def create_demo_object(): #=========================================================================== # Control variable #=========================================================================== e_arr = np.linspace(0, 0.012, 80) powers = np.linspace(1, math.log(20, 10), 6) n_int_range = np.array(np.power(10, powers), dtype=int) #=========================================================================== # Randomization #=========================================================================== tvars = dict( fu=RV('weibull_min', 1200.0e6, 200.), qf=1500.0, # qf = RV('uniform', 1500., 100.), L=0.02, # # L = RV('uniform', 0.02, 0.02 / 2.), A=RV('norm', 5.30929158457e-10, .03 * 5.30929158457e-10), E_mod=RV('uniform', 70.e9, 250.e9), z=RV('uniform', 0.0, 0.03), phi=0.0, # # phi = RV('cos_distr', 0.0, 1.0), # phi = RV('uniform', 0.0, 1.0), f=RV('uniform', 0.0, 0.03)) #=========================================================================== # Integrator object #=========================================================================== s = SPIRRID( q=ConstantFrictionFiniteFiber(), e_arr=e_arr, n_int=10, tvars=tvars, ) #=========================================================================== # Lab #=========================================================================== slab = SPIRRIDLAB(s=s, save_output=False, show_output=True, dpi=300, qname='fiber_po_8p', plot_mode='subplots', n_int_range=n_int_range, extra_compiler_args=True, le_sampling_lst=['LHS', 'PGrid'], le_n_int_lst=[10, 10], plot_sampling_idx=[ 0, 3, ]) return slab
def create_demo_object(): m_la, std_la = 10., 1.0 m_xi, std_xi = 1.0, 0.1 # discretize the control variable (x-axis) e_arr = np.linspace(0, 2.0, 80) # n_int range for sampling efficiency test powers = np.linspace(1, math.log(500, 10), 50) n_int_range = np.array(np.power(10, powers), dtype=int) #=========================================================================== # Randomization #=========================================================================== s = SPIRRID( q=fiber_tt_2p(), e_arr=e_arr, n_int=10, 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=False, show_output=True, dpi=300, exact_arr=mu_q_ex(e_arr, m_xi, std_xi, m_la), plot_mode='subplots', n_int_range=n_int_range, extra_compiler_args=True, le_sampling_lst=['LHS', 'PGrid'], le_n_int_lst=[440, 5000]) return slab
def setup_class(cls): np.random.seed(2356) #=========================================================================== # Control variable #=========================================================================== e_arr = np.linspace(0, 0.012, 80) cls.m_la, cls.std_la = 10., 1.0 cls.m_xi, cls.std_xi = 1.0, 0.1 #=========================================================================== # Randomization #=========================================================================== cls.s = SPIRRID( q=fiber_tt_2p(), evars={'e': e_arr}, codegen_type='numpy', n_int=10, tvars=dict(la=RV('norm', cls.m_la, cls.std_la), xi=RV('norm', cls.m_xi, cls.std_xi)), )
q - T * (abs(x) - l / 2.)) * Heaviside(abs(x) - l / 2.) q_x = q_x * Heaviside(x + Ll) * Heaviside(Lr - x) q_x = q_x * Heaviside(q_x) return q_x q = CBClampedFiberSP() s = SPIRRID( q=q, sampling_type='LHS', evars=dict(w=np.linspace(0.0, 0.4, 50), x=np.linspace(-20.1, 20.5, 100), Lr=np.linspace(0.1, 20.0, 50)), tvars=dict( tau=RV('uniform', 0.7, 1.0), l=RV('uniform', 5.0, 10.0), D_f=26e-3, E_f=72e3, theta=0.0, xi=RV('weibull_min', scale=0.017, shape=8, n_int=10), phi=1.0, Ll=50.0, # Lr = 1.0 ), n_int=5) e_arr = make_ogrid(s.evar_lst) n_e_arr = [e / np.max(np.fabs(e)) for e in e_arr] max_mu_q = np.max(np.fabs(s.mu_q_arr))
