Notebooks
Story: The song of the !korro-ssin !ku (or Pit-making Bushmen)
The song of the !korro-ssin !ku (or Pit-making Bushmen)
Title
The song of the !korro-ssin !ku (or Pit-making Bushmen)
Collection
Summary
The song of the Pit-making Bushmen (or !korro-ssin !ku) who sing of the burning or pain in their eyes.
Comments
1) See also Different Bushmen in !nanni and Tamme's country and The song of the !na !na'rishe (a bird) and A song of the |kam-ssin !ku (or Sun Bushmen) and Song of the ||ku and Song of the !kan ||ka ||karashe and The song of the Sauko and The song of the ||noruko djo-djo and The song of the mother of the little buck and The song of the ≠na≠n'arro and The song of the Ngogan-a (a little bird) and The song of the !ke tsa'ba (a bird), 2) This is found in Book XI & XII-3
Contributors
Date
8 December 1879 (given on or about)
Categories
Keywords
song (of the !korro-ssin !ku or 'Pit-making Bushmen') , song (of the !kun) , song (given by Tamme) , !korro-ssin !ku (the 'Pit-making Bushmen') , !korro-ssin !ku (a song of) , !kun (different groups of) , !kun (the !korro-ssin !ku or 'Pit-making Bushmen') , Pit-making Bushmen (or !korro-ssin !ku) , Pit-making Bushmen (a group of !kun) , Pit-making Bushmen (song of) , names (of groups of !kun) , names (!korro-ssin !ku or 'Pit-making Bushmen')
Story Pages
9182


System Information
CPU Info
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Uptime
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System Power
Atributions & Credits
Web Application created by Ben Parmeter, copyright 2020. Check out my other projects on GitHub.
If you enjoy this software and feel the need to donate a cup of coffee, a frosty beer or a bottle of wine to the developer you can click here.
Of course, none of this project would be available without the wonderful and amazing folks below...
- Bootstrap - WebUI Based on Bootstrap 4. Bootstrap is released under the MIT license and is copyright 2018 Twitter. (getbootstrap.com)
- JQuery - Required by Bootstrap. Copyright JS Foundation and other contributors. Released under MIT license. (jquery.org/license)
- Popper - Required by Bootstrap. Copyright 2016, 2018 FEDERICO ZIVOLO & CONTRIBUTORS. Released under MIT license. (popper.js.org)
- FontAwesome - Amazing FREE Icons that I use throughout this project. Copyright Font Awesome. Released under the Font Awesome Free License. (fontawesome.com)(github.com)
- BootSwatch - Bootstrap CSS styling. Copyright 2012-2020 Thomas Park. Licensed under MIT. (https://bootswatch.com/)


I'm glad you came here to learn about Granny's Favorite Pie Recipies! The rest of the this stuff is just fun filler!
All of the words in Lorem Ipsum have flirted with me - consciously or unconsciously. That's to be expected. When other websites give you text, they’re not sending the best. They’re not sending you, they’re sending words that have lots of problems and they’re bringing those problems with us. They’re bringing mistakes. They’re bringing misspellings. They’re typists… And some, I assume, are good words. All of the words in Lorem Ipsum have flirted with me - consciously or unconsciously. That's to be expected.
All of the words in Lorem Ipsum have flirted with me - consciously or unconsciously. That's to be expected. When other websites give you text, they’re not sending the best. They’re not sending you, they’re sending words that have lots of problems and they’re bringing those problems with us. They’re bringing mistakes. They’re bringing misspellings. They’re typists… And some, I assume, are good words. Lorem Ispum is a choke artist. It chokes!
Copyright © 2012 Granny's Pies A Granny's Pies Production All rights reserved


Watch Kanye West’s Sunday Service With Joel Osteen
Including live performances off ‘Jesus Is King.’
by Mallory ChinOver the weekend, Kanye West brought his Sunday Service to pastor Joel Osteen‘s Lakewood Church in Houston, Texas marking the multi-hyphenate’s largest audience to date.
During West’s appearance, the artist used some of his time on stage to defend the megachurch head who has come under fire in recent years for his prosperity gospel teachings and for not making his place of worship available for Hurricane Harvey victims. “There’s a lot of people in the Christian community that try to give Joel a hard time because when you turn on the radio, he just keeps telling you how good God is,” Kanye said. “That’s good, I love that. When you got Kanye defending you, you made it,” Osteen replied the endorsement.
West went on to condemn the music industry citing, “The Devil stole all the good producers. The Devil stole all the good musicians, all the good artists, all the good designers, all the good business people, and said, ‘You gotta come over and work for me.’ And now, the trend, the shift is going to change. Jesus has won the victory.” He added, “Now, the greatest artist God has ever created is now working for him.”
West and his choir also performed songs from Jesus Is King, such as “God Is” and “Follow God,” as well as secular-turned-gospel songs such as Destiny’s Child’s “Say My Name” and A$AP Ferg’s “New Level.” Check it out above.
Tickets to West’s highly-anticipated Sunday Service with Joel Osteen were resold online.
In related news, Kanye West and Dr. Dre reveal Jesus Is King Part II is “coming soon.”


javax.wsdl.extensions.soap12
Interfaces
SOAP12Address
SOAP12Binding
SOAP12Body
SOAP12Fault
SOAP12Header
SOAP12HeaderFault
SOAP12Operation


Mis-sense mutation Val----Ile in exon 17 of amyloid precursor protein gene in Japanese familial Alzheimer's disease.


OpenThem is a Chrome extension which lets you open your favorite websites when launching Google Chrome, with some cool options:
- Open websites in incognito mode
- Automatic synchronizations between all your Google Chromes
- Open links only on some devices
- ...and so on


MFC Support
Specify the application feature support for your application.
Overview
Application Type
Additional SDK Support
MFC Support
COM Options
.NET Options
MFC support:
N
o MFC Support
Regular DLL with MFC
S
tatically linked.
R
egular DLL using MFC Shared DLL
E
xtension DLL using MFC Shared DLL (recommended for MFC support)
A
utoCAD MFC Extension Support
F
inish
C
ancel
H
elp


Given mr flintstone is "hungry"
When he harasses bambam for food
Then bambam places a burger order with barney "a burger"
When barney gives to bambam "1 burger"
Then bambam gives to mr flintstone "1 burger"
Then mr flintstone shares his food with barney because he likes him
Test result:
Test passed
Sequence Diagram
Interesting Givens
String
burger
Bambam gets food for his dad repeated so we can check multiple scenarios per test method
Given mr flintstone is "starving"
When he harasses bambam for food
Then bambam places a burger order with barney "a burger"
When barney gives to bambam "1 burger"
Then bambam gives to mr flintstone "1 burger"
Then mr flintstone shares his food with barney because he likes him
Test result:
Test passed
Sequence Diagram
Interesting Givens
String
burger
Specification
Given mr flintstone is "peckish"
When he harasses bambam for food
Then bambam places a burger order with barney "a burger"
When barney gives to bambam "1 burger"
Then bambam gives to mr flintstone "1 burger"
Then mr flintstone shares his food with barney because he likes him


Wasif Izar
Computer Science and Engineering Student
Iam Wasif Izar, Pursuing B.E(Bachelor of Engineering) in Computer Science and Engineering
Iam 21 y/o and Moving forward to be a Software Developer
My Projects
Music Player
This is Music Player that can Play songs present in your local storage
User can use all basic functions like. Play, Pause, Forward, Rewind, Go 10sec backward or Forward
This Music Player is Developed with Android Studio and can work on android smartphones and emulators
Languages used to Develop is Java and XML
Check out my project's snapshots and source code by clicking below
GitHub LinkCourses and Certifications
TCSiON Certificate
TCS iON Career Edge - Young Professional
This course contains
Communication Skills │ Presentation Skills│ Soft Skills │ Career Guidance Framework │ Resume Writing │ Group Discussion Skills│ Interview Skills │ Business Etiquette │ Effective Email Writing │ Telephone Etiquette │ Accounting Fundamentals │ IT Foundational Skills │ Overview of Artificial Intelligence
Coursera - Crash Course on Python by Google
This course is provided by Google
Objecive of the course:
- Write short Python scripts to perform automated actions
- Understand how to use the basic Python structures: strings, lists, and dictionaries
Become a Software Developer
LinkedIn - Become a Software Developer
This course is of 29hrs+ which gives introduction to the programming skills needed for a career as a software developer
Top skills covered
Software Development | Full-Stack Development |
Web Design | JavaScript | Front-end Development
Education
S.L.N. COLLEGE OF ENGINEERING - Raichur
Affiliated to VTU, Approved by AICTE, Accredited by NAAC
B.E(Bachelor of Engineering) in
Computer Science & Engineering
From : 2019 - 2022
GOVERNMENT POLYTECHNIC - Raichur
Affiliated to Board of Technical Education
Diploma in Computer Science & Engineering
From : 2016 - 2019
TAGORE MEMORIAL HIGH SCHOOL - Raichur
Karnataka Secondary Education Examination Board
Higher Education and SSLC(10th)
Pass out : 2016


I would like to see my K/D for a certain amount of last played maps. To see my current performance.
I am recovering from a bad K/D from my begin game, and would like to see how I am doing right now,but I don't want to reset my stats. This can be applied to other stats also, like W/L ratio or accuracy.
BrainFooLong
Administrator
Use the bf3stats.com frame on your statistic overview page at the bottom.
It have a big history over all values.


