def get_measures_interpolate(me_id, locs, sexes, ages, inc_id, rms_id, emr_id, year_start, year_end):
inc = interpolate.interpolate(
gbd_id_type='modelable_entity_id',
gbd_id=me_id,
measure_id=inc_id,
location_id=locs,
sex_id=sexes,
age_group_id=ages,
reporting_year_start=year_start,
reporting_year_end=year_end,
status='best',
source='epi',
gbd_round_id=help.GBD_ROUND)
rms = interpolate.interpolate(
gbd_id_type='modelable_entity_id',
gbd_id=me_id,
measure_id=rms_id,
location_id=locs,
sex_id=sexes,
age_group_id=ages,
reporting_year_start=year_start,
reporting_year_end=year_end,
status='best',
source='epi',
gbd_round_id=help.GBD_ROUND)
emr = interpolate.interpolate(
gbd_id_type='modelable_entity_id',
gbd_id=me_id,
measure_id=emr_id,
location_id=locs,
sex_id=sexes,
age_group_id=ages,
reporting_year_start=year_start,
reporting_year_end=year_end,
status='best',
source='epi',
gbd_round_id=help.GBD_ROUND)
inc = inc.loc[inc['year_id'] < year_end]
rms = rms.loc[rms['year_id'] < year_end]
emr = emr.loc[emr['year_id'] < year_end]
dropcols = ['measure_id', 'metric_id', 'model_version_id', 'modelable_entity_id']
inc.drop(dropcols, axis=1, inplace=True)
rms.drop(dropcols, axis=1, inplace=True)
emr.drop(dropcols, axis=1, inplace=True)
indexcols = ['location_id', 'year_id', 'age_group_id', 'sex_id']
inc.set_index(indexcols, inplace=True)
rms.set_index(indexcols, inplace=True)
emr.set_index(indexcols, inplace=True)
m_dict = {'incidence': inc, 'remission': rms, 'emr': emr}
return m_dict
def year_chunk_interp(start_year, end_year, locid, measid, meid):
df = interpolate(gbd_id_type='modelable_entity_id',
gbd_id=meid,
source='epi',
measure_id=[measid],
location_id=locid,
reporting_year_start=start_year,
reporting_year_end=end_year,
status='best')
if start_year != 1980:
df = df[df.year_id != start_year]
return df
def year_chunk_interp(start_year, end_year, locid, meid):
df = interpolate(gbd_id_type='modelable_entity_id',
gbd_id=meid,
source='epi',
measure_id=18,
location_id=locid,
reporting_year_start=start_year,
reporting_year_end=end_year,
status='best')
if start_year != 1990:
df = df[df.year_id != start_year]
df = df[['location_id', 'year_id', 'age_group_id', 'sex_id'] + draws]
df['year_id'] = df['year_id'].astype(int)
df.set_index(['location_id', 'year_id', 'age_group_id', 'sex_id'],
inplace=True)
return df
if __name__ == '__main__':
(gbd_id, measure_id, gbd_round_id, sex_id, outdir,
decomp_step) = parse_arguments()
if not os.path.exists(outdir):
makedirs_safely(outdir)
end_year = int(gbd_round_from_gbd_round_id(gbd_round_id))
df = interpolate(gbd_id=gbd_id,
gbd_id_type='modelable_entity_id',
source='epi',
measure_id=measure_id,
reporting_year_start=1980,
reporting_year_end=end_year,
sex_id=sex_id,
gbd_round_id=gbd_round_id,
decomp_step=decomp_step,
num_workers=30)
id_cols = [col for col in df.columns if col.endswith('_id')]
for col in id_cols:
df[col] = df[col].astype('int64')
df.to_hdf(
os.path.join(outdir, 'interp_{}_{}.h5'.format(gbd_id, sex_id)),
key='draws',
mode='w',
format='table',
data_columns=['location_id', 'year_id', 'age_group_id', 'sex_id'])
me_id = int(me_id)
# get list of locations
locations = maternal_fns.get_locations()
yearlist = list(range(1980, 2018))
start_year = 1980
end_year = 2017
# call central function to interpolate
logger.info("Calling interpolate")
interp_df = interpolate(gbd_id_type='modelable_entity_id',
gbd_id=me_id,
source='epi',
reporting_year_start=start_year,
reporting_year_end=end_year,
measure_id=18,
age_group_id=list(range(7, 16)),
sex_id=2,
num_workers=45)
draw_cols = ["draw_{}".format(x) for x in range(0, 1000)]
data_cols = ['measure_id', 'location_id', 'year_id', 'age_group_id', 'sex_id']
interp_df = interp_df[data_cols + draw_cols]
for col in data_cols:
interp_df[col] = interp_df[col].astype(np.int64)
# save each of the files
for year in yearlist:
logger.info('saving interpolated draws for year %s' % year)
# get list of locations
locations = maternal_fns.get_locations()
start_year = 1980
end_year = 2019
yearlist = list(range(1980, end_year + 1))
# call central function to interpolate
logger.info("Calling interpolate the first time.")
