Here are the steps to get Console2 to open to a specific startup directory:

1. Launch Console2
2. Open settings through Edit > Settings
3. Click tabs from the tree on the left
4. Click the ‘Add’ button to add a new tab
5. Set the title to ‘Cygwin’ (or another appropriate name)
6. In the ‘Shell’ field put: C:\cygwin\bin\bash.exe –login -i -c “cd /cygdrive/c/Users/<username>/<path>/; exec /bin/bash”
• Replace username with your login name, and the path with the path you want to use relative your home directory.  This can also be used to start in other paths on the system.
• This launched cygwin with the command to change the path and then launches bash again so the console window will stay open.
• You don’t have to put anything in ‘Startup dir’
7. You should be set to open Cygwin tabs to a specific directory in Console2.

Feel free to do other customizations to the console, I like to make each console have a different color so it’s easy to tell which type of console I’m looking at very quickly.

Features:

• BSD License
• Supports python 2.6 – 3.2
• Output formatters
• Cross platform
• Fully tested

unitbench documentation

unitbench pypi page (with downloads)

unitbench source code

I’d love to hear any feedback.  All comments are appreciated.  If you notice any bugs or weird behavior post a bug report to bitbucket or leave a comment here.  I’ll try to address it as soon as possible.  The same goes for feature requests.

New Features include:

• Reporting of user and system time in addition to wall clock time.
  • User time is the amount of CPU time spent in user code.
  • System time is the amount of CPU time spent in the operating system kernel.
• XML output with all the information recorded by the system.
• Now tested on mingw and cygwin.

cppbench webpage: http://blog.quibb.org/cppbench/

cppbench on bitbucket: https://bitbucket.org/qbproger/cppbench/overview

cppbench api: http://blog.quibb.org/cppbench-api/

Some of the features include:

• Simplified BSD License
• High fidelity stopwatch
• Output formatters
• Cross platform

For a more complete list of features see the cppbench webpage.  With the mindset “release early, release often” here it is:

Webpage: http://blog.quibb.org/cppbench

Bitbucket: https://bitbucket.org/qbproger/cppbench/overview

API: http://blog.quibb.org/cppbench-api/

This being the first open source project I’ve released, any feedback is appreciated.

There are a few reasons you’d want to use a text based browser to crawl the web.  For example, it makes it easier to do natural language processing on web pages.  I was doing this a year or two ago, and at the time I was unable to find a Python library that would remove HTML reliably.  Since there was a looming deadline, I only tried BeautifulSoup and NLTK for removing HTML.  They eventually crashed on websites after running for a while.

Keep in mind this information is targeted at general crawling.  If you’re crawling a specific site or similarly formatted sites, different techniques can be used.  You’ll be able to use the formatting to your advantage, so HTML parsers, such as BeautifulSoup, html5lib, or lxml, become more useful.

Lynx

Using Lynx is pretty straight forward.  If you type lynx into a command prompt with lynx installed it will bring up the browser.  There is help to get you started, and you’ll be able to browse the Internet.  As you can see it does a good job of removing the formatting while keeping the text intact.  That’s the information we’re after.

Lynx has several command line parameters that we’ll use to do this.  Looking at the command line parameters can be done with the command: lynx -help.  The most important command line parameter is -dump.  It causes lynx to output the web page to standard out rather than in their browser interface.  It allows the information to be captured and processed.

Python Code

def kill_lynx(pid):
    os.kill(pid, signal.SIGKILL)
    os.waitpid(-1, os.WNOHANG)
    print("lynx killed")
 
def get_url(url):
    web_data = ""
 
    cmd = "lynx -dump -nolist -notitle \"{0}\"".format(url)
    lynx = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE)
    t = threading.Timer(300.0, kill_lynx, args=[lynx.pid])
    t.start()
 
    web_data = lynx.stdout.read()
    t.cancel()
 
    web_data = web_data.decode("utf-8", 'replace')
    return web_data

As you can see in addition to the -dump flag -nolist and -notitle are used too.  In most cases the title is included in the text of the website.  Another reason for excluding the title is that most of the time it isn’t a complete sentence.  The -nolist parameter removes the list of links from the bottom of the dump.  I wanted to just parse the information on the page, and on some pages this greatly decreases the amount of text to process.

One other thing to notice is lynx is killed after 300 seconds.  While crawling I found that some sites were huge and slow but wouldn’t timeout.  Killing lynx helped if it was taking too long on one site.  An alternate approach would have been to have different threads running lynx to capture information, so one thread wouldn’t block everything.  However, killing it worked well enough for my use case.

Clean Up

Now that the information has been obtained, it needs to be run through some cleanup to help with the natural language processing of it.  Before showing the code, let me just say if the project was ongoing I’d look into better ways of doing it.  The best way to describe it is, “It worked for me at the time.”

_LINK_BRACKETS = re.compile(r"\[\d+]", re.U)
_LEFT_BRACKETS = re.compile(r"\[", re.U)
_RIGHT_BRACKETS = re.compile(r"]", re.U)
_NEW_LINE = re.compile(r"([^\r\n])\r?\n([^\r\n])", re.U)
_SPECIAL_CHARS = re.compile(r"\f|\r|\t|_", re.U)
_WHITE_SPACE = re.compile(r" [ ]+", re.U)
 
MS_CHARS = {u"\u2018":"'",
            u"\u2019":"'",
            u"\u201c":"\"",
            u"\u201d":"\"",
            u"\u2020":" ",
            u"\u2026":" ",
            u"\u25BC":" ",
            u"\u2665":" "}
 
def clean_lynx(input):
 
    for i in MS_CHARS.keys():
        input = input.replace(i,MS_CHARS[i])
 
    input = _NEW_LINE.sub("\g<1> \g<2>", input)
    input = _LINK_BRACKETS.sub("", input)
    input = _LEFT_BRACKETS.sub("(", input)
    input = _RIGHT_BRACKETS.sub(")", input)
    input = _SPECIAL_CHARS.sub(" ", input)
    input = _WHITE_SPACE.sub(" ", input)
 
    return input

This cleanup was done for the natural language processing algorithms.  Certain algorithms had problems if there were line breaks in the middle of a sentence.  Smart quotes, or curved quotes, were also problematic.  I remember square brackets (ie- [ or ]) caused problems with the tagger or sentence tokenizer I was using, so I replaced them with parenthesis.  There were a few other rules, and you may need more for your projects.

I hope you find this useful.  It should get you started with crawling the web.  Be prepared to spent time debugging, and it’s a good idea to spot check.  The entire time I was working on the project, I was tweaking different parts of it.  The web is pretty good at throwing every case at you.