CORKY ROMANO RATING: 6/10 http://www.joblo.com/corkyromano.htm
For more reviews and movie screensavers, visit http://www.joblo.com/
THE THEORY: Is it just me or does there seem to be a very distinct pattern to all of these ex-Saturday Night Live comedians' movies? Let's break it down. They usually use the name of the main character (the ex-Saturday Night Liver himself) as the title of the film in two words. Check!  Check! They usually don't last more than 90 minutes. Check! They usually feature a total babe (a no-name) as the co-star, a woman who under any other circumstance (this being a Hollywood movie production) wouldn't cross the street to spit on the star of each film, for whom she inevitably falls in love. Check! They are usually produced by a man named Robert Simonds. Check! They usually have a cameo, or small role, from a once "high-profile" older guy. Check! They usually feature the main character in a "fish out of water" plotline. Check! They usually have the lead come to some kind of understanding near the end, and take part in an "emotional" scene. Check! Okay, okay...I think we get the picture. The folks behind these movies aren't exactly driving the originality boat in Hollywood, but I guess they have recognized some kind of niche in the marketplace and are ready to jam it full of movies like CORKY ROMANO, BILLY MADISON, JOE DIRT, HAPPY GILMORE and DEUCE BIGALOW, and hope to reap the rewards.
PLOT: Corky Romano's family is in the mob. Corky Romano is an assistant veterinarian and doesn't speak to his family anymore. But now that Pops Romano is about to be indicted on racketeering charges, the family needs a "nobody" to go undercover in the FBI and steal their evidence on Pops. Naturally, they call on their goofy relative Corky, to help them achieve their goal. Wackiness ensues?
CRITIQUE: Humor is a very subjective thing. Even more so, movies based on "stupid humor", which is the type of "funny" that this kind of flick is going for, is even more subjective, and usually dependent on your mood and your appreciation for the film's lead actor. In my case tonight, I was looking for some cheap laughs, I really like Chris Kattan (his fruity smile alone cracks me up) and I got enough guffaws out of this movie to recommend it to anyone looking for something very simple, very stupid and pretty funny, for the most part. The movie is basically Kattan.  I dug his take on this goofball and especially appreciated a few scenes in particular, one in which he sees a dead body for the first time, another in which he faces some skinheads and another, where he is high on coke. They really cracked me up. The rest of the film is filled with some laughs every now and then, helped by Chris Penn and Peter Berg, who played the brothers really well, and I want to especially thank the men behind this film for once again choosing an extra-sweet babe to star alongside the big-headed Kattan. Vinessa Shaw, I'm not sure where you've come from, or if you're actually the brunette sister of BILLY MADISON's own Bridgette Wilson, but thank you for wearing that sexy nurse's uniform for a good part of the movie. You've made my night that much easier to download.
The plotline of this film obviously does not have much of a stranglehold on its goings-on, but it was surprisingly passable. It did falter a little near the end, when they made things a little "too easy" on everyone (and that whole Night Vulture thing wasn't needed), and even went for a tear (what the hell?). See my SNL-movie theory at the top of this review for more on that.  I thought the film was pretty funny for the most part, but noticed that the biggest laughs came from the moments in which Kattan was given his space to do "his thing". I didn't laugh when there were big set pieces like when he was caught on the hood of the car, or when his head was placed in a shaker machine, and definitely not when the midget kicked him in the head a la MATRIX/CHARLIE'S ANGELS (guys...please, let's form a committee and stop with the completely unoriginal MATRIX parodies already!).  He also had an amazing nemesis on the FBI, who has his own moment to shine as well (the guy's name is Brick Davis and the scene in which he cracks up is hilarious!).
All in all, the movie does deliver the basics as described in my SNL-movie theory above, tosses in the obligatory gay, fart, drug and retard jokes, and I don't think that anyone should go into this movie expecting much else than that. Basically, if you dig Kattan and his skits on SNL, you will likely enjoy this movie. If you don't like Kattan or his skits on SNL, don't bother seeing this movie and that'll be that. I think I offered the same advice to Tom Green fans/non-fans for FREDDY GOT FINGERED and it just seems to make sense.
Where's JoBlo coming from? Billy Madison (7/10) - Freddy Got Fingered (5/10) - Happy Gilmore (8/10) - Joe Dirt (5/10) - The Ladies Man (6/10) - Little Nicky (6/10) - The Waterboy (4/10) - Zoolander (7/10)
Review Date: October 10, 2001 Director: Rob Pritts Writers: David Garrett & Jason Ward Producer: Robert Simonds Actors: Chris Kattan as Corky Romano Peter Berg Chris Penn Genre: Comedy Year of Release: 2001 ------------------------------------ JoBlo's Movie Emporium http://www.joblo.com/ ------------------------------------ (c) 2001 Berge Garabedian
========== X-RAMR-ID: 29795 X-Language: en X-RT-ReviewID: 254296 X-RT-TitleID: 1110376 X-RT-SourceID: 573 X-RT-AuthorID: 1021 X-RT-RatingText: 6/10
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.
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to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


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{% extends "base.html" %} {% block content %}
Use !pin <first few words of message>
to pin a single message.
!quote
(with newlines included) pins a message block.
<first few words of start message>
<first few words of end message>
The bot also accepts message IDs. You can copy any message's ID by turning on Developer Mode in the Appearance menu of Discord settings. This seems to be the only thing Developer Mode does.
Use !pinfile
to grab the current channel's pin file, or !pinfile #channel
to obtain another channel's pin file.
Use !pinfile all
or !omnipinfile
to grab a pinfile for the whole server.
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to unpin the last message(s) that you pinned, within ten minutes of pinning.
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As a (user),
I want (a feature)
so that (value)
Introduction
Policy #188 lives as a Technology Policy in the Office of the Chief Information Officer
OCIO Policy #101
State Agencies, Boards, Commissions & Higher Edu
Recommended for Legislative and Judiciary
Policy #188
Intent
The State’s Policy connects WA State IT, services and digital communications with the State’s
obligation under Title II of the Americans with Disability Act to not discriminate
against people with disabilities.
Accessible services for
For WA State 65,000 State Employees
7.4 million citizens
Agile
I am also...
Scrum Master and agile coach
Scrum for more than writing code
- Business Intelligence and Data Warehousing
- Procurement and Contracts
- Change Management
Agile is...
- Flexible
- Collaborative
- Iterative
Agile, the enemy of Accessibility
Rush to production, push to release customer value
Use Agile to Drive Accessibility
User Stories
“As a (user), I want (a feature) so that (value)”
- replace -
Classic waterfall "system shall" type requirements
Requirements & Tests
Test are requirements and requirements are testing
Common Guidelines for User Stories
- Bridge Tech and User Value
- Understand the users
- Think as a user
Empathy
the ability to understand and share the feelings of another.
WCAG User Stories
“As a (user), I want (a feature) so that (value)”
WCAG Layers
- Principles - perceivable, operable, understandable, and robust
- Guidelines - basic goals to make content more accessible - not testable
- Success Criteria - testable success criteria & more
WCAG
1. Perceivable
Information and user interface components must be presentable to users in ways they can perceive.
Guideline 1.1 Text Alternatives
Provide text alternatives for any non-text content so that it can be changed into other forms people need, such as large print, braille, speech, symbols or simpler language.
WCAG User Stories
“As a (Benefits), I want (Success Criteria) so that (Value that relates to your product)”
Sucess Criteria for Stories
User stories aren't testable
Behavior Driven Development (BDD) Criteria are testable
GIVEN Exact context
WHEN Action/Event
THEN Outcomes
AND/EXCEPT More of the same…
GIVEN a non-text element exists
WHEN a user's browser or AT focus in on the non-text elements
THEN text alternative that serves the equivalent purpose are available
Reuse and Overuse
Reusable - Absolutely!
Danger of overuse...
Definition Of Done
an agreed upon list of the activities necessary to get a product increment to a done state
Great for automated testing
Not a substitue for User Stories!


UK insurer Aviva unveils Asia overhaul
LONDON, Nov 20 — British insurance firm Aviva today announced a major overhaul that could see it leave several countries in Asia.
Aviva, whose chief executive Maurice Tulloch launched a strategic review to slash costs, said in a statement that it will exit Hong Kong — and explore options over divisions in Vietnam and Indonesia.
However, the London-listed company added that it will retain businesses in China and Singapore.
“Today, we are presenting our strategy to simplify Aviva into a leading international savings, retirement and insurance business delivering for our customers, shareholders and communities,” Aviva said in a statement.
“These actions will drive higher returns for our shareholders. At the same time, we remain committed to our progressive dividend policy, our deleveraging plan and maintaining the financial strength of the group.”
Aviva had already said in June that it planned to cut 1,800 jobs over the next three years, out of a global workforce of 30,000, as part of a restructuring spearheaded by Tulloch.
Today’s news sent Aviva’s share price sliding 4.21 per cent to stand at 400.70 pence in late morning deals on London’s falling FTSE 100 index. — AFP


jl777c
05-Jan-20 11:00 AM
it isnt a lossless protocol, but if a reliability layer is added on top of it, maybe it can. also there isnt a large amount of privacy with the dexp2p, so it would need to be carefully analyzed to see if point to point encryption will be sufficient
i made a new release, it is hardforking
now any chain with -dexp2p=2 (or 1) will activate the decentralized DEX p2p messages
txpow is active at a minimal level, tx/sec of blasters will likely be reduced by 10x to 50x
packet priority is supported. each priority level doubles the txpow needed, dont go much above 16 and for now priority is not affecting routing, so can just set it to 0
3 indices in parallel are maintained in realtime for all packets with tags (or destpubkey)
DEX_broadcast now will actually broadcast the hex specified and takes a lot more parameters
DEX_list is able to get a list of all the packets with matching tags
plz dont abuse the messaging side yet, it is not hardened and limited to just 64 different tags for now. also, it will likely crash after an hour if you are making DEX_list requests that needs to get a packet being purged
(edited)
destpubkey encryption and min/max amount filtering in DEX_list is not implemented yet, nor is minpriority filtering. DEX_stats and DEX_get is not implemented yet
however, enough should be work to be able to broadcast a specific message with a specific tag and then see it in a list
for example if tagA is KMD and tagB is BTC, then the packet will be in the indices for "KMD", "BTC" and "KMD/BTC". that will allow future DEX_list for BTC to find this along with any other that had BTC as tagA or tagB. if you DEX_list with both KMD and BTC, only the ones that match the order will be returned. so to get an orderbook, you can do a DEX_list KMD BTC and a DEX_list BTC KMD and construct it. however, i plan to add convenient orderbook functions as each packet also has volumeA and volumeB fields that can be used to calculate a price
i was just able to get this to work in a few cases and havent had a chance to find and fix all bugs, but too tired now and will wait for bug reports based on the above functionality


