def test01(ntest, prefix='fig-v01') :
"""Test for radial 1-d binning of entire image.
"""
from time import time
import algos.graph.GlobalGraphics as gg
from PSCalib.GeometryAccess import img_from_pixel_arrays
arr, geo = data_geo(ntest)
t0_sec = time()
iX, iY = geo.get_pixel_coord_indexes()
X, Y, Z = geo.get_pixel_coords()
mask = geo.get_pixel_mask(mbits=0377).flatten()
print 'Time to retrieve geometry %.3f sec' % (time()-t0_sec)
t0_sec = time()
hp = HPolar(X, Y, mask, nradbins=500, nphibins=1) # v1
print 'HPolar initialization time %.3f sec' % (time()-t0_sec)
t0_sec = time()
nda, title = arr, None
if ntest == 1 : nda, title = arr, 'averaged data'
elif ntest == 2 : nda, title = hp.pixel_rad(), 'pixel radius value'
elif ntest == 3 : nda, title = hp.pixel_phi(), 'pixel phi value'
elif ntest == 4 : nda, title = hp.pixel_irad() + 2, 'pixel radial bin index'
elif ntest == 5 : nda, title = hp.pixel_iphi() + 2, 'pixel phi bin index'
elif ntest == 6 : nda, title = hp.pixel_iseq() + 2, 'pixel sequential (rad and phi) bin index'
elif ntest == 7 : nda, title = mask, 'mask'
elif ntest == 8 : nda, title = hp.pixel_avrg(nda), 'averaged radial intensity'
elif ntest == 9 : nda, title = hp.pixel_avrg_interpol(arr) * mask , 'interpolated radial intensity'
else :
print 'Test %d is not implemented' % ntest
return
print 'Get %s n-d array time %.3f sec' % (title, time()-t0_sec)
img = img_from_pixel_arrays(iX, iY, nda) if not ntest in (21,) else nda[100:300,:]
da, ds = None, None
colmap = 'jet' # 'cubehelix' 'cool' 'summer' 'jet' 'winter'
if ntest in (2,3,4,5,6,7) :
da = ds = (nda.min()-1., nda.max()+1.)
else :
ave, rms = nda.mean(), nda.std()
da = ds = (ave-2*rms, ave+3*rms)
gg.plotImageLarge(img, amp_range=da, figsize=(14,12), title=title, cmap=colmap)
gg.save('%s-%02d-img.png' % (prefix, ntest))
gg.hist1d(nda, bins=None, amp_range=ds, weights=None, color=None, show_stat=True, log=False, \
figsize=(6,5), axwin=(0.18, 0.12, 0.78, 0.80), \
title=None, xlabel='Pixel value', ylabel='Number of pixels', titwin=title)
gg.save('%s-%02d-his.png' % (prefix, ntest))
gg.show()
print 'End of test for %s' % title
def test03(ntest, prefix='fig-v01') :
"""Test for 2-d binning of the restricted rad-phi range of entire image
"""
from time import time
import algos.graph.GlobalGraphics as gg
from PSCalib.GeometryAccess import img_from_pixel_arrays
arr, geo = data_geo(ntest)
iX, iY = geo.get_pixel_coord_indexes()
X, Y, Z = geo.get_pixel_coords()
mask = geo.get_pixel_mask(mbits=0377).flatten()
t0_sec = time()
#hp = HPolar(X, Y, mask, nradbins=5, nphibins=8, phiedges=(-20, 240), radedges=(10000,80000))
hp = HPolar(X, Y, mask, nradbins=3, nphibins=8, phiedges=(240, -20), radedges=(80000,10000)) # v3
print 'HPolar initialization time %.3f sec' % (time()-t0_sec)
#print 'bin_number_of_pixels:', hp.bin_number_of_pixels()
#print 'bin_intensity:', hp.bin_intensity(arr)
#print 'bin_avrg:', hp.bin_avrg(arr)
t0_sec = time()
nda, title = arr, None
if ntest == 41 : nda, title = arr, 'averaged data'
elif ntest == 44 : nda, title = hp.pixel_irad() + 2, 'pixel radial bin index'
elif ntest == 45 : nda, title = hp.pixel_iphi() + 2, 'pixel phi bin index'
elif ntest == 46 : nda, title = hp.pixel_iseq() + 2, 'pixel sequential (rad and phi) bin index'
#elif ntest == 47 : nda, title = mask, 'mask'
elif ntest == 48 : nda, title = hp.pixel_avrg(nda), 'averaged radial intensity'
