def estimate(self, spec_py=None, spec_var=None, spec_file=None):
t1 = time()
if spec_py is not None:
reload(spec_py)
spec_var = spec_py.specification
if spec_var is not None:
self.specification, variables, coefficents, equations, submodels = \
self.load_specification_from_variable(spec_var)
elif spec_file is not None:
self.specification = EquationSpecification(in_storage=self.storage)
self.specification.load(in_table_name=spec_file)
self.specification.set_dataset_name_of_variables("land_cover")
self.model_name = "land_cover_change_model"
choices = range(1, 15)
lccm = LandCoverChangeModel(choices, submodel_string="lct")
## 4. select (uncomment) from one the following choices of subsetted sampling files (agents_index)
# agents_index = where(self.lc1.get_attribute("sall_91_95_0"))[0]
# agents_index = where(self.lc1.get_attribute("sall_95_99_0"))[0]
agents_index = where(self.lc1.get_attribute("sall_99_02_0"))[0]
# agents_index = where(self.lc1.get_attribute("suburb91_95sample0"))[0]
# agents_index = where(self.lc1.get_attribute("suburb95_99sample0"))[0]
# agents_index = where(self.lc1.get_attribute("up91x95_old_samp0"))[0]
# agents_index = where(self.lc1.get_attribute("urbsamp95_99_0"))[0]
## need to include agents_index_all seperate for the calibration portion
## when using the dataset at the full extent, agents_index_all is needed as it is
## created from the lc1_all agents_set and matches the size of the input data
## 5. select (uncomment) from one the following choices of sampling files (agents_index) at full spatial extent
# agents_index_all = where(self.lc1_all.get_attribute("sall_91_95_0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("sall_95_99_0"))[0]
agents_index_all = where(self.lc1_all.get_attribute("sall_99_02_0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("suburb91_95sample0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("suburb95_99sample0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("up91x95_old_samp0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("urbsamp95_99_0"))[0]
coef, results = lccm.estimate(self.specification,
self.lc1,
self.lc2,
agents_index=agents_index,
debuglevel=4)
new_coef = lccm.calibrate(self.lc1_all, self.lc2_all, agents_index_all)
specification = lccm.specification
#save estimation results
out_suffix = spec_py.__name__[len(spec_py.__name__) - 11:]
specification.write(out_storage=self.storage,
out_table_name='lccm_specification_%sc' %
out_suffix)
new_coef.write(out_storage=self.storage,
out_table_name='lccm_coefficients_%sc' % out_suffix)
logger.log_status("Estimation done. %s s" % str(time() - t1))
def load_specification_from_variable(self, spec_var):
variables = []
coefficients = []
equations = []
submodels = []
try:
for sub_model, submodel_spec in spec_var.items():
if not isinstance(submodel_spec, dict):
raise ValueError, "Wrong specification format"
if submodel_spec.has_key("equation_ids"):
equation_ids = submodel_spec[
"equation_ids"] ## this retrieves eq_ids from spec.py - they're stored in equations then passed to the equation specifications
del submodel_spec["equation_ids"]
else:
equation_ids = None
for var, coefs in submodel_spec.items():
if not equation_ids:
equation_ids = range(1, len(coeffs) + 1)
for i in range(len(coefs)):
if coefs[i] != 0:
variables.append(var)
coefficients.append(coefs[i])
equations.append(equation_ids[i])
submodels.append(sub_model)
except:
raise ValueError, "Wrong specification format for submodel variable."
