def run(iter):
(Simulation().set().rand_seed(1928).pragma_autocompact(
True).pragma_live_info(True).pragma_live_info_ts(False).fn_group_setup(
grp_setup).done().add().rule(
FluProgressRule()).rule(FluLocationRule()).probe(
probe_flu_at(school_l, 'low-income')
). # the simulation output we care about and want monitored
probe(probe_flu_at(school_m, 'med-income')). # ^
done().db(fpath_db).gen_groups(
tbl='students',
attr_db=[],
rel_db=[GroupDBRelSpec(name='school', col='school_id')],
attr_fix={},
rel_fix={
'home': site_home
},
rel_at='school').done().run(iter))
def pop_db_gen():
# Add foreign keys to the 'people' table:
# BEGIN TRANSACTION;
# CREATE TABLE people_tmp (sp_id INTEGER, sp_hh_id INTEGER, age INTEGER, sex TEXT, race INTEGER, relate INTEGER, income INTEGER, school_id INTEGER, work_id INTEGER);
# INSERT INTO people_tmp SELECT sp_id, sp_hh_id, age, sex, race, relate, income, school_id, work_id FROM people;
# DROP TABLE people;
# CREATE TABLE people (sp_id INTEGER, sp_hh_id INTEGER, age INTEGER, sex TEXT, race INTEGER, relate INTEGER, income INTEGER, school_id INTEGER, work_id INTEGER, CONSTRAINT fk__people__households FOREIGN KEY (sp_hh_id) REFERENCES households (sp_id), CONSTRAINT fk__people__schools FOREIGN KEY (school_id) REFERENCES schools (sp_id), CONSTRAINT fk__people__workplaces FOREIGN KEY (work_id) REFERENCES workplaces (sp_id));
# INSERT INTO people SELECT sp_id, sp_hh_id, age, sex, race, relate, income, school_id, work_id FROM people_tmp;
# DROP TABLE people_tmp;
# COMMIT;
if not 'sim-flu-ac' in session:
return jsonify({ 'res': False, 'err': 'The simulation has not been initialized' })
json = request.get_json()
fpath = db_get_fpath(json['db'])
if not fpath:
return jsonify({ 'res': False, 'err': 'Incorrect database specified' })
attr_db = json['attr_db']
rel_db = json['rel_db']
site_home = Site('home')
stdout = io.StringIO()
with redirect_stdout(stdout):
sim = session['sim-flu-ac']
sim.gen_groups_from_db(
fpath_db = fpath,
tbl = json['tbl'],
attr_db = attr_db,
rel_db = [GroupDBRelSpec(name=rel['name'], col=rel['col']) for rel in rel_db],
attr_fix = {},
rel_fix = { 'home': site_home },
rel_at = 'school',
is_verbose = False
)
session['stdout'].append(stdout.getvalue())
return jsonify({ 'res': True, 'pop': sim.get_state()['pop'], 'stdout': session['stdout'] })
])
if te.is_db_empty and do_sim and (do_school or do_school_work):
for t in te.traj.values():
t.get_sim().gen_groups_from_db(locale_db,
schema=None,
tbl='people',
rel_at='home',
limit=0,
attr_fix={},
rel_fix={'home': site_home},
attr_db=['age_group'],
rel_db=[
GroupDBRelSpec(name='school',
col='school_id',
fk_schema=None,
fk_tbl='schools',
fk_col='sp_id',
sites=None)
])
if te.is_db_empty and do_sim and (do_work or do_school_work):
for t in te.traj.values():
t.get_sim().gen_groups_from_db(
locale_db,
schema=None,
tbl='people',
rel_at='home',
limit=0,
attr_fix={},
rel_fix={'home': site_home},
attr_db=['age_group'],
probe_grp_size_flu_school_m02 = probe_grp_size_flu_school(
'school-m02', school_m02, pp)
probe_grp_size_flu_school_m03 = probe_grp_size_flu_school(
'school-m03', school_m03, pp)
# ----------------------------------------------------------------------------------------------------------------------
# (3) Simulation:
(Simulation().set().rand_seed(rand_seed).pragma_autocompact(True).
pragma_live_info(pragma_live_info).pragma_live_info_ts(pragma_live_info_ts).
pragma_rule_analysis_for_db_gen(True).done().add().rule(
SimpleFluProgressRule()).
