Qt Designer is harmful, exhibit A

11/12/2014

Last week, iker j. de los mozos posted a Qt tutorial on his blog. The post was retweeted a number of times, so I figure people liked it.

The post exemplifies what is wrong with the Qt Designer, and also how a little more investment in learning can pay dividends for your career.

I know it’s unfair to give people code reviews on things they just put out for free, but I consider it even worse to allow people to continue to use the Qt Designer with a clear conscience. I thank Ike for his post, and for syndicating his feed on Planet Tech Art, and hope that no one takes my writeup below personally. It’s not an attack on a person, it’s trying to point out there there is a much better way to do things.

There are 117 lines of Python code in Ike’s tool for locking and unlocking transformation attributes. This sounds like a small amount, but is for an experienced Pythonista it indicates huge waste. For comparison, the entire ZMQ-based request/reply client and server I built for Practical Maya Programming with Python is the same size or smaller (depending on the version). If we take a closer look at his code (see link above), we can see a ton of copy and pasted functionality. This is technically a separate concern from the use of the Designer, but in my experience the two go hand-in-hand. The duplication inherent in GUI tools carries over to the way you program.

Let’s look at some refactored code where we build the GUI in code (warning, I haven’t tried this code since I don’t have Maya on this machine):

from functools import partial
from PySide import QtGui
import pymel.core as pmc
import qthelpers

OPTIONS = [
    dict(label='Position', btn='POS', attr='translate'),
    dict(label='Rotation', btn='ROT', attr='rotate'),
    dict(label='Scale', btn='SCALE', attr='scale')
]

def setLock(obj, attr, value):
    obj.setAttr(attr, lock=value, keyable=not value)

def isAttrLocked(obj, attr):
    return obj.getAttr(attr, q=True, lock=True)

def toggleRowCallback(attr):
    obj = pmc.ls()[0]
    value = isAttrLocked(obj, attr + 'X')
    for axis in 'XYZ':
        setLock(obj, attr + axis, value)

def toggleCellCallback(attr, state):
    obj = pmc.ls()[0]
    setLock(obj, attr, state)

def makeRow(options):
    return qthelpers.row(
        [QtGui.QLabel(options['label'])] +
        map(lambda axis: qhelpers.checkbox(onCheck=partial(toggleCellCallback, options['attr'] + axis)), 'XYZ') +
        qhelpers.button(label='lock ' + options['btn'], onClick=partial(toggleRowCallback, options['attr']))
    )

def main():
    window = qthelpers.table(map(makeRow, OPTIONS), title='lockAndHide UI', base=QtGui.QMainWindow)
    window.show()

Why is this code better? Well, for starters, it’s less than a third of the size (37 lines) and there’s less duplication. These are very good things. When we want to change behavior- such as auto-updating the checkboxes when our selection changes- we can put it in one place, not nine or more.

So the code is better, but what other benefits are there to not using the Designer?
– We pull common primitives, like a “row” (QWidget with HBoxLayout) and “table” into a qthelpers module, so we can use this across all GUIs. This saves huge amounts of boilerplate over the long run, especially since we can customize what parameters we pass to it (like onClick being a callback).
– The GUI is clear from the code because the UI is built declaratively. I do not even need to load the UI into the Designer or run the code to understand it. I can just read the bottom few lines of the file and know what this looks like.
– You learn new things. We use functools.partial for currying, instead of explicit callbacks. This is more complicated to someone that only knows simple tools, but becomes an indispensable tool as you get more advanced. We are not programming in Turtle Graphics. We are using the wonderful language of Python. We should take advantage of that.

Again, I thank Ike for his blog post, and hope I haven’t upset anyone. Ike’s code is pretty consistent with the type of code I’ve seen from Technical Artists. It’s time to do better. Start by ditching the Designer and focusing on clean code.

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Behavioral testing is the bee’s knees

10/11/2014

I have been doing Test Driven Development (TDD) with xUnit-based frameworks (like unittest and NUnit) for a number of years now, and started with RSpec in Ruby and Jasmine in JavaScript a few months ago. RSpec and Jasmine are behavioral testing frameworks that facilitate Behavioral Driven Development (BDD). BDD is really no different from “normal” TDD except for the frameworks used. BDD frameworks facilitate a different way of designing tests.

Behavioral testing is awesome and makes xUnit-style testing seem barbaric and uncivilized in comparison. I didn’t see the big deal for a long time. My xUnit style tests served me just fine. But now I’m hooked.

If you are a TDD person (or do any unit testing), I encourage you to try out BDD. You should get the hang of it quickly. It can take a little while to learn BDD idioms, but once you start doing things like custom matchers, it’s absolutely amazing.

In future posts I’ll try to discuss more of the differences between TDD and BDD, but for now I just provide a hearty endorsement of BDD and frameworks like RSpec and Jasmine.

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PracticalMayaPython: RuntimeError: Internal C++ object (PySide.QtGui.QStatusBar) already deleted.

19/10/2014

TLDR: If you get that error for the code on page 163, see the fix at https://github.com/rgalanakis/practicalmayapython/pull/2/files

In August, reader Ric Williams noted:

I’m running Maya 2015 with Windows 7 64-bit. On page 163 when we open the GUI using a Shelf button, the GUI status bar does not work, (it works when called from outside Maya). This RuntimeError appears: RuntimeError: Internal C++ object (PySide.QtGui.QStatusBar) already deleted.

I no longer have Maya installed so I couldn’t debug it, but reader ragingViking (sorry, don’t know your real name!) contributed a fix to the book’s GitHub repository. You can see the fix here: https://github.com/rgalanakis/practicalmayapython/pull/2/files
And you can see the issue which has more explanation here: https://github.com/rgalanakis/practicalmayapython/issues/1

Thanks again to Ric and ragingViking. I did my best to test the code with various versions of Maya but definitely missed some things (especially those which required manual testing). If you find any other problems, please don’t hesitate to send me an email!

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Metaprogramming with the type function

1/09/2014

In my book, Practical Maya Programming with Python, I use Python’s type function for dynamically creating custom Maya nodes based on specifications, such as input and output attributes. I really love the type function*, so I thought I’d post another cool use of it.

I recently wrote this gem as a way to dynamically create exception types from error codes (it was for a now-defunct REST client):

def get_errtype(code):
    errtype = _errtype_cache.get(code)
    if errtype is None:
        errtype = type('Error%s' % code, (FrameworkError,), {})
        _errtype_cache[code] = errtype
    return errtype

Next is an uncommon form of Python’s except statement. Its argument can be any expression that evaluates to a single exception type or sequence of exception types. You can actually call a function in the argument to except!

try:
    return framework.do_something()
except framework.catch(404):
    return None

The framework.catch function is below. It looks up (and potentially creates) error types based on the error codes being caught:

def catch(*codes):
    return [get_errtype(c) for c in codes]

This sort of utility is why I wrote the type of book I did. Learning how to program in Python instead of MEL is all well and good. But you need to really see what Python is capable of to make big strides. I hope that with a more advanced understanding of Python, 3D developers can start creating frameworks and libraries, just like PyMEL, that other developers will work with and on for many years to come.


* I love the type function for a vain reason. It’s more obscure than decorators, but not as difficult to understand as metaclasses.

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GeoCities and the Qt Designer

25/08/2014

In a review of my book, Practical Maya Programming with Python, reviewer W Boudville suggests my advice of avoiding the Qt Designer is backwards-looking and obsolete, such as writing assembler instead of C for better performance, or using a text file to design a circuit instead of a WYSIWYG editor. I am quite sure he (assuming it is a he) isn’t the only person with such reservations.

Unfortunately, the comparison is not at all fair. Here’s a more relevant allegory:

Hey, did you hear about this awesome thing called geocities? You can build a website by just dragging and dropping stuff, no programming required!

We’ve had WYSIWYG editors for the web for about two decades (or longer?), yet I’ve never run into a professional who works that way. I think WYSIWYG editors are great for people new to GUI programming or a GUI framework, or for mock-ups, but it’s much more effective to do production GUI work through code. Likewise, we’ve had visual programming systems for even longer, but we’ve not seen one that produces a result anyone would consider maintainable. Sure, we’ve had some luck creating state machine tools, but we are nowhere close for the more general purpose logic required in a UI. And even these state machine tools are only really useful when they have custom nodes written in code.

