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:
