Africa's best footballers nominated for 2019 CAF Awards - full list out
The Confederation of African Football (CAF) has named Africa's current top players for this year's CAF Awards ceremony to take place early next year.
CAF released the list of nominees for the 28th edition of its annual football awards with to players like Sadio Mane, Riyad Mahrez, Mohammed Salah and Ghanaian duo of Thomas Partey and Jordan Ayew for the top award.
Some of the best female players as well as coaches and countries were also selected for the various categories for event which takes place on Tuesday, 7 January 2020 in Egypt.
The CAF awards celebrate African footballers and officials who have distinguished themselves during 2019 and this year's event will be held at Citadel Azure, Hurghada, Egypt.
The list of nominees is compiled by a panel of media and technical professionals.
It features several award categories for men’s and women’s football including: African Player of the Year, Coach of the Year, Youth Player of the Year, National Team of the Year, Goal of the Year, Africa Finest XI, Federation of the Year and several new award categories which will recognise exceptional contributions to African football and inspiring individuals.
The Interclubs Player of the Year Award has been reintroduced and will be dedicated to honouring key actors in CAF’s club competitions
Full list of nominees for CAF Awards 2019:
African Player of the Year
· Achraf Hakimi (Morocco & Borussia Dortmund)
· André Onana (Cameroon & Ajax)
· Baghdad Bounedjah (Algeria& Al-Sadd)
· Carolus Andriamatsinoro (Madagascar & Al Adalah)
· Denis Onyango (Uganda & Mamelodi Sundowns)
· Eric Maxim Choupo-Moting (Cameroon & Paris Saint-Germain)
· Ferjani Sassi(Tunisia & Zamalek)
· Hakim Ziyech (Morocco & Ajax)
· Idrissa Gueye (Senegal & Paris Saint-Germain)
· Ismail Bennacer (Algeria & AC Milan)
· Jordan Ayew (Ghana & Crystal Palace)
· Kalidou Koulibaly (Senegal & Napoli)
· Kodjo Fo Doh Laba (Togo & Al Ain)
· Mahmoud Hassan"Trezeguet" (Egypt & Aston Villa)
· Mbwana Samatta (Tanzania & Genk)
· Mohamed Salah (Egypt & Liverpool)
· Moussa Marega (Mali & Porto)
· Naby Keita (Guinea & Liverpool)
· Nicolas Pepe (Côte d'Ivoire & Arsenal)
· Odion Ighalo (Nigeria & Shanghai Shenhua)
· Percy Tau (South Africa & Club Brugge)
· Pierre-Emerick Aubameyang (Gabon & Arsenal)
· Riyad Mahrez (Algeria & Manchester City)
· Sadio Mane (Senegal & Liverpool)
· Taha Yassine Khenissi (Tunisia & Esperance)
· Thomas Teye Partey (Ghana & Atlético Madrid)
· Victor Osimhen (Nigeria & Lille)
· Wilfred Ndidi (Nigeria & Leicester City)
· Wilfried Zaha (Côte d'Ivoire & Crystal Palace)
· Youcef Belaili (Algeria & Ahli Jeddah)
African Women’s Player of the Year
· Ajara Nchout (Cameroon & Valerenga )
· Ange N'Guessan (Côte d'Ivoire & Tenerife)
· Asisat Oshoala (Nigeria & Barcelona)
· Chiamaka Nnadozie (Nigeria & Rivers Angels)
· Elizabeth Addo (Ghana & Jiangsu Suning)
· Gabrielle Ouguene (Cameroon & CSKA Moscow)
· Refiloe Jane (South Africa & AC Milan)
· Tabitha Chawinga (Malawi & Jiangsu Suning)
· Thembi Kgatlana (South Africa & Beijing Phoenix FC)
· Uchenna Kanu (Nigeria & Southeastern Fire)
African Interclubs Player of the Year
· Ali Maaloul (Tunisia & Al Ahly)
· Anice Badri (Tunisia & Esperance)
· Denis Onyango (Uganda & Mamelodi Sundowns)
· Emmanuel Okwi (Uganda & Simba)
· Ferjani Sassi (Tunisia & Zamalek)
· Fousseny Coulibaly (Cote d'Ivoire & Esperance)
· Franck Kom (Cameroon & Esperance)
· Herenilson (Angola & Petro de Luanda)
· Ismail El Haddad (Morocco & Wydad Casablanca)
· Jean Marc Makusu (DR Congo & AS VITA)
· Kodjo Fo Doh Laba (Togo & RS Berkane / Al Ain)
· Mahmoud Alaa (Egypt & Zamalek)
· Meddie Kagere (Rwanda & Simba)
· Meschack Elia (DR Congo & TP Mazembe)
· Taha Yassine Khenissi (Tunisia & Esperance)
· Tarek Hamed (Egypt & Zamalek)
· Themba Zwane (South Africa & Mamelodi Sundowns)
· Trésor Mputu (DR Congo & TP Mazembe)
· Walid El Karti (Morocco & Wydad Casablanca)
· Youcef Belaïli (Algeria & Esperance / Ahli Jeddah)
African Youth Player of the Year
· Achraf Hakimi (Morocco & Borussia Dortmund)
· Amadou Haidara (Mali & RB Leipzig)
· Amadou Sagna (Senegal & Club Brugge)
· Enock Mwepu (Zambia & Red Bull Salzburg)
· Ismaïla Sarr (Senegal & Watford)
· Krépin Diatta (Senegal & Club Brugge)
· Moussa Djenepo (Mali & Southampton)
· Moussa Ndiaye (Senegal & Excellence Foot)
· Musa Barrow (Gambia & Atalanta)
· Osvaldo Pedro Capemba ‘Capita’ (Angola & Primeiro de Agosto)
· Patson Daka (Zambia & Red Bull Salzburg)
· Samuel Chukwueze (Nigeria & Villarreal)
· Sekou Koita (Mali & Red Bull Salzburg)
· Steve Regis Mvoue (Cameroon & AS Azur Star)
· Victor Osimhen (Nigeria & Lille)
· Zito Luvumbo (Angola & Primeiro de Agosto)
African Men’s Coach of the Year
· Aliou Cisse (Senegal - Senegal)
· Christian Gross (Switzerland - Zamalek)
· Corentin Martins (France - Mauritania)
· Djamel Belmadi (Algeria - Algeria)
· Faouzi Benzarti (Tunisia - Wydad Athletic Club)
· Gernot Rohr (Germany - Nigeria)
· Moïne Chaâbani (Tunisia - Esperance)
· Mounir Jaouani (Morocco - Berkane FC)
· Nicolas Dupuis (France - Madagascar)
· Pitso Mosimane (South Africa - Mamelodi Sundowns)
African Women’s Coach of the Year
· Alain Djeumfa (Cameroon)
· Bruce Mwape (Zambia)
· Clementine Toure (Côte d'Ivoire)
· Desiree Ellis (South Africa)
· David Ouma (Kenya)
· Thomas Dennerby (Nigeria)
African Men’s National Team of the Year
· Algeria
· Madagascar
· Nigeria
· Senegal
· Tunisia
African Women’s National Team of the Year
· Cameroon
· Côte d'Ivoire
· Kenya
· Nigeria
· South Africa
· Zambia


[VisAPI Index] [VisAPI Hierarchy]
Compare attribute access implementation. More...
#include <VisApi/VisApi.h>
Inherits: visAttributeAccess
This class implements the attribute access for use with two attributes. It can be used along with an attribute combiner to compare two attributes.
Initialize this class with fieldAccess and attribute val. The fieldAccess will be used to get an attribute factory for an attribute.
Set second attribute.
| Kdoc |


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Our goal is to create a resourceful space where teens can learn about AI and work on projects in Machine Learning. We hope to inspire the next generation through early exposure in AI by providing free GPU power, access to large datasets, as well as mentorship from experts in the field.
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| spark.fpGrowth {SparkR} | R Documentation |
A parallel FP-growth algorithm to mine frequent itemsets.
spark.fpGrowth
fits a FP-growth model on a SparkDataFrame. Users can
spark.freqItemsets
to get frequent itemsets, spark.associationRules
to get
association rules, predict
to make predictions on new data based on generated association
rules, and write.ml
/read.ml
to save/load fitted models.
For more details, see
FP-growth.
spark.fpGrowth(data, ...) spark.freqItemsets(object) spark.associationRules(object) ## S4 method for signature 'SparkDataFrame' spark.fpGrowth( data, minSupport = 0.3, minConfidence = 0.8, itemsCol = "items", numPartitions = NULL ) ## S4 method for signature 'FPGrowthModel' spark.freqItemsets(object) ## S4 method for signature 'FPGrowthModel' spark.associationRules(object) ## S4 method for signature 'FPGrowthModel' predict(object, newData) ## S4 method for signature 'FPGrowthModel,character' write.ml(object, path, overwrite = FALSE)
data |
A SparkDataFrame for training. |
... |
additional argument(s) passed to the method. |
object |
a fitted FPGrowth model. |
minSupport |
Minimal support level. |
minConfidence |
Minimal confidence level. |
itemsCol |
Features column name. |
numPartitions |
Number of partitions used for fitting. |
newData |
a SparkDataFrame for testing. |
path |
the directory where the model is saved. |
overwrite |
logical value indicating whether to overwrite if the output path already exists. Default is FALSE which means throw exception if the output path exists. |
spark.fpGrowth
returns a fitted FPGrowth model.
A SparkDataFrame
with frequent itemsets.
The SparkDataFrame
contains two columns:
items
(an array of the same type as the input column)
and freq
(frequency of the itemset).
A SparkDataFrame
with association rules.
The SparkDataFrame
contains four columns:
antecedent
(an array of the same type as the input column),
consequent
(an array of the same type as the input column),
condfidence
(confidence for the rule)
and lift
(lift for the rule)
predict
returns a SparkDataFrame containing predicted values.
spark.fpGrowth since 2.2.0
spark.freqItemsets(FPGrowthModel) since 2.2.0
spark.associationRules(FPGrowthModel) since 2.2.0
predict(FPGrowthModel) since 2.2.0
write.ml(FPGrowthModel, character) since 2.2.0
## Not run:
##D raw_data <- read.df(
##D "data/mllib/sample_fpgrowth.txt",
##D source = "csv",
##D schema = structType(structField("raw_items", "string")))
##D
##D data <- selectExpr(raw_data, "split(raw_items, ' ') as items")
##D model <- spark.fpGrowth(data)
##D
##D # Show frequent itemsets
##D frequent_itemsets <- spark.freqItemsets(model)
##D showDF(frequent_itemsets)
##D
##D # Show association rules
##D association_rules <- spark.associationRules(model)
##D showDF(association_rules)
##D
##D # Predict on new data
##D new_itemsets <- data.frame(items = c("t", "t,s"))
##D new_data <- selectExpr(createDataFrame(new_itemsets), "split(items, ',') as items")
##D predict(model, new_data)
##D
##D # Save and load model
##D path <- "/path/to/model"
##D write.ml(model, path)
##D read.ml(path)
##D
##D # Optional arguments
##D baskets_data <- selectExpr(createDataFrame(itemsets), "split(items, ',') as baskets")
##D another_model <- spark.fpGrowth(data, minSupport = 0.1, minConfidence = 0.5,
##D itemsCol = "baskets", numPartitions = 10)
## End(Not run)


reedsolomon module¶
-
reedsolomon.
decode_file
(in_file, out_file, k, n, size, err_loc)[source]¶ Decode file that coded by Reed-Solomon codes.
Parameters: - in_file (str) – file name that you wan to decode.
- out_file (str) – file name that you wan to output.
- k (int) – amount of original data.
- n (int) – total number of symbols after the erasure coding
- size (long) – file size of original file (not encoded file)
- err_loc (list) – error location in bytes in file.
Returns: 0 is success. others for error.
-
reedsolomon.
encode_file
(orig_file, encoded_file, k, n)[source]¶ Encode file by Reed-Solomon codes with interleaving.
For interleaving, data will be considered as K x (interleaving length) matrix (interleaving length= (file size % K)+1). Then Reed Solomon coding is adapted to each column, and add parity bits to the end of column.
For example, RS(K=200,N=208) is used to 1,000,000 bytes data, which means interleaving length=5,000 so you can recover data even if you cannot download sequential 5,000*8 bytes.
Parameters: - orig_file (str) – file name that you wan to encode.
- encoded_file (str) – file name that you wan to output.
- k (int) – amount of original data.
- n (int) – total number of symbols after the erasure coding
Returns: 0 is success. others for error.


