def identify_diagonal_maxima(phi_start, phi_end, phi_width,
wavelength, lattice, matrix):
'''Find phi rotation values where the diagonals of the matrix
[R][U] are maximised - logically this will maximise the derivative
of the spot positions as a function of the cell axes.'''
cell, A, U = parse_matrix(matrix)
# transform U matrix to xia2 coordinate frame
u1, u2, u3 = mat2vec(U)
ux1 = mosflm_to_xia2(u1)
ux2 = mosflm_to_xia2(u2)
ux3 = mosflm_to_xia2(u3)
UX = vec2mat((ux1, ux2, ux3))
# print the new U matrix
print 'UX Matrix:'
print '%.4f %.4f %.4f' % (UX[0], UX[1], UX[2])
print '%.4f %.4f %.4f' % (UX[3], UX[4], UX[5])
print '%.4f %.4f %.4f' % (UX[6], UX[7], UX[8])
# set up the rotations
phi = phi_start + 0.5 * phi_width
# initialize search variables
phi_a = phi_start
phi_b = phi_start
phi_c = phi_start
ru_a = 0.0
ru_b = 0.0
ru_c = 0.0
ia = 0
ib = 0
ic = 0
i = 0
# only consider the first 180 degrees of data...
if phi_end - phi_start > 180:
phi_end = phi_start + 180.0
while phi < phi_end:
RX = rot_x(phi)
# RX = rot_y(phi)
RXU = matmul(RX, UX)
if math.fabs(RXU[0]) > ru_a:
ru_a = math.fabs(RXU[0])
phi_a = phi
ia = i
if math.fabs(RXU[4]) > ru_b:
ru_b = math.fabs(RXU[4])
phi_b = phi
ib = i
if math.fabs(RXU[8]) > ru_c:
ru_c = math.fabs(RXU[8])
phi_c = phi
ic = i
phi += phi_width
i += 1
# return the closest positions and the angular offset from
# perpendicularity...
return phi_a, phi_b, phi_c, ru_a, ru_b, ru_c, ia, ib, ic
def identify_parallel_reciprocal_axes2(phi_start, phi_end, phi_width,
wavelength, lattice, matrix):
'''Find phi values which present the primitive reciprocal unit cell axes as
near as possible to being perpendicular to the beam vector.'''
# FIXME should add a test in here that the mosflm orientation matrix
# corresponds to the asserted lattice...
# find thee P1 reciprocal-space cell axes - note well am doing this
# for the matrix in whatever setting
cell, A, U = parse_matrix(matrix)
a, b, c = mat2vec(A)
# compute rotations of these and find minimum for axis.Z - that is the
# Z component of the rotated axis... check workings and definitions!
phi = phi_start + 0.5 * phi_width
# initialize search variables
phi_a = phi_start
phi_b = phi_start
phi_c = phi_start
dot_a = 0.0
dot_b = 0.0
dot_c = 0.0
ia = 0
ib = 0
ic = 0
i = 0
# only consider the first 180 degrees of data...
if phi_end - phi_start > 180:
phi_end = phi_start + 180
while phi < phi_end:
RX = rot_z(phi)
RXa = matvecmul(RX, a)
RXb = matvecmul(RX, b)
RXc = matvecmul(RX, c)
if math.fabs(RXa[0]) > dot_a:
dot_a = math.fabs(RXa[0])
phi_a = phi
ia = i
if math.fabs(RXb[0]) > dot_b:
dot_b = math.fabs(RXb[0])
phi_b = phi
ib = i
if math.fabs(RXc[0]) > dot_c:
dot_c = math.fabs(RXc[0])
phi_c = phi
ic = i
phi += phi_width
i += 1
length_a = math.sqrt(dot(a, a))
length_b = math.sqrt(dot(b, b))
length_c = math.sqrt(dot(c, c))
rtod = 180.0 / math.pi
angle_a = math.fabs(rtod * math.acos(dot_a / length_a))
angle_b = math.fabs(rtod * math.acos(dot_b / length_b))
angle_c = math.fabs(rtod * math.acos(dot_c / length_c))
# return the closest positions and the angular offset from
# perpendicularity...
return phi_a, phi_b, phi_c, angle_a, angle_b, angle_c, ia, ib, ic
