def k_COV_plots(k_arr, COV_arr):
sig_max_arr = np.zeros((len(k_arr), len(COV_arr)))
wmax_arr = np.zeros((len(k_arr), len(COV_arr)))
mu_r, mu_tau = 0.01, 0.1
for i, k in enumerate(k_arr):
for j, cov in enumerate(COV_arr):
Vf = Vf_k(k)
loc_r = mu_r * (1 - cov * np.sqrt(3.0))
scale_r = cov * 2 * np.sqrt(3.0) * mu_r
loc_tau = mu_tau * (1 - cov * np.sqrt(3.0))
scale_tau = cov * 2 * np.sqrt(3.0) * mu_tau
reinf = ContinuousFibers(r=RV('uniform', loc=loc_r, scale=scale_r),
tau=RV('uniform',
loc=loc_tau,
scale=scale_tau),
xi=WeibullFibers(shape=7.0, sV0=0.003),
V_f=Vf,
E_f=200e3,
n_int=100)
ccb_view.model.reinforcement_lst = [reinf]
sig_max, wmax = ccb_view.sigma_c_max
sig_max_arr[i, j] = sig_max / Vf
wmax_arr[i, j] = wmax
ctrl_vars = orthogonalize([np.arange(len(k_arr)), np.arange(len(COV_arr))])
print sig_max_arr
print wmax_arr
mlab.surf(ctrl_vars[0], ctrl_vars[1], sig_max_arr / np.max(sig_max_arr))
mlab.surf(ctrl_vars[0], ctrl_vars[1], wmax_arr / np.max(wmax_arr))
mlab.show()
def plot():
w_arr = np.linspace(0.0, 0.1, 20)
ll = 2.0
lr = 5.0
x = np.linspace(-ll, lr, 51)
#resm, resf = ccb_post.w_x_results(w_arr, x)
#eps_vars = orthogonalize([w_arr * 100., x])
#m.surf(eps_vars[0], eps_vars[1], resm * 500.)
#m.surf(eps_vars[0], eps_vars[1], resf * 500.)
#m.show()
sigma = ir.load_sigma_c_arr
eps_vars = orthogonalize([np.linspace(0.0, 0.0254229525299, len(sigma)) * 100., x])
# mu_q_nisp = nisp(P, x, Ll, Lr)[0]
mu_epsm = ir.interpolator_epsm(sigma, x, 100., 100.)
mu_epsf = ir.interpolator_mu_epsf(sigma, x, 100., 100.)
sig_c = ir.interpolator_mu_sigma_c(sigma, x, 100., 100.)
#mu_q_isp2 = ir(sigma, x, Ll, Lr)
# plt.plot(np.arange(len(sigma)), sigma/0.0103)
# plt.plot(np.arange(len(sigma)), np.max(mu_q_isp,axis = 0))
# plt.show()
# n_mu_q_arr = mu_q_nisp / np.max(np.fabs(mu_q_nisp))
m.surf(eps_vars[0], eps_vars[1], mu_epsm * 500.)
m.surf(eps_vars[0], eps_vars[1], mu_epsf * 500.)
m.surf(eps_vars[0], eps_vars[1], sig_c /(25e3*0.85 + 200e3*0.15)*500)
m.show()
def k_COV_plots(k_arr, COV_arr):
sig_max_arr = np.zeros((len(k_arr), len(COV_arr)))
wmax_arr = np.zeros((len(k_arr), len(COV_arr)))
mu_r, mu_tau = 0.01, 0.1
for i, k in enumerate(k_arr):
for j, cov in enumerate(COV_arr):
Vf = Vf_k(k)
loc_r = mu_r * (1 - cov * np.sqrt(3.0))
scale_r = cov * 2 * np.sqrt(3.0) * mu_r
loc_tau = mu_tau * (1 - cov * np.sqrt(3.0))
scale_tau = cov * 2 * np.sqrt(3.0) * mu_tau
reinf = ContinuousFibers(r=RV('uniform', loc=loc_r, scale=scale_r),
tau=RV('uniform', loc=loc_tau, scale=scale_tau),
xi=WeibullFibers(shape=7.0, sV0=0.003),
V_f=Vf, E_f=200e3, n_int=100)
ccb_view.model.reinforcement_lst = [reinf]
sig_max, wmax = ccb_view.sigma_c_max
sig_max_arr[i, j] = sig_max/Vf
wmax_arr[i, j] = wmax
ctrl_vars = orthogonalize([np.arange(len(k_arr)), np.arange(len(COV_arr))])
print sig_max_arr
print wmax_arr
mlab.surf(ctrl_vars[0], ctrl_vars[1], sig_max_arr / np.max(sig_max_arr))
mlab.surf(ctrl_vars[0], ctrl_vars[1], wmax_arr / np.max(wmax_arr))
mlab.show()
def det_tau_r(E_f, V_f, m_list, mu_xi, dp_tau, dp_r, tau_range, r_range, sV0):
w_arr = np.linspace(0, 20, 300)
tau_arr = np.linspace(tau_range[0], tau_range[1], dp_tau)
r_arr = np.linspace(r_range[0], r_range[1], dp_tau)
e_arr = orthogonalize([tau_arr, r_arr])
x_axis = e_arr[0]
y_axis = e_arr[1]
for mi in m_list:
res_array = np.zeros((dp_tau, dp_r))
for i_tau, taui in enumerate(tau_arr):
for i_r, ri in enumerate(r_arr):
s = get_scale(mu_xi, mi, taui, ri)
print s
#sV0 = float(s * (pi * mu_r ** 2) ** (1. / mi))
T = 2. * taui / ri
s0 = ((T * (mi + 1) * sV0 ** mi) / (2. * E_f * pi * ri ** 2)) ** (1. / (mi + 1))
print s0
k = np.sqrt(T / E_f)
ef0 = k * np.sqrt(w_arr)
G = 1 - np.exp(-(ef0 / s0) ** (mi + 1))
mu_int = ef0 * E_f * V_f * (1 - G)
I = s0 * gamma(1 + 1. / (mi + 1)) * gammainc(1 + 1. / (mi + 1), (ef0 / s0) ** (mi + 1))
mu_broken = E_f * V_f * I / (mi + 1)
result = mu_int + mu_broken
sigma_c = np.max(result)
if sigma_c == result[-1]:
print "w_arr too short"
pass
res_array[i_tau, i_r] = sigma_c
mlab.surf(x_axis, y_axis, res_array / 100.)
mlab.xlabel("det tau")
mlab.ylabel("det r")
mlab.zlabel("sigma")
mlab.show()
def rand_r_det_tau( E_f, V_f, tau_range, CoV_r_range, mu_r , dp_tau, dp_r, sV0 ):
m = m_list[0]
s0 = ( ( mu_tau * ( m + 1 ) * sV0 ** m ) / ( E_f * pi * mu_r ** 3 ) ) ** ( 1. / ( m + 1 ) )
mu_xi = s0 * gamma( 1. + 1. / ( 1. + m ) )
#loop with rand tau and rand r
for mi in m_list:
##############
Pf = RV( 'uniform', loc = 0.0, scale = 1.0 )
w_arr = np.linspace( 0, 2.0, 30 )
cb = CBResidual( include_pullout = True )
tau_arr = np.linspace( tau_range[0], tau_range[1], dp_tau )
CoV_r_arr = np.linspace( 0.0001, 0.5, dp_r )
loc_r_arr = mu_r - CoV_r_arr * mu_r * 3 ** 0.5
scale_r_arr = 2 * mu_r - 2 * loc_r_arr
e_arr = orthogonalize( [tau_arr, CoV_r_arr] )
x_axis = e_arr[0]
y_axis = e_arr[1]
stats_r = []
for s in range( dp_r ):
stats_r.append( RV( 'uniform', loc = loc_r_arr[s], scale = scale_r_arr[s] ) )
#gen Tuple of [loc,scale]
res_array = np.zeros( ( dp_tau, dp_r ) )
for i_tau, taui in enumerate( tau_arr ):
for i_r, ri in enumerate( stats_r ):
s0i = mu_xi / gamma( 1. + 1. / ( 1. + mi ) )
sV0i = ( ( s0i ** ( mi + 1 ) * E_f * pi * mu_r ** 3. ) / ( taui * ( mi + 1. ) ) ) ** ( 1. / mi )
total = SPIRRID( q = cb,
sampling_type = 'MCS',
evars = dict( w = w_arr ),
tvars = dict( tau = taui, E_f = E_f, V_f = V_f, r = ri,
m = mi, sV0 = sV0i, Pf = Pf ),
n_int = 60 )
if isinstance( ri, RV ):
r_arr = np.linspace( ri.ppf( 0.001 ), ri.ppf( 0.999 ), 200 )
Er = np.trapz( r_arr ** 2 * ri.pdf( r_arr ), r_arr )
else:
Er = ri ** 2
result = total.mu_q_arr / Er
sigma_c = np.max( result )
if sigma_c == result[-1]:
print "w_arr too short"
pass
res_array[i_tau, i_r] = sigma_c
#mayaviplot
print np.max( res_array )
mlab.surf( x_axis, y_axis, res_array )
#
mlab.view( 0., 0. )