Finally, WYSIWYG editors can be useful in extremely verbose frameworks or languages. I wouldn’t want to use WinForms in C# without the Visual Studio Designer. Fortunately for Pythonistas, PySide and PyQt are not WinForms!

I have no doubt that at some point WYSIWYG editors will become useful for GUI programming. Perhaps it will require 3D displays or massively better libraries. I don’t know. But for today and the foreseeable future, I absolutely discourage the use of the Qt Designer for creating production GUIs with Python.

12 Comments

Code separators and headers are more than a matter of style

4/08/2014

IDontCareIfYouPreferPascalCase, ifCamelCaseIsForYou, or_if_you_prefer_lowercase_underscores. They all have their merits. However, there is one element of many style guides that I have come to vehemently disagree with: function/class headers* and separators. I bring it up because I’ve encountered them in pretty much every codebase I’ve worked with, but never found it difficult to convince people to stop using them.

Many people have the belief that there’s no inherent superiority between having and not having headers and separators. Separators vs. no separators is like PascalCase vs. camelCase. “It’s just the way it is, follow the guide, be consistent.”

It’s a reasonable opinion, but wrong.

Headers and separators are more like the comments that say #Open the file directly above the line that says with open(somepath) as f. Or more likely, a comment that says #Don't write the file yet above the line that says requests.get(someurl). Wait, what does that comment refer to? No idea, because someone edited the code but not the comment.

We’ve known these sorts of comments are harmful for a long time. They involve out-of-band, redundant information that quickly rots. Having to create purely formal, redundant information (“a new function becomes here!”) is extra work that cannot be justified. Headers and separators are the same. “Added missing separator above function” is a commit message no one should ever have to write or read. Separators and headers are not a debate about readability. They are harmful because they are an impediment to changing code.

Please, if you use separators and headers, stop it immediately, or at least listen to the next jerk that comes in and wants to stop using them.


* : I am also against file headers, but I know they can be useful in some cases, such as when your code is mingled with client code and it can be important for people to know what code came from where. I can tolerate overhead that has a demonstrable benefit.

2 Comments

Practical Maya Programming with Python is Published

28/07/2014

My book, Practical Maya Programming with Python has been finally published! Please check it out and tell me what you think. I hope you will find it sufficiently-but-not-overly opinionated :) It is about as personal as a technical book can get, being distilled from years of mentoring many technical artists and programmers, which is a very intimate experience. It also grows from my belief and observation that becoming a better programmer will, due to all sorts of indirect benefits, help make you a better person.

If you are using Python as a scripting language in a larger application- a game engine, productivity software, 3D software, even a monolithic codebase that no longer feels like Python- there’s a lot of relevant material here about turning those environments into more standard and traditional Python development environments, which give you better tools and velocity. The Maya knowledge required is minimal for much of the book. Wrapping a procedural undo system with context managers or decorators is universal. A short quote from the Preface:

This book is not a reference. It is not a cookbook, and it is not a comprehensive guide to Maya’s Python API. It is a book that will teach you how to write better Python code for use inside of Maya. It will unearth interesting ways of using Maya and Python to create amazing things that wouldn’t be possible otherwise. While there is plenty of code in this book that I encourage you to copy and adapt, this book is not about providing recipes. It is a book to teach skills and enable.

Finally, to those who pre-ordered, I’m truly sorry for all the delays. They’re unacceptable. I hope you’ll buy and enjoy the book anyway. At least I now have a real-world education on the perils of working with the wrong publisher, and won’t be making that same mistake again.

Thanks and happy reading!
Rob Galanakis

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goless 0.7 released, with Python3 support and bug fixes

25/07/2014

goless version 0.7.0 is out on PyPI. goless facilitates writing Go language style concurrent programs in Python, including functionality for channels, select, and goroutines.

I forgot to blog about 0.6 at the start of July, which brought Python 3.3 and 3.4 support to goless (#17). I will support pypy3 as soon as Travis supports it.

Version 0.7 includes:
– A “fix” for a gevent problem on Windows (socket must be imported!). #28
– Errors in the case of a deadlock will be more informative. For example, if the last greenlet/tasklet tries to do a blocking send or recv, a DeadlockError will be raised, instead of the underlying error being raised. #25
– goless now has a small exception hierarchy instead of exposing the underlying errors.
– Better PyPy stackless support. #29
goless.select can be called with (case1, case2, case3), etc., in addition to a list of cases (ie, ([case1, case2, case3])). #22

Thanks to Michael Az for several contributions to this release.