def create_demo_object(): D = 26 * 1.0e-6 # m A = (D / 2.0)**2 * math.pi # set the mean and standard deviation of the two random variables la_mean, la_stdev = 0.0, 0.2 xi_mean, xi_stdev = 0.019027, 0.0022891 E_mean, E_stdev = 70.0e+9, 15.0e+9 th_mean, th_stdev = 0.0, 0.01 A_mean, A_stdev = A * 0.3, 0.7 * A do = 'norm' if do == 'general': # set the mean and standard deviation of the two random variables la_mean, la_stdev = 0.0, 0.2 xi_mean, xi_stdev = 0.019027, 0.0022891 E_mean, E_stdev = 70.0e+9, 15.0e+9 th_mean, th_stdev = 0.0, 0.01 A_mean, A_stdev = A * 0.3, 0.7 * A # construct the normal distributions and get the methods # for the evaluation of the probability density functions g_la = RV('uniform', la_mean, la_stdev) g_xi = RV('norm', xi_mean, xi_stdev) g_E = RV('uniform', E_mean, E_stdev) g_th = RV('uniform', th_mean, th_stdev) g_A = RV('uniform', A_mean, A_stdev) mu_ex_file = 'fiber_tt_5p_30.txt' delimiter = ',' elif do == 'uniform': # set the mean and standard deviation of the two random variables la_mean, la_stdev = 0.0, 0.2 xi_mean, xi_stdev = 0.01, 0.02 E_mean, E_stdev = 70.0e+9, 15.0e+9 th_mean, th_stdev = 0.0, 0.01 A_mean, A_stdev = A * 0.3, 0.7 * A # construct the uniform distributions and get the methods # for the evaluation of the probability density functions g_la = RV('uniform', la_mean, la_stdev) g_xi = RV('uniform', xi_mean, xi_stdev) g_E = RV('uniform', E_mean, E_stdev) g_th = RV('uniform', th_mean, th_stdev) g_A = RV('uniform', A_mean, A_stdev) mu_ex_file = 'fiber_tt_5p_40_unif.txt' delimiter = ' ' elif do == 'norm': # set the mean and standard deviation of the two random variables la_mean, la_stdev = 0.1, 0.02 xi_mean, xi_stdev = 0.019027, 0.0022891 E_mean, E_stdev = 70.0e+9, 15.0e+9 th_mean, th_stdev = 0.005, 0.001 A_mean, A_stdev = 5.3e-10, 1.0e-11 # construct the normal distributions and get the methods # for the evaluation of the probability density functions g_la = RV('norm', la_mean, la_stdev) g_xi = RV('norm', xi_mean, xi_stdev) g_E = RV('norm', E_mean, E_stdev) g_th = RV('norm', th_mean, th_stdev) g_A = RV('norm', A_mean, A_stdev) mu_ex_file = os.path.join(file_dir, 'fiber_tt_5p_n_int_40_norm_exact.txt') delimiter = ' ' # discretize the control variable (x-axis) e_arr = np.linspace(0, 0.04, 40) # n_int range for sampling efficiency test powers = np.linspace(1, math.log(20, 10), 15) n_int_range = np.array(np.power(10, powers), dtype=int) #=========================================================================== # Randomization #=========================================================================== s = SPIRRID( q=fiber_tt_5p(), e_arr=e_arr, n_int=10, tvars=dict(lambd=g_la, xi=g_xi, E_mod=g_E, theta=g_th, A=g_A), ) # Exact solution def mu_q_ex(e): data = np.loadtxt(mu_ex_file, delimiter=delimiter) x, y = data[:, 0], data[:, 1] f = interp1d(x, y, kind='linear') return f(e) #=========================================================================== # Lab #=========================================================================== slab = SPIRRIDLAB(s=s, save_output=False, show_output=True, dpi=300, exact_arr=mu_q_ex(e_arr), plot_mode='subplots', n_int_range=n_int_range, extra_compiler_args=True, le_sampling_lst=['LHS', 'PGrid'], le_n_int_lst=[25, 30]) return slab
def _random_changed(self): # get the default distribution if self.random: self.spirrid.rv_dict[ self.varname ] = RV(pd = self.pd, name = self.varname, n_int = self.n_int) else: del self.spirrid.rv_dict[ self.varname ]