1.2.5. Reusing code: scripts and modules¶
For now, we have typed all instructions in the interpreter. For longer sets of instructions we need to change track and write the code in text files (using a text editor), that we will call either scripts or modules. Use your favorite text editor (provided it offers syntax highlighting for Python), or the editor that comes with the Scientific Python Suite you may be using (e.g., Scite with Python(x,y)).
Let us first write a script, that is a file with a sequence of instructions that are executed each time the script is called. Instructions may be e.g. copied-and-pasted from the interpreter (but take care to respect indentation rules!).
The extension for Python files is
.py. Write or copy-and-paste the
following lines in a file called
message = "Hello how are you?" for word in message.split(): print word
Let us now execute the script interactively, that is inside the Ipython interpreter. This is maybe the most common use of scripts in scientific computing.
in Ipython, the syntax to execute a script is
%run script.py. For
In : %run test.py Hello how are you? In : message Out: 'Hello how are you?'
The script has been executed. Moreover the variables defined in the
script (such as
message) are now available inside the interpreter’s
Other interpreters also offer the possibility to execute scripts
execfile in the plain Python interpreter, etc.).
It is also possible In order to execute this script as a standalone program, by executing the script inside a shell terminal (Linux/Mac console or cmd Windows console). For example, if we are in the same directory as the test.py file, we can execute this in a console:
$ python test.py Hello how are you?
Standalone scripts may also take command-line arguments
import sys print sys.argv
$ python file.py test arguments ['file.py', 'test', 'arguments']
Don’t implement option parsing yourself. Use modules such as
18.104.22.168. Importing objects from modules¶
In : import os In : os Out: <module 'os' from '/usr/lib/python2.6/os.pyc'> In : os.listdir('.') Out: ['conf.py', 'basic_types.rst', 'control_flow.rst', 'functions.rst', 'python_language.rst', 'reusing.rst', 'file_io.rst', 'exceptions.rst', 'workflow.rst', 'index.rst']
In : from os import listdir
In : import numpy as np
from os import *
This is called the star import and please, Use it with caution
- Makes the code harder to read and understand: where do symbols come from?
- Makes it impossible to guess the functionality by the context and the name (hint: os.name is the name of the OS), and to profit usefully from tab completion.
- Restricts the variable names you can use: os.name might override name, or vise-versa.
- Creates possible name clashes between modules.
- Makes the code impossible to statically check for undefined symbols.
Modules are thus a good way to organize code in a hierarchical way. Actually, all the scientific computing tools we are going to use are modules:
>>> import numpy as np # data arrays >>> np.linspace(0, 10, 6) array([ 0., 2., 4., 6., 8., 10.]) >>> import scipy # scientific computing
In Python(x,y), Ipython(x,y) executes the following imports at startup:
>>> import numpy >>> import numpy as np >>> from pylab import * >>> import scipy
and it is not necessary to re-import these modules.
22.214.171.124. Creating modules¶
If we want to write larger and better organized programs (compared to simple scripts), where some objects are defined, (variables, functions, classes) and that we want to reuse several times, we have to create our own modules.
Let us create a module
demo contained in the file
"A demo module." def print_b(): "Prints b." print 'b' def print_a(): "Prints a." print 'a' c = 2 d = 2
In this file, we defined two functions
we want to call the
print_a function from the interpreter. We could
execute the file as a script, but since we just want to have access to
print_a, we are rather going to import it as a module.
The syntax is as follows.
In : import demo In : demo.print_a() a In : demo.print_b() b
Importing the module gives access to its objects, using the
module.object syntax. Don’t forget to put the module’s name before the
object’s name, otherwise Python won’t recognize the instruction.
In : demo? Type: module Base Class: <type 'module'> String Form: <module 'demo' from 'demo.py'> Namespace: Interactive File: /home/varoquau/Projects/Python_talks/scipy_2009_tutorial/source/demo.py Docstring: A demo module. In : who demo In : whos Variable Type Data/Info ------------------------------ demo module <module 'demo' from 'demo.py'> In : dir(demo) Out: ['__builtins__', '__doc__', '__file__', '__name__', '__package__', 'c', 'd', 'print_a', 'print_b'] In : demo. demo.__builtins__ demo.__init__ demo.__str__ demo.__class__ demo.__name__ demo.__subclasshook__ demo.__delattr__ demo.__new__ demo.c demo.__dict__ demo.__package__ demo.d demo.__doc__ demo.__reduce__ demo.print_a demo.__file__ demo.__reduce_ex__ demo.print_b demo.__format__ demo.__repr__ demo.py demo.__getattribute__ demo.__setattr__ demo.pyc demo.__hash__ demo.__sizeof__
Importing objects from modules into the main namespace
In : from demo import print_a, print_b In : whos Variable Type Data/Info -------------------------------- demo module <module 'demo' from 'demo.py'> print_a function <function print_a at 0xb7421534> print_b function <function print_b at 0xb74214c4> In : print_a() a
Modules are cached: if you modify
demo.pyand re-import it in the old session, you will get the old one.
In : reload(demo)
In Python3 instead
reload is not builtin, so you have to import the
importlib module first and then do:
In : importlib.reload(demo)
126.96.36.199. ‘__main__’ and module loading¶
Sometimes we want code to be executed when a module is
run directly, but not when it is imported by another module.
if __name__ == '__main__' allows us to check whether the
module is being run directly.
def print_b(): "Prints b." print 'b' def print_a(): "Prints a." print 'a' # print_b() runs on import print_b() if __name__ == '__main__': # print_a() is only executed when the module is run directly. print_a()
In : import demo2 b In : import demo2
In : %run demo2 b a
188.8.131.52. Scripts or modules? How to organize your code¶
Rule of thumb
- Sets of instructions that are called several times should be written inside functions for better code reusability.
