One more dawn; one more day; one day more!"
So the Climax of the musical is about to begin. Something may happen At the Opera Tonight, or fate and destiny could be working to bring the characters together for some sort of final showdown. Either way, the writers find it that it's important to remind us just how important what is happening is. So they introduce a song to bring everything dramatically together. A classic example of this type of song seems to be cutting between the heroes doing a Hero Song and the villains doing their Villain Song, as they each do their own particular preparations.
Frequently overlaps with Massive Multiplayer Ensemble Number, which can be about a wider range of subjects, but must include counterpoint and various character perspectives. Not to be confused with The Eleven O'Clock Number, which is sung near the end of a musical to initiate the climax.
Examples:
- The song "At the Opera Tonight" from Repo! The Genetic Opera is a pretty clear-cut example.
- In Hairspray, before the main characters crash the set of the nicest kids in town, Motormouth Maybelle sings "I Know Where I've Been" as a reminder of how much is at stake.
- By nature, virtually every film in the Disney Animated Canon will have its own;
- The Jungle Book (1967) has "What Friends Are For" sung by The British Invasion vultures leading up to the battle with Shere Khan.
- "Savages" from Pocahontas. Even overlaps with Massive Multiplayer Ensemble Number.
- Moulin Rouge! has "The Show Must Go On".
- Not Another Teen Movie parodies this trope with the song "Prom Tonight".
- "Making Christmas" is this for The Nightmare Before Christmas. All the cast save for Sally, are preparing creepy Christmas presents right before Jack's takeoff as Skeleton Claus.
- "La Resistance" from South Park: Bigger, Longer & Uncut, which is itself a take-off on "One Day More". This trope is even lampshaded within the song:
Why did our mothers start this war?

When did this song become a marathon?
- Battle Dawn, depicting Queen Selenay at the final confrontation of Mercedes Lackey's Arrows trilogy.
- "Walk Through the Fire" from the Buffy musical episode "Once More, With Feeling".
- In the Musical Episode of Even Stevens, there's "Sixth Period", in which the two lead characters of the show express their fears about their upcoming classes.
- "Teardrop" by Massive Attack at the end of the first season of Prison Break, a night before the breakout.
- Brigadoom from Lexx. It's really almost more of a Backstory Musical, but it does also fill this role because it sort of pumps them up as well, by showing them the history of how Kai fought so hard in the past and how he shouldn't let that go to waste, and that they all must once again struggle valiantly to defeat new Big Bad Mantrid rather than running.
- "Welcome to my Realm" from Food for the Gods by Fireaxe. Sung by the Devil, right before he and his army of demons and damned souls storm the walls of Heaven.
- Jeff Wayne's Musical Version of The War of the Worlds has "Eve Of The War" and the first part of "Horsell Common And The Heat Ray", covering the lead-up to the violent first contact with the Martians.
- The Megas: "History Repeating, Part 1," the first song on History Repeating - Blue, is a relatively slow and melancholy song that ramps up hard into the much more energetic "History Repeating, Part 2 (One More Time)", as a reluctant Mega Man commits to winning one last fight and moves onto a war footing. Then, in History Repeating - Red, "I'm Not the Breakman" has Proto Man lamenting his abandonment by Dr Light and resultant loss of purpose, just before the climax of the album as he and his brother fight in "Make Your Choice" and Mega Man saves the day in "I Refuse (To Believe)".
- "Soon It's Gonna Rain" from The Fantasticks.
- "One Day More" from Les Misérables, in which one of Enjolras' lines is literally: "One more day before the storm"
- "Tonight" (the "Quintet" version with Tony/Maria/Anita/The Jets/The Sharks, even though the last two are actually groups) from West Side Story is one of the classics, setting up for the big rumble that changes everything.
- The Phantom of the Opera:
- "Prima Donna" qualifies, certainly in the stage production.
- "Notes (Reprise) - Twisted Every Way" could apply as well (this song is called "We Have All Been Blind - Twisted Every Way in the movie version, as "Notes (Reprise)" was cut from this version).
- "Luck Be A Lady" from Guys and Dolls.
- "A New Argentina" from Evita, depicting the build-up to the 1946 Argentinian presidential election. It closes the first act, while the second act picks up right after the election, with Juan and Eva giving their victory speeches from the balcony of the Casa Rosada.
- Sweeney Todd: The Demon Barber of Fleet Street has the Fogg's Asylum sequence. As the final confrontation looms, the chorus asks "Who could see how the road would twist?" as the lunatics escape to echo the Beggar Woman's Cassandra Truth across the city.
- "I Hope I Get It" from A Chorusline.
- Little Shop of Horrors :
- Though it was a Cut Song, "We'll Have Tomorrow" would have been a perfect example of this.
- The start of "Suppertime Reprise (Sominex)" could count, but the storm sort of starts in the middle of it.
- The Act One finale of Urinetown. Just for extra emphasis, it goes into slow motion. "Why Did I Listen To That Man?" features no less than four alternating, then simultaneous takes on the collapsing situation... then The Hero Dies and things truly explode two songs later.
- Godspell subverts it with "On the Willows"note , which is light and soft, but the lyrics talk about the coming sacrifice as one by one the apostles say goodbye to Jesus after Judas has been exposed. Heartwrenching if done properly.
- "It's Time" from Evil Dead: The Musical. The gist of the song is that Ash has decided it's time to man-up and kick a whole mess of ass. In the next few scenes, he kills things that are immortal.
- A light-hearted, waltzy version appears in A Little Night Music: "A Weekend in the Country".
- "Comfort and Joy" from Bat Boy: The Musical
- Vanities: "I Can't Imagine" plays this straight, but its reprise, "The Argument" is a song during the storm (the breakdown of the characters' friendship).
- A Very Potter Musical
- "Voldemort is Going Down".
- "No Way" in the Sequel, in which Draco finally gets to be a part of a 4 part harmony.
- Jekyll & Hyde: "This is the Moment", sung right before Jekyll's first transformation into Hyde, and "Confrontation", sung right before the show's finale where Hyde comes out at Jekyll's wedding, leading to his death.
- Sera Myu: Transylvania no Mori and it's revised version have "We'll Be The Last Victory" which is basically both the heroes and villains declaring what they're fighting for and end with what "group" they belong to, even if that group is themselves.
- The reprise of "Checkmate" in Black Butler Musical II: A Thousand Souls and a Fallen Shinigami. All of the characters sing about what will happen At the Opera Tonight, ending with a dramatic "Yes, My Lord" from Sebastian.
- In the stage version of The Little Mermaid (1989), "Fathoms Below" is a literal example, with the bridge section telling of the storms that occur when King Triton gets mad, which happens shortly afterwards.
- Appropriately enough, the song in Hamilton just before the Reynolds Pamphlet is released is called Hurricane and perfectly fits this.
- The SpongeBob Musical has "Tomorrow Is" where the citizens of Bikini Bottom sing about what they hope to accomplish before Mt. Humongus erupts.
- A rarity in video games: Red Dead Redemption II has "That's the Way It Is", which is played near the end of Chapter 6 when Arthur Morgan, riddled with tuberculosis, goes on his final ride to camp, where he will face the Pinkertons before meeting up with Micah Bell for one final showdown in which he may or may not survive depending on his honor.
- "So They Say" from Dr. Horrible's Sing-Along Blog. Dr. Horrible prepares his Death Ray, Captain Hammer sings about doing the weird stuff and Penny sings about her future.
- The fanmade Phoenix Wright - The Musical features a song called Tomorrow's Trial, taking place the night before the final faceoff between Phoenix and Von Karma.
- "Where is the Fun?" from The Fairly Oddparents: School's Out! The Musical
- "This Day Aria" from the Season Two finale of My Little Pony: Friendship Is Magic.
- "The Ballad of the Crystal Empire" from the Season 3 premiere.
- The title song from Equestria Girls.
- "You'll Play Your Part" from the Season 4 finale, sung by the four Princesses just before Tirek makes his appearance.
- On Daria, "What If the Town Blew Away" is literally this—the characters are singing about the hurricane that's about to hit Lawndale.
- Tangled: The Series has "Ready As I'll Ever Be," with both the villain, Varian, and the heroes preparing for the first season's big finale battle.


