|
- import os
- import re
- import matplotlib
- import numpy as np
- import scipy.io as sio
- import TheArtist
- import matplotlib.pyplot as plt
-
-
- def main():
-
- plt.rc('text', usetex='True')
- plt.rc('font', family='Serif', size='6')
-
- fig_width_pt = 510
- inches_per_pt = 1.0 / 72.27
- ratio = 2 / (1 + 5 ** 0.5)
- fig_width = fig_width_pt * inches_per_pt
- fig_height = fig_width * ratio / 4 * 4 +0.3*2
-
- figure = plt.figure('panel15', figsize=(fig_width, fig_height))
-
- ax01 = figure.add_axes([0.040, 0.94, 0.42, 0.05])
- ax02 = figure.add_axes([0.520, 0.94, 0.42, 0.05])
-
- ax01.text(0.5, 0.5, 'Test Case 2', horizontalalignment='center', verticalalignment='center', transform=ax01.transAxes, fontsize=8)
- ax01.axis("off")
- ax02.text(0.5, 0.5, 'Test Case 4', horizontalalignment='center', verticalalignment='center', transform=ax02.transAxes, fontsize=8)
- ax02.axis("off")
-
- axa = figure.add_axes([0.010, 0.92, 0.12, 0.05])
- axb = figure.add_axes([0.010, 0.65, 0.12, 0.05])
-
- axa.text(0.05, 0.5, 'a)', horizontalalignment='center', verticalalignment='center', transform=axa.transAxes, fontsize=8)
- axa.axis("off")
- axb.text(0.05, 0.5, 'b)', horizontalalignment='center', verticalalignment='center', transform=axb.transAxes, fontsize=8)
- axb.axis("off")
-
- ax03 = figure.add_axes([0.040, 0.72, 0.2, 0.2])
- ax04 = figure.add_axes([0.280, 0.72, 0.2, 0.2])
- ax05 = figure.add_axes([0.520, 0.72, 0.2, 0.2])
- ax06 = figure.add_axes([0.760, 0.72, 0.2, 0.2])
-
- ax07 = figure.add_axes([0.060, 0.47, 0.40, 0.17])
- ax08 = figure.add_axes([0.540, 0.47, 0.40, 0.17])
- ax09 = figure.add_axes([0.060, 0.27, 0.40, 0.17])
- ax10 = figure.add_axes([0.540, 0.27, 0.40, 0.17])
- ax11 = figure.add_axes([0.060, 0.07, 0.40, 0.17])
- ax12 = figure.add_axes([0.540, 0.07, 0.40, 0.17])
-
- """
- Channel
- """
-
- <<<<<<< HEAD:plot/plot_panel05.py
- filename = f"../data/channel/ss08/results/predictions_architecture-01-noise-010.npz"
- =======
- filename = f"/storage/aguemes/gan-piv/channel/ss08/results/predictions_architecture-01-noise-010.npz"
- >>>>>>> d697111dba0b1b8df36000c0c120a92bf7e61e0e:plot/nature06.py
- data = np.load(filename)
- dns_target = data['dns_target'] / 512 / 0.013
- hr_predic = data['hr_predic']
- lr_target = data['lr_target']
- fl_target = data['fl_target']
- cbc_predic = data['cbc_predic'] / 512 / 0.013
- gap_predic = data['gap_predic'] / 512 / 0.013
-
- xhr = data['xhr']
- yhr = data['yhr'][:,0]
- <<<<<<< HEAD:plot/plot_panel05.py
- grid_path = f"../data/channel/ss01/piv_noise010/SS1_grid.mat"
- =======
- grid_path = f"/storage/aguemes/gan-piv/channel/ss01/piv_noise010/SS1_grid.mat"
- >>>>>>> d697111dba0b1b8df36000c0c120a92bf7e61e0e:plot/nature06.py
- grid = sio.loadmat(grid_path)
- xlr = np.array(grid['X']).T
- ylr = np.array(grid['Y']).T[0,:]
- yplus = yhr / 512 * 0.0499 / 0.00005
- yppiv = ylr / 512 * 0.0499 / 0.00005
-
- cmapR = matplotlib.cm.get_cmap('Reds')
- cmapB = matplotlib.cm.get_cmap('Blues')
- cmapY = matplotlib.cm.get_cmap('plasma')
- cmapV = matplotlib.cm.get_cmap('Purples')
- cmapG = matplotlib.cm.get_cmap('Greens')
-
- cpiv = cmapR(0.6)
- cgan = cmapB(1.0)
- cgan4 = cmapB(0.6)
- cgan2 = cmapB(0.2)
