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_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 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 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]
surface.actor.mapper.scalar_range = np.array( [ 6.97602671, 8.8533387 ] )
surface.actor.mapper.scalar_visibility = False
scene = engine.scenes[0]
scene.scene.background = ( 1.0, 1.0, 1.0 )
surface.actor.property.specular_color = ( 0.0, 0.0, 0.0 )
surface.actor.property.diffuse_color = ( 0.0, 0.0, 0.0 )
surface.actor.property.ambient_color = ( 0.0, 0.0, 0.0 )
surface.actor.property.color = ( 0.0, 0.0, 0.0 )
surface.actor.property.line_width = 1.
warp_scalar = engine.scenes[0].children[0].children[0]
module_manager = engine.scenes[0].children[0].children[0].children[0].children[0]
warp_scalar.filter.normal = np.array( [ 0. , 0. , 0.2] )
module_manager.scalar_lut_manager.scalar_bar.global_warning_display = 1
module_manager.scalar_lut_manager.scalar_bar.position2 = np.array( [ 0.8 , 0.17] )
module_manager.scalar_lut_manager.scalar_bar.position = np.array( [ 0.1 , 0.01] )
module_manager.scalar_lut_manager.data_range = np.array( [ 6.97602671, 8.8533387 ] )
module_manager.scalar_lut_manager.default_data_range = np.array( [ 6.97602671, 8.8533387 ] )
module_manager.vector_lut_manager.scalar_bar.global_warning_display = 1
module_manager.vector_lut_manager.scalar_bar.position2 = np.array( [ 0.8 , 0.17] )
module_manager.vector_lut_manager.scalar_bar.position = np.array( [ 0.1 , 0.01] )
module_manager.vector_lut_manager.data_range = np.array( [ 0., 1.] )
module_manager.vector_lut_manager.default_data_range = np.array( [ 0., 1.] )
scene.scene.isometric_view()
scene.scene.camera.position = [1.2836424071875543, 1.4371492101974028, 4.0390558511994534]
scene.scene.camera.focal_point = [0.17361105930834225, 0.21417386120592863, 3.4874067491767562]
scene.scene.camera.view_angle = 30.0
scene.scene.camera.view_up = [-0.21371002618964222, -0.23386371429877953, 0.94849132196367569]
scene.scene.camera.clipping_range = [0.79163912587841923, 2.7365159886347699]
scene.scene.camera.compute_view_plane_normal()
#mlab.surf( x_axis2, y_axis2, res2 )
#mlab.xlabel( "rand tau" )
#mlab.ylabel( "rand r" )
#mlab.zlabel( "z" )
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 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 mlab_plot():
w = np.linspace(0, 1.8, 30)
x = np.linspace(-50., 20., 30)
P = CBEMClampedFiberSP()
spirrid = SPIRRID(q = P,
sampling_type = 'LHS',
evars = dict(w = w,
x = x),
tvars = dict(Ll = 50.,
Lr = 20.,
tau = RV('uniform', 0.05, .15),
l = RV('uniform', 2.0, 15.0),
A_f = Af,
E_f = Ef,
theta = 0.01, #RV('uniform', 0.0, .02),
xi = RV('weibull_min', scale = 0.0179, shape = 5, n_int = 10),
phi = phi,
E_m = Em,
A_m = Am,
Nf = 1.
),
n_int = 20)
e_arr = make_ogrid([x, w])
n_e_arr = [ e / np.max(np.fabs(e)) for e in e_arr ]
mu_q_arr = spirrid.mu_q_arr
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)
from numpy import array
# -------------------------------------------
scene = engine.scenes[0]
scene.scene.background = (1.0, 1.0, 1.0)
scene.scene.camera.position = [1.5028781189276834, 3.5681173520859848, 1.9543753549631095]
scene.scene.camera.focal_point = [-0.29999999701976776, 0.5, 0.5]
scene.scene.camera.view_angle = 30.0
scene.scene.camera.view_up = [-0.14835983161589669, -0.35107055575000001, 0.92452086252733578]
scene.scene.camera.clipping_range = [1.9820957553309271, 6.1977861427731007]
scene.scene.camera.compute_view_plane_normal()
module_manager = engine.scenes[0].children[0].children[0].children[0].children[0]
module_manager.scalar_lut_manager.lut_mode = 'Greys'
m.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 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()
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()
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
#===========================================================================
# Prepare plotting
#===========================================================================
tdir = tempfile.mkdtemp()
n_img = n_mu_q_arr.shape[0]
fnames = [os.path.join(tdir, 'x%02d.jpg' % i) for i in range(n_img) ]
f = m.figure(1, size = (1000, 500), fgcolor = (0, 0, 0),
bgcolor = (1., 1., 1.))
