def _redraw( self ):
s = SPIRRID( rf = rf,
# min_eps = 0.00, max_eps = 20.00, n_eps = 80,
min_eps = 0.00, max_eps = 0.3, n_eps = 100,
cached_qg = True,
compiled_qg_loop = False,
compiled_eps_loop = False
)
# construct the random variables
n_int = 30
#s.add_rv( 'tau1', distribution='uniform', loc=2. / 1000, scale=10.0 / 1000, n_int=n_int )
#s.add_rv( 'l', distribution='uniform', loc=0.01, scale=.01, n_int=n_int )
#s.add_rv( 'xi', distribution='weibull_min', shape=8.64, scale=0.0287, n_int=n_int )
s.rv_dict['theta'] = RV( pd = self.pdf_theta, name = 'theta', n_int = n_int )
s.rv_dict['l'] = RV( pd = self.pdf_l, name = 'l', n_int = n_int )
s.rv_dict['phi'] = RV( pd = self.pdf_phi, name = 'phi', n_int = n_int )
s.mean_curve.plot( self.figure, linewidth = 2, label = 'mean CB per filament response' )
plt.xlabel( 'crack opening w[mm]' )
plt.ylabel( 'force P[N]' )
plt.legend( loc = 'best' )
plt.figure( 10 )
plt.show()
def run():
# Quantities for the response function
# and randomization
# construct a default response function for a single filament
rf = ConstantFrictionFreeLengthFiniteFiber( tau = 0.5e6,
l = 0.01,
E = 70e9,
A = 0.89e-6,
slack = 0.0,
L = 0.3,
sigma_fu = 200e6 )
# construct the integrator and provide
# it with the response function.
max_u = 1.8
s = SPIRRID( rf = rf,
min_eps = 0.00, max_eps = max_u, n_eps = 80,
cached_dG = True, compiled_QdG_loop = False,
compiled_eps_loop = False )
# construct the correlated integrator and
# provide it with the response function.
sc = CorSPIRRID( rf = rf, rho = -0.99,
min_eps = 0.00, max_eps = max_u, n_eps = 80,
cached_dG = True, compiled_QdG_loop = False,
compiled_eps_loop = False )
n_int = 100
s.add_rv( 'tau', distribution = 'norm', loc = 0.3e6, scale = 0.1e6, n_int = n_int )
s.add_rv( 'l', distribution = 'norm', loc = 0.03, scale = 0.01, n_int = n_int )
sc.add_rv( 'tau', distribution = 'norm', loc = 0.3e6, scale = 0.1e6, n_int = n_int )
sc.add_rv( 'l', distribution = 'norm', loc = 0.03, scale = 0.01, n_int = n_int )
# define a tables with the run configurations to start in a batch
print 'sum', sum( sc.dG_grid )
print 'sum', sum( s.dG_grid )
# z = sc.pdf_theta_grid
# x, y = mgrid[-4:4:50j, -4:4:50j]
#
# mlab.surf(x, y, z)
# mlab.show()
s.mean_curve.plot( plt, color = 'red', linewidth = 2, label = 'spirrid' )
sc.mean_curve.plot( plt, color = 'blue', linewidth = 2, label = 'cor spirrid' )
plt.xlabel( 'strain [-]' )
plt.ylabel( 'stress' )
plt.legend( loc = 'lower right' )
plt.title( s.rf.title )
plt.show()
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 _get_s_inner( self ):
s = SPIRRID( rf = self.rf,
cached_dG = True,
compiled_QdG_loop = False,
compiled_eps_loop = False )
# construct the random variables
n_int = 10
s.add_rv( 'xi', distribution = 'weibull_min', scale = 0.02,
shape = 10., n_int = n_int )
s.add_rv( 'theta', distribution = 'uniform', loc = self.loc_theta,
scale = 0.01, n_int = n_int )
#.add_rv('lambd', distribution = 'uniform', loc = 0.0, scale = 0.01, n_int = n_int)
return s
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)),
)
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',
evars = {'e' : [0, 1], 'la' : [0, 1]},
tvars = {'xi': RV('norm', 1.0, 0.1),
def run():
m_mu, std_mu = 3., 0.68
m_cov, std_cov = 0.18, 0.067
# discretize the control variable (x-axis)
x_arr = np.linspace(0, 6.0, 1000)[1:]
#===========================================================================
# Randomization
#===========================================================================
s = SPIRRID(q = BasicPDF(),
e_arr = x_arr,
n_int = 100,
tvars = dict(mu = RV('norm', m_mu, std_mu), #RV('beta', m_cov, std_cov),#BETA('beta', mu = m_mu, sig = std_mu, a = a, b = b),
cov = RV('norm', m_cov, std_cov)
),
sampling_type = 'TGrid',
codegen_type = 'numpy',
)
#===========================================================================
# Calculate
#===========================================================================
# calculate compounded distribution values
cpd_pdf = s.mu_q_arr
cpd_cdf = np.cumsum(cpd_pdf) * np.diff(x_arr)[0]
# calculate first raw moment
raw_1 = np.trapz(cpd_pdf * x_arr, x_arr)
# calculate second raw moment
raw_2 = np.trapz(cpd_pdf * x_arr ** 2, x_arr)
std = np.sqrt(raw_2 - raw_1 ** 2)
# calculate values of lognormal distrib
lognorm_pdf = lognormal_pdf(x_arr, raw_1, std)
lognorm_cdf = lognormal_cdf(x_arr, raw_1, std)
# calculate error
rms_err = rms_error(lognorm_pdf, cpd_pdf)
max_err = max_error(lognorm_pdf, cpd_pdf)
chi_err = chi_error(lognorm_pdf, cpd_pdf)
# generate samples
samples = s.sampling.get_samples(20)
sample_args = [ sam[:, np.newaxis] for sam in samples]
q_arr = s.q(x_arr[None, :], *sample_args)
#===========================================================================
# Print
#===========================================================================
print 'check unity\t', np.trapz(cpd_pdf, x_arr)
print 'mean\t\t', raw_1
print 'std\t\t', std
print 'rms error\t', rms_err
