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One of the strengths of Python is that there are many built-in add-ons - or modules - which allow you to do complex tasks in only a few lines of code. In addition, there are many other third-party modules (e.g. Numpy, Scipy, Matplotlib) that can be installed, and you can also develop your own modules that include functionalities you commonly use. From experience, it is advised to break down your workflow in the most basic and general elementary functions, that are responsible for one specific task (e.g. input/output). These can then easily be reused in later projects, or used to build complex programs.
The built-in modules are referred to as the Standard Library, and you can find a full list of the available functionality in the Python Documentation.
To use modules in your Python session or script, you need to import them. The following example shows how to import the built-in os module, which contains amongst other things many useful functions relating to files and paths:
In [1]: import os
This will give you access to functions available within this module, which you can now access if you use the module name as a prefix. For example, if we want to check if a file data/m31.fits exists, we can use the os.path.exists function:
In [2]: os.path.exists('data/m31.fits')
Out[2]: False
In this case, we can use the function in an if statement, since it returns a boolean:
In [3]: if os.path.exists('data/m31.fits'):
...: print("The file exists")
...: else:
...: print("The file does not exist")
...:
The file does not exist
Note
As with objects in Python, once you have imported a module, you can (in IPython) type the name of the module, followed by ., then press TAB to see the available functions!
If a module name is too long to be conveniently written each time you want to use a function, you can define a shortcut when you import it:
In [4]: import matplotlib.pyplot as plt
In [5]: fig = plt.figure()
In the following workshops we will look at a number of third-party modules in more detail, such as numpy for creating and manipulating high performance arrays, scipy for scientific computing and matplotlib.pyplot for plotting.
The main difference between scripts and modules is that if you execute scripts, typically a series of tasks will be performed, while if you execute modules, typically only a number of functions become available. Consider this small example of a script, where we happen to have also defined a function:
def add_two_numbers(x1,x2):
return x1+x2
y = add_two_numbers(4,3)
print(y)
If you save the contents to a file, you can execute it in a terminal:
$:> python myscript.py
7
Consider also this small example of a module, where nothing is executed:
def add_two_numbers(x1,x2):
return x1+x2
saving this to a file and executing it, will not do anything:
$:> python mymodule.py
Python does not make a clear distinction between the script and a module, in the sense that you can also import a script. What Python will do is execute the script, while at the same time the function becomes available:
In [1]: import myscript
7
In [2]: myscript.add_two_numbers(4,3)
7
It is not good practice to run a Python script by importing it! Still, you might want to make a certain script also available as a module, if you defined some generally useful functions in there. You can do this by adding the statement if __name__=="__main__":
def add_two_numbers(x1,x2):
return x1+x2
if __name__=="__main__":
y = add_two_numbers(4,3)
print(y)
This statement checks if a script is imported as a module or is run from a terminal. If you save this to a file, then you get the following behaviour from a terminal:
$:> python myscript2.py
7
and from a Python script:
In [1]: import myscript2
In [2]: myscript2.add_two_numbers(4,3)
7
Note that now, the import statement does not execute the part hidden in the if statement.
You can readily import any Python file that is located in the current working directory. If you want to make your own packages globally available or you want to share them with other people, there are two ways of advancing:
Simply add the directory where you package is located to the PYTHONPATH. For example in bash:
$:> export PYTHONPATH="$PYTHONPATH:/home/user/python"This statement can be added to your bash_profile, so that it is evaluated each time you open a terminal.
Use distutils to package your library. This allows users to install your package into their default Python installation. It is extremely powerful, in the sense that it can also automatically compile Fortran and C extensions.