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Friday, 24 February 2017

New Documentary: Hollywood's GreatestTrick

I just became aware of a 2-day old article about a documentary available in full, showing the current situation of visual effects workers in Hollywood's film industry.

Hollywood's Greatest Trick

It talks about how the industry has been able to continue to raise the bar on the quality and increased number of visual effects shots in an average Hollywood film.

The revenue generated by these films are also discussed. This is put in context when compared to the overheads and marginal profits that can be made from film to film. The documentary then shows that on top of struggling just to break even, what visual effects vendors and companies are up against when bidding for movies to be awarded to them from a limited and fixed pool of client studios.

At the artists level, the film talks about how artists are expected to work long hours, and their passion for their work was being taken for granted, oftentimes for the benefit of the clients.

More and more companies are hiring on a per-project basis, and they set-up offices in places that allow them to operate at the cheapest costs (labour costs and tax breaks offered to film-related business activities). Artists are forced to move with the companies, in order to find work, and that may only last for a few months to a year, as most companies hire on contract terms nowadays.

If you have friends, family or relatives working in the visual effects industry, or if you just enjoy Hollywood films, or maybe you enjoy watching behind the scenes featurettes, fascinated by what visual effects can do, it is definitely worth your time watching this film.

It is my hope that more people become aware that behind the beautifully magical visuals of Hollywood films lie not just advanced image manipulation technology and software, but also a huge team of highly skilled workers who work the software and craft their shots.

In the middle of the article, there is an excellent infographics animation that clearly introduces the major stages of how a visual effects shot is created. It is really enlightening and can be easily understood by the lay person. I highly recommend watching this!

Sunday, 15 January 2017

Maya Expressions Part 04 - Twisting Cubes

Video also available on Vimeo.com.

In Maya Expressions Part 4, we start to use expressions across multiple objects. This results in an interesting rig where the objects driven by expression can arrange themselves in interesting ways.

In my set up, every cube has a slight offset in translation and rotation to the one before, As the first cube moves along its x-axis, the spacing between cubes and each of their rotations change and react to reflect that change.

In the video I also show how I structure my expression so that as much as possible, the same code can be applied to each cube with minimal alteration. With the standardised code and minimal change for each of the cube, it will become relatively easy to replicate this behaviour to as many extra cubes as required without hassle.

Maya Expressions is my running series introducing and using the powerful expression function in Maya to achieve tasks that would otherwise be very manual or inefficient to set up and manage.

Watch the previous videos!
Maya Expressions Part 1
Maya Expressions Part 2
Maya Expressions Part 3

Tuesday, 13 December 2016

Python Sorting Part 2: Classes and Attributes

Python Sorting with Classes and Attributes

# Here we define a class, containing student names
# each student contains an list attribute of varying from 1 to 7 in length
# populated by random integer values from 0 to 500

import random # imports the random module

# declares the class object
class myClass(object):
    def __init__(self, inputName):
        self.myName = inputName # creates a myName attribute
        # create a list of random length
        self.myList = [random.randint(0,500) \
                        for x in range(random.randint(1, 7))]
    def greet(self):
        # prints out name and list of values the instance holds
        print 'Hi I am {}, my list is {}'.format(self.myName, self.myList)

random.seed(0) # providing a seed to generate consistent random values
# Now we create instances of myClass for 5 students,
# and store the result as a list in studentNames
studentNames = ('tommy', 'sally', 'summer', 'jane', 'jonathan')
# For each item in studentNames, create a myClass instance
classList = [myClass(x) for x in studentNames]

# What kind of data does the list contain? Let's find out
print classList
# Result: [<__main__.myClass object at 0x0000000057D90470>,
# <__main__.myClass object at 0x0000000057D90320>,
# <__main__.myClass object at 0x0000000057D90240>,
# <__main__.myClass object at 0x0000000057D906A0>,
# <__main__.myClass object at 0x0000000057D906D8>] # 

# They are instances of myClass, that each hold name attribute values of
# 'tommy', 'sally', 'summer', 'jane', 'jonathan' in that order respectively
# They will also each have a list containing 1 to 7 items which are integers

