Posts Tagged ‘Python’

There are wide choice of programming languages catering to a diverse application domain. This post is an attempt at classifying them based on how much the language/implementation tries to abstract the machine details from the programmer.

We can broadly classify languages as

  • Assembly language (x86 assembler AT&T syntax or Intel syntax)
  • Compiled (C)
  • Compiled to byte code of VM and compiled just in time (Java2)
  • Compiled to byte code of VM and interpreted (Python)
  • String interpreted (TCL)
Assembly Languages

Such languages need the least amount of work to make them executable. Assemblers like the x86 assembly language is closely tied to the machine code. It is basically a one is to one mapping between machine instruction to a human readable string called mnemonics. Assemblers also make it easy to assign labels to address locations to ease programming effort. Modern assemblers also support macros which can make programming repetitive code easier. There is little scope for optimization or higher order data structures in assembly languages, since they just mirror the target machine code. Such languages are the least portable since they are always tied to a target machine architecture. Few examples of assembly languages would be the x86 assembly language, Motorola 6800 assembly etc.

Compiled Languages

Such languages are a step taken for portability. They abstract the underlying machine instructions and give higher order constructs for arithmetic operations, branching, looping and basic data types. They are compiled in to target machine code directly. Once compiled, the binary can be natively run in the target machine. Arguably they are slower than assembler, since the actual machine code is generated by a compiler and will not be as optimized as hand coded assembler. But in practice, for any modern processor with multiple cores and pipelining, the compiler tends to generate more optimized code. Such languages can have support for higher order data structures like lists, maps etc either natively or via standard libraries. C, C++, Pascal etc are some examples of compiled languages.

Compiled to Byte Code of a VM (JIT compiled)

These languages are another step towards portability. First they are compiled to byte code of a virtual machine. The virtual machine executes the byte code by compiling it in to native machine code Just In Time (JIT). Practically these are slower than compiled languages since there is another layer of abstraction. But they are more portable than compiled languages. The same compiled byte code can be run on any platform that supports the VM, while for compiled languages, different binaries are required for different platforms. Typically they support a full range of higher order data structures. Examples include Java2, Ruby core 1.9.

Compiled to Byte Code of a VM (Interpreted)

The first step of these languages is the same as the above. They are compiled to byte code of a virtual machine. The virtual machine itself executes the byte code by interpreting it. They are generally slower than JIT implementations. They have same portability as JIT implementations. Performance also depends on the optimization effort gone in to the VM implementation. For example Python2 (byte code interpreted) is faster than Ruby core 1.9 (JIT) while Java2 (JIT) is way faster than compiled Lisp (SBCL). Examples include Python, Ruby.

String Interpreted

Such languages interpret the source code string directly. Because of this, these are usually the slowest of the lot. Consider this statement – a = 100 + 2. The 100 and 2 are strings and instead of doing the addition 100 + 2 natively, the interpreter knows how to add integers as strings. The interpreter is easier to implement than byte code compilation but performance is the least. TCL, JavaScript are examples of string interpreted languages.

We can see a pattern emerging. From assembly to string interpretation, the language/implementation abstracts machine details more and more from the programmer. As a result performance keeps decreasing while portability keeps increasing. Beyond a point, performance decreases without any increase in portability, but implementation becomes easier. Also more abstracted languages usually provide higher order data structures and automatic memory management for free.

Also the level of abstraction really depends on the implementation rather than the language itself. For example, Python2 is both byte code interpreted (official CPython) and JIT (PyPy). The implementation can vary not only in the VM. For example Common Lisp has a compiled implementation (SBCL), compiled to C/C++ or byte code interpreted implementation (ECL).

For anybody who loves programming, there is a problem of plenty. Naturally we are curious to learn more and there is simply too many options to pursue. When I started my career 4 years ago, the only language I knew fluently (well kinda) was C. I wanted to learn C++ and a scripting language for rapid prototyping. It has been 4 years, and I have learnt C++ and Python.

Now comes the predicament. What to study next. There are plenty of options. Should I study GUI programming via wxWidgets or learn Internet technologies like HTML and JavaScript. Both will not help me in my line of work (carrier class embedded software development in telecom domain). But it will be fun, and turn around time is much quicker. Applications always have this magical aura of being useful directly. Users interact with application directly and the developer gets feedback instantly. With infrastructure like Linux or the iPhone, we have to wait for some application to utilize the new goodies so that end users can appreciate the infrastructure.

