def post_install_setup_minimum(args):
"""If not all data are available, this scripts allows to
create dummy datas (temperature and service sector load profiles)
"""
path_config_file = args.config_file
config = data_loader.read_config_file(path_config_file)
path_local_data = config['PATHS']['path_local_data']
# ==========================================
# Post installation setup witout access to non publicy available data
# ==========================================
print(
"... running initialisation scripts with only publicly available data")
local_paths = data_loader.get_local_paths(path_config_file)
# Create folders to input data
raw_folder = os.path.join(path_local_data, '_raw_data')
basic_functions.create_folder(raw_folder)
basic_functions.create_folder(config['PATHS']['path_processed_data'])
basic_functions.create_folder(local_paths['path_post_installation_data'])
basic_functions.create_folder(local_paths['load_profiles'])
basic_functions.create_folder(local_paths['rs_load_profile_txt'])
basic_functions.create_folder(local_paths['ss_load_profile_txt'])
# Load data
data = {}
data['paths'] = data_loader.load_paths(path_config_file)
data['lookups'] = lookup_tables.basic_lookups()
data['enduses'], data['sectors'], data[
'fuels'], lookup_enduses, lookup_sector_enduses = data_loader.load_fuels(
data['paths'])
# Assumptions
data['assumptions'] = general_assumptions.Assumptions(
lookup_enduses=lookup_enduses,
lookup_sector_enduses=lookup_sector_enduses,
base_yr=2015,
weather_by=config['CONFIG']['user_defined_weather_by'],
simulation_end_yr=config['CONFIG']['user_defined_simulation_end_yr'],
paths=data['paths'],
enduses=data['enduses'],
sectors=data['sectors'])
# Read in residential submodel shapes
run(data['paths'], local_paths, config['CONFIG']['base_yr'])
# --------
# Dummy service sector load profiles
# --------
dummy_sectoral_load_profiles(local_paths, path_config_file)
print(
"Successfully finished post installation setup with open source data")
def test_assign_by_fuel_tech_p(config_file):
"""
"""
config = data_loader.read_config_file(config_file)
# Load data
data = {}
data['paths'] = config['CONFIG_DATA']
data['lookups'] = lookup_tables.basic_lookups()
data['enduses'], data['sectors'], data[
'fuels'], _, _ = data_loader.load_fuels(data['paths'])
data['local_paths'] = data_loader.get_local_paths(path_main)
#Load assumptions
base_yr = 2015
data['assumptions'] = general_assumptions.Assumptions(
submodels_names=['a'],
base_yr=base_yr,
curr_yr=None,
sim_yrs=None,
paths=data['paths'],
enduses=data['enduses'],
sectors=data['sectors'],
fueltypes=data['lookups']['fueltypes'],
fueltypes_nr=data['lookups']['fueltypes_nr'])
strategy_vars_def.load_param_assump(data['paths'], data['local_paths'],
data['assumptions'])
fuel_tech_p_by = fuel_shares.assign_by_fuel_tech_p(
data['enduses'], data['sectors'], data['lookups']['fueltypes'],
data['lookups']['fueltypes_nr'])
def load_data_before_simulation(data, sim_yrs, config, curr_yr):
# ---------
# Configuration
# -----------
base_yr = config['CONFIG']['base_yr']
weather_yr_scenario = config['CONFIG']['weather_yr_scenario']
path_new_scenario = config['PATHS']['path_new_scenario']
data['data_path'] = os.path.normpath(config['PATHS']['path_local_data'])
data['processed_path'] = os.path.normpath(
config['PATHS']['path_processed_data'])
data['result_path'] = os.path.normpath(config['PATHS']['path_result_data'])
data['paths'] = config['CONFIG_DATA']
# Downloaded (FTP) data
data['local_paths'] = config['DATA_PATHS']
# ------------------------------------------------
# Load Inputs
# ------------------------------------------------
data['enduses'], data['sectors'], data['fuels'], lookup_enduses, \
lookup_sector_enduses = data_loader.load_fuels(config['CONFIG_DATA'])
# ------------------------------------------------
# Load Assumptions
# ------------------------------------------------
data['assumptions'] = general_assumptions.Assumptions(
lookup_enduses=lookup_enduses,
lookup_sector_enduses=lookup_sector_enduses,
base_yr=base_yr,
weather_by=config['CONFIG']['user_defined_weather_by'],
simulation_end_yr=config['CONFIG']['user_defined_simulation_end_yr'],
curr_yr=curr_yr,
sim_yrs=sim_yrs,
paths=config['CONFIG_DATA'],
enduses=data['enduses'],
sectors=data['sectors'],
reg_nrs=len(data['regions']))
