CPython still needs to update reference counts after the fork even if your python code itself doesn't modify any data, making Copy on Write less helpful than it would first appear. Monolithic memory buffers like numpy arrays can be shared efficiently via CoW, but rich data structures with many PyObjects suffer from the refcounting issue.
The systems of differential equations produced by these types of models tend to be analytically intractable, so "numerical methods" are absolutely essential here. The phrase you want to look for is "initial value problem" (IVP): you know the initial values of the quantities described by the equations and you want to find out how those quantities evolve over time.
To make this more clear as I have had the same experience and want more people to enjoy it: Mamba is more or less a drop-in replacement for the conda tool with the guts rewritten in C++ and as a result is much faster to resolve dependencies -- I want to say an order of magnitude faster or more. It's even noticeably faster at downloading and installing. You just
conda install -c conda-forge mamba
in your base environment and then you can start running mamba instead of conda.
TIFF will support that size and larger. It can use JPEG or ZLIB compression as standard, or even WEBP or JPEG-XL which are unofficially supported by a small number of TIFF libraries.
I’ve used those Snappy brand cutters in a handheld cordless drill. It’s workable but tricky. You need to start with light pressure and high speed until the cutters score a clean circle, then gradually increase the pressure. The workpiece absolutely needs to be clamped down with zero movement, and it helps if you can find a way to brace your upper body and the drill as you work. The trickiest part is keeping the cutter perfectly square to the work in both axes —- if you tip it too far you’re likely to shatter the cutter as the metal is very brittle.
That species of moth doesn’t exist on the American continent but other similar and related ones do, whose range does overlap with that of hummingbirds. For example:
This post claims the history of which releases closed which holes is a bit more complicated and that 8u191 really is the first release to prevent this particular exploit. However it also points out that it’s still possible to achieve RCE via log4j template expansion in certain Tomcat and Websphere configurations:
Also if the upper tray is height-adjustable, check the rubber one-way valve flaps in the middle of the supply tube that runs up the inside rear wall of the basin. If any of those flaps are damaged or missing, water will leak out there instead of being directed to the spray arm depending on which height position you're using. If you're lucky, you might still have one usable height position without replacing anything. Otherwise you need a whole new supply tube ($60-100) as the rubber part doesn't seem to be sold separately.
Dask does the lazy evaluation and query planning thing on numpy arrays and pandas dataframes, and can execute in parallel. It mimics most of their native interfaces which makes it a pretty easy drop-in.
True, but running the %debug magic command in IPython immediately after an unhandled exception drops you into the debugger at the point of the raised exception, complete with the full interpreter state including the stack. It doesn’t permit edit-and-continue but you can still evaluate expressions (including calling any function) to explore what went wrong without having to recreate the situation inside an explicit pdb session.
I have fond memories of using Swarm in an undergrad course on agent based simulation ~20 years ago. Just tweaking the heatbugs example in various ways was already enlightening. Thanks for your work!
> The key insight is that file operations in separate directories don’t (for the most part) interfere with each other, enabling parallel execution.
It really is magically fast.
EDIT: Sorry, that tool is only for local copies. I just remembered you're doing remote copies. Still worth keeping in mind.