| java.lang.Object | |
| ↳ | com.google.inject.TypeLiteral<T> |
Represents a generic type T
. Java doesn't yet provide a way to
represent generic types, so this class does. Forces clients to create a
subclass of this class which enables retrieval the type information even at
runtime.
For example, to create a type literal for List<String>
, you can
create an empty anonymous inner class:
TypeLiteral<List<String>> list = new TypeLiteral<List<String>>() {
;}
Along with modeling generic types, this class can resolve type parameters.
For example, to figure out what type keySet()
returns on a Map<Integer, String>
, use this code:
TypeLiteral<Map<Integer, String>> mapType = new TypeLiteral<Map<Integer, String>>() {
; TypeLiteral> keySetType = mapType.getReturnType(Map.class.getMethod("keySet")); System.out.println(keySetType); // prints "Set"}
| Protected Constructors | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
Constructs a new type literal.
|
| Public Methods | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
Gets type literal for the given
Class instance. | |||||||||||
Gets type literal for the given
Type instance. | |||||||||||
Returns the resolved generic exception types thrown by
constructor . | |||||||||||
Returns the resolved generic type of
field . | |||||||||||
Returns the resolved generic parameter types of
methodOrConstructor . | |||||||||||
Returns the raw (non-generic) type for this type.
| |||||||||||
Returns the resolved generic return type of
method . | |||||||||||
Returns the generic form of
supertype . | |||||||||||
Gets underlying
Type instance. | |||||||||||
|
[Expand]
Inherited Methods | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
From class
java.lang.Object
|
Constructs a new type literal. Derives represented class from type parameter.
Clients create an empty anonymous subclass. Doing so embeds the type parameter in the anonymous class's type hierarchy so we can reconstitute it at runtime despite erasure.
Gets type literal for the given Class
instance.
Gets type literal for the given Type
instance.
Returns the resolved generic exception types thrown by constructor
.
| methodOrConstructor | a method or constructor defined by this or any supertype. |
|---|
Returns the resolved generic type of field
.
| field | a field defined by this or any superclass. |
|---|
Returns the resolved generic parameter types of methodOrConstructor
.
| methodOrConstructor | a method or constructor defined by this or any supertype. |
|---|
Returns the raw (non-generic) type for this type.
Returns the resolved generic return type of method
.
| method | a method defined by this or any supertype. |
|---|
Returns the generic form of supertype
. For example, if this is ArrayList<String>
, this returns Iterable<String>
given the input Iterable.class
.
| supertype | a superclass of, or interface implemented by, this. |
|---|


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Reasons to choose our name generator .
Allows for further customization.
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THE FANTASTICKS
Reviewed by Harvey Karten United Artists Director: Michael Ritchie Writer: Tom Jones, Harvey Schmidt Cast: Joel Grey, Barnard Hughes, Jean Louisa Kelly, Joseph McIntyre, Jonathon Morris, Brad Sullivan, Teller
When "The Fantasticks" opened off-Broadway on May 3, 1960 the two-act musical by Tom Jones, music by Harvey Schmidt (suggested by Edmond Rostand's play "Les Romanesques") was not yet dated. Broadway productions like "South Pacific" were already over the hill, but "The Fantasticks" was perhaps considered timeless--a benefit helped by its simple staging in the tiny Sullivan Street Playhouse in New York's Greenwich Village where it is still playing. The staged work got a nice boost from Jerry Orbach's position in the principal role of El Gallo, the handsome would-be bandit who creates a fairy-tale world for two teenagers to show them that you're best off looking for love and romance in your own back yard.
You'd think that once the Vietnam War dragged on, leading to increasing cynicism in the country, that such a fragile work would die out as hopelessly dated. Nonetheless the mood created by this gossamer show hooked the crowds, which, we are told, often remained in their seats several minutes after the final curtain, the middle-aged members of the audience remembering their youth and their first loves through rose-tinted spectacles.
Michael Ritchie's movie version opens up the play, still keeping the fairy-tale motif, with its Arizona setting reminding us of the vast reaches of farmland in the U.S. and helping to make the characters stand out as isolated individuals in a self-enclosed world. What Ritchie does not ultimately succeed in doing is re-creating the ambience that we felt in the small theater in the Village because, after all, this is not a grand and glorious celebration of life that made "Oklahoma" even better on the big screen than on the stage.
Ritchie re-arranged the songs, leaving the most famous yearning song of nostalgia, "Try to Remember," for last, perhaps because this is the one song recalled by anyone who had seen the play forty years ago. This is the strain that moviegoers today probably wait to hear in fond anticipation. While the term "rape" is mentioned only once or twice--its meaning in this play being the primary one of abduction and seizure--the Rape Ballet no longer has that name and there is no mention of the "First Class Rape" ordered by the fathers in a roundabout way to help persuade their two children to marry each other.
The story is of two fathers, Bellamy (Joel Grey) and Hucklebee (Brad Sullivan), who build a wall between the adjoining homes and pretend to feud so that their children, Luisa (Jean Louisa Lelly) and Matt (Joseph McIntyre) would act as teens always do--to defy their allegedly hostile parents and bond happily with each other. To further their plan, they pay El Gallo (Jonathon Morris)--who is the leader of a traveling carnival act--to kidnap the girl so that the boy could come to her rescue and be deemed a hero. But Luisa becomes infatuated with the handsome would-be bandit--who takes her on a smoke-and-mirrors trip around the world--while Matt, made to drink a bitter, magic solution, thinks like Judy Garland's Dorothy that he is traveling way outside his neighborhood. Realizing that the world is not as glamorous as they thought, the two young lovers are happily reunited.
What saves the movie from terminal obsolescence is the magical voice of Jean Louisa Kelly who can dance almost as well as she can vocalize and whose intense and lovely adolescent expressiveness should win most in the audience. Jonathon Morris makes a charming El Gallo but the two guys playing the fathers, Joel Grey and Brad Sullivan, are so irritating in the story that they become annoying for us in the audience to watch.
The movie does have innovations worthy of note. For one, thanks to digital technology, the actors sang live during the shooting rather than lip-synching with recorded tracks. For another, this sugary musical is an antidote to the overkill of vulgarity of the last two or three seasons. Even the kisses are innocent ones, over the eyes, on the hand and on the wrist. Also, the two performers playing the lovebirds are teens themselves, not the twenty-somethings usually cast in stories involving high-school kids. In the final analysis the movie could make one wonder about the longevity of the play, but then, "Cats" has already proved that box office is usually not directly related to quality.
Rated PG. Running time: 85 minutes. (C) 2000 by Harvey Karten<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


SARS-CoV- detection Control Disease Institute Department Health Center Hospital assay Infectious National Prevention using Diseases Laboratory View OpenUrl Public University designs RPA master mix species Medical fluorescent subspecies &; Abstract Virology protocol viral water China FREE Provincial RNA Table Text flow high lateral surveillance Centers Supplementary acid coronavirus plate respiratory synthetic testing Abudayyeh CRISPR-based Cas Centre Download Gootenberg Medicine, Pathology SARS-like SHERLOCK Services Viral Zhang based data design nucleic rapidly readout: related -well Academy Add Available: Chinese Clinical CrossRef Diseases, Division Hospital, Laboratory, Microbiology Microbiology, NTC: Pathogen RNase Science. Sciences Virology, Wuhan Yang assays available bat diagnostics diversity inline input machine outbreak patient popup predicted provided sample samples similar table: target targets


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Delhi's Air Pollution Levels Are Making People Really, Really Sick
Delhiites took to Twitter to complain about dizziness, sore throats and breathlessness.
by Rohini ChatterjiDelhi air pollution levels were the worst in the year on Sunday, even as the city continues to choke thanks to the hazardous levels of smog.
The air quality index breached the 900 mark, over 22 times the safe level. The Centre stepped in on Sunday, with the pollution levels spiralling.
For the latest news and more, follow HuffPost India on Twitter, Facebook, and subscribe to our newsletter.
The Indian Express reported a statement from the PMO as saying that the Cabinet Secretary will monitor the situation in Delhi, Haryana and Punjab and that Chief Secretaries had also been asked to monitor different districts.
While the citizens of Delhi who are exposed to such high levels of air pollution are at the risk of lung disease, heart disease and stroke in the long term, short-term effects include watery eyes and a sore throat.
Delhi Twitter was full of photos of people wearing masks and views of smog from people’s windows and balconies. Delhiites also complained of watery eyes and running noses.
Here’s what they said:
Even those who visited Delhi did not have respite.
People also Tweeted at Chief Minister Arvind Kejriwal and Deputy Chief Minister Manish Sisodia about their health problems because of air pollution.


Hot v.s. cold Tapping
Decoder's classification of what they see on photo
Companies' Cause
*Note:* Pairs with value count less than 10 are mainly omitted from this graph


UEFA carpet Feyenoord ahead of Rangers clash as fans pay the price for misbehaviour
The Dutch side have been punished following incidents during their Europa League clash with Young Boys.
by Record Sport OnlineFeyenoord have been banned from selling tickets to their fans for next month's Europa League game at Porto.
The punishment follows disturbances during a previous clash against Young Boys in Basel.
UEFA have fined the club €50,000 due to the behaviour of their supporters during the 2-0 defeat in Switzerland last month and an additional €5000 for "acts of damage".
Crowd disturbance charges were also levelled at Feyenoord after their supporters threw objects and set off fireworks.
UEFA confirmed they'd taken action via a statement on Monday.
It read: "The Control, Ethics and Disciplinary Body has decided to ban Feyenoord from selling tickets to its away supporters for its next (1) UEFA competition match."
The Dutch club were given 30 days to contact Young Boys for the settlement of damages caused by their supporters.
Dick Advocaat's side host Rangers at De Kuip on Thursday night in a make or break clash.
Steven Gerrard 's side can eliminate their Dutch foes with victory as Group G looks set to go to the wire.
Just three point separate all four sides heading into the final two matchdays.


UVdesk Community skeleton is a fully-functional support ticket system built on the top of the Symfony PHP framework, supporting organizations to provide better customer support. It comes with all the essential features like Email Piping, Knowledgebase support, automated-workflow, multiple-group-team-agent-customer creation, prepared responses, saved replies and many more. Features and functionality of community skeleton helpdesk enable the user to use or build custom helpdesk solution to make it for their purpose.
~~~ $ composer clear-cache ~~~
~~~ $ composer create-project uvdesk/community-skeleton helpdesk-project --stability dev ~~~
For example – http://domain/installed_folder/public/
Click on SETUP UVDESK
Evaluate your requirements and Click on the PROCEED Button.
Fill in the required fields and Click on the PROCEED Button.
Fill in the required fields and Click on the PROCEED Button.
Fill in the required fields and Click on the PROCEED Button.
The Helpdesk Has Been Installed Successfully.
The development of the UVDesk Community Edition is supported by Webkul, led by the UVDesk Team.
Visit our official website to learn more about the UVDesk Helpdesk System.
All libraries and bundles included in the UVDesk Community Edition are released under the MIT or BSD license.


URLs for this server include:
| URL | Purpose |
|---|---|
| %ROOT_URL% | this page |
| %TOPICS_URL% | topics in use |
| %FACTS_URL% | facts for given topic; POST to add a fact |
| %SUBSCRIPTION_URL% | POST to create subscription to a topic |
| %FROM_SUB_URL% | latest fact from subscription |
See the API documentation at http://docs.combo.apiary.io for further details.
Source code for this server is available under an open-source license.
Below are the last 10 facts published on this server. This table is refreshed once a second.


Christina Chen is a User Experience, User Interface Designer interested in expanding her role as a product manager. She is grounded by her passion in social justice; such as access to education and health. She was born and raised in Taiwan and had the privilege of going through puberty in Singapore. She is currently living in Sunny San Diego pursuing a degree in Cognitive Science, Human and Computer Interaction, minor in American Sign Languages at UC San Diego.


POST TITLE
2/7/16 at 12:00AM
This is where the first few characters of a post would show. Two line max?
100 Replies
Pin this Post
Username - Sat Feb 6, 2:15 PM
POST TITLE
This is where the first few characters of a post would show. Two line max?
100 Replies
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Username - Sat Feb 6, 2:15 PM
POST TITLE
2/7/16 at 12:00AM
This is where the first few characters of a post would show. Two line max?
100 Replies
Pin this Post
Username - Sat Feb 6, 2:15 PM
POST TITLE
2/7/16 at 12:00AM
This is where the first few characters of a post would show. Two line max?
100 Replies
Pin this Post
Username - Sat Feb 6, 2:15 PM


Enumerations | |
| enum | OAUTH2_HTTP_FOUND |
| enum | OAUTH2_HTTP_BAD_REQUEST |
| enum | OAUTH2_HTTP_UNAUTHORIZED |
| enum | OAUTH2_HTTP_FORBIDDEN |
"Bad Request" HTTP status code.
Definition at line 211 of file OAuth2.inc.
"Forbidden" HTTP status code.
Definition at line 226 of file OAuth2.inc.
| enum OAUTH2_HTTP_FOUND |
"Found" HTTP status code.
Definition at line 203 of file OAuth2.inc.
"Unauthorized" HTTP status code.
Definition at line 219 of file OAuth2.inc.


importance: 5
Output a single-linked list from the previous task info:task/output-single-linked-list in the reverse order.
Make two solutions: using a loop and using a recursion.


DEAD MAN WALKING
A film review by Chad Polenz
Copyright 1997 Chad Polenz
**** (out of 4 = excellent) 1995, R, 122 minutes [2 hours, 2 minutes] [drama] starring: Susan Sarandon (Sister Helen Prejean), Sean Penn (Matthew Poncelet), Raymond Barry (Earl Delacroix), R. Lee Ermey (Clyde Percy), produced by Jon Kilik, Tim Robbins, Rodd Simmons, written and directed by Tim Robbins, based on the novel by Sister Helen Prejean.
Capital punishment is one of the most difficult issues of all time. Many people base their opinion upon ethical and spiritual values, but we usually don't stop to consider the prisoner as a person do we? "Dead Man Walking" is quite an accomplishment as it sheds light on the issue from all angles. Susan Sarandon stars as Sister Helen Prejean, a very friendly and kind nun who suddenly finds herself drawn to a convicted murder and rapist named Matthew Poncelet (Penn) in his time of need. She believes anyone is capable of salvation and murder is wrong whether it be in combat, crime, or authorized by the government. Throughout the film Poncelet declares his innocence, claiming he was framed and the real killer is walking free. All criminals tend to say this, and after a while they convince themselves they really are innocent and feel no remorse for their crime. Robbins carefully places flashbacks of the night of the crime to show just how brutal the crime was and to prove Poncelet's guilt. This is a great method of storytelling because each person who views this film will form a different opinion. He obviously is a vicious criminal and he should pay, but because of the love that Sister Helen has for him we really start to consider other options. At first Sister Helen tries desperately to fight his death sentence, but since she is poor she cannot get a good lawyer. This is very realistic because we all know the rich and powerful are able to buy their freedom. In some ways it's difficult to relate to the situation of the story. Most of us are not family members of murder victims, and so we can only imagine the pain and rage the family members of Poncelet's victims must feel. In any other film we wouldn't get to hear their "side" at all, but this film does an excellent job in depicting all the possible viewpoints. Raymond Barry co-stars as Earl Delacroix, the father of one of the victims, who hates Poncelet and can't understand how a nun could defend that "monster." When Sister Helen tries to explain her reasoning, we realize she might be following a foolish heart. One of the most moving scenes occurs when Sister Helen visits the family of the other victim and hears them tell the horrible story of when they heard their daughter was dead. R. Lee Ermey and Celia Weston (Mr. and Mrs. Percy) give incredible performances. At first, they think Sister Helen is on "their side," but they become almost as furious with her as they are with Poncelet when they discover that she wants to save his life. It seems what everyone is missing is Sister Helen is not so much trying to save Poncelet's life so much as his soul. She frequently visits him in prison talking to him about the Bible and about the teachings of Jesus. "And the truth shall set you free" she tells him. At first he doesn't understand it, but after deep contemplation he confesses his sins and seems to make peace with himself, although I am not completely sure if he makes his peace with God. There are many unresolved themes and elements like this throughout the film, but this is probably the most significant. Maybe "Dead Man Walking" leans a tad to the left because of its detailed characterization of, and our sympathy for Sister Helen and Poncelet. But what it really wants to show is the criminal is a human being with a soul, and therefore is able to redeem themselves if they so choose.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


2011, with the start of battlelog, Better Battlelog has come to life. All beginned with a super small set of features.
BBLog was growing and growing over the years and now it's a worldwide used goody for battlelog, for advanced and casual users as well. It's possible only with the help of the community, great translators, awesome supporters and such kind of contributors.
Right now, as of March 2015, my spare time changed a bit and BBLog is now pretty much unchanged since some months. This have several reasons, one of this is my lack of spare time, another reason is that there is simply not that much to improve on battlelog as it was some years ago.
Some features that have been rejected for integration into BBLog have been done by plugin and theme developers. They make that stuff for that i don't have time or for things that simply doesn't fit my "style guideline" for BBLog features.
BBLog is used by about 23.000+ users, every single day. Cool, isn't it?
Now talk about the future topics
Mozilla Firefox Support - Maybe will be dropped
This reason is simple. Mozilla will reject all extensions that are not signed by AMO (Mozilla Addon Center) -
In general i support that idea, it helps us all to get rid of the unwanted extensions that come with several installers. Google chrome have done the same a year ago.
What i can do is to submit the addon to mozzila addon center. I've done so a few weeks ago. Right now, nothing happened. The sign process of mozilla is horrible, slow and so much strict.
BBLog is designed to work on several browser engines with the same code - That mostly not fit with mozilla addon rules because they say you should use mozilla native code whenever possible, you should not use that or this, etc...
That's not the biggest problem, it's fixable, the big hammer is simply that they need over a month to check a newly uploaded addon version. That's horrible. That haven't changed since years, i tried it at the beginning of BBLog - I decided to remove the addon from their addon center because of that behaviour.
Maybe it will be better in the near future but i don't have much hope for that.
Firefox usage shrink since years, 25% of all BBLog users currently use firefox, some years ago it was 75%.
Windows 10 - Spartan Browser - Maybe will be supported
I follow every news about that. Microsoft decided to integrate a similar extension system as google chrome has. I like that and whenever possible BBLog will support this browser from the beginning of his live.
This will possibly result in a huge grow of the userbase when W10 comes to life.
Battlefield Hardline
It will be fully supported by BBLog with mostly the same features as for Battlefield 4. Expected release a few days after BF Hardline release. However, i'll not buy that thing.
Star Wars Battlefront
I plan to support that as well because it will use battlelog as well. I'm excited about a new franchise on battlelog.
DaniluK4000
Community Manager
Okey, great news! I'm going to translate this into Russian Language
Topicstarter
BrainFooLong
Administrator
You don't need to, this board is english only, even for official news.
DaniluK4000
Community Manager
No no, in my board on Battlelog, I translate to it all BBLog news
Topicstarter
BrainFooLong
Administrator
DaniluK4000 wrote:
No no, in my board on Battlelog, I translate to it all BBLog news
All that info come from the DEVS and producers directly and i interpret this now:
1. There will be no battlelog for star wars battlefront, i think that's plausible.
2. They also said that there is "No browser game launching", also i think that's plausible - Browser game launching is one of the most controverse features in battlelog. Battlelog itself is cool for stats and stuff like that, but not for game launching, that should be a in-game browser, imho.
3. Uprise Dev said: "The thing we (Uprise) are working on is built from scratch." - That means to me that they work on a new web-based thingy for community stuff for star wars, i think that's plausible also. Star Wars as a way more different than the BF or MOHW series, using the battlelog for Star wars battlefront will be like using a cube for a round hole.
All in all i am pretty sure that we can expect a "starlog" community platform without game launching instead of a "battlelog" community with game launching. If yes, i will make a "Better Starlog" if possible and required :)
Nano
Global Moderator
Don't drop Firefox Support for all... :(
Can we mantain Firefox addon without stay in Marketplace/AMO?

That's the problem, mozilla decided to disable this ability.
I keep bblog awake for firefox as long as possible.
Currently waiting since 40!!!!!!! days for bblog addon review process from the AMO, horrible, it's crazy.
 :(
Topicstarter
BrainFooLong
Administrator
Nano wrote:
I read your post in reddit... lol
But if Mozilla remove completely this ability, i'll be forced to change my main browser... :(
:) We will see
Post edited 1 x times, last by
DaniluK4000
Community Manager
DaniluK4000
Community Manager
Spartan (Edge) is using combined engine of Mozilla and Chrome?
User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.71 Safari/537.36 Edge/12.0
BrowserEngine: Edge
BrowserEngineVersion: 12.0
BrowserName: Spartan
OSFamily: Windows
OSVersion: 10.0
isBrowser: yes
isMobile: yes
x64: no
normalizedBrowserName: Spartan
appCodeName: Mozilla
appMinorVersion: BETA
browserLanguage: ru-RU
cpuClass: x64
language: ru-RU
systemLanguage: ru-RU
userLanguage: ru-RU
appName: Netscape
appVersion: 5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.71 Safari/537.36 Edge/12.0
platform: Win64
product: Gecko
productSub: 20030107
userAgent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.71 Safari/537.36 Edge/12.0
vendor:
vendorSub:
Topicstarter
BrainFooLong
Administrator
Rumors say it will support firefox and chrome extensions or in a very similar system, but nothing of that has currently been confirmed by microsoft.
DaniluK4000
Community Manager
BrainFooLong wrote:
Rumors say it will support firefox and chrome extensions or in a very similar system, but nothing of that has currently been confirmed by microsoft.