The Collegeville organization is dedicated to studying, developing, teaching and promoting better computational science and engineering (CSE)
Scientists and engineers use computation as a part of their everyday discovery and design processes. In order to improve the effectiveness of computational approaches, the Collegeville Project is focused on studying, developing, teaching and promoting theory and best practices in CSE.
While many people work to improve the effectiveness of CSE while simultaneously working on a specific CSE project, we are looking at the process of
Collegeville Workshop Series
The Collegeville Project sponsors a series of workshops on scientific software. These events focus on specific subthemes related to the development and use of software for science. Participants come from industry, academic and laboratory institutions, enabling a broader understanding of common and complementary topics across these communities. Details about the workshops can be found here:
Capstone Origins
Some of the content in the Collegeville Project comes from a capstone course on computer science methodologies taught by Michael Heroux since 1998. This material draws upon and develops best practices in oral and written discourse, with the basic goal of always improving each scientist's personal skills for effective communication. Two sub-projects under the Collegeville Project are:
Lex: Effective Software
The Lex Project is focused on studying, developing, teaching and promoting best practices and tools for (scientific and engineering) software engineering.Sagatagan: R&D of R&D
The Sagatagan Project is focused on studying, developing, teaching and promoting theoretical models for CSE collaboration. Of particular interest is the characterization and improvement of
The Sagatagan Project is focused on model-driven approaches to improving team productivity for writing papers, developing software and more, and product sustainability for documents, software and tools.
Capturing Assignment
The Capturing Assignment Project is focused on studying, and developing the best way to collect and grade students' assignments in a GitHub repository.
Authors and Contributors
This site is developed and maintained by Michael Heroux @maherou with assistance from Duong Do @duongdo27 and Jim Willenbring @jwillenbring.
Its content is used as part of a Computer Science capstone course in research methodologies at the College of St. Benedict and St. John's University in rural Collegeville, Minnesota.


With a rise in technology, classrooms have changed from being a teacher lecturing at the front of the classroom to being more interactive with the help of technologies. The introduction of the radio sparked the technology movement, creating the overhead projector, films, calculators, scantrons, and introduced them into the common classroom.
These led to more advanced technologies, like the computer and the Internet, and led the classroom to be much more interactive. Today, we have technologies such as iPads, Microsoft Office, SMARTboards, variable speed tape recorders, and many others. In addition, we have opened up opportunities for those with disabilities to learn better in the classroom with assistive technologies, the most notable being phonetic spelling software, talking calculators, and even robots designed to help those with autism learn their social skills!
With the previously mentioned technologies currently being used, the possibilities for the future are endless! But, some new ideas that are currently being implemented are biometrics, augmented reality, touch screen technology, and even more ways to assist students with disabilities in the classroom.
Prior to the introduction of the radio in 1923, classrooms typically had students ranging from 1st through 8th grades being taught by one teacher. There were usually 6-40+ students, with the youngest sitting in the front and the oldest sitting in the back.
Examples of subjects taught are reading, writing, arithmetic, history, and geography. These lessons were memorized and recited by the students.
After the introduction of the radio, (but before the introduction of the computer) many forms of technology were used such as:
Overhead projectors, films, television, headphones, photocopiers, calculators, and scantrons
After the computer was introduced in 1984, many new technologies were developed and implemented such as:
Macbook, laptops, CD-ROMs, HTML, SMARTBoards, Internet, and servers
Computers have built the stepping stones to other technologies in the classroom, such as:
Canvas, YouTube, Milo the Autism Robot, and the modern computer
Biometrics are technologies that recognize people based on certain physical or behavioral traits. They can be used to recognize physical and emotional disposition of students in the classroom. Eventually, they can be used to analyze the students and alter the course material to tailor to each individual students' needs based on their own biometric signals.
Augmented reality glasses are already rumored to be on Google's release list, and they can be very useful not only in classrooms but in many aspects of everyday life. AR Glasses layer data on top of what we already naturally see, and will allow for a real-world learning experience.
Students wearing these glasses, or even contacts as technology advances, can potentially sit and have a conversation with historical figures about the subjects that they are learning. For example, it may even be possible to learn about electricity from Thomas Edison himself!
Multi-touch systems, more commonly known as devices with touch screens, are already a large part of our society today. But, there is a lot of room to grow from simply iPads, Smartphones, and touch-screen laptops.
If we keep expanding, we can use these multi-touch systems on a larger scale: touch screen desks, more advanced SmartBoards, or possibilities we haven't yet thought of. These large-scale workstations could allow students to collaborate, whether in the same classroom or in other parts of the world. These large workstations could even stream videos directly to the surface and enhance the classroom even further.
While we already have phonetic spelling software, talking calculators, and even a robot to help autistic children learn their social skills, there is still an endless amount of possibilities to help these children even further.
Milo, the Austism Robot, is definitely a step in the right direction, but it is very likely that he will become more advanced and be able to be of even more help. Whether it be him becoming more realistic-looking, having more features and interactions, or even helping those with other disabilities, the future is bright for assistive technologies.


---
layout: post
title: accessing your event arguments.
tags: []
status: publish
type: post
published: true
meta: {}
---
The Mach-ii framework conveniently passes your form and url variables into the event for you and you can access these values from within CFC by passing them into your method using:
[code][/code]
Now if you wanted to access the value for a submitted form field called "myOption", you could access it using the following syntax:
[code][/code]
And now this value can easily be used and passed into your queries or whatever you want to do with it within that method.


Condition will change the result of the build if the cyclomatic complexity of procedures exceeds the preset value.
Cyclomatic complexity limit - The procedures with cyclomatic complexity that exceeds this value will change the build result. [default 30]
Mark build as - the desired result if the condition is triggered.


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SetRed is a binary operator. A binary operator is one which takes data from two images and generates a third image.
It does this by taking the value of the corresponding pixels in each of the two input images and combining the
values using some operator to generate the apropriate pixel in the third image. Both input images must have the same
dimensions.
SetRed takes the red channel from one TrianaPixelMap and combines it with the green and blue channels of the
other. Mathematically it can be expressed as follows:
rr(i, j) = r1(i, j)
gr(i, j) = g2(i, j)
br(i, J) = b2(i, j)
See also
GetRed,
GetGreen,
SetGreen,
GetBlue,
SetBlue


--- title: Callback keywords: fastai sidebar: home_sidebar summary: "Basic callbacks for Learner" description: "Basic callbacks for Learner" ---
from nbdev.showdoc import *
The training loop is defined in Learner
a bit below and consists in a minimal set of instructions: looping through the data we:
Any tweak of this training loop is defined in a Callback
to avoid over-complicating the code of the training loop, and to make it easy to mix and match different techniques (since they'll be defined in different callbacks). A callback can implement actions on the following events:
begin_fit
: called before doing anything, ideal for initial setup.begin_epoch
: called at the beginning of each epoch, useful for any behavior you need to reset at each epoch.begin_train
: called at the beginning of the training part of an epoch.begin_batch
: called at the beginning of each batch, just after drawing said batch. It can be used to do any setup necessary for the batch (like hyper-parameter scheduling) or to change the input/target before it goes in the model (change of the input with techniques like mixup for instance).after_pred
: called after computing the output of the model on the batch. It can be used to change that output before it's fed to the loss.after_loss
: called after the loss has been computed, but before the backward pass. It can be used to add any penalty to the loss (AR or TAR in RNN training for instance).after_backward
: called after the backward pass, but before the update of the parameters. It can be used to do any change to the gradients before said update (gradient clipping for instance).after_step
: called after the step and before the gradients are zeroed.after_batch
: called at the end of a batch, for any clean-up before the next one.after_train
: called at the end of the training phase of an epoch.begin_validate
: called at the beginning of the validation phase of an epoch, useful for any setup needed specifically for validation.after_validate
: called at the end of the validation part of an epoch.after_epoch
: called at the end of an epoch, for any clean-up before the next one.after_fit
: called at the end of training, for final clean-up.tst_cb = Callback()
tst_cb.call_me = lambda: print("maybe")
test_stdout(lambda: tst_cb("call_me"), "maybe")
This is a shortcut to avoid having to write self.learn.bla
for any bla
attribute we seek, and just write self.bla
.
mk_class('TstLearner', 'a')
class TstCallback(Callback):
def batch_begin(self): print(self.a)
learn,cb = TstLearner(1),TstCallback()
cb.learn = learn
test_stdout(lambda: cb('batch_begin'), "1")
Note that it only works to get the value of the attribute, if you want to change it, you have to manually access it with self.learn.bla
. In the example below, self.a += 1
creates an a
attribute of 2 in the callback instead of setting the a
of the learner to 2. It also issues a warning that something is probably wrong:
learn.a
class TstCallback(Callback):
def batch_begin(self): self.a += 1
learn,cb = TstLearner(1),TstCallback()
cb.learn = learn
cb('batch_begin')
test_eq(cb.a, 2)
test_eq(cb.learn.a, 1)
A proper version needs to write self.learn.a = self.a + 1
:
class TstCallback(Callback):
def batch_begin(self): self.learn.a = self.a + 1
learn,cb = TstLearner(1),TstCallback()
cb.learn = learn
cb('batch_begin')
test_eq(cb.learn.a, 2)
test_eq(TstCallback().name, 'tst')
class ComplicatedNameCallback(Callback): pass
test_eq(ComplicatedNameCallback().name, 'complicated_name')
When writing a callback, the following attributes of Learner
are available:
model
: the model used for training/validationdata
: the underlying DataLoaders
loss_func
: the loss function usedopt
: the optimizer used to udpate the model parametersopt_func
: the function used to create the optimizercbs
: the list containing all Callback
sdl
: current DataLoader
used for iterationx
/xb
: last input drawn from self.dl
(potentially modified by callbacks). xb
is always a tuple (potentially with one element) and x
is detuplified. You can only assign to xb
.y
/yb
: last target drawn from self.dl
(potentially modified by callbacks). yb
is always a tuple (potentially with one element) and y
is detuplified. You can only assign to yb
.pred
: last predictions from self.model
(potentially modified by callbacks)loss
: last computed loss (potentially modified by callbacks)n_epoch
: the number of epochs in this trainingn_iter
: the number of iterations in the current self.dl
epoch
: the current epoch index (from 0 to n_epoch-1
)iter
: the current iteration index in self.dl
(from 0 to n_iter-1
)The following attributes are added by TrainEvalCallback
and should be available unless you went out of your way to remove that callback:
train_iter
: the number of training iterations done since the beginning of this trainingpct_train
: from 0. to 1., the percentage of training iterations completedtraining
: flag to indicate if we're in training mode or notThe following attribute is added by Recorder
and should be available unless you went out of your way to remove that callback:
smooth_loss
: an exponentially-averaged version of the training lossIt happens that we may want to skip some of the steps of the training loop: in gradient accumulation, we don't aways want to do the step/zeroing of the grads for instance. During an LR finder test, we don't want to do the validation phase of an epoch. Or if we're training with a strategy of early stopping, we want to be able to completely interrupt the training loop.
This is made possible by raising specific exceptions the training loop will look for (and properly catch).
You can detect one of those exceptions occurred and add code that executes right after with the following events:
after_cancel_batch
: reached imediately after a CancelBatchException
before proceeding to after_batch
after_cancel_train
: reached imediately after a CancelTrainException
before proceeding to after_epoch
after_cancel_valid
: reached imediately after a CancelValidException
before proceeding to after_epoch
after_cancel_epoch
: reached imediately after a CancelEpochException
before proceeding to after_epoch
after_cancel_fit
: reached imediately after a CancelFitException
before proceeding to after_fit
test_eq(event.after_backward, 'after_backward')
Here's the full list: begin_fit begin_epoch begin_train begin_batch after_pred after_loss after_backward after_step after_cancel_batch after_batch after_cancel_train after_train begin_validate after_cancel_validate after_validate after_cancel_epoch after_epoch after_cancel_fit after_fit.