class CensusTaker : Debuggable
Used to figure out population and economy stats by CityMap... |
|
class Constructor : Debuggable
Constructor is responsible for looking at the city... seeing what the demand is for each type of zone and then attempting to place them in the most desirable spots... |
|
class ContactFulfiller : Debuggable |
|
enum class CountType |
|
object CrimeUpdater |
|
class DesirabilityUpdater : Debuggable
This class is responsible for going over each zoned tile and figuring out the "Desirability score"... in other words how badly people want to live there... |
|
data class EconomyReport
Stores supply/demand stats, used by charts and so forth. |
|
object FireCoverageUpdater |
|
class GoodsConsumer |
|
class HappinessUpdater : Debuggable |
|
class LandValueUpdater : Debuggable |
|
class Liquidator : Debuggable |
|
class Manufacturer : Debuggable |
|
class PollutionUpdater : Debuggable |
|
class PowerCoverageAutomata |
|
object PowerCoverageUpdater |
|
class ResourceCounts |
|
class ResourceFinder : Debuggable |
|
class Shipper : Debuggable |
|
class TaxCollector : Debuggable |
|
class TrafficCalculator |
|
class Upgrader : Debuggable |
|
class ZotPopulator : Debuggable |


Home

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Projects

  List of projects:
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The FreeRADIUS Project
FreeRADIUS includes a RADIUS server, a BSD licensed client library, a PAM library, and an Apache module. In most cases, the word FreeRADIUS refers to the RADIUS server.
FreeRADIUS is the most widely deployed RADIUS server in the world. It is the basis for multiple commercial offerings. It supplies the AAA needs of many Fortune-500 companies and Tier 1 ISPs.
It is also widely used for Enterprise Wi-Fi and IEEE 802.1X network security, particularly in the academic community, including eduroam.
The server is fast, feature-rich, modular, and scalable.
The server has reached a stable .tar.bz2">Version .tar.bz2.sig">(sig) , with incremental improvements added in every release.
Documentation
Beginners to RADIUS should read
the FreeRADIUS
Technical Guide. Chapter 2 describes RADIUS concepts, such as
"authorization", "accounting", and how the NAS and server communicate.
i.e. how RADIUS works. Chapter 3 describes how to install FreeRADIUS.
Chapter 4 describes what configuration files are distributed with the
server, and what they are used for. This includes describing the
packet processing lists (request
, reply
,
etc.), and the various configuration sections
(authorize
, authenticate
, etc.)
We recommend reading Chapter 4 to anyone who wants to understand how the server works.
There is extensive documentation for the server available via the /">Network RADIUS Documentation page. The "introduction" and "concepts" chapters are not available there yet, as those subjects are covered in the Technical guide.
We recommend in particular the /raddb/mods-available/">modules page, which contains documentation for nearly every module distributed with the server. We also recommend the /unlang/keywords.html">Unlang keywords page, which describes what each keyword does, and how it is used.
There is also documentation available on the documentation page, and on the wiki.
Stable Release
The "stable release" of FreeRADIUS is version 3. The goal of this version is stability. Version 3 introduces a number of major changes over Version 2. The debugging output is clearer (and colourised!), more errors are found while the configuration files are parsed instead of at run-time, RADIUS over TLS (i.e. RadSec) and RADIUS over TCP are supported. The raddb/ directory has been re-arranged so that files are easier to find.
Download: .tar.bz2">
Upgrading to v3 from v2? Please read the upgrade guide.
Previous stable
The "previous stable" release of FreeRADIUS was version 2.
The v2.2.x series is now End Of Life. Only security fixes will be applied to v2.2.x. Users of 2.2.x are encouraged to migrate to the latest version 3 release
Download: .tar.bz2">
Follow @freeradius


Upload your lifeless Resume below
Put a good Display Picture here
Or click below to start right away!


