Author: tlinnet
Date: Wed Jun 11 19:37:43 2014
New Revision: 23851
URL: http://svn.gna.org/viewcvs/relax?rev=23851&view=rev
Log:
Changed the target function to handle the B14 model for faster numpy
computation.
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=23851&r1=23850&r2=23851&view=diff
==============================================================================
--- branches/disp_spin_speed/target_functions/relax_disp.py (original)
+++ branches/disp_spin_speed/target_functions/relax_disp.py Wed Jun 11
19:37:43 2014
@@ -395,7 +395,7 @@
self.func = self.func_ns_mmq_3site_linear
# Setup special numpy array structures, for higher dimensional
computation.
- if model in [MODEL_CR72, MODEL_CR72_FULL]:
+ if model in [MODEL_B14, MODEL_B14_FULL, MODEL_CR72, MODEL_CR72_FULL]:
# Get the shape of back_calc structure.
# If using just one field, or having the same number of
dispersion points, the shape would extend to that number.
# Shape has to be: [ei][si][mi][oi].
@@ -430,6 +430,9 @@
self.cpmg_frqs_a = deepcopy(ones_a)
self.num_disp_points_a = deepcopy(zeros_a)
+ self.inv_relax_times_a = deepcopy(zeros_a)
+ self.tau_cpmg_a = deepcopy(zeros_a)
+ self.power_a = ones(self.numpy_array_shape, int16)
self.frqs_a = deepcopy(zeros_a)
self.disp_struct = deepcopy(zeros_a)
@@ -459,6 +462,7 @@
# Extract cpmg_frqs and num_disp_points from
lists.
self.cpmg_frqs_a[ei][si][mi][oi][:num_disp_points] =
self.cpmg_frqs[ei][mi][oi]
self.num_disp_points_a[ei][si][mi][oi][:num_disp_points] =
self.num_disp_points[ei][si][mi][oi]
+
self.inv_relax_times_a[ei][si][mi][oi][:num_disp_points] = 1.0 /
self.relax_times[ei][mi]
# Extract the errors and values to numpy array.
self.errors_a[ei][si][mi][oi][:num_disp_points]
= self.errors[ei][si][mi][oi]
@@ -474,6 +478,9 @@
if self.missing[ei][si][mi][oi][di]:
self.has_missing = True
missing_a[ei][si][mi][oi][di] = 1.0
+ if model in [MODEL_B14, MODEL_B14_FULL]:
+ self.power_a[ei][si][mi][oi][di] =
int(round(self.cpmg_frqs[ei][mi][0][di] * self.relax_times[ei][mi]))
+ self.tau_cpmg_a[ei][si][mi][oi][di] =
0.25 / self.cpmg_frqs[ei][mi][0][di]
# Make copy of values structure.
self.back_calc_a = deepcopy(self.values_a)
@@ -508,40 +515,26 @@
k_BA = pA * kex
k_AB = pB * kex
- # Initialise.
- chi2_sum = 0.0
-
- # 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 + si*self.num_frq
-
- # Convert dw from ppm to rad/s.
- dw_frq = dw[si] * self.frqs[ei][si][mi]
-
- # Alias the dw frequency combinations.
- 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 = dw_frq
-
- # Back calculate the R2eff values.
- r2eff_B14(r20a=R20A[r20_index], r20b=R20B[r20_index],
pA=pA, pB=pB, dw=aliased_dw, kex=kex, k_AB=k_AB, k_BA=k_BA,
ncyc=self.power[ei][mi], inv_tcpmg=self.inv_relax_times[ei][mi],
tcp=self.tau_cpmg[ei][mi], back_calc=self.back_calc[ei][si][mi][0],
num_points=self.num_disp_points[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])
+ # Convert dw from ppm to rad/s. Use the out argument, to pass
directly to structure.
+ multiply( multiply.outer( asarray(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( asarray(R20A).reshape(self.NE,
self.NS, self.NM), self.no_nd_struct )
+ self.r20b_struct[:] = multiply.outer( asarray(R20B).reshape(self.NE,
self.NS, self.NM), self.no_nd_struct )
+
+ # Back calculate the R2eff values.
+ r2eff_B14(r20a=self.r20a_struct, r20b=self.r20b_struct, pA=pA,
pB=pB, dw=self.dw_struct, kex=kex, k_AB=k_AB, k_BA=k_BA, ncyc=self.power_a,
inv_tcpmg=self.inv_relax_times_a, 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_sum
+ return chi2_rankN(self.values_a, self.back_calc_a, self.errors_a)
def calc_CR72_chi2(self, R20A=None, R20B=None, dw=None, pA=None,
kex=None):
r23851 - /branches/disp_spin_speed/target_functions/relax_disp.py