For more info about sass - http://sass-lang.com/
We are using very easy to understand and compiles ruby, compass, and sass by command lines.
- 1- Download and Install Ruby from here:- http://rubyonrails.org/download/
- 2- After complete installation > open assan html folder > hold Shift + right click
- 3- type this command line - gem install sass
- 4- then type this - gem install compass
- 5- then type this - compass watch
- Now the compass is watching changes for your .scss file, when you make changes into the .scss file, the compass compile them into .css file
- Recommended editor - Netbeans IDE 8.01 (easy to use and compile files)


THtmlViewer, TFrameViewer components
property ServerRoot: string;
The ServerRoot property establishes an effective root directory on disk. URLs which begin with a slash (or backslash) are considered to be relative to this directory. If no ServerRoot is specified, the root is taken to be the root directory of the current drive.
ServerRoot := 'C:\MyDirectory';
The URL:
/images/new.gif
will be now be translated to:
C\MyDirectory\images\new.gif


WARRIORS OF VIRTUE A film review by Steve Rhodes Copyright 1997 Steve Rhodes
RATING (0 TO ****): ** 1/2
WARRIORS OF VIRTUE features five organic superheroes, who must rely on the natural weapons of earth, water, metal, wood, and fire to fight their battles. They possess the classic virtues of benevolence, righteousness, order, wisdom and loyalty, and their creed does not permit them to kill, even in the heat of battle. Their hokey latex suits make them look like donkeys standing on their hind legs, but actually they are supposed to be fighting kangaroos known as Roo-Warriors. As Dave Berry would say, "I am not making this up."
Four brothers, Chris, Dennis, Jeremy, and Ron Law, who are medical doctors from Hong Kong, had the idea for the film and are its producers. Although adults may be bored stiff by the confusing story and find the outfits laughable, the film will undoubtedly find an eager audience with the grade school group.
The film starts off strongly with a highly imaginative kung fu sequence inside the kitchen of a Chinese restaurant. Peter Pau's cinematography is stunning throughout the film, especially during the food photography. The images remind one of LIKE WATER FOR CHOCOLATE. A lavish food scene of sumptuous beauty is the last thing one expects in a kids' movie, especially a magical, superhero one. Another lavish scene has moving lanterns in the distance against a bluish night. This one appears as though lifted out of a Metropolitan Opera production.
Wide eyed Mario Yedidia, who was George in the movie JACK, plays Ryan Jeffers, a boy with big ambitions but with a metal brace on one of his legs. "It's pretty hard to fly if you have a broken wing," he explains to his friend and mentor from the restaurant. Ryan, whom we first meet reading on the toilet, is absorbed in his sword and sorcery books. His imaginary life has all the aspects missing from his real life and more.
One day, the school quarterback and local Lucifer, dares Ryan to walk on a pipe over a treacherous whirlpool. Ryan falls and "dies," but awakens in an underworld populated by the type of heroes whom he has read about. And, miracle of miracles, his leg has been cured and the brace has disappeared.
In Ryan's new and magical world, the Warriors of Virtue, led by Master Chung (Chao Li Chi) fight the forces of evil controlled by a warlord named Komodo, played by Angus MacFadyen, who was Robert the Bruce in BRAVEHEART. The less said about MacFadyen's atrocious acting, the better.
One of the people Ryan meets in this underworld kingdom is a gorgeous girl named Elysia, played by Marley Shelton, who was Tricia Nixon in NIXON. Shirley Chan's costumes for her are luxurious gowns, some in innocent white with high necks and some low cut and in voluptuous colors. Both are in strong contrast to the dull forest colors on the rest of the actors. My immediate reaction on seeing her was a desire to tell the kid to lose those donkeys and grab the girl.
Elysia gives Ryan a 30-second explanation of the characters and their places in his new environment, but it makes about as much sense as flipping through the Cliff's Notes version of "War and Peace." Eventually, the story comes together somewhat, but the confusion remains. It feels like you are watching the twenty-fourth episode of a dungeons and dragons series, where you missed the first twenty-three episodes.
As an adult, there is only one way to survive the film -- crank your brain down to third grade level. Ignoring the silly plot and funny rubber heads, you can begin to appreciate the majesty of the rest of the production. The sets are by Academy Award winner Eugenio Zanetti, who won the award this past year for his creations for RESTORATION. In both films, the sets are the most impressive parts of the movie. Zanetti's vision of a fantasy world has a forest of gigantic trees all shrouded in fog. A stream runs through the dense forest, and the houses are build amongst the trees. A tad too dark for the preschool set, but beautiful nevertheless. He and the director of photography clearly worked together closely on the effect they wanted to create.
The film is at its most magical in the choreographing of the fight sequences. They are done in a blur with the characters whirling around each other. Golden, autumnal leaves swirl with them in a dazzle of color.
Finally, I must comment at length on the PG rating of the film, which I fear may be too lenient. There is a single bad cuss word in the film, and it is used effectively for humor in one of the movie's best lines. Still, it is just the sort of inappropriate phrase that your kid will repeat endlessly. The film has several open mouth kissing scenes, where the kisses are carefully aborted just before the lips and tongues touch. The biggest problem comes in several violent scenes which would have worked just as well without the blood. If you make the mistake of taking kids younger than seven, they may repeat Ryan's plea: "I don't want to be here anymore. I don't want to see death. I just want to go home."
WARRIORS OF VIRTUE runs 1:41. It is rated PG. The film's target audience is the grade school crowd. Younger ones may be frightened, and older ones bored. My son, Jeffrey (age 8), says the film is "super," and wants me to take him to see it again soon. The beauty of the production convinced me to give the picture thumbs up and ** 1/2.
**** = A must see film. *** = Excellent show. Look for it. ** = Average movie. Kind of enjoyable. * = Poor show. Don't waste your money. 0 = Totally and painfully unbearable picture.
REVIEW WRITTEN ON: April 28, 1997
Opinions expressed are mine and not meant to reflect my employer's.
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


{% extends "storybase_story/widget_base.html" %}
{% comment %}
Template for embed widget that shows both a featured story and list of stories
By default, this template is empty because the required markup is included in
``storybase_story/widget_base.html``. This template is maintained as a hook
if this particular widget layout needs to be customized.
{% endcomment %}


Alex's portfolio
Work
Hejorama
About
Work
Some interesting things I've been working on...
Website:
listento.tv
Complexity: **
Skills: html5, css, jquery
Website:
cosmo-sapiens.com
Complexity: ***
Skills: css, wordpress
Website:
eyetime.fr
Complexity: ***
Skills: PHP, MySQL, Jquery
Hejorama
Hejorama is my main side project...
Website:
hejorama.com
Complexity: ***
Skills: project management
Project:
HejoMap
Complexity: **
Skills: css, openstreetmap, javascript
Project:
Instagram
Complexity: **
Skills: Jquery, Instagram API
About


Education
Ann Arbor, MI
B.S.E. in Computer Science
--Regents Merit Scholarship (Michigan Resident with High
Academic Achievement)
--Completed Coursework--
Data Structures and Algorithms, Web Databases and Information Systems,
Computer Organization, Discrete Mathematics, Linear Algebra,
Entreprenuerial Business Basics
--Current Coursework--
Machine Learning, Computer Security, Marketing Mgt
Sep '16 - May '20
Work Experience
Ann Arbor, MI
Intern
--Ensured swift resolution of all delivery system bugs and errors
--Developed and built online inventory system to expedite
expense tracking as well as tracking servers currently in use in the field,
built using a combination of AngularJS, C#, SQL and HTML5/CSS
Summer '18
Ann Arbor, Mi
Research Analyst for Dr. Iain Osgood
--Evaluate the broader key public policy issues regarding preferential
trade agreements that garner the most concern from industrial America
--Create python algorithms to quickly gather and present data in a logical format
--Analyze the coded data using R statistical software to evaluate results
Sep '16 - Present
Leadership Roles
Ann Arbor, MI
Director of Marketing
--Developed concrete branding plan of action that ensures the
community has a singular image of Kappa Theta Pi
--Created
an Alumni network allowing actives to capitalize on connections
within industry
--Participate in weekly meetings and discuss the
development of technology in the local community, the local
industry, and the greater world
Sep '17 - Present
Ann Arbor, MI
Account Director
--Managed team of five students as well as relationship with
individual client
--Conducted sales data breakdown for local
startup Wedit, identifying new avenues to increase revenue
and decrease costs
--Generated insights on the average customer
profile for Wedit by using Google Analytics, this resulted in
a pivot of the targeted marketing sectors
--Implemented and
tracked the effects of recommended marketing strategy for
local gelateria, Iorios, which increased in store foot traffic
by 7%
Sep '16 - Jan '18'
Languages
C++
Python
ReactJS | AngularJS
HTML5/CSS
Projects
Python
Regulations.gov Web-Scraper | github
--Created an application that allows users to easily download and
analyze comments on all legislature submitted to regulations.gov
--Users can download all data as a CSV file, a TXT file for text-classification
purposes, or simply download all files relating to the legislature for custom use
ReactJS | HTML5/CSS
KappaThetaPi.com | live website | github
--Oversaw front-end development and design.
--Continue to monitor google analytics to track
traffic and use the data to improve the website
R Statistical Software
Corporate Support for PTA's study | report
--Developed and analyzed database of frequently discussed topics in
submissions to the United Statess Trade Representative
--Found that sales, sourcing, and regulation are all significant drivers
of industrial preference on trade
--Firms and industries, even within the same sector, are very heterogenous
in their areas of main interest concerning PTA's
Contact
<EMAIL_ADDRESS>LinkedIn
Github


AddThis installation example
This is example native toolbox
This is example about sharing toolbox
Using AddThis only
Using facebook plugin only
Using both shoulb be conflicted


We sell blockchain!
- revolutionize integrated solutions
- unleash impactful synergies
- enable B2C infrastructures
- evolve cross-media web-readiness
- orchestrate granular markets
- synthesize cutting-edge e-tailers
- maximize web-enabled portals
- deliver efficient niches
- cultivate value-added web services
- unleash best-of-breed partnerships
- seize customized technologies
- matrix cross-media web-readiness
- expedite scalable content
- morph out-of-the-box vortals
- brand bleeding-edge convergence


aKDPhi
Photo Gallery
aKDPhi
Home
About Us
Our Sisters
Active House
Board Members
Full Roster
Photos
Recent Events
Photo Gallery


This page lets you select the desired language module and localized help file from those installed. To find out how to install or even create new language packs see Language packs.
Interface
Select the desired language module to be used for menus, dialogs and messages.
The English interface is built into the program, so it is always available.
Other modules can be downloaded and installed separately.
Help
Select the desired help file to be used. DeskPins originally contains only the English
help file.
Each option shows the language name and intended program version (e.g. German v1.30), and the corresponding file name than contains it (e.g. DeskPinsGerman.chm). The interface and help versions should match the version of the program, otherwise you may get erratic behavior.