Happy concurrent programming!

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goless now on PyPI

12/06/2014

goless is now available on the Python Package Index: https://pypi.python.org/pypi/goless . You can do pip install goless and get Go-like primitives to use in Python, that runs atop gevent, PyPy, or Stackless Python. You can write code like:

channel = goless.chan()

def goroutine():
    while True:
        value = channel.recv()
        channel.send(value ** 2)
goless.go(goroutine)

for i in xrange(2, 5):
    channel.send(i)
    squared = channel.recv()
    print('%s squared is %s' % (i, squared))

# Output:
# 2 squared is 4
# 3 squared is 9
# 4 squared is 16

I’ve also ported the goless benchmarks to Go, for some basic comparisons to using goless on various Python runtimes (PyPy, CPython) and backends (gevent, stackless): https://goless.readthedocs.org/en/latest/#a-benchmarks

Thanks to Rui Carmo we have more extensive examples of how to use goless (but if you’ve done or read any Go, it should be relatively straightforward). Check them out in the examples folder: https://github.com/rgalanakis/goless/tree/master/examples

And just a compatibility note (which is covered in the docs, and explained in the exception you get if you try to use goless without stackless or gevent available): goless works out of the box with PyPy (using stackless.py in its stdlib) and Stackless Python. It works seamlessly with gevent and CPython 2.7. It works with PyPy and gevent if you use the tip of gevent and PyPy 2.2+. It will support Python 3 as soon as gevent does.

Thanks and if you use goless, I’m eager to hear your feedback!

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goless Benchmarks

9/06/2014

I benchmarked how goless performs under different backends (goless is a library that provides a Go-like concurrency model for Python, on top of stackless, PyPy, or gevent). Here are the results, also available on the goless readthedocs page:

Platform Backend   Benchmark      Time
======== ========= ============== =======
PyPy     stackless chan_async     0.08400
CPython  stackless chan_async     0.18000
PyPy     gevent    chan_async     0.46800
CPython  gevent    chan_async     1.32000
~~~~~~~~ ~~~~~~~~~ ~~~~~~~~~~~~~~ ~~~~~~~
PyPy     stackless chan_buff      0.08000
CPython  stackless chan_buff      0.18000
PyPy     gevent    chan_buff      1.02000
CPython  gevent    chan_buff      1.26000
~~~~~~~~ ~~~~~~~~~ ~~~~~~~~~~~~~~ ~~~~~~~
PyPy     stackless chan_sync      0.04400
CPython  stackless chan_sync      0.18000
PyPy     gevent    chan_sync      0.44800
CPython  gevent    chan_sync      1.26000
~~~~~~~~ ~~~~~~~~~ ~~~~~~~~~~~~~~ ~~~~~~~
PyPy     stackless select         0.06000
CPython  stackless select         0.38000
PyPy     gevent    select         0.60400
CPython  gevent    select         1.94000
~~~~~~~~ ~~~~~~~~~ ~~~~~~~~~~~~~~ ~~~~~~~
PyPy     stackless select_default 0.00800
PyPy     gevent    select_default 0.01200
CPython  stackless select_default 0.19000
CPython  gevent    select_default 0.25000

The trends are that PyPy with its built-in stackless support is fastest, then Stackless Python (2-5x), then PyPy with gevent (5-10x), and finally CPython with gevent (15-30x).

Remember that these are benchmarks of goless itself; goless performance characteristics may be different in real applications. For example, if you have lots of C extensions, CPython/stackless may pull ahead due to stack switching.

If you’re using goless for anything, I’d love to get some benchmarks, and find out how it’s working, so please comment below, or create a GitHub issue.

Disclaimers: It’s possible that goless is inefficiently using gevent, but the backend-specific code is so simple I doubt it (see goless/goless/backends.py). These benchmarks are all done against Python 2.7. Also I am no benchmark master (especially with PyPy) so there may be problems, look over goless/benchmark.py for benchmark code. These were done mostly for curiosity.

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