- ccbc = cmapY(0.9)
- cgap = cmapV(0.9)
-
- cy1 = cmapG(1.0)
- cy2 = cmapG(0.7)
- cy3 = cmapG(0.4)
-
- ax03.plot([50, 50], [0, 30], color=cy1, linewidth=1, linestyle='-')
- ax03.plot([250, 250], [0, 30], color=cy2, linewidth=1, linestyle='-')
- ax03.plot([500, 500], [0, 30], color=cy3, linewidth=1, linestyle='-')
-
- ax04.plot([50, 50], [0, 25], color=cy1, linewidth=1, linestyle='-')
- ax04.plot([250, 250], [0, 25], color=cy2, linewidth=1, linestyle='-')
- ax04.plot([500, 500], [0, 25], color=cy3, linewidth=1, linestyle='-')
-
-
- mean_u_gan = np.mean(np.mean(hr_predic[:, ::-1, :, 0], axis=0), axis=1) / 0.0499
- mean_u_piv = np.mean(np.mean(lr_target[:, ::-1, :, 0], axis=0), axis=1) / 0.0499
- mean_u_cbc = np.mean(np.nanmean(cbc_predic[:, ::-1, :, 0], axis=0), axis=1) / 0.0499
- mean_u_gap = np.mean(np.nanmean(gap_predic[:, ::-1, :, 0], axis=0), axis=1) / 0.0499
-
- mean_uu_gan = np.mean(np.var(hr_predic[:, ::-1, :, 0], axis=0), axis=1) / 0.0499 / 0.0499
- mean_uu_piv = np.mean(np.var(lr_target[:, ::-1, :, 0], axis=0), axis=1) / 0.0499 / 0.0499
- mean_uu_cbc = np.mean(np.nanvar(cbc_predic[:, ::-1, :, 0], axis=0, ddof=-1), axis=1) / 0.0499 / 0.0499
- mean_uu_gap = np.mean(np.nanvar(gap_predic[:, ::-1, :, 0], axis=0, ddof=-1), axis=1) / 0.0499 / 0.0499
-
- filename = 're1000_full_stat.mat'
- data = sio.loadmat(filename)
- mean_u_dns = data['u']
- mean_uu_dns = data['uu']
- ydns = data['y']
-
- ax03.semilogx(ydns[0,:], mean_u_dns[0,:], label='HR', color='k', linewidth=2, linestyle='-', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
- ax03.semilogx(yplus, mean_u_cbc, color=ccbc, label='Cubic', linewidth=1, linestyle='-.', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
- ax03.semilogx(yplus, mean_u_gap, color=cgap, label='GappyPOD', linewidth=1, linestyle='-.', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
- ax03.semilogx(yplus, mean_u_gan, color=cgan, label='RaSeedGAN', linewidth=1, linestyle='--', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=1)
- ax03.semilogx(yppiv, mean_u_piv, color=cpiv, label='LR', linewidth=1, linestyle=':', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
-
- ax04.semilogx(ydns[0,:], mean_uu_dns[0,:], color='k', linewidth=2, linestyle='-', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
- ax04.semilogx(yplus, mean_uu_cbc, color=ccbc, linewidth=1, linestyle='-.', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
- ax04.semilogx(yplus, mean_uu_gap, color=cgap, linewidth=1, linestyle='-.', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
- ax04.semilogx(yplus, mean_uu_gan, color=cgan, linewidth=1, linestyle='--', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
- ax04.semilogx(yppiv, mean_uu_piv, color=cpiv, linewidth=1, linestyle=':', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
-
- data = sio.loadmat('channel_spectra.mat')
-
- puustar = (data['lstar']*data['utau']*data['utau'])[0][0]
- ax07.loglog(data['freq'][0]*data['lstar'][0], data['puuX_DNS'][:,data['idx_aux'][0,0]-1]/puustar, label='HR', color='k')
- ax07.loglog(data['freq'][0]*data['lstar'][0], data['puuX_CUBIC'][:,data['idx_aux'][0,0]-1]/puustar, label='Cubic', color=ccbc, linewidth=1, linestyle='-.')