mlab.xlabel( "det tau" )
mlab.ylabel( "rand r" )
mlab.zlabel( "sigma" )
mlab.show()
def rand_tau_det_r( E_f, V_f, CoV_tau_range, r_range, mu_tau , dp_tau, dp_r, sV0 ):
m = m_list[0]
s0 = ( ( mu_tau * ( m + 1 ) * sV0 ** m ) / ( E_f * pi * mu_r ** 3 ) ) ** ( 1. / ( m + 1 ) )
mu_xi = s0 * gamma( 1. + 1. / ( 1. + m ) )
for mi in m_list:
#######################################
Pf = RV( 'uniform', loc = 0.0, scale = 1.0 )
w_arr = np.linspace( 0, 5, 300 )
cb = CBResidual( include_pullout = True )
# CoV generation
CoV_tau_arr = np.linspace( CoV_tau_range[0], CoV_tau_range[1], dp_tau )
loc_tau_arr = mu_tau - CoV_tau_arr * mu_tau * 3 ** 0.5
scale_tau_arr = 2 * mu_tau - 2 * loc_tau_arr
r_arr = np.linspace( r_range[0], r_range[1], dp_r )
e_arr = orthogonalize( [CoV_tau_arr, r_arr] )
x_axis = e_arr[0]
y_axis = e_arr[1]
#TAU gen Tuple of [loc,scale]
stats_tau = []
for s in range( dp_tau ):
stats_tau.append( RV( 'uniform', loc = loc_tau_arr[s], scale = scale_tau_arr[s] ) )
#r gen Tuple of [loc,scale]
res_array = np.zeros( ( dp_tau, dp_r ) )
for i_tau, taui in enumerate( stats_tau ):
for i_r, ri in enumerate( r_arr ):
print i_tau, i_r
s0i = mu_xi / gamma( 1. + 1. / ( 1. + mi ) )
sV0i = ( ( s0i ** ( mi + 1 ) * E_f * pi * ri ** 3. ) / ( mu_tau * ( mi + 1. ) ) ) ** ( 1. / mi )
total = SPIRRID( q = cb,
sampling_type = 'MCS',
evars = dict( w = w_arr ),
tvars = dict( tau = taui, E_f = E_f, V_f = V_f, r = ri,
m = mi, sV0 = sV0i, Pf = Pf ),
n_int = 60 )
if isinstance( ri, RV ):
r_arr = np.linspace( ri.ppf( 0.001 ), ri.ppf( 0.999 ), 200 )
Er = np.trapz( r_arr ** 2 * ri.pdf( r_arr ), r_arr )
else:
Er = ri ** 2
#total(evars=dict(w=[3.]))
x = [2.]
x = np.array( x )
result = total.mu_q_arr / Er
sigma_c = np.max( result )
if sigma_c == result[-1]:
print "w_arr too short"
res_array[i_tau, i_r] = sigma_c
#mayaviplot
mlab.surf( x_axis, y_axis * 50, res_array, warpscale = 0.1 )
mlab.view( 0., 0. )
mlab.xlabel( "rand tau" )
mlab.ylabel( "det r" )
mlab.zlabel( "sigma" )
mlab.show()
def m_effect():
m_xi_arr = np.linspace(1., 20., 15)
m_tau_arr = np.linspace(0.3, 5.0, 10)
try:
res_file = open('res_m.pkl', 'rb')
res_arr = pickle.load(res_file)
res_file.close()
# for res_part in res_arr:
# plt.plot(m_xi_arr, res_part)
# plt.show()
eps_vars = orthogonalize([np.arange(15), np.arange(10)])
resCB = np.ones_like(res_arr)
resMC = res_arr
mlab.surf(eps_vars[0], eps_vars[1], resCB * 10)
mlab.surf(eps_vars[0], eps_vars[1], resMC * 10)
mlab.show()
except:
res_arr = []
for m_tau in m_tau_arr:
strength_CB = []
strength_MC = []
for m_xi in m_xi_arr:
wmCB, smCB = maxsigma(
1000.,
RV('weibull_min', shape=m_tau, scale=0.1, loc=0.0),
m_xi)
wmMC, smMC = maxsigma(
1., RV('weibull_min', shape=m_tau, scale=0.1, loc=0.0),
m_xi)
