def __init__(self, n_groups=1, pool_size=0.0):
'''
Parameters
----------
n_groups: int
Number of groups in the population.
pool_size: float
Upperbound on the range of the existing population groups. A random value
from 0 to the upperbound value will be assigned to each group.
'''
super().__init__()
self.port_names_expected = [
'probation', 'adjudication', 'arrested', 'prison', 'community'
]
quantities = list()
self.ode_params = dict()
self.initial_time = 0.0 * unit.day
self.end_time = 100 * unit.day
self.time_step = 0.5 * unit.day
unit.month = 30 * unit.day
unit.percent = 1 / 100
# Population groups
self.n_groups = n_groups
# Jail population groups
fjg_0 = np.random.random(self.n_groups) * pool_size
fjg = Quantity(name='fjg',
formal_name='jail-pop-grps',
latex_name='$n_j^{(g)}$',
unit='# offenders',
value=fjg_0,
info='Jail Population Groups')
quantities.append(fjg)
# Model parameters: commitment coefficients
# Jail to community
a = 35 * unit.percent / unit.year * np.ones(self.n_groups)
b = 40 * unit.percent / unit.year * np.ones(self.n_groups)
cj0g_0 = (a +
(b - a) * np.random.random(self.n_groups)) / self.n_groups
cj0g = Quantity(name='cj0g',
formal_name='commit-community-coeff-grps',
unit='1/s',
value=cj0g_0)
self.ode_params['commit-to-community-coeff-grps'] = cj0g_0
quantities.append(cj0g)
# Jail to prison
a = 60 * unit.percent / unit.year * np.ones(self.n_groups)
b = 65 * unit.percent / unit.year * np.ones(self.n_groups)
cjpg_0 = (a +
(b - a) * np.random.random(self.n_groups)) / self.n_groups
cjpg = Quantity(name='cjpg',
formal_name='commit-prison-coeff-grps',
unit='1/s',
value=cjpg_0)
self.ode_params['commit-to-prison-coeff-grps'] = cjpg_0
quantities.append(cjpg)
# Death term
a = 0.5 * unit.percent / unit.year * np.ones(self.n_groups)
b = 0.6 * unit.percent / unit.year * np.ones(self.n_groups)
djg_0 = (a + (b - a) * np.random.random(self.n_groups)) / self.n_groups
self.ode_params['jail-death-rates-coeff'] = djg_0
# Phase state
self.population_phase = Phase(self.initial_time,
time_unit='s',
quantities=quantities)
self.population_phase.SetValue('fjg', fjg_0, self.initial_time)
# Initialize inflows to zero
self.ode_params['arrested-inflow-rates'] = np.zeros(self.n_groups)
self.ode_params['probation-inflow-rates'] = np.zeros(self.n_groups)
self.ode_params['adjudication-inflow-rates'] = np.zeros(self.n_groups)
return
class Jail(Module):
'''
Jail Cortix module used to model criminal group population in a jail.
Notes
-----
These are the `port` names available in this module to connect to respective
modules: `probation`, `adjudication`, `arrested`, `prison`, and `community`.
See instance attribute `port_names_expected`.
'''
def __init__(self, n_groups=1, pool_size=0.0):
'''
Parameters
----------
n_groups: int
Number of groups in the population.
pool_size: float
Upperbound on the range of the existing population groups. A random value
from 0 to the upperbound value will be assigned to each group.