# Loop through each myClass instance and call their greet() method
for person in classList:
# Result:
# Hi I am tommy, my list is [379, 210, 129, 256, 202, 392]
# Hi I am sally, my list is [238, 292, 454]
# Hi I am summer, my list is [141, 378, 309, 125]
# Hi I am jane, my list is [492, 405, 451, 155, 365, 450, 342]
# Hi I am jonathan, my list is [50, 217, 306, 457] #

# return just the names stored in myClass.myName attribute
[classList[x].myName for x in range(len(classList))]
# Result: ['tommy', 'sally', 'summer', 'jane', 'jonathan'] # 

# sorting the classes (and returning their myName values 
# by the length of their myName strings sorted([x.myName for x in classList], key=len)
# Result: ['jane', 'tommy', 'sally', 'summer', 'jonathan'] # 
# 'jane' is the shortest string and 'jonathan' is the longest string, correct ascending order

# return the name stored in myClass.myName attribute
# in the order of the length of the list contained in their myList attribute
y.myName for y in sorted([x for x in classList], 
  key=lambda(student): len(student.myList))]
# Result: ['sally', 'summer', 'jonathan', 'tommy', 'jane'] # 
# 'sally' has a myList attribute containing 3 items
# 'jane' has a myList attribute containing 7 items
# correct ascending order

# we can find out the sum of the items in each instance's list
[(x.myName, sum(x.myList)) for x in classList]
# Result: [('tommy', 1568),
 ('sally', 984),
 ('summer', 953),
 ('jane', 2660),

 ('jonathan', 1030)] # 

# Now we shall try to sort the class instances 
# ordered by the sum of their list items
[y.myName for y in sorted([x for x in classList], key=lambda(inst):sum(inst.myList))]

# Result: ['summer', 'sally', 'jonathan', 'tommy', 'jane'] # 
# 'summer' has a list with the sum of 953
# 'jane' has a list with the sum of 2660
# This is the correct ascending order according to each instance's sum

# When we enclose list comprehensions like that it is very confusing 
# and may be difficult to read.

# Here's that statement again, with colour coded blocks that we can examine in parts
[y.myName for y in sorted([x for x in classList]

# Let's break it down, starting from the inner most list comprehension:
[x for x in classList]
# This gives us the list of myClass instances in the order of the 
# studentNames list, which the class instances were created

# Expanding from that, we come to this statement
sorted([x for x in classList], key=lambda(inst):sum(inst.myList))
# We sort this list of class instances by using the key argument
# The lambda function returns to the sum of the instance's myList items
# the inst variable passed into the lambda function in this case will contain
# the myClass instance as the list of instances are passed in to be sorted

# With that sum's value as the key argument, the sorted([x for x...]) part of the statement 
# will return a list containing the class instances ordered by sum of the list's items

# Finally, the outermost list comprehension
[y.myName for y in sorted([x for x...], key=lambda...)]
# This simply take the already ordered list of class instances 
# (ordered by the sum of each instance's list items)
# and return the myName attribute

Python Sorting Part 1: Dictionary Keys

Sorting Python Dictionary Keys

Recently I ran into the need to perform more involved operations on Python dictionaries and sorting of ordered lists. This led me to a deeper understanding of a side of the sorted() function that I used to shy away from. 

The biggest area that I had to learn about was sorting values by a key -- how it works and the practical applications.

So the following lines of code are my 'journal' in this little learning journey through dictionary operations and sorting. 

# Here's a dictionary d that has strings as keys, and a list of integers for each key's value.
d = {'m': [23, 42, 63], 'm800': [32, 53, 743, 8], 'm23': [3324,425,21], 'a132': [2, 2, 53, 64]}

# Here's a dictionary e that has integers as keys, and a list of integers for each key's value.
e = {1: [42, 43, 52], 200: [3, 53, 63, 2], 60: [4, 62, 96], 30: [63, 89], 500: [32]}

print d
# Result: {'a132': [2, 2, 53, 64], 'm800': [32, 53, 743, 8], 'm': [23, 42, 63], 'm23': [3324, 425, 21]}

# The order of the dictionary that Python prints out for us reflects the order stored internally, 
# which is in no particular order. 
# Indeed Python documentation states that dictionaries are not ordered types.

# However, starting Python 3.6 and above, 
# the default dictionary type will maintain their order in which keys are created.

print e
# Result: {200: [3, 53, 63, 2], 1: [42, 43, 52], 60: [4, 62, 96], 500: [32], 30: [63, 89]}

# Result: ['a132', 'm800', 'm', 'm23'] #
# The result returns a list of keys in the order stored internally, same as print d above.

# Result: [[2, 2, 53, 64], [32, 53, 743, 8], [23, 42, 63], [3324, 425, 21]] # 
# The result returns a list of the values stored respective to the order of the keys stored internally

# Finding the sum of all the elements in a list of d's keys
sum([len(d[i]) for i in d.keys()])
# Result: 14 #

# Finding the sum of all the elements in a list of e's keys
sum([len(e[i]) for i in e.keys()])
# Result: 13 #

# Sorts dictionary d by her keys (which are strings), in ascending alphabetical order
# Result: ['a132', 'm', 'm23', 'm800'] #
sorted(d) # yields the same result, but I feel this is less readable
# Result: ['a132', 'm', 'm23', 'm800'] #

# Sorts dictionary e by her keys (which are integers), in order of ascending numerical value
sorted(e.keys()) # sorts this dictionary which uses integers as keys, in ascending order
# Result: [1, 30, 60, 200, 500] #

# Using the reverse argument we can reverse the resulting list order of dictionary d keys
sorted(d, reverse=True) # returns d's keys in decending order
# Result: ['m800', 'm23', 'm', 'a132'] #

# Using the reverse argument to reverse the resulting list order of dictionary e keys
sorted(e, reverse=True)
# Result: [500, 200, 60, 30, 1] # 

When sorted()is used with the key argument, we can sort according to the result of a function / operation / method
sorted(d.keys(), key=str) # sort using  themselves (string type) as the ordering crieteria
# Result: ['a132', 'm', 'm23', 'm800'] # still in alphabetical order

# We can also sort dictionary e by the string representation of her integer keys
sorted(e.keys(), key=str)
# Result: [1, 200, 30, 500, 60] #
# see how the numbers are ordered by value of the first character, 
# regardless of actual integer value? (200 came before 30, 500 came before 60)

# now we want to sort the keys of d by string length instead
# for this, we use the special method __len__ of the string class which the keys belong to
sorted(d.keys(), key=str.__len__) # use the special method __len__ of the string class 
# Result: ['m', 'm23', 'a132', 'm800'] # arranged from shortest to longest string length

# We can achieve the same result by using the len() function
sorted(d.keys(), key=len) # arranged from shortest to longest string length
# Result: ['m', 'm23', 'a132', 'm800'] #

# dictionary e has integers as her keys, 
# int objects do not have the __len__ in their special methods
sorted(e.keys(), key=str.__len__) # this will cause an error
# Error: descriptor '__len__' requires a 'str' object but received a 'int'

# however we can pass in a function to the key argument. our function takes an input
def getStrLength(myInput):
    return len(str(myInput))
sorted(e.keys(), key=getStrLength)
# Result: [1, 60, 30, 200, 500] # correctly sorted in order of length
# note that 60 comes before 30, that is because 60 and 30 are both of length 2,
# it makes no difference which one comes first

# a one-line function like this is an excellent candidate for the lambda function
sorted(e.keys(), key=lambda(myInput): len(str(myInput)))
# Result: [1, 60, 30, 200, 500] #
# with lambda we do away from the need to create a named function just for this

# here we have the dictionary f, which has float values as her keys
f = {'m1': [23, 42, 63], 'm2': [32, 53, 743, 8], 'm23': [3324,425,21],
    'a132': [2, 2, 53, 64], 2001: [2, 5, 6, 32], 32: [24, 25], 4: [50],
    2.64: [2, 53, 6], 78.12526: [5, 60, 22]
sorted( f.keys(), key=lambda x: len(str(x)) )
# Result: [4, 32, 'm1', 'm2', 'm23', 2001, 'a132', 2.64, 78.12526] #
# because our lambda function takes any object type passed in and converted it into a string,
# it does not encounter an error, even with keys that are integers or float numbers