elif ntest == 49 : nda, title = hp.pixel_avrg_interpol(nda), 'averaged radial interpolated intensity'
elif ntest == 50 : nda, title = hp.bin_avrg_rad_phi(nda),'r-phi'
else :
print 'Test %d is not implemented' % ntest
return
print 'Get %s n-d array time %.3f sec' % (title, time()-t0_sec)
img = img_from_pixel_arrays(iX, iY, nda) if not ntest in (50,) else nda # [100:300,:]
colmap = 'jet' # 'cubehelix' 'cool' 'summer' 'jet' 'winter' 'gray'
da = (nda.min()-1, nda.max()+1)
ds = da
if ntest in (41,48,49,50) :
ave, rms = nda.mean(), nda.std()
da = ds = (ave-2*rms, ave+3*rms)
gg.plotImageLarge(img, amp_range=da, figsize=(14,12), title=title, cmap=colmap)
gg.save('%s-%02d-img.png' % (prefix, ntest))
gg.hist1d(nda, bins=None, amp_range=ds, weights=None, color=None, show_stat=True, log=False, \
figsize=(6,5), axwin=(0.18, 0.12, 0.78, 0.80), \
title=None, xlabel='Pixel value', ylabel='Number of pixels', titwin=title)
gg.save('%s-%02d-his.png' % (prefix, ntest))
gg.show()
print 'End of test for %s' % title
def test03(ntest, prefix='fig-v01'):
"""Test for 2-d binning of the restricted rad-phi range of entire image
"""
from time import time
import algos.graph.GlobalGraphics as gg
from PSCalib.GeometryAccess import img_from_pixel_arrays
arr, geo = data_geo(ntest)
iX, iY = geo.get_pixel_coord_indexes()
X, Y, Z = geo.get_pixel_coords()
mask = geo.get_pixel_mask(mbits=0377).flatten()
t0_sec = time()
#hp = HPolar(X, Y, mask, nradbins=5, nphibins=8, phiedges=(-20, 240), radedges=(10000,80000))
hp = HPolar(X,
Y,
mask,
nradbins=3,
nphibins=8,
phiedges=(240, -20),
radedges=(80000, 10000)) # v3
print 'HPolar initialization time %.3f sec' % (time() - t0_sec)
#print 'bin_number_of_pixels:', hp.bin_number_of_pixels()
#print 'bin_intensity:', hp.bin_intensity(arr)
#print 'bin_avrg:', hp.bin_avrg(arr)
t0_sec = time()
nda, title = arr, None
if ntest == 41: nda, title = arr, 'averaged data'
elif ntest == 44:
nda, title = hp.pixel_irad() + 2, 'pixel radial bin index'
elif ntest == 45:
nda, title = hp.pixel_iphi() + 2, 'pixel phi bin index'
elif ntest == 46:
nda, title = hp.pixel_iseq(
) + 2, 'pixel sequential (rad and phi) bin index'
#elif ntest == 47 : nda, title = mask, 'mask'
elif ntest == 48:
nda, title = hp.pixel_avrg(nda), 'averaged radial intensity'
elif ntest == 49:
nda, title = hp.pixel_avrg_interpol(
nda), 'averaged radial interpolated intensity'
elif ntest == 50:
nda, title = hp.bin_avrg_rad_phi(nda), 'r-phi'
else:
print 'Test %d is not implemented' % ntest
return
print 'Get %s n-d array time %.3f sec' % (title, time() - t0_sec)
img = img_from_pixel_arrays(
iX, iY, nda) if not ntest in (50, ) else nda # [100:300,:]
colmap = 'jet' # 'cubehelix' 'cool' 'summer' 'jet' 'winter' 'gray'
da = (nda.min() - 1, nda.max() + 1)
ds = da
if ntest in (41, 48, 49, 50):
ave, rms = nda.mean(), nda.std()
da = ds = (ave - 2 * rms, ave + 3 * rms)
gg.plotImageLarge(img,
amp_range=da,
figsize=(14, 12),
title=title,
cmap=colmap)
gg.save('%s-%02d-img.png' % (prefix, ntest))
gg.hist1d(nda, bins=None, amp_range=ds, weights=None, color=None, show_stat=True, log=False, \
figsize=(6,5), axwin=(0.18, 0.12, 0.78, 0.80), \
title=None, xlabel='Pixel value', ylabel='Number of pixels', titwin=title)
gg.save('%s-%02d-his.png' % (prefix, ntest))
gg.show()
print 'End of test for %s' % title
def test01(ntest, prefix='fig-v01'):
"""Test for radial 1-d binning of entire image.