'''
super().__init__()
self.port_names_expected = [
'probation', 'adjudication', 'arrested', 'prison', 'community'
]
quantities = list()
self.ode_params = dict()
self.initial_time = 0.0 * unit.day
self.end_time = 100 * unit.day
self.time_step = 0.5 * unit.day
unit.month = 30 * unit.day
unit.percent = 1 / 100
# Population groups
self.n_groups = n_groups
# Jail population groups
fjg_0 = np.random.random(self.n_groups) * pool_size
fjg = Quantity(name='fjg',
formal_name='jail-pop-grps',
latex_name='$n_j^{(g)}$',
unit='# offenders',
value=fjg_0,
info='Jail Population Groups')
quantities.append(fjg)
# Model parameters: commitment coefficients
# Jail to community
a = 35 * unit.percent / unit.year * np.ones(self.n_groups)
b = 40 * unit.percent / unit.year * np.ones(self.n_groups)
cj0g_0 = (a +
(b - a) * np.random.random(self.n_groups)) / self.n_groups
cj0g = Quantity(name='cj0g',
formal_name='commit-community-coeff-grps',
unit='1/s',
value=cj0g_0)
self.ode_params['commit-to-community-coeff-grps'] = cj0g_0
quantities.append(cj0g)
# Jail to prison
a = 60 * unit.percent / unit.year * np.ones(self.n_groups)
b = 65 * unit.percent / unit.year * np.ones(self.n_groups)
cjpg_0 = (a +
(b - a) * np.random.random(self.n_groups)) / self.n_groups
cjpg = Quantity(name='cjpg',
formal_name='commit-prison-coeff-grps',
unit='1/s',
value=cjpg_0)
self.ode_params['commit-to-prison-coeff-grps'] = cjpg_0
quantities.append(cjpg)
# Death term
a = 0.5 * unit.percent / unit.year * np.ones(self.n_groups)
b = 0.6 * unit.percent / unit.year * np.ones(self.n_groups)
djg_0 = (a + (b - a) * np.random.random(self.n_groups)) / self.n_groups
self.ode_params['jail-death-rates-coeff'] = djg_0
# Phase state
self.population_phase = Phase(self.initial_time,
time_unit='s',
quantities=quantities)
self.population_phase.SetValue('fjg', fjg_0, self.initial_time)
# Initialize inflows to zero
self.ode_params['arrested-inflow-rates'] = np.zeros(self.n_groups)
self.ode_params['probation-inflow-rates'] = np.zeros(self.n_groups)
self.ode_params['adjudication-inflow-rates'] = np.zeros(self.n_groups)
return
def run(self, *args):
self.__zero_ode_parameters()
time = self.initial_time
while time <= self.end_time:
# Interactions in the prison port
#--------------------------------
# one way "to" prison
message_time = self.recv('prison')
outflow_rates = self.__compute_outflow_rates(
message_time, 'prison')
self.send((message_time, outflow_rates), 'prison')
# Interactions in the adjudication port
#------------------------------------
# one way "from" adjudication
self.send(time, 'adjudication')
(check_time, adjudication_inflow_rates) = self.recv('adjudication')
assert abs(check_time - time) <= 1e-6
self.ode_params[
'adjudication-inflow-rates'] = adjudication_inflow_rates
# Interactions in the arrested port
#----------------------------------
# one way "from" arrested
self.send(time, 'arrested')
(check_time, arrested_inflow_rates) = self.recv('arrested')
assert abs(check_time - time) <= 1e-6
self.ode_params['arrested-inflow-rates'] = arrested_inflow_rates
# Interactions in the probation port
#-----------------------------------
# one way "from" probation
self.send(time, 'probation')
(check_time, probation_inflow_rates) = self.recv('probation')
assert abs(check_time - time) <= 1e-6
self.ode_params['probation-inflow-rates'] = probation_inflow_rates
# Interactions in the community port
#------------------------------
# one way "to" community
message_time = self.recv('community')
outflow_rates = self.__compute_outflow_rates(
message_time, 'community')
self.send((message_time, outflow_rates), 'community')
# Evolve jail group population to the next time stamp
#----------------------------------------------------
time = self.__step(time)
def __rhs_fn(self, u_vec, t, params):
fjg = u_vec # jail population groups
arrested_inflow_rates = params['arrested-inflow-rates']
probation_inflow_rates = params['probation-inflow-rates']
adjudication_inflow_rates = params['adjudication-inflow-rates']
inflow_rates = arrested_inflow_rates + probation_inflow_rates + \
adjudication_inflow_rates
cj0g = self.ode_params['commit-to-community-coeff-grps']
cjpg = self.ode_params['commit-to-prison-coeff-grps']
outflow_rates = (cj0g + cjpg) * fjg
death_rates_coeff = params['jail-death-rates-coeff']
death_rates = death_rates_coeff * fjg
dt_fjg = inflow_rates - outflow_rates - death_rates
return dt_fjg
def __step(self, time=0.0):
r'''
ODE IVP problem:
Given the initial data at :math:`t=0`,
:math:`u = (u_1(0),u_2(0),\ldots)`
solve :math:`\frac{\text{d}u}{\text{d}t} = f(u)` in the interval
:math:`0\le t \le t_f`.
