def __init__(self, t=TimeAlways()):
super().__init__(t=t, name='flu-spike-evt')
def apply(self, pop, group, iter, t):
return [
GroupSplitSpec(p=0.90, attr_set={ 'flu': 'i' }),
GroupSplitSpec(p=0.10)
]
def is_applicable(self, group, iter, t):
return super().is_applicable(group, iter, t) and iter == 30 and (group.ha({ 'flu': 's' }) or group.ha({ 'flu': 'r' }))
(Simulation().
add_probe(GroupSizeProbe.by_attr('flu', 'flu', ['s', 'i', 'r'], msg_mode=ProbeMsgMode.DISP)).
add_rule(SIRSModel('flu', beta=0.05, gamma=0.50, alpha=0.10)).
add_rule(FluSpikeEvent()).
add_group(Group(m=1000, attr={ 'flu': 's' })).
run(48)
)
# ----------------------------------------------------------------------------------------------------------------------
# dpath_cwd = os.path.dirname(__file__)
# fpath_db = os.path.join(dpath_cwd, f'sim.sqlite3')
#
# if os.path.isfile(fpath_db):
# os.remove(fpath_db)
#
# probe = GroupSizeProbe(
# name='flu',
'''
A test of the mass transfer graph.
'''
from pram.entity import Group
from pram.model.epi import SIRSModel
from pram.sim import Simulation
from pram.traj import Trajectory, TrajectoryEnsemble
# ----------------------------------------------------------------------------------------------------------------------
def get_out_dir(filename):
return os.path.join(os.path.dirname(__file__), 'out', filename)
te = (TrajectoryEnsemble().add_trajectories([
Trajectory(sim=(Simulation().add(
[SIRSModel('flu', beta, 0.50, 0.10),
Group(m=1000, attr={'flu': 's'})])),
name=f'SIR: b={round(beta,2)}') for beta in [0.05, 0.10]
]).set_group_names([(0, 'S', Group.gen_hash(attr={'flu': 's'})),
(1, 'I', Group.gen_hash(attr={'flu': 'i'})),
(2, 'R', Group.gen_hash(attr={'flu': 'r'}))]).run(100))
# te.plot_mass_locus_line ((1200,300), get_out_dir('_plot-line.png'), iter_range=(-1, -1))
# te.plot_mass_locus_line_aggr((1200,300), get_out_dir('_plot-aggr.png'), iter_range=(-1, -1))
# te.traj[1].plot_mass_locus_streamgraph((900,600), get_out_dir('_plot.png'), do_sort=True)
# te.traj[1].plot_heatmap((800,800), get_out_dir('_plot-heatmap.png'), (-1,20))
'''
from pram.data import ProbePersistenceDB, ProbeMsgMode, GroupSizeProbe
from pram.entity import Group, GroupQry, GroupSplitSpec
from pram.model.model import MCSolver
from pram.model.epi import SIRSModel
from pram.sim import Simulation
# ----------------------------------------------------------------------------------------------------------------------
(Simulation().add_probe(
GroupSizeProbe.by_attr('flu',
'flu', ['s', 'i', 'r'],
msg_mode=ProbeMsgMode.DISP)).add_rule(
SIRSModel('flu',
beta=0.05,
gamma=0.50,
alpha=0.10,
solver=MCSolver())).add_group(
Group(m=1000,
attr={'flu': 's'})).run(48))
# ----------------------------------------------------------------------------------------------------------------------
# dpath_cwd = os.path.dirname(__file__)
# fpath_db = os.path.join(dpath_cwd, f'sim.sqlite3')
#
# if os.path.isfile(fpath_db):
# os.remove(fpath_db)
#
# probe = GroupSizeProbe(
# name='flu',
# queries=[
fpath_db = os.path.join(os.path.dirname(__file__), f'trajectory.sqlite3')
# ----------------------------------------------------------------------------------------------------------------------
# (1) Create the database and run a trajectory ensemble:
if os.path.isfile(fpath_db):
os.remove(fpath_db)
te = (TrajectoryEnsemble(fpath_db).
add_trajectories([
Trajectory(
(Simulation().
add([
SIRSModel('flu', beta, 0.50, 0.00),
Group(m=1000, attr={ 'flu': 's' })
])
), f'SIR: b={round(beta,2)}'
) for beta in [0.05] # np.arange(0.05, 0.06, 0.01)
]).
set_group_names([
(0, 'S', Group.gen_hash(attr={ 'flu': 's' })),
(1, 'I', Group.gen_hash(attr={ 'flu': 'i' })),
(2, 'R', Group.gen_hash(attr={ 'flu': 'r' }))
]).
run(100)
)
# ----------------------------------------------------------------------------------------------------------------------
A simulation implementing the SIRS model of infectious disease spread in a population and in particular demonstrating
probe plotting.