Practice Questions 1 21/01/2018
1. A ____________ element includes the start tag, contents, sub-elements, and end tag.
- Container
- Standalone
- Emphasis
- Formatted
2. Which of the following DOCTYPE is used in HTML5?
- HTML
- Standalone
- Emphasis
- Formatted
3. What does URI stand for?
- Uniform Resource Informer
- Uniform Resource Identifier
- Uniform Reply Identifier
- Uniform Recourse Information
4. Which of the following formatting element emphasizes the text as compared to its surrounding text?
- B
- Strong
- I
- U
5. Which of the following tag is used to specify a definition term?
-
-
- <dfn>
-

6. Which of the following are the two elements used for creating an ordered list?
- OL
- UL
- LI
- DF
7. The _________ tag is used to apply preformatted text content to a Web page.
-

-
- <dfn>
-
8. Which of the following is the mobile-based operating system developed by Apple?
- Symbian
- iOS
- Windows 7
- Android
9. Which of the following attribute element is used to provide a hyperlink?
-
- <h>
-
-
10. Which of the following value loads the target URL in the same window of the current Web page?
- _blank
- _self
- _top
- _bottom
11. Which of the following paths has links that contain the complete address to get to a web page?
- Absolute
- Non-Absolute
- Relative
- Non-Relative
12. To automatically add a subject line in the new e-mail message, the ________ attribute must be inserted after the email address.
- ?subject=
- ?subjectline=
- ?subject line=
- ?topic=
13. The ________ attribute of the A element specifies the location where the linked web page will open when the link is clicked.
- subject
- object
- target
- pageopen
Exercise 1
-
Create a web page and insert the content provided containing the same formatting style as shown.
Sample Content
A multi-tier enterprise application consists of two types of objects: application logic components and persistent data objects. Application logic components represent actions and define methods that perform common tasks such as calculating the order price, customer credit card billing, and so on. Session beans model the application logic components as they contain the logic to perform the application tasks.
It is easy and simple to manage the business data present in large organizations as objects rather than as relational rows ina database. Business data when treated as objects are easy to handle and manage as they are compact in nature.
Entities are not associated with only EJBs and can be used in Java 2 Standard Edition (J2SE) environment. The difference between an Entity and a session bean are as follows:
- Entities have a client-visibility and a persistent identity that is distinct from their object reference. Each entity is disinct from the other because of their identities. Clients can use identities to pass the reference of an entity to other applications. However, this is not possible with Session beans.
- Entity bean cannot be accessed remotely, whereas Session beans can be accessed locally as well as remotely.
- Entity's lifetime is not dependent on the application's lifetime. Entity bean have a much longer lifetime than a client's session as it depends on how long the data is present in the dtabase.
-
Entities represent data in a storage that is permanent as well as fault-tolerant and hence, they are long lasting and can survive application server or database crash. This data can be reconstructed in the memory. Hence, it can be said that entity is an in-memory representation of persistent data that:
- Can be loaded from persistent store and its field populated with the stored data
- Can be modified in-memory to change the data values
- Can be saved and correspondingly database is updated
- Apply bold formatting for the required words as shown in the sample content.
- Apply an unordered bullet list to the text as shown in the sample content.
Exercise 2
-
Solution Anywhere is an open source software development organization headquarters at Chicago, USA. The company wants to make its presence felt worldwide by creating a web site which will highlight the activities performed by the organization. The organization also provides all the software that are available as freeware. You as a web site developer for the organization have been asked to create the following four separate web pages for the organization.
- Home
- About Us
- Downloads
- Contact Us
-
Link all four web pages to each other using hyperlinks on all four pages.
-
In the Downloads page, provide an e-mail address that is hyperlinked. This e-mail link must open a new e-mail with the subject line as 'New E-mail'


Located on a small archipelago, the Bawah Island Resort is a remote paradise off the coast of southern Malaysia.
Indulge yourself in an environment where nature has inspired both our design and our service. Our staff think and act holistically, and our healthy meals draw ingredients from nearby environments whenever possible. We care about our ecosystem and we welcome our guests to participate in our conservation efforts to protect this amazing environment.
Only accessible by seaplane, Bawah is true barefoot luxury, a sanctuary to slow down and unwind in. Your package includes full round-trip transfers from Singapore, daily meals (breakfast, lunch and a la carte dinner), soft drinks, wellness centre and surface water sports. No hidden costs.
Your journey begins at 7am (Singapore time) with a luxury car from your home, hotel or airport in Singapore, to Tanah Merah Ferry terminal. Here you will be met by a member of the Bawah team and escorted on a short ferry ride to Batam,Indonesia. With snacks and cold drinks provided. Fast tracked through immigration we then introduce you to our travel coordinator, Vio, who will guide you through the rest of your journey. Starting at our private lounge in Batam airport, where you will then take a 70-minute flight aboard our private plane, an amphibious plane, with 2 pilots, that takes off on land and then arrives on water, in the beautiful lagoon right in front of your suite. Your exciting journey will take around 3 1/2 hours.


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Builder Darren Rowe, 49, secretly took the images without her consent and uploaded them using a customer’s wifi.
He has now been prosecuted and received a suspended sentence.

‘One night 12 men came to the door. I was with my three-month-old baby at the time. Another time someone tried to get in the house.’
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Rowe, from Rowley Regis, was given a 12-month suspended jail sentence after admitting two counts of disclosing private sexual photographs and films with intent to cause distress at Wolverhampton Crown Court.
His 39-year-old victim told the BBC she was ‘disgusted’ by what she believes is a lenient punishment.
Over a four-month period in summer 2017, around 35 men came to her house, she said.
At first, her partner claimed the photos had been posted by one of her friends, but police uncovered the truth.
Customer services assistant Sharon said: ‘The police think he wanted to play the hero, whereas I think he had certain fetishes, which I’m not interested in.’
She added: ‘I believe he put me in danger for his own enjoyment and I would encourage any other woman going through something like this to report it.

‘You learn to live with it daily unfortunately, but time is a good healer. If anyone is going through this, I beg you to speak out, you are not alone.’
PC Lee Newell, of West Bromwich CID, said: ‘This has been a long and complex case.
‘But our investigations revealed that the only person linked to an email address used to transmit the material was in fact Rowe.
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‘His victim has suffered a distressing onslaught of online abuse. But following this conviction we now wish her well for the future.’
Got a story for Metro.co.uk?
Get in touch with our news team by emailing us at<EMAIL_ADDRESS>For more stories like this, check our news page.


Kidney Exchange Solver
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Preview Pane is contained in preview-pane
directive. This directive contains the following attributes:
Preview Pane is triggered by a Preview button defined in the preview-pane-toggle
directive. The attributes
of this directive are:
.hpe-divide-right
or hpe-checkbox
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directive. The directive defines the
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Define the tabIndex on each row to enable keyboard navigation.


--- layout: post title: Automating static website publishing using google cloud build categories: [automate, gcp, cloud, build] tags: [automate, gcp, cloud, build] image: cloud-build.png ---
Go to Goggle Cloud Build → Build Triggers section on the GCP Console and click “Add trigger”:
Select GitHub as the source of your repository. In the next screen, you may be asked to authorize access to your GitHub account and repositories. This is needed for Google Cloud Source Repositories to mirror and create commit hooks on your GitHub repositories.
Then, pick your repository from the list
In the next screen
create a cloudbuild.yaml file with the following contents in your repository.
I am using jekyll for static site builder, _site directory is where the generated site will be placed (by default) once Jekyll is done build. replace _site with your website content directory
{% highlight yml %} steps: - name: gcr.io/cloud-builders/gsutil args: ["-m", "rsync", "-r", "-c", "-d", "_site/", "gs://{BUCKET_NAME}"] {% endhighlight %}YAML file declares a build step with the gsutil -m rsync command and makes sure that the website is uploaded to the storage bucket. The -m flag accelerates upload by processing multiple files in parallel and the -c flag avoids re-uploading unchanged files.
Once you push the cloudbuild.yaml file to your repository and create the Build Trigger, you can kick off the first build manually. Head over to the GCP Console Build Triggers section, click “Run Trigger”


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string readdir ([ resource $dir_handle ] )
Returns the name of the next entry in the directory. The entries are returned in the order in which they are stored by the filesystem.
Returns the entry name on success or FALSE on failure. Warning: This function may return Boolean FALSE, but may also return a non-Boolean value which evaluates to FALSE, such as 0 or "". Please read the section on Booleans for more information. Use the === operator for testing the return value of this function.


--- title: Notes To Self permalink: notes/index.html layout: page ---
these are notes to me. I journal a few pages every morning (or at least I try). The interesting bits I'll post here.
{{post.content}}