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|
C++ Actor Framework 0.18
|
Identifies an untyped actor. More...
#include <actor.hpp>
Public Types | |
| using | signatures = none_t |
Public Member Functions | |
| actor (actor &&)=default | |
| actor (const actor &)=default | |
| actor & | operator= (actor &&)=default |
| actor & | operator= (const actor &)=default |
| actor (std::nullptr_t) | |
| actor (const scoped_actor &) | |
| template<class T , class = detail::enable_if_t<actor_traits<T>::is_dynamically_typed>> | |
| actor (T *ptr) | |
| template<class T , class = detail::enable_if_t<actor_traits<T>::is_dynamically_typed>> | |
| actor & | operator= (intrusive_ptr< T > ptr) |
| template<class T , class = detail::enable_if_t<actor_traits<T>::is_dynamically_typed>> | |
| actor & | operator= (T *ptr) |
| actor & | operator= (std::nullptr_t) |
| actor & | operator= (const scoped_actor &x) |
| operator bool () const | |
| Queries whether this actor handle is valid. | |
| bool | operator! () const |
| Queries whether this actor handle is invalid. | |
| actor_addr | address () const noexcept |
| Returns the address of the stored actor. | |
| actor_id | id () const noexcept |
| Returns the ID of this actor. | |
| node_id | node () const noexcept |
| Returns the origin node of this actor. | |
| actor_system & | home_system () const noexcept |
| Returns the hosting actor system. | |
| void | swap (actor &other) noexcept |
Exchange content of *this and other . | |
Static Public Attributes | |
| static constexpr bool | has_weak_ptr_semantics = false |
Friends | |
| class | local_actor |
| template<class , class , int > | |
| class | actor_cast_access |
| std::string | to_string (const actor &x) |
| void | append_to_string (std::string &x, const actor &y) |
| template<class Inspector > | |
| bool | inspect (Inspector &f, actor &x) |
| void | destroy (actor &x) |
Releases the reference held by handle x . | |
Related Functions | |
(Note that these are not member functions.) | |
| CAF_CORE_EXPORT bool | operator== (const actor &lhs, abstract_actor *rhs) |
| CAF_CORE_EXPORT bool | operator== (abstract_actor *lhs, const actor &rhs) |
| CAF_CORE_EXPORT bool | operator!= (const actor &lhs, abstract_actor *rhs) |
| CAF_CORE_EXPORT bool | operator!= (abstract_actor *lhs, const actor &rhs) |
Identifies an untyped actor.
Can be used with derived types of event_based_actor
, blocking_actor
, and actor_proxy
.
|
friend |
Releases the reference held by handle x
.
Using the handle after invalidating it is undefined behavior.


Organic molecules contain Carbon, inorganic molecules do not.
There are four basic organic compounds used to build cells:
Each organic macromolecule is reviewed in turn in the remainder of the lesson.


Let's start with a simple example. First we need to create our view handling class:
Go ahead and create a 'views' directory in the same folder as SimpleView.php. In the views folder create:
That's it. Point your web browser at SimpleView.php and you should see your Hello World page.
Just a quote note about that $CACHE flag. By default Nest doesn't cache output pages. In production you'll want to set the $CACHE flag to true to have it compile once and then reuse that file.
At this point we've just displayed an HTML document, which we could have done easily without the need for a specialized library. So let's add a line to our SimpleView.php file:
Now let's change the nest template:That's it. Values can be displayed in the view by simply adding them as attributes and then inserting them as a token into the nest template. If you view SimpleView.php in a web browser you should see "Hello Tim!".