- ax07.loglog(data['freq'][0]*data['lstar'][0], data['puuX_GAP'][:,data['idx_aux'][0,0]-1]/puustar, label='GappyPOD', color=cgap, linewidth=1, linestyle='-.')
- ax07.loglog(data['freq'][0]*data['lstar'][0], data['puuX_GAN'][:,data['idx_aux'][0,0]-1]/puustar, label='RaSeedGAN $\\times$8', color=cgan, linewidth=1, linestyle='--')
- ax07.loglog(data['freq4'][0]*data['lstar'][0], data['puuX_GAN4'][:,data['idx4_aux'][0,0]-1]/puustar, label='RaSeedGAN $\\times$4', color=cgan4, linewidth=1, linestyle='--')
- ax07.loglog(data['freq2'][0]*data['lstar'][0], data['puuX_GAN2'][:,data['idx2_aux'][0,0]-1]/puustar, label='RaSeedGAN $\\times$2', color=cgan2, linewidth=1, linestyle='--')
- ax07.loglog(data['freq_LR'][0]*data['lstar'][0], data['puuX_LR'][:,data['idx_LR_aux'][0,0]-1]/puustar, label='LR', color=cpiv, linewidth=1, linestyle=':')
-
- ax09.loglog(data['freq'][0]*data['lstar'][0], data['puuX_DNS'][:,data['idx_aux'][0,1]-1]/puustar, color='k')
- ax09.loglog(data['freq'][0]*data['lstar'][0], data['puuX_CUBIC'][:,data['idx_aux'][0,1]-1]/puustar, color=ccbc, linewidth=1, linestyle='-.')
- ax09.loglog(data['freq'][0]*data['lstar'][0], data['puuX_GAP'][:,data['idx_aux'][0,1]-1]/puustar, color=cgap, linewidth=1, linestyle='-.')
- ax09.loglog(data['freq'][0]*data['lstar'][0], data['puuX_GAN'][:,data['idx_aux'][0,1]-1]/puustar, color=cgan, linewidth=1, linestyle='--')
- ax09.loglog(data['freq4'][0]*data['lstar'][0], data['puuX_GAN4'][:,data['idx4_aux'][0,1]-1]/puustar, color=cgan4, linewidth=1, linestyle='--')
- ax09.loglog(data['freq2'][0]*data['lstar'][0], data['puuX_GAN2'][:,data['idx2_aux'][0,1]-1]/puustar, color=cgan2, linewidth=1, linestyle='--')
- ax09.loglog(data['freq_LR'][0]*data['lstar'][0], data['puuX_LR'][:,data['idx_LR_aux'][0,1]-1]/puustar, color=cpiv, linewidth=1, linestyle=':')
- ax11.loglog(data['freq'][0]*data['lstar'][0], data['puuX_DNS'][:,data['idx_aux'][0,2]-1]/puustar, color='k')
- ax11.loglog(data['freq'][0]*data['lstar'][0], data['puuX_CUBIC'][:,data['idx_aux'][0,2]-1]/puustar, color=ccbc, linewidth=1, linestyle='-.')
- ax11.loglog(data['freq'][0]*data['lstar'][0], data['puuX_GAP'][:,data['idx_aux'][0,2]-1]/puustar, color=cgap, linewidth=1, linestyle='-.')