Fake news Classification with Logistic Regression
With the maturity of the internet, fake news has become much more common in recent years. Fake news is disinformation which is disguised as “real” news to influence public opinion. Can we use machine learning to classify news articles fake or real? Text contains a lot of information let’s investigate if we can use it!
In this blog post, we construct a simple logistic regression model to classify news articles to be fake or real. We’ll be using bag-of-words and tf-idf representations to vectorize our textual data.
I try to recreate and improve the Bayesian model from George McIntire detailed in this post with a logistic regression model.
1. Load Data
Let’s load news data and look at the first few rows. We’ll be using only the core text of the articles to train our classifier. This will keep our model simple. You could also use the title or the title in combination with the full text or other extracted features.
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
news = pd.read_csv("https://s3.amazonaws.com/assets.datacamp.com/blog_assets/fake_or_real_news.csv").iloc[:, 1:]
news = news[["text", "label"]]
news.head()
| text | label | |
|---|---|---|
| 0 | Daniel Greenfield, a Shillman Journalism Fello... | FAKE |
| 1 | Google Pinterest Digg Linkedin Reddit Stumbleu... | FAKE |
| 2 | U.S. Secretary of State John F. Kerry said Mon... | REAL |
| 3 | — Kaydee King (@KaydeeKing) November 9, 2016 T... | FAKE |
| 4 | It's primary day in New York and front-runners... | REAL |
The data can be downloaded here and is provided by DataCamp.
In the cell above I display the text column which contains the text of the article and the label columns. This las one holds information on whether or not the text is fake.
2. Exploratory Data Analysis
Before modeling the data, let’s first take a look at the textual data! I would like to see if there is a simple difference between real and fake texts. There are several basic ways to look at our textual data. We could look at the number of words of our texts, the number of characters or the average length of words. Furthermore, it maybe could be useful to do part of speech (POS) tagging, do entity recognition or compute readability scores and identify differences between the text classes.
Let’s start by printing the first sentence of a fake and real news article to see if these text parts give us clues about fake or realness. We do this by using a string method to split on a new line.
print("FAKE ARTICLE: {} \n".format(news["text"].str.split('\n')[0][0]))
print("REAL ARTICLE: {}".format(news["text"].str.split('\n')[2][0]))
FAKE ARTICLE: Daniel Greenfield, a Shillman Journalism Fellow at the Freedom Center, is a New York writer focusing on radical Islam.
REAL ARTICLE: U.S. Secretary of State John F. Kerry said Monday that he will stop in Paris later this week, amid criticism that no top American officials attended Sunday’s unity march against terrorism.
I couldn’t really classify these articles to be real or fake, at least not based on their first sentences. Before diving deeper into the exploratory data analysis, let’s look at the distribution of our target labels.
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn-whitegrid')
sns.set_style('whitegrid')
sns.countplot(y ="label", data=news)
plt.title("Label count per target class", fontsize=14);
The figure above shows an even distribution of labels. There are as many fake as real news articles in our datasets. We set our baseline at around 50%. Anything above this score will be better than random guessing.
2.1 Number of words
Let’s see if there’s a difference between the number of words used in real articles compared to fake news texts. We first look at some simple summary statistics to get a feel for the data. We apply a lambda function that splits every word in the texts, stores it in a list and gets the length of the list. After that, we store word count back into a new column in our original data frame.
news["word_count"] = news['text'].apply(lambda x: len(x.split()))
news.head()
| text | label | word_count | |
|---|---|---|---|
| 0 | Daniel Greenfield, a Shillman Journalism Fello... | FAKE | 1296 |
| 1 | Google Pinterest Digg Linkedin Reddit Stumbleu... | FAKE | 446 |
| 2 | U.S. Secretary of State John F. Kerry said Mon... | REAL | 431 |
| 3 | — Kaydee King (@KaydeeKing) November 9, 2016 T... | FAKE | 404 |
| 4 | It's primary day in New York and front-runners... | REAL | 317 |
We can now compute several summary statistics on the word count grouped by there label. Let’s see if there are differences in word count between fake and real articles.
news.groupby('label')['word_count'].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| label | ||||||||
| FAKE | 3164.0 | 679.129267 | 958.962790 | 0.0 | 212.0 | 421.0 | 830.0 | 20891.0 |
| REAL | 3171.0 | 873.257647 | 722.483569 | 7.0 | 450.5 | 771.0 | 1123.0 | 7602.0 |
The output above suggests that real news articles have a greater average and median word count, with less variation (standard deviation and percentiles) than the fake news articles. The range of word count of fake news articles is far greater ranging from 0 to more than 20.000.
Let’s look at the distribution of words and whether or not there is a difference in word count between real and fake news. I will use seaborn
’s FaceGrid
and kdeplot
to display this distribution.
g = sns.FacetGrid(news, hue="label", aspect=2.5, height=2.5)
g.map(sns.kdeplot, "word_count", shade=True)
g.fig.suptitle("Word count distribution for Real and Fake News", y=1.05, size=14);
g.add_legend(title="");
The plot confirms our summary statistics inferences. Real news articles seem to have a greater word count than fake news articles. We could do statistical tests to confirm this. Further exploration could look at the number of characters or number of sentences. We, however, are going to do this but look at readability.
2.2 Readability
Are fake news articles easier to read real news articles? Let’s find out! We’ll use a readability score to quantify this. Readability tells us the ease with which the reader can understand a text. The readability depends on the contents of the texts. It measures the complexity of the vocabulary and syntax.
We’ll use the Gunning fog index to classify the readability of our news articles. This index takes into account two basic factors;
- The greater the average sentence length the harder the text is to read.
- The greater the percentage of complex words, the harder the text is to read.
The formula returns a score corresponding to the grade in which someone would be able to read the text. The higher the index the harder the text is to read. I’ll use the textstat
package to get the score of every text and store it in a new column.
import textstat
news['gunning_fog_score'] = news['text'].apply(lambda x: textstat.gunning_fog(x))
news[['text','label', 'gunning_fog_score']].head()
| text | label | gunning_fog_score | |
|---|---|---|---|
| 0 | Daniel Greenfield, a Shillman Journalism Fello... | FAKE | 12.30 |
| 1 | Google Pinterest Digg Linkedin Reddit Stumbleu... | FAKE | 13.32 |
| 2 | U.S. Secretary of State John F. Kerry said Mon... | REAL | 20.21 |
| 3 | — Kaydee King (@KaydeeKing) November 9, 2016 T... | FAKE | 19.53 |
| 4 | It's primary day in New York and front-runners... | REAL | 16.35 |
Let’s group by the target label and see if there is a difference between the readability score for real and fake news articles. I don’t know if textstat gives as the correct gunning fog score (the seem high), but we can use it for comparison.
news.groupby('label')['gunning_fog_score'].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| label | ||||||||
| FAKE | 3164.0 | 20.785307 | 15.188323 | 0.0 | 13.3075 | 17.27 | 23.595 | 325.13 |
| REAL | 3171.0 | 23.458212 | 14.673496 | 3.6 | 16.1600 | 20.67 | 26.950 | 440.66 |
The summary statistics of the Gunning fog index suggest that real texts are somewhat harder to read. They also have less variation in their readability score. Which if we think about it makes some intuitive sense. If we plot the distribution of scores we also see this difference.
g = sns.FacetGrid(news, hue="label", aspect=2.5, height=2.5)
g.map(sns.kdeplot, "gunning_fog_score", shade=True)
g.fig.suptitle("Gunning Fog Score score distribution for Real and Fake News", y=1.05, size=14);
g.add_legend(title="")
plt.xlim(0,100);
3. Train and Validation & First Model
Before we go and model the data we split the data into a train and validation set. We create a validation dataset to validate our classifier. We’ll use 75% of the data for training the logistic and 25% for validating our model. We’ll only use the text of the article, not the title, word count or readability score. Furthermore, we use stratification to ensure that we have even distribution of target label classes in the train and validation data.
from sklearn.model_selection import train_test_split
target = news.label.values
X_train, X_valid, y_train, y_valid = train_test_split(news, target, test_size=0.25, random_state=2, stratify=target)
f"Train shape = {X_train.shape} Test shape = {X_valid.shape}"
'Train shape = (4751, 4) Test shape = (1584, 4)'
Let’s use a train a logistic regression classifier. We use stratified cross validation to validate our model.
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold, cross_val_score
logreg = LogisticRegression()
kfold = StratifiedKFold(n_splits=10, random_state=2)
cv = cross_val_score(logreg, X_train[['word_count', 'gunning_fog_score']], y_train, cv=kfold)
cv.mean()
0.61734409553295
4. Feature Extraction and Modeling the Data
These simple statistics do not seem to really help us classify fake news. Accuracy is higher than random but I think we can do better. We need to find another way to represent text that does allow a model to make better classifications.
4.1 Bag-of-words model
We will start by building a bag-of-words model. A bag of words model is a simple way to represent text. In a bag-of-words model, we extract word tokens from the text and compute the word frequency of these tokens. Based on these frequencies and our corpus vocabulary we build word vector. The frequency of occurrence of each word in a text is a feature we train our classifier on.
Simply put, every row represents a text/article from the dataset, every column represents a term (word) of the dataset, and every cell contains the frequency count of that word in a particular text.
We can construct a bag of word model with CountVectorizer
from sklearn
. We fit the CountVecotrizer on the training data. After that, we transform the train and validation data. This will create a sparse matrix that we can transform into a dense matrix using the to toarray
function.
We also don’t take stop words into account, because they often do not provide useful information.
from sklearn.feature_extraction.text import CountVectorizer
count_vector = CountVectorizer(analyzer='word', stop_words='english')
count_vector_train = count_vector.fit_transform(X_train['text'])
count_vector_valid = count_vector.transform(X_valid['text'])
count_vector_train.toarray().shape
(4751, 60245)
We can see that based on training data this vector has 60245 features. Let’s look at these features by using get_feature_names
on the bag-of-words vectorizer.
bow_df = pd.DataFrame(data = count_vector_train.toarray(),
columns= count_vector.get_feature_names())
bow_df.iloc[:, 4595:4600].head()
| arguably | argue | argued | argues | arguing | |
|---|---|---|---|---|---|
| 0 | 0 | 0 | 1 | 0 | 0 |
| 1 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 0 |
| 3 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 0 | 0 | 0 |
We can see that the word “argued” occurs 1 time in the first document of our training data.
We’ll use this bag-of-words model to train our logistic regression.
logreg = LogisticRegression()
kfold = StratifiedKFold(n_splits=10, random_state=2)
cv = cross_val_score(logreg, count_vector_train, y_train, cv=kfold)
cv.mean()
0.9155939849624062
The logistic regression algorithm performs much better on this basic bag-of-words model with a cross-validation score of 91,55%.
4.2 Term Frequency and Inverse Data Frequency (tf-idf)
Another way to represent text is by using term frequency-inverse data frequency (tf-idf). This way of representing text quantifies how relative importance a word is to a text and in our dataset.
\[w_{i,j} = tf_{i,j} \times \log(\frac{N}{df_i})\]The first component (tf) calculates the word frequency $tf_{i,j}$ by counting how many times the word appears in a document $n_{i,j}$ compared to all the words in the document $\sum_kn_{i,j}$.
\[tf_{i,j} = \frac{n_{i,j}}{\sum_kn_{i,j}}\]The second component (idf) can be calculated as the logarithm of the number of texts in the dataset, divided by the total number of texts where the specific word appears. This gives weight to every word. Rare words get a high idf score, the more common words a low score.
\[idf(w) = \log(\frac{N}{df_t})\]tf-idf basically measures of original a word.
We can use TfidfVectorizer
to create a tf-idf representation of our news articles by fitting and transforming the data.
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf_vector = TfidfVectorizer(analyzer="word", stop_words='english')
tfidf_train = tfidf_vector.fit_transform(X_train['text'])
tfidf_valid = tfidf_vector.transform(X_valid['text'])
tfidf_train.toarray().shape
(4751, 60245)
Let’s also extract the feature names and look at their corresponding weights.
tfidf_df = pd.DataFrame(data = tfidf_train.toarray(),
columns= tfidf_vector.get_feature_names())
tfidf_df.iloc[:, 4595:4600].head()
| arguably | argue | argued | argues | arguing | |
|---|---|---|---|---|---|
| 0 | 0.0 | 0.0 | 0.030122 | 0.0 | 0.0 |
| 1 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 |
| 2 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 |
| 3 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 |
| 4 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 |
We can see that now there are weights assigned to the features per document.
Let’s use the same logistic regression algorithm on the tf-idf representation as on the bag-of-words model.
logreg = LogisticRegression()
kfold = StratifiedKFold(n_splits=10, random_state=2)
cv = cross_val_score(logreg, tfidf_train, y_train, cv=kfold)
cv.mean()
0.9103321539141973
When using tf-idf representation the logistic regression model has an accuracy of 91,03 %. This is a little less than the bag-of-words model.
4.3 Text processing
Both the bag-of-words and the tf-idf model have a lot of features (60,000). Let’s look at ways make to turn down the number of features.
Let’s apply lemmatization. This process converts words into there base form. We perform lemmatization by using nltk
’s lemmatization functionality.
First, we define a WordNetLemmatizer
. Then we define a lambda function that takes a string, loops over the words of the string (text), stores them in a list. Lastly, words are joined back together into a string.
from nltk.stem import WordNetLemmatizer
lemma = WordNetLemmatizer()
X_train_lemma = X_train['text'].apply(lambda x: ' '.join([lemma.lemmatize(word) for word in x.strip().split(' ')]))
X_valid_lemma = X_valid['text'].apply(lambda x: ' '.join([lemma.lemmatize(word) for word in x.strip().split(' ')]))
Let’s look at the dimensions of our bag-of-words model after applying lemmatization.
cv_lemma_train = count_vector.fit_transform(X_train_lemma)
cv_lemma_valid = count_vector.transform(X_train_lemma)
cv_lemma_train.toarray().shape
(4751, 58849)
We can see we lost about 2,000 features using lemmatization and removing stop words.
Let’s compare some models that we fit on the bag of words representation with lemmatization applied.
logreg = LogisticRegression()
kfold = StratifiedKFold(n_splits=10, random_state=2)
cv = cross_val_score(logreg, cv_lemma_train, y_train, cv=kfold)
cv.mean()
0.9160141530296328
We can see that we achieve a train accuracy score of validation score of 91.60% of the logistic regression. This model also has the lowest variance. But can we use other text representations to achieve better accuracy scores? Let’s look at tf-idf with lemmatization applied.
tfidf_lemma_train = tfidf_vector.fit_transform(X_train_lemma)
tfidf_lemma_valid = tfidf_vector.transform(X_valid_lemma)
logreg = LogisticRegression()
kfold = StratifiedKFold(n_splits=10, random_state=2)
cv = cross_val_score(logreg, tfidf_lemma_train, y_train, cv=kfold)
cv.mean()
0.9115944272445822
The tf-idf model also improved slightly. Overall lemmatization decreased the number of features of our data without decreasing the accuracy of our classifier.
We can also combine several representations. We can create a tf-idf representation of the characters in the articles and combine them with the tf-idf word representation. Let’s first create the character model. To keep the number of features smaller we set the max_features to 60000.
tfidf_char = TfidfVectorizer(analyzer='char', strip_accents='unicode',
stop_words='english', ngram_range=(2, 6),
max_features=60000)
tfidf_train_char = tfidf_char.fit_transform(X_train_lemma)
tfidf_valid_char = tfidf_char.transform(X_valid_lemma)
We use the hstack()
function from SciPy to horizontally stack the sparse word and character matrices.
from scipy.sparse import hstack
train_features = hstack([tfidf_lemma_train, tfidf_train_char])
valid_features = hstack([tfidf_lemma_valid, tfidf_valid_char])
5. Final model
We train our final logistic regression classifier on the features that combine the words and character tf-idf - representations
logreg = LogisticRegression()
kfold = StratifiedKFold(n_splits=10, random_state=2)
cv = cross_val_score(logreg, train_features, y_train, cv=kfold)
cv.mean()
0.9318018575851392
We get an accuracy score of around 93.18% on the validation set!
logreg.fit(train_features, y_train)
logreg.score(valid_features, y_valid)
0.9343434343434344
The final testing score is 93.34% on the testing.
6. Conclusion
We used a logistic regression model to classify real and fake news articles. We used a bag-of-words and a tf-idf model to represent the article’s texts.
This is a really simple way to classify articles, with good results. You could also use word embeddings, topic models and many more ways to represent text.
Furthermore, you could also other classifier models, like Naive Bayes classifiers, Support Vector Machines and (Deep) Neural Nets.
I also did not do any hyper parameter tuning to find the best model.


