FIX: Edge case where a resampled column was too-long-by-one #365
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…blow for larger deviations
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@yarikoptic also says (but he adds that him and I need to actually check whether this happens) that this in principle is not complete, and that there must be no resampling across runs when there are multiple. |
bids/variables/variables.py
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| num = len(self.index) | ||
| # _build_entity_index computation above does it per run, | ||
| # which possibly provides a more stable solution and yadadada | ||
| num_computed = int(np.ceil(n * sampling_rate / old_sr)) |
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Do we want to change this to np.round() a la #361 (comment)? And do we want to include f936aed as well?
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I would say that with any resampling it should be ceil (to not loose any point) and then analysis of the actual target duration based on the actual target temporal duration which might require trimming. But here in particular, given the comment below I think this all computation should be gone altogether (and thus warnings, and assertions)
bids/variables/variables.py
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| assert num_diff == 1 # the only way to get here | ||
| import warnings | ||
| warnings.warn( | ||
| "Probably due to multiple runs we ran into a slight " |
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This appears with a single run when TR=2.000001. I don't think this is a great error message. It's not clear that it's a warning that will do anything but cause confusion.
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we added it because there were no other way to inform user (e.g. logging) and we see how silence could be problematic to read down the road ;) again, with aforementioned suggested fix by just relying on index length this would be gone altogether
bids/variables/variables.py
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| "Probably due to multiple runs we ran into a slight " | ||
| "divergence between obtained number of samples in the index (computed " | ||
| "per each run) and overall estimate down/up-sampled samples. " | ||
| "But that is ok") |
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we looked at the code more, and our verdict is that we just need to get rid of num_computed here altogether and use the length of the index, estimated (by going through runs and using their actual duration which is not a subject of up/down sampling effects of "ints").
The reason is -- the n is not "reliable". It is already a result of applying ceil operation in to_dense function (called by the test first to upsample by 10). so there is really no reliable way to get back the original number of points.
But also what is important probably is that interp1d below should also be applied per run, similarly to how to_dense is doing it I guess, to not leak values across the run boundaries.... but that is a separate issue
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@effigies a The main reason for doing this is that we eventually want to be able to fit arbitrary mixed-effects structures without having to spend a lot of time restructuring the variables in complicated ways. For example, this approach makes it trivial to both do a 2-level-style analysis where you concatenate each subject's runs and add nuisance regressors for all runs to the same model (instead of fitting each run separately, and then aggregating the estimate within-subject), and to fit the full dataset in one shot. There are also some potential efficiency and interface benefits (e.g., nearly all transformers take an undocumented |
Codecov Report
@@ Coverage Diff @@
## master #365 +/- ##
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+ Coverage 73.27% 73.36% +0.09%
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Files 24 24
Lines 2604 2613 +9
Branches 640 642 +2
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+ Hits 1908 1917 +9
+ Misses 513 512 -1
- Partials 183 184 +1
Continue to review full report at Codecov.
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Codecov Report
@@ Coverage Diff @@
## master #365 +/- ##
=========================================
+ Coverage 73.27% 73.77% +0.5%
=========================================
Files 24 23 -1
Lines 2604 2494 -110
Branches 640 623 -17
=========================================
- Hits 1908 1840 -68
+ Misses 513 471 -42
Partials 183 183
Continue to review full report at Codecov.
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effigies
changed the title
| @@ -448,12 +448,13 @@ def resample(self, sampling_rate, inplace=False, kind='linear'): | |||
| self.index = self._build_entity_index(self.run_info, sampling_rate) | |||
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| x = np.arange(n) | |||
| num = int(np.ceil(n * sampling_rate / old_sr)) | |||
| num = len(self.index) | |||
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I worry that this will hide other potential edge cases where there's a bigger mismatch... I suggest checking for a discrepancy and issuing a warning either if any is found, or if a large one is found (like you did in one of the other PRs).
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I think that was an earlier version of this PR. I'm curious what kind of edge case you're talking about?
As I understand it, we have a run duration, which is the original TR * nvols.
For a given sampling rate, we find the nearest n such that n / sampling_rate == duration. So this line was a way of taking from duration ~= n_old / sr_old ~= n_new / sr_new, and calculating n_new from n_old, sr_old, and sr_new. Index instead calculates directly from duration and sr_new. So the discrepancy we were finding is when you get an off-by-one between the two ways of calculating n_new.
So it's not really clear where another discrepancy can arise.
Fixes the dimension mismatch after resampling described in #358 and #361.
@yarikoptic and I suspect that the problem lies within the recomputation of num with
num = int(np.ceil(n * sampling_rate / old_sr))-- if done per each run with up/downsampling adapted to the runs data it might explain why it results in failures for some runs of mine, but not for others.Its not an ideal solution, but for now we reassign num to the length of self.Index instead of recomputing and ignore it when there is a slight difference in length (of 1), and if there are larger deviations in length (>1) we blow up.
I put the commit on top of @effigies test in 8cfba06 (which passes with the change).