- ax11.loglog(data['freq'][0]*data['lstar'][0], data['puuX_GAN'][:,data['idx_aux'][0,2]-1]/puustar, color=cgan, linewidth=1, linestyle='--')
- ax11.loglog(data['freq4'][0]*data['lstar'][0], data['puuX_GAN4'][:,data['idx4_aux'][0,2]-1]/puustar, color=cgan4, linewidth=1, linestyle='--')
- ax11.loglog(data['freq2'][0]*data['lstar'][0], data['puuX_GAN2'][:,data['idx2_aux'][0,2]-1]/puustar, color=cgan2, linewidth=1, linestyle='--')
- ax11.loglog(data['freq_LR'][0]*data['lstar'][0], data['puuX_LR'][:,data['idx_LR_aux'][0,2]-1]/puustar, color=cpiv, linewidth=1, linestyle=':')
-
- ax07.text(0.8,0.8,"$y^+=50$", transform=ax07.transAxes, color=cy1)
- ax09.text(0.8,0.8,"$y^+=250$", transform=ax09.transAxes, color=cy2)
- ax11.text(0.8,0.8,"$y^+=500$", transform=ax11.transAxes, color=cy3)
-
- <<<<<<< HEAD:plot/plot_panel05.py
- """
- TBL
- """
-
- filename = f"../data/exptbl/ss04/results/predictions_architecture-01-noise-000.npz"
- =======
- filename = f"/storage/aguemes/gan-piv/exptbl/ss04/results/predictions_architecture-01-noise-000.npz"
- >>>>>>> d697111dba0b1b8df36000c0c120a92bf7e61e0e:plot/nature06.py
- data = np.load(filename)
- hr_predic = data['hr_predic']
- lr_target = data['lr_target']
- fl_target = data['fl_target']
- cbc_predic = data['cbc_predic']
- gap_predic = data['gap_predic']
- dns_target = data['dns_target']
- yhr = data['yhr'][:,0]
- ylr = data['ylr'][:,0]
-
- filename = 'PTV_Guemes_stat.mat'
- data = sio.loadmat(filename)
- utau = data['OutData'][0][0][3][0][0]
- nu = data['OutData'][0][0][4][0][0]
- mean_u_dns = np.mean(np.mean(dns_target[:, ::-1, :, 0], axis=0), axis=1) / (48440 * 0.000017 * utau)
- mean_uu_dns = np.mean(np.var(dns_target[:, ::-1, :, 0], axis=0), axis=1) / (48440 * 0.000017 * utau)**2
-
-
- mean_u_piv = np.mean(lr_target[:, ::-1, :, 0], axis=(0, 2)) / 48440 / 0.000017 / utau
- mean_uu_piv = np.mean(np.nanvar(lr_target[:, ::-1, :, 0], axis=0), axis=1) / (48440 * 0.000017 * utau)**2
-
- mean_u_gan = np.mean(hr_predic[:, ::-1, :, 0], axis=(0, 2)) / 48440 / 0.000017 / utau
- mean_u_cbc = np.nanmean(cbc_predic[:, ::-1, :, 0], axis=(0, 2)) / 48440 / 0.000017 / utau
- mean_u_gap = np.nanmean(gap_predic[:, ::-1, :, 0], axis=(0, 2)) / 48440 / 0.000017 / utau
- mean_uu_gan = np.mean(np.nanvar(hr_predic[:, ::-1, :, 0], axis=0), axis=1) / (48440 * 0.000017 * utau)**2
- mean_uu_cbc = np.mean(np.nanvar(cbc_predic[:, ::-1, :, 0], axis=0), axis=1) / (48440 * 0.000017 * utau)**2
- mean_uu_gap = np.mean(np.nanvar(gap_predic[:, ::-1, :, 0], axis=0), axis=1) / (48440 * 0.000017 * utau)**2
-
- yppiv = ylr / 48440 * utau / nu
- yplus = yhr / 48440 * utau / nu + (data['OutData'][0][0][0][0] - yhr[0] / 48440) * utau / nu
-
-
- ax05.plot([250, 250], [0, 30], color=cy1, linewidth=1, linestyle='-')
- ax05.plot([500, 500], [0, 30], color=cy2, linewidth=1, linestyle='-')
- ax05.plot([800, 800], [0, 30], color=cy3, linewidth=1, linestyle='-')
-
- ax06.plot([250, 250], [0, 30], color=cy1, linewidth=1, linestyle='-')
- ax06.plot([500, 500], [0, 30], color=cy2, linewidth=1, linestyle='-')
