Westlife are an Irish boy band group, formed in 1998 in Sligo, Ireland. They disbanded in 2012 and reunited in 2018. They were originally signed by Simon Cowell in the UK, Clive Davis in the US and managed by Louis Walsh and Sonny Takhar. Westlife is one of my favorite bands, their songs are just simply perfect, from the flow, the content of the message and the tune in every song, everything is just done in perfect harmony by this amazing band. Their passion to sing about love is one of the other things that makes stand out as one of my favorite bands.They really graced our childhood with songs that still date are still lingering in our minds.Westlife Forever!!
Shane Steven Filan[1] (born 5 July 1979) is an Irish singer and songwriter. He is one of the three lead singers of boy band Westlife, which was formed in 1998, disbanded in 2012, and regrouped in 2018. Westlife have released thirteen albums, embarked on twelve world tours, and won several awards, becoming one of the most successful musical groups of all time. Filan has released three solo albums: You and Me (2013), Right Here (2015) and Love Always (2017). He has appeared fourteen times in the UK Singles Chart with number-one hits, making him one of the most-appeared Irish music artists in British music number-one singles history.Read More
Kian John Francis Egan (born 29 April 1980) is an Irish singer, multi-instrumentalist, and songwriter, best known as a member of International vocal group Westlife. Westlife has since released twelve albums, embarked on thirteen world tours, and won several awards, becoming one of the most successful musical groups of all time. He was a coach on The Voice of Ireland and won the thirteenth series of I'm a Celebrity...Get Me Out of Here! in 2013.Read more
Markus Michael Patrick Feehily (commonly known as Mark Feehily, born 28 May 1980) is an Irish singer and songwriter. He is one of the two lead singers of the vocal group Westlife. He is the youngest in the band. Westlife has released twelve albums, embarked on thirteen world tours, and won several awards, becoming one of the most successful musical groups of all time. With 15 number one appearances, he is also the highest-charting LGBT performer and Irish LGBT performer on the UK Singles Chart.Read more
McFadden was born in Clonseagh Dublin, Ireland, and was raised Catholic.[1] Throughout his younger years, he always had an interest in singing, dancing and football.[2] Along with his sister Susan, McFadden attended the Billie Barry Stage School in Dublin,[2] which led him to doing stage and TV roles, including a role in the Irish TV show Finbar's Class, a comedy revolving around a group of student-swingers.[3] In early 1998, he formed a pop-R&B group with his friends Tim and Darragh, called Cartel, and performed live gigs in Dublin pubs.[2] When the band passed their credentials to prominent Irish music manager Louis Walsh, McFadden was asked to come to the audition for the formation of a new band that would later be called Westlife.Read more
Nicholas Bernard James Adam Byrne, Jr.[1] (born 9 October 1978) is an Irish singer, songwriter, radio presenter, dancer, television presenter and former professional footballer, best known for being a member of Irish music band Westlife; he is the band's oldest member. Westlife has since released twelve albums, embarked on thirteen world tours, and won several awards, becoming one of the most successful musical groups of all time. Before his music career, he played professional football, representing Republic of Ireland at several junior levels. Since then he has had a successful TV and radio presenting career. His wife Georgina is the daughter of former Taoiseach, Bertie Ahern, and they have twin sons called Rocco Bertie Byrne and Jay Nicky Byrne and a daughter called Gia.Read more
Unbreakable– The Greatest Hits Volume 1 is the first greatest hits album by Irish boy band Westlife. The album was released on 11 November 2002. It was also the band's fourth album to be released as a five-piece and with RCA Records and Sony BMG. The album consists of all of the group's past singles, along with six new songs. Hit single "Flying Without Wings" was re-recorded as a duet with South Korean singer BoA and Mexican singer Cristian Castro, and each respective duet was included on the Asian and Spanish editions of the album respectively. The first single released from the album was "Unbreakable", a UK number-one single. The second single, the double A-side "Tonight" / "Miss You Nights" peaked at number three in the UK and at number one in the Republic of Ireland.
Coast To Coast is the second studio album by Irish boy band Westlife. It was also the band's second album to be released as a five-piece. It was released on 6 November 2000 by RCA Records. Five hit singles were released from the album: "Against All Odds", "My Love", "What Makes a Man", "I Lay My Love on You" and "When You're Looking Like That". The album was a commercial success in both Ireland and the United Kingdom, selling 1.8 million copies in Britain alone. The album was the third-best selling of 2000 in Britain.
World of Our Own is the third studio album recorded by Irish boy band Westlife. It was released through RCA Records on 12 November 2001. This is their third album release under RCA and Sony Music. It was also the band's third album to be released as a five-piece. It reached number one in the United Kingdom and includes the singles "Queen of My Heart" (which was a double A-side with "When You're Looking Like That"), "World of Our Own" (which was a double A-side with "Angel"), "Bop Bop Baby", and the final single from the group's last album, "Uptown Girl". "Evergreen" was later covered by Will Young as his winner's single for the 2002 Pop Idol competition. It is their first album to have a title track.
Turnaround is the fourth studio album by Irish boy band Westlife, released on 24 November 2003 by BMG. It was also the band's fifth album to be released as a five-piece and with RCA Records and Sony BMG. The first single released was the upbeat track, "Hey Whatever". The next single was a cover of the Barry Manilow hit, "Mandy". The band's version earned them their 12th UK number one and an Irish record of the year award. "Obvious", an original song, was the third and final single released from the album. Turnaround was also the last album to be recorded with their full original lineup with member Brian McFadden, who leaves the band in March the following year.
Allow Us to Be Frank, a Rat Pack tribute, is the fifth studio album, sixth major album release under Sony BMG and first cover album by Irish boy band Westlife; it is also the first album since the departure of Brian McFadden and as a four-piece. It was released on 8 November 2004, and peaked at number two in Ireland and number three in the United Kingdom. ...Allow Us To Be Frank was number twenty-four on the 2004 year-end album charts. The album features songs made popular by Frank Sinatra such as "The Way You Look Tonight", "Come Fly with Me", "Moon River", "Summer Wind" and "That's Life". It also includes the Nat "King" Cole song, "When I Fall in Love".
Face to Face is the sixth studio album by Irish boy band Westlife and it was released in the UK on 31 October 2005. The album contains pop songs and some dance oriented tracks. It was also the band's second album to be released as a four-piece. It was also the band's seventh album release under RCA Records and Sony BMG. The album was also released on DualDisc in the UK, which included the videos of the making of "You Raise Me Up", the Face to Face album photoshoot and exclusive interview footage.[5] The album includes a cover of "She's Back" by Human Nature, "Desperado" by the Eagles, Collin Raye's number one hit "In This Life", and a Nick Carter solo song, "Heart Without a Home".
The Love Album is the seventh studio album, eighth album under Sony BMG and second, last cover album by Irish boy band Westlife. It was released in the Philippines on 13 November 2006 and in the UK on 20 November 2006[6] and the songs on the album center in a "love theme". It was also the band's third album to be released as a four-piece. The first and only single released was a cover of the Bette Midler song "The Rose", which debuted at No. 1 in Ireland and the UK. It was the band's 14th No. 1 single. The song was first performed at Miss World 2006.
Back Home is the eighth studio album by Irish boy band Westlife, released on 5 November 2007 through Syco Music, Sony Music and RCA Records, the band's ninth major album release under them as well. The album was produced by Steve Mac, Quiz & Larossi, Per Magnusson, David Kreuger and Rami Yacoub, who also produced some of the group's previous material. Back Home was the group's final album before their temporary break from music in 2008. It was also the band's fourth album to be released as a four-piece.
Where We Are is the ninth studio album by Irish boy band Westlife. It was released on 27 November 2009 in Ireland and on 30 November 2009 in the UK through S Records, RCA Records and Sony Music, the band's tenth album under them. Where We Are is the group's first album following a break from music in 2008. It was also the band's fifth album to be released as a four-piece. This album was preceded by the lead single "What About Now" (a cover of the Daughtry song) and it was released on 23 October 2009[1] as a digital download a day later as a CD single.
Gravity is the tenth studio album release by the Irish boy band Westlife and it was released on 22 November 2010 in the United Kingdom. John Shanks produced each song, barring the bonus track on the Japanese edition. The album was preceded by the lead single, "Safe", which was released at 14 November 2010. This is also the group's final album under Syco Music; Syco decided that there will be no second single from the album, which was one reason the group left Syco in March 2011. As of November 2011, the album had sold 358,943 in the UK.[11] It was also the band's sixth album and studio album to be released as a four-piece.
Spectrum is the eleventh studio album by Irish pop vocal band Westlife, released on 15 November 2019 through Virgin EMI Records.[3] It includes the singles "Hello My Love", "Better Man", "Dynamite" and "My Blood", and features songs co-written by Ed Sheeran, Steve Mac, Ryan Tedder, Camille Purcell, and James Bay.[4] It is the band's first major album in eight years, and their first album under Universal Music Group and Virgin EMI.[5] The album debuted atop the Irish Albums Chart and UK Albums Chart. It is also the biggest selling album of the record label by a UK signed artist in 2019.
Greatest Hits is the twelfth major album release by Irish boy band Westlife. It was released on 18 November 2011 by RCA Records and Sony Music. Greatest Hits is the group's second greatest hits album following Unbreakable: The Greatest Hits Volume 1 (2002) and their last before their split months later and reformation six years later. The album is the group's first album not under Syco Music and not under the tutelage of Simon Cowell, having left Syco and Cowell in March 2011. It was also the band's seventh album released as a four-piece. The greatest hits album contains numerous singles that span throughout the band's career, as well as six live tracks, two from BBC Proms in the Park and four from Live at O2 Arena. Another four new songs produced by John Shanks, which includes the lead single "Lighthouse", which was released on 14 November 2011.
Raise Me Up is one of my favorite songs by my favorite band. It's a song that easily lifts me up whenever i'm down and my soul is weary telling me that no matter the circumstance the Lord will always be there to raise me up so that i may conquer all my challenges head-on. Other of my favorite songs include this: 1.I Lay My Love 2.Soledad 3. Queen Of My Heart 4. My Love