# ------------------------------------------
# Make selection of regions to model
# ------------------------------------------
if config['CRITERIA']['reg_selection']:
region_selection = read_data.get_region_selection(
os.path.join(config['DATA_PATHS']['local_path_datafolder'],
"region_definitions",
config['CRITERIA']['reg_selection_csv_name']))
#region_selection = ['E02003237', 'E02003238']
setattr(data['assumptions'], 'reg_nrs', len(region_selection))
else:
region_selection = data['regions']
# Create .ini file with simulation parameter
write_data.write_simulation_inifile(path_new_scenario, data,
region_selection)
# -------------------------------------------
# Weather year specific initialisations
# -------------------------------------------
path_folder_weather_yr = os.path.join(
os.path.join(path_new_scenario,
str(weather_yr_scenario) + "__" + "all_stations"))
data['weather_result_paths'] = basic_functions.get_result_paths(
path_folder_weather_yr)
folders_to_create = [
path_folder_weather_yr, data['weather_result_paths']['data_results'],
data['weather_result_paths']['data_results_PDF'],
data['weather_result_paths']['data_results_validation'],
data['weather_result_paths']['data_results_model_run_results_txt']
]
for folder in folders_to_create:
basic_functions.create_folder(folder)
# ------------------------------------------------
# Load load profiles of technologies
# ------------------------------------------------
data['tech_lp'] = data_loader.load_data_profiles(
config['CONFIG_DATA'], config['DATA_PATHS'],
data['assumptions'].model_yeardays,
data['assumptions'].model_yeardays_daytype)
# Obtain population data for disaggregation
if config['CRITERIA']['msoa_crit']:
name_population_dataset = config['DATA_PATHS'][
'path_population_data_for_disaggregation_msoa']
else:
name_population_dataset = config['DATA_PATHS'][
'path_population_data_for_disaggregation_lad']
data['pop_for_disag'] = data_loader.read_scenario_data(
name_population_dataset, region_name='region', value_name='value')
# ------------------------------------------------
# Load building related data
# ------------------------------------------------
if config['CRITERIA']['virtual_building_stock_criteria']:
data['scenario_data']['floor_area']['rs_floorarea'], data[
'scenario_data']['floor_area']['ss_floorarea'], data[
'service_building_count'], rs_regions_without_floorarea, ss_regions_without_floorarea = data_loader.floor_area_virtual_dw(
data['regions'],
data['sectors'],
config['DATA_PATHS'],
data['scenario_data']['population'][
data['assumptions'].base_yr],
base_yr=data['assumptions'].base_yr)
# Add all areas with no floor area data
data['assumptions'].update("rs_regions_without_floorarea",
rs_regions_without_floorarea)
data['assumptions'].update("ss_regions_without_floorarea",
ss_regions_without_floorarea)
else:
# ------------------------------------------------
# Load floor area directly from scenario
# ------------------------------------------------
data['scenario_data']['floor_area']['rs_floorarea'] = {}
data['scenario_data']['floor_area']['rs_floorarea'] = data[
'scenario_data']['rs_floorarea']
data['scenario_data']['floor_area']['ss_floorarea'] = data[
'scenario_data']['ss_floorarea']
data['scenario_data']['service_building_count'][
data['assumptions'].base_yr] = {}
return data
data['regions'] = read_data.get_region_names(name_region_set)
data['reg_coord'] = basic_functions.get_long_lat_decimal_degrees(read_data.get_region_centroids(name_region_set))
data['scenario_data']['population'] = data_loader.read_scenario_data(name_population_dataset)
data['scenario_data']['gva_industry'] = data_loader.read_scenario_data_gva(name_gva_dataset, all_dummy_data=False)
data['scenario_data']['gva_per_head'] = data_loader.read_scenario_data(name_gva_dataset_per_head)
# -----------------------------
# Assumptions
# -----------------------------
data['assumptions'] = general_assumptions.Assumptions(
lookup_enduses=lookup_enduses,
lookup_sector_enduses=lookup_sector_enduses,
base_yr=config['CONFIG']['base_yr'],
weather_by=config['CONFIG']['user_defined_weather_by'],
simulation_end_yr=config['CONFIG']['user_defined_simulation_end_yr'],
curr_yr=2015,
sim_yrs=sim_yrs,
paths=data['paths'],
enduses=data['enduses'],
sectors=data['sectors'],
reg_nrs=len(data['regions']))