"""
from time import time
import algos.graph.GlobalGraphics as gg
from PSCalib.GeometryAccess import img_from_pixel_arrays
arr, geo = data_geo(ntest)
t0_sec = time()
iX, iY = geo.get_pixel_coord_indexes()
X, Y, Z = geo.get_pixel_coords()
mask = geo.get_pixel_mask(mbits=0377).flatten()
print 'Time to retrieve geometry %.3f sec' % (time() - t0_sec)
t0_sec = time()
hp = HPolar(X, Y, mask, nradbins=500, nphibins=1) # v1
print 'HPolar initialization time %.3f sec' % (time() - t0_sec)
t0_sec = time()
nda, title = arr, None
if ntest == 1: nda, title = arr, 'averaged data'
elif ntest == 2: nda, title = hp.pixel_rad(), 'pixel radius value'
elif ntest == 3: nda, title = hp.pixel_phi(), 'pixel phi value'
elif ntest == 4: nda, title = hp.pixel_irad() + 2, 'pixel radial bin index'
elif ntest == 5: nda, title = hp.pixel_iphi() + 2, 'pixel phi bin index'
elif ntest == 6:
nda, title = hp.pixel_iseq(
) + 2, 'pixel sequential (rad and phi) bin index'
elif ntest == 7:
nda, title = mask, 'mask'
elif ntest == 8:
nda, title = hp.pixel_avrg(nda), 'averaged radial intensity'
elif ntest == 9:
nda, title = hp.pixel_avrg_interpol(
arr) * mask, 'interpolated radial intensity'
else:
print 'Test %d is not implemented' % ntest
return
print 'Get %s n-d array time %.3f sec' % (title, time() - t0_sec)
img = img_from_pixel_arrays(
iX, iY, nda) if not ntest in (21, ) else nda[100:300, :]
da, ds = None, None
colmap = 'jet' # 'cubehelix' 'cool' 'summer' 'jet' 'winter'
if ntest in (2, 3, 4, 5, 6, 7):
da = ds = (nda.min() - 1., nda.max() + 1.)
else:
ave, rms = nda.mean(), nda.std()
da = ds = (ave - 2 * rms, ave + 3 * rms)
gg.plotImageLarge(img,
amp_range=da,
figsize=(14, 12),
title=title,
cmap=colmap)
gg.save('%s-%02d-img.png' % (prefix, ntest))
gg.hist1d(nda, bins=None, amp_range=ds, weights=None, color=None, show_stat=True, log=False, \
figsize=(6,5), axwin=(0.18, 0.12, 0.78, 0.80), \
title=None, xlabel='Pixel value', ylabel='Number of pixels', titwin=title)
gg.save('%s-%02d-his.png' % (prefix, ntest))
gg.show()
print 'End of test for %s' % title
def test02(ntest, prefix='fig-v01') :
"""Test for 2-d (default) binning of the rad-phi range of entire image
"""
#from Detector.GlobalUtils import print_ndarr
from time import time
import algos.graph.GlobalGraphics as gg
from PSCalib.GeometryAccess import img_from_pixel_arrays
arr, geo = data_geo(ntest)
iX, iY = geo.get_pixel_coord_indexes()
X, Y, Z = geo.get_pixel_coords()
mask = geo.get_pixel_mask(mbits=0377).flatten()
t0_sec = time()
rb = RadialBkgd(X, Y, mask) # v0
#rb = RadialBkgd(X, Y, mask, nradbins=500) # , nphibins=8, phiedges=(-20, 240), radedges=(10000,80000))
print 'RadialBkgd initialization time %.3f sec' % (time()-t0_sec)
#print 'npixels_per_bin:', rb.npixels_per_bin()
#print 'intensity_per_bin:', rb.intensity_per_bin(arr)
#print 'average_per_bin:', rb.average_per_bin(arr)
t0_sec = time()
nda, title = arr, None
if ntest == 21 : nda, title = arr, 'averaged data'
