# * Get indices of outlier volumes for each dataset. # * Write each as its own file and save in 'vol_std_outliers' folder # * Takes 15 min to run all_bands_outliers = [] all_sdevs = [] all_iqr_outliers = [] for data in all_data: sdev = dl.vol_std(data) all_sdevs.append(sdev) outlier, band = dl.iqr_outliers(sdev) all_iqr_outliers.append(outlier) all_bands_outliers.append(band) sv.save_all(all_sdevs, fileroot='sdevs', typ='data', folder_root='SDEVS' ext='txt') sv.save_all(all_iqr_outliers, fileroot='out_iqr', typ = 'data', folder_root='OUTLIER_IQRs', ext='txt') 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
