def simple(self):
progress_flu_rule = DiscreteInvMarkovChain('flu-status', { 's': [0.95, 0.05, 0.00], 'i': [0.00, 0.50, 0.50], 'r': [0.10, 0.00, 0.90] })
# s - susceptible
# i - infectious
# r - recovered
sites = { 'home': Site('h'), 'work': Site('w') }
probe_grp_size_flu = GroupSizeProbe.by_attr('flu', 'flu-status', progress_flu_rule.get_states(), msg_mode=ProbeMsgMode.DISP, memo='Mass distribution across flu status')
probe_grp_size_site = GroupSizeProbe.by_rel('site', Site.AT, sites.values(), msg_mode=ProbeMsgMode.DISP, memo='Mass distribution across sites')
# ----------------------------------------------------------------------------------------------------------------------
# (1) Simulations testing the basic operations on groups and rules:
# (1.1) A single-group, single-rule (1g.1r) simulation:
s = Simulation()
s.add_rule(progress_flu_rule)
s.add_probe(probe_grp_size_flu)
s.add_group(Group('g0', 1000, { 'flu-status': 's' }))
sys.stdout = open('out.dat', 'w')
s.run(24)
sys.stdout.close()
with open('out.dat') as file:
self.data = file.readlines()
self.data = str(self.data)
apple = self.data.replace('\\n', '<br>')
# print(apple)
return apple
def sim_flu_ac_init(session, do_force=False):
if 'sim-flu-ac' in session and not do_force:
return
if 'sim-flu-ac' in session:
session.pop('sim-flu-ac')
gc.collect()
school_l = Site(450149323) # 88% low income students
school_m = Site(450067740) # 7% low income students
session['sim-flu-ac'] = (
Simulation().
set().
pragma_autocompact(True).
pragma_live_info(True).
pragma_live_info_ts(False).
pragma_rule_analysis_for_db_gen(False).
done().
add().
probe(sim_flu_ac_init_get_probe(school_l, 'low-income')).
probe(sim_flu_ac_init_get_probe(school_m, 'med-income')).
done()
)
session['stdout'] = []
def run_the_app():
# Draw the UI elements to search for objects (pedestrians, cars, etc.)
gsize, s1, s2, s3, i1, i2, i3, r1, r2, r3, run = frame_selector_ui()
progress_flu_rule = DiscreteInvMarkovChain('flu-status',
{ 's': [s1, s2, s3], 'i': [i1, i2, i3], 'r': [r1, r2, r3] })
# s - susceptible
# i - infectious
# r - recovered
sites = { 'home': Site('h'), 'work': Site('w') }
probe_grp_size_flu = GroupSizeProbe.by_attr('flu', 'flu-status', progress_flu_rule.get_states(), msg_mode=ProbeMsgMode.DISP, memo='Mass distribution across flu status')
probe_grp_size_site = GroupSizeProbe.by_rel('site', Site.AT, sites.values(), msg_mode=ProbeMsgMode.DISP, memo='Mass distribution across sites')
data = ""
s = Simulation()
s.add_rule(progress_flu_rule)
s.add_probe(probe_grp_size_flu)
s.add_group(Group('g0', gsize, { 'flu-status': 's' }))
sys.stdout = open('out.dat', 'w')
s.run(int(run))
sys.stdout.close()
with open('out.dat') as file:
data = file.readlines()
for line in data:
st.write(line)
def sim_flu_init(session, do_force=False):
if 'sim-flu' in session and not do_force:
return
if 'sim-flu' in session:
session.pop('sim-flu')
sites = { s:Site(s) for s in ['home', 'school-a', 'school-b']}
probe_grp_size_site = GroupSizeProbe.by_rel('site', Site.AT, sites.values(), msg_mode=ProbeMsgMode.CUMUL)
session['sim-flu'] = (
Simulation().
set().
pragma_live_info(False).
pragma_live_info_ts(False).
done().
add().
rule(ResetSchoolDayRule(TimePoint(7))).
rule(GoToAndBackTimeAtRule(t_at_attr='t@school')).
probe(probe_grp_size_site).
done().
new_group(500).
set_rel(Site.AT, sites['home']).
set_rel('home', sites['home']).
set_rel('school', sites['school-a']).
done().
new_group(500).
set_rel(Site.AT, sites['home']).