s = m.surf(n_e_arr[1], n_e_arr[2], n_mu_q_arr[0, :, :])
ms = s.mlab_source
m.axes(s, color = (.7, .7, .7),
extent = (-1, 1, 0, 1, 0, 1),
ranges = (-0.21, 0.21, 0.1, 20, 0, max_mu_q),
xlabel = 'x[mm]', ylabel = 'Lr[mm]',
zlabel = 'f[N]',)
m.view(-60.0, 70.0, focalpoint = [0., 0.45, 0.45])
m.savefig(fnames[0])
for i, fname in enumerate(fnames[1:]):
ms.scalars = n_mu_q_arr[i, :, :]
m.savefig(fname)
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()
def w_m(m):
dataname = "wOPT20_with_m{}.npy".format(m)
res = np.load(dataname)
#res2 = np.load( "sigmaAN_with_m7.0.npy" )
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]
surface.actor.mapper.scalar_range = np.array([6.97602671, 8.8533387])
surface.actor.mapper.scalar_visibility = False
scene = engine.scenes[0]
scene.scene.background = (1.0, 1.0, 1.0)
surface.actor.property.specular_color = (0.0, 0.0, 0.0)
surface.actor.property.diffuse_color = (0.0, 0.0, 0.0)
surface.actor.property.ambient_color = (0.0, 0.0, 0.0)
surface.actor.property.color = (0.0, 0.0, 0.0)
surface.actor.property.line_width = 1.
warp_scalar = engine.scenes[0].children[0].children[0]
module_manager = engine.scenes[0].children[0].children[0].children[
0].children[0]
warp_scalar.filter.normal = np.array([0., 0., 15])
module_manager.scalar_lut_manager.scalar_bar.global_warning_display = 1
module_manager.scalar_lut_manager.scalar_bar.position2 = np.array(
[0.8, 0.17])
module_manager.scalar_lut_manager.scalar_bar.position = np.array(
[0.1, 0.01])
module_manager.scalar_lut_manager.data_range = np.array(
[6.97602671, 8.8533387])
module_manager.scalar_lut_manager.default_data_range = np.array(
[6.97602671, 8.8533387])
module_manager.vector_lut_manager.scalar_bar.global_warning_display = 1
module_manager.vector_lut_manager.scalar_bar.position2 = np.array(
[0.8, 0.17])
module_manager.vector_lut_manager.scalar_bar.position = np.array(
[0.1, 0.01])
module_manager.vector_lut_manager.data_range = np.array([0., 1.])
module_manager.vector_lut_manager.default_data_range = np.array([0., 1.])
scene.scene.isometric_view()
scene.scene.camera.position = [
1.2836424071875543, 1.4371492101974028, 4.0390558511994534
]
scene.scene.camera.focal_point = [
0.17361105930834225, 0.21417386120592863, 3.4874067491767562
]
scene.scene.camera.view_angle = 30.0
scene.scene.camera.view_up = [
-0.21371002618964222, -0.23386371429877953, 0.94849132196367569
]
scene.scene.camera.clipping_range = [
0.79163912587841923, 2.7365159886347699
]
scene.scene.camera.compute_view_plane_normal()
mlab.show()
def param_plot( self ):
fig = plt.figure()
ax = fig.gca( projection = '3d' )
values = self.results
p1 = self.values1
p2 = self.values2
n1 = self.name1
n2 = self.name2
n3 = self.name3
x = np.linspace( p1[0], p1[-1], 20 )
y = np.linspace( p2[0], p2[-1], 20 )
X = np.meshgrid( x, x )[0]
Y = np.meshgrid( y, y )[1]
val = np.array( values[0:9] ).reshape( 3, 3 )
spl = spline( p1, p2, val, kx = 2, ky = 2 )
Z = spl( x, y ) / 0.89
val2 = np.array( values[9:] ).reshape( 3, 3 )
spl2 = spline( p1, p2, val2, kx = 2, ky = 2 )
ZZ = spl2( x, y ) / 0.89
#
# ax.plot_wireframe( X, Y, Z, rstride = 5, cstride = 5, alpha = 1.,
# color = 'black', lw = 2 )
#
# i = 0
# for V in p2:
# for vorsp in p1:
# print vorsp, V, values[i]
# ax.plot( [vorsp, vorsp], [V, V], values[i] / 0.89, 'ko', lw = 5 )
# i += 1
# glas 1200tex 5cN: ax.plot( [100, 100], [30, 30], np.array( [588.32, 588.32] ) / 0.89, 'ro', label = '%.1f MPa' % ( 588.32 / 0.89 ) )
# carbon 1600tex 5cN: ax.plot( [70, 70], [60, 60], np.array( [1768., 1768.] ) / 0.89, 'ro', label = '%.1f MPa' % ( 1768. / 0.89 ) )
# ax.plot( [0, 0], [-1e15, -1e15], color = 'black', label = self.label1, lw = 2 )
ax.plot_wireframe( X, Y, ZZ, rstride = 5, cstride = 5, alpha = 1.,
color = 'black', lw = 2 )
i = 0
for V in p2:
for vorsp in p1:
print vorsp, V, values[9 + i]
ax.plot( [vorsp, vorsp], [V, V], values[9 + i] / 0.89, 'ko' )
m.points3d( vorsp, V, values[i] / 0.89 )
i += 1