|
Copyright 2013-01-06 Werner Randelshofer | ||||||||
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java.lang.Object org.monte.media.beans.AbstractStateModel org.monte.screenrecorder.ScreenRecorder
public class ScreenRecorder
A screen recorder written in pure Java.
Captures the screen, the mouse cursor and audio.
This class records mouse clicks occurring on other Java Windows running in the same JVM. Mouse clicks occurring in other JVM's and other processes are not recorded. This ability is useful for performing in-JVM recordings of an application that is being tested.
This recorder uses four threads. Three capture threads for screen, mouse cursor and audio, and one output thread for the movie writer.
FIXME - This class is a horrible mess.
| Nested Class Summary | |
|---|---|
protected static class |
ScreenRecorder.MouseGrabber
|
static class |
ScreenRecorder.State
|
| Field Summary | |
|---|---|
protected int |
audioTrack
Id of the audio track. |
static java.lang.String |
ENCODING_BLACK_CURSOR
"Encoding" for black mouse cursor. |
static java.lang.String |
ENCODING_WHITE_CURSOR
"Encoding" for white mouse cursor. |
static java.lang.String |
ENCODING_YELLOW_CURSOR
"Encoding" for yellow mouse cursor. |
protected java.util.concurrent.ScheduledThreadPoolExecutor |
mouseCaptureTimer
Timer for mouse captures. |
protected Format |
mouseFormat
The input video format for cursor capture. |
protected java.util.concurrent.ScheduledFuture |
mouseFuture
|
protected ScreenRecorder.MouseGrabber |
mouseGrabber
|
protected java.io.File |
movieFolder
Where to store the movie. |
protected long |
recordingStartTime
The start time of the recording. |
protected long |
recordingStopTime
The stop time of the recording. |
protected int |
videoTrack
Id of the video track. |
| Fields inherited from class org.monte.media.beans.AbstractStateModel |
|---|
changeEvent, listenerList |
| Constructor Summary | |
|---|---|
ScreenRecorder(java.awt.GraphicsConfiguration cfg)
Creates a screen recorder. |
|
ScreenRecorder(java.awt.GraphicsConfiguration cfg,
Format fileFormat,
Format screenFormat,
Format mouseFormat,
Format audioFormat)
Creates a screen recorder. |
|
ScreenRecorder(java.awt.GraphicsConfiguration cfg,
java.awt.Rectangle captureArea,
Format fileFormat,
Format screenFormat,
Format mouseFormat,
Format audioFormat)
Creates a screen recorder. |
|
ScreenRecorder(java.awt.GraphicsConfiguration cfg,
java.awt.Rectangle captureArea,
Format fileFormat,
Format screenFormat,
Format mouseFormat,
Format audioFormat,
java.io.File movieFolder)
Creates a screen recorder. |
| Method Summary | |
|---|---|
protected java.io.File |
createMovieFile(Format fileFormat)
Creates a file for recording the movie. |
protected MovieWriter |
createMovieWriter()
|
float |
getAudioLevelLeft()
Returns the audio level of the left channel or of the mono channel. |
float |
getAudioLevelRight()
Returns the audio level of the right channel. |
javax.sound.sampled.Mixer |
getAudioMixer()
Gets the audio mixer used for sound input. |
java.util.List<java.io.File> |
getCreatedMovieFiles()
Returns a list of all files that the screen recorder created. |
long |
getMaxFileSize()
Maximal file size. |
long |
getMaxRecordingTime()
Maximal recording time in milliseconds. |
long |
getStartTime()
|
ScreenRecorder.State |
getState()
Returns the state of the recorder. |
java.lang.String |
getStateMessage()
Returns the state of the recorder. |
void |
setAudioMixer(javax.sound.sampled.Mixer mixer)
Sets the audio mixer for sound input. |
void |
setMaxFileSize(long maxFileSize)
|
void |
setMaxRecordingTime(long maxRecordingTime)
Maximal recording time in milliseconds. |
void |
start()
Starts the screen recorder. |
protected void |
startMouseCapture()
Starts mouse capture. |
void |
stop()
Stops the screen recorder. |
protected void |
stopMouseCapture()
Stops mouse capturing. |
protected void |
waitUntilMouseCaptureStopped()
Waits until mouse capturing stopped. |
protected void |
write(Buffer buf)
Writes a buffer into the movie. |
| Methods inherited from class org.monte.media.beans.AbstractStateModel |
|---|
addChangeListener, fireStateChanged, removeChangeListener |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
public static final java.lang.String ENCODING_BLACK_CURSOR
public static final java.lang.String ENCODING_WHITE_CURSOR
public static final java.lang.String ENCODING_YELLOW_CURSOR
protected Format mouseFormat
protected long recordingStartTime
protected volatile long recordingStopTime
protected java.util.concurrent.ScheduledThreadPoolExecutor mouseCaptureTimer
protected int videoTrack
protected int audioTrack
protected ScreenRecorder.MouseGrabber mouseGrabber
protected java.util.concurrent.ScheduledFuture mouseFuture
protected java.io.File movieFolder
| Constructor Detail |
|---|
public ScreenRecorder(java.awt.GraphicsConfiguration cfg) throws java.io.IOException, java.awt.AWTException
cfg
- Graphics configuration of the capture screen.
java.io.IOException
java.awt.AWTException
public ScreenRecorder(java.awt.GraphicsConfiguration cfg, Format fileFormat, Format screenFormat, Format mouseFormat, Format audioFormat) throws java.io.IOException, java.awt.AWTException
cfg
- Graphics configuration of the capture screen.fileFormat
- The file format "AVI" or "QuickTime".screenFormat
- The video format for screen capture.mouseFormat
- The video format for mouse capture. The
EncodingKey
must be ENCODING_BLACK_CURSOR or
ENCODING_WHITE_CURSOR. The SampleRateKey
can be independent from
the screenFormat
. Specify null if you don't want to capture the
mouse cursor.audioFormat
- The audio format for audio capture. Specify null if
you don't want audio capture.
java.io.IOException
java.awt.AWTException
public ScreenRecorder(java.awt.GraphicsConfiguration cfg, java.awt.Rectangle captureArea, Format fileFormat, Format screenFormat, Format mouseFormat, Format audioFormat) throws java.io.IOException, java.awt.AWTException
cfg
- Graphics configuration of the capture screen.captureArea
- Defines the area of the screen that shall be captured.fileFormat
- The file format "AVI" or "QuickTime".screenFormat
- The video format for screen capture.mouseFormat
- The video format for mouse capture. The
EncodingKey
must be ENCODING_BLACK_CURSOR or
ENCODING_WHITE_CURSOR. The SampleRateKey
can be independent from
the screenFormat
. Specify null if you don't want to capture the
mouse cursor.audioFormat
- The audio format for audio capture. Specify null if
you don't want audio capture.
java.io.IOException
java.awt.AWTException
public ScreenRecorder(java.awt.GraphicsConfiguration cfg, java.awt.Rectangle captureArea, Format fileFormat, Format screenFormat, Format mouseFormat, Format audioFormat, java.io.File movieFolder) throws java.io.IOException, java.awt.AWTException
cfg
- Graphics configuration of the capture screen.captureArea
- Defines the area of the screen that shall be captured.fileFormat
- The file format "AVI" or "QuickTime".screenFormat
- The video format for screen capture.mouseFormat
- The video format for mouse capture. The
EncodingKey
must be ENCODING_BLACK_CURSOR or
ENCODING_WHITE_CURSOR. The SampleRateKey
can be independent from
the screenFormat
. Specify null if you don't want to capture the
mouse cursor.audioFormat
- The audio format for audio capture. Specify null if
you don't want audio capture.movieFolder
- Where to store the movie
java.io.IOException
java.awt.AWTException
| Method Detail |
|---|
protected MovieWriter createMovieWriter() throws java.io.IOException
java.io.IOException
public java.util.List<java.io.File> getCreatedMovieFiles()
protected java.io.File createMovieFile(Format fileFormat) throws java.io.IOException
This implementation creates a file in the users "Video" folder on Windows, or in the users "Movies" folders on Mac OS X.
You can override this method, if you would like to create a movie file at a different location.
fileFormat
-
java.io.IOException
public ScreenRecorder.State getState()
public java.lang.String getStateMessage()
public long getStartTime()
public void start() throws java.io.IOException
java.io.IOException
protected void startMouseCapture() throws java.io.IOException
java.io.IOException
protected void stopMouseCapture()
waitUntilMouseCaptureStopped()
to wait until the capturing stopped.
protected void waitUntilMouseCaptureStopped() throws java.lang.InterruptedException
stopMouseCapture()
.
java.lang.InterruptedException
public float getAudioLevelLeft()
public float getAudioLevelRight()
public void stop() throws java.io.IOException
Stopping the screen recorder may take several seconds, because audio capture uses a large capture buffer. Also, the MovieWriter has to finish up a movie file which may take some time depending on the amount of meta-data that needs to be written.
java.io.IOException
protected void write(Buffer buf) throws java.io.IOException, java.lang.InterruptedException
The buffer is copied and passed to the writer queue, which is consumed by the writer thread. See method startWriter().
AVI does not support a variable frame rate for the video track. Since we can not capture frames at a fixed frame rate we have to resend the same captured screen multiple times to the writer.
This method is called asynchronously from different threads.
You can override this method if you wish to process the media data.
buf
- A buffer with un-encoded media data. If
buf.track==videoTrack
, then the buffer contains a
BufferedImage
in buffer.data
and a Point
in
buffer.header
with the recorded mouse location. The header is
null if the mouse is outside the capture area, or mouse recording has not
been enabled.
java.io.IOException
java.lang.InterruptedException
public long getMaxRecordingTime()
public void setMaxRecordingTime(long maxRecordingTime)
public long getMaxFileSize()
public void setMaxFileSize(long maxFileSize)
public javax.sound.sampled.Mixer getAudioMixer()
public void setAudioMixer(javax.sound.sampled.Mixer mixer)
|
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cv
General Information
| Full Name | Swapneel Mehta |
| Summary | Ph.D. candidate with 2 years of full-time experience in SWE / ML Engineering seeking full-time roles in AI/ML. |
| Languages | English, Hindi, Gujarati, Spanish (basic), French (basic) |
Education
-
2023
(expected) Ph.D., Data Science
Center for Data Science, New York University
- Google Research Innovator Award
- Thesis - Tools to limit Online Disinformation
- Advisors - Richard Bonneau, Jonathan Nagler at Center for Social Media and Politics
- Machine Learning, Causal Inference, Social Networks, Probabilistic Programs
-
2018
B. Eng., Computer Engineering
D.J. Sanghvi College of Engg., University of Mumbai
- Applied Math, Graph Theory, Machine Learning, Artificial Intelligence, Algorithms
Experience
-
2021 - Present
Founder
SimPPL, New York
- Audit online disinformation, influence operations, and state-backed cyber warfare at parrot.report and simppl.org
- Digital strategy and audience analysis for The Sunday Times (UK), Deutsche Welle, Yale Daily News; GCHQ, USAF.
- Awarded USD 35,000 in grants from AWS, Google, the Wikimedia Fdn., the NYC Media Lab, and JournalismAI.
-
2018 - 2019
TSP Researcher
CERN, Geneva
- Built graph neural networks for particle track reconstruction using collision data from the Large Hadron Collider.
- Built, maintained, and deployed the ‘DeepJet’ deep learning package into production across 42 global sites.
- Google Cloud Award and the Oxford MLHEP Award for our work
-
2021 - Present
Graduate Researcher
Center for Social Media and Politics, NYU
- Estimating the causal effects of Reddit’s platform downtime on the spread of low quality news online (ongoing).
- Estimating the causal impact of Twitter’s interventions on Donald Trump’s tweets presented at Stanford T&S Conf.
- Modeling policy interventions for epidemiology models accepted at PROBPROG 2021, MIT.
Internships
-
2023
Ph.D. Product Data Science Intern
Slack (Salesforce) Inc., New York
- Working on Slack's Central DS Team to understand how 'social' learning affects product feature adoption among enterprise Slack users.
- Developing strategies for product growth using tools from machine learning and causal inference.
-
2022
Ph.D. Machine Learning Engineer Intern
Twitter Inc., New York
- Worked with Civic Integrity and sister teams at Twitter Health, improving the precision of their civic and health misinformation classifier (BigQuery ML) by 30% to be deployed into production to score millions of tweets a week.
-
2021 -
Visiting Researcher
University of Oxford, UK
- Simulating the impact of influence operations on ranking and recommender systems on social networks.
- First-author workshop paper presented at the AI4ABM Workshop at ICML 2022; Misinfocon at DEFCON 2022.
-
2020
Data Science Intern
Adobe Research, California
- Trending Hashtag Recommendation for Videos; web-scraping, weakly-supervised learning, graph neural networks.
- First-author paper accepted IEEE Intl. Symposium on Multimedia 2021; U.S. Patent App P10634-US.
-
2016 - 2017
Research Intern, Prof. Sameer Sahasrabudhe
Indian Institute of Technology, Bombay
- Automated the evaluation for 3D Modeling assignments in Blender; Course Instructor, edX Prize Finalist, 2019.
- Published a first-author short paper at IEEE International Conference on Advanced Learning Technologies, 2018.
-
2016 - 2017
Software Engineering Intern
Smokescreen Technologies (acq. ZScaler), Mumbai
- Combined low-latency network scans, hostname clustering, intelligent decoys, and automated deployment using custom-built modules integrated into the NASSCOM-DSCI award-winning IllusionBlack software, 3rd Gen., 2017.
-
2016 - 2016
Software Engineering Intern
Falkonry Software Solutions, Mumbai
- Developed and deployed real-time data streaming and subscription-based alerts in the three client connectors released as open-source codebases in Java, Python and Javascript.
-
2016 - 2016
Research Intern, Microsoft Research
Indian Institute of Technology, Bombay
- Created a Google Analytics Dashboard to track and monitor user interactions on the “Lokacart” app.
- Presented the project to Microsoft Research and a committee of IIT-B faculty (video available).
Honors and Awards
-
2023
- Google Research Innovator Award
- WikiCred Grant, Wikimedia Foundation (USD 10,000)
- 2nd place “Spam and Misinformation”, WSDM Smart City Challenge (USD 1000)
- Grant Funding, JournalismAI Team Parrot (USD 2000)
-
2022
- News Product Alliance Mentorship Program
- Digital Forensics Lab - Digital Sherlocks Program
- Algovera AI Research, Community Grant Rounds 5, 7, 8 (USD 3000)
- Amazon Web Services Research Grant (USD 5000)
- Google Cloud Research Award (USD 1000)
- NYU Tech Ventures Workshop (USD 1000)
- NYC Media Lab AI + Local News Challenge (USD 7500)
-
2021
- Google AI Research India (INR 85,000)
- IRIS - HEP Fellowship
- UK Research and Innovation Grant, Profs. Philip Torr and Günes Baydin (GBP 23,200)
-
2018-20
- 2nd place, Oxford MLHEP Challenge
- Economic Times Campus Stars, India's Top 33 Engineers


[gecode-users] Valid Domain Recomputation
Christian Schulte
schulte at imit.kth.se
Mon Aug 7 13:55:16 CEST 2006
No, unfortunately not. But you cannot expect miracles here: Invoking
complete search for each variable value pair is bound to cost something.
One could use ideas similar to branch-and-bound that records what
variable-value combinations have already been recorded. I know that Mikael
Lagerkvist (zayenz at kth.se) has been toying with this idea but he is
currently on holidays.
Cheers
Christian
--
Christian Schulte, http://www.imit.kth.se/~schulte/
-----Original Message-----
From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On Behalf
Of Efstratios Kalogirou
Sent: Monday, August 07, 2006 10:50 AM
To: users at gecode.org
Subject: [gecode-users] Valid Domain Recomputation
Hi all,
I am using Gecode to create a function that receives a variable x and a
value v for that variable from the user and returns only the valid values
for all the variables (some of them are excluded because of constraint
propagation after the assignment x=v). What I am doing is :
1. Assign variable x value v
2. For all the variables i of this new space which I call FATHER SPACE
3. For all the values j of each variable
4. Clone FATHER to TEMP Space and assign inside TEMP variable i= value
j
5. Get the Status of the TEMP Space. if SS_SOLVED do nothing, if
SS_FAILED post i!=j in the FATHER SPACE. If SS_BRANCH I am using a search
engine to check weather there is a solution or not. If there is, do nothing,
otherwise post again i!=j in the FATHER SPACe.
6. Delete TEMP
By posting i!=j I manage to exclude from my father space the values that are
no longer valid. This works fine, but steps 1-5 are repeated many times and
each time I perform this valid domain check, i have to go through all the
variables and all the values, copy the father space to temp, and get the
status of the temp. This slows things down a lot and I have been wondering
if there is a faster way to do this. Any ides?
Best,
Stratos Kalogirou
More information about the gecode-users
mailing list


{% extends "base.html" %} {% block title %}Welcome {% endblock title %} {% block content %}
Your account has been successfully created. Go to Login.


