By adding the property display: flex to any element, by default, it aligns them horizontally in the same row and gives them equal height (based on the element with the greatest height)
Adding display: flex to an element turns it into a flex container. This makes it possible to align any children of that element into rows or columns. You do this by adding the flex-direction property to the parent item and setting it to row or column. Creating a row will align the children horizontally, and creating a column will align the children vertically.
Other options for flex-direction are row-reverse and column-reverse.
Recall that setting a flex container as a row places the flex items side-by-side from left-to-right. A flex container set as a column places the flex items in a vertical stack from top-to-bottom. For each, the direction the flex items are arranged is called the main axis. For a row, this is a horizontal line that cuts through each item. And for a column, the main axis is a vertical line through the items.
There are several options for how to space the flex items along the line that is the main axis. One of the most commonly used is justify-content: center;, which aligns all the flex items to the center inside the flex container. Others options include:
Flex containers also have a cross axis which is the opposite of the main axis. For rows, the cross axis is vertical and for columns, the cross axis is horizontal.
CSS offers the align-items property to align flex items along the cross axis. For a row, it tells CSS how to push the items in the entire row up or down within the container. And for a column, how to push all the items left or right within the container.
The differenct values available include:
CSS flexbox has a feature to split a flex item into multiple rows (or columns). By default, a flex container will fit all flex items together. For example, a row will all be on one line.
However, using the flex-wrap property, it tells CSS to wrap items. This means extra items move into a new row or column. The break point of where the wrapping happens depends on the size of the items and the size of the container.
CSS also has options for the direction of the wrap:
This property allows an item to shrink if the flex container is too small.
Items shrink when parent.width < flex.totalWidth
It takes numbers as values. The higher the number the greater the shrinking. Example: If one item has a flex-shrink value of 1 and the other has a flex-shrink value of 5, the one with the value of 5 will shrink five times as much as the other.
The flex-grow property controls the size of items when the parent container expands.
Using a similar example from before, if one item has a flex-grow value of 1 and the other has a flex-grow value of 5, the one with the value of 5 will grow five times as much as the other.
The flex-basis property specifies the initial size of the item before CSS makes adjustments with flex-shrink or flex-grow.
The units used by the flex-basis property are the same as other size properties (px, em, %, etc.). The value auto sizes items based on the content.
The flex-grow, flex-shrink, and flex-basis properties can all be set together by using the flex property.
flex: 1 0 10px;The syntax goes as follows:
The default property settings are flex: 0 1 auto;.
The order property is used to tell CSS the order of how flex items appear in the flex container. The property takes numbers as values, and negative numbers can be used.
This property allows you to adjust each item's alignment individually, instead of setting them all at once.
align-self accepts the same values as align-items and will override any value set by the align-items property.
Page 14


Objective
A challenging software development position in a cutting-edge organization
Professional Experience
Software Developer II, Dell, Madison, WI Apr. 2012
– Present
- Worked with a team developing a SaaS email migration product C#, Azure,
IMAP, REST, SOAP, jQuery, HTML/CSS, JS
- Improved back-end code tying the product’s migration engine to its web UI and database by rewriting
the system for managing migration jobs and increasing test coverage C#,
Azure storage
- Designed and programmed enhancements to the UI for setting up migrations jQuery,
Kendo, HTML/CSS, JS
Software Developer I, Quest Software, Madison, WI Jun. 2010
– Mar. 2012
- Developed a community site for DBAs and imported existing content PHP, MediaWiki,
Python, Django, HTML/CSS, JS
- Built plugins/themes for a major product website moving to a new platform Jive
SBS, Java, Maven, Struts, HTML/CSS, JS
- Designed and implemented the UI for a web-based database analytics application Photoshop, HTML/CSS, JS
Software Development Intern, Quest Software, Madison, WI Apr. 2009
– May 2010
- Collaborated with marketing team to improve a community site for SQL Server DBAs and launch versions
for Oracle and NoSQL audiences PHP, MediaWiki, HTML/CSS, JS
- Created aggregator that combines wiki articles, blog posts, web links, and status updates and allows
users to vote on, comment on, and administer items in the content stream Python,
Django, HTML/CSS, JS
Web Developer, Press Office, ASM, Madison, WI Oct. 2008
– May 2009
- Consulted with chair and press team to modernize student government website Photoshop, HTML/CSS, JS
- Developed small feedback web app to enable communication between students and leaders PHP, HTML/CSS, JS
Web Developer, Student Elections Commission, ASM,
Madison, WI Oct. 2007
– May 2009
- Helped commission recruit candidates and host events for student government elections
- Maintained team website and designed graphics for web/email/print advertisementsJoomla, Photoshop, InDesign
- Built online candidate guide resulting in improved turnout and recognition from press PHP, HTML/CSS, JS, Photoshop
Freelance Web Developer, Procom Communications,
Phoenix, AZ Nov. 2003
– Jun. 2009
- Created/integrated custom forms, shopping carts, site designs, payment systems, forums, blogs, CMSs,
etc.
Education
B.S. in Computer Science, University of Wisconsin,
Madison, WI Aug. 2007
– May 2010
- Participated in service learning project to aid local non-profit InfoPath,
SharePoint
- Completed advanced coursework in computer science, including classes on AI, algorithms, computational photography, and compilers, as well as substantial coursework toward a second major in Spanish
Technical Skills
Proficient With
- Languages: C#, Python, JavaScript, HTML/CSS, SQL
- Libraries: .NET (MVC), Django, jQuery, Angular.js, Kendo UI
- Applications: Visual Studio, ReSharper, dotCover, Photoshop, Git, TFS
- Platforms: OS X, Windows, Linux, Azure, AWS
Familiar With
- Languages: Java, PHP, VB, C++, C
- Libraries: Node.js, Struts, Spring, WPF, Windows Forms
- Applications: Elasticsearch, PostgreSQL, SQLite, SQL Server, Jive, MediaWiki, WordPress, Subversion, Mercurial, Eclipse, IntelliJ, MySQL, InDesign
Interested In
- Designing UI/UX, including layouts, graphics, and interactions, and type
- Developing mobile apps for iOS, Android, or Windows
- Learning to structure code more effectively (DI, SOLID, clean APIs)
- Watching every film on the IMDB Top 250 list


Overview
Today we will learn the OOP concept of C#.
- Class
- Inheritance
- Composition
Class
Class is a template or blueprint of an object, it contain variables and method (behaviour). Class encapsulate date (stored in field and behaviour). Object is an instance of a class. Class don’t allocate memory, object does.
Constructor
A constructor is a method that is called when an instance of a class is created.
We use constructors to put an object in an early state.
As a best practice, define a constructor only when an object “needs” to be initialised or it won’t be able to do its job.
Constructors do not have a return type, not even void, and they should have the exact same name as the class.
public class Car {
public string Color;
public string manufacturer;
// constructor
public Car(string _color, string _manufacturer){
Color = _color;
manufacturer = _manufacturer;
}
public void Drive(){
// car can drive
}
}
Property
→ A property is a kind of class member that is used for providing access to fields of a class
→ A property encapsulates a get and a set method.
public class Car {
private string _color;
public string Manufacturer { get; set; }
public string Color
{
get{ return _color; }
set { _color = value; }
}
public void Drive(){
// car can drive
}
}
Inheritance
→ A kind of relationship between two classes that allows one to inherit code from the other class.
→ Is-A relationship.
→ Code reusability is the major benifit of using Inheritance.
→ Example : A car is a vehicle. (parent class vehicle, child class car).
Public class Vehicle{
// share common attribute
}
Public class speedBoat : Vehicle{
// code specific to speedBoat
}
Composition
Gold fish dont walk
→ Composition is just like Inheritance, another kind of relationship between two classes that allows one to contain other.
→ Has-a relationship
→ Example : Car/Speedboat has an engine.
public class Car{
public string name;
// has an engine
}
public class SpeedBoat{
private Engine engine;
// constructor
public speedBoat(Engine _engine){
engine = _engine;
}
public void Drive(){
engine.StartEngine(); // from Engine class
Console.WriteLine("Driving speed boat");
}
}
public class Engine(){
public void StartEngine(){
Console.WriteLine("Engine Started");
}
}
// form main class
SpeedBoat myboat = new SpeedBoat(new Engine()); // composition
Problems with inheritance
• Easily abused by amateur designers / developers
• Leads to large complex/large hierarchies
• Such hierarchies are very fragile and a change may affect many classes
• Results in tight coupling
Benefits of composition
• Flexible
• Leads to loose coupling
Having said all that, it doesn’t mean inheritance should be avoided at all times. In fact, it’s great to use inheritance when dealing with very stable classes on top of small hierarchies. As the hierarchy grows (or variations of classes increase), the hierarchy, however, becomes fragile. And that’s where composition can give you a better design.
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Whoops!
I'm sorry fine fellow, we cannot find what you were looking for. You were most likely looking for the
comic
, or the
cms
. Choose wisely and you'll be back on track.


Here's a regular unordered list:
Here's one with a variety of markers:
Here's one with white space around items:
Here's one with no space after markers:
-list item -another list item


In the last couple of weeks since we announced the Aurelia Release Candidate, we've closed over 150 issues. So, today we're pushing out patch releases which reflect those fixes.
Skeletons and The CLI
We've done major updates to all the skeletons, cleaning them up and making them more consistent and correct. We've got a brand new Webpack solution for both ESNext and TypeScript based on Easy Webpack , a new project created by Aurelia Core Team Member, Bazyli Brzóska, to simplify and modularize Webpack configuration. We've also updated our ASP.NET Core skeletons to the .NET Core release versions. Mads Kristensen of Microsoft graciously lent his time to review the skeletons, ensuring that they are correct.
The CLI is gearing up for some major work over the next couple of weeks. A big thanks goes out to everyone in the community that has been trying it out, reporting issues and expanding the use cases for us. We're excited about what we're going to be able to do with this tool.
Using Aurelia with Script Tags
We've got some new beginner kits we're working on. These new kits are based on a new Aurelia build we've created: a pre-concatenated version of Aurelia designed to be used without a build system. We're working out the final details and will have these new builds up on CDN soon. This will make progressive enhancement, hybrid app scenarios and "getting started" much simpler going forward.
One Minor Breaking Change
There was one minor breaking change we had to make in these releases. It was the result of a minor oversight during the Beta period. It's unlikely to affect you at all. If you used the Optional.of()
helper to indicate optional dependencies, the default was that it would only check the child container for the optional dependency, not the container hierarchy. We have switched the default to check the hierarchy instead. Again, this is unlikely to affect you at all, but we're calling it out here in case you made use of that capability.
Wrap Up
Thanks to everyone in the community that is helping to make Aurelia great. We're on schedule for release this month and can't wait to "put a ribbon on it" for you.
Changelog
In addition to the list below, various fixes were made througout to dts files and documentation. Additional performance optimization has been done as well.
pal-browser 1.0.0-rc.1.0.1
Bug Fixes
- dom: only use parentNode is provided ( eff0211 )
route-recognizer 1.0.0-rc.1.0.1
Bug Fixes
- route-recognizer: respect explicit href during generation ( 335f2a7 )
dependency-injection 1.0.0-rc.1.0.1
Bug Fixes
Features
binding 1.0.0-rc.1.0.3
Bug Fixes
- CheckedObserver: handle falsey model ( 9d39a1a )
router 1.0.0-rc.1.0.1
Bug Fixes
- navigation-instruction: incorrect base url extraction ( b8036e4 ), closes #309
- router: navigation model explicit href normalization ( e80a16c )
Features
templating 1.0.0-rc.1.0.1
Bug Fixes
- double-uid: ( #412 ) ( 37d282d )
- module-analyzer: address circular refs in non-globals ( 78cd3d3 ), closes #350
- view-factory: handle instructions directly on enhanced element ( b6630a9 ), closes #318
Features
- decorators: log exception in process attributes and process content ( a15773f ), closes #330
- view: add more typing info ( 1221383 ), closes #370
templating-binding 1.0.0-rc.1.0.1
Bug Fixes
- InterpolationBinding: regression issue in array handling ( 5d772ca )
templating-resources 1.0.0-rc.1.0.1
Bug Fixes
- ArrayRepeatStrategy: standard mutation processing ( 34111cd ), closes aurelia/framework#408 aurelia/templating#349
Performance Improvements
- repeat: provide skipAnimation hint to ViewSlot ( 48dbc02 )
templating-router 1.0.0-rc.1.0.1
Bug Fixes
- route-href: return null from promises ( fd1b2fc ), closes #31
- router-view: correct relative view strategies ( 3f92470 )
framework 1.0.0-rc.1.0.2
Bug Fixes
ui-virtualization v1.0.0-beta.1.0.2
Bug Fixes
- dist: restore release dist folder ( 45f74be )
- table-strategy: move buffer elements outside of table ( 1fe64a6 ), closes #46
- template-strategy: move views correctly ( 5b5e6e6 ), closes #68
Features
- all: support infinite scroll ( 9a3b965 )
- virtual-repeat: pass location state to scroll callback ( 29418bf )
dialog 1.0.0-beta.2.0.0
Bug Fixes
webpack-plugin 1.0.0-beta.4.0.1
Bug Fixes
- build-resources: Fix module resolution for symlinks ( 4a5536d )


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Figure 1
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The molecule of compound 1 a at ambient pressure and room te ... |
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Figure 2
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Top: a fragment of the infinite π -stack in the structure of ... |
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Figure 3
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Energy frameworks in the crystal structure of 1 a . Green, r ... |
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Figure 4
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Unit cell parameter changes with temperature ( left) and pre ... |
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Figure 5
|
Variation of the C=O out-of-plane rotation (purple squares) ... |
|
Figure 6
|
Variation of the ( a) inter-planar distances in a π -stack w ... |
|
Figure 7
|
Electronic Partial Density Of State (PDOS) plots in the vici ... |
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Figure 8
|
Microscopy images of a 1 a crystal in a Diamond Anvil Cell ( ... |
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Figure 9
|
Luminescence spectra of 1 a at selected pressures. Excitatio ... |
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Figure 10
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The variation of the theoretically-predicted wavelength of m ... |


Join our community!
If you identify as female and are interested in technology, you are eligible to be a part of the WiT community! There are no major, class, or experience requirements.
Fill out the membership form here to join our Slack and sign up for our newsletter.
Click here to email<EMAIL_ADDRESS>and send us suggestions or give us feedback on how we're doing.
Click here to email<EMAIL_ADDRESS>and send us suggestions or give us feedback on how we're doing.


Nucleator CLI Reference
nucleator-init(1) Manual Page
NAME
nucleator-init - Initialize nucleator local configuration files
SYNOPSIS
nucleator init
DESCRIPTION
This command creates a ~/.nucleator nucleator local configuration directory and deposits initial and template configuration files that affect nucleator’s behavior.
Running nucleator init multiple times is safe. If you have made local changes to initial or template file contents, nucleator init will not overwrite them. See also [FILES].
FILES
- ~/.nucleator/
-
Configuration directory for local nucleator configuration.
- ~/.nucleator/sources.yml
-
Source specification for Nucleator Stacksets that should be installed by nucleator update.
- ~/.nucleator/distkeys.yml
-
Distibution keys that should be used while accessing private repository(ies) used in sources.yml.
NUCLEATOR
Part of the nucleator(1) suite


Upload this database to the master database
This will transfer the local database and upload it, through the switch, to the master database. Please make sure that:
-
The coord is plugged into the switch, and into the macbook through the converter
-
The IP address is correct(ask the person operating the master database for their IP)
-
Stray cosmic rays from deep space alien wars are not interfering with your connection
BEWARNED - ON A SUCCESSFULL SUBMISSION, THIS WILL CLEAR YOUR LOCAL DATABASE.
This is in effort to prevent duplicate submissions in the master.
Example: <IP_ADDRESS>


maps
TileMill Base + Leaflet / Wax + Vector overlays
World, low-res
World, high-res
US States
javascript
|
data


public interface Mask extends java.lang.Iterable<Slot>, android.os.Parcelable
MaskImpl
- the main implementation of this interface.| Modifier and Type | Method and Description |
|---|---|
void |
clear()
Removes user input from this mask.
|
boolean |
filled()
Checks whether th whole mask is filled out.
|
int |
findCursorPositionInUnformattedString(int cursorPosition)
Finds cursor position in unformatted string that corresponds to passed cursor position
in a formatted string.
|
int |
getInitialInputPosition()
Looks for initial position for cursor since buffer can be predefined with starting
characters
|
java.lang.Character |
getPlaceholder() |
int |
getSize()
Returns current size of a mask in slots.
|
boolean |
hasUserInput()
Checks whether there're filled out slot except of 'hardcoded' ones.
|
int |
insertAt(int position,
java.lang.CharSequence input)
Convenience method for
MaskImpl.insertAt(int, CharSequence, boolean) that always
places cursor after trailing hardcoded sequence. |
int |
insertAt(int position,
java.lang.CharSequence input,
boolean cursorAfterTrailingHardcoded)
Method insert
input to the buffer. |
int |
insertFront(java.lang.CharSequence input)
Convenience method for
MaskImpl.insertAt(int, CharSequence, boolean) that inserts
text at the first position of a mask and always places cursor after trailing hardcoded
sequence. |
boolean |
isForbidInputWhenFilled() |
boolean |
isHideHardcodedHead() |
boolean |
isShowingEmptySlots() |
int |
removeBackwards(int position,
int count)
Removes available symbols from the buffer.
|
int |
removeBackwardsWithoutHardcoded(int position,
int count) |
void |
setForbidInputWhenFilled(boolean forbidInputWhenFilled)
Defines whether mask won't allow to perform further input if all the slots are already
filled out.
|
void |
setHideHardcodedHead(boolean shouldHideHardcodedHead)
Defines whether mask should hide leading hardcoded sequence when calling
#toString()
or toUnformattedString() . |
void |
setPlaceholder(java.lang.Character placeholder)
Sets character that will be put on empty position when calling
#toString() or
toUnformattedString() . |
void |
setShowingEmptySlots(boolean showingEmptySlots)
Defines whether mask allows to display slots with no value, putting placeholder
on those positions.
|
java.lang.String |
toUnformattedString() |
java.lang.String toUnformattedString()
Slot.TAG_DECORATION
tag on a corresponding
Slot
int getInitialInputPosition()
boolean hasUserInput()
boolean filled()
void clear()
int insertAt(int position, java.lang.CharSequence input, boolean cursorAfterTrailingHardcoded)
input
to the buffer. Only validated characters would be inserted.
Hardcoded slots are omitted. Method returns new cursor position that is affected by input
and cursorAfterTrailingHardcoded
flag. In most cases if input string is followed by
a sequence of hardcoded characters we should place cursor after them. But this behaviour can
be modified by cursorAfterTrailingHardcoded
flag.position
- from which position to begin inputinput
- string to insertcursorAfterTrailingHardcoded
- when input is followed by a hardcoded characters
sequence then this flag defines whether new cursor
position should be after or before themint insertAt(int position, java.lang.CharSequence input)
MaskImpl.insertAt(int, CharSequence, boolean)
that always
places cursor after trailing hardcoded sequence.position
- from which position to begin inputinput
- string to insertint insertFront(java.lang.CharSequence input)
MaskImpl.insertAt(int, CharSequence, boolean)
that inserts
text at the first position of a mask and always places cursor after trailing hardcoded
sequence.input
- string to insertint removeBackwards(int position, int count)
Method also updates showHardcodedTail
flag that defines whether tail of hardcoded
symbols (at the end of user's input) should be shown. In most cases it should not. The only
case when they are visible - buffer starts with them and deletion was inside them.
position
- from where to start deletioncount
- number of symbols to delete.int removeBackwardsWithoutHardcoded(int position, int count)
int getSize()
boolean isShowingEmptySlots()
setPlaceholder(Character)
void setShowingEmptySlots(boolean showingEmptySlots)
setPlaceholder(Character)
showingEmptySlots
- true if mask allows empty slots to be displayedjava.lang.Character getPlaceholder()
#toString()
or
toUnformattedString()
.
setShowingEmptySlots(boolean)
void setPlaceholder(java.lang.Character placeholder)
#toString()
or
toUnformattedString()
. Placeholder should not be null!
setShowingEmptySlots(boolean)
placeholder
- character that represents empty slotsboolean isHideHardcodedHead()
setHideHardcodedHead(boolean)
void setHideHardcodedHead(boolean shouldHideHardcodedHead)
#toString()
or toUnformattedString()
.
PredefinedSlots.RUS_PHONE_NUMBER
.shouldHideHardcodedHead
- the "hide hardcode head" valueboolean isForbidInputWhenFilled()
setForbidInputWhenFilled(boolean)
void setForbidInputWhenFilled(boolean forbidInputWhenFilled)
filled()
returns true).
forbidInputWhenFilled
- flag that forbids overwriting terminated filled out masks.int findCursorPositionInUnformattedString(int cursorPosition)
cursorPosition
- java.lang.IndexOutOfBoundsException
- if cursorPosition < 0 or cursorPosition > size


Open the dev tools to see the logs
The running code can be seen here: https://github.com/bkawk/scatter-test
Ensure you have the dektop app running and you are logged in: https://github.com/GetScatter/ScatterDesktop/releases/
Ensure you are not serving the html from file:// if you are using the Chrome plugin (Desktop is ok)
To make a basic transfer with Scatter click Transfer below
To call the `worbliworbli` contract enter security code below and select `Call Contract`
securitycode:Please be patient after selecting the button, Scatter can take a few seconds


These threee types of happiness give scale and context to the types o fulfillment we seek in life. Where pleasure is fleeting and attributed to the kind of lifestyle lived by rockstars, it is often thought of as the kind of happiness that will bring one personal sustainability and fulfillment. Motivation rooted in passion has more of a medium grade sustainability, as it's less focused on immediate gratification. Further, the purpose lens gives a lifetime of context and, when worked towards, can movivate someone to achieve the biggest picture happiness with their work.
Perhaps obviously I've rewritten this question, but I was inspired to answer the "my ideal life" invitation in the chapter. Here it is!
My ideal future is one comprised of meaningful creative relationships that function collaboratively, underwritten by a flexible and sustainable career that is companionate to my soul work in this life.
I identify my soul work as providing reflective, supportive, and affirming space particularly for young people on the margins of society, especially queer youth. I want to advance access to mindfulness tools, especially embodiment and the avenues of creative expression therefrom.
In my ideal future, I’ll hold a second passport from a politically stable country (other than the US) and perhaps own a home there. I’ll work with likeminded peers advancing access to information through technology, preferably the kind that promotes well-being. Even at times when the work seems tedious, I’ll be able to zoom out and see the big-picture features and goals of my life. This foundation will enable me to:
1).Travel, lecture, and teach as a mindfulness educator
2).Collaborate with peers both artistically and in veins of activism
3).Create a lifestyle within capitalism that is sustainable, giving me the tools to let the more spiritual, artistic, and community-development goals in my life develop organically.
The most important lesson I’ve learned in the last 2 years boils down to this: listen. If you have the intention to serve, you must first ask and listen to what is needed. One of the keys to my longer term mental health is to be able to ask the right people the right questions, and be able to deliver the right result.
I have a lot of learning to engage in before I understand my role and creative capabilities as a programmer. I really am unsure what direction this path will take me on, or how my career path will either divert, postpone, or compliment my soul work.
What I do feel clear on is that right now, my mission is to grow into this part of my awareness and adopt the skillset of a proficient programmer. In this journey so far I have not infrequently caught myself in doubt, frustration, or overwhelm. I relate deeply to the practice of bringing mindfulness and regulation to this craft. I have been guided to coding as the professional companion to my soul work in this life. I am committed to sticking with it!