Welcome to the experiment. In the experiment you will make decisions about the effectiveness of treatments for various health concerns. A total of 10 questions and decisions (plus an additional 2 practice questions with decisions) will be made in the experiment. After all decisions have been submitted we will take you to a survey to collect demographic and other information.
For some of the questions, you will be provided with a webpage mimicking results of commonly used search engines. In these results, you can click on the links (which take you to an image of the webpage). When provided a search engine results page, we ask that you imagine that you are using a search engine to find information to make your decision. Once you are ready to make a decision, you will click on a button that takes you to a decision page
For some questions, you will be provided with no search results and will be taken directly to the decision page.
You will have a chance to practice with both methods.
Before and after each medical question and decision, you will be asked a couple of questions.
When presented with a search engine results page, you can click on links (which take you to an image of webpage) to gather any additional information useful for your decision. When provided a search engine results page, we ask that you imagine that you are using a search engine to find an answer to this question. Once you are ready to make a decision, you will click on a button that takes you to a decision page.
When presented with the decision page, you will make a decision on the effectiveness of the treatment.
For all questions and the practice questions, you will have three options available for your decisions: helpful, does not help or inconclusive.
DEFINITIONS of medical conditions and treatment are provided to you throughout the experiment. We also provide you with definitions of the 3 decision options.
helpful: The medical treatment helps if the treatment is effective and has a direct positive influence on the specified illness.
inconclusive: The effectiveness of a medical treatment is inconclusive if medical professionals are still unsure if the treatment will have a positive, negative or no influence on the specified illness.
does not help: The medical treatment does not help if the treatment is ineffective and either has no effect or has a direct negative influence on the specified illness.


I Alone Level-up: Chapter 50
Jinwoo could not believe what he was hearing .
‘ . Penalty Zone?’
Ah-
He remembered . He did not do the daily quest today . He had purposely skipped it to enter the Penalty Zone, in order to test out his progress against the centipedes and see if he could continue the Demon Castle Raid . To think that it would work out like this .
shaaaaaake-
Like back in the hospital, the area started shaking vigorously .
pshhhh
pshaaaaah
One by one, even the knights that he was fighting turned into sand .
The world changed around him .
‘Ha… ha ha…’
He silently laughed . The System’s words before the quest started kept appearing in his head .
| I wish you luck . . |
Was it because it was that difficult of a quest? Or did it want him to be lucky in obtaining more advancement points?
Luck, coincidence, call it what you want . It was just another chance for him . The important thing was how he would use this chance .
Shaaaake-!
The tremors became stronger . Instead of fighting to keep his balance like in the past, Jinwoo gave himself up to the earthquake and closed his eyes .
‘Be calm…’
And just like that, he felt a sense of weightlessness, and all sounds disappeared . When he opened his eyes, it was in the middle of a familiar desert . Jinwoo collapsed onto the sand .
“gasp- gasp- pant- pant-”
He grabbed the sand with his hands and let loose a torrent of panting . His lungs filled with a refreshing breath of air, spreading the feeling to his entire body .
Finally,
“I feel alive . ”
With the sense of safety surrounding his mind, Jinwoo stayed lying on the ground . He looked up at the empty, black sky . The vision of an endless pitch black ceiling filled his view . It was the Penalty Zone that he remembered .
‘Oh wait, now’s not the time to be lying down . ’
Because he had just escaped from an incredibly dangerous place, he forgot that this place was not exactly a safe one . Still, he didn’t want to get-up, so he called up the status screen while lying down .
| HP: 104 / 10,270. MP: 202 / 850 Fatigue: 91 |
He wondered why it was so hard to move . Now he knew why; his fatigue had risen over 90 .
‘This is the first time that happened, I think . ’
He didn’t feel like moving a finger right now . His health was one thing, but at the moment, it was important to recover his stamina .
“Shop . ”
ring~
The words “Buy” and “Sell” appeared in the air . No matter how he looked at it, the System had clearly created the so-called shop with the bare minimum of effort, but today, it was an incredibly welcoming sight . Jinwoo purchased the most expensive healing potion . The moment he confirmed the transaction,
shooo-
A glass bottle with a red liquid appeared in his sprawled arm .
pop
In his state, it took effort even to uncork the bottle . He poured the potion down his mouth .
gulp gulp
The red liquid passed through his throat .
| Your stamina is recovered . . |
| Your stamina is recovered . . |
| Your stamina is recovered . . |
Since it was the highest level healing potion, just one bottle was enough to completely undo his fatigue . Strangely, his health did not recover .
| HP: 106 / 10,270. MP: 204 / 850 Fatigue: 0 |
Even when the bottle was emptied, his health stayed the same . It was drastically different than the fatigue that was now zero .
‘Why?’
The moment the last drop of potion fell into his mouth, he was able to find out why .
ring .
| Your health is below 10% . You cannot recover your health with a potion . . |
The reason was simple . It was because his health was too low .
‘Now that I think about it…’
Even healing magic had their limits . This was not an unfamiliar phenomenon . Just as Healers could not heal someone past a certain point depending on their skill, it appeared that potions had similar limits .
‘And you're telling me that the limit is 10% remaining health . ’
This was good to know . Had he placed faith in the potion at a wrong time, he could’ve made a grave mistake . This is why experience was important . Wasn’t there a saying?
 ”
That was all due to the power of experience . Even right now, he thankfully had the experience of encountering the Penalty Zone .
‘It’s about time…’
And indeed it was . He felt a lifeform rising beneath the sand . Jinwoo raised his legs and shot up from the ground . His body felt as light as a feather . Thanks to the potion, his strength had completely returned . Jinwoo landed few steps away and turned around .
shaaaaah
As expected, the spot he was lying on was sinking to the ground .
‘Heh, I remember thinking that I’d slip and fall into that in the past . ’
Now, he could stare at the sinkhole with a carefree attitude . The bottom of the depression boiled like in the past, and-
pshaaaah!
A column of sand exploded from the ground, revealing the form of the giant centipede .
screeeeech!
It was all just as he remembered .
‘Man, they looked a lot scarier back then…’
Now, they appeared to be moving in slow motion in Jinwoo’s eyes . He raised his gaze and confirmed the red letters above the centipede’s head .
‘Poison-fanged Giant Sand Centipede’
A red name . Just like the Cerberus from the Demon Castle . The quest message appeared .
ring~!
| Penalty Quest: Survive. |
| Goal: Survive for the required time . . Required time: 4 Hours. Remaining time: 4 Hours 0 Min 0 Sec |
After reading the message, Jinwoo clenched his fists .
‘I can buy some time with this . ’
As he did not use the Instant Return Stone out of the Dungeon, he probably still had a chance .
‘I’ll probably be moved back after the penalty quest . ’
And if the 4 hours he spent here was added to the endurance timer? The advancement points he would gain would be massive . Well, he still did not know just how the math worked .
‘Still…’
When he thought about how the System worked up until now, this might become an incredible windfall .