set_rel('home', sites['home']).
set_rel('school', sites['school-b']).
done()
)
def add_initial_rules(time_offset):
"Imports rules built into pram and inserts them into the rules dictionary"
global sites
sites = {
s: Site(s)
for s in ['home', 'work-a', 'work-b', 'work-c', 'store-a', 'store-b']
}
mall_sites_names = [
"big_theater", "down_store1", "down_store2", "down_store3",
"down_store4", "down_store5", "down_store6", "down_store7",
"down_store8", "down_store9", "big_down_store10", "big_down_store11",
"big_down_store12", "up_store1", "up_store2", "up_store3", "up_store4",
"up_store5", "up_store6", "up_store7", "up_store8", "up_store9",
"big_down_courtyard_1", "big_down_courtyard_2", "big_down_courtyard_3",
"big_down_courtyard_4"
]
mall_sites = [Site(s) for s in mall_sites_names]
for i in range(len(mall_sites)):
sites[mall_sites_names[i]] = mall_sites[i]
#rules["Mall Movement"] = [MallMovement(0.2, mall_sites)]
rules["Mall Flu"] = [MallFlu(1, 0.2, mall_sites)]
rules["Simple Flu Progress Rule"] = [
SimpleFluProgress(
'flu-status', {
's': [0.95, 0.05, 0.00],
'i': [0.00, 0.50, 0.50],
'r': [0.10, 0.00, 0.90]
}, sites['home'])
]
# rules["Home-Work-School Rules"] = [SimpleGoTo(TimeInt( (8 - time_offset)%24,(12 - time_offset)%24), 0.4, 'home', 'work', 'Some agents leave home to go to work'),
# SimpleGoTo(TimeInt((16- time_offset)%24,(20- time_offset)%24), 0.4, 'work', 'home', 'Some agents return home from work'),
# SimpleGoTo(TimeInt((16- time_offset)%24,(21- time_offset)%24), 0.2, 'home', 'store', 'Some agents go to a store after getting back home'),
# SimpleGoTo(TimeInt((17- time_offset)%24,(23- time_offset)%24), 0.3, 'store', 'home', 'Some shopping agents return home from a store'),
# SimpleGoTo(TimePoint((24- time_offset)%25), 1.0, 'store', 'home', 'All shopping agents return home after stores close'),
# SimpleGoTo(TimePoint( (2- time_offset)%25), 1.0, None, 'home', 'All still-working agents return home')]
global probe_grp_size_site
probe_grp_size_site = GroupSizeProbe.by_rel(
'site',
Site.AT,
sites.values(),
msg_mode=ProbeMsgMode.DISP,
memo='Mass distribution across sites')
return
def probes_ls():
school_l = Site(450149323) # 88% low income students
school_m = Site(450067740) # 7% low income students
return GroupSizeProbe(
name=name or str(school.name),
queries=[
GroupQry(attr={ 'flu': 's' }, rel={ 'school': school }),
GroupQry(attr={ 'flu': 'e' }, rel={ 'school': school }),
GroupQry(attr={ 'flu': 'r' }, rel={ 'school': school })
],
qry_tot=GroupQry(rel={ 'school': school }),
persistance=pp,
var_names=['ps', 'pe', 'pr', 'ns', 'ne', 'nr']
)
probes = [
{ 'name': 'Low-income school', 'inf': '88% low income students', 'persistenceName': 'low-income', 'id': 450149323 },
{ 'name': 'medium-income school', 'inf': '7% low income students', 'persistenceName': 'med-income', 'id': 450067740 }
]
return jsonify({ 'res': True, 'probes': probes })
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'] })
def sim01():
from pram.data import GroupSizeProbe, ProbeMsgMode
from pram.entity import Site
from pram.rule import GoToAndBackTimeAtRule, ResetSchoolDayRule, TimePoint
from pram.sim import Simulation
sites = {s: Site(s) for s in ['home', 'school-a', 'school-b']}
probe_grp_size_site = GroupSizeProbe.by_rel('site',
Site.AT,
sites.values(),
msg_mode=ProbeMsgMode.CUMUL)
(Simulation().add().rule(ResetSchoolDayRule(TimePoint(7))).rule(
GoToAndBackTimeAtRule(t_at_attr='t@school')).probe(
probe_grp_size_site).commit().new_group(500).set_rel(
Site.AT, sites['home']).set_rel('home', sites['home']).set_rel(
'school',
sites['school-a']).commit().new_group(500).set_rel(
Site.AT,
sites['home']).set_rel('home', sites['home']).set_rel(
'school', sites['school-b']).commit().run(18))
return probe_grp_size_site.get_msg()
This simulation is an extension of 'sim-02.py' and adds the eventual settlement of the migrating population in one of
the counties bordering the conflict country.
