def calculateIA(i): global unscalable_districts outputs = buildCostCalculator( unscalable_districts['district_latitude'][i], unscalable_districts['district_longitude'][i], unscalable_districts['build_bandwidth'][i], 0, 0, 0).costquestRequestWithDistance() return { 'esh_id': unscalable_districts['esh_id'][i], 'district_build_cost': outputs.build_cost, 'district_build_distance': outputs.distance}
def calculateAZ(i): global unscalable_campuses outputs = buildCostCalculator( unscalable_campuses['sample_campus_latitude'][i], unscalable_campuses['sample_campus_longitude'][i], unscalable_campuses['build_bandwidth'][i], unscalable_campuses['distance'][i], unscalable_campuses['district_latitude'][i], unscalable_campuses['district_longitude'][i]).costquestRequest() return { 'campus_id': unscalable_campuses['campus_id'][i], 'esh_id': unscalable_campuses['esh_id'][i], 'build_cost_az': outputs}
def calculateZPop(i): global unscalable_campuses outputs = buildCostCalculator( unscalable_campuses['district_latitude'][i], unscalable_campuses['district_longitude'][i], unscalable_campuses['build_bandwidth'][i], 0, 0, 0).costquestRequestWithDistance() return { 'campus_id': unscalable_campuses['campus_id'][i], 'esh_id': unscalable_campuses['esh_id'][i], 'build_cost_zpop': outputs.build_cost, 'build_distance_zpop': outputs.distance}
from classes import buildCostCalculator, cost_magnifier unscalable_districts = read_csv( GITHUB + '/Projects/funding_the_gap_2017/data/interim/unscalable_districts.csv', index_col=0) print("Unscalable districts imported") ##calculate Z-PoP cost for all unscalable districts and save into pandas dataframe district_costs = [] for i in range(0, unscalable_districts.shape[0]): outputs = buildCostCalculator( unscalable_districts['district_latitude'][i], unscalable_districts['district_longitude'][i], unscalable_districts['build_bandwidth'][i], 0, 0, 0).costquestRequestWithDistance() district_costs.append({ 'esh_id': unscalable_districts['esh_id'][i], 'district_build_cost': outputs.build_cost, 'district_build_distance': outputs.distance }) #print(outputs, file=open(GITHUB+'/Projects/funding_the_gap_2017/data/interim/costsfile_ia.csv', 'a')) if i % 250 == 0: print("Iteration {0} out of {1}".format(i, unscalable_districts.shape[0])) else: continue print("Costs calculated")
from numpy import where, arange from classes import buildCostCalculator, cost_magnifier #choose one or the other for 1: rerunning 2: static #from unscalable_campuses import unscalable_campuses unscalable_campuses = read_csv('unscalable_campuses.csv') print("Unscalable campuses imported") #calculate cost for all unscalable campuses and save into pandas dataframe campus_costs = [] for i in range(0, unscalable_campuses.shape[0]): cost_test = buildCostCalculator(unscalable_campuses['district_latitude'][i], unscalable_campuses['district_longitude'][i], unscalable_campuses['build_bandwidth'][i], 0, unscalable_campuses['sample_campus_latitude'][i], unscalable_campuses['sample_campus_longitude'][i]).costquestRequest() campus_costs.append({'build_cost': cost_test}) print(cost_test, file=open('./costsfile.csv', 'a')) if i % 250 == 0: print("Iteration {0} out of {1}".format(i,unscalable_campuses.shape[0])) else: continue print("Costs calculated") campus_costs = DataFrame(campus_costs) #use this code if there are timeouts campus_costs = read_csv('costsfile.csv') campus_costs = concat([unscalable_campuses, campus_costs], axis=1)
from classes import buildCostCalculator, cost_magnifier unscalable_campuses = read_csv( GITHUB + '/Projects/funding_the_gap_2017/data/interim/unscalable_campuses.csv', index_col=0) print("Unscalable campuses imported") ##calculate A-PoP cost for all unscalable campuses and save into pandas dataframe campus_costs_apop = [] for i in range(0, unscalable_campuses.shape[0]): outputs = buildCostCalculator( unscalable_campuses['sample_campus_latitude'][i], unscalable_campuses['sample_campus_longitude'][i], unscalable_campuses['build_bandwidth'][i], 0, 0, 0).costquestRequestWithDistance() campus_costs_apop.append({ 'campus_id': unscalable_campuses['campus_id'][i], 'esh_id': unscalable_campuses['esh_id'][i], 'build_cost_apop': outputs.build_cost, 'build_distance_apop': outputs.distance }) #print(outputs, file=open(GITHUB+'/Projects/funding_the_gap_2017/data/interim/costsfile_apop.csv', 'a')) if i % 250 == 0: print("Iteration {0} out of {1}".format(i, unscalable_campuses.shape[0])) else: continue