specification = EquationSpecification(variables=variables,
coefficients=coefficients,
equations=equations,
submodels=submodels)
return (specification, variables, coefficients, equations, submodels)
def estimate(self, spec_py=None, spec_var=None, spec_file=None):
t1 = time()
if spec_py is not None:
reload(spec_py)
spec_var = spec_py.specification
if spec_var is not None:
self.specification, variables, coefficents, equations, submodels = \
self.load_specification_from_variable(spec_var)
elif spec_file is not None:
self.specification = EquationSpecification(in_storage=self.storage)
self.specification.load(in_table_name=spec_file)
self.specification.set_dataset_name_of_variables("land_cover")
self.model_name = "land_cover_change_model"
choices = range(1,15)
lccm = LandCoverChangeModel(choices, submodel_string="lct")
## 4. select (uncomment) from one the following choices of subsetted sampling files (agents_index)
# agents_index = where(self.lc1.get_attribute("sall_91_95_0"))[0]
agents_index = where(self.lc1.get_attribute("sall_95_99_0"))[0]
# agents_index = where(self.lc1.get_attribute("sall_99_02_0b"))[0]
# agents_index = where(self.lc1.get_attribute("sa9902_9195_0"))[0]
# agents_index = where(self.lc1.get_attribute("sa9902_9599_0"))[0]
# agents_index = where(self.lc1.get_attribute("suburb91_95sample0"))[0]
# agents_index = where(self.lc1.get_attribute("suburb95_99sample0"))[0]
# agents_index = where(self.lc1.get_attribute("up91x95_old_samp0"))[0]
# agents_index = where(self.lc1.get_attribute("urbsamp95_99_0"))[0]
## need to include agents_index_all seperate for the calibration portion
## when using the dataset at the full extent, agents_index_all is needed as it is
## created from the lc1_all agents_set and matches the size of the input data
## 5. select (uncomment) from one the following choices of sampling files (agents_index) at full spatial extent
# agents_index_all = where(self.lc1_all.get_attribute("sall_91_95_0"))[0]
agents_index_all = where(self.lc1_all.get_attribute("sall_95_99_0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("sall_99_02_0b"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("sa9902_9195_0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("sa9902_9599_0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("suburb91_95sample0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("suburb95_99sample0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("up91x95_old_samp0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("urbsamp95_99_0"))[0]
coef, results = lccm.estimate(self.specification, self.lc1, self.lc2, agents_index=agents_index, debuglevel=4)
new_coef = lccm.calibrate(self.lc1_all, self.lc2_all, agents_index_all)
specification = lccm.specification
#save estimation results
# out_suffix = spec_py.__name__[len(spec_py.__name__) - 11:]
out_suffix = spec_py.__name__[30:]
specification.write(out_storage=self.storage, out_table_name='lccm_specification_%sc' % out_suffix)
new_coef.write(out_storage=self.storage, out_table_name='lccm_coefficients_%sc' % out_suffix)
logger.log_status("Estimation done. %s s" % str(time()-t1))
def run(self, base_directory, urbansim_cache_directory, years):
""" run the simulation
base_directory: directory contains all years folder of lccm.
urbansim_cache_directory: directory contains all years folder of urbansim cache.
years: lists of year to run."""
model = LandCoverChangeModel(self.possible_lcts, submodel_string=self.lct_attribute,
choice_attribute_name= self.lct_attribute, debuglevel=4)
coefficients = Coefficients()
storage = StorageFactory().get_storage('tab_storage',
storage_location=os.path.join(self.package_path, 'data'))
coefficients.load(in_storage=storage, in_table_name="land_cover_change_model_coefficients")
specification = EquationSpecification(in_storage=storage)
specification.load(in_table_name="land_cover_change_model_specification")
specification.set_variable_prefix("biocomplexity.land_cover.")
constants = Constants()
simulation_state = SimulationState()
simulation_state.set_cache_directory(urbansim_cache_directory)
attribute_cache = AttributeCache()
index = arange(100000)
for year in years:
simulation_state.set_current_time(year)
#land_cover_path = os.path.join(base_directory, str(year))
land_cover_path = base_directory
land_covers = LandCoverDataset(in_storage=StorageFactory().get_storage('flt_storage', storage_location=land_cover_path),
out_storage=StorageFactory().get_storage('flt_storage', storage_location=land_cover_path),
debuglevel=4)
land_covers.subset_by_index(index)
#land_covers.load_dataset()
gridcells = GridcellDataset(in_storage=attribute_cache, debuglevel=4)
agents_index = None
model.run(specification, coefficients, land_covers, data_objects={"gridcell":gridcells,
"constants":constants, "flush_variables":True},
chunk_specification = {'nchunks':1}
)
land_covers.flush_dataset()
del gridcells
del land_covers
def run(self, base_directory, urbansim_cache_directory, years):
""" run the simulation
base_directory: directory contains all years folder of lccm.
urbansim_cache_directory: directory contains all years folder of urbansim cache.
years: lists of year to run."""
model = LandCoverChangeModel(self.possible_lcts,
submodel_string=self.lct_attribute,
choice_attribute_name=self.lct_attribute,
debuglevel=4)
coefficients = Coefficients()
storage = StorageFactory().get_storage('tab_storage',
storage_location=os.path.join(
self.package_path, 'data'))
coefficients.load(in_storage=storage,
in_table_name="land_cover_change_model_coefficients")
specification = EquationSpecification(in_storage=storage)
specification.load(
in_table_name="land_cover_change_model_specification")
specification.set_variable_prefix("biocomplexity.land_cover.")