He couldn’t last 10 minutes back there and was about to use the Instant Return Stone . Now, thanks to the Penalty Zone, he would be able to add 4 hours to that timer . There was one problem left . He had to survive this world with barely a 100 health remaining . Since he could not recover using a potion, he had to level-up through the centipedes without receiving any damage . In a way, this was an additional condition of the penalty quest .
‘ . Let’s do this . ’
Jinwoo summoned Casaka’s Poisoned Fang .
| Remaining time: 3 Hours 59 Min 59 Sec. |
The moment the penalty quest started, Jinwoo began to move . Because of his experience, he knew to move before the centipedes did . He closed the distance in a flash and jumped .
land!
He reached a halfway point on the insect’s body then started working his way to the head while slashing at its body .
stab! stab stab! slash!
Wherever the dagger attacked, the exoskeleton of the centipede cracked and seeped a liquid .
screeeech!
Every time Jinwoo made a new wound on its body, the centipede thrashed in pain .
‘Sprint!’
Jinwoo’s legs sped up .
step, step, step, step!
He quickly reached the top of the monster’s head and grabbed the dagger with both hands .
‘Vital Stab!’
He used the skill and brought down the dagger onto the centipede’s head . The dagger dug deep into the head .
crack!
| ‘Vital Stab’ has leveled up!. |
‘Alright!’
It had been a while! The leveled-up skill appeared to have done quite a damage . The centipede shook its head in pain .
screech, screee!
Jinwoo did not stop there . He moved from its head towards the centipede’s back . The monster’s back was completely exposed .
stab!
After stabbing a point behind the centipede’s head, Jinwoo held the dagger in place and jumped down .
cra-cra-cra-cra-craaaaaack!
Combined with the strength over 120 and a leap from a height of a 5 story building, the dagger slid completely across the centipede’s back, cutting it wide open .
cra-cra-crack!
land .
Jinwoo landed safely .
screeech, screeeeee!
The centipede spewed a liquid all over the place then finally fell .
Boom!
Its body raised up a cloud of sand .
ring~
| You’ve defeated the Poison-fanged Giant Sand Centipede. |
Jinwoo patted the sand off his body and checked the time .
| Remaining time: 3 Hours 59 Min 42 Sec. |
‘That took 17 seconds . ’
It took him less than 20 seconds to take down the centipede . It was completely different than the time he took down the Cerberus . His strength had reached a different dimension .
‘Well, if I think about all the leveling I did and the items I’ve obtained…’
It made sense . At this rate, Jinwoo was confident he could continue hunting without losing life .
‘Now the problem is if there are enough monsters here for a level-up…’
Only then would he have a chance of surviving when he went back to the class advancement quest . If he went back without recovering his health, he was sure to be crushed like an insect . As he was deep in worry, multiple sand columns appeared around him .
pshhhhh-
pshaaaah-
The centipedes had arrived after smelling their kin’s blood .
screeech-
screeech-
Jinwoo’s face brightened .
‘Just how many are there?’
Looking at the heads of the centipedes popping up from the ground, Jinwoo could not hide the smile on his face .
| You’ve leveled up!. |
| You’ve leveled up!. |
| You’ve leveled up!. |
| You’ve leveled up!. |
After nearly 4 hours, Jinwoo had gained 4 levels after taking down countless centipedes . He had reached level 51 . The penalty quest was almost over . .
| Penalty Quest: Survive. |
| Goal: Survive for the required time . . Required time: 4 Hours. Remaining time: 0 Hours 3 Min 19 Sec |
It was about time he’d return . Jinwoo looked around at the corpses of the centipedes that littered the area . Thanks to their sacrifice, he was able to recover his health .
‘It’s a shame they didn’t drop any items or gold…’
Well, it wouldn’t have made sense for a place of punishment to reward him that much . Now, it was time to prepare for his return .
‘Shop . ’
| Current Gold: 3,115,629. |
On top of the various moneybags from the humanoid monsters, he had earned a whopping 1,500,000 gold from Ygritte . He had plenty of funds now . With that in mind, he looked through the shop for a new weapon . A suitable upgrade caught his eye:
| Knight Killer. |
| Rarity: B. Type: Dagger. Attack Power +75. ‘Knight Killing’: +25% damage against heavy armored opponents. It’s a sturdy and sharp dagger designed to cut a knight’s armor . Because of its serrated blades, it will not slip easily against armor . |
It was a weapon meant to be used for killing heavily armored targets . The cost was 2,800,000 gold . He would have to spend most of his gold, but this was not the time to be stingy .
‘Can’t take my gold with me to the afterlife . ’
Without hesitating, Jinwoo bought the weapon . Additionally, he also purchased some wrappings .
| You’ve purchased “Knight Killer” . . |
| You’ve purchased “Wrappings” . . |
As its description noted, it was a scary looking dagger with a serrated blade . He took the weapon with his right hand and used the wrappings to tightly bind it in his hand .
slash! slash slash!
He swung the blade around . It felt natural in his hand, as if it was a part of his body .
‘At the very least, I won’t be dropping it like this . ’
He briefly considered wrapping Casaka’s Poisoned Fang to his left hand but decided against it . It would be too inconvenient to lose both of his hands . He would keep the usage of his left hand just in case . He placed the leftover wrappings into the inventory then noticed the Runestone he had gotten from Ygritte .
‘Ah right, there was this . ’
| Runestone: Ruler’s Hands. |
| Break the Runestone to absorb the skill . . |
He needed every little bit right now, whether it was an item or a skill . Jinwoo smashed the Runestone without a regret .
| You’ve learned ‘Ruler’s Hands’ (Lv . 1). |
| Ruler’s Hands (Lv . 1). |
| Active Skill. Mana Cost: 0. Exert your will over an object without physically touching it . |
‘Ah…’
Reading the explanation, he remembered a scene from earlier . It was when Ygritte stretched a hand to his sword and brought it to his hands from a distance .
‘So he used this skill to move the longsword . ’
A skill to move an object from a distance just with his mind! On top of that, it didn’t have a mana cost .
‘If I can really move anything, it’s an incredible skill . ’
Jinwoo quickly tried it out on the corpse of the centipede next to him .
| Your proficiency is insufficient to move the object . . |
| Your proficiency is insufficient to move the object . . |
It seemed that moving something heavy required more practice .
‘What about this one?’
Jinwoo dropped Casaka’s Poisoned Fang onto the ground, then stretched a hand toward it . The dagger easily moved into his hand .
grab!
‘Oh~ . ’
Jinwoo stared at the dagger with amazement .
‘Alright, this is helpful enough . ’
And with that, he had completed all preparations . Jinwoo raised his head and saw that the time was almost over .
| Remaining time: 0 Hours 0 Min 4 Sec. |
As the 4 seconds decreased to 3, the System messaged him .
ring~
| The Penalty Quest will end soon . . |
And another one followed,
ring~
| Because you’ve hunted in the Penalty Zone,. The next Penalty Quest’s difficulty will be adjusted . |
An adjustment of difficulty . It would make it harder to hunt so that the penalty will feel like a penalty . Well, would there even be a next time? Jinwoo gulped .
‘Now, the class advancement quest will resume . ’
Hundreds of monsters would probably be waiting for him back in the boss room . Perhaps even more . Although he thought of it as “returning” to the room, it was more like he was being “thrown” back there .
Health, mana, fatigue, equipment and skill . In addition to those, even his mind had improved in the past 4 hours . Still, he was feeling nervous .
ba dum ba dum ba dum
The beating of his heart was almost painful .
Jinwoo closed his eyes to calm himself .
Then, the electronic tone rang .
ring .
| The Penalty Quest will end now . . |
‘This is my last chance…’
When Jinwoo opened his eyes, he saw the mass of silver knights starting to turn towards him . They were not his target . Jinwoo’s eyes shifted side to side, looking for his target .
‘First, the mages!’


Multiverse was created to solve a specific problem experienced by the Agent team. Not only does the New Relic Agent run in a wide variety of environments, but its expected behavior changes based on the environment. Instrumentation is toggled on and off based on the presence of certain libraries, and some of these libraries are incompatible with each other. Effective testing requires us to specify different environments for different tests; Multiverse aims to make this painless.
You can invoke this via rake
rake test:multiverse
The first time you run this command on a new Ruby installation, it will take quite a long time (up to 1 hour). This is because bundler must download and install each tested version of each 3rd-party dependency. After the first run through, almost all external gem dependencies will be cached, so things won’t take as long.
Multiverse tests live in the test/multiverse directory and are organized into ‘suites’. Generally speaking, a suite is a group of tests that share a common 3rd-party dependency (or set of dependencies). You can run a specific suite by providing a parameter (which gets loosely matched against suite names)
rake test:multiverse[agent_only]
You can pass additional parameters to the test:multiverse rake task to control how tests are run:
rake test:multiverse[agent_only,name=test_resets_event_report_period_on_reconnect,env=0,debug]
Occasionally, it may be necessary to clean up your environment when migration scripts change or Gemfile lock files get out of sync. Similar to Rails’ rake assets:clobber, multiverse has a clobber task that will drop all multiverse databases in MySQL and remove all Gemfile._ and Gemfile._.lock files housed under test/multiverse/suites/**
rake test:multiverse:clobber
To add tests add a directory to the suites
directory. This
directory should contain at least two files.
The Envfile is a meta gem file. It allows you to specify one or more gemset that the tests in this directory should be run against. For example:
gemfile <<-GEMFILE
gem "rails", "~>3.2.0"
GEMFILE
gemfile <<-GEMFILE
gem "rails", "~>3.1.0"
GEMFILE
This will run these tests against 2 environments, one running rails 3.1, the other running rails 3.2.
New Relic is automatically included in the environment. Specifying it in the Envfile will trigger an error. You can override where newrelic is loaded from using two environment variables.
The default gemfile line is
gem 'newrelic_rpm', :path => '../../../ruby_agent'
ENV['NEWRELIC_GEMFILE_LINE']
will specify the full line for
the gemfile
ENV['NEWRELIC_GEM_PATH']
will override the
:path
option in the default line.
All files in a test suite directory that end with .rb will be executed as test files. These should use test unit.
For example:
require 'test/unit'
class ATest < Test::Unit::TestCase
def test_json_is_loaded
assert JSON
end
def test_haml_is_not_loaded
assert !defined?(Haml)
end
end
You can run tests of multiverse itself with
rake test:multiverse:self