# w_CB = np.linspace(0.0,100./m**2,200)
# w_MC = np.linspace(0.0,1.5/m**2,200)
# plt.plot(wmCB, smCB, 'ro')
# plt.plot(wmMC, smMC, 'bo')
# sigma_c_CB = sigmac(w_CB, 1000., RV('weibull_min', shape=3.0, scale=0.1, loc=0.0), m)
# sigma_c_MC = sigmac(w_MC, 1.0, RV('weibull_min', shape=3.0, scale=0.1, loc=0.0), m)
# plt.plot(w_CB, sigma_c_CB, label='CB')
# plt.plot(w_MC, sigma_c_MC, label='MC')
# plt.show()
strength_CB.append(smCB)
strength_MC.append(smMC)
CB_arr = np.ones_like(np.array([strength_CB]))
MC_arr = np.array([strength_MC]) / np.array([strength_CB])
res_arr.append(MC_arr.flatten())
plt.plot(m_xi_arr, CB_arr.flatten(), lw=2., color='black')
plt.plot(m_xi_arr, MC_arr.flatten(), label=str(m_tau) + ' MC')
res_arr = np.array(res_arr)
file_res = open('res_m.pkl', 'wb')
pickle.dump(res_arr, file_res, -1)
file_res.close()
plt.legend(loc='best')
plt.ylim(0)
plt.show()
def random_domain(w):
Ef = 70e3
Fxi = weibull_min(5., scale = 0.02)
r = np.linspace(0.001, 0.005, 100)
tau = np.linspace(0., 1., 100)
e_arr = orthogonalize([np.arange(len(r)), np.arange(len(tau))])
tau = tau.reshape(1, len(tau))
r = r.reshape(len(r), 1)
eps0 = np.sqrt(w * 2 * tau / r / Ef)
F = Fxi.cdf(eps0)
a = r * Ef / 2. / tau
m.surf(e_arr[0], e_arr[1], 50 * F / np.max(F))
m.surf(e_arr[0], e_arr[1], 50 * eps0 / np.max(eps0))
m.surf(e_arr[0], e_arr[1], np.nan_to_num(a) / 100.)
m.show()
def m_effect():
m_xi_arr = np.linspace(1., 20., 15)
m_tau_arr = np.linspace(0.3, 5.0, 10)
try:
res_file = open('res_m.pkl', 'rb')
res_arr = pickle.load(res_file)
res_file.close()
# for res_part in res_arr:
# plt.plot(m_xi_arr, res_part)
# plt.show()
eps_vars = orthogonalize([np.arange(15), np.arange(10)])
resCB = np.ones_like(res_arr)
resMC = res_arr
mlab.surf(eps_vars[0], eps_vars[1], resCB * 10)
mlab.surf(eps_vars[0], eps_vars[1], resMC * 10)
mlab.show()
except:
res_arr = []
for m_tau in m_tau_arr:
strength_CB = []
strength_MC = []
for m_xi in m_xi_arr:
wmCB, smCB = maxsigma(1000., RV('weibull_min', shape=m_tau, scale=0.1, loc=0.0), m_xi)
wmMC, smMC = maxsigma(1., RV('weibull_min', shape=m_tau, scale=0.1, loc=0.0), m_xi)
# w_CB = np.linspace(0.0,100./m**2,200)
# w_MC = np.linspace(0.0,1.5/m**2,200)
# plt.plot(wmCB, smCB, 'ro')
# plt.plot(wmMC, smMC, 'bo')
# sigma_c_CB = sigmac(w_CB, 1000., RV('weibull_min', shape=3.0, scale=0.1, loc=0.0), m)
# sigma_c_MC = sigmac(w_MC, 1.0, RV('weibull_min', shape=3.0, scale=0.1, loc=0.0), m)
# plt.plot(w_CB, sigma_c_CB, label='CB')
# plt.plot(w_MC, sigma_c_MC, label='MC')
# plt.show()
strength_CB.append(smCB)
strength_MC.append(smMC)
CB_arr = np.ones_like(np.array([strength_CB]))
MC_arr = np.array([strength_MC]) / np.array([strength_CB])
res_arr.append(MC_arr.flatten())
plt.plot(m_xi_arr, CB_arr.flatten(), lw=2., color='black')
plt.plot(m_xi_arr, MC_arr.flatten(), label=str(m_tau) + ' MC')
res_arr = np.array(res_arr)
file_res = open('res_m.pkl', 'wb')
pickle.dump(res_arr, file_res, -1)
file_res.close()
plt.legend(loc='best')
plt.ylim(0)
plt.show()
def plot():
sigma = isp.adaption.load_sigma_c
Ll = 50.