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Chemistry: The Science in Context 5e
作者：
Thomas R. Gilbert
&
Rein V. Kirss
&
Natalie Foster
分类：
化学 - 基础化学
出版商：W. W. Norton & Company
语言：英语
百度盘下载
提取码是：wvcn
书籍目录
Particles of Matter: Measurement and the Tools of Science
Atoms, Ions, and Molecules: Matter Starts Here
Stoichiometry: Mass, Formulas, and Reactions
Reactions in Solution: Aqueous Chemistry in Nature
Thermochemistry: Energy Changes in Reactions
Properties of Gases: The Air We Breathe
A Quantum Model of Atoms: Waves, Particles, and Periodic Properties
Chemical Bonds: What Makes a Gas a Greenhouse Gas?
Molecular Geometry: Shape Determines Function
Intermolecular Forces: The Uniqueness of Water
Solutions: Properties and Behavior
Solids: Crystals, Alloys, and Polymers
Chemical Kinetics: Reactions in the Atmosphere
Chemical Equilibrium: How Much Product Does a Reaction Really Make?
Acid–Base Equilibria: Proton Transfer in Biological Systems
Additional Aqueous Equilibria: Chemistry and the Oceans
Thermodynamics: Spontaneous and Nonspontaneous Reactions and Processes
Electrochemistry: The Quest for Clean Energy
Nuclear Chemistry: Applications to Energy and Medicine
Organic and Biological Molecules: The Compounds of Life
The Main Group Elements: Life and the Periodic Table
Transition Metals: Biological and Medical Applications
书籍简介
本书pdf是
文字版
，
彩图版
，书签良好。
相关书籍：
Chemistry An Atoms-Focused Approach 2e
Chemistry The Science in Context 5e
Particles of Matter: Measurement and the Tools of Science
Atoms, Ions, and Molecules: Matter Starts Here
Stoichiometry: Mass, Formulas, and Reactions
Reactions in Solution: Aqueous Chemistry in Nature
Thermochemistry: Energy Changes in Reactions
Properties of Gases: The Air We Breathe
A Quantum Model of Atoms: Waves, Particles, and Periodic Properties
Chemical Bonds: What Makes a Gas a Greenhouse Gas?
Molecular Geometry: Shape Determines Function
Intermolecular Forces: The Uniqueness of Water
Solutions: Properties and Behavior
Solids: Crystals, Alloys, and Polymers
Chemical Kinetics: Reactions in the Atmosphere
Chemical Equilibrium: How Much Product Does a Reaction Really Make?
Acid–Base Equilibria: Proton Transfer in Biological Systems
Additional Aqueous Equilibria: Chemistry and the Oceans
Thermodynamics: Spontaneous and Nonspontaneous Reactions and Processes
Electrochemistry: The Quest for Clean Energy
Nuclear Chemistry: Applications to Energy and Medicine
Organic and Biological Molecules: The Compounds of Life
The Main Group Elements: Life and the Periodic Table
Transition Metals: Biological and Medical Applications


Game Designer / Programmer / Musician
Zed specializes in making things for human to play. Most of the things he makes are digital video games, but he also puts effort into crafting electronic musics and motion pictures about geometry beauty.
A simulation game where Pokemon meets Harverst Moon. Features voxel visual style, relaxing fantasy narrative and monster-driven systems.
一款模拟经营类游戏，灵感来源自精灵宝可梦和牧场物语。使用体素美术风格，拥有休闲的幻想故事，以可爱的小怪物为育成核心的模拟经营游戏。
Applied Scrum method, designed behaviours of multiple systems, AI programming, UI system, narrative, Animation, etc.
New York State Game Dev Challenge Prize winner.
Test 2


Ravens closing in on Patriots as Super Bowl favorite in AFC
by Jim Barnes Las Vegas Review-JournalWith each Lamar Jackson performance, the gap between the Super Bowl favorite New England Patriots and upstart Baltimore Ravens appears to narrow.
Jackson, the sensational second-year quarterback, has led the Ravens to six straight wins and an 8-2 record. The past four wins have included double-digit victories over likely playoff teams in Seattle (30-16), Houston (41-7) and, yes, New England (37-20).
The Patriots still hold the No. 1 seed in the AFC at 9-1, but two sportsbook directors said that’s the only thing keeping New England favored over Baltimore in the conference.
“All it is is home field,” William Hill sportsbook director Nick Bogdanovich said.
Sunset Station sportsbook director Chuck Esposito said: “The way the Ravens played against the Texans and Deshaun Watson (on Sunday), the way Lamar Jackson has developed, I’m super impressed.”
Esposito said the AFC had become a two-team race, with even Kansas City ranking “far below” the Patriots and Ravens at this point.
However, with their championship pedigree and their current edge for home-field advantage in the playoffs, the Patriots remain the favorites to win the AFC at various sportsbooks.
William Hill lists New England as minus 105 to win the conference and plus 250 to win the Super Bowl, with the Ravens at plus 190 to win the AFC and 5-1 to win the Super Bowl.
The prices were even closer at some other books. Circa Sports has the Patriots at plus 132 to win the AFC and plus 325 to win the Super Bowl, while the Ravens were at plus 175 and plus 400, respectively.
Looking ahead
Sunday’s marquee game has the Dallas Cowboys traveling to New England. The Patriots were installed as consensus 6½-point favorites with a total of 46, and the line hit 6 at a couple of books Monday afternoon.
Bogdanovich and Esposito said the line would likely trend lower and was extremely unlikely to get to 7, where sharps would see a clear “buy” sign on Dallas.
More Betting: Follow at reviewjournal.com/betting.
Contact Jim Barnes at<EMAIL_ADDRESS>Follow @JimBarnesLV on Twitter.


email<EMAIL_ADDRESS>github: github.com/lucagez
codepen: codepen.io/lucagez
twitter: twitter.com/lucagez
Currently I am available for freelance work.
Tell me what you would like to see on the web and we'll make it for real.
Yours,
Gez


