Add helper methods for timeseries support #56
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This adds in helper functions to detect equilibration in timeseries data, as well as trimming away the timeseries data that is not equilibrated. Unit tests are included to verify behavior.
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@chrisjonesBSU can you take a look at this and see if we can leverage utilities like these in your PR? This should provide a defined method we can all use in the study to then sample data without much code duplication for different properties. |
justinGilmer
changed the title
project/src/analysis/equlibration.py
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| discarding more data. | ||
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| """ | ||
| if not is_equilibrated(a_t, threshold=threshold, nskip=nskip): |
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When calling the is_equilibrated function, if it's True, could it also return t0 and g? That way we don't have to run timeseries.detectEquilibration a second time in trim_non_equilibrated
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I haven’t tried that in python, but would that be an issue for the false case?
Or I guess we can just return None for those values. Does None for the non-existent t0 and g seem reasonable in the false case?
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yeah, None seems reasonable-- in this case we would use it something like this?
eq, t0, g = is_equilibrated
if eq:
do something
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Ok cool. I just pushed those changes!
Co-authored-by: Jenny <39961845+jennyfothergill@users.noreply.github.com>
This adds in helper functions to detect equilibration in timeseries
data, as well as trimming away the timeseries data that is not
equilibrated.
Unit tests are included to verify behavior.