About Me
I am a junior web developer who was previously a Verification Engineer in the VLSI (Electronics & Communication) field. After moving to Australia, I realised that web development and web design was something I connected with. I took up volunteer graphic design projects and gradually transitioned to web development. I wanted to explore and learn more, so after my maternity break, I took up the Web Development Immersive at General Assembly, Sydney. It was such a great experience and I learnt so much valuable knowledge that I would love to apply to a new workplace!
Outside work, I enjoy cooking and connecting with nature. I try learning and creating new recipes and enjoy taking walks in the park.


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--- layout: post status: publish published: true title: Young Leaders in Recife and US Assistant Secretary of State image: feature: young-leaders-in-recife-and-us-assistant-secretary-of-state.jpg topPosition: -50px author: display_name: Fabio Pereira login: admin email<EMAIL_ADDRESS>url: http://fabiopereira.me author_login: admin author_email<EMAIL_ADDRESS>author_url: http://fabiopereira.me wordpress_id: 543 wordpress_url: http://fabiopereira.me/blog/?p=543 date: '2013-08-04 06:36:30 +1000' date_gmt: '2013-08-04 13:36:30 +1000' categories: - Uncategorized tags: [] comments: [] ---
On the 16th of July I was privileged to participate in a roundtable discussion as part of the group of Young Leaders in Recife with members of the consulate of the United States in Brazil and the US Assistant Secretary of State for Western Hemisphere Affairs, Roberta S. Jacobson, who came to visit Brazil. The objective of the round table was to give Roberta our view of the current scenario in the northeast of Brazil, since it was the first time she was visiting Recife.
Before the roundtable a member of the project Oi Kabum presented some posters that they had worked on to raise awareness racism as a huge issue we have to work on.
We all expressed our opinions and heard Roberta’s during the roundtable. The topics of the discussion were mainly about Brazil: education, including English as a second language, protests, corruption and the mindset shift that has happened recently in Brazil in regards of complacency with government issues. It was very interesting to hear everyone’s opinion about the topics.
Members of the Consulate:
Young Leaders:


South Gloucestershire Council paid out £1million in redundancies in 12 months
Chiefly because of large payments made to three individuals
by Amanda CameronSouth Gloucestershire Council racked up around £1million in redundancy payouts last year, despite laying of almost 100 fewer staff than the year before.
The authority made 125 people redundant in 2017/18, at a cost of roughly £1.6million.
Last year, there were only 30 compulsory redundancies, but they added up to around £1million - chiefly because of large payments made to three individuals.
The unnamed individuals received a payout of between £150,001 and 200,000 each in the year to April 2019, the council’s annual report for the year shows.
Another three got between £40,001 and £80,000, while the remaining 24 received up to £40,000.
By contrast, in 2017/18, the highest redundancy payout was in the range £80,001 to £100,000, and went to one person only. Twelve others received £40,001 to £80,000 and the remaining 113 got up to £40,000.
A council spokesman said: “There were a number of redundancies in 2018/19, following reorganisation within the council as part of our ongoing work to improve the value for money as we continue to deliver our priorities and the services people need.
“A number of individuals received contractual redundancy payments as part of this process, which are capped by the council’s policy.
“The total costs to the council (described as ‘exit packages’) includes pension costs payable to the pension fund to comply with pension rules.”
Altogether the council forked out almost £1.25million in severance payments in 2018/19, compared with nearly £2.05million the previous financial year.
As well as forced redundancies, exit packages of up to £40,000 for staff who left for other reasons were paid to 15 people last year and 22 the previous year.
The council was restructured in 2013.
It paid out nearly £15million making staff redundant in the seven years to 2017/18, but it saved the same amount from its annual wage bill.
For the latest news in and around Bristol, visit and bookmark Bristol Live's homepage .


Can you judge a book by its cover? Or how it might perform in Amazon reviews?
Let's see if we can use Machine Learning to find out.
We set out to create a ML model for the best and worst Amazon reviewed books and their covers. This site allows you to upload a book cover image, which is then processed and tagged through the GoogleVisionAPI, run through the model, and then receives predicted performance metrics based on previous Amazon reviews.
Upload up to 4 files and we'll make a prediction of its Amazon rating.