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public interface ReactionManager
This interface models the entity responsible of handling eco-laws.
After an initialization phase in which eco-laws can be added and/or removed it provides scheduling logic.
| Method Summary | |
|---|---|
ReactionManager |
addEcolaw(CompiledEcolaw law)
Stores an eco-law in order to be scheduled. |
ReactionManager |
addEcolaw(Ecolaw law)
Stores an eco-law in order to be scheduled. |
ReactionManager |
addReactionManagerObserver(ReactionManagerObserver obs)
Registers a new ReactionManagerObserver . |
CompiledEcolaw[] |
ecolaws()
Retrieves a list of all the eco-laws the ReactionManager is aware
of. |
ReactionManager |
removeEcolaw(CompiledEcolaw law)
Dismisses an eco-law in order to stop its scheduling. |
ReactionManager |
removeEcolaw(Ecolaw law)
Dismisses an eco-law in order to stop its scheduling. |
ReactionManager |
removeReactionManagerObserver(ReactionManagerObserver obs)
Removes the registration of a ReactionManagerObserver . |
| Methods inherited from interface it.apice.sapere.api.node.agents.SAPEREAgent |
|---|
getAgentURI, getLocalAgentId, isRunning, kill, spawn |
| Methods inherited from interface it.apice.sapere.api.node.agents.networking.Subscriber |
|---|
sendMessage |
| Method Detail |
|---|
ReactionManager addEcolaw(Ecolaw law)
Stores an eco-law in order to be scheduled.
law
- The eco-law
ReactionManager
itselfReactionManager addEcolaw(CompiledEcolaw law)
Stores an eco-law in order to be scheduled.
law
- The eco-law (compiled)
ReactionManager
itselfReactionManager removeEcolaw(Ecolaw law)
Dismisses an eco-law in order to stop its scheduling.
law
- The eco-law
ReactionManager
itselfReactionManager removeEcolaw(CompiledEcolaw law)
Dismisses an eco-law in order to stop its scheduling.
law
- The eco-law (compiled)
ReactionManager
itselfCompiledEcolaw[] ecolaws()
Retrieves a list of all the eco-laws the ReactionManager
is aware
of.
ReactionManager addReactionManagerObserver(ReactionManagerObserver obs)
Registers a new ReactionManagerObserver
.
obs
- The observer
ReactionManager
iteselfReactionManager removeReactionManagerObserver(ReactionManagerObserver obs)
Removes the registration of a ReactionManagerObserver
.
obs
- The observer
ReactionManager
iteself
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This is the course website for IS445(AOG) in Fall 2020
Feel free to check out Spring 2020’s notebook that uses police shooting data for visualization if you are interestedin other mappable datasets.
We will use a dataset of USA “mobility” which (I think comes from a a large census study from 1989-2015) and is collected in several places including right here. Here “mobility” is refering to how easy it is for a person to move up in economic status (more info can be found here) based on factors like parental income, location, race, etc.
We’ll be using Idyll to do some web-dev and we need to install another package manager, npm, to install Idyll and other Idyll-related packages.


Social Justice Coalition Leads ‘Right To Protest’ March To Parliament
by NT EditorThe right to protest was the issue taken up by hundreds of demonstrators in Cape Town’s CBD on Tuesday.
The Social Justice Coalition (SJC) led the march from Hanover Street in District Six to Parliament.
Dozens of chanting SJC members gathered outside Parliament dressed in white t-shirts and carried banners with the words “defend the right to protest”.
The demonstrators said they were taking to the streets because freedom of assembly and freedom of expression were still being restricted around the world.
The SJC called on President Cyril Ramaphosa to uphold the values of South Africa’s progressive and transformative Constitution by advocating for communities to be able to assemble and make their voices heard.
Among other issues highlighted was police brutality with the protesters referring to what they called excessive force by police and other law enforcement officials against peaceful demonstrators.


Banana Cabana is a 501(c) nonprofit organization dedicated to the promotion of all things banana.
We work in defense of top-to-bottom peelers, bottom-to-top peelers and yes, we even care about that puckery feeling you get when you lick the inside of a peel.
We stand for yellow.

We stand for sweet.


Documentation Style Guide #
This guide provides an overview of the essential style guidelines for writing and contributing to the Flink documentation. It’s meant to support your contribution journey in the greater community effort to improve and extend existing documentation — and help make it more accessible, consistent and inclusive.
Language #
The Flink documentation is maintained in US English and Chinese — when extending or updating the documentation, both versions should be addressed in one pull request. If you are not familiar with the Chinese language, make sure that your contribution is complemented by these additional steps:
- Open a JIRA ticket for the translation, tagged with the chinese-translation component;
- Link the ticket to the original contribution JIRA ticket.
Looking for style guides to contribute with translating existing documentation to Chinese? Go ahead and consult this translation specification.
Language Style #
Below, you find some basic guidelines that can help ensure readability and accessibility in your writing. For a deeper and more complete dive into language style, also refer to the General Guiding Principles.
Voice and Tone #
-
Use active voice. Active voice supports brevity and makes content more engaging. If you add by zombies after the verb in a sentence and it still makes sense, you are using the passive voice.
Active Voice"You can run this example in your IDE or on the command line."
Passive Voice
"This example can be run in your IDE or on the command line (by zombies)."
-
Use you, never we. Using we can be confusing and patronizing to some users, giving the impression that “we are all members of a secret club and you didn’t get a membership invite”. Address the user as you.
-
Avoid gender- and culture-specific language. There is no need to identify gender in documentation: technical writing should be gender-neutral. Also, jargon and conventions that you take for granted in your own language or culture are often different elsewhere. Humor is a staple example: a great joke in one culture can be widely misinterpreted in another.
-
Avoid qualifying and prejudging actions. For a user that is frustrated or struggling to complete an action, using words like quick or easy can lead to a poor documentation experience.
-
Avoid using uppercase words to highlight or emphasize statements. Highlighting key words with e.g. bold or italic font usually appears more polite. If you want to draw attention to important but not obvious statements, try to group them into separate paragraphs starting with a label, highlighted with a corresponding HTML tag:
Note
Warning
Danger
Using Flink-specific Terms #
Use clear definitions of terms or provide additional instructions on what something means by adding a link to a helpful resource, such as other documentation pages or the Flink Glossary. The Glossary is a work in progress, so you can also propose new terms by opening a pull-request.
Repository #
Markdown files (.md) should have a short name that summarizes the topic covered, spelled in lowercase and with dashes (-) separating the words. The Chinese version file should have the same name as the English version, but stored in the content.zh folder.
Syntax #
The documentation website is generated using Hugo and the pages are written in Markdown, a lightweight portable format for web publishing (but not limited to it).
Extended Syntax #
Markdown can also be used in combination with GitHub Flavored Markdown and plain HTML. For example, some contributors prefer to use HTML tags for images and are free to do so with this intermix.
Front Matter #
In addition to Markdown, each file contains a YAML front matter block that will be used to set variables and metadata on the page. The front matter must be the first thing in the file and must be specified as a valid YAML set between triple-dashed lines.
Apache License
For every documentation file, the front matter should be immediately followed by the Apache License statement. For both language versions, this block must be stated in US English and copied in the exact same words as in the following example.
---
title: Concepts
layout: redirect
---

Below are the front matter variables most commonly used along the Flink documentation.
| Layout | layout | {base,plain,redirect} | The layout file to use. Layout files are located under the _layouts directory. |
| Content | title | %s | The title to be used as the top-level (Level-1) heading for the page. |
| Navigation | nav-id | %s | The ID of the page. Other pages can use this ID as their nav-parent_id. |
| nav-parent_id | {root,%s} | The ID of the parent page. The lowest navigation level is root. | |
| nav-pos | %d | The relative position of pages per navigation level. | |
| nav-title | %s | The title to use as an override of the default link text (title). |
Documentation-wide information and configuration settings that sit under
_config.yml
are also available to the front matter through the site variable.
These settings can be accessed using the following syntax:
{{ "{{ site.CONFIG_KEY " }}}}
The placeholder will be replaced with the value of the variable named CONFIG_KEY
when generating the documentation.
Formatting #
Listed in the following sections are the basic formatting guidelines to get you started with writing documentation that is consistent and simple to navigate.
Headings #
In Markdown, headings are any line prefixed with a hash (#), with the number of hashes indicating the level of the heading. Headings should be nested and consecutive — never skip a header level for styling reasons!
| # Heading | The page title is defined in the Front Matter, so this level should not be used. | |
| ## Heading | Starting level for Sections. Used to organize content by higher-level topics or goals. | |
| ### Heading | Sub-sections. Used in each Section to separate supporting information or tasks. | |
| #### Heading |
Best Practice
Use descriptive language in the phrasing of headings. For example, for a documentation page on dynamic tables, “Dynamic Tables and Continuous Queries” is more descriptive than “Background” or “Technical Information”.
Table of Contents #
In the documentation build, the Table Of Contents (TOC) is automatically generated from the headings of the page using the following line of markup:
{{ "{:toc" }}}
All headings up to Level-3 are considered. To exclude a particular heading from the TOC:
{{ "# Excluded Heading
{:.no_toc" }}}
Best Practice
Write a short and concise introduction to the topic that is being covered and place it before the TOC. A little context, such an outline of key messages, goes a long way in ensuring that the documentation is consistent and accessible to all levels of knowledge.
Navigation #
In the documentation build, navigation is defined using properties configured in the front-matter variables of each page.
It’s possible to use Back to Top links in extensive documentation pages, so that users can navigate to the top of the page without having to scroll back up manually. In markup, this is implemented as a placeholder that is replaced with a default link when generating the documentation:
{{ "{% top " }}%}
Best Practice
It’s recommended to use Back to Top links at least at the end of each Level-2 section.
Annotations #
In case you want to include edge cases, tightly related information or nice-to-knows in the documentation, it’s a (very) good practice to highlight them using special annotations.
-
To highlight a tip or piece of information that may be helpful to know:

