import csv
from constants import constants
from models.base_model import ConstantBaseModel
from models.base_model import BaseModel
from utils.generators import generate_constants, generate_year_models, generate_dist_models, list_prod
f_type = constants.f_type
yr_models = generate_year_models(fuel_type=f_type,
start_year=constants.start_year,
end_year=constants.end_year,
path=constants.path)
rd_factor = generate_constants(fuel_type=f_type,
constant_type=constants.r_factor)
print(rd_factor)
rd_factor.sort(reverse=True)
em_dict = []
if f_type == 'diesel' or 'petrol':
for year_row in rd_factor:
year_row = list(map(float, year_row))
year = year_row[0]
less_than_1300cc = (year_row[1] + year_row[2] + year_row[3] +
year_row[4] + year_row[5]) / 5
between_1300cc_and_1900cc = (year_row[6] + year_row[7] + year_row[8] +
year_row[10] + year_row[11]) / 6
more_than_1900cc = (year_row[12] + year_row[13] + year_row[14]) / 3
em_dict.append({
import csv
from constants import constants
from models.base_model import ConstantBaseModel
from models.base_model import BaseModel
from utils.generators import generate_constants, generate_year_models, generate_dist_models, list_prod
f_type = constants.f_type
yr_models = generate_year_models(fuel_type=f_type,
start_year=constants.start_year,
end_year=constants.end_year,
path=constants.path)
eff_band = generate_constants(fuel_type=f_type, constant_type=constants.f_band)
print(eff_band)
eff_band.sort(reverse=True)
#vehicle stock 0 - 14 } aggregate 1-5, 5-11, 12-14
#distance 0-4 {sum of (vehicle stock (0)*eff band (0))+vehicle stock (1) *eff band (1) +.../(sum of total vehicle stock in group 0 - 4)}
em_dict = []
if f_type == 'diesel' or 'petrol':
for year_row in eff_band:
year_row = list(map(float, year_row))
year = year_row[0]
less_than_900cc = year_row[1]
between_900cc_and_1200cc = (year_row[2] + year_row[3] +
year_row[4]) / 3
between_1200cc_and_1500cc = (year_row[5] + year_row[6] +
year_row[7]) / 3
import csv
from utils.salespercentage import get_sales_percentage
from models.base_model import BaseModel
from constants import constants
from models.base_model import ConstantBaseModel
from utils.generators import generate_constants, generate_year_models, list_prod, get_model_by_year
f_type = constants.f_type
yr_models = generate_year_models(fuel_type=f_type,
start_year=constants.start_year,
end_year=constants.end_year,
path=constants.path)
em_band = generate_constants(fuel_type=f_type, constant_type=constants.em_band)
rd_factor = generate_constants(fuel_type=f_type,
constant_type=constants.r_factor)
dist_travelled = ConstantBaseModel(
generate_constants(fuel_type=f_type, constant_type=constants.d_travelled))
new_rd_factor = [row[1:] for row in rd_factor]
new_em_band = [row[1:] for row in em_band]
consumption_per_km = [[(1 + float(rf)) * float(emb)
for rf, emb in zip(r_fs, embs)]
for r_fs, embs, in zip(new_rd_factor, new_em_band)]
new_consumption_per_km = []
#emissions bands are only recorded from 2004 onwards
base_year = 1990
for row in consumption_per_km:
new_consumption_per_km.append([base_year] + row)
from constants import constants
from models.base_model import ConstantBaseModel
from utils.generators import generate_year_models, generate_constants, list_prod, read_file
f_type = constants.f_type
yr_models = generate_year_models(fuel_type=f_type,
start_year=constants.start_year,
end_year=constants.end_year,
path=constants.path)
dist_travelled = ConstantBaseModel(
generate_constants(fuel_type=f_type, constant_type=constants.d_travelled))
age_rate_path = f'static_constants/{f_type}/ageing_rates.csv'
age_rates = read_file(age_rate_path)
for sample_model in yr_models:
print(sample_model._year)
# Getting pkm per cat for every year
dist_for_yr = list(
map(int, dist_travelled.get_constant(year=sample_model._year)))
cars_per_cat = sample_model.get_cat_counts()
pkm_cat = list_prod(dist_for_yr, cars_per_cat)
# getting the share of a car age for that cat
car_share_weightings = [[
car_count / cat_count
for car_count, cat_count in zip(cars_per_age, cars_per_cat)
] for cars_per_age in sample_model.get_counts()]