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vignettes/articles/cmd_qc.Rmd
cmd_qc.Rmd
This pipeline will import data from single-cell preprocessing algorithms (e.g. CellRanger, HCA Optimus, Alevin), generate various quality control metrics (e.g. general metrics, doublet scores, contamination estimates) using multiple tools, and output results in standard data containers (e.g. SingleCellExperiment, Seurat object, AnnData).
For data generated with microfluidic devices, the first major step after UMI counting is to detect cell barcodes that represent droplets containing a true cell and exclude empty droplets that only contain ambient RNA.
The Droplet and Cell matrices have also been called “raw” and “filtered” matrices, respectively, by tools such as CellRanger. However, using the term “filtered” can be ambiguous as other forms of cell filtering can be applied beyond empty droplets (e.g. excluding poor-quality cells based on low number of UMIs). Both the original droplet matrix and the filtered cell matrix can be QC’ed in this pipeline. However, QC of the droplet matrix is specific for single cell data generated from microfluidic devices (e.g. 10X).
To run the pipeline, users can install the singleCellTK (SCTK) package along with Python dependencies that may be potentially used (mainly Scrublet and AnnData). Alternatively, users can run the Docker version of the pipeline which is described in detail further down in this page.
To run the pipeline script, users will need to download the SCTK_runQC.R
script file here. Also, the tutorial of installing singleCellTK package can found in Installation page.
 Also, the pipeline is compatible with datasets generated by other algorithms. Please refer to the section Importing data from different preprocessing tools for more details.
User can quantify expression of mitochondrial genes by passing a GMT files containing mitochondrial genes (with -g/--gmt
argument). User can also quantify expression of mitochondrial genes for human or mouse dataset by setting -M/--detectMitoLevel
argument. Please refer to the section Gene sets for more details.
Besides, the pipeline contains various parameters to control the process of quality control. Please refer to the section Parameters for more details.
If you have not used docker before, you can follow the instruction to install and set up docker in Windows, Mac or Linux.
The Docker image can be obtained by running:
docker pull campbio/sctk_qc:2.10.1
The usage of each argument is the same as running command line analysis. Here is an example code to perform QC on CellRangerV3 data with SCTK docker:
docker run --rm -v /path/to/data:/SCTK_docker \
-it campbio/sctk_qc:2.10.1 \
-b /SCTK_docker/cellranger_folder \
-P CellRangerV3 \
-s SampleName \
-o /SCTK_docker/Output_Directory \
-g /SCTK_docker/name_of_gmt_file.gmt \
-S TRUE \
-F SCE,AnnData,FlatFile,Seurat
The docker image will not access files in your host file system by default. To get access to the files on your machine, you can properly set up a mount volume. Noted that the transcriptome data and GMT file needed to be accessible to the container through mounted volume. In the above example, mount volume is enabled for accessing input and output directory using argument -v
. The transcriptome and GMT files stored in the path /path/to/data
of your machine file system is now available in /SCTK_docker
folder inside the docker. To learn more about mounted volumes, please check out this post.
Please refer to the section Parameters for more details about parameters.
Users who have not used Singularity before can install it following the instruction here. The Singularity image for SCTK-QC can be easily built using Docker Hub as a source:
singularity pull docker://campbio/sctk_qc:2.10.1
The usage of singleCellTK Singularity image is very similar to that of Docker. In Singularity 3.0+, the mount volume is automatically overlaid.
It’s recommended to re-set the home directory when you run singularity. Singularity will mount \$HOME
path on your file system by default, which might contain your personal R/Python library folder. If we don’t re-set the home to mount, singularity will try to use R/Python libraries which are not built within the singularity image and cause some conflicts. You can point to some “sanitized home”, which is different from \$HOME
path on your machine, using argument -H
/--home
(see more information). Or you can disable the \$HOME
binding by setting the argument --no-home
. Besides, you can use argument --bind
/-B
to specify your own mount volume, which is the path that contains the dataset and will be used to store the output of QC pipeline. The example is shown as below:
singularity run --home=/PathToSanitizedHome \
--bind /PathToData:/data sctk_qc_2.10.1.sif \
-b /SCTK_docker/cellranger_folder \
-P CellRangerV3 \
-s SampleName \
-o /SCTK_docker/Output_Directory \
-g /SCTK_docker/name_of_gmt_file.gmt \
-S TRUE \
-F SCE,AnnData,FlatFile,Seurat
One important note about this docker image: please run the docker image on a machine / node which has a CPU with the following architecture: broadwell, haswell, skylake, cascadelake or the latest architecture. This can avoid having the “illegal operation” issue from Scrublet package, because this Python package are compiled by SIMD instructions that are compatible with these CPU architectures. Please specify the CPU architecture, at the script header after #$ -l cpu_arch=
, as one of the following: broadwell
, haswell
, skylake
, cascadelake
or latest architecture. One of the example is shown below:
#!/bin/bash
#$ -cwd
#$ -j y
#$ -pe omp 16
#$ -l cpu_arch=broadwell|haswell|skylake|cascadelake
singularity run --home=/PathToSanitizedHome ### this also works for 'docker run'
--bind /PathToData:/data sctk_qc_2.10.1.sif \
-b /SCTK_docker/cellranger_folder \
-P CellRangerV3 \
-s SampleName \
-o /SCTK_docker/Output_Directory \
-g /SCTK_docker/name_of_gmt_file.gmt \
-S TRUE \
-F SCE,AnnData,FlatFile,Seurat
The pipeline contains various parameters to control the process of quality control. The function of each parameter is shown below:
The required arguments are as follows:
| Parameter | Description |
|---|---|
-b , --basePath (required) |
Base path for the output from the preprocessing algorithm. |
-P , --preproc (required) |
Algorithm used for preprocessing. One of CellRangerV2 , CellRangerV3 , BUStools , STARSolo , SEQC , Optimus , DropEst , SceRDS , Seurat , CountMatrix and AnnData . |
-s , --sample (required) |
Name of the sample. This will be prepended to the cell barcodes. |
-o , --directory (required) |
Output directory. A new subdirectory will be created with the name “sample”. R, Python, and FlatFile directories will be created under the “sample” directory containing the data containers with QC metrics. Default . . More information about output directory structure is explained in Outputs section below. |
-F , --outputFormat (required) |
The output format of this QC pipeline. Currently, it supports SCE , Seurat , FlatFile , AnnData and HTAN (manifest files that meets HTAN requirement). |
-S , --splitSample (required) |
Save a SingleCellExperiment object for each sample. Default is TRUE . If FALSE , the data of all samples will be combined into one SingleCellExperiment object and this object will be output. |
When running the pipeline with more than one sample to process, care should be exercised with the -S flag, and whether or not one wishes to combine the samples.
In addition, the following parameters will need multiple inputs, separated by a comma without any spacing:
| Parameter | Description |
|---|---|
-b , --basePath
|
Base path for the output from the preprocessing algorithm; one is required for each sample, even if they are in the same location. |
-P , --preproc
|
In multiple object mode, each entry should have a preprocessing algorithm associated with it; the pipeline supports serial processing of different sample types. |
-s , --sample
|
Names of the samples. |
An example execution is shown below:
The optional arguments are as follows. Their usage depend on type of data and user-defined behaviour.
| Parameter | Description |
|---|---|
-g , --gmt
|
GMT file containing gene sets for quality control. |
-t , --delim (required when -g is specified) |
Delimiter used in GMT file. Default "\t" . |
-G , --genome
|
The name of genome reference. This is only required for CellRangerV2 data. |
-y , --yaml
|
YAML file used to specify parameters of QC functions called by SCTK-QC pipeline. Please check Specify parameters using yaml file section for details. |
-c , --cellData
|
The full path of the RDS, H5AD or Matrix file of the cell matrix. This would be use only when --preproc is SceRDS , Seurat , AnnData , or CountMatrix . |
-r , --rawData
|
The full path of the RDS file or Matrix file of the droplet matrix. This would be provided only when --preproc is SceRDS , Seurat , AnnData , or CountMatrix . |
-C , --cellPath
|
The directory containing matrix.mtx.gz , features.tsv.gz and barcodes.tsv.gz files originally generated by 10x CellrangerV2 or CellrangerV3 (files in the filtered_feature_bc_matrix directory). This argument only works when --preproc is CellRangerV2 or CellRangerV3 . Default is NULL . If base_path is NULL , cellPath or rawPath should be specified. |
-R , --rawPath
|
The directory containing matrix.mtx.gz , features.tsv.gz and barcodes.tsv.gz files originally generated by 10x CellrangerV2 or CellrangerV3 (files in the raw_feature_bc_matrix directory). This argument only works when --preproc is CellRangerV2 or CellRangerV3 . Default is NULL . If base_path is NULL , cellPath or rawPath should be specified. |
-d , --dataType
|
Type of data as input. Default is Both , which means taking both droplet and cell matrix as input. If set as Droplet , it will only processes droplet data. If set as Cell , it will only processes cell data. |
-D , --detectCells
|
Detect cells from droplet matrix. Default is FALSE . This argument is only evaluated when -d is Droplet . If set as TRUE , cells will be detected and cell matrix will be subset from the droplet matrix. Also, QC will be performed on the detected cell matrix. |
-m , --cellDetectMethod
|
Methods to detect cells from droplet matrix. Default is EmptyDrops . This argument is only evaluated when -D is TRUE . Other options could be Knee or Inflection . More information is provided in the Droplet QC documentation. |
-n , --numCores
|
Number of cores used to run the pipeline. By default is 1 . Parallel computing is enabled if -n is greater than 1. |
-T , --parallelType
|
Type of parallel computing used for parallel computing. Parallel computing used in this pipeline depends on BiocParallel package. Default is MulticoreParam . It can be MulticoreParam or SnowParam . This argument will be evaluated only when --numCores is greater than 1. |
-i , --studyDesign
|
The TXT file containing the description of the study design. Default is NULL . This would be shown at the beginning of the HTML report of cell and droplet QC. |
-L , --subTitle
|
The subtitle used in the cell and droplet QC HTML report. Default is None . The subtitle can contain information of the sample, like sample name, etc. If -S is set as TRUE , the length of subsitle should be the same as the number of samples. If -S is set as FALSE , the length of subtitle should be one or NULL. |
-M , --detectMitoLevel
|
Detect mitochondrial gene expression level. If TRUE , the pipeline will examine mitochondrial gene expression level automatically without the need of importing user defined GMT file. Default is TRUE . |
-E , --mitoType
|
Type of mitochondrial gene-set to be loaded when --detectMitoLevel is set to TRUE . Possible choices are: human-ensembl , human-symbol , human-entrez , human-ensemblTranscriptID , mouse-ensembl , mouse-symbol , mouse-entrez and mouse-ensemblTranscriptID . |
Users can specify parameters for QC algorithms in this pipeline with a YAML file (supplied with -y
/--yamlFile
argument). The current supported QC algorithms including doublet detection (bcds
, cxds
, cxds_bcds_hybrid
, doubletFinder
, doubletCells
and scrublet
), decontamination (decontX
), emptyDrop detection (emptyDrops
) and barcodeRankDrops (barcodeRanks
). A summary of each function is shown below:
An example of QC parameters YAML file is shown below:
---
Params: ### should not be omitted
bcds:
ntop: 600
cxds:
ntop: 600
cxds_bcds_hybrid:
nTop: 600
decontX:
maxIter: 600
emptyDrops:
lower: 50
niters: 5000
testAmbient: True
barcodeRanks:
lower: 50
The format of YAML file can be found here. The parameters should be consistent with the parameters of each QC function in SCTK. Parameters that are not defined in this YAML file will use the default value. Please refer reference for detailed information about arguments of each QC function.
SCTK-QC pipeline enables parallel computing to speed up the analysis. Parallel computing is enabled by setting -n
/--numCores
greater than 1. The -n
/--numCores
is used to set the number of cores used for the pipeline.
The backend of parallel computing is supported by BiocParallel package. Therefore, users can select different types of parallel evaluation by setting -T
/--parallelType
argument. Default is MulticoreParam
. Currently, MulticoreParam
and SnowParam
are supported for -T
argument. However, MulticoreParam
is not supported by Windows system. Windows user can choose SnowParam
as the backend of parallel computing.
-d
argument is used to specify which type of count matrix is used in the pipeline. Default is Both
, which means the pipeline will run quality control on both droplet and cell count data.
Users can also choose to run SCTK-QC pipeline on only the droplet count or cell count matrix, instead of running on both. In this case, the pipeline will only take the single input and perform QC on it. An example is shown below:
Rscript SCTK_runQC.R \
-b /base/path \
-P Preprocessing_Algorithm \
-s SampleName \
-o Output_Directory \
-S TRUE \
-F SCE,AnnData,FlatFile,Seurat \
-d Droplet \
-D TRUE \
-m EmptyDrops
If -d
argument is set as Droplet
, the QC pipeline will only take droplet count matrix as input and perform quality control. You can choose whether to detect cells from the droplet matrix by setting -D
as TRUE
. If yes, cell count matrix will be detected and the pipeline will also perform quality control on this matrix and output the result. You could further define the method used to detect cells from droplet matrix by setting -m
argument. -m
could be one of EmptyDrops
, Knee
or Inflection
. EmptyDrops
will keep cells that pass the runEmptyDrops()
function test. Knee
and Inflection
will keep cells that pass the knee or inflection point returned from runBarcodeRankDrops()
function.
If -d
argument is set as Cell
, the QC pipeline will only take cell count matrix as input and perform quality control. A figure showing the analysis steps and outputs of different inputs is shown below:
This pipeline enables different ways to import CellrangerV2/CellrangerV3 data for flexibility.
For CellRangerV3:
Rscript SCTK_runQC.R \
-b /base/path \
-P CellRangerV3 \
-s SampleName \
-o Output_Directory \
-S TRUE \
-F SCE,AnnData,FlatFile,Seurat
For CellRangerV2, the reference used by cellranger needs to be specified by -G
/--genome
:
Rscript SCTK_runQC.R \
-b /base/path \
-P CellRangerV2 \
-s SampleName \
-o Output_Directory \
-S TRUE \
-G GenomeName \
-F SCE,AnnData,FlatFile,Seurat
-b
specifies the base path and usually it’s the output folder of 10x cellranger-count
. -s
specifies the sample name, which has to be the same as the name of the sample folder under the base folder. The folder layout would look like the following:
├── BasePath
└── SampleName
├── outs
| ├── filtered_feature_bc_matrix
| | ├── barcodes.tsv.gz
| | ├── features.tsv.gz
| | └── matrix.mtx.gz
| ├── raw_feature_bc_matrix
| | ├── barcodes.tsv.gz
| | ├── features.tsv.gz
| | └── matrix.mtx.gz
...
cellranger-count
output have been moved out of the default cellranger output directory, you can specified the path to droplet and cell count matrix using arguments -R
and -C
:Rscript SCTK_runQC.R \
-P CellRangerV2 \
-C /path/to/cell/matrix \
-R /path/to/droplet/matrix \
-s SampleName \
-o Output_Directory \
-S TRUE \
-F SCE,AnnData,FlatFile,Seurat
In this case, you must skip -b
arguments and you can also skip -G
argument for CellRangerV2 data.
SingleCellExperiment
object in RDS file, singleCellTK also supports this type of input. To run quality control with RDS file as input, run the following code:Rscript SCTK_runQC.R \
-P SceRDS \
-s Samplename \
-o Output_Directory \
-S TRUE \
-F SCE,AnnData,FlatFile,Seurat \
-r /path/to/rds/file/droplet.RDS \
-c /path/to/rds/file/cell.RDS
Seurat
object in RDS file, you may use the following code:Rscript SCTK_runQC.R \
-P Seurat \
-s Samplename \
-o Output_Directory \
-S TRUE \
-F SCE,AnnData,FlatFile,Seurat \
-r /path/to/rds/file/droplet.RDS \
-c /path/to/rds/file/cell.RDS
Warning: Both SingleCellExperiment
and Seurat
objects use the same RDS format. Be sure which one your data is formatted as or the pipeline may not successfully run.
AnnData
object in H5AD file,Rscript SCTK_runQC.R \
-P AnnData \
-s Samplename \
-o Output_Directory \
-S TRUE \
-F SCE,AnnData,FlatFile,Seurat \
-r /path/to/rds/file/droplet.RDS \
-c /path/to/rds/file/cell.RDS
SCTK-QC pipeline allows importing data from the following pre-processing tools or objects:
If your data is preprocessed by other algorithms, you might want to make sure the ‘-b’ argument matches the path storing the data and the ‘-P’ argument matches the right preprocessing tools. Basically, the templated is shown below:
Rscript SCTK_runQC.R \
-b /base/path \
-P Preprocessing_Algorithm \
-s SampleName \
-o Output_Directory \
-S TRUE \
-F SCE,AnnData,FlatFile,Seurat
The following table describes how SCTK expects the inputs to be structured and passed for each import function. In all cases, SCTK retains the standard output directory structure from upstream tools. All the import functions return the imported counts matrix as an assay
in a SingleCellExperiment
object, with associated information in respective colData
, rowData
, reducedDims
, and metadata
fields.
The table above also shows the R console functions for the QC algorithms. Detailed information about function parameters and defaults are available in the Reference section.
Quantifying the level of gene sets can be useful quality control. For example, the percentage of counts from mitochondrial genes can be an indicator of cell stress or death.
User can quantify the expression of mitochondrial genes for human or mouse dataset by setting -M
/--detectMitoLevel
as TRUE
. User needs to specify the correct --mitoType
argument for the dataset. The SCTK-QC pipeline has built-in mitochondrial gene sets for human and mouse genes. It supports four different type of gene id: gene symbol
, entrez ID
, ensembl ID
and ensembl transcript ID
. Therefore, there are eight options for --mitoType
arguments: human-ensembl
, human-symbol
, human-entrez
, human-ensemblTranscriptID
, mouse-ensembl
, mouse-symbol
, mouse-entrez
and mouse-ensemblTranscriptID
.
To quantify expression of other gene set, users can pass a GMT file (with -g
/--gmt
argument) to the pipeline with one row for each gene set. The first column should be the name of the gene set (e.g. mito
).
The second column for each gene set in the GMT file (i.e. the description) should contain the location of where to look for the matching IDs in the data. If set to rownames
, then the gene set IDs will be matched with the row IDs of the data matrix. If a character string or an integer index is supplied, then gene set IDs will be matched to the IDs in that column of feature table. Gene sets with mitochondrial genes can be found here.
The output directory is created under the path specified by -o
/--directory
argument. Each sample is stored in the subdirectory (named by -s
/--sample
argument) within this output direcotry. Within each sample directory, each output format will be separated into subdirectories. The output file hierarchy is shown below:
(root; output directory)
├── level3Meta.csv
├── level4Meta.csv
├── sample1_dropletQC.html
├── sample1_cellQC.html
└── sample1
├──sample1_cellQC_summary.csv
├── R
| ├── sample1_Droplets.rds
| └── sample1_Cells.rds
├── Python
| ├── Droplets
| | └── sample1.h5ad
| └── Cells
| └── sample1.h5ad
├── FlatFile
| ├── Droplets
| | ├── assays
| | | └── sample1_counts.mtx.gz
| | ├── metadata
| | | └── sample1_metadata.rds
| | ├── sample1_colData.txt.gz
| | └── sample1_rowData.txt.gz
| └── Cells
| ├── assays
| | └── sample1_counts.mtx.gz
| ├── metadata
| | └── sample1_metadata.rds
| ├── reducedDims
| | ├──sample1_decontX_UMAP.txt.gz
| | ├──sample1_scrublet_TSNE.txt.gz
| | └──sample1_scrublet_UMAP.txt.gz
| ├── sample1_colData.txt.gz
| └── sample1_rowData.txt.gz
└── sample1_QCparameters.yaml