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/linter.20doc_blame.20with.20old_structure_cmd.html ---
The following snippet triggers the "missing doc string" complaint from the linter:
set_option old_structure_cmd true /-- Foo structure -/ structure foo := (x : ℕ) /-- Bar structure -/ structure bar extends foo := (y : ℕ) #lint -- #print bar.to_foo /- def missing doc string -/
then don't use old_structure_cmd
I note that even though Lean automatically created bar.to_foo
regardless of whether I'm using the old_structure_cmd
, it is only when this option is true (as above) that the linter complains.
well it's not wrong
@Kenny Lau Oh really? It's much more convenient in some situations I think. I'd be happy to be corrected!
I was referring to the old joke "Doctor I feel painful when I breathe; doctor: then don't breathe"
I would hate for this to be the reason you choose one method over the other
I had scanned some old threads here before deciding to use it and I thought the current wisdom was that it's fine to use it, and even that the name is a bit of a misnomer but really I'm far from sure.
@Kenny Lau Ah I see. :laughing:
@Mario Carneiro Noted, thanks. Perhaps I'll add an appropriate nolint
statement and allow the advice to flow in the code review.
Thank you both.
you can also document it
there is a command like add_doc_string
for adding doc strings to automatic definitions
The linter generally ignores auto-generated stuff but I guess this doesn't get flagged as auto-generated.
It's called add_decl_doc
.
OK thanks. It feels a bit funny to have to add a doc string to an auto-generated function but I am still glad to learn about add_decl_doc
.
You're right that the name is terrible. It implies it's deprecated, whereas in fact for part of the structure heirarchy it's exactly what we want.
PR?
How about flat_structure
instead
I literally just created one here: https://github.com/leanprover-community/mathlib/pull/2404
(I'm currently scanning it to make sure I haven't done anything stupid before I request review.)
I have not previously seen flat_structure
. I'd love to learn about it. I just grepped the Mathlib source but didn't find it anywhere!
I just made the name up
as an alternative to old_structure_cmd
which has wrong connotations
Oh I see: my comment about the possible misnomer. Yeah, flat_structure
is nice
I'd vote for that over old_structure_cmd
for sure.
Last updated: Apr 24 2020 at 19:02 UTC


C F G H I Z
All Classes All Packages
All Classes All Packages
All Classes All Packages
C
- com.compuware.api.topaz.eclipse.hostcredentials - package com.compuware.api.topaz.eclipse.hostcredentials
-
Provides exceptions related to host credentials.
- com.compuware.api.topaz.eclipse.hostcredentials.zos - package com.compuware.api.topaz.eclipse.hostcredentials.zos
-
Provides classes related to z/OS host credentials.
- com.compuware.api.topaz.eclipse.hosts - package com.compuware.api.topaz.eclipse.hosts
-
Provides classes related to hosts.
- connectUIUser(IZOSHost) - Static method in class com.compuware.api.topaz.eclipse.hostcredentials.zos.ZOSUIUserCredentialsManager
-
Connects the TopazWorkbench UI user to the specified z/OS host in the Topaz Workbench client.
- CredentialsNotFoundException - Exception in com.compuware.api.topaz.eclipse.hostcredentials
-
CredentialsNotFoundException
is an unchecked exception thrown when an attempt to obtain or use credentials fails because they do not exist. - CredentialsNotFoundException(String, String) - Constructor for exception com.compuware.api.topaz.eclipse.hostcredentials.CredentialsNotFoundException
-
Constructs a new credentials not found exception with the specified detail messages.
- CredentialsNotFoundException(String, String, Throwable) - Constructor for exception com.compuware.api.topaz.eclipse.hostcredentials.CredentialsNotFoundException
-
Constructs a new credentials not found exception with the specified detail messages and cause.
All Classes All Packages


◆ get_header()
| std::string HttpRequest::get_header |
( |
std::string |
key | ) |
|
|
inline |
◆ get_int_header()
| int HttpRequest::get_int_header |
( |
std::string |
key | ) |
|
|
inline |
◆ body
| std::vector<char> HttpRequest::body |
◆ body_string
| std::string HttpRequest::body_string |
◆ headers
| std::unordered_map<std::string, std::string> HttpRequest::headers |
◆ http_method
| std::string HttpRequest::http_method |
◆ http_protocol
| std::string HttpRequest::http_protocol |
◆ path
| std::string HttpRequest::path |
The documentation for this struct was generated from the following file:


Lorem ipsum dolor sit amet, consectetur adipisici elit, sed eiusmod tempor incidunt ut labore et dolore magna aliqua.
<section data-title="First Page with Header" class='left'>
presentation.css:
span#zuehlke-header:before { content: "Demo - "; font-weight: normal; color: #dddddd; }
This is easy: use the suffix ?print-pdf in the address bar to enter print mode
Press 's' on the keyboard
Image with nice shadow effect:
see presentation.js for code and help yourself with chartjs
Apple 71%
Samsung 18%
HTC 2%
Sony 1.5%
Nokia 0.8%
Slides can be nested inside of other slides, try pressing down.
Press down or up to navigate.
That's it, time to go back up.
Press ESCto enter the slide overview.
Hold down alt and click on any element to zoom in on it using zoom.js. Alt + click anywhere to zoom back out.


{% load staticfiles %}
Optimizing exam timetables by maximizing revision time between exams
Download the template files
Modify the template files and upload them
Generate Time Table


