<jupyter_start><jupyter_text>Learning curves with and without different masks at training<jupyter_code>df_learning_curves = pd.read_csv('updnet_learning_curves.csv')
plt.figure()
sns.lineplot(data=df_learning_curves, x='n_samples', y='psnr', hue='fixed_masks')
plt.savefig('updnet_learning_curves.png')<jupyter_output><empty_output><jupyter_text>Denoising performance function of n params<jupyter_code>df_n_params = pd.read_csv('n_params_model.csv', index_col=0)
# df_denoising_res = pd.read_csv('denoising_results_None.csv', index_col=0)
# df_denoising_res = pd.read_csv('denoising_results_5.csv', index_col=0)
df_denoising_res = pd.read_csv('denoising_results_None_compound_mssim.csv', index_col=0)
# df_denoising_res_didn = pd.read_csv('denoising_results_None_DIDN.csv', index_col=0)
# df_denoising_res[df_denoising_res['model_name'] == 'DIDN'] = df_denoising_res_didn.values
df_denoisers = df_denoising_res.merge(df_n_params, on=['model_name', 'model_size'])
df_denoisers['model_denomination'] = df_denoisers[['model_name', 'model_size']].agg('-'.join, axis=1)
plt.figure()
plt.xscale('log')
p = sns.scatterplot(x='n_params', y='psnr', data=df_denoisers)
for line in range(0, df_denoisers.shape[0]):
    p.text(
        df_denoisers.n_params[line], 
        df_denoisers.psnr[line]+0.01, 
        df_denoisers.model_denomination[line], 
        horizontalalignment='left', 
        size='small', 
        color='black', 
    )<jupyter_output><empty_output><jupyter_text>Reconstruction performance function of denoising performance<jupyter_code>df_reconstruction = pd.read_csv('reconstruction_results_None.csv', index_col=0)
# df_reconstruction = pd.read_csv('reconstruction_results_5.csv', index_col=0)
# df_reconstruction = pd.read_csv('reconstruction_results_af_2.csv', index_col=0)
# df_reconstruction_didn = pd.read_csv('reconstruction_results_None_DIDN.csv', index_col=0)
# df_reconstruction[df_reconstruction['model_name'] == 'DIDN'] = df_reconstruction_didn.values
df_reconstruction['psnr_recon'] = df_reconstruction['psnr']
df_reconstruction = df_reconstruction.drop('psnr', axis=1)
df_reconstruction['ssim_recon'] = df_reconstruction['ssim']
df_reconstruction = df_reconstruction.drop('ssim', axis=1)
df_all = df_denoisers.merge(df_reconstruction, on=['model_name', 'model_size'])
df_all
plt.figure()
p = sns.scatterplot(x='psnr', y='psnr_recon', data=df_all)
for line in range(0, df_all.shape[0]):
    p.text(
        df_all.psnr[line]+0.01, 
        df_all.psnr_recon[line]+0.01, 
        df_all.model_denomination[line], 
        horizontalalignment='left', 
        size='small', 
        color='black', 
    )
plt.figure()
p = sns.scatterplot(x='ssim', y='ssim_recon', data=df_all)
for line in range(0, df_all.shape[0]):
    p.text(
        df_all.ssim[line]+0.0001, 
        df_all.ssim_recon[line]+0.0001, 
        df_all.model_denomination[line], 
        horizontalalignment='left', 
        size='small', 
        color='black', 
    )<jupyter_output><empty_output><jupyter_text>Reconstruction perfoamce function of n params<jupyter_code>plt.figure()
plt.xscale('log')
p = sns.scatterplot(x='n_params', y='psnr_recon', data=df_all)
for line in range(0, df_all.shape[0]):
    p.text(
        df_all.n_params[line], 
        df_all.psnr_recon[line]+0.01, 
        df_all.model_denomination[line], 
        horizontalalignment='left', 
        size='small', 
