Northern Ireland constituency profile - three
by Tommie Gorman, https://www.facebook.com/rtenews/The DUP won ten of Northern Ireland's 18 seats in the 2017 Westminster Election, called by Theresa May.
The party became the key to keeping her minority Conservative Party administration in power.
The DUP's decision to not back the Brexit deal of Boris Johnson - Mrs May's successor - pushed him to trigger next month's general election.
It is the latest chapter in an ongoing saga of unpredictable twists and turns.
Northern Ireland constituency profile - one
Northern Ireland constituency profile - two
Will Boris Johnson get an overall majority? Might he again be dependent on the DUP in a hung parliament? Could Jeremy Corbyn, helped by the Scottish National Party and/or the Liberal Democrats, become the next prime minister? How will European politics be affected by the first member state divorce in the history of the European Union?
Northern Ireland’s 18 constituencies are providing a juicy quota of subplots. In total 102 candidates are involved.
Voting takes place on Thursday. Counting will begin immediately after polls close at four centres in Belfast, Magherafelt, Omagh and Bangor.
Below is a taster of what's to come.
NORTH ANTRIM
Outgoing MP: Ian Paisley (DUP)
Brexit referendum result 2016: Remain 37.8%, Leave 62.2 %
The 2017 story:
Turnout was 64.21 %; six candidates contested; with 28,521 votes the DUP's Ian Paisley was 20,643 votes ahead of his nearest challenger, Cara McShane (Sinn Féin).
The 2019 contest:
Ian Paisley featured in a number of Westminster controversies. But he received 59% of the votes two years ago.
He will be boosted by the resolution of the Wrightbus factory problem in recent weeks and should comfortably retain his seat. The highest pro-Leave constituency in the Brexit referendum at 62.2%.
Candidates: Margaret Mckillop, SDLP; Cara McShane, Sinn Féin; Patricia O'Lynn, Alliance; Ian Paisley, DUP; Stephen Palmer, Independent; Robin Swann, UUP.
SOUTH ANTRIM
Outgoing MP: Paul Girvan (DUP)
Brexit referendum result 2016: Remain 49.4 %, Leave 50.6%
The 2017 story:
Turnout was 63.44 %; five candidates contested; with 16,508 votes the DUP's Paul Girvan was 3,208 votes ahead of his nearest challenger, the outgoing Ulster Unionist MP Danny Kinahan.
The 2019 contest:
If the Ulster Unionist party is to stage a comeback, this has to become a success story. Danny Kinahan returns to contest the seat he lost two years ago.
Candidates: John Blair, Alliance; Paul Girvan, DUP; Declan Kearney, Sinn Féin; Danny Kinahan, UUP; Roisin Lynch, SDLP.
EAST ANTRIM
Outgoing MP: Sammy Wilson (DUP)
Brexit referendum result 2016: Remain 44.8%, Leave 55.2%
The 2017 story:
Turnout was 60.83%; six candidates contested; with 21,873 votes the DUP's Sammy Wilson was 15,923 votes ahead of his nearest challenger, Stewart Dickson (Alliance).
The 2019 contest:
The DUP’s Brexit spokesman, Sammy Wilson, had a huge surplus and should retain his seat. Steve Aiken, the new Ulster Unionist party leader, is a candidate. This was the third highest Leave constituency in the Brexit referendum at 55.2%.
Candidates: Steve Aiken, UUP; Danny Donnelly, Alliance; Oliver McMullan, Sinn Féin; Angela Mulholland, SDLP; Philip Randle, Green Party; Aaron Rankin, Conservative; Sammy Wilson; DUP.
UPPER BANN
Outgoing MP: David Simpson (DUP) *retiring
Brexit referendum result 2016: Remain 47.4%, Leave 52.6%.
The 2017 story:
Turnout was 64.15%; five candidates contested; with 22,317 votes the DUP's David Simpson was 7,992 votes ahead of his nearest challenger, John O’Dowd (Sinn Féin).
The 2019 contest: The DUP chose 34-year-old Carla Lockhart, an Assembly member since 2016, to contest the seat vacated by party member, David Simpson.
Arlene Foster once worked as a solicitor in the area but she decided to stay focused on NI Assembly politics.
John O’Dowd will again be the Sinn Féin candidate, fresh from his challenge to Michelle O’Neill for the Sinn Féin vice-president role.
He received 14,325 votes in 2017. One of the leading UUP members, Doug Beattie, is also running.
He got 7,900 votes in 2017. SDLP stalwart Dolores Kelly will be seeking to improve on the 4,397 total of Declan McAlinden.
Candidates: Doug Beattie, UUP; Dolores Kelly, SDLP; Carla Lockhart, DUP; John O'Dowd, Sinn Fein; Eóin Tennyson, Alliance.
LAGAN VALLEY
Outgoing MP: Jeffrey Donaldson (DUP)
Brexit referendum result 2016: Remain 46.9%, Leave 53.1%
The 2017 story:
Turnout was 62.23 %, seven candidates contested, with 26,672 votes the DUP's Jeffrey Donaldson was 19,229 votes ahead of his nearest challenger, Robbie Butler (Ulster Unionist Party).
The 2019 contest:
The sitting DUP MP, Jeffrey Donaldson, should retain his seat with ease.
Candidates: Robbie Butler, UUP; Jeffrey Donaldson, DUP; Sorcha Eastwood, Alliance; Ally Haydock, SDLP; Gary Hynds, Conservative; Alan Love, UKIP; Gary McCleave, Sinn Féin.
STRANGFORD
Outgoing MP: Jim Shannon (DUP)
Brexit referendum result 2016: Remain 44.5%, Leave 55.5%
The 2017 story:
Turnout was 60.36 %; seven candidates contested; with 24,036 votes the DUP's Jim Shannon was 18,343 votes ahead of his nearest challenger, Kellie Armstrong (Alliance).
The 2019 contest:
Jim Shannon (DUP) should comfortably retain his seat. This is the second highest pro-Leave constituency at 55.5%.
Candidates: Grant Abraham, Conservative; Kellie Armstrong, Alliance; Joe Boyle, SDLP; Ryan Carlin, Sinn Féin; Maurice McCartney, Green Party; Jim Shannon, DUP; Philip Smith, UUP; Robert Stephenson, UKIP.
NORTH DOWN
Outgoing MP: Sylvia Hermon (Independent) *retiring
Brexit referendum result 2016: Remain 52.4%, Leave 47.6%
The 2017 story:
Turnout was 61.04 %; eight candidates contested; with 16,148 votes Independent Unionist Sylvia Hermon was 1,208 votes ahead of her nearest challenger, Alex Easton (DUP).
The 2019 contest:
The vacancy created by Sylvia Hermon's retirement probably opens the door for the DUP or Alliance.
Alex Easton (DUP) lost the 2017 contest by 1,208 votes.
The Alliance candidate Andrew Muir (3,639) was significantly behind Easton (14,940). Stephen Farry, the Alliance party deputy leader, should boost his party’s chances but the DUP will see North Down as a possible gain, even though it was a pro-Remain constituency (52.3%).
Candidates: Alan Chambers, UUP; Alex Easton, DUP; Stephen Farry, Alliance; Matthew Robinson, Conservative.
SOUTH DOWN
Outgoing MP: Chris Hazzard (Sinn Féin)
Brexit referendum result 2016: Remain 67.2% Leave 32.8%
The 2017 story:
Turnout was 67.49%, five candidates contested, with 20,328 votes Sinn Féin's Chris Hazzard was 2,446 votes ahead of his nearest challenger, the outgoing SDLP MP Margaret Ritchie.
The 2019 contest:
Margaret Ritchie moved to the House of Lords so Michael Savage is the SDLP candidate, trying to recapture the seat lost to Chris Hazzard two years ago. Sinn Féin will be confident they will hold what they have.
Candidates: Paul Brady, Aontú; Patrick Brown, Alliance; Glyn Hanna, DUP; Chris Hazzard, Sinn Féin; Jill Macauley, UUP; Michael Savage, SDLP.


"Chicago Graffiti Removal Request" is a Progressive Web App to let people search for and
map Graffiti Removal requests to the City's 311 system. The app could be used by
city workers heading out to remove the graffiti or by users who want to search
before entering a 311 service request.
Results are shown in a list and map view.
This was developed by Tian Zhou.
This is some text within a card body.


pips Red=55 White=151
(CorrectAnswer= 0/0, ErrorScore= 0)
This position occurs on page 190 of my latest book, “Backgammon in the Wind”, in the article entitled “Out of the Box”.
It is one of the most analysed back game positions of all time as it led to the 17-point match being decided in one game with the cube on 32! I have analysed it many times over the years, first by hand and then using various software programs. In preparing the book for publication this is the one position I did not roll out again but relied on an old rollout.
That was a mistake! What was a double a few years ago has now becaome a clear non-double. The position now obeys Kent Goulding's basic principle that you should not double such positions while you still have four points to clear from in front of the two back game anchors. White's timing is tenuous but sufficient for this rule to be obeyed. Walter Cooke (Red) should have held on to the cube. For Jesse Sammis (White) the take was trivial.
On the last roll of the game Sammis held a 16-cube and needed to roll 66 to win. Recognising that being down 16-0 to 17 was hopeless he redoubled to 32 and promptly rolled 66. The match thus entered the history books!
It is one of the most analysed back game positions of all time as it led to the 17-point match being decided in one game with the cube on 32! I have analysed it many times over the years, first by hand and then using various software programs. In preparing the book for publication this is the one position I did not roll out again but relied on an old rollout.
That was a mistake! What was a double a few years ago has now becaome a clear non-double. The position now obeys Kent Goulding's basic principle that you should not double such positions while you still have four points to clear from in front of the two back game anchors. White's timing is tenuous but sufficient for this rule to be obeyed. Walter Cooke (Red) should have held on to the cube. For Jesse Sammis (White) the take was trivial.
On the last roll of the game Sammis held a 16-cube and needed to roll 66 to win. Recognising that being down 16-0 to 17 was hopeless he redoubled to 32 and promptly rolled 66. The match thus entered the history books!
Cube analysis 2-ply cubeless equity +0.570 (Money: +0.559) 0.566 0.338 0.089 - 0.434 0.000 0.000 Cubeful equities: 1. Double, take +0.799 2. Double, pass +1.000 (+0.201) 3. No double +0.764 (-0.034) Proper cube action: Double, take