 // Info Message

-
To signal danger of pitfalls or call attention to an important piece of information that is crucial to follow:

 // Danger Message

Links #
Adding links to documentation is an effective way to guide the user into a better understanding of the topic being discussed without the risk of overwriting.
-
Links to sections in the page. Each heading generates an implicit identifier to directly link it within a page. This identifier is generated by making the heading lowercase and replacing internal spaces with hyphens.
- Heading: ## Heading Title
- ID: #heading-title
[Link Text](#heading-title)
-
Links to other pages of the Flink documentation.
[Link Text]({% link path/to/link-page.md %})
-
Links to external pages
[Link Text](external_url)
Best Practice
Use descriptive links that provide information on the action or destination. For example, avoid using “Learn More” or “Click Here” links.
Visual Elements #
Figures and other visual elements are placed under the root fig folder and can be referenced in documentation pages using a syntax similar to that of links:
Best Practice
Use flowcharts, tables and figures where appropriate or necessary for additional clarification, but never as a standalone source of information. Make sure that any text included in those elements is large enough to read and that the overall resolution is adequate.
Code #
-
Inline code. Small code snippets or references to language constructs in normal text flow should be highlighted using surrounding backticks ( ` ).
-
Code blocks. Code that represents self-contained examples, feature walkthroughs, demonstration of best practices or other useful scenarios should be wrapped using a fenced code block with appropriate syntax highlighting. One way of achieving this with markup is:
```java // Java Code ```
When specifying multiple programming languages, each code block should be styled as a tab:

