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New in Django 1.11.

Removes the index named name from the model with model_name .

Allows running of arbitrary SQL on the database - useful for more advanced features of database backends that Django doesn’t support directly, like partial indexes.

sql , and reverse_sql if provided, should be strings of SQL to run on the database. On most database backends (all but PostgreSQL), Django will split the SQL into individual statements prior to executing them. This requires installing the sqlparse Python library.

You can also pass a list of strings or 2-tuples. The latter is used for passing queries and parameters in the same way as cursor.execute() . These three operations are equivalent:

If you want to include literal percent signs in the query, you have to double them if you are passing parameters.

The reverse_sql queries are executed when the migration is unapplied, so you can reverse the changes done in the forwards queries:

The state_operations argument is so you can supply operations that are equivalent to the SQL in terms of project state; for example, if you are manually creating a column, you should pass in a list containing an AddField operation here so that the autodetector still has an up-to-date state of the model (otherwise, when you next run makemigrations , it won’t see any operation that adds that field and so will try to run it again). For example:

The optional hints argument will be passed as **hints to the allow_migrate() method of database routers to assist them in making routing decisions. See GG Marmont velvet small shoulder bag Brown Gucci xYQKbfxZT
for more details on database hints.

The optional elidable argument determines whether or not the operation will be removed (elided) when squashing migrations .

Pass the RunSQL.noop attribute to sql or reverse_sql when you want the operation not to do anything in the given direction. This is especially useful in making the operation reversible.

Runs custom Python code in a historical context. code (and reverse_code if supplied) should be callable objects that accept two arguments; the first is an instance of django.apps.registry.Apps containing historical models that match the operation’s place in the project history, and the second is an instance of SchemaEditor .

We should be wary of the Religion of Humanity, because subordinating our lives to utility hollows them out. But we have lots to learn from Mill’s fierce desire to add poetry to progress. Let’s rediscover the paradox that George Herbert – one of those poets excluded from Mill’s education – deftly expressed in 1633:

Without goods that explode utilitarianism and open us to the mystery of suffering, even the happiest life is miserable.

Seven Ways of Looking at Pointless Suffering by Scott Samuelson is out now via The University of Chicago Press.

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Scott Samuelson

is associate professor of philosophy and the humanities at Kirkwood Community College in Iowa. His latest book is (2018).

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, Adinolf, S. (2015). The effects of customization on motivation in an extended study with a massively multiplayer online roleplaying game. (3), article 2. doi: http://dx.doi.org/10.5817/CP2015-3-2

The effects of customization on motivation in an extended study with a massively multiplayer online roleplaying game

Selen Turkay, Sonam Adinolf
Harvard Initiative for Learning and Teaching, Harvard University, Cambridge, MA, United States Massachusetts Institute of Technology, Cambridge, MA, United States
Customization is a ubiquitous feature of games and can be perceived as a series of choices that may influence players’ experiences. Opportunities for customization may give users decision-making authority over the technology, enabling them to shape and create their own experiences, as well as provide ways to be a part of a group based on their needs and desires. This study aims to shed more light on the effects customization has on players’ motivation, and investigates how customization as a way of user control affects player motivation within an MMO. A mixed method study was designed with two conditions: customization (n = 33) and no customization (n = 33). Adult participants played Lord of the Rings Online (LotRO), a Massively Multiplayer Online game, for about ten hours over four sessions. Data was collected through surveys, interviews and observations. Results showed that players’ desire to replay the game increased over time, and customization affected players’ motivation positively. Findings are discussed through the lens of Self-Determination Theory (Deci Ryan, 1985).

Keywords: customization, motivation, MMOs, player retention

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Virtual worlds are immersive, simulated environments. They enable users to move and interact over the Internet through virtual bodies, called avatars. Virtual worlds are considered complex as they offer numerous activities for users (e.g., socializing, gaming, exploring, role-playing, etc.) and/or require consideration of several attributes (e.g., avatar appearance, character skills, navigating in the environment, enemies, etc.) with regard to user experiences (Bartle, 2003). Massively Multiplayer Online games (MMOs) are a type of gaming virtual world. MMOs are persistent, networked, and interactive environments, which support large numbers of people, either synchronously or asynchronously. MMOs also motivate players to play longer periods of time compared to other games (Ng Wiemer-Hastings, 2005).

MMO players' motivation and willingness to continue playing is encouraged by game design aspects such as challenge, fantasy, curiosity (Malone, 1981), narrative, character design (Dickey, 2007), feedback, clear goals and tasks, and by providing users ways to feel control in the virtual world (Przybylski, Rigby, Ryan, 2010; Rieber, 1996; Sweetsner Wyeth, 2005). These design aspects promote intrinsically-rewarding behaviors that make player experiences more enjoyable and more likely to be repeated in the future (Csikszentmihalyi, 1975).

Here’s how it works:

For each new sample, the best-candidate algorithm generates a fixed number of candidates, shown in gray. (Here, that number is 10.) Each candidate is chosen uniformly from the sampling area.

The best candidate , shown in red, is the one that is farthest away from all previous samples, shown in black. The distance from each candidate to the closest sample is shown by the associated line and circle: notice that there are no other samples inside the gray or red circles. After all candidates are created and distances measured, the best candidate becomes the new sample, and the remaining candidates are discarded.

Now here’s the code:

As I explained the algorithm above, I will let the code stand on its own. (And the purpose of this essay is to let you study code through visualization, besides.) But I will clarify a few details:

The external numCandidates defines the number of candidates to create per sample. This parameter lets you trade-off speed with quality. The lower the number of candidates, the faster it runs. Conversely, the higher the number of candidates, the better the sampling quality.

The distance function is simple geometry:

The findClosest function returns the closest sample to the current candidate. This can be done by brute force, iterating over every existing sample. Or you can accelerate the search, say by using a quadtree . Brute force is simple to implement but very slow (quadratic time, in O-notation ). The accelerated approach is much faster, but more work to implement.

Speaking of trade-offs: when deciding whether to use an algorithm, we evaluate it not in a vacuum but against other approaches. And as a practical matter it is useful to weigh the complexity of implementation — how long it takes to implement, how difficult it is to maintain —against its performance and quality.

The simplest alternative is uniform random sampling:

It looks like this:

Uniform random is pretty terrible. There is both severe under- and oversampling: many samples are densely-packed, even overlapping, leading to large empty areas. (Uniform random sampling also represents the lower bound of quality for the best-candidate algorithm, as when the number of candidates per sample is set to one.)

Dots patterns are one way of showing sample pattern quality, but not the only way. For example, we can attempt to simulate vision under different sampling strategies by coloring an image according to the color of the closest sample. This is, in effect, a Voronoi diagram of the samples where each cell is colored by the associated sample.

What does The Starry Night look like through 6,667 uniform random samples?

Hold down the mouse to compare to the original.

The lackluster quality of this approach is again apparent. The cells vary widely in size, as expected from the uneven sample distribution. Detail has been lost because densely-packed samples (small cells) are underutilized. Meanwhile, sparse samples (large cells) introduce noise by exaggerating rare colors, such as the pink star in the bottom-left.

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