sv.save_all(all_bands_outliers,fileroot='band',typ='data',folder_root='IQR_BANDS'
ext='txt')
#For each run, we have a plot of:
# * The volume standard deviation values;
# * The outlier points from the std values, marked on the plot with an 'o'
# marker;
# * A horizontal dashed line at the lower IRQ threshold;
# * A horizontal dashed line at the higher IRQ threshold
for index, sdevs in enumerate(all_sdevs):
outlier_sdevs = all_iqr_outliers[index]
outlier_interval = all_bands_outliers[index]
plt_fmri.plot_sdevs(sdevs, outlier_sdevs, outlier_interval)
sv.save_plt(fileroot='vol_std_plt', index=index, folder_root='VOL_STD_PLTS',ext='png')
plt.close()
#Do the same for the RMS
all_rms = []
all_outliers_rms = []
all_bands_rms = []
for data in all_data:
rms = dl.vol_rms_diff(data)
outliers_rms, rms_interval = dn.iqr_outliers(rms)
all_rms.append(rms)
all_outliers_rms.append(outliers_rms)
all_bands_rms.append(rms_interval)
for index, rms in enumerate(all_rms):
outlier_rms = all_outliers_rms[index]