Leancamp Way of Working
Organizer requirements
The organizer must be a community builder amongst entrepreneurs. He or she must have attended a Leancamp or Source Summit before. As an exception, we could agree that some other unconferences qualify as well. (ProductCamp, BarCamp etc).
Roles
The organizer must be able to form a team locally and that team should be capable of selling at least 30 tickets on personal title, preferably to entrepreneurs. All team members organize Leancamps as individuals and should volunteer their time.
The organizer will work with a Leancamp Facilitator, who is trained to run Leancamps, do session planning and helps curate the talks of ambassadors. They’ll meet once every two weeks (according to the checklist as well) to see if milestones have been met.
Centralized ops and finance
Tickets will be sold through our Central Eventbrite account and the organizing team can manage the event as a co-admin. Same goes for website content and promotions.
A Facilitator will be assigned by Leancamp and will help the team prepare as well as be the main facilitator for the day itself. He/she will get expenses covered.
A budget will be prepared and based on a point-of-no-return we’ll compensate some pre-payments to prepare for the event, limited to pre-payment to the catering company and other costs.
Any profit will be for the Leancamp global organization and will be earmarked to enable a Leancamp organizer to take more risk organizing a subsequent unconference.
Targeting potential leaders
Organizers can become Facilitators at other Leancamps. We must promote that during each Leancamp and make sure that we also promote the possibility of organizing your own Leancamp during a Leancamp day.
Onboarding
In order to qualify, an organizer must run an interest campaign and collect at least 50 to 150 email addresses. The Leancamp Facilitator will create a landing page for that purpose.
Once we have a substantial list of interestees we’ll jump in and set things up and follow the guide and checklist. We should be completely transparent on that status of such campaigns.


RCMP asking for help in finding missing Kelowna teen
by Rob MunroRCMP are asking for the public's help to find a missing 14-year-old girl.
Trinity Erickson was last seen in her Kelowna home just before 9 p.m. yesterday, Nov. 14, according to an RCMP media release.
Police say foul play is not suspected and she is believed to be in the Kelowna area. Family told police it’s out of character for her to be out of contact for this long.
Erickson is decribed as an Inigenous female, 5'5" tall, 150 lbs. with long dyed blonde hair and brown eyes. She was last seen wearing pyjamas but it’s presumed that she has changed her clothing.
Anyone with information can contact their local police, or Crime Stoppers at 1-800-222-8477.


Here you can find documentation from workshops we conducted at Brown, as well as documentation for our software packages, tutorials, and best practices.
This workshop is designed to introduce the QIIME and dada2 pipelines for analyzing metagenomics data. A full video of this workshop can be found at the following URLs: https://www.youtube.com/watch?v=LUS62N7tSUU https://www.youtube.com/watch?v=nWeRN2lKIto https://www.youtube.com/watch?v=wV5_z7rR6yw


