def __init__(self, como_version, location_id=[], year_id=[], sex_id=[]):
self.como_version = como_version
# set up draw source factory
self._ss_factory = SourceSinkFactory(como_version)
# set up the dimensions we are using
self.dimensions = self.como_version.nonfatal_dimensions
if location_id:
self.dimensions.simulation_index["location_id"] = location_id
if year_id:
self.dimensions.simulation_index["year_id"] = year_id
if sex_id:
self.dimensions.simulation_index["sex_id"] = sex_id
self.dimensions.simulation_index["age_group_id"] = [164]
def __init__(self, como_version):
# inputs
self.como_version = como_version
# set up draw source factory
self._ss_factory = SourceSinkFactory(como_version)
# set up the dimensions we are using
self.dimensions = self.como_version.nonfatal_dimensions
def __init__(self, como_version, location_id, year_id, sex_id):
self.como_version = como_version
self._estim_years = estimation_years_from_gbd_round_id(
self.como_version.gbd_round_id)
# set up draw source factory
self._ss_factory = SourceSinkFactory(como_version)
# set up the dimensions we are using
self.dimensions = self.como_version.nonfatal_dimensions
if location_id:
self.dimensions.simulation_index["location_id"] = location_id
if year_id:
self.dimensions.simulation_index["year_id"] = year_id
if sex_id:
self.dimensions.simulation_index["sex_id"] = sex_id
self.dimensions.simulation_index["age_group_id"] = [age.BIRTH]
def __init__(self, como_version, measure_id, year_id, sex_id):
self.como_version = como_version
self.measure_id = measure_id
self.dimensions = self.como_version.nonfatal_dimensions
if year_id:
self.dimensions.simulation_index["year_id"] = year_id
if sex_id:
self.dimensions.simulation_index["sex_id"] = sex_id
self._ss_factory = SourceSinkFactory(como_version)
class BirthPrevInputCollector:
def __init__(self, como_version, location_id, year_id, sex_id):
self.como_version = como_version
self._estim_years = estimation_years_from_gbd_round_id(
self.como_version.gbd_round_id)
# set up draw source factory
self._ss_factory = SourceSinkFactory(como_version)
# set up the dimensions we are using
self.dimensions = self.como_version.nonfatal_dimensions
if location_id:
self.dimensions.simulation_index["location_id"] = location_id
if year_id:
self.dimensions.simulation_index["year_id"] = year_id
if sex_id:
self.dimensions.simulation_index["sex_id"] = sex_id
self.dimensions.simulation_index["age_group_id"] = [age.BIRTH]
@property
def _birth_prev_set(self):
memv_df = self.como_version.mvid_list.merge(
self.como_version.birth_prev, on="modelable_entity_id")
arglist = list(zip(
list(memv_df.modelable_entity_id),
list(memv_df.model_version_id)))
return list(set(arglist))
def read_single_birth_prev(self, modelable_entity_id, model_version_id):
birth_source = self._ss_factory.get_birth_prev_modelable_entity_source(
modelable_entity_id, model_version_id)
dim = self.dimensions.get_simulation_dimensions(
measure_id=measures.PREVALENCE, at_birth=False)
# get filters w/ added years if interpolation is needed
filters = dim.index_dim.to_dict()["levels"]
req_years = filters["year_id"]
if not set(req_years).issubset(set(self._estim_years)):
filters["year_id"] = list(set(req_years + self._estim_years))
# read data
df = birth_source.content(filters=filters)
if df.empty:
raise Exception(f"No data returned for ME {modelable_entity_id}, "
f"model version {model_version_id}.")
