duf | a nice CLI disk-usage/free-space utility from @ muesli | Command Line Interface library

This solves fine (), but is indeed a complex solve mainly due to:

• underspecification
• lack of modularization

This particular environment specification ends up installing well over 300 packages. And there isn't a single one of those that are constrained by the specification. That is a huge SAT problem to solve and Conda will struggle with this. Mamba will help solve faster, but providing additional constraints can vastly reduce the solution space.

At minimum, specify a Python version (major.minor), such as python=3.9. This is the single most effective constraint.

Beyond that, putting minimum requirements on central packages (those that are dependencies of others) can help, such as minimum NumPy.

I assume the name "devenv" means this is a development environment. So, I get that one wants all these tools immediately at hand. However, Conda environment activation is so simple, and most IDE tooling these days (Spyder, VSCode, Jupyter) encourages separation of infrastructure and the execution kernel. Being more thoughtful about how environments (emphasis on the plural) are organized and work together, can go a long way in having a sustainable and painless data science workflow.

The environment at hand has multiple red flags in my book:

• conda-build should be in base and only in base
• snakemake should be in a dedicated environment
• notebook (i.e., Jupyter) should be in a dedicated environment, co-installed with nb_conda_kernels; all kernel environments need are ipykernel

I'd probably also have the linting/formatting packages separated, but that's less an issue. The real killer though is snakemake - it's just a massive piece of infrastructure and I'd strongly encourage keeping that separated.

By default, when you use mysqldump DB, the output includes table-creation statements, but no CREATE DATABASE statement. It just assumes you have created an empty schema first.

So you could do this to create the schema first:

You can use nested for-loop:

try this:

Recently, Firebase announced that version 9 of Firebase SDK JS is generally available. This was done to do some optimisations. Try using:

You're trying to use the block-local array argv, which is recreated for every command and, what's worse, doesn't even exist any longer after parse_cmd has returned. An array object with sufficient lifetime has to be used; you can do this by changing

I don't believe what you're asking for is possible, but there are several answers here that show all the detections you can do to use the correct type of clear. Usually, it's just best to either make your own window or not clear the screen, sadly.

Well, I'm not entirely sure what was wrong, but I think it came down to one process not finishing before another process started. I inserted a sleep 5 in the script - just before the last line where I'm trying to connect to the server, and that worked. I also wrapped the line in a while loop, giving it up to 5 attempts to connect to the server, and if it fails, it will wait a further 5 seconds. Currently this seems to work fairly reliably and I am able to connect to the MySQL server.

So either way the problem appears to be solved.

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