def test_nested_dict(self):
nested_dict = {'key': {'nested_dict': 'a value'}}
actual = crit_dim_var(nested_dict)
expected = False
assert actual == expected
nested_dict = {'another_key': {}, 'key': []}
actual = crit_dim_var(nested_dict)
expected = True
assert actual == expected
def replace_variable(_user_defined_vars, strategy_vars):
"""Replace default strategy variables with user defined variables
Arguments
----------
_user_defined_vars : dict
User defined strategy variables
strategy_vars : dict
Default strategy variables
Returns
-------
strategy_vars : dict
Updated strategy variables with user defined inputs
"""
for new_var, new_var_vals in _user_defined_vars.items():
crit_single_dim = narrative_related.crit_dim_var(new_var_vals)
if crit_single_dim:
strategy_vars[new_var] = new_var_vals
else:
for sub_var_name, sector_sub_var in new_var_vals.items():
if type(sector_sub_var) is dict:
strategy_vars[new_var][sub_var_name] = {}
for sector, sub_var in sector_sub_var.items():
strategy_vars[new_var][sub_var_name][sector] = sub_var
else:
strategy_vars[new_var][sub_var_name] = sector_sub_var
strategy_vars[new_var] = strategy_vars[new_var]
return strategy_vars
def test_nested_dict_in_list(self):
nested_dict_in_list = [{'key': {'nested_dict': 'a value'}}]
actual = crit_dim_var(nested_dict_in_list)
expected = False
print("AC ULT " + str(actual))
assert actual == expected
def spatial_explicit_modelling_strategy_vars(strategy_vars,
spatially_modelled_vars, regions,
fuel_disagg, f_reg, f_reg_norm,
f_reg_norm_abs):
"""
Spatial explicit modelling of scenario variables
based on narratives. Convert strategy variables
(e.g. country level) to regional variables.
Arguments
---------
strategy_vars : dict
All non_spatial strategy variables
spatially_modelled_vars : dict
All spatial variables
regions : dict
Regions
fuel_disagg : dict
Disaggregated fuel
f_reg, f_reg_norm, f_reg_norm_abs : dict
Spatial factors
"""
regional_vars = {}
# Iterate strategy variables and calculate regional variable
for var_name, strategy_var in strategy_vars.items():
logging.info("...Spatially explicit diffusion modelling %s", var_name)
single_dim_var = narrative_related.crit_dim_var(strategy_var)
if single_dim_var:
regional_vars[var_name] = get_regional_narrative(
var_name, strategy_var, spatially_modelled_vars, regions,
fuel_disagg, f_reg, f_reg_norm, f_reg_norm_abs)
else:
regional_vars[var_name] = {}
for sub_var_name, sector_sub_strategy_var in strategy_var.items():
regional_vars[var_name][sub_var_name] = {}
if type(sector_sub_strategy_var) is dict: # sectors defined
for sector, sub_strategy_var in sector_sub_strategy_var.items(
):
regional_vars[var_name][sub_var_name][
sector] = get_regional_narrative(
sub_var_name, sub_strategy_var,
spatially_modelled_vars, regions, fuel_disagg,
f_reg, f_reg_norm, f_reg_norm_abs)
else:
regional_vars[var_name][
sub_var_name] = get_regional_narrative(
sub_var_name, sector_sub_strategy_var,
spatially_modelled_vars, regions, fuel_disagg,
f_reg, f_reg_norm, f_reg_norm_abs)
regional_vars[var_name] = dict(regional_vars[var_name])
return regional_vars
def generate_default_parameter_narratives(
default_streategy_vars,
end_yr=2050,
base_yr=2015
):
"""Load default parameters and create default timesteps
Arguments
---------
default_streategy_vars : dict
Default parameter values
end_yr : int
Simulation end year
base_yr : int
Base year
Returns
-------
strategy_vars : dict
Strategy variables
