Author: tlinnet
Date: Mon Mar 3 12:47:14 2014
New Revision: 22391
URL: http://svn.gna.org/viewcvs/relax?rev=22391&view=rev
Log:
Added ":" to dictionary keys to match return from spin_loop in systemtest
Relax_disp.test_r1rho_kjaergaard.
Regarding bug #21344, (https://gna.org/bugs/index.php?21344) - Handling of in
sparse acquired R1rho dataset with missing combinations of time and spin-lock
field strengths.
Modified:
trunk/test_suite/system_tests/relax_disp.py
Modified: trunk/test_suite/system_tests/relax_disp.py
URL:
http://svn.gna.org/viewcvs/relax/trunk/test_suite/system_tests/relax_disp.py?rev=22391&r1=22390&r2=22391&view=diff
==============================================================================
--- trunk/test_suite/system_tests/relax_disp.py (original)
+++ trunk/test_suite/system_tests/relax_disp.py Mon Mar 3 12:47:14 2014
@@ -2712,38 +2712,38 @@
# Paper reference values
# Resi Resn R1 R1err R2
R2err kEX kEXerr phi phierr
ref = dict()
- ref['13@N'] = [13, 'L13N-HN', 1.32394, 0.14687, 8.16007, 1.01237,
13193.82986, 2307.09152, 58703.06446, 22413.09854]
- ref['15@N'] = [15, 'R15N-HN', 1.34428, 0.14056, 7.83256, 0.67559,
13193.82986, 2307.09152, 28688.33492, 13480.72253]
- ref['16@N'] = [16, 'T16N-HN', 1.71514, 0.13651, 17.44216, 0.98583,
13193.82986, 2307.09152, 57356.77617, 21892.44205]
- ref['25@N'] = [25, 'Q25N-HN', 1.82412, 0.15809, 9.09447, 2.09215,
13193.82986, 2307.09152, 143111.13431, 49535.80302]
- ref['26@N'] = [26, 'Q26N-HN', 1.45746, 0.14127, 10.22801, 0.67116,
13193.82986, 2307.09152, 28187.06876, 13359.01615]
- ref['28@N'] = [28, 'Q28N-HN', 1.48095, 0.14231, 10.33552, 0.691,
13193.82986, 2307.09152, 30088.0686, 13920.25654]
- ref['39@N'] = [39, 'L39N-HN', 1.46094, 0.14514, 8.02194, 0.84649,
13193.82986, 2307.09152, 44130.18538, 18104.55064]
- ref['40@N'] = [40, 'M40N-HN', 1.21381, 0.14035, 12.19112, 0.81418,
13193.82986, 2307.09152, 41834.90493, 17319.92156]
- ref['41@N'] = [41, 'A41N-HN', 1.29296, 0.14286, 9.29941, 0.66246,
13193.82986, 2307.09152, 26694.8921, 13080.66782]
- ref['43@N'] = [43, 'F43N-HN', 1.33626, 0.14352, 12.73816, 1.17386,
13193.82986, 2307.09152, 70347.63797, 26648.30524]
- ref['44@N'] = [44, 'I44N-HN', 1.28487, 0.1462, 12.70158, 1.52079,
13193.82986, 2307.09152, 95616.20461, 35307.79817]
- ref['45@N'] = [45, 'K45N-HN', 1.59227, 0.14591, 9.54457, 0.95596,
13193.82986, 2307.09152, 53849.7826, 21009.89973]
- ref['49@N'] = [49, 'A49N-HN', 1.38521, 0.14148, 4.44842, 0.88647,
