def save_hw(meid):
save_results_epi(input_dir='FILEPATH' + str(meid),
input_file_pattern='{year_id}.h5',
modelable_entity_id=meid,
description='result of Hardy - Weinberg calculation; ' +
today_string,
gbd_round_id=5,
mark_best=True,
birth_prevalence=True)
def run_save_results(me_id, year_id, measure_id, description, input_dir):
save_results_epi(input_dir=input_dir,
input_file_pattern="{location_id}.csv",
modelable_entity_id=me_id,
description=description,
year_id=year_id,
measure_id=measure_id,
gbd_round_id=5,
mark_best=True)
def save_custom(meid):
save_results_epi(input_dir='FILEPATH/str(meid)',
input_file_pattern="{measure_id}_{location_id}.h5",
modelable_entity_id=meid,
description='DESCRIPTION ' + today_string,
measure_id=5,
metric_id=3,
mark_best=True,
db_env='prod',
gbd_round_id=5)
def save_custom(meid):
save_results_epi(input_dir='FILEPATH/' + str(meid),
input_file_pattern='{location_id}.csv',
modelable_entity_id=meid,
description='malaria anemia pre process ' + today_string,
year_id=[1990, 1995, 2000, 2005, 2010, 2017],
measure_id=5,
metric_id=3,
mark_best=True,
db_env='prod',
gbd_round_id=5)
def save_hw(meid):
save_results_epi(
input_dir = 'FILEPATH',
input_file_pattern = 'FILEPATH',
modelable_entity_id = meid,
description='result of Hardy - Weinberg calculation; ' + today_string,
gbd_round_id = 6,
decomp_step = 'step1',
mark_best=False,
birth_prevalence=True
)
def upload():
folder = os.path.join('FILEPATH')
dems = db.get_demographics(gbd_team='epi', gbd_round_id=help.GBD_ROUND)
save_results_epi(input_dir=folder,
input_file_pattern='FILEPATH.h5',
modelable_entity_id=3136,
description='other msk adjusted for injuries fractures and dislocations',
year_id=dems['year_id'],
measure_id=5,
mark_best=True)
print('Successfully uploaded Other MSK')
def save_custom(meid):
save_results_epi(
input_dir='FILEPATH/hiv_prop_interp/'+str(meid),
input_file_pattern='{location_id}.h5',
modelable_entity_id=meid,
description='DESCRIPTION',
year_id = range(1990, 2018),
measure_id=18,
metric_id=3,
mark_best=True,
db_env='prod',
gbd_round_id=5
)
def save_custom(modelable_entity_id, year_id, gbd_round_id, decomp_step,
save_dir) -> int:
description = (
f"Anemia CA for GBD {gbd_round_from_gbd_round_id(gbd_round_id)} "
f"{decomp_step}, {datetime.date.today().strftime('%m/%d/%y')}")
save_results_epi(input_dir=save_dir,
input_file_pattern="FILEPATH",
modelable_entity_id=modelable_entity_id,
description=description,
year_id=year_id,
measure_id=5,
mark_best=True,
gbd_round_id=gbd_round_id,
decomp_step=decomp_step)
def save_anem_exposures(modelable_entity_id, year_id, gbd_round_id, decomp_step) -> int:
description = (
f"Calculated {pairings[modelable_entity_id]} exposure from anemia causal attribution results"
f" {decomp_step}, {datetime.date.today().strftime('%m/%d/%y')}"
)
save_results_epi(
input_dir='FILEPATH',
input_file_pattern='FILEPATH',
measure_id= 19,
metric_id= 3,
modelable_entity_id=modelable_entity_id,
description='Calculated {} exposure from anemia causal attribution results'.format(pairings[modelable_entity_id]),
year_id=year_id,
decomp_step=decomp_step,
gbd_round_id=gbd_round_id,
mark_best=True)
def save_worker(meid, meas_ids, description, input_dir, cnf_run_id):
print("saving {}...".format(description))
try:
success_df = save_results_epi(modelable_entity_id=meid,
description=description,
input_dir=input_dir,
