vec_space = 5 x = np.arange(0, len(mz[0]), vec_space) y = x.copy() mx_vec = mx[::vec_space, ::vec_space].copy() my_vec = my[::vec_space, ::vec_space].copy() plt.close('all') fig1, ax1 = plt.subplots(figsize=(3, 3)) fig1.set_size_inches(4, 4) im1 = plt.imshow(mz, cmap='jet') Q = plt.quiver(x, y, mx_vec, my_vec, units='width', scale=20, width=0.005) fig1.colorbar(im1, ax=ax1, fraction=0.046, pad=0.04) ax1.xaxis.set_ticklabels([]) ax1.yaxis.set_ticklabels([]) fp.format_plot(plt, 290, 290, 0, 0, no_axes=True) pylab.savefig('/Users/alec/UCSB/scan_images/mz_recon_' + str(scannum) + '.png', format='png') fig1, ax1 = plt.subplots() im1 = plt.imshow(mr, cmap='jet') Q = plt.quiver(x, y, mx_vec, my_vec, units='width') fig1.colorbar(im1, ax=ax1, fraction=0.046, pad=0.04) fp.format_plot(plt, 500, 500, 0, 320, no_axes=True) fig1, ax1 = plt.subplots() im1 = plt.imshow(phi_seed, cmap='jet') fig1.colorbar(im1, ax=ax1, fraction=0.046, pad=0.04) fp.format_plot(plt, 290, 290, 290, 0, no_axes=True) # fig1, ax1 = plt.subplots()
slen = len(domains) res = scansize / slen res_difference = 2e-9 thickness = 0.911e-9 Ms = 1.044e6 domains = np.add(np.multiply(2 / 255, domains), -1) domains1 = np.flipud(np.fliplr(domains)) domains2 = np.flipud(domains) domains3 = np.flipud(np.fliplr(domains)) domains4 = np.fliplr(domains) domains5 = np.fliplr(domains) domains6 = np.flipud(np.fliplr(domains)) domains7 = np.flipud(domains) domains8 = np.flipud(np.fliplr(domains)) domainstop = np.concatenate((domains1, domains2, domains3), axis=1) domainsmiddle = np.concatenate((domains4, domains, domains5), axis=1) domainsbottom = np.concatenate((domains6, domains7, domains8), axis=1) domains_mirror = np.concatenate((domainstop, domainsmiddle, domainsbottom), axis=0) plt.close('all') fig1, ax1 = plt.subplots() im1 = plt.imshow(domains_mirror, cmap='Greys', interpolation='nearest') fp.format_plot(plt, 800, 800, 50, 50) plt.show()
print(t2 - t1) # BLOCH dw = 30.0e-9 mz = np.tanh(grid_dist / dw) #---------------- PLOTS ------------------------------------------ #----------------------------------------------------------------- plt.close('all') fig2, ax2 = plt.subplots(figsize=(5, 5)) im2 = plt.imshow(mdata, cmap='gray', interpolation='nearest') plt.colorbar(im2, fraction=0.046, pad=0.04, use_gridspec=True) ax2.set_axis_off() fp.format_plot(plt, 350, 350, 450, 50) fig3, ax3 = plt.subplots() plt.plot(theta, theta_grad, 'g.') plt.plot(theta_int_list, theta_grad_int(theta_int_list), 'r-') fp.format_plot(plt, 350, 350, 50, 50) fig3, ax3 = plt.subplots() plt.plot(phis, r0s, 'g.') plt.plot(phis_int_list, r0s_int(phis_int_list), 'r-') fp.format_plot(plt, 350, 350, 50, 450) fig4, ax4 = plt.subplots() plt.plot(x0, y0, 'g.') plt.plot(x0_int, y0_int, 'r-') fp.format_plot(plt, 350, 350, 450, 450)
