Extremes | A jquery plug-in to lay out elements around a circle | Plugin library

You obviously need to define and evaluate the condition somewhere.

You could either do this in the event handler or before you raise the event assuming you have control over the code that actually raises the event.

If you are referring to the built-in KeyDown event, you don't control when this one is raised so then you should check your condition in the handler.

Edit:

You could use the Validation.GetErrors method to check whether there are any validation errors in the event handler:

I have tried examples, and the conclusion I can draw from them is range_color is working fine. However, your argument to range_color is what is causing the problem. Since it is a range, it is only sensible to define it in an increasing manner, i.e., [0,-6] is not a correct range. Instead, you should use [-6,0].

I can confirm this by the following:

Make sure that everything you use is a numpy array and NOT Python list and make sure that all of the members have the same data type. In your case this is true.

Now you can make use of a library called numba. It uses JIT.

A video demonstrating it can be seen here.

The documentation for numba can be seen here

Just don't use id2token.

Your model should be :

If you have a reference threshold value, you can just naively filter data using Numpy with something like data[data < threshold] with threshold set for example to 10_000. Alternatively, you can also put some NaN values if you want to keep these values (because is may not always make sense to just remove them) using data[data < threshold] = np.nan.

If you do not have a reference value, then things stat to be a bit more complex. They are fancy ways to detect efficiently such patterns but most are complex.

The simplest solution is analyse the standard deviation of your input data using a sliding window and detect outliers regarding the resulting local standard deviation. You can see how to do that here (you need to combine this with something like data[sdValues < threshold] to remove the outliers). Note however that this method is very sensitive for values near 0.

An alternative solution is to compute a Gaussian/median filter and then measure the relative difference (or another more advanced distance metric) with your input data (a bit like in a high-pass filter). You can take a look to this post to do that.

For these two methods, you need to define an arbitrary threshold. But unlike the naive method, this threshold is directly related to the data variation and not the raw data itself. It is up to you to find a good threshold regarding the data variations, the outliers and the expected final input.

Note: you might be interested in using scipy.signal (especially to compute filters).

You can add separate elements, probably elements, on either side of your input, then use CSS to position them appropriately.

As far as tick-marks are concerned, there isn't a standard way to add those. However, with the concept of progressive enhancement in mind, feel free to add the without the label attributes (since you'll have those set using the above-referenced elements) so that users who access your site on browsers that do support the tick marks can see them.

You just need to create some variables in a higher scope and assign the values to them:

Here is a way to proceed making an assumption about how you defined your diagonal lines. First create reproducible data and get the quantiles:

you can make scrollable by giving css property as following :-