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Hi, is there a method in Deeptime similar to Pyemma's " pyemma.coordinates.save_traj" which can be used to extract pdb structures representing macrostates after coarse-graining using PCCA+
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Hi, I was going through DeepTime documentation, Like in pyEmma I could source my data instead of loading and using the source object for TICA, but I couldn't use the same source object for TICA in D…
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For actual adaptive simulations, a flexible way to analyze the data is very important. Apart from choosing input parameters such as the lag time and msm states, it will be necessary to modify e.g. inp…
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So far this is has only happened in one of the three MSMs I'm building right now:
```
super(StreamingTransformer, self).estimate(X, **kwargs)
File "/cbio/jclab/home/hansons/opt/anaconda/lib/pyt…
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- Shall we copy the util.config module too and just change the path?
- Do we hardcode some defaults and try to use pyemma's config?
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I am trying to apply TICA to reduce dimensions to some data:
reader = pyemma.coordinates.source(files, top=pdb) # create reader
reader.featurizer.add_backbone_torsions(periodic=False) # add feat…
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`File "pipeline.py", line 72, in run_pipeline`
`cPickle.dump(tica, f)`
`cPickle.PicklingError: Can't pickle : attribute lookup pyemma._base.progress.reporter.dummy failed`
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Not quite sure how these are all related, but so far running `pipeline.py` on three different datasets gets three different errors (the first is in #12).
I did not get any of these problems running o…
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give user an estimate or runtime and disk space used by output files! --VInce
upodate: PYEMMA has a nice progress bar that maybe we can use