def get_envelope_draws(meid, loc):
df = get_draws('modelable_entity_id',
meid,
'dismod',
location_ids=loc,
year_ids=yids,
sex_ids=sids,
age_group_ids=agids,
gbd_round_id={GBD ROUND ID})
df = df.drop(['modelable_entity_id', 'model_version_id', 'measure_id'], axis=1)
renames = {'draw_%s' % d: 'env_%s' % d for d in range(1000)}
df.rename(columns=renames, inplace=True)
draws = [col for col in list(df) if col.startswith('env')]
df['mean'] = df[draws].mean(axis=1)
for col in df[draws]:
df[col] = df[col]/df['mean']
df.fillna(value=1, inplace=True)
df= df.drop(['mean'], axis=1)
return df
locations = stroke_fns.get_locations()
sexes = [1, 2]
loops = (len(locations) * 2) * 2
all_df = pd.DataFrame()
#all_df_list = []
count = 0
for me, mv in izip(modelable_entity_ids, models):
for geo in locations:
for sex in sexes:
print 'On loop %s of %s' % (count, loops)
draws = get_draws('modelable_entity_id',
me,
'epi',
location_ids=geo,
year_ids=year,
sex_ids=sex,
gbd_round_id=4)
d_ages = draws.age_group_id.unique()
if 235 in d_ages:
draws = draws[draws.age_group_id.isin(ages2)]
draws[['age_group_id']] = draws[['age_group_id'
]].replace(to_replace=235,
value=33)
elif 33 in d_ages:
draws = draws[draws.age_group_id.isin(ages1)]
# pull out incidence and EMR into seperate dfs
incidence = draws[draws.measure_id == 6]
emr = draws[draws.measure_id == 9]
def run_yld_compile(yld_tmp, yld_dir, yld_version, root_dir, location, ar,
n_draws):
if ar:
years = range(1990, 2017)
else:
years = [1990, 1995, 2000, 2005, 2010, 2016]
pops = pd.read_csv('{root_dir}/PATH/pop.csv'.format(root_dir=root_dir))
index_cols = ['location_id', 'age_group_id', 'sex_id', 'year_id']
if yld_version == 0:
yld_draws = get_draws("cause_id",
source="como",
gbd_id=294,
measure_ids=3,
location_ids=location,
year_ids=years,
sex_ids=[1, 2],
n_draws=n_draws,
resample=True)
else:
yld_draws = get_draws("cause_id",
source="como",
gbd_id=294,
measure_ids=3,
location_ids=location,
year_ids=years,
sex_ids=[1, 2],
version=yld_version,
n_draws=n_draws,
resample=True)
draw_cols = [col for col in yld_draws.columns if 'draw' in col]
yld_draws = yld_draws[index_cols + draw_cols]
yld_draws = yld_draws.merge(pops, on=index_cols)
yld_draws = yld_draws.set_index(index_cols)
yld_draws[draw_cols] = yld_draws[draw_cols].multiply(
yld_draws['population'], axis='index')
yld_draws.drop('population', axis=1, inplace=True)
yld_draws = yld_draws.reset_index()
yld_draws.loc[yld_draws['age_group_id'].isin(range(2, 5) + [164]),
'age_group_id'] = 28
ages = range(5, 21) + range(30, 33) + [28, 235]
yld_draws = yld_draws.loc[yld_draws['age_group_id'].isin(ages)]
yld_draws = yld_draws.groupby(index_cols).sum().reset_index()
sex_agg = yld_draws.groupby(['age_group_id', 'location_id',
'year_id']).sum().reset_index()
sex_agg['sex_id'] = 3
yld_draws = yld_draws.append(sex_agg)
yld_draws = yld_draws.merge(pops, on=index_cols)