"""
from pram.data import Probe, ProbePersistenceMem, Var
from pram.entity import Group, GroupQry, GroupSplitSpec, Site
from pram.rule import IterAlways, TimeAlways, Rule
from pram.sim import Simulation
import random
import statistics
from pram2mesa.pram2mesa import pram2mesa
# ----------------------------------------------------------------------------------------------------------------------
site_sudan = Site('Sudan')
site_ethiopia = Site('Ethiopia', attr={'travel-time': 8}) # [months]
site_chad = Site('Chad', attr={'travel-time': 9})
site_egypt = Site('Egypt', attr={'travel-time': 10})
site_libya = Site('Libya', attr={'travel-time': 11})
site_conflict = site_sudan
sites_dst = [site_egypt, site_ethiopia, site_chad,
site_libya] # migration destinations
# ----------------------------------------------------------------------------------------------------------------------
class ConflictRule(Rule):
"""
Conflict causes death and migration. The probability of death scales with the conflict's severity and scale
while the probability of migration scales with the conflict's scale only. Multipliers for both factors are exposed
'''
A test of the mass transfer graph for the segregation model.
'''
import math
import random
from scipy.stats import poisson
from pram.entity import Group, Site
from pram.rule import SegregationModel
from pram.sim import Simulation
from pram.traj import Trajectory, TrajectoryEnsemble
# ----------------------------------------------------------------------------------------------------------------------
loc = [Site('a'), Site('b')]
s = (Simulation().set().pragma_autocompact(True).pragma_live_info(
False).done().add([
SegregationModel('team', len(loc)),
Group(m=200, attr={'team': 'blue'}, rel={Site.AT: loc[0]}),
Group(m=300, attr={'team': 'blue'}, rel={Site.AT: loc[1]}),
Group(m=100, attr={'team': 'red'}, rel={Site.AT: loc[0]}),
Group(m=400, attr={'team': 'red'}, rel={Site.AT: loc[1]})
]))
# ----------------------------------------------------------------------------------------------------------------------
te = (TrajectoryEnsemble().add_trajectory(Trajectory(
'segregation', None, s)).set_group_names([
(0, 'Blue A',
Group.gen_hash(attr={'team': 'blue'}, rel={Site.AT: loc[0]})),
rand_seed = 1928
pragma_live_info = True
pragma_live_info_ts = False
fpath_db_in = os.path.join(os.path.dirname(__file__), '..', '..', '..', 'data',
'allegheny-county', 'allegheny-students.sqlite3')
# ----------------------------------------------------------------------------------------------------------------------
# (1) Sites:
sites = Simulation.gen_sites_from_db(
fpath_db_in,
lambda fpath_db:
{'school': Site.gen_from_db(fpath_db, 'schools', 'sp_id', 'school', [])},
pragma_live_info=pragma_live_info,
pragma_live_info_ts=pragma_live_info_ts)
site_home = Site('home')
# ----------------------------------------------------------------------------------------------------------------------
# (2) Probes:
# n_schools = 8
# few_schools = [sites['school'][k] for k in list(sites['school'].keys())[:n_schools]]
#
# probe_grp_size_few_schools = GroupSizeProbe('school', [GroupQry(rel={ Site.AT: s }) for s in few_schools], msg_mode=ProbeMsgMode.DISP)
fpath_db = os.path.join(os.path.dirname(__file__), 'out-test-03c.sqlite3')
'''
A simulation of agents going from home to work and then, sometimes, to a store and back home again.
