first version of new continuous_create_residuals #991
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We have a set of test for the Xmipp programs: . |
Ok |
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@jamesmkrieger we have to update to nolan de golds |
How do I get the input files to do this? |
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We need to upload the tmpLink/xmipp_continuous_create_residuals_01/output.xmd |
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How do I get input/aFewProjections.sel and input/phantomBacteriorhodopsin.vol to generate output.xmd? |
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ok, I think I found them |
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I have sent you an email to your cnb address with the 3 files and also a query about the command failing to run in one directory. |
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Are the test ready? can I upload the files:
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I think everything is ready, yes. We still need to regenerate the test outputs with the revised program though. |
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I have added the test outputs to this pull request. I don't know if that is a good idea or will work but I guess you can take them from here |
Is not necessary to upload the data test in the repository. I have uploaded it to nolan, the server where the test data is located. |
src/xmipp/tests/test_programs.py
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| def test_case3(self): | ||
| "Test output xmd and projections only" | ||
| self.runCase("-i input/aFewProjections.sel -o %o/output.xmd --ref input/phantomBacteriorhodopsin.vol--oprojections %o/projections.stk", |
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--ref input/phantomBacteriorhodopsin.vol--oprojections
needs a blank space
--ref input/phantomBacteriorhodopsin.vol --oprojections
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This is based on angular_continuous_assign2, but has 2 changes:
angular_continuous_assign2 already applies continuous A and B to the projection mP, not mIfilterp like I thought, so no change there, except for using mPp instead.
I can see with simulated data that the lack of low-pass filter definitely has a good effect in reducing positive log likelihoods.