yld_draws = yld_draws.set_index(index_cols)
yld_draws[draw_cols] = yld_draws[draw_cols].divide(yld_draws['population'],
axis='index')
yld_draws.drop('population', axis=1, inplace=True)
yld_draws = yld_draws.reset_index()
yld_draws.rename(columns=(lambda x: x.replace('draw_', '')
if x in draw_cols else x),
inplace=True)
new_draws = [col for col in yld_draws.columns if col.isdigit()]
yld_draws = pd.melt(yld_draws,
id_vars=index_cols,
value_vars=new_draws,
var_name='draw',
value_name='yld_rate')
csv_draws = yld_draws.set_index('location_id')
csv_draws.to_csv('{yld_tmp}/{location}_draws.csv'.format(
yld_tmp=yld_tmp, location=location))
summ_cols = ['yld_rate']
calc_summary(yld_draws, summ_cols, yld_dir, location)
def process_location_cc_draws(location_id, test=False):
"""Pull mortality numbers, limiting to desired ages by cause
Gets all years >1990 and ages for the location id as mortality numbers
from get_draws
"""
dfs = []
cause_age_sets = [[
dw.CC_ALL_AGE_CAUSE_IDS,
range(2, 21) + range(30, 33) + [235]
], [dw.CC_THIRTY_SEVENTY_CAUSE_IDS,
range(11, 19)], [dw.PRE_1990_CAUSES, [22]]]
if test:
years = [2016]
else:
years = []
for causes, ages in cause_age_sets:
gbd_ids = {'cause_ids': causes}
df = get_draws(gbd_id_field=['cause_id'] * len(causes),
gbd_id=causes,
source='codcorrect',
version=dw.CC_VERS,
location_ids=[location_id],
year_ids=years,
age_group_ids=ages,
sex_ids=[3],
measure_ids=1)
dfs.append(df)
df = pd.concat(dfs, ignore_index=True)
# keep relevant years
df = df.ix[(df['year_id'] >= 1990) |
((df['cause_id'].isin(dw.PRE_1990_CAUSES)) &
(df['year_id'] >= 1980))]
# make sure index variables are ints
for idvar in dw.CC_GROUP_COLS:
df[idvar] = df[idvar].astype(int)
# make sure it looks like we expect
assert set(df.ix[df['cause_id'].isin(dw.PRE_1990_CAUSES)].age_group_id) == set([22]), \
'unexpected age group ids found'
assert set(df.ix[~df['cause_id'].isin(dw.PRE_1990_CAUSES)].age_group_id) == \
set(range(2, 21) + range(30, 33) + [235]), \
'unexpected age group ids found'
assert set(df.sex_id) == set([3]), \
'unexpected sex ids found'
if not test:
assert set(df.ix[df['cause_id'].isin(dw.PRE_1990_CAUSES)].year_id) == \
set(range(1980, 2017)), \
'unexpected year ids found'
assert set(df.ix[
~df['cause_id'].isin(dw.PRE_1990_CAUSES)
].year_id) == \
set(range(1990, 2017)), \
'unexpected year ids found'
assert set(df.location_id) == set([location_id]), \
'unexpected location ids found'
# age standardize
df = age_standardize(df, 'codcorrect')
# write the output
df = df[dw.CC_GROUP_COLS + dw.DRAW_COLS]
write_output(df, 'codcorrect', location_id)
return df
def process_location_como_draws(location_id, measure_id, test=False):
"""Pull indidence rates, merging with population to make cases
Gets all years, ages, and sexes for the location id as incidence rates
from get_draws, and combines into all ages, both
sexes cases.