Kogi Election: Gov. Bello’s Aide Accuses Dino Of Been Responsible For Violence
by Enioluwa AdeniyiSenator Dino Melaye, has been accused by the Kogi State government of being responsible for the violence in the state during the recent electoral process.
The Chief Press Secretary to the state Governor, Yahaya Bello, Mohammed Onogwu, stated this while speaking on Channels Tv on Sunday.
Onogwu claimed that the state government has video evidence to prove that the Senator was distributing money on the day of the election.
“We have (video) clip evidence where he (Dino Melaye) was distributing money on the line where they were voting.”
“Going by the reports that various observers have turned in after the election, the Governorship election was largely adjudged as being fair and credible.”
“All the violence that took place in the last elections was orchestrated by Senator Dino Melaye who was a candidate in that election”.
The candidate of the All Progressives Congress, Smart Adeyemi will contest against Dino Melaye in the supplementary election on fixed for November 30 by the Independent National Electoral Commission.


{% extends 'base.html' %} {% block content %}
Now that we know that there is indeed a declining birth rate, the next big question to ask ourselves is why is there such a problem faced.
More people getting married doesn't necessarily mean that more people will have children
This could be attributed with couples, more specifically women, joining the workforce and deciding to put
their careers ahead of starting a family, which might have been something that the past generations did not
choose to do.
Another potential reason could be due to the increase in cost of living in Singapore and the increase
in cost of having children that deters the couples.


all files
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295/300
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128/132
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Class BlockType (v2.2.2)
Type of a block (text, image etc) as identified by OCR.
Protobuf type google.cloud.vision.v1.Block.BlockType
Methods
name
Parameter| Name | Description |
| value |
mixed
|
value
Parameter| Name | Description |
| name |
mixed
|
Constants
UNKNOWN
Value: 0
Unknown block type.
Generated from protobuf enum UNKNOWN = 0;
TEXT
Value: 1
Regular text block.
Generated from protobuf enum TEXT = 1;
TABLE
Value: 2
Table block.
Generated from protobuf enum TABLE = 2;
PICTURE
Value: 3
Image block.
Generated from protobuf enum PICTURE = 3;
RULER
Value: 4
Horizontal/vertical line box.
Generated from protobuf enum RULER = 4;
BARCODE
Value: 5
Barcode block.
Generated from protobuf enum BARCODE = 5;


--- layout: base title: Transparent Corporates (TRACE) short: We're making corporate data freely and publically available, to empower you to hold the right people in the corporate sector accountable when they do things that affect your everyday life. extra_js: - https://cdn.jsdelivr.net/g/uri.js@1.18.10,mark.js@8.9.1(jquery.mark.min.js+mark.min.js) - /js/underscore-1.8.3-min.js - /js/handlebars-v4.0.5.js - /js/PorterStemmer1980.min.js - /js/data-desk-search.js ---
We're making corporate data freely and publically available, to empower you to hold the right people in the corporate sector accountable when they do things that affect your everyday life.
We want to make corporate data truly accessible, because we believe you shouldn’t have to pay for company information that should legally and legitimately be public. We’re interrogating data around corporate structures and the relationships corporates have with the state, shareholders and communities.
We can help you find the important corporate data you're missing. Just send us a data request.
Request dataA rough OCR of tender bulletins for 2015 and 2016
The latest list of properties from the UK Land Registry.
Market announcements from publicly listed companies.
A list of blacklisted suppliers across all provinces
Get in touch to chat about what else can help your investigation.
We have datasets and have done work around tender information, beneficial ownership, company shareholder registries, illicit financial flows, potential conflicts of interest in the private sector, and the environmental and social impacts that the decisions corporates make have on everyday life. Read more about our work and the work other organisations are doing to improve corporate transparency and accountability here:
An investigation into corporate environmental compliance in South Africa. The report shows that some companies are committing serious compliance violations.
DownloadThe Open Society Foundation is conducting ongoing research into South African disclosure practice and the drivers behind it.
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module onnxrt.ops_cpu.op_reduce_min
#
Short summary#
module mlprodict.onnxrt.ops_cpu.op_reduce_min
Runtime operator.
Classes#
class |
truncated documentation |
|---|---|
ReduceMin ========= Computes the min of the input tensor’s element along the provided axes. The resulted tensor has the … |
Properties#
property |
truncated documentation |
|---|---|
|
Returns the list of arguments as well as the list of parameters with the default values (close to the signature). … |
|
Returns the list of modified parameters. |
|
Returns the list of optional arguments. |
|
Returns the list of optional arguments. |
|
Returns all parameters in a dictionary. |
Methods#
method |
truncated documentation |
|---|---|
Documentation#
Runtime operator.
- class mlprodict.onnxrt.ops_cpu.op_reduce_min.ReduceMin(onnx_node, desc=None, **options)#
Bases:
mlprodict.onnxrt.ops_cpu._op.OpRunReduceNumpy
Computes the min of the input tensor’s element along the provided axes. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.
The above behavior is similar to numpy, with the exception that numpy default keepdims to False instead of True.
Attributes
axes: A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data). default value cannot be automatically retrieved (INTS)
keepdims: Keep the reduced dimension or not, default 1 mean keep reduced dimension. Default value is
namekeepdimsi1typeINT
(INT)
Inputs
data (heterogeneous)T: An input tensor.
Outputs
reduced (heterogeneous)T: Reduced output tensor.
Type Constraints
T tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16), tensor(uint8), tensor(int8): Constrain input and output types to high-precision and 8 bit numeric tensors.
Version
Onnx name: ReduceMin
This version of the operator has been available since version 13.
Runtime implementation:
ReduceMin
- Parameters
onnx_node – onnx node
desc – internal representation
expected_attributes – expected attributes for this node
options – runtime options
- __init__(onnx_node, desc=None, **options)#
- Parameters
onnx_node – onnx node
desc – internal representation
expected_attributes – expected attributes for this node
options – runtime options
- _run(data)#
Should be overwritten.


The Moog company pioneered the commercial manufacture of modular voltage-controlled analog synthesizer systems in the early 1950s.
| Gödel, Escher, Bach: an Eternal Golden Braid, also known as GEB, is a 1979 book by Douglas Hofstadter. By exploring common themes in the lives and works of logician Kurt Gödel, artist M. C. Escher, and composer Johann Sebastian Bach, the book expounds concepts fundamental to mathematics, symmetry, and intelligence. Through illustration and analysis, the book discusses how, through self-reference and formal rules, systems can acquire meaning despite being made of "meaningless" elements. It also discusses what it means to communicate, how knowledge can be represented and stored, the methods and limitations of symbolic representation, and even the fundamental notion of "meaning" itself. In response to confusion over the book's theme, Hofstadter emphasized that Gödel, Escher, Bach is not about the relationships of mathematics, art, and music—but rather about how cognition emerges from hidden neurological mechanisms. One point in the book presents an analogy about how individual neurons in the brain coordinate to create a unified sense of a coherent mind by comparing it to the social organization displayed in a colony of ants. | $29.99 |
The Fender Mustang is a solid body electric guitar produced by the Fender Musical Instruments Corporation. It was introduced in 1964 as the basis of a major redesign of Fender's student models, the Musicmaster and Duo-Sonic. It was produced until 1982 and reissued in 1990. In the 1990s, the Mustang attained cult status largely as a result of its use by a number of alternative rock bands, in particular grunge bands. Early examples are generally seen as the most collectible of all the short-scale Fender guitars. The Mustang features two single-coil pickups, an unusual pickup switching configuration, and a unique vibrato system. It was originally available in two scale lengths, 24 and 22.5 inches.
The Fender Mustang is a solid body electric guitar produced by the Fender Musical Instruments Corporation. It was introduced in 1964 as the basis of a major redesign of Fender's student models, the Musicmaster and Duo-Sonic. It was produced until 1982 and reissued in 1990. In the 1990s, the Mustang attained cult status largely as a result of its use by a number of alternative rock bands, in particular grunge bands. Early examples are generally seen as the most collectible of all the short-scale Fender guitars. The Mustang features two single-coil pickups, an unusual pickup switching configuration, and a unique vibrato system. It was originally available in two scale lengths, 24 and 22.5 inches.
Moog
Moog synthesisers were created by Dr. Robert Moog under the company named Moog Music.
Moog
Moog synthesisers were created by Dr. Robert Moog under the company named Moog Music.
Moog
Moog synthesisers were created by Dr. Robert Moog under the company named Moog Music.
Moog
Moog synthesisers were created by Dr. Robert Moog under the company named Moog Music.
Moog
Moog synthesisers were created by Dr. Robert Moog under the company named Moog Music.
Moog
Moog synthesisers were created by Dr. Robert Moog under the company named Moog Music.
Wurlitzer Electric Piano
Wurlitzer Electric Piano
Wurlitzer Electric Piano
Wurlitzer Electric Piano
Wurlitzer Electric Piano
Wurlitzer Electric Piano
Wurlitzer Electric Piano
Wurlitzer Electric Piano
Analog synths produce a wave sound, whereas the sounds stored on a digital synth have been sampled and then turned into numbers.
Analog synths produce a wave sound, whereas the sounds stored on a digital synth have been sampled and then ...
Analog synthesizers are often said to have a "warmer" sound than their digital counterparts.
Analog synthesizers are often said to have a "warmer" sound than their digital counterparts.
Analog synthesizers are often said to have a "warmer" sound than their digital counterparts.
Analog synthesizers are often said to have a "warmer" sound than their digital counterparts.
Pet Sounds featured a number of unconventional instruments such as bicycle bells, buzzing organs, harpsichords, flutes, Electro-Theremin, dog whistles, trains, Hawaiian-sounding string instruments, Coca-Cola cans and barking dogs.


