Merge branch 'master' into new-counterfactual-scenario

average_dist_vintage_engine_cc.py

@@ -47,35 +47,18 @@
    # print(annual_passenger_kilometers)
     annual_pkm = list(map(int, annual_passenger_kilometers))
     print(annual_pkm)
-
-    # engine_cc_cat = 1
-    # while engine_cc_cat < 15:
-
-    #     annual_pkm_enginecat = annual_pkm[engine_cc_cat::15]
-    #     print(annual_pkm_enginecat)
-    #     annual_pkm_by_category = sum(annual_pkm_enginecat)
-    #     engine_cc_cat += 1
-    #     #print(annual_pkm_by_category)
 
 PATH = constants.path
 fuel_type = constants.f_type
 year = base_year
 readfile = f'{PATH}/{fuel_type}/average_distance_engine_cc/scenario_0_distance_travelled.csv'
-# with open(readfile, newline='', encoding='utf-8-sig') as f:
-#     data = [row for row in csv.reader(f)]
-
-#    # gets average distance for engine cc data[year_row][enginecc_catgory]
-#     #enginecc_category_starts at 1, because year data is stored in 0
 
-#     average_distance_for_engine_cc = data[0][year - 2000]
-#     #need to get list of cars of year and of engine cc specified in average_distance_for_engine_cc
 # #opening a file of all the years
 num_car_year = 0
 for year in range(2001,2018):
     readfile = f'{PATH}/{fuel_type}/{year}.csv'
     with open(readfile, newline='', encoding='utf-8-sig') as ff:
         data = [row for row in csv.reader(ff)]
-        #print(data)
 
     readfile = f'{PATH}/{fuel_type}/average_distance_engine_cc/scenario_0_distance_travelled.csv'
     with open(readfile, newline='', encoding='utf-8-sig') as f:
@@ -95,63 +78,9 @@
             average_distance_for_engine_cc = enginecc[(year- 2000)][enginecol]
 
             average_distance_list.append(int(average_distance_for_engine_cc))
-
-    #print(average_distance_list)
-    #print(average_distance_list[0])
-    #print(average_distance_list[0])
 
-
-            #print(average_distance_for_engine_cc)
     engineccpklist = []
 for listyear in average_distance_list:
     for engine_cc_mileage_average in average_distance_list:
         engineccpkitem = engine_cc_mileage_average*total_engine_cc_stock_for_year
         engineccpklist.append(engineccpkitem)
-
-
-    #this list prints a list of the average distances
-#print(average_distance_list)
-#print(engineccpklist)
-    #print(total_engine_cc_stock_for_year)
-
-
-
-
-
-   # gets average distance for engine cc data[year_row][enginecc_catgory]
-    #enginecc_category_starts at 1, because year data is stored in 0
-
-
-    #need to get list of cars of year and of engine cc specified in average_distance_for_engine_cc
-#opening a file of all the years
-
-    #print(average_distance_for_engine_cc)
-    #print(total_engine_cc_stock_for_year)
-
-    #print(total_engine_cc_stock_for_year)
-
-
-
-
-#average_pkm_for_given_engine_cc_and_year = [stock*average_distance_for_engine_cc for stock in 
-    #info = item[1]  
-#print(average_distance_for_engine_cc)
-
-# readfile = f'{PATH}/{fuel_type}/average_distance_engine_cc/scenario_0_distance_travelled.csv'
-# with open(readfile, newline='', encoding='utf-8-sig') as f:
-
-
-
-#     #print(sample_model._year, annual_pkm)
-#     dist_dict.append({"year": sample_model._year, "passenger_kilometers": annual_pkm})
-# #print(total_passenger_kilometers)
-#     csv_file = f'distance_cc_vintage/{f_type}/{sample_model._year}.csv'
-#     csv_columns = ["year","passenger_kilometers"]
-#     with open(csv_file, 'w', newline='') as csvfile:
-#             for col in csv.reader(csvfile):
-#                 total += int(col[1])
-#                 print(total)
-#             writer = csv.DictWriter(csvfile, fieldnames=csv_columns)
-#             writer.writeheader()
-#             for year in dist_dict:
-#                 writer.writerow(year)

breakout_avg_dist.py

@@ -16,32 +16,17 @@
 
     # Getting pkm per cat for every year
     dist_for_yr = list(map(int,dist_travelled.get_constant(year=sample_model._year)))
-    #print(dist_for_yr)
     cars_per_cat = sample_model.get_cat_counts()
-    #print(cars_per_cat) 
     pkm_cat = list_prod(dist_for_yr,cars_per_cat)
-    #print(pkm_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()]
-    #car_share_weightings = [[car_count/1 for car_count, cat_count in zip(cars_per_age,cars_per_cat)] for cars_per_age in  sample_model.get_counts()]
-    #print(car_share_weightings)
-    #print(car_share_weightings)
 
