def save_custom(meid):
save_custom_results(meid=meid, description='{description}',
input_dir="/{FILEPATH}/"+str(meid), years=[{YEAR IDS}],
sexes=[{SEX IDS}], mark_best=True,
in_counts=False, env="prod",
custom_file_pattern="{measure_id}_{location_id}.h5",
h5_tablename='draws', gbd_round={GBD ROUND})
def save_worker(meid, description, input_dir):
save_custom_results(meid=meid,
description=description,
input_dir=input_dir,
env='prod',
mark_best=True,
custom_file_pattern='{location_id}.h5',
h5_tablename='draws')
parser.add_argument("me_id", help="The me_id to upload", type=int)
parser.add_argument("cause_name",
help="The name of cause group for this upload")
parser.add_argument(
"model_version_ids",
help="The model versions used to calculate squeeze proportions")
args = parser.parse_args()
me_id = args.me_id
cause_name = args.cause_name
model_version_ids = args.model_version_ids
directory = "{root}/{proc}".format(root=root, proc=cause_name)
year_ids = [1990, 1995, 2000, 2005, 2010, 2016]
description = "Used model_version_ids " + model_version_ids + "."
upload_dir = os.path.join(directory, str(me_id))
sexes = [1, 2]
al.save_custom_results(meid=me_id,
description=description,
input_dir=upload_dir,
sexes=sexes,
birth_prev=True,
mark_best=True,
env='prod',
years=year_ids,
custom_file_pattern="{year_id}.h5",
h5_tablename="data")
# this part uploads the results of the HIV split to the epi database
from adding_machine import agg_locations as al
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)
args = parser.parse_args()
me_id = args.me_id
save_dir = args.save_dir
year_ids = [{YEAR IDS}]
description = 'Proportions calculated from anemia casual attribution'
sexes = [{SEX IDS}]
al.save_custom_results(meid=me_id, description=description,
input_dir=save_dir,
sexes=sexes, mark_best=True, env={ENV},
years=year_ids, custom_file_pattern='{year_id}.h5',
h5_tablename="draws")
locations = get_most_detailed(location_set, gbd_round)
years = [1990, 1995, 2000, 2005, 2010, 2016]
ages = [164]
sexes = [1, 2]
measure = 5
upload_me = 15803
# grab the u_2500 birth prevalence
prev_df = draws(source='dismod',
gbd_ids={"modelable_entity_ids": [me_id]},
location_ids=locations, year_ids=years,
age_group_ids=ages, sex_ids=sexes,
status='best', measure_ids=[measure],
gbd_round_id=gbd_round)
prev_df = index_draws_by_demographics(prev_df)
def mapping(x):
y = predict_for_simple_ols(x, mean_parameters, cov_matrix)
return y
mean_weight_df = prev_df.applymap(mapping)
mean_weight_df['age_group_id'] = 2
save_to_hdf(mean_weight_df, savefile)
description = ('Estimate of mean birth weight from simple linear'
'regression. Units in grams')
al.save_custom_results(meid=upload_me, description=description,
input_dir=output_dir,
sexes=sexes, mark_best=True, env='prod',
years=years, custom_file_pattern="all_draws.h5",
h5_tablename="draws")
from adding_machine import agg_locations as al
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("me_id", help="The me_id to upload", type=int)
parser.add_argument(
"model_version_ids",
help="The model versions of the input me_ids used in this custom code",
type=str)
parser.add_argument("out_dir", help="upload directory", type=str)
args = parser.parse_args()
me_id = args.me_id
model_version_ids = args.model_version_ids
out_dir = args.out_dir
year_ids = [1990, 1995, 2000, 2005, 2010, 2016]
if me_id == 16535:
description = "obstetric fistula DisMod {}".format(model_version_ids)
sexes = [1, 2]
else:
description = 'applied live births to incidence; applied duration'
sexes = [2]
al.save_custom_results(meid=me_id,
description=description,
input_dir=out_dir,
sexes=sexes,
mark_best=True,
env='prod',
years=year_ids)
def upload_to_me(save_dir, in_path, cause_id, cause_name, me_id, write_hdf,
save_Results, send_Slack, token, channel):
""" upload using save_results """
# out file name
out_file = "5_draws.h5"
out_hdf = save_dir + out_file
# table name
h5_tablename = "draws"
# get the time
time_stamp = UploadToME.timestamp()
# important columns
save_cols = [
'measure_id', 'location_id', 'year_id', 'age_group_id', 'sex_id'
]
years = range(1990, 2015, 5) + [2016]
sexes = [1, 2]
# if the user asked to write an HDF for this etiology
if write_hdf == "YES":
# draw columns
draw_cols = (['draw_%s' % i for i in range(0, 1000)])
# get the locations
locations_df = get_location_metadata(location_set_id=9)
# filter out locations not used in in Epi and non-admin0 locations
DEMOGRAPHICS = list(
get_demographics(gbd_team='epi')['location_ids'])
LOCATIONS = locations_df.query('location_id in {}'.format(
(DEMOGRAPHICS)))[['location_id']]
# location IDs
LOCATION_IDS = LOCATIONS.location_id.unique()
location_count = 0
results_list = []
for location_id in LOCATION_IDS:
location_count += 1
try:
df = pd.read_csv(
"{in_path}prevalence/corr_prev_draws_{location_id}.csv"
.format(in_path=in_path, location_id=location_id))
# filter to the one etiology being saved
temp = df.query('cause_id == {}'.format(cause_id)).copy()
# drop all unnecessary columns
temp = temp[save_cols + draw_cols]
# append the new df to the rest
results_list.append(temp)
except:
print "Missing location at {location_id} for {cause_id}".format(
location_id=location_id, cause_id=cause_id)
if send_Slack == "YES":
message = "Missing location at {}".format(location_id)
slack.chat.post_message(channel, message)
print " results for {} locations appended.".format(
location_count)
results = pd.concat(results_list)
results.reset_index(inplace=True)
results.drop('index', axis=1, inplace=True)
# Make sure there aren't any negative values (This will happen where Chagas is endemic)
results[results < 0] = 0
# save the results to a HDF
results.to_hdf(out_hdf,
h5_tablename,
format='table',
data_columns=save_cols)
#if the user asked to save the HDF for this etiology using "save_results"
if save_Results == "YES":
description = "heart failure due to {cause_name}; new upload {time_stamp}; corrected-IHD proportions.".format(
cause_name=cause_name, time_stamp=time_stamp)
al.save_custom_results(me_id,
description,
save_dir,
years=years,
sexes=sexes,
mark_best="best",
in_counts=False,
env="prod",
custom_file_pattern=out_file,
h5_tablename=h5_tablename,
gbd_round=2016)