# sorting the keys in ascending order of the number of items of the list in each key
sorted(f.keys(), key=lambda(myKey):len(f[myKey]))
# Result: [4, 32, 'm1', 78.12526, 2.64, 'm23', 2001, 'm2', 'a132'] #
# in the first returned key, f[4] has only 1 item [50]
# in the last item f['a132'] has 4 items [2, 2, 53, 64]
# thus the result is correctly ordered

# sorting the keys in ascending order of the sum of all items in each key's list
sorted(f.keys(), key=lambda(myKey):sum(f[myKey]))
# Result: [2001, 32, 4, 2.64, 78.12526, 'a132', 'm1', 'm2', 'm23'] #
# in the first returned key, the sum of f[2001] items is 45
# in the last returned key, the sum of f['m23'] items is 3770
# thus the result is correctly ordered

Saturday, 17 September 2016

PyMel: Creating and Accessing the Notes Attribute in an Object

Today I find myself wondering how I can access the 'notes' section of a Maya object using script.

I searched the internet and found out that to access the notes in an object are stored in an attribute called 'notes'. No surprises.

What surprised me, was when I went through the attributes of a newly created Maya object, I could not find that attribute!

So I do the following experiment.

Consider myObject which is a newly created object in the scene (an empty group).

from pymel.core import *
myObject = group(empty=True)

At this point, it does not contain the attribute 'notes'. The following list comprehension looks through each of myObject's attributes, and only includes attributes with 'notes' in its name in the resulting list:
[x for x in myObject.listAttr() if 'notes' in x.lower()]

This returned an empty list. It means that the attribute does not exist.
# Result: [] #

However, if I enter something into the notes section via the Maya Attribute Editor, and I run the same statement, [x for x in myObject.listAttr() if 'notes' in x.lower()]

the script editor will now display the following result:
# Result: [Attribute(u'null1.notes')] #

This means that the attribute now exists.

After further searching, I came to a useful blog post that mentions that we can create the 'notes' attribute ourselves through script:

The blog post used MEL examples, so I created the 'notes' attribute via PyMel.

if not [x for x in myObject.listAttr() if 'notes' in x.lower()]:
    myObject.addAttr('notes', dataType='string')

This block of code will create the notes attribute and set the value to 'hello'. In the Attribute Editor under the 'notes' section, I will be able to see the field update to contain 'hello'.

Here comes something unexpected.

At this point, if I use the attribute editor to delete the 'hello', leaving the notes field empty again, and then I run:
[x for x in myObject.listAttr() if 'notes' in x.lower()]
# Result: [] #

Maya returns an empty list again. The attribute got removed.

Now, I enter something in the notes field in the Attribute Editor again, so that it is not empty. Then I run the following to test if the 'notes' attribute exists. This time I use the attributeQuery() command:
attributeQuery('notes', node=myObject, exists=True)
# Result: True #

Maya created the 'notes' attribute again.

Now I try to give it an empty string by code (not using the Attribute Editor), to see if Maya will still remove the attribute automatically.

Now we try to query the existence of the 'notes' attribute:
attributeQuery('notes', node=myObject, exists=True)
# Result: True #

The attribute exists. Maya did not remove the attribute when we set the attribute to an empty string via script.

Thus we can conclude these things about the 'notes' attribute in Maya nodes.
- in Maya, objects are created without the 'notes' attribute by default
- to use the 'notes' attribute for the first time (via script), we need to create the 'notes' attribute.
- the 'notes' attribute is automatically created when we enter something the 'notes' field using the Attribute Editor
the 'notes' attribute is automatically remove when we clear the 'notes' field using the Attribute Editor

- when we assign an empty string to the 'notes' attribute via script, Maya does not remove the 'notes' attribute