Design Goals
This page outlines the core principles that guide Istio's design.
Istio’s architecture is informed by a few key design goals that are essential to making the system capable of dealing with services at scale and with high performance.
-
Maximize Transparency. To adopt Istio, an operator or developer should be required to do the minimum amount of work possible to get real value from the system. To this end, Istio can automatically inject itself into all the network paths between services. Istio uses sidecar proxies to capture traffic, and where possible, automatically program the networking layer to route traffic through those proxies without any changes to the deployed application code. In Kubernetes, the proxies are injected into pods and traffic is captured by programming iptables rules. Once the sidecar proxies are injected and traffic routing is programmed, Istio is able to mediate all traffic. This principle also applies to performance. When applying Istio to a deployment, operators should see a minimal increase in resource costs for the functionality being provided. Components and APIs must all be designed with performance and scale in mind.
-
Incrementality. As operators and developers become more dependent on the functionality that Istio provides, the system must grow with their needs. While we expect to continue adding new features ourselves, we expect the greatest need will be the ability to extend the policy system, to integrate with other sources of policy and control and to propagate signals about mesh behavior to other systems for analysis. The policy runtime supports a standard extension mechanism for plugging in other services. In addition, it allows for the extension of its vocabulary to allow policies to be enforced based on new signals that the mesh produces.
-
Portability. The ecosystem in which Istio will be used varies along many dimensions. Istio must run on any cloud or on-prem environment with minimal effort. The task of porting Istio-based services to new environments should be trivial, and it should be possible to operate a single service deployed into multiple environments (on multiple clouds for redundancy for example) using Istio.
-
Policy Uniformity. The application of policy to API calls between services provides a great deal of control over mesh behavior, but it can be equally important to apply policies to resources which are not necessarily expressed at the API level. For example, applying quota to the amount of CPU consumed by an ML training task is more useful than applying quota to the call which initiated the work. To this end, the policy system is maintained as a distinct service with its own API rather than being baked into the proxy/sidecar, allowing services to directly integrate with it as needed.


Class CreateDomainOptions
Options for creating a domain to use with MailSlurp. You must have ownership access to this domain in order to verify it. Domains will not function correctly until the domain has been verified. See https://www.mailslurp.com/guides/custom-domains for help. Domains can be either HTTP
or SMTP
type. The type of domain determines which inboxes can be used with it. SMTP
inboxes use a mail server running mx.mailslurp.com
while HTTP
inboxes are handled by AWS SES.
Inherited Members
Namespace: mailslurp.Model
Assembly: mailslurp.dll
Syntax
[DataContract(Name = "CreateDomainOptions")]
public class CreateDomainOptions : IEquatable<CreateDomainOptions>, IValidatableObject
Constructors
View SourceCreateDomainOptions()
Initializes a new instance of the CreateDomainOptions class.
Declaration
[JsonConstructor]
protected CreateDomainOptions()
CreateDomainOptions(string, string, bool?, DomainTypeEnum?)
Initializes a new instance of the CreateDomainOptions class.
Declaration
public CreateDomainOptions(string domain = null, string description = null, bool? createdCatchAllInbox = null, CreateDomainOptions.DomainTypeEnum? domainType = null)
Parameters
| Type | Name | Description |
|---|---|---|
| string | domain | The top level domain you wish to use with MailSlurp. Do not specify subdomain just the top level. So |
| string | description | Optional description of the domain.. |
| bool? | createdCatchAllInbox | Whether to create a catch all inbox for the domain. Any email sent to an address using your domain that cannot be matched to an existing inbox you created with the domain will be routed to the created catch all inbox. You can access emails using the regular methods on this inbox ID.. |
| CreateDomainOptions.DomainTypeEnum? | domainType | Type of domain. Dictates type of inbox that can be created with domain. HTTP means inboxes are processed using SES while SMTP inboxes use a custom SMTP mail server. SMTP does not support sending so use HTTP for sending emails.. |
Properties
View SourceCreatedCatchAllInbox
Whether to create a catch all inbox for the domain. Any email sent to an address using your domain that cannot be matched to an existing inbox you created with the domain will be routed to the created catch all inbox. You can access emails using the regular methods on this inbox ID.
Declaration
[DataMember(Name = "createdCatchAllInbox", EmitDefaultValue = true)]
public bool? CreatedCatchAllInbox { get; set; }
Property Value
| Type | Description |
|---|---|
| bool? | Whether to create a catch all inbox for the domain. Any email sent to an address using your domain that cannot be matched to an existing inbox you created with the domain will be routed to the created catch all inbox. You can access emails using the regular methods on this inbox ID. |
Description
Optional description of the domain.
Declaration
[DataMember(Name = "description", EmitDefaultValue = true)]
public string Description { get; set; }
Property Value
| Type | Description |
|---|---|
| string | Optional description of the domain. |
Domain
The top level domain you wish to use with MailSlurp. Do not specify subdomain just the top level. So test.com
covers all subdomains such as mail.test.com
. Don't include a protocol such as http://
. Once added you must complete the verification steps by adding the returned records to your domain.
Declaration
[DataMember(Name = "domain", IsRequired = true, EmitDefaultValue = true)]
public string Domain { get; set; }
Property Value
| Type | Description |
|---|---|
| string | The top level domain you wish to use with MailSlurp. Do not specify subdomain just the top level. So |
DomainType
Type of domain. Dictates type of inbox that can be created with domain. HTTP means inboxes are processed using SES while SMTP inboxes use a custom SMTP mail server. SMTP does not support sending so use HTTP for sending emails.
Declaration
[DataMember(Name = "domainType", EmitDefaultValue = true)]
public CreateDomainOptions.DomainTypeEnum? DomainType { get; set; }
Property Value
| Type | Description |
|---|---|
| CreateDomainOptions.DomainTypeEnum? | Type of domain. Dictates type of inbox that can be created with domain. HTTP means inboxes are processed using SES while SMTP inboxes use a custom SMTP mail server. SMTP does not support sending so use HTTP for sending emails. |
Methods
View SourceEquals(CreateDomainOptions)
Returns true if CreateDomainOptions instances are equal
Declaration
public bool Equals(CreateDomainOptions input)
Parameters
| Type | Name | Description |
|---|---|---|
| CreateDomainOptions | input | Instance of CreateDomainOptions to be compared |
Returns
| Type | Description |
|---|---|
| bool | Boolean |
Equals(object)
Returns true if objects are equal
Declaration
public override bool Equals(object input)
Parameters
| Type | Name | Description |
|---|---|---|
| object | input | Object to be compared |
Returns
| Type | Description |
|---|---|
| bool | Boolean |
Overrides
View SourceGetHashCode()
Gets the hash code
Declaration
public override int GetHashCode()
Returns
| Type | Description |
|---|---|
| int | Hash code |
Overrides
View SourceToJson()
Returns the JSON string presentation of the object
Declaration
public virtual string ToJson()
Returns
| Type | Description |
|---|---|
| string | JSON string presentation of the object |
ToString()
Returns the string presentation of the object
Declaration
public override string ToString()
Returns
| Type | Description |
|---|---|
| string | String presentation of the object |
Overrides
View SourceValidate(ValidationContext)
To validate all properties of the instance
Declaration
public IEnumerable<ValidationResult> Validate(ValidationContext validationContext)
Parameters
| Type | Name | Description |
|---|---|---|
| ValidationContext | validationContext | Validation context |
Returns
| Type | Description |
|---|---|
| IEnumerable<ValidationResult> | Validation Result |