'''
from pram.data import GroupSizeProbe
from pram.entity import GroupQry, Site
from pram.rule import GotoRule, TimeInt, TimePoint
from pram.sim import Simulation
rand_seed = 1928
# ----------------------------------------------------------------------------------------------------------------------
sites = {
'home' : Site('home'),
'work-a' : Site('work-a'),
'work-b' : Site('work-b'),
'work-c' : Site('work-c'),
'store-a' : Site('store-a'),
'store-b' : Site('store-b')
}
probe_grp_size_site = GroupSizeProbe.by_rel('site', Site.AT, sites.values(), memo='Mass distribution across sites')
(Simulation(6,1,24, rand_seed=rand_seed).
new_group('g0', 1000).
set_rel(Site.AT, sites['home']).
set_rel('home', sites['home']).
set_rel('work', sites['work-a']).
set_rel('store', sites['store-a']).
print(initialSusceptible,end=' ')
print(initialExposed,end=' ')
print(initialInfectious,end=' ')
print(initialRecovered,end=' ')
print(R0,end=' ')
print(latentPeriod,end=' ')
print(infectiousPeriod,end=' ')
print(numberOfSimulations,end=' ')
print(numberOfDays)
#
# Define flu progression rule:
#
siteName = 'Pittsburgh'
site = Site(siteName)
class FluProgressRule(Rule):
def __init__(self): super().__init__('flu-progress', TimeAlways())
def apply(self, pop, group, iter, t):
if group.has_attr({ 'flu': 's' }):
at = group.get_rel(Site.AT)
n = at.get_pop_size()
n_s = at.get_pop_size(GroupQry(attr={ 'flu': 's' }))
n_e = at.get_pop_size(GroupQry(attr={ 'flu': 'e' }))
n_i = at.get_pop_size(GroupQry(attr={ 'flu': 'i' }))
beta = R0 / (population*infectiousPeriod)
lambdat = beta*n_i
fractionInfected = float(n_i) / float(n)
numberOfNewlyInfected = np.random.binomial(n_s,lambdat)
p_infection = float(numberOfNewlyInfected+n_e)/float(n)
def main():
# SAMPLE SIMULATION FROM 09-segregation
import os
from pram.data import GroupSizeProbe, ProbeMsgMode, ProbePersistenceDB
from pram.entity import Group, GroupQry, Site, GroupSplitSpec
from pram.rule import SegregationModel
from pram.sim import Simulation
# -----------------------------------------------------------------------------------------------------------------
# (1) Simulation (two locations)
loc = [Site('a'), Site('b')]
def gs(pop, group):
return [
GroupSplitSpec(p=0.7, attr_set={'foobar': 'foo'}),
GroupSplitSpec(p=0.3, attr_set={'foobar': 'bar'})
]
probe_loc = GroupSizeProbe.by_rel('loc',
Site.AT,
loc,
msg_mode=ProbeMsgMode.DISP)
probe_sim = GroupSizeProbe(name='sim',
queries=[
GroupQry(attr={'team': 'blue'},
rel={Site.AT: loc[0]}),
GroupQry(attr={'team': 'red'},
rel={Site.AT: loc[0]}),
GroupQry(attr={'team': 'blue'},
rel={Site.AT: loc[1]}),
GroupQry(attr={'team': 'red'},
rel={Site.AT: loc[1]})
],
qry_tot=None,
persistence=ProbePersistenceDB(),
msg_mode=ProbeMsgMode.DISP)
s = (
Simulation().set().pragma_autocompact(True).pragma_live_info(False).
fn_group_setup(gs).done().add([
SegregationModel('team', len(loc)),
# TranslateEverything(),
# LambdasAndArguments(),
Group(m=200,
attr={'team': 'blue'},
rel={
Site.AT: loc[0],
'origin': loc[0]
}),
Group(m=300,
attr={'team': 'blue'},
rel={
Site.AT: loc[1],
'origin': loc[1]
}),
Group(m=100,
attr={'team': 'red'},
rel={
Site.AT: loc[0],
'origin': loc[0]
}),
Group(m=400,
attr={'team': 'red'},
rel={
Site.AT: loc[1],
'origin': loc[1]
}),
probe_loc, # the distribution should tend to 50%-50%
probe_sim # mass should tend to move towards two of the four sites
# fixed naming issue in pram/sim.py line 815
]))
pram2mesa(s, 'TestSimultaneousGrammar')
'''
Tests of simulation construction exceptions associated with (1) improper order of adding rules and groups and
(2) superfluous attributes or relations.
