def get_absolute_qs(self,line,verbosity=3):
"""
OLD!
determine two points on y-axis with known q-distance
(low-loss w-q reference with central spot and bragg spot)
line ... 1D array with N-points containing two peaks
"""
x = np.arange(N,dtype='float');
ref=gaussfilt1D(line, 5); peaks=[];
for i in range(2): # fit 2 peaks
imax = np.argmax(ref); # initial guess for peak
p, pconv = \
opt.curve_fit(models.gauss,x,ref,p0=(imax, np.sum(ref[imax-5:imax+5]), 10));
peaks.append(p); # gauss fit
imin = max(p[0]-5*p[2],0);
imax = min(p[0]+5*p[2],N);
ref[imin:imax]=0; # remove peak from line (5*fwhm around x0)
if verbosity>2:
plt.figure(); plt.title("DEBUG: Fit q-reference");
plt.plot(x,line,'k');
plt.plot(x,models.gauss(x,*peaks[0]),'r');
plt.plot(x,models.gauss(x,*peaks[1]),'g');
return peaks[0][0], peaks[1][0];
def get_absolute_qs(self, line, verbosity=3):
"""
OLD!
determine two points on y-axis with known q-distance
(low-loss w-q reference with central spot and bragg spot)
line ... 1D array with N-points containing two peaks
"""
x = np.arange(N, dtype='float')
ref = gaussfilt1D(line, 5)
peaks = []
for i in range(2): # fit 2 peaks
imax = np.argmax(ref)
# initial guess for peak
p, pconv = \
opt.curve_fit(models.gauss,x,ref,p0=(imax, np.sum(ref[imax-5:imax+5]), 10))
peaks.append(p)
# gauss fit
imin = max(p[0] - 5 * p[2], 0)
imax = min(p[0] + 5 * p[2], N)
ref[imin:imax] = 0
# remove peak from line (5*fwhm around x0)
if verbosity > 2:
plt.figure()
plt.title("DEBUG: Fit q-reference")
plt.plot(x, line, 'k')
plt.plot(x, models.gauss(x, *peaks[0]), 'r')
plt.plot(x, models.gauss(x, *peaks[1]), 'g')
return peaks[0][0], peaks[1][0]
def fit_aperture_borders(self,order=2,log=False,**kwargs):
"""
Determine the left and right border of the aperture as polynom x(y)
for the series of E-q maps.
order ... (opt) order of the polynomial to fit
log ... (opt) consider aperture images on log-scale
for further options, see fit_border.get_border_points()
RETURNS list of tuples(left,right) containing polynomials x = left(y)
"""
self.ap_order =order;
self.ap_log =log;
# illumination reference (smoothed version)
if self.ref_name is not None:
ref = np.abs(gaussfilt1D(self.ref_img,11)); # gauss-filter
#ref = np.abs(lorentzfit1D(self.ref_img,offset=offset)); # fit lorentz
ref[ref<np.mean(ref)] = np.mean(ref); # constant for low values
# to avoid 0 devision
if self.verbosity>9:
self.__dbg_fig.append(self.plot_reference(ref)); # draw figure and save in list
else:
ref = 1; # no reference given
# iterate over all aperture images
points = []; fit = []; lines=[]; stack=[]; info=[];
for i,image in enumerate(self.ap_stack):
# correct image by illu_ref
filtimg = image/ref;
if log: filtimg = np.log(np.abs(filtimg)+1);
