1.1. Scientific computing with tools and workflow

Authors: Fernando Perez, Emmanuelle Gouillart, Gaël Varoquaux, Valentin Haenel

1.1.1. Why Python?

1.1.1.1. The scientist’s needs

  • Get data (simulation, experiment control),
  • Manipulate and process data,
  • Visualize results (to understand what we are doing!),
  • Communicate results: produce figures for reports or publications, write presentations.

1.1.1.2. Specifications

  • Rich collection of already existing bricks corresponding to classical numerical methods or basic actions: we don’t want to re-program the plotting of a curve, a Fourier transform or a fitting algorithm. Don’t reinvent the wheel!
  • Easy to learn: computer science is neither our job nor our education. We want to be able to draw a curve, smooth a signal, do a Fourier transform in a few minutes.
  • Easy communication with collaborators, students, customers, to make the code live within a lab or a company: the code should be as readable as a book. Thus, the language should contain as few syntax symbols or unneeded routines as possible that would divert the reader from the mathematical or scientific understanding of the code.
  • Efficient code that executes quickly... but needless to say that a very fast code becomes useless if we spend too much time writing it. So, we need both a quick development time and a quick execution time.
  • A single environment/language for everything, if possible, to avoid learning a new software for each new problem.

1.1.1.3. Existing solutions

Which solutions do scientists use to work?

Compiled languages: C, C++, Fortran, etc.

  • Advantages:
    • Very fast. Very optimized compilers. For heavy computations, it’s difficult to outperform these languages.
    • Some very optimized scientific libraries have been written for these languages. Example: BLAS (vector/matrix operations)
  • Drawbacks:
    • Painful usage: no interactivity during development, mandatory compilation steps, verbose syntax (&, ::, }}, ; etc.), manual memory management (tricky in C). These are difficult languages for non computer scientists.

Scripting languages: Matlab

  • Advantages:
    • Very rich collection of libraries with numerous algorithms, for many different domains. Fast execution because these libraries are often written in a compiled language.
    • Pleasant development environment: comprehensive and well organized help, integrated editor, etc.
    • Commercial support is available.
  • Drawbacks:
    • Base language is quite poor and can become restrictive for advanced users.
    • Not free.

Other scripting languages: Scilab, Octave, Igor, R, IDL, etc.

  • Advantages:
    • Open-source, free, or at least cheaper than Matlab.
    • Some features can be very advanced (statistics in R, figures in Igor, etc.)
  • Drawbacks:
    • Fewer available algorithms than in Matlab, and the language is not more advanced.
    • Some software are dedicated to one domain. Ex: Gnuplot or xmgrace to draw curves. These programs are very powerful, but they are restricted to a single type of usage, such as plotting.

What about Python?

  • Advantages:
    • Very rich scientific computing libraries (a bit less than Matlab, though)
    • Well thought out language, allowing to write very readable and well structured code: we “code what we think”.
    • Many libraries for other tasks than scientific computing (web server management, serial port access, etc.)
    • Free and open-source software, widely spread, with a vibrant community.
  • Drawbacks:
    • less pleasant development environment than, for example, Matlab. (More geek-oriented).
    • Not all the algorithms that can be found in more specialized software or toolboxes.

1.1.2. Scientific Python building blocks

Unlike Matlab, Scilab or R, Python does not come with a pre-bundled set of modules for scientific computing. Below are the basic building blocks that can be combined to obtain a scientific computing environment:

  • Python, a generic and modern computing language

    • Python language: data types (string, int), flow control, data collections (lists, dictionaries), patterns, etc.
    • Modules of the standard library.
    • A large number of specialized modules or applications written in Python: web protocols, web framework, etc. ... and scientific computing.
    • Development tools (automatic testing, documentation generation)
    ../_images/snapshot_ipython.png

  • IPython, an advanced Python shell http://ipython.org/

  • Numpy : provides powerful numerical arrays objects, and routines to manipulate them. http://www.numpy.org/

1.1.3. The interactive workflow: IPython and a text editor

Interactive work to test and understand algorithms: In this section, we describe an interactive workflow with IPython that is handy to explore and understand algorithms.

Python is a general-purpose language. As such, there is not one blessed environment to work in, and not only one way of using it. Although this makes it harder for beginners to find their way, it makes it possible for Python to be used to write programs, in web servers, or embedded devices.

Reference document for this section:

IPython user manual: http://ipython.org/ipython-doc/dev/index.html

1.1.3.1. Command line interaction

Start ipython:

In [1]: print('Hello world')

Hello world

Getting help by using the ? operator after an object:

In [2]: print?

Type:               builtin_function_or_method

Base Class:         <type 'builtin_function_or_method'>

String Form:        <built-in function print>

Namespace:          Python builtin

Docstring:

    print(value, ..., sep=' ', end='\n', file=sys.stdout)



    Prints the values to a stream, or to sys.stdout by default.

