Author: tlinnet
Date: Mon Jun 16 22:11:36 2014
New Revision: 24002
URL: http://svn.gna.org/viewcvs/relax?rev=24002&view=rev
Log:
Moved the calculation of dw out of for loops for model ns mmq 2site.
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=24002&r1=24001&r2=24002&view=diff
==============================================================================
--- branches/disp_spin_speed/target_functions/relax_disp.py (original)
+++ branches/disp_spin_speed/target_functions/relax_disp.py Mon Jun 16
22:11:36 2014
@@ -1514,6 +1514,13 @@
k_BA = pA * kex
k_AB = pB * kex
+ # Convert dw and dwH from ppm to rad/s. Use the out argument, to
pass directly to structure.
+ multiply( multiply.outer( dw.reshape(self.NE, self.NS),
self.nm_no_nd_ones ), self.frqs, out=self.dw_struct )
+ multiply( multiply.outer( dwH.reshape(self.NE, self.NS),
self.nm_no_nd_ones ), self.frqs_H, out=self.dwH_struct )
+
+ # Reshape R20 to per experiment, spin and frequency.
+ self.r20_struct[:] = multiply.outer( R20.reshape(self.NE, self.NS,
self.NM), self.no_nd_ones )
+
# This is a vector that contains the initial magnetizations
corresponding to the A and B state transverse magnetizations.
self.M0[0] = pA
self.M0[1] = pB
@@ -1523,44 +1530,39 @@
# Loop over the experiment types.
for ei in range(self.num_exp):
- # 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 + ei*self.num_frq +
si*self.num_frq*self.num_exp
-
- # Convert dw from ppm to rad/s.
- dw_frq = dw[si] * self.frqs[ei][si][mi][0][0]
- dwH_frq = dwH[si] * self.frqs_H[ei][si][mi][0][0]
-
- # Alias the dw frequency combinations.
- aliased_dwH = 0.0
- if self.exp_types[ei] == EXP_TYPE_CPMG_SQ:
- aliased_dw = dw_frq
- elif self.exp_types[ei] == EXP_TYPE_CPMG_PROTON_SQ:
- aliased_dw = dwH_frq
- elif self.exp_types[ei] == EXP_TYPE_CPMG_DQ:
- aliased_dw = dw_frq + dwH_frq
- elif self.exp_types[ei] == EXP_TYPE_CPMG_ZQ:
- aliased_dw = dw_frq - dwH_frq
- elif self.exp_types[ei] == EXP_TYPE_CPMG_MQ:
- aliased_dw = dw_frq
- aliased_dwH = dwH_frq
- elif self.exp_types[ei] == EXP_TYPE_CPMG_PROTON_MQ:
- aliased_dw = dwH_frq
- aliased_dwH = dw_frq
-
- # Back calculate the R2eff values for each experiment
type.
- self.r2eff_ns_mmq[ei](M0=self.M0, m1=self.m1,
m2=self.m2, R20A=R20[r20_index], R20B=R20[r20_index], pA=pA, pB=pB,
dw=aliased_dw, dwH=aliased_dwH, k_AB=k_AB, k_BA=k_BA,
inv_tcpmg=self.inv_relax_times[ei][si][mi][0],
tcp=self.tau_cpmg[ei][si][mi][0], back_calc=self.back_calc[ei][si][mi][0],
num_points=self.num_disp_points[ei][si][mi][0],
power=self.power[ei][si][mi][0])
-
- # 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[ei][si][mi][0]):
- if self.missing[ei][si][mi][0][di]:
- self.back_calc[ei][si][mi][0][di] =
self.values[ei][si][mi][0][di]
-
- # Calculate and return the chi-squared value.
- chi2_sum += chi2(self.values[ei][si][mi][0],
self.back_calc[ei][si][mi][0], self.errors[ei][si][mi][0])
+
+ r20 = self.r20_struct[ei]
+ dw_frq = self.dw_struct[ei]
+ dwH_frq = self.dwH_struct[ei]
+
+ # Alias the dw frequency combinations.
+ aliased_dwH = 0.0
+ if self.exp_types[ei] == EXP_TYPE_CPMG_SQ:
+ aliased_dw = dw_frq
+ elif self.exp_types[ei] == EXP_TYPE_CPMG_PROTON_SQ:
+ aliased_dw = dwH_frq
+ elif self.exp_types[ei] == EXP_TYPE_CPMG_DQ:
+ aliased_dw = dw_frq + dwH_frq
+ elif self.exp_types[ei] == EXP_TYPE_CPMG_ZQ:
+ aliased_dw = dw_frq - dwH_frq
+ elif self.exp_types[ei] == EXP_TYPE_CPMG_MQ:
+ aliased_dw = dw_frq
+ aliased_dwH = dwH_frq
+ elif self.exp_types[ei] == EXP_TYPE_CPMG_PROTON_MQ:
+ aliased_dw = dwH_frq
+ aliased_dwH = dw_frq
+
+ # Back calculate the R2eff values for each experiment type.
+ self.r2eff_ns_mmq[ei](M0=self.M0, m1=self.m1, m2=self.m2,
R20A=r20, R20B=r20, pA=pA, pB=pB, dw=aliased_dw, dwH=aliased_dwH, k_AB=k_AB,
k_BA=k_BA, inv_tcpmg=self.inv_relax_times[ei], tcp=self.tau_cpmg[ei],
back_calc=self.back_calc[ei], num_points=self.num_disp_points[ei],
power=self.power[ei])
+
+ # 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.
+ mask_replace_blank_ei = masked_equal(self.missing[ei], 1.0)
+ self.back_calc[ei][mask_replace_blank_ei.mask] =
self.values[ei][mask_replace_blank_ei.mask]
+
+ # Calculate and return the chi-squared value.
+ chi2_sum += chi2_rankN(self.values[ei], self.back_calc[ei],
self.errors[ei])
# Return the total chi-squared value.
return chi2_sum
r24002 - /branches/disp_spin_speed/target_functions/relax_disp.py