#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
import dde
def model(Y, t, d):
x, y = Y(t)
xd, yd = Y(t-d)
return np.array([0.5*x*(1-yd), -0.5*y*(1-xd)])
g = lambda t : np.array([0, 0])
tt = np.linspace(2, 30, 20000)
(fig, ax) = plt.subplots(1)
for d in [0, 0.2]:
yy = dde.ddeint(model, g, tt, fargs=(d,))
# WE PLOT X AGAINST Y
ax.plot(yy[:, 0], yy[:, 1], lw=2, label='delay = {0:.01f}'.format(d))
ax.legend()
plt.show()
def main():
np.set_printoptions(suppress=True, precision=10)
np.seterr(all='raise')
args = docopt(__doc__)
runs = int(args['--runs'])
N0 = float(args['--initialpop'])
outbreak_start = 365 * float(args['--outbreak'])
control_years = float(args['--releasetime'])
pre_control_years = float(args['--timebefore'])
post_control_years = float(args['--timeafter'])
release_ratio = float(args['--releaseratio'])
control_start = 365.0 * pre_control_years
control_end = 365.0 * control_years + control_start
simulation_end = 365.0 * post_control_years + control_end
dt = 1.0
tt = np.linspace(0, simulation_end, simulation_end*dt, endpoint=True)
yys = []
parameters = generate_params(runs, release_ratio, N0, control_start,
control_end, outbreak_start)
start_states = generate_start_states(parameters, N0)
finished_runs = []
for r in range(runs):
#states for this run
ss = start_states if runs == 1 else start_states[r, :]
#history Function
g = lambda t : ss
#parameters for this run
par = parameters if runs == 1 else parameters[r, :]
print_run_info(ss, par, r)
try:
yys.append(dde.ddeint(RIDL_dde, g, tt, fargs=(par, )))
except FloatingPointError as err:
print("RUN {} FAILED: {}".format(r, err))
else:
finished_runs.append(r)
out = open('run_parameters.csv', 'w')
out.write(','.join(p_names)+'\n')
if len(finished_runs) == 1:
out.write(','.join(str(x) for x in parameters)+'\n')
else:
for r in finished_runs:
out.write(','.join(str(x) for x in parameters[r, :])+'\n')
out.close()
gr = len(finished_runs)
if gr >= 1:
vars_to_graph = [_F, _Yi, _S, _Ii, _Ri, _Iij, _R]
num_vars = len(vars_to_graph)
f, axarr = plt.subplots(num_vars)
graph_start = 0#int(control_start - 300)
graph_end = int(simulation_end)
for i, v in enumerate(vars_to_graph):
yy = np.array([y[graph_start:graph_end, v] for y in yys])
axarr[i].plot(tt[graph_start:graph_end], yy.T)
axarr[i].set_xlim(graph_start, graph_end)
y_lim = axarr[i].get_ybound()
axarr[i].set_ylim(0, y_lim[1])
axarr[i].axvline(control_start, color='black', linestyle='--')
axarr[i].axvline(control_end, color='black', linestyle='--')
if i+1 != num_vars:
axarr[i].set_xticklabels([])
else:
axarr[i].set_xlabel('Days')
axarr[i].set_ylabel(s_names[v], rotation='horizontal')
axarr[i].yaxis.set_label_position("right")
plt.figlegend(labels=('Run_{}'.format(x) for x in finished_runs),
handles= axarr[-1].get_lines(), loc=1)
plt.savefig('fig.pdf', orientation='landscape', papertype='letter',
transparent=True, format='pdf')
plt.show()