Parameters
----------
time: float
Time in SI unit.
Returns
-------
None
'''
u_vec_0 = self.population_phase.GetValue('fjg', time)
t_interval_sec = np.linspace(0.0, self.time_step, num=2)
(u_vec_hist, info_dict) = odeint(self.__rhs_fn,
u_vec_0,
t_interval_sec,
args=(self.ode_params, ),
rtol=1e-4,
atol=1e-8,
mxstep=200,
full_output=True)
assert info_dict['message'] == 'Integration successful.', info_dict[
'message']
u_vec = u_vec_hist[1, :] # solution vector at final time step
values = self.population_phase.GetRow(time) # values at previous time
time += self.time_step
self.population_phase.AddRow(time, values)
# Update current values
self.population_phase.SetValue('fjg', u_vec, time)
return time
def __compute_outflow_rates(self, time, name):
fjg = self.population_phase.GetValue('fjg', time)
assert np.all(fjg >= 0.0), 'values: %r' % fjg
if name == 'prison':
cjpg = self.ode_params['commit-to-prison-coeff-grps']
outflow_rates = cjpg * fjg
if name == 'community':
cj0g = self.ode_params['commit-to-community-coeff-grps']
outflow_rates = cj0g * fjg
return outflow_rates
def __zero_ode_parameters(self):
'''
If ports are not connected the corresponding outflows must be zero.
'''
zeros = np.zeros(self.n_groups)
p_names = [p.name for p in self.ports]
if 'community' not in p_names:
self.ode_params['commit-to-community-coeff-grps'] = zeros
if 'prison' not in p_names:
self.ode_params['commit-to-prison-coeff-grps'] = zeros
return
def __init__(self, n_groups=1, pool_size=0.0):
'''
Parameters
----------
n_groups: int
Number of groups in the population.
pool_size: float
Upperbound on the range of the existing population groups. A random value
from 0 to the upperbound value will be assigned to each group.
'''
super().__init__()
self.port_names_expected = ['parole','adjudication','jail','community']
quantities = list()
self.ode_params = dict()
self.initial_time = 0.0 * unit.day
self.end_time = 100 * unit.day
self.time_step = 0.5 * unit.day
unit.percent = 1/100
# Population groups
self.n_groups = n_groups
# Prison population groups
fpg_0 = np.random.random(self.n_groups) * pool_size
fpg = Quantity(name='npg', formal_name='prison-pop-grps',
latex_name = '$n_p^{(g)}$',
unit='# offenders', value=fpg_0, info='Prison Population Groups')
quantities.append(fpg)
# Model parameters: commitment coefficients
# Prison to community
a = 10*unit.percent/unit.year * np.ones(self.n_groups)
b = 15*unit.percent/unit.year * np.ones(self.n_groups)
cp0g_0 = (a + (b-a)*np.random.random(self.n_groups)) / self.n_groups
cp0g = Quantity(name='cp0g', formal_name='commit-community-coeff-grps',
unit='1/s', value=cp0g_0)
self.ode_params['commit-to-community-coeff-grps'] = cp0g_0
quantities.append(cp0g)
# Prison to parole
a = 20*unit.percent/unit.year * np.ones(self.n_groups)
b = 25*unit.percent/unit.year * np.ones(self.n_groups)
cpeg_0 = (a + (b-a)*np.random.random(self.n_groups)) / self.n_groups