measure_id=meas_ids,
mark_best=True,
input_file_pattern="{location_id}.h5")
except:
success_df = pd.DataFrame()
return(success_df)
def save_proportions(modelable_entity_id, gbd_round_id, decomp_step, year_ids,
save_dir) -> int:
mv = save_results_epi(
input_dir=save_dir,
input_file_pattern='FILEPATH',
modelable_entity_id=modelable_entity_id,
description='Proportions calculated from anemia casual attribution',
year_id=year_ids,
measure_id=18,
mark_best=True,
gbd_round_id=gbd_round_id,
decomp_step=decomp_step)
return mv
def save_worker(meid, meas_ids, description, input_dir, cnf_run_id):
print("saving {}...".format(description))
d_step = utils.get_gbd_parameter('current_decomp_step')
gbd_id = utils.get_gbd_parameter('current_gbd_round')
estimation_yrs = [1990, 2000, 2017] # temporary for fauxcorrect
try:
success_df = save_results_epi(modelable_entity_id=meid,
description=description,
input_dir=input_dir,
measure_id=meas_ids,
mark_best=True,
n_draws=1000,
decomp_step=d_step,
gbd_round_id=gbd_id,
input_file_pattern="{location_id}.h5")
except:
success_df = pd.DataFrame()
return (success_df)
# this part uploads the results of the HIV split to the epi database
from save_results import save_results_epi
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("me_id", help="The me_id to upload", type=int)
parser.add_argument("save_dir", help="upload directory", type=str)
parser.add_argument("year_id", nargs='*', type=int, help='year ids to include')
args = parser.parse_args()
me_id = args.me_id
save_dir = args.save_dir
year_id = args.year_id
description = 'Proportions calculated from anemia casual attribution'
sexes = [1, 2]
save_results_epi(input_dir=save_dir,
input_file_pattern='{year_id}.h5',
modelable_entity_id=me_id,
description=description,
year_id=year_id,
measure_id=[18],
mark_best=True,
db_env='prod',
gbd_round_id=5)
def save(risk, date, mark_best=False):
file_pattern = "{location_id}.csv"
if risk == "adult_ov":
save_results_epi(input_dir=os.path.join("FILEPATH", date,
"expanded_adult_draws_ow"),
input_file_pattern=file_pattern,
description="overweight model {}".format(date),
metric_id=3,
measure_id=18,
mark_best=mark_best,
modelable_entity_id=9363)
with open(
os.path.join("FILEPATH", date, "step_10_{}.txt".format(risk)),
'w') as f:
f.write("completed")
elif risk == "adult_ob":
save_results_epi(input_dir=os.path.join("FILEPATH", date,
"draws_transformed_adult"),
input_file_pattern=file_pattern,
description="obesity model {}".format(date),
metric_id=3,
measure_id=18,
mark_best=mark_best,
modelable_entity_id=9364)
with open(
os.path.join("FILEPATH", date, "step_10_{}.txt".format(risk)),
'w') as f:
f.write("completed")
elif risk == "child_ov":
save_results_epi(
input_dir=os.path.join("FILEPATH", date, "child_draws_ow"),
input_file_pattern=file_pattern,
description="childhood overweight model {}".format(date),
metric_id=3,
measure_id=18,
mark_best=mark_best,
modelable_entity_id=10344)
with open(
os.path.join("FILEPATH", date, "step_10_{}.txt".format(risk)),
'w') as f:
f.write("completed")
elif risk == "child_ob":
save_results_epi(
input_dir=os.path.join("FILEPATH", date, "child_draws"),
input_file_pattern=file_pattern,
description="childhood obesity model {} (zeta by density)".format(
date),
metric_id=3,
measure_id=18,
mark_best=mark_best,
modelable_entity_id=10345)
with open(