- Functions (or other bits of code) that are called from several scripts should be written inside a module, so that only the module is imported in the different scripts (do not copy-and-paste your functions in the different scripts!).
184.108.40.206.1. How modules are found and imported¶
import mymodule statement is executed, the module
is searched in a given list of directories. This list includes a list
of installation-dependent default path (e.g.,
well as the list of directories specified by the environment variable
The list of directories searched by Python is given by the
In : import sys In : sys.path Out: ['', '/home/varoquau/.local/bin', '/usr/lib/python2.7', '/home/varoquau/.local/lib/python2.7/site-packages', '/usr/lib/python2.7/dist-packages', '/usr/local/lib/python2.7/dist-packages', ...]
Modules must be located in the search path, therefore you can:
write your own modules within directories already defined in the search path (e.g.
$HOME/.local/lib/python2.7/dist-packages). You may use symbolic links (on Linux) to keep the code somewhere else.
modify the environment variable
PYTHONPATHto include the directories containing the user-defined modules.
On Linux/Unix, add the following line to a file read by the shell at startup (e.g. /etc/profile, .profile)
On Windows, http://support.microsoft.com/kb/310519 explains how to handle environment variables.
or modify the
sys.pathvariable itself within a Python script.
import sys new_path = '/home/emma/user_defined_modules' if new_path not in sys.path: sys.path.append(new_path)
This method is not very robust, however, because it makes the code less portable (user-dependent path) and because you have to add the directory to your sys.path each time you want to import from a module in this directory.
See https://docs.python.org/tutorial/modules.html for more information about modules.
A directory that contains many modules is called a package. A package
is a module with submodules (which can have submodules themselves, etc.).
A special file called
__init__.py (which may be empty) tells Python
that the directory is a Python package, from which modules can be
$ ls cluster/ io/ README.txt@ stsci/ __config__.py@ LATEST.txt@ setup.py@ __svn_version__.py@ __config__.pyc lib/ setup.pyc __svn_version__.pyc constants/ linalg/ setupscons.py@ THANKS.txt@ fftpack/ linsolve/ setupscons.pyc TOCHANGE.txt@ __init__.py@ maxentropy/ signal/ version.py@ __init__.pyc misc/ sparse/ version.pyc INSTALL.txt@ ndimage/ spatial/ weave/ integrate/ odr/ special/ interpolate/ optimize/ stats/ $ cd ndimage $ ls doccer.py@ fourier.pyc interpolation.py@ morphology.pyc setup.pyc doccer.pyc info.py@ interpolation.pyc _nd_image.so setupscons.py@ filters.py@ info.pyc measurements.py@ _ni_support.py@ setupscons.pyc filters.pyc __init__.py@ measurements.pyc _ni_support.pyc tests/ fourier.py@ __init__.pyc morphology.py@ setup.py@
In : import scipy In : scipy.__file__ Out: '/usr/lib/python2.6/dist-packages/scipy/__init__.pyc' In : import scipy.version In : scipy.version.version Out: '0.7.0' In : import scipy.ndimage.morphology In : from scipy.ndimage import morphology In : morphology.binary_dilation? Type: function Base Class: <type 'function'> String Form: <function binary_dilation at 0x9bedd84> Namespace: Interactive File: /usr/lib/python2.6/dist-packages/scipy/ndimage/morphology.py Definition: morphology.binary_dilation(input, structure=None, iterations=1, mask=None, output=None, border_value=0, origin=0, brute_force=False) Docstring: Multi-dimensional binary dilation with the given structure. An output array can optionally be provided. The origin parameter controls the placement of the filter. If no structuring element is provided an element is generated with a squared connectivity equal to one. The dilation operation is repeated iterations times. If iterations is less than 1, the dilation is repeated until the result does not change anymore. If a mask is given, only those elements with a true value at the corresponding mask element are modified at each iteration.
220.127.116.11. Good practices¶
Use meaningful object names
Indentation: no choice!
Indenting is compulsory in Python! Every command block following a colon bears an additional indentation level with respect to the previous line with a colon. One must therefore indent after
while:. At the end of such logical blocks, one decreases the indentation depth (and re-increases it if a new block is entered, etc.)
Strict respect of indentation is the price to pay for getting rid of
;characters that delineate logical blocks in other languages. Improper indentation leads to errors such as
------------------------------------------------------------ IndentationError: unexpected indent (test.py, line 2)
All this indentation business can be a bit confusing in the beginning. However, with the clear indentation, and in the absence of extra characters, the resulting code is very nice to read compared to other languages.
Indentation depth: Inside your text editor, you may choose to indent with any positive number of spaces (1, 2, 3, 4, ...). However, it is considered good practice to indent with 4 spaces. You may configure your editor to map the
Tabkey to a 4-space indentation. In Python(x,y), the editor is already configured this way.
Long lines: you should not write very long lines that span over more than (e.g.) 80 characters. Long lines can be broken with the
>>> long_line = "Here is a very very long line \ ... that we break in two parts."
Write well-spaced code: put whitespaces after commas, around arithmetic operators, etc.:
>>> a = 1 # yes >>> a=1 # too cramped
A certain number of rules for writing “beautiful” code (and more importantly using the same conventions as anybody else!) are given in the Style Guide for Python Code.
If you want to do a first quick pass through the Scipy lectures to learn the ecosystem, you can directly skip to the next chapter: NumPy: creating and manipulating numerical data.
The remainder of this chapter is not necessary to follow the rest of the intro part. But be sure to come back and finish this chapter later.