The Table API and SQL are integrated in a joint API. The central concept of this API is a Table which serves as input and output of queries. This document shows the common structure of programs with Table API and SQL queries, how to register a Table, how to query a Table, and how to emit a Table.
All Table API and SQL programs for batch and streaming follow the same pattern. The following code example shows the common structure of Table API and SQL programs.
Note: Table API and SQL queries can be easily integrated with and embedded into DataStream or DataSet programs. Have a look at the Integration with DataStream and DataSet API section to learn how DataStreams and DataSets can be converted into Tables and vice versa.
The TableEnvironment is a central concept of the Table API and SQL integration. It is responsible for:
Registering a Table in the internal catalog
Registering an external catalog
Executing SQL queries
Registering a user-defined (scalar, table, or aggregation) function
Converting a DataStream or DataSet into a Table
Holding a reference to an ExecutionEnvironment or StreamExecutionEnvironment
A Table is always bound to a specific TableEnvironment. It is not possible to combine tables of different TableEnvironments in the same query, e.g., to join or union them.
A TableEnvironment is created by calling the static TableEnvironment.getTableEnvironment() method with a StreamExecutionEnvironment or an ExecutionEnvironment and an optional TableConfig. The TableConfig can be used to configure the TableEnvironment or to customize the query optimization and translation process (see Query Optimization).
A TableEnvironment maintains a catalog of tables which are registered by name. There are two types of tables, input tables and output tables. Input tables can be referenced in Table API and SQL queries and provide input data. Output tables can be used to emit the result of a Table API or SQL query to an external system.
An input table can be registered from various sources:
an existing Table object, usually the result of a Table API or SQL query.
a TableSource, which accesses external data, such as a file, database, or messaging system.
An output table can be registered using a TableSink.
Register a Table
A Table is registered in a TableEnvironment as follows:
Note: A registered Table is treated similarly to a VIEW as known from relational database systems, i.e., the query that defines the Table is not optimized but will be inlined when another query references the registered Table. If multiple queries reference the same registered Table, it will be inlined for each referencing query and executed multiple times, i.e., the result of the registered Table will not be shared.
A TableSource provides access to external data which is stored in a storage system such as a database (MySQL, HBase, …), a file with a specific encoding (CSV, Apache [Parquet, Avro, ORC], …), or a messaging system (Apache Kafka, RabbitMQ, …).
Flink aims to provide TableSources for common data formats and storage systems. Please have a look at the Table Sources and Sinks page for a list of supported TableSources and instructions for how to build a custom TableSource.
A TableSource is registered in a TableEnvironment as follows:
A registered TableSink can be used to emit the result of a Table API or SQL query to an external storage system, such as a database, key-value store, message queue, or file system (in different encodings, e.g., CSV, Apache [Parquet, Avro, ORC], …).
Flink aims to provide TableSinks for common data formats and storage systems. Please see the documentation about Table Sources and Sinks page for details about available sinks and instructions for how to implement a custom TableSink.
A TableSink is registered in a TableEnvironment as follows:
An external catalog can provide information about external databases and tables such as their name, schema, statistics, and information for how to access data stored in an external database, table, or file.
An external catalog can be created by implementing the ExternalCatalog interface and is registered in a TableEnvironment as follows:
Once registered in a TableEnvironment, all tables defined in a ExternalCatalog can be accessed from Table API or SQL queries by specifying their full path, such as catalog.database.table.
Currently, Flink provides an InMemoryExternalCatalog for demo and testing purposes. However, the ExternalCatalog interface can also be used to connect catalogs like HCatalog or Metastore to the Table API.
The Table API is a language-integrated query API for Scala and Java. In contrast to SQL, queries are not specified as Strings but are composed step-by-step in the host language.
The API is based on the Table class which represents a table (streaming or batch) and offers methods to apply relational operations. These methods return a new Table object, which represents the result of applying the relational operation on the input Table. Some relational operations are composed of multiple method calls such as table.groupBy(...).select(), where groupBy(...) specifies a grouping of table, and select(...) the projection on the grouping of table.
The Table API document describes all Table API operations that are supported on streaming and batch tables.
The following example shows a simple Table API aggregation query:
Note: The Scala Table API uses Scala Symbols, which start with a single tick (') to reference the attributes of a Table. The Table API uses Scala implicits. Make sure to import org.apache.flink.api.scala._ and org.apache.flink.table.api.scala._ in order to use Scala implicit conversions.
Table API and SQL queries can be easily mixed because both return Table objects:
A Table API query can be defined on the Table object returned by a SQL query.
A SQL query can be defined on the result of a Table API query by registering the resulting Table in the TableEnvironment and referencing it in the FROM clause of the SQL query.
A Table is emitted by writing it to a TableSink. A TableSink is a generic interface to support a wide variety of file formats (e.g. CSV, Apache Parquet, Apache Avro), storage systems (e.g., JDBC, Apache HBase, Apache Cassandra, Elasticsearch), or messaging systems (e.g., Apache Kafka, RabbitMQ).
A batch Table can only be written to a BatchTableSink, while a streaming Table requires either an AppendStreamTableSink, a RetractStreamTableSink, or an UpsertStreamTableSink.
Please see the documentation about Table Sources & Sinks for details about available sinks and instructions for how to implement a custom TableSink.
The Table.insertInto(String tableName) method emits the Table to a registered TableSink. The method looks up the TableSink from the catalog by the name and validates that the schema of the Table is identical to the schema of the TableSink.
Table API and SQL queries are translated into DataStream or DataSet programs depending on whether their input is a streaming or batch input. A query is internally represented as a logical query plan and is translated in two phases:
optimization of the logical plan,
translation into a DataStream or DataSet program.
A Table API or SQL query is translated when:
a Table is emitted to a TableSink, i.e., when Table.insertInto() is called.
a SQL update query is specified, i.e., when TableEnvironment.sqlUpdate() is called.
Once translated, a Table API or SQL query is handled like a regular DataStream or DataSet program and is executed when StreamExecutionEnvironment.execute() or ExecutionEnvironment.execute() is called.
Table API and SQL queries can be easily integrated with and embedded into DataStream and DataSet programs. For instance, it is possible to query an external table (for example from a RDBMS), do some pre-processing, such as filtering, projecting, aggregating, or joining with meta data, and then further process the data with either the DataStream or DataSet API (and any of the libraries built on top of these APIs, such as CEP or Gelly). Inversely, a Table API or SQL query can also be applied on the result of a DataStream or DataSet program.
This interaction can be achieved by converting a DataStream or DataSet into a Table and vice versa. In this section, we describe how these conversions are done.
Implicit Conversion for Scala
The Scala Table API features implicit conversions for the DataSet, DataStream, and Table classes. These conversions are enabled by importing the package org.apache.flink.table.api.scala._ in addition to org.apache.flink.api.scala._ for the Scala DataStream API.
Register a DataStream or DataSet as Table
A DataStream or DataSet can be registered in a TableEnvironment as a Table. The schema of the resulting table depends on the data type of the registered DataStream or DataSet. Please check the section about mapping of data types to table schema for details.
Note: The name of a DataStreamTable must not match the ^_DataStreamTable_[0-9]+ pattern and the name of a DataSetTable must not match the ^_DataSetTable_[0-9]+ pattern. These patterns are reserved for internal use only.
Instead of registering a DataStream or DataSet in a TableEnvironment, it can also be directly converted into a Table. This is convenient if you want to use the Table in a Table API query.
A Table can be converted into a DataStream or DataSet. In this way, custom DataStream or DataSet programs can be run on the result of a Table API or SQL query.
When converting a Table into a DataStream or DataSet, you need to specify the data type of the resulting DataStream or DataSet, i.e., the data type into which the rows of the Table are to be converted. Often the most convenient conversion type is Row. The following list gives an overview of the features of the different options:
Row: fields are mapped by position, arbitrary number of fields, support for null values, no type-safe access.
POJO: fields are mapped by name (POJO fields must be named as Table fields), arbitrary number of fields, support for null values, type-safe access.
Case Class: fields are mapped by position, no support for null values, type-safe access.
Tuple: fields are mapped by position, limitation to 22 (Scala) or 25 (Java) fields, no support for null values, type-safe access.
Atomic Type: Table must have a single field, no support for null values, type-safe access.
Convert a Table into a DataStream
A Table that is the result of a streaming query will be updated dynamically, i.e., it is changing as new records arrive on the query’s input streams. Hence, the DataStream into which such a dynamic query is converted needs to encode the updates of the table.
There are two modes to convert a Table into a DataStream:
Append Mode: This mode can only be used if the dynamic Table is only modified by INSERT changes, i.e, it is append-only and previously emitted results are never updated.
Retract Mode: This mode can always be used. It encodes INSERT and DELETE changes with a boolean flag.
Note: A detailed discussion about dynamic tables and their properties is given in the Dynamic Tables document.
Flink’s DataStream and DataSet APIs support very diverse types. Composite types such as Tuples (built-in Scala and Flink Java tuples), POJOs, Scala case classes, and Flink’s Row type allow for nested data structures with multiple fields that can be accessed in table expressions. Other types are treated as atomic types. In the following, we describe how the Table API converts these types into an internal row representation and show examples of converting a DataStream into a Table.
The mapping of a data type to a table schema can happen in two ways: based on the field positions or based on the field names.
Position-based Mapping
Position-based mapping can be used to give fields a more meaningful name while keeping the field order. This mapping is available for composite data types with a defined field order as well as atomic types. Composite data types such as tuples, rows, and case classes have such a field order. However, fields of a POJO must be mapped based on the field names (see next section). Fields can be projected out but can’t be renamed using an alias as.
When defining a position-based mapping, the specified names must not exist in the input data type, otherwise the API will assume that the mapping should happen based on the field names. If no field names are specified, the default field names and field order of the composite type are used or f0 for atomic types.
Name-based Mapping
Name-based mapping can be used for any data type including POJOs. It is the most flexible way of defining a table schema mapping. All fields in the mapping are referenced by name and can be possibly renamed using an alias as. Fields can be reordered and projected out.
If no field names are specified, the default field names and field order of the composite type are used or f0 for atomic types.
Atomic Types
Flink treats primitives (Integer, Double, String) or generic types (types that cannot be analyzed and decomposed) as atomic types. A DataStream or DataSet of an atomic type is converted into a Table with a single attribute. The type of the attribute is inferred from the atomic type and the name of the attribute can be specified.
Tuples (Scala and Java) and Case Classes (Scala only)
Flink supports Scala’s built-in tuples and provides its own tuple classes for Java. DataStreams and DataSets of both kinds of tuples can be converted into tables. Fields can be renamed by providing names for all fields (mapping based on position). If no field names are specified, the default field names are used. If the original field names (f0, f1, … for Flink Tuples and _1, _2, … for Scala Tuples) are referenced, the API assumes that the mapping is name-based instead of position-based. Name-based mapping allows for reordering fields and projection with alias (as).
POJO (Java and Scala)
Flink supports POJOs as composite types. The rules for what determines a POJO are documented here.
When converting a POJO DataStream or DataSet into a Table without specifying field names, the names of the original POJO fields are used. The name mapping requires the original names and cannot be done by positions. Fields can be renamed using an alias (with the as keyword), reordered, and projected.
Row
The Row data type supports an arbitrary number of fields and fields with null values. Field names can be specified via a RowTypeInfo or when converting a RowDataStream or DataSet into a Table. The row type supports mapping of fields by position and by name. Fields can be renamed by providing names for all fields (mapping based on position) or selected individually for projection/ordering/renaming (mapping based on name).
Apache Flink leverages Apache Calcite to optimize and translate queries. The optimization currently performed include projection and filter push-down, subquery decorrelation, and other kinds of query rewriting. Flink does not yet optimize the order of joins, but executes them in the same order as defined in the query (order of Tables in the FROM clause and/or order of join predicates in the WHERE clause).
It is possible to tweak the set of optimization rules which are applied in different phases by providing a CalciteConfig object. This can be created via a builder by calling CalciteConfig.createBuilder()) and is provided to the TableEnvironment by calling tableEnv.getConfig.setCalciteConfig(calciteConfig).
Explaining a Table
The Table API provides a mechanism to explain the logical and optimized query plans to compute a Table.
This is done through the TableEnvironment.explain(table) method. It returns a String describing three plans:
the Abstract Syntax Tree of the relational query, i.e., the unoptimized logical query plan,
the optimized logical query plan, and
the physical execution plan.
The following code shows an example and the corresponding output:


val nextAction: Map<String, Any?>?
If present, this property tells you what actions you need to take in order for your customer to fulfill a payment using the provided source.


Document System Tutorial Part 11 - Document and Folder Permissions¶
In this part, we will talk about setting and getting permissions for Document and Folder records.
Go to document.go inside the models folder and create a function named GetPermissions that holds the user parameter with the data type uadmin.User and returns Read, Add, Edit, and Delete parameters with the data type bool. If the user is an admin, all permission levels should be given.
// GetPermissions !
func (d Document) GetPermissions(user uadmin.User) (Read bool, Add bool, Edit bool, Delete bool) {
// Check whether the user is an admin
if user.Admin {
// Set all permissions to true
Read = true
Add = true
Edit = true
Delete = true
}
}
Inside the GetPermissions folder, let’s check the Folder permissions.
// Since Folder is a foreign key to the Document model, we need to check
// whether there is a Folder specified in the Document model.
// We will check for folder permissions first
// Then we will check for document permissions after that
if d.FolderID != 0 {
// Initialize the FolderGroup model
folderGroup := FolderGroup{}
// Get data by GroupID and FolderID
uadmin.Get(&folderGroup, "group_id = ? AND folder_id = ?", user.UserGroupID, d.FolderID)
// Check whether there is a FolderGroup record
if folderGroup.ID != 0 {
// Assign FolderGroup permission values to the variables
Read = folderGroup.Read
Add = folderGroup.Add
Edit = folderGroup.Edit
Delete = folderGroup.Delete
}
// Initialize the FolderUser model
folderUser := FolderUser{}
// Get data by UserID and FolderID
uadmin.Get(&folderUser, "user_id = ? AND folder_id = ?", user.ID, d.FolderID)
// Check whether there is a FolderUser record
if folderUser.ID != 0 {
// Assign FolderUser permission values to the variables
Read = folderUser.Read
Add = folderUser.Add
Edit = folderUser.Edit
Delete = folderUser.Delete
}
}
Now we will check for Document permissions after validating the Folder specified in the Document model.
// Document Permissions
// Initialize the DocumentGroup model
documentGroup := DocumentGroup{}
// Get data by GroupID and DocumentID
uadmin.Get(&documentGroup, "group_id = ? AND document_id = ?", user.UserGroupID, d.ID)
// Check whether there is a DocumentGroup record
if documentGroup.ID != 0 {
// Assign DocumentGroup permission values to the variables
Read = documentGroup.Read
Add = documentGroup.Add
Edit = documentGroup.Edit
Delete = documentGroup.Delete
}
// Initialize the DocumentUser model
documentUser := DocumentUser{}
// // Get data by UserID and DocumentID
uadmin.Get(&documentUser, "user_id = ? AND document_id = ?", user.ID, d.ID)
// Check whether there is a DocumentUser record
if documentUser.ID != 0 {
// Assign DocumentUser permission values to the variables
Read = documentUser.Read
Add = documentUser.Add
Edit = documentUser.Edit
Delete = documentUser.Delete
}
// Return Read, Add, Edit, and Delete values
return
Click here to view our progress so far.
In the next part, we will discuss about creating a custom Count function that checks the query and the UserID.


