n = 2j
#y1, y2, y3 = np.mgrid[0.1:0.9:n, 0.1:0.9:n, 0.1:0.9:n]
y1, y2, y3 = np.mgrid[0.15:0.17:n, 0.15:0.17:n, 0.15:0.17:n]
starting_yy1 = y1.flatten()
starting_yy2 = y2.flatten()
starting_yy3 = y3.flatten()
final_y = list()
responses = list()
for i in range(len(starting_yy1)):
res, y = simulate((starting_yy1[i], starting_yy2[i], starting_yy3[i]),
t_begin=0.0, t_end=0.02, dt=.0001, stop=False)
final_y.append(y)
responses.append(res)
final_y = np.array(final_y)
end_points = np.array([y[-1] for y in final_y])
print("Endpoints in the plot.")
plot_scatter(end_points)
print("last 25 points in the plot")
fig = plt.figure()
for y in final_y:
plot2(y[-25:], fig)
plt.show()
print("traces in the plot")
fig = plt.figure()
for y in final_y:
plot2(y, fig)
plt.show()
try:
res = float(res)
except:
res = np.nan
if res == np.nan:
print "NaN"
return res
n = 4j
y1, y2, y3 = np.mgrid[0.1:0.9:n, 0.1:0.9:n, 0.1:0.9:n]
starting_yy1 = y1.flatten()
starting_yy2 = y2.flatten()
starting_yy3 = y3.flatten()
starting_values = np.array([(starting_yy1[i], starting_yy2[i], starting_yy3[i]) for i in
range(len(starting_yy1))])
bounds = ((0.0001, 0.9999), (0.0001, 0.9999), (0.0001, 0.9999))
x = list()
results = list()
for starting in starting_values:
res = optimize.minimize(fe, starting, method='L-BFGS-B', bounds=bounds)
x.append(res["x"])
results.append(res)
yy = np.array(x)
print("\n\nPlot is ready!")
plot_scatter(yy)
