problem SAGE result reprocessing

[wraff]

When running two identifications using the files available (unitil 31 apr25) from
https://filesender.renater.fr/?s=download&token=f2292947-5146-4f99-8042-7112d44b0f85

we got the following from MS2rescore:

Reading PSMs from PSM file (1/2): 'D:/wraff/pxd001819_ups1/testSearchGUI4apr25/UPS1_125amol_R2_uncalibrated.sage.tsv'...
Reading PSMs from PSM file (2/2): 'D:/wraff/pxd001819_ups1/testSearchGUI4apr25/UPS1_125amol_R3_uncalibrated.sage.tsv'...
Removed 0 PSMs with rank >= 10.
Found 2444 PSMs, of which 24.06% are decoys.
Non-mapped modifications found: {'+15.994915', '+57.021465'}
This can be ignored if they are Unimod modification labels.
Found 649 identified PSMs with rank <= 1 at 0.01 FDR before rescoring.
Adding basic features to PSMs.
Adding MS²PIP-derived features to PSMs.
Running MS²PIP for PSMs from run (1/2) UPS1_125amol_R2_uncalibrated...
Downloading model_20210416_HCD2021_B.xgboost to C:\Users\wraff.ms2pip\model_20210416_HCD2021_B.xgboost...
Downloading model_20210416_HCD2021_Y.xgboost to C:\Users\wraff.ms2pip\model_20210416_HCD2021_Y.xgboost...
Processing spectra and peptides...
Running MS²PIP for PSMs from run (2/2) UPS1_125amol_R3_uncalibrated...
Processing spectra and peptides...
Adding DeepLC-derived features to PSMs.
Running DeepLC for PSMs from run (1/2): UPS1_125amol_R2_uncalibrated...
Running DeepLC for PSMs from run (2/2): UPS1_125amol_R3_uncalibrated...
Removed 0 PSMs with rank >= 1.
Failed in nopython mode pipeline (step: nopython frontend)
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File "mokapot\qvalues.py", line 176:�[0m
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Traceback (most recent call last):
File "ms2rescore\gui\function2ctk.py", line 301, in run
self.fn(*self.fn_args, **self.fn_kwargs)
File "ms2rescore\gui\app.py", line 809, in function
rescore(configuration=config)
File "ms2rescore\core.py", line 159, in rescore
psm_list = _calculate_confidence(psm_list)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "ms2rescore\core.py", line 307, in _calculate_confidence
new_confidence = lin_psm_data.assign_confidence(scores=psm_list["score"])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "mokapot\dataset.py", line 607, in assign_confidence
return LinearConfidence(
^^^^^^^^^^^^^^^^^
File "mokapot\confidence.py", line 375, in init
self._assign_confidence(desc=desc)
File "mokapot\confidence.py", line 455, in _assign_confidence
data["mokapot q-value"] = qvalues.tdc(scores, targets, desc=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "mokapot\qvalues.py", line 99, in tdc
qvals = _fdr2qvalue(fdr, num_total, unique_metric, indices)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "numba\core\dispatcher.py", line 423, in _compile_for_args
File "numba\core\dispatcher.py", line 364, in error_rewrite
numba.core.errors.TypingError: Failed in nopython mode pipeline (step: nopython frontend)
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File "mokapot\qvalues.py", line 176:�[0m
�[1mdef crosslink_tdc(scores, num_targets, desc=True):

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