Author: tlinnet
Date: Fri Jun 20 19:13:16 2014
New Revision: 24218
URL: http://svn.gna.org/viewcvs/relax?rev=24218&view=rev
Log:
Implemented multidimensional r1rho 3d 3site 3D exchange matrix.
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=24218&r1=24217&r2=24218&view=diff
==============================================================================
--- branches/disp_spin_speed/lib/dispersion/ns_matrices.py (original)
+++ branches/disp_spin_speed/lib/dispersion/ns_matrices.py Fri Jun 20
19:13:16 2014
@@ -977,4 +977,288 @@
matrix[5, 8] = k_CB
matrix[6, 3] = k_BC
matrix[7, 4] = k_BC
- matrix[8, 5] = k_BC
+ matrix[8, 5] = k_BC
+
+
+def rr1rho_3d_3site_rankN(R1=None, r1rho_prime=None, pA=None, pB=None,
pC=None, dw_AB=None, dw_AC=None, omega=None, offset=None, w1=None, k_AB=None,
k_BA=None, k_BC=None, k_CB=None, k_AC=None, k_CA=None, relax_time=None):
+ """Definition of the 3D exchange matrix.
+
+ @keyword R1: The longitudinal, spin-lattice relaxation rate.
+ @type R1: numpy float array of rank [NE][NS][NM][NO][ND]
+ @keyword r1rho_prime: The R1rho transverse, spin-spin relaxation rate
in the absence of exchange.
+ @type r1rho_prime: 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 pC: The population of state C.
+ @type pC: float
+ @keyword omega: The chemical shift for the spin in rad/s.
+ @type omega: numpy float array of rank [NS][NM][NO][ND]
+ @keyword offset: The spin-lock offsets for the data.
+ @type offset: numpy float array of rank [NE][NS][NM][NO][ND]
+ @keyword dw_AB: The chemical exchange difference between states
A and B in rad/s.
+ @type dw_AB: numpy float array of rank [NE][NS][NM][NO][ND]
+ @keyword dw_AC: The chemical exchange difference between states
A and C in rad/s.
+ @type dw_AC: numpy float array of rank [NE][NS][NM][NO][ND]
+ @keyword w1: The spin-lock field strength in rad/s.
+ @type w1: 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 k_BC: The forward exchange rate from state B to state
C.
+ @type k_BC: float
+ @keyword k_CB: The reverse exchange rate from state C to state
B.
+ @type k_CB: float
+ @keyword k_AC: The forward exchange rate from state A to state
C.
+ @type k_AC: float
+ @keyword k_CA: The reverse exchange rate from state C to state
A.
+ @type k_CA: float
+ @keyword relax_time: The total relaxation time period for each
spin-lock field strength (in seconds).
+ @type relax_time: numpy float array of rank [NE][NS][NM][NO][ND]
+ """
+
+ # The AB auto-block.
+ #matrix[0, 0] = -r1rho_prime - k_AB - k_AC
+ #matrix[0, 1] = -wA
+ #matrix[1, 0] = wA
+ #matrix[1, 1] = -r1rho_prime - k_AB - k_AC
+ #matrix[1, 2] = -w1
+ #matrix[2, 1] = w1
+ #matrix[2, 2] = -R1 - k_AB - k_AC
+
+ # The AC auto-block.
+ #matrix[3, 3] = -r1rho_prime - k_BA - k_BC
+ #matrix[3, 4] = -wB
+ #matrix[4, 3] = wB
+ #matrix[4, 4] = -r1rho_prime - k_BA - k_BC
+ #matrix[4, 5] = -w1
+ #matrix[5, 4] = w1
+ #matrix[5, 5] = -R1 - k_BA - k_BC
+
+ # The BC auto-block.
+ #matrix[6, 6] = -r1rho_prime - k_CA - k_CB
+ #matrix[6, 7] = -wC
+ #matrix[7, 6] = wC
+ #matrix[7, 7] = -r1rho_prime - k_CA - k_CB
+ #matrix[7, 8] = -w1
+ #matrix[8, 7] = w1
+ #matrix[8, 8] = -R1 - k_CA - k_CB
+
+ # The AB cross-block.
+ #matrix[0, 3] = k_BA
+ #matrix[1, 4] = k_BA
+ #matrix[2, 5] = k_BA
+ #matrix[3, 0] = k_AB
+ #matrix[4, 1] = k_AB
+ #matrix[5, 2] = k_AB
+
+ # The AC cross-block.
