if verbosity>9:
info=[];
info.append({'desc': 'DEBUG: rebinned image', 'yperchan':ybin,
'xperchan': dq, 'xunits':'1/A', 'xlabel':'q', 'xoffset':q[0]});
info.append({'desc': 'DEBUG: rebinned image (reversed q-axis)', 'yperchan':ybin,
'xperchan': -dq, 'xunits':'1/A', 'xlabel':'-q', 'xoffset':q[-1]});
fig = WQStackBrowser([ret,ret],info,aspect='auto');
plt.show();
# 5. save undistorted w-q map
# 5.1 save as TIFF (readable by DM3, ImageJ)
tiffile = '%s-rebinned%dx%d.tif'%(filename.split('/')[-1].split('.tif')[0],
ret.shape[0],ret.shape[1]);
print 'write to file %s'%tiffile;
tiff.imsave(tiffile,ret.astype(np.float32));
# 5.2 pickle array and history for extract_spectrum
pklfile = tiffile.replace('.tif','.pkl');
print 'write to file %s'%pklfile;
FILE=open(pklfile,'w');
data={'yqmap':ret, 'qaxis':q, 'qdisp':qcal,
'rawfile':filename,'descr':descr, 'history':history};
pickle.Pickler(FILE).dump(data);
FILE.close();
# spectrum
spectra[s, Ny] = E[s]
# energy offset
# DEBUG
if s == 0:
plt.imshow(image, vmin=0, vmax=1000)
plt.figure()
plt.plot(xline, label='projected Tiff')
plt.plot(gauss(range(Nx), *param), label='Gauss fit')
window = np.zeros(Nx)
window[xmin:xmax] = xline.max()
plt.plot(window, label='selected region')
plt.legend()
plt.show(block=False)
# test if crop region has been too small
if x0.max() - x0.min() > dx.min():
print 'WARNING: atomatic crop region might be too small'
# save spectra to TIFF
print "SAVE EELS spectra to file '%s'" % outfile
tiff.imsave(outfile, spectra.astype(np.float32))
# plot all spectra
plt.figure()
for i, s in enumerate(spectra):
plt.plot(s / float(s[0:Ny].max()) - i * 0.1, 'k', linewidth=1)
plt.show()
# crop image and extract EELS
xmin = max(x0[s]-dx[s],0); xmax = min(x0[s]+dx[s], Ny);
spectra[s,0:Ny] = np.sum(image[:,xmin:xmax],axis=1); # spectrum
spectra[s,Ny] = E[s]; # energy offset
# DEBUG
if s==0:
plt.imshow(image,vmin=0,vmax=1000);
plt.figure()
plt.plot(xline,label='projected Tiff');
plt.plot(gauss(range(Nx),*param),label='Gauss fit');
window=np.zeros(Nx); window[xmin:xmax]=xline.max();
plt.plot(window,label='selected region');
plt.legend();
plt.show(block=False);
# test if crop region has been too small
if x0.max()-x0.min() > dx.min():
print 'WARNING: atomatic crop region might be too small'
# save spectra to TIFF
print "SAVE EELS spectra to file '%s'"%outfile;
tiff.imsave(outfile,spectra.astype(np.float32));
# plot all spectra
plt.figure()
for i,s in enumerate(spectra):
plt.plot(s/float(s[0:Ny].max()) -i*0.1,'k',linewidth=1);
plt.show()
EQmap.append(spectrum);
EQmap=np.asarray(EQmap).T; # first index E, second q
ehistory+="\nRebinning for Emin=%8.5f, Emax=%8.5f, dE=%8.5f"%(E.min(), E.max(),dE);
# DEBUG: plot calibrated Eqmap
if verbosity>9:
info = {'desc': 'DEBUG: rebinned WQmap',
'xperchan':dE, 'ylabel':'E', 'yunits':'eV',
'yperchan':dqx,'ylabel':'q', 'yunits':'1/A',
'yoffset' :qaxis[0]};
WQB = WQBrowser(EQmap.T,info,aspect='auto');
plt.show();
# 5. save E-q map as readable tif
outfile = '%s-calibrated.tif'%(filename.split('/')[-1].split('.pkl')[0]);
print 'write to file %s'%outfile;
tiff.imsave(outfile,EQmap.astype(np.float32));
# 6. save energy-loss function
for iq, q in enumerate(qaxis):
if qmin > q or q > qmax: continue
E0_offset,_ = e2y.inverse(y0_fit(iq),y0_fit(iq)); # keV, beam energy offset
# calculate aperture correction function for given q (rectangular aperture)
if apply_APC==True:
aperture = TEM_wqslit(q*conv.bohr,dqx*conv.bohr,dqy*conv.bohr,E0);
APC = [aperture.get_APC(_E) for _E in E];
else: APC = 1;
elf = EQmap[:,iq]/APC