Lr = 50.
x = np.linspace(-Ll, Lr, 201)
eps_vars = orthogonalize([np.arange(len(sigma)), np.arange(len(x))])
# mu_q_nisp = nisp(P, x, Ll, Lr)[0]
mu_q_isp = isp(sigma, x, 1., 14.)
mu_q_isp2 = isp(sigma, x, Ll, Lr)
# plt.plot(np.arange(len(sigma)), sigma/0.0103)
# plt.plot(np.arange(len(sigma)), np.max(mu_q_isp,axis = 0))
# plt.show()
# n_mu_q_arr = mu_q_nisp / np.max(np.fabs(mu_q_nisp))
mlab.surf(eps_vars[0], eps_vars[1], mu_q_isp / 10.)
mlab.surf(eps_vars[0], eps_vars[1], mu_q_isp2 / 10.)
mlab.show()
def plot():
sigma = isp.adaption.load_sigma_c
Ll = 50.
Lr = 50.
x = np.linspace(-Ll, Lr, 201)
eps_vars = orthogonalize([np.arange(len(sigma)), np.arange(len(x))])
# mu_q_nisp = nisp(P, x, Ll, Lr)[0]
mu_q_isp = isp(sigma, x, 1., 14.)
mu_q_isp2 = isp(sigma, x, Ll, Lr)
# plt.plot(np.arange(len(sigma)), sigma/0.0103)
# plt.plot(np.arange(len(sigma)), np.max(mu_q_isp,axis = 0))
# plt.show()
# n_mu_q_arr = mu_q_nisp / np.max(np.fabs(mu_q_nisp))
mlab.surf(eps_vars[0], eps_vars[1], mu_q_isp / 10.)
mlab.surf(eps_vars[0], eps_vars[1], mu_q_isp2 / 10.)
mlab.show()
def det_tau_r( E_f, V_f, m_list, dp_tau, dp_r, tau_range, r_range, sV0 ):
m = m_list[0]
s0 = ( ( mu_tau * ( m + 1 ) * sV0 ** m ) / ( E_f * pi * mu_r ** 3 ) ) ** ( 1. / ( m + 1 ) )
mu_xi = s0 * gamma( 1. + 1. / ( 1. + m ) )
################
w_arr = np.linspace( 0, 100, 300 )
tau_arr = np.linspace( tau_range[0], tau_range[1], dp_tau )
r_arr = np.linspace( r_range[0], r_range[1], dp_tau )
e_arr = orthogonalize( [tau_arr, r_arr] )
x_axis = e_arr[0]
y_axis = e_arr[1]
for i_m, mi in enumerate( m_list ):
res_array = np.zeros( ( dp_tau, dp_r ) )
for i_tau, taui in enumerate( tau_arr ):
for i_r, ri in enumerate( r_arr ):
s0i = mu_xi / gamma( 1. + 1. / ( 1. + mi ) )
sV0i = ( ( s0i ** ( mi + 1 ) * E_f * pi * ri ** 3. ) / ( taui * ( mi + 1. ) ) ) ** ( 1. / mi )
T = 2. * taui / ri
s0 = ( ( T * ( mi + 1 ) * sV0i ** mi ) / ( 2. * E_f * pi * ri ** 2 ) ) ** ( 1. / ( mi + 1 ) )
k = np.sqrt( T / E_f )
ef0 = k * np.sqrt( w_arr )
G = 1 - np.exp( -( ef0 / s0 ) ** ( mi + 1 ) )
mu_int = ef0 * E_f * V_f * ( 1 - G )
I = s0 * gamma( 1 + 1. / ( mi + 1 ) ) * gammainc( 1 + 1. / ( mi + 1 ), ( ef0 / s0 ) ** ( mi + 1 ) )
mu_broken = E_f * V_f * I / ( mi + 1 )
result = mu_int + mu_broken
sigma_c = np.max( result )
if sigma_c == result[-1]:
print "w_arr too short"
pass
res_array[i_tau, i_r] = sigma_c
mlab.surf( x_axis, y_axis, res_array / 100. )
mlab.xlabel( "det tau" )
mlab.ylabel( "det r" )
mlab.zlabel( "sigma" )
mlab.show()
x = np.linspace(0, 20, 20000)
#print x[35]
x -= x[70]
#print x
#crack_x = x -
#x = np.linspace( -10 , 10 , 100 )
# eps = sb.get_eps_x_reinf( x )
#
# plt.plot( x, eps, lw = 2, color = 'black' )
# plt.show()
import enthought.mayavi.mlab as m
from stats.spirrid import orthogonalize
resolution = 200
profiles_list = []
forces_arr = np.linspace(0, 6200, resolution)
for i, p in enumerate(forces_arr):
sb.P = p
profiles_list.append(sb.get_eps_x_reinf(x))
profiles_arr = np.array(profiles_list)