"""
db_pops = qry.get_pops()
if measure_id == 6:
causes = dw.COMO_INC_CAUSE_IDS
elif measure_id == 5:
causes = dw.COMO_PREV_CAUSE_IDS
else:
raise ValueError("bad measure_id: {}".format(measure_id))
dfs = []
if test:
years = [2016]
else:
years = []
for cause_id in causes:
print("pulling {c}".format(c=cause_id))
if test:
df = get_draws(gbd_id_field='cause_id',
gbd_id=cause_id,
source='como',
version=dw.COMO_VERS,
location_ids=[location_id],
year_ids=years,
age_group_ids=[],
sex_ids=[1, 2],
measure_ids=[measure_id])
else:
df = interpolate(gbd_id_field='cause_id',
gbd_id=cause_id,
source='como',
version=dw.COMO_VERS,
reporting_year_start=1990,
reporting_year_end=2016,
location_ids=[location_id],
age_group_ids=[],
sex_ids=[1, 2],
measure_ids=[measure_id])
# these pull in as rates
df['metric_id'] = 3
# make sure it looks like we expect
assert set(df.age_group_id) == set(range(2, 21) + range(30, 33) + [235]), \
'unexpected age group ids found'
assert set(df.sex_id) == set([1, 2]), \
'unexpected sex ids found'
if not test:
assert set(df.year_id) == set(range(1990, 2017)), \
'unexpected year ids found'
assert set(df.location_id) == set([location_id]), \
'unexpected location ids found'
# compile
dfs.append(df[dw.COMO_GROUP_COLS + dw.DRAW_COLS])
df = pd.concat(dfs, ignore_index=True)
# merge with pops to transform to cases
df = df.merge(db_pops, how='left')
assert df.population.notnull().values.all(
), 'merge with populations failed'
# concatenate the metadata with the draw cols times the pop
# this multiplies each draw column by the population column
df = pd.concat([
df[dw.COMO_GROUP_COLS],
df[dw.DRAW_COLS].apply(lambda x: x * df['population']),
df['population']
],
axis=1)
# now its numbers (this line is for readability)
df['metric_id'] = 1
# aggregate sexes
df['sex_id'] = 3
# collapse sexes together
df = df.groupby(dw.COMO_GROUP_COLS,
as_index=False)[dw.DRAW_COLS + ['population']].sum()
df = pd.concat([
df[dw.COMO_GROUP_COLS],
df[dw.DRAW_COLS].apply(lambda x: x / df['population'])
],
axis=1)
df['metric_id'] = 3
# AGE STANDARDIZE
print("age standardizing")
wgts = custom_age_weights(0, 125)
df = df.merge(wgts, on=['age_group_id'], how='left')
assert df.age_group_weight_value.notnull().values.all(), \
'merge w wgts failed'
df = pd.concat([
df[dw.COMO_GROUP_COLS],
df[dw.DRAW_COLS].apply(lambda x: x * df['age_group_weight_value'])
],
axis=1)
df['age_group_id'] = 27
df = df.groupby(dw.COMO_GROUP_COLS, as_index=False)[dw.DRAW_COLS].sum()
df = df[dw.COMO_GROUP_COLS + dw.DRAW_COLS]
write_output(df, 'como', location_id)
return df
def process_location_risk_exposure_draws(location_id, test=False):
"""Return yearly age standardized estimates of each rei_id.
Arguments:
location_id: the location_id to process
Returns:
pandas dataframe like so:
[ID_COLS] : [dw.DRAW_COLS]
"""
dfs = []
# version_df = pd.DataFrame()
risks = set(dw.RISK_EXPOSURE_REI_IDS).union(
set(dw.RISK_EXPOSURE_REI_IDS_MALN))
if test:
years = [2016]
else:
years = []
for rei_id in risks:
print("pulling {r}".format(r=rei_id))
if test or rei_id == 166:
df = get_draws(gbd_id_field='rei_id',
gbd_id=rei_id,
source='risk',
location_ids=[location_id],
year_ids=years,
age_group_ids=[],
sex_ids=[],
draw_type='exposure')
elif not test and rei_id == 86:
df = interpolate(gbd_id_field='rei_id',
gbd_id=rei_id,
source='risk',
reporting_year_start=1990,
reporting_year_end=2016,
location_ids=[location_id],
age_group_ids=[],
sex_ids=[],
measure_ids=19,
draw_type='exposure')
else:
df = interpolate(gbd_id_field='rei_id',
gbd_id=rei_id,
source='risk',
reporting_year_start=1990,
reporting_year_end=2016,
location_ids=[location_id],
age_group_ids=[],
sex_ids=[],
draw_type='exposure')
# remove any other ages besides main gbd ages
df = df.query(
'(age_group_id >= 2 & age_group_id <= 20) or age_group_id in [30, 31, 32, 235] and sex_id in [1, 2]'
)
df = df.query('year_id >= 1990')
if rei_id == 166:
# only keep 10+ for smoking
df = df.query('age_group_id >= 7')
df = df.query('parameter=="cat1"')
# set the rei_id because it isnt in the get_draws pull
df['rei_id'] = rei_id
# these are prevalence rates
df['metric_id'] = 3
if rei_id == 86:
df['measure_id'] = 19
else:
df['measure_id'] = 5
dfs.append(df[dw.RISK_EXPOSURE_GROUP_COLS + dw.DRAW_COLS])
df = pd.concat(dfs, ignore_index=True)
# COLLAPSE SEX
print("collapsing sex")
df = df.merge(qry.get_pops(), how='left')
assert df.population.notnull().values.all(), 'merge with pops fail'
# overriding the sex variable for collapsing
df['sex_id'] = df.rei_id.apply(lambda x: 2 if x == 167 else 3)
# for stunting and wasting (where we only have under-5), keep only under-5 and aggregate ages
df.ix[df['rei_id'].isin(dw.RISK_EXPOSURE_REI_IDS_MALN), 'age_group_id'] = 1
# make all ages for PM 2.5
df.ix[df['rei_id'] == 86, 'age_group_id'] = 22
df = pd.concat([
df[dw.RISK_EXPOSURE_GROUP_COLS],
df[dw.DRAW_COLS].apply(lambda x: x * df['population'])
],
axis=1)
# so unnecessary programmatically but good for documentation -
# these are now prev cases
df['metric_id'] = 1
# now that its in cases it is possible to collapse sex
df = df.groupby(dw.RISK_EXPOSURE_GROUP_COLS, as_index=False).sum()
# RETURN TO RATES
print("returning to rates")
df = df.merge(qry.get_pops(), how='left')
assert df.population.notnull().values.all(), 'merge with pops fail'
df = pd.concat([
df[dw.RISK_EXPOSURE_GROUP_COLS],
df[dw.DRAW_COLS].apply(lambda x: x / df['population'])
],
axis=1)
df['metric_id'] = 3
# AGE STANDARDIZE
print("age standardizing")
wgts = custom_age_weights(10, 125) # FOR SMOKING ONLY
df = df.merge(wgts, on=['age_group_id'], how='left')
assert df.age_group_weight_value.notnull().values.all(), \
'merge w wgts failed'
df = pd.concat([
df[dw.RISK_EXPOSURE_GROUP_COLS],
df[dw.DRAW_COLS].apply(lambda x: x * df['age_group_weight_value'])
],
axis=1)
df['age_group_id'] = 27
df = df.groupby(dw.RISK_EXPOSURE_GROUP_COLS,
as_index=False)[dw.DRAW_COLS].sum()
df = df[dw.RISK_EXPOSURE_GROUP_COLS + dw.DRAW_COLS]
write_output(df, 'risk_exposure', location_id)
return df
def process_location_risk_burden_draws(location_id, test=False):
''' Given a list of rei_ids, use gopher to get attributable burden draws
and save to out directory.
'''
dfs = []
for rei_id in dw.RISK_BURDEN_REI_IDS + dw.RISK_BURDEN_DALY_REI_IDS:
print(rei_id)
if rei_id in dw.RISK_BURDEN_REI_IDS:
measure_id = 1
elif rei_id in dw.RISK_BURDEN_DALY_REI_IDS:
measure_id = 2
else:
raise ValueError("no measure found")
print('Getting draws')
df = get_draws(gbd_id_field=['cause_id', 'rei_id'],
gbd_id=[294, rei_id],
source='burdenator',
version=dw.BURDENATOR_VERS,
location_ids=location_id,
year_ids=[],
age_group_ids=[],
sex_ids=[],
num_workers=3,
n_draws=1000,
