def run_local():
if os.path.isdir('H:/') == True:
j = 'J:'
h = 'H:'
#cod_dict, cod_raw, cod_missingness = run_local()
elif os.path.isdir('/home/j/') == True:
j = '/home/j'
h = '/homes/abertozz'
else:
print_and_log('What am I supposed to do?')
cod_dict = {}
cod_raw = {}
cod_missingness = {}
nrows = None # number of rows of file to read; make it small for running local jobs
map_dir = '%s/Project/us_counties/mortality/data_prep/counties/01_clean_microdata/state_map.csv' % j
yearvals = [1992]
for year in yearvals:
if year in range(1968, 1989):
us_indir = "%s/DATA/USA/VR/%d/USA_VITAL_STATISTICS_%d_MORTALITY.TXT" % (
j, year, year)
ps_indir = 'NONE'
elif year in range(1989, 1994):
fname = get_filepaths(year)
us_indir = '%s/LIMITED_USE/PROJECT_FOLDERS/USA/NVSS_MORTALITY/%d/%s' % (
j, year, fname)
ps_indir = 'NONE'
else:
fname = get_filepaths(year)
us_indir = '%s/LIMITED_USE/PROJECT_FOLDERS/USA/NVSS_MORTALITY/%d/%s' % (
j, year, fname['US'])
ps_indir = '%s/LIMITED_USE/PROJECT_FOLDERS/USA/NVSS_MORTALITY/%d/%s' % (
j, year, fname['PS'])
rlog.open(
'%s/temp/amelia/counties/parse_death_files/debug_parse_%d.log' %
(j, year))
rlog.log('Hello, world')
rlog.log('Initializing')
cod_data, cod_data_raw, cod_missingness = parse_cod_mortality(
year, us_indir, ps_indir, map_dir, nrows)
return cod_data, cod_data_raw, cod_missingness
year = int(year)
env_id = int(env_id)
late_id = int(late_id)
cause = [env_id, late_id]
# get list of locations
locations = maternal_fns.get_locations()
# set up columns we want to subset
columns = maternal_fns.filter_cols()
index_cols = [col for col in columns if not col.startswith('draw_')]
# logging
rlog.open('FILEPATH.log' % (log_dir, year))
rlog.log('')
rlog.log('Starting to get late cause fractions')
##############################################
# GET LATE CAUSE FRACTIONS:
##############################################
codcorrect_df = draws(gbd_ids={'cause_ids': [env_id, late_id]},
source='codcorrect', year_ids=[year], sex_ids=[2],
measure_ids=[1])
codcorrect_df['measure_id'] = 1
codcorrect_df = codcorrect_df[codcorrect_df.age_group_id.isin(range(7, 16))]
envelope_df = codcorrect_df[codcorrect_df.cause_id == env_id]
late_df = codcorrect_df[codcorrect_df.cause_id == late_id]
# we only want index_cols and draws as columns
log_dir, year, dalynator_dir, env_id, late_id, out_dir = sys.argv[1:7]
year = int(year)
env_id = int(env_id)
late_id = int(late_id)
cause = [env_id, late_id]
# get list of locations
locations = maternal_fns.get_locations()
# set up columns we want to subset
columns = maternal_fns.filter_cols()
# logging
rlog.open('%s/dalynator_late_%s.log' % (log_dir, year))
rlog.log('')
rlog.log('Starting to get late cause fractions')
##############################################
# GET LATE CAUSE FRACTIONS:
##############################################
for geo in locations:
fname = 'draws_%s_%s.h5' % (geo, year)
# dalynator files are saved as loc/year, with age, sex and cause inside
try:
dalynator_df = pd.read_hdf('%s/%s/%s' % (dalynator_dir, geo, fname),
'data',
where=[("'cause_id'==%s & 'measure_id'==1"
"& 'metric_id'==1 & 'sex_id'==2"
"& 'rei_id'==0") % cause])