cpeg = Quantity(name='cpeg', formal_name='commit-parole-coeff-grps',
unit='1/s', value=cpeg_0)
self.ode_params['commit-to-parole-coeff-grps'] = cpeg_0
quantities.append(cpeg)
# Death term
a = 0.8*unit.percent/unit.year * np.ones(self.n_groups)
b = 0.9*unit.percent/unit.year * np.ones(self.n_groups)
dpg_0 = (a + (b-a)*np.random.random(self.n_groups)) / self.n_groups
self.ode_params['death-rates-coeff'] = dpg_0
# Phase state
self.population_phase = Phase(self.initial_time, time_unit='s',
quantities=quantities)
self.population_phase.SetValue('npg', fpg_0, self.initial_time)
# Initialize inflows to zero
self.ode_params['parole-inflow-rates'] = np.zeros(self.n_groups)
self.ode_params['adjudication-inflow-rates'] = np.zeros(self.n_groups)
self.ode_params['jail-inflow-rates'] = np.zeros(self.n_groups)
return
def __init__(self, n_groups=1, pool_size=0.0):
super().__init__()
self.port_names_expected = ['prison','community']
quantities = list()
self.ode_params = dict()
self.initial_time = 0.0 * unit.day
self.end_time = 100 * unit.day
self.time_step = 0.5 * unit.day
unit.percent = 1/100
# Population groups
self.n_groups = n_groups
# Parole population groups
feg_0 = np.random.random(self.n_groups) * pool_size
feg = Quantity(name='feg', formal_name='parole-pop-grps',
latex_name = '$n_e^{(g)}$',
unit='# offenders', value=feg_0, info='Parole Population Groups')
quantities.append(feg)
# Model parameters: commitment coefficients
# Parole to community
a = 50*unit.percent/unit.year * np.ones(self.n_groups)
b = 70*unit.percent/unit.year * np.ones(self.n_groups)
ce0g_0 = (a + (b-a)*np.random.random(self.n_groups)) / self.n_groups
ce0g = Quantity(name='ce0g', formal_name='commit-community-coeff-grps',
unit='1/s', value=ce0g_0)
self.ode_params['commit-to-community-coeff-grps'] = ce0g_0
quantities.append(ce0g)
# Parole to prison
a = 30*unit.percent/unit.year * np.ones(self.n_groups)
b = 40*unit.percent/unit.year * np.ones(self.n_groups)
cepg_0 = (a + (b-a)*np.random.random(self.n_groups)) / self.n_groups
cepg = Quantity(name='cepg', formal_name='commit-prison-coeff-grps',
unit='1/s', value=cepg_0)
self.ode_params['commit-to-prison-coeff-grps'] = cepg_0
quantities.append(cepg)
# Death term
a = 0.5*unit.percent/unit.year * np.ones(self.n_groups)
b = 0.8*unit.percent/unit.year * np.ones(self.n_groups)
deg_0 = (a + (b-a)*np.random.random(self.n_groups)) / self.n_groups
self.ode_params['parole-death-rates-coeff'] = deg_0
# Phase state
self.population_phase = Phase(self.initial_time, time_unit='s',
quantities=quantities)
self.population_phase.SetValue('feg', feg_0, self.initial_time)
# Initialize inflows to zero
self.ode_params['prison-inflow-rates'] = np.zeros(self.n_groups)
return
def __init__(self,
n_groups=1,
non_offender_adult_population=100,
offender_pool_size=0.0,
free_offender_pool_size=0.0):
"""Constructor.
Parameters
----------
n_groups: int
Number of groups in the population.
non_offender_adult_population: float
Pool of individuals reaching the adult age (SI) unit. Default: 100.
offender_pool_size: float
Upperbound on the range of the existing population groups. A random value
from 0 to the upperbound value will be assigned to each group. This is
typically a small number, say a fraction of a percent.
"""
super().__init__()
self.port_names_expected = [
'probation', 'adjudication', 'jail', 'prison', 'arrested', 'parole'
]
quantities = list()
self.ode_params = dict()
self.initial_time = 0.0 * unit.day
self.end_time = 100 * unit.day
self.time_step = 0.5 * unit.day
self.show_time = (False, 10 * unit.day)
self.log = logging.getLogger('cortix')
unit.percent = 1 / 100
# Population groups
self.n_groups = n_groups
# Community non-offender population
n0_0 = np.array([float(non_offender_adult_population)])
n0 = Quantity(name='n0',
formal_name='non-offender-adult-pop',
latex_name='$n_0$',
unit='# adults',
value=n0_0,
info='Non-Offender Adult Population')
quantities.append(n0)
# Community free-offender population groups
f0g_0 = np.random.random(self.n_groups) * offender_pool_size
f0g = Quantity(name='f0g',
formal_name='free-offender-pop-grps',
latex_name='$n_0^{(g)}$',
unit='# offenders',
value=f0g_0,
info='Free-Offender Population Groups')
quantities.append(f0g)
# Model parameters: commitment coefficients
# Community non-offenders to offenders (arrested)
a = 0.6 * unit.percent / unit.year * np.ones(self.n_groups)
b = 0.8 * unit.percent / unit.year * np.ones(self.n_groups)
c00g_0 = (a +
(b - a) * np.random.random(self.n_groups)) / self.n_groups
c00g = Quantity(name='c00g',
formal_name='non-offenders-commit-arrested-coeff-grps',
unit='1/s',
value=c00g_0)
self.ode_params['non-offenders-commit-to-arrested-coeff-grps'] = c00g_0
quantities.append(c00g)
# Community free-offenders to arrested (recidivism)
a = 0.8 * unit.percent / unit.year * np.ones(self.n_groups)
b = 0.9 * unit.percent / unit.year * np.ones(self.n_groups)
c0rg_0 = (a +
(b - a) * np.random.random(self.n_groups)) / self.n_groups
c0rg = Quantity(
name='c0rg',
formal_name='free-offenders-commit-arrested-coeff-grps',
value=c0rg_0,
unit='1/s')
self.ode_params[
'free-offenders-commit-to-arrested-coeff-grps'] = c0rg_0
quantities.append(c0rg)
# Death term for community offenders
a = 0.8 * unit.percent / unit.year * np.ones(self.n_groups)
b = 1.0 * unit.percent / unit.year * np.ones(self.n_groups)
d0g_0 = (a + (b - a) * np.random.random(self.n_groups)) / self.n_groups
self.ode_params['free-offenders-death-rates-coeff'] = d0g_0
# Death term for community non-offenders
a = 0.5 * unit.percent / unit.year
b = 1.0 * unit.percent / unit.year
d0_0 = a + (b - a) * np.random.random()
self.ode_params['non-offenders-death-rate-coeff'] = d0_0
# Maturity term for community non-offenders
a = 1.5 * unit.percent / unit.year
b = 2.5 * unit.percent / unit.year
s0_0 = a + (b - a) * np.random.random()
self.ode_params['non-offenders-maturity-rate-coeff'] = s0_0
self.ode_params[
'non_offender_adult_population'] = non_offender_adult_population
# Phase state
self.population_phase = Phase(self.initial_time,
time_unit='s',
quantities=quantities)
self.population_phase.SetValue('n0', n0_0, self.initial_time)
self.population_phase.SetValue('f0g', f0g_0, self.initial_time)
# Initialize inflows to zero
self.ode_params['prison-inflow-rates'] = np.zeros(self.n_groups)
self.ode_params['parole-inflow-rates'] = np.zeros(self.n_groups)
self.ode_params['arrested-inflow-rates'] = np.zeros(self.n_groups)
self.ode_params['jail-inflow-rates'] = np.zeros(self.n_groups)
self.ode_params['adjudication-inflow-rates'] = np.zeros(self.n_groups)
self.ode_params['probation-inflow-rates'] = np.zeros(self.n_groups)
class Community(Module):
"""Community model with criminal group population.