Efficiency tops agenda for NI Chamber's energy forum
ENERGY efficiency and business competitiveness will be top of the agenda for the Northern Ireland Chamber of Commerce and Industry's upcoming energy forum.
Hosted in partnership with SONI, the event will be held in Invest NI's Belfast headquarters on Thursday December 5.
The economic support agency's energy and resource efficient programme manager, Rachel Sankannawar, will discuss the benefits of energy efficiency for business and outline the practical ways local companies can save money and increase productivity though sustainable energy use.
Delegates will also hear from Paul Friar, general manager at Smufit Kappa, a market leader in the production of packaging and point of purchase solutions.
Paul will share the background and most effective elements of Smurfit Kappa's sustainability strategy.
Other speakers will include Alistair Jinks, director of business services at NI Water and Eddie McGoldrick, founder of PowerOn Technologies, an innovative start-up in the emerging energy services market in Ireland.
Speaking ahead of the event, NI Chamber chief executive Ann McGregor said: “The final energy forum of 2019 is an opportunity for both energy users and suppliers to come together and hear about the practical ways they can adapt their energy use to reduce costs and improve the overall bottom-line.
“At the close of the year, with companies looking to 2020 plans and budgets, the timing of this event is particularly relevant, so we hope as many companies as possible make the most of the opportunity.”


The pace of technologies and software updates is ever-accelerating. Stay up-to-date without compromising your productivity with the Toolbox App
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What if I already have some JetBrains tools installed? In most cases JetBrains Toolbox App can detect these IDEs and work with them, without supporting certain features. However, if you decide to stay with JetBrains Toolbox App, we'd recommend uninstalling these previously installed tools and re-installing them through Toolbox App.


Download
$1 source code: JavaScript, C#
Dynamic time warping: C#
Rubine classifier: C#
Pseudocode: $1, Protractor
Unistroke gesture logs: XML
Paper: PDF
This software is distributed under the New BSD License agreement.
About
The $1 Unistroke Recognizer is a 2-D single-stroke recognizer designed for rapid prototyping of gesture-based
user interfaces. In machine learning terms, $1 is an instance-based nearest-neighbor classifier with a 2-D Euclidean
distance function, i.e., a geometric template matcher. $1 is a significant extension of the proportional shape matching
approach used in SHARK2, which itself is
an adaptation of Tappert's elastic matching approach
with zero look-ahead. Despite its simplicity, $1 requires very few templates to perform well and is only about
100 lines of code, making it easy to deploy.
An optional enhancement called Protractor improves $1's speed.
The $N Multistroke Recognizer extends $1 to gestures with multiple strokes.
The $P Point-Cloud Recognizer performs unistroke and multistroke recognition without the
combinatoric overhead of $N, as it ignores stroke number, order, and direction. The $Q Super-Quick Recognizer
extends $P for use on low-powered mobiles and wearables, as it is a whopping 142× faster and slightly more accurate.
The $-family recognizers have been built into numerous projects and even industry prototypes,
and have had many follow-ons by others. Read about the $-family's impact.
Demo
In the demo below, only one unistroke template is loaded for each of the 16 gesture types. You can add additional
unistrokes as you wish, and even define your own custom unistrokes.
|
Make strokes on this canvas. If a misrecognition occurs,
add the misrecognized unistroke as an example of the intended gesture.
|
Our Gesture Software Projects
- $Q: Super-quick multistroke recognizer - optimized for low-power mobiles and wearables
- $P+: Point-cloud multistroke recognizer - optimized for people with low vision
- $P: Point-cloud multistroke recognizer - for recognizing multistroke gestures as point-clouds
- $N: Multistroke recognizer - for recognizing simple multistroke gestures
- $1: Unistroke recognizer - for recognizing unistroke gestures
- AGATe: AGreement Analysis Toolkit - for calculating agreement in gesture-elicitation studies
- GHoST: Gesture HeatmapS Toolkit - for visualizing variation in gesture articulation
- GREAT: Gesture RElative Accuracy Toolkit - for measuring variation in gesture articulation
- GECKo: GEsture Clustering toolKit - for clustering gestures and calculating agreement
Our Gesture Publications
-
Vatavu, R.-D., Anthony, L. and Wobbrock, J.O. (2018).
$Q: A super-quick, articulation-invariant stroke-gesture recognizer for low-resource devices.
Proceedings of the ACM Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI '18).
Barcelona, Spain (September 3-6, 2018).
New York: ACM Press. Article No. 23.
-
Vatavu, R.-D. (2017).
Improving gesture recognition accuracy on touch screens for users with low vision.
Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI '17).
Denver, Colorado (May 6-11, 2017).
New York: ACM Press, pp. 4667-4679.
-
Vatavu, R.-D. and Wobbrock, J.O. (2016).
Between-subjects elicitation studies: Formalization and tool support.
Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI '16).
San Jose, California (May 7-12, 2016).
New York: ACM Press, pp. 3390-3402.
-
Vatavu, R.-D. and Wobbrock, J.O. (2015).
Formalizing agreement analysis for elicitation studies: New measures, significance test, and toolkit.
Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI '15).
Seoul, Korea (April 18-23, 2015).
New York: ACM Press, pp. 1325-1334.
-
Vatavu, R.-D., Anthony, L. and Wobbrock, J.O. (2014).
Gesture heatmaps: Understanding gesture performance with colorful visualizations.
Proceedings of the ACM International Conference on Multimodal Interfaces (ICMI '14).
Istanbul, Turkey (November 12-16, 2014).
New York: ACM Press, pp. 172-179.
-
Vatavu, R.-D., Anthony, L. and Wobbrock, J.O. (2013).
Relative accuracy measures for stroke gestures.
Proceedings of the ACM International Conference on Multimodal Interfaces (ICMI '13).
Sydney, Australia (December 9-13, 2013).
New York: ACM Press, pp. 279-286.
-
Anthony, L., Vatavu, R.-D. and Wobbrock, J.O. (2013).
Understanding the consistency of users' pen and finger stroke gesture articulation.
Proceedings of Graphics Interface (GI '13).
Regina, Saskatchewan (May 29-31, 2013).
Toronto, Ontario: Canadian Information Processing Society, pp. 87-94.
-
Vatavu, R.-D., Anthony, L. and Wobbrock, J.O. (2012).
Gestures as point clouds: A $P recognizer for user interface prototypes.
Proceedings of the ACM International Conference on Multimodal Interfaces (ICMI '12).
Santa Monica, California (October 22-26, 2012).
New York: ACM Press, pp. 273-280.
-
Anthony, L. and Wobbrock, J.O. (2012).
$N-Protractor: A fast and accurate multistroke recognizer.
Proceedings of Graphics Interface (GI '12).
Toronto, Ontario (May 28-30, 2012).
Toronto, Ontario: Canadian Information Processing Society, pp. 117-120.
-
Anthony, L. and Wobbrock, J.O. (2010).
A lightweight multistroke recognizer for user interface prototypes.
Proceedings of Graphics Interface (GI '10).
Ottawa, Ontario (May 31-June 2, 2010).
Toronto, Ontario: Canadian Information Processing Society, pp. 245-252.
-
Wobbrock, J.O., Wilson, A.D. and Li, Y. (2007).
Gestures without libraries, toolkits or training: A $1 recognizer for user interface prototypes.
Proceedings of the ACM Symposium on User Interface Software and Technology (UIST '07).
Newport, Rhode Island (October 7-10, 2007).
New York: ACM Press, pp. 159-168.
Background Publications by Others
-
Li, Y. (2010).
Protractor: A fast and accurate gesture recognizer.
Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI '10).
Atlanta, Georgia (April 10-15, 2010).
New York: ACM Press, pp. 2169-2172.
-
Kristensson, P. and Zhai, S. (2004).
SHARK2: A large vocabulary shorthand writing system for pen-based computers.
Proceedings of the ACM Symposium on User Interface Software and Technology (UIST '04).
Santa Fe, New Mexico (October 24-27, 2004).
New York: ACM Press, pp. 43-52.
-
Rubine, D. (1991).
Specifying gestures by example.
Proceedings of the ACM Conference on Computer Graphics and Interactive Techniques (SIGGRAPH '91).
Las Vegas, Nevada (July 28 - August 2, 1991).
New York: ACM Press, pp. 329-337.
-
Tappert, C.C. (1982).
Cursive script recognition by elastic matching.
IBM Journal of Research and Development 26 (6), pp. 765-771.
Copyright © 2007-2019 Jacob O. Wobbrock. All rights reserved.
Last updated June 30, 2019.


Selenium is a powerful open-source tool for automated web testing. To demonstrate the use of JBehave with Selenium, we'll draw upon the fully-functional Ajax Email example webapp from Pico Web project.
Let's see how we can easily use Selenium to run web-based scenarios. As usual, we'll have a Scenario class that maps to a textual scenario, e.g.
Scenario: User logs in successfully Given nobody is logged in When user Gill Bates with password 1234 attempts to log in Then the Inbox is selected And there are some messages listed Scenario: User fails to login with invalid password Given nobody is logged in When user Gill Bates with password 99999 attempts to log in And there are no messages listed And the text "Invalid Login" should be visible Scenario: User fails to login with invalid username Given nobody is logged in When user Fred with password 1234 attempts to log in Then the Inbox should not be visible And the text "Invalid Login" should be visible
The objective in creating a DSL is to represent the business-domain functionality whilst abstracting away the details of the implementation, in this case the access to the web layer via a specific testing tool, such as Selenium. The same DSL should be re-usable with different testing tools.
The Scenario class will specify the Steps class is where all the Selenium magic happens:
public class AjaxEmailSteps extends SeleniumSteps { private Main main; private String[] lastFormValues; private String prefix; @Given("nobody is logged in") public void nobodyLoggedIn() { Main.logout(selenium); } @Given("user is viewing their Inbox") public void theyreInTheirInbox() { nobodyLoggedIn(); logIn("Gill Bates", "1234"); boxIsSelected("Inbox"); } @When("user $userName with password $password attempts to log in") public void logIn(String userName, String password) { main = new LoginForm(selenium, runner).login(userName, password); } @Then("the Inbox should not be visible") public void inBoxIsNotVisible() { main.textIsNotVisible("Instant Millionaire"); } @Then("the text \"$text\" should be visible") public void textIsVisible(String text) { main.waitFor(new Text(text)); } @Then("the text \"$text\" should not be visible") public void textIsNotVisible(String text) { waitFor(new Not(new Text(text))); } private void waitFor(Condition condition) { runner.waitFor(condition); waitFor(1); } }
The @BeforeScenario and @AfterScenario annotations in the SeleniumSteps methods allow to start and stop the selenium server before and after scenario. Users can of course override the default behaviour, but generally don't need to bother for most use cases.
NOTE: To get the Selenium based tests running in an automated way, you need to run both a webapp server (e.g. Jetty) and the Selenium server. See the Ajax Email example webapp for a way to do this using Maven.


