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 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 sim01(iter_cnt):
sim = (Simulation().add([
ProgressFluRule(),
GroupSizeProbe.by_attr('flu',
'flu', ['s', 'e', 'r'],
msg_mode=ProbeMsgMode.CUMUL),
Group(m=1000)
]).run(iter_cnt))
print(sim.probes[0].get_msg())
print()
def sim02(iter_cnt):
sim = (Simulation().add([
ProgressFluIncomeRule(),
GroupSizeProbe.by_attr('flu',
'flu', ['s', 'e', 'r'],
msg_mode=ProbeMsgMode.CUMUL),
Group(m=500, attr={'income': 'l'}),
Group(m=500, attr={'income': 'm'})
]).run(iter_cnt))
print(sim.probes[0].get_msg())
print()
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())
print(f'Attributes : {s.last_run.attr_used}')
print(f'Relations : {s.last_run.rel_used}')
print()
]
if group.has_attr({'flu': 'I'}):
return [
GroupSplitSpec(p=0.5, attr_set={'flu': 'I'}),
GroupSplitSpec(p=0.5, attr_set={'flu': 'R'}),
]
if group.has_attr({'flu': 'R'}):
return [
GroupSplitSpec(p=0.7, attr_set={'flu': 'R'}),
GroupSplitSpec(p=0.3, attr_set={'flu': 'S'}),
]
probe_grp_size_flu = GroupSizeProbe.by_attr(
'flu',
'flu', ['S', 'I', 'R'],
msg_mode=ProbeMsgMode.DISP,
persistance=ProbePersistanceDB(),
memo='Mass distribution across flu status')
p = GroupSizeProbe.by_attr('flu',
'flu', ['S', 'I', 'R'],
persistance=ProbePersistanceDB())
(Simulation().add_probe(
GroupSizeProbe.by_attr('flu',
'flu', ['S', 'I', 'R'],
msg_mode=ProbeMsgMode.DISP)).add_probe(p).add_rule(
SIRRule()).add_group(
Group(m=1000, attr={'flu': 'S'})).run(26))
series = [{
class FluSpikeEvent(Event):
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(
# (0) Init:
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)
def setup(self, pop, group):
return [GroupSplitSpec(p=1.0, attr_set={'stage': 's'})]
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
if __name__ == "__main__":
i = 1
eq = 50
print(eq * "=", "starting sim {}".format(i), eq * "=")
sim = (Simulation().add([
StartupGrowthRule(),
GroupSizeProbe.by_attr('stage',
'stage',
['s', 'a', 'b', 'c', 'success', 'failure'],
msg_mode=ProbeMsgMode.CUMUL),
Group(m=10000)
]).run(10))
print(sim.probes[0].get_msg())
print()
i += 1
print(eq * "=", "starting sim {}".format(i), eq * "=")
sim = (Simulation().add([
StartupLocationRule(),
GroupSizeProbe(
'startup-location',
[
GroupQry(attr={
'location': 'usa',
# 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})
],
qry_tot=GroupQry(rel={'school': site}),
var_names=['ps', 'pi', 'pr', 'ns', 'ni', 'nr'],
persistence=probe_persistence,
memo=f'Flu at school {s.name.upper()}'))
probe_grp_size_flu = GroupSizeProbe.by_attr(
'flu',
'flu', ['s', 'i', 'r'],
msg_mode=ProbeMsgMode.DISP,
memo='Mass distribution across flu stages')
probe_grp_size_site = GroupSizeProbe.by_rel(
'site',
Site.AT,
sites.values(),
msg_mode=ProbeMsgMode.DISP,
memo='Mass distribution across sites')
# ----------------------------------------------------------------------------------------------------------------------
# (2) Simulation:
if __name__ == '__main__':
sim = (
Simulation().