elif ntest == 22 : nda, title = rb.pixel_rad(), 'pixel radius value'
elif ntest == 23 : nda, title = rb.pixel_phi(), 'pixel phi value'
elif ntest == 24 : nda, title = rb.pixel_irad() + 2, 'pixel radial bin index'
elif ntest == 25 : nda, title = rb.pixel_iphi() + 2, 'pixel phi bin index'
elif ntest == 26 : nda, title = rb.pixel_iseq() + 2, 'pixel sequential (rad and phi) bin index'
elif ntest == 27 : nda, title = mask, 'mask'
elif ntest == 28 : nda, title = rb.pixel_avrg(nda), 'averaged radial background'
elif ntest == 29 : nda, title = rb.subtract_bkgd(nda) * mask, 'background-subtracted data'
elif ntest == 30 : nda, title = rb.bin_avrg_rad_phi(nda),'r-phi'
elif ntest == 31 : nda, title = rb.pixel_avrg_interpol(nda), 'averaged radial interpolated background'
elif ntest == 32 : nda, title = rb.subtract_bkgd_interpol(nda, method='linear', verb=True) * mask, 'interpol-background-subtracted data'
else :
print 'Test %d is not implemented' % ntest
return
print 'Get %s n-d array time %.3f sec' % (title, time()-t0_sec)
img = img_from_pixel_arrays(iX, iY, nda) if not ntest in (30,) else nda # [100:300,:]
colmap = 'jet' # 'cubehelix' 'cool' 'summer' 'jet' 'winter' 'gray'
da = (nda.min()-1, nda.max()+1)
ds = da
if ntest in (21,28,29,30,31) :
ave, rms = nda.mean(), nda.std()
da = ds = (ave-2*rms, ave+3*rms)
elif ntest in (32,) :
colmap = 'gray'
ds = da = (-20, 20)
gg.plotImageLarge(img, amp_range=da, figsize=(14,12), title=title, cmap=colmap)
gg.save('%s-%02d-img.png' % (prefix, ntest))
gg.hist1d(nda, bins=None, amp_range=ds, weights=None, color=None, show_stat=True, log=False, \
figsize=(6,5), axwin=(0.18, 0.12, 0.78, 0.80), \
title=None, xlabel='Pixel value', ylabel='Number of pixels', titwin=title)
gg.save('%s-%02d-his.png' % (prefix, ntest))
gg.show()
print 'End of test for %s' % title
def make_index_table(prefix='./v01-') :
from algos.core.GlobalUtils import str_tstamp
fname = '%s**t-cxif5315-r0169-%s.txt' % (prefix, str_tstamp())
fout = open(fname,'w')
fout.write('# file name: %s\n' % fname)
#------------------------------
# Photon energy
Egamma_eV = 6003.1936 # eV SIOC:SYS0:ML00:AO541
wavelen_nm = wavelength_nm_from_energy_ev(Egamma_eV) # nm
evald_rad = wave_vector_value(Egamma_eV) # 1/A
#-------
sigma_ql = 0.002 * evald_rad
sigma_qt = 0.002 * evald_rad
#-------
rec = '\n# photon energy = %.4f eV' % (Egamma_eV)\
+ '\n# wavelength = %.4f A' % (wavelen_nm*10)\
+ '\n# wave number/Evald radius k = 1/lambda = %.6f 1/A' % (evald_rad)\
+ '\n# sigma_ql = %.6f 1/A (approximately = k * <pixel size>/' % (sigma_ql)\
+ '\n# sigma_qt = %.6f 1/A (approximately = k * <pixel size>/' % (sigma_qt)\
+ '<sample-to-detector distance> = k*100um/100mm)'\
+ '\n# 3*sigma_ql = %.6f 1/A\n' % (3*sigma_ql)\
+ '\n# 3*sigma_qt = %.6f 1/A\n' % (3*sigma_qt)
print rec
fout.write(rec)
#------------------------------
# Lattice parameters
# from previous analysis note:
#a, b, c = 18.36, 26.65, 4.81 # Angstrom
#alpha, beta, gamma = 90, 90, 77.17 # 180 - 102.83 degree
a= 18.55 # Angstrom
b, c = 1.466*a, 0.262*a # Angstrom
alpha, beta, gamma = 90, 90, 78.47 # 180 - 101.53 degree
hmax, kmax, lmax = 4, 6, 0 # size of lattice to consider
a1, a2, a3 = triclinic_primitive_vectors(a, b, c, alpha, beta, gamma)
b1, b2, b3 = reciprocal_from_bravias(a1, a2, a3)
msg1 = '\n# Triclinic crystal cell parameters:'\
+ '\n# a = %.2f A\n# b = %.2f A\n# c = %.2f A' % (a, b, c)\
+ '\n# alpha = %.2f deg\n# beta = %.2f deg\n# gamma = %.2f deg' % (alpha, beta, gamma)
msg2 = '\n# 3-d space primitive vectors:\n# a1 = %s\n# a2 = %s\n# a3 = %s' %\
(str(a1), str(a2), str(a3))
msg3 = '\n# reciprocal space primitive vectors:\n# b1 = %s\n# b2 = %s\n# b3 = %s' %\
(str(b1), str(b2), str(b3))
rec = '%s\n%s\n%s\n' % (msg1, msg2, msg3)
print rec
fout.write(rec)
fout.write('\n# %s\n\n' % (89*'_'))
#for line in triclinic_primitive_vectors.__doc__.split('\n') : fout.write('\n# %s' % line)
test_lattice (b1, b2, b3, hmax, kmax, lmax, cdtype=np.float32)
lattice_node_radius(b1, b2, b3, hmax, kmax, lmax, cdtype=np.float32)
lattice_node_radius(b1, b2, b3, hmax, kmax, 1, cdtype=np.float32)
#------------------------------
#return
#------------------------------
# binning for look-up table and plots
# bin parameters for q in units of k = Evald's sphere radius [1/A]
bpq = BinPars((-0.25, 0.25), 1000, vtype=np.float32, endpoint=False)
# bin parameters for omega [degree] - fiber rotation angle around axis
bpomega = BinPars((0., 180.), 360, vtype=np.float32, endpoint=False)
# bin parameters for beta [degree] - fiber axis tilt angle
#bpbeta = BinPars((15., 195.), 2, vtype=np.float32, endpoint=True)
#bpbeta = BinPars((15., 15.), 1, vtype=np.float32, endpoint=False)
#bpbeta = BinPars((5., 25.), 2, vtype=np.float32, endpoint=True)
bpbeta = BinPars((0., 50.), 11, vtype=np.float32, endpoint=True)
bpbeta2 = BinPars((180., 230.), 11, vtype=np.float32, endpoint=True)
str_beta = 'for-beta:%s' % (bpbeta.strrange)
print '\n%s\nIndexing lookup table\n' % (91*'_')
lut = make_lookup_table(b1, b2, b3, hmax, kmax, lmax, np.float32, evald_rad, sigma_ql, sigma_qt, fout, bpq, bpomega, bpbeta)
lut2 = make_lookup_table(b1, b2, b3, hmax, kmax, lmax, np.float32, evald_rad, sigma_ql, sigma_qt, fout, bpq, bpomega, bpbeta2)
fout.close()
print '\nIndexing lookup table is saved in the file: %s' % fname
#------------------------------
# produce and save plots
import algos.graph.GlobalGraphics as gg
img = lut # or lut2
img = lut + lut2
img_range = (bpq.vmin, bpq.vmax, bpomega.vmax, bpomega.vmin)
axim = gg.plotImageLarge(lut, img_range=img_range, amp_range=None, figsize=(15,13),\
title='Non-symmetrized for beta', origin='upper', window=(0.05, 0.06, 0.94, 0.94))
axim.set_xlabel('$q_{H}$ ($1/\AA$)', fontsize=18)
axim.set_ylabel('$\omega$ (degree)', fontsize=18)
gg.save('%splot-img-prob-omega-vs-qh-%s.png' % (prefix, str_beta), pbits=1)