constants = Constants()
simulation_state = SimulationState()
simulation_state.set_cache_directory(urbansim_cache_directory)
attribute_cache = AttributeCache()
index = arange(100000)
for year in years:
simulation_state.set_current_time(year)
#land_cover_path = os.path.join(base_directory, str(year))
land_cover_path = base_directory
land_covers = LandCoverDataset(
in_storage=StorageFactory().get_storage(
'flt_storage', storage_location=land_cover_path),
out_storage=StorageFactory().get_storage(
'flt_storage', storage_location=land_cover_path),
debuglevel=4)
land_covers.subset_by_index(index)
#land_covers.load_dataset()
gridcells = GridcellDataset(in_storage=attribute_cache,
debuglevel=4)
agents_index = None
model.run(specification,
coefficients,
land_covers,
data_objects={
"gridcell": gridcells,
"constants": constants,
"flush_variables": True
},
chunk_specification={'nchunks': 1})
land_covers.flush_dataset()
del gridcells
del land_covers
class LCCMEstimator(Estimator):
def __init__(self, **kargs):
# Estimator.__init__(self, settings=None, run_land_price_model_before_estimation=False, **kargs) # <-- old __init__
# Estimator.__init__(self, config=None, save_estimation_results=True) # <-- new __init__ doesn't work, but not needed
parent_dir_path = package().get_package_parent_path()
package_path = OpusPackage().get_path_for_package("biocomplexity")
self.storage = StorageFactory().get_storage(
'tab_storage', storage_location=os.path.join(package_path, 'data'))
## 1. directory path of full (4 county spatial extent) dataset
flt_directory = os.path.join(parent_dir_path, "biocomplexity", "data",
"LCCM_4County")
## 2. select (uncomment) from one the following choices of directory pathes of subsetted sample input data/variables
# flt_directory_est = os.path.join(parent_dir_path, "biocomplexity", "data", "LCCM_small_test_set_opus")
flt_directory_est = os.path.join(parent_dir_path, "biocomplexity",
"data", "data_for_estimation_all")
# flt_directory_est = os.path.join(parent_dir_path, "biocomplexity", "data", "data_for_estimation_all_orig")
# flt_directory_est = os.path.join(parent_dir_path, "biocomplexity", "data", "data_for_suburban_orig")
# flt_directory_est = os.path.join(parent_dir_path, "biocomplexity", "data", "data_for_urban")
# flt_directory_est = os.path.join(parent_dir_path, "biocomplexity", "data", "data_for_urban_orig")
## note - must rename lct-forusewith91sample.Float32 to lct.lf4 if doing 1991-1995
## note - must rename lct-forusewith95sample.Float32 to lct.lf4 if doing 1995-1999
## 3. select (uncomment) from one the following choices of land cover data (input data) date pairs (years)
# years = [1991, 1995]
years = [1995, 1999]
# years = [1999, 2002]
self.lc1 = LandCoverDataset(in_storage=StorageFactory().get_storage(
"flt_storage",
storage_location=os.path.join(flt_directory_est, str(years[0]))),
resources=Resources({"lowercase": 1}))
self.lc2 = LandCoverDataset(in_storage=StorageFactory().get_storage(
"flt_storage",
storage_location=os.path.join(flt_directory_est, str(years[1]))),
resources=Resources({"lowercase": 1}))
self.lc1_all = LandCoverDataset(
in_storage=StorageFactory().get_storage(
"flt_storage",
storage_location=os.path.join(flt_directory, str(years[0]))),
resources=Resources({"lowercase": 1}))
self.lc1_all.flush_dataset()
self.lc2_all = LandCoverDataset(
in_storage=StorageFactory().get_storage(
"flt_storage",
storage_location=os.path.join(flt_directory, str(years[1]))),
resources=Resources({"lowercase": 1}))
self.lc2_all.flush_dataset()
def estimate(self, spec_py=None, spec_var=None, spec_file=None):
t1 = time()
if spec_py is not None:
reload(spec_py)