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What is SQL?
- Stands for structured query language.
- Access data in a database.
- Is a standard across many different vendors.
What is a database
- Structured data of any kind in a central place.
- SQL is particularly for a relational database.
- Some columns relate to each other.
Tools
- Lots of different tools.
- We will be using Microsoft SQL Server management Studio.
- Find it in your start menu.
Data
- We are going to use some basic Tropical Storm data.
- Atlantic data which can be downloaded here
- Pacific data which can be downloaded here


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CasedLetterParser Class
Definition at line
127
of
Primitive.hpp
Constructors
CasedLetterParser
()
Member Functions
void
Accept
(
Visitor
& visitor)
override
Match
Parse
(
Scanner
& scanner,
ObjectStack
& stack,
ParsingData
* parsingData)
override
Constructor Details
CasedLetterParser Constructor
cmajor::parsing::CasedLetterParser::CasedLetterParser()
Definition at line
304
of
Primitive.cpp
:
304
:
Parser
(
U"cased_letter"
,
U"cased_letter"
)
305
{
306
}
Declaration at line
130
of
Primitive.hpp
Member Function Details
Accept Member Function
void
cmajor::parsing::CasedLetterParser::Accept(
Visitor
& visitor)
override
Definition at line
321
of
Primitive.cpp
:
322
{
323
visitor
.
Visit
(
*
this
)
;
324
}
Declaration at line
132
of
Primitive.hpp
Base class overridden functions:
cmajor::parsing::ParsingObject::Accept
Calls:
cmajor::parsing::Visitor::Visit
Parse Member Function
Match
cmajor::parsing::CasedLetterParser::Parse(
Scanner
& scanner,
ObjectStack
& stack,
ParsingData
* parsingData)
override
Definition at line
308
of
Primitive.cpp
Declaration at line
131
of
Primitive.hpp
Base class overridden functions:
cmajor::parsing::Parser::Parse
Calls:
cmajor::parsing::Match::Nothing
,
cmajor::parsing::Match::One
,
cmajor::parsing::Scanner::AtEnd
,
cmajor::parsing::Scanner::GetChar
,
cmajor::unicode::IsCasedLetter
index
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up
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prev
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next


Firefox and Safari focus test
This test is to determine if Firefox and Safari will allow focus on links, form controls and buttons.
Example
Code
<form action="#">

Name
<input id="form-name" type="text" autocomplete="name">

<button>Submit</button>
</form>


We are a Kasaragod local area online travel company based in Vidya Nagar, Kasaragod. We are collaborating with local taxi and
auto-rickshaw drivers in a mutual co-operation to ease the travel within the district. During this era of internet, people can now book taxis
and auto-rickshaws, for immediate or further service, with us and pay the fares online as well. We provide service through 8:00 AM and
10:00 PM. You, the customer, are at the heart of our business, and we are dedicated to serve you the best.
YUGENKSD.COM


RG Service
RG Service Admin
Chennai... Ithu enga area...
Menu Designer
Type Designer
Document Manager
Route Manager


Small Swamp [ab02], swamp, in Diamond Island, wilderness
Last updated Turn #128
Small Swamp [ab02], swamp, in Diamond Island, wilderness
------------------------------------------------------------------------
5: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
5: 16% damaged, arrived from Ocean [ab01], owner:
5: Phantasm [n4b], Imperial Empire, duke, with two workers,
5: eight sailors
6: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
6: 16% damaged, departed for Ocean [aa02].
15: Stack of nazgul [di1o], nazgul, number: 2, arrived from the east.
17: Stack of nazgul [di1o], nazgul, number: 2, went east.
Routes leaving Small Swamp:
North, to Ocean [aa02], Crystal Sea, 2 days
East, swamp, to Small Swamp [ab03], 14 days
South, plain, to Concha Valley [ac02], 7 days
West, to Ocean [ab01], Crystal Sea, 2 days
Seen here:
Stack of nazgul [ez3x], nazgul, number: 4
Stack of nazgul [di1o], nazgul, number: 2


July 2019 - Present
The visionary company Field Information Solutions GmbH is
my rite of passage into software engineering professionalism.
The mobile app team has been working on building in-depth,
sector-specific modules
on one powerful platform, Field Buzz.
Developers and designers blew me away with their willingness
to share their time and wisdom.
May 2016 - Jan 2017
I was a lucky intern to work with Durbin Lab's small, brilliant android team.
I learned about project management, Android & web app development,
testing, using software development tools etc.
This tenure during my varsity years flooded me with opportunities
to osmose bits of wisdom from software developers, system designers,
testers from the industry.
Dec 2016 - Jan 2017
I worked with Young Bangla who launched a program with
Microsoft Bangladesh to inspire the young developers to come up with something
that would make a mark on the socio-economic state of Bangladesh.
Working with a company with such an immense calibre taught me to surround myself
with people
who push me to my limits.


Across
1)
a
rainbow
of butterflies
2)
a
band
of musicians
3)
a
bed
of flowers
4)
a
flight
of dragons
5)
a
school
of whales
Down
1)
a
range
of mountains
2)
a
bunch
of grapes
3)
a
waddle
of penguins
4)
a
string
of pearls
5)
a
litter
of kittens


Our Story
How we met
WE SWIPED RIGHT!!!
Yep!!! That's how it all started.
The Proposal
Initially, my plan was to go to the highest point in Prospect Park to propose, but Roli foiled that idea by not wanting to leave the house. I finally convinced her to go out to get vegetables at the farmers market a block away, so we could have fresh produce for dinner. It worked!!! On the way back home, we walked through Washington Park, also known as Stone Park. I found a nice secluded area with a bench and signed my life away!!!