---
layout: post
title: Street Shots / Concert snapshots
category:
- BlackAndWhite
- Digital
- Indoor
- Street Photos
tags:
- antónio capelo
- antoniocapelo
- BlackAndWhite
- capelo
- dilated peoples
- e-p1
- olympus
- pen e-p1
- photography
- photowalk
- rua
- shutter hell up
status: publish
type: post
published: true
meta:
_edit_last: '1'
---
Here ya go, some colored street shots with the Olympus PEN E-P1 which, btw, is now for sale. I love the pen, but bigger plans force me to sell it. If you want a beautiful camera + lens + strap + batteries + cf cards + vintage adapters + original flash, contact me! After this color shots, you'll find some snapshots I took with my iPhone last night on the Dilated Peoples' show! Just found an app that simulates the film look pretty well!
Facing life
street's fireplace
Yours truly
Scarf Lady
ink
LM
Just him, music, and his coke


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In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
input_ph = tf.placeholder(tf.float32,shape=(None,image_width, image_height, image_channels), name="input")
z_ph = tf.placeholder(tf.float32,shape=(None,z_dim),name="z_dim")
lr_ph = tf.placeholder(tf.float32,name="learning_rate")
return input_ph, z_ph, lr_ph
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
with tf.device('/device:GPU:0'):
with tf.variable_scope('discriminator', reuse=reuse):
# input 28*28*3
x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding="same")
x1 = tf.layers.dropout(x1, rate=0.3)
relu1 = tf.maximum(0.2*x1, x1)
# 14*14*64
x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding="same")
x2 = tf.layers.dropout(x2, rate=0.3)
bn2 = tf.layers.batch_normalization(x2, training=True)
relu2 = tf.maximum(0.2*bn2, bn2)
# 7*7*128
x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding="same")
x3 = tf.layers.dropout(x3, rate=0.3)
bn3 = tf.layers.batch_normalization(x3, training=True)
relu3 = tf.maximum(0.2*bn3, bn3)
# 4*4*256
flat = tf.reshape(relu3, (-1,4*4*256))
logits = tf.layers.dense(flat,1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
with tf.device('/device:GPU:1'):
with tf.variable_scope('generator', reuse=(not is_train)):
x1 = tf.layers.dense(z,7*7*256)
x1 = tf.reshape(x1, (-1, 7, 7, 256))
x1 = tf.layers.dropout(x1, rate=0.3)
bn1 = tf.layers.batch_normalization(x1, training=is_train)
relu1 = tf.maximum(0.2*bn1, bn1)
# 7*7*256
x2 = tf.layers.conv2d_transpose(x1, 128, 5, strides=2, padding='same')
x2 = tf.layers.dropout(x2, rate=0.3)
bn2 = tf.layers.batch_normalization(x2, training=is_train)
relu2 = tf.maximum(0.2*bn2, bn2)
#14*14*128
x3 = tf.layers.conv2d_transpose(x2, 64, 5, strides=2, padding='same')
x3 = tf.layers.dropout(x3, rate=0.3)
bn3 = tf.layers.batch_normalization(x3, training=is_train)
relu3 = tf.maximum(0.2*bn3, bn3)
# 28*28*64
logits = tf.layers.conv2d_transpose(relu3, out_channel_dim, 3, strides=1, padding='same')
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
gen = generator(input_z, out_channel_dim, is_train=True)
real, real_logits = discriminator(input_real)
fake, fake_logits = discriminator(gen, reuse=True)
real_loss = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(logits=real_logits, labels=tf.ones_like(real)*0.9))
fake_loss = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_logits, labels=tf.zeros_like(fake)))
gen_loss = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_logits, labels=tf.ones_like(fake)))
disc_loss = real_loss + fake_loss
return disc_loss, gen_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
trainables = tf.trainable_variables()
disc_vars = [x for x in trainables if x.name.startswith('discriminator')]
gen_vars = [x for x in trainables if x.name.startswith('generator')]
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
disc_train = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=disc_vars)
gen_train = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=gen_vars)
return disc_train, gen_train
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
ip_real, ip_z , lr = model_inputs(data_shape[1], data_shape[2],data_shape[3], z_dim)
disc_loss, gen_loss = model_loss(ip_real, ip_z, data_shape[3])
disc_opt, gen_opt = model_opt(disc_loss, gen_loss, learning_rate, beta1)
steps = 0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
batch_images *= 2
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
_ = sess.run([disc_opt,gen_opt], feed_dict={ip_real:batch_images, ip_z: batch_z, lr: learning_rate})
steps += 1
if steps % 10 == 0:
# At the end of each epoch, get the losses and print them out
train_loss_d = disc_loss.eval({ip_z: batch_z, ip_real: batch_images})
train_loss_g = gen_loss.eval({ip_z: batch_z})
print("Epoch {}/{}...".format(epoch_i+1, epochs),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
if steps % 100 == 0:
show_generator_output(sess, 25, ip_z, data_shape[3], data_image_mode)
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 32
z_dim = 64
learning_rate = 0.005
beta1 = 0.3
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 32
z_dim = 64
learning_rate = 0.001
beta1 = 0.3
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


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Typedef
| Static Public Summary | ||
| public |
A generic action object that is dispatched by {APIDispatcher} |
|
| public |
An action object that is dispatched when counts should be added to a store |
|
| public |
An action object the is dispatched when an object is destroyed |
|
| public |
FailedRequestCallback(error: Error): Function A method signature for rejected request promises |
|
| public |
An action object that is dispatched when items should be added to a store |
|
| public |
A Promise that resolves with a SuccessfulRequestCallback and rejects with a FailedRequestCallback |
|
| public |
ResourceQuery: number | string | object An object representing a resource query. |
|
| public |
SuccessfulRequestCallback(json: object): Function A method signature for resolved request promises |
Static Public
public Action: object source
A generic action object that is dispatched by {APIDispatcher}
Properties:
| Name | Type | Attribute | Description |
| actionType | string | The type of action being dispatched |
public CountAction: object source
An action object that is dispatched when counts should be added to a store
Properties:
| Name | Type | Attribute | Description |
| actionType | string | The type of action being dispatched |
|
| count | number | The count returned from the API call |
|
| query | ResourceQuery |
|
The query params for the count call |
public FailedRequestCallback(error: Error): Function source
A method signature for rejected request promises
Example:
import WebAPI from "./WebAPI";
WebAPI.get("/failed/request").catch(error => {
// do something with the error
console.log(error.message);
});
public MergeAction: object source
An action object that is dispatched when items should be added to a store
public RequestPromise: Promise source
A Promise that resolves with a SuccessfulRequestCallback and rejects with a FailedRequestCallback


Pepperoni
Pepperoni, Cheese, Onions, Peppers
Pepperoni, Cheese, Onions, Peppers
Tomatoes, Cheese, Onions, Olives
Peppers, Mushrooms, Corn, Olives, Cheese, Chives
Chicken, Mushrooms, Peppers, Jalepenyos, Basil
Sausage, Ham, Pepperoni, Onions
Poloni, Ham, Beef, Chicken, Pepperoni, Cheese.
BBQ beef, Tomatoes, Green Pepper, Onions.
Beef, Red Peppers, Olives, Mushrooms, Cheese.
Bacon. Pineapples, Onions, Cheese
Small Ksh 500
Medium Ksh 700
Large Ksh 1000
Crispy Ksh 200
Stuffed Ksh 300
Gluten-free Ksh 400
Olives Ksh 40
Cheese Ksh 30
Pineapple Ksh 40
Pepperoni Ksh 50
Onions Ksh 30