This Privacy Policy describes how your personal information is collected, used, and shared when you visit or make a purchase from https://aranyx63.github.io/PROG8110_A2_PWA/ (the “Site”).
PERSONAL INFORMATION WE COLLECTWhen you visit the Site, we automatically collect certain information about your device, including information about your web browser, IP address, time zone, and some of the cookies that are installed on your device. Additionally, as you browse the Site, we collect information about the individual web pages or products that you view, what websites or search terms referred you to the Site, and information about how you interact with the Site. We refer to this automatically-collected information as “Device Information.”
We collect Device Information using the following technologies:
When we talk about “Personal Information” in this Privacy Policy, we are talking both about Device Information and Order Information.
HOW DO WE USE YOUR PERSONAL INFORMATION?We use the Order Information that we collect generally to fulfill any orders placed through the Site (including processing your payment information, arranging for shipping, and providing you with invoices and/or order confirmations). Additionally, we use this Order Information to: Communicate with you;Screen our orders for potential risk or fraud; and When in line with the preferences you have shared with us, provide you with information or advertising relating to our products or services.
We use the Device Information that we collect to help us screen for potential risk and fraud (in particular, your IP address), and more generally to improve and optimize our Site (for example, by generating analytics about how our customers browse and interact with the Site, and to assess the success of our marketing and advertising campaigns).
SHARING YOUR PERSONAL INFORMATIONWe share your Personal Information with third parties to help us use your Personal Information, as described above. For example, we use Shopify to power our online store--you can read more about how Shopify uses your Personal Information here: https://www.shopify.com/legal/privacy. We also use Google Analytics to help us understand how our customers use the Site--you can read more about how Google uses your Personal Information here: https://www.google.com/intl/en/policies/privacy/. You can also opt-out of Google Analytics here: https://tools.google.com/dlpage/gaoptout.
Finally, we may also share your Personal Information to comply with applicable laws and regulations, to respond to a subpoena, search warrant or other lawful request for information we receive, or to otherwise protect our rights.
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DO NOT TRACKPlease note that we do not alter our Site’s data collection and use practices when we see a Do Not Track signal from your browser.
YOUR RIGHTSIf you are a European resident, you have the right to access personal information we hold about you and to ask that your personal information be corrected, updated, or deleted. If you would like to exercise this right, please contact us through the contact information below.Additionally, if you are a European resident we note that we are processing your information in order to fulfill contracts we might have with you (for example if you make an order through the Site), or otherwise to pursue our legitimate business interests listed above. Additionally, please note that your information will be transferred outside of Europe, including to Canada and the United States.
DATA RETENTIONWhen you place an order through the Site, we will maintain your Order Information for our records unless and until you ask us to delete this information.
MINORSThe Site is not intended for individuals under the age of 14.
CHANGESWe may update this privacy policy from time to time in order to reflect, for example, changes to our practices or for other operational, legal or regulatory reasons.CONTACT US For more information about our privacy practices, if you have questions, or if you would like to make a complaint, please contact us by e-mail at<EMAIL_ADDRESS>or by mail using the details provided below:
108 University Ave. East, Waterloo, ON, N2J 2W2, Canada


Flopzilla like tool to evaluate hand ranges against flops.
you may enter a hand range by either clicking hand types in the grid
or by using the slider / spin or by entering a hand range into the editbox.
in case you entered a hand range in the editbox, hitting
return
will set the grid to all complete hand types in the range entered, that is,
single cards are not being reflected.
next you can either select a flop in the grid to the right and hit
Evaluate
flop or hit
Evaluate random to evaluate the range against all
possible flops.
- evaluated is always the content of the editbox. slider, spinbox and the grid
are mere tools to fill the editbox in some predefined ways.
the meters to the bottom show the result of an evaluation, that is the percentages
a hand range hits on a certain or all possible flops:
- StraightFlush: percent straight flushes
- Quads: percent quads
- FullHouse: percent full houses (excluding flops that are a set)
- Flush: percent flushes
- Sets: percent sets
- TwoPair: percent two pairs (excluding paired flops)
- OverPair: percent overpairs (including flops that are a set)
- TopPair: percent top pairs (excluding overpairs)
- Pair: percent pair (excluding overpairs and top pairs)
- FlushDraws: percent flush draws
- StraightDraws: percent straight draws
the
editbox recognizes the following (comma separated)
patterns:
- JhQh: a single two card combination
- TT: a single pair
- TT+: all pairs X and above
- TT-22: all pairs X to Y
- KTs: a suited two card combination
- KTs+: all suited two card combinations XY and above
- KTs-K7s: all suited two card combination XY to XZ
- KTo: an offsuit two card combination
- KTo+: all offsuit two card combinations XY and above
- KTo-K7o: all offsuit two card combination XY to XZ
- KT: a two card combination suited and offsuit
- KT+: all suited and offsuit two card combinations XY and above
- KT-K7: all suited and offsuit two card combination XY to XZ
- random: all hands
- suited: all suited hands
- offuit: all offsuit hands
- pairs: all pairs
- broadways: all brodway hands


Methods
Included Modules
- TestHelper START:includes
Instance Public methods
Source: show
# File test/tc_gdata_http_request.rb, line 26 def test_google_is_live request = GData::HTTP::Request.new('http://www.google.com') service = GData::HTTP::DefaultService.new response = service.make_request(request) assert_equal(200, response.status_code) end
Source: show
# File test/tc_gdata_http_request.rb, line 22 def test_world_is_sane assert(true, 'World is not sane.') end


Catherine Ordun, November 2019
Find Out MoreData generated by edge devices and sensors require special consideration for networking and data pipelines. It is unclear which of the multiple frameworks for processing data streams (queues) in real-time is optimal. The hardware requirements remain a challenge for processing AI data from the edge devices and sensors. Integration with existing legacy systems or the cloud- remains a worthy challenge.
Edge AI will review frameworks suitable for edge compute and stream processing. We will define the architecture requirements for an edge server, as well as ancillary edge devices. There will be a thorough review of both software and hardware options.
Two unique containerized applications functioning on separate edge devices (including processing and sending data).
Data processed and collected by a master node.
Alerting and visualization of input across edge devices.
Insert Use Case One documents, file, pictures, video, etc.
Insert Use Case Two documents, file, pictures, video, etc.
Insert Use Case Three documents, file, pictures, video, etc.
Questions? Give us a call or send us an email and we will get back to you as soon as possible!


Vodafone threatens to withdraw from India amid dispute over taxes
by James WarringtonVodafone has warned it may pull out of India unless the government grants it a series of remedies, after a court ruling in the country dragged the firm to a €1.9bn (£1.6bn) half-year loss.
Chief executive Nick Read today said the firm was in a “critical” situation in the country amid a fierce battle with regulators over fines and tax liabilities.
Read more: Vodafone expands full-fibre network with Openreach deal
The Indian Supreme Court last month ruled that telecoms operators in the country must pay back roughly $14bn ($10.9bn) in taxes and penalties following a change in the way spectrum charges were calculated.
Vodafone said its own liabilities were at least $4bn, but could be “substantially higher”.
The firm has pumped $20bn into India and is the country’s largest foreign direct investor. However, Read said the company has written off this amount, adding that there was now “zero value” for its share price in the country.
Even ahead of the court ruling, Vodafone had been battling fierce competition in the Indian telecoms market.
Its joint venture in the company – Vodafone Idea – has suffered a drop in its share price to just 3.95 rupees, compared to roughly 22 rupees in January.
Read said he recently travelled to India to outline a series of proposed remedies to the government.
The boss asked for a two-year moratorium on spectrum payments, a reduction in the licence fees and taxes placed on the sector, a waiving of interest fees and penalties linked to the ruling, and to spread the cost over 10 years.
While Read insisted the saga was an “industry issue, not a Vodafone issue”, he made it clear that the company would not invest in India any further unless its demands were met.
“We’ve reached a point where we cannot put any new equity into India,” he said. “We now need to see the government come forward with a relief package to provide confidence that they want a vibrant, competitive environment.”
It came as Vodafone posted a €1.9bn (£1.6bn) loss for the six months to the end of September, largely caused by its troubles in India.
However, the telecoms giant returned to growth in its second quarter and lifted its full-year profit forecast to between €14.8bn and €15bn, up from its previous range of €13.8bn to €14.2bn.
The firm’s net debt soared to €48.1bn after the $22bn acquisition of assets from Liberty Global over the summer.
Read more: Virgin Media to ditch BT Mobile for Vodafone network
But Vodafone has announced plans to spin off its mobile masts business into a separate company, with a potential initial public offering in the next 15 months. The company plans to use the proceeds from this move to pay down its debt.
Analysts at Citi said Vodafone’s results showed it was on an “improving path”, and shares in the company rose almost four per cent.
Main image credit: Getty


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|
Franklin D. RooseveltRoosevelt presided over the greatest expansion of centralized federal power in the history of the United States, laying the foundations for the bloated bureaucracy that survives to this day. When he didn't get his way, he tried to pack the Supreme Court; and when that backfired there was still enough pressure applied to the Court to get his blatantly unconstitutional powers instantiated. Among the worst of these is the 1942 decision that pioneered abuse of the Interstate Commerce Clause to enforce Federal will outside of the enumerated powers of the Constitution. Also ordered the illegal internment of over 100,000 Japanese-American citizens during World War II.See also Celebrity Deformities. Timeline
|


