SCV | Space Construction Vehicle

1. Open the CSV in Excel
2. Select all the data
3. Choose the Data tab atop the ribbon.
4. Select Text to Columns.
5. Ensure Delimited is selected and click Next.
6. Clear each box in the Delimiters section and instead choose Semi Colon.
7. Click Finish.

How to convert it?

There's a couple of problems here:

First of all, service() turns the builder into an actual Service composed of its layers while consuming the builder. If you really want to return a builder, you'll need to defer calling service() at a different place.

If you remove that call, you'll still end up with a different signature than you specified, i.e. the type of tower::ServiceBuilder::new().rate_limit(1, std::time::Duration::new(10, 0)) is not ServiceBuilder but rather ServiceBuilder>. Note that your compile error came from the RateLimit in your example requiring a type parameter L. RateLimitLayer does not require this.

Combined it would look something like this:

Argument ranefstart

Function lmertree iterates between estimating the LM tree-part of the model (here log(y) ~ -1 + time | TD + EP2 + DFA + DTCX + TIF + SCV) and the random-effects part of the model (here log(y) ~ ((-1+time)|HC)). Argument ranefstart controls the initialization, but this will often be inconsequential. It might be consequential when there is variation in the response (here log(y)) that can potentially be explained by both the LM tree as well as the random effects:

• By default, estimation is initialized with the LM tree part; then this variation will likely be captured by the tree.

• If you override the default and specify ranefstart = TRUE, lmertree will initialize estimation with the random-effects part; then this variation will likely be captured by the random effects.

You obtained quite similar results using ranefstart = NULL and ranefstart = TRUE, indicating that the final model is not sensitive to initialization with the tree versus the random effects. In that case, using the default is fine.

By specifying ranefstart = z0l[,"value_t0"], variable z0l[,"value_t0"] will be included as an offset in the linear predictor in the first iteration of the estimation. In further iterations, this offset will no longer be used. Because you already specified offset = log(z0l[,"value_t0"]), in the first iteration the offset is doubled; this could send the iterative estimation into a wrong direction, and further iterations might not be able to correct for this anymore. This could explain why you got such different results.

The use of the ranefstart argument is useful only, when you expect that a substantial part of the variance in the response can be potentially accounted for by the tree, as well as by the random effects, and you prefer this variation to be accounted for by the random-effects part of the model, not by the tree.

Argument cluster

In addition: You mention you have subject-level (level-II) partitioning covariates. I would advise the use of the cluster argument in that case, because by default the parameter-stability tests employed for selecting the splitting variables assume the partitioning variables to be measured at the lowest level (level I). By specifying cluster = HC, the parameter-stability tests will account for the partitioning variables being measured at level II. When the parameter-stability tests are performed at level I, the tests may be overpowered.

Plotting level-II group size in terminal nodes

In a future version of glmertree it would be nice to have an argument of the plotting function to specify whether n should reflect sample size at level I, II, etc. For now, I can suggest the following approach:

remove:

Usually, what I like to do when collecting data through webscraping is to build either :

• A list of dictionaries (which contains metadata) (option 1)
• Lists of metadata in a single dictionary with corresponding column names (data, title, price, etc.) (option 2)

(What I call "metadata" is all the pieces of information that describes a single item : in your case, that would be : the item price, the extraction date, the reviews on a specific item, and so on.)

When scraping is done, I build the DataFrame only as a final step.

As a final note, I didn't want to mess with your original script too much, but I think you should consider two things :

• Build a function to wrap your scraping steps (or maybe even a class so you can add a single function that handles all the similar things you're doing : collecting metadata on a product)
• You should probably replace "Sin Marc", "Sin Reviews" with np.nan, it will make your data processing and analysis easier

In the modification I did in your script, I choose option 2. I'm not sure about that, but I would guess that option 2 is more efficient than option 1. However, I find it useful, sometimes, when you handle more complex data to build a dictionary first and then, happen the dictionary corresponding to a single item into a list of dictionaries (that would be option 1) : it can make it easier to track each item at a time.

Qt5+ How to set default audio device for QMediaPlayer

This the one your are searching for. Its in C++, so you need to think out the python way out from it. Its not that difficult. I'm currently not on my PC, else I would have written the python code here.

UPDATE

os.environ['QT_MULTIMEDIA_PREFERRED_PLUGINS'] = 'windowsmediafoundation'

Add the above line of code at the top of your code. This will help your media player to use the latest media API of windows instead of DirectShow, so QAudioDeviceInfo will work correctly.

There are several ways you can do this, you could use with_items on each task but this is not so reusable.

Create generateReport.yml with two variables organization and pat, with those variables the playbook generates a file to attach with name [organization]_file.csv and sends the mail. Then create a playbook that includes the tasks of generateReport.yml sending the values of organization and pat.

generateReport.yml