First Name
Last Name
Booking-Ref
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To
Flight Operator
Flight Number
Date
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Class
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Raw Data:
hello
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aztec
pdf417


--- layout: page title: About header-img: "img/about-bg.png" ---
Trying to chase my dreams in this evolving world


Type in first name and last name, full name will be placed in the end.
Click the buttons below, and see how to use watch monitoring the router changes.
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- An organization for the support of women in the Gr8 (Groovy, Grails, Gradle, etc) community
- Education
- Professional Development
- Networking
- Retention
- Chapters in Minneapolis, MN and looking to expand
Gr8Workshops
- Free or low-cost weekend courses
- Audience
- Beginners
- Experienced developers
- Volunteers
Goals of this session
- Evaluate current teaching models
- Raise questions about how to solve the "skills shortage"1
- Discuss the role of diversity in the community and in education
- Navigate solutions
- Reveal Lessons Learned from Gr8Ladies Gr8Workshops
Backgrounds
How did you first learn to program?
- Traditional University (Computer Science, IT, or related)
- Other University major and on-the-job training
- Two-year technical, vocational, or community college
- Online University
- Code School/Bootcamp
How did you first learn Groovy?
- University
- On the job
- Books, blog posts, other media
- Workshop, conference, or other training
Challenges
- "Skills shortage"
- Lack of Diversity
"Skills Shortage" Solutions
- Free and Low-Cost Courses
- Employer paid skills training
- Hire new grads and offer training
Diversity Exercise
I am _____,______,______,______,_____
Traits
- Physical (tall, short, blond hair, brown eyes)
- Personality (smart, funny, extroverted, loyal, dedicated)
- Gender Identity(male, female, bigender, genderqueer)
- Race, ethnicity, heritage, country
- Profession
- Education
- Socioeconomic status/background
The importance of diversity in the workplace and education is about embracing the value that comes from our individual differences and perspectives
Tackling Diversity in Education
What can we do?
Potential Students
- University students
- Working professionals looking for a career change
- Experienced developers looking to learn new skills
- Parents, caregivers, etc returning to the workforce after an extended leave
- Please let everyone have a chance to speak
- Avoid generalizations
- Stay within the Code of Conduct
Do you think you would have benefited from another format for learning Groovy?
Does Groovy have potential as a language for beginners?
What content should be included when teaching Groovy?
- Syntax
- Frameworks
- Testing
- Specific Plugins
- IDEs
What teaching styles work best for learning/teaching Groovy?
- Examples vs projects
- Self guided vs group settings
For experienced teachers:
- What is your typical audience?
- Is that what you expect?
Lessons learned from Gr8Ladies Gr8Workshops
Identify the audience
- Experience Level
- No programming experience
- Introductory course experience
- Experienced developers looking to learn something new
- Goal of education
- Job specific skills
- Theoretical knowledge
- Career change
Assess Technical Background Knowledge
- Basic Web Skills
- Tools
- Source control, Command line, Integrated Development Environment(IDE)
- General Programming Concepts
- Object Oriented Programming (OOP)
- Functional programming
- REST/APIs
- Define all jargon and acronyms
- Simplify exercises
- avoid industry specific examples (even math)
Setting Up a Development Environment
- Provide instructions for multiple machine configurations
- Assume students don't know how any programs(IDE's etc) work
- Offer supervised time to assist with setup
- Pick a location for your event with good Wi-Fi
- Bring a backup USB drive with all necessary setup files
- If all else fails, try a web console
Beginner Specific Hurdles
- Learning something new can be intimidating
- Remember a welcoming attitude
- Watch for assumptions and tone of voice
- The more TA(teaching assistant)s the better
- Small student to teacher ratios are important
- Multiple viewpoints
- Different analogies and explanations
- Languages
- Java is practical, but beginners often get frustrated with the syntax
- Groovy is dynamic, flexible, and easier to learn!
Current and Ongoing Challenges
- Wait for everyone to catch up or keep moving on?
- Budget enough time for questions and assistance
- One hour sessions aren't enough for true beginners
- Consider a follow-up session if there are still outstanding questions
- Consider offering an intermediate level for repeat students who don't quite have enough experience for the advanced class


We, at NGO ‘We Listen’ (registration process underway), envision a safe space for adolescents and young people, where they are listened to and are addressed about their feelings. Our vision is to create an atmosphere where mental health is not considered a taboo and therapy procedures are easily accessible to all.
A series of online creatives posted on all our social media platforms creating awareness on mental health, the myths associated with them, the issues & problems, their types, causes, and the grounding techniques.
A series of weekly online posts in which motivational & inspirational quotes written either by famous personalities or contributed to us by our audience, revolving around mental health, are restated by us.
We organized events spanning the entire month with the goal to educate people, fight the stigma and provide support related to mental health and the mental health issues.
A forum connecting the audience to specialists to address their queries, myths, or doubts regarding mental health and the problems through anonymous posts.
We collaborated with established Mental Health Experts, and conducted informative sessions on topics including, Positives And Prohibitions of Lockdown, Empowering Strategies to Fight Cravings, The Role of Nutrition on Mental Health, Suicide Awareness & Prevention and Impact of Pets on Mental Health
This series consisted of 3 module-based sessions to be headed by the Mental Health & Wellness Coach - Sarah Baxter. These sessions were designed for anyone and everyone who had an interest in understanding themselves better.
We collaborated with Wysa, an AI chat that makes you feel heard and supports you in difficult times. Wysa has helped over a million people last year and we too hope to help numerous people through the collaboration.
We released an online creative by Richa Vashista (she/her), a queer affirmative mental health professional, who shares how we can help the LGBTQIA+ community, to create a better and supportive environment for them.
A platform for enthusiastic people to be a part of freelance projects related to social causes by contributing through various articles, PPTs, research-related work, etc thereby building a strong network with the contributors.
We are trying to capture the journey of people who've fought bravely with mental health issues & spread their word through videos to encourage people to give and seek help and break the stigma.
We will release a number of online creatives, organize activities and webinars revolving around different aspects of the CoVID-19 and lockdown situation with respect to mental health and well being.
We will be coming up with a student mentorship program in which the seniors from schools & colleges will mentor their fellow juniors regarding various aspects related to career, stream, and subject selections, considering the impact of the pandemic.
The Discord Forum is intended to create a safe space and a sense of community, encouraging discussions on various sensitive issues and give a sense of support to the community.
The team is documenting the different categories of medicines used in mental health treatments. It will also contain a module on mental health counseling programs running in various schools, suggesting various reforms, improvements, and scope for the future.
The team is working on developing surveys for parents of pre-teens (9-12 years old) and interviewing the parents & children to get a fair idea about the issue of high suicide rate in India due to rough family relationships.
Konsciously provides its users with a series of interactive and transformational journeys that contain concepts derived from the fields of cognitive psychology, philosophy, and spirituality. They are designed to help users facing emotional distress to achieve holistic well-being.
Through this collaboration, We Listen and Konsciously would be working towards achieving the shared aims of helping individuals attain psychological resilience and emotional well being.


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The FCPXML document as a properly formatted string.
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Swift
public var fcpxmlString: String
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Undocumented
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Swift
class XMLDocument : XMLNode
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An array of all resources in the FCPXML document.
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Swift
public var fcpxResources: [XMLElement]
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The library XMLElement in the FCPXML document.
Declaration
Swift
public var fcpxLibrary: XMLElement?
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An array of all event elements in the FCPXML document.
Declaration
Swift
public var fcpxEvents: [XMLElement]
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An array of format resources in the FCPXML document.
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Swift
public var fcpxFormatResources: [XMLElement]
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An array of asset resources in the FCPXML document.
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Swift
public var fcpxAssetResources: [XMLElement]
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An array of multicam resources in the FCPXML document.
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Swift
public var fcpxMulticamResources: [XMLElement]
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An array of compound clip resources in the FCPXML document.
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Swift
public var fcpxCompoundResources: [XMLElement]
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An array of effect resources in the FCPXML document.
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An array of all projects in all events in the FCPXML document.
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Swift
public var fcpxAllProjects: [XMLElement]
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An array of all clips in all events in the FCPXML document.
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public var fcpxAllClips: [XMLElement]
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An array of all roles used in the FCPXML document.
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Swift
public var fcpxRoles: [String]
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The highest resource ID number used in the FCPXML document.
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Swift
public var fcpxLastResourceID: Int
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The highest text style ID number used in the FCPXML document.
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Swift
public var fcpxLastTextStyleID: Int
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Swift
class XMLDocument : XMLNode
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The names of all events as a String array.
Declaration
Swift
public var fcpxEventNames: [String]
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All items from all events as a XMLElement array.
Declaration
Swift
public var fcpxAllEventItems: [XMLElement]
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The names of all items from all events as a XMLElement array.
Declaration
Swift
public var fcpxAllEventItemNames: [String]
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The names of all projects from all events as a XMLElement array.
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Swift
public var fcpxAllProjectNames: [String]
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Initializes a new XMLDocument using the contents of an existing FCPXML file.
Throws
An error object that, on return, identifies any parsing errors and warnings or connection problems.Declaration
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public convenience init(contentsOfFCPXML URL: Foundation.URL) throws
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Initializes a new XMLDocument as FCPXML.
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Swift
public convenience init(resources: [XMLElement], events: [XMLElement], fcpxmlVersion: Float)
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Parses the resource IDs, text style IDs, and roles, refreshing the fcpxLastResourceID, fcpxLastTextStyleID, and fcpxRoles properties. Call this method when initially loading an FCPXML document and when the IDs or roles change.
Declaration
Swift
public func parseFCPXIDsAndRoles()
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Swift
public func parser(_ parser: XMLParser, didStartElement elementName: String, namespaceURI: String?, qualifiedName qName: String?, attributes attributeDict: [String : String])
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Returns the resource that matches the given ID string.
Declaration
Swift
public func resource(matchingID ID: String) -> XMLElement?
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Returns asset resources that match the given URL.
Declaration
Swift
public func assetResources(matchingURL url: URL, usingFilenameOnly: Bool, omittingExtension: Bool, caseSensitive: Bool) -> [XMLElement]
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Adds a resource XMLElement to the FCPXML document.
Declaration
Swift
public func add(resourceElement: XMLElement)
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Adds an array of resource XMLElements to the FCPXML document.
Declaration
Swift
public func add(resourceElements: [XMLElement])
-
Removes the resource at the specified index. - Important: This method will remove all associated clips from all events in the FCPXML document. However, it will not remove synchronized clips, compound clips and multicams that contain the resource.
Declaration
Swift
public func remove(resourceAtIndex index: Int)
-
Removes all resources from the FCPXML document.
Declaration
Swift
public func removeAllResources()
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Adds an event to the library XMLElement of the FCPXML document.
Declaration
Swift
public func add(event: XMLElement)
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Adds an array of event XMLElements to the FCPXML document.
Declaration
Swift
public func add(events: [XMLElement])
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Removes the event at the specified index.
Declaration
Swift
public func remove(eventAtIndex index: Int)
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Removes all events from the library.
Declaration
Swift
public func removeAllEvents()
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Returns the version numbers of the DTD documents included in this framework bundle.
Declaration
Swift
public func fcpxmlDTDVersions() -> [String]
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Validates the XMLDocument against the DTD of the latest FCPXML version included in this framework. The XMLDocument is valid if no error is thrown.
Throws
An error describing the reason for the XML being invalid or another error, such as not being able to read or set the associated DTD file.Declaration
Swift
public func validateFCPXMLAgainstLatestVersion() throws
-
Validates the XMLDocument against the DTD of the FCPXML version specified. The version number must match a DTD resource included in the bundle. The XMLDocument is valid if no error is thrown.
Throws
An error describing the reason for the XML being invalid or another error, such as not being able to read or set the associated DTD file.Declaration
Swift
public func validateFCPXMLAgainst(version: String) throws


