Stim | A fast stabilizer circuit library

Here is a data.table approach.. could not reproduce your desired output, since it contains values that are not in your sample data?

You can add a label to each of the curves and then use mplcursors to show an annotation while hovering (or when clicking with hover=False).

Note that to have an interactive plot in a Jupyter notebook, %matplotlib notebook (this might depend on how Jupyter is installed) is needed instead of just %matplotlib inline (which generates static images). See Docs.

Here is an example showing the general idea with some test data:

If you want a more generalized/elaborated replacement, bear in mind DataFrame.replace also takes a dict as a parameter:

At a complete guess, but I am guessing that what you are saying is that data for the last date is missing. For example, if you select 2021-01-01 and 2021-01-31 for your input parameters, a row with a value of 2021-01-31T17:00:00 is not being returned.

The reason for that is quite simple, because 2021-01-31T17:00:00 is not BETWEEN the values 2021-01-01 and 2021-01-31; as 2021-01-31 is effectively 2021-01-31T00:00:00.

When dealing with date and time values, you are better off using date explicit date boundaries with >= and <. This means your query should probably be the following:

The different p-values you are seeing reflect unadjusted p-values vs p-values that were adjusted for multiple comparisons.

The ?emmeans::pairs documentation tells us:

Ordinarily, when simple is a list or "each", the return value is an emm_list object with each entry in correspondence with the entries of simple. However, with combine = TRUE, the elements are all combined into one family of contrasts in a single emmGrid object using rbind.emmGrid.. In that case, the adjust argument sets the adjustment method for the combined set of contrasts.

So, with your reproducible example, you can combine all the simple main effects into one data frame with the combine argument set to TRUE. And you can choose between unadjusted vs adjusted p-values by setting the adjust argument.

So, you want to have a predicate that takes a structure as argument and that should do two things:

1. call all goals matching stim(Structure, _)
2. delete all clauses headed by stim(Structure, Options), where delete is a member of Options from the database.

First of all, you need stim to be dynamic, otherwise clauses can't be dynamically added to/deleted from the database:

When you want to call a python function from another file, you should not try to run another python process by calling os.system. Just import that function: from plotrdm import plot_rdm

Instead of using os.filewalk and a file.startswith check, we can cleanup the code a lot by using the nice python library glob. I throw in a enumerate for good measure.

Your new rdmsave.py

Your a represents 10 different stims, yet your b only seems to look at 2 of them (anchor and apple). Further, table(a) is only going to return lots of 1s, so kable is only going to group one row at a time.

The mechanism we need it to be able to identify somehow the anchor in anchor1-photo-colour to extract that. If there is a means to do this without regexes earlier in your generation of b, then it might be better to do it there, since regexes can introduce problems if done incorrectly.

If we assume that all anchor[0-9].* photographs are concurrent (and same for other stims), then we can do something like this:

Maybe try this approach: