#!/usr/bin/env python
# -*- coding: utf-8 -*-
# This file is part of the Cortix toolkit environment.
# https://cortix.org
import numpy as np
import scipy.constants as const
from scipy.integrate import odeint
from cortix import Module
from cortix import Phase
from cortix import Quantity
[docs]class Parole(Module):
'''
Parole Cortix module used to model criminal group population in a parole system.
Notes
-----
These are the `port` names available in this module to connect to respective
modules: `prison` and `community`.
See instance attribute `port_names_expected`.
'''
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 * const.day
self.end_time = 100 * const.day
self.time_step = 0.5 * const.day
# Population groups
self.n_groups = n_groups
# Parole population groups
feg_0 = np.random.random(self.n_groups) * pool_size
feg = Quantity(name='feg', formalName='parole-pop-grps',
unit='individual', value=feg_0)
quantities.append(feg)
# Model parameters: commitment coefficients and their modifiers
# Parole to community
ce0g_0 = np.random.random(self.n_groups) / const.day
ce0g = Quantity(name='ce0g', formalName='commit-community-coeff-grps',
unit='individual', value=ce0g_0)
self.ode_params['commit-to-community-coeff-grps'] = ce0g_0
quantities.append(ce0g)
me0g_0 = np.random.random(self.n_groups)
me0g = Quantity(name='me0g', formalName='commit-community-coeff-mod-grps',
unit='individual', value=me0g_0)
self.ode_params['commit-to-community-coeff-mod-grps'] = me0g_0
quantities.append(me0g)
# Parole to prison
cepg_0 = np.random.random(self.n_groups) / const.day
cepg = Quantity(name='cepg', formalName='commit-prison-coeff-grps',
unit='individual', value=cepg_0)
self.ode_params['commit-to-prison-coeff-grps'] = cepg_0
quantities.append(cepg)
mepg_0 = np.random.random(self.n_groups)
mepg = Quantity(name='mepg', formalName='commit-prison-coeff-mod-grps',
unit='individual', value=mepg_0)
self.ode_params['commit-to-prison-coeff-mod-grps'] = mepg_0
quantities.append(mepg)
# Death term
self.ode_params['parole-death-rates'] = np.zeros(self.n_groups)
# 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
[docs] def run(self, *args):
self.__zero_ode_parameters()
time = self.initial_time
while time < self.end_time:
# Interactions in the prison port
#--------------------------------
# two way "to" and "from" prison
# to
message_time = self.recv('prison')
outflow_rates = self.__compute_outflow_rates( message_time, 'prison' )
self.send( (message_time, outflow_rates), 'prison' )
# from
self.send( time, 'prison' )
(check_time, prison_inflow_rates) = self.recv('prison')
assert abs(check_time-time) <= 1e-6
self.ode_params['prison-inflow-rates'] = prison_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 parole group population to the next time stamp
#------------------------------------------------------
time = self.__step( time )
def __rhs_fn(self, u_vec, t, params):
feg = u_vec # parole population groups
prison_inflow_rates = params['prison-inflow-rates']
inflow_rates = prison_inflow_rates
ce0g = self.ode_params['commit-to-community-coeff-grps']
me0g = self.ode_params['commit-to-community-coeff-mod-grps']
cepg = self.ode_params['commit-to-prison-coeff-grps']
mepg = self.ode_params['commit-to-prison-coeff-mod-grps']
outflow_rates = ( ce0g * me0g + cepg * mepg ) * feg
death_rates = params['parole-death-rates']
dt_feg = inflow_rates - outflow_rates - death_rates
return dt_feg
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('feg', 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('feg', u_vec, time)
return time
def __compute_outflow_rates(self, time, name):
feg = self.population_phase.GetValue('feg',time)
assert np.all(feg>=0.0), 'values: %r'%feg
if name == 'prison':
cepg = self.ode_params['commit-to-prison-coeff-grps']
mepg = self.ode_params['commit-to-prison-coeff-mod-grps']
outflow_rates = cepg * mepg * feg
if name == 'community':
ce0g = self.ode_params['commit-to-community-coeff-grps']
me0g = self.ode_params['commit-to-community-coeff-mod-grps']
outflow_rates = ce0g * me0g * feg
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
self.ode_params['commit-to-community-coeff-mod-grps'] = zeros
if 'prison' not in p_names:
self.ode_params['commit-to-prison-coeff-grps'] = zeros
self.ode_params['commit-to-prison-coeff-mod-grps'] = zeros
return