print 'max error\t', max_err
print 'chi error\t', chi_err
#===========================================================================
# Plot
#===========================================================================
p.figure()
# plot samples
#p.plot(x_arr, q_arr.T, color = 'gray')
# plot compounded distribution
p.plot(x_arr, cpd_pdf, 'b-')
# plot lognormal distribution, method of moments
p.plot(x_arr, lognorm_pdf, color = 'red', linewidth = 2)
data = np.loadtxt('maple_cpd.txt', delimiter = ',')
p.plot(data[:, 0], data[:, 1], color = 'violet')
p.figure()
p.plot(x_arr, cpd_cdf, 'b-')
# plot lognormal CDF, method of moments
p.plot(x_arr, lognorm_cdf, color = 'red', linewidth = 2)
p.show()
q = 0.0;
}else{
q = la * eps;
}
'''
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,
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 = True, show_output = True,
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, "xi": RV("norm", 1.0, 0.1)}, # RV( 'norm', 10.0, 1.0 ),
)
print "tvar_names", s.tvar_names
print "tvars", s.tvar_list
print "evar_names", s.evar_names
print "evars", s.evar_list
print "var_defaults", s.var_defaults
# print 'mu_q', s.mu_q_arr
s = SPIRRID(
q=fiber_tt_2p(),
sampling_type="LHS",
evars={"e": [0, 1], "la": [0, 1]},
def run(mu, cov, plot = True):
# discretize the control variable (x-axis)
x_arr = np.linspace(0, 10.0, 1000)[1:]
#===========================================================================
# Randomization
#===========================================================================
s = SPIRRID(q = BasicPDF(),
e_arr = x_arr,
n_int = 100,
tvars = dict(mu = mu,
cov = cov
),
sampling_type = 'TGrid',
codegen_type = 'numpy',
)
#s.codegen.cached_dG = False
#s.codegen.compiled_eps_loop = False
#===========================================================================
# Calculate
#===========================================================================
# calculate compounded distribution values
cpd_pdf = s.mu_q_arr
cpd_cdf = np.cumsum(cpd_pdf) * np.diff(x_arr)[0]
# calculate first raw moment
raw_1 = np.trapz(cpd_pdf * x_arr, x_arr)
# calculate second raw moment
raw_2 = np.trapz(cpd_pdf * x_arr ** 2, x_arr)
std = np.sqrt(raw_2 - raw_1 ** 2)
# calculate values of lognormal distrib
lognorm_pdf = lognormal_pdf(x_arr, raw_1, std)
lognorm_cdf = lognormal_cdf(x_arr, raw_1, std)
# calculate error
rms_err = rms_error(lognorm_pdf, cpd_pdf)
max_err = max_error(lognorm_pdf, cpd_pdf)
chi_err = chi_error(lognorm_pdf, cpd_pdf)
ks_err = ks_error(lognorm_cdf, cpd_cdf)
# generate samples
samples = s.sampling.get_samples(50)
sample_args = [ sam[:, np.newaxis] for sam in samples]
q_arr = s.q(x_arr[None, :], *sample_args)
emp_data = np.loadtxt('data_emp.txt', delimiter = '\t')
#===========================================================================
# Print
#===========================================================================
print 'check unity\t', np.trapz(cpd_pdf, x_arr)
print 'mean\t\t', raw_1
print 'std\t\t', std
print 'rms error\t', rms_err
print 'max error\t', max_err
print 'chi error\t', chi_err
print 'ks error\t', ks_err
#===========================================================================
# Plot
#===========================================================================
if plot:
p.figure()
p.title(mu.type)
# plot samples
#p.plot(x_arr, q_arr.T, color = 'gray')
for s in q_arr:
p.plot(x_arr, s, color = 'gray', linewidth = mu.pdf((s * x_arr).sum() / 100.) * 5)
# plot compounded distribution
p.plot(x_arr, cpd_pdf, 'b-', label = 'cpd')
# plot lognormal distribution, method of moments
p.plot(x_arr, lognorm_pdf, color = 'red', linewidth = 2, label = 'lognorm')
p.plot(x_arr, lognormal_pdf(x_arr, np.mean(emp_data[:, 0]), np.std(emp_data[:, 0])), color = 'cyan', linewidth = 2, label = 'lognorm_data')
p.plot(x_arr, mu.pdf(x_arr), color = 'green', label = 'mu_dist')
p.legend(loc = 0)
p.figure()
p.title(mu.type)
p.plot(emp_data[:, 0], emp_data[:, 1], 'k-', linewidth = 3, label = 'emp')
p.plot(x_arr, cpd_cdf, 'b-', label = 'cpd')
# plot lognormal CDF, method of moments
p.plot(x_arr, lognorm_cdf, color = 'red', linewidth = 2, label = 'lognorm')
p.plot(x_arr, lognormal_cdf(x_arr, np.mean(emp_data[:, 0]), np.std(emp_data[:, 0])), color = 'cyan', linewidth = 2, label = 'lognorm_data')
p.legend(loc = 0)
p.figure()
p.title(mu.type)
p.loglog(emp_data[:, 0], emp_data[:, 1], 'k-', linewidth = 3, label = 'emp')
p.loglog(x_arr, cpd_cdf, 'b-', label = 'cpd')
p.loglog(x_arr, lognorm_cdf, color = 'red', linewidth = 2, label = 'lognorm')
p.loglog(x_arr, lognormal_cdf(x_arr, np.mean(emp_data[:, 0]), np.std(emp_data[:, 0])), color = 'cyan', linewidth = 2, label = 'lognorm_data')
p.ylim(1e-4, 1)
p.xlim(1, 10)
p.legend(loc = 0)
#p.show()
return rms_err
def run():
# Quantities for the response function
# and randomization
#
xx = 10 # Pa
yy = 20 # Pa
zz = 30
aa = 40
# construct a default response function for a single filament
rf = Filament( x=xx, y=yy, z=zz, a=aa )