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Honor 9X Full Day Battery Test
by A Word From Our SponsorsThe Honor 9X packs a seriously large battery considering the low budget price of the phone. The 4000mAh battery keeps the phone running effortlessly throughout a day’s worth of usage. The ultra efficient Kirin 9X chipset keeps the phone running fast with minimum battery drain. In this article, we are going to look at the usage of the phone over the course of a day and see how it does.
| Honor 9X | Specs |
|---|---|
| Display | 6,59″ 1080 x 2340p (391 ppi) |
| Chipset | HiSilicon Kirin 710F |
| RAM | 6GB |
| Storage | 128GB |
| Main Camera | 48MP/8MP (Ultrawide)/2MP (Depth Sensor) |
| Selfie Camera | 16MP Motorized Pop-up |
| Battery | 4,000mAh |
| Operating System | Android 9.1.0 EMUI 9.1.0 |
An Hour of Minecraft 9%
The first hour of use on my Honor 9X was spent playing some Minecraft. When gaming, I like to use the max settings on whatever game I’m playing, to get the best visual experience. In Minecraft, I set the render distance up to 11 chunks and all the other graphics settings on high. With most budget phones, Minecraft can seriously stress their hardware to it’s limits. With the Honor 9X I played a solid hour of Minecraft with no heat issues, and no performance dips. The game was as smooth of a gaming experience as you can have on Android.
To help the phone in its gaming performance, I like to set the phone to Performance Mode in the battery menu. This mode prioritizes performance over battery conservation. With performance mode enabled, and the screen brightness set to 50%, I ended up using 9% in an hour of gameplay.
An Hour of Music 3%
For me, an hour of music a day is really overkill. In reality I’ll probably listen to a few tracks while I’m headed out for lunch, just to find out that Popeye’s is once again sold out of their chicken sandwich. But just to be thorough in my testing, I’ll assume an average user will stream one hour of music on their Honor 9X. For this test, we will use Google Play Music to stream, and have the screen off. Streaming music is the best time to enable the Power Saver Option, since this isn’t a graphics intensive task and wont affect the music playback in any way.
After an hour of streaming songs on Google Play Music, the Honor 9X only used 3% of its battery. This means that even if you’re low on battery, you can still get hours of music time out of your Honor 9X. Long car rides is the most logical time to toggle on Power Save Option and just let your music play. This battery saving feature from EMUI is very effective.
An Hour of YouTube 11%
Aside from some intensive 3D gaming, streaming video is usually most stress you’ll put on your smartphone battery. For the video streaming test, I’ll play a one hour long YouTube video with the screen at 70% brightness. I will not have Power Save Option or Performance Mode on.
After an hour of streaming a YouTube video, I was able to get the battery down to 78% capacity. The video played in full-screen, only being interrupted by a few advertisements. In the hour of YouTube I only lost 11% battery in total. You should be able to comfortably watch several movies throughout your day, or binge a handful of episode of a show before needing to recharge.
Ten Minutes of Filming and 100 48MP Photos 7%
For this next test I’ll take a look at camera usage. I filmed 10 minutes of footage at 1080p resolution and 60FPS. These are the max video settings supported by the Honor 9X. Next, I took 100 photos with the 48MP sensor.
Fun Fact: 100 photos at 48MP takes up 674MB
Ten minutes of filming is probably way more than you’d use on an average day, but it’s important to know how much battery this will use if you’re going to be using your Honor 9X to document a vacation, or shoot some videos for the internet. After filming for 10 minutes and shooting 100 photos at 48MP, I managed to kill about 7% of the battery over the course of 14 minutes.
An Hour of GPS 6%
GPS on Android phones is probably way more efficient than most people think. If you’ve got GPS running and have your screen off when you’re not looking at it, GPS will barely touch your battery over the course of an hour. For this test, I left my screen on at 25% brightness and no battery save features were used. I took my phone out for a one hour ride on my electric scooter (which makes me look very cool btw) in the freezing cold temperatures. The app I used for GPS navigation was Google Maps.
After a full hour of usage, I drained another 6% from the battery on the Honor 9X.
Eight Hours of Sleep 6%
Alright it’s bedtime. Nothing too significant will be done to prepare the Honor 9X for the night. The display will be off and the phone will be set to silent to test the standby drain.
In the morning, the battery had dropped to 59% while losing 6% overall during the night. With 59% we have a whole second day of use ahead of us before needing to recharge.
The Honor 9X has a very impressive battery life. You’ll easily be able to get through an entire day on one charge, and most people will be able to go through two. See what others are saying about the Honor 9X in the community forums.


A x B = ?
A negative times a negative is a positive.
A negative times a positive is a negative.
A positive times a negative is a negative.
C / B = ?
A negative divided by a negative is a positive.
A negative divided by a positive is a negative.
A positive divided by a negative is a negative.


Dave Smith | April 2013
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{% extends "layout.html" %} {% block bar_heading %} Get an annual MOT reminder {% endblock %} {% block content %} {% include "includes/phase_banner_beta.html" %} Back Information You can’t sign up right now MOT has expired so you can’t sign up. Book a test at an authorized garage. You will be able to sign up for a reminder after the vehicle passes the MOT. What is the vehicle’s registration number? Registration (number plate) {% endblock %}
MOT has expired so you can’t sign up. Book a test at an authorized garage.
You will be able to sign up for a reminder after the vehicle passes the MOT.


Daisy Ridley isn't scared of the Star Wars curse
by Zara WoodcockStar Wars actress Daisy Ridley won’t be going down the same cursed and regrettable path as the previous leading men and women.
The 27-year-old actress avoids parties, drinking and drugs at all costs as she’s ‘pretty responsible’.
Daisy said: ‘I’m not really scared. I get to do a really great thing with a great group of people.
‘I’m already involved with other awesome things but I’m sure I’ll always be compared to Star Wars, and I’ll always be thankful for what I’ve been able to do.’
Considering Daisy prefers tea to alcohol and knows being a party animal while being part of the Star Wars franchise is difficult, we don’t think the ‘curse’ will be a problem.
She revealed: ‘I think I’m pretty responsible, which is a good thing. I’m just not that person to go out and get crazy.
‘I like to be at home and drink tea. I’m not a massive drinker, I don’t take drugs — all of those things that I think are so boring.
‘If you like to party and you get a job like this, that’s tricky, because you have to figure that out.’
Previous Star Wars actors – including Mark Hamill, Jake Lloyd, Hayden Christensen and Carrie Fisher – struggled with the fame and turned to sex, drugs, and C-list movies.
Jake played a young Anakin Skywalker in 1999’s The Phantom Menace, unfortunately, ended up in a psychiatric hospital.
Hayden, on the other hand, played a teen Skywalker in Revenge of the Sith and ended up starring in endless unmemorable movies.
Carrie, who died aged 60 in 2016, became addicted to drugs after playing Princess Leia.
A post mortem found she had taken cocaine, heroin and ecstasy before her death.
Being part of such a giant universe would normally lead to amazing roles, but Mark (the original Skywalker from the first trilogy) ended up doing voice-overs until director JJ Abrams brought him back in the rebooted movies in 2015.
Although Daisy has been leading a clean lifestyle, it hasn’t come without backlash.
The star was recently criticised for insisting she isn’t more privileged than co-star John Boyega.
The British actress was asked if her privileged upbringing – which included a boarding school education – played a part in her confidence as an actress and her ability to navigate her high level of celebrity.
But Daisy was seemingly thrown by the question in her Guardian chat and instantly refuted the interviewer’s claim that wealth, class and education allowed her to decode the rules in certain spaces.
‘Well no, because, no…’, Daisy replied.
If you've got a celebrity story, video or pictures get in touch with the Metro.co.uk entertainment team by emailing us<EMAIL_ADDRESS>calling 020 3615 2145 or by visiting our Submit Stuff page - we'd love to hear from you.


