def __init__(self, t=TimeAlways()):
super().__init__(t=t, name='rec-flu-gamma-proc')
self.p_iter_0 = None
self.p = lambda iter: gamma(a=5.0, loc=5.0, scale=25.0).pdf(
iter - self.p_iter_0) * 125
self.wave = var_frq_sine_wave(1, 0, 1, n_iter / 1000 + 2, 1,
n_iter + 1)
self.last_applied_iter = None
def __init__(self,
var,
tm,
home,
name='markov-chain',
t=TimeAlways(),
memo=None):
super().__init__(var, tm)
self.home = home
self.t = t
def __init__(self, tx_matrix, t=TimeAlways(), memo=None):
super().__init__('startup-growth', t, memo)
# self.initial_population = tx_matrix.get("initial_population")
self.round_seed_a = tx_matrix.get("round_seed_a")
self.round_seed_failure = tx_matrix.get("round_seed_failure")
self.round_a_b = tx_matrix.get("round_a_b")
self.round_a_failure = tx_matrix.get("round_a_failure")
self.round_b_c = tx_matrix.get("round_b_c")
self.round_b_failure = tx_matrix.get("round_b_failure")
self.round_c_success = tx_matrix.get("round_c_success")
self.round_c_failure = tx_matrix.get("round_c_failure")
self.round_success_success = tx_matrix.get("round_success_success")
self.round_success_failure = tx_matrix.get("round_success_failure")
self.round_failure_success = tx_matrix.get("round_failure_success")
self.round_failure_failure = tx_matrix.get("round_failure_failure")
def __init__(self,
env_harshness,
env_harshness_death_mult=0.001,
migration_death_mult=0.05,
name='migration',
t=TimeAlways(),
i=IterAlways()):
super().__init__(
name,
t,
i,
group_qry=GroupQry(
cond=[lambda g: g.has_attr({'is-migrating': True})]))
self.env_harshness = env_harshness # [0=benign .. 1=harsh]
self.env_harshness_death_mult = env_harshness_death_mult
self.migration_death_mult = migration_death_mult
def __init__(self,
severity,
scale,
severity_death_mult=0.0001,
scale_migration_mult=0.01,
name='conflict',
t=TimeAlways(),
i=IterAlways()):
super().__init__(
name,
t,
i,
group_qry=GroupQry(
cond=[lambda g: g.has_attr({'is-migrating': False})]))
self.severity = severity # [0=benign .. 1=lethal]
self.scale = scale # [0=contained .. 1=wide-spread]
self.severity_death_mult = severity_death_mult
self.scale_migration_mult = scale_migration_mult
def make_sir(beta, gamma, t=TimeAlways(), i=IterAlways(), dt=0.1):
def f_sir_model(t, state):
'''
[1] Kermack WO & McKendrick AG (1927) A Contribution to the Mathematical Theory of Epidemics. Proceedings of the
Royal Society A. 115(772), 700--721.
http://www.public.asu.edu/~hnesse/classes/sir.html
'''
s, i, r = state
n = s + i + r
return [-beta * s * i / n, beta * s * i / n - gamma * i, gamma * i]
return ODESystemMass(f_sir_model, [
DotMap(attr={'flu': 's'}),
DotMap(attr={'flu': 'i'}),
DotMap(attr={'flu': 'r'})
],
t=t,
i=i,
dt=dt)
def __init__(self, t=TimeAlways(), memo=None):
super().__init__('startup-location', t, memo)
def __init__(self, t=TimeAlways()):
super().__init__(t=t, name='climate-calamity-proc')
def __init__(self, t=TimeAlways()):
super().__init__(t=t, name='flu-gamma-proc')
self.p = lambda iter: gamma(a=5.0, loc=5.0, scale=100.0).pdf(
iter - 1000) * 500
def __init__(self, t=TimeAlways()):
super().__init__(t=t, name='flu-exp-proc')
def __init__(self, t=TimeAlways()):
super().__init__('attr-rule', t)
def __init__(self, t=TimeAlways()):
super().__init__(t=t, name='prey-disease-evt')
def __init__(self, t=TimeAlways(), memo=None):
super().__init__('valuation', t, memo)
def __init__(self):
super().__init__('sir-time-compress-proc', TimeAlways())
self.wave = var_frq_sine_wave(1, 0, 1, (n_iter / 1000) + 3, 1, n_iter + 1)
self.last_applied_iter = None
def __init__(self):
super().__init__('flu-progress', TimeAlways())
def __init__(self, t=TimeAlways()):
super().__init__(t=t, name='flu-spike-evt')
def __init__(self):
super().__init__('tree-to-wood', TimeAlways())
def __init__(self, t=TimeAlways(), memo=None):
super().__init__('progress-flu', t, memo)
def __init__(self):
super().__init__('hour-rule', TimeAlways())
def __init__(self):
super().__init__('wood-to-lumber', TimeAlways())
def __init__(self):
super().__init__('double-incidence-of-ad', TimeAlways())
def __init__(self): super().__init__('flu-progress', TimeAlways())
def apply(self, pop, group, iter, t):
def __init__(self):
super().__init__('flu-location', TimeAlways())
def __init__(self, p, sites):
super().__init__(name="SimpleMallMovement", t=TimeAlways())
self.sites = sites
self.p = p
def __init__(self):
super().__init__('flu-random-beta-proc', TimeAlways())
def __init__(self, p, move_p, sites):
super().__init__(name="SimpleMallFlu", t=TimeAlways())
self.p = p
self.move_p = move_p
self.sites = sites
def __init__(self, t=TimeAlways(), memo=None):
super().__init__('startup-growth', t, memo)
def __init__(self):
super().__init__('get-ad-prob', TimeAlways())