# construct the integrator and provide it with the response function.
s = SPIRRID( rf=rf,
min_eps=0.00, max_eps=0.05, n_eps=1 )
# construct the random variables
n_int = 80
s.add_rv( 'x', distribution='norm', scale=10., shape=2., n_int=n_int )
s.add_rv( 'y', distribution='norm', loc=10., scale=2., n_int=n_int )
s.add_rv( 'z', distribution='norm', loc=10., scale=2., n_int=n_int )
s.add_rv( 'a', distribution='norm', loc=10., scale=2., n_int=n_int )
# define a tables with the run configurations to start in a batch
run_list = [
(
{'cached_qg' : False,
'compiled_qg_loop' : True,
'compiled_eps_loop' : True },
'bx-',
'$\mathrm{C}_{e,\\theta} ( q(e,\\theta) \cdot g[\\theta_1] g[\\theta_2] \dots g[\\theta_n] ) $ - %4.2f sec'
),
(
{'cached_qg' : False,
'compiled_qg_loop' : True,
'compiled_eps_loop' : False },
'r-2',
'$\mathrm{P}_{e} ( \mathrm{C}_{\\theta} ( q(e,\\theta) \cdot g[\\theta_1] g[\\theta_2] \dots g[\\theta_n] ) ) $ - %4.2f sec',
),
(
{'cached_qg' : True,
'compiled_qg_loop' : True,
'compiled_eps_loop' : True },
'go-',
'$\mathrm{C}_{e,\\theta} ( q(e,\\theta) \cdot G[\\theta] ) $ - %4.2f sec',
),
(
{'cached_qg' : True,
'compiled_qg_loop' : False,
'compiled_eps_loop' : False },
'y--',
'$\mathrm{P}_{e} ( \mathrm{N}_{\\theta} ( q(e,\\theta) \cdot G[\\theta] ) ) $ - %4.2f sec'
),
]
legend = []
for idx, run in enumerate( run_list ):
run_options, plot_options, legend_string = run
print 'run', idx,
s.set( **run_options )
s.mean_curve.plot( plt, plot_options )
print 'execution time', s.exec_time
legend.append( legend_string % s.exec_time )
plt.xlabel( 'strain [-]' )
plt.ylabel( 'stress' )
plt.legend( legend )
plt.title( s.rf.title )
plt.show()
def run():
# Quantities for the response function
# and randomization
# construct a default response function for a single filament
rf = ConstantFrictionFiniteFiber( fu=1200e15, qf=1200,
L=0.02, A=0.00000002,
E_mod=210.e9, z=0.004,
phi=0.5, f=0.01 )
# construct the integrator and provide it with the response function.
s = SPIRRID( rf=rf,
min_eps=0.00, max_eps=0.0008, n_eps=80 )
# construct the random variables
n_int = 15
s.add_rv( 'E_mod', distribution='uniform', loc=170.e9, scale=250.e9, n_int=n_int )
s.add_rv( 'L', distribution='uniform', loc=0.02, scale=0.03, n_int=n_int )
s.add_rv( 'phi', distribution='cos_distr', loc=0., scale=1., n_int=n_int )
s.add_rv( 'z', distribution='uniform', loc=0, scale=rf.L / 2., n_int=n_int )
# define a tables with the run configurations to start in a batch
run_list = [
(
{'cached_qg' : False,
'compiled_qg_loop' : True,
'compiled_eps_loop' : True },
'bx-',
'$\mathrm{C}_{e,\\theta} ( q(e,\\theta) \cdot g[\\theta_1] g[\\theta_2] \dots g[\\theta_n] ) $ - %4.2f sec'
),
(
{'cached_qg' : False,
'compiled_qg_loop' : True,
'compiled_eps_loop' : False },
'r-2',
'$\mathrm{P}_{e} ( \mathrm{C}_{\\theta} ( q(e,\\theta) \cdot g[\\theta_1] g[\\theta_2] \dots g[\\theta_n] ) ) $ - %4.2f sec',
),
(
{'cached_qg' : True,
'compiled_qg_loop' : True,
'compiled_eps_loop' : True },
'go-',
'$\mathrm{C}_{e,\\theta} ( q(e,\\theta) \cdot G[\\theta] ) $ - %4.2f sec',
),
(
{'cached_qg' : True,
'compiled_qg_loop' : False,
'compiled_eps_loop' : False },
'b--',
'$\mathrm{P}_{e} ( \mathrm{N}_{\\theta} ( q(e,\\theta) \cdot G[\\theta] ) ) $ - %4.2f sec'
),
]
for idx, run in enumerate( run_list ):
run_options, plot_options, legend_string = run
print 'run', idx,
s.set( **run_options )