# -----------------------------------------------------------------------------
# Calculate population density for base year
# -----------------------------------------------------------------------------
region_objects = read_data.get_region_objects(name_region_set)
data['pop_density'] = {}
for region in region_objects:
region_name = region['properties']['name']
region_area = region['properties']['st_areasha']
data['pop_density'][region_name] = data['scenario_data']['population'][data['assumptions'].base_yr][region_name] / region_area
config.read(os.path.join(path_config, 'wrapperconfig.ini'))
# --------
# Other configuration
# --------
base_yr = 2015
sim_yrs = [2015, 2050]
paths = data_loader.load_paths(path_main)
data_path = os.path.normpath(config['PATHS']['path_local_data'])
local_paths = data_loader.get_local_paths(data_path)
data = {}
data['enduses'], data['sectors'], data['fuels'] = data_loader.load_fuels(paths)
assumptions = general_assumptions.Assumptions(base_yr=user_defined_base_yr,
curr_yr=2015,
sim_yrs=sim_yrs,
paths=paths,
enduses=data['enduses'],
sectors=data['sectors'])
# Write parameters to YAML file
_, _ = strategy_vars_def.load_param_assump(assumptions)
print("==========")
print("Finishged generating YAML file with scenario parameters")
print("==========")
def post_install_setup(args):
"""Run this function after installing the energy_demand
model with smif and putting the data folder with all necessary
data into a local drive. This scripts only needs to be
executed once after the energy_demand model has been installed
Arguments
----------
args : object
Arguments defined in ``./cli/__init__.py``
"""
print("... start running initialisation scripts", flush=True)
path_config_file = args.local_data
config = data_loader.read_config_file(path_config_file)
local_data_path = config['PATHS']['path_local_data']
path_results = resource_filename(Requirement.parse("energy_demand"),
"results")
local_data_path = args.local_data
path_config = config['PATHS']['path_energy_demand_config']
base_yr = config['CONFIG']['base_yr']
data = {}
data['paths'] = config['CONFIG_DATA']
data['local_paths'] = config['DATA_PATHS']
data['result_paths'] = config['RESULT_DATA']
data['lookups'] = lookup_tables.basic_lookups()
data['enduses'], data['sectors'], data['fuels'], lookup_enduses, \
lookup_sector_enduses = data_loader.load_fuels(data['paths'])
data['assumptions'] = general_assumptions.Assumptions(
lookup_enduses=lookup_enduses,
lookup_sector_enduses=lookup_sector_enduses,
base_yr=base_yr,
paths=data['paths'],
enduses=data['enduses'],
sectors=data['sectors'])
# Delete all previous data from previous model runs
basic_functions.del_previous_setup(data['local_paths']['data_processed'])
basic_functions.del_previous_setup(data['result_paths']['data_results'])
basic_functions.del_previous_setup(
data['local_paths']['path_post_installation_data'])
# Create folders and subfolder for data_processed
folders_to_create = [
data['local_paths']['data_processed'],
data['local_paths']['path_post_installation_data'],
data['local_paths']['load_profiles'],
data['local_paths']['rs_load_profile_txt'],
data['local_paths']['ss_load_profile_txt']
]
for folder in folders_to_create:
basic_functions.create_folder(folder)
print("... Read in residential submodel load profiles", flush=True)
s_rs_raw_shapes.run(data['paths'], data['local_paths'], base_yr)
print("... Read in service submodel load profiles", flush=True)
s_ss_raw_shapes.run(data['paths'], data['local_paths'], data['lookups'])
# Input data preparation
print("Generate additional data", flush=True)
# Extract NISMOD population data
path_to_zip_file = os.path.join(
local_data_path, "population-economic-smif-csv-from-nismod-db.zip")
path_extraction = os.path.join(local_data_path, 'scenarios',
"MISTRAL_pop_gva")
zip_ref = zipfile.ZipFile(path_to_zip_file, 'r')
zip_ref.extractall(path_extraction)
zip_ref.close()
# Complete gva and pop data for every sector
data_pop = os.path.join(local_data_path, "scenarios", "MISTRAL_pop_gva",
"data")
path_geography = os.path.join(
local_data_path, "scenarios",
"uk_pop_principal_2015_2050_MSOA_england.csv")
geography_name = "region" # "lad_uk_2016"
script_data_preparation_MISTRAL_pop_gva.run(
path_to_folder=data_pop,
path_MSOA_baseline=path_geography,
MSOA_calculations=False,
geography_name="region") # "lad_uk_2016"
print("... successfully finished setup")
return