{% from "components/inset-text/macro.njk" import govukInsetText %}
To put a heading in your template, use a hash:
{{ govukInsetText({ "text": "# This is a heading", "classes": "govuk-!-margin-top-0 govuk-!-margin-bottom-3"}) }}To make bullet points, use asterisks:
{{ govukInsetText({ "html": "* point 1To insert a page break, use three asterisks:
{{ govukInsetText({ "html": "Content on page 1


Import tooltip module into your application
import { TooltipModule } from '@sbb-esta/angular-public';
Tooltip allows the user to display explanations as needed.
You can use tooltip for context specific explanations of interaction elements or text.
The tooltip consists of a question icon and the overlay.
The overlay has got the following characteristics:
An example of tooltip component is shown below:

Single Tooltip

<sbb-tooltip (opened)="onOpen($event)" (closed)="onClose($event)">
{{ tooltipContent }}
</sbb-tooltip>
You can close the overlay in four modes:
The overlay is displayed:


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xml.sax.handler.ContentHandler --+ | scanner_t
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Receive notification of the end of a document. The SAX parser will invoke this method only once, and it will be the last method invoked during the parse. The parser shall not invoke this method until it has either abandoned parsing (because of an unrecoverable error) or reached the end of input.
|
Signals the start of an element in namespace mode. The name parameter contains the name of the element type as a (uri, localname) tuple, the qname parameter the raw XML 1.0 name used in the source document, and the attrs parameter holds an instance of the Attributes class containing the attributes of the element. The uri part of the name tuple is None for elements which have no namespace.
|
Signals the end of an element in namespace mode. The name parameter contains the name of the element type, just as with the startElementNS event.
|
Signals the start of an element in non-namespace mode. The name parameter contains the raw XML 1.0 name of the element type as a string and the attrs parameter holds an instance of the Attributes class containing the attributes of the element.
|
Signals the end of an element in non-namespace mode. The name parameter contains the name of the element type, just as with the startElement event.
|
| Home | Trees | Indices | Help |
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| Generated by Epydoc 3.0.1 on Mon Oct 20 09:00:21 2008 | http://epydoc.sourceforge.net |


CommitTwo is designed to help two individuals achieve mutually shared goals by tying the success of the two together. Each individual will gain points when they or their partner make steps productive to their goal and likewise, both partners lose points when they or their partner engage in activity counterproductive to their goal.
As partners accumulate points, they become eligible for rewards. Beneficial and Detrimental action is dependent on the duo's shared goal. For example, if the teammates goal is to avoid unhealthy foods, they would get points for eating at healthy restaurants, but lose points if either of them chose to eat a meal at a fast food restaurant. Additionally, when the team members commit to each other, they also agree to check in and log their actions at a certain frequency, also dependent on the specific goal. A check in simply requires that the team member open the app at a certain time or frequency, thereby allowing for verification that they are staying true to their commitment. If the team member does not fulfill this check in requirement, both they and their partner automatically lose points, regardless of whether they actually kept their commitment. As each partner makes an effort to help their teammate succeed, they will ultimately end up succeeding themselves!
CommitTwo users may directly choose to their partner if they have a friend working towards a shared goal. For users who are not directly selecting their partner, CommitTwo will find an individual of a similar age and residence to be their partner. Additionally, as users spend more time selecting rewards, CommitTwo is able to learn more about the users interests and pair together individuals with shared interests and hobbies. Not only can CommitTwo be a great way to reach your goals, but it becomes an opportunity to meet friends along the way!


Reset
Palette
Image Tools
Add Default Image
Pixel Tools
Pixelate
Fill Matrix
C code will show here after matrix is filled


I ordered some calamus root from an online herbal shop. Worst idea ever. I remember reading that if you chew 2in of the root, you will experience stimulation. I tried it, and felt a little more upbeat. Then a few weeks later, (yesterday) I wanted to 'trip-out' from it. I have done weed, alc, dxm, methadone, demerol, soma, salvia, and nitrous.
4:00pm I chewed and swallowed about 7 inches of the dried root, which tasted spicy and bitter. I also took some tums with it to ease any heartburn that might follow. I went to the mall and two hours later I still wasn't feeling anything.
6:00pm I said screw this and got some panda express. 10 min later it started my whole stomach turned upside-down.  Soon after every 10-15 min i puked again. I puked in the parking lot, then got a ride home.
  I say we all stick to weed and alc.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


--- layout: post status: publish published: true title: Forthcoming Aural Delight author: display_name: Andy login: Andy email<EMAIL_ADDRESS>url: '' author_login: Andy author_email<EMAIL_ADDRESS>excerpt: "Hey Cubs fans--Get ready for a steady diet of\n\n\n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\nAs the Cubs face the Sox at \"The Cell\"\n\n\n\n \n\n \n\n \n\n \n\n \n\n \n\nHere's J-lo in The Cell. She must be hanging out in the locker rooms or something.\n\nAnd if all of this wasn't enough to make me have a joy shiver, " wordpress_id: 107 wordpress_url: http://ivyenvy.wordpress.com/?p=107 date: '2008-06-27 14:54:39 +0000' date_gmt: '2008-06-27 14:54:39 +0000' categories: - Broadcasters/Networks tags: - Hawk and DJ - Jon Miller and Joe Morgan - Ryan Dempster - The Cell comments: [] ---
Hey Cubs fans--Get ready for a steady diet of
As the Cubs face the Sox at "The Cell"
Here's J-lo in The Cell. She must be hanging out in the locker rooms or something.
And if all of this wasn't enough to make me have a joy shiver, Sunday night we get the crackerjack commentary of Joe and Jon. This weekend is making the rifftrax-esque rogue baseball broadcast idea of Corey's look better and better.
Well, today we have "Demps" on the mound. Let's see if he can do it in a place devoid of soul and sans-ivy.


--- title: Clean run directories type: docs repo: https://github.com/rstudio/tfruns menu: main: name: Clean run directories - tfruns parent: tfruns-reference aliases: - /reference/tfruns/clean_runs.html - /tfruns/reference/clean_runs.html - /guide/tfruns/reference/clean_runs.html - /tools/tools/tfruns/reference/clean_runs.html - /installation/tfruns/reference/clean_runs.html - /tutorials/tfruns/reference/clean_runs.html - /guide/tools/tfruns/reference/clean_runs.html - /deploy/tfruns/reference/clean_runs.html - /tools/tfruns/reference/clean_runs.html - /tutorials/tools/tfruns/reference/clean_runs.html ---
Remove run directories from the filesystem.
clean_runs(runs = ls_runs(runs_dir = runs_dir),
runs_dir = getOption("tfruns.runs_dir", "runs"),
confirm = interactive())
purge_runs(runs_dir = getOption("tfruns.runs_dir", "runs"),
confirm = interactive())
| runs | Runs to clean. Can be specified as a data frame
(as returned by |
| runs_dir | Directory containing runs. Defaults to "runs" beneath the
current working directory (or to the value of the |
| confirm |
|
The clean_runs()
function moves the specified runs (by default,
all runs) into an "archive" subdirectory of the "runs" directory.
The purge_runs()
function permanently deletes the "archive"
subdirectory.
Other run management: copy_run


Woman sets constable on fire, arrested
by Special CorrespondentA woman was arrested for attempting to murder a police constable by setting him on fire in Thirumullaivoyal here on Sunday.
K. Venkatesan, 31, was living with a woman named Asha in Thirumullaivoyal after his first wife left him.
On Saturday night, Venkatesan and Asha had a quarrel. She poured petrol on him and set him on fire.
On hearing his cries, neighbours rushed him to the Government Kilpauk Medical College Hospital.
He suffered more than 80% burns and is undergoing treatment, said police.


Multimedia Meeting Minutes
Meeting Reminders
All Meeting Minutes
All Reminders
Create Meeting Minutes
Create Reminder
Audio List
play
pause
stop


Pixel Zoom 1 2 3 4 5 6 7 8
To draw here, it costs 1 satoshi per pixel. You need to be running C-Lightning v0.8.0+ and follow instructions in the README.md
If you like the mixture of art and the Lightning Network, follow Sparkshot.io for more beast code shipping soon.


The name of each LAPACK routine is a coded specification of its function (within the very tight limits of standard Fortran 77 6-character names).
All driver and computational routines have names of the form XYYZZZ, where for some driver routines the 6th character is blank.
The first letter, X, indicates the data type as follows:
S REAL D DOUBLE PRECISION C COMPLEX Z COMPLEX*16 or DOUBLE COMPLEX
When we wish to refer to an LAPACK routine generically, regardless of data type, we replace the first letter by ``x''. Thus xGESV refers to any or all of the routines SGESV, CGESV, DGESV and ZGESV.
The next two letters, YY, indicate the type of matrix (or of the most significant matrix). Most of these two-letter codes apply to both real and complex matrices; a few apply specifically to one or the other, as indicated in Table 2.1.
| BD | bidiagonal |
| DI | diagonal |
| GB | general band |
| GE | general (i.e., unsymmetric, in some cases rectangular) |
| GG | general matrices, generalized problem (i.e., a pair of general matrices) |
| GT | general tridiagonal |
| HB | (complex) Hermitian band |
| HE | (complex) Hermitian |
| HG | upper Hessenberg matrix, generalized problem (i.e a Hessenberg and a |
| triangular matrix) | |
| HP | (complex) Hermitian, packed storage |
| HS | upper Hessenberg |
| OP | (real) orthogonal, packed storage |
| OR | (real) orthogonal |
| PB | symmetric or Hermitian positive definite band |
| PO | symmetric or Hermitian positive definite |
| PP | symmetric or Hermitian positive definite, packed storage |
| PT | symmetric or Hermitian positive definite tridiagonal |
| SB | (real) symmetric band |
| SP | symmetric, packed storage |
| ST | (real) symmetric tridiagonal |
| SY | symmetric |
| TB | triangular band |
| TG | triangular matrices, generalized problem (i.e., a pair of triangular matrices) |
| TP | triangular, packed storage |
| TR | triangular (or in some cases quasi-triangular) |
| TZ | trapezoidal |
| UN | (complex) unitary |
| UP | (complex) unitary, packed storage |
When we wish to refer to a class of routines that performs the same function on different types of matrices, we replace the first three letters by ``xyy''. Thus xyySVX refers to all the expert driver routines for systems of linear equations that are listed in Table 2.2.
The last three letters ZZZ indicate the computation performed. Their meanings will be explained in Section 2.4. For example, SGEBRD is a single precision routine that performs a bidiagonal reduction (BRD) of a real general matrix.
The names of auxiliary routines follow a similar scheme except that the 2nd and 3rd characters YY are usually LA (for example, SLASCL or CLARFG). There are two kinds of exception. Auxiliary routines that implement an unblocked version of a block algorithm have similar names to the routines that perform the block algorithm, with the sixth character being ``2'' (for example, SGETF2 is the unblocked version of SGETRF). A few routines that may be regarded as extensions to the BLAS are named according to the BLAS naming schemes (for example, CROT, CSYR).