s.mean_curve.plot( plt, plot_options, linewidth=2, label=legend_string % s.exec_time )
print 'execution time', s.exec_time, s.mu_q_peak
plt.xlabel( 'strain [-]' )
plt.ylabel( 'stress' )
plt.legend( loc='lower right' )
plt.title( s.rf.title )
plt.show()
# discretize the control variable (x-axis)
e_arr = np.linspace(0, 1.2, 40)
# 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
mu_q_ex_arr = mu_q_ex(e_arr, m_xi, std_xi, m_la)
g_la = RV('norm', m_la, std_la)
g_xi = RV('norm', m_xi, std_xi)
s = SPIRRID(
q=fiber_tt_2p,
e_arr=e_arr,
n_int=10,
tvars=dict(la=g_la, xi=g_xi),
)
mu_q_ex_arr = mu_q_ex(e_arr, m_xi, std_xi, m_la)
slab = SPIRRIDLAB(s=s,
save_output=False,
show_output=True,
exact_arr=mu_q_ex(e_arr, m_xi, std_xi, m_la))
slab.configure_traits(kind='live')
# powers = np.linspace(1, math.log(200, 10), 50)
# n_int_range = np.array(np.power(10, powers), dtype = int)
#
from stats.spirrid.sampling import PGrid, TGrid, FunctionRandomization
from stats.spirrid import SPIRRID
class C(FunctionRandomization):
sampling_type = Trait('T-grid', {'T-grid' : TGrid,
'P-grid' : PGrid}, input_change = True)
sampling = Property(depends_on = '+input_change')
@cached_property
def _get_sampling(self):
print '... recalculating result ...',
return self.sampling_type_(randomization = self)
c = C()
print 'got', c.sampling
c.sampling_type = 'P-grid'
print 'got', c.sampling
c.sampling_type = 'T-grid'
print 'got', c.sampling
print 'got', c.sampling
print '====='
s = SPIRRID()
print 'got', s.sampling
s.sampling_type = 'P-grid'
print 'got', s.sampling
s.sampling_type = 'T-grid'
print 'got', s.sampling
print 'got', s.sampling
print 'input', s.cv, 'output', r
return r
if __name__ == '__main__':
rf = Yarn()
# control variable - strains
# can be a float number or array
cv = linspace( 0.0, 0.06, 10 )
# construct the outer integrator and provide it with the response function.
s_outer = SPIRRID( rf = rf,
cv = cv,
cached_dG = True,
compiled_QdG_loop = False,
compiled_eps_loop = False )
# construct the random variables
n_int = 3
s_outer.add_rv( 'loc_theta', distribution = 'uniform', loc = 0.00,
scale = 0.01, n_int = n_int )
# plot the mean filament and mean yarn response
plt.plot( cv, rf( cv, 0.01 ), linewidth = 2,
label = 'random filament properties' )
plt.plot( cv, s_outer.results[0], linewidth = 2,
label = 'random bundle properties' )
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 = 'fiber_tt_5p_40_norm.txt'
delimiter = ' '
# discretize the control variable (x-axis)
e_arr = np.linspace(0, 0.04, 1000)
#===========================================================================
# Randomization
#===========================================================================
s = SPIRRID(q = fiber_tt_5p(),
e_arr = e_arr,
n_int = 30,
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 = True, show_output = True,
exact_arr = mu_q_ex(e_arr))
def run():
# Quantities for the response function
# and randomization
#
E_mod = 70 * 1e+9 # Pa
sig_u = 1.25 * 1e+9 # Pa
D = 26 * 1.0e-6 # m
A = ( D / 2.0 ) ** 2 * pi
xi_u = sig_u / E_mod
dict_var = {'E_mod':70.e9,
'xi':0.02,
'A':A,
'theta':0,
'lambd':0}
outfile = open( 'filament_comb_stat.dat', 'w' )
comb = 0
while_cond = 1
while while_cond == 1: #for comb in range( 1, n_com + 1 ):
comb += 1
for i in range( 0, 2 ):
# construct a default response function for a single filament
rf = Filament()
rf.set( **dict_var )