French volunteers find Airbus engine fragments from mid-air explosion
by EuronewsFrench volunteer search teams have found fragments from an engine that exploded in mid-air over eastern France in July, officials have said.
The discovery came in an unprecedented search operation involving 150 members of the public after French air accident investigation agency BEA asked volunteers to help comb a forest for a titanium engine part following two engine explosions on Airbus A220 jets operated by the airline Swiss since July.
A third engine suffered a similar blowout last month, forcing it to divert to Paris where it scattered some parts during landing, according to French officials.
"Three fragments from the engine have been found by the ... volunteer team ... Thanks to all participants of this first day," BEA said on its Twitter account.
The engine incidents have prompted worldwide engine checks on Airbus A220 and some Embraer aircraft, but no flaws have been found.
They have heightened scrutiny of a new generation of fuel-saving engines built by US manufacturer Pratt & Whitney, a unit of United Technologies, after a series of reliability problems.
But a version of the engines used on Airbus's larger and more widely used A320 series is not affected by the checks.
Reuters reported last month that a US-led investigation was focusing on a recent update in engine software that may have inadvertently allowed parts to resonate or vibrate destructively during certain settings.
Finding the engine fragment could help confirm the theory, though investigators have not ruled out titanium manufacturing problems.
A220 pilots have been told to avoid placing unusually high demands on the engine above 29,000 feet, while a new version of the software is finalised for the first quarter of next year.


When we wrote our Vec
class, we noted that we did not want to use the built-in new
and
delete
operations to manage our memory. The reason is that if we relied on these operations,
our Vec
would be more restrictive than the standard vector
. The new
operator
does too much for our purposes: It both allocates and initializes memory. When used to
allocate an array of type T
, it needs the default constructor for T
. This approach prevents
us from offering our users as much flexibility as we would like to offer.
Using new
would also be unduly expensive. If we use new
, it always initializes every
element of a T
array by using T::T()
. If we wanted to initialize the Vec
elements ourselves,
we would have to initialize each element twice—once by new
, and again to install
the value that our user supplied. Even worse, consider the allocation strategy that we
propose to use for push_back
. This strategy implies that we'll double the size of the Vec
each time we need to get more storage. We have no reason to want the extra elements initialized.
They'll be used only by push_back
, which will use the space only when we
have a new element to construct in that space. If we used new
to allocate the underlying
array, these elements would be initialized regardless of whether we ever use them.
Instead of using the built-in new
and delete
operators, we can do better by using
standard-library facilities designed to support flexible memory management. The core
language itself does not have any notion of memory allocation, because the properties of
memory are too variable to wire into the language itself.
For example, modern computers have many kinds of memory. There may be many different speeds of memory on the machine. There may be memory with special properties, such as graphical buffers or shared memory. There may be memory that is persistent across power failures. Because users might want to allocate any of these (or other) kinds of memory, it is best left to the library to specify how we allocate and manage memory. The standard library isn't required to support all these kinds of memory either. However, by offering a library facility to manage memory, the standard also offers a uniform interface to memory managers, even if those managers themselves are tied to specific implementations. As with the decision to make input-output a library rather than a language facility, the decision to make memory management part of the library gives us greater flexibility in using these different kinds of memory.
The <memory>
header provides a class, called
, that allocates a block
of uninitialized memory that is intended to contain objects of type T, and returns a
pointer to the initial element of that memory. Such pointers are dangerous, because their
type suggests that they point to objects, but the memory doesn't really contain those
objects yet. The library also provides a way to construct objects in that memory, and to
destroy the objects again—all without deallocating the memory itself. It is up to the programmer
using the allocator
<T>allocator
class to keep track of which space holds constructed
objects and which space is still uninitialized.
The interesting part of the allocator
class, for our purposes, comprises four member
functions and two associated nonmember functions:
template<class T> class allocator { public: T* allocate(size_t); void deallocate(T*, size_t); void construct(T*, const T&) ; void destroy(T*); // ... }; template<class Out, class T> void uninitialized_fill(Out, Out, const T&); template<class In, class Out> Out uninitialized_copy(In, In, Out);
The allocate
member allocates typed but uninitialized storage sufficient to hold the
requested number of elements. By typed storage, we mean that it will be used to hold values
of type T
, and that we will use pointers of type T*
to address it. By uninitialized storage,
we mean that the memory is raw storage, and no objects have yet been constructed in
it. The deallocate
member frees this uninitialized storage. It takes a pointer to storage
that was allocated by allocate
, and a size that indicates how many elements were allocated.
 We call construct
, passing it a pointer into space that was allocated
by allocate
, and a value to copy into that space. The destroy
function runs the
destructor for T
on the element indicated by its argument.
The two companion functions that we need are named uninitialized_copy
and
uninitialized_fill
. These functions initialize elements in space that is allocated by
allocate
. The uninitialized_fill
function fills this uninitialized space with the
specified value. When the function completes, each of the elements in the range specified
by the first two arguments will be a newly constructed copy of the value specified in the
third argument. The uninitialized_copy
function operates like the library copy
function, in that it copies values from a sequence specified by its first two arguments into
a target sequence denoted by its third argument. Like uninitialized_fill
, it
assumes that the target range contains raw storage, rather than elements that already hold
values, and it constructs new objects in that memory.
As with any template, the actual type bound to T
will be instantiated at compile time.
This instantiation will generate an appropriate allocator
class for each type that uses
the class. In order to obtain an allocator
of the right type, we'll add to our Vec<T>
class an allocator<T>
member that will know how to allocate objects of type T
. By
adding this member, and using its associated library functions, we can provide the same
kind of efficient, flexible memory management as the standard vector
class provides.
Our complete Vec
class, including declarations but not definitions for the memory management
functions, now looks like
template <class T> class Vec { public: typedef T* iterator; typedef const T* const_iterator; typedef size_t size_type; typedef T value_type; Vec() { create(); } explicit Vec(size_type n, const T& t = T()) { create(n, t); } Vec(const Vec& v) { create(v.begin(), v.end()); } Vec& operator=(const Vec&); // as defined in §11.3.2/196 ~Vec() { uncreate(); } T& operator[](size_type i) { return data[i]; } const T& operator[](size_type i) const { return data[i]; } void push_back(const T& t) { if (avail == limit) grow(); unchecked_append(t); } size_type size() const { return avail - data; } iterator begin() { return data; } const_iterator begin() const { return data; } iterator end() { return avail; } const_iterator end() const { return avail; } private: iterator data; // first element in the Vec iterator avail; // (one past) the last element in the Vec iterator limit; // (one past) the allocated memory // facilities for memory allocation allocator<T> alloc; // object to handle memory allocation // allocate and initialize the underlying array void create() ; void create(size_type, const T&); void create(const_iterator, const_iterator); // destroy the elements in the array and free the memory void uncreate(); // support functions for push_back void grow(); void unchecked_append(const T&); };
All that remains is to implement the private
members that handle memory allocation.
As we write these members, our program will be easier to understand if we remember
that whenever we have a valid Vec
object, four things are always true:
data
points at our initial data element, if we have any, and is zero otherwise.data <= avail <= limit
.data
, avail
).avail, limit
).We shall call these conditions the class invariant. Much as we did with loop invariants in §2.3.2/20, we intend to establish the class invariant as soon as we construct an object of that class. If we do so, and we ensure that none of our member functions falsifies the class invariant, we can be assured that the invariant will always be true.
Note that none of the public
members is capable of falsifying the invariant, because
the only way to do so would be to change the value of data
, avail
, or limit
, and none
of those member functions does so.
We shall begin by looking at the various create
functions, which are responsible for
allocating memory, initializing elements in that memory, and setting the pointers appropriately.
In each case, we initialize whatever memory is allocated and so, after running
create
, the pointers limit
and avail
are always equal: The last constructed element is
the same as the last allocated element. You should verify for yourself that the class invariant
is true after we have executed any of the following functions:
template <class T> void Vec<T>::create() { data = avail = limit = 0; } template <class T> void Vec<T>::create(size_type n, const T& val) { data = alloc.allocate(n); limit = avail = data + n; uninitialized_fill(data, limit, val); } template <class T> void Vec<T>::create(const_iterator i, const_iterator j) { data = alloc.allocate(j - i); limit = avail = uninitialized_copy(i, j, data); }
The version of create
that takes no arguments creates an empty Vec
, so its job is to
ensure that the pointers start out with zero values.
The version that takes a size and a value uses the size to allocate
the appropriate
amount of memory. The allocate
member of class allocator<T>
allocates enough
memory to hold the specified number of objects of type T. Thus, alloc.allocate(n)
allocates enough space to hold n objects. The allocate
function returns a pointer to the
initial element, which we store in data
. The memory returned by allocate
is uninitialized,
so we arrange to initialize it by calling uninitialized_fill
, which copies its
third argument into the sequence of uninitialized elements specified by its first two arguments.
When the function completes, it will have constructed new elements in the space
obtained by allocate
and will have initialized each of these elements to val
.
The final version of create
operates similarly to the other two, except that it calls
uninitialized_copy
to initialize the space obtained from allocate
. That function
copies elements from the sequence denoted by its first two arguments into a target
sequence of uninitialized elements denoted by its third argument. It returns a pointer to
(one past) the last element that it initialized, which is exactly the value that we need for
limit
and avail
.
The uncreate
member has to undo what the create
members did: It must run the
destructors on the elements, and return the space that the Vec
used:
template <class T> void Vec<T>::uncreate() { if (data) { // destroy (in reverse order) the elements that were constructed iterator it = avail; while (it != data) alloc.destroy(--it); // return all the space that was allocated alloc.deallocate(data, limit - data); } // reset pointers to indicate that the Vec is empty again data = limit = avail = 0; }
If data
is zero, there's no work to do. If we were using delete
, we might not bother to
compare data
to zero, knowing that executing delete
on a zero pointer is harmless.
However, unlike delete
, the alloc.deallocate
function requires a nonzero pointer,
even if no memory is being freed. Therefore, we must check whether data
is zero.
If we have work to do, we march the iterator it
through the constructed elements of
the Vec
, calling destroy
to destroy each element. We go backward through the Vec
to
match the behavior of delete[]
, which destroys elements in reverse order. Once we've
destroyed the elements, we free all the space in the call to deallocate
. This function
takes a pointer to the first element of the memory to free, and an integral value that indicates
how many elements of type T
are to be freed. Because we want to return all the
space that was allocated, we deallocate
the space between data
and limit
.
What's left is to implement the members used by push_back
:
template <class T> void Vec<T>::grow() { // when growing, allocate twice as much space as currently in use size_type new_size = max(2 * (limit - data), ptrdiff_t(1)); // allocate new space and copy existing elements to the new space iterator new_data = alloc.allocate(new_size); iterator new_avail = uninitialized_copy(data, avail, new_data); // return the old space uncreate(); // reset pointers to point to the newly allocated space data = new_data; avail = new_avail; limit = data + new_size; } // assumes avail points at allocated, but uninitialized space template <class T> void Vec<T>::unchecked_append(const T& val) { alloc.construct(avail++, val); }
The job of grow
is to allocate
enough space to hold at least another element. It allocates
more than it needs, so that subsequent calls to push_back
can use the excess, avoiding
the overhead of frequent memory allocations. In §11.4/202, we said that our strategy
would be to double the amount of space for each new allocation. Of course, the Vec
might currently be empty, so we cater to this possibility by allocating the max
of one element
and twice the existing space. Remembering from §8.1.3/142 that the two arguments
to max
must have exactly the same type, we explicitly construct an object with value 1 of
type ptrdiff_t
, which we know from §10.1.4/175 is the type of limit - data
.
We start by remembering in new_size
how many elements we will allocate. We
allocate
the appropriate space, and then call uninitialized_copy
to copy the elements
from the current space into the newly allocated space. We then return the old
memory, and destroy the elements there by calling uncreate
. Finally, we reset the
pointers so that data
points to the first element in the newly allocated array,
limit
points to (one past) the last constructed element in the Vec
, and avail
points to
(one past) the last allocated but as yet uninitialized element.
Note that it is essential that we save the values returned by allocate
and
uninitialized_copy
. The reason is that if we used those values immediately to reset
data
and limit
, then the subsequent calls to uncreate
would destroy and free the
memory that we just allocated, rather than getting rid of the old space!
The unchecked_append
function builds an element in the first location after the constructed
elements. It assumes that avail
points at space that was allocated, but has not
yet been used to hold a constructed element. Because we call unchecked_append
only
immediately after a previous call to grow
, we know that this call is safe.


