def run_m2nc(self):
"""
First: Read in the m-map
Second: Using the "mapping", m-maps are converted to grid
Third: Output this grid as a netCDF file
"""
print("\tReading in m-map")
if self.timexist:
self.mmap, self.time = read_mym(self.mmap_in, path= InputDir.m_in_dir)
else:
self.mmap = read_mym(self.mmap_in, path= InputDir.m_in_dir)
# Create map linking m-maps to lat/lon matrix
print("\tCreating gridded map")
gridmap = self.get_gridmap(self.mmap, self.mapping, self.timexist)
# *** WRITE OUTPUT ***
print("\tWriting netCDF output")
writemap = WriteMaps(gridmap, self.map_title, self.map_var, self.map_unit, self.map_outname, self.timexist)
writemap.maptime2nc()
self.rate_average = np.average(self.rate[0:t - TSCEN, ...], axis=0)
if t == TSCEN + 1:
self.rate_average_historic = np.average(
self.rate[0:(TSCEN + 1 - YEAR_IN), ...], axis=0)
else:
self.rate_average_historic = 0
return self.rate, self.rate_average, self.rate_average_historic
# ---------------------------------------------------------------------------- #
if __name__ == "__main__":
# Import variables
pym.read_mym("floorspace.out")
pym.read_mym("Dwel_LT.dat")
pym.read_mym("pop_q.out")
pym.read_mym("U_Values.dat")
pym.read_mym("Costs_UValues.dat")
floorspace = pym.read_mym("floorspace.out")[0]
population = pym.read_mym("pop_q.out")[0]
HDD = pym.read_mym("HDD.out")[0]
HDD_annual = HDD[:, :, 12]
household_floor = pym.read_mym("HHFloor.out")[0]
u_value_help = pym.read_mym("U_Values.dat")
u_value = np.reshape(u_value_help, ((5, 4, 6)))
cost_u_value_help = pym.read_mym("Costs_UValues.dat")
cost_u_value = np.reshape(cost_u_value_help, ((5, 4, 6)))
lifetime_help = pym.read_mym("Dwel_LT.dat")
build_lifetime = lifetime_help[0, :]
return (self.new_buildings, abandoned_buildings)
def calculate_stock(self):
if t == 0:
building_stock[t, ...] = initial_building_stock
else:
building_stock[t, ...] = building_stock[t-1, ...] + new_buildings[t, ...] + \
abandoned_buildings[t, ...] - decommissioned[t, ...]
return building_stock
# ---------------------------------------------------------------------------- #
if __name__ == "__main__":
# Import variables
pym.read_mym("floorspace.out")
pym.read_mym("Dwel_LT.dat")
pym.read_mym("pop_q.out")
floorspace_per_capita, time = pym.read_mym("floorspace.out")
population, time = pym.read_mym("pop_q.out")
lifetime_help = pym.read_mym("Dwel_LT.dat")
build_lifetime = lifetime_help[0, :]
floorspace = floorspace_per_capita * population
initial_building_stock = floorspace[0, :, :]
# Definitions
new_buildings = np.zeros((YEARS, NREGIONS, TURQ))
decommissioned = np.zeros((YEARS, NREGIONS, TURQ))
abandoned_buildings = np.zeros((YEARS, NREGIONS, TURQ))
abandoned_residual = np.zeros((YEARS, NREGIONS, TURQ))
return (self.new_buildings, self.abandoned_buildings)
def calculate_stock(self, t, initial_stock):
if t == 0:
self.stock[t, ...] = initial_stock
else:
self.stock[t, ...] = self.stock[t-1, ...] + self.new_buildings[t, ...] + \
self.abandoned_buildings[t, ...] - self.decommissioned[t, ...]
return self.stock
# ---------------------------------------------------------------------------- #
if __name__ == "__main__":
# Import variables
floorspace, time = pym.read_mym("floorspace.out", "Buildings/imports")
population = pym.read_mym("pop_q.out", "Buildings/imports")[0]
build_lifetime = pym.read_mym("Dwel_LT.dat", "Buildings/imports")[0]
initial_stock = floorspace[0, :, :]
print(build_lifetime.shape)
print(floorspace)
# Definitions
# increased_floor = np.zeros((YEARS, NREGIONS, TURQ))
# decreased_floor = np.zeros((YEARS, NREGIONS, TURQ))
buildings = BuildingStock(floorspace, build_lifetime)
for t in range(YEARS):
buildings.floorspace_change()
decommission_initial, decommissioned, abandoned_residual = buildings.decommission(
def interpolation_function_1D(xp_data_points, interpolated_variable, x_coordinates):
# this function builds on the numpy 1D interpolation function
# numpy.interp(x, xp, fp, left=None, right=None, period=None)
fp_data_points = np.array(interpolated_variable[xp_data_points])
y_coordinates = np.interp(x_coordinates, xp_data_points, fp_data_points)
return y_coordinates
x = np.arange(1971, 2050)
data_path = os.path.join("demand_functions", "appliance", "imports", "data")
unit_energy_consumption_unit = pym.load_mym("UEC_unit.dat", data_path, x)
satur_price = pym.read_mym("Saturation.dat", data_path)[0]
# Fraction of cooling appliances which are air coolers
air_cooler_fraction = pym.read_mym("AirCoolFrac.dat", data_path)[0]
# Beta for cooling devices. Only 1-3 of 3rd dim used.
beta = pym.read_mym("Beta_global.dat", data_path)
# Gamma for cooling devices. Only 1-3 of 3rd dim used.
gamma = pym.read_mym("Gamma_global.dat", data_path)
# coefficient for quintile allocation of TV, washing machine and refrigerator
appliance_a = pym.read_mym("Appliance_a.dat", data_path)[0]
# Alpha coefficient for UEC development (kWh/normalising unit)
unit_energy_consumption_alpha = pym.read_mym("UEC_alpha.dat", data_path)[0]
# Beta coefficient (reduction rate) for UEC development (TV,washing,refrig)
unit_energy_consumption_beta = pym.read_mym("UEC_beta.dat", data_path)[0]
# Assumption on minimum UEC (kWh/normalising unit)
unit_energy_consumption_min_in = pym.read_mym("UEC_min.dat", data_path)[0]
unit_energy_consumption_min_scen = pym.read_mym("UEC_min.dat", data_path)[0]