def run_scenario(scenario, result_dir):
# scenario name
sce = scenario.__name__
sce_nice_name = sce.replace('_', ' ').title()
# prepare input data
data = rivus.read_excel(data_spreadsheet)
vertex = pdshp.read_shp(vertex_shapefile)
edge = prepare_edge(edge_shapefile, building_shapefile)
# apply scenario function to input data
data, vertex, edge = scenario(data, vertex, edge)
log_filename = os.path.join(result_dir, sce+'.log')
# create & solve model
model = rivus.create_model(
data, vertex, edge,
peak_multiplier=lambda x:scale_peak_demand(x, peak_demand_prefactor))
# scale peak demand according to pickled urbs findings
#reduced_peak = scale_peak_demand(model, peak_demand_prefactor)
#model.peak = reduced_peak
prob = model.create()
optim = SolverFactory('gurobi')
optim = setup_solver(optim, logfile=log_filename)
result = optim.solve(prob, tee=True)
prob.load(result)
# report
rivus.save(prob, os.path.join(result_dir, sce+'.pgz'))
rivus.report(prob, os.path.join(result_dir, sce+'.xlsx'))
# plot without buildings
rivus.result_figures(prob, os.path.join(result_dir, sce))
# plot with buildings and to_edge lines
more_shapefiles = [{'name': 'to_edge',
'color': rivus.to_rgb(192, 192, 192),
'shapefile': to_edge_shapefile,
'zorder': 1,
'linewidth': 0.1}]
rivus.result_figures(prob, os.path.join(result_dir, sce+'_bld'),
buildings=(building_shapefile, False),
shapefiles=more_shapefiles)
return prob
def replot(directory):
"""Recreate result figures for all pickled rivus results in directory
Args:
directory: a directory with 1 or multiple pickled rivus instances
Returns:
Nothing
"""
glob_pattern = os.path.join(directory, '*.pgz')
pickle_filenames = glob.glob(glob_pattern)
data_dir = os.path.join('data', os.path.basename(directory).split('-')[0])
# if directory = 'result/moosh' try to find a suitable building shapefile
# in 'data/moosh'
buildings = None
building_filename = os.path.join(data_dir, 'building')
if os.path.exists(building_filename + '.shp'):
buildings = (building_filename, False) # if True, color buildings
# if data/.../to_edge exists, paint it
shapefiles = None
to_edge_filename = os.path.join(data_dir, 'to_edge')
if os.path.exists(to_edge_filename + '.shp'):
shapefiles = [{
'name': 'to_edge',
'color': rivus.to_rgb(192, 192, 192),
'shapefile': to_edge_filename,
'zorder': 1,
'linewidth': 0.1
}]
for pf in pickle_filenames:
prob = rivus.load(pf)
figure_basename = os.path.splitext(pf)[0]
if buildings:
figure_basename += '_bld'
rivus.result_figures(prob,
figure_basename,
buildings=buildings,
shapefiles=shapefiles)
def replot(directory):
"""Recreate result figures for all pickled rivus results in directory
Args:
directory: a directory with 1 or multiple pickled rivus instances
Returns:
Nothing
"""
glob_pattern = os.path.join(directory, '*.pgz')
pickle_filenames = glob.glob(glob_pattern)
data_dir = os.path.join('data', os.path.basename(directory).split('-')[0])
# if directory = 'result/moosh' try to find a suitable building shapefile
# in 'data/moosh'
buildings = None
building_filename = os.path.join(data_dir, 'building')
if os.path.exists(building_filename+'.shp'):
buildings = (building_filename, False) # if True, color buildings
# if data/.../to_edge exists, paint it
shapefiles = None
to_edge_filename = os.path.join(data_dir, 'to_edge')
if os.path.exists(to_edge_filename+'.shp'):
shapefiles = [{'name': 'to_edge',
'color': rivus.to_rgb(192, 192, 192),
'shapefile': to_edge_filename,
'zorder': 1,
'linewidth': 0.1}]
for pf in pickle_filenames:
prob = rivus.load(pf)
figure_basename = os.path.splitext(pf)[0]
if buildings:
figure_basename += '_bld'
rivus.result_figures(prob, figure_basename,
buildings=buildings,
shapefiles=shapefiles)
# load nodes
vertex = pdshp.read_shp(vertex_shapefile)
# load spreadsheet data
data = rivus.read_excel(data_spreadsheet)
# create & solve model
prob = rivus.create_model(data, vertex, edge)
if PYOMO3:
prob = prob.create() # no longer needed in Pyomo 4
optim = SolverFactory('glpk')
optim = setup_solver(optim)
result = optim.solve(prob, tee=True)
if PYOMO3:
prob.load(result) # no longer needed in Pyomo 4
# load results
costs, Pmax, Kappa_hub, Kappa_process = rivus.get_constants(prob)
source, flows, hub_io, proc_io, proc_tau = rivus.get_timeseries(prob)
result_dir = os.path.join('result', os.path.basename(base_directory))
# create result directory if not existing already
if not os.path.exists(result_dir):
os.makedirs(result_dir)
rivus.save(prob, os.path.join(result_dir, 'prob.pgz'))
rivus.report(prob, os.path.join(result_dir, 'prob.xlsx'))
rivus.result_figures(prob, os.path.join(result_dir, 'plot'))
# load nodes
vertex = pdshp.read_shp(vertex_shapefile)
# load spreadsheet data
data = rivus.read_excel(data_spreadsheet)
# create & solve model
prob = rivus.create_model(data, vertex, edge)
if PYOMO3:
prob = prob.create() # no longer needed in Pyomo 4
optim = SolverFactory('glpk')
optim = setup_solver(optim)
result = optim.solve(prob, tee=True)
if PYOMO3:
prob.load(result) # no longer needed in Pyomo 4
# load results
costs, Pmax, Kappa_hub, Kappa_process = rivus.get_constants(prob)
source, flows, hub_io, proc_io, proc_tau = rivus.get_timeseries(prob)
result_dir = os.path.join('result', os.path.basename(base_directory))
# create result directory if not existing already
if not os.path.exists(result_dir):
os.makedirs(result_dir)
rivus.save(prob, os.path.join(result_dir, 'prob.pgz'))
rivus.report(prob, os.path.join(result_dir, 'prob.xlsx'))
rivus.result_figures(prob, os.path.join(result_dir, 'plot'))
