def subtract_background_from_ydats(scanfile, indir, outdir, scannumber=-1, highqnorm=False):
"""Subtract backround from SAXS data in .ydat files.
If `highqnorm` is True, normalize the buffer to the sample intensity
in q-range [4.0, 5.0] 1/nm and adjust with a constant before subtracting.
"""
scans = read_yaml(scanfile)
if scannumber > 0:
scannos = [ scannumber ]
else:
scannos = scans.keys()
scannos.sort()
for scanno in scannos:
print("Scan #%03d" % scanno)
try:
bufscan = scans[scanno][0]
except TypeError:
print("Scan #%03d is a buffer" % scanno)
continue
try:
conc = scans[scanno][1]
except TypeError:
print("No concentration for scan #02d." % scanno)
conc = 1.0
print("Using concentration %g g/l." % conc)
filelist = glob.glob(indir+"/s%03d.*.fil.ydat" % scanno)
for posno in xrange(len(filelist)):
bufname = indir + "/bufs%03d.p%02d.out.ydat" % (bufscan, posno)
buf, dbuf = read_ydat(bufname, addict=1)
fname = indir + "/s%03d.p%02d.fil.ydat" % (scanno, posno)
sam, dsam = read_ydat(fname, addict=1)
outname = os.path.basename(fname)
outname = outdir+'/'+outname[:outname.find('.fil.ydat')]+'.sub.ydat'
ad = {
'samfile': [os.path.basename(fname), md5_file(fname)],
'buffile': [os.path.basename(bufname), md5_file(bufname)],
'position' : dsam.get('inputposition', "unknown"),
'q~unit' : dsam.get('q~unit', "unknown"),
'I~unit' : dsam.get('I~unit', "unknown"),
'Ierr~unit' : dsam.get('Ierr~unit', "unknown"),
}
if highqnorm:
# 1 + 0.007 1/(g/l) is the excess of scattered intensity
# in a protein sample versus buffer in the q-range
# used [4.0, 5.0] 1/nm per concentration.
scale = highq_scale(sam, buf)
bufscale = scale * 1.0/(1.0 + 0.007*conc)
print("scale: %g, bufscale: %g" % (scale, bufscale))
buf[1,:] = bufscale * buf[1,:]
buf[2,:] = bufscale * buf[2,:]
ad['normalization'] = float(bufscale)
else:
ad['normalization'] = 'transmission'
# Assumes the standard q, I, Ierr ordering in index 0 columns
sub = errsubtract(sam, buf)
sub[1:3,:] = sub[1:3,:] / conc
write_ydat(sub, outname, addict=ad, attributes=['~unit'])
print(os.path.basename(outname))
def excess_ratio(scanfile, qrange=[4.0, 5.0], cnorm=True):
"""Return ratio of (sam/buf)-1 in subtractions in the `qrange` given.
Subtractions are made from data given in `scanfile` as in
'subtract_background_from_ydats()', but only the ratios of
sample / buffer intensity are returned in a list of arrays.
If `cnorm` is True (default) then the ratio is normalized to the
concentration read from the scanfile.
Results from this function can be used to calibrate high-q normalized
subtraction.
"""
scans = read_yaml(scanfile)
scannos = scans.keys()
scannos.sort()
indir = '.'
mlist = []
indlist = []
clist = []
for scanno in scannos:
try:
bufscan = scans[scanno][0]
except TypeError:
logging.warning("Scan #%03d is a buffer" % scanno)
continue
try:
conc = scans[scanno][1]
except TypeError:
print("No concentration for scan #02d." % scanno)
raise TypeError()
logging.warning("Scan #%03d" % scanno)
filelist = glob.glob(indir+"/s%03d.*.fil.ydat" % scanno)
marr= np.zeros((len(filelist)))
for posno in xrange(len(filelist)):
bufname = indir + "/bufs%03d.p%02d.out.ydat" % (bufscan, posno)
buf, dbuf = read_ydat(bufname, addict=1)
fname = indir + "/s%03d.p%02d.fil.ydat" % (scanno, posno)
sam, dsam = read_ydat(fname, addict=1)
# Assumes the standard q, I, Ierr ordering in index 0 columns
q = sam[0,:]
qind = np.logical_and(q > qrange[0], q < qrange[1])
ratio = np.mean(sam[1,qind]) / np.mean(buf[1,qind]) - 1.0
if cnorm:
ratio = ratio / conc
marr[posno] = ratio
mlist.append(marr)
indlist.append(scanno)
clist.append(conc)
return mlist, indlist, clist
def read_filtered(fname):
"""Return dat-array, inclusion map and first data read from file `fname`.
"""
dat, yd = read_ydat(fname, addict=1)
first = None
aver = None
if dat.shape[0] >= 5:
first = np.zeros((3, dat.shape[1]))
first[0,:] = dat[0,:]
first[1:3,:] = dat[3:5,:]
if dat.shape[0] >= 7:
aver = np.zeros((3, dat.shape[1]))
aver[0,:] = dat[0,:]
aver[1:3,:] = dat[5:7,:]
incmap = strings_to_incmap(yd['incmap']).T
return (dat[0:3,:], first, aver, incmap)
def read_outliers(fname):
dat, yd = read_ydat(fname, addict=1)
first = None
aver = None
if dat.shape[0] >= 5:
first = np.zeros((3, dat.shape[1]))
first[0,:] = dat[0,:]
first[1:3,:] = dat[3:5,:]
if dat.shape[0] >= 7:
aver = np.zeros((3, dat.shape[1]))
aver[0,:] = dat[0,:]
aver[1:3,:] = dat[5:7,:]
cdm = np.array(yd['chi2matrix'])
incinds = np.array(yd['incinds'])
threshold = yd['chi2cutoff']
return (dat[0:3,:], first, aver, incinds, cdm, threshold)
def get_bg(indir, scanno, posno):
"""Read background SAXS curve from an ydat-file.
"""
fname = indir + "/bufs%03d.p%02d.out.ydat" % (scanno, posno)
return read_ydat(fname)