# write file containing energy-loss function:
# required parameters
param = { 'title' : title + ', ELF',
'desc': 'DEBUG: rebinned WQmap',
'xperchan': dE,
'xlabel': 'E',
'xunits': 'eV',
'yperchan': dqx,
'ylabel': 'q',
'yunits': '1/A',
'yoffset': qaxis[0]
}
WQB = WQBrowser(EQmap.T, info, aspect='auto')
plt.show()
# 5. save E-q map as readable tif
outfile = '%s-calibrated.tif' % (filename.split('/')[-1].split('.pkl')[0])
print 'write to file %s' % outfile
tiff.imsave(outfile, EQmap.astype(np.float32))
# 6. save energy-loss function
for iq, q in enumerate(qaxis):
if qmin > q or q > qmax: continue
# calibrate offset in energy axis (so far E=0eV at x=0px)
E_ZLP, _ = e2y.inverse(y0_fit(q), y0_fit(q))
Ecorr = E + E0_offset - E_ZLP
# calculate aperture correction function for given q (rectangular aperture)
# note: APC for negative energies is well defined (energy gain)
if apply_APC == True:
aperture = TEM_wqslit(q * conv.bohr, dqx * conv.bohr, dqy * conv.bohr,
E0)
APC = [aperture.get_APC(_E) for _E in Ecorr]
else:
import glob;
import numpy as np
import scipy.signal as sig
from TEMareels.tools.img_filter import binning
from TEMareels.tools import tvips
import TEMareels.tools.tifffile as tiff
# define infile name and binning along y-direction (E-axis)
pattern = "/path/to/q_calibration/img%d.tif";
files = [pattern%i for i in range(344,345)]
ybin = 64;
for filename in files:
image = tvips.load_TVIPS(filename) # img[iy,ix]
#image = tiff.imread(filename).astype(np.float64);
if ~np.allclose(image.shape, (4096,4096)):
raise IOError("Unexpected image size in file '%s'"%filename);
medimg= sig.medfilt2d(image,kernel_size=3); # filtering
binimg= binning(medimg,ybin); # binning along y
#binimg = binning(binimg.T,32).T; # binning along x
outfile = filename.split(".tif")[0]+"_filt_bin%d.tif"%ybin;
tiff.imsave(outfile,binimg.astype(np.float32));
"""
# use TEMareels package specified in _set_pkgdir (overrides PYTHONPATH)
import _set_pkgdir
import glob
import numpy as np
import scipy.signal as sig
from TEMareels.tools.img_filter import binning
from TEMareels.tools import tvips
import TEMareels.tools.tifffile as tiff
# define infile name and binning along y-direction (E-axis)
pattern = "/path/to/q_calibration/img%d.tif"
files = [pattern % i for i in range(344, 345)]
ybin = 64
for filename in files:
image = tvips.load_TVIPS(filename) # img[iy,ix]
#image = tiff.imread(filename).astype(np.float64);
if ~np.allclose(image.shape, (4096, 4096)):
raise IOError("Unexpected image size in file '%s'" % filename)
medimg = sig.medfilt2d(image, kernel_size=3)
# filtering
binimg = binning(medimg, ybin)
# binning along y
#binimg = binning(binimg.T,32).T; # binning along x
outfile = filename.split(".tif")[0] + "_filt_bin%d.tif" % ybin
tiff.imsave(outfile, binimg.astype(np.float32))
import glob;
import numpy as np
import scipy.signal as sig
from TEMareels.tools.img_filter import binning
from TEMareels.tools import tvips
import TEMareels.tools.tifffile as tiff
# define infile name and binning along y-direction (E-axis)
pattern = "/path/to/q_calibration/img%d.tif";
files = [pattern%i for i in range(344,345)]
ybin = 64;
for filename in files:
image = tvips.load_TVIPS(filename) # img[iy,ix]
#image = tiff.imread(filename).astype(np.float64);
if ~np.allclose(image.shape, (4096,4096)):
raise IOError("Unexpected image size in file '%s'"%filename);
medimg= sig.medfilt2d(image,kernel_size=3); # filtering
binimg= binning(medimg,ybin); # binning along y
#binimg = binning(binimg.T,32).T; # binning along x
outfile = filename.split(".tif")[0]+"_filt_bin%d.tif"%ybin;
tiff.imsave(outfile,image.astype(np.float32));
