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Wrapper runs in smif 0.6
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@RoaldL
RoaldL committed Feb 2, 2018
1 parent 934fa25 commit 2e7f7c2
Showing 1 changed file with 60 additions and 62 deletions.
122 changes: 60 additions & 62 deletions run.py
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Expand Up @@ -36,22 +36,21 @@
class EDWrapper(SectorModel):
"""Energy Demand Wrapper
"""
def before_model_run(self, data=None):
"""Runs prior to any ``simulate()`` step
def __init__(self, name):
super().__init__(name)

Writes scenario data out into the scenario files
self.user_data = {}

Saves useful data into the ``self.user_data`` dictionary for access
in the ``simulate()`` method
def before_model_run(self, data_handle):
"""Implement this method to conduct pre-model run tasks
Data is accessed using the `get_scenario_data()` method is provided
as a numpy array with the dimensions timesteps-by-regions-by-intervals.
Info
-----
`self.user_data` allows to pass data from before_model_run to main model
Arguments
---------
data_handle: smif.data_layer.DataHandle
Access parameter values (before any model is run, no dependency
input data or state is guaranteed to be available)
"""
data = defaultdict(dict, data)
data = defaultdict(dict)

# Criteria
data['criterias']['mode_constrained'] = False # True: Technologies are defined in ED model and fuel is provided, False: Heat is delievered not per technologies
Expand All @@ -65,7 +64,7 @@ def before_model_run(self, data=None):
data['criterias']['plot_crit'] = True
data['criterias']['plot_tech_lp'] = True

data['sim_param']['base_yr'] = 2015 # Base year
data['sim_param']['base_yr'] = data_handle.timesteps[0] # Base year
data['sim_param']['curr_yr'] = data['sim_param']['base_yr']
self.user_data['base_yr'] = data['sim_param']['base_yr']

Expand Down Expand Up @@ -121,12 +120,17 @@ def before_model_run(self, data=None):
# -----------------------------
# Obtain external scenario data
# -----------------------------
pop_array = self.get_scenario_data('population')
pop_dict = self.array_to_dict(pop_array)
data['population'][data['sim_param']['base_yr']] = pop_dict[2015] # Get only population of base year

gva_array = self.get_scenario_data('gva')
data['gva'] = self.array_to_dict(gva_array)
pop_array = data_handle.get_base_timestep_data('population')
pop_dict = {}
for r_idx, region in enumerate(self.get_region_names(REGION_SET_NAME)):
pop_dict[region] = pop_array[r_idx, 0]
data['population'][data['sim_param']['base_yr']] = pop_dict

gva_array = data_handle.get_base_timestep_data('gva')
gva_dict = {}
for r_idx, region in enumerate(self.get_region_names(REGION_SET_NAME)):
gva_dict[region] = gva_array[r_idx, 0]
data['gva'][data['sim_param']['base_yr']] = gva_dict

# Get building related data
if data['criterias']['virtual_building_stock_criteria']:
Expand Down Expand Up @@ -197,8 +201,10 @@ def before_model_run(self, data=None):
# ------------------------
# Load all SMIF parameters and replace data dict
# ------------------------

parameters = data_handle.get_parameters()
data['assumptions'] = self.load_smif_parameters(
data,
parameters,
data['assumptions'])

# Update technologies after strategy definition
Expand All @@ -210,8 +216,8 @@ def before_model_run(self, data=None):
# ------------------------
# Pass along to simulate()
# ------------------------
self.user_data['gva'] = self.array_to_dict(gva_array)
self.user_data['population'] = self.array_to_dict(pop_array)
self.user_data['gva'] = data['gva']
self.user_data['population'] = data['population']
self.user_data['rs_floorarea'] = rs_floorarea
self.user_data['ss_floorarea'] = ss_floorarea
self.user_data['data_pass_along'] = {}
Expand Down Expand Up @@ -245,11 +251,9 @@ def before_model_run(self, data=None):
pop_array[t_idx])

def initialise(self, initial_conditions):
"""
"""
pass

def simulate(self, timestep, data=None):
def simulate(self, data_handle):
"""Runs the Energy Demand model for one `timestep`
Arguments
Expand Down Expand Up @@ -284,8 +288,8 @@ def simulate(self, timestep, data=None):
"""
time_start = datetime.datetime.now()

# Convert data to default dict
data = defaultdict(dict, data)
# Init default dict
data = defaultdict(dict)

# Paths
path_main = resource_filename(Requirement.parse("energy_demand"), "")
Expand All @@ -311,7 +315,7 @@ def simulate(self, timestep, data=None):
logger_setup.set_up_logger(os.path.join(data['local_paths']['data_results'], "logger_smif_run.log"))

data['sim_param']['base_yr'] = self.user_data['base_yr'] # Base year definition
data['sim_param']['curr_yr'] = timestep # Read in current year from smif
data['sim_param']['curr_yr'] = data_handle.current_timestep # Read in current year from smif
data['sim_param']['simulated_yrs'] = self.timesteps # Read in all simulated years from smif

# ---------------------------------------------
Expand All @@ -331,15 +335,26 @@ def simulate(self, timestep, data=None):
# ---------------------------------------------
# Scenario data
# ---------------------------------------------
pop_array = self.get_scenario_data('population') #for simulation year
pop_dict = self.array_to_dict(pop_array)
pop_array_current = data_handle.get_data('population') #for simulation year
gva_array_current = data_handle.get_data('gva') #for simulation year

gva_dict_current = {}
pop_dict_current = {}

for r_idx, region in enumerate(self.get_region_names(REGION_SET_NAME)):
pop_dict_current[region] = pop_array_current[r_idx, 0]
gva_dict_current[region] = gva_array_current[r_idx, 0]

pop_by_cy = {}
pop_by_cy[data['sim_param']['base_yr']] = pop_dict[2015] # Get population of by
pop_by_cy[data['sim_param']['curr_yr']] = pop_dict[data['sim_param']['curr_yr']] # Get population of cy
pop_by_cy[data['sim_param']['base_yr']] = self.user_data['population'][data_handle.base_timestep] # Get population of by
pop_by_cy[data['sim_param']['curr_yr']] = pop_dict_current # Get population of cy

gva_by_cy = {}
gva_by_cy[data['sim_param']['base_yr']] = self.user_data['gva'][data_handle.base_timestep] # Get gva of by
gva_by_cy[data['sim_param']['curr_yr']] = gva_dict_current # Get gva of cy

data['scenario_data'] = {
'gva': self.user_data['gva'],
'gva': gva_by_cy,
'population': pop_by_cy,

# Only add newcastle floorarea here
Expand All @@ -366,7 +381,7 @@ def simulate(self, timestep, data=None):
# ------------------------------------------------
# Validation base year: Hourly temporal validation
# ------------------------------------------------
if data['criterias']['validation_criteria'] == True and timestep == data['sim_param']['base_yr']:
if data['criterias']['validation_criteria'] == True and data_handle.current_timestep == data['sim_param']['base_yr']:
lad_validation.tempo_spatial_validation(
data['sim_param']['base_yr'],
data['assumptions']['model_yearhours_nrs'],
Expand Down Expand Up @@ -395,7 +410,7 @@ def simulate(self, timestep, data=None):
# Plot individual enduse
# ----
crit_plot_enduse_lp = True
if crit_plot_enduse_lp and timestep == 2015:
if crit_plot_enduse_lp and data_handle.current_timestep == 2015:

