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
