That commit message might be a bit misleading when I present it to all
relax users. It sounds like the 'NS R1rho 3-site' model is new. You
have to be a bit more careful what you write, as I present your text
in all of the relax announcements.
Regards,
Edward
On 17 June 2014 19:27, <tlinnet@xxxxxxxxxxxxx> wrote:
Author: tlinnet
Date: Tue Jun 17 19:27:10 2014
New Revision: 24056
URL: http://svn.gna.org/viewcvs/relax?rev=24056&view=rev
Log:
Implemented the target function for ns r1rho 3site.
Task #7807 (https://gna.org/task/index.php?7807): Speed-up of
dispersion models for Clustered analysis.
Modified:
branches/disp_spin_speed/target_functions/relax_disp.py
Modified: branches/disp_spin_speed/target_functions/relax_disp.py
URL:
http://svn.gna.org/viewcvs/relax/branches/disp_spin_speed/target_functions/relax_disp.py?rev=24056&r1=24055&r2=24056&view=diff
==============================================================================
--- branches/disp_spin_speed/target_functions/relax_disp.py
(original)
+++ branches/disp_spin_speed/target_functions/relax_disp.py Tue Jun
17 19:27:10 2014
@@ -784,35 +784,26 @@
k_AC = pC * kex_AC / pA_pC
dw_AC = dw_AB + dw_BC
- # Initialise.
- chi2_sum = 0.0
-
- # Loop over the spins.
- for si in range(self.num_spins):
- # Loop over the spectrometer frequencies.
- for mi in range(self.num_frq):
- # The R20 index.
- r20_index = mi + si*self.num_frq
-
- # Convert dw from ppm to rad/s.
- dw_AB_frq = dw_AB[si] * self.frqs[0, si, mi, 0, 0]
- dw_AC_frq = dw_AC[si] * self.frqs[0, si, mi, 0, 0]
-
- # Loop over the offsets.
- for oi in range(self.num_offsets[0, si, mi]):
- # Back calculate the R2eff values for each
experiment type.
- ns_r1rho_3site(M0=self.M0, matrix=self.matrix,
r1rho_prime=r1rho_prime[r20_index], omega=self.chemical_shifts[0, si, mi,
oi, 0], offset=self.offset[0, si, mi, oi, 0], r1=self.r1[0, si, mi, oi, 0],
pA=pA, pB=pB, pC=pC, dw_AB=dw_AB_frq, dw_AC=dw_AC_frq, k_AB=k_AB,
k_BA=k_BA, k_BC=k_BC, k_CB=k_CB, k_AC=k_AC, k_CA=k_CA,
spin_lock_fields=self.spin_lock_omega1[0, si, mi, oi],
relax_time=self.relax_times[0, si, mi, oi],
inv_relax_time=self.inv_relax_times[0, si, mi, oi],
back_calc=self.back_calc[0, si, mi, oi], num_points=self.num_disp_points[0,
si, mi, oi])
-
- # 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 di in range(self.num_disp_points[0, si, mi,
oi]):
- if self.missing[0, si, mi, oi, di]:
- self.back_calc[0, si, mi, oi, di] =
self.values[0, si, mi, oi, di]
-
- # Calculate and return the chi-squared value.
- chi2_sum += chi2(self.values[0, si, mi, oi],
self.back_calc[0, si, mi, oi], self.errors[0, si, mi, oi])
+ # Convert dw from ppm to rad/s. Use the out argument, to pass
directly to structure.
+ multiply( multiply.outer( dw_AB.reshape(1, self.NS),
self.nm_no_nd_ones ), self.frqs, out=self.dw_AB_struct )
+ multiply( multiply.outer( dw_AC.reshape(1, self.NS),
self.nm_no_nd_ones ), self.frqs, out=self.dw_AC_struct )
+
+ # Reshape R20 to per experiment, spin and frequency.
+ self.r20_struct[:] = multiply.outer(
r1rho_prime.reshape(self.NE, self.NS, self.NM), self.no_nd_ones )
+
+ # Back calculate the R2eff values for each experiment type.
+ ns_r1rho_3site(M0=self.M0, matrix=self.matrix,
r1rho_prime=self.r20_struct, omega=self.chemical_shifts,
offset=self.offset, r1=self.r1, pA=pA, pB=pB, pC=pC,
dw_AB=self.dw_AB_struct, dw_AC=self.dw_AC_struct, k_AB=k_AB, k_BA=k_BA,
k_BC=k_BC, k_CB=k_CB, k_AC=k_AC, k_CA=k_CA,
spin_lock_fields=self.spin_lock_omega1, relax_time=self.relax_times,
inv_relax_time=self.inv_relax_times, back_calc=self.back_calc,
num_points=self.num_disp_points)
+
+ # Clean the data for all values, which is left over at the end
of arrays.
+ self.back_calc = self.back_calc*self.disp_struct
+
+ ## 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.
+ if self.has_missing:
+ # Replace with values.
+ self.back_calc[self.mask_replace_blank.mask] =
self.values[self.mask_replace_blank.mask]
# Return the total chi-squared value.
- return chi2_sum
+ return chi2_rankN(self.values, self.back_calc, self.errors)
def experiment_type_setup(self):
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