Integrates R and the 'PostgreSQL/PostGIS' database system to build and manage animal trajectory (movement) data sets. The package relies on 'ltraj' objects from the R package 'adehabitatLT', building the analogous 'pgtraj' data structure in 'PostGIS'. Functions allow users to seamlessly transfer between 'ltraj' and 'pgtraj', as well as build new 'pgtraj' directly from location data stored in the database.
| Version: | 0.6.0 |
| Depends: | R (≥ 3.3.0), DBI, RPostgreSQL, rpostgis (≥ 1.0.3), adehabitatLT (≥ 0.3.12) |
| Imports: | sp, lubridate, shiny, leaflet, htmltools, mapview, shinyWidgets, magrittr, sf |
| Suggests: | knitr, raster, rmarkdown, testthat |
| Published: | 2018-03-02 |
| Author: | Balázs Dukai [aut, cre] (Package creator during Google Summer of Code 2016 and 2017), Mathieu Basille [aut], David Bucklin [aut], Clément Calenge [ctb] |
| Maintainer: | Balázs Dukai <balazs.dukai at gmail.com> |
| BugReports: | https://github.com/mablab/rpostgisLT/issues |
| License: | GPL (≥ 3) |
| URL: | https://github.com/mablab/rpostgisLT |
| NeedsCompilation: | no |
| SystemRequirements: | PostgreSQL with PostGIS extension |
| Citation: | rpostgisLT citation info |
| Materials: | README NEWS |
| CRAN checks: | rpostgisLT results |
| Reference manual: | rpostgisLT.pdf |
| Vignettes: |
rpostgisLT: Database model rpostgisLT: Explore a pgtraj rpostgisLT: Use cases |
| Package source: | rpostgisLT_0.6.0.tar.gz |
| Windows binaries: | r-devel: rpostgisLT_0.6.0.zip, r-release: rpostgisLT_0.6.0.zip, r-oldrel: rpostgisLT_0.6.0.zip |
| OS X binaries: | r-release: rpostgisLT_0.6.0.tgz, r-oldrel: rpostgisLT_0.6.0.tgz |
| Old sources: | rpostgisLT archive |
Please use the canonical form https://CRAN.R-project.org/package=rpostgisLT to link to this page.


A web app for calculating bit budgets
This resource requires a JavaScript-enabled browser.
Calculate
Value 1
Value 2
Value 3
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The BBox class is used to quickly and easily clip an object in a 3D scene.
It creates a bounding box that contains the whole object.
Example
This example is taken from the Shape3D class. It is used in
the
updateBoundingVolumes()
method:
_oBBox = BBox.create( m_oGeometry.aVertex );
public var maxEdge:Point3D
A Point3D representing the highest point of the cube volume.
public var minEdge:Point3D
A Point3D representing the lowest point of the cube volume.
public var uptodate:Boolean = false
Specifies if this object's boundaries are up to date with the object it is enclosing.
If false
, this object's transform()
method must be called to get its updated boundaries in the current frame.
public function BBox(p_min:Point3D = null, p_max:Point3D = null)
Creates a new BBox instance.
Parameters
| p_min:Point3D (default = null ) — A Point3D representing the lowest point of the cube volume.
|
| |
| p_max:Point3D (default = null ) — A Point3D representing the highest point of the cube volume.
|
public function addInternalPoint(p_oPoint:Point3D):void
Parameters
public function addInternalPointXYZ(x:Number, y:Number, z:Number):void
Parameters
| x:Number |
| |
| y:Number |
| |
| z:Number |
public function clone():BBox
Returns a new BBox object that is a clone of the original instance.
Returns
| BBox —
A new BBox object that is identical to the original.
|
public static function create(p_aVertices:Array):BBox
Creates a bounding box that encloses a 3D from an Array of the object's vertices.
Parameters
| p_aVertices:Array — The vertices of the 3D object the bounding box will contain.
|
Returns
| BBox —
The BBox instance.
|
public function getCenter():Point3D
Returns the center of the bounding box volume.
Returns
| Point3D —
A Point3D representing the center of the bounding box.
|
public function getEdges(edges:Array):void
Parameters
public function getSize():Point3D
Returns the size of the bounding box.
Returns
| Point3D —
A Point3D object representing the size of the volume in three dimensions.
|
public function intersectsBox(box:BBox):Boolean
Parameters
Returns
public function isPointInsideXYZ(x:Number, y:Number, z:Number):Boolean
Parameters
| x:Number |
| |
| y:Number |
| |
| z:Number |
Returns
public function isPointTotalInside(p_oPoint:Point3D):Boolean
Parameters
Returns
public function merge(box:BBox):void
Merge the current BoundingBox with the one given in argument
Parameters
| box:BBox — The BBox object to merge the current BBox with
|
public function reset():void
Reset the current bounding box to an empoty box with 0,0,0 as max and min values
public function toString(decPlaces:Number = 0):String
Returns a string representation of this object.
Parameters
| decPlaces:Number (default = 0 )
|
Returns
| String — The fully qualified name of this object.
|
public function transform(p_oMatrix:Matrix4):BBox
Applies a transformation to the bounding box.
Parameters
| p_oMatrix:Matrix4 — The transformation matrix.
|
Returns
| BBox —
the transformed Bounding box
|
API documentation for Sandy 3D Engine, Ver. 3.1.2


Qt Quick Examples - Touch Interaction
Touch Interaction is a collection of small QML examples relating to touch interaction methods. For more information, visit Important Concepts In Qt Quick - User Input.
Running the Example
To run the example from Qt Creator, open the Welcome mode and select the example from Examples. For more information, visit Building and Running an Example.
Multipoint Flames Example
Multipoint Flames demonstrates distinguishing different fingers in a MultiPointTouchArea, by assigning a different colored flame to each touch point.
The MultipointTouchArea sets up multiple touch points:
MultiPointTouchArea { anchors.fill: parent minimumTouchPoints: 1 maximumTouchPoints: 5 touchPoints: [ TouchPoint { id: touch1 }, TouchPoint { id: touch2 }, TouchPoint { id: touch11 }, TouchPoint { id: touch21 }, TouchPoint { id: touch31 } ] }
The flames are then simply bound to the coordinates of the touch point, and whether it is currently pressed, as follows:
ParticleFlame { color: "red" emitterX: touch1.x emitterY: touch1.y emitting: touch1.pressed }
Bear-Whack Example
Bear-Whack demonstrates using MultiPointTouchArea to add multiple finger support to a simple game. The interaction with the game is done through a SpriteGoal that follows the TouchPoint. The TouchPoints added to the MultiPointTouchArea are a component with the relevant logic embedded into it:
TouchPoint { id: container property ParticleSystem system onPressedChanged: { if (pressed) { timer.restart(); child.enabled = true; system.explode(x,y); } } property QtObject obj: Timer { id: timer interval: 100 running: false repeat: false onTriggered: child.enabled = false } property Item child: SpriteGoal { enabled: false x: container.area.x - 16 y: container.area.y - 16 width: container.area.width + 32 height: container.area.height + 32 //+32 so it doesn't have to hit the exact center system: container.system parent: container.system goalState: "falling" } }
Flick Resize Example
Flick Resize uses a PinchArea to implement a pinch-to-resize behavior. This is easily achieved by listening to the PinchArea signals and responding to user input.
onPinchStarted: { initialWidth = flick.contentWidth initialHeight = flick.contentHeight } onPinchUpdated: { // adjust content pos due to drag flick.contentX += pinch.previousCenter.x - pinch.center.x flick.contentY += pinch.previousCenter.y - pinch.center.y // resize content flick.resizeContent(initialWidth * pinch.scale, initialHeight * pinch.scale, pinch.center) } onPinchFinished: { // Move its content within bounds. flick.returnToBounds() }
Flickable Example
Flickable is a simple example demonstrating the Flickable type.
Rectangle { width: 320 height: 480 Flickable { anchors.fill: parent contentWidth: 1200 contentHeight: 1200 Rectangle { width: 1000 height: 1000
Corkboards Example
Corkboards shows another use for Flickable, with QML types within the flickable object that respond to mouse and keyboard interaction. This behavior does not require special code as the Qt Quick types already cooperate with the Flickable type for accepting touch events.
Files:
- touchinteraction/touchinteraction.qml
- touchinteraction/flickable/basic-flickable.qml
- touchinteraction/flickable/corkboards.qml
- touchinteraction/flickable/content/Panel.qml
- touchinteraction/multipointtouch/bearwhack.qml
- touchinteraction/multipointtouch/multiflame.qml
- touchinteraction/multipointtouch/content/AugmentedTouchPoint.qml
- touchinteraction/multipointtouch/content/BearWhackParticleSystem.qml
- touchinteraction/multipointtouch/content/ParticleFlame.qml
- touchinteraction/pincharea/flickresize.qml
- touchinteraction/main.cpp
- touchinteraction/touchinteraction.pro
- touchinteraction/touchinteraction.qmlproject
- touchinteraction/touchinteraction.qrc


