def filter_matfile(fname, outstem):
stack = read_mat(fname)
for pos in range(stack.shape[0]):
print("File: %s, pos %d" % (fname, pos))
sys.stdout.flush()
first = stack[pos,0,...]
aver = mean_stack(stack[pos,...])
filt, inds = chifilter_points(stack[pos,...])
outname = "%s.p%02d.fil.ydat" % (outstem, pos)
write_filtered(filt, first, aver, inds, outname, \
os.path.basename(fname), pos)
print(outname)
def filter_matfile(fname, outstem, p_reject=0.001, plot=1):
stack = read_mat(fname)
md5 = md5_file(fname)
print("Rejection probability: %0.3g" % p_reject)
N = np.sum(np.logical_not(np.isnan(stack[0,0,1,:])))
print("Number of valid channels: %d" % N)
threshold = chi2.ppf(1.0 - p_reject, N) / N
print("Chisq rejection threshold: %0.3g" % threshold)
for pos in range(stack.shape[0]):
reps = stack[pos,...]
incinds, cdm = filter_outliers(reps, threshold=threshold, plot=plot)
ms = mean_stack(reps[incinds,...])
disinds = range(reps.shape[0])
for i in incinds:
disinds.remove(i)
print("Pos %d, discarded: %s" % (pos, str(disinds)))
ad = { 'chi2cutoff' : float(threshold),
'rejection_prob' : float(p_reject),
'incinds' : map(int, list(incinds)),
'disinds' : map(int, list(disinds)),
'chi2matrix' : map(float, list(cdm)),
'method' : "filter_outliers",
'inputfile' : [ fname, md5 ],
'inputposition' : int(pos),
'q~unit' : '1/nm',
'I~unit' : 'arb.',
'Ierr~unit' : 'arb.',
'I_first~unit' : 'arb.',
'Ierr_first~unit' : 'arb.',
'I_all~unit' : 'arb.',
'Ierr_all~unit' : 'arb.',
}
outarr = np.zeros((7, ms.shape[1]))
outarr[0:3,:] = ms
outarr[3:5,:] = reps[0,1:3,:]
outarr[5:7,:] = mean_stack(reps)[1:3,:]
outname = "%s.p%02d.out.ydat" % (outstem, pos)
print(outname)
write_ydat(outarr, outname, addict=ad,
cols=['q','I','Ierr','I_first','Ierr_first','I_all','Ierr_all'],
attributes=['~unit'])