interpolate(gbd_id_type='modelable_entity_id',
gbd_id=2519,
source='epi',
reporting_year_start=1980,
reporting_year_end=2019,
measure_id=18,
age_group_id=7,
location_id=101,
sex_id=2,
num_workers=45,
decomp_step=decomp.decomp_step_from_decomp_step_id(decomp_step_id),
gbd_round_id=maternal_fns.GBD_ROUND_ID)
logger.info("Calling interpolate a second time.")
interp_df = interpolate(
gbd_id_type='modelable_entity_id',
gbd_id=me_id,
source='epi',
reporting_year_start=start_year,
reporting_year_end=end_year,
measure_id=18,
age_group_id=list(range(7, 16)),
sex_id=2,
def get_measures_interpolate(ecode, locs, sexes, ages, inc_id, rms_id, emr_id, year_start, year_end, decomp):
me_id = help.get_me(ecode)
best_version = db.get_best_model_versions(entity="modelable_entity",
ids=me_id,
status="best",
decomp_step=decomp,
gbd_round_id=help.GBD_ROUND)
me_id = int(me_id)
inc_id = int(inc_id)
rms_id = int(rms_id)
emr_id = int(emr_id)
n_workers = 30
start = time.time()
inc = interpolate.interpolate(
gbd_id_type='modelable_entity_id',
gbd_id=me_id,
measure_id=inc_id,
location_id=locs,
sex_id=sexes,
age_group_id=ages,
reporting_year_start=year_start,
reporting_year_end=year_end,
status='best',
source='epi',
gbd_round_id=help.GBD_ROUND,
decomp_step=decomp,
num_workers=n_workers, # Make sure we have 40 threads
)
start = time.time()
rms = interpolate.interpolate(
gbd_id_type='modelable_entity_id',
gbd_id=me_id,
measure_id=rms_id,
location_id=locs,
sex_id=sexes,
age_group_id=ages,
reporting_year_start=year_start,
reporting_year_end=year_end,
status='best',
source='epi',
gbd_round_id=help.GBD_ROUND,
decomp_step=decomp,
num_workers=n_workers # Make sure we have 40 threads
)
start = time.time()
emr = interpolate.interpolate(
gbd_id_type='modelable_entity_id',
gbd_id=me_id,
measure_id=emr_id,
location_id=locs,
sex_id=sexes,
age_group_id=ages,
reporting_year_start=year_start,
reporting_year_end=year_end,
status='best',
source='epi',
gbd_round_id=help.GBD_ROUND,
decomp_step=decomp
)
inc = inc.loc[inc['year_id'] < year_end]
rms = rms.loc[rms['year_id'] < year_end]
emr = emr.loc[emr['year_id'] < year_end]
dropcols = ['measure_id', 'metric_id', 'model_version_id',
'modelable_entity_id']
inc.drop(dropcols, axis=1, inplace=True)
rms.drop(dropcols, axis=1, inplace=True)
emr.drop(dropcols, axis=1, inplace=True)
indexcols = ['location_id', 'year_id', 'age_group_id', 'sex_id']
inc.set_index(indexcols, inplace=True)
rms.set_index(indexcols, inplace=True)
emr.set_index(indexcols, inplace=True)
emr = emr.reindex(inc.index)
m_dict = {'incidence': inc, 'remission': rms, 'emr': emr}
return m_dict