Tools that I find useful with mac

Here is my list of useful tools on mac:

Notational Velocity is a cool way to keep textual notes.
      
I always had issue of manually deleting the archive after extraction, Unarchiver helps with that.
  
Want to have your favorite websites into Mac desktop applications, use fluidinfo.

Using buildbot for continuos integration development

Continuos integration in it's simplicity embodies certain agile tenets like frequent integration of code and automated verification of the integrated code to provide continuous feedback to the team on development and reducing heart burns during large integrations. It also avoids silent creeping in of broken builds into the code repository. At the heart of this process is a tool that can be integrated with the workflow of code check-in to trigger automated testing of frequently checked in development artifacts.

This helps in providing the developer an immediate feedback and assurance that things are moving in a positive direction.

Buildbot is a "continuos integration" tool.

BuildBot can automate the compile/test cycle required by most software projects to validate code changes.

I had a chance to set it up some time back. What follows is a snippet of that experience on getting it up and running quickly.

Ubantu on Mac OSX using VirtualBox

I installed ubantu on mac osx using VirtualBox some time back. Installation went fairly easy except for the fact that I had to look out for increasing the resolution from the default 800X600.

Here is an step by step approach to install and use VirtualBox.

Python and binary data - Part 3

Normal file operations that we use are line oriented

FILE = open(filename,"w")
FILE.writelines(linelist)
FILE .close()
FILE = open(filename,"r")
for line in FILE.readlines(): print line
FILE .close()

We can also use byte oriented I/O operations on these files.

FILE = open(filename,"r")
FILE.read(numBytes)  # This reads up to numBytes of Bytes from the file.

But if the file does not contain textual data, the contents may not be meaningful.

It is much better to open the file in binary mode

FILE = open(filename,"rb")
FILE.read(numBytes)

Python and binary data - Part 2

In the previous post, I discussed about numbers (floating, signed and whole) representation on a computer. In "C", the bits used for representation are limited. This means that there is inherent limitation on what can be represented. It also means that there is danger of overflow when you can't hold all the bit values after an operation to represent a number that exceeds the bit limits.

How about Python? How does it represent these numbers?

Even if Python underlying implementation is in "C" or "C++" types, Python integers are not like typical "C" integers.

Python integer have arbitrary precision.

It creates a higher level of abstraction for number representation.
Python represents integers by allocating the memory that is required to hold the number value. Initially, the size is set to 32 or 64 bits and increased when required. They can pretty much store a very large value or an astronomical figure. Arbitrary precision operations on these integers can be very slow.

Algorithms in Python - Smallest free number

I have recently started reading the book "pearls of functional algorithm design" by Richard Bird. The book details various problems and functional algorithmic solutions.
I thought it would be a good exercise to solve them in Python and attempt to solve them using a functional programming language in future.

Task: find the smallest natural number not in finite set of numbers - X

The first problem illustrates the programming task in which objects are named by numbers and X represents the set of objects currently in use. The task is to find the object with the smallest name not in use. I.e. it is not in X.

Of course, there are multiple ways to solve this problem and I am not even trying to look at what may be an elegant solution.

Python and binary data - Part 1

All data is represented by ones and zeroes. How ever, the stream of binary data (ones and zeroes) can represent anything. Practically any thing can be represented with multiple ones and zeroes strung together along with the means for interpretation. The most common interpretation is textual data or Ascii data.

If the representation format is not known then we simply refer to it as binary data.

This interpretation process is called decoding and reverse transformation to binary data is called encoding.
If the binary data is not an ascii representation, you can't manipulate it in a textual editor.
Python has a specific module called 'binascii' for transformation of binary data to ascii representation and back and forth.

Learning Twisted (part 6) : Understanding protocol factory

Since most of us will be reusing the transport implementations that are already provided by Twisted. Our focus will be to create protocols and protocol factory that ties up a transport with protocol instance.

from twisted.web import proxy
from twisted.internet import reactor
from twisted.python import log
import sys
log.startLogging(sys.stdout)

class myProtocolFactory():
    protocol = proxy.Proxy

reactor.connectTCP('localhost', 80, myProtocolFactory())
reactor.run()

This barebones throws a whole lot of trace backs which helps understand the code flow a little bit easily. You can keep supplying the functions and rerun to see all the required methods of protocol factory and how the code flows or is structured.