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python_scicomp_notes.lyx
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Informal introduction to Python 3 for scientific computing
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Juha Jeronen
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Department of
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Laboratory of
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Mathematical Information Technology
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Electrical Energy Engineering
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University of Jyväskylä
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Tampere University of Technology
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juha.jeronen@{jyu,tut}.fi
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LECTURE NOTES
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Version 1.0.0
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Foreword
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These lecture notes are aimed mostly at students and researchers interested
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No previous background in computer science is assumed or required, but
basic or intermediate programming skill is useful.
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We assume no previous background in computer science or software engineering,
but basic or intermediate programming skill is useful.
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This does
\emph on
not
\emph default
aim to be a comprehensive general guide to Python for general-purpose programmi
ng.
For that, see the book by Mark Lutz
\emph on
: Learning Python, 5th ed., O'Reilly, 2013
\emph default
.
For newcomers to programming in general, there is also Zed A.
Shaw:
\emph on
Learn Python
\begin_inset space ~
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3 the Hard Way, Addison–Wesley, 2017
\emph default
.
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Instead, these notes bring together — in an extremely compressed form —
various aspects of Python as seen through the lens of scientific computing.
Unfortunately, it is impossible to cover everything at a hands-on level,
because that would expand this material into a proper book, which I don't
have the time to write right now.
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Hence, links to existing online material are provided, and the reader is
expected to follow them where appropriate.
All the links are clickable.
Although the text does show the raw URLs so that no information is lost
when printing, it may be more convenient to read this material digitally
(and while connected to the internet) than to print it out.
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Still, this material is rather long.
Wherever this does not stem simply from my own failure to summarize adequately,
it
\begin_inset space ~
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is because programming in Python, even when restricted to the use case of
scientific computing, is a large topic.
The task of explaining Python briefly is akin to that of explaining large-defor
mation elasticity in five minutes — up to, and including, tensor calculus
in general coordinate systems.
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Some aspects not traditionally emphasized in a scientific computing context
are explored, including the very basics of software engineering.
From my somewhat unusual vantage point, which is a superposition state,
\begin_inset Formula ${\scriptstyle 1/\sqrt{3}}$
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each, of information technology, pure and applied mathematics, and physics
and engineering sciences, I feel that IT and CS have a lot to offer to
scientists who program, and especially to those who either apply or study
numerics.
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Of all things, why software engineering? In an online essay on Python, physicist
Steve Byrnes
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Appendix 3 in
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makes the point that scientists often write code, but many scientists do
not know how to write
\emph on
good, correct software quickly
\emph default
.
But this is a solved problem! Software engineering is, precisely, a set
of tools and practices to do exactly that.
It is not only for software professionals, and contrary to popular belief,
it's not scary.
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Of course, an awareness of the very basics is more than enough to get started
in a scientific setting, and that is the extent to which we will touch
upon this topic in the final advanced section of these notes.
In fact, with MATLAB, you may have already used some software engineering
tools: a debugger, a performance profiler, and a (realtime) static code
analyzer that alerts you to problems such as assigned to but unused variables.
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In my own research work, I have used MATLAB in 2008–2012, and Python since
2012, at which point I felt it provided all the tools that my work needed,
and that the benefits outweighed the cost of switching.
The practical motivating factor behind writing these notes is that there
has now been interest toward Python for scientific computing, at both Universit
y of Jyväskylä and at Tampere University of Technology.
\end_layout
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Why teach Python right now? In my personal opinion: Python's capabilities
have for the most part equalled, and in some respects, surpassed MATLAB;
Python
\begin_inset space ~
\end_inset
3 can be considered a stable platform to build science on; and Python's
popularity shows not the faintest sign of waning — having observed, for
the last 15 years, Python take the programming world by storm in slow motion.
I'm by no means an early adopter of new programming languages!
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Although there are those who swear by the names of even more advanced languages
such as Haskell and Scheme, Python has the advantage that it has entered
the mainstream, and comes with ample software libraries and community support.
Someone, somewhere on the internet always knows how to do the particular
technical thing you want — and likely, also how to obtain optimal performance
while at it.
\end_layout
\begin_layout Standard
Also, Python is a huge step up in a scientist's productivity when compared
to C, C++ and Fortran.
Simultaneously, Python is a proper general-purpose programming language,
in contrast to a domain-specific collection of tools like MATLAB.
\emph on
Python is a general high-level language done right (at least to a large
extent)
\emph default
.
\end_layout
\begin_layout Standard
This material contains a large number of links to the internet.
If any links have gone dead, or if you find mistakes, please do tell me.
Or even, feel free to send a PR on GitHub.
\end_layout
\begin_layout Standard
Oh, and, welcome to the revolution.
\end_layout
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–JJ 2017-10-07
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License
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BY-CC-SA 4.0
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Homepage
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(Please excuse the temporary lack of proper branding.)
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Changelog
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This project adheres to
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...as far as it makes sense for a set of lecture notes to adhere to a standard
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software
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Thus, we interpret the rules as follows:
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The API
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\emph on
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Version history below.
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v1.0.0 [2017-10-07]: initial release
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Motivation
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What is Python?
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Let's hear it from the horse's mouth:
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begin{verbatim}
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jje@arcturus:~$ man python
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PYTHON(1) General Commands Manual PYTHON(1)
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[...]
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DESCRIPTION Python is an interpreted, interactive, object-orie
nted
\end_layout
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programming language that combines remarkable
power
\end_layout
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with very clear syntax.
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Why use Python?
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Of course the answer depends on the scientist, but let's see if we can
find a common theme.
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Paraphrasing an online essay by physicist Steve Byrnes,
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Free and open source
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Cleaner syntax compared to MATLAB
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General-purpose programming language, with extensive examples already on
the internet on how to do
\emph on
almost anything a computer can do
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Very high abstraction level, but easy(-ish) tools available also for writing
performance-critical parts
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Python is a valuable career skill (true for MATLAB, too)
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Rising trend; more and more of your current and future colleagues know Python
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Easy to learn; many learning resources available
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From a blog post by data scientist Steve Tjoa,
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Completely free
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General–purpose language
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Beautiful syntax
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