jj | A Git-compatible DVCS

You need to set RAKULIB to wherever your libraries were initially installed, as is done here:

If you have an array like

Not much of an improvement, but using abind:

There's a few misunderstandings in this attempt.

1. There is one instance of a meta-class per type. Thus if we want to allow a given type to only be instantiated once, the correct scoping is an attribute in the meta-class, not a my. A my would mean there's one global object no matter which type we create.
2. The compose method, when subclassing ClassHOW, should always call back up to the base compose method (which can be done using callsame). Otherwise, the class will not be composed.
3. The method_table method returns the table of methods for this exact type. However, most classes won't have a new method. Rather, they will inherit the default one. If we wrap that, however, we'd be having a very global effect.

While new is relatively common to override to change the interface to construction, the bless method - which new calls after doing any mapping work - is not something we'd expect language users to be overriding. So one way we could proceed is to just try installing a bless method that does the required logic. (We could also work with new, but really we'd need to check if there was one in this class, wrap it if so, and add a copy of the default one that we then wrap if not, which is a bit more effort.)

Here's a solution that works:

Create nested lists and convert to DataFrame:

you can change this line:

We may create a function

here is one option with strsplit - replace the , after the digit followed by the . and one or more digits (\\d+) with a new delimiter using gsub and then split with strsplit in base R

One main issue is that the second function call compile the function again. Indeed, the types of the provided arguments change: in the first call the third argument is an integer (int transformed to a np.int_) while in the second call the third argument (k) is a floating point number (float transformed to a np.float64). Numba recompiles the function for different parameter types because they are deduced from the type of the arguments and it does not know you want to use a np.float64 type for the third argument (since the first time the function is compiled with for a np.int_ type). One simple solution to fix the problem is to change the first call to: