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I am not a full fledged programmer (yet) but am getting pretty good at no and low code. I use Zoho Deluge scripts a lot.

Zoho spits out the ugliest, most unformatted JSON responses and I want to know the best environment I should use to copy the response and paste into to get a clean, formatted JSON.

Thanks :)

My versions are:

I have an xyz point cloud (pcd) as a matrix of size (N, 3) and an image (img) as a matrix of size (H, W). I want the points where the image is projecting so I have the reunion of the following masks:

true_where_x_on_img = (0 < pcd[:, 0]) & ( pcd[:, 0] < img.shape[1])

true_where_y_on_img = (0 < pcd[:, 1]) & ( pcd[:, 1] < img.shape[0])

true_where_point_on_img = true_where_x_on_img & true_where_y_on_img

This mask is of size N and works as intended (using pcd[true_where_point_on_img]). Now I filter these values using another mask that tells me whether the pixels in the image are background or not:

true_where_not_background = mask[pcd[:, 1], pcd[:, 0]] != 0

true_where_not_background is of size M because mask is H x W. Finally, I want to project these results into column 4 of a bigger matrix, aug_pcd, of size N x 4. This matrix was initialized with zeros and pcd was copied into the first 3 columns of it. I now want to put the masked image (img[true_where_not_background]) into column 4. Something like aug_pcd[true_where_not_background, 3:] = img[true_where_not_background]. The problem is true_where_not_background has size M, and aug_pcd has row size N and is already a full-fledge slice. Slicing the slice would make a copy to which I would not be able to assign values to. How can I blend true_where_point_on_img and true_where_not_background so I can have a mask of size N?

I've a php page which returns a full-fledge js snippet when we query that page. Let say that page is get_js.php and looks something like this:

Trying to code as efficient as possible object-oriented implementation of priority queue in Python, I faced an interesting behavior. The following code works fine

I would like to run Snakemake workflows from within a Docker container, i.e. the container itself should contain Snakemake, but also all required executables preinstalled. The executables are installed in various ways, from system package manager over downloadable binaries to Bioconda. Some Bioconda tools need to go into their own environment, because they are incompatible with the rest of the installation. But since Snakemake can't use preexisting environments, I'm left with either using conda directives (installation at runtime from within the Docker image?!) or container directives (Docker within Docker?!). Is there really no way for me to create a fully-fledged pre-built all-in-one image? Am I overlooking some best practices?

Thank you, Andreas

JupyterLab 3.0 comes with a full-fledged visual debugger. But using it is only posssible if you're running a xeus-python kernel. Other sources report that there are a few differences between xeus-python and ipython, but that the list of features that differ between the two is growing shorter. So that's not really what I'm asking for here. Rather, what is it that distinguishes xeus-python from ipython that makes it suitable for a visual debugger as the one featured by JupyterLab 3.0?

Recently I was trying out some code snippets from "Dart For Absolute Beginners" on DartPad. Specifically:

Might sound a little silly but I'm not proficient in Java so wanted to make sure:

If there are two code points

I: