def run(
self,
resources,
write_datasets_to_cache_at_end_of_year=True,
log_file_name="run_model_system.log",
cleanup_datasets=True,
):
"""Entries in resources: (entries with no defaults are required)
models - a list containing names of models to be run. Each name
must correspond to the name of the module/class of that model. Default(object): None
years - a tuple (start year, end year)
debuglevel - an integer. The higher the more output will be printed. Default: 0
expression_library - a dictionary. The keys in the dictionary are pairs (dataset_name, variable_name)
and the values are the corresponding expressions. The model system needs to set the expression library
(if it isn't None) in DatasetFactory for DatasetFactory to know about variables defined as expressions
in the xml expression library. Default: None
This method is called both to start up the simulation for all years, and also for each year
when running with one process per year. In the latter case, 'years' consists of just
(current_year, current_year) rather than the real start and end years for the simulation.
"""
if not isinstance(resources, Resources):
raise TypeError, "Argument 'resources' must be of type 'Resources'."
logger_settings = resources.get("log", {"tags": [], "verbosity_level": 3})
logger.set_tags(logger_settings.get("tags", []))
logger.set_verbosity_level(logger_settings.get("verbosity_level", 3))
self.simulation_state = SimulationState()
self.simulation_state.set_low_memory_run(resources.get("low_memory_mode", False))
self.simulation_state.set_start_time(resources.get("base_year", 0))
self.run_year_namespace = {}
if resources.get("cache_directory", None) is not None:
self.simulation_state.set_cache_directory(resources["cache_directory"])
if "expression_library" in resources:
VariableFactory().set_expression_library(resources["expression_library"])
if resources.get("sample_input", False):
self.update_config_for_multiple_runs(resources)
cache_directory = self.simulation_state.get_cache_directory()
log_file = os.path.join(cache_directory, log_file_name)
logger.enable_file_logging(log_file, verbose=False)
try:
logger.log_status("Cache Directory set to: " + cache_directory)
with logger.block("Start simulation run"):
models = resources.get("models", [])
models_in_years = resources.get("models_in_year", {})
resources.check_obligatory_keys(["years"])
years = resources["years"]
if (not isinstance(years, tuple)) and (not isinstance(years, list)):
raise TypeError, "Entry 'years' in resources must be a tuple."
if len(years) < 2:
print years
raise StandardError, "Entry 'years' in resources must be of length at least 2."
start_year = years[0]
end_year = years[-1]
debuglevel = resources.get("debuglevel", 0)
seed_values = resources.get("seed", NO_SEED)
logger.log_status("random seed = %s" % str(seed_values))
seed(seed_values)
for year in range(start_year, end_year + 1):
with logger.block("Starting simulation for year " + str(year)):
self.simulation_state.set_current_time(year)
SessionConfiguration().get_dataset_pool().remove_all_datasets()
logger.disable_file_logging(log_file)
try:
if models_in_years.get(year, None) is not None:
models_to_run = models_in_years[year]
else:
models_to_run = models
self._run_year(
year=year,
models=models_to_run,
simulation_state=self.simulation_state,
debuglevel=debuglevel,
resources=resources,
write_datasets_to_cache_at_end_of_year=write_datasets_to_cache_at_end_of_year,
cleanup_datasets=cleanup_datasets,
)
finally:
logger.enable_file_logging(log_file, verbose=False)
collect()
finally:
logger.disable_file_logging(log_file)
def run(self,
resources,
write_datasets_to_cache_at_end_of_year=True,
log_file_name='run_model_system.log',
cleanup_datasets=True):
"""Entries in resources: (entries with no defaults are required)
models - a list containing names of models to be run. Each name
must correspond to the name of the module/class of that model. Default(object): None
years - a tuple (start year, end year)
debuglevel - an integer. The higher the more output will be printed. Default: 0
expression_library - a dictionary. The keys in the dictionary are pairs (dataset_name, variable_name)
and the values are the corresponding expressions. The model system needs to set the expression library
(if it isn't None) in DatasetFactory for DatasetFactory to know about variables defined as expressions
in the xml expression library. Default: None
This method is called both to start up the simulation for all years, and also for each year
when running with one process per year. In the latter case, 'years' consists of just
(current_year, current_year) rather than the real start and end years for the simulation.
"""
if not isinstance(resources, Resources):
raise TypeError, "Argument 'resources' must be of type 'Resources'."
logger_settings = resources.get("log", {
"tags": [],
"verbosity_level": 3
})
logger.set_tags(logger_settings.get("tags", []))
logger.set_verbosity_level(logger_settings.get("verbosity_level", 3))
self.simulation_state = SimulationState()
self.simulation_state.set_low_memory_run(
resources.get("low_memory_mode", False))
self.simulation_state.set_start_time(resources.get("base_year", 0))
self.run_year_namespace = {}
if resources.get('cache_directory', None) is not None:
self.simulation_state.set_cache_directory(
resources['cache_directory'])
if 'expression_library' in resources:
VariableFactory().set_expression_library(
resources['expression_library'])
if resources.get('sample_input', False):
self.update_config_for_multiple_runs(resources)
cache_directory = self.simulation_state.get_cache_directory()
log_file = os.path.join(cache_directory, log_file_name)
logger.enable_file_logging(log_file, verbose=False)
try:
logger.log_status("Cache Directory set to: " + cache_directory)
with logger.block('Start simulation run'):
models = resources.get("models", [])
models_in_years = resources.get("models_in_year", {})
resources.check_obligatory_keys(["years"])
years = resources["years"]
if (not isinstance(years, tuple)) and (not isinstance(
years, list)):
raise TypeError, "Entry 'years' in resources must be a tuple."
if len(years) < 2:
print years
raise StandardError, "Entry 'years' in resources must be of length at least 2."
start_year = years[0]
end_year = years[-1]
debuglevel = resources.get("debuglevel", 0)
seed_values = resources.get('seed', NO_SEED)
logger.log_status("random seed = %s" % str(seed_values))
seed(seed_values)
for year in range(start_year, end_year + 1):
with logger.block("Starting simulation for year " +
str(year)):
self.simulation_state.set_current_time(year)
SessionConfiguration().get_dataset_pool(
).remove_all_datasets()
logger.disable_file_logging(log_file)
try:
if models_in_years.get(year, None) is not None:
models_to_run = models_in_years[year]
else:
models_to_run = models
self._run_year(
year=year,
models=models_to_run,
simulation_state=self.simulation_state,
debuglevel=debuglevel,
resources=resources,
write_datasets_to_cache_at_end_of_year=
write_datasets_to_cache_at_end_of_year,
cleanup_datasets=cleanup_datasets)
finally:
logger.enable_file_logging(log_file, verbose=False)
collect()
finally:
logger.disable_file_logging(log_file)
def optimize(bform,prices,costdiscount,dataset_pool):
btype = bform.btype
ieqcons = None
if btype == 1: ieqcons = bform.sfoneoff_bounds
elif btype == 2: ieqcons = bform.sfbuilder_bounds
elif btype in [3,4]: ieqcons = bform.mf_bounds
elif btype in [5,6]: ieqcons = bform.mf_bounds
elif btype in COMMERCIALTYPES_D: ieqcons = bform.commercial_bounds
else: assert 0
#if btype == 2: return [], -1.0
if btype in [4,6]: nf = 5
elif btype in COMMERCIALTYPES_D: nf = 1
else: nf = 4
x0 = array([0 for i in range(nf)])
proposal_comp = dataset_pool['proposal_component']
proposal_comp['sales_absorption_ratio'] = copy.copy(proposal_comp['sales_absorption'])
bform.sales_absorption = proposal_comp['sales_absorption']
bform.rent_absorption = proposal_comp['rent_absorption']
bform.leases_absorption = proposal_comp['leases_absorption']
bform.sales_vacancy_rates = proposal_comp['sales_vacancy_rates']
bform.vacancy_rates = proposal_comp['vacancy_rates']
bldg_type = devmdltypes[btype-1]
#throttle development when annual vacancy > 10%
#residential vacancy = min(own, rent)
max_residential_vacancy = min( proposal_comp['sales_vacancy_rates'][0],
proposal_comp['vacancy_rates'][0]) * 12