param_arr = orthogonalize([x, forces_arr])
norm_param_arr = [ e / np.max(np.fabs(e)) for e in param_arr ]
norm_profiles_arr = profiles_arr / np.max(np.fabs(profiles_arr))
m.surf(norm_param_arr[0], norm_param_arr[1], norm_profiles_arr)
m.show()
from stats.spirrid import make_ogrid as orthogonalize
import numpy as np
from etsproxy.mayavi import mlab
import mayavi
#x,y data generation
CoV_tau_range = [1e-7, 0.5]
CoV_r_range = [1e-7, 0.5]
dp_tau = 20
dp_r = 20
CoV_tau_arr = np.linspace(CoV_tau_range[0], CoV_tau_range[1], dp_tau)
#CoV_tau_arr = CoV_tau_arr[::-1]
CoV_r_arr = np.linspace(CoV_r_range[0], CoV_r_range[1], dp_r)
e_arr = orthogonalize([CoV_tau_arr, CoV_r_arr])
x_axis1 = e_arr[0]
y_axis1 = e_arr[1]
list(CoV_tau_arr)
#x,y,z plot
# -------------------------------------------
def sigma_m(m):
dataname = "sigmaOPT20_with_m{}.npy".format(m)
res = np.load(dataname)
from mayavi.api import Engine
engine = Engine()
engine.start()
if len(engine.scenes) == 0:
engine.new_scene()
mlab.surf(x_axis1, y_axis1, res, representation='wireframe')
def rand_tau_r(E_f, V_f, mu_tau, mu_r, mu_xi, m_list, CoV_tau_range, CoV_r_range, dp_tau, dp_r, sV0):
#rand tau and rand r
for mi in m_list:
s = get_scale(mu_xi, mi, mu_tau, mu_r)
sV0 = float(s * (pi * mu_r ** 2) ** (1. / mi))
Pf = RV('uniform', loc=0.0, scale=1.0)
w_arr = np.linspace(0, 1.2, 30)
cb = CBResidual(include_pullout=True)
# loc scale generation for specific CoV
#CoVtau
CoV_tau_arr = np.linspace(CoV_tau_range[0], CoV_tau_range[1], dp_tau)
loc_tau_arr = mu_tau - CoV_tau_arr * mu_tau * 3 ** 0.5
scale_tau_arr = 2 * mu_tau - 2 * loc_tau_arr
#CoVr
CoV_r_arr = np.linspace(CoV_r_range[0], CoV_r_range[1], dp_r)
loc_r_arr = mu_r - CoV_r_arr * mu_r * 3 ** 0.5
scale_r_arr = 2 * mu_r - 2 * loc_r_arr
#shaping for mayavi
e_arr = orthogonalize([CoV_tau_arr, CoV_r_arr])
x_axis = e_arr[0]
y_axis = e_arr[1]
#TAU gen Tuple of [loc,scale]
stats_tau = []
for s in range(dp_tau):
stats_tau.append(RV('uniform', loc=loc_tau_arr[s], scale=scale_tau_arr[s]))
stats_r = []
for s in range(dp_r):
stats_r.append(RV('uniform', loc=loc_r_arr[s], scale=scale_r_arr[s]))
#r gen Tuple of [loc,scale]
res_array = np.zeros((dp_tau, dp_r))
for i_tau, taui in enumerate(stats_tau):
for i_r, ri in enumerate(stats_r):
total = SPIRRID(q=cb,
sampling_type='MCS',
evars=dict(w=w_arr),
tvars=dict(tau=taui, E_f=E_f, V_f=V_f, r=ri,
m=mi, sV0=sV0, Pf=Pf),
n_int=70)
if isinstance(ri, RV):
r_arr = np.linspace(ri.ppf(0.001), ri.ppf(0.999), 200)
Er = np.trapz(r_arr ** 2 * ri.pdf(r_arr), r_arr)
else:
Er = ri ** 2
result = total.mu_q_arr / Er
sigma_c = np.max(result)
if sigma_c == result[-1]:
print "w_arr too short"
res_array[i_tau, i_r] = sigma_c
#mayaviplot
mlab.surf(x_axis, y_axis, res_array)
#mlab.xlabel("rand tau")
#mlab.ylabel("rand r")
#mlab.zlabel("sigma")
mlab.show()
from stats.spirrid import make_ogrid as orthogonalize
import numpy as np
from etsproxy.mayavi import mlab
import mayavi
#x,y data generation