# rule(ProgressFluSimpleAlleghenyRule()).
# rule(FluLocationAlleghenyRule()).
# probe(probe_grp_size_few_schools).
probe(probe_grp_size_flu_school_l01).probe(probe_grp_size_flu_school_l02).
probe(probe_grp_size_flu_school_l03).probe(
probe_grp_size_flu_school_m01).probe(probe_grp_size_flu_school_m02).probe(
probe_grp_size_flu_school_m03).done().gen_groups_from_db(
fpath_db_in,
tbl='students',
attr_fix={
'flu': 's'
},
rel_fix={
'home': site_home
},
attr_db=[],
rel_db=[GroupDBRelSpec('school', 'school_id', sites['school'])],
rel_at='school').run(3).summary(True, 0, 0, 0, 0, (1, 0)))
GroupSplitSpec(p=0.9, attr_set={'flu': 's'}),
GroupSplitSpec(p=0.1, attr_set={'flu': 'i'})
]
(Simulation().set().rand_seed(1928).pragma_autocompact(True).pragma_live_info(
True).pragma_live_info_ts(False).fn_group_setup(
grp_setup).done().add().rule(FluProgressRule()).
rule(FluLocationRule()).probe(probe_flu_at(
school_l,
'low-income')). # the simulation output we care about and want monitored
probe(probe_flu_at(school_m, 'med-income')). # ^
done().db(fpath_db).gen_groups(
tbl='students',
attr_db=[],
rel_db=[GroupDBRelSpec(name='school', col='school_id')],
attr_fix={},
rel_fix={
'home': site_home
},
rel_at='school').done().run(2))
# ----------------------------------------------------------------------------------------------------------------------
# Key points
# Static rule analysis - Automatically form groups based on rules
# Dynamic rule analysis - Alert the modeler they might have missed something
#
# After 100 iterations
# Low income school - 24% of infected kids
# Medium income school - 14% of infected kids
# probe(probe_flu_at(school_m, 'm.p')).
commit().
# gen_sites_from_db(
# fpath_db = fpath_db,
# name = 'school',
# tbl = 'schools',
# name_col = 'sp_id'
# ).
gen_groups_from_db(
fpath_db = fpath_db,
tbl = 'students',
attr_fix = {},
rel_fix = { 'home': site_home },
attr_db = [],
# rel_db = [GroupDBRelSpec(name='school', col='school_id', entities=sites['school'])],
rel_db = [GroupDBRelSpec(tbl='schools', col_from='school_id', col_to='sp_id', name='school')],
rel_at = 'school' # TODO: move to group_setup()
)
)
# print(s.pop.sites[450149323])
# print(s.pop.sites[450149323])
#
sys.exit(77)
(s.
add().
probe(probe_flu_at(s.pop.sites[450149323], 'l.p')).
probe(probe_flu_at(s.pop.sites[450149323], 'm.p')).
commit().
setup_groups(setup_group).
(Simulation().
set().
rand_seed(rand_seed).
pragma_autocompact(True).
pragma_live_info(pragma_live_info).
pragma_live_info_ts(pragma_live_info_ts).
pragma_rule_analysis_for_db_gen(True).
done().
add().
rule(SEIRModel()).
rule(ResetSchoolDayRule(TimePoint(7))).
rule(GoToAndBackTimeAtRule(t_at_attr='t@school')).
# rule(AttrRule()).
probe(probe_grp_size_schools).
done().
gen_groups_from_db(
fpath_db_in,
tbl = 'people',
attr_fix = {},
rel_fix = { 'home': site_home },
attr_db = [],
rel_db = [
GroupDBRelSpec('school', 'school_id', sites['school'])
],
rel_at = 'home'
).
run(1).
summary()
)
os.remove(fpath_groups)
if os.path.isfile(fpath_groups):
print('Loading groups... ', end='')
with gzip.GzipFile(fpath_groups, 'rb') as f:
gc.disable()
groups = pickle.load(f)
gc.enable()
print('done.')
else:
print('Generating groups... ', end='')
groups = Group.gen_from_db(fpath_db,
tbl='people',
attr=[],
rel=[
GroupDBRelSpec('home', 'sp_hh_id',
sites['home']),
GroupDBRelSpec('school', 'school_id',
sites['school'])
],
rel_at='home')
print('done.')
print('Saving groups... ', end='')
with gzip.GzipFile(fpath_groups, 'wb') as f:
pickle.dump(groups, f)
print('done.')
inf('groups', groups, do_calc_size_groups)
# groups: 164,459 (175.2M)
# file 5.1M