2.5 More examples 2
Let’s keep going with exmaples using flights
data.
Previously, we find that the congestion at the airports is unlikely the cause of delays. Then, what else may explain the patterns of delays? Recall that earlier we observe that some airlines have longer delays than others for NYC-MSP flights. Are some airlines responsible for the delays? Let’s take a look at the average delays by carrier.
stat_carrier <- flights %>%
group_by(carrier) %>%
summarise(n_obs = n(),
dep_delay = mean(dep_delay, na.rm = TRUE),
arr_delay = mean(arr_delay, na.rm = TRUE)
) %>%
left_join(airlines, by="carrier") %>%
arrange(desc(n_obs))
stat_carrier %>% kable(digit=2)
| carrier | n_obs | dep_delay | arr_delay | name |
|---|---|---|---|---|
| UA | 58665 | 12.11 | 3.56 | United Air Lines Inc. |
| B6 | 54635 | 13.02 | 9.46 | JetBlue Airways |
| EV | 54173 | 19.96 | 15.80 | ExpressJet Airlines Inc. |
| DL | 48110 | 9.26 | 1.64 | Delta Air Lines Inc. |
| AA | 32729 | 8.59 | 0.36 | American Airlines Inc. |
| MQ | 26397 | 10.55 | 10.77 | Envoy Air |
| US | 20536 | 3.78 | 2.13 | US Airways Inc. |
| 9E | 18460 | 16.73 | 7.38 | Endeavor Air Inc. |
| WN | 12275 | 17.71 | 9.65 | Southwest Airlines Co. |
| VX | 5162 | 12.87 | 1.76 | Virgin America |
| FL | 3260 | 18.73 | 20.12 | AirTran Airways Corporation |
| AS | 714 | 5.80 | -9.93 | Alaska Airlines Inc. |
| F9 | 685 | 20.22 | 21.92 | Frontier Airlines Inc. |
| YV | 601 | 19.00 | 15.56 | Mesa Airlines Inc. |
| HA | 342 | 4.90 | -6.92 | Hawaiian Airlines Inc. |
| OO | 32 | 12.59 | 11.93 | SkyWest Airlines Inc. |
While we see some differences, the simple average of delays over various routes, days, and hours of flights may not be a good measure for the comparison across carriers. For example, some carriers may serve the routes and hours that tend to have more delays. Also, given that our dataset covers only the flights from New York City, the comparison may not be nationally representative since carriers use different airports around the country as their regional hubs.
For our purposes, instead of delays, let’s compare the average air time among carriers, for whihch we can account for flight’s destination and timing. The differences in air time may indicate some efficiency differences.
Let’s first check how air time relates to flight distance.
flights %>%
filter (month == 1, day == 1, !is.na(air_time)) %>%
ggplot(aes(x = distance, y = air_time)) +
geom_point(alpha = 0.05) +
geom_smooth()
## `geom_smooth()` using method = 'loess'
air_time
and distance
show a general linear relationship. We can better account for this relationship if we calculate the average air time for each flight destination from New York City.
First, consider using a simple linear regression model of air time to account for varying destinations. The idea is to estimate the regression-mean air time for each destination, remove the destination effects from the total variation of air time, and then compare that across carriers.
# make a copy of flights data
flights2 <- flights
flights2 <- flights2 %>% # handle missing data as NA
mutate( idx_na1 = is.na(air_time) | is.na(dest) )
res <- flights2 %>% # estimate a linear model and obtain residuals
with(
lm( air_time ~ 0 + as.factor(dest), subset=!idx_na1)
# lm() estimates a linear model.
# "y ~ x"" is the formula for regressing y on x.
# "y ~ 0 + x" removes the intercept from the model.
# as.factor() converts "dest" to a factor (categorical) class
# which is used as a set of dummy variables in the regression.
) %>%
residuals()
summary(res)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -57.574 -6.909 -0.953 0.000 5.862 144.317
# define a variable containing a vector of NA
# replace rows with idx_na1 == FALSE
flights2$res <- NA
flights2$res[!flights2$idx_na1] <- res
flights2 %>%
group_by(carrier) %>%
summarise(
mean_res = mean(res, na.rm = TRUE), # mean residual by carrier
sd_res = sd(res, na.rm = TRUE)
) %>%
left_join(stat_carrier, by="carrier") %>%
select(carrier, name, n_obs, dep_delay, arr_delay, mean_res, sd_res) %>%
arrange(-n_obs) %>%
kable(digit=2)
| carrier | name | n_obs | dep_delay | arr_delay | mean_res | sd_res |
|---|---|---|---|---|---|---|
| UA | United Air Lines Inc. | 58665 | 12.11 | 3.56 | -0.87 | 14.59 |
| B6 | JetBlue Airways | 54635 | 13.02 | 9.46 | 0.28 | 11.55 |
| EV | ExpressJet Airlines Inc. | 54173 | 19.96 | 15.80 | -0.37 | 8.94 |
| DL | Delta Air Lines Inc. | 48110 | 9.26 | 1.64 | -0.20 | 12.32 |
| AA | American Airlines Inc. | 32729 | 8.59 | 0.36 | 0.68 | 13.86 |
| MQ | Envoy Air | 26397 | 10.55 | 10.77 | 0.45 | 8.87 |
| US | US Airways Inc. | 20536 | 3.78 | 2.13 | -0.42 | 9.43 |
| 9E | Endeavor Air Inc. | 18460 | 16.73 | 7.38 | 0.84 | 8.76 |
| WN | Southwest Airlines Co. | 12275 | 17.71 | 9.65 | 0.16 | 12.55 |
| VX | Virgin America | 5162 | 12.87 | 1.76 | 3.26 | 17.58 |
| FL | AirTran Airways Corporation | 3260 | 18.73 | 20.12 | 1.16 | 8.75 |
| AS | Alaska Airlines Inc. | 714 | 5.80 | -9.93 | -2.13 | 16.17 |
| F9 | Frontier Airlines Inc. | 685 | 20.22 | 21.92 | 3.12 | 15.16 |
| YV | Mesa Airlines Inc. | 601 | 19.00 | 15.56 | -0.05 | 7.06 |
| HA | Hawaiian Airlines Inc. | 342 | 4.90 | -6.92 | 5.64 | 20.69 |
| OO | SkyWest Airlines Inc. | 32 | 12.59 | 11.93 | 1.02 | 7.26 |
It appears to make sense that the average air time is longer for low-cost carriers such as Virgin America, Frontier Airlines, and Hawaiian Airlines. The differences across other carriers, on the other hand, appear small relative to standard errors. To get a sense of whether these differences have any statistical significance, let’s use t-test to compare the mean residual between United Airlines and American Airlines.
# t-test comparing UA vs AA for the mean air time
flights2 %>%
with({
idx_UA <- carrier == "UA"
idx_AA <- carrier == "AA"
t.test(res[idx_UA], res[idx_AA])
})
##
## Welch Two Sample t-test
##
## data: res[idx_UA] and res[idx_AA]
## t = -15.722, df = 68826, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.741133 -1.355142
## sample estimates:
## mean of x mean of y
## -0.8689523 0.6791852
With a large number of observations, a seemingly small difference in the means often turns out to be statistically significant. Nonetheless, the average difference of about 1.5 minute air time per flight appears very small.
In fact, we can do this sort of pair-wise comparison all at once using a regression. Using carrier fixed effects in addition to destination fixed effects, we can directly compare the mean effects across carriers. We will set United Airlines to be a reference of the carrier fixed effects, so that the fixed effect for United Airlines is set to zero (i.e., omitted category), from which the fixed effects of all other airlines are estimated.
flights2$carrier <- relevel(factor(flights2$carrier), ref="UA")
# reference level is United Airlines
flights2$carrier %>% table()
## .
## UA 9E AA AS B6 DL EV F9 FL HA MQ OO
## 58665 18460 32729 714 54635 48110 54173 685 3260 342 26397 32
## US VX WN YV
## 20536 5162 12275 601
flights2 %>%
with({
n_carrier <- unique(carrier) %>% length()
n_dest <- unique(dest) %>% length()
print(paste('There are', n_carrier, 'distinct carriers and',
n_dest,'distinct destinations in the data.' ))
})
## [1] "There are 16 distinct carriers and 105 distinct destinations in the data."
With 16 carriers and 105 destinations minus 2 reference levels for carriers and destinations, the total of 119 coefficients will be estimated for the fixed effects.
f1 <- flights2 %>%
with(
lm( air_time ~ as.factor(carrier) + as.factor(dest) )
# fixed effects for carriers and destinations
)
tidy(f1)[1:20,] # show the first 20 coefficients
## term estimate std.error statistic p.value
## 1 (Intercept)<PHONE_NUMBER> 0.75069658<PHONE_NUMBER> 0.000000e+00
## 2 as.factor(carrier)9E 1.8015498 0.12723996 14.1586788 1.702649e-45
## 3 as.factor(carrier)AA 1.9326712 0.09731105 19.8607572 1.002388e-87
## 4 as.factor(carrier)AS -1.9071536 0.49596319 -3.8453531 1.204017e-04
## 5 as.factor(carrier)B6 1.1808039 0.08495098 13.8998267 6.535025e-44
## 6 as.factor(carrier)DL 0.7531812 0.08722600 8.6348244 5.907432e-18
## 7 as.factor(carrier)EV 0.4174574 0.11044837 3.7796605 1.570702e-04
## 8 as.factor(carrier)F9 3.8891981 0.48090201 8.0872985 6.120836e-16
## 9 as.factor(carrier)FL 2.6434074 0.27600661 9.5773336 1.002386e-21
## 10 as.factor(carrier)HA 11.0125104 0.89821710 12.2604106 1.503557e-34
## 11 as.factor(carrier)MQ 1.4592669 0.11892133 12.2708590 1.321669e-34
## 12 as.factor(carrier)OO 1.8091432 2.21222472 0.8177936 4.134757e-01
## 13 as.factor(carrier)US 0.1319337 0.13826299 0.9542230 3.399715e-01
## 14 as.factor(carrier)VX 4.5298528 0.18441295 24.5636378 4.086448e-133
## 15 as.factor(carrier)WN 1.2226161 0.17520980 6.9780125 2.999500e-12
## 16 as.factor(carrier)YV 0.5167461 0.52737831 0.9798395 3.271661e-01
## 17 as.factor(dest)ACK -207.1011095 1.04478912 -198.2228803 0.000000e+00
## 18 as.factor(dest)ALB -216.6188634 0.95130943 -227.7059972 0.000000e+00
## 19 as.factor(dest)ANC<PHONE_NUMBER> 4.26930687 38.6799351 0.000000e+00
## 20 as.factor(dest)ATL -136.1282095 0.75641976 -179.9638460 0.000000e+00
# a function to clean up the coefficient table above
clean_lm_rlt <- function(f) {
# keep only rows for which column "term" contains "carrier" e.g., rows 2 to 16 above
rlt <- tidy(f) %>% filter(grepl("carrier",term))
# create column named carrier
rlt <- rlt %>% mutate(carrier = gsub('as.factor\\(carrier\\)','', term))
# drop column term
rlt <- rlt %>% select(-term)
# add columns of carrier, name, and n_obs from the stat_carrier data frame
stat_carrier %>%
select(carrier, name, n_obs) %>%
left_join(rlt, by="carrier")
}
clean_lm_rlt(f1) %>% kable(digit=2)
| carrier | name | n_obs | estimate | std.error | statistic | p.value |
|---|---|---|---|---|---|---|
| UA | United Air Lines Inc. | 58665 | NA | NA | NA | NA |
| B6 | JetBlue Airways | 54635 | 1.18 | 0.08 | 13.90 | 0.00 |
| EV | ExpressJet Airlines Inc. | 54173 | 0.42 | 0.11 | 3.78 | 0.00 |
| DL | Delta Air Lines Inc. | 48110 | 0.75 | 0.09 | 8.63 | 0.00 |
| AA | American Airlines Inc. | 32729 | 1.93 | 0.10 | 19.86 | 0.00 |
| MQ | Envoy Air | 26397 | 1.46 | 0.12 | 12.27 | 0.00 |
| US | US Airways Inc. | 20536 | 0.13 | 0.14 | 0.95 | 0.34 |
| 9E | Endeavor Air Inc. | 18460 | 1.80 | 0.13 | 14.16 | 0.00 |
| WN | Southwest Airlines Co. | 12275 | 1.22 | 0.18 | 6.98 | 0.00 |
| VX | Virgin America | 5162 | 4.53 | 0.18 | 24.56 | 0.00 |
| FL | AirTran Airways Corporation | 3260 | 2.64 | 0.28 | 9.58 | 0.00 |
| AS | Alaska Airlines Inc. | 714 | -1.91 | 0.50 | -3.85 | 0.00 |
| F9 | Frontier Airlines Inc. | 685 | 3.89 | 0.48 | 8.09 | 0.00 |
| YV | Mesa Airlines Inc. | 601 | 0.52 | 0.53 | 0.98 | 0.33 |
| HA | Hawaiian Airlines Inc. | 342 | 11.01 | 0.90 | 12.26 | 0.00 |
| OO | SkyWest Airlines Inc. | 32 | 1.81 | 2.21 | 0.82 | 0.41 |
The “estimate” column shows the mean difference in air time comapred to United Airlines, accounting for the flight destination. The estimate tends to be more precise (i.e., smaller standard errors) for carriers with a larger number of observations. This time, we find that Virgin America, Air Tran, Frontier Airlines, and Hawaiian Airlines tend to show particularly longer air times than United Airlines.
Next, let’s take a step further to account for flight timing as well. We can do this by adding fixed effects for flight dates and hours.
flights2 <- flights2 %>%
mutate( date_id = month*100 + day )
flights2$date_id %>% unique() %>% length()
## [1] 365
f2 <- flights2 %>%
with(
lm( air_time ~ as.factor(carrier) + as.factor(dest) +
+ as.factor(date_id) + as.factor(hour) )
)
lm_rlt2 <- clean_lm_rlt(f2)
lm_rlt2 %>% kable(digit=2)
| carrier | name | n_obs | estimate | std.error | statistic | p.value |
|---|---|---|---|---|---|---|
| UA | United Air Lines Inc. | 58665 | NA | NA | NA | NA |
| B6 | JetBlue Airways | 54635 | 1.60 | 0.07 | 22.50 | 0.00 |
| EV | ExpressJet Airlines Inc. | 54173 | 0.61 | 0.09 | 6.67 | 0.00 |
| DL | Delta Air Lines Inc. | 48110 | 0.95 | 0.07 | 13.03 | 0.00 |
| AA | American Airlines Inc. | 32729 | 1.84 | 0.08 | 22.81 | 0.00 |
| MQ | Envoy Air | 26397 | 1.45 | 0.10 | 14.70 | 0.00 |
| US | US Airways Inc. | 20536 | 0.17 | 0.11 | 1.51 | 0.13 |
| 9E | Endeavor Air Inc. | 18460 | 1.57 | 0.11 | 14.72 | 0.00 |
| WN | Southwest Airlines Co. | 12275 | 1.14 | 0.15 | 7.82 | 0.00 |
| VX | Virgin America | 5162 | 4.85 | 0.15 | 31.57 | 0.00 |
| FL | AirTran Airways Corporation | 3260 | 2.19 | 0.23 | 9.58 | 0.00 |
| AS | Alaska Airlines Inc. | 714 | -2.55 | 0.41 | -6.21 | 0.00 |
| F9 | Frontier Airlines Inc. | 685 | 3.31 | 0.40 | 8.29 | 0.00 |
| YV | Mesa Airlines Inc. | 601 | 0.32 | 0.44 | 0.73 | 0.46 |
| HA | Hawaiian Airlines Inc. | 342 | 11.79 | 0.75 | 15.80 | 0.00 |
| OO | SkyWest Airlines Inc. | 32 | 7.63 | 1.83 | 4.17 | 0.00 |
lm_rlt2 %>% filter(carrier!='UA') %>%
ggplot(aes(x = carrier, y = estimate)) + geom_col() +
labs(title = "Mean Air Time Compared to United Airlines")
The results are similar to the previous linear mode except that this time SkyWest Airlines shows much longer air time.
Our final model is a check for the robustness of the above results. Let’s replace the date and hour fixed effects in the previous model with date-hour fixed effects (i.e., the interaction between date and hour). We could add such fixed effects using time_hour
variable defined above. However, that would mean adding nearly 7,000 dummy variables to our linear regression, which is computationally intensive.
To work around this issue, we approximate this estimation by pre-processing the dependent variable. Specifically, we calculate the average air time for each combination of time_hour
and dest
and define a new dependent variable by subtracting this average value from the original air time variable (i.e., the new variable is centered at zero-mean for each combination of time_hour
and dest
). Then, we estimate a linear model with carrier and destination fixed effects.
## Adding time_hour fixed effects is computationally intensive
# f1 <- flights %>%
# with(
# lm( air_time ~ as.factor(carrier) + as.factor(dest) + as.factor(time_hour))
# )
unique(flights2$time_hour) %>% length() # 6,936 unique time_hour
## [1] 6936
flights2 <- flights2 %>%
group_by(dest, time_hour) %>%
mutate(
air_time_centered = air_time - mean(air_time, na.rm=TRUE)
)
f3 <- flights2 %>%
with(
lm( air_time_centered ~ as.factor(carrier) + as.factor(dest) )
)
lm_rlt3 <- clean_lm_rlt(f3)
lm_rlt3 %>% kable(digit=2) # Note: standard errors, t-stat, and p-val are incorrect
| carrier | name | n_obs | estimate | std.error | statistic | p.value |
|---|---|---|---|---|---|---|
| UA | United Air Lines Inc. | 58665 | NA | NA | NA | NA |
| B6 | JetBlue Airways | 54635 | 0.82 | 0.03 | 32.24 | 0.00 |
| EV | ExpressJet Airlines Inc. | 54173 | 0.88 | 0.03 | 26.50 | 0.00 |
| DL | Delta Air Lines Inc. | 48110 | 0.52 | 0.03 | 19.85 | 0.00 |
| AA | American Airlines Inc. | 32729 | 1.20 | 0.03 | 41.06 | 0.00 |
| MQ | Envoy Air | 26397 | 1.00 | 0.04 | 27.84 | 0.00 |
| US | US Airways Inc. | 20536 | -0.09 | 0.04 | -2.21 | 0.03 |
| 9E | Endeavor Air Inc. | 18460 | 1.27 | 0.04 | 33.07 | 0.00 |
| WN | Southwest Airlines Co. | 12275 | 1.30 | 0.05 | 24.70 | 0.00 |
| VX | Virgin America | 5162 | 3.47 | 0.06 | 62.59 | 0.00 |
| FL | AirTran Airways Corporation | 3260 | 1.78 | 0.08 | 21.48 | 0.00 |
| AS | Alaska Airlines Inc. | 714 | -2.86 | 0.15 | -19.15 | 0.00 |
| F9 | Frontier Airlines Inc. | 685 | 1.99 | 0.14 | 13.73 | 0.00 |
| YV | Mesa Airlines Inc. | 601 | 0.78 | 0.16 | 4.89 | 0.00 |
| HA | Hawaiian Airlines Inc. | 342 | 1.34 | 0.27 | 4.96 | 0.00 |
| OO | SkyWest Airlines Inc. | 32 | 3.50 | 0.67 | 5.26 | 0.00 |
lm_rlt3 %>% filter(carrier!='UA') %>%
ggplot(aes(x = carrier, y = estimate)) + geom_col() +
labs(title = "Mean Air Time Compared to United Airlines: Robustness Check")
The point estimates should be approximately what we would obtain if we regress air_time
on the fixed effects of carrier
, dest
, and time_hour
. However, the standard errors are not correctly displayed in the table because the centered variable has a smaller total variation compared to the original air_time
variable. (Correct standard errors can be obtained, for example, through a bootstrapping technique.)
Overall, we see again a tendency that lower-cost carriers like Sky West Airlines, Virgin America, Frontier Airlines, and Air Tran show particularly longer air time than United Airlines. Jet Blue Airways, another low-cost carrier, shows a less obvious difference from United Airlines, possibly suggesting that their operation focused on the East Cost is efficient for the flights departing from New York City. Hawaiian Airlines and Alaskan Airlines appear to be somewhat different from other carriers perhaps because they are more specialized in particular flight destinations compared to their rivals. In particular, the flights to Hawaii may have distinct delay patterns that are concentrated on peak vacation seasons.


