Scientific Computing with Python 3

There are currently two major versions of Python; the 2.x branch and the new 3.x branch. There are language incompatibilities between these branches and one has to be aware of which one to use. This book is based on the 3.x branch, considering the language up to release 3.5.

For this book you need to install the following:

The installation of these is eased by the so-called distribution packages. We recommend that you use Anaconda. The default screen of Spyder consists of an editor window on left, a console window in the lower right corner which gives access to an IPython shell and a help window in the upper right corner as shown in the following figure:

Figure 1.1: The default screen of Spyder consists of an editor window on left, a console window in the lower right corner which gives access to an IPython shell and a help window in the upper right corner.

Even if you have Python pre-installed on your computer, we recommend that you create your personal Python environment that allows you to work without the risk of accidentally affecting the software on which your computer's functionality might depend. With a virtual environment, such as Anaconda, you are free to change language versions and install packages without the unintended side-effects.

If the worst happens and you screw things up totally, just delete the Anaconda directory and start again. Running the Anaconda installer will install Python, a Python development environment and editor (Spyder), the shell IPython, and the most important packages for numerical computations, for example SciPy, NumPy, and matplotlib.

You can install additional packages with conda install within your virtual environment created by Anaconda (refer for official documentation from[2]).

Most Python codes will be collected in files. We recommend that you use the following header in all your Python files:

from scipy import *
from matplotlib.pyplot import *

With this, you make sure that all standard modules and functions used in this book, such as SciPy, are imported. Without this step, most of the examples in the book would raise errors. Many editors, such as Spyder, provide the possibility to create a template for your files. Look for this feature and put the preceding header into a template.

The Python shell is good but not optimal for interactive scripting. We therefore recommend using IPython instead (refer to [26] for the official documentation). IPython can be started in different ways:

You often want to execute the contents of a file. Depending on the location of the file on your computer, it is necessary to navigate to the correct location before executing the contents of a file.

      run myfile

Here are some tips on how to use IPython:

The Jupyter notebook is a fantastic tool for demonstrating your work. Students might want to use it to make and document homework and exercises and teachers can prepare lectures with it, even slides and web pages.

If you have installed Python via Anaconda, you already have everything for Jupyter in place. You can invoke the notebook by running the following command in the terminal window:

jupyter notebook

A browser window will open and you can interact with Python through your web browser.

If a line in a program contains the symbol #, everything following on the same line is considered as a comment:

# This is a comment of the following statement
a = 3  # ... which might get a further comment here  

A backslash \ at the end of the line marks the next line as a continuation line, that is, explicit line joining. If the line ends before all the parentheses are closed, the following line will automatically be recognized as a continuation line, that is, implicit line joining.

A number may be an integer, a real number, or a complex number. The usual operations are:

Here is an example:

2 ** (2 + 2) # 16
1j ** 2 # -1
1. + 3.0j

Strings are sequences of characters, enclosed by simple or double quotes:

'valid string'
"string with double quotes"
"you shouldn't forget comments"
'these are double quotes: ".." '

You can also use triple quotes for strings that have multiple lines:

"""This is
 a long,
 long string"""

A variable is a reference to an object. An object may have several references. One uses the assignment operator = to assign a value to a variable:

x = [3, 4] # a list object is created
y = x # this object now has two labels: x and y
del x # we delete one of the labels
del y # both labels are removed: the object is deleted

The value of a variable can be displayed by the print function:

x = [3, 4] # a list object is created
print(x)

Lists are a very useful construction and one of the basic types in Python. A Python list is an ordered list of objects enclosed by square brackets. One can access the elements of a list using zero-based indexes inside square brackets:

L1 = [5, 6]
L1[0] # 5
L1[1] # 6
L1[2] # raises IndexError
L2 = ['a', 1, [3, 4]]
L2[0] # 'a'
L2[2][0] # 3
L2[-1] # last element: [3,4]
L2[-2] # second to last: 1

Indexing of the elements starts at zero. One can put objects of any type inside a list, even other lists. Some basic list functions are as follows:

      print(list(range(5))) # returns [0, 1, 2, 3, 4]

      len(['a', 1, 2, 34]) # returns 4

      L = ['a', 'b', 'c']
      L[-1] # 'c'
      L.append('d')
      L # L is now ['a', 'b', 'c', 'd']
      L[-1] # 'd'

A Boolean expression is an expression that may have the value True or False. Some common operators that yield conditional expressions are as follow:

One combines different Boolean values with or and and. The keyword not , gives the logical negation of the expression that follows. Comparisons can be chained so that, for example, x < y < z is equivalent to x < y and y < z. The difference is that y is only evaluated once in the first example. In both cases, z is not evaluated at all when the first condition, x < y, evaluates to False:

2 >= 4  # False
2 < 3 < 4 # True
2 < 3 and 3 < 2 # False
2 != 3 < 4 or False # True
2 <= 2 and 2 >= 2 # True
not 2 == 3 # True
not False or True and False # True!

One typical use of a for loop is to repeat a certain task a fixed number of times:

n = 30
for iteration in range(n):
    do_something # this gets executed n times

The for statement has two important keywords: break and else. break quits the for loop even if the list we are iterating is not exhausted:

for x in x_values:
   if x > threshold:
     break
   print(x)

The finalizing else checks whether the for loop was broken with the break keyword. If it was not broken, the block following the else keyword is executed:

for x in x_values:
    if x > threshold:
       break
else:
    print("all the x are below the threshold")

Often one collects functions in a script. This creates a module with additional Python functionality. To demonstrate this, we create a module by collecting functions in a single file, for example smartfunctions.py:

def f(x):
    return 2*x + 1
def g(x):
    return x**2 + 4*x - 5
def h(x):
    return 1/f(x)

Again, the command exec(open('smartfunctions.py').read()) makes these functions available to your IPython shell (note that there is also the IPython magic function run). In Python terminology, one says that they are put into the actual namespace.

Alternatively, the modules  can be imported by the command import. It creates a named namespace. The command from puts the functions into the general namespace:

import smartfunctions
print(smartfunctions.f(2))      # 5

from smartfunctions import g    #import just this function
print(g(1)) # 0
  
from smartfunctions import *    #import all
print(h(2)*f(2))                # 1.0