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Commit Date  
[r26104] by bugman

Created the Structure.test_align system test.

This will be used to test the yet to be implemented structure.align user function. This user
function will be similar to the structure.superimpose user function but will be designed so that
structures with different primary and atomic sequences can be superimposed.

2014-10-01 17:44:08 Tree
[r26103] by bugman

Implemented the backend of the structure.mean user function.

This primarily occurs within the internal structural object in the new mean() method. The
pipe_control.structure.main.mean() function simply checks if the current data pipe is correctly set
up and then calls the structural object mean() method.

2014-09-30 16:08:57 Tree
[r26102] by bugman

Some fixes and additional checks for the Structure.test_mean system test.

2014-09-30 16:07:02 Tree
[r26101] by bugman

Import fix for r26099.

2014-09-30 15:32:11 Tree
[r26100] by bugman

Converted all of the pipe_control.strucuture.main functions to use the check_structure() object.

This standardises and improves all of the checks.

2014-09-30 15:31:30 Tree
[r26099] by bugman

Created the pipe_control.structure.main.check_structure() checking object.

This will be used for providing much more detailed feedback for when structural information is
missing.

2014-09-30 15:26:24 Tree
[r26098] by bugman

Fixes and simplifications for the pipe_control.pipes.check_pipe() checking object.

One of the RelaxError classes were not initialised and the docstring was incorrect.

2014-09-30 14:56:28 Tree
[r26097] by bugman

Implemented the structure.mean user function frontend.

The backend is currently just a stub function.

2014-09-30 13:50:08 Tree
[r26096] by bugman

Created the Structure.test_mean system test.

This is to test the functionality of a planned new feature, the structure.mean user function. This
is an analysis aid that will calculate the mean structure from all loaded models.

2014-09-30 13:32:36 Tree
[r26095] by tlinnet

Implemented counting of outliers for statistics.

This is to get a better feeling why some statistics are very much different between NI.

Task #7826 (https://gna.org/task/index.php?7826): Write an python class for the repeated analysis of dispersion data.

2014-09-30 08:41:07 Tree
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