DM-44025: Update ptc turnoff computation code and improve tests with real data. #240
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| dtype=float, | ||
| doc="If there are any outliers in the initial fit above " | ||
| "maxSignalInitialPtcOutlierFit, then no points that have this delta " | ||
| "from the previous ``good`` point are allowed. If " |
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Can you clarify that this delta is in terms of the rawMeans vector (or finalMeans). The units being to first order implies this (so that it's not based on the variance).
| @@ -1236,8 +1276,10 @@ def errFunc(p, x, y): | |||
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| count += 1 | |||
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Do we not warn if we've reached the iteration threshold and are still finding outliers? I'm guessing we converge very quickly, so it's possible 2 is always sufficient, but I wanted to check if this was an oversight.
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We never have, but I can add a warning.
tests/test_ptc.py
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| solvedDataset = solveTask.fitMeasurementsToModel(dataset) | ||
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| # Check that the ptcTurnoff is what is expected. | ||
| self.assertFloatsAlmostEqual(solvedDataset.ptcTurnoff[ampName], ptcTurnoff) |
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Can you add msg arguments to all of these asserts with the dense and mode parameters? That should help identify problems when these tests fail in the future.
| if dense and mode == "normal": | ||
| # Taken from dense run 13591, detector 94, amplifier C02 | ||
| ptcTurnoff = 92239.4794 | ||
| rawMeans = np.array([ |
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I have a slight preference for storing these as external PTC dataset files in tests/data, but it's probably not worth the effort on this ticket.
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Since we didn't have a test data directory I was too lazy to make one ...
This also adds a computation of the "sampling error" on the ptc turnoff.