Introduction to Python for Computational Science and Engineering [pdf]
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Re: Introduction to Python for Computational Science and Engineering [pdf]
#2This is a very good guide. I thought I knew python ecosystem well and I found something new for myself ('visual' package for 3D illustrations).
I am wondering if there are guides for the "reverse direction": I already know how to program, but I want to learn new scientific domain that is interesting to me: e.g. material science, climate modeling, etc. Something like Rosalind[1] does for bioinformatics.
Re: Introduction to Python for Computational Science and Engineering [pdf]
#3Python3 as default dialect!
Re: Introduction to Python for Computational Science and Engineering [pdf]
#4> As Python 2.x is still the default Python on many system and there are a fair number of research codes out there based on Python 2, we will use Python 2.x in this book.
This is so unfortunate. Scientific computing is riddled with technical debt and starting with Python 2 today is fairly irresponsible. If you're already invested in Python 2 and have code/training written up, fine. But if you're learning it just now, as the book's audience obviously is, picking Python 3 should be a no brainer.
Re: Introduction to Python for Computational Science and Engineering [pdf]
#5> As Python 2.x is still the default Python on many system and there are a fair number of research codes out there based on Python 2, we will use Python 2.x in this book. This is so unfortunate. Scientific computing is riddled with technical debt and starting with Python 2 today is fairly irresponsible. If you're already invested in Python 2 and have code/training written up, fine. But if you're learning it just now,…
I'm a hardcore proponent of Python 3 (because it's the only Python I've ever used/learned), but I'm OK with this book accepting the realities and for at least committing to writing 3.x compatible code:
> However, we will write code that is as much as possible in the Python 3 style (and understood by Python 2). The most prominent example is that in Python 2.x, the print command is special where as in Python 3 it is an ordinary function.
I don't specialize in the purported domain of this book, but if the author thinks that `print` will be the most prominent differentiator, then I'm guessing that there aren't a ton of situations where it'll be hard to make his examples 3.x compatible (for this domain, I'm assuming behavior of the division operator will be another prominent, but easy-to-fix difference).
Re: Introduction to Python for Computational Science and Engineering [pdf]
#6> As Python 2.x is still the default Python on many system and there are a fair number of research codes out there based on Python 2, we will use Python 2.x in this book. This is so unfortunate. Scientific computing is riddled with technical debt and starting with Python 2 today is fairly irresponsible. If you're already invested in Python 2 and have code/training written up, fine. But if you're learning it just now,…
I'd be happy to hear examples how Python 3 helps us in computational sciences. I've barely even tried it before and have used Python 2 a lot.
Re: Introduction to Python for Computational Science and Engineering [pdf]
#7> As Python 2.x is still the default Python on many system and there are a fair number of research codes out there based on Python 2, we will use Python 2.x in this book. This is so unfortunate. Scientific computing is riddled with technical debt and starting with Python 2 today is fairly irresponsible. If you're already invested in Python 2 and have code/training written up, fine. But if you're learning it just now,…
I'm a hardcore proponent of Python 3 (because it's the only Python I've ever used/learned), but I'm OK with this book accepting the realities and for at least committing to writing 3.x compatible code: > However, we will write code that is as much as possible in the Python 3 style (and understood by Python 2). The most prominent example is that in Python 2.x, the print command is special where as in Python 3 it is an…
Reduce is easy to fix also, but that ignores the entire reasoning for it being removed and only serves to highlight the massive chasm between 2 and 3.
The clean break of 3 and the sanity it brings to the language is undeniable.
Re: Introduction to Python for Computational Science and Engineering [pdf]
#8> As Python 2.x is still the default Python on many system and there are a fair number of research codes out there based on Python 2, we will use Python 2.x in this book. This is so unfortunate. Scientific computing is riddled with technical debt and starting with Python 2 today is fairly irresponsible. If you're already invested in Python 2 and have code/training written up, fine. But if you're learning it just now,…
I'd be happy to hear examples how Python 3 helps us in computational sciences. I've barely even tried it before and have used Python 2 a lot.
It's not that it helps in a particular way (though more on that below), it's just that starting with Python 2 now necessarily leads to rewrites later on. While you can see packages not supporting Python 3 (there are fewer and fewer of those), in the future you will see the opposite, some project already announcing end dates for their Py2 support (IPython to give an example).
I have to stress that I used 2to3 to tackle the vast majority of conversion issues. It was on a small codebase and, but still, it worked rather well. While I feel the transition has not gone terribly smoothly, I truly believe Python 3 is a better language - and as I noted in a different thread [0] - the unicode support alone is worth it (for me). While not significantly beneficial in the vast majority of computational sciences, it is helpful in some areas - linguistics to give an example.
Re: Introduction to Python for Computational Science and Engineering [pdf]
#9> As Python 2.x is still the default Python on many system and there are a fair number of research codes out there based on Python 2, we will use Python 2.x in this book. This is so unfortunate. Scientific computing is riddled with technical debt and starting with Python 2 today is fairly irresponsible. If you're already invested in Python 2 and have code/training written up, fine. But if you're learning it just now,…
IMO, Python 3's support for matrix multiplication using the @ operator is itself worth the cost of admission. Much of technical computing is just implementing algorithms that use linear algebra extensively, and if you're coming from matlab littering your code with dot(dot(X,Y),Z) is a real pain
Re: Introduction to Python for Computational Science and Engineering [pdf]
#10This is a very good guide. I thought I knew python ecosystem well and I found something new for myself ('visual' package for 3D illustrations). I am wondering if there are guides for the "reverse direction": I already know how to program, but I want to learn new scientific domain that is interesting to me: e.g. material science, climate modeling, etc. Something like Rosalind[1] does for bioinformatics. [1] http://ros…
Well if you want to learn the science, you can just pick up a science book. I know many users here have good suggestions. But if you are specifically looking for scientific computing those generally talk about code basics. There are also numeric programming books. If you're trying to get into those studies I suggest going through a numerics book because the techniques will be similar among any of the code. But you'll have to spend time learning the non-coding parts as well. In science coding is just a tool.