Lets show how we can recover a region of interest that is planted in a set of 10k trajectories from a Beijing taxi data set.
In [2]:
import pyscan
import matplotlib.pyplot as plt
import random
import math
import statistics
import csv
import itertools
import numpy as np
def plot_points(ax, pts, c):
xs = []
ys = []
for pt in pts:
xs.append(pt[0] )
ys.append(pt[1])
ax.scatter(xs, ys, color=c)
def plot_points_traj(ax, pts, c):
xs = []
ys = []
for pt in pts:
xs.append(pt[0])
ys.append(pt[1])
ax.plot(xs, ys, color=c)
def plot_approx(ax, traj_pts, core_set_pts):
ax.set_xlim([-.01, 1.01])
ax.set_ylim([-.01, 1.01])
plot_points_traj(ax, traj_pts, "g")
plot_points(ax, core_set_pts, "b")
ax.set_axis_off()
def plot_full_trajectories(red, blue, ax):
plot_set = [(reg, True) for reg in blue] + [(reg, False) for reg in red]
random.shuffle(plot_set)
for traj, is_blue in plot_set:
if is_blue:
plot_points_traj(ax, traj, "b")
else:
plot_points_traj(ax, traj, "r")
def remove_long_trajectories(trajectories, percent=.9):
def toTraj(pts):
return pyscan.Trajectory([pyscan.Point(p[0], p[1], 1.0) for p in pts])
ltraj = sorted(toTraj(traj).get_length() for traj in trajectories)
perc_len_traj = ltraj[int(percent * len(trajectories))]
del ltraj
return [traj for traj in trajectories if toTraj(traj).get_length() <= perc_len_traj]
def normalize(pt, mxx, mnx, mxy, mny):
return (pt[0] - mnx) / (mxx - mnx), (pt[1] - mny) / (mxy - mny)
def read_csv(fname, filter_long=True):
traj_id_set = []
with open(fname) as f:
curr_id = None
mnx = float("inf")
mxx = -float("inf")
mny = float("inf")
mxy = -float("inf")
for row in csv.reader(f, delimiter=" "):
try:
if row[0] != curr_id:
curr_id = row[0]
traj_id_set.append([])
if math.isnan(float(row[1])) or math.isinf(float(row[1])):
continue
if math.isnan(float(row[2])) or math.isinf(float(row[2])):
continue
x, y = (float(row[1]), float(row[2]))
mnx = min(mnx, x)
mny = min(mny, y)
mxx = max(mxx, x)
mxy = max(mxy, y)
traj_id_set[-1].append((float(row[1]), float(row[2])))
except ValueError:
continue
norm_traces = []
while traj_id_set:
norm_trace = []
trace = traj_id_set.pop()
for pt in trace:
norm_trace.append(normalize(pt, mxx, mnx, mxy, mny))
norm_traces.append(norm_trace)
if filter_long:
return remove_long_trajectories(norm_traces)
else:
return norm_traces
In [3]:
# Ok we finally have the trajectories scaled to be in a [0,1]x[0,1] box with the extremely
#long trajectories removed
trajectories = read_csv("./bjtaxi_samples_10k.tsv")
Now that we have a set of trajectories we will plant a region of interest of size between 1/50 and and 1/500 of the domain size containing roughly 5% of the trajectories where 20% will be from the blue set and 80% from the red set.
In [4]:
r = .1
p = .5
q = .2
alpha = 1 / 100
max_r = 1 / 50
n = 100
s = 4000
min_r = alpha
We will start out with halfplane scanning which is the simplest and fastest method.
In [15]:
n = 400
s = 10000
red, blue, mx_plane, mx_planted = pyscan.plant_full_halfplane(trajectories, r, p, q, pyscan.DISC)
red_sample = pyscan.my_sample(red, s)
blue_sample = pyscan.my_sample(blue, s)
red_net = pyscan.my_sample(red, n)
blue_net = pyscan.my_sample(blue, n)
net = red_net + blue_net
#Sketch the trajectories down to simplify them.
m_sample = [pyscan.halfplane_kernel([pyscan.Point(pt[0], pt[1], 1.0) for pt in traj], alpha) for traj in red_sample]
b_sample = [pyscan.halfplane_kernel([pyscan.Point(pt[0], pt[1], 1.0) for pt in traj], alpha) for traj in blue_sample]
pt_net = [pyscan.halfplane_kernel([pyscan.Point(pt[0], pt[1], 1.0) for pt in traj], alpha) for traj in net]
#Now attempt to recover the region.
m_sample = list(pyscan.trajectories_to_labels(m_sample))
b_sample = list(pyscan.trajectories_to_labels(b_sample))
net_set = list(itertools.chain.from_iterable(pt_net))
reg, mx = pyscan.max_halfplane_labeled(net_set, m_sample, b_sample, pyscan.DISC)
actual_mx = pyscan.evaluate_range_trajectory(reg,
[pyscan.Trajectory([pyscan.Point(pt[0], pt[1], 1.0) for pt in traj]) for traj in red],
[pyscan.Trajectory([pyscan.Point(pt[0], pt[1], 1.0) for pt in traj]) for traj in blue], pyscan.DISC)
In [16]:
_, ax = plt.subplots(figsize=(16, 12))
plot_full_trajectories(red_net, blue_net, ax)
print("Planted Region: " + str(mx_plane) + " " + str(mx_planted))
print("Found Region: " + str(reg) + " " + str(actual_mx))
#Now lets plot what we got.
xs = np.arange(0, 1, .01)
ys = (-1 - mx_plane.get_coords()[0] * xs) * 1 / mx_plane.get_coords()[1]
ax.plot(xs, ys, color="g", linewidth=4)
plot_full_trajectories(red_net, blue_net, ax)
ys = (-1 - reg.get_coords()[0] * xs) * 1 / reg.get_coords()[1]
ax.plot(xs, ys, color="k", linewidth=4)
ax.set_ylim(0, 1)
ax.set_xlim(0, 1)
ax.set_axis_off()
plt.show()
Planted Region: HalfSpace(pyscan::Point<2>(-1.54218, 0.53373, 1.16071)) 0.10840026221554296
Found Region: HalfSpace(pyscan::Point<2>(0.963318, -0.268361, -0.71975)) 0.11572822538248437
The found and planted region are in the lower corner. The found region is actually more anomalous then the planted region, but also overlaps quite a lot with the planted region.
We will first plant a disk in the domain and attempt to recover it. Plotting a subset of the data it isn’t particularly clear where this region of interest is. We can use the max_disk_trajectory method to recover the region. It scans at multiple scales using multiple different coresets to minimize the number of regions that need to be considered.
In [5]:
n = 25
s = 400
trajectories = [[pyscan.Point(p[0], p[1], 1.0) for p in traj] for traj in trajectories]
while True:
red, blue, planted_reg , planted_mx = pyscan.plant_full_disk(trajectories, r, p, q, pyscan.DISC)
print(planted_reg.get_radius())
if min_r < planted_reg.get_radius() < max_r:
break
disc = pyscan.DISC
red_sample = pyscan.my_sample(red, s)
blue_sample = pyscan.my_sample(blue, s)
red_net = pyscan.my_sample(red, n)
blue_net = pyscan.my_sample(blue, n)
net = red_net + blue_net
reg, mx = pyscan.max_disk_trajectory(net, red_sample, blue_sample, min_r, max_r, alpha, disc)
0.027356629856841952
0.011568124074419107
In [7]:
_, ax = plt.subplots(figsize=(18, 12))
plot_full_trajectories(red_sample, blue_sample, ax)
actor = plt.Circle((planted_reg.get_origin()[0], planted_reg.get_origin()[1]), planted_reg.get_radius(),
alpha=.6,
color="g")
ax.add_artist(actor)
actor = plt.Circle((reg.get_origin()[0], reg.get_origin()[1]), reg.get_radius(),
color="k",
alpha=.6)
ax.add_artist(actor)
print("Planted Region: " + str(planted_reg))
print("Found Region: " + str(reg))
ax.set_axis_off()
plt.show()
Planted Region: Disk(pyscan::Point<2>(0.267767, 0.473677, 1), 0.0115681)
Found Region: Disk(pyscan::Point<2>(0.259012, 0.471483, 1), 0.0151749)
It is hard to tell whether we actually recovered the anomaly, but we can zoom in on the region of interest to see.
In [8]:
_, ax = plt.subplots(figsize=(18, 12))
plot_full_trajectories(red_sample, blue_sample, ax)
actor = plt.Circle((planted_reg.get_origin()[0], planted_reg.get_origin()[1]), planted_reg.get_radius(),
alpha=.6,
color="g")
ax.add_artist(actor)
actor = plt.Circle((reg.get_origin()[0], reg.get_origin()[1]), reg.get_radius(),
color="k",
alpha=.6)
ax.add_artist(actor)
ax.set_xlim([.2, .4])
ax.set_ylim([.3, .5])
ax.set_axis_off()
plt.show()
Again we plant a region and attempt to recover it.
In [ ]:
while True:
red, blue, planted_reg, _ = pyscan.plant_full_square(trajectories, r, p, q, disc)
print
if planted_reg.upX() - planted_reg.lowX() < max_r * 2:
break
red_sample = pyscan.my_sample(red, s)
blue_sample = pyscan.my_sample(blue, s)
red_net = pyscan.my_sample(red, n)
blue_net = pyscan.my_sample(blue, n)
net = red_net + blue_net
m_sample = [pyscan.even_sample_error(traj, alpha, False) for traj in red_sample]
b_sample = [pyscan.even_sample_error(traj, alpha, False) for traj in blue_sample]
pt_net = [pyscan.even_sample_error(traj, alpha, False) for traj in net]
m_sample = list(pyscan.trajectories_to_labels(m_sample))
b_sample = list(pyscan.trajectories_to_labels(b_sample))
net_set = list(itertools.chain.from_iterable(pt_net))
#Ok run the algorithm.
rect, mx = pyscan.max_rect_labeled(n, 2 * max_r, m_sample, b_sample, disc)
actor = plt.Rectangle((planted_reg.upX(), planted_reg.upY()),
planted_reg.upX() - planted_reg.lowX(),
planted_reg.upY() - planted_reg.lowY())
actor = plt.Rectangle((rect.upX(), rect.upY()), rect.upX() - rect.lowX(), rect.upY() - rect.lowY())
ax.add_artist(actor)


Some facts to keep in mind when thinking about length of pages, paragraphs, and sentences, and the complexity of ideas:
[Feel free to add more such statistics, fun facts]
Keep your pages short and on point.
Stay with one topic per page.
Don't put too much in a page, and only go as detailed as necessary: how much detail you include depends on the context.
Always make it clear to the reader where to find further, related information.
Don't say everything on one page, otherwise the reader will lose focus. Don't say too little, otherwise the reader will need to hop around the docs looking for a complete answer.
Landing pages. Introduce a subject and describe the most important aspects of a topic, with just enough detail to give an overview. Going deeper requires one of the next 2 types.
How-To Guides. Provide detailed information necessary to do something, usually code samples, examples of data, and/or dashboard screenshots. How-to pages are normally created to solve a single problem/use case.
In-Depth Guides. Go into more detail that the landing page left out due to the need for brevity on the landing page. They're not about doing anything (that's the purpose of the how-tos); instead, they're about understanding.
The idea is to have one Landing Page per main topic. It should be a starting point. For some users, it might also be a stopping point. The information on a landing page should cover the full subject in a clear but condensed way - which might enough for some readers.
However, many readers will need to see and copy/paste code, or see how to implement some particular use case, or require a bit more explanation on a particular point.
In sum, the landing page frames both the doing (how-to) and deeper learning (in-depth).
To learn more about these page types, check out the reasoning behind the different kinds of pages.
In terms of language and the progressive approach, all 3 types follow pretty much the same guidelines as described in this style guide.
One significant difference is that the landing page is written for all readers, with any level of technical expertise. Therefore, you need to write with the most appeal - plain English, little technical detail, higher-level, no code.
On the other hand, the other pages have more flexibility. You need to be clear about who your audience is and then write for them. Make good use of code and screen shots on these pages.
The How-To page usually follows the same pattern:
For the In-Depth, it is mostly free form. Again, think of your audience. And keep the progressive approach in mind.
H1 = Pager Title H2 = Main page headers or sections H3 = subsections within H2 H4 = subsections within H3
All pages follow the pattern of one H1 at the top of the page and a few well-chosen H2 subsections.
In between the H1 and the very first H2, you can have one or two introductory paragraphs. This is not an obligation, nor does it need to be consistent for every page. Avoid creating an H2 with the words "Introduction" or "Overview".
H3 and especially H4 should be used sparingly. H3s should be used when the H2 gets too long or there's a natural sub-grouping. Likewise for H3/H4. The goal is to keep our pages short. If you have an H2-H3-H3 construction, it's good to keep the H3 content very small. And see if it makes sense to turn an H3 into an H2, or if the additional detail of an H3 cannot be turned into an entirely new page.
Titles are the visible structure of your page, they frame the page for our users. It is for this reason that the H2s are used to create the table of contents.
Read your H2s vertically, from top-to-bottom, and see if this flow tells a story.
Some vertical/storytelling patterns we've seen:
Keep it consistent. For example, if you've started with action verbs (Do X, Build Y), don't switch into the "ing" form (Doing X, Building Y). On the other hand, mixing questions with phrases can be quite effective.
There's not much to say here - keep it reasonable, keep it to one topic, don't go beyond 3 page downs on a normal desktop screen. If it starts getting too detailed, or other related subjects start showing up on the page, move them over to a new page (a new landing page, in-depth, or how-to).
It's difficult for a reader to make sense of one page with too much detail or too many different ideas. It's easier to read 2 pages, where each page is clearly defined and shorter.
1 to 3 paragraphs. Otherwise, start using subsections (Hn+1), or create a new section, or a new page.
3 to 5 sentences on the average.
Keep it to one topic.
1 sentence is ok from time to time.
Organize the paragraph, by thinking of the whole page. it's really the primary unit of documentation. It conveys a full chunk of information.
Ask yourself - What is the point of the paragraph? Make sure every sentence addresses that point. Make the paragraph's point clear up front, usually in the first sentence. And then illustrate, elaborate, and then repeat first sentence, a bit rephrased.
10 to 20 words.
One thought per sentence. Break up complex sentences whenever possible.
Vary sentence length. Otherwise, it's boring. Short sentences are smart. Sometimes. But if they all are short it's choppy. Torturous to read.  Sometimes a longer sentence, one that begins with an indisputable claim, but immediately adds nuance and attempts to synthesis on the spot, this kind of longer flow can be more satisfying to a reader, as it allows them to wade a bit and let the new idea seep slowly in. Or not.
Sentences are all about rhythm. In English, if you can speak what you write, then it's normally good.
3 syllables at most. Joking. See more on Jargon.


---
layout: default
title: Home
---
I'm glad you're here.
I'm a coach and leadership group facilitator
with extensive tech and startup experience. Get in touch with me
here.
Services:
- Executive Coaching: Working with business owners, CEOs and executives to unblock them
- Group facilitation: Facilitate offsite, leadership groups or manager workshops
- Organizational assessments: Assess organizational needs and help facilitate change
My story:
I started as a community organizer, and went into education leading large
programs until I started struggling. I saw my own internal barriers as the
biggest obstacles to my growth and through a variety of trainings.
Now I coach and facilitate groups while I'm pursuing my master's in
Organizational Development at Pepperdine.
Have questions? Schedule some time here.