os.path.join("FILEPATH", date, "step_10_{}.txt".format(risk)),
'w') as f:
f.write("completed")
elif risk == "child_not_ob":
save_results_epi(
input_dir=os.path.join("FILEPATH", date, "child_ow_not_ob"),
input_file_pattern=file_pattern,
description="childhood overweight not obese for PAFs".format(date),
metric_id=3,
measure_id=18,
mark_best=mark_best,
modelable_entity_id=20018)
with open(
os.path.join("FILEPATH", date, "step_10_{}.txt".format(risk)),
'w') as f:
f.write("completed")
else:
save_results_epi(
input_dir=os.path.join("FILEPATH", date, "mean_bmi"),
input_file_pattern=file_pattern,
description="adult mean BMI estimates {}".format(date),
metric_id=3,
measure_id=19,
mark_best=mark_best,
modelable_entity_id=2548)
with open(
os.path.join("FILEPATH", date, "step_10_{}.txt".format(risk)),
'w') as f:
f.write("completed")
input_file_pattern=file_pattern,
cause_id=model_id,
description=description,
sex_id=2,
year_id=list(range(start_year, end_year + 1)),
mark_best=True,
gbd_round_id=maternal_fns.GBD_ROUND_ID,
decomp_step=decomp.decomp_step_from_decomp_step_id(decomp_step_id))
elif model_type == "epi":
print("Saving results for EPI")
save_results_epi(
input_dir=directory,
input_file_pattern=file_pattern,
modelable_entity_id=model_id,
description=description,
sex_id=2,
year_id=list(range(start_year, end_year + 1)),
measure_id=18,
mark_best=True,
gbd_round_id=maternal_fns.GBD_ROUND_ID,
decomp_step=decomp.decomp_step_from_decomp_step_id(decomp_step_id))
elif model_type == "sdg":
print("Saving results for SDG")
save_results_sdg(input_dir=directory,
input_file_pattern=file_pattern,
indicator_component_id=47,
gbd_id=366,
gbd_id_type='cause_id',
input_version_id=model_id,
n_draws=1000,
gbd_round_id=maternal_fns.GBD_ROUND_ID)
parser.add_argument("in_dir", help="The directory where the draws are stored")
parser.add_argument("decomp_step", help='The decomp step')
parser.add_argument(
"description",
help="A description of the model versions used in the congenital core code"
)
args = parser.parse_args()
me_id = args.me_id
in_dir = args.in_dir
decomp_step = args.decomp_step
description = args.description
year_ids = gbd.ESTIMATION_YEARS
upload_dir = os.path.join(in_dir, str(me_id))
sexes = [2]
model_version_df = save_results_epi(input_dir=upload_dir,
input_file_pattern="{location_id}.h5",
modelable_entity_id=me_id,
description=description,
year_id=year_ids,
sex_id=sexes,
mark_best=True,
measure_id=[5, 6],
metric_id=3,
n_draws=1000,
db_env='prod',
gbd_round_id=gbd.GBD_ROUND_ID,
decomp_step=decomp_step)
sex_id = [gbd.sex.FEMALE]
else:
sex_id = [gbd.sex.MALE, gbd.sex.FEMALE]
if args.modelable_entity_id in MEIDS.PREVALENCE_ONLY:
measure_id = [gbd.measures.PREVALENCE]
else:
measure_id = args.measure_id
if args.modelable_entity_id in MEIDS.INCLUDE_BIRTH_PREV:
birth_prev = True
else:
birth_prev = False
# call save results
mvid_df = save_results_epi(input_dir=args.input_dir,
input_file_pattern=args.input_file_pattern,
modelable_entity_id=args.modelable_entity_id,
description=args.description,
measure_id=measure_id,
year_id=args.year_id,
sex_id=sex_id,
metric_id=[gbd.metrics.RATE],
mark_best=True,
birth_prevalence=birth_prev,
decomp_step=args.decomp_step,
n_draws=args.n_draws)
SaveFactory.save_model_metadata(
args.parent_dir, args.modelable_entity_id,
int(mvid_df[Params.MODEL_VERSION_ID].iat[0]), args.decomp_step)