Community here is the system at large with all possible
adult individuals included in any given society.
Notes
-----
These are the `port` names available in this module to connect to respective
modules:
+ `probation`,
+ `adjudication`,
+ `jail`, `prison`,
+ `arrested`, and
+ `parole`.
See instance attribute `port_names_expected`.
"""
def __init__(self,
n_groups=1,
non_offender_adult_population=100,
offender_pool_size=0.0,
free_offender_pool_size=0.0):
"""Constructor.
Parameters
----------
n_groups: int
Number of groups in the population.
non_offender_adult_population: float
Pool of individuals reaching the adult age (SI) unit. Default: 100.
offender_pool_size: float
Upperbound on the range of the existing population groups. A random value
from 0 to the upperbound value will be assigned to each group. This is
typically a small number, say a fraction of a percent.
"""
super().__init__()
self.port_names_expected = [
'probation', 'adjudication', 'jail', 'prison', 'arrested', 'parole'
]
quantities = list()
self.ode_params = dict()
self.initial_time = 0.0 * unit.day
self.end_time = 100 * unit.day
self.time_step = 0.5 * unit.day
self.show_time = (False, 10 * unit.day)
self.log = logging.getLogger('cortix')
unit.percent = 1 / 100
# Population groups
self.n_groups = n_groups
# Community non-offender population
n0_0 = np.array([float(non_offender_adult_population)])
n0 = Quantity(name='n0',
formal_name='non-offender-adult-pop',
latex_name='$n_0$',
unit='# adults',
value=n0_0,
info='Non-Offender Adult Population')
quantities.append(n0)
# Community free-offender population groups
f0g_0 = np.random.random(self.n_groups) * offender_pool_size
f0g = Quantity(name='f0g',
formal_name='free-offender-pop-grps',
latex_name='$n_0^{(g)}$',
unit='# offenders',
value=f0g_0,
info='Free-Offender Population Groups')
quantities.append(f0g)
# Model parameters: commitment coefficients
# Community non-offenders to offenders (arrested)
a = 0.6 * unit.percent / unit.year * np.ones(self.n_groups)
b = 0.8 * unit.percent / unit.year * np.ones(self.n_groups)
c00g_0 = (a +
(b - a) * np.random.random(self.n_groups)) / self.n_groups
c00g = Quantity(name='c00g',
formal_name='non-offenders-commit-arrested-coeff-grps',
unit='1/s',
value=c00g_0)
self.ode_params['non-offenders-commit-to-arrested-coeff-grps'] = c00g_0
quantities.append(c00g)
# Community free-offenders to arrested (recidivism)
a = 0.8 * unit.percent / unit.year * np.ones(self.n_groups)
b = 0.9 * unit.percent / unit.year * np.ones(self.n_groups)
c0rg_0 = (a +
(b - a) * np.random.random(self.n_groups)) / self.n_groups
c0rg = Quantity(
name='c0rg',
formal_name='free-offenders-commit-arrested-coeff-grps',
value=c0rg_0,
unit='1/s')
self.ode_params[
'free-offenders-commit-to-arrested-coeff-grps'] = c0rg_0
quantities.append(c0rg)
# Death term for community offenders
a = 0.8 * unit.percent / unit.year * np.ones(self.n_groups)
b = 1.0 * unit.percent / unit.year * np.ones(self.n_groups)
d0g_0 = (a + (b - a) * np.random.random(self.n_groups)) / self.n_groups
self.ode_params['free-offenders-death-rates-coeff'] = d0g_0
# Death term for community non-offenders
a = 0.5 * unit.percent / unit.year
b = 1.0 * unit.percent / unit.year
d0_0 = a + (b - a) * np.random.random()
self.ode_params['non-offenders-death-rate-coeff'] = d0_0
# Maturity term for community non-offenders
a = 1.5 * unit.percent / unit.year