"""
strategy_vars = defaultdict(dict)
# Create default narrative for every simulation parameter
for var_name, var_entries in default_streategy_vars.items():
crit_single_dim = narrative_related.crit_dim_var(var_entries)
if crit_single_dim:
# Create default narrative with only one timestep from simulation base year to simulation end year
strategy_vars[var_name] = narrative_related.default_narrative(
end_yr=end_yr,
value_by=var_entries['default_value'],
value_ey=var_entries['default_value'],
diffusion_choice=var_entries['diffusion_type'],
base_yr=base_yr,
regional_specific=var_entries['regional_specific']) # Criteria whether the same for all regions or not
else:
# Standard narrative for multidimensional narrative
for sub_var_name, sub_var_entries in var_entries.items():
try:
scenario_value = sub_var_entries['scenario_value']
except TypeError:
raise TypeError("{}: Var_entries dict: {}".format(var_name, var_entries))
# Crate single-step default narratives (up to end_year)
strategy_vars[var_name][sub_var_name] = narrative_related.default_narrative(
end_yr=end_yr,
value_by=sub_var_entries['default_value'],
value_ey=scenario_value,
diffusion_choice=sub_var_entries['diffusion_type'],
base_yr=base_yr,
regional_specific=sub_var_entries['regional_specific'])
return dict(strategy_vars)
def test_crit_list(self):
a_list = []
actual = crit_dim_var(a_list)
expected = True
assert actual == expected
the_bug = {
'enduse': [],
'diffusion_type': 'linear',
'default_value': 15.5,
'name': 'ss_t_base_heating',
'sector': True,
'regional_specific': False,
'description': 'Base temperature',
'scenario_value': 15.5
}
actual = crit_dim_var(the_bug)
expected = True
assert actual == expected
def test_crit_dim_var_buggy(self):
fixture = [{
'sig_steepness': 1,
'sector': 'dummy_sector',
'diffusion_choice': 'linear',
'sig_midpoint': 0,
'regional_specific': True,
'base_yr': 2015,
'value_ey': 0.05,
'value_by': 0.05,
'regional_vals_ey': {
'W06000023': 0.05,
'W06000010': 0.05
},
'regional_vals_by': {
'W06000023': 0.05,
'W06000010': 0.05
},
'end_yr': 2030,
'enduse': [],
'default_by': 0.05
}, {
'sig_steepness': 1,
'sector': 'dummy_sector',
'diffusion_choice': 'linear',
'sig_midpoint': 0,
'regional_specific': True,
'base_yr': 2030,
'value_ey': 0.05,
'value_by': 0.05,
'regional_vals_ey': {
'W06000023': 0.05,
'W06000010': 0.05
},
'regional_vals_by': {
'W06000023': 0.05,
'W06000010': 0.05
},
'end_yr': 2050,
'enduse': [],
'default_by': 0.05
}]
actual = crit_dim_var(fixture)
expected = True
assert actual == expected
def test_crit_dim_var_ordered(self):
the_bug = OrderedDict()
the_bug['a'] = {
'diffusion_type': 'linear',
'default_value': 15.5,
'name': 'ss_t_base_heating',
'sector': True,
'regional_specific': False,
'description': 'Base temperature',
'scenario_value': 15.5,
'enduse': []
}
the_bug['b'] = 'vakye'
actual = crit_dim_var(the_bug)
expected = False
assert actual == expected
def test_crit_in_list(self):
crit_in_list = [{
'sig_midpoint': 0,
'value_by': 5,
'diffusion_choice': 'linear',
'fueltype_replace': 0,
'regional_specific': False,
'base_yr': 2015,
'value_ey': 5,
'sig_steepness': 1,
'end_yr': 2050,
'fueltype_new': 0
}]
actual = crit_dim_var(crit_in_list)
expected = True
print("AC ULT " + str(actual))
assert actual == expected
def autocomplete_strategy_vars(
strategy_vars,
narrative_crit=False
):
"""Autocomplete all narratives or strategy variables with
and 'enduse' or 'sector' in case they are not defined.