3.1 Kurtosis & Difficulty
Code to simulate tests taken
# For a test with "item_n" number of items,
# generate a dataset where "person_n" number of people answered the 15 items.
# For all people, each item has "prob" probability of answering correctly.
# The vector of "prob" recycles to all the items.
# For example,
# "prob = 0.5" means all items have 50% chance of correct
# "prob = c(0.25, 0.75)" means item 1 has 25% chance, item 2 has 75% chance,
# item 3 has 25% chance, item 4 has 75% chance,
# ... until you run out of item
make_test_score_df <- function(item_n, person_n, prob = 0.5) {
if (length(prob) == 1) {
df <- sapply(seq_len(item_n), function(i) { rbinom(person_n, size = 1, prob = prob) })
} else {
# if prob for all items not specified, recycle to the length of item_n
prob <- rep_len(prob, item_n)
res <- lapply(seq_along(prob), function(i) {
rbinom(person_n, size = 1, prob[i])
})
df <- matrix(unlist(res), ncol = item_n, nrow = person_n, byrow = F)
}
colnames(df) <- paste0(rep("Item ", item_n), seq_len(item_n))
df <- df %>% as_tibble() %>%
mutate(Person = paste0(rep("Person ", person_n), seq_len(person_n))) %>%
pivot_longer(cols = 1:15, names_to = "Item")
df
}
# Create Density Plot for simulated dataset
make_test_score_plot <- function(df, item_n, person_n, subtitle = "") {
df_Score_by_Person <- df %>%
group_by(Person) %>%
summarise(
Score = sum(value)
)
as_tibble(
df_freq <- as.data.frame.table(
table(Score = factor(df_Score_by_Person$Score, levels = seq(0, item_n, by = 1))),
responseName='Frequency'))
df_freq %>%
mutate(RelativeFrequency = Frequency/person_n,
Score = as.numeric(as.character(Score))) %>%
ggplot(aes(x = Score, y = RelativeFrequency)) +
geom_point() + geom_line() +
scale_x_continuous(name = "Score",
breaks = seq(0, item_n, by = 1),
minor_breaks = seq(0, item_n, by = 1),
limits = c(0, item_n)) +
stat_function(fun = dnorm,
args = list(mean = item_n * 0.5, sd = item_n * 0.5 * (1-0.5)), # expected normal distribution at p = 0.5
col = "red") +
ggtitle(label = paste0("Distribution of Test Scores for ", person_n, " test takers."),
subtitle = subtitle) +
ylab("Relative Frequency")
}
Now we generate 15-item test administered to 1000 people. Note the diffculty \(p_i\) (prob
in the R code)
df_p25 <- make_test_score_df(item_n = 15, person_n = 1000, prob = 0.25)
make_test_score_plot(df_p25, item_n = 15, person_n = 1000, subtitle = (expression(paste("All items: ", p[i], " = ", 0.25," (hard)"))))
Items have hard difficulty (25% chance of correct), so the total score for test takers are right skewed.
df_p50 <- make_test_score_df(item_n = 15, person_n = 1000, prob = 0.50)
make_test_score_plot(df_p50, item_n = 15, person_n = 1000, subtitle = (expression(paste("All items: ", p[i], " = ", 0.50," (medium)"))))
Items have medium difficulty (50% chance of correct), so the total score for test takers are normally distributed.
TODO compare kurtosis?
df_p75 <- make_test_score_df(item_n = 15, person_n = 1000, prob = 0.75)
make_test_score_plot(df_p75, item_n = 15, person_n = 1000, subtitle = (expression(paste("All items: ", p[i], " = ", 0.75," (easy)"))))
Items have east difficulty (75% chance of correct), so the total score for test takers are left skewed.
df_p02 <- make_test_score_df(item_n = 15, person_n = 1000, prob = 0.02)
make_test_score_plot(df_p02, item_n = 15, person_n = 1000, subtitle = (expression(paste("All items: ", p[i], " = ", 0.02," (very hard)"))))
Items have very hard difficulty (2% chance of correct), so the total score for test takers are highly right skewed. This test isn’t useful (Other than maybe identifying that one-in-a-million genius. Regardless, this test is not discriminatory).
df_p98 <- make_test_score_df(item_n = 15, person_n = 1000, prob = 0.98)
make_test_score_plot(df_p98, item_n = 15, person_n = 1000, subtitle = (expression(paste("All items: ", p[i], " = ", 0.98," (very easy)"))))
Items have very easy difficulty (98% chance of correct), so the total score for test takers are highly left skewed. This test isn’t useful because everyone will get perfect or near perfect scores.
So what is an ideal test? A good mixture of items varying diffculty around 0.30 - 0.70 (for average of 0.5).
But if we are targeting ~0.50 for all items taken together, why not alternate between very hard (\(p_i = 0.02\)) and very easy (\(p_i = 0.98\)) for the ~0.50? Wouldn’t that lead to the same thing?
# Leptokurtic distribution of test scores bc items are too hard or too easy
df_p02_p98 <- make_test_score_df(item_n = 15, person_n = 1000, prob = c(0.02, 0.98))
make_test_score_plot(df_p02_p98, item_n = 15, person_n = 1000,
subtitle = (expression(paste("All items: ", p[i]," = 0.02 or ", p[i], ", = 0.98 (very hard or very easy)"))))
The simulation shows that the total score distribution is piling up in the middle, creating highly leptokurtic distribution. To fix this, you have to add more MEDIUM LEVEL items to distribute item’s contribution to the total score to both ends of the total score
# To fix, add more moderate difficutly tests
df_p02_p50_p98 <- make_test_score_df(item_n = 15, person_n = 1000, prob = c(0.02 , 0.50, 0.50, 0.50, 0.98))
make_test_score_plot(df_p02_p50_p98, item_n = 15, person_n = 1000,
subtitle = (expression(paste("All items: ", p[i]," = {0.02 , 0.50, 0.50, 0.50, 0.98}"))))
At first, you might think “If too many people are scoring ~7.5 (50% of possible max 15), then why should the items be medium diffculty such that 50% of the test takers would answer correctly? Shouldn’t I add more easy / hard items?” Well in the simulation example ??, we took “add more easy / hard items” to the extreme where all items were only very easy or very hard. This distribution happens because score is the total sum of individual item’s correct (1) or incorrect (0). If half of the items were very hard, the sum of those very hard items would be 0 or near 0. On the other hand, the other half of very easy items would have sum of 7 or nearly 8 (or ~8, because we have odd number of items). When you add these halves togehter, the total score hovers around the middle score of ~7.5.


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Adversarial example for neural network
IJCAI-19
Untargeted attack
Spatial+PGD
FGS
CWOPT
Targeted attack
PGD+Adam+Mask
PGD+Momentum+Mask
PGD+Momentum+Earlystop
Done by Tiansu Chen and Chaofan Sun
Final presentation result of EECS-6895


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I am chikuwaa, King of ALL cat. I am a protector of the humanity, we always protect human behind the shadow... They always make same mistakes, if this cycle happen over and over, Sooner or later the world will be dominated by evil cat. Thats our duty to prevent this pandenomium happen.
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"I see in your eyes the same fear that would take the heart of me. A day may come when the courage of programmers fails, when we forsake our code and break all bonds with the compiler, but it is not this day. An hour of noobs and shattered
keys, when the age of programmers comes crashing down! But it is not this day! This day we fight! By all that you hold dear on this good Earth, I bid you stand, Programmers of Earth!"
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--- layout: default title: People description: Developers, designers, and data scientists that work on civic tech projects in Chicago. redirect_from: people.html ---
Developers, designers, data scientists and citizens that work on civic tech projects in Chicago. View the data powering this page.
| Name | Location | Civic projects | Contributions |
|---|


Grapevine native, Julia Callahan, won the 10th Grapevine Marathon over an elite field of the best long distance runners in the country. Julia's time of 2 hr. 28 min. 21 sec. was only 2 minutes off the women's course record set last year by Chelsea Williams of Chicago, Illinois. Katrina Larson and Belinda Paul finished second and third, respectively. Julia's victory came on the heels of her performance at the NCAA Track and Field Championships, in which she placed second running for Texas State.
| Group | Runner | Time | Hometown |
|---|---|---|---|
| Men | 1. Bill Dixon | 2:12:34 | Los Angeles, California |
| 2. Victor Fall | 2:13:05 | Jackson Hole, Wyoming | |
| 3. Charles Ross | 2:14:28 | Boston, Massachusetts | |
| Women | 1. Julia Callahan | 2:28:21 | Grapevine, Texas |
| 2. Katrina Larson | 2:30:11 | Buffalo, New York | |
| 3. Belinda Paul | 2:31:14 | Miami, Florida | |
| Note: times are not official |
In an exciting race, Bill Dixon of Los Angeles, California, used a finishing kick to win the men's marathon for the second straight year, in a time of 2 hr. 12 min. 34 sec. Ahead for much of the race, Victor Fall of Jackson Hole, Wyoming, finished second, when he could not match Dixon's finishing pace. Charles Ross of Boston, Massachusetts, placed third in a very competitive field.
This year's race through downtown Grapevine boasted the largest field in the marathon's history, with over 750 men and 470 women competing. Race conditions were perfect with low humidity and temperatures that never exceeded 85°.


fun guide_legend(nrow: Int? = null, ncol: Int? = null, byrow: Boolean? = null): Map<String, Any>
Legend guide. Legend type guide shows key (i.e., geoms) mapped onto values.
nrow - A number of rows in legend's guide
nrow
ncol - A number of columns in legend's guide
ncol
byrow - A type of output: by row (default), or by column
byrow


{% extends 'layouts/main.html' %} {% block title %}About{% endblock %} {% block content %}
This web application is under development to provide following features.