mx = np.max(scandata) scandata = (scandata - mn) / (mx - mn) scandataint = ndimage.interpolation.zoom(scandata, 2, order=1) plt.close('all') my_dpi = 96 size_inches = size / my_dpi if save: fig = plt.figure(frameon=False) fig.set_size_inches(size_inches, size_inches) ax = plt.Axes(fig, [0., 0., 1., 1.]) fig.add_axes(ax) pcol = ax.pcolormesh(scandataint, cmap='bone', linewidth=0, edgecolor='none') pcol.axes.set_xlim(0, len(scandata[0])) pcol.axes.set_ylim(0, len(scandata)) plt.gca().invert_yaxis() ax.set_axis_off() fig.savefig(savepath, format=filetype, dpi=my_dpi) else: fig, ax = plt.subplots() im = plt.imshow(scandataint, cmap='bone', interpolation='nearest') cbar = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04) cbar.set_ticks([0, 1]) cbar.set_ticklabels([0, 1]) fp.format_plot(plt, size, size, 0, 50) plt.show()
(delta_total_wall_length_norm * thicknessSI) ) print('domain_wall_energy_density = ' + str(domain_wall_energy_density)) As = (domain_wall_energy_density**2)/(16*KeffSI) DW_width = domain_wall_energy_density/(4*KeffSI) print('DW_width (no pi) = ' + str(DW_width)) print('As = ' + str(As)) plt.close('all') fig, ax = plt.subplots() im = plt.imshow(demagH, cmap='jet', interpolation='nearest') plt.colorbar(im, fraction=0.046, pad=0.04) fp.format_plot(plt, 500, 500, 50, 50) fig, ax = plt.subplots() im = plt.imshow(domains, cmap='jet', interpolation='nearest') plt.colorbar(im, fraction=0.046, pad=0.04) fp.format_plot(plt, 500, 500, 550, 50) # # fig1, ax1 = plt.subplots() # # im1 = plt.imshow(domains[1:slen-1,1:slen-1], cmap='Greys', interpolation='nearest',alpha=0.5) # # for n, contour in enumerate(smoothed_contours): # ax1.plot(contour[:, 1], contour[:, 0], linewidth=1) # # ax1.axis('image') # ax1.set_xticks([]) # ax1.set_yticks([])
color='black', linewidth=1.3, linestyle='-', label='ARG-1200-TR') if sig_flag == 'tex': ex_run.ex_type._plot_tex_stress_strain_asc(p, xscale=1000., color='black', linewidth=1.3, linestyle='-', label='2D-01-08') if sig_flag == 'tex': format_plot(p, xlabel='Dehnung [1E-3]', ylabel='Textilspannung [MPa]', xlim=14., ylim=1400.) if sig_flag == 'comp': format_plot(p, xlabel='Dehnung [1E-3]', ylabel='Kompositspannung [MPa]', xlim=14., ylim=25.) font.set_size('12') p.legend(prop=font) if save_fig_to_file: # create a report-subfolder with the name of the script (without file extension '.py') # and save it in the test-type-subfolders with the name and path as
'k', 'k', 'k', ] fig = p.figure(facecolor='white') fig.set_size_inches(8, 6) plot_method_str = '_plot_' + sig_flag + '_stress_strain_asc' for i, ex_path in enumerate(path_list): ex_run = ExRun(ex_path) plot_method = getattr(ex_run.ex_type, plot_method_str) plot_method(p, k_rho=k_rho, color=color_list[i], linewidth=1.2, linestyle='-', label=label_list[i], xscale=1000., plot_analytical_stiffness=True, interpolated=True) # set limits and labels for axis # if sig_flag == 'tex': format_plot(p, xlabel='strain [1E-3]', ylabel='textile stress [MPa]', xlim=8., ylim=1700.) if sig_flag == 'comp': format_plot(p, xlabel='strain [1E-3]', ylabel='composite stress [MPa]', xlim=8., ylim=25.) # plot grid lines # axes = p.gca() axes.xaxis.grid(True, which='major') axes.yaxis.grid(True, which='major') # plot legend # p.legend(prop=font, loc=4, frameon=False) # (4: lower right) (7: center right) # -------------------------------- # save figure
