start = pd.to_datetime('2010-12-13 13:54:10.5-05:00') end = pd.to_datetime('2010-12-13 13:54:11.5-05:00') window_sizes = [32, 64, 128] raw = slicer.series['raw'][start:end] raw.plot() for ws in window_sizes: slicer.extract_rolling_median(seriesname = 'raw', window_size = ws) rm = slicer.series['raw_rolling_median_' + str(ws)][start:end] rm.plot(xticks=[i for i in rm.index]) plt.legend(['512Hz EEG']+[ 'Rolling Median %d window size' % ws \ for ws in window_sizes] ,loc='best') plt.ylabel(r"Potential ($\mu$V)") plt.xlabel(r"Time ($\mu$Sec)") #plt.title('10 Hz rolling median, compared to 512Hz signal') ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%S.%f')) ax.set_ylim(ax.get_ylim()[::-1]) pdfpages.savefig() #plt.show() if __name__=="__main__": slicer = Slicer() print 'loading raw from list of csvfiles' slicer.load_series_from_csv('raw', sys.argv[1:]) pp = PdfPages('rolling_median.pdf') do_charts(slicer, pp) pp.close()
slicer.extract_rolling_median(seriesname='raw', window_size=ws) rm = slicer.series['raw_rolling_median_' + str(ws)][start:end] rm_x = [ int(j.microseconds / 1000) for j in [i - rm.index[0] for i in rm.index] ] rm_y = [i for i in rm] #rm.plot(xticks=rm.index) plt.plot(rm_x, rm_y) plt.legend(['512Hz EEG']+[ 'Window size: %d' % ws \ for ws in window_sizes] ,loc='best') plt.ylabel(r"Potential ($\mu$V)") plt.xlabel(r"Time after stimulus (ms)") plt.grid() #plt.title('10 Hz rolling median, compared to 512Hz signal') ax.set_ylim(ax.get_ylim()[::-1]) pdfpages.savefig() #plt.show() #debug if __name__ == "__main__": slicer = Slicer() print 'loading raw from list of csvfiles' slicer.load_series_from_csv('raw', sys.argv[1:]) pp = PdfPages('rolling_median.pdf') do_charts(slicer, pp) pp.close()
if args.interpolate: process_series_files.process_all_in_dir(args.indir[0], join(out_dir, 'data')) data_dir = join(out_dir, 'data') """ else: #just copy the files print "Copying data files to ", data_dir for csvf in glob.iglob(join(args.indir[0],"*.csv")): shutil.copyfile(csvf, join(data_dir, os.path.basename(csvf))) """ print "Instantiating Slicer and loading series" slicer = Slicer(taskfile=join(data_dir, 'task.xls')) filelist=[join(data_dir,f) for f in os.listdir(data_dir) if \ re.compile(".*\.csv").match(f)] num_subjects = len(filelist) slicer.load_series_from_csv('raw', filelist) if args.stats: pp = PdfPages(join(report_dir, 'stats.pdf')) stats.plot_all(slicer, pp) fig, ax = plt.subplots() ax.plot(range(1, num_subjects + 1)) plt.title("Number of subjects") pp.savefig(fig) pp.close() if args.kernelsvm: kernel_svm.do_kernelsvm_slicer(slicer) if args.chartsforpaper:
process_series_files.process_all_in_dir(args.indir[0], join(out_dir,'data')) data_dir = join(out_dir,'data') """ else: #just copy the files print "Copying data files to ", data_dir for csvf in glob.iglob(join(args.indir[0],"*.csv")): shutil.copyfile(csvf, join(data_dir, os.path.basename(csvf))) """ print "Instantiating Slicer and loading series" slicer = Slicer(taskfile=join(data_dir,'task.xls')) filelist=[join(data_dir,f) for f in os.listdir(data_dir) if \ re.compile(".*\.csv").match(f)] num_subjects = len(filelist) slicer.load_series_from_csv('raw', filelist) if args.stats: pp = PdfPages(join(report_dir, 'stats.pdf')) stats.plot_all(slicer, pp) fig, ax = plt.subplots() ax.plot(range(1,num_subjects+1)) plt.title("Number of subjects") pp.savefig(fig) pp.close() if args.kernelsvm: kernel_svm.do_kernelsvm_slicer(slicer) if args.chartsforpaper: