Want to select a non rectangular area in xy graph( ellipse, oval)

I have a xy graph that is the combination of 1 histogram in the x plane and a different histogram in the y plane. I would like to be able to draw a shape around the resulting smear to be able to see how many events occured in this area of interest. I can get close with 2 cursors, but my application requires to be a tighter fit.
Thanks,
kubs23

Here's an idea:
Draw the ellipse on top of the graph using the plot image feature. Here are some references.
How to draw on top of an xy graphs: http://forums.ni.com/ni/board/message?board.id=170&message.id=202702#M202702
(Instead of cursor positions, use mouse-down and mouse-move events. Of course an ellipse has more degrees of freedom, so you need extra inputs to e.g. set the excentricity, or keep that fixed at e.g. 3:1.). Now just draw an ellipse between the mouse-down and mouse-move coordinates which has the major axis connecting the two points.
How to draw a rotated ellipse (see attached, modified from: http://forums.ni.com/ni/board/message?board.id=170&message.id=140604#M140604).
LabVIEW Champion . Do more with less code and in less time .
Attachments:
Ellipse3.vi ‏36 KB

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quote:
Originally posted by:
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SKEW                                     80 .013973812          75
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              9             28
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Execution Plan
Plan hash value: 4273422929
| Id | Operation         | Name | Rows | Bytes | Cost (%CPU)| Time     |
|   0 | SELECT STATEMENT |       |     1 |     3 |     1   (0)| 00:00:01 |
|   1 | SORT AGGREGATE   |       |     1 |     3 |            |          |
|* 2 |   INDEX RANGE SCAN| T1_I1 |    86 |   258 |     1   (0)| 00:00:01 |
Predicate Information (identified by operation id):
   2 - access("SKEW"=75)Skipped the Statistics information for keep example short.
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COUNT(*)
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Execution Plan
Plan hash value: 4273422929
| Id | Operation         | Name | Rows | Bytes | Cost (%CPU)| Time     |
|   0 | SELECT STATEMENT |       |     1 |     3 |     1   (0)| 00:00:01 |
|   1 | SORT AGGREGATE   |       |     1 |     3 |            |          |
|* 2 |   INDEX RANGE SCAN| T1_I1 |    29 |    87 |     1   (0)| 00:00:01 |
Predicate Information (identified by operation id):
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COUNT(*)
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Plan hash value: 4273422929
| Id | Operation         | Name | Rows | Bytes | Cost (%CPU)| Time     |
|   0 | SELECT STATEMENT |       |     1 |     3 |     1   (0)| 00:00:01 |
|   1 | SORT AGGREGATE   |       |     1 |     3 |            |          |
|* 2 |   INDEX RANGE SCAN| T1_I1 |    29 |    87 |     1   (0)| 00:00:01 |
Predicate Information (identified by operation id):
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So how the value is calculated for these values?

Your example seems to be based on Jonathan Lewis's article:
http://jonathanlewis.wordpress.com/2012/01/03/newdensity/
In this article, he does walk through the calculation of selectivity for non-popular values.
The calculation is not density but NewDensity, as seen in a 10053 trace, which takes into account the number of non-popular values AND the number of non-popular buckets
The article describes exactly how 29 is derived.Hi Dom,
Yes i used the same sample script of create the data sets. I should have checked Jonathan's blog for new density calculations. So selectivity works out as two either ways
1) selectivity(non popular) = Newdensity(took from 10053 traces) * rum_rows
or
2) non-popular rows/ non_popular values (where non-popular values can be derived from 10053 traces and non popular rows are (3240 * (74-31)/74 = ) 1883
Thanks for pointing to right blog

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