Select Tickets
♦ STEP 1Please select the day you want to join this meeting
| Ticket | 1-Day-Ticket | 2-Day-Ticket | CC Affiliate/Partners | |
|---|---|---|---|---|
| Date: | 15. Oct(Thu) | 16. Oct(Fri) | 15.-16. Oct(Thu-Fri) | 15.-16. Oct(Thu-Fri) |
| Normal: | $100 | $200 | $300 | $0 |
| Students: | $100 | $200 | $300 | $0 |
Select Your Lunch
♦ STEP 2Please select your lunch for registered day.


(1) By default, blocks of text
just folow each other
(2) One after another.
(3) A relative position moves a block away from where it would normally
lay out.
(4) And text after that just lays out. So be somewhat
careful with relative.
(5) Fixed text is relative to the window.
It maintains its position even when the window is resized
or scrolled.
A link that never goes away.
(6) Absolute text is relative to its parent element.
In this case the div is positioned
relative to the body tag.
It maintains its position even when the window is resized
or scrolled.
(7) In a sense, the fixed and
absolute elements are
"plucked out" of the the normal render stream
and as such take up no vertical space.
there is not any padding or margin
left behind by these tags.


TIL: Listen to your phone through your PC speakers
#!/usr/bin/env bash # Directs audio input (e.g. mic) to audio output (e.g. speakers), # then sleeps forever. Stops audio redirection when it is killed. # So, for example, plug your phone into the PC's mic, run 'listen', # and listen to phone audio through your computer's speakers. # # Requires: # sudo apt-get install pactl # Ubuntu set -e module=$(pactl load-module module-loopback) function cleanup { pactl unload-module $module } trap cleanup EXIT sleep infinity


Create and keep your grocery list handy. You can add to your list right from your recipe.
Search:


| 006:007 Khan | : |
And even if We had sent down unto you (O Muhammad SAW) a Message written on paper so that they could touch it with their hands, the disbelievers would have said: "This is nothing but obvious magic!" |
| 006:007 Maulana | : |
And if We had sent down to thee a writing on paper, then they had touched it with their hands, those who disbelieve would have said: This is nothing but clear enchantment. |
| 006:007 Pickthal | : |
Had we sent down unto thee (Muhammad) (actual) writing upon parchment, so that they could feel it with their hands, those who disbelieve would have said: This is naught else than mere magic. |
| 006:007 Rashad | : |
Even if we sent down to them a physical book, written on paper, and they touched it with their hands, those who disbelieved would have said, "This is no more than clever magic." |
| 006:007 Sarwar | : |
(Muhammad), had We sent you a Book on paper, the unbelievers would have touched it with their hands but would still have said, "It is no more than plain sorcery". |
| 006:007 Shakir | : |
And if We had sent to you a writing on a paper, then they had touched it with their hands, certainly those who disbelieve would have said: This is nothing but clear enchantment. |
| 006:007 Sherali | : |
And if WE had sent down to thee a writing upon the parchment and they had felt it with their hands even then the disbelievers would have surely said, 'This is nothing but manifest sorcery.' |
| 006:007 Yusufali | : |
If We had sent unto thee a written (message) on parchment, so that they could touch it with their hands, the Unbelievers would have been sure to say: "This is nothing but obvious magic!" |
Concepts/Themes Covered: أركان الإسلام > التوحيد > الكافرون > صفات الكفار Pillars of Islam> Islamic> The Disbelievers> The Characteristics of the Disbelievers أركان الإسلام > (محمد (صلى الله عليه وسلم > الوحي Pillars of Islam> The Blessed Muhammad(PBUH)> The Revelation القرآن الكريم > حقيقته وتصديقه للكتب الأوائل The Holy Quran> The Quran's Reality and its confirmation of the Previous Books
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


--- layout: page title: Mozilla Appmaker date: 2015-02-25 12:58:59.000000000 +05:30 type: page published: true status: publish categories: [] tags: [] meta: _publicize_pending: '1' _rest_api_published: '1' _rest_api_client_id: "-1" _oembed_a6309084c00938f3addac9edad45ac18: "{{unknown}}" _oembed_3c7d55dac40df34d3cfe4d203508c4ea: "{{unknown}}" _oembed_7dab3cd48c923544562e908f68a12732: "{{unknown}}" _oembed_ca0ae0e3c093f00380a91f28797dac59: "{{unknown}}" _oembed_a42720ae70f7443665283a2a0abeaa6b: "{{unknown}}" _oembed_f7c5692565f8be449c172e35d6559791: "{{unknown}}" _oembed_e086fc32c43e6c68cadc80ab31494edd: "{{unknown}}" _oembed_7f454573a86d5da29afe6a1d256b16d9: "{{unknown}}" _oembed_879f866e76deb3af5dea885517951384: "{{unknown}}" _oembed_dbe565583c461835361cae56ca10246c: "{{unknown}}" author: login: viswaprasath email<EMAIL_ADDRESS>display_name: Vitchu first_name: '' last_name: '' ---
Mozilla Appmaker is one of the amazing tool developed for developing apps without knowing coding. You can learn more about Mozilla Appmaker in this post. And below are some of the introduction post about the bricks
https://technorulerin.wordpress.com/2014/07/26/utility-components-for-appmaker-for-firefox-os/
https://technorulerin.wordpress.com/2014/07/29/connection-bricks-of-appmaker-for-firefox-os/
https://technorulerin.wordpress.com/2014/07/29/audio-bricks-by-app-maker-for-firefox-os-apps/
https://technorulerin.wordpress.com/2014/08/03/application-using-number-bricks-in-appmaker/
https://technorulerin.wordpress.com/2014/07/29/fun-bricks-are-app-maker-for-firefox-os-apps
https://technorulerin.wordpress.com/2014/07/30/media-bricks-by-appmaker-for-firefox-os/
To Learn more about bricks (demo)
https://technorulerin.wordpress.com/2014/07/24/building-mozilla-firefox-os-app-using-basic-bricks/
https://technorulerin.wordpress.com/2014/08/03/example-for-connection-bricks-firefox-os-appmaker/


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 It is also the ability to use that intelligence in your actions / reactions.
Emotional intelligence is different from IQ in the sense that it is able to be changed and worked on. IQ does not have any effect on your ability to work with people and or how to act in specific moments such as moments of high stress. IQ and EQ have no relation, there are separate unrelated metrics. IQ does not tend to change over time.
EQ is arguably more important than IQ. EQ is key to being able to work with and manage a group of people. EQ can open doors that you never thought possible. Even in everyday relationships out of a workplace high EQ will allow you to have diplomatic conversations rather than unnecessary arguments. EQ lets you be calm under pressure.