```java
// Java Code
```

```scala
// Scala Code
```

These code blocks are often used to learn and explore, so there are a few best practices to keep in mind:
-
Showcase key development tasks. Reserve code examples for common implementation scenarios that are meaningful for users. Leave more lengthy and complicated examples for tutorials or walkthroughs.
-
Ensure the code is standalone. Code examples should be self-contained and not have external dependencies (except for outlier cases such as examples on how to use specific connectors). Include all import statements without using wildcards, so that newcomers can understand and learn which packages are being used.
-
Avoid shortcuts. For example, handle exceptions and cleanup as you would in real-world code.
-
Include comments, but don’t overdo it. Provide an introduction describing the main functionality of the code and possible caveats that might not be obvious from reading it. Use comments to clarify implementation details and to describe the expected output.
-
Commands in code blocks. Commands can be documented using
bash
syntax highlighted code blocks. The following items should be considered when adding commands to the documentation:- Use long parameter names. Long parameter names help the reader to understand the purpose of the command. They should be preferred over their short counterparts.
- One parameter per line. Using long parameter names makes the command
possibly harder to read. Putting one parameter per line improves the
readability. You need to add a backslash
\
escaping the newline at the end of each intermediate line to support copy&paste. - Indentation. Each new parameter line should be indented by 6 spaces.
- Use
$
prefix to indicate command start. The readability of the code block might worsen having multiple commands in place. Putting a dollar sign$
in front of each new commands helps identifying the start of a command.
A properly formatted command would look like this:
$ ./bin/flink run-application \
--target kubernetes-application \
-Dkubernetes.cluster-id=my-first-application-cluster \
-Dkubernetes.container.image=custom-image-name \
local:///opt/flink/usrlib/my-flink-job.jar
General Guiding Principles #
This style guide has the overarching goal of setting the foundation for documentation that is Accessible, Consistent, Objective, Logical and Inclusive.
Accessible #
The Flink community is diverse and international, so you need to think wide and globally when writing documentation. Not everyone speaks English at a native level and the level of experience with Flink (and stream processing in general) ranges from absolute beginners to experienced advanced users. Ensure technical accuracy and linguistic clarity in the content you produce so that it can be understood by all users.
Consistent #
Stick to the basic guidelines detailed in this style guide and use your own best judgment to uniformly spell, capitalize, hyphenate, bold and italicize text. Correct grammar, punctuation and spelling are desirable, but not a hard requirement — documentation contributions are open to any level of language proficiency.
Objective #
Keep your sentences short and to the point. As a rule of thumb, if a sentence is shorter than 14 words, readers will likely understand 90 percent of its content. Sentences with more than 25 words are usually harder to understand and should be revised and split, when possible. Being concise and using well-known keywords also allows users to navigate to relevant documentation with little effort.
Logical #
Be mindful that most users will scan through online content and only read around 28 percent of it. This underscores the importance of grouping related ideas together into a clear hierarchy of information and using focused, descriptive headings. Placing the most relevant information in the first two paragraphs of each section is a good practice that increases the “return of time invested” for the user.
Inclusive #
Use positive language and concrete, relatable examples to ensure the content is findable and welcoming to all users. The documentation is translated to other languages, so using simple language and familiar words also helps reduce the translation effort.


Writing Graph Transformations on ATen IR¶
Passes¶
Since the ATen IR sits at the FX Graph/GraphModule level, any transformations written for FX Graphs can be easily applied onto the ATen IR. If you’re familiar with writing FX graph transformations, then this will be the same.
The most direct way of writing transformations is by looping through the given graph and directly manipulating the nodes within the graph.
For example, let’s say we want to replace
torch.ops.aten.add.Tensor()
calls with
torch.ops.aten.mul.Tensor()
calls:
import torch
def replace_add_with_mul(gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
for node in gm.graph.nodes:
if node.op == "call_function" and node.target == torch.ops.aten.add.Tensor:
node.target = torch.ops.aten.mul.Tensor
We can also delete and append new nodes through FX utility functions
that can be found in the
Graph
documentation. For example, if we want to insert a
torch.ops.aten.relu.default()
after the add
call:
import torch
def insert_relu_after_add(gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
for node in gm.graph.nodes:
if node.op == "call_function" and node.target == torch.ops.aten.add.Tensor:
# Specifies the insertion point. Any nodes added to the graph within
# this scope will be inserted after `node`
with gm.graph.inserting_after(node):
# Insert a new `call_function` node with op `torch.ops.aten.relu.default`
new_relu_node = gm.graph.call_function(torch.ops.aten.relu.default, args=(node,))
# Replace all the places that use `node` to now use the `new_relu_node`
node.replace_all_uses_with(new_relu_node)
In general, transformations can be roughly categorized into a couple of axis:
Axis A: 1. Creating one-to-X mapping (eg. decomposition) 2. Creating many-to-one mapping (eg. fusion)
Axis B: 1. Doing forwards iteration (eg. shape propagation) 2.  Dependent on local node information (eg. out-variant conversion) 2. Dependent on global graph information (eg. memory planning)
Our projection on the frequency of these use cases are: 1. A.1, B.1, C.1 2. A.2 3. B.2, C.2
Although we can make all graph transformations through directly manipulating the graph, we also provide some helper utilities for some ease of use for the level 1 and 2 use-cases.
Transformer¶
For level 1 uses cases (creating one-to-X mappings, doing forwards iterations, and looking at local node information), we can utilize the Transformer class to execute each node and recreate a graph, except with the transformations specified.
One-to-One Pass¶
An example for one-to-one mappings, if we wanted to replace an op A with another op B, we can run the GraphModule, and very time we see op A, return op B.
An example is:
class ReplaceAddWithMul(torch.fx.Transformer):
def call_function(self, target, args, kwargs):
if target != torch.ops.aten.add.Tensor:
return super().call_function(target, args, kwargs)
return super().call_function(torch.ops.aten.mul.Tensor, args, kwargs)
transformed_graph_module = ReplaceAddWithMul(graph_module).transform()
The super().call_function(target, args, kwargs, meta)
call creates a
call_function
FX node, and returns the result of running the
operator with the given arguments.
One-to-X Pass¶
If we wanted to do one-to-X mappings, like replacing op A with 2 other
ops B and C, we would then make 2 calls to super().call_function
to
create 2 FX nodes, one with op B and another with op C, and return the
result of running op C.
For example:
class ReplaceAddWithMulSub(torch.fx.Transformer):
"""
Original:
def f(x, y):
return x + y
After pass:
def f(x, y):
z = x * y
return z - y
"""
def call_function(self, target, args, kwargs):
if target != torch.ops.aten.add.Tensor:
return super().call_function(target, args, kwargs)
x, y = args
mul_res = super().call_function(torch.ops.aten.mul.Tensor, args, {})
return super().call_function(torch.ops.aten.sub.Tensor, (mul_res, y), {})
transformed_graph_module = ReplaceAddWithMulSub(graph_module).transform()
One-to-None Pass¶
If we wanted to remove an op, we can just return the value passed into the function:
class RemoveDetachPass(torch.fx.Transformer):
def call_function(self, target, args, kwargs):
if target not in (
torch.ops.aten.detach.default,
torch.ops.aten.detach_copy.default,
):
return super().call_function(target, args, kwargs, meta)
assert len(args) == 1
return args[0]
transformed_graph_module = RemoveDetachPass(graph_module).transform()
Utilizing Local Information¶
An example of utilizing local node information is, if we wanted to
convert all the scalars within the graph to tensors, we can run the
given fx.GraphModule
, and for every argument that contains a scalar,
we convert it to a tensor. It might look something like:
def args_map(target, fn, args, kwargs):
assert isinstance(args, tuple)
assert isinstance(kwargs, dict)
args = list(args)
kwargs = kwargs.copy()
# Update the argument based on the function passed
def update(key, args, schema):
args[key] = fn(args[key], schema)
# Update each argument in the schema
for i, schema in enumerate(target._schema.arguments):
if schema.name in kwargs:
update(schema.name, kwargs, schema)
elif not schema.kwarg_only and i < len(args):
update(i, args, schema)
return tuple(args), kwargs
class ScalarToTensorPass(torch.fx.Transformer):
def call_function(self, target, args, kwargs):
breakpoint()
def try_coerce(value, arg):
return (
torch.tensor(value)
if isinstance(value, (float, int, bool))
and type(arg.type) == torch.TensorType
else value
)
args, kwargs = args_map(target, try_coerce, args, kwargs)
return super().call_function(target, args, kwargs)
transformed_graph_module = ScalarToTensorPass(graph_module).transform()
Subgraph Rewriter¶
For creating many-to-one mappings, we can utilize FX’s subgraph
rewriter.
Given a pattern
, it creates a subgraph of operators matching to the
pattern, and then replaces each matched subgraph with the
replacement
.
Note:
This is an inplace operation.
The pattern
and replacement
inputs must be callable functions or
GraphModules containing the same operators that are used within the
graph (ATen ops) so that the subgraph rewriter can find the correct
pattern in the graph. Inputs to the pattern/replacement callables will
be treated as wildcards when matching.
An example:
from torch.fx import subgraph_rewriter
def replace_patterns(graph_module):
def pattern(x, y):
x = torch.ops.aten.add.Tensor(x, y)
x = torch.ops.aten.mul.Tensor(x, y)
return x
def replacement(x, y):
return torch.ops.aten.sub.Tensor(x, y)
replaced_patterns = subgraph_rewriter.replace_pattern_with_filters(
traced_module, pattern, replacement
)
The subgraph rewriter returns a list of ReplacedPatterns
:
@dataclass
class ReplacedPatterns:
# Node from which the match was found
anchor: Node
# Maps nodes in the pattern subgraph to nodes in the larger graph
nodes_map: Dict[Node, Node]
# List of nodes that were added into the graph
replacements: List[Node]
Note:
The nodes created by the subgraph rewriter will not have the metadata that
is populated in the matched nodes, but you can use
`ReplacedPatterns.nodes_map` to find the nodes in the original graph that
were matched, and `ReplacedPatterns.replacements` to find the nodes that
were replaced in the transformed graph.
Pass Manager¶
The
`PassManager
<https://github.com/pytorch/pytorch/blob/main/torch/fx/passes/infra/pass_manager.py>`__
is a class used to run multiple passes on a given graph module. When
initializing a PassManager
instance, we pass in a list of passes
that we want to run and set a couple of flags. To run the collection of
passes on a graph module, we can pass the graph module directly to the
PassManager
instance.
An example:
from torch.fx.passes.infra.pass_manager import PassManager
pm = PassManager(
passes=[replace_add_with_div, replace_div_with_mul],
run_checks_after_each_pass=True,
suppress_check_failures=False,
)
graph_module_out = pm(graph_module)
To add a common set of checks that are run after each pass, we can call
the function set_checks(check: Callable)
which takes in a callable
function as input. If the run_checks_after_each_pass
flag is set,
the check
will be called after each pass is run on the graph module.
An example:
pm = PassManager(passes=[replace_add_with_div, replace_div_with_mul])
def check_div_target(graph_module):
for node in graph_module.graph.nodes:
if node.op == "call_function" and node.target != torch.div:
raise ValueError("Target should be div!")
pm.add_checks(check_div_target)
pm(graph_module) # raises ValueError after replace_div_with_mul pass
Partitioner¶
There are a couple of common FX graph based partitioners we can use to partition the graph.
Subgraph Matcher¶
For finding subgraphs within a graph that match a specific pattern, we
can utilize FX’s
`SubgraphMatcher
<https://github.com/pytorch/pytorch/blob/main/torch/fx/passes/utils/matcher_utils.py>`__.
Class Attributes:
pattern (Graph)
: The targeted matching pattern. Placeholder nodes in the graph will be treated as wildcards when matching.match_output (bool)
: If True, output node in the pattern graph will be treated as a part of the targeted pattern. If False, output node is ignored during match.match_placeholder (bool)
: If True, placeholder node in the pattern graph will be treated as a part of the targeted pattern. If False, placeholder nodes will be used a wildcard.remove_overlapping_matches (bool)
: If True, in the case of overlapping matches, only the first match will be returned.ignore_literals (bool)
: If True, will not check if literals are equal and will instead treat them as wildcards.
An example:
from torch.fx.passes.utils.matcher_utils import SubgraphMatcher
class LargeModel(torch.nn.Module):
def __init__(self):
super().__init__()
self._weight = torch.nn.Parameter(torch.ones(3, 3))
self._bias = torch.nn.Parameter(torch.ones(3, 3))
def forward(self, x):
return torch.ops.aten.addmm.default(self._bias, x, self._weight)
large_model_graph = torch.export(LargeModel(), inputs).graph
class PatternModel(torch.nn.Module):
def __init__(self):
super().__init__()
self._weight_1 = torch.nn.Parameter(torch.ones(5, 5))
self._bias_1 = torch.nn.Parameter(torch.ones(5, 5))
def forward(self, x):
return torch.ops.aten.addmm.default(self._bias_1, x, self._weight_1)
pattern_graph = torch.export(PatternModel(), inputs).graph
subgraph_matcher = SubgraphMatcher(pattern_graph)
match_result = subgraph_matcher.match(large_model_graph)
The match
function returns a list of InternalMatch
:
@dataclass
class InternalMatch():
# Nodes from which the match was found
anchors: List[Node]
# Maps nodes in the pattern subgraph to nodes in the larger graph
nodes_map: Dict[Node, Node] = field(default_factory=dict)
# Nodes in target graph that are matched placeholder in pattern
placeholder_nodes: List[Node] = field(default_factory=list)
# Nodes in matched subgraph returned by output
returning_nodes: List[Node] = field(default_factory=list)
Capability Based Partitioner¶
To find the largest subgraphs of nodes that support a specific
invariant, we can utilize FX’s
`CapabilityBasedPartitioner
<https://github.com/pytorch/pytorch/blob/main/torch/fx/passes/infra/partitioner.py#L34>`__.
Class Attributes
graph_module (torch.fx.GraphModule)
: The graph module we are partitioning on.operator_support (OperatorSupportBase)
: The object used to determine if a node in the graph is supported in the partition.allows_single_node_partition (bool)
: If True, allows single node partitions to be formed.non_compute_ops (Optional[Sequence[str]])
: A set of ops that are considered to be “non-compute” (extorch.ops.aten.view
and_operator.getitem
, so that the partitioner will not create graphs that only contain these non-compute opsallowed_single_node_partition_ops (Optional[Sequence[str]])
: A set of ops that are allowed to be in a single node partition.
The
`OperatorSupportBase
<https://github.com/pytorch/pytorch/blob/main/torch/fx/passes/operator_support.py#LL28C1-L28C1>`__
class is used by the partitioner to determine if a specific node in the
graph belongs in the partition. This is done by overriding the
is_node_supported
function. You can chain multiple
OperatorSuppportBase
by using
`chain
<https://github.com/pytorch/pytorch/blob/main/torch/fx/passes/operator_support.py#L150>`__(which
returns False if any of the OperatorSupportBase return False) and
`any_chain
<https://github.com/pytorch/pytorch/blob/main/torch/fx/passes/operator_support.py#L164>`__
(which returns True if any of the OperatorSupportBase returns True).
An example:
from torch.fx.passes.infra.partitioner import CapabilityBasedPartitioner
