Author: bugman
Date: Mon Jul 15 10:57:48 2013
New Revision: 20295
URL: http://svn.gna.org/viewcvs/relax?rev=20295&view=rev
Log:
Completed the conversion of the ground and excited states (G, E) to the A and
B states.
This follows from
http://thread.gmane.org/gmane.science.nmr.relax.devel/4132/focus=4154.
Modified:
branches/relax_disp/lib/dispersion/ns_2site_star.py
branches/relax_disp/lib/dispersion/ns_matrices.py
branches/relax_disp/target_functions/relax_disp.py
Modified: branches/relax_disp/lib/dispersion/ns_2site_star.py
URL:
http://svn.gna.org/viewcvs/relax/branches/relax_disp/lib/dispersion/ns_2site_star.py?rev=20295&r1=20294&r2=20295&view=diff
==============================================================================
--- branches/relax_disp/lib/dispersion/ns_2site_star.py (original)
+++ branches/relax_disp/lib/dispersion/ns_2site_star.py Mon Jul 15 10:57:48
2013
@@ -41,7 +41,7 @@
from lib.linear_algebra.matrix_power import square_matrix_power
-def r2eff_ns_2site_star(r20a=None, r20b=None, fg=None, kge=None, keg=None,
tcpmg=None, cpmg_frqs=None, back_calc=None, num_points=None):
+def r2eff_ns_2site_star(r20a=None, r20b=None, fA=None, pA=None, pB=None,
kex=None, k_AB=None, k_BA=None, tcpmg=None, cpmg_frqs=None, back_calc=None,
num_points=None):
"""The 2-site numerical solution to the Bloch-McConnell equation using
complex conjugate matrices.
This function calculates and stores the R2eff values.
@@ -51,12 +51,18 @@
@type r20a: float
@keyword r20b: The R2 value for state A in the absence of
exchange.
@type r20b: float
- @keyword fg: The frequency of the ground state.
- @type fg: float
- @keyword kge: The forward exchange rate from state A to state
B.
- @type kge: float
- @keyword keg: The reverse exchange rate from state B to state
A.
- @type keg: float
+ @keyword fA: The frequency of state A.
+ @type fA: float
+ @keyword pA: The population of state A.
+ @type pA: float
+ @keyword pB: The population of state B.
+ @type pB: float
+ @keyword kex: The kex parameter value (the exchange rate in
rad/s).
+ @type kex: float
+ @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 tcpmg: Total duration of the CPMG element (in seconds).
@type tcpmg: float
@keyword cpmg_frqs: The CPMG nu1 frequencies.
@@ -70,11 +76,11 @@
# The matrix that contains only the R2 relaxation terms ("Redfield
relaxation", i.e. non-exchange broadening).
Rr = -1.0 * matrix([[r20b, 0.0], [0.0, r20a]])
- # The matrix that contains the exchange terms between the two states G
and E.
- Rex = -1.0 * matrix([[kge, -keg], [-kge, keg]])
+ # The matrix that contains the exchange terms between the two states A
and B.
+ Rex = -1.0 * matrix([[k_AB, -k_BA], [-k_AB, k_BA]])
# The matrix that contains the chemical shift evolution. It works here
only with X magnetization, and the complex notation allows to evolve in the
transverse plane (x, y).
- RCS = complex(0.0, -1.0) * matrix([[0.0, 0.0], [0.0, fg]])
+ RCS = complex(0.0, -1.0) * matrix([[0.0, 0.0], [0.0, fA]])
# The matrix that contains all the contributions to the evolution, i.e.
relaxation, exchange and chemical shift evolution.
R = Rr + Rex + RCS
@@ -82,17 +88,8 @@
# This is the complex conjugate of the above. It allows the chemical
shift to run in the other direction, i.e. it is used to evolve the shift
after a 180 deg pulse. The factor of 2 is to minimise the number of
multiplications for the prop_2 matrix calculation.
cR2 = conj(R) * 2.0
- # Conversion of kinetic rates.
- kex = kge + keg
-
- # Calculate relative populations - will be used for generating the
equilibrium magnetizations.
- IGeq = keg / kex
-
- # As the preceding line but for the excited state.
- IEeq = kge / kex
-
- # This is a vector that contains the initial magnetizations
corresponding to ground (G) and excited (E) state transverse magnetizations.
