<jupyter_start><jupyter_text>Flywing n20: SEG scores on validation data<jupyter_code>alpha0_5_n20 = read_Noise2Seg_results('alpha0.5', 'flywing_n20', measure='SEG', runs=[1,2,3,4,5],
                           fractions=[0.125, 0.25, 0.5, 1.0, 2.0], score_type = '',
                           path_str='/home/prakash/Desktop/fileserver_Noise2Seg/experiments/{}_{}_run{}/fraction_{}/{}scores.csv')
baseline_flywing_n20 = read_Noise2Seg_results('fin', 'flywing_n20', measure='SEG', runs=[1,2,3,4,5], 
                           fractions=[0.125, 0.25, 0.5, 1.0, 2.0], score_type = '',
                           path_str='/home/prakash/Desktop/fileserver_Noise2Seg/experiments/{}_{}_run{}/fraction_{}/{}scores.csv')

sequential_flywing_n20 = read_Noise2Seg_results('finSeq', 'flywing_n20', measure='SEG', runs=[1,2,3,4,5], 
                           fractions=[0.125, 0.25, 0.5, 1.0, 2.0], score_type = '',
                           path_str='/home/prakash/Desktop/fileserver_Noise2Seg/experiments/{}_{}_run{}/fraction_{}/{}scores.csv')
plt.rc('font', family = 'serif', size = 16)
fig = plt.figure(figsize=cm2inch(12.2/2,3)) # 12.2cm is the text-widht of the MICCAI template
plt.rcParams['axes.axisbelow'] = True

plt.plot(fraction_to_abs(alpha0_5_n20[:, 0], max_num_imgs = 1428), 
         alpha0_5_n20[:, 1],
         color = '#8F89B4', alpha = 1, linewidth=2)
plt.fill_between(fraction_to_abs(alpha0_5_n20[:, 0], max_num_imgs = 1428), 
                 y1 = alpha0_5_n20[:, 1] + alpha0_5_n20[:, 2], 
                 y2 = alpha0_5_n20[:, 1] - alpha0_5_n20[:, 2], 
                 color = '#8F89B4', alpha = 0.5, label = r'\textsc{DenoiSeg} ($\alpha = 0.5$)')

plt.plot(fraction_to_abs(sequential_flywing_n20[:, 0], max_num_imgs = 1428), 
         sequential_flywing_n20[:, 1],
         color = '#526B34', alpha = 1, linewidth=2)
plt.fill_between(fraction_to_abs(sequential_flywing_n20[:, 0], max_num_imgs = 1428), 
                 y1 = sequential_flywing_n20[:, 1] + sequential_flywing_n20[:, 2], 
                 y2 = sequential_flywing_n20[:, 1] - sequential_flywing_n20[:, 2], 
                 color = '#526B34', alpha = 0.5, label = r'Sequential Baseline')

plt.plot(fraction_to_abs(baseline_flywing_n20[:, 0], max_num_imgs = 1428), 
         baseline_flywing_n20[:, 1],
         color = '#6D3B2B', alpha = 1, linewidth=2)
plt.fill_between(fraction_to_abs(baseline_flywing_n20[:, 0], max_num_imgs = 1428), 
                 y1 = baseline_flywing_n20[:, 1] + baseline_flywing_n20[:, 2], 
                 y2 = baseline_flywing_n20[:, 1] - baseline_flywing_n20[:, 2], 
                 color = '#6D3B2B', alpha = 0.25, label = r'Baseline ($\alpha = 0$)')

plt.semilogx()

plt.ylabel(r'\textbf{SEG}')
plt.xlabel(r'\textbf{Number of Annotated Training Images}')

plt.grid(axis='y')

plt.xticks(ticks=fraction_to_abs(baseline_flywing_n20[:, 0], max_num_imgs = 1428), 
           labels=fraction_to_abs(baseline_flywing_n20[:, 0], max_num_imgs = 1428).astype(np.int),
           rotation=45)
plt.minorticks_off()

plt.yticks(rotation=45)

plt.xlim([1.86, 31])

plt.tight_layout();

plt.savefig('Flywing_SEG_n20_area.pdf', pad_inches=0.0);
plt.savefig('Flywing_SEG_n20_area.svg', pad_inches=0.0);<jupyter_output><empty_output>
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