def test_init_dict_brackets():
"""Test
"""
first_level_keys = ["a", "b"]
one_level_dict = helpers.init_dict_brackets(first_level_keys)
expected = {"a": {}, "b": {}}
assert one_level_dict == expected
def get_s_fueltype_tech(
enduses,
fuel_p_tech_by,
fuels,
technologies,
sector=False
):
"""Calculate total energy service fractions per technology.
This calculation converts fuels into energy services (e.g. heating
for fuel into heat demand) and then calculates how much an invidual
technology contributes as a fraction of total energy service demand.
This is calculated to determine how much the technology
has already diffused up to the base year to define the
first point on the sigmoid technology diffusion curve.
Arguments
----------
enduses : dict
Enduses
fuel_p_tech_by : dict
Assumed fraction of fuel for each technology within a fueltype
fuels : array
Base year fuel demand
technologies : object
Technology of base year (region dependent)
Return
------
s_tech_by_p : dict
Percentage of total energy service per technology for base year
s_fueltype_by_p : dict
Percentage of energy service per fueltype
"""
fueltypes = lookup_tables.basic_lookups()['fueltypes']
service = init_nested_dict_brackets(fuels, fueltypes.values()) # Energy service per technology for base year
s_tech_by_p = helpers.init_dict_brackets(fuels) # Percentage of total energy service per technology for base year
s_fueltype_by_p = init_nested_dict_zero(sector, s_tech_by_p.keys(), range(len(fueltypes))) # Percentage of service per fueltype
for enduse in enduses:
# Depending if sector or not sector specific
fuel = fuels[enduse][sector]
for fueltype, fuel_fueltype in enumerate(fuel):
tot_s_fueltype = 0
# Initialise
for tech in fuel_p_tech_by[enduse][sector][fueltype]:
service[enduse][fueltype][tech] = 0
# Iterate technologies to calculate share of energy service depending on fuel and efficiencies
for tech, fuel_alltech_by in fuel_p_tech_by[enduse][sector][fueltype].items():
# Fuel share based on defined shares within fueltype (share of fuel * total fuel)
fuel_tech = fuel_alltech_by * fuel_fueltype
# Get efficiency for base year
if technologies[tech].tech_type == 'heat_pump':
eff_tech = tech_related.eff_heat_pump(
temp_diff=10,
efficiency_intersect=technologies[tech].eff_by)
else:
eff_tech = technologies[tech].eff_by
# Energy service: Service == Fuel of technoloy * efficiency
s_fueltype_tech = fuel_tech * eff_tech
# Add energy service demand
service[enduse][fueltype][tech] += s_fueltype_tech
# Total energy service demand within a fueltype
tot_s_fueltype += s_fueltype_tech
# Calculate percentage of service enduse within fueltype
for tech in fuel_p_tech_by[enduse][sector][fueltype]:
if tot_s_fueltype == 0: # No fuel in this fueltype
s_fueltype_by_p[enduse][sector][fueltype] += 0
else:
s_fueltype_by_p[enduse][sector][fueltype] += service[enduse][fueltype][tech]
# Calculate percentage of service of all technologies
total_s = sum_2_level_dict(service[enduse])
# Percentage of energy service per technology
for fueltype, technology_s_enduse in service[enduse].items():
for technology, s_tech in technology_s_enduse.items():
with np.errstate(divide='ignore'):
if total_s == 0:
s_p_tech = 0
else:
# Calculate service
s_p_tech = s_tech / total_s
# Do not add dummy technology with zero service
if technology == 'placeholder_tech' and s_p_tech == 0:
pass
else:
try:
s_tech_by_p[enduse][technology] += s_p_tech
except:
s_tech_by_p[enduse][technology] = s_p_tech
# Convert service per enduse
for fueltype in s_fueltype_by_p[enduse][sector]:
with np.errstate(divide='ignore'):
s_fueltype_by_p[enduse][sector][fueltype] = s_fueltype_by_p[enduse][sector][fueltype] / total_s
#warnings.filterwarnings('ignore') # Ignore warnings
# Test if the energy service for all technologies is 100%
# Test if within fueltype always 100 energy service
for enduse, s_p_techs in s_tech_by_p.items():
sum_enduse = sum(s_p_techs.values())
if round(sum_enduse, 2) != 0: # if total is zero, no demand provided
assert round(sum_enduse, 2) == 1.0
return s_tech_by_p, s_fueltype_by_p