def filter_pairs( h1h2, c2a, B, BI, tol = 1e-5):
order = np.argsort( c2a ) # increasing
h1, h2 = h1h2[order[0]]
BT = BTmat( h1, h2, B, BI )
UBI = np.array( (np.dot(B, h1), np.dot(B,h2), (0,0,0)), float )
cImageD11.quickorient( UBI, BT )
UB = np.linalg.inv( UBI )
pairs = [ (h1, h2), ]
cangs = [ c2a[order[0]], ]
matrs = [ BT, ]
gvecs = [ np.dot( UB, HKL0 ).T.copy(), ]
nhkl = len(HKL0[0])
#import pdb;pdb.set_trace()
for i in order[1:]:
h1, h2 = h1h2[i]
BT = BTmat( h1, h2, B, BI )
UBI = np.array( (np.dot(B, h1), np.dot(B,h2), (0,0,0)), float )
cImageD11.quickorient( UBI, BT )
j = len(pairs)-1
while j>=0 and fabs(c2a[order[i]] - cangs[j]) < 1e-6:
if cImageD11.score( UBI, gvecs[j], 1e-8 ) < nhkl+1:
pairs += [ (h1, h2), ]
cangs += [ c2a[i], ]
matrs += [ BT, ]
gvecs += [ np.dot( np.linalg.inv(UBI) , HKL0 ).T.copy(), ]
j = -1 # break
else:
j -= 1
return pairs, cangs, matrs
def gridgrains(
ul,
flt,
pars,
minx=-750,
maxx=750,
stepx=25,
miny=-750,
maxy=750,
stepy=25,
tol=0.05,
):
trn = transformer.transformer()
trn.loadfiltered(flt)
trn.parameterobj = parameters.parameters(**pars)
peaks = [trn.getcolumn(trn.xname), trn.getcolumn(trn.yname)]
peaks_xyz = transform.compute_xyz_lab(peaks,
**trn.parameterobj.get_parameters())
omega = trn.getcolumn(trn.omeganame)
trn.updateparameters()
tx = minx - stepx
n = 0
sys.stderr.write("Using tol = %f\n" % (tol))
while tx <= maxx:
tx = tx + stepx
ty = miny - stepy
while ty <= maxy:
ty = ty + stepy
trn.parameterobj.set_parameters({'t_x': tx, 't_y': ty})
pars = trn.parameterobj.get_parameters()
tth, eta = transform.compute_tth_eta_from_xyz(
peaks_xyz, omega, **pars)
if 'omegasign' in pars:
om_sgn = float(pars["omegasign"])
else:
om_sgn = 1.0
gv = transform.compute_g_vectors(tth, eta, omega * om_sgn, **pars)
print(tx, ty, end=' ')
for ubi in ul:
ns = score(ubi, gv.T, tol)
print(ns, end=' ')
n += ns
print()
return n
uniq_ubis = []
names = ubi_all.keys()
#dsu = [ (int( np.split(".")[0].split("_")[-1] ), n) for n in names ]
#dsu.sort()
#names = [d[1] for d in dsu]
tol = 0.05
for name in names[15:]:
this_list = ubi_all[name]
for ubi, i in zip(this_list, range(len(this_list))):
gv = np.dot(np.linal.inv(ubi), hkl)
seen = 0
for j in range(len(uniq_ubis)):
u = uniq_ubis[j][0]
npk = cImageD11.score(u, gv.T, tol)
if npk == len(pks):
# seen you before
uniq_ubis[j][1].append((name, i))
seen += 1
if npk > 12 and npk < len(pks):
print "close...", npk,
if seen == 0:
uniq_ubis.append([ubi, [(name, i)]])
if seen > 1:
print "uniq seen more than once", ubi, i, name
print "Found", len(uniq_ubis), "unique ubi matrices"
z = {}
for line in open("job_z.txt", "r").readlines():
def pairs(self, hkl1, hkl2, cos_tol=0.02, hkl_tol=0.1):
"""
We only look for reflection pairs matching a single hkl pairing
"""
import time
start = time.time()
w = self.transformpars.get("wavelength")
tth1 = get_tth(self.unitcell.ds(hkl1), w)
tth2 = get_tth(self.unitcell.ds(hkl2), w)
tthtol = self.transformpars.get("fit_tolerance")
allinds = np.arange(self.cf.nrows)
ind1 = allinds[abs(self.cf.tth - tth1) < tthtol]
ind2 = allinds[abs(self.cf.tth - tth2) < tthtol]
angle, cosangle = self.unitcell.anglehkls(hkl1, hkl2)
print("Angle, cosangle", angle, cosangle, hkl1, hkl2)
assert angle > 1 and angle < 179, "hkls are parallel"
g = np.array((self.cf.gx, self.cf.gy, self.cf.gz), np.float)
n = g / self.cf.modg
gvf = g.T.copy()
n1 = n[:, ind1]
n2 = n[:, ind2]
pairs = []
j = np.arange(n2.shape[1])
# from unitcell.orient
h1c = unit(np.dot(self.unitcell.B, hkl1))
h2c = unit(np.dot(self.unitcell.B, hkl2))
t1c = unit(h1c)
t3c = unit(np.cross(h1c, h2c))
t2c = unit(np.cross(h1c, t3c))
T_c = np.array([t1c, t2c, t3c])
T_g = np.zeros((3, 3))
for i, this_n1 in enumerate(n1.T):
cosa = np.dot(this_n1, n2)
goodones = j[abs(cosa - cosangle) < cos_tol]
# t1 is along g1
# t2 is plane of both: g1x(g1xg2)
# t3 is perpendicular to both
#if i%100==0:
# print i,time.time()-start,len(n1.T)
for k in goodones:
this_n2 = n2[:, k]
T_g[0] = this_n1
T_g[2] = unit(np.cross(this_n1, this_n2))
T_g[1] = unit(np.cross(this_n1, T_g[2]))
U = np.dot(T_g.T, T_c)
ub = np.dot(U, self.unitcell.B)
ubi = np.linalg.inv(ub)
ubio = ubi.copy()
npks = cImageD11.score(ubi, gvf, hkl_tol)
pairs.append((ind1[i], ind2[k], U, ubi))
print(npks, end=' ')
ubi, trans = self.refine(ubi,
np.zeros(3, np.float),
tol=hkl_tol)
inds, hkls = self.assign(ubi, trans, hkl_tol)
ubi, trans = self.refine(ubi,
trans,
inds=inds,
hkls=hkls,
tol=hkl_tol)
print(npks, ubi)
print("cell: ", 6 * "%.6f " % (indexing.ubitocellpars(ubi)))
print("position: ", trans)
print()
self.pairscache = pairs
print(time.time() - start, "for", len(pairs), n1.shape, n2.shape)
return pairs
def filter_pairs( h1, h2, c2a, B, BI, tol = 1e-5):
""" remove duplicate pairs for orientation searches
h1 = reflections of ring1, N1 peaks
h2 = reflections of ring2, N2 peaks
c2a = cos angle between them, N1xN2
B = B matrix in reciprocal space
BI = inverse in real space
"""
assert c2a.shape == (len(h1), len(h2))
order = np.argsort( c2a.ravel() ) # increasing in cosine of angle
c2as = c2a.flat[order]
hi, hj= np.mgrid[0:len(h1),0:len(h2)]
hi = hi.ravel()[order] # to get back the peaks
hj = hj.ravel()[order]
# Results holders:
pairs = [ ]
cangs = [ ]
matrs = [ ]
# cluster the cangs assuming a sensible threshold
dc = (c2as[1:] - c2as[:-1]) > 1e-8 # differences
inds = list(np.arange( 1, len(dc) + 1, dtype=int )[dc]) + [len(c2as)-1,]
p = 0 # previous
for i in inds:
c = c2as[p:i] # block is p:i
if abs(c2as[p]) < 0.98: # always keep the first one
ha = h1[hi[p]]
hb = h2[hj[p]]
pairs.append( (ha, hb) )
cangs.append( c2as[p] )
BT = BTmat( ha, hb, B, BI)
matrs.append( BT )
else:
p = i
continue
if len(c) == 1:
p = i
continue
assert (c.max()-c.min()) < 2.1e-8, "Angles blocking error in filter_pairs"
# here we have a bunch of hkl pairs which all give the same angle
# between them. They are not all the same. We generate a pair of peaks
# from the first one and see which other pairs index differently
ga = np.dot(B, ha )
gb = np.dot(B, hb )
assert abs( np.dot(ga,gb)/np.sqrt(np.dot(ga,ga)*np.dot(gb,gb)) - c2as[p] ) < 2e-8, "mixup in filter_pairs"
gobs = np.array( (ga, gb, (0,0,0)), float)
UBI = gobs.copy()
cImageD11.quickorient( UBI, BT )
gtest = [ np.dot( np.linalg.inv(UBI), HKL0 ).T, ]
for j in range(p+1,i):
ha = h1[hi[j]]
hb = h2[hj[j]]
BT = BTmat( ha, hb, B, BI)
newpair = True
for gt in gtest:
UBI = gobs.copy()
cImageD11.quickorient( UBI, BT )
npk = cImageD11.score(UBI, gt, 1e-6)
if npk == len(HKL0[0]):
newpair = False
break
if newpair:
pairs.append( (ha, hb) )
cangs.append( c2as[j] )
matrs.append( BT )
gtest.append( np.dot( np.linalg.inv(UBI), HKL0 ).T )
p = i
return pairs, cangs, matrs
#
ubi0 = np.linalg.inv( ub0 )
hkl0 = np.array((h0,k0,l0),float)
unitH = hkl0 / np.sqrt( np.dot(hkl0,hkl0) )
unitG = gtest / np.sqrt( np.dot(gtest,gtest) )
angles_to_test = np.linspace( -np.pi, np.pi, 360 )
testmats = [ np.dot( ubi0, Rotation.from_rotvec( unitG*x ).as_dcm() ) for x in angles_to_test ]
rmats = [ Rotation.from_rotvec( unitG*x ).as_dcm() for x in angles_to_test ]
print(np.dot(testmats[0],gtest))
gobs = gcalc.T.copy()
import timeit
start = timeit.default_timer()
npk = [cImageD11.score( m, gobs, 0.05) for m in testmats]
print("Scoring takes", timeit.default_timer()-start)
pl.figure()
pl.plot( angles_to_test, npk,"-")
# Conclusion: This appears to work. It takes a while to test all the potential orientations however.
# Perhaps we can expand the mathematics to try to figure out some contributions a bit quicker?
#
# Our equation is:
# hkl = integer = UBI0 . rotation_on_g0 . gvobs
#
# Each g-vector has a component along the g0 axis which will be invariant.
#
# The other components could be found in an axis/angle expansion.
# - gaxis + gs.sin(angle) + gc.cos(angle)
