def fire_gaus_optimize(self, ):
xs, ys = self.fire_shift_optimize()
if self.costs.min() < 0.1:
min_val = [4, 4, -15, xs - 2, ys - 2]
max_val = [40, 40, 15, xs + 2, ys + 2]
self.bounds = [4, 40], [4, 40], [-15,
15], [xs - 2,
xs + 2], [ys - 2, ys + 2]
ps, distributions = create_training_set(self.parameters,
min_val,
max_val,
n_train=50)
print('Start solving...')
self.gaus_solved = parmap(self.gaus_optimize, ps, nprocs=5)
result = np.array(
[np.hstack((i[0], i[1])) for i in self.gaus_solved])
print(
'solved psf',
dict(
zip(self.parameters + [
'cost',
], result[np.argmin(result[:, -1])])))
return result[np.argmin(result[:, -1]), :]
else:
print('Cost is too large, plese check!')
return []
def fire_shift_optimize(self, ):
#self.S2_PSF_optimization()
self._preprocess()
if self.lh_mask.sum() == 0:
self.costs = np.array([
100000000000.,
])
return 0, 0
min_val = [-50, -50]
max_val = [50, 50]
ps, distributions = create_training_set(['xs', 'ys'],
min_val,
max_val,
n_train=50)
self.shift_solved = parmap(self.shift_optimize, ps)
self.paras, self.costs = np.array([i[0] for i in self.shift_solved]), \
np.array([i[1] for i in self.shift_solved])
if (1 - self.costs.min()) >= 0.6:
xs, ys = self.paras[self.costs == np.nanmin(self.costs)][0].astype(
int)
else:
xs, ys = 0, 0
#print 'Best shift is ', xs, ys, 'with the correlation of', 1-self.costs.min()
return xs, ys