I'm trying to come up with a simple example. I think that for bunch of flying hard spheres in a box, any measurement that requires sufficiently time spaced samples of their positions will have trouble being differentiated.
All these seem nice but I never seem to find one that will interface with rootless nodes, amd link back to wherever my laptop is. There's always a requirement for a kernel module or mounting a new interface. For now I use chisel, but it's a hack and I need to manage addresses and ports manually.
I don't suppose there's type checking on indexing via fields... IE df.at(field_value) typechecks no matter what type the first field of the record class is.
"Python 2.7 puts “at our disposal an advanced programming language that is guaranteed not to evolve anymore”, Rougier writes1."
Oh no. That's not at all what was intended.
Regarding my own research: I'm doing theoretical biophysics. Often I do simulations. If conda stays stable enough, my code should be reproducible. There's however some external binaries(like lammps) I did not turn into a conda package yet. There's no official package that fits my use-case in conda since compilation is fine-grained to each user's needs.
Just from the introduction, seems like they wanted to have abstractions for the amalgamation of tools one uses when doing science that uses computing. Git+interpreter+reproducibility, but like a graph that is manipulated by the user doing data/code mutation and invoking functions.
My master thesis was on a magnetic traps for microfluidics. I'm not really up to speed on the field anymore, but it's exciting to recognize much of what's being used, and with such a direct application.
I'm trying to come up with a simple example. I think that for bunch of flying hard spheres in a box, any measurement that requires sufficiently time spaced samples of their positions will have trouble being differentiated.