def analysis_simple(tx_dict=None, population=10000, iteration=16):
if tx_dict is None:
tx_dict = dict(
round_seed_a=0.3,
round_seed_failure=0.67,
round_a_b=0.2,
round_a_failure=0.6,
round_b_c=0.3,
round_b_failure=0.4,
round_c_success=0.5,
round_c_failure=0.5,
round_success_success=1,
round_success_failure=0,
round_failure_success=0,
round_failure_failure=1,
# round_a_a=0.2,
# round_b_b=0.2,
# round_c_c=0.2,
)
old_p = GroupSizeProbe.by_attr(
'stage',
'stage', ['seed', 'a', 'b', 'c', 'success', 'failure'],
persistance=ProbePersistanceMem(),
msg_mode=ProbeMsgMode.CUMUL)
p = GroupSizeProbe.by_attr('stage',
'stage',
['seed', 'a', 'b', 'c', 'success', 'failure'],
persistance=ProbePersistanceDB())
sim = (Simulation().add(
[UserStartupGrowthRule(tx_dict), p,
Group(m=population)]).run(iteration))
series = [
{
'var': 'n0'
},
{
'var': 'n1'
},
{
'var': 'n2'
},
{
'var': 'n3'
},
{
'var': 'n4'
},
{
'var': 'n5'
},
]
col_names = {
'n0': 'seed',
'n1': 'a',
'n2': 'b',
'n3': 'c',
'n4': 'success',
'n5': 'failure'
}
probe_data = p.probe_data(series)
probe_frame: pd.DataFrame
probe_frame = pd.DataFrame.from_dict(probe_data)
probe_frame.rename(columns=col_names, inplace=True)
probe_frame["i"] = probe_frame["i"] + 1
initial_condition = {
'seed': population,
'a': 0,
'b': 0,
'c': 0,
'success': 0,
'failure': 0,
'i': 0
}
probe_frame = pd.concat(
[pd.DataFrame(initial_condition, index=[0]),
probe_frame[:]]).reset_index(drop=True)
probe_frame.drop(columns=['i'], inplace=True)
print(probe_frame)
d = probe_frame.iloc[0]
print(d)
# for chart
plot_data = probe_frame.iloc[0]
# probe_names = probe_frame
print(plot_data.values)
print(list(plot_data.index))
# data_source = ColumnDataSource(probe_frame)
return probe_frame
def analysis_Complex_version2(tx_dict=None, population=100, iteration=20):
np.set_printoptions(suppress=True)
if tx_dict is None:
tx_dict = dict(
round_seed_a=0.3,
round_seed_failure=0.67,
round_a_b=0.2,
round_a_failure=0.3,
round_b_c=0.3,
round_b_failure=0.2,
round_c_success=0.3,
round_c_failure=0.4,
round_success_success=1,
round_success_failure=0,
round_failure_success=0,
round_failure_failure=1,
round_a_a=0.4,
round_b_b=0.3,
round_c_c=0.3,
)
# seed = 1, a = 0, b = 0, c = 0, success = 0, failure = 0
tx_matrix = np.array([
[
0, tx_dict['round_seed_a'], 0, 0,
1 - tx_dict['round_seed_failure'] - tx_dict['round_seed_a'],
tx_dict['round_seed_failure']
],
[
0, tx_dict['round_a_a'], tx_dict['round_a_b'], 0,
1 - tx_dict['round_a_failure'] - tx_dict['round_a_a'] -
tx_dict['round_a_b'], tx_dict['round_a_failure']
],
[
0, 0, tx_dict['round_b_b'], tx_dict['round_b_c'],
1 - tx_dict['round_b_failure'] - tx_dict['round_b_c'] -
tx_dict['round_b_b'], tx_dict['round_b_failure']
],
[
0, 0, 0, tx_dict['round_c_c'],
1 - tx_dict['round_c_failure'] - tx_dict['round_c_c'],
tx_dict['round_c_failure']
],
[
0, 0, 0, 0, 1 - tx_dict['round_success_failure'],
tx_dict['round_success_failure']
],
[
0, 0, 0, 0, 1 - tx_dict['round_failure_failure'],
tx_dict['round_failure_failure']
],
])
old_p = GroupSizeProbe.by_attr(
'stage',
'stage', ['seed', 'a', 'b', 'c', 'success', 'failure'],
persistance=ProbePersistanceMem(),
msg_mode=ProbeMsgMode.CUMUL)
p = GroupSizeProbe.by_attr('stage',
'stage',
['seed', 'a', 'b', 'c', 'success', 'failure'],
persistance=ProbePersistanceDB())
sim = (Simulation().add(
[UserStartupGrowthRuleVersion2(tx_dict), p,
Group(m=population)]).run(iteration))
print(sim.probes[0].get_msg())
print()
