Mostly cat /dev/urandom
Sometimes I find myself wanting to transfer one (or several) file to a different host or mobile device, and getting the USB cable or using SSH is not always possible. In those cases I just use this super-silly Python one-liner:
[gist]https://gist.github.com/676079[/gist]
And there you go.
In Python we have several types of objects for storing values in a
array-like way: lists, tuples, dictionaries (they are really more like
hash tables) and sets. Lists and sets might look alike but they are
different. Lets do a quick test:
[gist]https://gist.github.com/569053[/gist]
In the above snippet, we can see that iterating over all items in a
list takes just a little longer tan a a set: 8.99us vs 6.87us we are
talking micro-seconds here!
Now, if we look at how long it takes to verify is the list or set
contains an object, we can see the difference: 1.99us vs 140ns. Sets
are an order of magnitude faster!
Why is that? We can think that a python list is like a C linked list.
It’s time complexity when checking for a value is O(n). Sets, on the
other hand, are implemented as hash tables, so the key is hashed and
instantaneously found (or not) with a time complexity of O(1).
For the curious, this is the list object declaration in Include/listobject.h
[gist]https://gist.github.com/569078[/gist]
and this is the set object declaration, in Include/setobject.h
[gist]https://gist.github.com/569085[/gist]
More on python and hash tables here:
http://sites.google.com/site/usfcomputerscience/hash-tables-imp
:wq
When I download a library for using whatever API I usually see that people tend to use different JSON libraries, and sometimes I just don’t have it installed, but I know I got another one which could do the job just fine.
A while ago I ran across this while I was having a look at the Tropo WebAPI library (http://github.com/tropo/tropo-webapi-python) and ended up adding the following code:
try:
import cjson as jsonlib
jsonlib.dumps = jsonlib.encode
jsonlib.loads = jsonlib.decode
except ImportError:
try:
from django.utils import simplejson as jsonlib
except ImportError:
try:
import simplejson as jsonlib
except ImportError:
import json as jsonlib
Most used JSON libraries are ordered regarding speed. You might want to read a nice comparison between libraries here:
http://blog.hill-street.net/?p=7
:wq
A while ago I was bored at home and decided to play a bit with the Twitter API. As a result I created a simple Twitter Bot which retewitted everything with the *#asterisk* hashtag (http://twitter.com/asteriskbot).>
It’s functionality was very simple andI didn’t even touch the (crappy) code for months, but Twitter decided to drop the basic authentication support in favor of OAuth so it was the right time for updating the bot.
Since the bot was created lots of other bots have, and it feels annoying to get the same tweet over and over again, so I though I’d try to make the bot a bit *smarter*. The main goal was to detect if a tweet was already tweeted (or RTd) not to repeat things. AFAIK there are 2 types of retweets:
The current bot detects both formats by using regular expressions and extracts the tweet itself. However, I felt this could not be enough, because we could get the retweet *before* the real tweet so it’s be great to save the entire tweet and then look for it.
As the bot was using a SQLite database I decided to use its *Full Text Search* (fts3) capability, inspired by a colleague. With FTS searching for an entire string is amazingly faster than doing a regular SELECT query. Here is an example taken from the SQLite website http://www.sqlite.org/fts3.html
CREATE VIRTUAL TABLE enrondata1 USING fts3(content TEXT); /* FTS3 table */
CREATE TABLE enrondata2(content TEXT); /* Ordinary table */
SELECT count(*) FROM enrondata1 WHERE content MATCH 'linux'; /* 0.03 seconds */
SELECT count(*) FROM enrondata2 WHERE content LIKE '%linux%'; /* 22.5 seconds */
The (silly) code for this bot is available on GitHub: http://github.com/saghul/twitterbot
Happy tweeting!
Since I used dmesg for the first time I felt there was something wrong about it. Having very accurate timestamps might of course be helpful for many people, but sometimes you just want to know when something happened.
dmesg prints timestamps in the form of seconds.nanoseconds since the system booted. And no, there seems to be no -h option to make it human readable.
Today I felt like writing some Python for that, and pydmesg is the result. Is a simple script that fetches the uptime from /proc/uptime and uses it to print nice dmesg timestamps (timestamp format can be changed by editing the file).
Before:
[499902.343696] uvcvideo: Failed to query (1) UVC ...
[499902.354633] uvcvideo: Failed to query (1) UVC ...
[530442.358520] npviewer.bin[8818]: segfault at ...
After:
[2010-08-21 13:12:37] uvcvideo: Failed to query (1) UVC ...
[2010-08-21 13:12:37] uvcvideo: Failed to query (1) UVC ...
[2010-08-21 21:41:37] npviewer.bin [8818]: segfault at ...
By default precision is set to the second, which I guess is ok for
human beings ;–)
[gist]https://gist.github.com/542780[/gist]