class
cudaFlowclass to create a cudaFlow task dependency graph
A cudaFlow is a high-level interface over CUDA Graph to perform GPU operations using the task dependency graph model. The class provides a set of methods for creating and launch different tasks on one or multiple CUDA devices, for instance, kernel tasks, data transfer tasks, and memory operation tasks. The following example creates a cudaFlow of two kernel tasks, task1
and task2
, where task1
runs before task2
.
tf::Taskflow taskflow; tf::Executor executor; taskflow.emplace([&](tf::cudaFlow& cf){ // create two kernel tasks tf::cudaTask task1 = cf.kernel(grid1, block1, shm_size1, kernel1, args1); tf::cudaTask task2 = cf.kernel(grid2, block2, shm_size2, kernel2, args2); // kernel1 runs before kernel2 task1.precede(task2); }); executor.run(taskflow).wait();
A cudaFlow is a task (tf::
Please refer to GPU Tasking (cudaFlow) for details.
Constructors, destructors, conversion operators
Public functions
- auto operator=(cudaFlow&&) -> cudaFlow& defaulted
- default move assignment operator
- auto empty() const -> bool
- queries the emptiness of the graph
- auto num_tasks() const -> size_t
- queries the number of tasks
- void clear()
- clears the cudaFlow object
-
void dump(std::
ostream& os) const - dumps the cudaFlow graph into a DOT format through an output stream
-
void dump_native_graph(std::
ostream& os) const - dumps the native CUDA graph into a DOT format through an output stream
- auto noop() -> cudaTask
- creates a no-operation task
-
template<typename C>auto host(C&& callable) -> cudaTask
- creates a host task that runs a callable on the host
-
template<typename C>void host(cudaTask task, C&& callable)
- updates parameters of a host task
-
template<typename F, typename... ArgsT>auto kernel(dim3 g, dim3 b, size_t s, F f, ArgsT... args) -> cudaTask
- creates a kernel task
-
template<typename F, typename... ArgsT>void kernel(cudaTask task, dim3 g, dim3 b, size_t shm, F f, ArgsT... args)
- updates parameters of a kernel task
- auto memset(void* dst, int v, size_t count) -> cudaTask
- creates a memset task that fills untyped data with a byte value
- void memset(cudaTask task, void* dst, int ch, size_t count)
- updates parameters of a memset task
- auto memcpy(void* tgt, const void* src, size_t bytes) -> cudaTask
- creates a memcpy task that copies untyped data in bytes
- void memcpy(cudaTask task, void* tgt, const void* src, size_t bytes)
- updates parameters of a memcpy task
-
template<typename T, std::enable_if_t<is_pod_v<T> && (sizeof(T)==1||sizeof(T)==2||sizeof(T)==4), void>* = nullptr>auto zero(T* dst, size_t count) -> cudaTask
- creates a memset task that sets a typed memory block to zero
-
template<typename T, std::enable_if_t<is_pod_v<T> && (sizeof(T)==1||sizeof(T)==2||sizeof(T)==4), void>* = nullptr>void zero(cudaTask task, T* dst, size_t count)
- updates parameters of a memset task to a zero task
-
template<typename T, std::enable_if_t<is_pod_v<T> && (sizeof(T)==1||sizeof(T)==2||sizeof(T)==4), void>* = nullptr>auto fill(T* dst, T value, size_t count) -> cudaTask
- creates a memset task that fills a typed memory block with a value
-
template<typename T, std::enable_if_t<is_pod_v<T> && (sizeof(T)==1||sizeof(T)==2||sizeof(T)==4), void>* = nullptr>void fill(cudaTask task, T* dst, T value, size_t count)
- updates parameters of a memset task to a fill task
-
template<typename T, std::enable_if_t<!std::is_same_v<T, void>, void>* = nullptr>auto copy(T* tgt, const T* src, size_t num) -> cudaTask
- creates a memcopy task that copies typed data
-
template<typename T, std::enable_if_t<!std::is_same_v<T, void>, void>* = nullptr>void copy(cudaTask task, T* tgt, const T* src, size_t num)
- updates parameters of a memcpy task to a copy task
- void run(cudaStream_t stream)
- offloads the cudaFlow onto a GPU asynchronously via a stream
- auto native_graph() -> cudaGraph_t
- acquires a reference to the underlying CUDA graph
- auto native_executable() -> cudaGraphExec_t
- acquires a reference to the underlying CUDA graph executable
-
template<typename C>auto single_task(C c) -> cudaTask
- runs a callable with only a single kernel thread
-
template<typename C>void single_task(cudaTask task, C c)
- updates a single-threaded kernel task
-
template<typename I, typename C>auto for_each(I first, I last, C callable) -> cudaTask
- applies a callable to each dereferenced element of the data array
-
template<typename I, typename C>void for_each(cudaTask task, I first, I last, C callable)
- updates parameters of a kernel task created from tf::
cudaFlow:: for_each -
template<typename I, typename C>auto for_each_index(I first, I last, I step, C callable) -> cudaTask
- applies a callable to each index in the range with the step size
-
template<typename I, typename C>void for_each_index(cudaTask task, I first, I last, I step, C callable)
- updates parameters of a kernel task created from tf::
cudaFlow:: for_each_index -
template<typename I, typename O, typename C>auto transform(I first, I last, O output, C op) -> cudaTask
- applies a callable to a source range and stores the result in a target range
-
template<typename I, typename O, typename C>void transform(cudaTask task, I first, I last, O output, C c)
- updates parameters of a kernel task created from tf::
cudaFlow:: transform -
template<typename I1, typename I2, typename O, typename C>auto transform(I1 first1, I1 last1, I2 first2, O output, C op) -> cudaTask
- creates a task to perform parallel transforms over two ranges of items
-
template<typename I1, typename I2, typename O, typename C>void transform(cudaTask task, I1 first1, I1 last1, I2 first2, O output, C c)
- updates parameters of a kernel task created from tf::
cudaFlow:: transform -
template<typename C>auto capture(C&& callable) -> cudaTask
- constructs a subflow graph through tf::
cudaFlowCapturer -
template<typename C>void capture(cudaTask task, C callable)
- updates the captured child graph
Function documentation
void tf:: cudaFlow:: dump_native_graph(std:: ostream& os) const
dumps the native CUDA graph into a DOT format through an output stream
The native CUDA graph may be different from the upper-level cudaFlow graph when flow capture is involved.
cudaTask tf:: cudaFlow:: noop()
creates a no-operation task
| Returns | a tf:: |
|---|
An empty node performs no operation during execution, but can be used for transitive ordering. For example, a phased execution graph with 2 groups of n
nodes with a barrier between them can be represented using an empty node and 2*n
dependency edges, rather than no empty node and n^2
dependency edges.
template<typename C>
cudaTask tf:: cudaFlow:: host(C&& callable)
creates a host task that runs a callable on the host
| Template parameters | |
|---|---|
| C | callable type |
| Parameters | |
| callable | a callable object with neither arguments nor return (i.e., constructible from std::function<void()> ) |
| Returns | a tf:: |
A host task can only execute CPU-specific functions and cannot do any CUDA calls (e.g., cudaMalloc
).
template<typename C>
void tf:: cudaFlow:: host(cudaTask task,
C&& callable)
updates parameters of a host task
The method is similar to tf::
template<typename F, typename... ArgsT>
cudaTask tf:: cudaFlow:: kernel(dim3 g,
dim3 b,
size_t s,
F f,
ArgsT... args)
creates a kernel task
| Template parameters | |
|---|---|
| F | kernel function type |
| ArgsT | kernel function parameters type |
| Parameters | |
| g | configured grid |
| b | configured block |
| s | configured shared memory size in bytes |
| f | kernel function |
| args | arguments to forward to the kernel function by copy |
| Returns | a tf:: |
template<typename F, typename... ArgsT>
void tf:: cudaFlow:: kernel(cudaTask task,
dim3 g,
dim3 b,
size_t shm,
F f,
ArgsT... args)
updates parameters of a kernel task
The method is similar to tf::
cudaTask tf:: cudaFlow:: memset(void* dst,
int v,
size_t count)
creates a memset task that fills untyped data with a byte value
| Parameters | |
|---|---|
| dst | pointer to the destination device memory area |
| v | value to set for each byte of specified memory |
| count | size in bytes to set |
| Returns | a tf:: |
A memset task fills the first count
bytes of device memory area pointed by dst
with the byte value v
.
void tf:: cudaFlow:: memset(cudaTask task,
void* dst,
int ch,
size_t count)
updates parameters of a memset task
The method is similar to tf::
cudaTask tf:: cudaFlow:: memcpy(void* tgt,
const void* src,
size_t bytes)
creates a memcpy task that copies untyped data in bytes
| Parameters | |
|---|---|
| tgt | pointer to the target memory block |
| src | pointer to the source memory block |
| bytes | bytes to copy |
| Returns | a tf:: |
A memcpy task transfers bytes
of data from a source location to a target location. Direction can be arbitrary among CPUs and GPUs.
void tf:: cudaFlow:: memcpy(cudaTask task,
void* tgt,
const void* src,
size_t bytes)
updates parameters of a memcpy task
The method is similar to tf::
template<typename T, std::enable_if_t<is_pod_v<T> && (sizeof(T)==1||sizeof(T)==2||sizeof(T)==4), void>* = nullptr>
cudaTask tf:: cudaFlow:: zero(T* dst,
size_t count)
creates a memset task that sets a typed memory block to zero
| Template parameters | |
|---|---|
| T | element type (size of T must be either 1, 2, or 4) |
| Parameters | |
| dst | pointer to the destination device memory area |
| count | number of elements |
| Returns | a tf:: |
A zero task zeroes the first count
elements of type T
in a device memory area pointed by dst
.
template<typename T, std::enable_if_t<is_pod_v<T> && (sizeof(T)==1||sizeof(T)==2||sizeof(T)==4), void>* = nullptr>
void tf:: cudaFlow:: zero(cudaTask task,
T* dst,
size_t count)
updates parameters of a memset task to a zero task
The method is similar to tf::
The source/destination memory may have different address values but must be allocated from the same contexts as the original source/destination memory.
template<typename T, std::enable_if_t<is_pod_v<T> && (sizeof(T)==1||sizeof(T)==2||sizeof(T)==4), void>* = nullptr>
cudaTask tf:: cudaFlow:: fill(T* dst,
T value,
size_t count)
creates a memset task that fills a typed memory block with a value
| Template parameters | |
|---|---|
| T | element type (size of T must be either 1, 2, or 4) |
| Parameters | |
| dst | pointer to the destination device memory area |
| value | value to fill for each element of type T |
| count | number of elements |
| Returns | a tf:: |
A fill task fills the first count
elements of type T
with value
in a device memory area pointed by dst
. The value to fill is interpreted in type T
rather than byte.
template<typename T, std::enable_if_t<is_pod_v<T> && (sizeof(T)==1||sizeof(T)==2||sizeof(T)==4), void>* = nullptr>
void tf:: cudaFlow:: fill(cudaTask task,
T* dst,
T value,
size_t count)
updates parameters of a memset task to a fill task
The method is similar to tf::
The source/destination memory may have different address values but must be allocated from the same contexts as the original source/destination memory.
template<typename T, std::enable_if_t<!std::is_same_v<T, void>, void>* = nullptr>
cudaTask tf:: cudaFlow:: copy(T* tgt,
const T* src,
size_t num)
creates a memcopy task that copies typed data
| Template parameters | |
|---|---|
| T | element type (non-void) |
| Parameters | |
| tgt | pointer to the target memory block |
| src | pointer to the source memory block |
| num | number of elements to copy |
| Returns | a tf:: |
A copy task transfers num*sizeof(T)
bytes of data from a source location to a target location. Direction can be arbitrary among CPUs and GPUs.
template<typename T, std::enable_if_t<!std::is_same_v<T, void>, void>* = nullptr>
void tf:: cudaFlow:: copy(cudaTask task,
T* tgt,
const T* src,
size_t num)
updates parameters of a memcpy task to a copy task
The method is similar to tf::
void tf:: cudaFlow:: run(cudaStream_t stream)
offloads the cudaFlow onto a GPU asynchronously via a stream
| Parameters | |
|---|---|
| stream | stream for performing this operation |
Offloads the present cudaFlow onto a GPU asynchronously via the given stream.
An offloaded cudaFlow forces the underlying graph to be instantiated. After the instantiation, you should not modify the graph topology but update node parameters.
template<typename C>
cudaTask tf:: cudaFlow:: single_task(C c)
runs a callable with only a single kernel thread
| Template parameters | |
|---|---|
| C | callable type |
| Parameters | |
| c | callable to run by a single kernel thread |
| Returns | a tf:: |
template<typename C>
void tf:: cudaFlow:: single_task(cudaTask task,
C c)
updates a single-threaded kernel task
This method is similar to cudaFlow::
template<typename I, typename C>
cudaTask tf:: cudaFlow:: for_each(I first,
I last,
C callable)
applies a callable to each dereferenced element of the data array
| Template parameters | |
|---|---|
| I | iterator type |
| C | callable type |
| Parameters | |
| first | iterator to the beginning (inclusive) |
| last | iterator to the end (exclusive) |
| callable | a callable object to apply to the dereferenced iterator |
| Returns | a tf:: |
This method is equivalent to the parallel execution of the following loop on a GPU:
for(auto itr = first; itr != last; itr++) { callable(*itr); }
template<typename I, typename C>
void tf:: cudaFlow:: for_each(cudaTask task,
I first,
I last,
C callable)
updates parameters of a kernel task created from tf::
The type of the iterators and the callable must be the same as the task created from tf::
template<typename I, typename C>
cudaTask tf:: cudaFlow:: for_each_index(I first,
I last,
I step,
C callable)
applies a callable to each index in the range with the step size
| Template parameters | |
|---|---|
| I | index type |
| C | callable type |
| Parameters | |
| first | beginning index |
| last | last index |
| step | step size |
| callable | the callable to apply to each element in the data array |
| Returns | a tf:: |
This method is equivalent to the parallel execution of the following loop on a GPU:
// step is positive [first, last) for(auto i=first; i<last; i+=step) { callable(i); } // step is negative [first, last) for(auto i=first; i>last; i+=step) { callable(i); }
template<typename I, typename C>
void tf:: cudaFlow:: for_each_index(cudaTask task,
I first,
I last,
I step,
C callable)
updates parameters of a kernel task created from tf::
The type of the iterators and the callable must be the same as the task created from tf::
template<typename I, typename O, typename C>
cudaTask tf:: cudaFlow:: transform(I first,
I last,
O output,
C op)
applies a callable to a source range and stores the result in a target range
| Template parameters | |
|---|---|
| I | input iterator type |
| O | output iterator type |
| C | unary operator type |
| Parameters | |
| first | iterator to the beginning of the input range |
| last | iterator to the end of the input range |
| output | iterator to the beginning of the output range |
| op | the operator to apply to transform each element in the range |
| Returns | a tf:: |
This method is equivalent to the parallel execution of the following loop on a GPU:
while (first != last) { *output++ = callable(*first++); }
template<typename I, typename O, typename C>
void tf:: cudaFlow:: transform(cudaTask task,
I first,
I last,
O output,
C c)
updates parameters of a kernel task created from tf::
The type of the iterators and the callable must be the same as the task created from tf::
template<typename I1, typename I2, typename O, typename C>
cudaTask tf:: cudaFlow:: transform(I1 first1,
I1 last1,
I2 first2,
O output,
C op)
creates a task to perform parallel transforms over two ranges of items
| Template parameters | |
|---|---|
| I1 | first input iterator type |
| I2 | second input iterator type |
| O | output iterator type |
| C | unary operator type |
| Parameters | |
| first1 | iterator to the beginning of the input range |
| last1 | iterator to the end of the input range |
| first2 | iterato |
| output | iterator to the beginning of the output range |
| op | binary operator to apply to transform each pair of items in the two input ranges |
| Returns | cudaTask handle |
This method is equivalent to the parallel execution of the following loop on a GPU:
while (first1 != last1) { *output++ = op(*first1++, *first2++); }
template<typename I1, typename I2, typename O, typename C>
void tf:: cudaFlow:: transform(cudaTask task,
I1 first1,
I1 last1,
I2 first2,
O output,
C c)
updates parameters of a kernel task created from tf::
The type of the iterators and the callable must be the same as the task created from tf::
template<typename C>
cudaTask tf:: cudaFlow:: capture(C&& callable)
constructs a subflow graph through tf::
| Template parameters | |
|---|---|
| C | callable type constructible from std::function<void(tf::cudaFlowCapturer&)> |
| Parameters | |
| callable | the callable to construct a capture flow |
| Returns | a tf:: |
A captured subflow forms a sub-graph to the cudaFlow and can be used to capture custom (or third-party) kernels that cannot be directly constructed from the cudaFlow.
Example usage:
taskflow.emplace([&](tf::cudaFlow& cf){ tf::cudaTask my_kernel = cf.kernel(my_arguments); // create a flow capturer to capture custom kernels tf::cudaTask my_subflow = cf.capture([&](tf::cudaFlowCapturer& capturer){ capturer.on([&](cudaStream_t stream){ invoke_custom_kernel_with_stream(stream, custom_arguments); }); }); my_kernel.precede(my_subflow); });
template<typename C>
void tf:: cudaFlow:: capture(cudaTask task,
C callable)
updates the captured child graph
The method is similar to tf::


