def get_model_category_configs():
"""
Returns dictionary where key is model category name and value is dictionary
of model category attributes, including individual model config filename(s)
"""
with open(os.path.join(misc.configs_dir(), 'yaml_configs.yaml')) as f:
yaml_configs = yaml.load(f)
with open(os.path.join(misc.configs_dir(), 'model_structure.yaml')) as f:
model_category_configs = yaml.load(f)['models']
for model_category, category_attributes in model_category_configs.items():
category_attributes['config_filenames'] = yaml_configs[model_category]
return model_category_configs
def settings():
with open(os.path.join(misc.configs_dir(), "settings.yaml")) as f:
settings = yaml.load(f)
# monkey patch on the settings object since it's pretty global
# but will also be available as injectable
orca.settings = settings
return settings
def settings():
with open(os.path.join(misc.configs_dir(), "settings.yaml")) as f:
settings = yaml.load(f)
# monkey patch on the settings object since it's pretty global
# but will also be available as injectable
orca.settings = settings
return settings
def ual_settings():
"""
This step loads the UAL settings, which are kept separate for clarity.
Data expectations
-----------------
- 'configs' folder contains a file called 'ual_settings.yaml'
- 'os.path' is expected to provide the root level of the urbansim instance, so be sure
to either (a) launch the python process from that directory, or (b) use os.chdir to
switch to that directory before running any model steps
"""
with open(os.path.join(misc.configs_dir(), 'ual_settings.yaml')) as f:
return yaml.load(f)
def mapping():
with open(os.path.join(misc.configs_dir(), "mapping.yaml")) as f:
return yaml.load(f)
def inputs():
with open(os.path.join(misc.configs_dir(), "inputs.yaml")) as f:
return yaml.load(f)
def policy():
with open(os.path.join(misc.configs_dir(), "policy.yaml")) as f:
return yaml.load(f)
def hazards():
with open(os.path.join(misc.configs_dir(), "hazards.yaml")) as f:
return yaml.load(f)
def settings():
with open(os.path.join(misc.configs_dir(), "legacy_settings.yaml")) as f:
settings = yaml.load(f)
orca.settings = settings
return settings
def tables_in_base_year():
h5store = pd.HDFStore(os.path.join(misc.data_dir(),
yamlio.yaml_to_dict(str_or_buffer=os.path.join(misc.configs_dir(),
"settings.yaml"))['store']), mode="r")
store_table_names = orca.get_injectable('store_table_names_dict')
return [t for t in orca.list_tables() if t in h5store or store_table_names.get(t, None) in h5store]
def func():
with open(os.path.join(misc.configs_dir(), yaml_file)) as f:
config = yaml.load(f)
return config
def settings():
with open(os.path.join(misc.configs_dir(), "settings.yaml")) as f:
settings = yaml.load(f)
orca.settings = settings
return settings