def __init__(self,
ap_series,
illu_ref=None,
reverse_y=False,
N=4096,
verbosity=1):
"""
ap_series ... names of tif-images of the shifted aperture, shape (Nap,)
illu_ref ... (opt) name of reference to correct non-homogeneous illumination
reverse_y ... (opt) True if y-axis should be inversed
N ... (opt) number of pixels of camera
verbosity ... (opt) 0: silent, 1: minimal, 2: verbose, >10: debug
"""
self.Npx = N
self.ap_names = ap_series
self.ref_name = illu_ref
self.verbosity = verbosity
self.history = []
# load image files
self.ref_img = tif.imread(illu_ref,verbosity=verbosity) \
if illu_ref is not None else None
# Ny,Nx
self.ap_stack = tif.imread(ap_series, verbosity=verbosity)
# Nap,Ny,Nx
# reverse images
if reverse_y:
print "WARNING: in QDispersion: reverse_y==True"
self.ref_img = self.ref_img[::-1]
self.ap_stack = self.ap_stack[:, ::-1]
# set image parameters
self.Nap, self.Ny, self.Nx = self.ap_stack.shape
self.ybin, self.xbin = self.Npx / float(self.Ny), self.Npx / float(
self.Nx)
self.crop_img()
# transformations (identity by default)
self.u2x = trafo.I()
# normalised coordinates
self.s2u = trafo.I()
# slit coordinates = distortions of iso-q-lines
self.q2s = trafo.I()
# non-linear dispersion on q-axis
# History + DEBUG
self.history = [
"momentum_dispersion.py, version %s (%s)" %
(Release.version, Release.version_id)
]
self.__dbg_fig = []
def __init__(self,ap_series,illu_ref=None,reverse_y=False,N=4096,verbosity=1):
"""
ap_series ... names of tif-images of the shifted aperture, shape (Nap,)
illu_ref ... (opt) name of reference to correct non-homogeneous illumination
reverse_y ... (opt) True if y-axis should be inversed
N ... (opt) number of pixels of camera
verbosity ... (opt) 0: silent, 1: minimal, 2: verbose, >10: debug
"""
self.Npx = N;
self.ap_names = ap_series;
self.ref_name = illu_ref;
self.verbosity= verbosity
self.history = [];
# load image files
self.ref_img = tif.imread(illu_ref,verbosity=verbosity) \
if illu_ref is not None else None; # Ny,Nx
self.ap_stack = tif.imread(ap_series,verbosity=verbosity); # Nap,Ny,Nx
# reverse images
if reverse_y:
print "WARNING: in QDispersion: reverse_y==True";
self.ref_img= self.ref_img[::-1];
self.ap_stack=self.ap_stack[:,::-1];
# set image parameters
self.Nap, self.Ny, self.Nx = self.ap_stack.shape;
self.ybin,self.xbin = self.Npx/float(self.Ny), self.Npx/float(self.Nx);
self.crop_img();
# transformations (identity by default)
self.u2x = trafo.I(); # normalised coordinates
self.s2u = trafo.I(); # slit coordinates = distortions of iso-q-lines
self.q2s = trafo.I(); # non-linear dispersion on q-axis
# History + DEBUG
self.history = ["momentum_dispersion.py, version %s (%s)" %
(Release.version, Release.version_id)];
self.__dbg_fig=[]; # list of figures
def get_peak_pos(filename, refname=None, medfilt_radius=5, sort=False, border=10, ampl_cut=0.5, verbosity=1):
"""
calculate the position-dependent energy dispersion from
the distance between two peaks (ZLP and plasmon reference)
filename ... file containing the spectrum image (Nspectra, Npx)
refname ... (opt) filename of reference spectrum for second peak
medfilt_radius... (opt) median filter radius for smoothing of spectra
sort ... (opt) if True, sort spectra according to ZLP position
border ... (opt) skip peaks which are too close to the border (in pixel)
ampl_cut ... (opt) skip peaks with amplitude smaller than ampl_cut*maximum
verbosity... (opt) 0 (silent), 1 (minimal), 2 (plot), 3 (debug)
RETURNS
x(N), zl(N) or
x(N), zl(N), pl(N) which are one-dimensional arrays of length N
containing the x-value of the spectrum, the zero-loss and
plasmon-peak position.