phi = np.loadtxt(path + 'phi_c_test.txt', delimiter=',') phi_seed = np.loadtxt(path + 'phi_seed_c_test.txt', delimiter=',') phi_seed = phi_seed % (2 * pi) phi = phi % (2 * pi) mr = np.sqrt(1 - mz**2) mx_seed = np.multiply(np.cos(phi_seed), mr) my_seed = np.multiply(np.sin(phi_seed), mr) mx = np.multiply(np.cos(phi), mr) my = np.multiply(np.sin(phi), mr) plt.close('all') fig1, ax1 = plt.subplots() im1 = plt.imshow(mz, cmap='jet') fig1.colorbar(im1, ax=ax1, fraction=0.046, pad=0.04) fp.format_plot(plt, 400, 400, 50, 50) fig1, ax1 = plt.subplots() im1 = plt.imshow(mr, cmap='jet') fig1.colorbar(im1, ax=ax1, fraction=0.046, pad=0.04) fp.format_plot(plt, 400, 400, 450, 50) fig1, ax1 = plt.subplots() im1 = plt.imshow(phi, cmap='jet') fig1.colorbar(im1, ax=ax1, fraction=0.046, pad=0.04) fp.format_plot(plt, 400, 400, 50, 450) fig1, ax1 = plt.subplots() im1 = plt.imshow(phi_seed, cmap='jet') fig1.colorbar(im1, ax=ax1, fraction=0.046, pad=0.04) fp.format_plot(plt, 400, 400, 450, 450)
hlist[2 * j + 1] = rpopt[0] dwlist[2 * j] = popt[2] dwlist[2 * j + 1] = rpopt[2] # philist[2*j] = popt[2] # philist[2*j+1] = rpopt[2] # thetalist[2*j] = popt[2] # thetalist[2*j+1] = rpopt[2] # bz0list[2*j] = popt[4] # bz0list[2*j+1] = rpopt[4] plt.figure(1) csubplot = plt.subplot(gs[(j % 5), int(np.floor(j / 5) * 2)]) plt.plot(xShort, cal_func(xShort, *guess), 'g-') plt.errorbar(xShort, yShort, yerr=yeShort, color='#000000', fmt='.') plt.plot(xShort, cal_func(xShort, *popt), 'r-') csubplot = plt.subplot(gs[(j % 5), int(np.floor(j / 5) * 2 + 1)]) plt.plot(xShort, cal_func(xShort, *rguess), 'g-') plt.errorbar(xShort, ryShort, yerr=ryeShort, color='#000000', fmt='.') plt.plot(xShort, cal_func(xShort, *rpopt), 'r-') fp.format_plot(plt, 1200, 900, 0, 50, tight=False) plt.show() print('h mean = ' + str(np.mean(hlist)) + ' +/- ' + str(np.std(hlist))) # print('theta mean = '+str(np.mean(thetalist))+' +/- '+str(np.std(thetalist))) # print('phi mean = '+str(np.mean(philist))+' +/- '+str(np.std(philist))) print('edge width mean = ' + str(np.mean(dwlist)) + ' +/- ' + str(np.std(dwlist))) #print('bz0 mean = '+str(np.mean(bz0list))+' +/- '+str(np.std(bz0list)))