# We do this for subcauses. Next, we proportionately scale the cause
# fractions so they sum to one across subcauses. Timing scaling and
# interpolation is done in Step 3, after codcorrect.
##########################################################################
interp_yearvals = {
start_year: start_year + 5
for start_year in range(1990, 2011, 5)
}
# dismod files: model_vers_id/full/draws/{location_id}_{year_id}_{sex_id}.h5
dismod_dir = '/ihme/epi/panda_cascade/prod'
# 'step 1' refers both to interpolation and fraction scaling.
print maternal_fns.check_dependencies(1)
if maternal_fns.check_dependencies(1):
rlog.log("On Step 1")
step_df = dep_map.ix[dep_map.step == 1]
# make output directories
for target_id in pd.unique(step_df.target_id):
maternal_fns.check_dir('%s/%s' % (cluster_dir, target_id))
##############################
# INTERPOLATION
###############################
rlog.log('Interpolating subcause cause fractions')
# set in and out directories for interpolation
for index, row in step_df.iterrows():
if row['source_id'] != 9015: # don't run for HIV!
dismod_me_id = row['source_id']
dismod_model_vers = maternal_fns.get_model_vers(
print 'Where am I supposed to go?'
##############################################
# PREP WORK:
# set directories and other preliminary data
##############################################
print 'starting job!'
log_dir, jobname, dismod_dir, cluster_dir, year = sys.argv[1:6]
year = int(year)
# logging
rlog.open('%s/%s.log' % (log_dir, jobname))
rlog.log('Starting scale fractions step')
# get list of locations
locations = maternal_fns.get_locations()
geo_length = len(locations)
# set up database
enginer = dbapis.engine_factory()
# set up columns we want to subset
columns = maternal_fns.filter_cols()
# get dependency_map
dep_map = pd.read_csv(
"dependency_map.csv", header=0).dropna(axis='columns', how='all')
if os.path.isdir('J:/'):
j = 'J:'
elif os.path.isdir('/home/j/'):
j = '/home/j'
else:
print 'Where am I supposed to go?'
log_dir, jobname, source_dir, out_dir, start_year_str, end_year_str = sys.argv[
1:7]
start_year = int(start_year_str)
end_year = int(end_year_str)
# logging
rlog.open('%s/%s.log' % (log_dir, jobname))
rlog.log('source_dir is %s' % source_dir)
rlog.log('out_dir is %s' % out_dir)
# get list of locations
locations = maternal_fns.get_locations()
# set up columns we want to subset
columns = maternal_fns.filter_cols()
for geo_idx, geo in enumerate(locations):
rlog.log('interpolating for place %s' % geo)
rlog.log('place is number %s of %s' % (geo_idx, len(locations)))
rlog.log('getting data')
start_dir = '%s/%s_%s_2.h5' % (source_dir, geo, start_year)
def print_and_log(phrase):
from PyJobTools import rlog
print phrase
rlog.log(phrase)
if os.path.isdir('J:/'):
j = 'J:'
elif os.path.isdir('/home/j/'):
j = '/home/j'
else:
print 'Where am I supposed to go?'