parser = argparse.ArgumentParser()
parser.add_argument("me_id", help="The me_id to upload", type=int)
parser.add_argument("in_dir", help="The directory where the draws are stored")
parser.add_argument(
"description",
help="A description of the model versions used in the congenital core code"
)
args = parser.parse_args()
me_id = args.me_id
in_dir = args.in_dir
description = args.description
year_ids = [1990, 1995, 2000, 2005, 2010, 2017]
upload_dir = os.path.join(in_dir, str(me_id))
sexes = [1, 2]
model_version_df = save_results_epi(input_dir=upload_dir,
input_file_pattern="{location_id}.h5",
modelable_entity_id=me_id,
description=description,
year_id=year_ids,
sex_id=sexes,
measure_id=[5],
metric_id=3,
mark_best=True,
birth_prevalence=True,
n_draws=1000,
db_env='prod',
gbd_round_id=5)
# Save results
########################################################################
if model_type == "cod":
print("Saving results for COD")
save_results_cod(input_dir=directory,
input_file_pattern=file_pattern,
cause_id=model_id,
description=description,
sex_id=2,
year_id=range(start_year, end_year + 1),
mark_best=True)
elif model_type == "epi":
print("Saving results for EPI")
save_results_epi(input_dir=directory,
input_file_pattern=file_pattern,
modelable_entity_id=model_id,
description=description,
sex_id=2,
year_id=range(start_year, end_year + 1),
measure_id=18,
mark_best=True)
elif model_type == "sdg":
print("Saving results for SDG")
save_results_sdg(input_dir=directory,
input_file_pattern=file_pattern,
indicator_component_id=47,
gbd_id=366,
gbd_id_type='cause_id',
input_version_id=model_id,
n_draws=1000)
year_ids = [1990, 1995, 2000, 2005, 2010, 2017]
if me_id == 16535:
description = "Obstetric fistula DisMod {}".format(model_version_ids)
else:
description = """
Applied live births to incidence; applied duration to prevalence.
Used the following modelable_entity_ids and
model_version_ids as inputs: {}""".format(model_version_ids)
print(description)
if (me_id == 3620) | (me_id == 3629):
measids = 6
else:
measids = [5, 6]
sexes = [2]
model_version_df = save_results_epi(
input_dir=out_dir,
input_file_pattern="{measure_id}_{location_id}_{year_id}_{sex_id}.csv",
modelable_entity_id=me_id,
description=description,
year_id=year_ids,
sex_id=sexes,
mark_best=True,
measure_id=measids,
metric_id=3,
n_draws=1000,
db_env='prod',
gbd_round_id=5)
default=3)
parser.add_argument(
'--file_pattern',
type=str,
help=('string specifying the general pattern used in draw filenames, '
'where special idenifying fields (e.g. location_id, or sex_id) '
'are enclosed in curly braces {}. For example, a valid file '
'pattern might be: {location_id}_{year_id}_{sex_id}.csv. Note '
'that if you are using h5 files, you will also need to specify '
'an h5_tablename'),
default="{location_id}.h5")
parser.add_argument('--env',
type=str,
help='dev/prod environment',
default='prod')
parser.add_argument('--gbd_round_id', type=int, help='gbd round id', default=5)
args = vars(parser.parse_args())
model_version_df = save_results_epi(modelable_entity_id=args['meid'],
input_dir=args['input_dir'],
description=args['description'],
input_file_pattern=args['file_pattern'],
year_id=args['years'],
sex_id=args['sexes'],
mark_best=args['best'],
measure_id=args['meas_ids'],
metric_id=args['metric_id'],
n_draws=1000,
db_env=args['env'],
gbd_round_id=args['gbd_round_id'])