spec_var = spec_py.specification
if spec_var is not None:
self.specification, variables, coefficents, equations, submodels = \
self.load_specification_from_variable(spec_var)
elif spec_file is not None:
self.specification = EquationSpecification(in_storage=self.storage)
self.specification.load(in_table_name=spec_file)
self.specification.set_dataset_name_of_variables("land_cover")
self.model_name = "land_cover_change_model"
choices = range(1, 15)
lccm = LandCoverChangeModel(choices, submodel_string="lct")
## 4. select (uncomment) from one the following choices of subsetted sampling files (agents_index)
# agents_index = where(self.lc1.get_attribute("sall_91_95_0"))[0]
agents_index = where(self.lc1.get_attribute("sall_95_99_0"))[0]
# agents_index = where(self.lc1.get_attribute("sall_99_02_0b"))[0]
# agents_index = where(self.lc1.get_attribute("sa9902_9195_0"))[0]
# agents_index = where(self.lc1.get_attribute("sa9902_9599_0"))[0]
# agents_index = where(self.lc1.get_attribute("suburb91_95sample0"))[0]
# agents_index = where(self.lc1.get_attribute("suburb95_99sample0"))[0]
# agents_index = where(self.lc1.get_attribute("up91x95_old_samp0"))[0]
# agents_index = where(self.lc1.get_attribute("urbsamp95_99_0"))[0]
## need to include agents_index_all seperate for the calibration portion
## when using the dataset at the full extent, agents_index_all is needed as it is
## created from the lc1_all agents_set and matches the size of the input data
## 5. select (uncomment) from one the following choices of sampling files (agents_index) at full spatial extent
# agents_index_all = where(self.lc1_all.get_attribute("sall_91_95_0"))[0]
agents_index_all = where(self.lc1_all.get_attribute("sall_95_99_0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("sall_99_02_0b"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("sa9902_9195_0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("sa9902_9599_0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("suburb91_95sample0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("suburb95_99sample0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("up91x95_old_samp0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("urbsamp95_99_0"))[0]
coef, results = lccm.estimate(self.specification,
self.lc1,
self.lc2,
agents_index=agents_index,
debuglevel=4)
new_coef = lccm.calibrate(self.lc1_all, self.lc2_all, agents_index_all)
specification = lccm.specification
#save estimation results
# out_suffix = spec_py.__name__[len(spec_py.__name__) - 11:]
out_suffix = spec_py.__name__[30:]
specification.write(out_storage=self.storage,
out_table_name='lccm_specification_%sc' %
out_suffix)
new_coef.write(out_storage=self.storage,
out_table_name='lccm_coefficients_%sc' % out_suffix)
logger.log_status("Estimation done. %s s" % str(time() - t1))
def load_specification_from_variable(self, spec_var):
variables = []
coefficients = []
equations = []
submodels = []
try:
for sub_model, submodel_spec in spec_var.items():
if not isinstance(submodel_spec, dict):
raise ValueError, "Wrong specification format"
if submodel_spec.has_key("equation_ids"):
equation_ids = submodel_spec[
"equation_ids"] ## this retrieves eq_ids from spec.py - they're stored in equations then passed to the equation specifications
del submodel_spec["equation_ids"]
else:
equation_ids = None
for var, coefs in submodel_spec.items():
if not equation_ids:
equation_ids = range(1, len(coeffs) + 1)
for i in range(len(coefs)):
if coefs[i] != 0:
variables.append(var)
coefficients.append(coefs[i])
equations.append(equation_ids[i])
submodels.append(sub_model)
except:
raise ValueError, "Wrong specification format for submodel variable."
