Author: bugman
Date: Wed Jul 23 11:47:41 2008
New Revision: 6942
URL: http://svn.gna.org/viewcvs/relax?rev=6942&view=rev
Log:
Split the rdc module in half, shifting the alignment tensor components into
their own module.
Added:
branches/rdc_analysis/maths_fns/alignment_tensor.py
- copied, changed from r6940, branches/rdc_analysis/maths_fns/rdc.py
Modified:
branches/rdc_analysis/maths_fns/rdc.py
Copied: branches/rdc_analysis/maths_fns/alignment_tensor.py (from r6940,
branches/rdc_analysis/maths_fns/rdc.py)
URL:
http://svn.gna.org/viewcvs/relax/branches/rdc_analysis/maths_fns/alignment_tensor.py?p2=branches/rdc_analysis/maths_fns/alignment_tensor.py&p1=branches/rdc_analysis/maths_fns/rdc.py&r1=6940&r2=6942&rev=6942&view=diff
==============================================================================
--- branches/rdc_analysis/maths_fns/rdc.py (original)
+++ branches/rdc_analysis/maths_fns/alignment_tensor.py Wed Jul 23 11:47:41
2008
@@ -21,132 +21,10 @@
###############################################################################
# Module docstring.
-"""Module containing functions for the calculation of RDCs."""
+"""Module for the manipulation of alignment tensors."""
# Python imports.
-from numpy import sum
from numpy.linalg import eigvals
-
-
-def average_rdc_5D(vect, K, A, weights=None):
- """Calculate the average RDC for an ensemble set of XH bond vectors,
using the 5D notation.
-
- This function calculates the average RDC for a set of XH bond vectors
from a structural
- ensemble, using the 5D vector form of the alignment tensor. The formula
for this ensemble
- average RDC value is::
-
- _K_
- 1 \
- <RDC_i> = - > RDC_ik (theta),
- K /__
- k=1
-
- where K is the total number of structures, k is the index over the
multiple structures, RDC_ik
- is the back-calculated RDC value for spin system i and structure k, and
theta is the parameter
- vector consisting of the alignment tensor parameters {Axx, Ayy, Axy,
Axz, Ayz}. The
- back-calculated RDC is given by the formula::
-
- RDC_ik(theta) = (x**2 - z**2)Axx + (y**2 - z**2)Ayy + 2x.y.Axy +
2x.z.Axz + 2y.z.Ayz.
-
-
- @param vect: The unit XH bond vector matrix. The first dimension
corresponds to the
- structural index, the second dimension is the
coordinates of the unit
- vector.
- @type vect: numpy matrix
- @param K: The total number of structures.
- @type K: int
- @param A: The 5D vector object. The vector format is {Axx,
Ayy, Axy, Axz, Ayz}.
- @type A: numpy 5D vector
- @param weights: The weights for each member of the ensemble. The
last weight is assumed to
- be missing, and is calculated by this function.
Hence the length should be
- one less than K.
- @type weights: numpy rank-1 array
- @return: The average RDC value.
- @rtype: float
- """
-
- # Initial back-calculated RDC value.
- val = 0.0
-
- # Averaging factor.
- if weights == None:
- c = 1.0 / K
-
- # Loop over the structures k.
- for k in xrange(K):
- # The weights.
- if weights != None:
- if k == K-1:
- c = 1.0 - sum(weights, axis=0)
- else:
- c = weights[k]
-
- # Back-calculate the RDC.
- val = val + c * (vect[k,0]**2 - vect[k,2]**2)*A[0] + (vect[k,1]**2 -
vect[k,2]**2)*A[1] + 2.0*vect[k,0]*vect[k,1]*A[2] +
2.0*vect[k,0]*vect[k,2]*A[3] + 2.0*vect[k,1]*vect[k,2]*A[4]
-
- # Return the average RDC.
- return val
-
-
-def average_rdc_tensor(vect, K, A, weights=None):
- """Calculate the average RDC for an ensemble set of XH bond vectors,
using the 3D tensor.
-
- This function calculates the average RDC for a set of XH bond vectors
from a structural
- ensemble, using the 3D tensorial form of the alignment tensor. The
formula for this ensemble
- average RDC value is::
-
- _K_
- 1 \
- <RDC_i> = - > RDC_ik (theta),
- K /__
- k=1
-
- where K is the total number of structures, k is the index over the
multiple structures, RDC_ik
- is the back-calculated RDC value for spin system i and structure k, and
theta is the parameter
- vector consisting of the alignment tensor. The back-calculated RDC is
given by the formula::
-
- RDC_ik(theta) = muT . A . mu,
-
- where mu is the unit XH bond vector, T is the transpose, and A is the
alignment tensor matrix.
-
-
- @param vect: The unit XH bond vector matrix. The first dimension
corresponds to the
- structural index, the second dimension is the
coordinates of the unit
- vector.
- @type vect: numpy matrix
- @param K: The total number of structures.
- @type K: int
- @param A: The alignment tensor.
- @type A: numpy rank-2 3D tensor
- @param weights: The weights for each member of the ensemble. The
last weight is assumed to
- be missing, and is calculated by this function.
Hence the length should be
- one less than K.
- @type weights: numpy rank-1 array
- @return: The average RDC value.
- @rtype: float
- """
-
- # Initial back-calculated RDC value.
- val = 0.0
-
- # Averaging factor.
- if weights == None:
- c = 1.0 / K
-
- # Loop over the structures k.
- for k in xrange(K):
- # The weights.
- if weights != None:
- if k == K-1:
- c = 1.0 - sum(weights, axis=0)
- else:
- c = weights[k]
-
- # Back-calculate the RDC.
- val = val + c * dot(vect[k], dot(A, vect[k]))
-
- # Return the average RDC.
- return val
def maxA(tensor):
Modified: branches/rdc_analysis/maths_fns/rdc.py
URL:
http://svn.gna.org/viewcvs/relax/branches/rdc_analysis/maths_fns/rdc.py?rev=6942&r1=6941&r2=6942&view=diff
==============================================================================
--- branches/rdc_analysis/maths_fns/rdc.py (original)
+++ branches/rdc_analysis/maths_fns/rdc.py Wed Jul 23 11:47:41 2008
@@ -21,11 +21,10 @@
###############################################################################
# Module docstring.
-"""Module containing functions for the calculation of RDCs."""
+"""Module for the calculation of RDCs."""
# Python imports.
from numpy import sum
-from numpy.linalg import eigvals
def average_rdc_5D(vect, K, A, weights=None):
@@ -147,54 +146,3 @@
# Return the average RDC.
return val
-
-
-def maxA(tensor):
- """Find the maximal alignment - the Azz component in the alignment frame.
-
- @param tensor: The alignment tensor object.
- @type tensor: numpy rank-2 3D tensor
- @return: The Azz component in the alignment frame.
- """
-
- # Return the value.
- return max(abs(eigvals(tensor)))
-
-
-def to_5D(vector_5D, tensor):
- """Convert the rank-2 3D alignment tensor matrix to the 5D vector format.
-
- @param vector_5D: The 5D vector object to populate. The vector format
is {Axx, Ayy, Axy, Axz,
- Ayz}.
- @type vector_5D: numpy 5D vector
- @param tensor: The alignment tensor object.
- @type tensor: numpy rank-2 3D tensor
- """
-
- # Convert the matrix form to the vector form.
- vector_5D[0] = tensor[0, 0]
- vector_5D[1] = tensor[1, 1]
- vector_5D[2] = tensor[0, 1]
- vector_5D[3] = tensor[0, 2]
- vector_5D[4] = tensor[1, 2]
-
-
-def to_tensor(tensor, vector_5D):
- """Convert the 5D vector alignment tensor form to the rank-2 3D matrix
from.
-
- @param tensor: The alignment tensor object, in matrix format, to
populate.
- @type tensor: numpy rank-2 3D tensor
- @param vector_5D: The 5D vector object. The vector format is {Axx,
Ayy, Axy, Axz, Ayz}.
- @type vector_5D: numpy 5D vector
- """
-
- # Convert the vector form to the matrix form.
- tensor[0, 0] = vector_5D[0]
- tensor[0, 1] = vector_5D[2]
- tensor[0, 2] = vector_5D[3]
- tensor[1, 0] = vector_5D[2]
- tensor[1, 1] = vector_5D[1]
- tensor[1, 2] = vector_5D[4]
- tensor[2, 0] = vector_5D[3]
- tensor[2, 1] = vector_5D[4]
- tensor[2, 2] = -vector_5D[0] -vector_5D[1]
r6942 - in /branches/rdc_analysis/maths_fns: alignment_tensor.py rdc.py