- M0 = matrix([[IGeq], [IEeq]])
+ # This is a vector that contains the initial magnetizations
corresponding to the A and B state transverse magnetizations.
+ M0 = matrix([[pA], [pB]])
# Replicated calculations for faster operation.
inv_tcpmg = 1.0 / tcpmg
Modified: branches/relax_disp/lib/dispersion/ns_matrices.py
URL:
http://svn.gna.org/viewcvs/relax/branches/relax_disp/lib/dispersion/ns_matrices.py?rev=20295&r1=20294&r2=20295&view=diff
==============================================================================
--- branches/relax_disp/lib/dispersion/ns_matrices.py (original)
+++ branches/relax_disp/lib/dispersion/ns_matrices.py Mon Jul 15 10:57:48 2013
@@ -79,75 +79,75 @@
return R
-def rcpmg_2d(R2E=None, R2G=None, df=None, kGE=None, kEG=None):
+def rcpmg_2d(R2A=None, R2B=None, df=None, k_AB=None, k_BA=None):
"""Definition of the 2D exchange matrix.
- @keyword R2E: The transverse, spin-spin relaxation rate for state A.
- @type R2E: float
- @keyword R2G: The transverse, spin-spin relaxation rate for state B.
- @type R2G: float
+ @keyword R2A: The transverse, spin-spin relaxation rate for state A.
+ @type R2A: float
+ @keyword R2B: The transverse, spin-spin relaxation rate for state B.
+ @type R2B: float
@keyword df: FIXME - add description.
@type df: float
- @keyword kGE: The forward exchange rate from state A to state B.
- @type kGE: float
- @keyword kEG: The reverse exchange rate from state B to state A.
- @type kEG: float
+ @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
@return: The relaxation matrix.
@rtype: numpy rank-2, 4D array
"""
# Parameter conversions.
- fG = 0
- fE = fG + df
+ fA = 0
+ fB = fA + df
# Create the matrix.
temp = matrix([
- [-R2G-kGE, -fG, kEG, 0.0],
- [ fG, -R2G-kGE, 0.0, kEG],
- [ kGE, 0.0, -R2E-kEG, -fE],
- [ 0.0, kGE, fE, -R2E-kEG]
+ [ -R2A-k_AB, -fA, k_BA, 0.0],
+ [ fA, -R2A-k_AB, 0.0, k_BA],
+ [ k_AB, 0.0, -R2B-k_BA, -fB],
+ [ 0.0, k_AB, fB, -R2B-k_BA]
])
# Return the matrix.
return temp
-def rcpmg_3d(R1E=None, R1G=None, R2E=None, R2G=None, df=None, kGE=None,
kEG=None):
+def rcpmg_3d(R1A=None, R1B=None, R2A=None, R2B=None, df=None, k_AB=None,
k_BA=None):
"""Definition of the 3D exchange matrix.
- @keyword R1E: The longitudinal, spin-lattice relaxation rate for state
A.
- @type R1E: float
- @keyword R1G: The longitudinal, spin-lattice relaxation rate for state
B.
- @type R1G: float
- @keyword R2E: The transverse, spin-spin relaxation rate for state A.
- @type R2E: float
- @keyword R2G: The transverse, spin-spin relaxation rate for state B.
- @type R2G: float
+ @keyword R1A: The longitudinal, spin-lattice relaxation rate for state
A.
+ @type R1A: float
+ @keyword R1B: The longitudinal, spin-lattice relaxation rate for state
B.
+ @type R1B: float
+ @keyword R2A: The transverse, spin-spin relaxation rate for state A.
+ @type R2A: float
+ @keyword R2B: The transverse, spin-spin relaxation rate for state B.
+ @type R2B: float
@keyword df: FIXME - add description.
@type df: float
- @keyword kGE: The forward exchange rate from state A to state B.
- @type kGE: float
- @keyword kEG: The reverse exchange rate from state B to state A.
- @type kEG: float
+ @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
@return: The relaxation matrix.