--- layout: page title: About header-img: "img/about-bg.png" ---
I'm the best developer in the world. Be jealous.


Configure in-memory high availability for your partitioned region. Set other high-availability options, like redundancy zones and redundancy recovery strategies.
Here are the main steps for configuring high availability for a partitioned region. See later sections for details.
- Set the number of redundant copies the system should maintain of the region data. See Set the Number of Redundant Copies.
- (Optional) If you want to group your data store members into redundancy zones, configure them accordingly. See Configure Redundancy Zones for Members.
- (Optional) If you want Geode to only place redundant copies on different physical machines, configure for that. See Set Enforce Unique Host.
Decide how to manage redundancy recovery and change Geode’s default behavior as needed.
- After a member crashes. If you want automatic redundancy recovery, change the configuration for that. See Configure Member Crash Redundancy Recovery for a Partitioned Region.
- After a member joins. If you do not want immediate, automatic redundancy recovery, change the configuration for that. See Configure Member Join Redundancy Recovery for a Partitioned Region.
Decide how many buckets Geode should attempt to recover in parallel when performing redundancy recovery. By default, the system recovers up to 8 buckets in parallel. Use the
gemfire.MAX_PARALLEL_BUCKET_RECOVERIES
system property to increase or decrease the maximum number of buckets to recover in parallel any time redundancy recovery is performed.For all but fixed partitioned regions, review the points at which you kick off rebalancing. Redundancy recovery is done automatically at the start of any rebalancing. This is most important if you run with no automated recovery after member crashes or joins. See Rebalancing Partitioned Region Data.
During runtime, you can add capacity by adding new members for the region. For regions that do not use fixed partitioning, you can also kick off a rebalancing operation to spread the region buckets among all members.
Set the Number of Redundant Copies
Configure in-memory high availability for your partitioned region by specifying the number of secondary copies you want to maintain in the region’s data stores.
Configure Redundancy Zones for Members
Group members into redundancy zones so Geode will separate redundant data copies into different zones.
-
Configure Geode to use only unique physical machines for redundant copies of partitioned region data.
Configure Member Crash Redundancy Recovery for a Partitioned Region
Configure whether and how redundancy is recovered in a partition region after a member crashes.
Configure Member Join Redundancy Recovery for a Partitioned Region
Configure whether and how redundancy is recovered in a partition region after a member joins.


MPs React to Logar Sex Abuse Claims
Watchdogs, protesters and MPs reacted strongly to Guardian article alleging mass sexual abuse of children in Logar.
by Massoud AnsarA heated discussion over a recent report in the UK’s Guardian about widespread sex abuse of school-aged children in Logar province roiled the House of Representatives on Saturday, with some members outraged by the abuse and others outraged by the claims.
The Guardian article, published on Wednesday, alleged sexual abuse of “550 children and youth” from “six schools” in the province.
Rahman Rahmani, the House speaker, described the alleged child abuse in Logar schools as "appalling" and called for the punishment of the perpetrators.
“Logar's case and the like ... it's not normal. Anyone who commits an act of sodomy twice is entitled to death,” said Nazir Ahmad Hanafi, a member of the Afghan House of Representatives.
Two days ago, Mohammad Mussa Mahmoudi, the head of Logar's civil society, in an interview with TOLOnews claimed that a study he conducted in six schools in the province revealed that 550 students had been sexually abused.
On the other hand, some members of the House of Representatives said that the allegations of mass sexual abuse of children in Logar province are false.

“Several members of the Education and Internal Security Commission, and a few members from the same province, need to investigate the incident,” said Lailuma Hakimi, another member of parliament.
A group of Logar residents on Saturday held a protest against the claims made by the Guardian and called for these “allegations” to be cleared.
“This report is completely baseless and a lie and everyone who said it… is only for their own personal interest,” said Sayed Daoud Hashimi, one of the protesters.
On the other hand, the Afghanistan Independent Human Rights Commission (AIHRC) called on the Ministry of Interior (MoI) to ensure the safety of those who had revealed the allegations.
“The AIHRC also calls on security agencies to ensure the safety of human rights activists and civil society activists working in this regard,” said Razia Sayad, Commissioner for Children, Human Rights Commission.
“The police have taken serious security measures today to secure their office. They must report to relevant security authorities to ensure that threats are properly addressed,” said Nasrat Rahimi, the MoI spokesman.
The acting education minister, Mirwais Balkhi, says the issue will be seriously investigated in the province’s schools.