# Maybe move to result folder in a later step
path_folder_lp = os.path.join(data['local_paths']['data_results'], 'individual_enduse_lp')
Expand All @@ -421,23 +436,23 @@ def simulate(self, timestep, data=None):

path_run = data['local_paths']['data_results_model_runs']
write_data.write_supply_results(
timestep, "result_tot_yh", path_run, sim_obj.ed_fueltype_regs_yh, "result_tot_submodels_fueltypes")
data_handle.current_timestep, "result_tot_yh", path_run, sim_obj.ed_fueltype_regs_yh, "result_tot_submodels_fueltypes")
write_data.write_enduse_specific(
timestep, path_run, sim_obj.tot_fuel_y_enduse_specific_yh, "out_enduse_specific")
data_handle.current_timestep, path_run, sim_obj.tot_fuel_y_enduse_specific_yh, "out_enduse_specific")
write_data.write_max_results(
timestep, path_run, "result_tot_peak_enduses_fueltype", sim_obj.tot_peak_enduses_fueltype, "tot_peak_enduses_fueltype")
data_handle.current_timestep, path_run, "result_tot_peak_enduses_fueltype", sim_obj.tot_peak_enduses_fueltype, "tot_peak_enduses_fueltype")
write_data.write_lf(
path_run, "result_reg_load_factor_y", [timestep], sim_obj.reg_load_factor_y, 'reg_load_factor_y')
path_run, "result_reg_load_factor_y", [data_handle.current_timestep], sim_obj.reg_load_factor_y, 'reg_load_factor_y')
write_data.write_lf(
path_run, "result_reg_load_factor_yd", [timestep], sim_obj.reg_load_factor_yd, 'reg_load_factor_yd')
path_run, "result_reg_load_factor_yd", [data_handle.current_timestep], sim_obj.reg_load_factor_yd, 'reg_load_factor_yd')
write_data.write_lf(
path_run, "result_reg_load_factor_winter", [timestep], sim_obj.reg_seasons_lf['winter'], 'reg_load_factor_winter')
path_run, "result_reg_load_factor_winter", [data_handle.current_timestep], sim_obj.reg_seasons_lf['winter'], 'reg_load_factor_winter')
write_data.write_lf(
path_run, "result_reg_load_factor_spring", [timestep], sim_obj.reg_seasons_lf['spring'], 'reg_load_factor_spring')
path_run, "result_reg_load_factor_spring", [data_handle.current_timestep], sim_obj.reg_seasons_lf['spring'], 'reg_load_factor_spring')
write_data.write_lf(
path_run, "result_reg_load_factor_summer", [timestep], sim_obj.reg_seasons_lf['summer'], 'reg_load_factor_summer')
path_run, "result_reg_load_factor_summer", [data_handle.current_timestep], sim_obj.reg_seasons_lf['summer'], 'reg_load_factor_summer')
write_data.write_lf(
path_run, "result_reg_load_factor_autumn", [timestep], sim_obj.reg_seasons_lf['autumn'], 'reg_load_factor_autumn')
path_run, "result_reg_load_factor_autumn", [data_handle.current_timestep], sim_obj.reg_seasons_lf['autumn'], 'reg_load_factor_autumn')
logging.info("... finished writing results to file")

# ------------------------------------
Expand Down Expand Up @@ -517,23 +532,7 @@ def simulate(self, timestep, data=None):
return supply_results

def extract_obj(self, results):
"""Implement this method to return a scalar value objective function
This method should take the results from the output of the `simulate`
method, process the results, and return a scalar value which can be
used as the objective function
Arguments
=========
results : :class:`dict`
The results from the `simulate` method
Returns
=======
float
A scalar component generated from the simulation model results
"""
pass
return 0

def pass_to_simulate(self, dict_to_copy_into, dict_to_pass_along):
"""Pass dict defined in before_model_run() to simlate() function
Expand Down Expand Up @@ -599,7 +598,6 @@ def load_smif_parameters(self, data, assumptions):

# Get narrative variable from input data dict
strategy_variables[var_name] = data[var_name]
del data[var_name]

# Add to assumptions
assumptions['strategy_variables'] = strategy_variables
Expand Down

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