series = [
{
'var': 'p0'
},
{
'var': 'p1'
},
{
'var': 'p2'
},
{
'var': 'p3'
},
{
'var': 'p4'
},
{
'var': 'p5'
},
]
col_names = {
"p0": "p_seed",
"p1": "p_a",
"p2": "p_b",
"p3": "p_c",
"p4": "p_success",
"p5": "p_failure"
}
names = ["p0", "p1", "p2", "p3", "p4", "p5"]
probe_data: dict
probe_data = p.probe_data(series)
probe_data_ordered = OrderedDict()
for name in names:
probe_data_ordered[col_names.get(name)] = probe_data.pop(name)
# pp(probe_data_ordered)
probe_data_frame = pd.DataFrame.from_dict(probe_data_ordered)
pp(probe_data_frame)
value_df = pd.DataFrame(columns=list(probe_data_ordered.keys()))
# print(value_df)
# proportion of investment
investment_proportion = OrderedDict(seed=1,
a=0,
b=0,
c=0,
success=0,
failure=0)
# hyper-parameters
# stage_multiplier = {"seed": 3, "a": 5, "b": 7, "c": 5, "success": 5, "failure": 0}
# stage_multiplier_dict = OrderedDict(seed=3, a=5, b=7, c=5, success=5, failure=0)
stage_multiplier_list = [1, 1.1, 1.25, 1.3, 1, 0]
# stage_multiplier_list = [1, 2, 3, 4, 5, 0]
inital_investment_dict = OrderedDict(
seed=population * investment_proportion.get("seed"),
a=population * investment_proportion.get("a"),
b=population * investment_proportion.get("b"),
c=population * investment_proportion.get("c"),
success=population * investment_proportion.get("success"),
failure=population * investment_proportion.get("failure"),
)
sim_length = len(probe_data_ordered.get('p_a'))
valuation_list = [population]
for i in range(sim_length):
# print(valuation_list[i] * probe_data_frame.iloc[[i]])
val = valuation_list[i] * probe_data_frame.iloc[i]
val = stage_multiplier_list * val
valuation_list.append(np.sum(val))
print(valuation_list)
# print("sim_length",sim_length)
#
# # total_investment = sum(inital_investment)
# print("inital_investment",inital_investment_dict)
probe_frame: pd.DataFrame
probe_frame = pd.DataFrame.from_dict(probe_data)
probe_frame.rename(columns=col_names, inplace=True)
probe_frame["i"] = probe_frame["i"] + 1
initial_condition = {
'seed': population,
'a': 0,
'b': 0,
'c': 0,
'success': 0,
'failure': 0,
'i': 0,
"p_seed": 1,
"p_a": 0,
"p_b": 0,
"p_c": 0,
"p_success": 0,
"p_failure": 0
}
probe_frame = pd.concat(
[pd.DataFrame(initial_condition, index=[0]),
probe_frame[:]]).reset_index(drop=True)
probe_frame.drop(columns=['i'], inplace=True)
print(probe_frame)
# d = probe_frame.iloc[0]
# print(d)
# for chart
plot_data = probe_frame.iloc[0]
# probe_names = probe_frame
print(plot_data.values)
print(list(plot_data.index))
# data_source = ColumnDataSource(probe_frame)
probe_frame.to_csv("data_file.csv")
return probe_frame
def analysis_Complex_version1(tx_dict=None, population=10000, iteration=20):
if tx_dict is None:
tx_dict = dict(
round_seed_a=0.3,
round_seed_failure=0.67,
round_a_b=0.2,
round_a_failure=0.3,
round_b_c=0.3,
round_b_failure=0.2,
round_c_success=0.3,
round_c_failure=0.4,
round_success_success=1,
round_success_failure=0,
round_failure_success=0,
round_failure_failure=1,
round_a_a=0.4,
round_b_b=0.3,
round_c_c=0.3,
)
tx_numpy_array = np.array([])
old_p = GroupSizeProbe.by_attr(
'stage',
'stage', ['seed', 'a', 'b', 'c', 'success', 'failure'],
persistance=ProbePersistanceMem(),
msg_mode=ProbeMsgMode.CUMUL)
p = GroupSizeProbe.by_attr('stage',
'stage',
['seed', 'a', 'b', 'c', 'success', 'failure'],
persistance=ProbePersistanceDB())
sim = (Simulation().add(
[UserStartupGrowthRuleVersion2(tx_dict), p,
Group(m=population)]).run(iteration))