bpopt, bpcov = curve_fit(fit_tanh, bxf, bzphi[i], p0=bguesses[i]) bfits[i] = fit_tanh(xf, *bpopt) bwidths[i] = np.abs(bpopt[3]) #---------------- PLOTS ------------------------------------------ #----------------------------------------------------------------- plt.close('all') fig1, ax1 = plt.subplots() fig1.set_size_inches(4, 4) im1 = plt.imshow(datas0, cmap='gray', interpolation='nearest') fig1.colorbar(im1, ax=ax1, fraction=0.046, pad=0.04) ax1.xaxis.set_ticklabels([]) ax1.yaxis.set_ticklabels([]) fp.format_plot(plt, 250, 250, 50, 50) pylab.savefig('/Users/alec/UCSB/scan_images/datas_' + str(scannum) + filespec + '.png', format='png') fig1, ax1 = plt.subplots() fig1.set_size_inches(4, 4) im1 = plt.imshow(bxdata, cmap='gray', interpolation='nearest') fig1.colorbar(im1, ax=ax1, fraction=0.046, pad=0.04) ax1.xaxis.set_ticklabels([]) ax1.yaxis.set_ticklabels([]) fp.format_plot(plt, 250, 250, 50, 50) pylab.savefig('/Users/alec/UCSB/scan_images/bx_' + str(scannum) + filespec + '.png', format='png')
ffcut = ffxcut x0, x1, y0, y1 = cutcrop[0], cutcrop[1], yconstant, yconstant # ---------------------- PLOTS ------------------------------------------------------- # ------------------------------------------------------------------------------------ plt.close('all') fig1, ax1 = plt.subplots() im1 = plt.imshow(ffdata[0], cmap='bone', interpolation='nearest') fig1.colorbar(im1, ax=ax1, fraction=0.046, pad=0.04) plt.plot([x0, x1], [y0, y1], 'r-') plt.axis('image') fp.format_plot(plt, 450, 450, 450, 50) ploth = 1 plotr = 0 fig1, ax1 = plt.subplots() plt.plot(blochnv[ploth][0], blochnv[ploth][1], color='#2D7DD2', linewidth=2.0, label="Bloch") plt.plot(nleftnv[ploth][0], nleftnv[ploth][1], color='#F97304', linewidth=2.0, label=u'left-handed Néel') plt.plot(nrightnv[ploth][0],
plot_method(p, k_rho=k_rho, color=color_list[i], linewidth=1.2, linestyle='-', label=label_list[i], xscale=1000., plot_analytical_stiffness=True, interpolated=True) # set limits and labels for axis # if sig_flag == 'tex': format_plot(p, xlabel='strain [1E-3]', ylabel='textile stress [MPa]', xlim=8., ylim=1700.) if sig_flag == 'comp': format_plot(p, xlabel='strain [1E-3]', ylabel='composite stress [MPa]', xlim=8., ylim=25.) # plot grid lines # axes = p.gca() axes.xaxis.grid(True, which='major') axes.yaxis.grid(True, which='major')
# ## for figure compare pretest with barrelshell (TTb_2cm) only: # xarr = np.array([0.3 * eps_u_avg, 1.2 * eps_u_avg]) # sig_tex_avg = 1077 # yarr = np.array([sig_tex_avg, sig_tex_avg]) # p.plot(xarr, yarr, linestyle='--', color='k', linewidth=1.) # p.text(0.3 * eps_u_avg, sig_tex_avg * 1.03, r'$\sigma_\mathrm{tex,u}\,=\,%0.0f\,\mathrm{MPa}$' % (sig_tex_avg), fontsize=16) # , bbox={'facecolor':'white', 'edgecolor':'none'}) # sig_tex_avg = 1483 # yarr = np.array([sig_tex_avg, sig_tex_avg]) # p.plot(xarr, yarr, linestyle='--', color='grey', linewidth=1.) # p.text(0.3 * eps_u_avg, sig_tex_avg * 1.03, r'$\sigma_\mathrm{tex,u}\,=\,%0.0f\,\mathrm{MPa}$' % (sig_tex_avg), color='grey', fontsize=16) # , bbox={'facecolor':'white', 'edgecolor':'none'}) # p.text(0.10 * eps_u_avg, sig_tex_avg * .04, r'$E_\mathrm{tex}$', color='k', fontsize=16) # , bbox={'facecolor':'white', 