A simple way to add page numbers to base/ggplot/lattice graphics.
| Version: | 1.1 |
| Imports: | grid |
| Suggests: | ggplot2, knitr, lattice, testthat |
| Published: | 2017-07-13 |
| Author: | Kevin Wright [aut, cre] |
| Maintainer: | Kevin Wright <kw.stat at gmail.com> |
| BugReports: | https://github.com/kwstat/pagenum/issues |
| License: | GPL-3 |
| URL: | https://github.com/kwstat/pagenum |
| NeedsCompilation: | no |
| CRAN checks: | pagenum results |
| Reference manual: | pagenum.pdf |
| Vignettes: |
Using the pagenum package |
| Package source: | pagenum_1.1.tar.gz |
| Windows binaries: | r-devel: pagenum_1.1.zip, r-release: pagenum_1.1.zip, r-oldrel: pagenum_1.1.zip |
| OS X binaries: | r-release: pagenum_1.1.tgz, r-oldrel: pagenum_1.1.tgz |
| Old sources: | pagenum archive |
Please use the canonical form https://CRAN.R-project.org/package=pagenum to link to this page.


tic;
I1 = zeros(300,300);
I2 = zeros(300,300);
tx = -30; ty = 70;
for i = 50:99
for j = 50:119
I1(i,j) = 255;
end
end
for i = 50+ty:99+ty
for j = 50+tx:119+tx
I2(i,j) = 255;
end
end
figure;
imagesc(I1)
title("Image1 without noise");colormap(gray);
figure;
imagesc(I2)
title("Image2 without noise");colormap(gray);
F1 = fft2(I1);
F2 = fft2(I2);
CPS = (F1.*conj(F2));
aCPS = abs(CPS);
aCPS(aCPS == 0) = 1;
CPS = CPS./aCPS;
figure; imagesc(log(abs(CPS)+1)); colormap(gray);
title("Cross Power Spectrum without noise");
I3 = real(ifft2(CPS));
[ttx,tty]=find(I3==max(I3(:)));
fprintf("found translation (without noise) tx = %f, ty = %f\n",-tty+1,300-ttx+1);
I1 = I1+randn(size(I1))*20;
I2 = I2+randn(size(I2))*20;
figure;
imagesc(I1)
title("Image1 with noise");colormap(gray);
figure;
imagesc(I2)
title("Image2 with noise");colormap(gray);
F1 = fft2(I1);
F2 = fft2(I2);
CPS = (F1.*conj(F2));
aCPS = abs(CPS);
aCPS(aCPS == 0) = 1;
CPS = CPS./aCPS;
figure; imagesc(log(abs(CPS)+1)); colormap(gray);
title("Cross Power Spectrum with noise");
I3 = real(ifft2(CPS));
[ttx,tty]=find(I3==max(I3(:)));
fprintf("found translation (with noise) tx = %f, ty = %f\n",-tty+1,300-ttx+1);
toc;
found translation (without noise) tx = -30.000000, ty = 70.000000
found translation (with noise) tx = -30.000000, ty = 70.000000
Elapsed time is 0.293254 seconds.


Datasets¶
The pymde.datasets
module provides functions that download and return
some datasets. You can use these datasets to experiment with custom
MDE problems, or while learning how to use PyMDE.
Each function returns a pymde.datasets.Dataset
object. The data
member of this object holds the raw data. The attributes
member
is a dictionary whose values are (held-out) attributes associated with
the items; you can use these attributes to color your embeddings, when
visualizing them. Other data related to the dataset is in the other_data
dict, and metadata about the dataset (eg, its authors) is available in
metadata
.
The first time one of these functions is called, it will download the dataset and
cache it locally, in the current directory (change the directory with
the root
keyword argument). Subsequent calls will use the cached data.
PyMDE currently provides the below datasets. If you would like to add an additional dataset, please reach out to us on Github.
MNIST¶
-
pymde.datasets.
MNIST
(root='./', download=True) → pymde.datasets.Dataset MNIST dataset (LeCun, et al.).
The MNIST dataset contains 70,000, 28x28 images of handwritten digits.
data
:torch.Tensor
with 70,000 rows, each of length 784 (representing the pixels in the image).attributes
dict: the keydigits
holds an array in which each entry gives the digit depicted in the corresponding row ofdata
.
Fashion MNIST¶
-
pymde.datasets.
FashionMNIST
(root='./', download=True) → pymde.datasets.Dataset Fashion-MNIST dataset (Xiao, et al.).
The Fashion-MNIST dataset contains 70,000, 28x28 images of Zalando fashion articles.
data
:torch.Tensor
with 70,000 rows, each of length 784 (representing the pixels in the image).attributes
dict: the keyclass
holds an array in which each entry gives the fashion article in the corresponding row ofdata
.
Google Scholar¶
-
pymde.datasets.
google_scholar
(root='./', download=True, full=False) → pymde.datasets.Dataset Google Scholar dataset (Agrawal, et al.).
The Google Scholar dataset contains an academic coauthorship graph: the nodes are authors, and two authors are connected by an edge if either author listed the other as a coauthor on Google Scholar. (Note that if two authors collaborated on a paper, but neither has listed the other as a coauthor on their Scholar profiles, then they will not be connected by an edge).
If
full
is False, obtains a small version of the dataset, on roughly 40,000 authors, each with h-index at least 50. Iffull
is True, obtains the whole dataset, on roughly 600,000 authors. The full dataset is roughly 1GB in size.data
: apymde.Graph
, with nodes representing authorsattributes
: thecoauthors
key has an array holding the number of coauthors of each other, normalized to be a percentile.other_data
: holds a dataframe describing the dataset, keyed bydataframe
.
Academic interests¶
-
pymde.datasets.
google_scholar_interests
(root='./', download=True) → pymde.datasets.Dataset Google Scholar academic interests
This dataset contains a cooccurrence matrix of the 5000 most popular academic interests listed by authors on Google Scholar
data
: a cooccurrence matrix, counting the number of times two two interests appeared among a single author’s listed interests, withdata[i, j]
giving the cooccurrence count ofinterests[i]
andinterests[j]
attributes
: one key,interests
, whereinterests[i]
is the interest corresponding to the ith row/column ofdata
.
scRNA transcriptomes from COVID-19 patients¶
-
pymde.datasets.
covid19_scrna_wilk
(root='./', download=True) → pymde.datasets.Dataset COVID-19 scRNA data (Wilk, et al.).
The COVID-19 dataset includes a PCA embedding of single-cell mRNA transcriptomes of roughly 40,000 cells, taken from some patients with COVID-19 infections and from healthy controls.
Instructions on how to obtain the full dataset are available in the Wilk et al. paper: https://www.nature.com/articles/s41591-020-0944-y,
data
: the PCA embeddingattributes
: two keys,cell_type
andhealth_status
.
Population genetics¶
-
pymde.datasets.
population_genetics
(root='./', download=True) → pymde.datasets.Dataset Population genetics dataset (Nelson, et al)
The population genetics dataset includes a PCA embedding (in R^20) of single nucleotide polymorphism data associated with 1,387 individuals thought to be of European descent. (The data is from the Population Reference Sample project by Nelson, et al.)
It also contains a “corrupted” version of the data, in which 154 additional points have been injected; the first 10 coordinates of these synthetic points are generated using a discrete uniform distribution on {0, 1, 2}, and the last 10 are generated using a discrete uniform distritubtion on {1/12, /18}.
A study of Novembre et al (2008) showed that a PCA embedding in R^2 roughly resembles the map of Europe, suggesting that the genes encode geographical information. But PCA does not produce interesting visualizations of the corrupted data. If distortion functions are chosen to be robust (eg, using the Log1p or Huber attractive penalties), we can create embeddings that preserve the geographical structure, while placing the synthetic points to the side, in their own cluser.
data
: the PCA embedding of the clean genetic data, in R^20corrupted_data
: the corrupted data, in R^20attributes
: two keys,clean_colors
andcorrupted_colors
.
US counties¶
-
pymde.datasets.
counties
(root='./', download=True) → pymde.datasets.Dataset US counties (2013-2017 ACS 5-Year Estimates)
This dataset contains 34 demographic features for each of the 3,220 US counties. The features were collected by the 2013-2017 ACS 5-Year Estimates longitudinal survey, run by the US Census Bureau.
data
: the PCA embedding of the clean genetic data, in R^20county_dataframe
: the raw ACS datavoting_dataframe
: the raw 2016 voting dataattributes
: one key,democratic_fraction
, the fraction of of voters who voted Democratic in each county in the 2016 presidential election


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Given a word $w$, rearrange the letters of $w$ to construct another word $s$ in such a way that $s$ is lexicographically greater than $w$. In case of multiple possible answers, find the lexicographically smallest one among them.
Input Format
The first line of input contains $t$, the number of test cases. Each of the next $t$ lines contains $w$.
Constraints
$1 \le t \le 10^5$
$1 \le |w| \le 100$
$w$ will contain only lower-case English letters and its length will not exceed $100$.
Output Format
For each testcase, output a string lexicographically bigger than $w$ in a separate line. In case of multiple possible answers, print the lexicographically smallest one, and if no answer exists, print no answer
.
Sample Input
5
ab
bb
hefg
dhck
dkhc
Sample Output
ba
no answer
hegf
dhkc
hcdk
Explanation
ab
which can be built by rearranging ab
. That is ba
. bb
and get a lexicographically greater string. hegf
is the next string lexicographically greater than hefg
.dhkc
is the next string lexicographically greater than dhck
.hcdk
is the next string lexicographically greater than dkhc
.


