patrick-kidger / patrick-kidger/diffrax
Treating Channels that only have missing values in interpolations
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Description
While using "diffrax.backward_hermite_coefficients", I noticed that the interpolation technique doesn't take into consideration the case when all the entries are missing (i.e. NaN), even if "replace_nans_at_start" is specified.
I beleive that the logical thing to have is just a constant value in these channels (equal to the one specified by replace_nans_at_start), but the interpolation technique is returning NaNs as coefficients.
As a reproducable example, I used a function that has been used in the neural_cde example (get_data), and I updated the last couple of lines to stack channels with only NaN values.
The result of the print statement when I run get_data(1000, False, key=jrandom.PRNGKey(0)) is as follows:
Thank you in advance!
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Research direction
Start at diffrax.backward_hermite_coefficients and reproduce the issue through get_data(1000, False, key=jrandom.PRNGKey(0)), including channels containing only NaNs. Done means channels with all missing values produce constant interpolation coefficients equal to replace_nans_at_start rather than NaNs.
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Assessment
- Tech stack
- python
- Domain
- backend
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100