Author: tlinnet
Date: Tue Jun 10 01:01:53 2014
New Revision: 23762
URL: http://svn.gna.org/viewcvs/relax?rev=23762&view=rev
Log:
Removed all looping over spin and spectrometer frequency.
This is the last loop!
Wuhu.
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=23762&r1=23761&r2=23762&view=diff
==============================================================================
--- branches/disp_spin_speed/target_functions/relax_disp.py (original)
+++ branches/disp_spin_speed/target_functions/relax_disp.py Tue Jun 10
01:01:53 2014
@@ -417,8 +417,6 @@
# 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 = deepcopy(self.ones_a)
- self.R20B_a = deepcopy(self.ones_a)
self.cpmg_frqs_a = deepcopy(self.ones_a)
self.num_disp_points_a = deepcopy(self.ones_a)
self.back_calc_a = deepcopy(self.ones_a)
@@ -538,7 +536,7 @@
# Expand dw to number of axis.
dw_axis = dw[None,:,None,None,None]
- # Tile tw according to dimensions.
+ # Tile dw according to dimensions.
dw_axis = np.tile(dw_axis, (self.numpy_array_shape[0],
self.numpy_array_shape[2],self.numpy_array_shape[3],
self.numpy_array_shape[4]))
# Convert dw from ppm to rad/s.
@@ -547,23 +545,21 @@
# Calculate pA and kex per frequency.
pA_arr = pA*self.spins_a
kex_arr = kex*self.spins_a + self.not_spins_a
-
- # Loop over the spectrometer frequencies.
- for mi in range(self.num_frq):
- # Extract number of dispersion points. Always the same per sin.
- num_disp_points = self.num_disp_points[0][0][mi][0]
-
- # Loop over the spins.
- for si in range(self.num_spins):
- # The R20 index.
- r20_index = mi + si*self.num_frq
-
- # Store r20a and r20b values per disp point.
- self.R20A_a[0][si][mi][0][:num_disp_points] = array(
[R20A[r20_index]] * num_disp_points, float64)
- self.R20B_a[0][si][mi][0][:num_disp_points] = array(
[R20B[r20_index]] * num_disp_points, float64)
+
+ # Reshape R20A and R20B to per experiment, spin and frequency.
+ R20A_axis = R20A.reshape(self.numpy_array_shape[0],
self.numpy_array_shape[1], self.numpy_array_shape[2])
+ R20B_axis = R20B.reshape(self.numpy_array_shape[0],
self.numpy_array_shape[1], self.numpy_array_shape[2])
+
+ # Expand R20A and R20B axis to offset and dispersion points.
+ R20A_axis = R20A_axis[:,:,:,None,None]
+ R20B_axis = R20B_axis[:,:,:,None,None]
+
+ # Tile R20A and R20B according to maximum of dispersion points.
Multiply with spin ON array. Add 1.
+ R20A_axis = np.tile(R20A_axis, (1, 1, 1, 1,
self.max_num_disp_points)) * self.spins_a + self.not_spins_a
+ R20B_axis = np.tile(R20B_axis, (1, 1, 1, 1,
self.max_num_disp_points)) * self.spins_a + self.not_spins_a
## Back calculate the R2eff values.
- r2eff_CR72(r20a=self.R20A_a, r20b=self.R20B_a, pA=pA_arr,
dw=dw_frq_a, kex=kex_arr, cpmg_frqs=self.cpmg_frqs_a,
back_calc=self.back_calc_a, num_points=self.num_disp_points_a)
+ r2eff_CR72(r20a=R20A_axis, r20b=R20B_axis, pA=pA_arr, dw=dw_frq_a,
kex=kex_arr, cpmg_frqs=self.cpmg_frqs_a, back_calc=self.back_calc_a,
num_points=self.num_disp_points_a)
## 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:
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Re: r23762 - /branches/disp_spin_speed/target_functions/relax_disp.py