Author: tlinnet
Date: Sat Jun 7 23:18:18 2014
New Revision: 23727
URL: http://svn.gna.org/viewcvs/relax?rev=23727&view=rev
Log:
Moved the creation of special numpy structures outside target function.
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=23727&r1=23726&r2=23727&view=diff
==============================================================================
--- branches/disp_spin_speed/target_functions/relax_disp.py (original)
+++ branches/disp_spin_speed/target_functions/relax_disp.py Sat Jun 7
23:18:18 2014
@@ -394,6 +394,43 @@
if model == MODEL_NS_MMQ_3SITE_LINEAR:
self.func = self.func_ns_mmq_3site_linear
+ # Setup special numpy array structures, for higher dimensional
computation.
+ if model == MODEL_CR72_FULL:
+ # Get the shape of back_calc structure.
+ back_calc_shape = list( np.asarray(self.back_calc).shape )
+
+ # Find which frequency has the maximum number of disp points.
+ # To let the numpy array operate well together, the broadcast
size has to be equal for all shapes.
+ self.max_num_disp_points = np.max(self.num_disp_points)
+
+ # Create numpy arrays to pass to the lib function.
+ # All numpy arrays have to have same shape to allow to
multiply together.
+ # The dimensions should be [ei][si][mi][oi][di].
[Experiment][spins][spec. frq][offset][disp points].
+ # The number of disp point can change per spectrometer, so we
make the maximum size.
+ self.R20A_a = np.ones(back_calc_shape +
[self.max_num_disp_points])
+ self.R20B_a = np.ones(back_calc_shape +
[self.max_num_disp_points])
+ self.pA_a = np.ones(back_calc_shape +
[self.max_num_disp_points])
+ self.dw_frq_a = np.ones(back_calc_shape +
[self.max_num_disp_points])
+ self.kex_a = np.ones(back_calc_shape +
[self.max_num_disp_points])
+ self.cpmg_frqs_a = np.ones(back_calc_shape +
[self.max_num_disp_points])
+ self.num_disp_points_a = np.ones(back_calc_shape +
[self.max_num_disp_points])
+ self.back_calc_a = np.ones(back_calc_shape +
[self.max_num_disp_points])
+
+ # 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):
+ # Loop over the offsets.
+ for oi in range(self.num_offsets[ei][si][mi]):
+ # Extract number of dispersion points.
+ num_disp_points =
self.num_disp_points[ei][si][mi][oi]
+
+ # 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]
+
def calc_B14_chi2(self, R20A=None, R20B=None, dw=None, pA=None,
kex=None):
"""Calculate the chi-squared value of the Baldwin (2014) 2-site
exact solution model for all time scales.
@@ -471,26 +508,6 @@
@rtype: float
"""
- # Get the shape of back_calc structure.
- back_calc_shape = list( np.asarray(self.back_calc).shape )
-
- # Find which frequency has the maximum number of disp points.
- # To let the numpy array operate well together, the broadcast size
has to be equal for all shapes.
- max_num_disp_points = np.max(self.num_disp_points)
-
- # Create numpy arrays to pass to the lib function.
- # All numpy arrays have to have same shape to allow to multiply
together.
- # The dimensions should be [ei][si][mi][oi][di].
[Experiment][spins][spec. frq][offset][disp points].
- # The number of disp point can change per spectrometer, so we make
the maximum size.
- R20A_a = np.ones(back_calc_shape + [max_num_disp_points])
- R20B_a = np.ones(back_calc_shape + [max_num_disp_points])
- pA_a = np.ones(back_calc_shape + [max_num_disp_points])
- dw_frq_a = np.ones(back_calc_shape + [max_num_disp_points])
- kex_a = np.ones(back_calc_shape + [max_num_disp_points])
- cpmg_frqs_a = np.ones(back_calc_shape + [max_num_disp_points])
- num_disp_points_a = np.ones(back_calc_shape +
[max_num_disp_points])
- back_calc_a = np.ones(back_calc_shape + [max_num_disp_points])
-
# Loop over the experiment types.
for ei in range(self.num_exp):
# Loop over the spins.
@@ -506,25 +523,21 @@
r20_index = mi + si*self.num_frq
# Store r20a and r20b values per disp point.
- R20A_a[ei][si][mi][oi] = np.array(
[R20A[r20_index]] * max_num_disp_points, float64)
- R20B_a[ei][si][mi][oi] = np.array(
[R20B[r20_index]] * max_num_disp_points, float64)
+ self.R20A_a[ei][si][mi][oi] = np.array(
[R20A[r20_index]] * self.max_num_disp_points, float64)
+ self.R20B_a[ei][si][mi][oi] = np.array(
[R20B[r20_index]] * self.max_num_disp_points, float64)
# Convert dw from ppm to rad/s.
dw_frq = dw[si] * self.frqs[ei][si][mi]
# Store dw_frq per disp point.
- dw_frq_a[ei][si][mi][oi] = np.array( [dw_frq] *
max_num_disp_points, float64)
+ self.dw_frq_a[ei][si][mi][oi] = np.array( [dw_frq]
* self.max_num_disp_points, float64)
# Store pA and kex per disp point.
- pA_a[ei][si][mi][oi] = np.array( [pA] *
max_num_disp_points, float64)
- kex_a[ei][si][mi][oi] = np.array( [kex] *
max_num_disp_points, float64)
-
- # Extract cpmg_frqs and num_disp_points from lists.
- cpmg_frqs_a[ei][si][mi][oi][:num_disp_points] =
self.cpmg_frqs[ei][mi][oi]
-
num_disp_points_a[ei][si][mi][oi][:num_disp_points] =
self.num_disp_points[ei][si][mi][oi]
+ self.pA_a[ei][si][mi][oi] = np.array( [pA] *
self.max_num_disp_points, float64)
+ self.kex_a[ei][si][mi][oi] = np.array( [kex] *
self.max_num_disp_points, float64)
## Back calculate the R2eff values.
- r2eff_CR72(r20a=R20A_a, r20b=R20B_a, pA=pA_a, dw=dw_frq_a,
kex=kex_a, cpmg_frqs=cpmg_frqs_a, back_calc = back_calc_a,
num_points=num_disp_points_a)
+ r2eff_CR72(r20a=self.R20A_a, r20b=self.R20B_a, pA=self.pA_a,
dw=self.dw_frq_a, kex=self.kex_a, cpmg_frqs=self.cpmg_frqs_a,
back_calc=self.back_calc_a, num_points=self.num_disp_points_a)
# Initialise.
chi2_sum = 0.0
@@ -542,7 +555,7 @@
# Extract number of dispersion points.
num_disp_points =
self.num_disp_points[ei][si][mi][oi]
- self.back_calc[ei][si][mi][oi] =
back_calc_a[ei][si][mi][oi][:num_disp_points]
+ self.back_calc[ei][si][mi][oi] =
self.back_calc_a[ei][si][mi][oi][:num_disp_points]
for di in
range(self.num_disp_points[ei][si][mi][oi]):
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Re: r23727 - /branches/disp_spin_speed/target_functions/relax_disp.py