axim = gg.plotImageLarge(img, img_range=img_range, amp_range=None, figsize=(15,13),\
title='Symmetrized for beta (beta, beta+pi)', origin='upper', window=(0.05, 0.06, 0.94, 0.94))
axim.set_xlabel('$q_{H}$ ($1/\AA$)', fontsize=18)
axim.set_ylabel('$\omega$ (degree)', fontsize=18)
gg.save('%splot-img-prob-omega-vs-qh-sym-%s.png' % (prefix, str_beta), pbits=1)
arrhi = np.sum(img,0)
fighi, axhi, hi = gg.hist1d(bpq.binedges, bins=bpq.nbins-1, amp_range=(bpq.vmin, bpq.vmax), weights=arrhi,\
color='b', show_stat=True, log=False,\
figsize=(15,5), axwin=(0.05, 0.12, 0.85, 0.80),\
title=None, xlabel='$q_{H}$ ($1/\AA$)', ylabel='Intensity', titwin=None)
gg.show()
gg.save_fig(fighi, '%splot-his-prob-vs-qh-%s.png' % (prefix, str_beta), pbits=1)
qh_weight = zip(bpq.bincenters, arrhi)
fname = '%sarr-qh-weight-%s.npy' % (prefix, str_beta)
print 'Save qh:weigt array in file %s' % fname
np.save(fname, qh_weight)
def make_index_table(prefix='./v01-'):
from algos.core.GlobalUtils import str_tstamp
fname = '%s**t-cxif5315-r0169-%s.txt' % (prefix, str_tstamp())
fout = open(fname, 'w')
fout.write('# file name: %s\n' % fname)
#------------------------------
# Photon energy
Egamma_eV = 6003.1936 # eV SIOC:SYS0:ML00:AO541
wavelen_nm = wavelength_nm_from_energy_ev(Egamma_eV) # nm
evald_rad = wave_vector_value(Egamma_eV) # 1/A
#-------
sigma_ql = 0.002 * evald_rad
sigma_qt = 0.002 * evald_rad
#-------
rec = '\n# photon energy = %.4f eV' % (Egamma_eV)\
+ '\n# wavelength = %.4f A' % (wavelen_nm*10)\
+ '\n# wave number/Evald radius k = 1/lambda = %.6f 1/A' % (evald_rad)\
+ '\n# sigma_ql = %.6f 1/A (approximately = k * <pixel size>/' % (sigma_ql)\
+ '\n# sigma_qt = %.6f 1/A (approximately = k * <pixel size>/' % (sigma_qt)\
+ '<sample-to-detector distance> = k*100um/100mm)'\
+ '\n# 3*sigma_ql = %.6f 1/A\n' % (3*sigma_ql)\
+ '\n# 3*sigma_qt = %.6f 1/A\n' % (3*sigma_qt)
print rec
fout.write(rec)
#------------------------------
# Lattice parameters
# from previous analysis note:
#a, b, c = 18.36, 26.65, 4.81 # Angstrom
#alpha, beta, gamma = 90, 90, 77.17 # 180 - 102.83 degree
a = 18.55 # Angstrom
b, c = 1.466 * a, 0.262 * a # Angstrom
alpha, beta, gamma = 90, 90, 78.47 # 180 - 101.53 degree
hmax, kmax, lmax = 4, 6, 0 # size of lattice to consider
a1, a2, a3 = triclinic_primitive_vectors(a, b, c, alpha, beta, gamma)
b1, b2, b3 = reciprocal_from_bravias(a1, a2, a3)
msg1 = '\n# Triclinic crystal cell parameters:'\
+ '\n# a = %.2f A\n# b = %.2f A\n# c = %.2f A' % (a, b, c)\
+ '\n# alpha = %.2f deg\n# beta = %.2f deg\n# gamma = %.2f deg' % (alpha, beta, gamma)
msg2 = '\n# 3-d space primitive vectors:\n# a1 = %s\n# a2 = %s\n# a3 = %s' %\
(str(a1), str(a2), str(a3))
msg3 = '\n# reciprocal space primitive vectors:\n# b1 = %s\n# b2 = %s\n# b3 = %s' %\
(str(b1), str(b2), str(b3))
rec = '%s\n%s\n%s\n' % (msg1, msg2, msg3)
print rec
fout.write(rec)
fout.write('\n# %s\n\n' % (89 * '_'))