resample=True)
# keep years we want
df = df.query('measure_id == {}'.format(measure_id))
df = df.query('metric_id == 1')
df = df.query('age_group_id in {} and sex_id in [1, 2]'.format(
range(2, 21) + range(30, 33) + [235]))
df = df.query('year_id in {}'.format(range(1990, 2011, 5) + [2016]))
# aggregate to both sexes
df['sex_id'] = 3
df = df.groupby(dw.RISK_BURDEN_GROUP_COLS,
as_index=False)[dw.DRAW_COLS].sum()
pops = qry.get_pops(both_sexes=True)
df = df.merge(pops,
how='left',
on=['location_id', 'age_group_id', 'sex_id', 'year_id'])
df = pd.concat([
df[dw.RISK_BURDEN_GROUP_COLS],
df[dw.DRAW_COLS].apply(lambda x: x / df['population'])
],
axis=1)
df['metric_id'] = 3
# keep the right columns
df = df[dw.RISK_BURDEN_GROUP_COLS + dw.DRAW_COLS]
# interpolate years
print('Interpolating')
df = custom_interpolate(df)
# age-standardize
age_weights = qry.get_age_weights(4)
df = df.merge(age_weights)
df = pd.concat([
df[dw.RISK_BURDEN_GROUP_COLS],
df[dw.DRAW_COLS].apply(lambda x: x * df['age_group_weight_value'])
],
axis=1)
df['age_group_id'] = 27
df = df.groupby(dw.RISK_BURDEN_GROUP_COLS,
as_index=False)[dw.DRAW_COLS].sum()
dfs.append(df)
df = pd.concat(dfs)
write_output(df, 'risk_burden', location_id)
return df
def draw_deaths(cause_id, location_id, out_path, send_Slack, slack, channel):
"""
draw_deaths: For a given location and Cause grab death estimates at the
draw-level. Collapse it mean, upper, and lower.
"""
# year IDs
YEAR_IDS = range(1990, 2017)
# All ages that could be used
ALL_AGES = range(2, 22) + [28] + range(30, 33) + [235]
# Early and late ages of DisMod
DISMOD_AGES = [2, 3, 4, 30, 31, 32, 235]
# DisMod old ages
OLD_DISMOD_AGES = [30, 31, 32, 235]
# DisMod young ages
YOUNG_DISMOD_AGES = [2, 3, 4]
# Ages
AGES = range(2, 5) + range(30, 34)
# old ages
OLD_AGES = range(30, 34)
# young ages
YOUNG_AGES = range(2, 5)
# necessary columns
group_cols = ['cause_id', 'sex_id', 'age_group_id', 'location_id']
measure_cols = ['mean_death', 'upper_death', 'lower_death']
# Draw columns
draw_cols = (['draw_%s' % i for i in range(0, 1000)])
hf_death_cols = (['hf_deaths_%s' % i for i in range(0, 1000)])
# split <1 age group into early neonatal, late neonatal, and post neonatal
young_age_groups = pd.DataFrame({'age_group_id':YOUNG_DISMOD_AGES,
'temp_id':1})
# split 80+ age group into early neonatal, late neonatal, and post neonatal
old_age_groups = pd.DataFrame({'age_group_id':OLD_DISMOD_AGES,
'temp_id':1})
# Query draws for the cause/location combo
print cause_id, location_id
try:
DF = get_draws('cause_id',
cause_id,
'codcorrect',
location_ids=location_id,
age_group_ids=ALL_AGES,
gbd_round_id=4,
#status="best",
status="latest",
#version_id=64,
sexes=[1,2],
location_set_id=35,
measure_ids=1,
numworkers=5)
print DF['output_version_id'].unique(), " ", DF.shape
DF = DF.query('year_id in {}'.format(YEAR_IDS))
DF = DF.query('output_version_id == {}'.format(64))
DF.fillna(0, inplace=True)
except:
if send_Slack == "YES":
message = ("get_draws query FAILED for location_id={location_id} "
"and cause_id={cause_id}").format(\
location_id=location_id,
cause_id=cause_id)
slack.chat.post_message(channel, message)
print message
if send_Slack == "YES" and not len(DF):
message = "Missing data for get_draws for location_id={location_id}" +\
" and cause_id={cause_id}".format(location_id=location_id,