# construct the integrator and provide it with the response function.
s = SPIRRID( rf=rf,
min_eps=0.00, max_eps=0.05, n_eps=80 )
# construct the random variables
n_int = 32
dict_rv = {
'xi':{'variable':'xi', 'distribution':'weibull_min', 'scale':0.02, 'shape':10., 'n_int':n_int},
'E_mod':{'variable':'E_mod', 'distribution':'uniform', 'loc':70e+9, 'scale':15e+9, 'n_int':n_int},
'theta':{'variable':'theta', 'distribution':'uniform', 'loc':0.0, 'scale':0.01, 'n_int':n_int},
'lambd':{'variable':'lambd', 'distribution':'uniform', 'loc':0.0, 'scale':.2, 'n_int':n_int},
'A':{'variable':'A', 'distribution':'uniform', 'loc':A * 0.3, 'scale':0.7 * A, 'n_int':n_int},
}
n_var = len( dict_rv )
n_com = 0
for k in range( 0, n_var ):
n_com += choose( n_var, k )
print 'The only last combination'
comb = n_com
# end of the while loop
if comb == n_com:
while_cond = 0
list_rv_comb = list( powerset( dict_rv ) )
for rv in list_rv_comb[comb]:
s.add_rv( **dict_rv[rv] )
# define a tables with the run configurations to start in a batch
run_list = [
(
{'cached_dG' : False,
'compiled_QdG_loop' : True,
'compiled_eps_loop' : True },
'bx-',
'$\mathrm{C}_{e,\\theta} ( q(e,\\theta) \cdot g[\\theta_1] g[\\theta_2] \dots g[\\theta_n] ) $ - %4.2f sec'
),
(
{'cached_dG' : False,
'compiled_QdG_loop' : True,
'compiled_eps_loop' : False },
'r-2',
'$\mathrm{P}_{e} ( \mathrm{C}_{\\theta} ( q(e,\\theta) \cdot g[\\theta_1] g[\\theta_2] \dots g[\\theta_n] ) ) $ - %4.2f sec',
),
(
{'cached_dG' : True,
'compiled_QdG_loop' : False,
'compiled_eps_loop' : False },
'y--',
'$\mathrm{P}_{e} ( \mathrm{N}_{\\theta} ( q(e,\\theta) \cdot G[\\theta] ) ) $ - %4.2f sec'
),
(
{'cached_dG' : True,
'compiled_QdG_loop' : True,
'compiled_eps_loop' : True },
'go-',
'$\mathrm{C}_{e,\\theta} ( q(e,\\theta) \cdot G[\\theta] ) $ - %4.2f sec',
),
]
legend = []
print 'Combination', list_rv_comb[comb], ' -- ', i
outfile.write( "Combination %s -- %i\n" % ( list_rv_comb[comb], i ) )
outfile.write( "%s \t %s\n" % ( "Run", "Time" ) )
for idx, run in enumerate( run_list ):
run_options, plot_options, legend_string = run
print 'run', idx,
s.set( **run_options )
plt.figure( comb )
s.mean_curve.plot( plt, plot_options )
print 'execution time', s.exec_time
outfile.write( "%s \t %s\n" % ( idx, s.exec_time ) )
legend.append( legend_string % s.exec_time )
outfile.write( "=================\n" )
plt.xlabel( 'strain [-]' )
plt.ylabel( 'stress' )
plt.legend( legend )
plt.title( s.rf.title )
del s
plt.show()
def run():
sv = SPIRRIDModelView(model = SPIRRID(),
rf_values = [ Filament() ])
sv.configure_traits(view = 'traits_view_tabbed')
def run():
# Quantities for the response function
# and randomization
# construct a default response function for a single filament
dict_var = {'fu':1200.0e6,
'qf':1500.,
'L':0.02,
'A':5.30929158457e-10,
'E_mod':70.0e9,
'z':0.0,
'phi':0.,
'f':0.0}
outfile = open( 'pullout_comb_stat.dat', 'w' )
comb = 0
while_cond = 1
while while_cond == 1: #for comb in range( 1, n_com + 1 ):
comb += 1
for i in range( 0, 2 ):
# construct a default response function for a single filament
rf = ConstantFrictionFiniteFiber()
rf.set( **dict_var )
# construct the integrator and provide it with the response function.
s = SPIRRID( rf=rf,
min_eps=0.00, max_eps=0.05, n_eps=80 )
# construct the random variables
n_int = 10
dict_rv = {
'fu':{'variable':'fu', 'distribution':'weibull_min', 'loc':1200.0e6, 'scale':200., 'n_int':n_int },
'qf':{'variable':'qf', 'distribution':'uniform', 'loc':1500., 'scale':100., 'n_int':n_int },
'L':{'variable':'L', 'distribution':'uniform', 'loc':0.02, 'scale':rf.L / 2., 'n_int':n_int},
'A':{'variable':'A', 'distribution':'uniform', 'loc':5.30929158457e-10, 'scale':.03 * 5.30929158457e-10, 'n_int':n_int },
'E_mod':{'variable':'E_mod', 'distribution':'uniform', 'loc':70.e9, 'scale':250.e9, 'n_int':n_int},
'z':{'variable':'z', 'distribution':'uniform', 'loc':0., 'scale':.03, 'n_int':n_int },
'phi':{'variable':'phi', 'distribution':'cos_distr', 'loc':0., 'scale':1., 'n_int':n_int},
'f':{'variable':'f', 'distribution':'uniform', 'loc':0., 'scale':.03, 'n_int':n_int },
}
n_var = len( dict_rv )
n_com = 0
for k in range( 6, 7 ):
n_com += choose( n_var, k )
# end of the while loop
if comb == n_com:
while_cond = 0
list_rv_comb = list( powerset( dict_rv, par=6 ) )
