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'])