Активация virtualenv персонально для меня несколько геморная штука. И даже дело не в том, что активировать сложно — это неудобно. Так что ниже небольшой скрипт, который поможет активировать virtualenv прямо при заходе в папку с проектом.
Даёшь Django в народные массы!
This buildout extension will be helpful for those developers, who actively uses different VCS/DVCS in their everyday life and deployment.
Script to migrate from SQLite to MySQL or PostgreSQL. Uses SQLAlchemy as middleware.
I was very busy last month because of a deadline of my current project. And getting things done method was especially useful for me these days.
During last two or three weeks, I tried almost all time management programs for Mac OS X, but have not found any convinient. All of them are overloaded with uneeded features, their interfaces are cluttered with tags, projects, areas and etc..
So, I decided to write my own GTD tool, simple, flexible and convinient. I wrote it in one holiday using python and sqlalchemy. It's name is GTDZen.
GTDZen operates with two item types: Task and Tag. Any Task have title, priority and tags. You can get Tasks by tags, using simple AND-NOT logic. For example, you can show all tasks, related to "work" but not to "today".
Now GTDzen exists as a python module, which is the middleware between user interface and database. Also, it includes a command line interface. I think that this is enough, but you easily could write you own GUI interface for Windows or Mac OS X.
You can find a short tutorial in the README file, on the project's page at GitHub.
Code is licensed under the New BSD License, so feel free to fork it and use it. But I would be very appreciated if you send me some patches.
I was very busy last month because of a deadline of my current project. And getting things done method was especially useful for me these days.
During last two or three weeks, I tried almost all time management programs for Mac OS X, but have not found any convinient. All of them are overloaded with uneeded features, their interfaces are cluttered with tags, projects, areas and etc..
So, I decided to write my own GTD tool, simple, flexible and convinient. I wrote it in one holiday using python and sqlalchemy. It's name is GTDZen.
GTDZen operates with two item types: Task and Tag. Any Task have title, priority and tags. You can get Tasks by tags, using simple AND-NOT logic. For example, you can show all tasks, related to "work" but not to "today".
Now GTDzen exists as a python module, which is the middleware between user interface and database. Also, it includes a command line interface. I think that this is enough, but you easily could write you own GUI interface for Windows or Mac OS X.
You can find a short tutorial in the README file, on the project's page at GitHub.
Code is licensed under the New BSD License, so feel free to fork it and use it. But I would be very appreciated if you send me some patches.
Today I found an interesting project "rope". It is a python library for refactoring python code. Also, it has frontends for vim and emacs. As I am active user of the vim, I wrote a simple script, to simplify installation and update of the ropevim. To try ropevim, you need a Vim with support for python plugins and mercurial, to fetch sources of the rope from repository. If you work under Linux Ubuntu, like me, then you need to do few simple commands, to install the ropevim:
That's it! Enjoy python refactoring with vim and rope. And don't forget to read a ropevim manual with default keybindings.
Do you know anything about python's slots construction? I did't before this day.
But slots in the python exists from version 2.2. It's main purpose is memory optimisation because it allow to get rid of dict object, which created by interpreter for each instance of class.
If you create many small objects, with predefined structure, and meet a memory limit, than slots can help you to overcome that limitation.
But remember, that early optimisation is the root of all evil. Furthermore, slots has many limitations that you need to be aware. For example, you can't use multiple inheritance and must be very careful if usual inheritance too.
As I am very interested in the different optimisation technics, I desided to make a small test.
I have found an interesting python library, that helps to measure memory usage. It's name is Guppy.
So, I write this simple test, which create tuples of one million instances of simple class. Every object hold two attibute 'a' and 'b'. First class is standart, second uses slots to optimise memory footprint.
import sys import guppy import time class Test1(object): def __init__(self): self.a = 1 self.b = 2 class Test2(object): __slots__ = ('a', 'b') def __init__(self): self.a = 1 self.b = 2 if __name__ == '__main__': num = 1000000 cls = Test1 if len(sys.argv) == 2: if sys.argv[1] == '2': cls = Test2 l = tuple(cls() for i in xrange(num)) h = guppy.hpy() print h.heap()
If you run this program without params, it would create a 1000000 usual instances and output something like that:
Partition of a set of 2020446 objects. Total size = 169426216 bytes. Index Count % Size % Cumulative % Kind (class / dict of class) 0 1000000 49 136000000 80 136000000 80 dict of __main__.Test1 1 1000000 49 28000000 17 164000000 97 __main__.Test1 2 4901 0 4178264 2 168178264 99 tuple 3 9527 0 611920 0 168790184 100 str 4 1416 0 96288 0 168886472 100 types.CodeType 5 61 0 89704 0 168976176 100 dict of module 6 155 0 85784 0 169061960 100 dict of type 7 1342 0 75152 0 169137112 100 function 8 170 0 73360 0 169210472 100 type 9 119 0 68408 0 169278880 100 dict of class
Now run program with one argument '2'. In this case, output will look like that:
Partition of a set of 1020446 objects. Total size = 33426216 bytes. Index Count % Size % Cumulative % Kind (class / dict of class) 0 1000000 98 28000000 84 28000000 84 __main__.Test2 1 4901 0 4178264 12 32178264 96 tuple 2 9527 1 611920 2 32790184 98 str 3 1416 0 96288 0 32886472 98 types.CodeType 4 61 0 89704 0 32976176 99 dict of module 5 155 0 85784 0 33061960 99 dict of type 6 1342 0 75152 0 33137112 99 function 7 170 0 73360 0 33210472 99 type 8 119 0 68408 0 33278880 100 dict of class 9 65 0 49928 0 33328808 100 dict (no owner)
Pay attention at the top line in the first stats. It states, that 136000000 was reserved to the "dict of main.Test1". Seconds stats does not countain such line because of slots was used for optimisation.
Now we can calculate a profit of the slots in that particular case. It is (136000000 + 28000000) / 28000000 or 5 times! Second run reqire a 5 times less memory than first! It's amazing! But remember about warning you was given to.
Also see these links:
Ну вот. Началось. Java to Python - простой и эффективный способ трансляции кода из Java в Python. [>>>]
Помяните мое слово - скоро появятся человекоподобные роботы, которые пишут на Java и транслируют это дело в Python.
