Hi Ed.
I think I have broken something somewhere?
It must be something with:
specific_analyses.relax_disp.data
loop_offset_point
Best
Troels
---------- Forwarded message ----------
From: <tlinnet@xxxxxxxxxxxxx>
Date: 2014-06-13 17:31 GMT+02:00
Subject: r23942 - /branches/disp_spin_speed/target_functions/relax_disp.py
To: relax-commits@xxxxxxx
Author: tlinnet
Date: Fri Jun 13 17:31:40 2014
New Revision: 23942
URL: http://svn.gna.org/viewcvs/relax?rev=23942&view=rev
Log:
Replaced target function for model ns_cpmg_2site_expanded, to use higher
dimensional numpy array structures.
That makes the model much faster.
I cannot get system test: Relax_disp.test_cpmg_synthetic_dx_map_points
to pass.
-------
File
"/Users/tlinnet/software/disp_spin_speed/test_suite/system_tests/relax_disp.py",
line 1671, in test_cpmg_synthetic_dx_map_points
self.assertEqual(res_file[i], lines[i])
AssertionError: '0.76981 3.9169 0.41353 1\n' !=
'0.0098838 1.4654 18.661 1\n'
-------
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=23942&r1=23941&r2=23942&view=diff
==============================================================================
--- branches/disp_spin_speed/target_functions/relax_disp.py (original)
+++ branches/disp_spin_speed/target_functions/relax_disp.py Fri Jun 13
17:31:40 2014
@@ -396,7 +396,7 @@
# Setup special numpy array structures, for higher dimensional
computation.
- test_models = [MODEL_B14, MODEL_B14_FULL, MODEL_CR72,
MODEL_CR72_FULL, MODEL_DPL94, MODEL_IT99, MODEL_LM63, MODEL_M61,
MODEL_M61B, MODEL_MP05, MODEL_TAP03, MODEL_TP02, MODEL_TSMFK01]
+ test_models = [MODEL_B14, MODEL_B14_FULL, MODEL_CR72,
MODEL_CR72_FULL, MODEL_DPL94, MODEL_IT99, MODEL_LM63, MODEL_M61,
MODEL_M61B, MODEL_MP05, MODEL_NS_CPMG_2SITE_EXPANDED, MODEL_TAP03,
MODEL_TP02, MODEL_TSMFK01]
if model in test_models + [MODEL_NOREX]:
# Get the shape of back_calc structure.
@@ -457,10 +457,10 @@
self.phi_ex_struct = deepcopy(zeros_a)
if model in [MODEL_B14, MODEL_B14_FULL, MODEL_MMQ_CR72,
MODEL_NS_CPMG_2SITE_3D, MODEL_NS_CPMG_2SITE_3D_FULL,
MODEL_NS_CPMG_2SITE_EXPANDED, MODEL_NS_CPMG_2SITE_STAR,
MODEL_NS_CPMG_2SITE_STAR_FULL, MODEL_NS_MMQ_2SITE, MODEL_NS_MMQ_3SITE,
MODEL_NS_MMQ_3SITE_LINEAR, MODEL_TSMFK01, MODEL_NS_R1RHO_2SITE,
MODEL_NS_R1RHO_3SITE, MODEL_NS_R1RHO_3SITE_LINEAR]:
- # Expand relax times.
- self.inv_relax_times_a = 1.0 / multiply.outer(
tile(self.relax_times[:,None],(1, 1, self.NS)).reshape(self.NE, self.NS,
self.NM), self.no_nd_struct )
- self.power_a = ones(self.numpy_array_shape, int16)
+ self.relax_times_a = deepcopy(zeros_a)
+ self.inv_relax_times_a = deepcopy(zeros_a)
self.tau_cpmg_a = deepcopy(zeros_a)
+ self.power_a = zeros(self.numpy_array_shape, int16)
# For R1rho data.
if model in MODEL_LIST_R1RHO_FULL:
@@ -501,8 +501,13 @@
self.has_missing = True
missing_a[ei][si][mi][oi][di] = 1.0
if model in [MODEL_B14, MODEL_B14_FULL,
MODEL_MMQ_CR72, MODEL_NS_CPMG_2SITE_3D, MODEL_NS_CPMG_2SITE_3D_FULL,
MODEL_NS_CPMG_2SITE_EXPANDED, MODEL_NS_CPMG_2SITE_STAR,
MODEL_NS_CPMG_2SITE_STAR_FULL, MODEL_NS_MMQ_2SITE, MODEL_NS_MMQ_3SITE,
MODEL_NS_MMQ_3SITE_LINEAR, MODEL_TSMFK01, MODEL_NS_R1RHO_2SITE,
MODEL_NS_R1RHO_3SITE, MODEL_NS_R1RHO_3SITE_LINEAR]:
- 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]
+ self.relax_times_a[ei][si][mi][oi][di]
= self.relax_times[ei][mi]
+ self.tau_cpmg_a[ei][si][mi][oi][di] =
self.tau_cpmg[ei][mi][di]
+ self.power_a[ei][si][mi][oi][di] =
self.power[ei][mi][di]
+
+ if model != MODEL_TSMFK01:
+
self.inv_relax_times_a[ei][si][mi][oi][di] = self.inv_relax_times[ei][mi]
+
# For R1rho data.
if model in MODEL_LIST_R1RHO_FULL and
model != MODEL_NOREX:
self.disp_struct[ei][si][mi][oi][di] =
1.0
@@ -1500,37 +1505,25 @@
pA = params[self.end_index[1]]
kex = params[self.end_index[1]+1]
- # Once off parameter conversions.
- pB = 1.0 - pA
- k_BA = pA * kex
- k_AB = pB * kex
-
- # Chi-squared initialisation.
- 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]
-
- # Back calculate the R2eff values.
- r2eff_ns_cpmg_2site_expanded(r20=R20[r20_index], pA=pA,
dw=dw_frq, k_AB=k_AB, k_BA=k_BA, relax_time=self.relax_times[0][mi],
inv_relax_time=self.inv_relax_times[0][mi], tcp=self.tau_cpmg[0][mi],
back_calc=self.back_calc[0][si][mi][0],
num_points=self.num_disp_points[0][si][mi][0], num_cpmg=self.power[0][mi])
-
- # 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][0]):
- if self.missing[0][si][mi][0][di]:
- self.back_calc[0][si][mi][0][di] =
self.values[0][si][mi][0][di]
-
- # Calculate and return the chi-squared value.
- chi2_sum += chi2(self.values[0][si][mi][0],
self.back_calc[0][si][mi][0], self.errors[0][si][mi][0])
-
- # Return the total chi-squared value.
- return chi2_sum
+ # 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_a, out=self.dw_struct )
+
+ # Reshape R20A and R20B 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 R2eff values.
+ r2eff_ns_cpmg_2site_expanded(r20=self.r20_struct, pA=pA,
dw=self.dw_struct, dw_orig=dw, kex=kex, relax_time=self.relax_times_a,
inv_relax_time=self.inv_relax_times_a, tcp=self.tau_cpmg_a,
back_calc=self.back_calc_a, num_cpmg=self.power_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]
+
+ ## Calculate the chi-squared statistic.
+ return chi2_rankN(self.values_a, self.back_calc_a, self.errors_a)
def func_ns_cpmg_2site_star(self, params):
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Fwd: r23942 - /branches/disp_spin_speed/target_functions/relax_disp.py