non_residential_vacancy = proposal_comp['vacancy_rates'][4] * 12
if bldg_type in residential_building_types:
if max_residential_vacancy > .1:
return [], -1.0
elif non_residential_vacancy > .1:
return [], -1.0
logger.set_verbosity_level(0)
#proposal.compute_variables(["property_tax = proposal.disaggregate(parcel.property_tax)",
# "land_cost = proposal.disaggregate(parcel.land_cost)"],
# dataset_pool=dataset_pool)
#r = fmin_l_bfgs_b(_objfunc,x0,approx_grad=1,bounds=bounds,epsilon=1.0,factr=1e16)
if 0: #DEBUG:
r = fmin_slsqp(_objfunc,x0,f_ieqcons=ieqcons,iprint=0,full_output=1,epsilon=1,args=[btype,prices,dataset_pool],iter=150,acc=.01)
print r
#r2[0] = numpy.round(r2[0], decimals=1)
#r2[1] = _objfunc(r2[0],btype)
r = fmin_slsqp(_objfunc2,x0,f_ieqcons=ieqcons,iprint=0,full_output=1,epsilon=1,args=[bform,btype,prices,costdiscount,dataset_pool],iter=150,acc=.01)
r = list(r)
logger.log_status("type r: %s" % (type(r)))
logger.log_status("len r: %s" % (len(r)))
logger.log_status("r0: %s" % (r[0]))
logger.log_status("r1: %s" % (r[1]))
#if DEBUG > 0: print r
#print r
if r[3] <> 0: return r[0], -1
r[0] = numpy.round(r[0], decimals=1)
r[1] = _objfunc2(r[0],bform,btype,prices,costdiscount,dataset_pool)
if 0: #DEBUG:
print r2
print r
numpy.testing.assert_approx_equal(r2[1],r[1],significant=1)
r[1] *= -1*100000
return r[0], r[1]
def _objfunc2(params,btype,saveexcel=0,excelprefix=None):
global proforma_inputs
e = None
if DEBUG: print "PARAMS", params
if btype == 1: # single family one-off
assert len(params) == 4
for i in range(4): set_value(e,sp,'Bldg Form','K%d' % (63+i), params[i])
elif btype == 2: # single family builder
assert len(params) == 4
for i in range(4): set_value(e,sp,'Bldg Form','K%d' % (58+i), params[i])
elif btype == 3: # mf-rental
assert len(params) == 4
for i in range(4): set_value(e,sp,'Bldg Form','K%d' % (68+i), params[i])
elif btype == 5: # mf-condo
assert len(params) == 4
for i in range(4): set_value(e,sp,'Bldg Form','K%d' % (73+i), params[i])
elif btype == 6: # mxd-condo
assert len(params) == 5
for i in range(4): set_value(e,sp,'Bldg Form','K%d' % (73+i), params[i])
set_value(e,sp,'Bldg Form','K78', params[4])
elif btype in COMMERCIALTYPES_D: # commercial types
assert len(params) == 1
set_value(e,sp,'Bldg Form','K%d'%(COMMERCIALTYPES_D[btype]), params[0]*SQFTFACTOR)
d = proforma_inputs['proposal_component']
logger.set_verbosity_level(0)
d['sales_revenue'] = array([ 0, 0, 0, 0, 0])
d['rent_revenue'] = array([ 0, 0, 0, 0, 0])
d['leases_revenue'] = array([ 0, 0, 0, 0, 0])
if btype in [1,2]:
for i in range(4):
d['sales_revenue'][i] = \
max(sp.evaluate('Proforma Inputs!B%d' % (60+i)),0)
elif btype in [5,6]:
for i in range(4):
d['sales_revenue'][i] = \
max(sp.evaluate('Proforma Inputs!B%d' % (48+i)),0)
elif btype in [3,4]:
for i in range(4):
d['rent_revenue'][i] = \
max(sp.evaluate('Proforma Inputs!B%d' % (74+i)),0)
else:
d['leases_revenue'][4] = \
max(sp.evaluate('Proforma Inputs!B%d' % (92)),0)
d['sales_absorption'] = .2*d['sales_revenue']
d['rent_absorption'] = array([ 8, 4, 4, 8, 8])
d['leases_absorption'] = array([ 1, 1, 1, 1, 6])
proforma_inputs['proposal']['construction_cost'] = array(sp.evaluate('Proforma Inputs!B40'))
if DEBUG: print "COST:",proforma_inputs['proposal']['construction_cost']
if DEBUG: print "SALES REVENUE", d['sales_revenue']
if DEBUG: print d['rent_revenue']
if DEBUG: print d['leases_revenue']
if DEBUG: print d['sales_absorption']
if DEBUG: print d['rent_absorption']
if DEBUG: print d['leases_absorption']
from opus_core.tests.utils import variable_tester
po=['urbansim_parcel','urbansim']
v = variable_tester.VariableTester('proforma.py',po,proforma_inputs)
npv = v._get_attribute('npv')
#print npv, "\n\n"
return -1*npv/100000.0