cause_id=cause_id)
slack.chat.post_message(channel, message)
# Compute 25 and 75 percentiles of the distribution for each row
stats = DF[draw_cols].transpose().describe(
percentiles=[.25, .75]).transpose()[['mean', '25%', '75%']]
stats.rename(
columns={'mean':'mean_death',
'25%': 'lower_death',
'75%': 'upper_death'}, inplace=True)
# add these percentiles to the dataset
DF['mean_death'] = stats['mean_death']
DF['lower_death'] = stats['lower_death']
DF['upper_death'] = stats['upper_death']
# To make this script robust filter by what ever the age groups are
# available if age group ID 235 is available go with that otherwise find
# another way to get/make an 80+ age group
if pd.Series(DISMOD_AGES).isin(DF.age_group_id.unique()).all():
ages = range(2,21) + [30, 31, 32, 235]
DF = DF.query('age_group_id in {}'.format(ages))
print "DISMOD_AGES"
elif pd.Series(YOUNG_DISMOD_AGES).isin(DF.age_group_id.unique()).all() and \
not pd.Series(OLD_DISMOD_AGES).isin(DF.age_group_id.unique()).all():
ages = range(2, 21) + range(30, 34)
DF = DF.query('age_group_id in {}'.format(ages))
DF['age_group_id'].replace(to_replace=33, value=235, inplace=True)
print "YOUNG_DISMOD_AGES"
elif pd.Series(OLD_DISMOD_AGES).isin(DF.age_group_id.unique()).all() and \
not pd.Series(YOUNG_DISMOD_AGES).isin(DF.age_group_id.unique()).all():
ages = range(5, 21) + [28] + [30, 31, 32, 235]
DF = DF.query('age_group_id in {}'.format(ages))
print "OLD_DISMOD_AGES"
elif pd.Series(AGES).isin(DF.age_group_id.unique()).all():
ages = range(2, 21) + range(30, 34)
DF = DF.query('age_group_id in {}'.format(ages))
print "AGES"
elif pd.Series(OLD_AGES).isin(DF.age_group_id.unique()).all() and \
not pd.Series(YOUNG_AGES).isin(DF.age_group_id.unique()).all():
ages = range(5, 21) + [28] + range(30, 34)
DF = DF.query('age_group_id in {}'.format(ages))
print "OLD_AGES"
elif pd.Series(YOUNG_AGES).isin(DF.age_group_id.unique()).all() and \
not pd.Series(OLD_AGES).isin(DF.age_group_id.unique()).all():
ages = range(2, 22)
DF = DF.query('age_group_id in {}'.format(ages))
print "YOUNG_AGES"
else:
ages = range(5, 22) + [28]
DF = DF.query('age_group_id in {}'.format(ages))
"A lot is missing"
# split out neotals age groups if needed
if pd.Series([28]).isin(DF.age_group_id.unique()).all():
temp = DF.query('age_group_id == 28').copy()
DF = DF.query('age_group_id != 28')
# Make a temporary id to merge with age groups DataFrame
temp.drop('age_group_id', axis=1, inplace=True)
temp['temp_id'] = 1
temp = temp.merge(young_age_groups, on='temp_id', how='inner')
temp.drop('temp_id', axis=1, inplace=True)
# Append the new df w/ age groups to the original
# (excluding the <1 age composite).
DF = DF.append(temp)
# split out 80+ age groups if needed
if pd.Series([21]).isin(DF.age_group_id.unique()).all():
temp = DF.query('age_group_id == 21').copy()
DF = DF.query('age_group_id != 21')
# Make a temporary id to merge with age groups DataFrame
temp.drop('age_group_id', axis=1, inplace=True)
temp['temp_id'] = 1
temp = temp.merge(old_age_groups, on='temp_id', how='inner')
temp.drop('temp_id', axis=1, inplace=True)
# Append the new df w/ age groups to the original
# (excluding the 80+ age composite).
DF = DF.append(temp)
# The columns that are added up
DF = DF.groupby(group_cols)[measure_cols].sum().reset_index()
# fill in any missing data
DF = make_square_matrix(DF)