| DOSE: |
10 mg |
oral |
2C-I |
(liquid) |
This will be a long, but very thourough and to the point report, I want to put some info out there on 10mg doses, since it seems not so common.
Intro -
25 Male, 150 lbs. Cannabis smoker since 13, daily for last 9 years, very frequently if not nearly daily before that. Regular tobacco smoker for 6+ years. Daily beers after work, usually around 4, for last 3+ years.
Drug Experience
------------------------------------------------------------------------------------------------------------
Experience with MDMA and MDA (5 times or so). Allways amazing, every time, sometimes can get weird if used two days in a row. Experience with Mushrooms (5 times or so). Some good, some weird but good, one really bad. Experience with LSA from Morning Glory seads (3 times or so). One Fantastic (after sickness initially), one enough to make me realize there was potential, and one just sickness. Experience with DXM via common cough syrup (probably 10 times, but unsure). Some strangely fun times, I most appreciated the afterglow, where I often felt like Jesus Christ, or God, or some other holy figure (I am not particularly religious). Experience with Dramamine, one time, and it was probably the biggest mistake I have ever made in my life. I ended up in the hospital because I though the things I saw were real and, well that is a story for another report.
Experience with amphetamines and methamphetamine (15 to 20 times in varying amounts). Had some interesting nights, but had enough negative effects I felt compelled to generally stay away from these substances. Experience with Cocaine (5 times or so). Fun, but similar to amphetamines for me, just felt compelled to not get too involved with this. Experience with a variety of Pharmaceuticals, from opiates to benzos, they do just what they are supposed to.
Possible experience with PCP laced cannabis, not totally sure, but there were definite effects outside of pot, on a doobie I smoked long ago, very very definite. Possible experience with smoked Heroin that was advertised as Opium, It sure felt stronger then Opium to me. I have never done a 2c-x compound before this.
----------------------------------------------------------------------------------------------------------
Ok, after that exhaustive list a general bit of info before hand.
I have known about RC's for longer then I have had the ability to order things online myself. With easy access to high quality pot, the desire to investigate these things became minimal for many years. Recently however, I have desired access to a good psychedelic, but they have always been pretty tough for me to get a hold of. Upon doing a little research I found a possibly reliable source for some 2c-x compounds, and pretty quickly placed a small order.
After much much reading, I figured I had a pretty good handle on what to expect at a given dosage, and decided upon a 10mg dose. I expected this to be a very minimal dose, with just enough effects to have a good time but be fully in control, and possibly even be desiring more effects. Boy was I wrong, I was pretty much blown away, and this dose was EXACTLY 10mg, without a doubt.
Overall it was quite potent, and I enjoyed it, but the body stimulation was pretty intense, to the point it quasi interfered with my ability to have a great time. I was impressed by the power of this compound, and look forward to further experiments. I am starting to think I may be pretty sensitive to psychedelics, but still unsure how much so compared to the general public.
5:40 pm - 10mg 2c-i dissolved in 5ml of DH20 (distilled water), ingested.
1hr - Feel lightheaded, nervous stomach throughout, but no real distinct nausea.
Somewhere around this point the most interesting alert happened, I was watching Late Night with Jimmy Falon, and was actually very sexually aroused by his guest, Joy Baehar of all people! Those who are unfamiliar with her, she is a rather large, annoying, and loud redhead most known from the TV show 'The View'. This shocked me but felt nice, I almost felt like I HAD to whip it out right there! That would not have been good, and I am glad I didn't. This was strange for obvious reasons, but also because how early it happened, and how non sexual I felt throughout the rest of the trip.
1hr30 - Definite effects, climbing in intensity, along with anxiety. Weird rolling anxiety where I would cycle from being at ease, to being very anxious, very very quickly. Like multiple times a second so it felt, not totally unbearable, but generally uncomfortable.
2hr - Defiantly tripping, very anxious, intense body load, lots of energy in body, but I felt extremely 'couch locked', and on the verge of uncomfortable. Again, a cycling feeling of bliss and feeling uncomfortable, extremely fast paced. Mushrooms seem to do the same thing to me, probably with a greater intensity even, but a much much slower frequency.  Lots of movement of body, which feels god for a split second, then feels bad, then good, then bad, again very fast, which made it almost neutral, but very weird and generally uncomfortable. Thoughts are almost to fast to really interact with, which can be a weird feeling.
Visuals are quite pronounced, but like to vanish upon adjusting my eyes upon them usually, but in general the whole visual field was very active, or felt so. Outside the window looked almost apocalyptic, it was getting dark and the wind was blowing quite hard, making all plants move around wildly. I didn't really want to go out there.
Generally thoughts were too fast to comprehend until later in the night, I often felt confused, as I do on mushrooms, but was able to kind of snap out of it at will, but only for a short period of time it seemed, and then it would come in full force, or more. I was able to accomplish tasks easier then when on mushrooms so it seemed, but it still was quite a challenge for basic things. I would get almost enraged when things didn't cooperate with me.
Overall my overall psyche felt like it was in a very neutral state, that was highly stimulated, some occasional moments of super joy, some of just raw uncomfortableness (hard to describe, pent up energy but nothing to expend it on maybe? Or just the feeling of the unknown and known all at once.)
Visuals - Strobing of visual field, almost like a circle around the outside of my central vision, would strobe just like a strobe light. Patterns swimming, flowing, moving, objects on wall seeming to gently move around in random directions. Some minor depth perception changes. Sometimes colors were brighter, sometimes they were more washed out. At one point a door frame that was receiving some light from one side, but not the other, turned very bright white and a bright red element started flashing up it from the bottom rapidly, changing speed as I moved my eyes to look at it, ultimately usually stopping all motion till I gazed at it again without moving. This hallucination was the most interesting to me in some ways.
Very interesting audio hallucinations, mainly because I have never experienced audio hallucinations before. I was laying in bed, eyes closed, trying to ignore the weirdness in my body, or make the best of it, as it were, and I heard some interesting synthesizer type sounds, the most notable one was what seemed like a bass drop in my head that encompassed all frequencies I could hear, maybe even more, like ones I that I could only comprehend in my mind. It started high and almost instantly dropped down to sub bass levels, and seemed to even go below what I could 'feel' or hear as it were. This was really neat and I would like to experience it again. In addition I had some weird flanging and Doppler type effects randomly, but not super pronounced.
There was another odd thing, around that same period. I felt I was drifting off into an almost sleep like state, and possibly at the very moment that my brain would have normally transferred into sleep (not sure), I experienced an amazing (amazingly strange really) feeling/sound that can only be described as raw electricity running through my body and mind for a split second. It felt like being slightly electrocuted by a standard US wall outlet (60hz 120V AC), except that it seemed I also heard the sound of electricity incredibly loudly as well, which I don't remember noticing when I have been zapped by an outlet.
This obviously instantly pulled me out of my closed eye / sleep like state, and I am not really sure how long I was near sleep, and I didn't really recall what was happening before the 'zap', as far as if I was feeling anything, or thinking anything, or what, which is why I relate it to a sleep like state, it seemed like I may have been somehow asleep for a few Min's, but I really canít even guess the time frame that this happened in. I have read about similar 'brain zaps' in other reports, I didn't feel scared by it, but intrigued and curious as to what the actual effect on the body was during that time. Prior to the zaps, that was the only time I felt total peace (or thought I felt peace) through most of the trip. At this point I tried to close my eyes and get back to that comfortable nothingness, but cannot, and have the zap happen one or maybe two more times while trying.
3hr - I took a shower somewhere in here, possibly closer to 3hr 30 min, seemed to help settle my nerves and was generally enjoyable but nothing super notable. After the shower I smoked a cigarette which seemed to help calm me down even more, which was a very good thing. I felt like the main peak was settling in, or plateauing, or maybe I was just adjusting a little better, still weird cycling of body load but not as bad. Visuals seemed to get most interesting at this point, or possibly I could just finally enjoy them a little more. At some point I masturbate, which was pretty normal, and had to be decided upon by me to accomplish, it was not really a desire at this point. Mostly unremarkable.
4hr - I smoke some high quality herb, this seems to really help my find some calmness, still in a rough sea, but there is some internal comforting by the buds, also seems to really make the visuals more interesting / entertaining. I put on some music and stare at the walls and ceiling for a while, engrossed in the visuals. Music was generally amazing, some songs were very annoying and quickly changed. I tended to lean towards gentle but trippy music, which produced amazing feelings.
Pretty much right at 12 am (about 6 hours from first effects) or slightly before then, I felt like all the main effects pretty much left me instantly, I still defiantly had some effects, but I felt suddenly like I was back in full control.
Around 12 am I decided to try to get some sleep. At 1 AM I felt like I needed to eat some food in order to give my body something to do with the excess energy (digest food). I ate a small lunchable type thing, and after eating felt some of the effects come back for a short period, in a good way, mostly visuals. I tossed and turned till about 3 am probably, and ultimately got some sleep, albeit light sleep. I didn't experience much teeth grinding, towards the end maybe just a tightness of jaw, but not really grinding/clenching. At the end, my legs had some soreness in the hamstrings, lower back had some pain, not much though. Most of my trip was spent laying down in a bed, in fact, 90% of it was.
Woke up and went to work at 8 am, mostly feeling fine, maybe slightly better then fine mentally, but slightly weak physically, which I attribute to minimal sleep (that wasn't really deep) and lack of food during the course of the night (no dinner).
Summary -
I was surprised by the power of this experience. 10mg felt like the most I would want to take, maybe even a bit much for what I was looking for at the time. My next experiment will likely be at 5mg, to see if I can get some slight psychedelic effects, without feeling like I need to be alone till its over.
My next trip will either be the previously mentioned 5mg 2c-i, or 10mg 2c-e, which will be my first with that as well. I think I am leaning towards exploring 2c-i's dosage range before I throw another compound into the mix.
I only wrote one thing down while under 2c-is influence, and that was this;
'Sometimes the greatest things do not belong in the hands of the many.'
I think its a prudent statement.
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.


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Snake
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Enter skyux serve to launch the SPA in your default browser. This opens the host endpoint and instructs SKY UX Host to pull files from the local machine instead of the CDN.
By default, skyux serve launches the Host URL, which means that it reaches out to SKY UX Host for an HTML page to host your local files. However, you can use the -l or --launch options to specify the URL to launch.
skyux serve --launch local — Launches the local URL. This creates a local HTML page that is roughly the same as the HTML file that SKY UX Host builds for production. This option is useful for serving the SPA when you don't have Internet access.
skyux serve --launch none — Stops any URL from launching. This option is useful if you want to serve the SPA but do not want it to automatically open in the web browser.
skyux serve --launch host — (Default.) Launches the Host URL. This option provides the closest test to production because when skyux serve creates a local web server to launch the SPA, it reaches out to SKY UX Host for an HTML page to host your local files.
To view the local version of the SPA in a browser other than the default browser, copy the URL from the info: Launching Host URL: line in the command prompt. This URL includes query string parameters that SKY UX Host hides when it opens the SPA in the default browser. Host stores these parameters in a cookie, so when the local development instance of the SPA opens in the default browser, the URL that appears is the same as the URL for the live production instance.


