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DM-8982: Incorrect binning in overscan spline interpolation #25

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Jan 13, 2017
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DM-8982: Incorrect binning in overscan spline interpolation #25

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f72963f
overscan: fix binning of spline ordinate
PaulPrice Jan 12, 2017
4e287d2
overscan: plot bad data as well as good
PaulPrice Jan 12, 2017
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6 changes: 5 additions & 1 deletion python/lsst/ip/isr/isr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -345,6 +345,8 @@ def overscanCorrection(ampMaskedImage, overscanImage, fitType='MEDIAN', order=1,
diff = numpy.abs(biasArray - medianBiasArr[:, numpy.newaxis])
biasMaskedArr = numpy.ma.masked_where(diff > collapseRej*stdevBiasArr[:, numpy.newaxis], biasArray)
collapsed = numpy.mean(biasMaskedArr, axis=1)
if collapsed.mask.sum() > 0:
collapsed.data[collapsed.mask] = numpy.mean(biasArray.data[collapsed.mask], axis=1)
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I realize you are adding the original values back into the masked pixels in order to plot them below, but might this be a gotcha for anyone later using collapsed.data and expecting the non-modified values? I'm not familiar with the object here, so am not sure what the masked pixels would've been assigned in collapsed.data, so forgive my naïveté if this is a non-issue!

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Without this change, the masked pixels have a value of zero and are therefore useless.

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That's sort of my point, i.e. someone expecting a value of zero in collapsed.data for masked pixels (I don't imagine a use case here...but you never know!)

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I don't know why someone would want a zero value for masked pixels, except for my particular use of this below! I use collapsed.data*~collapsedMask, which provides exactly that (collapsedMask is True for bad pixels). So the possibility is still there just in case.

del biasArray, percentiles, stdevBiasArr, diff, biasMaskedArr

if shortInd == 0:
Expand Down Expand Up @@ -398,7 +400,9 @@ def overscanCorrection(ampMaskedImage, overscanImage, fitType='MEDIAN', order=1,
figure = plot.figure(1)
figure.clear()
axes = figure.add_axes((0.1, 0.1, 0.8, 0.8))
axes.plot(indices, collapsed, 'k+')
axes.plot(indices[~collapsedMask], collapsed[~collapsedMask], 'k+')
if collapsedMask.sum() > 0:
axes.plot(indices[collapsedMask], collapsed.data[collapsedMask], 'b+')
axes.plot(indices, fitBiasArr, 'r-')
figure.show()
prompt = "Press Enter or c to continue [chp]... "
Expand Down