- ax06.plot([800, 800], [0, 30], color=cy3, linewidth=1, linestyle='-')
-
-
- data = sio.loadmat('computed_vel_3270_dns.prof.mat')
-
- ax05.semilogx(yplus, mean_u_dns, label='HR', color='k', linewidth=2, linestyle='-', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
- ax05.semilogx(data['yplus'], data['Uplus'], label='Schlatter \& Örlü (2010)', color='dimgrey', linewidth=2, linestyle='-', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
- ax05.semilogx(yplus, mean_u_cbc, label='Cubic', color=ccbc, linewidth=1, linestyle='-.', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=3)
- ax05.semilogx(yplus, mean_u_gap, label='GappyPOD', color=cgap, linewidth=1, linestyle='-.', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=3)
- ax05.semilogx(yplus, mean_u_gan, label='RaSeedGAN', color=cgan4, linewidth=1, linestyle='--', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=3)
- ax05.semilogx(yppiv, mean_u_piv, label='LR', color=cpiv, linewidth=1, linestyle=':', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=3)
-
-
- ax06.semilogx(yplus, mean_uu_dns, color='k', linewidth=2, linestyle='-', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
- ax06.semilogx(data['yplus'], data['urmsplus']**2, color='dimgrey', linewidth=2, linestyle='-', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
- ax06.semilogx(yplus, mean_uu_cbc, color=ccbc, linewidth=1, linestyle='-.', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
- ax06.semilogx(yplus, mean_uu_gap, color=cgap, linewidth=1, linestyle='-.', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
- ax06.semilogx(yplus, mean_uu_gan, color=cgan4, linewidth=1, linestyle='--', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
- ax06.semilogx(yppiv, mean_uu_piv, color=cpiv, linewidth=1, linestyle=':', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
-
- data = sio.loadmat('exp_spectra.mat')
- puustar = (data['lstar']*data['utau']*data['utau'])[0][0]
- ax08.loglog(data['freq'][0]*data['lstar'][0], data['puuX_DNS'][:,data['idx_aux'][0,0]-1]/puustar, label='HR', color='k')
- ax08.loglog(data['freq'][0]*data['lstar'][0], data['puuX_CUBIC'][:,data['idx_aux'][0,0]-1]/puustar, label='Cubic', color=ccbc, linewidth=1, linestyle='-.')
- ax08.loglog(data['freq'][0]*data['lstar'][0], data['puuX_GAP'][:,data['idx_aux'][0,0]-1]/puustar, label='GappyPOD', color=cgap, linewidth=1, linestyle='-.')
- ax08.loglog(data['freq'][0]*data['lstar'][0], data['puuX_GAN'][:,data['idx_aux'][0,0]-1]/puustar, label='RaSeedGAN $\\times$4', color=cgan4, linewidth=1, linestyle='--')
- ax08.loglog(data['freq_LR'][0]*data['lstar'][0], data['puuX_LR'][:,data['idx_LR_aux'][0,0]-1]/puustar, label='LR', color=cpiv, linewidth=1, linestyle=':')
- ax10.loglog(data['freq'][0]*data['lstar'][0], data['puuX_DNS'][:,data['idx_aux'][0,1]-1]/puustar, color='k')
- ax10.loglog(data['freq'][0]*data['lstar'][0], data['puuX_CUBIC'][:,data['idx_aux'][0,1]-1]/puustar, color=ccbc, linewidth=1, linestyle='-.')
