dynamically changing raddiagram based on changes done on a radgrid
hi eric, - - can you elaborate how you are getting a reference to the diagram? are you doing it in the way shown in the demo, in the diagram\_load() handler? the code below can be found in the scripts.js tab of this demo: - - var jsontextbox, diagram; -   -   function diagram\_load(sender) { -       diagram = sender.get\_kendowidget(); -   -       diagram.layout({ -           type: tree, -           subtype: left, -           roots: [diagram.getshapebyid(nadal\_winner)], -           verticalseparation: 20, -           horizontalseparation: 30 -       }); -   } - - - - - regards, - vessy - progress telerik - get quickly onboarded and successful with your telerik and/or kendo ui products with the virtual classroom free technical training, available to all active customers. learn more.
there is a slider labeled hubigraph tile installation, turn that on. then a device is created that can be placed on the dashboard. monthly energy graph in this case

[[grpah2]](https://community.hubitat.com/uploads/default/original/3x/1/f/1f9bb3212a9c5f09119badcf87284edd06a27c55.png "grpah2")

then add that device to you dashboard using attribute as the template. then pick an attribute over to the right.

[[grp…](https://community.hubitat.com/uploads/default/original/3x/4/e/4e9f3c1689dbd166447a7d8b3d76c3d90c3f3263.png "grpah") vertical and horizontal grafting – layering Hi JimCreech,


It is possible to achieve a similar result in Dataiku by going through the following steps:


* run a [Window](https://knowledge.dataiku.com/latest/courses/visual-recipes/window.html) recipe using id as partitioning column, event as ordering column, and compute the lagdiff values on days
* run a [Prepare](https://knowledge.dataiku.com/latest/courses/basics/prepare-data/concept-prepare-recipe.html) recipe which will:
	+ run the assertions on the specific conditions you mentioned and create a dummy\_tmp column we'll reuse at the final step
	+ filter out the records where event == 01 (since we won't need it for the aggregation)
* run a [Groupby](https://knowledge.dataiku.com/latest/courses/basics/group-data/group-summary.html) recipe to aggregate at the id level and get the minimum of the dummy\_tmp column (which should be 1 if all conditions you described are verified, 0 otherwise).


You will find attached a sample DSS project with an example of implementation of those steps using the small example data mentioned in your post. If you are working on a regular basis with time series data, you may find this [tutorial](https://knowledge.dataiku.com/latest/courses/visual-recipes/window/window.html) on the Window recipe capabilities useful.


Hope this helps !


Best,


Harizo




[View solution in original post](/t5/General-Discussion/Using-Lag-and-Lead-in-Dataiku/m-p/19308#M1146) [@raja](/u/raja)

this could inspire you:

[[image]](https://aws1.discourse-cdn.com/business6/uploads/dynamobim/original/3x/9/b/9bcef06d0c489d0ac566fe7dad2328002f964445.png "detail-lines-by-offset.png")

here, i recreate lines by offsetting the room finish boundaries, but any other method to select the needed curves could work as well… :roll\_eyes:

[[image]](https://aws1.discourse-cdn.com/business6/uploads/dynamobim/original/3x/6/3/6393f7d9277c8a0ff2b48f07b1e2352f69f463b0.png "image")

t1 = [9, 9, 99, 9, 6, 7, 8];

t9 = dscore.list.chop(t1, 3);

a = t9;

t2 = get.valueatindex(a, 0);

t4 = [3, 2];

t10 = dscore.list.ofrepeateditem(t4, 3);

a1 = t10;

t3 = get.valueatindex(a1, 0);

t11 = t2<1l> + t3<1l>;

a2 = t9;

t5 = get.valueatindex(a2, 1);

a3 = t10;

t6 = get.valuea… if you do not need the "wave" contents for a further use, just drop out (k) from all its entries.
if you need it for a further use, initialize it as 
wave=zeros(3,numel(t));
before the loop and address it as wave(k,:) within the loop.
commands "stem" are definitely incorrect. if you want to see plots of the dependences on t, they should look something like stem(t,combined) or plot(t,combined), etc. however, the t range in the second version (0,3) is much wider than the one of the first version (0,0.01): consequently, such a graph will show much (hugely) more oscillations--you will probably see nothing but a unifromly painted square.
i do not see any need in the "nold on" and "hold off" commands in the both version. @pankajkumardas, 

converting geophysical data from the space (or time) domain to the frequency domain (via fast fourier transform) is common practice in geophysics, particularly when wanting to apply some sort of filter to that data. 

we do this because it is more computationally efficient to filter data in the frequency domain compared to the space domain. for example, applying a filter such as a first vertical derivative to a grid of magnetic data in the space domain requires convolution using a two-dimensional filter. depending on the type of filter, that process can be complex and therefore computationally expensive. 

transforming the data and the filter to the frequency domain allows us to simply multiply the two to get a filtered spectrum, which is then transformed back to a filtered grid in the space domain. this process is far more computationally efficient than convolution.

the following diagram describes the distinction between space and frequency domain filtering. it is taken from reeves (2005), which you can access here: http://www.geosoft.com/media/uploads/resources/technical-papers/aeromagnetic\_survey\_reeves.pdf




i'd urge you to download the document and review chapter 6, from which the diagram is taken.

best regards, basically, you could use picking from the camera to the center of the tag position to see if it is behind something.

if yes, you could manually adapt the style. method for handling data structures where the first element is processed first and the newest element is processed last.