# do all the prep work
dep_map = pd.read_csv("FILEPATH" % dep_map_type,
header=0).dropna(axis='columns', how='all')
step_df = dep_map.ix[dep_map.step == 2].reset_index()
columns = maternal_fns.filter_cols()
columns.remove('measure_id')
index_cols = [col for col in columns if not col.startswith('draw_')]
# logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger("maternal_custom.02_adjust_parent")
logger.info("Correcting underreporting for late maternal deaths")
# get list of locations
locs = maternal_fns.get_locations()
# get list of location/years that don't need correction from USER
logger.info('Pulling in adjustment csv from USER.')
adjust_df = pd.read_csv('%s/FILEPATH.csv' % (os.getcwd()))
adjust_df = adjust_df[['location_id', 'year_to_start']]
# get original codem envelope
logger.info("Pulling in codem envelope")
env = get_draws(gbd_id=366,
gbd_id_type='cause_id',
source='codem',
version_id=int(env_model_vers),
location_id=locs,
age_group_id=list(range(7, 16)))
env = env[columns + ['envelope']]
def main(year):
year = int(year)
current_date = maternal_fns.get_time()
# shorten to just today
current_date = current_date[0:10]
out_dir = 'FILEPATH' % current_date
if not os.path.exists(out_dir):
os.makedirs(out_dir)
diag_out_dir = '%s/diagnostics' % out_dir
if not os.path.exists(diag_out_dir):
os.makedirs(diag_out_dir)
############
# Prep Work
############
locations = maternal_fns.get_locations()
columns = maternal_fns.filter_cols()
rr_cols = []
for i in range(0, 1000):
rr_cols.append('rr_%d' % i)
all_diag = pd.DataFrame()
############
# Load data
############
# load relative risks
rr = pd.read_stata('FILEPATH')
rr.drop('dummy', axis=1, inplace=True)
rr = rr.reindex(range(0, 9))
# propagate the relative risks down to all ages of interest
rr['age_group_id'] = range(7, 16)
rr.fillna(method='ffill', axis=0, inplace=True)
rr.set_index('age_group_id', inplace=True)
cols = rr.columns
# rename all draw columns to be draw_0 to draw_999
for idx, i in enumerate(cols):
rr.rename(columns={'%s' % i: 'draw_%s' % idx}, inplace=True)
print("pulling draws")
all_pafs = pd.DataFrame()
for geo in locations:
hiv_prev = get_draws(gbd_id_type='modelable_entity_id',
gbd_id=9313,
source='epi',
sex_id=2,
location_id=geo,
year_id=year,
status='best',
age_group_id=range(7, 16),
gbd_round_id=6,
decomp_step='step2')
hiv_prev = hiv_prev[columns]
# we only want maternal age groups for hiv_prev
hiv_prev.set_index('age_group_id', inplace=True)
# replace all nulls with zeroes
hiv_prev.fillna(0, inplace=True)
# assert that HIV prevalence in pregnancy is between 0 and 1
if (hiv_prev.values.any() < 0):
raise ValueError(
'HIV Prev values are below 0 for geo %s and year %s' %
(geo, year))
continue
elif (hiv_prev.values.any() > 1):
raise ValueError(
'HIV Prev values are above 1 for geo %s and year %s' %
(geo, year))
continue
############
# Math
############
# multiply hiv prev for every loc/year/age by every rr draw using this
pafs = (hiv_prev * (rr - 1)) / ((hiv_prev * (rr - 1)) + 1)
# assert that all pafs are bounded between 0 and 1
if (pafs.values.any() < 0):
raise ValueError('PAF values are below 0 for geo %s and year %s' %
(geo, year))
continue
elif (pafs.values.any() > 1):
raise ValueError('PAF values are above 1 for geo %s and year %s' %
(geo, year))
continue
pafs['location_id'] = geo
pafs['year'] = year
all_pafs = all_pafs.append(pafs)
############
# Diagnostics
############
# create paf_mean and find countries where paf is high to output seperately
diagnostics = pafs.copy()
diagnostics['paf_mean'] = diagnostics.mean(axis=1).to_frame()
if diagnostics.paf_mean.any() >= .01:
diagnostics['add'] = 0
else:
diagnostics['add'] = 1
diagnostics['location_id'] = geo
diagnostics.reset_index(inplace=True)
all_diag = all_diag.append(diagnostics)
############
# Save
############
# output pafs as hdf
print("saving")
all_pafs.fillna(0, inplace=True)
all_pafs.reset_index(inplace=True)
all_pafs.convert_objects()
all_pafs.to_hdf('%s/pafs_%s.h5' % (out_dir, year),
'pafs',
mode='w',
format='table',
data_columns=['location_id', 'age_group_id', 'year'])
all_diag.to_csv('%s/diagnostics_%s.csv' % (diag_out_dir, year),
index=False,
encoding='utf-8')