(N=Nspectra)
"""
# 1. read EELS spectra of series
if verbosity>0: print "Loading spectra from file '%s'"%filename;
IN = tiff.imread(filename); # Ny, Ns+1
data = IN[:,:-1];
x = IN[:,-1]; # last line in image corresponds
# to energie values
# 2. fit ZLP to spectra
zl,spectra = fit_zlp(data, border=border, medfilt_radius=medfilt_radius,
ampl_cut=ampl_cut, verbosity=verbosity, sort=sort);
if refname is None:
if verbosity>2: plot_peaks(spectra, None, zl, None, filename=filename);
return x,zl;
# 3. fit second peak from correlation with reference spectrum
spectra_noZLP=spectra.copy();
for s in range(len(spectra)): # for each spectrum, we remove the ZLP
x0,I,fwhm = zl[s]; # parameters from ZLP
xmin,xmax = max(0,x0-5*fwhm), min(len(spectra[s]),x0+5*fwhm);
spectra_noZLP[s,xmin:xmax]=0;
REF = MSA(refname).get_data();
pl = fit_plasmon(spectra_noZLP, REF, border=border,
ampl_cut=ampl_cut, medfilt_radius=medfilt_radius, verbosity=verbosity);
if verbosity>2: plot_peaks(spectra, REF, zl, pl, filename=filename);
return x,zl,pl
# 1. get calibration object
qdisp_name = 'QDisp.pkl';
FILE = open(qdisp_name,'r');
qcal= pickle.Unpickler(FILE).load();
FILE.close();
s2u,q2s = qcal['s2u'],qcal['q2s']; # unpack the single trafos
history = qcal['history'];
history+= "\n\nremove_qdistortion.py, version %s (%s)" % \
(Release.version, Release.version_id);
print 'READ QDispersion: ' + qcal['descr'];
# 2. distorted E-q map and normalize q-axis
# TODO: automatic fit for border ?
filename= '../tests/qseries_sum.tif';
img = tiff.imread(filename);
Ny,Nx = img.shape;
ybin,xbin=N/float(Ny), N/float(Nx);
x0,y0 = 2377,967; # position of reference point in WQmap
xl,xr = 0,N; # (opt) slit borders in WQmap (fine-tuning of spec-mag)
Gl,Gr = 1193,3527; # coordinates of left+right Bragg spot at y0
G = 2.96; # length |G| corresponding to x0-Gl and Gr-x0
u2x = trafo.Normalize(x0,y0,xl,xr);# (u,v) -> (x,y)
s2x = trafo.Seq(u2x,s2u); # s -> u -> x
sl,_= s2x.inverse(Gl,y0);
sr,_= s2x.inverse(Gr,y0);
q2s = calibrate_qaxis(q2s,sl,sr,G);
history+="\nRawfile: %s"%filename;
history+="\n"+u2x.info(3);
# -- main ----------------------------------------
if __name__ == '__main__':
import TEMareels.tools.tifffile as tiff
coeff = [[[8.53185497e-06, 5.74373869e-02, 6.63128635e+02],
[7.69501010e-06, 4.65273936e-02, 7.89253445e+02]],
[[6.45514206e-06, 4.10327089e-02, 9.00378881e+02],
[6.12716673e-06, 2.82385482e-02, 1.07283189e+03]]]
stack = []
info = []
lines = []
for i in (1, 2):
# read image from file
filename = '../tests/qseries%d.tif' % i
stack.append(tiff.imread(filename))
# info for image
info.append({
'desc': 'WQStackBrowser: ' + filename,
'filename': filename,
'xperchan': 4.,
'yperchan': 64.
})
# additional line plots
y = np.arange(150, 4096)
l1 = plt.Line2D(np.poly1d(coeff[i - 1][0])(y), y, color='r', ls='-')
l2 = plt.Line2D(np.poly1d(coeff[i - 1][1])(y), y, color='g', ls='-')
lines.append([l1, l2])
# -- main ----------------------------------------
if __name__ == '__main__':
import TEMareels.tools.tifffile as tiff
coeff = [
[[ 8.53185497e-06, 5.74373869e-02, 6.63128635e+02],
[ 7.69501010e-06, 4.65273936e-02, 7.89253445e+02]],
[[ 6.45514206e-06, 4.10327089e-02, 9.00378881e+02],
[ 6.12716673e-06, 2.82385482e-02, 1.07283189e+03]]];
stack = []; info = []; lines = [];
for i in (1,2):
# read image from file
filename = '../tests/qseries%d.tif'%i;
stack.append(tiff.imread(filename));
# info for image
info.append( {'desc': 'WQStackBrowser: '+filename,
'filename':filename,
'xperchan':4., 'yperchan':64.} );
# additional line plots
y = np.arange(150,4096);
l1 = plt.Line2D(np.poly1d(coeff[i-1][0])(y),y,color='r',ls='-');
l2 = plt.Line2D(np.poly1d(coeff[i-1][1])(y),y,color='g',ls='-');
lines.append([l1,l2]);
# show single image
IB =WQBrowser(stack[0],{'desc': 'WQBrowser'},aspect='auto',verbosity=4);
image[:,i] = image[:,i]-ref_line;
offset += sector_width;
#print offset
image[:,0:5]= image[:,-5:] = 0;
if verbosity > 0:
plt.title("Remove Stripes: difference between old and new image");
plt.imshow(image - old_image, aspect='auto')
plt.show();
return image;
# -- main ----------------------------------------
if __name__ == '__main__':
import TEMareels.tools.tifffile as tiff
from TEMareels.tools import tvips
image_file = '../tests/wqmap.tif';
image = tiff.imread(image_file).astype(float);
binning = 8;
intstart= np.array([0,1025,2900,3800])/binning;
img = remove_stripes(image, intwidth=100/binning,
intstart=intstart, sector_width=1024/binning, verbosity=1);
#outfile = "script_test_.tif";
#tvips.write_tiff(img, outfile);
y_start = y0;
for i in range(0,Nx):
peak_value = img[:,i].max();
peak_index = img[:,i].argmax();
if np.abs(peak_index - y_start)<delta:
peaks.append([i,peak_index]);
if verbosity > 9:
plt.plot(i,peak_index, 'ro', linewidth=5);
peaks = np.asarray(peaks);
fit = np.polyfit(peaks[:,0],peaks[:,1],2);
fitFunc = np.poly1d(fit)
if verbosity > 9:
plt.title('Find Zero-Loss Peak');
plt.plot(x,fitFunc(x));
plt.imshow(img, vmin=0, vmax=vmax, cmap='gray', aspect='auto');
return fitFunc;
# -- main ----------------------------------------
if __name__ == '__main__':
import TEMareels.tools.tifffile as tiff
image = "../tests/wqmap.tif";
img = tiff.imread(image)
find_zlp(img, delta=10, vmax=img.max(), verbosity=11);
plt.show();
image[:, i] = image[:, i] - ref_line
offset += sector_width
#print offset
image[:, 0:5] = image[:, -5:] = 0
if verbosity > 0:
plt.title("Remove Stripes: difference between old and new image")
plt.imshow(image - old_image, aspect='auto')
plt.show()
return image
# -- main ----------------------------------------
if __name__ == '__main__':
import TEMareels.tools.tifffile as tiff
from TEMareels.tools import tvips
image_file = '../tests/wqmap.tif'
image = tiff.imread(image_file).astype(float)
binning = 8
intstart = np.array([0, 1025, 2900, 3800]) / binning
img = remove_stripes(image,
intwidth=100 / binning,
intstart=intstart,
sector_width=1024 / binning,
verbosity=1)
#outfile = "script_test_.tif";
#tvips.write_tiff(img, outfile);
peak_value = img[:,iq].max();
peak_index = ybin*img[:,iq].argmax();
if np.abs(peak_index-y_start) < delta:
peaks.append([q,peak_index]);
peaks = np.asarray(peaks);
fit = np.polyfit(peaks[:,0],peaks[:,1],2);
fitFunc = np.poly1d(fit)
if verbosity > 9:
info = {'desc': 'Find Zero-Loss Peak',
'yperchan':ybin, 'xperchan':qaxis[1]-qaxis[0],
'xlabel':'q', 'xunits':'1/A','xoffset' :qaxis[0]};
WQB = WQBrowser(img,info,aspect='auto');
WQB.axis.plot(peaks[:,0],peaks[:,1], 'ro');
WQB.axis.plot(qaxis,fitFunc(qaxis));
return fitFunc;
# -- main ----------------------------------------
if __name__ == '__main__':
import TEMareels.tools.tifffile as tiff
image = "../tests/wqmap.tif";
img = tiff.imread(image);
N,nq= img.shape;
qaxis = np.linspace(-3,3,nq);
find_zlp(img, qaxis, delta=10, vmax=img.max(),verbosity=11);
plt.show();