    Optional keyword arguments:

    file: a file-like object (stream); defaults to the current sys.stdout.

    sep:  string inserted between values, default a space.

    end:  string appended after the last value, default a newline.

1.1.3.2. Elaboration of the algorithm in an editor

Create a file my_file.py in a text editor. Under EPD (Enthought Python Distribution), you can use Scite, available from the start menu. Under Python(x,y), you can use Spyder. Under Ubuntu, if you don’t already have your favorite editor, we would advise installing Stani’s Python editor. In the file, add the following lines:

s = 'Hello world'

print(s)

Now, you can run it in IPython and explore the resulting variables:

In [1]: %run my_file.py

Hello world



In [2]: s

Out[2]: 'Hello world'



In [3]: %whos

Variable   Type    Data/Info

----------------------------

s          str     Hello world

From a script to functions

While it is tempting to work only with scripts, that is a file full of instructions following each other, do plan to progressively evolve the script to a set of functions:

  • A script is not reusable, functions are.
  • Thinking in terms of functions helps breaking the problem in small blocks.

1.1.3.3. IPython Tips and Tricks

The IPython user manual contains a wealth of information about using IPython, but to get you started we want to give you a quick introduction to four useful features: history, magic functions, aliases and tab completion.

Like a UNIX shell, IPython supports command history. Type up and down to navigate previously typed commands:

In [1]: x = 10



In [2]: <UP>



In [2]: x = 10

IPython supports so called magic functions by prefixing a command with the % character. For example, the run and whos functions from the previous section are magic functions. Note that, the setting automagic, which is enabled by default, allows you to omit the preceding % sign. Thus, you can just type the magic function and it will work.

Other useful magic functions are:

  • %cd to change the current directory.

    In [2]: cd /tmp

    /tmp

  • %timeit allows you to time the execution of short snippets using the timeit module from the standard library:

    In [3]: timeit x = 10

    10000000 loops, best of 3: 39 ns per loop

  • %cpaste allows you to paste code, especially code from websites which has been prefixed with the standard Python prompt (e.g. >>>) or with an ipython prompt, (e.g. in [3]):

    In [5]: cpaste

    Pasting code; enter '--' alone on the line to stop or use Ctrl-D.

    :In [3]: timeit x = 10

    :--

    10000000 loops, best of 3: 85.9 ns per loop

    In [6]: cpaste

    Pasting code; enter '--' alone on the line to stop or use Ctrl-D.

    :>>> timeit x = 10

    :--

    10000000 loops, best of 3: 86 ns per loop

  • %debug allows you to enter post-mortem debugging. That is to say, if the code you try to execute, raises an exception, using %debug will enter the debugger at the point where the exception was thrown.

    In [7]: x === 10

      File "<ipython-input-6-12fd421b5f28>", line 1

        x === 10

            ^

    SyntaxError: invalid syntax

    

    

    In [8]: debug

    > /.../IPython/core/compilerop.py (87)ast_parse()

         86         and are passed to the built-in compile function."""

    ---> 87         return compile(source, filename, symbol, self.flags | PyCF_ONLY_AST, 1)

         88

    

    ipdb>locals()

    {'source': u'x === 10\n', 'symbol': 'exec', 'self':

    <IPython.core.compilerop.CachingCompiler instance at 0x2ad8ef0>,

    'filename': '<ipython-input-6-12fd421b5f28>'}

IPython help

  • The built-in IPython cheat-sheet is accessible via the %quickref magic function.
  • A list of all available magic functions is shown when typing %magic.

Furthermore IPython ships with various aliases which emulate common UNIX command line tools such as ls to list files, cp to copy files and rm to remove files. A list of aliases is shown when typing alias:

In [1]: alias

Total number of aliases: 16

Out[1]:

[('cat', 'cat'),

('clear', 'clear'),

('cp', 'cp -i'),

('ldir', 'ls -F -o --color %l | grep /$'),

('less', 'less'),

('lf', 'ls -F -o --color %l | grep ^-'),

('lk', 'ls -F -o --color %l | grep ^l'),

('ll', 'ls -F -o --color'),

('ls', 'ls -F --color'),

('lx', 'ls -F -o --color %l | grep ^-..x'),

('man', 'man'),

('mkdir', 'mkdir'),

('more', 'more'),

('mv', 'mv -i'),

('rm', 'rm -i'),

('rmdir', 'rmdir')]

Lastly, we would like to mention the tab completion feature, whose description we cite directly from the IPython manual:

Tab completion, especially for attributes, is a convenient way to explore the structure of any object you’re dealing with. Simply type object_name.<TAB> to view the object’s attributes. Besides Python objects and keywords, tab completion also works on file and directory names.

In [1]: x = 10



In [2]: x.<TAB>

x.bit_length   x.conjugate    x.denominator  x.imag         x.numerator

x.real



In [3]: x.real.

x.real.bit_length   x.real.denominator  x.real.numerator

x.real.conjugate    x.real.imag         x.real.real



In [4]: x.real.