GPG-PRESET-PASSPHRASE
Section: GNU Privacy Guard (1)Updated: 2010-11-11
Index Return to Main Contents
NAME
gpg-preset-passphrase - Put a passphrase into gpg-agent's cacheSYNOPSIS
gpg-preset-passphrase [options] [command] cache-idDESCRIPTION
The gpg-preset-passphrase is a utility to seed the internal cache of a running gpg-agent with passphrases. It is mainly useful for unattended machines, where the usual pinentry tool may not be used and the passphrases for the to be used keys are given at machine startup.Passphrases set with this utility don't expire unless the --forget option is used to explicitly clear them from the cache --- or gpg-agent is either restarted or reloaded (by sending a SIGHUP to it). It is necessary to allow this passphrase presetting by starting gpg-agent with the --allow-preset-passphrase.
gpg-preset-passphrase is invoked this way:
-
gpg-preset-passphrase [options] [command] cacheid
cacheid is either a 40 character keygrip of hexadecimal characters identifying the key for which the passphrase should be set or cleared. The keygrip is listed along with the key when running the command: gpgsm --dump-secret-keys. Alternatively an arbitrary string may be used to identify a passphrase; it is suggested that such a string is prefixed with the name of the application (e.g foo:12346).
One of the following command options must be given:
- --preset
-
Preset a passphrase. This is what you usually will
use. gpg-preset-passphrase will then read the passphrase from
stdin.
- --forget
-
Flush the passphrase for the given cache ID from the cache.
The following additional options may be used:
- -v
- --verbose
-
Output additional information while running.
- -P string
- --passphrase string
-
Instead of reading the passphrase from stdin, use the supplied
string as passphrase. Note that this makes the passphrase visible
for other users.
SEE ALSO
gpg(1), gpgsm(1), gpg-agent(1), scdaemon(1)The full documentation for this tool is maintained as a Texinfo manual. If GnuPG and the info program are properly installed at your site, the command
-
info gnupg
should give you access to the complete manual including a menu structure and an index.
Index
This document was created by man2html, using the manual pages.
Time: 05:29:04 GMT, December 24, 2015


AlpineStayAll the information you need about alpine refuges in ItalyMY OBJECTIVES
Unite outdoors lovers
Tidy up infos and laws
Get more people into hiking
Drawing together like-minded people, forming a community of nature lovers where everyone will be able to share their experiences.Bringing order to the chaotic Italian legislations regarding wilderness, making clear what can and what can't be done in each region.Attracting newcomers to the hiking world by having experienced walkers giving advice and tips suitable for beginners.
Check all the info you need on the interactive map!go to map


--- title: Sentiment Analysis weight: 4 chapter: true ---
In the beginning we wanted to assess if the sentiment scores the different sentiment lexicons provided for tweets were actually comparable. For that purpose we computed the Spearman rank correlation coefficient between tweet sentiment scores provided by each pair of sentiment lexicons to assess whether the ranking of the tweets according to one sentiment lexicon agrees with the ranking of the tweets according to another sentiment lexicon.
Since the sentiment scores were computed on a per-tweet basis we first had to aggregate the sentiment scores accordingly in order to capture the overall social media vibe players were receiving before games in a single number. For that purpose we considered the sentiment scores of all tweets a respective player received in a 24-hour window before a respective game and aggregated them as follows:
Looking at the individual density curves we observed that the distributions of the average sentiment scores rougly fit the bell curve of a Normal distribution despite a few exceptions (esp. for the averaged sentiments for the emoji sentiment lexicon by Novak). Besides the sentiment aggregates we also studied how the BPM perfomance indicator values are distributed for the different players. Similar to the unweighted and weighted sentiment averages before, BPM was also normally distributed
Knowing that the BPM values were normally distributed for the different players it was sufficient to simply construct boxplots for the performance indicator to get a sense how the individual players performed in general in the two considered seasons and how their performance fluctuated.
As one can see, the points of the different scatterplots appeared rather scattered and for the different sentiment lexicons and players there was neither a strong nor directly visible (linear) relationship between the average tweet sentiment and the BPM performance indicator. Even though some of the linear regression lines suggested a correlation, the correlations themselves were rather weak or even neglectable as indicated by the respective Pearson correlation coefficients r that were relatively small (mostly less than 0.1). Additionally most of the p-values of the associated Pearson correlation coefficients were rather high which suggested that the observed strength of the correlations were not significantly different from 0 (and might have appeared due to random chance).
Nevertheless, there were also some counter examples where the Pearson correlation coefficient appeared rather significant. The player Jaylen Brown for example showed a positive correlation for the Afinn lexicon with a p-value below 0.05. However, since the correlations were rather weak, not significant and somehow contradicting for other sentiment lexicons (compare that the correlation was negative for the nrc lexicon), it is debatable if the positive correlation is generalizable for the entire population or even the single player alone.
Due to these reasons we had to conclude that there is no evidence of a significantly strong linear correlation between the average sentiment of tweets players receive within 24 hours before games and their performance within the games.
There was however another interesting observation the scatterplots revealed, namely the prominent outliers. For almost every player there was at least one game day in which the average tweet sentiment was vastly more positive compared to other days. Additionally there were some players with game days associated with an extremely negative average tweet sentiment in comparison to other days. To investigate these outliers more closely we created two word clouds for each player, one for the smallest average tweet sentiment the player received and one for the highest. We used the tweet sentiments created from the Jockers-Rinker lexicon for this purpose.
On extremely positive sentiment values (outliers) the player had birthday.


