def run_all(fit_empirical_priors=False):
""" Enqueues all jobs necessary to fit model
Example
-------
>>> import run_all
>>> run_all.run_all()
"""
if fit_empirical_priors:
# fit empirical priors (by pooling data from all regions)
import emp_priors, graphics
emp_priors.admin_err_and_bias(recompute=True)
emp_priors.llin_discard_rate(recompute=True)
emp_priors.neg_binom(recompute=True)
emp_priors.survey_design(recompute=True)
graphics.plot_neg_binom_fits()
#fit each region individually for this model
from data import Data
data = Data()
post_names = []
dir = settings.PATH
for ii, r in enumerate(sorted(data.countries)):
o = '%s/%s-stdout.txt' % (dir, r[0:3])
e = '%s/%s-stderr.txt' % (dir, r[0:3])
name_str = 'ITN%s-%d' % (r[0:3].strip(), ii)
post_names.append(name_str)
call_str = 'qsub -cwd -o %s -e %s ' % (o,e) \
+ '-N %s ' % name_str \
+ 'fit.sh %d' % ii
subprocess.call(call_str, shell=True)
# TODO: after all posteriors have finished running, notify me via email
hold_str = '-hold_jid %s ' % ','.join(post_names)
o = '%s/summarize.stdout' % dir
e = '%s/summarize.stderr' % dir
call_str = 'qsub -cwd -o %s -e %s ' % (o,e) \
+ hold_str \
+ '-N netsdone ' \
+ 'fit.sh summarize'
subprocess.call(call_str, shell=True)
def run_all(fit_empirical_priors=False):
""" Enqueues all jobs necessary to fit model
Example
-------
>>> import run_all
>>> run_all.run_all()
"""
if fit_empirical_priors:
# fit empirical priors (by pooling data from all regions)
import emp_priors, graphics
emp_priors.admin_err_and_bias(recompute=True)
emp_priors.llin_discard_rate(recompute=True)
emp_priors.neg_binom(recompute=True)
emp_priors.survey_design(recompute=True)
graphics.plot_neg_binom_fits()
#fit each region individually for this model
from data import Data
data = Data()
post_names = []
dir = settings.PATH
for ii, r in enumerate(sorted(data.countries)):
o = '%s/%s-stdout.txt' % (dir, r[0:3])
e = '%s/%s-stderr.txt' % (dir, r[0:3])
name_str = 'ITN%s-%d' % (r[0:3].strip(), ii)
post_names.append(name_str)
call_str = 'qsub -cwd -o %s -e %s ' % (o,e) \
+ '-N %s ' % name_str \
+ 'fit.sh %d' % ii
subprocess.call(call_str, shell=True)
# TODO: after all posteriors have finished running, notify me via email
hold_str = '-hold_jid %s ' % ','.join(post_names)
o = '%s/summarize.stdout' % dir
e = '%s/summarize.stderr' % dir
call_str = 'qsub -cwd -o %s -e %s ' % (o,e) \
+ hold_str \
+ '-N netsdone ' \
+ 'fit.sh summarize'
subprocess.call(call_str, shell=True)
return json.load(f)
obs = [d['itncomplex_to_simpleratio'] for d in data.design] + \
[d['llincomplex_to_simpleratio'] for d in data.design if d['llincomplex_to_simpleratio']]
emp_prior_dict = dict(mu=mean(obs), std=std(obs), tau=1 / var(obs))
f = open(settings.PATH + fname, 'w')
json.dump(emp_prior_dict, f)
graphics.plot_survey_design_prior(emp_prior_dict, obs)
return emp_prior_dict
if __name__ == '__main__':
import optparse
usage = 'usage: %prog [options]'
parser = optparse.OptionParser(usage)
(options, args) = parser.parse_args()
if len(args) != 0:
parser.error('incorrect number of arguments')
admin_err_and_bias()
llin_discard_rate()
neg_binom()
survey_design()
graphics.plot_neg_binom_fits()
return json.load(f)
obs = [d['itncomplex_to_simpleratio'] for d in data.design] + \
[d['llincomplex_to_simpleratio'] for d in data.design if d['llincomplex_to_simpleratio']]
emp_prior_dict = dict(mu=mean(obs), std=std(obs), tau=1/var(obs))
f = open(settings.PATH + fname, 'w')
json.dump(emp_prior_dict, f)
graphics.plot_survey_design_prior(emp_prior_dict, obs)
return emp_prior_dict
if __name__ == '__main__':
import optparse
usage = 'usage: %prog [options]'
parser = optparse.OptionParser(usage)
(options, args) = parser.parse_args()
if len(args) != 0:
parser.error('incorrect number of arguments')
admin_err_and_bias()
llin_discard_rate()
neg_binom()
survey_design()
graphics.plot_neg_binom_fits()