Arguments
----------
strategy_vars
Returns
-------
narrative_crit : bool
Criteria wheter inputs are a narrative or not
"""
if not narrative_crit:
out_dict = defaultdict(dict)
for var_name, var_entries in strategy_vars.items():
crit_single_dim = narrative_related.crit_dim_var(var_entries)
if crit_single_dim:
out_dict[var_name] = var_entries
# If no 'enduse' defined, add empty list of affected enduses
out_dict[var_name]['scenario_value'] = var_entries['default_value']
if 'enduse' not in var_entries:
out_dict[var_name]['enduse'] = []
if 'sector' not in var_entries:
out_dict[var_name]['sector'] = True # All sector
else:
for sub_var_name, sub_var_entries in var_entries.items():
out_dict[var_name][sub_var_name] = sub_var_entries
out_dict[var_name][sub_var_name]['scenario_value'] = sub_var_entries['default_value']
# If no 'enduse' defined, add empty list of affected enduses
if 'enduse' not in sub_var_entries:
out_dict[var_name][sub_var_name]['enduse'] = []
if 'sector' not in sub_var_entries:
out_dict[var_name][sub_var_name]['sector'] = True # All sector
else:
# Same but narratives which need to be iterated
out_dict = {}
for var_name, var_entries in strategy_vars.items():
out_dict[var_name] = {}
crit_single_dim = narrative_related.crit_dim_var(var_entries)
if crit_single_dim:
updated_narratives = []
for narrative in var_entries:
#If no 'enduse' defined, add empty list of affected enduses
if 'enduse' not in narrative:
narrative['enduse'] = []
if 'sector' not in narrative:
narrative['sector'] = 'dummy_sector' # All sector
updated_narratives.append(narrative)
out_dict[var_name] = updated_narratives
else:
#print(" ...user defined variable: %s", var_name)
for sub_var_name, sector_sub_var_entries in var_entries.items():
if type(sector_sub_var_entries) is dict: # If sectors are defined
for sector, sub_var_entries in sector_sub_var_entries.items():
out_dict[var_name][sub_var_name] = {}
updated_narratives = []
for narrative in sub_var_entries:
# If no 'enduse' defined, add empty list of affected enduses
if 'enduse' not in narrative:
narrative['enduse'] = []
if 'sector' not in narrative:
narrative['sector'] = sector
updated_narratives.append(narrative)
out_dict[var_name][sub_var_name][sector] = updated_narratives
else: # no sectors defined
updated_narratives = []
for narrative in sector_sub_var_entries:
if 'enduse' not in narrative:
narrative['enduse'] = [sub_var_name]
updated_narratives.append(narrative)
out_dict[var_name][sub_var_name] = updated_narratives
return dict(out_dict)
def generate_annual_param_vals(
regions,
strategy_vars,
sim_yrs,
):
"""
Calculate parameter values for every year based
on defined narratives and also add a generic
container of other information necessary
for parameter.
Inputs
-------
regions : dict
Regions
strategy_vars : dict4
Strategy variable infirmation
sim_yrs : list
Simulated years
Returns
-------
container_reg_param : dict
Values for all simulated years for every region (all parameters for which values
are provided for every region)
container_non_reg_param : dict
Values for all simulated years (all the same for very region)
"""
reg_param = {}
for region in regions:
reg_param[region] = {}
non_reg_param = {}
for var_name, strategy_vars_values in strategy_vars.items():
for region in regions:
reg_param[region][var_name] = {}
non_reg_param[var_name] = {}
single_dim_var = narrative_related.crit_dim_var(
strategy_vars_values)
logging.debug("Calculating annual values for parameter: {} {}".format(var_name, single_dim_var))
if single_dim_var:
# Additional dictionary passed along with every variable containing additional information
param_info = {}
try:
param_info['fueltype_replace'] = strategy_vars_values[0]['fueltype_replace']
param_info['fueltype_new'] = strategy_vars_values[0]['fueltype_new']
except KeyError:
pass
# Calculate annual parameter value
regional_strategy_vary = generate_general_parameter(
regions=regions,
narratives=strategy_vars_values,
sim_yrs=sim_yrs)
# Test if regional specific or not based on first narrative
for narrative in strategy_vars_values[:1]:
reg_specific_crit = narrative['regional_specific']
if reg_specific_crit:
for region in regions:
reg_param[region][var_name] = regional_strategy_vary[region]
reg_param[region][var_name]['param_info'] = param_info
else:
non_reg_param[var_name] = regional_strategy_vary
non_reg_param[var_name]['param_info'] = param_info
else:
if not hasattr(strategy_vars_values, 'items'):
msg = "Error in {}. Object is not a dict, but a {}: {}"
raise TypeError(msg.format(var_name,
type(strategy_vars_values),
strategy_vars_values))
for sub_var_name, sector_sub_var_values in strategy_vars_values.items():
for region in regions:
reg_param[region][var_name][sub_var_name] = {}
non_reg_param[var_name][sub_var_name] = {}
if type(sector_sub_var_values) is dict:
for sector, sub_var_values in sector_sub_var_values.items():
if var_name == 'generic_fuel_switch':
all_new_fueltypes = []
for narrative in sub_var_values:
all_new_fueltypes.append(narrative['fueltype_new'])
for new_fueltype in all_new_fueltypes:
# Get all narratives of this fueltype
narratives = []
for narrative in sub_var_values:
if narrative['fueltype_new'] == new_fueltype:
narratives.append(narrative)
param_info = {}
param_info['fueltype_new'] = int(new_fueltype)
param_info['fueltype_replace'] = narrative['fueltype_replace']
# Calculate annual parameter value
regional_strategy_vary = generate_general_parameter(
regions=regions,
narratives=narratives,
sim_yrs=sim_yrs)
if narrative['regional_specific']:
for region in regions:
reg_param[region][var_name][sub_var_name][sector][param_info['fueltype_new']] = dict(regional_strategy_vary[region])
reg_param[region][var_name][sub_var_name][sector][param_info['fueltype_new']]['param_info'] = param_info
else:
non_reg_param[var_name][sub_var_name][sector][param_info['fueltype_new']] = regional_strategy_vary
non_reg_param[var_name][sub_var_name][sector][param_info['fueltype_new']]['param_info'] = param_info
else:
# Calculate annual parameter value
regional_strategy_vary = generate_general_parameter(
regions=regions,
narratives=sub_var_values,
sim_yrs=sim_yrs)
# Test if regional specific or not based on first narrative
for narrative in sub_var_values[:1]:
reg_specific_crit = narrative['regional_specific']
if reg_specific_crit:
for region in regions:
reg_param[region][var_name][sub_var_name][sector] = dict(regional_strategy_vary[region])
else:
non_reg_param[var_name][sub_var_name][sector] = regional_strategy_vary
# Single dimensional
else:
param_info = {}
if var_name == 'generic_fuel_switch':
all_new_fueltypes = []
for narrative in sector_sub_var_values:
all_new_fueltypes.append(narrative['fueltype_new'])
for new_fueltype in all_new_fueltypes:
narratives = []
for narrative in sector_sub_var_values:
if narrative['fueltype_new'] == new_fueltype:
narratives.append(narrative)
# Calculate annual parameter value
regional_strategy_vary = generate_general_parameter(
regions=regions,
narratives=narratives,
sim_yrs=sim_yrs)
param_info = {}
param_info['fueltype_replace'] = narrative['fueltype_replace']
param_info['fueltype_new'] = new_fueltype
if narrative['regional_specific']:
for region in regions:
reg_param[region][var_name][sub_var_name][param_info['fueltype_new']] = dict(regional_strategy_vary[region])
reg_param[region][var_name][sub_var_name][param_info['fueltype_new']]['param_info'] = param_info
else:
non_reg_param[var_name][sub_var_name][param_info['fueltype_new']] = regional_strategy_vary
non_reg_param[var_name][sub_var_name][param_info['fueltype_new']]['param_info'][param_info['fueltype_replace']] = param_info
else:
# Calculate annual parameter value
regional_strategy_vary = generate_general_parameter(
regions=regions,
narratives=sector_sub_var_values,
sim_yrs=sim_yrs)
# Test if regional specific or not based on first narrative
for narrative in sector_sub_var_values[:1]:
reg_specific_crit = narrative['regional_specific']
if reg_specific_crit:
for region in regions:
reg_param[region][var_name][sub_var_name] = dict(regional_strategy_vary[region])
else:
non_reg_param[var_name][sub_var_name] = regional_strategy_vary
return dict(reg_param), dict(non_reg_param)