@* * Copyright 2023 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. *@ @(params: Map[String,Any]) @uk.gov.hmrc.hmrcemailrenderer.templates.helpers.html.template_main(params,"Unsuccessful receipt of Online Agent Authorisation") {
Thank you for sending your Online Agent Authorisation.
Unfortunately the Authorisation was not accepted as it failed HMRC data checks.
Please use the Help provided with the software or online forms service you used to complete and send this Authorisation to find out how to retrieve and view details of the reason(s) your Authorisation failed then correct the Authorisation and send it again.
Online Services Helpdesk Telephone 0300 200 3600, Textphone 0300 200 3603 or email helpdesk@@ir-efile.gov.uk
}


Survey Form
We want to know more about our Foodies!
Name:
Email:
Age:
What is your favorite style of cuisine? (Check all that apply).
American
Chinese
Italian
Indian
Mediterranian
Mexican
How likely are you to recommend our restaurant to someone else?
Definitely
Maybe
Not Really
What is your favorite menu item?
Pizza
Burrito
Burger
Lasagna
Sushi
What is your favorite drink?
Water
Coca-cola
Pepsi
Lemonade
Any comments or suggestions?
Submit


Hannah Hart is YouTube royalty, and she understands the value of a perfect Internet aesthetic. What does this mean for her? It's a combination of branding, ease-of-access, and feel-good imagery for her. Come hear her speak on the evolution of her YouTube channel for insight on the progression of such an influential platform.
Ingrid Burrington is an expert on the Internet's infrastructure and the design, politics, and artistry of the Web. She'll be contributing to Creator Aesthetic's understanding of the Internet as a structurally artistic platform for creators everywhere.
Susan Kare was one of the formatic Apple designers during the 1980s, and today she has left her influence on the Web from interface design to typefaces. Her work allows creators the freedom to explore the artistry of the Internet becauase of her own creator aesthetics.
Laura Poitras, 2015 Academy Award winner, has directed films and documentaries that have heavily influenced the American ideal of documentary work. She's thrilled to explore the aesthetics of truth and the visuals that allow one to tell a story.
Zoë Quinn is a video game developer, programmer, writer, and artist that contributes to the most interactive of content design. She thrives on the immersive, artistic elements of game design, and at Creator Aesthetics she'll unveil some of her most valuable lessons from designing immersive experiences.


Benchmark results (Clang 12 / AMD5700G)
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Encode + Decode Results
Benchmark results (MSVC 2019/19.28.29335 x64 / Intel Xeon W-2133 @ 3.6GHz)
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Encode + Decode Results


This textarea is stretchable in all browsers now, including Internet Explorer! ;-)
This is a textarea. It has text in it. On 3 lines.