# get aperture border as point list in px (correct for binning!)
c = self.crop;
l,r = fit_border.get_border_points(filtimg, interp_out=True,
xmin=int(c['xmin']/self.xbin), xmax=int(c['xmax']/self.xbin),
ymin=int(c['ymin']/self.ybin), ymax=int(c['ymax']/self.ybin),
verbosity=self.verbosity-10, **kwargs);
l = ((l+0.5).T*(self.xbin,self.ybin)).T; # convert to px, points (x,y)
r = ((r+0.5).T*(self.xbin,self.ybin)).T;
points.append([l,r]);
# fit polynom x=p(y) to left and right border
polyl = np.poly1d(np.polyfit(l[1],l[0],order));
polyr = np.poly1d(np.polyfit(r[1],r[0],order));
fit.append((polyl,polyr));
# DEBUG: drawing fit points and polynoms
if self.verbosity>2:
y = np.arange(self.crop['ymin'],self.crop['ymax']);
stack.append(filtimg);
info.append({'desc': 'DEBUG: '+self.ap_names[i],
'xperchan':self.xbin, 'yperchan':self.ybin});
p1 = plt.Line2D(l[0],l[1],marker='x',ls='',c='b');
p2 = plt.Line2D(r[0],r[1],marker='x',ls='',c='b')
p3 = plt.Line2D(polyl(y),y,color='r');
p4 = plt.Line2D(polyr(y),y,color='r');
lines.append([p1,p2,p3,p4]);
if self.verbosity>2:
self.__dbg_fig.append( self.plot_aperture_images(stack,info,lines) );
# store results
self.ap_points=points; # contains Nap (l,r) tuples; l,r are a lists of (x,y) points
self.ap_poly =fit; # contains Nap (pl,pr) tuples; pl,pr are polynoms y(x)
# comments
self.history.append("Fit aperture borders");
self.history.append("|- order=%d, log=%s"%(order,log));
params = ", ".join([key+": "+str(val) for key,val in kwargs.items()]);
self.history.append("|- " + params);
return fit;
def fit_aperture_borders(self, order=2, log=False, **kwargs):
"""
Determine the left and right border of the aperture as polynom x(y)
for the series of E-q maps.
order ... (opt) order of the polynomial to fit
log ... (opt) consider aperture images on log-scale
for further options, see fit_border.get_border_points()
RETURNS list of tuples(left,right) containing polynomials x = left(y)
"""
self.ap_order = order
self.ap_log = log
# illumination reference (smoothed version)
if self.ref_name is not None:
ref = np.abs(gaussfilt1D(self.ref_img, 11))
# gauss-filter
#ref = np.abs(lorentzfit1D(self.ref_img,offset=offset)); # fit lorentz
ref[ref < np.mean(ref)] = np.mean(ref)
# constant for low values
# to avoid 0 devision
if self.verbosity > 9:
self.__dbg_fig.append(self.plot_reference(ref))
# draw figure and save in list
else:
ref = 1
# no reference given
# iterate over all aperture images
points = []
fit = []
lines = []
stack = []
info = []
for i, image in enumerate(self.ap_stack):
# correct image by illu_ref
filtimg = image / ref
if log: filtimg = np.log(np.abs(filtimg) + 1)
# get aperture border as point list in px (correct for binning!)
c = self.crop
l, r = fit_border.get_border_points(
filtimg,
interp_out=True,
xmin=int(c['xmin'] / self.xbin),
xmax=int(c['xmax'] / self.xbin),
ymin=int(c['ymin'] / self.ybin),
ymax=int(c['ymax'] / self.ybin),
verbosity=self.verbosity - 10,
**kwargs)
l = ((l + 0.5).T * (self.xbin, self.ybin)).T
# convert to px, points (x,y)
r = ((r + 0.5).T * (self.xbin, self.ybin)).T
points.append([l, r])
# fit polynom x=p(y) to left and right border
polyl = np.poly1d(np.polyfit(l[1], l[0], order))
polyr = np.poly1d(np.polyfit(r[1], r[0], order))
fit.append((polyl, polyr))
# DEBUG: drawing fit points and polynoms
if self.verbosity > 2:
y = np.arange(self.crop['ymin'], self.crop['ymax'])
stack.append(filtimg)
info.append({
'desc': 'DEBUG: ' + self.ap_names[i],
'xperchan': self.xbin,
'yperchan': self.ybin
})
p1 = plt.Line2D(l[0], l[1], marker='x', ls='', c='b')
p2 = plt.Line2D(r[0], r[1], marker='x', ls='', c='b')
p3 = plt.Line2D(polyl(y), y, color='r')
p4 = plt.Line2D(polyr(y), y, color='r')
lines.append([p1, p2, p3, p4])
if self.verbosity > 2:
self.__dbg_fig.append(self.plot_aperture_images(
stack, info, lines))
# store results
self.ap_points = points
# contains Nap (l,r) tuples; l,r are a lists of (x,y) points
self.ap_poly = fit
# contains Nap (pl,pr) tuples; pl,pr are polynoms y(x)
# comments
self.history.append("Fit aperture borders")
self.history.append("|- order=%d, log=%s" % (order, log))
params = ", ".join(
[key + ": " + str(val) for key, val in kwargs.items()])
self.history.append("|- " + params)
return fit
