Forum Discussion
Grid smoothing DAX
I am trying to figure out a way to come up with a way to take a 2D XY grid of table values and come up with smoothed values by using the neighboring cells. Perhaps this would use some sort of distance weighted algorithm. I would use this to look for anomolies in the data and replace them with the estimated value if it exceeds a threshold.
Thanks.
11 Replies
That should be rather simple, but you won't be able to tune it nicely - you can use a parameter value to twist the knob but it is not nearly as interactive as a slicer. Let's try both ways and compare the outcome.
In Power BI you can use the Play Axis to show multiple generations...
You can calculate the second generation table thusly:
Table2 = SELECTCOLUMNS('Table',"X",'Table'[X],"Y",'Table'[Y],"Generation",2) Value = var x = Table2[X] var y = Table2[Y] return CALCULATE(max('Table'[Value]),'Table'[X]=x,'Table'[Y]=y) -- plus the weighting of our choice of the neighboring cells)That looks like it is going to work. Thanks!
sounds like a fun exercise. Please provide sample data in usable format (not as a picture - maybe insert into a table?) and show the expected outcome.
Here is a sample table:
X Y Value
-5 5 0.82
-4 5 1.15
-3 5 1.44
-2 5 1.33
-1 5 1.46
0 5 1.51
1 5 1.48
2 5 1.67
3 5 1.62
4 5 0.82
5 5 0.59
-5 4 1.09
-4 4 1.28
-3 4 1.42
-2 4 1.4
-1 4 1.58
0 4 1.49
1 4 1.7
2 4 1.52
3 4 1.52
4 4 1.15
5 4 0.89
-5 3 1.3
-4 3 1.56
-3 3 1.46
-2 3 1.56
-1 3 1.4
0 3 1.36
1 3 1.48
2 3 1.33
3 3 1.51
4 3 1.1
5 3 0.92
-5 2 1.26
-4 2 1.72
-3 2 1.66
-2 2 1.4
-1 2 1.25
0 2 1.32
1 2 1.31
2 2 1.33
3 2 1.48
4 2 1.33
5 2 1.2
-5 1 0.91
-4 1 1.56
-3 1 1.72
-2 1 1.19
-1 1 1.02
0 1 1.07
1 1 1.21
2 1 1.19
3 1 1.48
4 1 1.79
5 1 1.52
-5 0 0.52
-4 0 0.24
-3 0 0.29
-2 0 0.37
-1 0 0.11
0 0 0
1 0 0.43
2 0 0.95
3 0 1.3
4 0 1.3
5 0 1.16Below is a dashbboard image. The tables were too big to add; I'll have to cut them down somehow. I was able to do a simple average of immediately adjacent cells but haven't figured out anything more complex. I would like to be able to do some sort of Inverse Distance Weighting or other interpolation.
In my example, I am replacing values outside and expected range with an average of the 4 or less adjacent cells.
Thanks.
Ever played the "Game of Life"? Of the eight adjacent cells do you assume they all have the same weight, or is the weight of the corner cells less than the edge cells?
This feels a little too harsh.
I think it should start at 1 in the middle and then use twice the distance
or even more agressive.
Perhaps I shouldn't have used the word smoothing but rather prediction based on surrounding cells. I am sure there are more and less aggressive ways of doing it. It would be nice to be able to change the aggressiveness with a filter.
The part I can't figure out is how do I use the surrounding cell values and distances to come up with an estimate of each cell.
That's the easy part ( and why I mentioned the Game Of Life). You maintain two separate surfaces. One for display and one for computation. Once you have computed a new generation you blip it onto the screen.
Of course in Power BI you can't do that, it has no memory. You will need to show the new generation screen (table) next to the old one, and feed it with measures.
I'll play with your sample data to see what I can come up with