Based on the simulation from: sim/04-flu-prog/sim.py
'''
from pram.data import GroupSizeProbe, ProbeMsgMode
from pram.entity import AttrFluStage, Group, GroupSplitSpec, Site
from pram.rule import GoToRule, GoToAndBackTimeAtRule, TimeInt
from pram.sim import Simulation, SimulationConstructionError, SimulationConstructionWarning, TimeHour
from rules import ProgressFluRule
# ----------------------------------------------------------------------------------------------------------------------
sites = {'home': Site('h'), 'work': Site('w')}
probe_grp_size_flu = GroupSizeProbe.by_attr(
'flu',
'flu-stage',
AttrFluStage,
msg_mode=ProbeMsgMode.DISP,
memo='Mass distribution across flu stages')
# ----------------------------------------------------------------------------------------------------------------------
# (1) A proper simulation:
print('(1)')
s = (Simulation(TimeHour(6), 16).add_rule(ProgressFluRule()).add_rule(
GoToAndBackTimeAtRule()).add_probe(probe_grp_size_flu).new_group(
'0', 1000).set_attr('flu-stage', AttrFluStage.NO).commit())
'''
A simulation of agents going from home to work and then, sometimes, to a store and back home again. Apart from
testing the mechanics of a simulation, this module mainly tests the GoToRule rule.
'''
from pram.data import GroupSizeProbe, ProbeMsgMode
from pram.entity import GroupQry, Site
from pram.rule import GoToRule, TimeInt, TimePoint
from pram.sim import Simulation
# ----------------------------------------------------------------------------------------------------------------------
sites = {
s: Site(s)
for s in ['home', 'work-a', 'work-b', 'work-c', 'store-a', 'store-b']
}
probe_grp_size_site = GroupSizeProbe.by_rel(
'site',
Site.AT,
sites.values(),
msg_mode=ProbeMsgMode.DISP,
memo='Mass distribution across sites')
(Simulation().add().rule(
GoToRule(0.4,
'home',
'work',
TimeInt(8, 12),
memo='Some agents leave home to go to work')).rule(
GoToRule(0.4,
'work',
if self.is_verbose: print('begins to eat')
return [GroupSplitSpec(p=1.0, attr_set={ self.ATTR: self.State.EATING})]
def is_applicable(self, group, iter, t):
return (
super().is_applicable(group, iter, t) and
group.get_mass() > 0 and
group.has_rel(self.FORK_L) and
group.has_rel(self.FORK_R)
)
# ----------------------------------------------------------------------------------------------------------------------
forks = {
'f1' : Site('f1'),
'f4' : Site('f4'),
'f6' : Site('f6'),
'f8' : Site('f8'),
'f11' : Site('f11'),
}
(Simulation().
set_rand_seed(rand_seed).
add_rule(TERule(is_verbose=True)).
new_group('p0', 1).set_attr(TERule.ATTR, TERule.State.THINKING).set_rel(TERule.FORK_L, forks['f1' ]).set_rel(TERule.FORK_R, forks['f11']).done().
new_group('p2', 1).set_attr(TERule.ATTR, TERule.State.THINKING).set_rel(TERule.FORK_L, forks['f4' ]).set_rel(TERule.FORK_R, forks['f1' ]).done().
new_group('p5', 1).set_attr(TERule.ATTR, TERule.State.THINKING).set_rel(TERule.FORK_L, forks['f6' ]).set_rel(TERule.FORK_R, forks['f4' ]).done().
new_group('p7', 1).set_attr(TERule.ATTR, TERule.State.THINKING).set_rel(TERule.FORK_L, forks['f8' ]).set_rel(TERule.FORK_R, forks['f6' ]).done().
new_group('p10', 1).set_attr(TERule.ATTR, TERule.State.THINKING).set_rel(TERule.FORK_L, forks['f11']).set_rel(TERule.FORK_R, forks['f8' ]).done().