CoV_tau_range = [1e-7, 0.5]
CoV_r_range = [1e-7, 0.5]
dp_tau = 20
dp_r = 20
CoV_tau_arr = np.linspace( CoV_tau_range[0], CoV_tau_range[1], dp_tau )
#CoV_tau_arr = CoV_tau_arr[::-1]
CoV_r_arr = np.linspace( CoV_r_range[0], CoV_r_range[1], dp_r )
e_arr = orthogonalize( [CoV_tau_arr, CoV_r_arr] )
x_axis1 = e_arr[0]
y_axis1 = e_arr[1]
list( CoV_tau_arr )
#x,y,z plot
# -------------------------------------------
def sigma_m( m ):
dataname = "sigmaOPT20_with_m{}.npy".format( m )
res = np.load( dataname )
from mayavi.api import Engine
engine = Engine()
engine.start()
if len( engine.scenes ) == 0:
engine.new_scene()
mlab.surf( x_axis1 , y_axis1 , res , representation = 'wireframe' )
surface = engine.scenes[0].children[0].children[0].children[0].children[0].children[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=3,
)
from stats.spirrid import orthogonalize
from time import sleep
e_arr = orthogonalize(s.evar_list)
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
import enthought.mayavi.mlab as m
f = m.figure(1, size=(1000, 500), fgcolor=(0, 0, 0), bgcolor=(1.0, 1.0, 1.0))
s = m.surf(n_e_arr[1], n_e_arr[2], n_mu_q_arr[0, :, :])
m.axes(
s,
color=(0.7, 0.7, 0.7),
),
n_int = 20)
ctt = CTT(length = length,
nx = nx,
matrix_strength = matrix_strength,
cb_randomization = rand,
cb_type = 'mean',
)
P = np.linspace(0.1, 400, 200)
matrix = []
reinf = []
for p in P:
ctt.applied_force = p
matrix.append(ctt.eps_r_x)
reinf.append(ctt.eps_m_x)
e_arr = orthogonalize([ctt.x_arr, P])
n_e_arr = [ e / np.max(np.fabs(e)) for e in e_arr ]
mu_m_arr = np.array(matrix)
mu_r_arr = np.array(reinf)
n_mu_m_arr = mu_m_arr / np.max(np.fabs(mu_m_arr))
n_mu_r_arr = mu_r_arr / np.max(np.fabs(mu_r_arr))
#m.surf(n_e_arr[0], n_e_arr[1], n_mu_m_arr)
m.surf(n_e_arr[0], n_e_arr[1], n_mu_r_arr)
plt.plot(ctt.eps, P)
plt.show()
m.show()
## plt.show()
# e_arr = orthogonalize([x, P])
# n_e_arr = [ e / np.max(np.fabs(e)) for e in e_arr ]
#
# mu_q_arr = np.array(forces)
#
# n_mu_q_arr = mu_q_arr / np.max(np.fabs(mu_q_arr))
# m.surf(n_e_arr[0], n_e_arr[1], n_mu_q_arr)
mfy = MultifilamentYarn()
mfy.Ll = 10.
mfy.Lr = 20.
x = np.linspace(-30, 30, 300)
P = np.linspace(0., 580, 200)
forces = []
for p in P:
mfy.P = p
forces.append(mfy.get_force_x_reinf(x))
# plt.plot( x, mfy.get_force_x_reinf( x ) )
# plt.show()
e_arr = orthogonalize([x, P])
n_e_arr = [ e / np.max(np.fabs(e)) for e in e_arr ]
mu_q_arr = np.array(forces)
n_mu_q_arr = mu_q_arr / np.max(np.fabs(mu_q_arr))
m.surf(n_e_arr[0], n_e_arr[1], n_mu_q_arr)
m.show()
def rand_tau_r( E_f, V_f, mu_tau, mu_r, m_list, CoV_tau_range, CoV_r_range, dp_tau, dp_r, sV0 ):
#rand tau and rand r
m = m_list[0]
s0 = ( ( mu_tau * ( m + 1 ) * sV0 ** m ) / ( E_f * pi * mu_r ** 3 ) ) ** ( 1. / ( m + 1 ) )
mu_xi = s0 * gamma( 1. + 1. / ( 1. + m ) )
for i_m, mi in enumerate( m_list ):
s0i = mu_xi / gamma( 1. + 1. / ( 1. + mi ) )
sV0i = ( ( s0i ** ( mi + 1 ) * E_f * pi * mu_r ** 3. ) / ( mu_tau * ( mi + 1. ) ) ) ** ( 1. / mi )
