tall | A TALL | Plugin library

I am making an interface to store character and dinosaur variables to a list and then print them out when asked.

There is a switch statement to take the users' inputs:

• Case 1: The function allows the user to input character data and then it's put into a list.
• Case 2.: The function allows the user to input dinosaur data and then it put into a list.
• Case 3 The function prints the objects in each list. However, whenever I run it it doesn't print anything and comes up with no errors. If I run the code to print each list in their respective case then they print out fine.

Main Question:

Say I have some algorithm, that runs in O(n^2).

I get that, if I input an n twice the size of the original n I get 4 doubling of the maximal time complexity.

But how does it relate to running the algorithm twice with an input size of n?

Why I am asking:

The reason for why I'm asking, is because I made an algorithm whose big O notation was O(log_2(n)). But running it and comparing it to O(log_2(n)) showed, that it performed FAR better for inputs at the magnitude of 1 billion. The algorithm would only be used on inputs the size of 10k-20k. Meaning that it performed wastly better than $C_1*log_2(n)$

I get that big O notation is the maximum, but it felt like I asked "How tall are you?" and the big O notation answered "I am shorter than 40 meters".

I googled around a bit and found, that big O is - "in industry" - primarily used for scalability. I.e you expect your n to reach that point where it will get close to its big O.

Then, in my case, does it even make sense to use big O notation? How does running the same algorithm multiple times, rather than increasing the input size, relate to big O notation?

I have a dataset that has this structure (lots of questions asked to survey respondents) that I want to reshape from wide to long:

I want to generate a rank 5 100x600 matrix in numpy with all the entries sampled from np.random.uniform(0, 20), so that all the entries will be uniformly distributed between [0, 20). What will be the best way to do so in python?

I see there is an SVD-inspired way to do so here (https://math.stackexchange.com/questions/3567510/how-to-generate-a-rank-r-matrix-with-entries-uniform), but I am not sure how to code it up. I am looking for a working example of this SVD-inspired way to get uniformly distributed entries.

I have actually managed to code up a rank 5 100x100 matrix by vertically stacking five 20x100 rank 1 matrices, then shuffling the vertical indices. However, the resulting 100x100 matrix does not have uniformly distributed entries [0, 20).

Here is my code (my best attempt):

I use a ZStack to display a fullscreen background image underneath the main UI. The main UI consists of a VStack with multiple views separated by flexible Spacers to scale down or up on different device sizes. Now I experience that the Spacers will not scale down on small devices because the background image on small devices remains bigger than the screen size and keeps the ZStack tall, see screenshot of the preview of iPhone 8. What am I doing wrong here?

Code:

I have a plot with labelled rectangles. To ensure text stays inside the rectangle I'm using the ggfittext package. Example below:

I have a dataframe that I want to gather so that it is in tall format, and then mutate on another column with values based on membership of a string from another column in a list of lists. For example, I have the following data frame and list of lists:

I am currently taking a laravel 8 framework class and each time I install a laravel/ui (Bootstrap 4, TALL stack, etc) I get an error after running:

npm install && npm run dev

This is fixed by running the command again the second time, without having to troubleshoot or change anything. I don't have an issue with the error but as a freshman and a curious coder, I find myself wondering about what causes the error on the first run.

Here is an image of the error that stops the command the first time.

Can someone please explain what is it that happens the first time the commands are executed that causes the error to stop the execution of the command.

Many of you faced the error while coding in vs code unexpectedly. I had also faced the issue while coding and it seems to be a tall-order task to fix the issue.

Many of you tried even I also tried the following methods

1. removed git folder
2. uninstalled all extensions
3. disabled the git in the settings.json file
4. removed cached files of the software
5. even uninstalled VS code and installed it again, but it gives me the same error that says: "Extension host terminated unexpectedly"

so what's the solution for this problem ?