for rv in list_rv_comb[comb]:
s.add_rv( **dict_rv[rv] )
# define a tables with the run configurations to start in a batch
run_list = [
(
{'cached_dG' : False,
'compiled_QdG_loop' : True,
'compiled_eps_loop' : True },
'bx-',
'$\mathrm{C}_{e,\\theta} ( q(e,\\theta) \cdot g[\\theta_1] g[\\theta_2] \dots g[\\theta_n] ) $ - %4.2f sec'
),
(
{'cached_dG' : False,
'compiled_QdG_loop' : True,
'compiled_eps_loop' : False },
'r-2',
'$\mathrm{P}_{e} ( \mathrm{C}_{\\theta} ( q(e,\\theta) \cdot g[\\theta_1] g[\\theta_2] \dots g[\\theta_n] ) ) $ - %4.2f sec',
),
(
{'cached_dG' : True,
'compiled_QdG_loop' : True,
'compiled_eps_loop' : True },
'go-',
'$\mathrm{C}_{e,\\theta} ( q(e,\\theta) \cdot G[\\theta] ) $ - %4.2f sec',
),
(
{'cached_dG' : True,
'compiled_QdG_loop' : False,
'compiled_eps_loop' : False },
'b--',
'$\mathrm{P}_{e} ( \mathrm{N}_{\\theta} ( q(e,\\theta) \cdot G[\\theta] ) ) $ - %4.2f sec'
),
]
print 'Combination', list_rv_comb[comb], ' -- ', i
outfile.write( "Combination %s -- %i\n" % ( list_rv_comb[comb], i ) )
outfile.write( "%s \t %s\n" % ( "Run", "Time" ) )
for idx, run in enumerate( run_list ):
run_options, plot_options, legend_string = run
print 'run', idx,
s.set( **run_options )
plt.figure( comb )
s.mean_curve.plot( plt, plot_options, linewidth=2, label=legend_string % s.exec_time )
print 'execution time', s.exec_time, s.mu_q_peak
outfile.write( "%s \t %s\n" % ( idx, s.exec_time ) )
outfile.write( "=================\n" )
plt.xlabel( 'strain [-]' )
plt.ylabel( 'stress' )
plt.legend( loc='lower right' )
plt.title( s.rf.title )
del s
plt.show()
def run():
# Quantities for the response function
# and randomization
#
E_mod = 70 * 1e+9 # Pa
sig_u = 1.25 * 1e+9 # Pa
D = 26 * 1.0e-6 # m
A = ( D / 2.0 ) ** 2 * pi
xi_u = sig_u / E_mod
# construct a default response function for a single filament
rf = Filament( E_mod=70.e9, xi=0.02, A=A, theta=0, lambd=0 )
# construct the integrator and provide it with the response function.
s = SPIRRID( rf=rf,
min_eps=0.00, max_eps=0.05, n_eps=80 )
# construct the random variables
n_int = 70
s.add_rv( 'xi', distribution='weibull_min', scale=0.02, shape=10., n_int=n_int )
# s.add_rv( 'E_mod', distribution = 'uniform', loc = 70e+9, scale = 15e+9, n_int = n_int )
s.add_rv( 'theta', distribution='uniform', loc=0.0, scale=0.01, n_int=n_int )
s.add_rv( 'lambd', distribution='uniform', loc=0.0, scale=.2, n_int=n_int )
s.add_rv( 'A', distribution='uniform', loc=A * 0.3, scale=0.7 * A, n_int=n_int )
# define a tables with the run configurations to start in a batch
run_list = [
(
{'cached_dG' : False,
'compiled_QdG_loop' : True,
'compiled_eps_loop' : True },
'bx-',
'$\mathrm{C}_{e,\\theta} ( q(e,\\theta) \cdot g[\\theta_1] g[\\theta_2] \dots g[\\theta_n] ) $ - %4.2f sec'
),
(
{'cached_dG' : False,
'compiled_QdG_loop' : True,
'compiled_eps_loop' : False },
'r-2',
'$\mathrm{P}_{e} ( \mathrm{C}_{\\theta} ( q(e,\\theta) \cdot g[\\theta_1] g[\\theta_2] \dots g[\\theta_n] ) ) $ - %4.2f sec',
),
(
{'cached_dG' : True,
'compiled_QdG_loop' : True,
'compiled_eps_loop' : True },
'go-',
'$\mathrm{C}_{e,\\theta} ( q(e,\\theta) \cdot G[\\theta] ) $ - %4.2f sec',
),
(
{'cached_dG' : True,
'compiled_QdG_loop' : False,
'compiled_eps_loop' : False },
'y--',
'$\mathrm{P}_{e} ( \mathrm{N}_{\\theta} ( q(e,\\theta) \cdot G[\\theta] ) ) $ - %4.2f sec'
),
]
legend = []
for idx, run in enumerate( run_list ):
run_options, plot_options, legend_string = run
print 'run', idx,
s.set( **run_options )
s.mean_curve.plot( plt, plot_options )
print 'execution time', s.exec_time
legend.append( legend_string % s.exec_time )
plt.xlabel( 'strain [-]' )
plt.ylabel( 'stress' )
plt.legend( legend )
plt.title( s.rf.title )
plt.show()
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))
n_mu_q_arr = s.mu_q_arr / max_mu_q
n_std_q_arr = np.sqrt(s.var_q_arr) / max_mu_q
def run():
# Quantities for the response function
# and randomization
#
dict_var = {'x' : 10,
'y' : 20,
'z' : 30,
'a' : 40}
n_var = len( dict_var )
n_com = 0
for k in range( 0, n_var ):
n_com += choose( n_var, k )
outfile = open( 'simple_comb_stat.dat', 'w' )
for comb in range( 1, n_com + 1 ):
for i in range( 0, 2 ):
# construct a default response function for a single filament
rf = Filament()
rf.set( **dict_var ) # (x=10, y=20, ...)