+ #matrix[0, 6] = k_CA
+ #matrix[1, 7] = k_CA
+ #matrix[2, 8] = k_CA
+ #matrix[6, 0] = k_AC
+ #matrix[7, 1] = k_AC
+ #matrix[8, 2] = k_AC
+
+ # The BC cross-block.
+ #matrix[3, 6] = k_CB
+ #matrix[4, 7] = k_CB
+ #matrix[5, 8] = k_CB
+ #matrix[6, 3] = k_BC
+ #matrix[7, 4] = k_BC
+ #matrix[8, 5] = k_BC
+
+ #m_x = 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, 0.0, 0.0, 0.0],
+ # [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ # [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ # [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ # [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]])
+
+ # Repetitive calculations (to speed up calculations).
+ # The chemical shift offset of state A from the spin-lock. Larmor
frequency for state A [s^-1].
+ Wa = omega
+ # The chemical shift offset of state B from the spin-lock. Larmor
frequency for state B [s^-1].
+ Wb = omega + dw_AB
+ # The chemical shift offset of state C from the spin-lock. Larmor
frequency for state C [s^-1].
+ Wc = omega + dw_AC
+
+ # Population-averaged Larmor frequency [s^-1].
+ #W = pA*Wa + pB*Wb + pC*Wc
+ # Offset of spin-lock from A.
+ dA = Wa - offset
+ # Offset of spin-lock from B.
+ dB = Wb - offset
+ # Offset of spin-lock from C.
+ dC = Wc - offset
+ # Offset of spin-lock from population-average
+ #d = W - offset_i
+
+ # Parameter alias.
+ wA=dA
+ wB=dB
+ wC=dC
+
+ m_R1 = 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, -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, -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, -1.0]])
+
+ m_r1rho_prime = array([
+ [-1.0, 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],
+ [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, 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, -1.0, 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, 0.0, 0.0, 0.0]])
+
+ m_wA = array([
+ [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, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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, -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],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]])
+
+ m_wC = 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, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 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, 0.0, 0.0, 0.0]])
+
+ m_w1 = 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],
+ [0.0, 0.0, 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, 0.0, 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]])
+
+ m_k_AB = array([
+ [-1.0, 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],
+ [0.0, 0.0, -1.0, 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, 1.0, 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, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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_k_BA = array([
+ [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, 1.0, 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, -1.0, 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, 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]])
+
+ m_k_BC = 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, -1.0, 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, 0.0, 0.0, -1.0, 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, 1.0, 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]])
+
+ m_k_CB = 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, 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, 1.0],
+ [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, -1.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0]])
+
+ m_k_AC = array([
+ [-1.0, 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],
+ [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],
+ [1.0, 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],
+ [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]])
+
+ m_k_CA = array([
+ [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, 1.0, 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, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 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, 0.0, 0.0, -1.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0]])
+
+ # Multiply and expand.
+ mat_R1 = multiply.outer( R1 * relax_time, m_R1 )
+ mat_r1rho_prime = multiply.outer( r1rho_prime * relax_time,
m_r1rho_prime )
+
+ mat_wA = multiply.outer( wA * relax_time, m_wA )
+ mat_wB = multiply.outer( wB * relax_time, m_wB )
+ mat_wC = multiply.outer( wC * relax_time, m_wC )
+ mat_w1 = multiply.outer( w1 * relax_time, m_w1 )
+
+ mat_k_AB = multiply.outer( k_AB * relax_time, m_k_AB )
+ mat_k_BA = multiply.outer( k_BA * relax_time, m_k_BA )
+ mat_k_BC = multiply.outer( k_BC * relax_time, m_k_BC )
+
+ mat_k_CB = multiply.outer( k_CB * relax_time, m_k_CB )
+ mat_k_AC = multiply.outer( k_AC * relax_time, m_k_AC )
+ mat_k_CA = multiply.outer( k_CA * relax_time, m_k_CA )
+
+ # Collect matrix.
+ matrix = (mat_R1 + mat_r1rho_prime
+ + mat_wA + mat_wB + mat_wC + mat_w1
+ + mat_k_AB + mat_k_BA + mat_k_BC
+ + mat_k_CB + mat_k_AC + mat_k_CA )
+
+ # Return the matrix.
+ return matrix
r24218 - /branches/disp_spin_speed/lib/dispersion/ns_matrices.py