def test_e_arr(self):
'''Check the convenience constructor for evars
'''
#===========================================================================
# Control variable
#===========================================================================
e_arr = np.linspace(0, 0.012, 2)
m_la, std_la = 10., 1.0
m_xi, std_xi = 1.0, 0.1
#===========================================================================
# Randomization
#===========================================================================
s = SPIRRID(
q=fiber_tt_2p(),
e_arr=e_arr,
codegen_type='numpy',
n_int=10,
tvars=dict(la=m_la, xi=m_xi),
)
max_mu_q = np.max(s.mu_q_arr)
self.assertAlmostEqual(max_mu_q, 0.12000000000000001, 10)
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 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)),
)
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 run():
sv = SPIRRIDModelView(model = SPIRRID(),
rf_values = [ Filament() ])
sv.configure_traits(view = 'traits_view_tabbed')
if __name__ == '__main__':
from stats.spirrid import SPIRRID, Heaviside
class fiber_tt_2p:
la = Float(0.1)
def __call__(self, e, la, xi=0.017):
''' Response function of a single fiber '''
return la * e * Heaviside(xi - e)
s = SPIRRID(
q=fiber_tt_2p(),
sampling_type='LHS',
evars={'e': [0, 1]},
tvars={
'la': 1.0, # RV( 'norm', 10.0, 1.0 ),
'xi': RV('norm', 1.0, 0.1)
})
print 'tvar_names', s.tvar_names
print 'tvars', s.tvar_lst
print 'evar_names', s.evar_names
print 'evars', s.evar_lst
print 'var_defaults', s.var_defaults
#print 'mu_q', s.mu_q_arr
s = SPIRRID(
q=fiber_tt_2p(),
sampling_type='LHS',