from torch.fx.passes.operator_support import any_chain, OperatorSupportBase
class AddMulOperatorSupport(OperatorSupportBase):
def is_node_supported(self, submodules, node: torch.fx.Node) -> bool:
return node.op == "call_function" and node.target in [
torch.ops.aten.add.Tensor, torch.ops.aten.mul.Tensor,
]
capability_partitioner = CapabilityBasedPartitioner(
graph_module,
op_support,
)
# Returns a list of partitions (list of nodes that belong in each partition)
partition_list = capability_partitioner.propose_partitions()
# Fuses the partitions into graph modules and inserts `call_module` nodes in the graph
fused_graph_module = capability_partitioner.fuse_partitions(partition_list)


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Problem Solving
Tell your non-tech friend a story about a time you (1.) Got blocked on a simple problem
The JavaScript Racer game is my most recent example of getting stuck on a seemingly simple problem.
My JS didn’t seem to run on my first attempt, the alert box didn’t even pop up to get the ball rolling with setting up the track size. One my second attempt, I got as far as being able to have the track built, and some CSS saying on the background running, but not everything. No space ship, no planet target.
The problem solving techniques I used were pseudocode, googling, Rubber ducking, reading what scant error codes there were, did i mention googling? Asking my peers for help in the end, and from a coach for un-blocking.
But the last two I’ve felt bad about doing because of pride I think. I’ve just been trying to box-on. “If others can do it so can I…?”
I’ve felt terrible to be honest. I usually like to feel capable, and able to work through stuff. But I’ve felt stumped, and ashamed to look at other peers work, and to ask for help.
I’m glad for the meditation techniques though. I think I need to get to that accurate assessment place, to see my current weakness, and be in a confident place to ask for help when needed. Ask for help earlier I think is the thing I’ve learnt most.
2. Solved a problem in an elegant way This is a non-coding problem, as I don’t think I’ve solved anything elegantly yet.
This was a problem of wastage at my workplace. We had paper slips being made at great effort to use as place holders for paying orders ‘once’, and then thrown away.
I proposed a couple of solutions, the first of which didn’t fly, but with a bit of team work, came up with laminated colour-coded slips that could be re-used. saved the old production time and saved the wastage. Problem solved elegantly and effectively.
I felt great through the process. Challenged at first, but euphoric the closer we got to the final solution.
Reflect on how confident you feel using the problem solving techniques and process:
Psuedocode - not too confident, as I’m still finding my feet with the vocabulary.
Trying something - I did that, but it’s hard not knowing which way is up.
Rubber ducky method - this was good, I found some problems using this.
Reading error messages - it’s more the absence of error messages that I found hard.
Console.logging - I’m finding this difficult, as it doesn’t seem to connect for me sometimes.
Googling - so so, knowing the question to ask is the first step.
Asking your peers for help - this was hard. I felt whakama about it.
Asking coaches for help - this is a great step, but it took me a long time to get comfortable with the idea.
Improving your process with reflection - yes, I think this is good, but i can get stuck in loops sometimes.
Explain to your non-tech friend , using the JS Array docs as a starting point, describe what these functions do in your own words:
.map() - This function makes a new array of results from each element having a function run over each item in the original array.
.filter() - This function makes a new array of just the filtered items, named as the filter.
.reduce()This function reduces arrays to single numbers, or flattens nested arrays, with the use of a reducer and a function. This reducer runs the function across the array, calling over the array until the single result is achieved.


| Package | Description |
|---|---|
| java.net |
Provides the classes for implementing networking applications.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BindException
Signals that an error occurred while attempting to bind a
socket to a local address and port.
|
class |
ConnectException
Signals that an error occurred while attempting to connect a
socket to a remote address and port.
|
class |
NoRouteToHostException
Signals that an error occurred while attempting to connect a
socket to a remote address and port.
|
class |
PortUnreachableException
Signals that an ICMP Port Unreachable message has been
received on a connected datagram.
|
| Modifier and Type | Method and Description |
|---|---|
protected abstract void |
DatagramSocketImpl.bind(int lport,
InetAddress laddr)
Binds a datagram socket to a local port and address.
|
void |
DatagramSocket.bind(SocketAddress addr)
Binds this DatagramSocket to a specific address & port.
|
protected void |
DatagramSocketImpl.connect(InetAddress address,
int port)
Connects a datagram socket to a remote destination.
|
void |
DatagramSocket.connect(SocketAddress addr)
Connects this socket to a remote socket address (IP address + port number).
|
protected abstract void |
DatagramSocketImpl.create()
Creates a datagram socket.
|
boolean |
DatagramSocket.getBroadcast()
Tests if SO_BROADCAST is enabled.
|
static NetworkInterface |
NetworkInterface.getByIndex(int index)
Get a network interface given its index.
|
static NetworkInterface |
NetworkInterface.getByInetAddress(InetAddress addr)
Convenience method to search for a network interface that
has the specified Internet Protocol (IP) address bound to
it.
|
static NetworkInterface |
NetworkInterface.getByName(String name)
Searches for the network interface with the specified name.
|
byte[] |
NetworkInterface.getHardwareAddress()
Returns the hardware address (usually MAC) of the interface if it
has one and if it can be accessed given the current privileges.
|
InetAddress |
MulticastSocket.getInterface()
Retrieve the address of the network interface used for
multicast packets.
|
boolean |
Socket.getKeepAlive()
Tests if SO_KEEPALIVE is enabled.
|
boolean |
MulticastSocket.getLoopbackMode()
Get the setting for local loopback of multicast datagrams.
|
int |
NetworkInterface.getMTU()
Returns the Maximum Transmission Unit (MTU) of this interface.
|
NetworkInterface |
MulticastSocket.getNetworkInterface()
Get the multicast network interface set.
|
static Enumeration<NetworkInterface> |
NetworkInterface.getNetworkInterfaces()
Returns all the interfaces on this machine.
|
boolean |
Socket.getOOBInline()
Tests if OOBINLINE is enabled.
|
Object |
SocketOptions.getOption(int optID)
Fetch the value of an option.
|
int |
DatagramSocket.getReceiveBufferSize()
Get value of the SO_RCVBUF option for this DatagramSocket, that is the
buffer size used by the platform for input on this DatagramSocket.
|
int |
Socket.getReceiveBufferSize()
Gets the value of the SO_RCVBUF option for this Socket,
that is the buffer size used by the platform for
input on this Socket.
|
int |
ServerSocket.getReceiveBufferSize()
Gets the value of the SO_RCVBUF option for this ServerSocket,
that is the proposed buffer size that will be used for Sockets accepted
from this ServerSocket.
|
boolean |
DatagramSocket.getReuseAddress()
Tests if SO_REUSEADDR is enabled.
|
boolean |
Socket.getReuseAddress()
Tests if SO_REUSEADDR is enabled.
|
boolean |
ServerSocket.getReuseAddress()
Tests if SO_REUSEADDR is enabled.
|
int |
DatagramSocket.getSendBufferSize()
Get value of the SO_SNDBUF option for this DatagramSocket, that is the
buffer size used by the platform for output on this DatagramSocket.
|
int |
Socket.getSendBufferSize()
Get value of the SO_SNDBUF option for this Socket,
that is the buffer size used by the platform
for output on this Socket.
|
int |
Socket.getSoLinger()
Returns setting for SO_LINGER.
|
int |
DatagramSocket.getSoTimeout()
Retrieve setting for SO_TIMEOUT.
|
int |
Socket.getSoTimeout()
Returns setting for SO_TIMEOUT.
|
boolean |
Socket.getTcpNoDelay()
Tests if TCP_NODELAY is enabled.
|
int |
DatagramSocket.getTrafficClass()
Gets traffic class or type-of-service in the IP datagram
header for packets sent from this DatagramSocket.
|
int |
Socket.getTrafficClass()
Gets traffic class or type-of-service in the IP header
for packets sent from this Socket
|
boolean |
NetworkInterface.isLoopback()
Returns whether a network interface is a loopback interface.
|
boolean |
NetworkInterface.isPointToPoint()
Returns whether a network interface is a point to point interface.
|
boolean |
NetworkInterface.isUp()
Returns whether a network interface is up and running.
|
void |
DatagramSocket.setBroadcast(boolean on)
Enable/disable SO_BROADCAST.
|
void |
MulticastSocket.setInterface(InetAddress inf)
Set the multicast network interface used by methods
whose behavior would be affected by the value of the
network interface.
|
void |
Socket.setKeepAlive(boolean on)
Enable/disable SO_KEEPALIVE.
|
void |
MulticastSocket.setLoopbackMode(boolean disable)
Disable/Enable local loopback of multicast datagrams
The option is used by the platform's networking code as a hint
for setting whether multicast data will be looped back to
the local socket.
|
void |
MulticastSocket.setNetworkInterface(NetworkInterface netIf)
Specify the network interface for outgoing multicast datagrams
sent on this socket.
|
void |
Socket.setOOBInline(boolean on)
Enable/disable OOBINLINE (receipt of TCP urgent data)
By default, this option is disabled and TCP urgent data received on a
socket is silently discarded.
|
void |
SocketOptions.setOption(int optID,
Object value)
Enable/disable the option specified by optID.
|
void |
DatagramSocket.setReceiveBufferSize(int size)
Sets the SO_RCVBUF option to the specified value for this
DatagramSocket.
|
void |
Socket.setReceiveBufferSize(int size)
Sets the SO_RCVBUF option to the specified value for this
Socket.
|
void |
ServerSocket.setReceiveBufferSize(int size)
Sets a default proposed value for the SO_RCVBUF option for sockets
accepted from this ServerSocket.
|
void |
DatagramSocket.setReuseAddress(boolean on)
Enable/disable the SO_REUSEADDR socket option.
|
void |
Socket.setReuseAddress(boolean on)
Enable/disable the SO_REUSEADDR socket option.
|
void |
ServerSocket.setReuseAddress(boolean on)
Enable/disable the SO_REUSEADDR socket option.
|
void |
DatagramSocket.setSendBufferSize(int size)
Sets the SO_SNDBUF option to the specified value for this
DatagramSocket.
|
void |
Socket.setSendBufferSize(int size)
Sets the SO_SNDBUF option to the specified value for this
Socket.
|
void |
Socket.setSoLinger(boolean on,
int linger)
Enable/disable SO_LINGER with the specified linger time in seconds.
|
void |
DatagramSocket.setSoTimeout(int timeout)
Enable/disable SO_TIMEOUT with the specified timeout, in
milliseconds.
|
void |
Socket.setSoTimeout(int timeout)
Enable/disable SO_TIMEOUT with the specified timeout, in
milliseconds.
|
void |
ServerSocket.setSoTimeout(int timeout)
Enable/disable SO_TIMEOUT with the specified timeout, in
milliseconds.
|
void |
Socket.setTcpNoDelay(boolean on)
Enable/disable TCP_NODELAY (disable/enable Nagle's algorithm).
|
void |
DatagramSocket.setTrafficClass(int tc)
Sets traffic class or type-of-service octet in the IP
datagram header for datagrams sent from this DatagramSocket.
|
void |
Socket.setTrafficClass(int tc)
Sets traffic class or type-of-service octet in the IP
header for packets sent from this Socket.
|
boolean |
NetworkInterface.supportsMulticast()
Returns whether a network interface supports multicasting or not.
|
| Constructor and Description |
|---|
DatagramPacket(byte[] buf,
int offset,
int length,
SocketAddress address)
Constructs a datagram packet for sending packets of length
length with offset ioffset to the
specified port number on the specified host. |
DatagramPacket(byte[] buf,
int length,
SocketAddress address)
Constructs a datagram packet for sending packets of length
length to the specified port number on the specified
host. |
DatagramSocket()
Constructs a datagram socket and binds it to any available port
on the local host machine.
|
DatagramSocket(int port)
Constructs a datagram socket and binds it to the specified port
on the local host machine.
|
DatagramSocket(int port,
InetAddress laddr)
Creates a datagram socket, bound to the specified local
address.
|
DatagramSocket(SocketAddress bindaddr)
Creates a datagram socket, bound to the specified local
socket address.
|
Socket(SocketImpl impl)
Creates an unconnected Socket with a user-specified
SocketImpl.
|
Submit a bug or feature
For further API reference and developer documentation, see Java SE Documentation. That documentation contains more detailed, developer-targeted descriptions, with conceptual overviews, definitions of terms, workarounds, and working code examples.
Copyright © 1993, 2015, Oracle and/or its affiliates. All rights reserved.


Intro
Dynamic Global Properties represents a set of values that are calculated during normal chain operations and reflect the current values of global blockchain properties.
The API call returns an object containing information that changes every block interval such as the head block number, the total vesting fund, etc.
Sections
head_block_number
Block height at the head of the blockchain. This represents the latest block produced by witnesses.
- example:
24155032
head_block_id
Used to implement TaPoS (Transaction as Proof of Stake). The first 4 bytes (8 hex digits) of the block ID represents the block number. E.g., 01709398
in hex is 24155032
in decimal.
- example:
0170939865fa4e3aa7fca8f8df35d23333fe0bee
- see: RIPEMD-160 hashes
time
Point in time (UTC) that the block was included in the chain.
Used to synchronize events like Hard Fork activation.
When attempting to calculate the validity of a transaction we need to lookup a past block and check its block hash and the time it occurred so we can calculate whether the current transaction is valid and at what time it should expire.
For new transactions, expirations originate from this time.
- example:
2018-07-14T01:19:51
current_witness
Account name of the current witness.
- example:
blocktrades
current_supply
SPHTX currently in existence.
- example:
271546371.129 SPHTX
total_vesting_shares
VESTS that are invested in SPHTX.
- example:
390950506702.452773 SPHTX
witness_required_vesting
VESTS that are required to be invested in SPHTX to in order to become a witness.
- example:
300000.000000 SPHTX
maximum_block_size
Maximum block size is decided by the set of active witnesses which change every round. Each witness posts what they think the maximum size should be as part of their witness properties, the median size is chosen to be the maximum block size for the round.
Note: the minimum value for maximum_block_size
is defined by the protocol to prevent the network from getting stuck by witnesses attempting to set this too low.
- example:
65536
current_aslot
The current absolute slot number. Equal to the total number of slots since genesis. Also equal to the total number of missed slots plus head_block_number
.
- example:
24231997
recent_slots_filled
Used to compute witness participation.
- example:
340282366920938463463374607431768211455
last_irreversible_block_num
The latest block number that has been confirmed by two thirds of all block producers and is thus irreversible.
- example:
24155017
average_block_size
Removed
Average block size is updated every block to be: average_block_size = (99 * average_block_size + new_block_size) / 100
. This property is used to update the current_reserve_ratio
to maintain approximately * 50% or less utilization of network capacity.
- example:
9309
Not Covered
Fields not covered in this recipe are:
participation_count
Example Method Call
To retrieve the current results for alexandria_api.get_dynamic_global_properties
, we can retrieve the current state information using curl
:
curl -s --data '{"jsonrpc":"2.0", "method":"alexandria_api.get_dynamic_global_properties", "params":{}, "id":1}' https://API_URL
Example Output
{
"jsonrpc": "2.0",
"result": {
"properties": {
"head_block_number": 58019,
"head_block_id": "0000e2a396fb66a55ae156bf48ce26105b984826",
"time": "1970-01-01T00:00:00",
"current_witness": "initminer",
"current_supply": "350015744.660303 SPHTX",
"total_vesting_shares": "4169.375986 VESTS",
"witness_required_vesting": "250000.000000 VESTS",
"maximum_block_size": 262144,
"current_aslot": 7835941,
"recent_slots_filled": "340282366920938463463374607431768211455",
"participation_count": 128,
"last_irreversible_block_num": 57968,
"average_block_size": 49
}
},
"id": 1
}