run(20, do_disp_t=True)
def test_comparisons(self):
eq = self.assertEqual
ne = self.assertNotEqual
eq(Site('a'), Site('a')) # different objects, same name
ne(Site('a'), Site('b')) # different objects, different name
Spec('s4', 500),
Spec('s5', 5000),
Spec('s6', 50000)
]
dpath_cwd = os.path.dirname(__file__)
fpath_db = os.path.join(dpath_cwd, f'probes-{sim_dur_days}d.sqlite3')
if os.path.isfile(fpath_db):
os.remove(fpath_db)
probe_persistence = ProbePersistenceDB(fpath_db)
probes_grp_size_flu_school = []
sites = {'home': Site('home')}
for s in school_specs:
# Site:
site_name = f'school-{s.name}'
site = Site(site_name)
sites[site_name] = site
# Probe:
probes_grp_size_flu_school.append(
GroupSizeProbe(name=f'flu-{s.name}',
queries=[
GroupQry(attr={'flu': 's'}, rel={'school': site}),
GroupQry(attr={'flu': 'i'}, rel={'school': site}),
GroupQry(attr={'flu': 'r'}, rel={'school': site})
],
from pram.entity import Group, GroupDBRelSpec, GroupQry, Site
from pram.rule import SimpleFluLocationRule, SimpleFluProgressRule, SimpleFluProgressMoodRule
from pram.sim import Simulation
# ----------------------------------------------------------------------------------------------------------------------
rand_seed = 1928
fpath_db_out = os.path.join(os.path.dirname(__file__), 'sim-test-03b.sqlite3')
if os.path.isfile(fpath_db_out):
os.remove(fpath_db_out)
pp = ProbePersistenceDB(fpath_db_out)
home = Site('home')
school_l = Site('school-l')
school_m = Site('school-m')
probe_grp_size_flu_school_l = GroupSizeProbe(
name='flu-school-l',
queries=[
GroupQry(attr={ 'flu': 's' }, rel={ 'school': school_l }),
GroupQry(attr={ 'flu': 'e' }, rel={ 'school': school_l }),
GroupQry(attr={ 'flu': 'r' }, rel={ 'school': school_l })
],
qry_tot=GroupQry(rel={ 'school': school_l }),
persistence=pp,
var_names=['ps', 'pe', 'pr', 'ns', 'ne', 'nr'],
memo='Population size across flu states for low-income school'
)
from pram.entity import Site
# ----------------------------------------------------------------------------------------------------------------------
# Parameters:
N = 100_000
attr = {
'flu': 's',
'age_group': '10_19',
'is_migrating': True,
't-migration': 3,
'history': [2, 4, 8.0]
}
rel = {
'home': Site('home-x').__hash__(),
'school': Site('school-01').__hash__()
}
cond = [lambda x: x > 1, lambda x: x < 1, lambda x: x == 1]
full = False
# ----------------------------------------------------------------------------------------------------------------------
# Algorithms:
# (1) hash + str + inspect.getsource (64):
t0 = time.time()
for i in range(N):
hash(str((attr, rel, str([inspect.getsource(i) for i in cond]), full)))
print(f'01: {time.time() - t0}')
# (2) hash + json + inspect.getsource (64):
''' '''
import os,sys
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..'))
from pram.data import GroupSizeProbe, ProbeMsgMode
from pram.entity import Site
from pram.rule import GoToAndBackTimeAtRule, ResetSchoolDayRule, TimePoint
from pram.sim import Simulation
# ----------------------------------------------------------------------------------------------------------------------
sites = {s:Site(s) for s in ['home', 'school-a', 'school-b']}
probe_grp_size_site = GroupSizeProbe.by_rel('site', Site.AT, sites.values(), msg_mode=ProbeMsgMode.DISP, memo='Mass distribution across sites')
