Nothing. Your heat pump will still work. Modern heat pumps typically have a COP of 4 with some of the newest ones approaching 5.5. With a COP of 4, you can still heat your home when the outside temperature is -30C. The colder the outside air is, the less efficient a heat pump is so below -30C, the heat pump will still work, but the COP may have fallen to 1 or below and so may not be as energy efficient as resistive heating at that point.
Another point to consider is the capacity of your heat pump. If it's capacity is not enough to heat the room or home at -30C, it may not be able to keep up, even though it keeps up fine at -10. Make sure you have enough capacity to heat the area you need, down to the lowest outside temperatures you typically get in your area.
Absolute zero is -273C at which point there is no heat left, so at -30C, there is still a lot of heat left for a heat pump to extract to heat your home.
I use my 15 year old Panasonic heatpump down to -20 C. Add some wood to the fire on the coldest days. Works great although the «free energy» is really low at those temperatures.
It works less efficiently but it doesn't stop working.
From the article: “As a result, it boasts a reliable heating performance, enabling it to deliver a 100% heating performance in temperatures as low as -10 C.“
The freezing point of propane is -188C. So, it should continue to compress and expand just fine at extreme temperatures. It's just that the heat transfer probably gets a bit less efficient and it might not heat all the way to 70 degrees anymore or require more energy to do so.
Using exceptions in python isn't any more expensive than not using exceptions because the interpreter pays the same cost no matter what. It's a core design decision. Changing it would probably break some things.
I've been using pip-compile from https://github.com/jazzband/pip-tools for this use case; a standard project Makefile defines "make update" which pip-compiles the current requirements, and "make install" installs the frozen requirements list.
This way I can install the same bill of materials every time
I think we have different motivations. pip-compile can only fetch and install dependencies which have been declared.
For example, let's say I have a malicious yaml parser package. It should not need requests as a dependency. The odds are that a project may have requests already installed as a sub-dependency of another dependency. I can then just try and import requests in a try catch block and if available, and fetch malicious artefacts, for example. Panoptisch would report this.
Also, the usage of operating system or builtin modules such as socket, sys or importlib is not something which is analyzed by pip-compile.
An alternative approach is to use jsonpath; for python, the jsonpath-ng implementation is useful but could use more friendly documentation.
it contains XPath-style selectors and the ability to directly modify matched nodes in a Dict, including filtering (as indirect deletion) and upserts; the only thing that it's missing is sufficient addressability with regards to Lists (arrays), so I see myself building a similar 'op' processing but using jsonpath instead of jsonpatch language.
My personal approach has been to use conda for binary management and a place to stuff libraries (could just as easily use venv here), but the key tool I'm using to pin libraries and avoid transitive dependency drift is pip-compile.
Frankly the invocations are so arcane for these various supporting tools that I have been distributing a standard Makefile to projects to hide the complexity - "What's old is new again"!
> ipfs resolve -r /ipns/nauseam.eth/coding/random/how-side-effects-work-in-fp/: no link named "coding" under QmdzzonFE9eX6FGs8UbCyoC2XS5NQjaG6gaqhgeUyTHnag
