New-style PyQt Signals and Slots

I was to lazy to take a look at the new-style signal and slot support which was introduced in PyQt 4.5 until yesterday. I did know that there were something called new-style signals and slots but that was the end of the story. Now I have taken the time and I think it's a cleaner solution than the old-style.

I'll just give you a short intro to whet your appetite, find all details here yourself.

import sys
from PyQt4 import QtCore
from PyQt4 import QtGui

def clicked():
    print "Button Clicked"

qApp = QtGui.QApplication(sys.argv)

button = QtGui.QPushButton("Click Me")
QtCore.QObject.connect(button, QtCore.SIGNAL('clicked()'), clicked)
button.show()

sys.exit(qApp.exec_())

This is the old way of connecting a signal to a slot. To use the new-style support just replace line 11 with following code

button.clicked.connect(clicked)

The new-style support introduces an attribute with the same name as the signal, in this case clicked.

If you need to define your own signal you'll do something like this (off the top of my head):

class X(QtCore.QObject):
    mySignal = QtCore.pyqtSignal(int)

    def emitMySignal(self):
        self.mySignal.emit(100)

And the old way:

class X(QtCore.QObject):
    def emitMySignal(self):
        self.emit(QtCore.SIGNAL('mySignal'), 100)

IMHO the new-style support is more pythonic and you don't have to specify your signals as strings when connecting. If you use pydev (eclipse) you'll also have completion support for signals.

Digikam - Light table

This post is somewhat related to the post about ctypes and gphoto.

Background
I consider myself a pre-amateur photographer and that means I'm taking a lot of shots just to learn how the camera works. I own a Canon EOS 1000D and have recently moved from the fully automatic exposure modes to the advanced exposure modes, and to be more specific, the programmed auto exposure mode.

So, I needed an application that could load a couple of pictures, showing two pictures side-by-side for visual comparison and at the same time presenting the more important meta-data attributes such as focal length, exposure time, ISO and aperture. I considered doing such application myself and that's why I came across and started to explore gphoto. But luckily, I managed to stop myself and took yet another look at what Digikam offers. I'm a die-hard KDE-fan and have been using digikam, which is a KDE photo manager application, for quite some time to organize my photos but never actually used any other features except removing red-eyes.

To my surprise digikam included exactly the feature I was looking for! It's called the Light Table.

The Light Table
Digikam allows you to select a couple of pictures which can be placed onto the light table, as shown in the screenshot below.

By choosing Place onto Light Table a new window is opened with, in this case, the three pictures loaded and two of them shown side-by-side.

The light table also displays the meta-data for the respective picture (sorry about that some of the attributes are in Swedish) that are currently shown. You can synchronize operations like zooming and panning. When you zoom, both pictures will be zoomed to the same level and when panning, both pictures will be panned to the same area, really nice.

This was exactly what I was looking for. Now I can take a couple of shots with different settings, for example, changing the white balance, ISO and/or other parameters and compare the result rather easily. I get both a visual and a meta-data diff in the same view.

I just wanted to share my findings and hope this post was to some use for others that are looking for something similar.

Let's timeit!

Every now and then you might want to time snippets just to make sure that you choose the more efficient solution. In those cases you can use the timeit module to measure execution time for snippets.

Introduction
It's very easy to setup and measure the execution time for a snippet with timeit. The module contains a class, Timer, which is used to perform the measurement. The class has one constructor and three methods:

• Timer([stmt='pass'[, setup='pass'[, timer=<timer function>]]]) - stmt is the statement to be timed and setup is called once before executing the main statement. A timer function can be specified and default is time.time() for all platforms but windows which is set to time.clock() instead (according to my timeit.py)
  
• timeit([number=1000000]) - Executes the main statement passed to the constructor number of times and returns the result in seconds as a float.
  
• repeat([repeat=3[, number=1000000]]) - Convenience function that calls timeit(number) repeat times. Returns a list with the results.
• print_exc([file=None]) - Helper to print a traceback from the timed snippet.
  

Starting with Python 2.6 the timeit module also defines two convenience functions, timeit.timeit() and timeit.repeat(). They are basically wrappers around the Timer class.

Example
Suppose that I would like to create a list containing 100 'c':s like this ['c', 'c', ...]. There are at least two ways of doing this:

lst = ['c'] * 100
# or
lst = ['c' for i in xrange(100)]

Which one should I choose? Well, let's execute both statements with timeit and measure the execution time.

>>> import timeit
>>> t = timeit.Timer(stmt="lst = ['c'] * 100")
>>> print t.timeit()
1.10580182076
>>> t = timeit.Timer(stmt="lst = ['c' for x in xrange(100)]")
>>> print t.timeit()
7.66900897026

Ok, I think I'll stick with the first snippet :)

The result returned is the total execution time in seconds. In this particular case when we are executing the snippet 1000000 times the result is also the execution time in microseconds for one single pass (1000000*exe_time/1000000 == exe_time).

Normally, the timeit module doesn't have access to things that you have defined in your module. If you would like to measure a function that you have defined in your module you can specify the import statement in the setup parameter:

>>> def create_lst(size):
...    return ['c'] * size
...
>>> t = timeit.Timer(stmt="create_lst(100)", setup="from __main__ import create_lst")
>>> print t.timeit()
1.21339488029

This will introduce a little overhead since the create_lst() function is called in the measurement loop instead of just executing an inlined snippet.

Note: Timer.timeit() will by default disable garbage collection during timing. To enable GC you can pass 'gc.enable()' as a setup statement.

I find the timeit module as a simple and convenient way to measure execution time for small snippets.