The Attribute Query support allows developers to query the IdP for additional attributes for a user. This is done using the class dk.itst.oiosaml.sp.UserAttributeQuery. This class can be instantiated without any arguments. In that case, it is assumed that there is an assertion set in the session, and that the query will be performed against the IdP which has issued the assertion.
If username/password authentication is required, use the constructor which takes two strings. If the default constructor is used, or the username is set to null, no authentication will be performed.
Requests are always signed, and the usual validation policy is used for the response. That means that the response must be signed, and the returned assertion must be encrypted unless the property oiosaml-sp.encryption.force is set to false.
Queries are executed by calling UserAttributeQuery.query. This method takes the nameId of the principal to request attributes for, and a list of attributes to retrieve.
If the request fails validation, a ValidationException will be thrown. If the communication fails, an IOException will be thrown. Validation will also fail if the IdP does not respond with a Success status code - this can happen if the requested attribute does not exist, or if the IdP is unwilling to send the attribute value.


@* * Copyright 2021 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import uk.gov.hmrc.agentoverseasfrontend.config.AppConfig @import uk.gov.hmrc.agentoverseasfrontend.controllers.application.routes @import uk.gov.hmrc.agentoverseasfrontend.models.OverseasAddress @import uk.gov.hmrc.agentoverseasfrontend.views.html._ @import uk.gov.hmrc.agentoverseasfrontend.views.html.helpers._ @import uk.gov.hmrc.play.views.html.helpers._ @this(mainTemplate: main_template, errorPrefix: error_prefix, errorSummary: ErrorSummary, form: FormWithCSRF, input: Input) @(mainBusinessAddressForm: Form[OverseasAddress], countries: Map[String, String], backLink: Option[String] = None)(implicit messages: Messages, request: Request[_], appConfig: AppConfig) @value = @{ mainBusinessAddressForm("countryCode").value.getOrElse("")} @mainTemplate(title = errorPrefix(mainBusinessAddressForm) + Messages("mainBusinessAddress.title")) { @Messages("button.back") @if(mainBusinessAddressForm.hasErrors) { @errorSummary(Messages("error.summary.heading"), mainBusinessAddressForm, Seq.empty, Some("main-business-address")) } @form(action = routes.TradingAddressController.submitMainBusinessAddress, 'class -> "form js-form") {
} }


class: center, middle, inverse, title-slide .title[ # Visual perception and effective plot construction ] .subtitle[ ## SISBID 2023
https://github.com/dicook/SISBID
] .author[ ### Di Cook (
<EMAIL_ADDRESS>)
Heike Hofmann (
<EMAIL_ADDRESS>)
Susan Vanderplas (
<EMAIL_ADDRESS>) ] .date[ ### 07/26-28/2023 ] --- background-image: \url(images/who_wore_it_better.jpg) background-size: 20% background-position: 99% 50% # Let's play a game: Which plot wears it better?  - Conversely, in arrangement B, separate plots are made for sex, and age is mapped to the x axis. If you were to answer the question: .orange[At which age(s) are the counts for males and females relatively the same?] Which plot makes this easier? --- 🔮 👽 👼 **TWO MINUTE CHALLENGE**   We've got two different rearrangements of the same information. .orange[At which age(s) are the counts for males and females relatively the same?] Which plot makes this easier? What do we learn? That is different from each? What's the focus of each? What's easy, what's harder?  --- 🔮 👽 👼 **TWO MINUTE CHALLENGE**
Try to write out a question that would be easier to answer from arrangement B.
Go to www.menti.com and use the code 2821 2021.

 ??? - Arrangement A makes it easier to directly compare male and female counts, separately for each age group. Generally, male counts are higher than female counts. There is a big difference between counts in the 45-54 age group, and over 65 counts are almost the same. - Arrangement B makes it easier to directly compare counts by age group, separately for females and males. For females, incidence drops in the middle years. For males, it is pretty consistently high across age groups.
   --- On the next slide we have made **two different plots** of TB incidence in the USA, based on three variables: ``` # A tibble: 10 × 4 year sex age count <dbl> <chr> <chr> <dbl> 1 1997 m 1524 330 2 1997 m 2534 701 3 1997 m 3544 1127 4 1997 m 4554 979 5 1997 m 5564 679 6 1997 m 65 944 7 1997 f 1524 269 8 1997 f 2534 449 9 1997 f 3544 447 10 1997 f 4554 254 ``` - In plot type A, a line plot of counts is drawn separately by age and sex, and year is mapped to the x axis. - Conversely, in plot type B, counts for sex, and age are stacked into a bar chart, separately by age and sex, and year is mapped to the x axis If you were to answer the question: .orange[Is the trend for females generally decreasing over time?] Which plot makes this easier? ---   🔮 👽 👼 **TWO MINUTE CHALLENGE** Which type of plot makes it easier to answer: .orange[Is the trend for females generally decreasing over time?]
−
+
01
:
50
--- 🔮 👽 👼 **TWO MINUTE CHALLENGE** What are the pros and cons of each way of displaying the same information? Should specific limits on axes be made?
Should the limits of the y axes in plot type A have included 0 (zero)?

−
+
00
:
30
--- 🔮 👽 👼 **TWO MINUTE CHALLENGE** Plot A computes the proportion and displays this as a line plot. Plot B uses a 100% chart of stacked bars for females and males. .orange[Is there an age effect in the proportion of incidence by gender? Is there also a temporal trend in the proportions?]
−
+
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:
05
  --- # Perceptual principles - Hierarchy of mappings - Pre-attentive: some elements are noticed before you even realise it. - Color palettes: qualitative, sequential, diverging. - Proximity: Place elements for primary comparison close together. - Change blindness: When focus is interrupted differences may not be noticed. --- # Hierarchy of mappings .pull-left[ 1. Position - common scale (BEST) 2. Position - nonaligned scale 3. Length, direction, angle 4. Area 5. Volume, curvature 6. Shading, color (WORST) (Cleveland, 1984; Heer and Bostock, 2009) 🔮 👽 👼 **TWO MINUTE CHALLENGE** Come up with a plot type for each of the mappings.
−
+
01
:
40
] .pull-right[  ] --- # Color palettes .left-column[ ```r display.brewer.all() ``` Sequential, diverging, qualitative: [Color Brewer web site](http://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3) annotates the palettes indicating attributes of the palettes. ] .right-column[  ] --- # Sequential ```r dsamp <- diamonds[sample(nrow(diamonds), 1000), ] (d <- ggplot(dsamp, aes(carat, price)) + geom_point(aes(colour = clarity))) ```  Map quantitative variable into color scheme that emphasizes one ened, either high or low. Rainbow scheme with viridis palette gives true grey scale. --- # Sequential ```r d + scale_colour_brewer() ```  Default brewer sequential scale, blues. Focus is on the dark blue. --- # Diverging ```r d + scale_colour_brewer(palette="PRGn") ```  Map quantitative variable into color scheme that emphasizes both ends, either high AND low, de-emphasizing middle. --- # Qualitative ```r d + scale_colour_brewer(palette="Set1") ```  Map quantitative variable into color scheme to most differentiated set. It's possible to have too many colours to perceive the differences. --- class: inverse middle 🔮 👽 👼 **TWO MINUTE CHALLENGE** Of the previous three colour schemes on the same data, which would be the most appropriate? And why do you think so?
−
+
00
:
50
--- # Color blind-proofing ```r library(scales) library(dichromat) clrs <- hue_pal()(9) d + theme(legend.position = "none") clrs <- dichromat(hue_pal()(9)) d + scale_colour_manual("", values=clrs) + theme(legend.position = "none") ``` Online checking tool [coblis](https://www.color-blindness.com/coblis-color-blindness-simulator/) allows you to upload an image and it will re-map the colors for different colour perception issues. The package `colorblind` has color blind friendly palettes (Susan VanderPlas: but the colours are awful, to my eye). --- .pull-left[ Original colours  ] .pull-right[ Color blind view  ] --- # Pre-attentive Can you find the odd one out?  --- Is it easier now?  --- # Proximity Place elements that you want to compare close to each other. If there are multiple comparisons to make, you need to decide which one is most important.   --- # Mapping and proximity .left-column[ Same proximity is used, but different geoms. Is one better than the other to determine the relative ratios of males to females by age? ] .right-column[   ] --- # Mapping and proximity .left-column[ Same proximity is used, but different geoms. Is one better than the other to determine the relative ratios of ages by sex? ] .right-column[   ] --- # Change blindness ```r ggplot(dsamp, aes(x=carat, y=price, colour = clarity)) + geom_point() + geom_smooth(se=FALSE) + scale_color_brewer(palette="Set1") + facet_wrap(~clarity, ncol=4) ```  Which has the steeper slope, VS1 or VS2? --- Making comparisons across plots requires the eye to jump from one focal point to another. It may result in not noticing differences. ```r ggplot(dsamp, aes(x=carat, y=price, colour = clarity)) + geom_point() + geom_smooth(se=FALSE) + scale_color_brewer(palette="Set1") ```  --- # Core principles - Make a plot of your **data**! The hierarchy matters primarily if the structure is weak, or if differences between groups is small. - Knowing how to use proximity is an extremely valuable skill, and not well utilised. - Use of colour is a very valuable skill, and there are many bad habits to over-use, too many colours or precariously mapping to a continuous variable to add another dimension.
 - Show the data! There are a lot of examples where the statistics are plotted, but the magic comes when you plot the data. Plot the statistics if the volume of data is overwhelming, to tighten the message, but still plot the data for yourself and to keep track of the variability. - One plot is never enough! Plot the data in different ways, it will help digest the relationships between variables, to gain a better understanding of the data. --- class: inverse middle # Your turn This builds on the exercise from the previous session. - Using your choice of country, for example, Australia, make a set of plots to explore the TB incidence among males relative to females over different age groups for 2012. - Choose your best plot to answer this question: .orange[Is there a higher prevalence of TB among younger women in 2012?]
−
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:
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--- # Resources - [Claus O. Wilke, Fundamentals of Data Visualization](https://clauswilke.com/dataviz/) - [Naomi Robbins, Creating More Effective Graphs](http://www.nbr-graphs.com) - [Cleveland WS, McGill R. 1984. Graphical perception: Theory, experimentation, ...](https://www.tandfonline.com/doi/abs/10.1080/01621459.1984.10478080) - [Heer J, Bostock M. 2010. Crowdsourcing graphical perception](http://vis.stanford.edu/files/2010-MTurk-CHI.pdf) - [Antony Unwin, Graphical Data Analysis with R](https://www.crcpress.com/Graphical-Data-Analysis-with-R/Unwin/9781498715232) - [Wagemans J et al. 2012. A Century of Gestalt Psychology in Visual Perception: I. Perceptual Grouping and Figure-Ground Organization. Psychological Bulletin 138:1172–1217](http://dx.doi.org/10.1037/a0029333) - [Wagemans J, Feldman J, Gepshtein S, Kimchi R, Pomerantz JR, et al. 2012. A Century of Gestalt psychology in Visual Perception: II. Conceptual and Theoretical Foundations. Psychological Bulletin 138:1218–1252](https://doi.org/10.1037/a0029334) - [Wickham H. 2013. Graphical criticism](https://vita.had.co.nz/papers/stat-graph-hist.pdf) - [VanderPlas S, Goluch R, Hofmann H. 2019. Framed!](https://amstat.tandfonline.com/doi/full/10.1080/10618600.2018.1562937#.XS41dS1L21s) - [VanderPlas S, Hofmann H. 2015 Signs of the Sine Illusion](https://amstat.tandfonline.com/doi/abs/10.1080/10618600.2014.951547?scroll=top&needAccess=true&journalCode=ucgs20#.XS413i1L21s) --- # Share and share alike
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.