#for line in triclinic_primitive_vectors.__doc__.split('\n') : fout.write('\n# %s' % line)
test_lattice(b1, b2, b3, hmax, kmax, lmax, cdtype=np.float32)
lattice_node_radius(b1, b2, b3, hmax, kmax, lmax, cdtype=np.float32)
lattice_node_radius(b1, b2, b3, hmax, kmax, 1, cdtype=np.float32)
#------------------------------
#return
#------------------------------
# binning for look-up table and plots
# bin parameters for q in units of k = Evald's sphere radius [1/A]
bpq = BinPars((-0.25, 0.25), 1000, vtype=np.float32, endpoint=False)
# bin parameters for omega [degree] - fiber rotation angle around axis
bpomega = BinPars((0., 180.), 360, vtype=np.float32, endpoint=False)
# bin parameters for beta [degree] - fiber axis tilt angle
#bpbeta = BinPars((15., 195.), 2, vtype=np.float32, endpoint=True)
#bpbeta = BinPars((15., 15.), 1, vtype=np.float32, endpoint=False)
#bpbeta = BinPars((5., 25.), 2, vtype=np.float32, endpoint=True)
bpbeta = BinPars((0., 50.), 11, vtype=np.float32, endpoint=True)
bpbeta2 = BinPars((180., 230.), 11, vtype=np.float32, endpoint=True)
str_beta = 'for-beta:%s' % (bpbeta.strrange)
print '\n%s\nIndexing lookup table\n' % (91 * '_')
lut = make_lookup_table(b1, b2, b3, hmax, kmax, lmax, np.float32,
evald_rad, sigma_ql, sigma_qt, fout, bpq, bpomega,
bpbeta)
lut2 = make_lookup_table(b1, b2, b3, hmax, kmax, lmax, np.float32,
evald_rad, sigma_ql, sigma_qt, fout, bpq, bpomega,
bpbeta2)
fout.close()
print '\nIndexing lookup table is saved in the file: %s' % fname
#------------------------------
# produce and save plots
import algos.graph.GlobalGraphics as gg
img = lut # or lut2
img = lut + lut2
img_range = (bpq.vmin, bpq.vmax, bpomega.vmax, bpomega.vmin)
axim = gg.plotImageLarge(lut, img_range=img_range, amp_range=None, figsize=(15,13),\
title='Non-symmetrized for beta', origin='upper', window=(0.05, 0.06, 0.94, 0.94))
axim.set_xlabel('$q_{H}$ ($1/\AA$)', fontsize=18)
axim.set_ylabel('$\omega$ (degree)', fontsize=18)
gg.save('%splot-img-prob-omega-vs-qh-%s.png' % (prefix, str_beta), pbits=1)
axim = gg.plotImageLarge(img, img_range=img_range, amp_range=None, figsize=(15,13),\
title='Symmetrized for beta (beta, beta+pi)', origin='upper', window=(0.05, 0.06, 0.94, 0.94))
axim.set_xlabel('$q_{H}$ ($1/\AA$)', fontsize=18)
axim.set_ylabel('$\omega$ (degree)', fontsize=18)
gg.save('%splot-img-prob-omega-vs-qh-sym-%s.png' % (prefix, str_beta),
pbits=1)
arrhi = np.sum(img, 0)
fighi, axhi, hi = gg.hist1d(bpq.binedges, bins=bpq.nbins-1, amp_range=(bpq.vmin, bpq.vmax), weights=arrhi,\
color='b', show_stat=True, log=False,\
figsize=(15,5), axwin=(0.05, 0.12, 0.85, 0.80),\
title=None, xlabel='$q_{H}$ ($1/\AA$)', ylabel='Intensity', titwin=None)
gg.show()
gg.save_fig(fighi,
'%splot-his-prob-vs-qh-%s.png' % (prefix, str_beta),
pbits=1)
qh_weight = zip(bpq.bincenters, arrhi)
fname = '%sarr-qh-weight-%s.npy' % (prefix, str_beta)
print 'Save qh:weigt array in file %s' % fname
np.save(fname, qh_weight)