Or I can take MIT Open Course Ware course on Operating Systems or strengthen my knowledge of algorithms. Both will help me in my line of work and should be tons of fun. But people around me (friends and relatives) identify with Internet technologies much better. Imagine showing my dad a terminal shell on the basic OS I wrote as part of MIT OCW or red black trees in action. Now imagine showing my dad an iGoogle widget written in HTML and JavaScript that he can interact with. But having a better understanding of the infrastructure OS or the algorithms can make us better programmers in general. This will benefit application programming too.

Another fascinating thread to follow is to learn Lisp. It may not give us any practical benefits. But I trust people when they say that learning Lisp is a profoundly enlightening experience for a programmer. I want to experience it first hand. But should I take it up now? If not now when?

Apart from technology, processes and paradigms are also a good candidate to delve into. Modern software engineering principles, design patterns, architectures, refactoring, software metrics, aspect oriented programming, functional programming, concurrent programming, literate programming etc are also promising. All of them, like investing in infrastructure, will help in general.

One of the things to consider while choosing is, how long will it be relevant. Ideally, we do not want to invest in something that will become irrelevant in the future. Till now I have invested in time proven technologies/tools like C, C++, Python, Vim, Linux… which are here to stay for good. Another aspect of relevancy is that it should be universally useful. It should still be relevant outside of my current organization, without licensing issues. Copyrights and licenses can make a current technology irrelevant for our purposes. Sticking with open standards and open source technologies with strong communities and vision ensures that our investment will not be obsolete quickly. HTML, JavaScript, OS concepts, algorithms and Lisp all are here to stay for good. Processes and paradigms may or may not stand the test of time and can quickly become a fad. Plus they can be picked up while focus is on learning the others.

So we have 3 main streams to pursue. Internet technologies (HTML and JavaScript), infrastructure (OS and algorithms) and Lisp. Well investing in infrastructure seems to be the most logical choice for me. It will directly help me in my line of work. It will help me be a better programmer by providing me a better understanding of the underlying universal principles of computer science. Both will be around for a long time (forever is more like it). Both will help me in my pursuit of the other two streams. So this can be a foundation for the other two streams ūüôā

I am not the only on faced with this predicament. I quote Frederick P Brooks Jr from The Mythical Man Month.

ÔĽŅThe computer-related intellectual discipline has exploded as has the technology. When I was a graduate student in the mid-1950s, I could read all the journals and conference proceedings; I could stay current in all the discipline. Today my intellectual life has seen me regretfully kissing sub discipline interests goodbye one by one, as my portfolio has continuously overflowed beyond mastery. Too many interests, too many exciting opportunities for learning, research, and thought. What a marvelous predicament! Not only is the end not in sight, the pace is not slackening. We have many future joys.

So it is not really a predicament. We are just spoilt for choice. As Brooks predicts, we have many future joys. Amen.

In this post, we will explore how to set different TAB settings while editing C/C++ and Python code. For C/C++, we want 2 spaces for indendation, while for Python, we will stick to PEP 8 recommendations (4 spaces for indendation, 8 spaces for TAB, 4 spaces for expandtab). Vim being such a powerful and versatile editor, there are many ways to achieve this. We will explore the most portable way – with changes just to the .vimrc.

" Python options
let python_highlight_all = 1
function! s:set_python_settings()
  set tabstop=8
  set softtabstop=4
  set shiftwidth=4
function! s:unset_python_settings()
  set tabstop=2
  set softtabstop=2
  set shiftwidth=2
autocmd BufNewFile,BufEnter *.{py} call set_python_settings()
autocmd BufLeave *.{py} call unset_python_settings()

What we are doing here is define two functions, set_python_settings() which will change tab settings as per PEP 8 recommendations and unset_python_settings() which will default to our C/C++ settings. Autocmd upon entering a new buffer (BufEnter) or a new file is opened (BufNewFile) of type *py, we call set_python_settings() to set tab settings for Python. When we leave a buffer (BufLeave) of type *.py, we get back to original settings by calling unset_python_settings().

Exuberant ctags is a pretty nifty utility for source code browsing. Especially since it integrates so well with vim. Also exuberant ctags can understand many languages (41 as per the official website). So this is relevant not only for C/C++ or Python but for all 41 languages supported (and future ones too). In this post, we explore yet another popular plugin using exuberant ctags – TagList.

TagList is capable of showing a list of functions/global variables/class/struct towards one side of the vim. This is similar to some IDEs or event editors like Notepad++. This makes browsing and navigation pretty easy. Our aim will be to be able to navigate current file using TagList window and to always display current function name in the status line.

Screenshot of TagList plugin showing current function name in status line

For this, we will need:

TagList will work only with exuberant ctags and not with any other ctags (specifically GNU ctags). Install exuberant ctags if required. Also ensure that ctags command should actually invoke exuberant ctags.

Install TagList plugin (see elaborate steps in the link). Typically vim plugins are installed by simply copying to $HOME/.vim/plugin directory. If any documentation is there, copy that to $HOME/.vim/doc and re-index vim help by giving “:helptags $HOME/.vim/doc” command in vim.

Now we need to customize the TagList plugin options to get what we want.

" TagList options
let Tlist_Close_On_Select = 1 "close taglist window once we selected something
let Tlist_Exit_OnlyWindow = 1 "if taglist window is the only window left, exit vim
let Tlist_Show_Menu = 1 "show Tags menu in gvim
let Tlist_Show_One_File = 1 "show tags of only one file
let Tlist_GainFocus_On_ToggleOpen = 1 "automatically switch to taglist window
let Tlist_Highlight_Tag_On_BufEnter = 1 "highlight current tag in taglist window
let Tlist_Process_File_Always = 1 "even without taglist window, create tags file, required for displaying tag in statusline
let Tlist_Use_Right_Window = 1 "display taglist window on the right
let Tlist_Display_Prototype = 1 "display full prototype instead of just function name
"let Tlist_Ctags_Cmd = /path/to/exuberant/ctags

nnoremap <F5> :TlistToggle
nnoremap <F6> :TlistShowPrototype

set statusline=[%n]\ %<%f\ %([%1*%M%*%R%Y]%)\ \ \ [%{Tlist_Get_Tagname_By_Line()}]\ %=%-19(\LINE\ [%l/%L]\ COL\ [%02c%03V]%)\ %P

Notice the call to Tlist_Get_Tagname_By_Line() within the statuline. This is what displays the current function name (actually tag which can be class name or struct name or any tag) in the status line. With the above settings, pressing F6 will show the prototype of current function at the bottom.

Pressing F5 will open up the TagList window which will show the list of functions/classes/structs/define etc. The focus is automatically switched to the list window and we can quickly jump to the function/class/struct definition we want to. Upon selecting an entry, the list window is automatically closed.shrink the window back to default size.

Screenshot of TagList window

Now with the full prototype display, it is not very easy to read the actual function name. TagList has a zoom feature to overcome this. Press “x” and the TagList window will enlarge, occupying almost the complete vim screen. Selection of an entry or pressing F5 again will close the window. Pressing x again will shrink the window back to default size.

Screenshot of TagList window zoomed using "x" key

TagList generates its own tags file and does not require the user to provide a tags file. This file is generated every time we switch to a buffer. The tags file is created somewhere in /tmp/ folder. TagList cannot work with a user generated tags file.

In my previous post, I came up with an enhanced pygenie, which could count total complexity of a Python project as a whole. As explained earlier, my motivation is purely for fun. I took a test run of the enhanced pygenie on some of the popular open-source Python projects. And the results are out ūüôā

Django 1.2.1

time ./pygenie/ Django-1.2.1/ -r
Total Cumulative Statistics
Type         Count      Complexity
X            1431             1629
C            2393             2408
M            6299            13000
F            1144             3600
T           11267            20637

real    0m11.840s
user    0m11.489s
sys     0m0.056s

Zope 2.11.4

time ./pygenie/ Zope-2.11.4-final/ -r
Total Cumulative Statistics
Type         Count      Complexity
X            3005             4549
C            6453             6500
M           23137            45392
F            4985            12960
T           37580            69401

Totally 5 files failed
FAILED to process file: /home/aufather/Downloads/Zope-2.11.4-final/lib/python/ZEO/scripts/
FAILED to process file: /home/aufather/Downloads/Zope-2.11.4-final/lib/python/zope/app/testing/
FAILED to process file: /home/aufather/Downloads/Zope-2.11.4-final/lib/python/zope/app/component/
FAILED to process file: /home/aufather/Downloads/Zope-2.11.4-final/lib/python/zope/app/component/
FAILED to process file: /home/aufather/Downloads/Zope-2.11.4-final/lib/python/zope/rdb/gadfly/

real    0m37.042s
user    0m36.302s
sys     0m0.140s

Twisted 10.1.0

time ./pygenie/ Twisted-10.1.0/ -r
Total Cumulative Statistics
Type         Count      Complexity
X             935             1282
C            3736             3922
M           16782            26142
F            1323             3268
T           22776            34614

Totally 6 files failed
FAILED to process file: /home/aufather/Downloads/Twisted-10.1.0/doc/core/howto/listings/udp/
FAILED to process file: /home/aufather/Downloads/Twisted-10.1.0/doc/core/howto/listings/udp/
FAILED to process file: /home/aufather/Downloads/Twisted-10.1.0/doc/historic/2003/pycon/deferex/
FAILED to process file: /home/aufather/Downloads/Twisted-10.1.0/doc/historic/2003/pycon/deferex/
FAILED to process file: /home/aufather/Downloads/Twisted-10.1.0/doc/historic/2003/pycon/deferex/
FAILED to process file: /home/aufather/Downloads/Twisted-10.1.0/twisted/python/test/

real    0m20.794s
user    0m20.525s
sys     0m0.036s

Even though I started this activity for fun, I was surprised by the speed and utility of pygenie. Some of the uses I can think of

  • Identify syntax errors within a project. Since pygenie uses the same python compiler, if pygenie could not process a file, the file cannot be processed by Python byte code compiler. Zope has 5 files which will not run while Twisted has 6. Django is clean in this front. When we take a closer look, it is test cases, demo code, tutorials, historical code etc that are broken.
  • Object orientation used within a project. The ratio between methods to functions is a fair indicator. The scores for the three projects are Django(5.5), Zope (4.6) and Twisted (12.7). This is a good indicator of existing style of code.
  • Overall size of a project. Pygenie report indicates that Zope is 3.4 times and Twisted is 1.7 times as big as Django.

The original pygenie is available here. Enhanced pygenie is available from my dropbox here.

Recently I measured some code metrics of a C/C++ project at work. I used C and C++ Code Counter(CCCC) and it is pretty good. There was a similar project at work which was almost completely in Python. I wanted to compare the two and searched for a suitable tool to measure McCabe’s Cyclomatic Complexity(CC) for Python code and found pygenie.

Original pygenie, by default, prints Files(X), Classes(C), Methods(M), Functions(F) only if their CC exceeds 7. If –verbose or -v is given, it prints CC irrespective of whether it crosses the threshold of 7 or not. But I wanted overall CC. My main motivation is purely for fun or for interesting numbers. Kind of like the calories burnt counter on a treadmill or a cycle computer for a bicycle. But overall CC can be useful in estimating testing effort.

Here is a sample run of original pygenie on its own source code.

aufather@simstudio:~/Downloads/pygenie_orig$ ./ complexity *.py
File: /home/aufather/Downloads/pygenie_orig/
This code looks all good!

File: /home/aufather/Downloads/pygenie_orig/
Type Name Complexity
F    main 8          

File: /home/aufather/Downloads/pygenie_orig/
This code looks all good!

And here is a sample run of modified pygenie on its own source code.

aufather@simstudio:~/Downloads/pygenie$ ./ * -c
File: /home/aufather/Downloads/pygenie/
This code looks all good!

File: /home/aufather/Downloads/pygenie/
This code looks all good!

File: /home/aufather/Downloads/pygenie/
This code looks all good!

Total Cumulative Statistics
Type         Count      Complexity
X               3                4
C               8                8
M              31               70
F              13               26
T              55              108

I added the total cumulative statistics and a summary for each file. Total is represented by ‘T’. This gives a fair idea of how complex the overall project is. ‘F’ stands for functions, ‘M’ stands for methods, ‘C’ stands for classes and ‘X’ stands for files. Also while I was at it, added a whole bunch of command line options to pygenie.

Here is the help for original pygenie. Valid commands are “complexity” or “all”.

aufather@simstudio:~/Downloads/pygenie_orig$ ./ all -h
Usage: ./ command [options] *.py

 -h, --help     show this help message and exit
 -v, --verbose  print detailed statistics to stdout

And the new options added.

aufather@simstudio:~/Downloads/pygenie$ ./ -h
Usage: [options] *.py

 --version             show program's version number and exit
 -h, --help            show this help message and exit
 -c, --complexity      print complexity details for each file/module
 -t THRESHOLD, --threshold=THRESHOLD
 threshold of complexity to be ignored (default=7)
 -a, --all             print all metrics
 -s, --summary         print cumulative summary for each file/module
 -r, --recurs          process files recursively in a folder
 -d, --debug           print debugging info like file being processed
 -o OUTFILE, --outfile=OUTFILE
 output to OUTFILE (default=stdout)

You can get the full zipped source code from my dropbox here

PS: I based my modification on revision 44 of pygenie. I got the original from

PPS: I could not find a better way of sharing the code in wordpress. I have updated the too so that unit test do not fail. If any one knows a better way of sharing code, please let me know. Dropbox works well for me.