'edgecolor':'none'}) # format plot # if sig_flag == 'comp': format_plot(p, fontsize=15, xlabel='Dehnung $\epsilon$ [1E-3]', ylabel='Kompositspannung $\sigma_\mathrm{c}$ [MPa]', xlim=xlim, ylim=ylim) if sig_flag == 'tex': format_plot(p, fontsize=15, xlabel='Dehnung $\epsilon$ [1E-3]', ylabel='Textilspannung $\sigma_\mathrm{tex}$ [MPa]', xlim=xlim, ylim=ylim) axes = p.gca() axes.xaxis.grid(True, which='major') axes.yaxis.grid(True, which='major') # plot legend # if legend_flag: font = FontProperties() font.set_family('serif') font.set_style('normal') font.set_size(14) font.set_variant('normal') # font.set_weight('ultralight')
plt.plot(radcutdiff[2], color='#E58A25', linewidth=2.0, label=u'left-handed Néel') plt.plot(radcutdiff[3], color='#000000', linewidth=2.0, label=r'$\gamma_h$ = 76.8 degrees') plt.plot(radcutdiff[4], color='#97CC04', linewidth=2.0, label=u'right-handed Néel') plt.legend(loc=2, borderaxespad=0., prop={'size': 12}) plt.ylabel('residual') plt.xlabel(r'radial cut angle ($\times\pi/8$)') fp.format_plot(plt, 600, 600, 0, 50) pylab.savefig(savepath + 'residuals.png', format='png') fig1, ax1 = plt.subplots() fig1.set_size_inches(6, 6) ax1.set_axis_off() im1 = plt.imshow(nleftnv, cmap='bone', interpolation='nearest') fig1.colorbar(im1, ax=ax1, fraction=0.046, pad=0.04) # fp.format_plot(plt, 450, 450, 0, 50) pylab.savefig(savepath + 'nv_sim_' + str(filenum) + filespec + '.png', format='png', dpi=my_dpi) fig1, ax1 = plt.subplots() fig1.set_size_inches(8, 8) ax1.set_axis_off()
rpcov = np.zeros((4, 4)) print('fit fail') thetalist[2 * j] = popt[2] thetalist[2 * j + 1] = rpopt[2] # mstlist[2*j] = popt[3] # mstlist[2*j+1] = rpopt[3] # bz0list[2*j] = popt[4] # bz0list[2*j+1] = rpopt[4] # hlist[2 * j] = popt[0] hlist[2 * j + 1] = rpopt[0] plt.figure(fig1.number) csubplot = plt.subplot(gs[(j % 5), int(np.floor(j / 5) * 2)]) plt.plot(x, cal_func(x, *guess), 'g-') plt.errorbar(x, y, yerr=ye, color='#000000', fmt='.') plt.plot(x, cal_func(x, *popt), 'r-') # plt.plot(yargmax*dres,ymax,'co') csubplot = plt.subplot(gs[(j % 5), int(np.floor(j / 5) * 2 + 1)]) # plt.plot(x,cal_func(x,*rguess),'g-') plt.errorbar(x, ry, yerr=rye, color='#000000', fmt='.') plt.plot(x, cal_func(x, *rpopt), 'r-') fp.format_plot(plt, 1400, 850, 50, 50, tight=False) plt.show() # print('Ms*t mean = '+str(np.mean(mstlist))+' +/- '+str(np.std(mstlist))) print('h mean = ' + str(np.mean(hlist)) + ' +/- ' + str(np.std(hlist))) print('theta mean = ' + str(np.mean(thetalist)) + ' +/- ' + str(np.std(thetalist))) #print('bz0 mean = '+str(np.mean(bz0list))+' +/- '+str(np.std(bz0list)))
