model)
self.error = None
def doWork(self):
self.gravity.apply()
self.emit(SIGNAL("finished_threaded_procedure( PyQt_PyObject )"), 0)
class ApplyGravityProcedure(WorkerThread):
def __init__(self, parentThread, **kwargs):
WorkerThread.__init__(self, parentThread)
self.gravity = GravityApplication(**kwargs)
self.error = None
def doWork(self):
self.gravity.apply()
self.report = self.gravity.report
self.emit(SIGNAL("finished_threaded_procedure( PyQt_PyObject )"), 0)
class ApplyGravityProcedure(WorkerThread):
def __init__(self, parentThread, **kwargs):
WorkerThread.__init__(self, parentThread)
self.gravity = GravityApplication(**kwargs)
self.error = None
self.report = []
def doWork(self):
try:
self.gravity.apply()
self.report = self.gravity.report
except ValueError as e:
self.error = e
self.finished_threaded_procedure.emit(0)
def test_apply(self):
args = {'impedance': matrix,
'rows': row_vector,
'row_field': 'rows',
'columns': column_vector,
'column_field': 'columns'}
models = [('EXPO', model_expo), ('POWER', model_power), ('GAMMA', model_gamma)]
for model_name, model_obj in models:
args['model'] = model_obj
distributed_matrix = GravityApplication(**args)
distributed_matrix.apply()
if distributed_matrix.gap > distributed_matrix.parameters['convergence level']:
self.fail('Gravity application did not converge for model ' + model_name)
def test_apply(self):
args = {
"impedance": matrix,
"rows": row_vector,
"row_field": "rows",
"columns": column_vector,
"column_field": "columns",
}
models = [("EXPO", model_expo), ("POWER", model_power), ("GAMMA", model_gamma)]
for model_name, model_obj in models:
args["model"] = model_obj
distributed_matrix = GravityApplication(**args)
distributed_matrix.apply()
if distributed_matrix.gap > distributed_matrix.parameters["convergence level"]:
self.fail("Gravity application did not converge for model " + model_name)
def test_apply(self):
args = {
'impedance': matrix,
'rows': row_vector,
'row_field': 'rows',
'columns': column_vector,
'column_field': 'columns'
}
models = [('EXPO', model_expo), ('POWER', model_power),
('GAMMA', model_gamma)]
for model_name, model_obj in models:
args['model'] = model_obj
distributed_matrix = GravityApplication(**args)
distributed_matrix.apply()
if distributed_matrix.gap > distributed_matrix.parameters[
'convergence level']:
self.fail('Gravity application did not converge for model ' +
model_name)
def __init__(self, parentThread, impedance, rows, columns, model):
WorkerThread.__init__(self, parentThread)
self.gravity = GravityApplication(rows, columns, impedance, model)
self.error = None
def __init__(self, parentThread, **kwargs):
WorkerThread.__init__(self, parentThread)
self.gravity = GravityApplication(**kwargs)
self.error = None
self.report = []
# Impedance
imped = proj_matrices.get_matrix("base_year_assignment_skims")
imped.computational_view(["final_time_with_intrazonals"])
# If we wanted the main diagonal to not be considered...
# np.fill_diagonal(imped.matrix_view, np.nan)
# %%
for function in ["power", "expo"]:
model = SyntheticGravityModel()
model.load(join(fldr, f"{function}_model.mod"))
outmatrix = join(proj_matrices.fldr, f"demand_{function}_model.aem")
apply = GravityApplication()
args = {
"impedance": imped,
"rows": vectors,
"row_field": "origins",
"model": model,
"columns": vectors,
"column_field": "destinations",
"nan_as_zero": True,
}
gravity = GravityApplication(**args)
gravity.apply()
# We get the output matrix and save it to OMX too,
gravity.save_to_project(name=f"demand_{function}_modeled",