Cognitive Biases
Cognitive Biases are a set of unwritten guidelines that nudge us to make decisions and take actions. They reduce our mental or intellectual effort to process information and make rational choices. We generally follow them without our awareness and in total submission. We follow them as they reduce our cognitive overload.
We, very commonly, fall into the trap of these biases as they are easy to follow, reduce the stress and efforts to decide and take an action. Our brain is wired to process information only when necessary. It wants to off-load as much of its work as possible and go into the ‘chill zone’ as soon and as often as possible. It doesn’t want to be disturbed. It wants to be left alone!
Hence, we form some heuristics with which we process information without troubling the brain very much and very often.
Confirmation Bias
One such heuristic, one such bias, is the Confirmation Bias. Let me give you a situation.
In fact, go through this situation and think about what would you do in such a situation. Don’t lie to yourself — more likely than not, you are biased ;)
Ok, let’s see.
 The group, which was formed 4 years ago, is moderately active. Members generally post birthday or anniversary wishes, news on current affairs, some memes and jokes.
One fine day, a discussion starts on Football. You don’t watch much Football and are just aware of only a few famous leagues and players. The discussion picks up heat, and some of your friends are madly debating about who is the best: Messi or Ronaldo!
 You go berserk. Stunned by this madness, you like to figure out who is the best yourself! And you have the greatest resort one can have to aid you in this endeavour, the all-knowing master of the universe: Google!
So, if you had to ask Google to find out who is actually ‘The Best’, what would you type? What would you search for?
Now, pause for a moment and think.
No, really, pause and think. What is the phrase that you’d search in Google?
I am serious. Pause and think!
Ok, did you think? Ready now? Fine.
Before reading further, write the phrase you’d search for in the comments below (or write it down somewhere) so that you have proof and you can lock it down. We will come back to it in a moment.
You can continue reading after you comment. Trust me, it will be fun.
Done commenting?
Ok. Now that you’ve decided what you’d search for, let’s see the possibilities. I have written some possible searches and their results:
- is messi better than ronaldo
- is ronaldo better than messi
- why is messi better than ronaldo
- why is ronaldo better than messi
- who is better, messi or ronaldo
- who is the best between messi and ronaldo
- football statistics of messi and ronaldo
Of course, there could be many other combinations, but let us look at these.
Based on the order in which you read those articles, you form some initial opinion about Messi and Ronaldo. You start to subscribe to what you read. And as each subsequent fact reinforces the previously read facts, your belief in it strengthens. Once you read a good number of articles (could just be 2 or 3), you will form a very strong opinion about each player.
Options 1, 2, 3, 4:
Please go through the below Google results carefully…
Clearly, as you can see, these results are already biased towards one player (Messi) than the other! Google filters out articles for you that tend to argue that Messi is better than Ronaldo! And as you read one article after the other, the reinforcement (the confirmation of your beliefs) happens, you tend to subscribe to one school of thought. So, if you search ‘is messi better than ronaldo’, you are more likely to end up in the ‘Messi Clan’ than the ‘Ronaldo Clan’ as you read more articles that favour Messi over Ronaldo.
And once you end up in that clan, you do not question your allegiance at all! Would you?  And you wouldn’t doubt the ‘all-knowing master’, would you? Hence you are right! Or, so you think!
Well, a while ago, you did not have any connection with Football. You do not watch much of Football and do not have much knowledge about the players and probably have a piece of very poor knowledge about what’s happening in the Football world. Yet, here you are, almost convinced that Messi is better than Ronaldo.
Similar will be the search results for options 2, 3, and 4 — and you tend to form a strong opinion on either Messi or Ronaldo based on those articles.
Options 5, 6:
Please read these results carefully now…
On the other hand, searches 5 and 6 are not targeted to either Messi or Ronaldo and they give a set of partially unbiased results. Yet, these results are someone’s opinions about both the players. So, if the author of those articles is partially biased towards any player, that would reflect in the article as well.
Option 7:
The search results for 7 give statistics about the players. They give details about the number of matches, goals, awards, possession time, win % etc. These will help you do an easy number-by-number comparison. However, comparing different kinds of numbers is also not easy. I mean, it is not possible to compare the number of goals to the ball possession time or the number of assists or the number of awards etc. Also, if you know a little about Football, you will know that the game is not just about numbers, after all!
How to actually compare then?
If you really wanted to make such a comparison, you’d have to watch 100s of Football matches, read through 100s of player stats and study the hardships each player faced in their life among other things. And the best answer you could probably arrive at is that the comparison itself is futile! Each of them is ‘best’ in their own way… Or if you had to conclude, you’d be very specific and categorical in your answer instead of giving a generic statement like ‘Messi is better than Ronaldo’. Because the harder question people forget to ask here is what does ‘better’ actually mean?
And, who really wants to go through this gruelling process of analysing the information in such an impartial way just to decide who is a better player, right! Instead, we tend to spend some time going through various videos and articles on the internet and form our own opinion as soon as possible.
Unfortunately, it is not just for discussions like these but also for many important decisions in our life that we ignore such thinking. We do not want to burden ourselves so much in our daily decision making. We off-load as much thinking as possible and spend our mental energy only in certain situations — typically the office work!
This kind of confirmation bias happens all the time:
- Religious affiliations: Is Hinduism better than Christianity
- Political affiliations: Is Biden better than Trump
- National Affairs: Is the farm bill bad for the farmers
- Fanbases: Is Marvel better than DC
- Diet plans: Is Vegan better than Keto
- Global warming is not real
- Covid vaccines do not work
The list is endless!
As we are no experts in certain situations, we rely on the views or opinions of others on the subject and seek answers. However, rather than looking for answers, we should question what we are looking for! Sadly, that kind of questioning requires a lot of rationality and our mind is super lazy to do that! The mind would rather know the answer ready-made than actually probe further. It would take the answer on the face of it than question the correctness of the question itself!
So, Confirmation Bias, basically, reinforces your already existing notions, beliefs, opinions, views, and expectations about anything. This helps us assess a situation faster, make decisions quickly and take necessary actions immediately. With that, we can spend our ‘mental energy’ on things that actually matter to us.
This happens because our mind doesn’t like to be questioned. It doesn’t want to be challenged. It doesn’t want to lose. It wants to win. It wants to be satisfied. The moment it finds that what it thinks is true, it becomes happy and reinforces that thought and tends to relax without any effort to validate it further. It requires an extra push to take the next step and validate further and we don’t want to give that extra push!
On the other hand, if it finds out anything contrary to the existing beliefs and expectations, it denies, resists, becomes restless and unhappy. And the mind tries very hard to avoid such situations and the easy way to do that is to comply with anything that reinforces the existing beliefs without questioning further.
So, the next time, when you think you are sure of something, that you are not biased in what you are saying and that you make total sense… Think again!
Thank you for reading! 🙏🏼
👍🏼 ➡ If you liked it, please share it with others who might find it interesting!
💬 Also, please leave your comment or feedback below.


To scale the Stage you have to setup your HTML and also define some properties of your Stage object. Let's start with some basic properties of the Canvas element and the Stage object.
The Html Canvas element takes the width and height properties to define the horizontal and vertical pixels within the Canvas. For a simple Canvas whose size doesn't change, the markup will look like the example below.
<canvas id="myStage" width="800" height="600"></canvas>
To control the size of the Canvas, you have to set the width and height stylesheet properties of the Canvas. If you changed those stylesheet properties you will see that the size of the Canvas changes accordingly, but Html does not change the width and height properties of the Canvas automatically. As a result the content of the Canvas would be distorted. To avoid this distortion the Stage object adjusts the width and height properties to match the size of the Canvas on the screen.
<canvas id="myStage" style="width:600px; height: 300px;" width="600" height="300"></canvas>
After the width and height stylesheet properties have changed, the Stage will automatically change the width and height properties of the Canvas.
The Stage adapts the Canvas element to the Display List. If the Canvas size is changing, the Stage will automatically scale the Display List to match with the new Canvas size. You can control how the Display List is scaled with the stageScaleMode and align property. The exmample below shows the default configuration of the Stage.
var canvas = html.query('#myStage');
var stage = new Stage('myStage', canvas);
stage.scaleMode = StageScaleMode.SHOW\_ALL;
stage.align = StageAlign.NONE;
It is also important to be aware of the coordinate system of the Stage and Display List. The Stage constructor takes two optional parameters to define the default coordinate system, if those parameters are not supplied the current width and height properties of the Canvas element are used.
var canvas = html.query('#myStage');
var stage = new Stage('myStage', canvas, 800, 600);
This way the Stage is using a coordinate system of 800x600 pixels, regardless of the size of the Stage. Even if the Canvas is much smaller or bigger, your Display Objects are still placed within a 800x600 grid. Please note that the size of this grid can change dependent on the scaleMode property. To get the current size of the Stage, you can query the contentRectangle property.
var rectangle = stage.contentRectangle;
The StageScaleMode defines how the Stage is scaled inside of the Canvas.
According to the StageScaleMode setting and the aspect ratio of the Stage and Canvas it is necessary to extended or truncated the available Stage area to fit inside the available Canvas area. To align the new Stage area you can set the Stage.align property to one of the following values.
A common use case is a Stage that fills the whole browser window. The content shouldn't be scaled if the browser window size changes. But the Stage should be extended to cover the available display size. To do this we set the StageScaleMode to 'NO_SCALE' and the StageAlign to 'TOP_LEFT'.
HTML/CSS (without enclosing head and body tags):
<style type="text/css">
body { margin: 0; padding: 0; overflow: hidden; }
#stage { position: absolute; width: 100%; height: 100%; }
</style>
<canvas id="stage"></canvas>
Dart:
var canvas = html.query('#stage');
var stage = new Stage('myStage', canvas);
stage.scaleMode = StageScaleMode.NO_SCALE;
stage.align = StageAlign.TOP_LEFT;
This way you will get a Stage that fits exactly to the display area of the window. We didn't define the width and height attribute of the canvas element, because those attributes will change automatically to match the available display size.
Here are two other frequently used configurations:
Scale the Stage to fit within the available Canvas area. Show borders on the left/right or top/bottom side if the aspect ratio does not match. Never truncate any content from the Stage.
var stage = new Stage('myStage', canvas);
stage.scaleMode = StageScaleMode.SHOW_ALL;
stage.align = StageAlign.NONE;
Scale the Stage to fill the whole Canvas area. If the aspect ratio does not match, truncate parts from the Stage, but never show any borders.
var stage = new Stage('myStage', canvas);
stage.scaleMode = StageScaleMode.NO_BORDER;
stage.align = StageAlign.NONE;
To understand the different StageScaleMode and StageAlign settings, it's best to try a little demo and play with the different settings. This exmaple shows a blue background Bitmap and a red circle Shape in the center. Resize the browser window to see the effect.
<!DOCTYPE html>
<html>
<head>
<title>Stage Scale Test</title>
<style type="text/css">
body { margin: 0; padding: 0; overflow: hidden; }
#stage { position: absolute; width: 100%; height: 100%; }
</style>
</head>
<body>
<canvas id="stage"></canvas>
<script type="application/dart" src="test.dart"></script>
<script src="packages/browser/dart.js"></script>
</body>
</html>
import 'dart:html' as html;
import 'package:stagexl/stagexl.dart';
void main() {
var canvas = html.query('#stage');
var stage = new Stage('stage', canvas, 800, 600);
stage.scaleMode = StageScaleMode.EXACT_FIT;
stage.align = StageAlign.NONE;
var renderLoop = new RenderLoop();
renderLoop.addStage(stage);
var bitmapData = new BitmapData(800, 600, true, Color.Blue);
var bitmap = new Bitmap(bitmapData);
stage.addChild(bitmap);
var shape = new Shape();
shape.graphics.ellipse(400, 300, 150, 150);
shape.graphics.fillColor(Color.Red);
stage.addChild(shape);
stage.onResize.listen((e) => print(stage.contentRectangle));
}