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Adding CPM Constraints
One of the strengths of the CPM is that it can incorporate different processes of interest as separate "rules" given to the model. These rules can be mixed and matched to get a simulation of interest, making the CPM a very flexible framework. Artistoo implements a diverse set of model rules, called Constraints. This tutorial will walk you through how to use these constraints in your models.
Soft versus hard constraints
In Artistoo, we distinguish two types of constraints: "hard" and "soft" constraints.
Soft constraints
"Soft" constraints are the classical CPM energy rules. They are the terms of the model's Hamiltonian, or energy equation. When a copy attempt would break such a rule, this becomes visible as an energetic penalty making that copy attempt less likely. But a soft constraint is more a guideline than an actual rule; the model will mostly adhere to it, but not always.
For example, suppose we have a cell with a volume constraint, and that cell is already larger than its target volume. A copy attempt that would add another pixel to that cell would be associated with a $ΔH_\text{vol} \gt 0$. However, that does not mean that this copy attempt will always fail to go through. First, it may be that there are other constraints acting on the model for which this copy attempt is favourable, compensating for the volume penalty such that the global $ΔH$ is still negative. Second, even if the global $ΔH$ is positive, a copy attempt may still be accepted with a chance $e^{-ΔH T}$. Thus, soft constraints make some changes more likely to happen than others, but they can balance each other out and are not always listened to.
Most CPM constraints are soft constraints. But sometimes, there are things we would like to forbid completely. For that, we can use a hard constraint.
Hard constraints
Whereas soft constraints are more guidelines than rules, "hard" constraints are set in stone. Before the $ΔH$ of a copy attempt is even computed, Artistoo checks whether it violates one of the hard constraints. If it does, it won't go through - ever.
Not many constraints are hard constraints, and in a typical CPM, you should be careful using them. But examples are the HardVolumeRangeConstraint, the BarrierConstraint, and the hard ConnectivityConstraint. See also this example of a 'particle' following a random walk, where we constrained the volume such that the cell can only be one or two pixels big:
Adding a constraint to a model
There are two ways of adding a constraint to your model, which we will explain here.
Method 1: via the conf object
The first method is to add the relevant constraint parameters to the conf
object. This is the easiest method to add a constraint, but it only works for a few of the
most used constraints (see this page for a list of the constraints for which this works).
For example:
let myCPM = new CPM.CPM(
[ 200, 200 ],
{
T : 20,
J : [[0,20],[20,0]],
V : [0,500],
LAMBDA_V: [ 0, 50]
}
)
will automatically create a CPM with an adhesion and a volume constraint inside it. If we open the browser console from a page with an Artistoo simulation in it, e.g. this one, we can check this. Go to the page, right click anywhere on the page outside of the grid, select "inspect" and then "console". Copy-paste the above code in the console and hit enter. If you now type:
myCPM.soft_constraints
and hit enter, you should see that there are two soft constraints in the model now:
an Adhesion
and a VolumeConstraint
.
This works the same if we use the
Simulation
class,
because the config.conf
is passed along internally to the CPM constructor.
So if we type:
let mySim = new CPM.Simulation(
{
field_size : [200,200],
conf : {
T : 20,
J : [[0,20],[20,0]],
V : [0,500],
LAMBDA_V: [ 0, 50]
},
simsettings : {
NRCELLS : [1]
}
}
)
mySim.C.soft_constraints
we again see our two constraints appear.
Method 2: via the add() method
Note that, as mentioned, the first method does not work for all constraints. If we try to add
an
AttractionPointConstraint
(another soft constraint) in this way:
let myCPM = new CPM.CPM(
[ 200, 200 ],
{
T : 20,
J : [[0,20],[20,0]],
V : [0,500],
LAMBDA_V: [ 0, 50],
LAMBDA_ATTRACTIONPOINT : [0,100],
ATTRACTIONPOINT: [[0,0],[100,100]]
}
)
then if we type:
myCPM.soft_constraints
we still only have our Adhesion
and a VolumeConstraint
.
The AttractionPointConstraint
has not been added.
To add this constraint, we must use the
add()
method of the CPM class. If we have already put the relevant parameters
in the conf
object, this is easy:
//add constraint
myCPM.add( new CPM.AttractionPointConstraint( myCPM.conf ) )
myCPM.soft_constraints
you should now see an AttractionPointConstraint
in the list of
soft constraints. We've given the entire conf
object of the CPM to the
constraint's constructor, but it will just ignore the parameters that are not relevant
to this constraint. However, we can also just give it it's own conf
object.
For example, we might add a second attraction point like this:
//add another constraint
myCPM.add( new CPM.AttractionPointConstraint( {
LAMBDA_ATTRACTIONPOINT : [0,100],
ATTRACTIONPOINT: [[0,0],[150,150]]
} ) )
myCPM.soft_constraints
You should now see two attraction point constraints in your list.
A word of warning: when you add a new constraint, the model will use it. It won't check
if another constraint of the same type was already present. For the example above, that's
perfectly fine because we might want to have two different points of attraction.
However, for other constraints such as the
Adhesion
,
this might not be what you want (basically, this will be the same as having only one
adhesion constraint where the parameters are twice as large. But someone checking your
code may overlook your second adhesion constraint and think that the adhesion parameters
are set lower than they actually are...).
Accessing a constraint in your model
Once you've put a constraint in your model, you may want to access it from outside.
The method getConstraint()
allows you to do that. For
example:
//get volumeconstraint
myCPM.getConstraint( "VolumeConstraint" )
will return the VolumeConstraint
that is currently in your model.
If there are multiple constraints of the same name in your model, this method automatically gives you the first one:
//get the first attraction point constraint
myCPM.getConstraint( "AttractionPointConstraint" )
This yields the first attraction point we added (from the browser console, you can
click the little triangle on the left to see more about the object. If you look at its
conf
object, you see that this is the settings we gave to our first
attraction point constraint).
If we want to access the second attraction point, we can type:
//get the second attraction point constraint
myCPM.getConstraint( "AttractionPointConstraint", 1 )
(Note here that JavaScript uses zero-based indexing, so an element with number
1
is actually the second element)
Implementation details
Constraints in Artistoo are implemented as separate classes, of which an instance
can be added to a CPM model
(See above for an explanation how). The base
Constraint
class underlies all of them, and has two subclasses: SoftConstraint
and
HardConstraint
.
These are never used by themselves, but can be extended to create a constraint
(see also this tutorial to see how to develop custom modules).