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fun DataFrame.filterNotNull(columnSelect: ColumnSelector = { all() }): DataFrame
removed record that have one or moremissing values in the selected columns. Per default all columns are selected.


|
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object com.googlecode.objectify.helper.ObjectifyWrapper com.googlecode.objectify.helper.CachingObjectify
public class CachingObjectify
Experimental session cache. When used to wrap an Objectify instance, all fetches will put key/values in a session cache (hashmap) that will be referenced in future calls to get(). Potentially skips calls to the datastore (or even the memcache).
| Constructor Summary | |
|---|---|
CachingObjectify(Objectify ofy)
|
| Method Summary | ||
|---|---|---|
|
delete(java.lang.Class<T> clazz,
long id)
A convenience method, shorthand for creating a key and deleting it. |
|
|
delete(java.lang.Class<T> clazz,
java.lang.String name)
A convenience method, shorthand for creating a key and deleting it. |
|
void |
delete(java.lang.Iterable<?> keysOrEntities)
Deletes the specified entities in a parallel batch operation. |
|
void |
delete(java.lang.Object keyOrEntity)
Deletes the specified entity. |
|
|
find(java.lang.Class<? extends T> clazz,
long id)
Same as get(Class, long) but returns null instead of throwing EntityNotFoundException |
|
|
find(java.lang.Class<? extends T> clazz,
java.lang.String name)
Same as get(Class, name) but returns null instead of throwing EntityNotFoundException |
|
|
find(Key<? extends T> key)
Same as get(Key) but returns null instead of throwing EntityNotFoundException |
|
|
get(java.lang.Class<? extends T> clazz,
java.lang.Iterable<S> idsOrNames)
A convenience method that prevents you from having to assemble all the Keys yourself and calling get(Iterable<Key>) . |
|
|
get(java.lang.Class<? extends T> clazz,
long id)
A convenience method, shorthand for creating a key and calling get() |
|
|
get(java.lang.Class<? extends T> clazz,
java.lang.String name)
A convenience method, shorthand for creating a key and calling get() |
|
|
get(java.lang.Iterable<? extends Key<? extends T>> keys)
Performs a parallel batch get, returning your entities. |
|
|
get(Key<? extends T> key)
Gets one instance of your entity. |
|
|
put(java.lang.Iterable<? extends T> objs)
Saves multiple entities to the datastore in a single parallel batch operation. |
|
|
put(T obj)
Puts an entity in the datastore. |
|
|
query()
Create a typesafe query across all kinds of entities. |
|
|
query(java.lang.Class<T> clazz)
Create a typesafe query across one specific kind of entity. |
| Methods inherited from class com.googlecode.objectify.helper.ObjectifyWrapper |
|---|
getDatastore, getFactory, getTxn |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public CachingObjectify(Objectify ofy)
| Method Detail |
|---|
public <T> java.util.Map<Key<T>,T> get(java.lang.Iterable<? extends Key<? extends T>> keys)
Objectify
Performs a parallel batch get, returning your entities. This is faster and more efficient than fetching entities one at a time.
You can fetch entities of many different kinds in a single call. Entities not present in the datastore will be absent from the returned map. Otherwise, the iteration order of the result will match the order in the parameter.
get
in interface Objectify
get
in class ObjectifyWrapper
keys
- are the keys to fetch; you can mix and match the types of objects.
keys
argument.
A empty map is returned if no keys are found in the datastore.DatastoreService.get(Iterable)
public <T> T get(Key<? extends T> key) throws com.google.appengine.api.datastore.EntityNotFoundException
Objectify
Gets one instance of your entity.
get
in interface Objectify
get
in class ObjectifyWrapper
com.google.appengine.api.datastore.EntityNotFoundException
- if the key does not exist in the datastoreDatastoreService.get(Key)
public <T> T get(java.lang.Class<? extends T> clazz, long id) throws com.google.appengine.api.datastore.EntityNotFoundException
Objectify
A convenience method, shorthand for creating a key and calling get()
get
in interface Objectify
get
in class ObjectifyWrapper
com.google.appengine.api.datastore.EntityNotFoundException
- if the key does not exist in the datastorepublic <T> T get(java.lang.Class<? extends T> clazz, java.lang.String name) throws com.google.appengine.api.datastore.EntityNotFoundException
Objectify
A convenience method, shorthand for creating a key and calling get()
get
in interface Objectify
get
in class ObjectifyWrapper
com.google.appengine.api.datastore.EntityNotFoundException
- if the key does not exist in the datastorepublic <S,T> java.util.Map<S,T> get(java.lang.Class<? extends T> clazz, java.lang.Iterable<S> idsOrNames)
Objectify
A convenience method that prevents you from having to assemble all the Keys
yourself and calling get(Iterable<Key>)
.
Note that unlike the standard batch get method, this method only gets a homogeneous set of objects.
get
in interface Objectify
get
in class ObjectifyWrapper
idsOrNames
- must be of type Iterablepublic <T> T find(Key<? extends T> key)
Objectify
get(Key)
but returns null instead of throwing EntityNotFoundException
find
in interface Objectify
find
in class ObjectifyWrapper
public <T> T find(java.lang.Class<? extends T> clazz, long id)
Objectify
get(Class, long)
but returns null instead of throwing EntityNotFoundException
find
in interface Objectify
find
in class ObjectifyWrapper
public <T> T find(java.lang.Class<? extends T> clazz, java.lang.String name)
Objectify
get(Class, name)
but returns null instead of throwing EntityNotFoundException
find
in interface Objectify
find
in class ObjectifyWrapper
public <T> Key<T> put(T obj)
Objectify
Puts an entity in the datastore.
If your entity has a null Long id, a fresh id will be generated and a new entity will be created in the database. If your entity already has an id (either long, Long, or String) value, any existing entity in the datastore with that id will be overwritten.
Generated ids are stored in the entity itself. If you put() an entity with a null Long id, it will be set before the method returns.
put
in interface Objectify
put
in class ObjectifyWrapper
obj
- must be an object of a registered entity type.
DatastoreService.put(com.google.appengine.api.datastore.Entity)
public <T> java.util.Map<Key<T>,T> put(java.lang.Iterable<? extends T> objs)
Objectify
Saves multiple entities to the datastore in a single parallel batch operation.
All the rules regarding generated ids in put()
apply.
Note that the iteration order of the return value will be the same as the order of the parameter.
put
in interface Objectify
put
in class ObjectifyWrapper
objs
- must all be objects of registered entity type
DatastoreService.put(Iterable)
public void delete(java.lang.Object keyOrEntity)
Objectify
delete
in interface Objectify
delete
in class ObjectifyWrapper
keyOrEntity
- can be either a Key>, a datastore Key, or a pojo entity.
If it is an entity, only the id fields are relevant.public void delete(java.lang.Iterable<?> keysOrEntities)
Objectify
delete
in interface Objectify
delete
in class ObjectifyWrapper
keysOrEntities
- can contain any mix of Key>, datastore Key, or pojo
entities. They need not be of the same type. If a pojo is used, only its
id fields are relevant.DatastoreService.delete(Iterable)
public <T> void delete(java.lang.Class<T> clazz, long id)
Objectify
delete
in interface Objectify
delete
in class ObjectifyWrapper
public <T> void delete(java.lang.Class<T> clazz, java.lang.String name)
Objectify
delete
in interface Objectify
delete
in class ObjectifyWrapper
public <T> Query<T> query()
Objectify
Create a typesafe query across all kinds of entities.
query
in interface Objectify
query
in class ObjectifyWrapper
public <T> Query<T> query(java.lang.Class<T> clazz)
Objectify
Create a typesafe query across one specific kind of entity.
query
in interface Objectify
query
in class ObjectifyWrapper
|
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


