Python tutorial - Steve Byrnes`s Homepage

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Python tutorial for scientific computing Steve Byrnes 16 March 2012

Terminology • Python is a general-purpose programming language – From NASA to YouTube…

• Python “package”/ “module” / “library” is what you download (or write) to get additional functions and definitions. Examples: – NumPy for fast array computations and manipulations and linear algebra. – SciPy for optimization, image-processing, interpolation, numerical integration, etc. – Matplotlib for making graphs.

• “Python distribution” is a way to download Python and various packages, either a la carte or all at once. – There’s a standard one, “Cpython”, at Others include EnThought, SAGE, Python(x,y), PortablePython, …

Terminology • “Python 2” and “Python 3” are different versions, not compatible with each other. NumPy only exists in Python 2 [so far], so use that. • “Integrated development environment” (IDE) is a program for writing programs: Text-editor, debugger, etc. – e.g. “Spyder” – Python IDEs are often, themselves, written in Python, i.e. they are Python packages.

Spyder, an IDE

Installation • As with everything opensource, installation is one of the most confusing parts for beginners (at least, it was for me.) • id=67 • Watch out Mac OS X users: NumPy is incompatible with your built-in Python.

Nice things about Python (vs MATLAB) • Lots of little things… – You don’t have to end every command with a semicolon – If you want to write a function, it doesn’t need to be in its own file, where the filename is the same as the function name. – etc. etc.…

• Free

Nice things about Python (vs MATLAB) • MATLAB becomes increasingly useless as you get farther away from matrices. Python is equally good at everything. – Someday maybe I’ll want to… • • • • • • •

turn something into a stand-alone program with a GUI pull data out of a pdf interface with hardware and instruments draw a 3D picture write a script to reorganize files on my computer put some interactive feature on my website …

– Python always has a professional-quality module for it!


Google search “numpy matlab” to find this chart.


Pretty graphics with Matplotlib • Awfully similar to Matlab, even down to arbitrary details like terminology, default color-scheme, etc.



I couldn’t (at the time) figure out how to refine the colormap in Matlab, although I got it later. Main reason I ported: Matlab SVG output wasn’t working for me. [Not built-in.]

Define a function • Define a function

• Use a function

def f(a,b): c = a * b return abs(c**2)

x = f(3,5)

Console versus Modules • Simplest calculations can be done directly in an interactive console. (“Consoles” are usually powered by “IPython”.)

Modules • For more complicated things, you write one or more programs / functions in a “module”, “” In: from temptest import * In: myfunction1(10) Out: 15 In: myfunction2(10) Out: 16

Saving isn’t enough! Also need to reload the module every time you change it.

In: reload(temptest); myfunction1(10) Out: Error… In: reload(temptest); myfunction1(10) Out: 15

Modules • Most normal math stuff like cos, conjugate, pi, etc., are actually in NumPy, not Python itself. Can use… “Best practices” Everyday use

(Avoids the risk that you’ll accidentally define a function with the same name as a numpy function you forgot about; Easier for other people to identify the functions.)

In: from numpy import *

In: import numpy

In: import numpy as np

In: cos(0) Out: 1.

In: numpy.cos(0) Out: 1.

In: np.cos(0) Out: 1.

In Spyder, “from numpy import *” is run automatically every time you open the program. So you can use cos, pi, etc., in the console. But inside a module, you still have to import these functions yourself.

Modules • The functions you usually want for plotting are in the module matplotlib.pyplot [a “submodule” of matplotlib]. Again, in the Spyder console, In: from matplotlib.pyplot import * In: plot([1,2,4])

you don’t need the first line, because Spyder runs it automatically every time you open the program.

Actually I think Spyder runs: from matplotlib.pylab import * instead of importing pyplot and numpy functions separately.

Scripts • Scripts are an alternative to modules, good for everyday use. – The command is “run” (in IPython) or “execfile” (in general). In the console… In: run

In the file… a += 1 print a

4 In: run 5 In: run 6

NumPy arrays versus Python lists • Python lists: Very general – a = [1,2] – b = [[1,2],[3,4]] – c = [[1,2, ’duh’],[3,[4]]]

• NumPy arrays: – – – –

x = array([1,2]) y = array([[1,2],[3,4]]) All rows must have same length, etc. All entries must have same data-type, e.g. all real or all complex.

• Always use NumPy arrays when you’re doing something with the data: – Math – Matrix addition, dot-product, conjugation… – Manipulating elements of the array – Reshaping, rearranging, combining, pulling out a sub-array, etc.

Warning: Integer division In: 7/3 Out: 2

Integer division is rounded down (towards negative infinity).

In: 7./3 Out: 2.3333333 In: 7/3. Out: 2.3333333

This unfortunate default gets fixed in Python 3. In the meantime, start every module and console session with: from __future__ import division

Warning: Array copying In: a=array([[1,2],[3,4]])

In: b=a In: b[0,0] = 100 In: a Out: array([[100,2],[3,4]]) In: a=array([[1,2],[3,4]]) In: b=a[:,0]

The behavior makes sense if you think of “a” as NOT a list of numbers but INSTEAD as a description of where I should look, in the computer’s RAM, to find a certain list of numbers. In the bottom-left example, b is a “view” of the data in a. FIXED In: a=array([[1,2],[3,4]])

In: b Out: array([1, 3])

In: b=a.copy()

In: b[0] = 100

In: b[0,0] = 100

In: a Out: array([[100,2],[3,4]])

In: a Out: array([[1,2],[3,4]])

Warning: Arrays in functions FIXED def messwitharray(a): a[0] = 57 return a[1]+a[2] In: a = array([1,2,3]) In: b = messwitharray(a) In: b Out: 5 In: a Out: array([57,2,3])

Solution: Put a2=a.copy() at the start of the function, then you can freely mess around with a2.

def messwitharray(a_temp): a = a_temp.copy() a[0] = 57 return a[1]+a[2]

........OR........ In: b = messwitharray(a.copy())

The behavior makes sense if you think of “a” as NOT a list of numbers but INSTEAD as a description of where I should look, in the computer’s RAM, to find a certain list of numbers.

When in doubt, copy()!! #define an array a = array([[1,2],[3,4],[5,6]]) #pull out the first two rows b = a[0:2].copy()

#also need the transpose c = b.T.copy() #run a function d = f(b.copy(), c.copy())

You can always take them out later on! The exact same warnings and suggestions apply to any “mutable object”, including built-in python arrays.

Define a function with multiple return values. A few options: Return a “Python list”

Return a “Python tuple”

import numpy as np

import numpy as np

def polar(z): phi = np.angle(z) abs_val = abs(z) return [phi, abs_val]

def polar(z): phi = np.angle(z) abs_val = abs(z) return (phi, abs_val)

[t,r] = polar(4+8j)

t,r = polar(4+8j)

import numpy as np def polar(z): phi = np.angle(z) abs_val = abs(z) return {’angle’:phi, ’abs’:abs_val} results = polar(4+8j) t = results[’angle’] r = results[’abs’]

My favorite: Return a “Python dictionary”. Code is easier to understand and less prone to error. [Even fancier options: Return an “object” in a custom “class”; return a “named tuple”]

Python programming: if

Python programming: for, range

There are “continue” and “break” commands for for loops too.

Python programming: “White space” • For “if”, “for”, “def”, “else”, etc. [commands ending in ‘:’ ], the associated code is whatever is indented afterwards.

“else” goes with “for” not “if”.

Python programming: “White space” • The end of a line is the end of a command. – For longer commands, use parentheses…when there’s dangling parentheses / brackets, Python assumes the command is continuing to the next line. [Alternative: End a line with a backslash.]

Importing data – an example. [File has unknown number of header rows.] “csv” is a standard Python package for reading data-files. Good practice: Start each function with a block-quote describing it.

Consecutive spaces are treated as just one separator.

Convert each item to a real number and put it in a list, then append the list as a new row in “A”. If you can’t convert to a real number, then it’s a header row. Don’t do anything. Convert “A” from a list-of-lists to a NumPy 2D array.

More information • has links at the bottom to the standard Python and NumPy tutorials, and also listings of Python packages you can freely use.

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