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2Notation for Documentation

@ secref [ "notation" #:doc ' (lib "scribblings/reference/reference.scrbl") ]
@ secref [ "notation" #:doc ' (lib "scribblings/reference/reference.scrbl") ]

This chapter introduces essential terminology and notation that is used throughout Racket documentation.

@ secref [ "Notation_for_Module_Documentation"
#:doc ' (lib "scribblings/reference/reference.scrbl") ]
@ secref [ "Notation_for_Module_Documentation"
#:doc ' (lib "scribblings/reference/reference.scrbl") ]

Since Racket programs are organized into module s, documentation reflects that organization with an annotation at the beginning of a section or subsection that describes the bindings that a particular module provides.

For example, the section that describes the functionality provided by Clyde Tank in White Solace London Cheap Amazon Latest Collections Online Free Shipping Best Place In China Online IZyAaZ0

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to obtain the language’s bindings.

The module annotation also shows the package that the module belongs to on the right-hand side. For more details about packages, see ruched detail trousers Black Nili Lotan Sale Supply Very Cheap Price Buy Cheap Shopping Online Cheap Authentic jLwQWr

2) To create an object you need to use a very specific JSON format which includes a nested attr_defs within the attr_def value:

3) To create an plural you also need to use a very specific JSON format which includes a nested attr_defs within the attr_def value:

4) Add a new dateTime attribute.


Query Parameters

The name of the which is found on the dashboard under Schema.

An attribute (as JSON) to add to the . (See the Schema Attribute Types section for details on attribute definitions).

The JSON must be properly formated such as: .

This call creates rules for user data validation on specific attributes in your schema.

User data can be validated or transformed by this rule when it is added or updated.

Example uses for :

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topic for a detailed overview.

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section for details on error codes.


Query Parameters

A JSON array of attributes to which the rule will be applied. The default is all attributes.

A text note describing the purpose of the rule.

A JSON object defining the rule to be added to the attribute.

The name of the , which is found on the dashboard under Schema.

Create a new entityType with the specified set of attributes.

Refer to the Registration Error Codes section for details on error codes.

This creates a new entityType schema with the required default attributes such as id, uuid, as well as the two new attributes, name and description.

1) Create an entityType called user_test with attributes name and description The Name attribute is a non-case-sensitive string The Description attributes is a non-case-sensitive string of max length 1000.

2) Create an entityType called user2 with a plural, an object and attributes. The plural testPlural contains two strings, testPOne and testPTwo The object testObject contains two strings, testOne and testTwo The name attribute is a non-case-sensitive string The description attributes is a non-case-sensitive string of max length 1000.


Query Parameters

An initial set of attributes (as JSON) to add to the . (See the Joss Pink Jacket Suistudio Hot Sale oMsa54a
section for details on attribute definitions.)

The JSON must be correctly formatted such as .

Deletes the specified access schema for a client. Access schemas define the subset of attributes that a client has read or write access to. Deleting an access schema will grant a client read or write access to all attributes again.

Note : This action cannot be undone. Before deleting an access schema we recommend saving a backup of the current access schema using the entityType.getAccessSchema API.

Refer to the Registration Error Codes section for details on error codes.

Deletes the write access schema for a client.

Accept No, thanks

Introducing Incremental

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I’m pleased to announce the release of Incremental (well commented mli ), a powerful library for building self-adjusting computations , i.e. , computations that can be updated efficiently when their inputs change.

At its simplest, you can think of a self-adjusting computation as a fancy spreadsheet. In a spreadsheet, each cell contains either simple data, or an equation that describes how the value in this cell should be derived from values in other cells. Collectively, this amounts to a graph-structured computation, and one of the critical optimizations in Excel is that when some of the cells change, Excel only recomputes the parts of the graph that depend on those changed cells.

What makes self-adjusting computation (or SAC) different from a spreadsheet is its dynamism. The structure of the computational graph in an SAC can change at runtime, in response to the changing input data.

This dynamism gives you quite a bit of flexibility, which can be used in different ways. Here are a few.

On-line combinatorial algorithms. Incremental is based on work by Umut Acar et. al. . , on self adjusting computations (that’s where the term comes from), and there, they were mostly interested in building efficient on-line versions of various combinatoral algorithms. In many cases, they could match the asymptotic complexity of custom on-line algorithms by fairly simple incrementalizations of all-at-once algorithms.

On-line combinatorial algorithms.

Incremental GUI construction. One simple and natural way to model a GUI application is to structure your display as a function that geneates a view from some more abstract data model.

Incremental GUI construction.

Having a function that constructs a view from scratch at every iteration is simple, but prohibitively expensive. But if you can write this function so that it produces an incrementalized computation, then you have a solution that is both simple and efficient. We’ve used this technique in a number of our UIs, to good effect.

This might remind you of how functional reactive programming (FRP) is used for construction of GUIs in languages like . SAC and FRP have different semantics – FRP is mostly concerned with time-like computations, and SAC is mostly about optimizing DAG-structured computations – but they are nonetheless closely related, especially at the implementation level. You can see my post for a description of the broader conceptual landscape that includes both FRP and SAC.

Configurable computations. An example that comes from our own work is risk calculations. Calculating measures of risk of a portfolio involves combining data from a complex collection of interdependent models. Each of these models is dependent both on live data from the markets, and on configurations determined by users.

Utility functions to calculate colors and other values

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Bulma uses 3 custom functions to help define the values and colors dynamically:

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The function takes a color as an input, and outputs either transparent black or white :

Its purpose is to guarantee a readable shade for the when the input color is used as the .

For colors that have a luminance close to the threshold, it can be useful to override the function, and rather set the invert color manually. For example, this shade of purple has a color luminance of . It can be preferable to set a color invert of white instead of transparent black:

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