Spring App API
Hello, World! This is the API page
Below is a description of the API routes
Topics Route
Route: /api/topics
- GET /api/topics Gets all topics
-
GET /api/topics/{id} Gets a topic by id
- POST /api/topics Create a new topic
- PUT /api/topics/{id} Updates a topic
-
DELETE /api/topics/{id} Deletes a topic
Return Home


Home
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Ranking
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Gym info
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{% extends "_wrapper.html" %} {% block main %}
Pomodoro Timer - Work until the timer is up, have a short break, repeat!
Initialising...


Alexander Alexandrovich Ivanec Pechatnikov pereulok, 22 Moscow Russia 107045 tel +7 913 457 50 07 email<EMAIL_ADDRESS>Received date: #RECEIVED_DATE#
Invoice #: #INVOICE_NUMBER#
Negative return
LAB USE ONLY
NAME: #FULL_NAME#
JOB NAME: #JOB_NAME#
EMAIL: #EMAIL#
ADDRESS: #ADDRESS#
CITY: #CITY#
STATE: #STATE#
ZIP CODE: #ZIP#
PHONE: #PHONE#
Scanner
#SCANNER#
Skin Tones
#SKIN_TONES#
Contrast
#CONTRAST#
B&W Contrast
#BW_CONTRAST#
Express Scanning
#EXPRESS_SCANNING#
SPECIAL INSTRUCTIONS
#SPECIAL#
QTY
PROCESS
FILM SIZE
PUSH/PULL
SCAN SIZE


Parses provided HTML as popup template
template(data, id, options = {})
| data |
string, HTML test with popup data |
| id |
string (optional), if there are multiple templates, id of the template |
| options |
obhect (optional), options for the popup |
Description
It will take HTML text, parse it, extracting [rel=title], [rel=body], and [rel=buttons] and open a popup based on this data. If you have following html
You can open it with
This function is used internally, when contetn is loaded with w2popup.load


This tests that following code works in Chrome:
var w = window.open(...); w.opener = null;
After new page finishes loading, its opener should stay as null.
Running ...


$page->title($inst_name." Events Calendar: " . $calendars[$_REQUEST['id']]['title'] . " Search Results") ->header($inst_name." Events Calendar: " . $calendars[$_REQUEST['id']]['title'] ); $page->content_begin(); ?> $i = 0; foreach ($eventFeed as $event) { $i++; } // have no idea why i'm not doing a count() here... if ($i == 1) { echo("
".$i." match found:
"); } else if ($i < 30) { echo("".$i." matches were found:
"); } else { echo("The first 30 matches found:
"); } ?>There were no results returned.
"); } else { foreach ($eventFeed as $event) { include("p_item.html"); } } ?>


the only bot you'll ever need for all your Shrek related content and memes we have loads of commands and we are constantly adding more you can use the navigation bar to see a list of all current commands and to join the support server we would be happy to see you so come and chat and get some Shrek support.
some photos of command usage.


@t header
fn main() { areas := ['game' ,'web' ,'tools' ,'science' ,'systems' ,'embedded' ,'drivers' ,'GUI' ,'mobile' ]for areain areas { println('Hello, $areadevelopers!' ) } }
You can learn the entire language by going through the documentation in half an hour, and in most cases there's only one way to do something.
This results in simple, readable, and maintainable code.
Despite being simple, V gives a lot of power to the developer. Anything you can do in other languages, you can do in V.
{{ raw (T "fast_comp2") }}
The entire language and its standard library are less than 2 MB. V is written in V, and can be built in 0.09 - 1 seconds.
For comparison:
| Space required | Build time | |
| Go | 525 MB | 1m 33s |
| Rust | 30 GB | 45m |
| GCC | 8 GB | 50m |
| Clang | 90 GB [0] | 25m |
| Swift | 70 GB [1] | 90m |
| V | < 2 MB | 0.09s - 1s |
V has only one dependency: a C compiler. If you are doing development, most likely you already have a C compiler installed.
It's a small dependency, and it's not going to be needed once x64 generation is mature enough.
Building V in 0.4 seconds and then using the resulting binary to build itself again:
std::vector<std::string> s; s.push_back("V is "); s.push_back("awesome"); std::cout << s.size();
mut s := [] s << 'V is ' s << 'awesome' println(s.len)
A blog post about translating DOOM will be published soon.
C++ to V translation is at an early stage.
You can follow the progress and read translated code here: github.com/vlang/doom
Get your changes instantly without recompiling.
Since you also don't have to get to the state you are working on after every compilation, this can save a lot of precious minutes of your development time.
DirectX, Vulkan, and Metal support is planned.
A simple example of the graphics library in action is tetris.v.
V has a ui
module that uses native GUI toolkits: WinAPI/GDI+ on Windows, Cocoa on macOS.
On Linux custom drawing is used.
Coming soon:
- a Delphi-like visual editor for building native GUI apps
- iOS/Android support with native controls
- a declarative API similar to SwiftUI and React Native.
v -os windows .
or
v -os linux .
No extra steps required, even for GUI and graphical apps!
(Compiling macOS software only works on macOS for now.)
Building V for Windows using V for macOS, and then testing resulting v.exe on a Windows VM:
v .
No more build environments, makefiles, headers, virtual environments, etc.
You get a single statically linked binary that is guaranteed to work on all operating systems (provided you cross compile) without any dependencies. *
Installing new libraries is as simple as v install sqlite
.
Emitting C will always be an option, even after direct machine code generation matures.
V can call C code, and calling V code is possible in any language that has C interop.
v >>> import http >>> data := http.get('https://vlang.io/utc_now') or { panic(err) } >>> data.text<PHONE_NUMBER>
for file in ls('build/') { rm(file) } mv('v.exe', 'build/') v run deploy.vshRead more about V script
['/post/:id'] fn (b Blog) show_post(id int) vweb.Result { post := b.posts_repo.retrieve(id)or {return vweb.not_found() }return vweb.view(post) }
V forum is running on Vorum (built with V/vweb).
struct Customer { id int name string nr_orders int country string } db := pg.connect(db_name, db_user) nr_customers := db.select count from Customer println('number of all customers: $nr_customers')// V syntax can be used to build queries uk_customers = db.select from Customer where country == 'uk' && nr_orders > 0 for customer in uk_customers { println('$customer.id - $customer.name') }// by adding `limit 1` we tell V that there will be // only one object customer = db.select from Customer where id == 1 limit 1 println(customer.name)// insert a new customer new_customer := Customer{name: 'Bob', nr_orders: 10} db.insert(new_customer)
V. The compiler can compile itself. The original version was written in Go.
No. V uses C as its backend and will compile directly to machine code by the time of the 1.0 release.
For now V emits C and uses GCC/Clang for optimized production builds. This way you get access to sophisticated optimization.
Such builds are compiled ≈150 times slower than V development builds (but are still an order of magnitude faster than C++ production builds).
This can be a problem for industries where optimization is required during development (for example AAA games). In this case, hot code reloading can be used.
In the future V will have its own optimizer.
No. V manages memory at compilation, like Rust:
vlang.io/docs#memory
Yes! V is a very modular language and encourages creation of modules that are easy to reuse.
Submitting your V module takes a couple of seconds.
Installing modules is as easy as
v install sqlite
It's going to be the same as in Go. To run foo()
concurrently, just call it with
go foo()
. Right now it launches the function in a new system thread. Soon coroutines and
the scheduler will be implemented.
Yes! Simply run with -freestanding
to exclude libc and vlib. See an example here.
Yes. This hasn't been implemented yet.
Right now V is under heavy development, lots of things change.
A stable 0.2 version will be released in early December 2019.
1.0 is going to have forward compatibility, meaning that V 1.0 programs will continue to compile and run without change.
It's very important to give developers certainty and stability, and not to be in beta for years. This means that we need to be careful with the 1.0 release. It's going to happen in early 2020.
Windows, macOS, Linux, FreeBSD, OpenBSD, NetBSD, DragonflyBSD, Solaris, Android (Termux).
Android and iOS later this year.
Clang parser is used for translating C/C++ to V.
There are basic plugins for VS Code and Vim. Also check out the Vid editor, which is written in V. It has V support built in.
Plugins for Emacs, and other popular editors will be available soon.
V is a small and simple language, it doesn't need a powerful IDE.
Initially the language had the same name as the product it was created for: Volt. The extension was ".v", I didn't want to mess up git history, so I decided to name it V :)
It's a simple name that reflects the simplicity of the language, and it's easy to pronounce for everyone in the world.
Please note that the name of the language is "V", not "Vlang" or "V-Lang" etc.
The name is not very searchable (like Go), so use #vlang on Twitter, vlang on Google etc.
No, sorry. Macros can be really useful, but they complicate the code significantly. Every company, team, developer can extend the language, and it's no longer possible to jump into a new codebase and immediately understand what's going on.
V will have sophisticated code generation.
MIT.
The V forum runs on Vorum.


We study fluid dynamics phenomena using computer simulations,
particularly fluid flows involving chemical reactions such as combustion.
Our work extends from chemical kinetic reaction models for transportation
fuels to computationally expensive, direct numerical simulations of
turbulent combustion.
Our research focuses on numerical methods to reduce the computational expense
of accurately simulating combustion and engineering fluid flows,
as well as the actual physical phenomena like turbulence-chemistry interactions
and smoldering combustion.


|
|
IFXMotionManager Class ReferenceThis class works with the IFXMotionMixer and works with motions in an array indexed by motionid.
More...
Inheritance diagram for IFXMotionManager:
List of all members.
Detailed Description
This class works with the IFXMotionMixer and works with motions in an array indexed by motionid.
Each IFXMotionMixer draws from one IFXMotionManager.
There can probably be just one master motion manager, but it might be useful to group independent motion sets into different managers.
- Note:
- The motions are stored in an array indexed by the motionid. Try to keep your requested id's small so that the array is not terribly sparse.
Member Function Documentation
| virtual IFXRESULT IFXAPI IFXMotionManager::FindMotion |
( |
const IFXString & |
rName, |
|
|
U32 * |
pMotionId |
|
) |
[pure virtual] |
|
| virtual IFXRESULT IFXAPI IFXMotionManager::GetMotion |
( |
I32 |
motionid, |
|
|
IFXMotion ** |
ppMotion, |
|
|
IFXString * |
pSourceName |
|
) |
[pure virtual] |
|
| virtual IFXRESULT IFXAPI IFXMotionManager::PopulateMotionMixer |
( |
const IFXString & |
rMixerName, |
|
|
IFXMotionMixer * |
pMotionMixer |
|
) |
[inline, virtual] |
|
| virtual IFXRESULT IFXAPI IFXMotionManager::Reset |
( |
void |
|
) |
[pure virtual] |
|
© 1999 - 2006 Intel Corporation. All rights reserved.
* Other names and brands may be claimed as the property of others.
|
|