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Scalable Data Science With FHIR
The FHIR standard started as a better way to exchange healthcare data, but it also provides a solid basis for deep analytics and Machine Learning at scale. This post looks at an example from the recent FHIR DevDays conference that does just that. You can also run the interactive FHIR data engineering tutorial used in the conference yourself.
Our first step is to bring FHIR data into a data lake — a computational environment where our analysis can easily and efficiently work through petabytes of data. We’ll look at some patterns for doing so, with concrete examples using the open source Bunsen and Apache Spark projects.
FHIR StructureDefinitions Define the Schema
The schema for every dataset you see here was generated from a FHIR StructureDefinition. There is a big gap between building a FHIR-based schema by hand and generating it directly from the source. Every field in every query here is fully documented as a FHIR resource, making the FHIR documentation itself the primary reference to our datasets. This means the data is well-defined, curated, and familiar to those who have used FHIR.
Data Catalogs over Filesystems
Organizing data in files and directories is convenient, but it becomes unwieldy when working with a large number of complex datasets. Data catalogs can meet this need — and to offer a foundation for further data governance. The Apache Hive metastore is the most common way to catalog data in Hadoop-based environments and has native integration with Spark, so we organize data as one FHIR resource per table. Here’s an example from the tutorial used at FHIR DevDays:
1 2 |
|
Which prints a table like this:
| database | tableName | isTemporary | |
|---|---|---|---|
| tutorial_small | allergyintolerance | false | |
| tutorial_small | careplan | false | |
| tutorial_small | claim | false | |
| tutorial_small | condition | false |
…and so on. This makes it trivial to use intuitive database metaphors like use tutorial_small
and select * from condition
.
First-class ValueSet Support
FHIR ValueSets — collections of code values for a specific purpose — are essential to querying or working with FHIR data. Therefore they should be a first-class construct in our healthcare data lake. Here’s a look at using some FHIR valuesets in our queries as supported by Bunsen.
1 2 3 4 5 6 7 8 |
|
Now we can use these valuesets in our SQL queries via the in_valueset user-defined function:
1 2 3 4 5 6 7 8 |
|
| reference | system | code | onsetDateTime | |
|---|---|---|---|---|
| urn:uuid:f88c… | http://snomed.info/sct | 53741008 | 2014-09-14T07:45:47 | |
| urn:uuid:d9ac… | http://snomed.info/sct | 53741008 | 2017-05-22T06:56:19 | |
| urn:uuid:7460… | http://snomed.info/sct | 53741008 | 1974-08-06T06:50:32 | |
| urn:uuid:5a28… | http://snomed.info/sct | 53741008 | 2015-08-28T01:17:20 |
It’s worth looking at what’s going on here: in a few lines of SQL, we are going from the rich (but somewhat complicated) FHIR Condition data model to a simple table of onset times of Coronary Heart Disease conditions.
FHIR Data in Columnar Storage
Users see a clear catalog of FHIR datasets, but something important is happening behind the scenes. Most data stores or serialization encodings like JSON keep data in a row-wise format. This means all columns from a given record are physically adjacent on disk, like this:
This is a good fit for many workloads, but often not for analysis at scale. For instance, we may want to query the “code” column of several billion observation rows, and retrieve only those in a certain valueset. This is more efficient if columns are grouped together, like this:
This is completely transparent to the user; she simply sees FHIR data from the specification.
So while users see the FHIR data model, it is encoded in a columnar file like Parquet. In such files, all of these “code” columns next to one another, allowing the queries to do tight scans over columns of interest without expensive seeking past unneeded data.
Creating For-Purpose Views
These are the building blocks that simplify otherwise complex analysis. For instance, if we want to identify people with diabetes-related risks, we can create a collection of simple views of the underlying data customized for that purpose. You can see the full example in the Bunsen data engineering tutorial, but we’ll start with a dataframe of people with diabetes-related conditions as defined by a provided ValueSet:
1 2 3 4 5 6 7 8 9 10 |
|
| condition_id | person_ref | system | code | display | |
|---|---|---|---|---|---|
| urn:uuid:9c72… | urn:uuid:5a28… | http://snomed.info/sct | 44054006 | Diabetes | |
| urn:uuid:56d5… | urn:uuid:214f… | http://snomed.info/sct | 15777000 | Prediabetes | |
| urn:uuid:69de… | urn:uuid:7f4d… | http://snomed.info/sct | 15777000 | Prediabetes |
We can inspect and validate this dataframe, and then move onto the next part of our analysis. Let’s say we want to exclude anyone who has had a wellness visit in the last two years from our analysis. We just build a dataframe with them:
1 2 3 4 5 6 7 8 |
|
| person_ref | encounter_start | encounter_end | |
|---|---|---|---|
| urn:uuid:f88c… | 2016-08-21T07:45:47 | 2016-08-21T07:45:47 | |
| urn:uuid:f88c… | 2017-08-27T07:45:47 | 2017-08-27T07:45:47 | |
| urn:uuid:d9ac… | 2016-05-16T06:56:19 | 2016-05-16T06:56:19 |
Now that we’ve loaded and analyzed our dataframes, we can simply exclude those with wellness visits by doing an anti join between them:
1 2 3 |
|
The result is a simple table containing the cohort we’re looking for! Check out the complete tutorial notebook for the full story.
Reproducible Results from Immutable Data
Repeatability is an essential property for deep analysis. Re-running the same notebook in the future must load exactly the same data and produce exactly the same results. This gives us the controls needed to build on and iteratively improve previous analysis over time. Fortunately, using immutable data partitions are a common pattern in this type of system. We won’t go into depth here, but will touch on a couple good practices:
- Data is never mutated. Updates coming into our data lake are appended to previous data, and we can reproduce previous results by only working with data that was available at a given processing time.
- If necessary, a policy to archive or remove previous views of data from the data catalog is used to manage size.
Finally, building on such a FHIR-based data lake enables portability. The predictive model or analysis output is fully captured starting with portable data — which means it can be more easily deployed into other systems. FHIR has made great progress in exchanging data in online systems, and we see a lot of promise for data science at scale as well.


The models in this package should not be used by the user. They are designed to build up other models in the MultiBody library and some of them cannot be used in an arbitrary way and require particular knowledge how to set the options in the parameter menu. Don't use the models of this package.
| Name | Description |
|---|---|
| RevoluteWithLengthConstraint | Revolute joint where the rotation angle is computed from a length constraint (1 degree-of-freedom, no potential state) |
| PrismaticWithLengthConstraint | Prismatic joint where the translational distance is computed from a length constraint (1 degree-of-freedom, no potential state) |
| RollingConstraintVerticalWheel | Rolling constraint for wheel that is always perpendicular to x-y plane |
| InitPosition | Internal model to initialize r_rel_a for Joints.FreeMotionScalarInit |
| InitAngle | Internal model to initialize the angels for Joints.FreeMotionScalarInit |
| InitAngularVelocity | Internal model to initialize w_rel_b for Joints.FreeMotionScalarInit |


Package algorithms.statistics
Class Gumbel
- java.lang.Object
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- algorithms.statistics.Gumbel
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public class Gumbel extends java.lang.Object
the Gumbel distribution (Generalized Extreme Value distribution Type-I) is used to model the distribution of the maximum (or the minimum) of a number of samples of various distributions.parameters: mu is the location sigma is the scale k is the shape For Gumbel distribution, k = 0, sigma .gt. 0 Let z = (x-mu)/sigma PDF = f(x) = (1/sigma)*exp(-z)*exp(-exp(-z) ) CDF = F(x) = exp( -exp(-z) ) inverse of CDF = G(alpha) = mu - sigma*log( log(1/alpha) where alpha is the random variate drawn from U(0,1)) references: "Statistical Distibutions", 2011, ed 4 Merran Evans, Nicholas Hastings, Brian Peacock, and Catherine Forbes https://www.statisticshowto.com/gumbel-distribution/ https://en.m.wikipedia.org/wiki/Gumbel_distribution
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Constructor Summary
Constructors Constructor Description Gumbel()
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Method Summary
All Methods Static Methods Concrete Methods Modifier and Type Method Description static double
cdf(double x, double location, double scale)
generate a point in the Gumbel cumulative distributionstatic double[]
empiricalCdf(double[] x)
static double[]
fitUsingMaximumLikelihood(double[] x)
estimate the parameters mu and sigma (location and scale, respectively) using method of maximum likelihood simultaneous solution.static double[]
fitUsingMethodOfMoments(double[] x)
calculate a rough estimate of Gumbel distribution parameters for the given x.static double[]
generateCurve(double[] x, double location, double scale)
static double[]
generateGumbelCurve(double[] x1, double mu, double sigma)
generate the generalized Gumbel probability density curve (GEV Type I).static double
inverseCdf(double alpha, double location, double scale)
calculate the inverse CDF of the Gumbel, that is, a random variate x given the probability alpha.static double
pdf(double x, double location, double scale)
calculate the probability of a point in the Gumbel discrete probability density function This uses the maximum Gumbel distribution and has a left leaning distribution.static double
pdfForMinimum(double x, double location, double scale)
calculate the probability of a point in the Gumbel discrete probability density function This uses the minimum Gumbel distribution and has a right leaning distribution.static double[]
sampleRandomlyFrom(double location, double scale, int nDraws, java.security.SecureRandom rand)
sample from a Gumbel distribution G(location, scale).
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Method Detail
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cdf
public static double cdf(double x, double location, double scale)
generate a point in the Gumbel cumulative distribution- Parameters:
x
- a general element, that is a quantile, of the larger range of general variate Xlocation
- parameter of the distribution functionscale
- parameter of the distribution function- Returns:
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pdfForMinimum
public static double pdfForMinimum(double x, double location, double scale)
calculate the probability of a point in the Gumbel discrete probability density function This uses the minimum Gumbel distribution and has a right leaning distribution.- Parameters:
x
- a general element, that is a quantile, of the larger range of general variate Xlocation
- parameter of the distribution functionscale
- parameter of the distribution function- Returns:
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pdf
public static double pdf(double x, double location, double scale)
calculate the probability of a point in the Gumbel discrete probability density function This uses the maximum Gumbel distribution and has a left leaning distribution.- Parameters:
x
- a general element, that is a quantile, of the larger range of general variate Xlocation
- parameter of the distribution functionscale
- parameter of the distribution function- Returns:
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inverseCdf
public static double inverseCdf(double alpha, double location, double scale)
calculate the inverse CDF of the Gumbel, that is, a random variate x given the probability alpha.- Parameters:
alpha
- random variate drawn from U(0,1) where U is the uniform distribution.location
- parameter of the distribution functionscale
- parameter of the distribution function- Returns:
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sampleRandomlyFrom
public static double[] sampleRandomlyFrom(double location, double scale, int nDraws, java.security.SecureRandom rand)
sample from a Gumbel distribution G(location, scale).- Parameters:
location
-scale
-nDraws
- number of random draws to makerand
-- Returns:
- a fair sampling from a Gumbel distribution G(location, scale).
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empiricalCdf
public static double[] empiricalCdf(double[] x)
- Parameters:
x
- ordered statistic of an observed Gumbel distribution.- Returns:
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generateCurve
public static double[] generateCurve(double[] x, double location, double scale)
- Parameters:
x
-location
-scale
-- Returns:
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fitUsingMethodOfMoments
public static double[] fitUsingMethodOfMoments(double[] x)
calculate a rough estimate of Gumbel distribution parameters for the given x. A more precise estimate can be obtained from fitGumbelUsingML or fitGumbelUsingBayesian when they are implemented.references: Chap 19 of "Statistical Distributions" by Evans et al. and https://www.itl.nist.gov/div898/handbook/eda/section3/eda366g.htm
- Parameters:
x
- ordered statistic of an observed Gumbel distribution.- Returns:
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fitUsingMaximumLikelihood
public static double[] fitUsingMaximumLikelihood(double[] x)
estimate the parameters mu and sigma (location and scale, respectively) using method of maximum likelihood simultaneous solution.reference is Chap 19 of "Statistical Distributions" by Evans et al. and https://www.itl.nist.gov/div898/handbook/eda/section3/eda366g.htm
- Parameters:
x
- ordered values for which to find the best fitting Gumbel distribution parameters- Returns:
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generateGumbelCurve
public static double[] generateGumbelCurve(double[] x1, double mu, double sigma)
generate the generalized Gumbel probability density curve (GEV Type I).reference is Chap 19 of "Statistical Distributions" by Evans et al.
- Parameters:
x1
-mu
-sigma
-- Returns:
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