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author
Sylvia Lutze
committed
forgot isort and black
1 parent 1a185e8 commit 4418b90

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2 files changed

+50
-44
lines changed

2 files changed

+50
-44
lines changed

‎src/vivarium_gates_nutrition_optimization/data/extra_gbd.py‎

Lines changed: 0 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -97,7 +97,6 @@ def get_anemia_yld_rate(location: str):
9797

9898

9999
@vi_utils.cache
100-
101100
def get_hemoglobin_rr_data(key: str, location: str):
102101
data = gbd.get_draws(
103102
release_id=33,
@@ -113,4 +112,3 @@ def get_hemoglobin_rr_data(key: str, location: str):
113112
data = data[data["cause_id"] == 367]
114113
data["year_id"] = 2023
115114
return data
116-

‎src/vivarium_gates_nutrition_optimization/data/loader.py‎

Lines changed: 50 additions & 42 deletions
Original file line numberDiff line numberDiff line change
@@ -177,8 +177,8 @@ def load_metadata(
177177

178178

179179
def load_categorical_paf(
180-
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
181-
) -> pd.DataFrame:
180+
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
181+
) -> pd.DataFrame:
182182
try:
183183
risk = {
184184
# todo add keys as needed
@@ -216,12 +216,14 @@ def load_categorical_paf(
216216

217217

218218
def get_pregnancy_end_incidence(
219-
location: str, years: Optional[Union[int, str, List[int]]] = None
220-
) -> pd.DataFrame:
219+
location: str, years: Optional[Union[int, str, List[int]]] = None
220+
) -> pd.DataFrame:
221221
asfr = get_data(data_keys.PREGNANCY.ASFR, location, years)
222222
sbr = get_data(data_keys.PREGNANCY.SBR, location, years)
223223
sbr = sbr.reset_index(level="year_end", drop=True).reindex(asfr.index, level="year_start")
224-
incidence_c995 = get_data(data_keys.PREGNANCY.RAW_INCIDENCE_RATE_MISCARRIAGE, location, years)
224+
incidence_c995 = get_data(
225+
data_keys.PREGNANCY.RAW_INCIDENCE_RATE_MISCARRIAGE, location, years
226+
)
225227
incidence_c374 = get_data(data_keys.PREGNANCY.RAW_INCIDENCE_RATE_ECTOPIC, location, years)
226228
pregnancy_end_rate = (
227229
asfr + asfr.multiply(sbr["value"], axis=0) + incidence_c995 + incidence_c374
@@ -230,8 +232,8 @@ def get_pregnancy_end_incidence(
230232

231233

232234
def load_asfr(
233-
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
234-
) -> pd.DataFrame:
235+
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
236+
) -> pd.DataFrame:
235237
asfr = load_standard_data(key, location, years)
236238
asfr = asfr.reset_index()
237239
asfr_pivot = asfr.pivot(
@@ -286,17 +288,17 @@ def load_lbwsg_exposure(
286288

287289

288290
def load_maternal_csmr(
289-
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
290-
) -> pd.DataFrame:
291+
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
292+
) -> pd.DataFrame:
291293
key = EntityKey(key)
292294
entity = get_entity(key)
293295
entity.restrictions.yll_age_group_id_end = 15
294296
return interface.get_measure(entity, key.measure, location, years).droplevel("location")
295297

296298

297299
def load_maternal_disorders_ylds(
298-
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
299-
) -> pd.DataFrame:
300+
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
301+
) -> pd.DataFrame:
300302
groupby_cols = ["age_group_id", "sex_id", "year_id"]
301303
draw_cols = vi_globals.DRAW_COLUMNS
302304

@@ -329,9 +331,11 @@ def load_maternal_disorders_ylds(
329331

330332

331333
def load_pregnant_maternal_disorders_incidence(
332-
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
333-
) -> pd.DataFrame:
334-
total_incidence = get_data(data_keys.MATERNAL_DISORDERS.RAW_INCIDENCE_RATE, location, years)
334+
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
335+
) -> pd.DataFrame:
336+
total_incidence = get_data(
337+
data_keys.MATERNAL_DISORDERS.RAW_INCIDENCE_RATE, location, years
338+
)
335339
pregnancy_end_rate = get_pregnancy_end_incidence(location, years)
336340
maternal_disorders_incidence = total_incidence / pregnancy_end_rate
337341
## We have to normalize, since this comes to a probability with some values > 1
@@ -342,17 +346,19 @@ def load_pregnant_maternal_disorders_incidence(
342346

343347

344348
def load_maternal_disorders_mortality_probability(
345-
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
346-
) -> pd.DataFrame:
349+
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
350+
) -> pd.DataFrame:
347351
total_csmr = get_data(data_keys.MATERNAL_DISORDERS.CSMR, location, years)
348-
total_incidence = get_data(data_keys.MATERNAL_DISORDERS.RAW_INCIDENCE_RATE, location, years)
352+
total_incidence = get_data(
353+
data_keys.MATERNAL_DISORDERS.RAW_INCIDENCE_RATE, location, years
354+
)
349355
mortality_probability = total_csmr / total_incidence
350356
return mortality_probability.fillna(0)
351357

352358

353359
def load_pregnant_maternal_hemorrhage_incidence(
354-
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
355-
) -> pd.DataFrame:
360+
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
361+
) -> pd.DataFrame:
356362
mh_incidence = get_data(data_keys.MATERNAL_HEMORRHAGE.RAW_INCIDENCE_RATE, location, years)
357363
mh_csmr = get_data(data_keys.MATERNAL_HEMORRHAGE.CSMR, location, years)
358364
pregnancy_end_rate = get_pregnancy_end_incidence(location, years)
@@ -365,8 +371,8 @@ def load_pregnant_maternal_hemorrhage_incidence(
365371