Hi! My name is Karla Matthews and I'm happy that you stumbled upon my long scrolling website!
This website was made for my 3870 Web Authoring class. The idea behind this website was that
this was made for my grandparents. When this website was created, I had lost my grandmother over the
previous summer. My grandparents house had an area in the backyard with these huge sunflowers and they always
had butterflies around them. My grandmother was known as the butterfly lady by many people, so putting both the sunflowers
that they loved so much, as well as the butterfly was a good representation of my grandparents.
Even though my grandfather passed away 13 years ago, with my grandmother's more recent passing,
I knew that I wanted to make something as a tribute to them.
References:
Background and sunflower was made in Adobe Illustrator by me.
Butterfly Sprite: butterfly


topics_metric.Rd
Returns topic data for a given filter. The filter must filter on the brand's topic_tree.
topics_metric(code, filter, file = NULL, save = FALSE, truncateAt = NULL, showParents = TRUE, showChildren = TRUE, forParent = NULL)
| code | An account code |
|---|---|
| filter | A filter for data |
| file | An optional file name to save a CSV file to. |
| save | Set to TRUE if you'd like a dialog file to choose where to save your CSV. |
| truncateAt | Optional number of results - rest will become "Others". This takes the top topics based on the volume of mentions from the site. |
| showParents | Set this to FALSE if you don't want any parent topics included in the results. |
| showChildren | Set this to FALSE if you don't want any child topics included in the results. |
| forParent | An ID of a parent topic that you want data specifically for. Adds an extra column of percentage values specifically against that parent. |
A tibble of your data
One thing to note is that your filter should contain a topic tree id. This
is a tag ID that identifies the topics assigned to a particular brand. See
below for more details. Using the showParents
and
showChildren
parameters, its easy to only show parent and child topics, respectively.
It is also possible to show only a single parent and its children. See the forParent
parameter.
Essentially, you should pass it the ID of the parent topic whose children you
are interested in.
The major difficulty in using this function is that your filter must contain a topic tree id. This is a tag ID that identifies the topics that have been assigned to the brand(s) that you're querying.
There are two ways to find the ID, both using brandseyer2. The first is to ask for all root brand information, like so:
library(brandseyer2) account("BEUB03AA") %>% root_brands(short = FALSE)
The column topic_tree_id
will give the topic tree ID for the brand that you are interested in.
Another way is to ask for the topic trees in an account:
library(brandseyer2) account("BEUB03AA") %>% topic_trees()
This returns regular tag information (since topic trees are only tags).
Assuming the topic tree that you're interested in has an ID of 1001, and your filter is "published inthelast month and relevancy isnt irrelevant", your new filter should be "(published inthelast month and relevancy isnt irrelevant) and tag is 1001".
# NOT RUN { topics_metric("QUIR01BA", "published inthelast week and brand isorchildof 10006 and tag is 1001") # }


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By now, you definitely know how to handle a list, the most popular collection in Python. Now let's discover an equally useful data type — tuples. You should remember that they are almost identical to lists. What sets them apart is their immutability.
Since tuples cannot be changed, tuple creation is similar to opening a box of a fixed size, then putting several values into this box and sealing it. Once the box has been sealed, you cannot modify its size or content.
Use a pair of parentheses to define a tuple:
empty_tuple = ()
print(type(empty_tuple)) # <class 'tuple'>
Empty tuples are easy to create. Then what went wrong in the following example?
not_a_tuple = ('cat')
print(not_a_tuple) # 'cat'
print(type(not_a_tuple)) # <class 'str'>
As you can see, the variable we created stores a string. It's actually a comma that makes a tuple, not parentheses. Let's fix this piece of code:
now_a_tuple = ('cat',)
print(now_a_tuple) # ('cat',)
print(type(now_a_tuple)) # <class 'tuple'>
So, always use a comma when defining a singleton tuple. In fact, even if your tuple contains more than one element, separating items with commas will be enough:
weekend = 'Saturday', 'Sunday'
print(weekend) # ('Saturday', 'Sunday')
print(type(weekend)) # <class 'tuple'>
The built-in function tuple()
turns strings, lists and other iterables into a tuple. With this function, you can create an empty tuple as well.
# another empty tuple
empty_tuple = tuple()
print(empty_tuple) # ()
print(type(empty_tuple)) # <class 'tuple'>
# a list turned into a tuple
bakers_dozen = tuple([12, 1])
print(bakers_dozen == (12, 1)) # True
# a tuple from a string
sound = tuple('meow')
print(sound) # ('m', 'e', 'o', 'w')
First, let's examine what characteristics lists and tuples have in common.
Both lists and tuples are ordered, that is, when passing elements to these containers, you can expect that their order will remain the same. Tuples are also indifferent to the nature of data stored in them, so you can duplicate values or mix different data types:
tiny_tuple = (0, 1, 0, 'panda', 'sloth')
print(len(tiny_tuple)) # 5
print(tiny_tuple) # (0, 1, 0, 'panda', 'sloth')
Just like lists, tuples support indexing. Be careful with indexes though, if you want to get along without IndexErrors
.
empty_tuple = ()
print(empty_tuple[0]) # IndexError
numbers = (0, 1, 2)
print(numbers[0]) # 0
print(numbers[1]) # 1
print(numbers[2]) # 2
print(numbers[3]) # IndexError
And here the first distinctive feature of tuples comes into play. What they don't support is item assignment. While you can change an element in a list referring to this element by its index, it's not the case for tuples:
# ex-capitals
capitals = ['Philadelphia', 'Rio de Janeiro', 'Saint Petersburg']
capitals[0] = 'Washington, D.C.'
capitals[1] = 'Brasília'
capitals[2] = 'Moscow'
print(capitals) # ['Washington, D.C.', 'Brasília', 'Moscow']
former_capitals = tuple(capitals)
former_capitals[0] = 'Washington, D.C.' # TypeError
In the example above, we tried to update the tuple and it didn't end well. You can't add an item to a tuple or remove it from there (unless you delete the entire tuple). However, immutability has a positive side. We'll discuss it in the next section.
By this time, one question might have come to your mind: why use tuples when we have lists? Predictably, all answers conduce to immutability. Let's dwell on its upsides:
TypeError
.Those were the very basics of tuples in Python. Just like lists, tuples are ordered and iterable. Unlike lists, they are immutable. You'll learn more of tuple features in the next topics, now it's time to write your first programs with them!
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Vexil (named for Vexillology) is a Swift package for managing feature flags
(also called feature toggles) in a flexible, multi-provider way.
Features ¶
- Define your flags in a structured tree
- Extensible to support any backend flag storage or platform
- Take and apply snapshots of flag states
- Get real-time flag updates using Combine
- Vexillographer: A simple SwiftUI interface for editing flags
Documentation ¶
In addition to this README, which covers basic usage and installation, you can find more documentation:
- On our documentation website: https://unsignedapps.github.io/Vexil/
- Detailed documentation and guides in the Documentation subdirectory
Usage ¶
Defining Flags ¶
If you’ve ever used swift-argument-parser defining flags in Vexil will be a familiar experience.
Vexil supports a tree of flags, so we need a structure to hold them:
import Vexil
struct LoginFlags: FlagContainer {
@Flag("Enables the forgot password button on the login screen and associated flows")
var forgotPassword: Bool
}
Side Note: Vexil requires descriptions for all of its flags and flag groups. This is used by Vexillographer for providing context for the flags you are enabling/disabling in the UI, but it also provides context for future developers (especially yourself in 12 months time) as to what flags mean and what their intended use is.
See the full documentation for how to define flags to read more
Checking flags ¶
To check your flags, you need to run them up a Flag Pole:
import Vexil
let flagPole = FlagPole(hoist: AppFlags.self)
// should we show the change password screen?
if flagPole.profile.password.changePassword {
// ...
}
Mutating flags ¶
By default access to flags on the FlagPole is immutable from your source code. This is a deliberate design decision: flags should not be easily mutatable from your app as it can lead to mistakes (eg. flag = true
instead of flag == true
).
That said, it is still very easy to mutate any flags if you need to using a snapshot:
import Vexil
let flagPole = FlagPole(hoist: AppFlags.self)
var snapshot = flagPole.emptySnapshot()
snapshot.profile.password.changePassword = true
// insert it at the top of the hierarchy
flagPole.insert(snapshot: snapshot, at: 0)
For more info see Snapshots .
Flag Value Sources ¶
The Vexil FlagPole
supports multiple backend flag sources, and ships with the following sources built-in:
| Name | Description |
|---|---|
UserDefaults |
Any UserDefaults instance automatically conforms to FlagValueSource |
Snapshot |
All snapshots taken of a FlagPole can be used as a source. |
See the full documentation on Flag Value Sources for more on working with sources and how to define your own.
Snapshots ¶
Vexil provides a mechanism to mutate, save, load and apply snapshots of flag states and values.
Important: Snapshots only reflect values and states that have been mutated. That is, a snapshot is only applied to values that have been explicitly set within it. Any values that have not been set will defer to the next source in the list, or the default value. The exception is when you take a full snapshot of a FlagPole, which captures the value of every flag.
Snapshots are implemented as a FlagValueSource
, so you can easily apply multiple snapshots in a prioritised order.
Snapshots can do a lot. See our Snapshots Guide for more.
Creating snapshots ¶
You can manually create snapshots and specify which flags are affected:
import Vexil
// create an empty snapshot
var snapshot = flagPole.emptySnapshot()
// update some values and states
snapshot.login.forgotPassword = false
snapshot.profile.password = false
// apply that snapshot - only the two values above will change
flagPole.insert(snapshot: snapshot, at: 0)
You can also take a snapshot of the current state of your FlagPole:
import Vexil
let flagPole = FlagPole(hoist: AppFlags.self)
// snapshot the current state - this will get the state of *all* flags
let snapshot = flagPole.snapshot()
// save them, mutate them, whatever you like
// ...
Installing Vexil ¶
To use Vexil in your project add it as a dependency in a Swift Package, add it to your Package.swift
:
dependencies: [
.package(url: "https://github.com/unsignedapps/Vexil.git", from: "1.0.0")
]
And add it as a dependency of your target:
targets: [
.target(name: "MyTarget", dependencies: [
.product(name: "Vexil", package: "Vexil")
])
]
In Xcode 11+ ¶
To use Vexil in Xcode 11 or higher, navigate to the File menu and choose Swift Packages -> Add Package Dependency…, then enter the repository URL and version details for the release as desired.
Vexillographer: A SwiftUI Flag Manipulation Tool ¶
The second library product of Vexil is Vexillographer, a small SwiftUI tool for displaying and manipulating flags.
Read more about Vexillographer .
Contributing ¶
We welcome all contributions! Please read the Contribution Guide for details on how to get started.
License ¶
Vexil is available under the MIT license. See the LICENSE file for more info.


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