def generate_artificial_data(xdata,tdata,x,t,I_xt,v,sigma_d):
lim = {'x':xdata,'y':x,'t':tdata,'u':t}
for s in lim.keys():
lim['N'+s] = len(lim[s])
lim['d'+s] = lim[s][1]- lim[s][0]
lim['v'] = v
R = past_light_cone_matrix_area(lim=lim)
s = np.exp(-np.dot(R,I_xt.ravel()))
# s reshaped as a Nx*Nt matrix:
true_s = s.reshape(len(xdata),len(tdata))
# add gaussian noise
data = true_s + np.random.normal(scale=sigma_d, size = true_s.shape)
return true_s, data
def _compute_theta_MAP(self, verbose=False):
"""Utility function.
Compute the hidden attribute theta_MAP (from which
I_MAP can be computed).
"""
tic = time.clock()
lim={'v':self.v, 'x':self.xdata, 't':self.tdata, 'u':self.t}
lim['du'] = lim['u'][1]-lim['u'][0]
# if FixedOris, I_iu (ori i, time u) is in ini/min, RI = integral of I*(dy=1)*du
lim['y'] = self.x_oris if self.FixedOris else self.x
lim['dy'] = 1 if self.FixedOris else lim['y'][1]-lim['y'][0]
self._param['R'] = past_light_cone_matrix_area(lim)
if verbose: print "R computed in {}s".format(time.clock()-tic)
tic = time.clock()
compute_C0_y_C0_u_for_param(param = self._param)
if verbose: print "C0 computed in {}s".format(time.clock()-tic)
tic = time.clock()
self._theta_MAP = compute_theta_MAP_estimate(param = self._param)
if verbose: print "theta_MAP found in {}s".format(time.clock()-tic)