September 4 world heard Night Visions, the first full album. He reached the 2 position in the chart Billboard 200. The single «It's Time» took 22 th place in the Billboard Hot 100, 4th in the Billboard Alternative and Billboard Rock, and now went platinum.
2010
Hell and Silence
Hell and Silence is an EP by Las Vegas rock group , released in March 2010 in the United States. It was recorded at Battle Born Studios. All songs were written by Imagine Dragons and self-produced. The EP was in part mixed by Grammy nominated engineer Mark Needham. To promote the album the band performed five shows at SXSW 2010 including at the BMI Official Showcase.While at SXSW they were endorsed by Blue Microphones. They also toured the western United States with Nico Vega and Saint Motel. They also performed at Bite of Las Vegas Festival 2010, New Noise Music Festival, Neon Reverb Festival, and Fork Fest.
2012
Night Visions
It was released on September 4, 2012 through Interscope Records. The extended track was released on February 12, 2013, adding three more songs. Recorded between 2010 and 2012, the album was primarily produced by the band themselves, as well as English hip-hop producer Alex da Kid and Brandon Darner from the American indie rock group The Envy Corps. It was mastered by Joe LaPorta. According to frontman Dan Reynolds, the album took three years to finish ...
2015
Smoke + Mirrow
Self-produced by members of the band along with English hip-hop producer Alexander Grant, known by his moniker Alex da Kid, the album was released by Interscope Records and Grant's KIDinaKORNER label on February 17, 2015, in the United States.
Before the release of Night Visions, Imagine Dragons made appearences on American radio and television to promote their extended play, Continued Silence and debut single it's Time. The band perfofmed "It's Time" on the July 16, 2012 airing of NBC late-night talk show The Tonight Show with Jay Leno"
03.08.2015
Smoke + mirrors tour
At Lollapalooza in Sao Paulu the last date on the Into the Night Tour, the band announced a rest, and complemented saying, "This is last show for a while, and no better place to end this tour". The conclusion of the Into the Night Tour signaled the end of the Night Visions album cycle. Lead singer Dan Reynolds joked about the end of the Night Visions cycle, saying that "We're always writing on the road, [so] that second album will come, unless we die. next year.  I don't know when it will be, but it may come"
"It's Time" was released as the lead single from Continued Silence and It's Time, both extended plays preseding Night Vision's releace.
03.04.2015
Indian summer
"Radioactive" is a song recorded by American rock band Imagine Dragons for their major-label debut EP Continued Silence and later on their debut studio album, Night Vision (2012), as the opening track.
20.07.2015
Indian summer remix
Amsterdam's Sam Feldt earned attention in Europe and then worldwide for his melodic house remixes, mixtapes, and collaborations including 2014's "Bloesem" with De Hofnar and "Hot
03.04.2015
Chill nation
A soulful singer/songwriter from a small town with a penchant for crafting moving and evocative folk-pop confections in the vein of Ed Sheeran, For Vance, and Ben Hovard, James Bay hails from North
03.04.2015
chill nation
A soulful singer/songwriter from a small English town with a penchant for crafting moving and evocative folk-pop confections in the vein of Ed Sheeran, Foy Vance, and Ben Howard, James Bay hails from the North Hert
01
01.03.2015
chill nation
Hard Rock Cafe teamed with the band, granting them the first ever full access to take control of Hard Rock Cafe's internal video system (more than 20,000 screens at all 151 locations worldwide) on February 17, 2014.[37] Also, autographed t-shirts will be
02
04.04.2015
chill nation
In 2015 a world tour, entitled the Smoke and Mirrors Tour is scheduled in promotion of the world-wide release of Smoke and Mirrors.
03
01.07.2015
chill nation
A soulful singer/songwriter from a small English town with a penchant for crafting moving and evocative folk-pop confections in the vein of Ed Sheeran, Foy Vance, and Ben Howard, James Bay hails from the North Hertf shire market town of Hitchin. Bay honed his skills regionally,
04
02.11.2015
chill nation
Hell and Silence is an EP by Las Vegas rock group Imagine Dragons, released in March 2010 in the United States. It was recorded at Battle Born Studios.[1] All songs were written by Imagine Dragons and self-produced. The EP was in part mixed Grammy nominated engi
05
06.09.2015
chill nation
A soulful singer/songwriter from a small English town with a penchant for crafting moving and evocative folk-pop confections in the vein of Ed Sheeran, Foy Vance, and Ben Howard, James Bay hails from the North Hertf shire market town
06
2-2-15 Hokukoryokuchi, Konohana Ward, 554-0042 Osaka Prefecture Osaka Japan
When the last-minute request is for a focus group, it’s usually a sign that he request originated in Marketing. When Web sites are being designed, thefolks inMarketing often feel like they don’t have much clout. Even though they’re the ones who spend the most time trying to figure out who the site’s audiences and what they want, the designers and developers are the ones with most of the hands-on control over how the site actually gets put together.
In 2008, lead singer Dan Reynolds met drummer Andrew Tolman at Brigham Young University where they were both students.Tolman recruited longtime high school friend Daniel Wayne "Wing" Sermon, who had graduated from Berklee College of Music