# construct the integrator and provide it with the response function.
s = SPIRRID( rf=rf,
min_eps=0.00, max_eps=0.05, n_eps=1 )
# construct the random variables
n_int = 35
dict_rv = {
'x':{'variable':'x', 'distribution':'norm', 'loc':10., 'scale':2., 'n_int':n_int},
'y':{'variable':'y', 'distribution':'norm', 'loc':10., 'scale':2., 'n_int':n_int},
'z':{'variable':'z', 'distribution':'norm', 'loc':10., 'scale':2., 'n_int':n_int},
'a':{'variable':'a', 'distribution':'norm', 'loc':10., 'scale':2., 'n_int':n_int},
}
list_rv_comb = list( powerset( dict_rv ) )
for rv in list_rv_comb[comb]:
s.add_rv( **dict_rv[rv] )
# define a tables with the run configurations to start in a batch
run_list = [
(
{'cached_dG' : False,
'compiled_QdG_loop' : True,
'compiled_eps_loop' : True },
'bx-',
'$\mathrm{C}_{e,\\theta} ( q(e,\\theta) \cdot g[\\theta_1] g[\\theta_2] \dots g[\\theta_n] ) $ - %4.2f sec'
),
(
{'cached_dG' : False,
'compiled_QdG_loop' : True,
'compiled_eps_loop' : False },
'r-2',
'$\mathrm{P}_{e} ( \mathrm{C}_{\\theta} ( q(e,\\theta) \cdot g[\\theta_1] g[\\theta_2] \dots g[\\theta_n] ) ) $ - %4.2f sec',
),
(
{'cached_dG' : True,
'compiled_QdG_loop' : True,
'compiled_eps_loop' : True },
'go-',
'$\mathrm{C}_{e,\\theta} ( q(e,\\theta) \cdot G[\\theta] ) $ - %4.2f sec',
),
(
{'cached_dG' : True,
'compiled_QdG_loop' : False,
'compiled_eps_loop' : False },
'y--',
'$\mathrm{P}_{e} ( \mathrm{N}_{\\theta} ( q(e,\\theta) \cdot G[\\theta] ) ) $ - %4.2f sec'
),
]
legend = []
print 'Combination', list_rv_comb[comb], ' -- ', i
outfile.write( "Combination %s -- %i\n" % ( list_rv_comb[comb], i ) )
outfile.write( "%s \t %s\n" % ( "Run", "Time" ) )
for idx, run in enumerate( run_list ):
run_options, plot_options, legend_string = run
print 'run', idx,
s.set( **run_options )
s.mean_curve.plot( plt, plot_options )
print 'execution time', s.exec_time, s.mu_q_peak
outfile.write( "%s \t %s\n" % ( idx, s.exec_time ) )
legend.append( legend_string % s.exec_time )
outfile.write( "=================\n" )
plt.xlabel( 'strain [-]' )
plt.ylabel( 'stress' )
plt.legend( legend )
plt.title( s.rf.title )
del s
plt.show()
outfile.close()
def run():
# Quantities for the response function
# and randomization
#
dict_var = {"x": 10, "y": 20, "z": 30, "a": 40}
n_var = len(dict_var)
n_com = 0
for k in range(0, n_var):
n_com += choose(n_var, k)
outfile = open("simple_comb_loop_stat.dat", "w")
for comb in range(1, n_com + 1):
for i in range(0, 2):
# construct a default response function for a single filament
rf = Filament()
rf.set(**dict_var) # (x=10, y=20, ...)
# construct the integrator and provide it with the response function.
s = SPIRRID(rf=rf, min_eps=0.00, max_eps=0.05, n_eps=1)
# construct the random variables
n_int = 35
dict_rv = {
"x": {"variable": "x", "distribution": "norm", "loc": 10.0, "scale": 2.0, "n_int": n_int},
"y": {"variable": "y", "distribution": "norm", "loc": 10.0, "scale": 2.0, "n_int": n_int},
"z": {"variable": "z", "distribution": "norm", "loc": 10.0, "scale": 2.0, "n_int": n_int},
"a": {"variable": "a", "distribution": "norm", "loc": 10.0, "scale": 2.0, "n_int": n_int},
}
list_rv_comb = list(powerset(dict_rv))
for rv in list_rv_comb[comb]:
s.add_rv(**dict_rv[rv])
# define a tables with the run configurations to start in a batch
run_list = [
(
{"cached_qg": False, "compiled_qg_loop": True, "compiled_eps_loop": True},
"bx-",
"$\mathrm{C}_{e,\\theta} ( q(e,\\theta) \cdot g[\\theta_1] g[\\theta_2] \dots g[\\theta_n] ) $ - %4.2f sec",
),
(
{"cached_qg": False, "compiled_qg_loop": True, "compiled_eps_loop": False},
"r-2",
"$\mathrm{P}_{e} ( \mathrm{C}_{\\theta} ( q(e,\\theta) \cdot g[\\theta_1] g[\\theta_2] \dots g[\\theta_n] ) ) $ - %4.2f sec",
),
(
{"cached_qg": True, "compiled_qg_loop": True, "compiled_eps_loop": True},
"go-",
"$\mathrm{C}_{e,\\theta} ( q(e,\\theta) \cdot G[\\theta] ) $ - %4.2f sec",
),
(
{"cached_qg": True, "compiled_qg_loop": False, "compiled_eps_loop": False},
"y--",
"$\mathrm{P}_{e} ( \mathrm{N}_{\\theta} ( q(e,\\theta) \cdot G[\\theta] ) ) $ - %4.2f sec",
),
]
legend = []
print "Combination", list_rv_comb[comb], " -- ", i
outfile.write("Combination %s -- %i\n" % (list_rv_comb[comb], i))