'''
from pram.data import ProbePersistenceMem, GroupSizeProbe
from pram.entity import Group
from pram.model.epi import SIRSModel
from pram.sim import Simulation
# ----------------------------------------------------------------------------------------------------------------------
p = GroupSizeProbe.by_attr('flu',
'flu', ['s', 'i', 'r'],
persistence=ProbePersistenceMem())
(Simulation().add_probe(p).add_rule(
SIRSModel('flu', beta=0.05, gamma=0.50,
alpha=0.10)).add_group(Group(m=1000, attr={'flu':
's'})).run(100))
series = [{
'var': 'p0',
'lw': 0.75,
'ls': 'solid',
'marker': 'o',
'color': 'red',
'ms': 0,
'lbl': 'S'
}, {
'var': 'p1',
'lw': 0.75,
'ls': 'dashed',
'marker': '+',
from pram.entity import Group, GroupQry, GroupSplitSpec
from pram.model.model import MCSolver
from pram.model.epi import SIRSModel
from pram.sim import Simulation
import matplotlib.pyplot as plt
import time
# ----------------------------------------------------------------------------------------------------------------------
sir_probe = GroupSizeProbe.by_attr('flu',
'flu', ['s', 'i', 'r'],
persistence=ProbePersistenceDB(),
msg_mode=ProbeMsgMode.DISP)
s = (Simulation().add_probe(sir_probe).add_rule(
SIRSModel('flu', beta=0.05, gamma=0.50, alpha=0.10,
solver=MCSolver())).add_group(Group(m=1000, attr={'flu': 's'})))
runs = 48
t0 = time.time()
s.run(runs)
time_elapsed = time.time() - t0
print(f'Time elapsed in {runs} runs: {time_elapsed} seconds')
series = [
{
'var': 'p0',
'lw': 2,
'linestyle': '-',
'marker': '',
'color': 'blue',
]
def is_applicable(self, group, iter, t):
return super().is_applicable(
group, iter, t) and 1000 <= iter <= 3000 and group.ha({'flu': 'r'})
# ----------------------------------------------------------------------------------------------------------------------
# if os.path.isfile(fpath_db): os.remove(fpath_db)
te = TrajectoryEnsemble(fpath_db)
if te.is_db_empty: # generate simulation data if the trajectory ensemble database is empty
te.set_pragma_memoize_group_ids(True)
te.add_trajectory((Simulation().add([
SIRSModel('flu', beta=0.20, gamma=0.02, solver=ODESolver()),
FluGammaProcess(),
Group(m=950, attr={'flu': 's'}),
Group(m=50, attr={'flu': 'i'})
])))
te.set_group_names(group_names)
te.run(3000)
# te.traj[1].plot_mass_flow_time_series(filepath=get_out_dir('_plot.png'), iter_range=(-1,10), v_prop=False, e_prop=True)
# te.traj[1].plot_mass_locus_streamgraph((1200,600), get_out_dir('_plot.png'))
# te.traj[1].plot_heatmap((800,800), get_out_dir('_plot.png'), (-1,20))
# te.traj[1].plot_mass_locus_recurrence((16,8), get_out_dir('_plot.png'), Group.gen_hash(attr={ 'flu': 's' }), iter_range=(-1, -1))
# te.traj[1].plot_mass_locus_recurrence((16,8), get_out_dir('_plot.png'), Group.gen_hash(attr={ 'flu': 'i' }), iter_range=(-1, 4000))
# te.traj[1].plot_mass_locus_recurrence((16,8), get_out_dir('_plot.png'), Group.gen_hash(attr={ 'flu': 'r' }), iter_range=(-1, 4000))
# ----------------------------------------------------------------------------------------------------------------------
# Simulations:
if os.path.isfile(fpath_db): os.remove(fpath_db)
# te = TrajectoryEnsemble(fpath_db)
# te = TrajectoryEnsemble(fpath_db, cluster_inf=ClusterInf(address='auto'))
te = TrajectoryEnsemble(fpath_db, cluster_inf=ClusterInf(num_cpus=6, memory=500*1024*1024, object_store_memory=500*1024*1024, include_webui=False))
if te.is_db_empty: # generate simulation data if the trajectory ensemble database is empty
te.set_pragma_memoize_group_ids(True)
te.add_trajectories([
(Simulation().
add([
SIRSModel('flu', beta=0.10, gamma=0.05, solver=MCSolver()), # model 1
SIRSModel('flu', beta=0.50, gamma=U(0.01, 0.15), solver=MCSolver(), i=[int(5 + TN(0,50, 5,10)), 50]), # model 2
MakeSusceptibleProcess(i=[50,0], a=3.0, scale=flu_proc_scale), # model 3
Group(m=1000, attr={ 'flu': 's' })
])
) for flu_proc_scale in U(1,5, 20) # a 20-trajectory ensemble
])
te.set_group_names(group_names)
te.run(120)
te.plot_mass_locus_line ((1200,300), os.path.join(os.path.dirname(__file__), 'out', '_plot-line.png'), opacity_min=0.2)
te.plot_mass_locus_line_aggr((1200,300), os.path.join(os.path.dirname(__file__), 'out', '_plot-iqr.png'))
# fpath_diag = os.path.join(os.path.dirname(__file__), 'out', 'sim-03.diag')
# fpath_pdf = os.path.join(os.path.dirname(__file__), 'out', 'sim-03.pdf')
# te.traj[2].sim.gen_diagram(fpath_diag, fpath_pdf)
# ----------------------------------------------------------------------------------------------------------------------
fpath_db = os.path.join(os.path.dirname(__file__), 'sim-01.sqlite3')
group_names = [(0, 'S', Group.gen_hash(attr={'flu': 's'})),
(1, 'I', Group.gen_hash(attr={'flu': 'i'})),
(2, 'R', Group.gen_hash(attr={'flu': 'r'}))]
# ----------------------------------------------------------------------------------------------------------------------
# if os.path.isfile(fpath_db): os.remove(fpath_db)
te = TrajectoryEnsemble(fpath_db)
if te.is_db_empty:
te.add_trajectories([
Trajectory((Simulation().add([
SIRSModel('flu', 0.10, 0.50, 0.00, solver=MCSolver()),
Group(m=900, attr={'flu': 's'}),
Group(m=100, attr={'flu': 'i'})
])))
])
te.set_group_names(group_names)
te.run(100)
# ----------------------------------------------------------------------------------------------------------------------
# te.traj[1].plot_mass_locus_line((1200,300), os.path.join(os.path.dirname(__file__), 'sim-01.png'))
print(te.traj[1].gen_agent(4)) # a single agent in a four-iteration simulation
print(te.traj[1].gen_agent_pop(
2, 3)) # two-agent population simulated for three iterations