draw_cols = [col for col in df.columns if "draw_" in col]
# add indices to dimensions object from draw source transforms
dim.index_dim.add_level("sequela_id", df.sequela_id.unique().tolist())
dim.index_dim.add_level("cause_id", df.cause_id.unique().tolist())
# interpolate missing years or filter if annual was found
if not set(df.year_id.unique()).issuperset(set(req_years)):
interp_df = pchip_interpolate(
df=df,
id_cols=dim.index_names,
value_cols=draw_cols,
time_col="year_id",
time_vals=req_years)
df = df[df.year_id.isin(req_years)]
df = df.append(interp_df)
else:
df = df[df.year_id.isin(req_years)]
# resample
if len(dim.data_list()) != len(draw_cols):
gbdizer = gbdize.GBDizeDataFrame(dim)
df = gbdizer.correlated_percentile_resample(df)
df["measure_id"] = measures.INCIDENCE
return df
def _q_read_single_birth_prev(self, inq, outq):
for arglist in iter(inq.get, SENTINEL):
try:
result = self.read_single_birth_prev(*arglist)
except Exception as e:
print(arglist)
print(e)
result = ExceptionWrapper(e)
outq.put(result)
def collect_birth_prev_inputs(self, n_processes=20):
_set = self._birth_prev_set
# spin up xcom queues
inq = Queue()
outq = Queue()
# Create and feed reader procs
read_procs = []
for i in range(n_processes):
p = Process(target=self._q_read_single_birth_prev,
args=(inq, outq))
read_procs.append(p)
p.start()
for readkey in _set:
inq.put(readkey)
# make the workers die after
for _ in read_procs:
inq.put(SENTINEL)
# get results
result_list = []
for _ in _set:
proc_result = outq.get()
result_list.append(proc_result)
# close up the queue
for p in read_procs:
p.join()
return result_list
def __init__(self, como_version, location_id, sex_id):
self.como_version = como_version
self._location_id = location_id
self._sex_id = sex_id
self._ss_factory = SourceSinkFactory(como_version)
class ENInjuryInputCollector(object):
_estim_years = [1990, 1995, 2000, 2005, 2010, 2017]
_draw_cols = ["draw_{}".format(i) for i in range(1000)]
def __init__(self,
como_version,
location_id=[],
year_id=[],
age_group_id=[],
sex_id=[]):
self.como_version = como_version
# set up draw source factory
self._ss_factory = SourceSinkFactory(como_version)
# set up the dimensions we are using
self.dimensions = self.como_version.nonfatal_dimensions
if location_id:
self.dimensions.simulation_index["location_id"] = location_id
if year_id:
self.dimensions.simulation_index["year_id"] = year_id
if sex_id:
self.dimensions.simulation_index["sex_id"] = sex_id
if age_group_id:
self.dimensions.simulation_index["age_group_id"] = age_group_id
@property
def _injury_set(self):
memv_df = self.como_version.mvid_list.merge(
self.como_version.injury_sequela, on='modelable_entity_id')
arglist = zip(list(memv_df.modelable_entity_id),
list(memv_df.model_version_id))
return list(set(arglist))
def read_single_en_injury(self,
modelable_entity_id,
model_version_id,
measure_id=[3, 6, 35, 36]):
injury_source = (
self._ss_factory.get_en_injuries_modelable_entity_source(
modelable_entity_id, model_version_id))
dim = self.dimensions.get_simulation_dimensions(measure_id)
# get filters w/ added years if interpolation is needed
filters = dim.index_dim.to_dict()["levels"]
req_years = filters["year_id"]
if not set(req_years).issubset(set(self._estim_years)):
filters["year_id"] = list(set(req_years + self._estim_years))
# read data
df = injury_source.content(filters=filters)
if df.empty:
raise Exception("No data returned for meid:{} and mvid:{}".format(
modelable_entity_id, model_version_id))
# add indices to dimensions object from draw source transforms
dim.index_dim.add_level("sequela_id", df.sequela_id.unique().tolist())
dim.index_dim.add_level("cause_id", df.cause_id.unique().tolist())
dim.index_dim.add_level("healthstate_id",
df.healthstate_id.unique().tolist())
dim.index_dim.add_level("rei_id", df.rei_id.unique().tolist())
# interpolate missing years
if not set(df.year_id.unique()).issuperset(set(req_years)):
interp_df = pchip_interpolate(df=df,
id_cols=dim.index_names,
value_cols=self._draw_cols,
time_col="year_id",
time_vals=req_years)
df = df[df.year_id.isin(req_years)]
df = df.append(interp_df)
else:
df = df[df.year_id.isin(req_years)]
# resample if ndraws is less than 1000
if len(dim.data_list()) != 1000:
gbdizer = gbdize.GBDizeDataFrame(dim)
df = gbdizer.correlated_percentile_resample(df)
return df
def _q_read_single_en_injury(self, inq, outq):
for arglist in iter(inq.get, sentinel):
try:
result = self.read_single_en_injury(*arglist)
except Exception as e:
print(arglist)
print(e)
result = ExceptionWrapper(e)
outq.put(result)
def collect_injuries_inputs(self,
n_processes=20,
measure_id=[3, 6, 35, 36]):
_set = self._injury_set
# spin up xcom queues
inq = Queue()
outq = Queue()
# Create and feed reader procs
read_procs = []
for i in range(min([n_processes, _set])):
p = Process(target=self._q_read_single_en_injury, args=(inq, outq))
read_procs.append(p)
p.start()
for readkey in _set:
args = readkey + (measure_id, )
inq.put(args)
# make the workers die after
for _ in read_procs:
inq.put(sentinel)
# get results
result_list = []
for _ in _set:
proc_result = outq.get()
result_list.append(proc_result)
# close up the queue
for p in read_procs:
p.join()
return result_list