Agile PM
Agile is a way to manage projects by implementing sprints or deliverables that can be handled within a few weeks to create a working product. Using agile the team is open to communication working close together to achieve goals. The tasks are created at the start of a project and are shared between team members and piece by piece the project comes together. Agile is a great way to keep organized and knowing what is needed from the start to the end to deliver the final project in a reasonable amount of time.
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To add or remove agents to the CRM you will need to raise a ticket with ICT. Please make sure that you remove any agents who no longer work in the Contact Centre.


--- title: The way all good websites are built excerpt: 'The way all good websites are built these days: http://bit.ly/lZMPjE and http://bit.ly/kK6GNh. Accessibility and UX as part of our DNA.' date: '2011-06-01 18:00:29 +0200' date_gmt: '2011-06-01 16:00:29 +0200' categories: - Webdev tags: - tweets - web - design - development ---
The way all good websites are built these days: http://bit.ly/lZMPjE and http://bit.ly/kK6GNh. Accessibility and UX as part of our DNA.


---
layout: post
title: Startups death = Death from 1,000 cuts
date: 2010-12-06 11:51:00.000000000 -05:00
comments: true
categories: []
published: true
---
You hardly ever hear of a startup that just goes *poof* one day and suddenly implodes. The more common occurrence are a series of bad decisions by management and/or the founders. This also applies to larger companies too.
One of the things I have encountered while building
Comp Versions is that it is easy to become overwhelmed by the amount of work ahead of you. Especially when it is now common mantra to get something out as quickly as possible.
Even more especially when you have to be learning programming as you go along, are doing it by yourself and bootstrapping - talk about bad odds.
Although I have completed a major milestone (the majority of the Rails back-end is done), I have now started the front-end.
Let me tell you, it is quite daunting when I look at my stylesheet and it is ~600 lines long, and I know that I still have about 80% of the application left of the front-end to complete.
This is further compounded when I spend an entire day (or two days) being stumped by some alignment issues of
items. Although, I must confess, once I complete that it feels SOOO good and there are cumulative effects - i.e. I will never have that problem again, and the issue is fixed everywhere else in the app that is needed.
Each of these 'stumpings' is a cut.  In the cumulative, I am quite scarred (note: not 'scared') at the moment and I know I will be further scarred in the weeks ahead.
But I have no choice but to get out my box of band-aids and press ahead.
This is one of the reasons why many people recommend going all-in (i.e. quitting your day-job, and giving up everything else). When you are all-in, each cut is a milestone.
So don't look at it like I am one more cut closer to quitting, but rather - one more cut closer to the 1,000 before I get to launch.
So here is to about 450 more.