- ax10.loglog(data['freq'][0]*data['lstar'][0], data['puuX_GAP'][:,data['idx_aux'][0,1]-1]/puustar, color=cgap, linewidth=1, linestyle='-.')
- ax10.loglog(data['freq'][0]*data['lstar'][0], data['puuX_GAN'][:,data['idx_aux'][0,1]-1]/puustar, color=cgan4, linewidth=1, linestyle='--')
- ax10.loglog(data['freq_LR'][0]*data['lstar'][0], data['puuX_LR'][:,data['idx_LR_aux'][0,1]-1]/puustar, color=cpiv, linewidth=1, linestyle=':')
- ax12.loglog(data['freq'][0]*data['lstar'][0], data['puuX_DNS'][:,data['idx_aux'][0,2]-1]/puustar, color='k')
- ax12.loglog(data['freq'][0]*data['lstar'][0], data['puuX_CUBIC'][:,data['idx_aux'][0,2]-1]/puustar, color=ccbc, linewidth=1, linestyle='-.')
- ax12.loglog(data['freq'][0]*data['lstar'][0], data['puuX_GAP'][:,data['idx_aux'][0,2]-1]/puustar, color=cgap, linewidth=1, linestyle='-.')
- ax12.loglog(data['freq'][0]*data['lstar'][0], data['puuX_GAN'][:,data['idx_aux'][0,2]-1]/puustar, color=cgan4, linewidth=1, linestyle='--')
- ax12.loglog(data['freq_LR'][0]*data['lstar'][0], data['puuX_LR'][:,data['idx_LR_aux'][0,2]-1]/puustar, color=cpiv, linewidth=1, linestyle=':')
-
- ax08.text(0.8,0.8,"$y^+=250$", transform=ax08.transAxes, color=cy1)
- ax10.text(0.8,0.8,"$y^+=500$", transform=ax10.transAxes, color=cy2)
- ax12.text(0.8,0.8,"$y^+=800$", transform=ax12.transAxes, color=cy3)
-
- """
- Settings
- """
-
- ax03.set_ylabel("$U^+$", labelpad=2)
- ax04.set_ylabel("$u^{\\prime}u^{\\prime ^+}$", labelpad=-1)
- ax05.set_xlabel("$y^+$", labelpad=2)
- ax05.set_ylabel("$U^+$", labelpad=2)
- ax06.set_xlabel("$y^+$", labelpad=2)
- ax04.set_xlabel("$y^+$", labelpad=2)
- ax03.set_xlabel("$y^+$", labelpad=2)
- ax06.set_ylabel("$u^{\\prime}u^{\\prime ^+}$", labelpad=-1)
- ax11.set_xlabel("$f_x^+$", labelpad=2)
- ax12.set_xlabel("$f_x^+$", labelpad=2)
- ax07.set_ylabel("$\\phi_{uu_x}$", labelpad=0)
- ax09.set_ylabel("$\\phi_{uu_x}$", labelpad=0)
- ax11.set_ylabel("$\\phi_{uu_x}$", labelpad=0)
- ax08.set_ylabel("$\\phi_{uu_x}$", labelpad=0)
- ax10.set_ylabel("$\\phi_{uu_x}$", labelpad=0)
- ax12.set_ylabel("$\\phi_{uu_x}$", labelpad=0)
-
- ax03.set_xlim([1, 2000])
- ax04.set_xlim([1, 2000])
- ax05.set_xlim([1, 2000])
- ax06.set_xlim([1, 2000])
- ax03.set_ylim([0, 30])
- ax04.set_ylim([0, 10])
- ax05.set_ylim([0, 30])
- ax06.set_ylim([0, 10])
- ax07.set_ylim([0.005, 1100])
- ax08.set_ylim([0.005, 1100])
- ax09.set_ylim([0.005, 1100])
- ax10.set_ylim([0.005, 1100])
- ax11.set_ylim([0.005, 1100])
- ax12.set_ylim([0.005, 1100])
-
- ax04.set_yticks([0, 1, 2, 3, 4])
- ax04.set_yticks([0, 2, 4, 6, 8, 10])
- ax06.set_yticks([0, 2, 4, 6, 8, 10])
-
- ax08.set_xticks([])
- ax07.set_xticks([])
- ax09.set_xticks([])
- ax10.set_xticks([])
-
- ax03.tick_params(axis="both", direction="in", which="both", pad=2, bottom=True, top=False, left=True, right=False, labelbottom=True, labelleft=True)
- ax04.tick_params(axis="both", direction="in", which="both", pad=2, bottom=True, top=False, left=True, right=False, labelbottom=True, labelleft=True)
