Changes between Initial Version and Version 1 of Ticket #835, comment 7
- Timestamp:
- Feb 8, 2017 12:37:33 PM (7 years ago)
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Ticket #835, comment 7
initial v1 1 Putting the correction in each model means that some models will forget to implement it, and sasview will fail inconsistently over a very long period of time (the problem only shows up when the resolution is unusually large at low q **AND** the user happens to try the failing model on that data set **AND** that the particular model parameters happen to give !NaN results in that case. Even worse, some models will silently seem to work even though they give bad values for negative q. The user never sees the calculated I(q) before smearing (even if we add the option to show it, they won't necessarily know that they should be looking at it for their particular data set).1 Putting the correction in each model means that some models will forget to implement it, and sasview will fail inconsistently over a very long period of time (the problem only shows up when the resolution is unusually large at low q **AND** the user happens to try the failing model on that data set **AND** that the particular model parameters happen to give !NaN results in that case. Even worse, some models will silently seem to work even though they give bad values for negative q. The user never sees the calculated I(q) before smearing, and even if we add the option to show it they won't necessarily know that they should be looking at it for their particular data set. 2 2 3 Resolution is the best place to put the correct since it already incorporates intimate details of the geometry. Compare Pinhole1D vs. Slit1D for example. Both of them make assumptions about how the data is being measured when determining how to weight the q values. They can also assume that q values on the opposite side of the beam center have the same circular average as the positive q values, and thus return abs(q).3 Resolution is the best place to put the correction since it already incorporates intimate details of the geometry. Compare Pinhole1D vs. Slit1D for example. Both of them make assumptions about how the data is being measured when determining how to weight the q values. They can also assume that q values on the opposite side of the beam center have the same circular average as the positive q values, and thus return abs(q). 4 4 5 5 We should not put the correction in the 1D modelling wrapper---somebody may be using sasview to fit data which is not SANS, and which has true y for negative x. In this hypothetical case we would need to use a different resolution function appropriate for the data, which for gaussian resolution happens to look like Pinhole1D without the absolute q correction.
