def bc_factors(part, f):
bc_tmp = deepcopy(filled)
for sex in ['m', 'f', 'p']:
bc_tmp['{}_{}'.format(part, sex)] = bc_tmp['{}_{}'.format(part, sex)] * f
output = bc_calc(bc_tmp, components=components)
output['rate'] = f
output['part'] = part
print 'B/C Ratio for {} and factor {} calculated.'.format(part, f)
return output
def bc_factors(part, f):
bc_tmp = deepcopy(filled)
for sex in ['m', 'f', 'p']:
bc_tmp['{}_{}'.format(part,
sex)] = bc_tmp['{}_{}'.format(part, sex)] * f
output = bc_calc(bc_tmp, etype=etype, components=components)
output['rate'] = f
output['part'] = part
print 'B/C Ratio for {} and factor {} calculated.'.format(part, f)
return output
import os
import sys
import pandas as pd
import numpy as np
from copy import deepcopy
# Paths
sys.path.extend([os.path.join(os.path.dirname(__file__), '..')])
filedir = os.path.join(os.path.dirname(__file__))
plots = os.path.join(filedir, '..', 'rslt', 'sensitivity')
from cba_setup import bc_calc, makeflows
etype = 2
filled = makeflows(etype=etype)
# vary discount rate
bc_discount = pd.DataFrame([])
for r in np.arange(0,0.16,0.01) :
bc_tmp = deepcopy(filled)
output = bc_calc(bc_tmp, rate=r)
output['rate'] = r
bc_discount = pd.concat([bc_discount, output], axis=0)
print 'B/C ratio for discount rate {} calculated.'.format(r)
bc_discount.sort_index(inplace=True)
bc_discount.to_csv(os.path.join(plots, 'bc_discount.csv'), index=True)
## vary DWL: BC
bc_dwl = pd.DataFrame([])
for d in rate_range:
bc_tmp = deepcopy(filled)
for sex in ['m', 'f', 'p']:
for part in [
'inc_trans_pub_{}'.format(sex), 'diclaim_{}'.format(sex),
'ssclaim_{}'.format(sex), 'ssiclaim_{}'.format(sex)
]:
bc_tmp[part] = bc_tmp[part] * (d / 0.5)
for part in ['edu_{}'.format(sex)]:
bc_tmp[part].loc[(sex, slice(None), slice(None)), slice('c0','c18')] = \
bc_tmp[part].loc[(sex, slice(None), slice(None)), slice('c0','c18')] * ((1 + d)/1.5)
for part in [
'ccpublic_{}'.format(sex), 'crimepublic_{}'.format(sex),
'health_public_{}'.format(sex), 'costs_{}'.format(sex)
]:
bc_tmp[part] = bc_tmp[part] * ((1 + d) / 1.5)
output = bc_calc(bc_tmp)
output['rate'] = d
bc_dwl = pd.concat([bc_dwl, output], axis=0)
print 'B/C Ratio for MCW {} calculated.'.format(d)
bc_dwl.sort_index(inplace=True)
bc_dwl.to_csv(os.path.join(plots, 'bc_dwl.csv'), index=True)
print bc_dwl
import sys
import pandas as pd
import numpy as np
from copy import deepcopy
# Paths
sys.path.extend([os.path.join(os.path.dirname(__file__), '..')])
filedir = os.path.join(os.path.dirname(__file__))
plots = os.path.join(filedir, '..', 'rslt', 'sensitivity')
from cba_setup import bc_calc, makeflows
from cba_N import N
etype = 2
filled = makeflows(etype=etype)
# vary discount rate
bc_discount = pd.DataFrame([])
for r in np.arange(0,0.16,0.01) :
bc_tmp = deepcopy(filled)
output = bc_calc(bc_tmp, etype=etype, rate=r)
output['rate'] = r
bc_discount = pd.concat([bc_discount, output], axis=0)
print 'B/C ratio for discount rate {} calculated.'.format(r)
bc_discount.sort_index(inplace=True)
bc_discount.to_csv(os.path.join(plots, 'bc_discount.csv'), index=True)
7: P=1, "ITT" with controls and weights
8: P=1, matching
'''
etype = 2
filled = makeflows(etype=etype)
## vary DWL: BC
bc_dwl = pd.DataFrame([])
for d in rate_range:
bc_tmp = deepcopy(filled)
for sex in ['m', 'f', 'p']:
for part in ['inc_trans_pub_{}'.format(sex), 'diclaim_{}'.format(sex), 'ssclaim_{}'.format(sex), 'ssiclaim_{}'.format(sex)]:
bc_tmp[part] = bc_tmp[part] * (d/0.5)
for part in ['edu_{}'.format(sex)]:
bc_tmp[part].loc[(sex, slice(None), slice(None)), slice('c0','c18')] = \
bc_tmp[part].loc[(sex, slice(None), slice(None)), slice('c0','c18')] * ((1 + d)/1.5)
for part in ['ccpublic_{}'.format(sex), 'crimepublic_{}'.format(sex), 'health_public_{}'.format(sex), 'costs_{}'.format(sex)]:
bc_tmp[part] = bc_tmp[part] * ((1+d)/1.5)
output = bc_calc(bc_tmp)
output['rate'] = d
bc_dwl = pd.concat([bc_dwl, output], axis=0)
print 'B/C Ratio for MCW {} calculated.'.format(d)
bc_dwl.sort_index(inplace=True)
bc_dwl.to_csv(os.path.join(plots, 'bc_dwl.csv'), index=True)
print bc_dwl
8: P=1, matching
'''
etype = 2
filled = makeflows(etype=etype)
## vary DWL: BC
bc_dwl = pd.DataFrame([])
for d in rate_range:
bc_tmp = deepcopy(filled)
for sex in ['m', 'f', 'p']:
for part in ['inc_trans_pub_{}'.format(sex), 'diclaim_{}'.format(sex), 'ssclaim_{}'.format(sex), 'ssiclaim_{}'.format(sex)]:
bc_tmp[part] = bc_tmp[part] * (d/0.5)
for part in ['edu_{}'.format(sex), 'm_ed_{}'.format(sex)]:
bc_tmp[part].loc[(sex, slice(None), slice(None)), slice('c0','c18')] = \
bc_tmp[part].loc[(sex, slice(None), slice(None)), slice('c0','c18')] * ((1 + d)/1.5)
for part in ['ccpublic_{}'.format(sex), 'crimepublic_{}'.format(sex), 'health_public_{}'.format(sex), 'costs_{}'.format(sex)]:
bc_tmp[part] = bc_tmp[part] * ((1+d)/1.5)
output = bc_calc(bc_tmp, etype=etype)
output['rate'] = d
bc_dwl = pd.concat([bc_dwl, output], axis=0)
print 'B/C Ratio for MCW {} calculated.'.format(d)
bc_dwl.sort_index(inplace=True)
bc_dwl.to_csv(os.path.join(plots, 'bc_dwl.csv'), index=True)
print bc_dwl