Author: tlinnet
Date: Fri Aug 22 14:48:19 2014
New Revision: 25216
URL: http://svn.gna.org/viewcvs/relax?rev=25216&view=rev
Log:
Modified target function of relax disp, to use the the new list of time
points, which are of higher dimension.
bug #22461(https://gna.org/bugs/?22461): NS R1rho 2-site_fit_r1 has extremely
high chi2 value in systemtest Relax_disp.test_r1rho_kjaergaard_missing_r1.
Modified:
trunk/target_functions/relax_disp.py
Modified: trunk/target_functions/relax_disp.py
URL:
http://svn.gna.org/viewcvs/relax/trunk/target_functions/relax_disp.py?rev=25216&r1=25215&r2=25216&view=diff
==============================================================================
--- trunk/target_functions/relax_disp.py (original)
+++ trunk/target_functions/relax_disp.py Fri Aug 22 14:48:19 2014
@@ -26,7 +26,7 @@
# Python module imports.
from copy import deepcopy
-from numpy import arctan2, cos, dot, float64, int16, multiply, ones,
rollaxis, pi, sin, version, zeros
+from numpy import all, arctan2, cos, dot, float64, int16, isfinite, max,
multiply, ones, rollaxis, pi, sin, sum, version, zeros
from numpy.ma import masked_equal
# relax module imports.
@@ -156,8 +156,8 @@
@type tilt_angles: rank-5 list of floats
@keyword r1: The R1 relaxation rates. This is only
used for off-resonance R1rho models. The dimensions are {Si, Mi}.
@type r1: rank-2 list of floats
- @keyword relax_times: The experiment specific fixed time
period for relaxation (in seconds). The dimensions are {Ei, Mi}.
- @type relax_times: rank-2 list of floats
+ @keyword relax_times: The experiment specific fixed time
period for relaxation (in seconds). The dimensions are {Ei, Mi, Oi, Di, Ti}.
+ @type relax_times: rank-4 list of floats
@keyword scaling_matrix: The square and diagonal scaling matrix.
@type scaling_matrix: numpy rank-2 float array
@keyword recalc_tau: A flag which if True will cause tau_CPMG
to be recalculated to remove user input truncation.
@@ -270,7 +270,8 @@
self.frqs_squared = deepcopy(numpy_array_zeros)
self.frqs_H = deepcopy(numpy_array_zeros)
self.relax_times = deepcopy(numpy_array_zeros)
- self.inv_relax_times = deepcopy(numpy_array_zeros)
+ if model in MODEL_LIST_INV_RELAX_TIMES:
+ self.inv_relax_times = deepcopy(numpy_array_zeros)
self.tau_cpmg = deepcopy(numpy_array_zeros)
self.power = deepcopy(numpy_array_zeros)
self.r1 = deepcopy(numpy_array_zeros)
@@ -297,10 +298,6 @@
frq_H = frqs_H[ei][si][mi]
self.frqs_H[ei, si, mi, :] = frq_H
- # Fill the relaxation time.
- relax_time = relax_times[ei, mi]
- self.relax_times[ei, si, mi, :] = relax_time
-
# Fill r1.
if r1 != None:
r1_l = r1[si][mi]
@@ -310,10 +307,6 @@
if chemical_shifts != None:
chemical_shift = chemical_shifts[ei][si][mi]
self.chemical_shifts[ei, si, mi, :] = chemical_shift
-
- # The inverted relaxation delay.
- if model in MODEL_LIST_INV_RELAX_TIMES:
- self.inv_relax_times[ei, si, mi, :] = 1.0 /
relax_time
# The number of offset data points.
if len(offset[ei][si][mi]):
@@ -377,6 +370,26 @@
if spin_lock_nu1 != None and
len(spin_lock_nu1[ei][mi][oi]):
self.spin_lock_omega1[ei, si, mi, oi, di] =
2.0 * pi * spin_lock_nu1[ei][mi][oi][di]
self.spin_lock_omega1_squared[ei, si, mi,
oi, di] = self.spin_lock_omega1[ei, si, mi, oi, di] ** 2
+
+ # The relax times
+ # Fill the relaxation time.
+ if relax_times != None and
len(relax_times[ei][mi][oi]):
+ relax_time = max(relax_times[ei][mi][oi][di])
+ self.relax_times[ei, si, mi, oi, di] =
relax_time
+
+ # The inverted relaxation times.
+ if model in MODEL_LIST_INV_RELAX_TIMES:
+ self.inv_relax_times[ei, si, mi, oi, di]
= 1.0 / relax_time
+
+ # Sanity checks.
+ if model in MODEL_LIST_INV_RELAX_TIMES:
+ # Check if values have been inserted.
+ if sum(self.inv_relax_times) == 0.0:
+ raise RelaxError("The inverted relaxation time data array
all contains 0.0. Please check your setup.")
+
+ # Check if array contains 'nan'='Not a Number', positive
infinity or negative infinity values, which could stem from 1/0.0 division.
+ if not all(isfinite(self.inv_relax_times)):
+ raise RelaxError("The inverted relaxation time data array
contains not finite values. Please check your setup.")
# Create the structure for holding the back-calculated R2eff values
(matching the dimensions of the values structure).
self.back_calc = deepcopy(self.values)
r25216 - /trunk/target_functions/relax_disp.py