tvars = dict(la = RV('norm', m_la, std_la),
xi = RV('norm', m_xi, std_xi)
),
)
#===========================================================================
# Exact solution
#===========================================================================
def mu_q_ex(e, m_xi, std_xi, m_la):
return e * (0.5 - 0.5 *
erf(0.5 * math.sqrt(2) * (e - m_xi) / std_xi)) * m_la
#===========================================================================
# Lab
#===========================================================================
slab = SPIRRIDLAB(s = s, save_output = True, show_output = True,
exact_arr = mu_q_ex(e_arr, m_xi, std_xi, m_la))
#===========================================================================
# Compare efficiency of sampling types
#===========================================================================
powers = np.linspace(1, math.log(1000, 10), 80)
n_int_range = np.array(np.power(10, powers), dtype = int)
# slab.sampling_efficiency(n_int_range = n_int_range)
#===========================================================================
# Compare the structure of sampling
#===========================================================================
# slab.sampling_structure(ylim = 18.0, xlim = 1.2,)
f = RV('uniform', 0.0, 0.03))
#===========================================================================
# Integrator object
#===========================================================================
s = SPIRRID(q = ConstantFrictionFiniteFiber(),
e_arr = e_arr,
n_int = 30,
tvars = tvars,
)
#===========================================================================
# Lab
#===========================================================================
slab = SPIRRIDLAB(s = s, save_output = True, show_output = True,
qname = 'fiber_po_8p',
)
#===========================================================================
# Compare efficiency of sampling types
#===========================================================================
powers = np.linspace(1, math.log(20, 10), 6)
n_int_range = np.array(np.power(10, powers), dtype = int)
#slab.sampling_efficiency(n_int_range = n_int_range)
#===========================================================================
# Compare the structure of sampling
#===========================================================================
#slab.sampling_structure(ylim = 10.0, xlim = 0.012, plot_idx = [0, 3])