Author: tlinnet
Date: Thu Jun 19 17:41:41 2014
New Revision: 24151
URL: http://svn.gna.org/viewcvs/relax?rev=24151&view=rev
Log:
Implemented the collection of the 3D exchange matrix, for rank
[NE][NS][NM][NO][ND][7][7].
Task #7807 (https://gna.org/task/index.php?7807): Speed-up of dispersion
models for Clustered analysis.
Modified:
branches/disp_spin_speed/lib/dispersion/ns_matrices.py
Modified: branches/disp_spin_speed/lib/dispersion/ns_matrices.py
URL:
http://svn.gna.org/viewcvs/relax/branches/disp_spin_speed/lib/dispersion/ns_matrices.py?rev=24151&r1=24150&r2=24151&view=diff
==============================================================================
--- branches/disp_spin_speed/lib/dispersion/ns_matrices.py (original)
+++ branches/disp_spin_speed/lib/dispersion/ns_matrices.py Thu Jun 19
17:41:41 2014
@@ -31,7 +31,7 @@
# Python module imports.
from math import cos, sin, pi
-from numpy import array, float64, matrix
+from numpy import array, float64, matrix, multiply
def r180x_2d(flip=pi):
@@ -158,6 +158,166 @@
# Return the matrix.
return temp
+
+
+def rcpmg_3d_rankN(R1A=None, R1B=None, R2A=None, R2B=None, pA=None, pB=None,
dw=None, k_AB=None, k_BA=None, tcp=None):
+ """Definition of the 3D exchange matrix, for rank
[NE][NS][NM][NO][ND][7][7].
+
+ @keyword R1A: The longitudinal, spin-lattice relaxation rate for state
A.
+ @type R1A: numpy float array of rank [NE][NS][NM][NO][ND]
+ @keyword R1B: The longitudinal, spin-lattice relaxation rate for state
B.
+ @type R1B: numpy float array of rank [NE][NS][NM][NO][ND]
+ @keyword R2A: The transverse, spin-spin relaxation rate for state A.
+ @type R2A: numpy float array of rank [NE][NS][NM][NO][ND]
+ @keyword R2B: The transverse, spin-spin relaxation rate for state B.
+ @type R2B: numpy float array of rank [NE][NS][NM][NO][ND]
+ @keyword pA: The population of state A.
+ @type pA: float
+ @keyword pB: The population of state B.
+ @type pB: float
+ @keyword dw: The chemical exchange difference between states A and B
in rad/s.
+ @type dw: numpy float array of rank [NE][NS][NM][NO][ND]
+ @keyword k_AB: The forward exchange rate from state A to state B.
+ @type k_AB: float
+ @keyword k_BA: The reverse exchange rate from state B to state A.
+ @type k_BA: float
+ @keyword tcp: The tau_CPMG times (1 / 4.nu1).
+ @type tcp: numpy float array of rank [NE][NS][NM][NO][ND]
+ @return: The relaxation matrix.
+ @rtype: numpy float array of rank [NE][NS][NM][NO][ND][7][7]
+ """
+
+ # The omega frequencies for states A and B (state A is assumed to be at
zero frequency).
+ wA = 0.0
+ wB = dw
+
+ r10a_tcp = R1A * tcp
+ r10b_tcp = R1B * tcp
+ r20a_tcp = R2A * tcp
+ r20b_tcp = R2B * tcp
+ pA_tcp = pA * tcp
+ pB_tcp = pB * tcp
+ dw_tcp = dw * tcp
+ k_AB_tcp = k_AB * tcp
+ k_BA_tcp = k_BA * tcp
+ wA_tcp = wA * tcp
+ wB_tcp = wB * tcp
+
+ # Create the matrix.
+ m_r10a = array([
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [1.0, 0.0, 0.0, -1.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ])
+
+ m_pA = array([
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ])
+
+ m_r10b = array([
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0], ])
+
+ m_pB = array([
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ])
+
+ m_r20a = array([
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ])
+
+ m_r20b = array([
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, -1.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, -1.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ])
+
+ m_k_AB = array([
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, -1.0, 0.0, 0.0, 0.0],
+ [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], ])
+
+ m_k_BA = array([
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],
+ [0.0, 0.0, 0.0, 0.0, -1.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, -1.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0], ])
+
+ m_wA = array([
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ])
+
+ m_wB = array([
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, -1.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ])
+
+ # Multiply and expand.
+ m_r10a_tcp = multiply.outer( r10a_tcp, m_r10a )
+ m_pA_tcp = multiply.outer( pA_tcp, m_pA )
+
+ m_r10b_tcp = multiply.outer( r10b_tcp, m_r10b )
+ m_pB_tcp = multiply.outer( pB_tcp, m_pB )
+
+ m_r20a_tcp = multiply.outer( r20a_tcp, m_r20a )
+ m_r20b_tcp = multiply.outer( r20b_tcp, m_r20b )
+
+ m_k_AB_tcp = multiply.outer( k_AB_tcp, m_k_AB )
+ m_k_BA_tcp = multiply.outer( k_BA_tcp, m_k_BA )
+
+ m_wA_tcp = multiply.outer( wA_tcp, m_wA )
+ m_wB_tcp = multiply.outer( wB_tcp, m_wB )
+
+ # Collect matrix.
+ c_mat = (m_r10a_tcp * m_pA_tcp + m_r10b_tcp * m_pB_tcp
+ + m_r20a_tcp + m_r20b_tcp
+ + m_k_AB_tcp + m_k_BA_tcp
+ + m_wA_tcp + m_wB_tcp )
+
+ # Return the matrix.
+ return c_mat
def rr1rho_3d_3site(matrix=None, R1=None, r1rho_prime=None, pA=None,
pB=None, pC=None, wA=None, wB=None, wC=None, w1=None, k_AB=None, k_BA=None,
k_BC=None, k_CB=None, k_AC=None, k_CA=None):
r24151 - /branches/disp_spin_speed/lib/dispersion/ns_matrices.py