import numpy as np import matplotlib.pyplot as plt import format_plot as fp import stray_field_calc as sfc pi = np.pi path = '/Users/alec/UCSB/cofeb_analysis_data/m_reconstruction/' mx = np.loadtxt(path + "mbx_test.dat") my = np.loadtxt(path + "mby_test.dat") mz = np.loadtxt(path + "mbz_test.dat") sfc_result = sfc.stray_field_calc(mx, my, mz, 1.0e-3, 4000, 80e-9) hz = sfc_result[4][2] hzc = np.loadtxt(path + 'stray_field_test_c.txt', delimiter=',') plt.close('all') fig1, ax1 = plt.subplots() im1 = plt.imshow(hz, cmap='jet') fig1.colorbar(im1, ax=ax1, fraction=0.046, pad=0.04) fp.format_plot(plt, 400, 400, 50, 50) fig1, ax1 = plt.subplots() im1 = plt.imshow(hzc, cmap='jet') fig1.colorbar(im1, ax=ax1, fraction=0.046, pad=0.04) fp.format_plot(plt, 400, 400, 50, 50) plt.show()
# sig_tex_avg = 1077 # yarr = np.array([sig_tex_avg, sig_tex_avg]) # p.plot(xarr, yarr, linestyle='--', color='k', linewidth=1.) # p.text(0.3 * eps_u_avg, sig_tex_avg * 1.03, r'$\sigma_\mathrm{tex,u}\,=\,%0.0f\,\mathrm{MPa}$' % (sig_tex_avg), fontsize=16) # , bbox={'facecolor':'white', 'edgecolor':'none'}) # sig_tex_avg = 1483 # yarr = np.array([sig_tex_avg, sig_tex_avg]) # p.plot(xarr, yarr, linestyle='--', color='grey', linewidth=1.) # p.text(0.3 * eps_u_avg, sig_tex_avg * 1.03, r'$\sigma_\mathrm{tex,u}\,=\,%0.0f\,\mathrm{MPa}$' % (sig_tex_avg), color='grey', fontsize=16) # , bbox={'facecolor':'white', 'edgecolor':'none'}) # p.text(0.10 * eps_u_avg, sig_tex_avg * .04, r'$E_\mathrm{tex}$', color='k', fontsize=16) # , bbox={'facecolor':'white', 'edgecolor':'none'}) # format plot # if sig_flag == 'comp': format_plot(p, fontsize=15, xlabel='Dehnung $\epsilon$ [1E-3]', ylabel='Kompositspannung $\sigma_\mathrm{c}$ [MPa]', xlim=xlim, ylim=ylim) if sig_flag == 'tex': format_plot(p, fontsize=15, xlabel='Dehnung $\epsilon$ [1E-3]', ylabel='Textilspannung $\sigma_\mathrm{tex}$ [MPa]', xlim=xlim, ylim=ylim) axes = p.gca() axes.xaxis.grid(True, which='major') axes.yaxis.grid(True, which='major') # plot legend #
"/Users/alec/UCSB/mathematica/CoFeB-MgO/linecut_simulations/linecut_30_h/neelright_y_" + str(heightnm) + "_30._400_10..dat") hintz = np.loadtxt( "/Users/alec/UCSB/mathematica/CoFeB-MgO/linecut_simulations/linecut_30_h/neelright_z_" + str(heightnm) + "_30._400_10..dat") hintx = np.multiply(hintx, (1e-3) * MstSInm) hinty = np.multiply(hinty, (1e-3) * MstSInm) hintz = np.multiply(hintz, (1e-3) * MstSInm) flen = len(hr[0]) ilen = len(hintx) ires = 10.0 fres = 2500 / flen ix = np.arange(-ilen * ires / 2, ilen * ires / 2, ires) fx = np.arange(-flen * fres / 2, flen * fres / 2, fres) plt.close('all') fig1, ax1 = plt.subplots() plt.plot(ix, hintx, 'r-') plt.plot(fx, hr[0][int(flen / 2), :], 'b-') fig1, ax1 = plt.subplots() plt.plot(ix, hintz, 'r-') plt.plot(fx, hr[2][int(flen / 2), :], 'b-') fp.format_plot(plt, 650, 400, 0, 450) plt.show()
