def ml_full_3d(noise_var=1, fct=lib.ll_redux_2d):
"""Graphs a 3d surface of the specified ML fct"""
p = ml_pinit_no_pulse_shape()
points = 10
t0 = 0
d0 = 0
t_halfwidth = 10
d_halfwidth = 10
t_min = t0-t_halfwidth
t_max = t0+t_halfwidth
d_min = d0-d_halfwidth
d_max = d0+d_halfwidth
t_step = t_halfwidth*2/points # int(round()) because integer samples
theta_range = np.arange(t_min, t_max, t_step)
deltaf_range = np.arange(d_min, d_max,d_halfwidth*2/points)*1e-6 # Deltaf in ppm
#deltaf_range = np.zeros(len(deltaf_range))
loglike = fct(p,0,0,theta_range,deltaf_range, var_w=noise_var)
#deltaf_range = np.arange(-1,1.1, 0.1)*1e-6
# Plot preparations
x = theta_range
y = deltaf_range*1e6
z = loglike*1e-3
# Plot prameters
fig, ax = graphs.surface3d(x, y, z, density=40)
ax.set_xlabel('Time offset (samples)')
ax.set_ylabel('CFO (ppm)')
ax.set_zlabel('Log likelihood (1e3)')
graphs.plt.tight_layout()
graphs.show()
def ml_full_one(variable='CFO',noise_var=1):
"""Graphs only 1 axis of the log ML"""
p = ml_pinit()
points = 1000
t0 = 10
d0 = 0
t_halfwidth = 100
d_halfwidth = 1
t_min = t0-t_halfwidth
t_max = t0+t_halfwidth
d_min = d0-d_halfwidth
d_max = d0+d_halfwidth
t_step = max(int(round(t_halfwidth*2/points)),1) # int(round()) because integer samples
theta_range = np.arange(t_min, t_max, t_step)
deltaf_range = np.arange(d_min, d_max,d_halfwidth*2/points)*1e-6 # Deltaf in ppm
# x & z preparation
if variable == 'CFO':
loglike = lib.loglikelihood_1d_CFO(p,t0,d0,deltaf_range, var_w=noise_var)
x = deltaf_range*1e6
xlabel = 'CFO (ppm)'
elif variable == 'TO':
loglike = lib.loglikelihood_1d_TO(p,t0,d0,theta_range, var_w=noise_var)
x = theta_range
xlabel = 'TO (samples)'
z = loglike*1e-3
# Plot prametersZ
ax = graphs.continuous(x, z)
ax.set_xlabel(xlabel)
ax.set_ylabel('Log likelihood (1e3)')
graphs.plt.tight_layout()
graphs.show()
p = ml_pinit()
points = 40
t0 = 0
d0 = 0
t_halfwidth = 10
d_halfwidth = 1
t_min = t0-t_halfwidth
t_max = t0+t_halfwidth
d_min = d0-d_halfwidth
d_max = d0+d_halfwidth
t_step = t_halfwidth*2/points # int(round()) because integer samples
theta_range = np.arange(t_min, t_max, t_step)
deltaf_range = np.arange(d_min, d_max,d_halfwidth*2/points)*1e-6 # Deltaf in ppm
#deltaf_range = np.zeros(len(deltaf_range))
loglike = lib.ll_redux_2d(p,0,0,theta_range,deltaf_range, var_w=noise_var)
#deltaf_range = np.arange(-1,1.1, 0.1)*1e-6
# Plot preparations
x = theta_range
y = deltaf_range*1e6
z = loglike*1e-3
# Plot prameters
fig, ax = graphs.surface3d(x, y, z, density=40)
ax.set_xlabel('Time offset (samples)')
ax.set_ylabel('CFO (ppm)')
ax.set_zlabel('Log likelihood (1e3)')
graphs.plt.tight_layout()
graphs.show()