# Save it to a CSV on the cluster to be read back into the main script.
DF.to_csv('{out_path}/codcorrect_{cause}_{location}.csv'.format(\
out_path=out_path,
cause=cause_id,
location=location_id),
index=False, encoding='utf-8')
all_cols = keep_cols + index_cols
ages1 = range(2,21) + [30,31,32,33]
ages2 = range(2,21) + [30,31,32,235]
# ages1 = [22,27]
# ages2 = [22,27]
all_acute_isch_list = []
all_acute_hem_list = []
all_chronic_isch_list = []
all_chronic_hem_list = []
count = 0
location_count = len(locations)
for geo in locations:
# get acute isch
csmr_isch = get_draws('modelable_entity_id', isch_me, 'epi', location_ids=geo,
year_ids=year, sex_ids=[1,2], gbd_round_id=4)
csmr_isch = csmr_isch[csmr_isch['measure_id']==15]
isch_ages = csmr_isch.age_group_id.unique()
if 235 in isch_ages:
csmr_isch = csmr_isch[csmr_isch.age_group_id.isin(ages2)]
csmr_isch[['age_group_id']] = csmr_isch[['age_group_id']].replace(to_replace=235,value=33)
elif 33 in isch_ages:
csmr_isch = csmr_isch[csmr_isch.age_group_id.isin(ages1)]
csmr_isch = csmr_isch[all_cols]
#get acute hem
csmr_hem = get_draws('modelable_entity_id', hem_me, 'epi', location_ids=geo,
year_ids=year, sex_ids=[1,2], gbd_round_id=4)
csmr_hem = csmr_hem[csmr_hem['measure_id']==15]
hem_ages = csmr_hem.age_group_id.unique()
if 235 in hem_ages:
def get_envelope(self):
"""get the envelope"""
print "get envelope"
# columns
draw_cols = (['draw_%s' % i for i in range(0, 1000)])
env_cols = (['env_prev_%s' % i for i in range(0, 1000)])
hf_cols = (['hf_prev_%s' % i for i in range(0, 1000)])
# get overall prevalence of heart failure
hf = get_draws('modelable_entity_id',
2412,
'dismod',
sex_ids=[1,2],
status="best",
measure_ids=5,
location_ids=self.location_id,
gbd_round_id=4,
num_workers=5)
# drop unneeded columns
hf = hf[self.group_cols + draw_cols]
# drop unneeded age groups
hf = hf.query('age_group_id in {}'.format((self.AGE_GROUPS_IDS)))
# delete the prevalence due to Chagas
chagas = get_draws('modelable_entity_id',
2413,
'dismod',
sex_ids=[1,2],
status="best",
measure_ids=5,
location_ids=self.location_id,
gbd_round_id=4,
num_workers=5)
# drop unneeded columns
chagas = chagas[self.group_cols + draw_cols]
# drop unneeded age groups
chagas = chagas.query('age_group_id in {}'.format((self.AGE_GROUPS_IDS)))
# make sure the size of HF and Chagas is the same
if len(hf) != len(chagas) and self.send_Slack == "YES":
message = "Chagas and HF envelople have different number of rows for {location_id}"\
.format(location_id=location_id)
self.slack.chat.post_message(self.channel, message)
#assert len(hf) == len(chagas), "matrices are not the same size."
# rename HF prev draws
for i in xrange(1000):
hf.rename(columns={'draw_'+str(i):'env_prev_'+str(i)}, inplace=True)
# merge the HF-chagas prev draws and the HF prev draws
hf = hf.merge(chagas, on=self.group_cols, how='inner')
# subtract prevalence of HF due to Chagas from total HF prevalence
for i in xrange(1000):
hf['nonchagas_prev_'+str(i)] = hf['env_prev_'+str(i)] - hf['draw_'+str(i)]
hf.rename(columns={'nonchagas_prev_'+str(i):'hf_prev_'+str(i)}, inplace=True)
# drop unneeded columns
hf = hf[self.group_cols + hf_cols]
hf[hf < 0] = 0
# Make sure the Matrix is square
assert_env_df_is_square(hf, self.send_Slack, self.slack, self.channel, self.location_id)
# Make sure there aren't any duplicates
if hf[self.group_cols].duplicated().any() and self.send_Slack == "YES":
message = "The Chagas deleted HF envelope has duplicates for {location_id}"\
.format(location_id=location_id)
self.slack.chat.post_message(self.channel, message)
#assert not hf[self.index_cols].duplicated().any(), 'duplicates introduced in custom cause generation'
self.envelope = hf