b = 2.5 * unit.percent / unit.year
s0_0 = a + (b - a) * np.random.random()
self.ode_params['non-offenders-maturity-rate-coeff'] = s0_0
self.ode_params[
'non_offender_adult_population'] = non_offender_adult_population
# Phase state
self.population_phase = Phase(self.initial_time,
time_unit='s',
quantities=quantities)
self.population_phase.SetValue('n0', n0_0, self.initial_time)
self.population_phase.SetValue('f0g', f0g_0, self.initial_time)
# Initialize inflows to zero
self.ode_params['prison-inflow-rates'] = np.zeros(self.n_groups)
self.ode_params['parole-inflow-rates'] = np.zeros(self.n_groups)
self.ode_params['arrested-inflow-rates'] = np.zeros(self.n_groups)
self.ode_params['jail-inflow-rates'] = np.zeros(self.n_groups)
self.ode_params['adjudication-inflow-rates'] = np.zeros(self.n_groups)
self.ode_params['probation-inflow-rates'] = np.zeros(self.n_groups)
def run(self, *args):
self.__zero_ode_parameters()
time = self.initial_time
while time <= self.end_time:
if self.show_time[0] and abs(time % self.show_time[1] -
0.0) <= 1.e-1:
self.log.info('Community::time[d] = ' +
str(round(time / unit.day, 1)))
self.__call_ports(time)
# Evolve offenders group population to the next time stamp
#---------------------------------------------------------
time = self.__step(time)
def __call_ports(self, time):
# Interactions in the jail port
#--------------------------------
# one way "from" jail
self.send(time, 'jail')
(check_time, inflow_rates) = self.recv('jail')
assert abs(check_time - time) <= 1e-6
self.ode_params['jail-inflow-rates'] = inflow_rates
# Interactions in the adjudication port
#--------------------------------------
# one way "from" adjudication
self.send(time, 'adjudication')
(check_time, inflow_rates) = self.recv('adjudication')
assert abs(check_time - time) <= 1e-6
self.ode_params['adjudication-inflow-rates'] = inflow_rates
# Interactions in the probation port
#--------------------------------
# one way "from" probation
self.send(time, 'probation')
(check_time, inflow_rates) = self.recv('probation')
assert abs(check_time - time) <= 1e-6
self.ode_params['probation-inflow-rates'] = inflow_rates
# Interactions in the prison port
#--------------------------------
# one way "from" prison
self.send(time, 'prison')
(check_time, inflow_rates) = self.recv('prison')
assert abs(check_time - time) <= 1e-6
self.ode_params['prison-inflow-rates'] = inflow_rates
# Interactions in the parole port
#--------------------------------
# one way "from" parole
self.send(time, 'parole')
(check_time, inflow_rates) = self.recv('parole')
assert abs(check_time - time) <= 1e-6
self.ode_params['parole-inflow-rates'] = inflow_rates
# Interactions in the arrested port
#--------------------------------
# two way "to" and "from" arrested
# to
message_time = self.recv('arrested')
outflow_rates = self.__compute_outflow_rates(message_time, 'arrested')
self.send((message_time, outflow_rates), 'arrested')
# from
self.send(time, 'arrested')
(check_time, inflow_rates) = self.recv('arrested')
assert abs(check_time - time) <= 1e-6
self.ode_params['arrested-inflow-rates'] = inflow_rates
def __step(self, time=0.0):
r'''
ODE IVP problem:
Given the initial data at :math:`t=0`,
:math:`u = (u_1(0),u_2(0),\ldots)`
solve :math:`\frac{\text{d}u}{\text{d}t} = f(u)` in the interval
:math:`0\le t \le t_f`.