366372

367373
def load_hemoglobin_maternal_hemorrhage_rr(
368-
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
369-
) -> pd.DataFrame:
374+
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
375+
) -> pd.DataFrame:
370376
if key != data_keys.MATERNAL_HEMORRHAGE.RR_ATTRIBUTABLE_TO_HEMOGLOBIN:
371377
raise ValueError(f"Unrecognized key {key}")
372378

@@ -388,21 +394,23 @@ def load_hemoglobin_maternal_hemorrhage_rr(
388394

389395

390396
def load_hemoglobin_maternal_hemorrhage_paf(
391-
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
392-
) -> pd.DataFrame:
397+
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
398+
) -> pd.DataFrame:
393399
if key != data_keys.MATERNAL_HEMORRHAGE.PAF_ATTRIBUTABLE_TO_HEMOGLOBIN:
394400
raise ValueError(f"Unrecognized key {key}")
395401

396-
rr = get_data(data_keys.MATERNAL_HEMORRHAGE.RR_ATTRIBUTABLE_TO_HEMOGLOBIN, location, years)
402+
rr = get_data(
403+
data_keys.MATERNAL_HEMORRHAGE.RR_ATTRIBUTABLE_TO_HEMOGLOBIN, location, years
404+
)
397405
proportion = get_data(
398406
data_keys.HEMOGLOBIN.PREGNANT_PROPORTION_WITH_HEMOGLOBIN_BELOW_70, location, years
399407
)
400408
return (rr * proportion + (1 - proportion) - 1) / (rr * proportion + (1 - proportion))
401409

402410

403411
def load_hemoglobin_maternal_disorders_rr(
404-
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
405-
) -> pd.DataFrame:
412+
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
413+
) -> pd.DataFrame:
406414
if key != data_keys.MATERNAL_DISORDERS.RR_ATTRIBUTABLE_TO_HEMOGLOBIN:
407415
raise ValueError(f"Unrecognized key {key}")
408416

@@ -415,8 +423,8 @@ def load_hemoglobin_maternal_disorders_rr(
415423

416424

417425
def load_hemoglobin_maternal_disorders_paf(
418-
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
419-
) -> pd.DataFrame:
426+
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
427+
) -> pd.DataFrame:
420428
location_id = utility_data.get_location_id(location)
421429
demography = get_data(data_keys.POPULATION.DEMOGRAPHY, location, years)
422430

@@ -433,8 +441,8 @@ def load_hemoglobin_maternal_disorders_paf(
433441

434442

435443
def get_moderate_hemorrhage_probability(
436-
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
437-
) -> pd.DataFrame:
444+
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
445+
) -> pd.DataFrame:
438446
hemorrhage_dist_params = data_values.PROBABILITY_MODERATE_MATERNAL_HEMORRHAGE
439447
# Clip a bit higher than zero to avoid underflow error
440448
dist = sampling.get_truncnorm_from_quantiles(*hemorrhage_dist_params, lower_clip=0.1)
@@ -456,8 +464,8 @@ def get_moderate_hemorrhage_probability(
456464

457465

458466
def load_background_morbidity(
459-
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
460-
) -> pd.DataFrame:
467+
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
468+
) -> pd.DataFrame:
461469
all_cause_yld_rate = extra_gbd.get_all_cause_yld_rate(location)
462470
all_cause_yld_rate = all_cause_yld_rate[
463471
vi_globals.DEMOGRAPHIC_COLUMNS + vi_globals.DRAW_COLUMNS
@@ -521,8 +529,8 @@ def get_hemoglobin_data(
521529

522530

523531
def get_hemoglobin_csv_data(
524-
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
525-
) -> pd.DataFrame:
532+
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
533+
) -> pd.DataFrame:
526534
location_id = utility_data.get_location_id(location)
527535
demography = get_data(data_keys.POPULATION.DEMOGRAPHY, location, years)
528536

@@ -543,8 +551,8 @@ def get_hemoglobin_csv_data(
543551

544552

545553
def load_bmi_prevalence(
546-
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
547-
) -> pd.DataFrame:
554+
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
555+
) -> pd.DataFrame:
548556
location_id = utility_data.get_location_id(location)
549557
demography = get_data(data_keys.POPULATION.DEMOGRAPHY, location, years)
550558

@@ -576,8 +584,8 @@ def load_bmi_prevalence(
576584

577585

578586
def load_ifa_coverage(
579-
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
580-
) -> pd.DataFrame:
587+
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
588+
) -> pd.DataFrame:
581589
df = pd.read_csv(
582590
paths.CSV_RAW_DATA_ROOT / "baseline_ifa_coverage" / (location + ".csv"), index_col=0
583591
)
@@ -586,8 +594,8 @@ def load_ifa_coverage(
586594

587595

588596
def load_ifa_effect_size(
589-
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
590-
) -> pd.DataFrame:
597+
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
598+
) -> pd.DataFrame:
591599
loc, scale = data_values.IFA_EFFECT_SIZE
592600
dist = stats.norm(loc, scale)
593601
rng = np.random.default_rng(get_hash(f"ifa_effect_size_{location}"))
@@ -601,8 +609,8 @@ def load_ifa_effect_size(
601609

602610

603611
def load_supplementation_stillbirth_rr(
604-
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
605-
) -> pd.DataFrame:
612+
key: str, location: str, years: Optional[Union[int, str, List[int]]] = None
613+
) -> pd.DataFrame:
606614
try:
607615
distribution = data_values.INTERVENTION_STILLBIRTH_RRS[key]
608616
except KeyError:

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