log_dir, jobname, envelope_dir, prop_dir, out_dir = sys.argv[1:6]
# get list of locations
locations = maternal_fns.get_locations()
# logging
rlog.open('%s/%s.log' % (log_dir, jobname))
rlog.log('out_dir is %s' % out_dir)
# set up columns we want to subset
columns = maternal_fns.filter_cols()
columns.append('year_id')
index_cols = ['year_id', 'age_group_id']
# concatenate dalynator draws into one df, if we're doing timings
if "timing" in jobname:
files = []
for root, dirnames, filenames in os.walk('%s' % envelope_dir):
for filename in fnmatch.filter(filenames, '*.h5'):
files.append(os.path.join(root, filename))
def read_file(f):
return pd.read_hdf(f,
from __future__ import division
import sys
from PyJobTools import rlog
import maternal_fns
from transmogrifier.gopher import draws
log_dir, jobname, env_model_vers, source_id, target_id, out_dir = sys.argv[1:7]
# get list of locations
locations = maternal_fns.get_locations()
# logging
rlog.open('%s/%s.log' % (log_dir, jobname))
rlog.log('out_dir is %s' % out_dir)
# set up columns we want to subset
columns = maternal_fns.filter_cols()
columns.remove('measure_id')
index_cols = [col for col in columns if not col.startswith('draw_')]
# read maternal disorders envelope
# CAUSES get multiplied by the Late corrected env from codem
# TIMINGS get multiplied by the CoDcorrect env
rlog.log("reading in envelope draws")
if 'timing' in jobname:
env = draws(gbd_ids={'cause_ids': [366]}, source='codcorrect',
measure_ids=[1], sex_ids=[2], location_ids=locations)
else:
env = draws(gbd_ids={'cause_ids': [366]}, source='codem', sex_ids=[2],
header=0).dropna(axis='columns', how='all')
# subset dep_map for the step that we're on
if "timing" in jobname:
step_df = dep_map[(dep_map.step == 4)
& (dep_map.source_id != 'codcorrect')]
held_constant_me = 376
else:
step_df = dep_map.ix[dep_map.step == 1]
held_constant_me = 9015
#######################################################################
# STEP 1: FOR EACH CAUSE, EXTRACT FILES, GET SUM BY GROUP + TOTAL SUM
#######################################################################
print 'getting data'
rlog.log('getting data')
all_data = {}
summed_idx = 0
for index, row in step_df.iterrows():
target_id = row['target_id']
try:
subtype_df = draws(
gbd_ids={'modelable_entity_ids': [row['source_id']]},
source='dismod',
measure_ids=[18],
sex_ids=[2],
year_ids=[year])
except (ValueError, OSError): # pull data from where interp saves it
subtype_df = pd.read_hdf(
'%s/%s/%s_2.h5' % (cluster_dir, row['source_id'], year), 'draws')
j = '/home/j'
else:
print 'Where am I supposed to go?'
log_dir, jobname, envelope_dir, out_dir = sys.argv[1:5]
# do all the prep work
enginer = dbapis.engine_factory()
dep_map = pd.read_csv("dependency_map.csv", header=0).dropna(axis='columns',
how='all')
step_df = dep_map.ix[dep_map.step == 2].reset_index()
index_cols = ['location_id', 'year_id', 'age_group_id', 'sex_id']
# logging
rlog.open('%s/%s.log' % (log_dir, jobname))
rlog.log("Correcting for the underreporting of Late Maternal deaths")
rlog.log('out_dir is %s' % out_dir)
# get list of locations
locs = maternal_fns.get_locations()
# get list of location/years that don't need correction
rlog.log('Pulling in adjustment csv')
adjust_df = pd.read_csv('%s/late_maternal_correction.csv' % (os.getcwd()))
adjust_df = adjust_df[['location_id', 'year_id', 'subnationals', 'adj_factor']]
# get the adjustment factor for most-detailed level, not just countries
only_subnats = adjust_df[adjust_df.subnationals == 1]
only_subnats.rename(columns={'location_id': 'parent_id'}, inplace=True)
query = '''SELECT
from __future__ import division
import sys
from PyJobTools import rlog
from transmogrifier.draw_ops import interpolate
import maternal_fns
log_dir, jobname, me_id, out_dir, start_year_str, end_year_str = sys.argv[1:7]
start_year = int(start_year_str)
end_year = int(end_year_str)
# logging
rlog.open('%s/%s.log' % (log_dir, jobname))
rlog.log('out_dir is %s' % out_dir)
# get list of locations
locations = maternal_fns.get_locations()
if start_year == 1990:
yearlist = range(1980, 1990) + range(1991, 1995)
start_year = 1980
else:
yearlist = range(start_year + 1, end_year)
# call central function to interpolate
rlog.log("Calling interpolate")
interp_df = interpolate(gbd_id_field='modelable_entity_id',
gbd_id=int(me_id),
source='dismod',