# glas 1200ax.plot( [50, 50], [30, 30], np.array( [594.15, 594.15] ) / 0.89, 'ro', label = '%.1f MPa' % ( 594.15 / 0.89 ) )
ax.plot( [70, 70], [40, 40], np.array( [1773., 1773.] ) / 0.89, 'ro', label = '%.1f MPa' % ( 1773. / 0.89 ) )
m.points3d( 100, 30, 588.32 / 0.89, color = ( 0.5, 0.7, 0.2 ) )
ax.plot( [0, 0], [-1e15, -1e15], color = 'black', label = self.label2, lw = 2 )
ax.legend( loc = 'upper left' )
ax.set_title( self.title )
ax.set_xlabel( n1 )
ax.set_xlim3d( self.xlim[0], self.xlim[1] )
ax.set_ylabel( n2 )
ax.set_ylim3d( self.ylim[0], self.ylim[1] )
ax.set_zlabel( n3 )
ax.set_zlim3d( self.zlim[0], self.zlim[1] )
#plt.show()
e_arr = orthogonalize( [x, y] )
#n_e_arr = [ e / np.max(np.fabs(e)) for e in e_arr ]
strength = Z
#strength_n = strength / np.max(np.fabs(strength))
m.surf( e_arr[0], e_arr[1], strength )
m.show()
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
#===========================================================================
# Prepare plotting
#===========================================================================
tdir = tempfile.mkdtemp()
n_img = n_mu_q_arr.shape[0]
fnames = [os.path.join(tdir, 'x%02d.jpg' % i) for i in range(n_img)]
f = m.figure(1, size=(1000, 500), fgcolor=(0, 0, 0), bgcolor=(1., 1., 1.))
s = m.surf(n_e_arr[1], n_e_arr[2], n_mu_q_arr[0, :, :])
ms = s.mlab_source
m.axes(
s,
color=(.7, .7, .7),
extent=(-1, 1, 0, 1, 0, 1),
ranges=(-0.21, 0.21, 0.1, 20, 0, max_mu_q),
xlabel='x[mm]',
ylabel='Lr[mm]',
zlabel='f[N]',
)
m.view(-60.0, 70.0, focalpoint=[0., 0.45, 0.45])
m.savefig(fnames[0])
def plot3d_surf(self):
# set background color to white and forground color to black
#
m.figure(fgcolor=(0, 0, 0), bgcolor=(1, 1, 1), size=(1600, 900))
#----------------------------------------------------
# plot 'plot_var' as 3d-surface plot
#----------------------------------------------------
# @todo: is this comment still relavant? "why is moved in the y direction?"
if self.use_mask == 'true':
mask_arr = self.grid_mask_w
else:
mask_arr = None
plot3d_var = getattr(self, self.plot3d_var_[0])
vmax = self.plot3d_var_[1]
# m.surf(self.x_arr, self.y_arr, plot3d_var, vmax = vmax, mask = mask_arr, warp_scale = warp_scale)
m.surf(self.x_arr_avg, self.y_arr_avg, plot3d_var, vmax=vmax, mask=mask_arr, warp_scale=warp_scale)
# use average coordinate values (1D-arrays)
#
# m.surf(self.x_arr_avg, self.y_arr_avg, plot3d_var, vmax = vmax, mask = mask_arr, warp_scale = warp_scale)
# plot scalarbar
#
m.scalarbar(orientation='horizontal', title=self.plot3d_var)
# plot axes
#
# m.axes()
# figure title
# uses directory path by default
#
if plot_title == 'true':
m.title(os.path.join(self.data_dir, self.basename))
# get mayavi engine
#
engine = m.get_engine()
# rotate scene
#
scene = engine.scenes[0]
scene.scene.parallel_projection = True
if self.warp_scale == 1:
scene.scene.z_plus_view()
else:
scene.scene.isometric_view()
scene.scene.camera.position = [-319.07443227647047, -469.29101319337502, 434.40818470843612]
scene.scene.camera.focal_point = [-66.983721840860625, -79.447548413824947, 1.1328650920308507]
scene.scene.camera.view_angle = 30.0
scene.scene.camera.view_up = [0.49290441759866288, 0.48449293877207339, 0.72271144130401255]
scene.scene.camera.clipping_range = [325.28180250321782, 995.97136098229157]
scene.scene.camera.compute_view_plane_normal()
scene.scene.render()
# predefine the position of the scalarbar
#
module_manager = engine.scenes[0].children[0].children[0].children[0].children[0]
module_manager.scalar_lut_manager.scalar_bar_representation.minimum_size = np.array([1, 1])
module_manager.scalar_lut_manager.scalar_bar_representation.position2 = np.array([ 0.27780226, 0.11638842])
module_manager.scalar_lut_manager.scalar_bar_representation.position = np.array([ 0.59707606, 0.16105125])
module_manager.scalar_lut_manager.scalar_bar_representation.maximum_size = np.array([100000, 100000])
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()