path = '/Users/alec/UCSB/cofeb_analysis_data/m_reconstruction/' mz = np.loadtxt(path + 'mzdata_test.txt') phi_seed = np.loadtxt(path + 'phi_seed_test.txt', delimiter=',') phi = np.loadtxt(path + 'phi_test.txt', delimiter=',') mr = np.sqrt(1 - mz**2) mx_seed = np.multiply(np.cos(phi_seed), mr) my_seed = np.multiply(np.sin(phi_seed), mr) mx = np.multiply(np.cos(phi), mr) my = np.multiply(np.sin(phi), mr) plt.close('all') fig1, ax1 = plt.subplots() im1 = plt.imshow(mz, cmap='jet') fig1.colorbar(im1, ax=ax1, fraction=0.046, pad=0.04) fp.format_plot(plt, 400, 400, 50, 50) fig1, ax1 = plt.subplots() im1 = plt.imshow(mr, cmap='jet') fig1.colorbar(im1, ax=ax1, fraction=0.046, pad=0.04) fp.format_plot(plt, 400, 400, 450, 50) fig1, ax1 = plt.subplots() im1 = plt.imshow(phi, cmap='jet') fig1.colorbar(im1, ax=ax1, fraction=0.046, pad=0.04) fp.format_plot(plt, 400, 400, 50, 450) fig1, ax1 = plt.subplots() im1 = plt.imshow(phi_seed, cmap='jet') fig1.colorbar(im1, ax=ax1, fraction=0.046, pad=0.04) fp.format_plot(plt, 400, 400, 450, 450)
r0s = np.append(r0s, r0s[0]) np.savetxt(path + 'stray_field_sim/' + 'radiusphi_' + filespec + '.txt', (angles, r0s), delimiter=',') #---------------- PLOTS ------------------------------------------ #----------------------------------------------------------------- plt.close('all') fig1, ax1 = plt.subplots() im1 = ax1.imshow(wimdatafilter, cmap='gray', interpolation='nearest') fig1.colorbar(im1, ax=ax1, fraction=0.046, pad=0.04) fp.format_plot(plt, 400, 400, 50, 50) fig2, ax2 = plt.subplots(figsize=(5, 5)) im2 = plt.imshow(mdata, cmap='jet', interpolation='nearest') plt.colorbar(im2, fraction=0.046, pad=0.04, use_gridspec=True) ax2.set_axis_off() # for i in range(0,phinum): # phi = i*2*pi/phinum # x1, y1 = x0+lclen*np.cos(phi), y0+lclen*np.sin(phi) # plt.plot([x0, x1], [y0, y1], 'k-') # plt.axis('image') fp.format_plot(plt, 350, 350, 450, 50) pylab.savefig('/Users/alec/UCSB/scan_images/mdata_' + str(scannum) + filespec + '.png', format='png')
path = join(simdb.exdata_dir, 'tensile_tests', 'dog_bone', '2010-03-17_TT-8g-3cm-0-TR') # , 'TT-8g-3cm-0-TR-V2.DAT', 'TT-8g-3cm-0-TR-V3.DAT'] tests = ['TT-8g-3cm-0-TR-V2.DAT'] for t in tests: ex_path = join(path, t) ex_run = ExRun(ex_path) if sig_flag == 'comp': ex_run.ex_type._plot_comp_stress_strain_asc( p, xscale=1000., color='black', linewidth=1.3, linestyle='-', label='ARG-1200-TR') if sig_flag == 'tex': ex_run.ex_type._plot_tex_stress_strain_asc( p, xscale=1000., color='black', linewidth=1.3, linestyle='-', label='2D-01-08') if sig_flag == 'tex': format_plot( p, xlabel='Dehnung [1E-3]', ylabel='Textilspannung [MPa]', xlim=14., ylim=1400.) if sig_flag == 'comp': format_plot( p, xlabel='Dehnung [1E-3]', ylabel='Kompositspannung [MPa]', xlim=14., ylim=25.) font.set_size('12') p.legend(prop=font) if save_fig_to_file: # create a report-subfolder with the name of the script (without file extension '.py') # and save it in the test-type-subfolders with the name and path as # ex_type test_series_name = os.path.basename(__file__)[:-3] subfolder_list = __file__.split(os.path.sep) devproc_idx = np.where(np.array(subfolder_list) == 'devproc')[0] subfolder_path = subfolder_list[