Introduction
Welcome to the OnboardFlow API.
We have language examples in Shell (with other platform examples coming soon)! You can view code examples in the dark area to the right, and you can switch the programming language of the examples with the tabs in the top right.
Getting Started
Authentication
OnboardFlow uses API Keys to allow access to the API. You can find your API Key on the API Settings page, when logged into your OnboardFlow account.
The API Key needs to be included in ALL API requests to the server in a header that looks like the following:
Authorization: Bearer e3c0c748fe9b55386eecc07c339ec4099a8b9b0e
Rate Limits
API calls are rate limited to ensure that we are able to provide a consistent quality of service and availability to all of our users. The limits on total calls are illustrated below:
- Free Plan - 50 API calls/day
- Launch Plan - 1,000 API calls/day
- Growth Plan - 5,000 API calls/day
- Expansion Plan - 10,000 API calls/day
Error Codes
The OnboardFlow API uses the following error codes:
| Error Code | Meaning |
|---|---|
| 400 | Bad Request -- Your request is invalid. |
| 401 | Unauthorized -- Your API key is wrong. |
| 404 | Not Found -- The specified record or endpoint could not be found. |
| 405 | Method Not Allowed -- You tried to access an endpoint with an invalid method. |
| 406 | Not Acceptable -- You requested a format that isn't json. |
| 429 | Too Many Requests -- You're requesting too many records! Slow down! |
| 500 | Internal Server Error -- We had a problem with our server. Try again later. |
| 503 | Service Unavailable -- We're temporarily offline for maintenance. Please try again later. |
Customers
Creating a Customer
curl "https://gateway.onboardflow.com/api/v1/customer/"
-X POST
-d<EMAIL_ADDRESS>-H "Content-Type: application/json"
-H "Authorization: Bearer e3c0c748fe9b55386eecc07c339ec4099a8b9b0e"
The above command returns JSON structured like this:
{
"success": true,
"data": {
"id": 979924,
"event_provider": "API",
"site_user": {},
"site_hash": "f37F2PPh",
"session_id": "f37F2PPh-1585922303",
"event_date":<PHONE_NUMBER>000,
"event_value": "1923c497cf0fc93ff63414795ec58c59d4ccab38",
"event_type": "PROVIDER_EVENT",
"event_meta": {
"data": {
"customer": {
"id": "cus_123XYZ",
"email"<EMAIL_ADDRESS>"user_id": "user_123XYZ",
"image_url": null,
"last_name": null,
"first_name": "Joe",
"has_payment_method": true
},
"custom_properties": {
"qty_tasks": 2,
"has_created_project": true
}
},
"type": "CUSTOMER_CREATED",
"created":<PHONE_NUMBER>
},
"updated":<PHONE_NUMBER>000,
"created":<PHONE_NUMBER>000
}
}
This method should be called when a new customer is created (typically at the point at which they start a trial).
HTTP Request
POST https://gateway.onboardflow.com/api/v1/customer/
Data Parameters
| Parameter | Description | Type | Required |
|---|---|---|---|
| site_key | The unique site key of the OnboardFlow site | String | Yes |
| event_date | The date the customer was created | DateTime (Unix Timestamp) | Yes |
| user_id | The unique ID of the user this customer record belongs to (typically this will be the ID of your internal user record) | String | Yes |
| customer_id | The unique ID of the customer being created (if you use a Payment Platform like Stripe this must be the exact same customer ID stored there) | String | Yes |
| customer_email | The email address of the customer being created | String | Yes |
| customer_first_name | The first name of the customer being created | String | No |
| customer_last_name | The last name of the customer being created | String | No |
| customer_image_url | The absolute URL of the customers profile image (if applicable) | String | No |
| customer_has_payment_method | A boolean indicating whether this customer has attached a payment method or not | Boolean | Yes |
| custom_properties | An optional dictionary containing custom properties for this Customer | Dict | No |
Updating a Customer
curl "https://gateway.onboardflow.com/api/v1/customer/"
-X PUT
-d<EMAIL_ADDRESS>-H "Content-Type: application/json"
-H "Authorization: Bearer e3c0c748fe9b55386eecc07c339ec4099a8b9b0e"
The above command returns JSON structured like this:
{
"success": true,
"data": {
"id": 979924,
"event_provider": "API",
"site_user": {},
"site_hash": "f37F2PPh",
"session_id": "f37F2PPh-1585922303",
"event_date":<PHONE_NUMBER>000,
"event_value": "1923c497cf0fc93ff63414795ec58c59d4ccab38",
"event_type": "PROVIDER_EVENT",
"event_meta": {
"data": {
"customer": {
"id": "cus_123XYZ",
"email"<EMAIL_ADDRESS>"user_id": "user_123XYZ",
"image_url": null,
"last_name": null,
"first_name": "Joe",
"has_payment_method": true
},
"custom_properties": {
"qty_tasks": 2,
"has_created_project": true
}
},
"type": "CUSTOMER_UPDATED",
"created":<PHONE_NUMBER>
},
"updated":<PHONE_NUMBER>000,
"created":<PHONE_NUMBER>000
}
}
This method should be called when a customer has updated their details (such as their name, email address, profile picture URL or when they've added a payment method).
HTTP Request
PUT https://gateway.onboardflow.com/api/v1/customer/
Data Parameters
| Parameter | Description | Type | Required |
|---|---|---|---|
| site_key | The unique site key of the OnboardFlow site | String | Yes |
| event_date | The date the customer was updated | DateTime (Unix Timestamp) | Yes |
| user_id | The unique ID of the user this customer record belongs to (typically this will be the ID of your internal user record) | String | Yes |
| customer_id | The unique ID of the customer being updated (if you use a Payment Platform like Stripe this must be the exact same customer ID stored there) | String | Yes |
| customer_email | The email address of the customer being updated | String | Yes |
| customer_first_name | The first name of the customer being updated | String | No |
| customer_last_name | The last name of the customer being updated | String | No |
| customer_image_url | The absolute URL of the customers profile image (if applicable) | String | No |
| customer_has_payment_method | A boolean indicating whether this customer has attached a payment method or not | Boolean | Yes |
| custom_properties | An optional dictionary containing custom properties for this Customer | Dict | No |
Deleting a Customer
curl "https://gateway.onboardflow.com/api/v1/customer/"
-X DELETE
-d '{"site_key":"f37F2PPh","user_id":"user_123XYZ", "customer_id":"cus_123XYZ"}'
-H "Content-Type: application/json"
-H "Authorization: Bearer e3c0c748fe9b55386eecc07c339ec4099a8b9b0e"
HTTP Request
DELETE https://gateway.onboardflow.com/api/v1/customer/
Data Parameters
| Parameter | Description | Type | Required |
|---|---|---|---|
| site_key | The unique site key of the OnboardFlow site | String | Yes |
| user_id | The unique ID of the user this customer record belongs to (typically this will be the ID of your internal user record) | String | Yes |
| customer_id | The unique ID of the customer being created (if you use a Payment Platform like Stripe this must be the exact same customer ID stored there) | String | Yes |
Subscriptions
Creating a Subscription
curl "https://gateway.onboardflow.com/api/v1/subscription/"
-X POST
-d '{"site_key":"f37F2PPh","event_date":1583020800,"subscription_name":"Pro","customer_id":"cus_123XYZ","subscription_id":"sub_123XYZ","subscription_value":49.99,"subscription_started_at":1583020800,"subscription_trial_start":1583020800,"subscription_trial_end":1583020800,"subscription_currency":"USD","subscription_status":"active","subscription_interval":"MONTH"}'
-H "Content-Type: application/json"
-H "Authorization: Bearer e3c0c748fe9b55386eecc07c339ec4099a8b9b0e"
The above command returns JSON structured like this:
{
"success": true,
"data": {
"id": 979925,
"event_provider": "API",
"site_user": {},
"session_id": "f37F2PPh-1585922793",
"event_date":<PHONE_NUMBER>000,
"event_value": "1ec049ab69e84b58d7466816a791a6fd0fe36fa9",
"event_type": "PROVIDER_EVENT",
"event_meta": {
"data": {
"customer": {
"id": "cus_123XYZ"
},
"subscription": {
"id": "sub_123XYZ",
"name": "Pro",
"value": 49.99,
"status": "active",
"currency": "USD",
"interval": "MONTH",
"trial_end":<PHONE_NUMBER>,
"product_id": null,
"started_at":<PHONE_NUMBER>,
"trial_start":<PHONE_NUMBER>
}
},
"type": "SUBSCRIPTION_CREATED",
"created":<PHONE_NUMBER>
},
"updated":<PHONE_NUMBER>000,
"site_hash": "f37F2PPh",
"created":<PHONE_NUMBER>000
}
}
This method should be called when a customer starts a new subscription (typically at the point at which they start a trial).
HTTP Request
POST https://gateway.onboardflow.com/api/v1/subscription/
Data Parameters
| Parameter | Description | Type | Required |
|---|---|---|---|
| site_key | The unique site key of the OnboardFlow site | String | Yes |
| event_date | The date the subscription was created | DateTime (Unix Timestamp) | Yes |
| customer_id | The unique ID of the customer this subscription is linked to (if you use a Payment Platform like Stripe this must be the exact same customer ID stored there) | String | Yes |
| subscription_id | The unique ID of the subscription (if you use a Payment Platform like Stripe this must be the exact same subscription ID stored there) | String | Yes |
| subscription_name | The name of the subscription (usually this is the name of the plan, such as "Pro") | String | Yes |
| subscription_status | The status of the subscription (possible values are trialing, active and cancelled). | String | Yes |
| subscription_interval | What is the renewal cycle of this subscription? Possible values are DAY, WEEK, MONTH and YEAR | String | Yes |
| subscription_value | The value of the subscription (this must take into account any discounts applied - if applicable) | Decimal | Yes |
| subscription_currency | The currency is this subscription charged in? This must be a valid 3 character ISO 4217 code | String | Yes |
| subscription_started_at | The date and time that the subscription was created | DateTime (Unix Timestamp) | Yes |
| subscription_trial_start | The date and time that the trial started (or is due to start) | DateTime (Unix Timestamp) | Yes |
| subscription_trial_end | The date and time that the trial ended (or is due to end) | DateTime (Unix Timestamp) | Yes |
Updating a Subscription
curl "https://gateway.onboardflow.com/api/v1/subscription/"
-X PUT
-d '{"site_key":"f37F2PPh","event_date":1583020800,"subscription_name":"Pro","customer_id":"cus_123XYZ","subscription_id":"sub_123XYZ","subscription_value":49.99,"subscription_started_at":1583020800,"subscription_trial_start":1583020800,"subscription_trial_end":1583020800,"subscription_currency":"USD","subscription_status":"active","subscription_interval":"MONTH"}'
-H "Content-Type: application/json"
-H "Authorization: Bearer e3c0c748fe9b55386eecc07c339ec4099a8b9b0e"
The above command returns JSON structured like this:
{
"success": true,
"data": {
"id": 979925,
"event_provider": "API",
"site_user": {},
"session_id": "f37F2PPh-1585922793",
"event_date":<PHONE_NUMBER>000,
"event_value": "1ec049ab69e84b58d7466816a791a6fd0fe36fa9",
"event_type": "PROVIDER_EVENT",
"event_meta": {
"data": {
"customer": {
"id": "cus_123XYZ"
},
"subscription": {
"id": "sub_123XYZ",
"name": "Pro",
"value": 49.99,
"status": "active",
"currency": "USD",
"interval": "MONTH",
"trial_end":<PHONE_NUMBER>,
"product_id": null,
"started_at":<PHONE_NUMBER>,
"trial_start":<PHONE_NUMBER>
}
},
"type": "SUBSCRIPTION_UPDATED",
"created":<PHONE_NUMBER>
},
"updated":<PHONE_NUMBER>000,
"site_hash": "f37F2PPh",
"created":<PHONE_NUMBER>000
}
}
This method should be called when a customer's subscription has changed (for example, this could be when its status changes or if they switch to another plan).
HTTP Request
PUT https://gateway.onboardflow.com/api/v1/subscription/
Data Parameters
| Parameter | Description | Type | Required |
|---|---|---|---|
| site_key | The unique site key of the OnboardFlow site | String | Yes |
| event_date | The date the subscription was updated | DateTime (Unix Timestamp) | Yes |
| customer_id | The unique ID of the customer this subscription is linked to (if you use a Payment Platform like Stripe this must be the exact same customer ID stored there) | String | Yes |
| subscription_id | The unique ID of the subscription (if you use a Payment Platform like Stripe this must be the exact same subscription ID stored there) | String | Yes |
| subscription_name | The name of the subscription (usually this is the name of the plan, such as "Pro") | String | Yes |
| subscription_status | The status of the subscription (possible values are trialing, active and cancelled). | String | Yes |
| subscription_interval | What is the renewal cycle of this subscription? Possible values are DAY, WEEK, MONTH and YEAR | String | Yes |
| subscription_value | The value of the subscription (this must take into account any discounts applied - if applicable) | Decimal | Yes |
| subscription_currency | The currency is this subscription charged in? This must be a valid 3 character ISO 4217 code | String | Yes |
| subscription_started_at | The date and time that the subscription was created | DateTime (Unix Timestamp) | Yes |
| subscription_trial_start | The date and time that the trial started (or is due to start) | DateTime (Unix Timestamp) | Yes |
| subscription_trial_end | The date and time that the trial ended (or is due to end) | DateTime (Unix Timestamp) | Yes |
Events
Logging a UX Event
curl "https://gateway.onboardflow.com/api/v1/tracker/event/"
-X POST
-d '{"s":"f37F2PPh","site_user":{"id":"user_123XYZ","customer_id":"cus_123XYZ","custom_properties":{"projects_created":7}},"event":"PAGE_VIEW","value":"/manage/settings/","meta":{"title":"App Settings Page"}}'
-H "Content-Type: application/json"
-H "Authorization: Bearer e3c0c748fe9b55386eecc07c339ec4099a8b9b0e"
The above command returns JSON structured like this:
{
"success": true,
"data": {
"event_date":<PHONE_NUMBER>000,
"event_provider": "JSTRACKER",
"event_value": "/manage/settings/",
"id": 979927,
"site_hash": "f37F2PPh",
"event_type": "PAGE_VIEW",
"event_meta": {
"title": "App Settings Page"
},
"site_user": {
"id": "user_123XYZ",
"customer_id": "cus_123XYZ",
"custom_properties": {
"projects_created": 7
}
},
"created":<PHONE_NUMBER>000,
"updated":<PHONE_NUMBER>000
}
}
Once installed, our JavaScript Tracker will automatically log page views as well as any custom UX events you've setup. However, sometimes it can be useful to log custom UX events via a simple API call. It might also be required to post UX events via the API if you are unable to install the JavaScript Tracker on your app (i.e. if you run a Mobile or Desktop app, rather than a Web app).
HTTP Request
POST https://gateway.onboardflow.com/api/v1/tracker/event/
Data Parameters
| Parameter | Description | Type | Required |
|---|---|---|---|
| s | The unique site key of the OnboardFlow site | String | Yes |
| event | The type of UX event you are logging. Possible options are PAGE_VIEW, CLICK or CUSTOM | String | Yes |
| event_date | The date the UX event took place | DateTime (Unix Timestamp) | Yes |
| session_id | A unique ID representing the session ID for that user. I.e. you might want to create a new ID each logged in session the user undertakes. | String | Yes |
| site_user | A dictionary containing details of the user that should be linked to this UX event. At a minimum this should contain an id and customer_id (i.e. {"id": "user_123XYZ", "customer_id":"cus_123XYZ"}). Read more about setting User and Customer ID's here. | Dictionary | Yes |
| value | If this is a page view event, this should be the URL of the page being viewed. If it's a click it could be the ID of the element or video being watched. No matter what the event is being logged here, this should basically be a value that helps us group similar events in the future (i.e. the URL of the page, ID of the video being watched, class or ID of the button being clicked etc). | String | Yes |
| meta | If logging a page view or perhaps a video view, then you can set additional properties here about the event. I.e. setting the title would be achieved by setting this to {"title":"App Settings Page"}. Please note that currently, only 'title' is officially supported. | Dictionary | Yes |


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