specification = EquationSpecification(variables=variables,
coefficients=coefficients,
equations=equations,
submodels=submodels)
return (specification, variables, coefficients, equations, submodels)
class LCCMEstimator(Estimator):
def __init__(self, **kargs):
# Estimator.__init__(self, settings=None, run_land_price_model_before_estimation=False, **kargs) # <-- old __init__
# Estimator.__init__(self, config=None, save_estimation_results=True) # <-- new __init__ doesn't work, but not needed
parent_dir_path = package().get_package_parent_path()
package_path = OpusPackage().get_path_for_package("biocomplexity")
self.storage = StorageFactory().get_storage('tab_storage',
storage_location=os.path.join(package_path, 'data'))
## 1. directory path of full (4 county spatial extent) dataset
flt_directory = os.path.join(parent_dir_path, "biocomplexity", "data", "LCCM_4County")
## 2. select (uncomment) from one the following choices of directory pathes of subsetted sample input data/variables
# flt_directory_est = os.path.join(parent_dir_path, "biocomplexity", "data", "LCCM_small_test_set_opus")
flt_directory_est = os.path.join(parent_dir_path, "biocomplexity", "data", "data_for_estimation_all")
# flt_directory_est = os.path.join(parent_dir_path, "biocomplexity", "data", "data_for_estimation_all_orig")
# flt_directory_est = os.path.join(parent_dir_path, "biocomplexity", "data", "data_for_suburban_orig")
# flt_directory_est = os.path.join(parent_dir_path, "biocomplexity", "data", "data_for_urban")
# flt_directory_est = os.path.join(parent_dir_path, "biocomplexity", "data", "data_for_urban_orig")
## note - must rename lct-forusewith91sample.Float32 to lct.lf4 if doing 1991-1995
## note - must rename lct-forusewith95sample.Float32 to lct.lf4 if doing 1995-1999
## 3. select (uncomment) from one the following choices of land cover data (input data) date pairs (years)
# years = [1991, 1995]
years = [1995, 1999]
# years = [1999, 2002]
self.lc1 = LandCoverDataset(in_storage = StorageFactory().get_storage("flt_storage",
storage_location = os.path.join(flt_directory_est, str(years[0]))),
resources=Resources({"lowercase":1}))
self.lc2 = LandCoverDataset(in_storage = StorageFactory().get_storage("flt_storage",
storage_location = os.path.join(flt_directory_est, str(years[1]))),
resources=Resources({"lowercase":1}))
self.lc1_all = LandCoverDataset(in_storage = StorageFactory().get_storage("flt_storage",
storage_location = os.path.join(flt_directory, str(years[0]))),
resources=Resources({"lowercase":1}))
self.lc1_all.flush_dataset()
self.lc2_all = LandCoverDataset(in_storage = StorageFactory().get_storage("flt_storage",
storage_location = os.path.join(flt_directory, str(years[1]))),
resources=Resources({"lowercase":1}))
self.lc2_all.flush_dataset()
def estimate(self, spec_py=None, spec_var=None, spec_file=None):
t1 = time()
if spec_py is not None:
reload(spec_py)
spec_var = spec_py.specification
if spec_var is not None:
self.specification, variables, coefficents, equations, submodels = \
self.load_specification_from_variable(spec_var)
elif spec_file is not None:
self.specification = EquationSpecification(in_storage=self.storage)
self.specification.load(in_table_name=spec_file)
self.specification.set_dataset_name_of_variables("land_cover")
self.model_name = "land_cover_change_model"
choices = range(1,15)
lccm = LandCoverChangeModel(choices, submodel_string="lct")
## 4. select (uncomment) from one the following choices of subsetted sampling files (agents_index)
# agents_index = where(self.lc1.get_attribute("sall_91_95_0"))[0]
agents_index = where(self.lc1.get_attribute("sall_95_99_0"))[0]
# agents_index = where(self.lc1.get_attribute("sall_99_02_0b"))[0]
# agents_index = where(self.lc1.get_attribute("sa9902_9195_0"))[0]
# agents_index = where(self.lc1.get_attribute("sa9902_9599_0"))[0]
# agents_index = where(self.lc1.get_attribute("suburb91_95sample0"))[0]
# agents_index = where(self.lc1.get_attribute("suburb95_99sample0"))[0]
# agents_index = where(self.lc1.get_attribute("up91x95_old_samp0"))[0]
# agents_index = where(self.lc1.get_attribute("urbsamp95_99_0"))[0]
## need to include agents_index_all seperate for the calibration portion
## when using the dataset at the full extent, agents_index_all is needed as it is
## created from the lc1_all agents_set and matches the size of the input data
## 5. select (uncomment) from one the following choices of sampling files (agents_index) at full spatial extent
# agents_index_all = where(self.lc1_all.get_attribute("sall_91_95_0"))[0]
agents_index_all = where(self.lc1_all.get_attribute("sall_95_99_0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("sall_99_02_0b"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("sa9902_9195_0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("sa9902_9599_0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("suburb91_95sample0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("suburb95_99sample0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("up91x95_old_samp0"))[0]
# agents_index_all = where(self.lc1_all.get_attribute("urbsamp95_99_0"))[0]
coef, results = lccm.estimate(self.specification, self.lc1, self.lc2, agents_index=agents_index, debuglevel=4)
new_coef = lccm.calibrate(self.lc1_all, self.lc2_all, agents_index_all)
specification = lccm.specification
#save estimation results
# out_suffix = spec_py.__name__[len(spec_py.__name__) - 11:]
out_suffix = spec_py.__name__[30:]
specification.write(out_storage=self.storage, out_table_name='lccm_specification_%sc' % out_suffix)
new_coef.write(out_storage=self.storage, out_table_name='lccm_coefficients_%sc' % out_suffix)
logger.log_status("Estimation done. %s s" % str(time()-t1))
def load_specification_from_variable(self, spec_var):
variables = []
coefficients = []
equations = []
submodels = []
try:
for sub_model, submodel_spec in spec_var.items():
if not isinstance(submodel_spec, dict):
raise ValueError, "Wrong specification format"
if submodel_spec.has_key("equation_ids"):
equation_ids = submodel_spec["equation_ids"] ## this retrieves eq_ids from spec.py - they're stored in equations then passed to the equation specifications
del submodel_spec["equation_ids"]
else:
equation_ids = None
for var, coefs in submodel_spec.items():
if not equation_ids:
equation_ids = range(1, len(coeffs)+1)
for i in range(len(coefs)):
if coefs[i] != 0:
variables.append(var)
coefficients.append(coefs[i])
equations.append(equation_ids[i])
submodels.append(sub_model)
except:
raise ValueError, "Wrong specification format for submodel variable."
