relax SVN archive

Speed-up of model DPL94.

task #7793: (https://gna.org/task/?7793) Speed-up of dispersion models.

Change in speed is:

test_dpl94_data_to_dpl94
19.412s -> 4.427s

Added missing imports of "numpy.array".

task #7793: (https://gna.org/task/?7793) Speed-up of dispersion models.

Speed-up of model IT99.

task #7793: (https://gna.org/task/?7793) Speed-up of dispersion models.

Change in speed is:
test_hansen_cpmg_data_auto_analysis
9.74s -> 8.330s

test_hansen_cpmg_data_to_it99
4.928s -> 3.138s

Speed-up of model LM63.

task #7793: (https://gna.org/task/?7793) Speed-up of dispersion models.

Change in systemtest was:
test_hansen_cpmg_data_auto_analysis
13.731s -> 9.971s

test_hansen_cpmg_data_auto_analysis_r2eff
13.370s -> 9.510s

test_hansen_cpmg_data_to_lm63
3.254s -> 2.080s

Speed-up for model M61.

task #7793: (https://gna.org/task/?7793) Speed-up of dispersion models.

Change in speed is:
test_m61_data_to_m61
6.692s -> 3.480s

Speed-up of model MMQ CR72.

task #7793: (https://gna.org/task/?7793) Speed-up of dispersion models.

Change in systemtest:
test_sprangers_data_to_mmq_cr72
9.892s -> 4.121s

Speed-up of model MP05.

task #7793: (https://gna.org/task/?7793) Speed-up of dispersion models.

The change in systemtest is:

test_tp02_data_to_mp05
10.750s -> 6.644s

Huge speed-up for model TAP03.

task #7793: (https://gna.org/task/?7793) Speed-up of dispersion models.

The change for running systemtest is:
test_tp02_data_to_tap03
13.869s -> 7.263s

This is won by not checking single values in the R1rho array for math domain
errors, but calculating all steps, and in one single round check for finite values.
If just one non-finite value is found, the whole array is returned with a large
penalty of 1e100.

This makes all calculations be the fastest numpy array way.

Speed-up of model TP02.

task #7793: (https://gna.org/task/?7793) Speed-up of dispersion models.

The change for running systemtest is:
test_curve_type_r1rho_fixed_time
0.057s -> 0.049s

test_tp02_data_to_ns_r1rho_2site
10.539s -> 10.456s

test_tp02_data_to_tp02
8.608s -> 5.727s

This is won by not checking single values in the R1rho array for math domain
errors, but calculating all steps, and in one single round check for finite values.
If just one non-finite value is found, the whole array is returned with a large
penalty of 1e100.

This makes all calculations be the fastest numpy array way.

Huge speed-up of model B14.

task #7793: (https://gna.org/task/?7793) Speed-up of dispersion models.

Time test for systemtests:

test_baldwin_synthetic
2.626s -> 1.990s

test_baldwin_synthetic_full
18.326s -> 13.742s

This is won by not checking single values in the R2eff array for math domain
errors, but calculating all steps, and in one single round check for finite values.
If just one non-finite value is found, the whole array is returned with a large
penalty of 1e100.

This makes all calculations be the fastest numpy array way.