While my experience is mostly C++, I assume these mocking libraries are similar in allowing you to create a class that can report it's own usage, and allow for arbitrary returns values and side effects, which is incredibly useful, especially in conjunction with dependency injection.
What patch lets you do is directly overwrite the functionality of private member functions on the fly, which if Java/JavaScript can do I'd love to know, I thought this was a uniquely Pythonic magic.
Oh I'm fully aware that python lets you cheat dependency injection with patch, its one of the many things python teaches developers to do wrong, which leads them to be unable to use any other language.
Exactly, have fun trying to get test coverage without dependency injection
This is a pet project I've been meaning to try for a while, especially to get some more rust on the github, but I was caught up on whether it made sense without git compatibility.
Thanks, being a software engineer and working in interferometry I was familiar with some of the details - enough to want to jump in when you were getting downvoted - but I will admit I only found and read the actual paper for the first time because of this thread, as I wanted to be sure on the facts!
You're welcome. I think calling it the output of an 'AI model' triggers thoughts of the current generative image models, i.e. entirely fictional which is not accurate, but it is important to recognise the difference between an image and a photo.
I also by no means want to downplay the achievement that the image represents, it's an amazing result and deserves the praise. Defending criticism and confirming conclusions will always be vital parts of the scientific method.
Most of what you said is correct but there is a final step you are missing, the image is not entirely constructed from raw data. The interferometry data is sparse and the 'gaps' are filled with mathematical solutions from theoretical models, and using statistical models trained on simulation data.
Paper: https://arxiv.org/pdf/2408.10322
We recently developed PRIMO (Principal-component Interferometric Modeling; Medeiros et al. 2023a) for in- terferometric image reconstruction and used it to obtain a high-fidelity image of the M87 black hole from the 2017 EHT data (Medeiros et al. 2023b). In this approach, we decompose the image into a set of eigenimages, which the algorithm “learned” using a very large suite of black- hole images obtained from general relativistic magneto- hydrodynamic (GRMHD) simulations
Its not hard to find that there are legitimate academic criticism of this 'photo'. For example here. The comparison you made is not correct, more like I gave a blurry photo to an AI trained on paintings of Donald Trump and asked it to make an image of him. Even if the original image was not of Trump, the chances are the output will be because that's all the model was trained on.
This is the trouble with using this as 'proof' that the. Theory and the simulations are correct, because while that is still likely, there is a feedback loop causing confirmation bias here, especially when people refer to this image as a 'photo'.
Using Latin for Latin based languages has the benefit that the words feel appropriate for their effects somehow, since most people don't know Latin but would be familiar with words that have similar roots.
Translating them into alternative historical languages gives the same effect for other cultures.
To be fair, its been like that since November
Reminds me of
Torpenhow Hill is a hill in Cumbria, England. Its name consists of the Old English ‘Tor’, the Welsh ‘Pen’, and the Danish ‘How’ - all of which translate to modern English as ‘Hill’. Therefore, Torpenhow Hill would translate as hill-hill-hill hill