//  [python]
def make_van_opstal_plot_individual_networks(regex=None):
    if regex is None:
       regex = ("/om2/user/francl/grp-om2/gahlm/dataset_pipeline_test/"
                "arch_number_[0-9][0,1,3-9][0-9]_init_0/"
                "batch_conditional_broadbandNoiseRecords"
                "_convolvedCIPIC*iter100000.npy")
    pd_cipic = make_dataframe(regex,fold_data=False,
                              elevation_predictions=True)
    pd_cipic_filtered = get_elevation_error_CIPIC(pd_cipic,subject=None)
    subjects_sorted = (pd_cipic_filtered.groupby("subject_num").mean().
                       sort_values(by="elev_error").reset_index()['subject_num'])
    quartile_subjects_idx = [round(x*subjects_sorted.shape[0]) for x in [.05,.25,.75,.95]]
    quartile_sujects = subjects_sorted.iloc[quartile_subjects_idx]
    num_quartile_sub = quartile_sujects.shape[0]
    num_total_sub = pd_cipic_filtered['subject_num'].unique().shape[0]-1
    pd_dataframe_cipic_subject_quartile = pd_cipic_filtered.query("subject_num in @quartile_sujects")
    pd_dataframe_cipic_new_ears = pd_cipic_filtered.query("subject_num != 999")
    plt.clf()
    plt.figure(figsize=(1.8,1.8))
    ax1=sns.regplot(x='elev',y='predicted_elev',data=pd_cipic_filtered,
                     hue="Ears Used",
                     scatter_kws={"s":2,"lw":0.15},
                     line_kws={"lw":1.25},ci=None,ax=plt.gca())
    handles,labels = plt.gca().get_legend_handles_labels()
    ax2 = sns.regplot(x='elev',y='predicted_elev',hue="subject_num",
                  data=pd_dataframe_cipic_subject_quartile,
                 palette=sns.color_palette(n_colors=1)*num_quartile_sub,
                 scatter_kws={"s":.75,"lw":0.15},
                 line_kws={"lw":0.5},ci=None,alpha=0.8,ax=ax1)
    ax3 = sns.regplot(x='elev',y='predicted_elev',hue="subject_num",
                  data=pd_dataframe_cipic_new_ears,
                 palette=sns.color_palette(n_colors=1)*num_total_sub,
                 scatter_kws={"s":0.35,"lw":0.05},
                 line_kws={"lw":0.35},ci=None,alpha=0.2,ax=ax2)
    #plt.legend(handles,labels)
    plt.gca().legend().remove()
    plt.xlabel("Elevation (Degrees)")
    plt.ylabel("Predicted Elevation (Degrees)")
    plt.xticks([0,10,20,30,40,50],rotation=75)
    plt.yticks([0,10,20,30,40,50])
    plt.tight_layout()
    plt.savefig(plots_folder+"/"+"elev_predicted_vs_label_all_subjects_vs_CIPIC_elev_collapsed.svg")
    plt.clf()
    ax1=sns.regplot(x='azim',y='predicted_azim',hue="Ears Used",data=pd_cipic_filtered,
                     scatter_kws={"s":2,"lw":0.15},
                     line_kws={"lw":1.25},ci=68,ax=plt.gca())
    handles,labels = plt.gca().get_legend_handles_labels()
    ax2 = sns.regplot(x='azim',y='predicted_azim',hue="subject_num",
                  data=pd_dataframe_cipic_subject_quartile,
                 palette=sns.color_palette(n_colors=1)*num_quartile_sub,
                 scatter_kws={"s":1.0,"lw":0.075},
                 line_kws={"lw":0.5},ci=68,alpha=0.8,ax=ax1)
    ax3 = sns.regplot(x='azim',y='predicted_azim',hue="subject_num",
                  data=pd_dataframe_cipic_new_ears,
                 palette=sns.color_palette(n_colors=1)*num_total_sub,
                 scatter_kws={"s":0.35,"lw":0.05},
                 line_kws={"lw":0.35},ci=68,alpha=0.2,ax=ax2)
    plt.xlabel("Azimuth (Degrees)")
    plt.ylabel("Predicted Azimuth (Degrees)")
    plt.xticks([-75,-50,-25,0,25,50,75],rotation=75)
    plt.yticks([-75,-50,-25,0,25,50,75])
    plt.gca().legend().remove()
    plt.tight_layout()
    pd_cipic_filtered.to_csv(plots_folder+"/"+"predicted_vs_label_all_subjects_vs_CIPIC.csv")
    plt.savefig(plots_folder+"/"+"azim_predicted_vs_label_all_subjects_vs_CIPIC_elev_collapsed.svg")