Author: tlinnet
Date: Fri Jun 13 08:18:27 2014
New Revision: 23906
URL: http://svn.gna.org/viewcvs/relax?rev=23906&view=rev
Log:
Replaced target function for model TP02, to use higher dimensional numpy
array structures.
That makes the model much faster.
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=23906&r1=23905&r2=23906&view=diff
==============================================================================
--- branches/disp_spin_speed/target_functions/relax_disp.py (original)
+++ branches/disp_spin_speed/target_functions/relax_disp.py Fri Jun 13
08:18:27 2014
@@ -1952,62 +1952,14 @@
# Once off parameter conversions.
pB = 1.0 - pA
- # 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_frq = dw[si] * self.frqs[0][si][mi]
-
- # Loop over the offsets.
- for oi in range(self.num_offsets[0][si][mi]):
- # Back calculate the R1rho values.
- r1rho_TP02(r1rho_prime=R20[r20_index],
omega=self.chemical_shifts[0][si][mi], offset=self.offset[0][si][mi][oi],
pA=pA, pB=pB, dw=dw_frq, kex=kex, R1=self.r1[si, mi],
spin_lock_fields=self.spin_lock_omega1[0][mi][oi],
spin_lock_fields2=self.spin_lock_omega1_squared[0][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])
-
- # Return the total chi-squared value.
- return chi2_sum
-
-
- def func_TSMFK01(self, params):
- """Target function for the the Tollinger et al. (2001) 2-site
very-slow exchange model, range of microsecond to second time scale.
-
- @param params: The vector of parameter values.
- @type params: numpy rank-1 float array
- @return: The chi-squared value.
- @rtype: float
- """
-
- # Scaling.
- if self.scaling_flag:
- params = dot(params, self.scaling_matrix)
-
- # Unpack the parameter values.
- R20A = params[:self.end_index[0]]
- dw = params[self.end_index[0]:self.end_index[1]]
- k_AB = params[self.end_index[1]]
-
# Convert dw 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_struct ), self.frqs_struct, out=self.dw_struct )
- # Reshape R20A and R20B to per experiment, spin and frequency.
- self.r20a_struct[:] = multiply.outer( R20A.reshape(self.NE, self.NS,
self.NM), self.no_nd_struct )
-
- # Back calculate the R2eff values.
- r2eff_TSMFK01(r20a=self.r20a_struct, dw=self.dw_struct, dw_orig=dw,
k_AB=k_AB, tcp=self.tau_cpmg_a, back_calc=self.back_calc_a,
num_points=self.num_disp_points_a)
+ # Reshape R20 to per experiment, spin and frequency.
+ self.r20_struct[:] = multiply.outer( R20.reshape(self.NE, self.NS,
self.NM), self.no_nd_struct )
+
+ # Back calculate the R1rho values.
+ r1rho_TP02(r1rho_prime=self.r20_struct,
omega=self.chemical_shifts_a, offset=self.offset_a, pA=pA, pB=pB,
dw=self.dw_struct, kex=kex, R1=self.r1_a,
spin_lock_fields=self.spin_lock_omega1_a,
spin_lock_fields2=self.spin_lock_omega1_squared_a,
back_calc=self.back_calc_a, num_points=self.num_disp_points_a)
# Clean the data for all values, which is left over at the end of
arrays.
self.back_calc_a = self.back_calc_a*self.disp_struct
@@ -2019,3 +1971,43 @@
# Return the total chi-squared value.
return chi2_rankN(self.values_a, self.back_calc_a, self.errors_a)
+
+
+
+ def func_TSMFK01(self, params):
+ """Target function for the the Tollinger et al. (2001) 2-site
very-slow exchange model, range of microsecond to second time scale.
+
+ @param params: The vector of parameter values.
+ @type params: numpy rank-1 float array
+ @return: The chi-squared value.
+ @rtype: float
+ """
+
+ # Scaling.
+ if self.scaling_flag:
+ params = dot(params, self.scaling_matrix)
+
+ # Unpack the parameter values.
+ R20A = params[:self.end_index[0]]
+ dw = params[self.end_index[0]:self.end_index[1]]
+ k_AB = params[self.end_index[1]]
+
+ # Convert dw 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_struct ), self.frqs_struct, out=self.dw_struct )
+
+ # Reshape R20A and R20B to per experiment, spin and frequency.
+ self.r20a_struct[:] = multiply.outer( R20A.reshape(self.NE, self.NS,
self.NM), self.no_nd_struct )
+
+ # Back calculate the R2eff values.
+ r2eff_TSMFK01(r20a=self.r20a_struct, dw=self.dw_struct, dw_orig=dw,
k_AB=k_AB, tcp=self.tau_cpmg_a, back_calc=self.back_calc_a,
num_points=self.num_disp_points_a)
+
+ # Clean the data for all values, which is left over at the end of
arrays.
+ self.back_calc_a = self.back_calc_a*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_a[self.mask_replace_blank.mask] =
self.values_a[self.mask_replace_blank.mask]
+
+ # Return the total chi-squared value.
+ return chi2_rankN(self.values_a, self.back_calc_a, self.errors_a)
r23906 - /branches/disp_spin_speed/target_functions/relax_disp.py