regions | Is it possible to extend region inference to System F | Computer Vision library

You need to loop through arrays and on each iteration you can append htmls inside some variable using += .Then , append this html generated inside your ul tag .

I have taken some codes from this post as we need to control each submenu click you can use jquery code so on each click add/remove show class from other submenu .

Demo Code :

Here is a minimal JS fiddle example: HTML:

• your sample data is not really usable for doing what you describe. Have used Northern Ireland geometry, population and COVID cases to demonstrate
• used dissolve() as you describe, have not bothered with fact some of the attributes cannot be summed (long and lat)
• simpler to see through visualisation, so have provided plots as each stage
• updated to use pandas cumsum() functionality to sub-divide regions for each time population exceeds 300K
• this dissolves C into 3 areas and E into 2 areas

You could draw the TGanttSeries at OnBeforeDrawAxes, then hide it at OnBeforeDrawSeries and finally make it visible again at OnAfterDraw for the next drawing cycle:

Restating the problem - My understanding given the different comments and your updates is the following:

• someone other than you is in possession of data, which as it happens is 2D data, i.e. an Nx2 matrix;
• using the covariance matrix, they are effectively saying something about the joint distribution of these two dimensions, specifically about the variance;
• if they assume a Gaussian distribution, as is implied by your comment regarding 68%, 95% and 99.7% for 1sigma, 2sigma and 3sigma, they can draw ellipses which represent the 2D-normal distribution: these are in fact some of the contour lines associated with the 3D "bell" surface;
• you have obtained the contour lines in a graph and are trying to obtain the covariance matrix (not the original data...);
• you are concerned about the complexity of having to extract the information from each ellipsis.

Partial answer:

• It is impossible to recover the original data, I hope you are already aware of that, but in case you are not let's just note that the covariance matrix is a summary statistic of the data, much like the average, and although it says something about the data many different datasets could happen to have the same summary statistic (the same way many different sets of numbers can give you an average of 10).
• It is possible to somewhat recover the covariance matrix, i.e. the 3 numbers a, b and c in the matrix [a,b;b,c], though the error in doing so will likely be large because of how imprecise the pixel representation is. Essentially, you will be looking for the dimensions of the two axes, for the variances, as well as the angle of one of the axes, for the covariance.
• Unless I am mistaken, under the Gaussian assumption above, you only need to measure this for one of the three ellipses, and then factor by whatever number of sigmas that contour represents. Here you might want to either use the best-defined ellipse, or attempt to use the largest one, which will provide the maximum precision for your measurements (cf. pixelization).
• Also, the problem of finding the axes and angle for the ellipse need not be as complex as what it seems like in your first trials: instead of trying to find the contour of the ellipses, find the bounding rectangle.
• In order to further simplify this process, if your images are color-coded the way you show, then a filter on blue pixels might be enough in terms of image processing. Then simply take the minimum and maximum (x,y) coordinates in order to obtain the bounding rectangle.
• Once the bounding rectangle is obtained, find the equation to your ellipse (that's a question for a math group, but you could start here for example).

Happy filtering!

S3 buckets are region specific. When you create a new bucket you need to select the target region for that bucket.

For DR reasons, you can keep backups in another region. Should the primary region fail in a way that the entire region is affected, then you could restore in the backup region.

Your DR strategy will depend on your use case, and your needs for returning services back to normal in case of region wide failure.

For example, let's say you rely on ec2/ebs to operate your service and those services suffer region wide outage for 5 hours. In order to recover your service you would need to move to a region where the resources are available. Assuming you need S3 data for operational processing you would want to have that data ready in the Target recovery region.

Setting up ingress for internal load balancing requires you to configure a proxy-only subnet on the same VPC used by your GKE cluster. This subnet will be used for the load balancers proxies. You'll also need to create a fw rule to allow traffic as well.

Have a look at the prereqs for ingress and then look here for info on how to setup the proxy-only subnet for your VPC.

You can try withh groupby and unstack:

I'd use .pivot: