def calc_anisotropy(U):
"""Calculates the anisotropy of a atomic ADP tensor U. Anisotropy is
defined as the smallest eigenvalue of U divided by the largest eigenvalue
of U.
"""
evals = linalg.eigenvalues(U)
return min(evals) / max(evals)
def eigenvalues(self):
"Returns the eigenvalues of a rank-2 tensor in an array."
if self.rank == 2:
from numpy.oldnumeric.linear_algebra import eigenvalues
return eigenvalues(self.array)
else:
raise ValueError, 'Undefined operation'
def calc_anisotropy(U):
"""Calculates the anisotropy of a atomic ADP tensor U. Anisotropy is
defined as the smallest eigenvalue of U divided by the largest eigenvalue
of U.
"""
evals = linalg.eigenvalues(U)
return min(evals) / max(evals)
def glr_Uellipse(self, position, U, prob):
"""Renders the ellipsoid enclosing the given fractional probability
given the gaussian variance-covariance matrix U at the given position.
C=1.8724 = 68%
"""
## rotate U
R = self.matrix[:3, :3]
Ur = numpy.dot(numpy.dot(R, U), numpy.transpose(R))
Umax = max(linalg.eigenvalues(Ur))
try:
limit_radius = Gaussian.GAUSS3C[prob] * MARGIN * math.sqrt(Umax)
except ValueError:
limit_radius = 2.0
try:
Q = linalg.inverse(Ur)
except linalg.LinAlgError:
return
self.object_list.append(
(
14,
dot43(self.matrix, position),
limit_radius,
self.material_color_r,
self.material_color_g,
self.material_color_b,
Q,
-Gaussian.GAUSS3C[prob] ** 2,
)
)
def glr_Uellipse(self, position, U, prob):
"""Renders the ellipsoid enclosing the given fractional probability
given the gaussian variance-covariance matrix U at the given position.
C=1.8724 = 68%
"""
## rotate U
R = self.matrix[:3,:3]
Ur = numpy.matrixmultiply(numpy.matrixmultiply(R, U), numpy.transpose(R))
Umax = max(linalg.eigenvalues(Ur))
try:
limit_radius = Gaussian.GAUSS3C[prob] * MARGIN * math.sqrt(Umax)
except ValueError:
limit_radius = 2.0
try:
Q = linalg.inverse(Ur)
except linalg.LinAlgError:
return
self.object_list.append(
(14,
matrixmultiply43(self.matrix, position),
limit_radius,
self.material_color_r,
self.material_color_g,
self.material_color_b,
Q,
-Gaussian.GAUSS3C[prob]**2))
H[5,3] = v_xy*g3s
H[3,5] = v_xy*g3
H[6,3] = v_xx*g0s
H[3,6] = v_xx*g0
H[7,3] = v_xy*g1s
H[3,7] = v_xy*g1
H[5,4] = v_xy*g2s
H[4,5] = v_xy*g2
H[6,4] = v_xy*g1s
H[4,6] = v_xy*g1
H[7,4] = v_xx*g0s
H[4,7] = v_xx*g0
enarray = eigenvalues(H)
enarray = sort_eigenvalues(enarray)
for en in enarray:
gfile.write("%13.8f" % en)
gfile.write("\n")
gfile.close()
label_y = 0.05
L_x = 0
gamma_x = n
X_x = 2*n
K_x = 2*n+1
gamma2_x = 3*n+1
pfile = open('chadicohen.gnu','w')