- ax05.tick_params(axis="both", direction="in", which="both", pad=2, bottom=True, top=False, left=True, right=False, labelbottom=True, labelleft=True)
- ax06.tick_params(axis="both", direction="in", which="both", pad=2, bottom=True, top=False, left=True, right=False, labelbottom=True, labelleft=True)
- ax07.tick_params(axis="both", direction="in", which="both", pad=4, bottom=True, top=False, left=True, right=False, labelbottom=True, labelleft=True)
- ax08.tick_params(axis="both", direction="in", which="both", pad=4, bottom=True, top=False, left=True, right=False, labelbottom=True, labelleft=True)
- ax09.tick_params(axis="both", direction="in", which="both", pad=4, bottom=True, top=False, left=True, right=False, labelbottom=True, labelleft=True)
- ax10.tick_params(axis="both", direction="in", which="both", pad=4, bottom=True, top=False, left=True, right=False, labelbottom=True, labelleft=True)
- ax11.tick_params(axis="both", direction="in", which="both", pad=4, bottom=True, top=False, left=True, right=False, labelbottom=True, labelleft=True)
- ax12.tick_params(axis="both", direction="in", which="both", pad=4, bottom=True, top=False, left=True, right=False, labelbottom=True, labelleft=True)
-
- axins = ax06.inset_axes([0.65, 0.65, 0.3, 0.3])
- data = sio.loadmat('computed_vel_3270_dns.prof.mat')
- axins.semilogx(yplus, mean_uu_dns, color='k', linewidth=2, linestyle='-', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
- axins.semilogx(data['yplus'], data['urmsplus']**2, color='dimgrey', linewidth=2, linestyle='-', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
- axins.semilogx(yplus, mean_uu_cbc, color=ccbc, linewidth=1, linestyle='-.', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
- axins.semilogx(yplus, mean_uu_gan, color=cgan4, linewidth=1, linestyle='--', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
- axins.semilogx(yppiv, mean_uu_piv, color=cpiv, linewidth=1, linestyle=':', marker=None, markeredgecolor=None, markerfacecolor=None, markersize=2)
- axins.set_xlim([1000, np.max(yplus)])
- axins.set_ylim([0,1])
- axins.set_xticklabels([],minor=True)
- axins.set_xticklabels([])
- axins.set_yticklabels([])
- axins.tick_params(axis="both", direction="in", which="both", pad=4, bottom=True, top=False, left=True, right=False, labelbottom=True, labelleft=True)
- ax06.indicate_inset_zoom(axins, edgecolor="black", alpha=1)
- <<<<<<< HEAD:plot/plot_panel05.py
- figure.savefig(f"panel05.pdf", dpi=1000)
- =======
-
- ax03.legend(fontsize=4, framealpha=0.0, frameon=False)
- ax07.legend(fontsize=4, framealpha=0.0, frameon=False, loc=6)
- ax05.legend(fontsize=4, framealpha=0.0, frameon=False, loc=2)
- ax08.legend(fontsize=4, framealpha=0.0, frameon=False, loc=6)
-
- figure.savefig(f"../figs/nature06rev.pdf", dpi=1000)
- >>>>>>> d697111dba0b1b8df36000c0c120a92bf7e61e0e:plot/nature06.py
-
- return
-
-
- if __name__ == '__main__':
-
- main()
|