print(sim.probes[0].get_msg())
print()
series = [
{
'var': 'p0'
},
{
'var': 'p1'
},
{
'var': 'p2'
},
{
'var': 'p3'
},
{
'var': 'p4'
},
{
'var': 'p5'
},
{
'var': 'n0'
},
{
'var': 'n1'
},
{
'var': 'n2'
},
{
'var': 'n3'
},
{
'var': 'n4'
},
{
'var': 'n5'
},
]
col_names = {
'n0': 'seed',
'n1': 'a',
'n2': 'b',
'n3': 'c',
'n4': 'success',
'n5': 'failure',
"p0": "p_seed",
"p1": "p_a",
"p2": "p_b",
"p3": "p_c",
"p4": "p_success",
"p5": "p_failure"
}
probe_data = p.probe_data(series)
probe_frame: pd.DataFrame
probe_frame = pd.DataFrame.from_dict(probe_data)
probe_frame.rename(columns=col_names, inplace=True)
probe_frame["i"] = probe_frame["i"] + 1
initial_condition = {
'seed': population,
'a': 0,
'b': 0,
'c': 0,
'success': 0,
'failure': 0,
'i': 0,
"p_seed": 1,
"p_a": 0,
"p_b": 0,
"p_c": 0,
"p_success": 0,
"p_failure": 0
}
probe_frame = pd.concat(
[pd.DataFrame(initial_condition, index=[0]),
probe_frame[:]]).reset_index(drop=True)
probe_frame.drop(columns=['i'], inplace=True)
print(probe_frame)
# d = probe_frame.iloc[0]
# print(d)
# for chart
plot_data = probe_frame.iloc[0]
# probe_names = probe_frame
print(plot_data.values)
print(list(plot_data.index))
# data_source = ColumnDataSource(probe_frame)
probe_frame.to_csv("data_file.csv")
return probe_frame
Original PRAM Simulation from:
pram/src/sim/04-flu-prog-01/sim.py
"""
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
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 = [
{
'''
A test simulation involving the SEIR flu model in isolation.
'''
from pram.data import GroupSizeProbe, ProbeMsgMode
from pram.entity import Group, Site
from pram.rule import SEIRFluRule
from pram.sim import Simulation
rand_seed = 1928
probe_grp_size_flu = GroupSizeProbe.by_attr(
'flu',
SEIRFluRule.ATTR,
SEIRFluRule.State,
msg_mode=ProbeMsgMode.DISP,
memo='Mass distribution across flu states')
(Simulation().set().rand_seed(rand_seed).done().add().rule(
SEIRFluRule()).probe(probe_grp_size_flu).done().new_group(
1000).done().summary(True, 0, 0, 0, 0,
(0, 1)).run(16).compact().summary(
False, 8, 0, 0, 0, (1, 0)))
# (Simulation().
# set().
# rand_seed(rand_seed).
# pragma_analyze(False).
# pragma_autocompact(True).
# done().
# add().
# rule(SEIRFluRule()).
return [
GroupSplitSpec(p=0.75, attr_set={'value': 0 * failure_value}),
GroupSplitSpec(p=0.2, attr_set={'value': 2 * failure_value}),
GroupSplitSpec(p=0.05, attr_set={'value': 1.5 * failure_value})
]
def is_applicable(self, group, iter, t):
return super().is_applicable(group, iter, t) and group.has_attr(
['stage', 'value'])
def setup(self, pop, group):
return [GroupSplitSpec(p=1.0, attr_set={"value": 1000})]
old_p = GroupSizeProbe.by_attr('stage',
'stage', ['c', 'success', 'failure'],
persistance=ProbePersistanceMem(),
msg_mode=ProbeMsgMode.CUMUL)
probe2 = GroupSizeProbe.by_attr("value",
"value", ['c', 'success', 'failure'],
persistance=ProbePersistanceMem(),
msg_mode=ProbeMsgMode.DISP)
# c_group = Group(name="c", m = 100, attr = {"stage" : "c", "value": 1000})
# success_group = Group(name="success", m = 100, attr ={"stage":"success", "value" : 1000})
# failure_group = Group(name="failure", m = 100, attr = {"stage": "failure", "value" : 1000})
c_group = Group(name="c", m=100, attr={"stage": "c"})
success_group = Group(name="success", m=100, attr={"stage": "success"})
failure_group = Group(name="failure", m=100, attr={"stage": "failure"})
'''
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,
