def make_k_vecs(self):
"""
Generate the k vectors from the experiment parameters
given in constructor
"""
xim, yim = self.spatial.make_pixel_lut(self.dims)
peaks = [numpy.ravel(xim), numpy.ravel(yim)]
# First, x, is the slow pixel direction, should not change
# when raveled
#for i in range(10):
# print "slow, fast"
# print peaks[0][i],peaks[1][i]
assert abs(peaks[0][10] - peaks[0][0]) < 3
# Second, y, is the fast, should change by ~ 1 pixel per pixel
assert abs(peaks[1][10] - peaks[1][0] - 10) < 3
tth, eta = transform.compute_tth_eta(peaks,
**self.pars.get_parameters())
self.k = transform.compute_k_vectors(tth, eta,
self.pars.get('wavelength'))
# FIXME
# This should be something like domega/dk where
# dk [ k(omega=0) - k(omega=1) ]
self.lorfac = numpy.ones(self.dims[0] * self.dims[1], numpy.float32)
def test_0_0_0(self):
""" wedge, chi = 0 """
SANITY = False
om = np.zeros(self.np, np.float)
g_old = transform.compute_g_vectors(self.tth,
self.eta,
om,
self.wvln,
wedge=0.0,
chi=0.0)
if SANITY: print g_old.shape, g_old[:, 0]
k = transform.compute_k_vectors(self.tth, self.eta, self.wvln)
if SANITY: print "k=", k[:, 0]
g_new = gv_general.k_to_g(k, om)
if SANITY: print g_new.shape, g_new[:, 0]
if SANITY: print "end routine"
self.assertAlmostEqual(array_diff(g_new, g_old), 0, 6)
def testhklcalc_many(wedge, chi):
# hklcalc_many
# apply wedge, chi to axis if necessary
axis = np.array( [0,0,-1], np.float )
UBI = np.eye(3).ravel()
hkl = np.zeros( (len(omega), 3), np.float)
XL = np.ones( (len(omega), 3), np.float)*1e3
T = np.array([100,200,300],np.float)
wvln = 0.254
pre = np.eye(3).ravel()
post = gv_general.chiwedge( wedge=wedge, chi=chi ).ravel()
kc = XL.copy()
wripaca.hklcalc_many( XL, axis, omega*np.pi/180, UBI, T, pre, post, hkl, wvln, kc)
print "axis is",axis
t, e = transform.compute_tth_eta_from_xyz( XL.T,
t_x = T[0],
t_y = T[1],
t_z = T[2],
omega = omega,
wedge = wedge,
chi = chi
)
#print t,e,omega
ori = transform.compute_grain_origins( omega, wedge=wedge, chi=chi,
t_x = T[0], t_y = T[1], t_z = T[2] )
print "last origin",ori.T[-1],ori.T[0]
print "last xl",XL[-1],XL[0]
kve = transform.compute_k_vectors( t, e, wvln )
print "last k",kve.T[-1],kve.T[0]
gve = transform.compute_g_vectors( t, e, omega, wvln, wedge=wedge, chi=chi )
print "last g",gve.T[-1],gve.T[0]
htest = np.dot( UBI.reshape(3,3), gve )
for i in range(len(t)):
# print gve[:,i]
# print hkl[i]
if ((gve[:,i]-hkl[i])**2).sum() > 1e-10:
print "FAIL",
print i,gve[:,i],hkl[i],((gve[:,i]-hkl[i])**2).sum()
print wedge,chi
sys.exit()
print "OK for wedge",wedge,"chi",chi
def test_0_0(self):
""" wedge, chi = 0 """
SANITY = False
if SANITY: print("*" * 80)
om = np.zeros(self.np, np.float)
g_old = transform.compute_g_vectors(self.tth,
self.eta,
self.omega,
self.wvln,
wedge=0.0,
chi=0.0)
if SANITY: print(g_old.shape, g_old[:, :3])
k = transform.compute_k_vectors(self.tth, self.eta, self.wvln)
if SANITY: print("k=", k[:, :3])
g_new = gv_general.k_to_g(k, self.omega, axis=[0, 0, -1])
if SANITY: print(g_new.shape, g_new[:, :3])
if SANITY: print("end routine")
if SANITY: print("*" * 80)
self.assertAlmostEqual(array_diff(g_new, g_old), 0, 6)
def test_25_30(self):
""" wedge, chi = 25,30 """
w, c = 25, 30
SANITY = False
if SANITY: print "*" * 80
om = np.zeros(self.np, np.float)
g_old = transform.compute_g_vectors(self.tth,
self.eta,
self.omega,
self.wvln,
wedge=w,
chi=c)
if SANITY: print g_old.shape, "\n", g_old[:, :3]
k = transform.compute_k_vectors(self.tth, self.eta, self.wvln)
if SANITY: print "k=", k[:, :3]
post = gv_general.chiwedge(chi=c, wedge=w)
g_new = gv_general.k_to_g(k, self.omega, axis=[0, 0, -1], post=post)
if SANITY: print g_new.shape, g_new[:, :3]
if SANITY: print "end routine"
if SANITY: print "*" * 80
self.assertAlmostEqual(array_diff(g_new, g_old), 0, 6)
def test1():
np.random.seed(42)
w = 40 / 12.
nv = 1024 * 1024
xyz = 1e5 * (np.random.random((nv, 3)) + (0.2, -0.5, -0.5)).T
old = []
new = []
for i in range(5):
start = timer()
t, e = compute_tth_eta_from_xyz(xyz, None)
k = compute_k_vectors(t, e, w)
old.append(timer() - start)
knew = k_from_xyz(xyz, w)
start = timer()
ok = np.allclose(k, knew)
new.append(timer() - start)
if ok:
print("Matches", nv)
else:
print("Fails")
print("old perf", np.min(old), np.mean(old), np.std(old))
print("new perf", np.min(new), np.mean(new), np.std(new))
print("speedup", np.min(old) / np.min(new))