specification = EquationSpecification(variables=variables,
coefficients=coefficients,
equations = equations,
submodels=submodels)
return (specification, variables, coefficients, equations, submodels)
def run(self, base_directory, urbansim_cache_directory, years, output_directory, temp_folder,
coefficients_name, specification_name, convert_flt=True, convert_input=False):
""" run the simulation
base_directory: directory contains all years folder of lccm.
urbansim_cache_directory: directory contains all years folder of urbansim cache.
years: lists of year to run."""
model = LandCoverChangeModel(self.possible_lcts, submodel_string=self.lct_attribute,
choice_attribute_name=self.lct_attribute, debuglevel=4)
coefficients = Coefficients()
storage = StorageFactory().get_storage('tab_storage',
storage_location=os.path.join(self.package_path, 'data'))
coefficients.load(in_storage=storage, in_table_name=coefficients_name)
specification = EquationSpecification(in_storage=storage)
specification.load(in_table_name=specification_name)
specification.set_variable_prefix("biocomplexity.land_cover.")
constants = Constants()
simulation_state = SimulationState()
simulation_state.set_cache_directory(urbansim_cache_directory)
attribute_cache = AttributeCache()
SessionConfiguration(new_instance=True,
package_order=['biocomplexity', 'urbansim', 'opus_core'],
in_storage=AttributeCache())
ncols = LccmConfiguration.ncols
if temp_folder is None:
self.temp_land_cover_dir = tempfile.mkdtemp()
else:
self.temp_land_cover_dir = temp_folder
for year in years:
land_cover_path = self._generate_input_land_cover(year, base_directory, urbansim_cache_directory,
years, output_directory, convert_flt, convert_input)
#max_size = 174338406 (orig) - act. int: 19019944 (37632028 incl NoData)
max_size = self._get_max_index(land_cover_path) # 1st instance of lc_dataset - but looks like a 'lite' version
offset = min(LccmConfiguration.offset, max_size)
s = 0
t = offset
while (s < t and t <= max_size):
logger.log_status("Offset: ", s, t)
index = arange(s,t)
land_cover_cache_path=os.path.join(urbansim_cache_directory,str(year),'land_covers')
self._clean_up_land_cover_cache(land_cover_cache_path)
simulation_state.set_current_time(year)
# 2nd instance of lc_dataset
land_covers = LandCoverDataset(in_storage=StorageFactory().get_storage('flt_storage', storage_location=land_cover_path),
out_storage=StorageFactory().get_storage('flt_storage', storage_location=land_cover_path),
debuglevel=4)
land_covers.subset_by_index(index)
# land_covers.load_dataset()
gridcells = GridcellDataset(in_storage=attribute_cache, debuglevel=4)
agents_index = None
model.run(specification, coefficients, land_covers, data_objects={"gridcell":gridcells,
"constants":constants, "flush_variables":True},
chunk_specification = {'nchunks':5}) ## chunk size set here
land_covers.flush_dataset()
del gridcells
del land_covers
# self._generate_output_flt(year, urbansim_cache_directory, output_directory, convert_flt)
self._generate_output_flt2(year, urbansim_cache_directory, output_directory, convert_flt)
if t >= max_size: break
s = max(t-10*ncols,s)
t = min(t+offset-10*ncols,max_size)
# clean up temp storage after done simulation
shutil.rmtree(self.temp_land_cover_dir)