This documentation is deprecated please refer to sg-orbit
| A | A | A | A | A | A | A | A | A | A | A |
| 6rem (96px) | 5rem (80px) | 3rem (48px) | 2.5rem (40px) | 2rem (32px) | 1.5rem (24px) | 1.25rem (20px) | 1.125rem (18px) | 1rem (16px) | .875rem (14px) | .75rem (12px) |
| f-headline | f-subheadline | f1 | f2 | f3 | f4 | f5 | f6 | f7 | f8 | f9 |
fw1 - 100
fw2 - 200
fw3 - 300
fw4 - 400
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fw6 - 600
fw7 - 700
fw8 - 800
fw9 - 900
1 : 1
2 : 1.25
3 : 1.33
4 : 1.5lh1
'I do not think your law was made for such a chance,' said Aragorn. 'Nor indeed am I a stranger; for I have been in this land before, more than once, and ridden with the host of the Rohirrim, though under other name and in other guise. You I have not seen before, for you are young, but I have spoken with to Éomund your father, and with Théoden son of Thengel. Never in former days would any high lord of this land have constrained a man to abandon such a quest as mine. My duty at least is clear, to go on. Come now, son of Éomund, the choice must be made at last.  Or seek to carry out your law. If you do so there will be fewer to return to your war or to your king.'
lh2'I do not think your law was made for such a chance,' said Aragorn. 'Nor indeed am I a stranger; for I have been in this land before, more than once, and ridden with the host of the Rohirrim, though under other name and in other guise. You I have not seen before, for you are young, but I have spoken with to Éomund your father, and with Théoden son of Thengel. Never in former days would any high lord of this land have constrained a man to abandon such a quest as mine. My duty at least is clear, to go on. Come now, son of Éomund, the choice must be made at last.  Or seek to carry out your law. If you do so there will be fewer to return to your war or to your king.'
lh3'I do not think your law was made for such a chance,' said Aragorn. 'Nor indeed am I a stranger; for I have been in this land before, more than once, and ridden with the host of the Rohirrim, though under other name and in other guise. You I have not seen before, for you are young, but I have spoken with to Éomund your father, and with Théoden son of Thengel. Never in former days would any high lord of this land have constrained a man to abandon such a quest as mine. My duty at least is clear, to go on. Come now, son of Éomund, the choice must be made at last.  Or seek to carry out your law. If you do so there will be fewer to return to your war or to your king.'
lh4'I do not think your law was made for such a chance,' said Aragorn. 'Nor indeed am I a stranger; for I have been in this land before, more than once, and ridden with the host of the Rohirrim, though under other name and in other guise. You I have not seen before, for you are young, but I have spoken with to Éomund your father, and with Théoden son of Thengel. Never in former days would any high lord of this land have constrained a man to abandon such a quest as mine. My duty at least is clear, to go on. Come now, son of Éomund, the choice must be made at last.  Or seek to carry out your law. If you do so there will be fewer to return to your war or to your king.'
Deprecated in 3.0
solid : 1
title : 1.25
copy : 1.5lh-solid
'I do not think your law was made for such a chance,' said Aragorn. 'Nor indeed am I a stranger; for I have been in this land before, more than once, and ridden with the host of the Rohirrim, though under other name and in other guise. You I have not seen before, for you are young, but I have spoken with to Éomund your father, and with Théoden son of Thengel. Never in former days would any high lord of this land have constrained a man to abandon such a quest as mine. My duty at least is clear, to go on. Come now, son of Éomund, the choice must be made at last.  Or seek to carry out your law. If you do so there will be fewer to return to your war or to your king.'
lh-title'I do not think your law was made for such a chance,' said Aragorn. 'Nor indeed am I a stranger; for I have been in this land before, more than once, and ridden with the host of the Rohirrim, though under other name and in other guise. You I have not seen before, for you are young, but I have spoken with to Éomund your father, and with Théoden son of Thengel. Never in former days would any high lord of this land have constrained a man to abandon such a quest as mine. My duty at least is clear, to go on. Come now, son of Éomund, the choice must be made at last.  Or seek to carry out your law. If you do so there will be fewer to return to your war or to your king.'
lh-copy'I do not think your law was made for such a chance,' said Aragorn. 'Nor indeed am I a stranger; for I have been in this land before, more than once, and ridden with the host of the Rohirrim, though under other name and in other guise. You I have not seen before, for you are young, but I have spoken with to Éomund your father, and with Théoden son of Thengel. Never in former days would any high lord of this land have constrained a man to abandon such a quest as mine. My duty at least is clear, to go on. Come now, son of Éomund, the choice must be made at last.  Or seek to carry out your law. If you do so there will be fewer to return to your war or to your king.'
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alpha
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bravo
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charlie
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delta
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echo
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foxtrot
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golf
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hotel
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india
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juliett
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kilo
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lima
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13
mike
[80px]
.w1 [4px]
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.w9 [48px]
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.w10 [56px]
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.br0
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.br-pill
.br-100
.shadow-1
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Box Sizing border-box is the default behaviour of our elements
In some cases you might want to live in the 1990's and accept that the width of an element can be affected by border and padding.


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If you have nested components, it would be better to use tuiProjectClassDirective that projects classes from parent to host element
tuiProjectClassDirective


|
C Training
1.0
|
https://www.hackerearth.com/practice/data-structures/hash-tables/basics-of-hash-tables/tutorial/
Data Structures are the programmatic way of storing data so that data can be used efficiently. Almost every enterprise application uses various types of data structures in one or the other way.
An overview of data structures and algorithm
Data structure include
To understand the list implementation. What variable sits what section of the memory. What goes where?
These three sections of the memory is decided at compile time.
malloc()
or new operator etc.Pointers and dynamic memory - stack vs heap
Local arrays are created on the stack, and have automatic storage duration – you don't need to manually manage memory, but they get destroyed when the function they're in ends. They necessarily have a fixed size.
Arrays created with operator new[] have dynamic storage duration and are stored on the heap (technically the "free store"). They can have any size, but you need to allocate and free them yourself since they're not part of the stack frame.


Jost Van Dyke or JVD, named for an early Dutch colonist, is the 4th largest of the Britisih Virgin Islands and is home to a copule hundred residents.
You can learn more about Jost Van Dyke by visiting the B.V.I. Tourists Board's website.


Class: RowList
| Defined in: | src/views/row_list.coffee |
| Inherits: | Marionette.CompositeView |
Overview
Render the table rows and columns
Property Summary
- (String) sortColumn
- the attribute name of the current sort column
- (String) sortDirection
- (asc|desc) the current sort direction direction
Variables Summary
- template =
-
template
- childView =
-
Row
- collectionEvents =
-
{ sync: 'fetched', reset: 'fetched' }
- ui =
-
{ selectAllCheckbox: 'thead th.select-all input', rowCheckboxes: 'td.checkbox input', sortableColHeader: 'thead th.sortable', thead: 'thead', table: 'table' }
- events =
-
{ 'click @ui.selectAllCheckbox': 'toggleSelectAll', 'click @ui.rowCheckboxes': 'selectIntermediateCheckboxes', 'click th.sortable': 'sortChanged' }
- childViewContainer =
-
'tbody'
- attributes =
-
{ "class": 'wrap' }
Instance Method Summary
- # (void) initialize(opts = {})
- # (void) sortChanged(e) Bound Callback for when the sort direction was changed by the user.
- # (void) setSort(sortColumn, sortDirection, opts = {}) Bound Sets the sort options on the collection and adds CSS classes as necessary
- # (void) setSearch(filter) Trigger a search of the models in the table based on the state of the {Entities.Filter}.
- # (Array<Object>) getRowCheckboxes() Fetch the checkbox elements within the table rows.
- # (void) toggleSelectAll() Toggle selection state of every model in the collection, and reset the state selections object.
-
#
(void)
selectIntermediateCheckboxes(e)
If the shift key is held down, set all checkboxes between the target checkbox and the nearest also-selected checkbox to the new state of the target checkbox.
- # (void) handleRemoveMultiple() If there are no entities left in the collection, deselect the select all checkbox.
- # (void) fetched() Bound If the EmptyView was replaced with a LoadingView, revert that replacement to actually show a message on empty collection.
- # (void) buildChildView(item, ItemView) Overriden to allow passing options to the rendered ItemView
- # (void) serializeData()
- # (void) updateClasses() Bound
- # (void) onRender()
Instance Method Details
#
(void)
initialize(opts = {})
#
(void)
sortChanged(e)
Bound
Callback for when the sort direction was changed by the user. Triggers a method on the controller, which determines what to do and calls back to #setSort.
#
(void)
setSort(sortColumn, sortDirection, opts = {})
Bound
Sets the sort options on the collection and adds CSS classes as necessary
#
(void)
setSearch(filter)
Trigger a search of the models in the table based on the state of the {Entities.Filter}.
#
(Array<Object>)
getRowCheckboxes()
Fetch the checkbox elements within the table rows.
#
(void)
toggleSelectAll()
Toggle selection state of every model in the collection, and reset the state selections object. Fire an event on the collection.
#
(void)
selectIntermediateCheckboxes(e)
If the shift key is held down, set all checkboxes between the target checkbox and the nearest also-selected checkbox to the new state of the target checkbox.
#
(void)
handleRemoveMultiple()
If there are no entities left in the collection, deselect the select all checkbox.
#
(void)
fetched()
Bound
If the EmptyView was replaced with a LoadingView, revert that replacement to actually show a message on empty collection. Called after any change to the collection.
#
(void)
buildChildView(item, ItemView)
Overriden to allow passing options to the rendered ItemView
#
(void)
serializeData()
#
(void)
updateClasses()
Bound
#
(void)
onRender()


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BJP helped me win in 2018, says Narayana Gowda
by Special CorrespondentDisqualified MLA K.C. Narayana Gowda, formerly with the JD(S) and now contesting on BJP ticket from K.R. Pet, said on Saturday that the BJP had “unofficially” supported him in the 2018 elections.
Revealing this to presspersons in K.R. Pet on Friday evening, the businessman-turned-politician said the saffron party had unofficially backed him and that is why Chief Minister B.S. Yediyurappa had not campaigned for the BJP’s own candidate, Manjunath. “In fact, Manju’s father Bookahalli Chandrappa blessed me and offered ₹1,100 when I visited his residence,” he said.
Interestingly, the Congress, during its campaign for the 2018 Assembly polls, had described the JD(S) as the BJP’s “B-team”. However, the Congress and JD(S) later went on to form a coalition government that lasted 14 months. It was brought down by 17 defectors from both parties, 13 of whom are now contesting the byelections on BJP ticket.