13193.82986, 2307.09152, 40686.65286, 18501.20774]
- ref['52@N'] = [52, 'V52N-HN', 1.57531, 0.15042, 6.51945, 1.43418,
13193.82986, 2307.09152, 93499.92172, 33233.23039]
- ref['53@N'] = [53, 'A53N-HN', 1.27214, 0.13823, 4.0705, 0.85485,
13193.82986, 2307.09152, 34856.18636, 17505.02393]
+ ref[':13@N'] = [13, 'L13N-HN', 1.32394, 0.14687, 8.16007, 1.01237,
13193.82986, 2307.09152, 58703.06446, 22413.09854]
+ ref[':15@N'] = [15, 'R15N-HN', 1.34428, 0.14056, 7.83256, 0.67559,
13193.82986, 2307.09152, 28688.33492, 13480.72253]
+ ref[':16@N'] = [16, 'T16N-HN', 1.71514, 0.13651, 17.44216, 0.98583,
13193.82986, 2307.09152, 57356.77617, 21892.44205]
+ ref[':25@N'] = [25, 'Q25N-HN', 1.82412, 0.15809, 9.09447, 2.09215,
13193.82986, 2307.09152, 143111.13431, 49535.80302]
+ ref[':26@N'] = [26, 'Q26N-HN', 1.45746, 0.14127, 10.22801, 0.67116,
13193.82986, 2307.09152, 28187.06876, 13359.01615]
+ ref[':28@N'] = [28, 'Q28N-HN', 1.48095, 0.14231, 10.33552, 0.691,
13193.82986, 2307.09152, 30088.0686, 13920.25654]
+ ref[':39@N'] = [39, 'L39N-HN', 1.46094, 0.14514, 8.02194, 0.84649,
13193.82986, 2307.09152, 44130.18538, 18104.55064]
+ ref[':40@N'] = [40, 'M40N-HN', 1.21381, 0.14035, 12.19112, 0.81418,
13193.82986, 2307.09152, 41834.90493, 17319.92156]
+ ref[':41@N'] = [41, 'A41N-HN', 1.29296, 0.14286, 9.29941, 0.66246,
13193.82986, 2307.09152, 26694.8921, 13080.66782]
+ ref[':43@N'] = [43, 'F43N-HN', 1.33626, 0.14352, 12.73816, 1.17386,
13193.82986, 2307.09152, 70347.63797, 26648.30524]
+ ref[':44@N'] = [44, 'I44N-HN', 1.28487, 0.1462, 12.70158, 1.52079,
13193.82986, 2307.09152, 95616.20461, 35307.79817]
+ ref[':45@N'] = [45, 'K45N-HN', 1.59227, 0.14591, 9.54457, 0.95596,
13193.82986, 2307.09152, 53849.7826, 21009.89973]
+ ref[':49@N'] = [49, 'A49N-HN', 1.38521, 0.14148, 4.44842, 0.88647,
13193.82986, 2307.09152, 40686.65286, 18501.20774]
+ ref[':52@N'] = [52, 'V52N-HN', 1.57531, 0.15042, 6.51945, 1.43418,
13193.82986, 2307.09152, 93499.92172, 33233.23039]
+ ref[':53@N'] = [53, 'A53N-HN', 1.27214, 0.13823, 4.0705, 0.85485,
13193.82986, 2307.09152, 34856.18636, 17505.02393]
guess = dict()
- guess['13@N'] = [13, 'L13N-HN', 1.0, 0.1, 8.00, 1.0, 10000.0,
2000.0, 50000.00, 20000.0]
- guess['15@N'] = [15, 'R15N-HN', 1.0, 0.1, 8.00, 0.6, 10000.0,
2000.0, 20000.00, 10000.0]
- guess['16@N'] = [16, 'T16N-HN', 1.0, 0.1, 17.0, 0.9, 10000.0,
2000.0, 50000.00, 20000.0]
- guess['25@N'] = [25, 'Q25N-HN', 1.0, 0.1, 9.00, 2.0, 10000.0,
2000.0, 140000.0, 40000.0]
- guess['26@N'] = [26, 'Q26N-HN', 1.0, 0.1, 10.0, 0.6, 10000.0,
2000.0, 20000.00, 10000.0]
- guess['28@N'] = [28, 'Q28N-HN', 1.0, 0.1, 10.0, 0.6, 10000.0,
2000.0, 30000.00, 10000.0]
- guess['39@N'] = [39, 'L39N-HN', 1.0, 0.1, 8.00, 0.8, 10000.0,
2000.0, 40000.00, 10000.0]
- guess['40@N'] = [40, 'M40N-HN', 1.0, 0.1, 12.0, 0.8, 10000.0,
2000.0, 40000.00, 10000.0]
- guess['41@N'] = [41, 'A41N-HN', 1.0, 0.1, 9.00, 0.6, 10000.0,
2000.0, 20000.00, 10000.0]
- guess['43@N'] = [43, 'F43N-HN', 1.0, 0.1, 12.0, 1.1, 10000.0,
2000.0, 70000.00, 20000.0]
- guess['44@N'] = [44, 'I44N-HN', 1.0, 0.1, 12.0, 1.5, 10000.0,
2000.0, 90000.00, 30000.0]
- guess['45@N'] = [45, 'K45N-HN', 1.0, 0.1, 9.00, 0.9, 10000.0,
2000.0, 50000.00, 20000.0]
- guess['49@N'] = [49, 'A49N-HN', 1.0, 0.1, 4.00, 0.8, 10000.0,
2000.0, 40000.00, 10000.0]
- guess['52@N'] = [52, 'V52N-HN', 1.0, 0.1, 6.00, 1.4, 10000.0,
2000.0, 90000.00, 30000.0]
- guess['53@N'] = [53, 'A53N-HN', 1.0, 0.1, 4.00, 0.8, 10000.0,
2000.0, 30000.00, 10000.0]
+ guess[':13@N'] = [13, 'L13N-HN', 1.0, 0.1, 8.00, 1.0, 10000.0,
2000.0, 50000.00, 20000.0]
+ guess[':15@N'] = [15, 'R15N-HN', 1.0, 0.1, 8.00, 0.6, 10000.0,
2000.0, 20000.00, 10000.0]
+ guess[':16@N'] = [16, 'T16N-HN', 1.0, 0.1, 17.0, 0.9, 10000.0,
2000.0, 50000.00, 20000.0]
+ guess[':25@N'] = [25, 'Q25N-HN', 1.0, 0.1, 9.00, 2.0, 10000.0,
2000.0, 140000.0, 40000.0]
+ guess[':26@N'] = [26, 'Q26N-HN', 1.0, 0.1, 10.0, 0.6, 10000.0,
2000.0, 20000.00, 10000.0]
+ guess[':28@N'] = [28, 'Q28N-HN', 1.0, 0.1, 10.0, 0.6, 10000.0,
2000.0, 30000.00, 10000.0]
+ guess[':39@N'] = [39, 'L39N-HN', 1.0, 0.1, 8.00, 0.8, 10000.0,
2000.0, 40000.00, 10000.0]
+ guess[':40@N'] = [40, 'M40N-HN', 1.0, 0.1, 12.0, 0.8, 10000.0,
2000.0, 40000.00, 10000.0]
+ guess[':41@N'] = [41, 'A41N-HN', 1.0, 0.1, 9.00, 0.6, 10000.0,
2000.0, 20000.00, 10000.0]
+ guess[':43@N'] = [43, 'F43N-HN', 1.0, 0.1, 12.0, 1.1, 10000.0,
2000.0, 70000.00, 20000.0]
+ guess[':44@N'] = [44, 'I44N-HN', 1.0, 0.1, 12.0, 1.5, 10000.0,
2000.0, 90000.00, 30000.0]
+ guess[':45@N'] = [45, 'K45N-HN', 1.0, 0.1, 9.00, 0.9, 10000.0,
2000.0, 50000.00, 20000.0]
+ guess[':49@N'] = [49, 'A49N-HN', 1.0, 0.1, 4.00, 0.8, 10000.0,
2000.0, 40000.00, 10000.0]
+ guess[':52@N'] = [52, 'V52N-HN', 1.0, 0.1, 6.00, 1.4, 10000.0,
2000.0, 90000.00, 30000.0]
+ guess[':53@N'] = [53, 'A53N-HN', 1.0, 0.1, 4.00, 0.8, 10000.0,
2000.0, 30000.00, 10000.0]
# The dispersion models.
MODELS = ['R2eff', 'No Rex', 'DPL94']
r22391 - /trunk/test_suite/system_tests/relax_disp.py