        color='black', 
    )
plt.figure()
plt.xscale('log')
p = sns.scatterplot(x='n_params', y='ssim_recon', data=df_all)
for line in range(0, df_all.shape[0]):
    p.text(
        df_all.n_params[line], 
        df_all.ssim_recon[line]+0.0001, 
        df_all.model_denomination[line], 
        horizontalalignment='left', 
        size='small', 
        color='black', 
    )<jupyter_output><empty_output><jupyter_text>Reconstruction performance at af 4 function of af 2<jupyter_code>df_reconstruction = pd.read_csv('reconstruction_results_None.csv', index_col=0)
df_reconstruction_af2 = pd.read_csv('reconstruction_results_af_2.csv', index_col=0)
df_reconstruction_af2['psnr_af2'] = df_reconstruction_af2['psnr']
df_reconstruction_af2 = df_reconstruction_af2.drop('psnr', axis=1)
df_reconstruction_af2['ssim_af2'] = df_reconstruction_af2['ssim']
df_reconstruction_af2 = df_reconstruction_af2.drop('ssim', axis=1)
df_all_recon = df_reconstruction.merge(df_reconstruction_af2, on=['model_name', 'model_size'])
df_all_recon['model_denomination'] = df_all_recon[['model_name', 'model_size']].agg('-'.join, axis=1)
df_all_recon
plt.figure()
p = sns.scatterplot(x='psnr_af2', y='psnr', data=df_all_recon)
for line in range(0, df_all_recon.shape[0]):
    p.text(
        df_all_recon.psnr_af2[line]+0.01, 
        df_all_recon.psnr[line]+0.01, 
        df_all_recon.model_denomination[line], 
        horizontalalignment='left', 
        size='small', 
        color='black', 
    )
plt.figure()
p = sns.scatterplot(x='ssim_af2', y='ssim', data=df_all_recon)
for line in range(0, df_all_recon.shape[0]):
    p.text(
        df_all_recon.ssim_af2[line]+0.0001, 
        df_all_recon.ssim[line]+0.0001, 
        df_all_recon.model_denomination[line], 
        horizontalalignment='left', 
        size='small', 
        color='black', 
    )<jupyter_output><empty_output><jupyter_text>Reconstruction performance function of dealiasing performance<jupyter_code>df_reconstruction = pd.read_csv('reconstruction_results_None.csv', index_col=0)
df_dealiasing = pd.read_csv('dealiasing_results_None_compound_mssim.csv', index_col=0)
df_dealiasing['psnr_dealias'] = df_dealiasing['psnr']
df_dealiasing = df_dealiasing.drop('psnr', axis=1)
df_dealiasing['ssim_dealias'] = df_dealiasing['ssim']
df_dealiasing = df_dealiasing.drop('ssim', axis=1)
df_all_recon = df_reconstruction.merge(df_dealiasing, on=['model_name', 'model_size'])
df_all_recon['model_denomination'] = df_all_recon[['model_name', 'model_size']].agg('-'.join, axis=1)
df_all_recon
plt.figure()
p = sns.scatterplot(x='psnr_dealias', y='psnr', data=df_all_recon)
for line in range(0, df_all_recon.shape[0]):
    p.text(
        df_all_recon.psnr_dealias[line]+0.01, 
        df_all_recon.psnr[line]+0.01, 
        df_all_recon.model_denomination[line], 
        horizontalalignment='left', 
        size='small', 
        color='black', 
    )
plt.figure()
p = sns.scatterplot(x='ssim_dealias', y='ssim', data=df_all_recon)
# for line in range(0, df_all_recon.shape[0]):
#     p.text(
#         df_all_recon.ssim_dealias[line]+0.0001, 
#         df_all_recon.ssim[line]+0.0001, 
#         df_all_recon.model_denomination[line], 
#         horizontalalignment='left', 
#         size='small', 
#         color='black', 
#     )<jupyter_output><empty_output>
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