# s = (Simulation().
# add().
# rule(ResetSchoolDayRule(TimePoint(7))).
# rule(GoToAndBackTimeAtRule(t_at_attr='t@school')).
# probe(probe_grp_size_site).
# done().
# new_group(500).
# set_rel(Site.AT, sites['home']).
# set_rel('home', sites['home']).
# set_rel('school', sites['school-a']).
# done().
# new_group(500).
# set_rel(Site.AT, sites['home']).
# set_rel('home', sites['home']).
do_remove_file_sites = False
do_remove_file_groups = False
if do_remove_file_sites and os.path.isfile(fpath_sites):
os.remove(fpath_sites)
if do_remove_file_groups and os.path.isfile(fpath_groups):
os.remove(fpath_groups)
# ----------------------------------------------------------------------------------------------------------------------
# (1) Sites:
sites = Simulation().gen_sites_from_db(
fpath_db_in, lambda fpath_db: {
'school': Site.gen_from_db(fpath_db, 'schools', 'sp_id', 'school', []),
'work': Site.gen_from_db(fpath_db, 'workplaces', 'sp_id', 'work', [])
}, fpath_sites)
site_home = Site('home')
# ----------------------------------------------------------------------------------------------------------------------
# (2) Probes:
fpath_db_out = os.path.join(dpath_cwd, 'sim.sqlite3')
if os.path.isfile(fpath_db_out):
os.remove(fpath_db_out)
pp = ProbePersistenceDB(fpath_db_out, flush_every=1)
# Recovered:
if group.has_attr({'flu': 'r'}):
return [
GroupSplitSpec(p=0.8,
rel_set={Site.AT: group.get_rel('school')}),
GroupSplitSpec(p=0.2)
]
return None
# ----------------------------------------------------------------------------------------------------------------------
# (1) Sites:
site_home = Site('home')
school_l = Site(450149323) # 88% low income students
school_m = Site(450067740) # 7% low income students
# ----------------------------------------------------------------------------------------------------------------------
# (2) Simulation:
def grp_setup(pop, group):
return [
GroupSplitSpec(p=0.9, attr_set={'flu': 's'}),
GroupSplitSpec(p=0.1, attr_set={'flu': 'i'})
]
def run(iter):
do_remove_file_sites = False
do_remove_file_groups = False
if do_remove_file_sites and os.path.isfile(fpath_sites):
os.remove(fpath_sites)
if do_remove_file_groups and os.path.isfile(fpath_groups):
os.remove(fpath_groups)
# ----------------------------------------------------------------------------------------------------------------------
# (1) Sites:
sites = Simulation.gen_sites_from_db(
fpath_db_in, lambda fpath_db: {
'hosp':
Site.gen_from_db(fpath_db, 'hospitals', 'hosp_id', 'hospital',
['workers', 'physicians', 'beds']),
'home_gq':
Site.gen_from_db(fpath_db, 'gq', 'sp_id', 'home',
['gq_type', 'persons']),
'home':
Site.gen_from_db(fpath_db, 'households', 'sp_id', 'home',
['hh_income']),
'school':
Site.gen_from_db(fpath_db, 'schools', 'sp_id', 'school', []),
'work':
Site.gen_from_db(fpath_db, 'workplaces', 'sp_id', 'work', [])
}, fpath_sites)
site_home = Site('home')
# ----------------------------------------------------------------------------------------------------------------------
n_weeks = 8
do_sim = True # set to False to plot only (probe DB must exist)
do_plot = True
do_rm_traj_ens_db = True
do_school = False
do_work = False
do_school_work = True
disease_name = 'COVID-19'
fpath_traj_ens_db = os.path.join(os.path.dirname(__file__), 'out', sim_name,
'te.sqlite3')
site_home = Site('home')
# ----------------------------------------------------------------------------------------------------------------------
def TN(a, b, mu, sigma, n=None):
return truncnorm((a - mu) / sigma, (b - mu) / sigma, mu, sigma).rvs(n)
# ----------------------------------------------------------------------------------------------------------------------
# Population and environment source:
locale_db = SQLiteDB(
os.path.join(os.path.dirname(__file__), '..', '..', '..', 'data',
'allegheny-county', 'allegheny.sqlite3'))
locale_db.open_conn()
def is_applicable(self, group, t):
return (super().is_applicable(t)
and group.has_attr({'is-student': True})
and group.has_rel(['home', 'school']))
@staticmethod
def setup(pop, group):
return [
GroupSplitSpec(p=1.0, attr_set={'did-attend-school-today': False})
] # attr_del=['t-at-school'],
# ----------------------------------------------------------------------------------------------------------------------
sites = {
'home': Site('home'),
'school-a': Site('school-a'),
'school-b': Site('school-b')
}
probe_grp_size_site = GroupSizeProbe.by_rel(
'site', Site.AT, sites.values(), memo='Mass distribution across sites')
(Simulation(7, 1, 10, rand_seed=rand_seed).new_group('0', 500).set_attr(
'is-student', True).set_rel(Site.AT, sites['home']).set_rel(
'home',
sites['home']).set_rel('school', sites['school-a']).commit().new_group(
'1', 500).set_attr('is-student', True).set_rel(
Site.AT, sites['home']).set_rel('home', sites['home']).set_rel(
'school', sites['school-b']).commit().add_rule(
ResetDayRule(TimePoint(7))).add_rule(