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()
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
#===========================================================================
# 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.))
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
#===========================================================================
# 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.))
