Even though Python can call out to BLAS or whatever, it's still usually worth it to code in Fortran or C or maybe Julia.
I write in a mix of C++ and Python, and have also dabbled with tokamaks, and I think this is a (common) misunderstanding.
Fundamentally, you are optimizing the speed of project progress. Now, if you don't need your results in real time, e.g. because you are building a simulator which is not in a control loop for instance, you are often better at taking the easy language and ignoring the compute performance of the language. The compute performance of languages is a fixed multiplier, and with numerical code you might see things up to 10x.
But having readable code, which is easy to manipulate and change to test ideas, is speeding up the project progress by such a large factor, that it is hard to keep up with other languages. The reason python is omnipresent in machine learning, is not for a lack of trying of other languages. Python is just very good at allowing you to keep up with a fast-moving field.
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I write in a mix of C++ and Python, and have also dabbled with tokamaks, and I think this is a (common) misunderstanding.
Fundamentally, you are optimizing the speed of project progress. Now, if you don't need your results in real time, e.g. because you are building a simulator which is not in a control loop for instance, you are often better at taking the easy language and ignoring the compute performance of the language. The compute performance of languages is a fixed multiplier, and with numerical code you might see things up to 10x.
But having readable code, which is easy to manipulate and change to test ideas, is speeding up the project progress by such a large factor, that it is hard to keep up with other languages. The reason python is omnipresent in machine learning, is not for a lack of trying of other languages. Python is just very good at allowing you to keep up with a fast-moving field.