DM-27783: Add improvements to getInitialGoodPoints in ptc.py #67
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Change defauls for getInitialGoodPoints
| Minimum signal value (in ADU) after which to start examining | ||
| the ratios var/mean. | ||
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| minVarPivotSearch : `float` |
| if len(pivot) > 0: | ||
| # For small values, sometimes the variance decreases slightly | ||
| # Only look when var > self.config.minVarPivotSearch | ||
| pivot = [p for p in pivot if variances[p] > minVarPivotSearch] |
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Just confirming that applying the minimum in the variance space is correct. Cutting on the mean seems better to me, as this is a saturation effect, and we want to not cut for saturation before we think we're at that point. This also feels odd because we're looking for a turnover but only if the turnover is above that minimum value. I'm fine either way, so feel free to ignore this.
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"This also feels odd because we're looking for a turnover but only if the turnover is above that minimum value"---> Right, this is what this minVarPivotSearch cut is meant to help with.
In addition, at this point, the mean vector has been sorted, and the variance vector too using the indices of the mean vector. The code at this point assumes that the variance, after being sorted by the index of the sorted mean, increases monotonically.
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| ptcFitType : `str` | ||
| Fit a 'POLYNOMIAL' (degree: 'polynomialFitDegree') or | ||
| 'EXPAPPROXIMATION' (Eq. 16 of Astier+19) to the PTC. |
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Can this not happen with FULLCOVARIANCE?
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This helper function is used by fitPtc, which is used only by POLYNOMIAL or EXPAPPROXIMATION. It was written just to facilitate the reading of fitPtc.
tests/test_ptc.py
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| @@ -314,19 +314,19 @@ def test_meanVarMeasurementAllNan(self): | |||
| def test_getInitialGoodPoints(self): | |||
| xs = [1, 2, 3, 4, 5, 6] | |||
| ys = [2*x for x in xs] | |||
| points = self.defaultTask._getInitialGoodPoints(xs, ys, 0.1, 0.25) | |||
| points = self.defaultTask._getInitialGoodPoints(xs, ys, 0.1, 0.25, 0., 0.) | |||
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Can these explicitly set the control parameters? _getInitialGoodPoints(xs, ys, maxDeviationPositive=0.2, maxDeviationNegative=0.25, minMeanRatioTest=0.0, minVarPivotSearch=0.0)? This is easier to read and understand than a long list of floats.
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