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.))