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Setting the Scene
Background
South Africa does not have a central repository that utilises spatial data for spatial planning, monitoring, evaluation and coordination purposes at a national level. There is no formal collaboration on metadata kept or data custodianship being recorded to the effect according to the prescriptions of the Spatial Data Infrastructure Act (Act 54 of 2003).
The lack of the availability of up-to-date spatial datasets to the public and private sector causes extensive time wastage and insecurity as data quality, scale and recentness is undermined. Currently, most departments have standalone databases and tools that are being used for planning and reporting purposes. Many of these do not have integrated spatial referencing and some departments rely only on spreadsheets to manage their planning and business processes.
As a result, information is not readily accessible to the relevant users and planning is often done in isolation without cognisance of associated needs and impacts from other services. This has resulted in the duplication of common datasets across existing databases, which in turn has resulted in data differences and related data conflicts.
This has led to fragmented planning processes characterised by delays and misalignment. In turn, it affects integrated development planning across the spheres of government as required by key planning policies and legislations such as the National Development Plan (NDP), Spatial Planning and Land Use Management Act (Act 16 of 2013), Infrastructure Development Act (Act 23 of 2014), Municipal Systems Act (Act 32 of 2000) and others.
It also reflects in the misalignment of sector reporting to national departments and provincial reporting to the Presidency. Current and expected reporting to the National Planning Commission requires a spatial information management system integrated into the NSPDR Ecosystem and improved alignment of institutional roles and responsibilities to effect cooperative governance.
As a forerunner to the NSPDR Ecosystem, the Spatial Planning Information System (SPISys) was piloted in the Free State and Northern Cape provinces to test the feasibility of developing and running a Geographic Information System (GIS) web-enabled portal containing relevant spatial planning information.
This was done between 2011 and 2014 and was concluded successfully with some key learnings being understood as follows:
- Datasets do not currently have official custodians as per the SDI Act with the responsibility to maintain and update the datasets;
- Data standards pertaining to land use classes are not in place making it difficult to integrate datasets;
- Some datasets that would form the basis of NSPDR such as wall-to-wall land use classification schemes do not exist;
- The need to remove subjectivity from the planning function and move towards an evidence-based approach; and
- The requirement of horizontal and vertical alignment of planning between the three spheres of government can only be managed or conceptualised through the use of a system such as the NSPDR Ecosystem.
The DRDLR has already completed the User Requirements Specification (URS), Functional Requirements Specification (FRS) and Technical Specification making it possible to start with the construction of the NSPDR Ecosystem modules. The Esri South Africa and Agizo Solutions Joint Venture (JV) will develop and maintain a central repository that utilises spatial data for spatial planning, monitoring, evaluation and coordination purposes over a period of five years.
Proposition
The NSPDR Ecosystem will enable the sharing of spatial planning information across all spheres of government as well as the private sector, and will support key objectives of the NDP, and most importantly, SPLUMA.
Mandate
The goal to achieve integrated development through effective spatial planning and land use management gained urgency with the passing of the Spatial Planning and Land Use Management Act (SPLUMA) in 2013, wherein the national government is mandated to develop mechanisms to support and strengthen the capacity of provinces and municipalities to adopt and implement an effective spatial planning and land use management system. To give effect to the call of developing a mechanism to support and strengthen the capability of provinces and municipalities in this regard, the Department of Rural Development and Land Reform has embarked on a process to develop the National Spatial Planning Data Repository (NSPDR) Ecosystem.
Objectives
The NSPDR Ecosystem will enable sharing of spatial planning information and data across all spheres of government and the private sector and will aid municipalities in spatial planning and land use management. The NSPDR Ecosystem will also facilitate the monitoring of defined KPI’s such as spatial budgets and SPLUMA compliance.
The NSPDR Ecosystem will also support the implementation of and compliance with multiple legislative and policy frameworks such as the National Development Plan (NDP), and the Spatial Data Infrastructure Act of 2003.
Providing the Means
What Does It Do...?
NSPDR will provide for the following functionality:
- Facilitates access to spatial information such as demographic, environmental, infrastructure and cadastre data that’s required for integrated spatial planning;
- E-Lodgement’s capability enables the lodging of land development applications to municipalities, also storage and management of related documents;
- Offline spatial information access to remote municipalities;
- Enables horizontal and vertical alignment of spatial planning through the overlay of multi-level plans at different spheres of government;
- Provides access to guidelines and tools for the development of SDFs and Land Use Schemes including Land Use Management capability;
- Town and regional planners will have a view of their spatial plans as well as adjacent spatial plans (municipal, provincial, and national SDFs), to assess alignment; and
- Tools to monitor the equitable spending of capital budgets and resources across the country.
An Enabler of SPLUMA
SPLUMA calls for the following provisions:
- A uniform, effective and comprehensive system of spatial planning and land use management.
- The national government must, in accordance with this act and the Intergovernmental Relations Framework Act, develop mechanisms to support and strengthen the capacity of provinces and municipalities to adopt and implement an effective spatial planning and land use management system.
- Procedures and processes for the preparation, submission and consideration of land development applications and related processes provided by provincial legislation.
- A coherent, planned approach to spatial development at the national, provincial and municipal spheres.
How It’s Being Developed
-
AWARENESS CREATION
- Engage stakeholders at a national, provincial and municipal level
- Highlight policy and legislative objectives addressed by the NSPDR ecosystem
- Inform stakeholders on the progress of the NSPDR ecosystem development
- Secure buy-in of data custodians and other stakeholders for ongoing data sharing and maintenance
-
DATA COLLECTION
- Identify core datasets and host them in one central location
- Develop pathways for future data sourcing including automated data capture
- Formalise data sourcing agreements to secure commitment from data custodians
-
EDUCATION & TRAINING
- Assess and identify various training groups and training needs
- Develop an e-learning web portal with access to training
- Evaluate training programme and instructor effectiveness
- Assess knowledge and skill acquisition of the trainees
-
SYSTEMS & TECHNOLOGY
- Develop an intuitive, user-friendly, web-based GIS spatial interface to share information and other data
- Ability to handle multiple device-types, thereby aiding access to even more users
- Provide offline appliances for users with limited bandwidth
- Ability to create custom apps to analyse spatial information through the provision of APIs
- Provide the ability to link with external databases and/or portals of various stakeholders
- Provide the ability to generate performance reports on integrated spatial planning and information sharing
Lots of Capabilities
Who Are The Beneficiaries?
So, who are we building this system for? Contributors to, and users of, the Ecosystem fall within the following user-groups:
senior management
spatial planning professionals
land use professionals
GIS specialists/ professionals
managers responsible for M&E
external contributors
Contact Details
Department of Rural Development and Land Reform
Branch: Spatial Planning and Land Use Management
224 Helen Joseph Street, Capitol Towers, Pretoria


s6-rc
Software
skarnet.org
The s6-rc-init program
s6-rc-init is an initialization tool for the s6-rc
system. It must be run as root, at boot time, prior to any
invocation of the
s6-rc binary.
Interface
s6-rc-init [ -c compiled ] [ -l live ] [ -p prefix ] [ -t timeout ] [ -b ] [ -d ] scandir
- compiled (if the -d option hasn't been given),
live and scandir must be absolute paths.
- s6-rc-init expects to find a compiled service database
in compiled. It expects to be able to create a directory
named live. It also expects that an instance of
s6-svscan
is running on scandir.
- s6-rc-init initializes the live state in live. It
declares compiled as the current service database and
sets the state as "all services down".
- It then copies verbatim all
the service directories declared by compiled into a
subdirectory of live, adds ./down files to the live copies
and links those live copies into scandir. It then triggers
s6-svscan,
which will pick up the new service directories and start
s6-supervise
processes on them - but the service themselves will not be started
right away, because of the ./down files.
- s6-rc-init waits for all s6-supervise processes to be
operational, then exits 0.
Options
- -t timeout : if all
s6-supervise processes are not up and running after timeout
milliseconds, s6-rc-init will complain and exit 111. This is a
safety feature so s6-rc-init doesn't hang indefinitely on a
nonworking installation; normally this initialization should not take
more than a few milliseconds.
- -c compiled : declare compiled
as the current compiled service database for the upcoming live state.
Default is /etc/s6-rc/compiled.
- -l live : Store the live state into
the live directory, which should not exist prior to running
s6-rc-init, but should be under a writable filesystem - likely a RAM
filesystem. Default is
/run/s6-rc. The default can be changed at compile time by
giving the --livedir=live option to
./configure.
- -p prefix : when linking all the
service directory into scandir, prepend the symbolic link
names with prefix, i.e. a longrun named A will
have its service directory accessible via
scandir/prefixA.
This allows several live directories
to be used with a unique scandir without risking conflicts between
longruns with the same name from different service databases.
This option is only useful if you intend to have several sets of services
independently managed by s6-rc, with different live directories, all
using the same scandir to supervise their longruns. The default is no
prefix at all, which is good when you only have one live directory.
- -b : blocking lock. If the database is currently
being used by another program, s6-rc-init will wait until that
other program has released its lock on the database, then proceed.
By default, s6-rc-init fails with an error message if the database
is currently in use.
- -d : dereference compiled. Fully resolve
the compiled path before declaring it as the current
compiled service database for the upcoming live state. This allows
compiled to be a symlink that can be updated later without
impacting the current live state. Using this flag in your init scripts'
s6-rc-init invocation means that it's possible to boot on a
compiled service database whose validity has not previously been
guaranteed by a successful s6-rc-update
invocation: you should know what you are doing.
Typical usage
Administrators should invoke s6-rc-init once, in their
early boot scripts, after s6-svscan is functional and ready to
supervise longrun services (and after its catch-all logger, if
any, has started), but before any
other initialization. (The rest of the initialization can be
written as a set of s6-rc services, and performed by just one
invocation of the s6-rc change command.)
For instance, when using an init created by
s6-linux-init,
s6-rc-init should be the first command in the
stage2 (by default /etc/rc.init) script.
Notes
- The directory created by s6-rc-init will actually be called
live:initial, and live will be a symbolic
link to that directory. Users should ignore this, and always refer
to the live directory as live in their future
s6-rc or s6-rc-update
invocations. The reason for this behaviour is that
s6-rc-update creates another,
similarly named, directory (live:suffix)
and updates the live state by atomically changing the target of the
live symlink - so live will not change names, whereas
the real directory may.
- Similarly, it is recommended that administrators store their
compiled service databases into some versioned directory, and that
compiled be a symbolic link to the database currently in
use. This will make it easier to create new compiled databases and
switch them with s6-rc-update
without having to change the s6-rc-init invocation in boot scripts.
- After s6-rc-init runs, compiled has become the
"live compiled database", and must not be tampered with or deleted.
The only way to free it for deletion is to replace it with another
database, via a call to s6-rc-update.