Subject age timedelta

[bendichter]

Motivation

fix #1554

How to test the behavior?

subject = Subject(subject_id='RAT123', age=timedelta(seconds=99999))

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[codecov]

Codecov Report

Merging #1590 (cd0e3e9) into dev (d435aa6) will increase coverage by 0.00%.
The diff coverage is 100.00%.

@@           Coverage Diff           @@
##              dev    #1590   +/-   ##
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  Coverage   91.30%   91.31%           
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  Files          25       25           
  Lines        2508     2510    +2     
  Branches      471      472    +1     
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+ Hits         2290     2292    +2     
  Misses        138      138           
  Partials       80       80           

Flag	Coverage Δ
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unit	84.42% <100.00%> (+0.01%)	⬆️

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Impacted Files	Coverage Δ
src/pynwb/file.py	88.00% <100.00%> (+0.06%)	⬆️

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[rly]

I hesitate to homebrew ship our own solution for a timedelta -> ISO 8601 duration conversion, but this seems to be the best available and doesn't require adding a dependency.

[CodyCBakerPhD]

I hesitate to homebrew ship our own solution for a timedelta -> ISO 8601 duration conversion, but this seems to be the best available and doesn't require adding a dependency.

PyNWB requires pandas: https://github.com/NeurodataWithoutBorders/pynwb/blob/dev/setup.py#L25

pandas has a Timedelta to ISO functioanlity we would want:

from pandas import Timedelta

time_delta = Timedelta(days=2,weeks=1, minutes=50, seconds=12)
iso_string = time_delta.isoformat()

so all you would have to do to accommodate both that class type and datetime.timedelta is map a datetime.timedelta onto the pandas.Timedelta and it's out of our hands and safely the responsibility of upstream dependencies.

[CodyCBakerPhD]

And actually, as far as I can tell, pandas.Timedelta can take datetime.timedelta objects as the input.

import datetime
import pandas

time_delta = pandas.Timedelta(datetime.timedelta(days=2,weeks=1, minutes=50, seconds=12))
iso_string = time_delta.isoformat()

[bendichter]

I think this is a pretty simple function and I'm pretty confident it's going to work but I'd also be fine with using pandas

[rly]

Looks good to me. Thanks!