outfile.write("%s \t %s\n" % ("Run", "Time"))
for idx, run in enumerate(run_list):
run_options, plot_options, legend_string = run
print "run", idx,
s.set(**run_options)
s.mean_curve.plot(plt, plot_options)
print "execution time", s.exec_time, s.mu_q_peak
outfile.write("%s \t %s\n" % (idx, s.exec_time))
legend.append(legend_string % s.exec_time)
outfile.write("=================\n")
plt.xlabel("strain [-]")
plt.ylabel("stress")
plt.legend(legend)
plt.title(s.rf.title)
del s
plt.show()
outfile.close()
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 = 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)
def run():
# Quantities for the response function
# and randomization
#
#E_mod = 70 * 1e+9 # Pa
#sig_u = 1.25 * 1e+9 # Pa
#D = 26 * 1.0e-6 # m
#A = ( D / 2.0 ) ** 2 * pi
#xi_u = sig_u / E_mod
# construct a default response function for a single filament
dict_var = {'fu':1200.0e6,
'qf':1500.,
'L':0.02,
'A':5.30929158457e-10,
'E_mod':70.0e9,
'z':0.0,
'phi':0.0,
'f':0.0}
e_arr = np.linspace(0, 0.012, 40)
# random variable dictionary
tvars = dict(fu = RV('weibull_min', 1200.0e6, 200.),
qf = RV('uniform', 1500., 100.),
L = RV('uniform', 0.02, 0.02 / 2.),
A = RV('uniform', 5.30929158457e-10, .03 * 5.30929158457e-10),
E_mod = RV('uniform', 70.e9, 250.e9),
z = RV('uniform', 0.0, 0.03),
phi = RV('sin_distr', 0.0, 1.0),
f = RV('uniform', 0.0, 0.03))
# list of all combinations of response function parameters
rv_comb_list = list(powerset(dict_var))
# define a tables with the run configurations to start in a batch
run_list = [
('c',
{'cached_dG' : False,
'compiled_eps_loop' : True },
'bx-',
'$\mathrm{C}_{e,\\theta} ( q(e,\\theta) \cdot g[\\theta_1] g[\\theta_2] \dots g[\\theta_n] ) $ - %4.2f sec'
),
('c',
{'cached_dG' : False,
'compiled_eps_loop' : False },
'r-2',
'$\mathrm{P}_{e} ( \mathrm{C}_{\\theta} ( q(e,\\theta) \cdot g[\\theta_1] g[\\theta_2] \dots g[\\theta_n] ) ) $ - %4.2f sec',
),
('numpy',
{ },
'go-',
'$\mathrm{C}_{e,\\theta} ( q(e,\\theta) \cdot G[\\theta] ) $ - %4.2f sec',
),
('c',
{'cached_dG' : True,
'compiled_eps_loop' : False },
'y--',
'$\mathrm{P}_{e} ( \mathrm{N}_{\\theta} ( q(e,\\theta) \cdot G[\\theta] ) ) $ - %4.2f sec'
),
]
outfile = open('pullout_comb_stat_new.dat', 'w')
sp1_outfile = open('pullout_comb_1_new.dat', 'w')
sp2_outfile = open('pullout_comb_2_new.dat', 'w')
plot = True
for id, rv_comb in enumerate(rv_comb_list[163:219]):#[0,-1]
for i in range(0, 2):
num_rv = len(rv_comb) # number of randomized variables
if i == 0:
sp1_outfile.write('%i \t %i' % (id, num_rv))
else:
sp2_outfile.write('%i \t %i' % (id, num_rv))
# construct a default response function
# construct the integrator and provide it with the response function.
tvars_up = dict_var
for rv in rv_comb:
tvars_up[rv] = tvars[rv]
if plot == True:
legend = []
s = SPIRRID(q = ConstantFrictionFiniteFiber(),
e_arr = e_arr,
n_int = 20,
tvars = tvars_up,
)
s.sampling_type = 'TGrid'
s.recalc = True
s.sampling.recalc = True
print 'Combination', rv_comb, ' -- ', i
outfile.write("Combination %s -- %i\n" % (rv_comb, i))
outfile.write("%s \t %s\n" % ("Run", "Time"))
for idx, run in enumerate(run_list):
code, run_options, plot_options, legend_string = run
s.codegen_type = code
s.codegen.set(**run_options)
print 'run', idx,
if plot == True:
plt.figure(id)
plt.plot(s.evar_list[0], s.mu_q_arr, plot_options)
legend.append(legend_string % s.exec_time)
print 'execution time', s.exec_time
outfile.write("%s \t %.6f\n" % (idx, s.exec_time))
if i == 0:
sp1_outfile.write("\t %.6f" % (s.exec_time))
else:
sp2_outfile.write("\t %.6f" % (s.exec_time))
#plt.show()
outfile.write("=================\n")
outfile.flush()
if i == 0:
sp1_outfile.write('\t %s\n' % str(rv_comb))
sp1_outfile.flush()
else:
sp2_outfile.write('\t %s\n' % str(rv_comb))
sp2_outfile.flush()
if plot == True:
plt.xlabel('strain [-]')
plt.ylabel('stress')
plt.legend(legend)
#plt.title(s.rf.title)
del s
outfile.close()
sp1_outfile.close()
sp2_outfile.close()
if plot == True:
plt.show()
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 _get_s(self):
return SPIRRID(rf=self.rf,
min_eps=0.00,
max_eps=1.0,
n_eps=20,
compiler_verbose=0)
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',