from embarrassingly.shy import Shy, slow_and_pointless

from scipy.optimize import shgo

import matplotlib.pyplot as plt

labels = list()

bounds = [(-0.5, 0.5), (-0.5, 0.5)]

# See https://www.microprediction.com/blog/robust-optimization for explanation

def rround(x,ndigits):

try:

return str(round(x,ndigits=ndigits))

except:

return ''

for t_unit in [None, 0.2, 0.3, 0.4]:

for d_unit in [None, 0.01, 0.02, 0.03]:

SAP = Shy(slow_and_pointless, bounds=bounds, t_unit=t_unit, d_unit=d_unit)

res = shgo(func=SAP, bounds=bounds, n=10, iters=4, options={'minimize_every_iter': False, 'ftol': 0.00001})

plt.plot(SAP.found_tau, SAP.found_y)

plt.xlabel('CPU Time')

plt.ylabel('Minimum')

plt.yscale('log')

plt.pause(0.00001)

label = "$\delta t="+rround(t_unit,4)+'$ $\delta d='+rround(d_unit,3)+'$'

labels.append(label)

plt.legend(labels)

plt.pause(0.00001)

pass

pass