     # multiply the two weightings together
     overall_weightings = [[float(ar)*car_share for ar,car_share in zip(ar_row, cs_row)] for ar_row,cs_row in zip(age_rates,car_share_weightings)]
-    #print(overall_weightings)
+
     # multiply the total pkm on top of each weighting to get the final avg dist
     total_dist_breakout_by_car = [[o_w*pkm for o_w,pkm in zip(weight_row,pkm_cat)] for weight_row in overall_weightings]
-    #print(total_dist_breakout_by_car)
-    #breakpoint()
-
-    #print(car_share_weightings)
-
-
-    avg_dist_per_car = [[fadb*car_share for fadb, car_share in zip(cs_row,fadb_row)] for fadb_row,cs_row in zip(final_avg_dist_breakout,car_share_weightings)]
-    #avg_dist_per_car_ans = [[avg_dist_per_car*car_coun for avg_dist_per_car, car_coun in zip(avg_dist_per_car,cars_per_cat)]]
-
+    avg_dist_per_car = [[fadb*car_share for fadb, car_share in zip(cs_row,fadb_row)] for fadb_row,cs_row in zip(final_avg_dist_breakout,car_share_weightings)]    
 
-    print(avg_dist_per_car)
-    #print(final_avg_dist_breakout)
+    print(avg_dist_per_car)

calc_band_emissions.py

@@ -44,25 +44,16 @@
     total_passenger_kilometers = [[float(numcar)*1*float(dt) for numcar,cpk,dt in zip(numcars,cpks, dist_for_yr_cat)] for numcars, cpks, dist_for_yr_cat in zip(sample_model._data, yr_consumption_per_km, dist_for_yr)]
     total_consumption_unweighted_list = [[float(numcar)*1*float(cpk) for numcar,cpk,dt in zip(numcars,cpks, dist_for_yr_cat)] for numcars, cpks, dist_for_yr_cat in zip(sample_model._data, yr_consumption_per_km, dist_for_yr)]
 
-    #print(total_passenger_kilometers)
-    #totalvehicles = [float(numcars) for numcars in zip(sample_model._data)]
-    #sumtotalvehicles = sum(sum(totalvehicles))
     annual_grams = sum(sum(total_consumption_grams,[]))
     total_travel = sum(sum(total_passenger_kilometers,[]))
     total_consumption_unweighted = sum(sum(total_consumption_unweighted_list,[]))
-    #totalnumcars = get_counts(self)
     numlist = BaseModel.get_counts(self=sample_model)
     numcars = sum(sum(numlist,[]))
     emissions_intensity = annual_grams/(numcars)
-
     emissions_intensity_per_km = annual_grams/(total_travel)
 
     emissions_intensity_per_km_unweighted = total_consumption_unweighted/(numcars)
-
-
-
     print(f'{f_type} Emissions for year: {sample_model._year} = {annual_grams} grams_CO2- number of cars :{numcars}')
-    # em_dict.append({"year": str(sample_model._year), "grams_CO2" : {annual_grams})
     em_dict.append({"year": str(sample_model._year), "grams_CO2" : annual_grams, "number_cars":numcars, "g_per_car_average": emissions_intensity, "g_per_km_per_car_weighted": emissions_intensity_per_km, "g_per_km_per_car_unweighted":emissions_intensity_per_km_unweighted })
 
 # this code outputs the year emissions to a csv
@@ -73,12 +64,3 @@
     writer.writeheader()
     for year in em_dict:
         writer.writerow(year)
-
-# total_consumption_grams = []
-# # this for loop will run through a 14 length row 17 team times (per age)
-# for numcars_per_age, cpks_per_age in zip(sample_model._data, yr_consumption_per_km):
-#     row_per_age = []
-#     # this for loop will run through every category on a row
-#     for numcars_per_age_cat, cpks_per_age_cat, dist_for_yr_per_cat in zip(numcars_per_age,cpks_per_age, dist_for_yr):
-#         row_per_age.append(float(cpks_per_age_cat)*float(cpks_per_age_cat)*float(dist_for_yr_per_cat)) 
-#     total_consumption_grams.append(row_per_age)

calc_emissions.py

@@ -24,7 +24,6 @@
     bio_fuel_dict = {2001: 0, 2002:0, 2003: 0, 2004:0, 2005:0, 2006:0, 2007:0, 2008:0, 2009:0, 2010:0.027, 2011: 0.038, 2012: 0.03, 2013: 0.038, 2014: 0.044, 2015: 0.044, 2016: 0.036, 2017: 0.054, 2018: 0.05}
 
 print(bio_fuel_dict)
-#dist_travelled = ConstantBaseModel(generate_constants(fuel_type=f_type,constant_type=constants.d_travelled))
 #need to figure out a way to chance how the distances are stored
 new_rd_factor = [row[1:] for row in rd_factor]
 new_eff_band = [row[1:] for row in eff_band]