Parameters
----------
time: float
Time in SI unit.
Returns
-------
None
'''
# Get state values
a_vec = self.population_phase.GetValue('n0', time)
b_vec = self.population_phase.GetValue('f0g', time)
u_0 = np.concatenate((a_vec, b_vec))
t_interval_sec = np.linspace(0.0, self.time_step, num=2)
(u_vec_hist, info_dict) = odeint(self.__rhs_fn,
u_0,
t_interval_sec,
args=(self.ode_params, ),
rtol=1e-4,
atol=1e-8,
mxstep=200,
full_output=True)
assert info_dict['message'] == 'Integration successful.', info_dict[
'message']
u_vec = u_vec_hist[1, :] # solution vector at final time step
time += self.time_step
# Update state variables
values = self.population_phase.GetRow() # values existing values
self.population_phase.AddRow(
time, values) # copy on new time for convenience
self.population_phase.SetValue('n0', u_vec[:1],
time) # insert new values
self.population_phase.SetValue('f0g', u_vec[1:],
time) # insert new values
return time
def __rhs_fn(self, u_vec, t, params):
n0 = u_vec[:1] # non-offender population
# source of non-offenders
s0_coeff = params['non-offenders-maturity-rate-coeff']
n0_0 = params['non_offender_adult_population']
s0 = s0_coeff * n0_0
# outflow rate to Arrested
c00g = params['non-offenders-commit-to-arrested-coeff-grps']
outflow_rate = np.sum(c00g * n0)
# death rate
death_rate_coeff = params['non-offenders-death-rate-coeff']
death_rate = death_rate_coeff * n0
dt_n0 = s0 - outflow_rate - death_rate
f0g = u_vec[1:] # free-offender population groups
prison_inflow_rates = params['prison-inflow-rates']
parole_inflow_rates = params['parole-inflow-rates']
arrested_inflow_rates = params['arrested-inflow-rates']
jail_inflow_rates = params['jail-inflow-rates']
adjudication_inflow_rates = params['adjudication-inflow-rates']
probation_inflow_rates = params['probation-inflow-rates']
# free-offenders inflows
inflow_rates = prison_inflow_rates + parole_inflow_rates +\
arrested_inflow_rates + jail_inflow_rates +\
adjudication_inflow_rates + probation_inflow_rates
assert np.all(inflow_rates >= 0.0), 'values: %r' % inflow_rates
c0rg = params['free-offenders-commit-to-arrested-coeff-grps']
# free-offenders outflow (recidivism)
outflow_rates = c0rg * f0g
assert np.all(outflow_rates >= 0.0), 'values: %r' % outflow_rates
death_rates_coeff = params['free-offenders-death-rates-coeff']
death_rates = death_rates_coeff * f0g
assert np.all(death_rates >= 0.0), 'values: %r' % death_rates
dt_f0g = inflow_rates - outflow_rates - death_rates
dt_u = np.concatenate((dt_n0, dt_f0g))
return dt_u
def __compute_outflow_rates(self, time, name):
n0 = self.population_phase.GetValue('n0', time)
f0g = self.population_phase.GetValue('f0g', time)
if name == 'arrested':
c0rg = self.ode_params[
'free-offenders-commit-to-arrested-coeff-grps']
c00g = self.ode_params[
'non-offenders-commit-to-arrested-coeff-grps']
# Recidivism and new offenders
outflow_rates = c0rg * f0g + c00g * n0
return outflow_rates
def __zero_ode_parameters(self):
'''
If ports are not connected the corresponding outflows must be zero.
'''
zeros = np.zeros(self.n_groups)
p_names = [p.name for p in self.ports]
if 'arrested' not in p_names:
self.ode_params['commit-to-arrested-coeff-grps'] = zeros
self.ode_params['general-commit-to-arrested-coeff-grps'] = zeros
return