@rtype: numpy rank-2, 7D array
"""
# Parameter conversions.
- fG = 0.0
- fE = df
- IGeq = kEG / (kEG + kGE)
- IEeq = kGE / (kEG + kGE)
+ fA = 0.0
+ fB = df
+ pA = k_BA / (k_BA + k_AB)
+ pB = k_AB / (k_BA + k_AB)
# Create the matrix.
temp = matrix([
- [ 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0],
- [ 0.0, -R2G-kGE, -fG, 0.0, kEG,
0.0, 0.0],
- [ 0.0, fG, -R2G-kGE, 0.0, 0.0,
kEG, 0.0],
- [2.0*R1G*IGeq, 0.0, 0.0, -R1G-kGE, 0.0,
0.0, kEG],
- [ 0.0, kGE, 0.0, 0.0, -R2E-kEG,
-fE, 0.0],
- [ 0.0, 0.0, kGE, 0.0, fE,
-R2E-kEG, 0.0],
- [2.0*R1E*IEeq, 0.0, 0.0, kGE, 0.0,
0.0, -R1E-kEG]
+ [ 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0],
+ [ 0.0, -R2A-k_AB, -fA, 0.0, k_BA,
0.0, 0.0],
+ [ 0.0, fA, -R2A-k_AB, 0.0, 0.0,
k_BA, 0.0],
+ [ 2.0*R1A*pA, 0.0, 0.0, -R1A-k_AB, 0.0,
0.0, k_BA],
+ [ 0.0, k_AB, 0.0, 0.0, -R2B-k_BA,
-fB, 0.0],
+ [ 0.0, 0.0, k_AB, 0.0, fB,
-R2B-k_BA, 0.0],
+ [ 2.0*R1B*pB, 0.0, 0.0, k_AB, 0.0,
0.0, -R1B-k_BA]
])
# Return the matrix.
Modified: branches/relax_disp/target_functions/relax_disp.py
URL:
http://svn.gna.org/viewcvs/relax/branches/relax_disp/target_functions/relax_disp.py?rev=20295&r1=20294&r2=20295&view=diff
==============================================================================
--- branches/relax_disp/target_functions/relax_disp.py (original)
+++ branches/relax_disp/target_functions/relax_disp.py Mon Jul 15 10:57:48
2013
@@ -497,8 +497,8 @@
# Once off parameter conversions.
pB = 1.0 - pA
- kge = pA * kex
- keg = pB * kex
+ k_AB = pA * kex
+ k_BA = pB * kex
# Initialise.
chi2_sum = 0.0
@@ -514,15 +514,15 @@
dw_frq = dw[spin_index] * self.frqs[spin_index, frq_index]
# Back calculate the R2eff values.
- r2eff_ns_2site_star(r20a=R20A[r20_index],
r20b=R20B[r20_index], pA=pA, kge=kge, keg=keg, cpmg_frqs=self.cpmg_frqs,
back_calc=self.back_calc[spin_index, frq_index],
num_points=self.num_disp_points)
-
- # For all missing data points, set the back-calculated value
to the measured values so that it has no effect on the chi-squared value.
- for point_index in range(self.num_disp_points):
- if self.missing[spin_index, frq_index, point_index]:
- self.back_calc[spin_index, frq_index, point_index] =
self.values[spin_index, frq_index, point_index]
-
- # Calculate and return the chi-squared value.
- chi2_sum += chi2(self.values[spin_index, frq_index],
self.back_calc[spin_index, frq_index], self.errors[spin_index, frq_index])
-
- # Return the total chi-squared value.
- return chi2_sum
+ r2eff_ns_2site_star(r20a=R20A[r20_index],
r20b=R20B[r20_index], pA=pA, pB=pB, kex=kex, k_AB=k_AB, k_BA=k_BA,
cpmg_frqs=self.cpmg_frqs, back_calc=self.back_calc[spin_index, frq_index],
num_points=self.num_disp_points)
+
+ # For all missing data points, set the back-calculated value
to the measured values so that it has no effect on the chi-squared value.
+ for point_index in range(self.num_disp_points):
+ if self.missing[spin_index, frq_index, point_index]:
+ self.back_calc[spin_index, frq_index, point_index] =
self.values[spin_index, frq_index, point_index]
+
+ # Calculate and return the chi-squared value.
+ chi2_sum += chi2(self.values[spin_index, frq_index],
self.back_calc[spin_index, frq_index], self.errors[spin_index, frq_index])
+
+ # Return the total chi-squared value.
+ return chi2_sum
r20295 - in /branches/relax_disp: lib/dispersion/ns_2site_star.py lib/dispersion/ns_matrices.py target_functions/relax_disp.py