-
Tokyo, Japan’s busy capital, mixes the ultramodern and the traditional, from neon-lit skyscrapers to historic temples.
-
Kyoto is famous for its numerous classical Buddhist temples, gardens, imperial palaces, Shinto shrines and traditional wooden houses.
-
Osaka is a large port city and commercial center known for its modern architecture, nightlife and hearty street food.
-
Hokkaido, the northernmost of Japan’s main islands, is known for its volcanoes, natural hot springs ("onsen") and ski areas.


An extension of 'ggplot2' to provide quiver plots to visualise vector fields. This functionality is implemented using a geom to produce a new graphical layer, which allows aesthetic options. This layer can be overlaid on a map to improve visualisation of mapped data.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.2.0), ggplot2 |
| Imports: | dplyr |
| Suggests: | ggmap, maps, testthat |
| Published: | 2017-08-15 |
| Author: | Mitchell O'Hara-Wild [aut, cre] |
| Maintainer: | Mitchell O'Hara-Wild <mail at mitchelloharawild.com> |
| BugReports: | https://github.com/mitchelloharawild/ggquiver/issues |
| License: | GPL-3 |
| URL: | https://github.com/mitchelloharawild/ggquiver |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | ggquiver results |
| Reference manual: | ggquiver.pdf |
| Package source: | ggquiver_0.1.0.tar.gz |
| Windows binaries: | r-devel: ggquiver_0.1.0.zip, r-release: ggquiver_0.1.0.zip, r-oldrel: ggquiver_0.1.0.zip |
| OS X binaries: | r-release: ggquiver_0.1.0.tgz, r-oldrel: ggquiver_0.1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=ggquiver to link to this page.


QDrag Class
The QDrag class provides support for MIME-based drag and drop data transfer. More...
| Header: | #include <QDrag> |
| qmake: | QT += gui |
| Inherits: | QObject |
Public Functions
| QDrag(QObject *dragSource) | |
| ~QDrag() | |
| Qt::DropAction | defaultAction() const |
| QPixmap | dragCursor(Qt::DropAction action) const |
| Qt::DropAction | exec(Qt::DropActions supportedActions = Qt::MoveAction) |
| Qt::DropAction | exec(Qt::DropActions supportedActions, Qt::DropAction defaultDropAction) |
| QPoint | hotSpot() const |
| QMimeData * | mimeData() const |
| QPixmap | pixmap() const |
| void | setDragCursor(const QPixmap &cursor, Qt::DropAction action) |
| void | setHotSpot(const QPoint &hotspot) |
| void | setMimeData(QMimeData *data) |
| void | setPixmap(const QPixmap &pixmap) |
| QObject * | source() const |
| Qt::DropActions | supportedActions() const |
| QObject * | target() const |
- 32 public functions inherited from QObject
Signals
| void | actionChanged(Qt::DropAction action) |
| void | targetChanged(QObject *newTarget) |
- 2 signals inherited from QObject
Static Public Members
| void | cancel() |
- 10 static public members inherited from QObject
Additional Inherited Members
- 1 property inherited from QObject
- 1 public slot inherited from QObject
- 1 public variable inherited from QObject
- 9 protected functions inherited from QObject
- 2 protected variables inherited from QObject
Detailed Description
The QDrag class provides support for MIME-based drag and drop data transfer.
Drag and drop is an intuitive way for users to copy or move data around in an application, and is used in many desktop environments as a mechanism for copying data between applications. Drag and drop support in Qt is centered around the QDrag class that handles most of the details of a drag and drop operation.
The data to be transferred by the drag and drop operation is contained in a QMimeData object. This is specified with the setMimeData() function in the following way:
QDrag *drag = new QDrag(this); QMimeData *mimeData = new QMimeData; mimeData->setText(commentEdit->toPlainText()); drag->setMimeData(mimeData);
Note that setMimeData() assigns ownership of the QMimeData object to the QDrag object. The QDrag must be constructed on the heap with a parent QObject to ensure that Qt can clean up after the drag and drop operation has been completed.
A pixmap can be used to represent the data while the drag is in progress, and will move with the cursor to the drop target. This pixmap typically shows an icon that represents the MIME type of the data being transferred, but any pixmap can be set with setPixmap(). The cursor's hot spot can be given a position relative to the top-left corner of the pixmap with the setHotSpot() function. The following code positions the pixmap so that the cursor's hot spot points to the center of its bottom edge:
drag->setHotSpot(QPoint(drag->pixmap().width()/2, drag->pixmap().height()));
Note: On X11, the pixmap may not be able to keep up with the mouse movements if the hot spot causes the pixmap to be displayed directly under the cursor.
The source and target widgets can be found with source() and target(). These functions are often used to determine whether drag and drop operations started and finished at the same widget, so that special behavior can be implemented.
QDrag only deals with the drag and drop operation itself. It is up to the developer to decide when a drag operation begins, and how a QDrag object should be constructed and used. For a given widget, it is often necessary to reimplement mousePressEvent() to determine whether the user has pressed a mouse button, and reimplement mouseMoveEvent() to check whether a QDrag is required.
See also Drag and Drop, QClipboard, QMimeData, QMacPasteboardMime, Draggable Icons Example, Draggable Text Example, Drop Site Example, and Fridge Magnets Example.
Member Function Documentation
QDrag::QDrag(QObject *dragSource)
Constructs a new drag object for the widget specified by dragSource.
QDrag::~QDrag()
Destroys the drag object.
[signal]
void QDrag::actionChanged(Qt::DropAction action)
This signal is emitted when the action associated with the drag changes.
See also targetChanged().
[static]
void QDrag::cancel()
Cancels a drag operation initiated by Qt.
Note: This is currently implemented on Windows and X11.
This function was introduced in Qt 5.7.
See also exec().
Qt::DropAction QDrag::defaultAction() const
Returns the default proposed drop action for this drag operation.
See also exec() and supportedActions().
QPixmap QDrag::dragCursor(Qt::DropAction action) const
Returns the drag cursor for the action.
This function was introduced in Qt 5.0.
See also setDragCursor().
Qt::DropAction QDrag::exec(Qt::DropActions supportedActions = Qt::MoveAction)
Starts the drag and drop operation and returns a value indicating the requested drop action when it is completed. The drop actions that the user can choose from are specified in supportedActions. The default proposed action will be selected among the allowed actions in the following order: Move, Copy and Link.
Note: On Linux and macOS, the drag and drop operation can take some time, but this function does not block the event loop. Other events are still delivered to the application while the operation is performed. On Windows, the Qt event loop is blocked during the operation.
This function was introduced in Qt 4.3.
See also cancel().
Qt::DropAction QDrag::exec(Qt::DropActions supportedActions, Qt::DropAction defaultDropAction)
Starts the drag and drop operation and returns a value indicating the requested drop action when it is completed. The drop actions that the user can choose from are specified in supportedActions.
The defaultDropAction determines which action will be proposed when the user performs a drag without using modifier keys.
Note: On Linux and macOS, the drag and drop operation can take some time, but this function does not block the event loop. Other events are still delivered to the application while the operation is performed. On Windows, the Qt event loop is blocked during the operation. However, QDrag::exec() on Windows causes processEvents() to be called frequently to keep the GUI responsive. If any loops or operations are called while a drag operation is active, it will block the drag operation.
This function was introduced in Qt 4.3.
QPoint QDrag::hotSpot() const
Returns the position of the hot spot relative to the top-left corner of the cursor.
See also setHotSpot().
QMimeData *QDrag::mimeData() const
Returns the MIME data that is encapsulated by the drag object.
See also setMimeData().
QPixmap QDrag::pixmap() const
Returns the pixmap used to represent the data in a drag and drop operation.
See also setPixmap().
void QDrag::setDragCursor(const QPixmap &cursor, Qt::DropAction action)
Sets the drag cursor for the action. This allows you to override the default native cursors. To revert to using the native cursor for action pass in a null QPixmap as cursor.
The action can only be CopyAction, MoveAction or LinkAction. All other values of DropAction are ignored.
See also dragCursor().
void QDrag::setHotSpot(const QPoint &hotspot)
Sets the position of the hot spot relative to the top-left corner of the pixmap used to the point specified by hotspot.
Note: on X11, the pixmap may not be able to keep up with the mouse movements if the hot spot causes the pixmap to be displayed directly under the cursor.
See also hotSpot().
void QDrag::setMimeData(QMimeData *data)
Sets the data to be sent to the given MIME data. Ownership of the data is transferred to the QDrag object.
See also mimeData().
void QDrag::setPixmap(const QPixmap &pixmap)
Sets pixmap as the pixmap used to represent the data in a drag and drop operation. You can only set a pixmap before the drag is started.
See also pixmap().
QObject *QDrag::source() const
Returns the source of the drag object. This is the widget where the drag and drop operation originated.
Qt::DropActions QDrag::supportedActions() const
Returns the set of possible drop actions for this drag operation.
See also exec() and defaultAction().
QObject *QDrag::target() const
Returns the target of the drag and drop operation. This is the widget where the drag object was dropped.
[signal]
void QDrag::targetChanged(QObject *newTarget)
This signal is emitted when the target of the drag and drop operation changes, with newTarget the new target.
See also target() and actionChanged().