#
#print 'Nr', i_m, 's0i', s0i, 'sV0i', sV0i, 'mu_xi', s0i * gamma(1. + 1. / (1. + mi))
#
#Pf = RV( 'uniform', loc = 0.0, scale = 1.0 )
#w_arr = np.linspace(0, 1.2, 30)
# loc scale generation for specific CoV
#CoVtau
CoV_tau_arr = np.linspace( CoV_tau_range[0], CoV_tau_range[1], dp_tau )
loc_tau_arr = mu_tau - CoV_tau_arr * mu_tau * 3 ** 0.5
scale_tau_arr = 2 * mu_tau - 2 * loc_tau_arr
#CoVr
CoV_r_arr = np.linspace( CoV_r_range[0], CoV_r_range[1], dp_r )
loc_r_arr = mu_r - CoV_r_arr * mu_r * 3 ** 0.5
scale_r_arr = 2 * mu_r - 2 * loc_r_arr
#shaping for mayavi
e_arr = orthogonalize( [CoV_tau_arr, CoV_r_arr] )
x_axis = e_arr[0]
y_axis = e_arr[1]
#TAU gen Tuple of [loc,scale]
stats_tau = []
for s in range( dp_tau ):
stats_tau.append( RV( 'uniform', loc = loc_tau_arr[s], scale = scale_tau_arr[s] ) )
stats_tau[0] = mu_tau
stats_r = []
for s in range( dp_r ):
stats_r.append( RV( 'uniform', loc = loc_r_arr[s], scale = scale_r_arr[s] ) )
stats_r[0] = mu_r
#r gen Tuple of [loc,scale]
sigma_array = np.zeros( ( dp_tau, dp_r ) )
w_array = np.zeros( ( dp_tau, dp_r ) )
##grid
sigma_array_grid = np.zeros( ( dp_tau, dp_r ) )
w_array_grid = np.zeros( ( dp_tau, dp_r ) )
for i_tau, taui in enumerate( stats_tau ):
for i_r, ri in enumerate( stats_r ):
total = SPIRRID( q = cb,
sampling_type = 'PGrid',
evars = dict(),
tvars = dict( tau = taui, E_f = E_f, V_f = V_f, r = ri,
m = mi, sV0 = sV0i ),
n_int = 70 )
if isinstance( ri, RV ):
r_arr = np.linspace( ri.ppf( 0.001 ), ri.ppf( 0.999 ), 200 )
Er = np.trapz( r_arr ** 2 * ri.pdf( r_arr ), r_arr )
else:
Er = ri ** 2
#Optimization taui, E_f, V_f, ri, mi, sV0i, Pf, Er
def spirrid_func( w_in, Er ):
w_arr = np.array( w_in )
#print 'w', w_arr
total.evars = dict( w = w_arr )
g = -1.*total.mu_q_arr / Er
#print 'res', g
total.evars = dict( w = w_arr )
return g
#w_test = np.linspace(0.001, 1, 1000)
#total.evars = dict(w=w_test)
# plt.plot(w_test, -1.*total.mu_q_arr / Er)
#plt.show()
print i_tau, 'von', dp_tau
#Optimierung
w_ult, sigma_ult, trash1, trash2, trash3 = fmin( spirrid_func, x0 = 0.1, args = [Er], ftol = 0.2, full_output = 1 )
sigma_array[i_tau, i_r] = np.float( -1.*sigma_ult )
w_array[i_tau, i_r] = np.float( w_ult )
#grid
w_grid = np.linspace( 0.2, 0.28, 50 )
res_grid = -1.* spirrid_func( w_grid, Er )
index_max_grid = np.argmax( res_grid )
sigma_array_grid[i_tau, i_r] = res_grid[index_max_grid]
w_array_grid[i_tau, i_r] = w_grid[index_max_grid]
#print w_grid[index_max_grid]
#print np.max( sigma_array )
s_dataname = 'sigmaOPT20_with_m{}.npy'.format( mi )
np.save( s_dataname, sigma_array )
w_dataname = 'wOPT20_with_m{}.npy'.format( mi )
np.save( w_dataname, w_array )
#Grid Datasave
s_gridname = 'sigmaGRID20_with_m{}.npy'.format( mi )
np.save( s_gridname , sigma_array_grid )
w_gridname = 'wGRID20_with_m{}.npy'.format( mi )
np.save( w_gridname, w_array_grid )
#mayaviplot
mlab.surf( x_axis, y_axis, w_array )
mlab.xlabel( "rand tau" )
mlab.ylabel( "rand r" )
mlab.zlabel( "sigma" )
mlab.show()