A buddy of mine was perscribed Lorazepam, but said it made him queasy and dizzy. I'd heard Lorazepam was a cousin of Diazepam (Valium) and having always wanted to try Valium was eager to give Lorazepam a try.
0:00 I took 2mg the first time. The instructions on the bottle advised dissolving the pill under my tongue. This was one of the most bitter and scathing tastes my mouth has been subjected to. I had a friend who bit his nails, so he bought this stuff that you paint on your nails that makes them taste vulgar so he would stop. As a joke, he smeared the stuff allover my hockey mouthguard. The taste never left my mouthguard and made me wretch for thirty minutes. This was how the Lorazepam tasted, and whats worse i had to do it twice because the pills were 1mg each.
0:30 When the horrid taste eventually left my mouth, my stomach felt somewhat weakened and kind of bubbly. It had been half an hour since I'd taken the pills but I was starting to regret it. I lay down and watched some TV and started to relax a little and the burning sensation in my stomach became only a memory.
1:00 This stuff takes ages to come up on. After a full hour I still felt nothing.
1:20 There was a shift in the way both my body and mind felt. However, it was only very slight. It wasn't even pleasant. It was sort of a slightly headachy, stressed feeling in my head mixed with a weak pleasant sensation in my body. I was hoping that these were just unpleasant coming up sensations
1:40-2:30 This would be the peak, if you could call it that. The feelings in both my body and mind were similar. Although I started to feel a little bit relaxed, and my body felt good, and I definitely felt a shift from normality, I was still very underwhelmed. In fact I was bored.  And lying down on the couch with the tv on was all I was capable of, and it wasn't exciting.
Around 2:00 it intensified a little bit, sending me into a brief hazy stupor. When I stood up to go to the washroom I found my motor skills somewhat affected, and my body heavy and cumbersome.
3:00 It was hard to tell if the effects were from the Lorazepam or just fatigue, but I just felt stupid and sleepy, and thus I slept. I had no problem sleeping, but didn't feel very refreshed when I woke up, despite the fact I had seven hours of sleep.
Aftermath: None. I achieved nothing because of this trip. The effects weren't significant enough to be called bad, but they certainly weren't good enough to even be called pleasant. I tried Lorazepam twice after this, and was subjected to the same godawful taste and boring effects. I have some more pills, but I'm pretty sure I'll throw them away or just forget about them.
I heard that Lorazepam is about 1/10th the strength of Valium. I could still imagine Valium being good, but Lorazepam has certainly taken away a lot of my excitement. Altogether I'd give Lorazepam a thumbs down as a recreational drug.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Exception: Aws::Errors::ServiceError
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
RuntimeError
|
|
|
Instance Chain:
self,
RuntimeError
|
|
| Inherits: |
RuntimeError
|
| Defined in: | gems/aws-sdk-core/lib/aws-sdk-core/errors.rb |
Overview
The base class for all errors returned by an Amazon Web Service. All ~400 level client errors and ~500 level server errors are raised as service errors. This indicates it was an error returned from the service and not one generated by the client.
Class Attribute Summary
Class Method Summary
Instance Attribute Summary
- #code ⇒ String readonly
- #context ⇒ Seahorse::Client::RequestContext readonly
- #data ⇒ Aws::Structure readonly
- #retryable? ⇒ Boolean readonly Internal use only
- #throttling? ⇒ Boolean readonly Internal use only
Constructor Details
.new(context, message, data = Aws::EmptyStructure.new) ⇒ ServiceError
Class Attribute Details
.code ⇒ String
(rw)
[ GitHub ]
# File 'gems/aws-sdk-core/lib/aws-sdk-core/errors.rb', line 38
attr_accessor :code
Instance Attribute Details
#code ⇒ String
(readonly)
[ GitHub ]
# File 'gems/aws-sdk-core/lib/aws-sdk-core/errors.rb', line 26
attr_reader :code
#context ⇒ Seahorse::Client::RequestContext (readonly)
# File 'gems/aws-sdk-core/lib/aws-sdk-core/errors.rb', line 30
attr_reader :context
#data ⇒ Aws::Structure (readonly)
[ GitHub ]# File 'gems/aws-sdk-core/lib/aws-sdk-core/errors.rb', line 33
attr_reader :data
#retryable? ⇒ Boolean
(readonly)
This method is for internal use only.
# File 'gems/aws-sdk-core/lib/aws-sdk-core/errors.rb', line 43
def retryable? false end
#throttling? ⇒ Boolean
(readonly)
This method is for internal use only.
# File 'gems/aws-sdk-core/lib/aws-sdk-core/errors.rb', line 48
def throttling? false end


New Request Form
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The Dream-Eaters
Dreamlands：§
Packs: From Core Set (2) to Before the Black Throne
Assets§
- 2x Clarity of Mind [3] (Return to the Dunwich Legacy)
- 1x Scavenging (Core Set)
- 2x Liquid Courage (The Dunwich Legacy)
- 2x Peter Sylvestre:Big Man on Campus [2] (The Dunwich Legacy)
- 1x Stick to the Plan [3] (Black Stars Rise)
- 2x St. Hubert's Key:Cleansing Fire (Black Stars Rise)
- 1x Hypnotic Therapy (The Circle Undone)
- 2x Fingerprint Kit (The Circle Undone)
- 1x Four of Cups:Chalice of the Heart [1] (The Circle Undone)
- 2x Hawk-Eye Folding Camera (The Wages of Sin)
- 1x Hallowed Mirror (Before the Black Throne)
- 3x Solemn Vow (The Dream-Eaters)
- 1x Versatile [2] (A Thousand Shapes of Horror)
- 2x Safeguard [2] (Dark Side of the Moon)
Events§
- 2x Dynamite Blast (Core Set)
- 2x Stand Together [3] (The Essex County Express)
- 1x Emergency Cache [2] (Blood on the Altar)
- 1x Ambush [1] (The Unspeakable Oath)
- 2x "I've had worse…" [2] (Before the Black Throne)
- 1x Astounding Revelation (The Dream-Eaters)
Skills§
- 2x Deduction (Core Set)
- 2x Guts (Core Set)
- 2x Perception (Core Set)
Treacheries§
- 1x Random Basic Weakness (Core Set)
- 1x Rational Thought (The Circle Undone)


Other name: 마법사의 딸
Authors: Moon Seora , Saebyeokae ,
Genres: Adventure Manga, Drama Manga, Fantasy Manga, Webtoons Manga,
Summary:
Maybelle had lived a life of abuse under her parents. Upon discovery that these 'parents' aren't her true biological parents and that somewhere, her father exists, with a sad heart, Maybelle sets off in search of him.
KissManga.org. Copyrights and trademarks for the manga, and other promotional materials are held by their respective owners and their use is allowed under the fair use clause of the Copyright Law. Privacy Policy


Chocolate Pizza
POSTED ON 15 DEC 2013 / DESSERTS
PRINT
For the fig-swirl: Melt butter over medium heat in a saucepan. Add brown sugar and stir to dissolve. Halve all of the figs and toss in the saucepan with water and lemon juice. Cook over medium heat, stirring frequently, until you have a chunky-jammy mixture. Add salt with one or two stirs, set aside and let cool completely.
Ice cream: In a small pot over medium heat, combine milk, and granulated sugar until sugar is completely dissolved and the milk is just barely lukewarm. Whisk in the egg yolks. Set mixture in the fridge and wait until the fig mixture is coded.
Using an ice cream machine, pour liquids into the frozen basin and process according to manufacturer instructions, i.e., let spin and thicken for 20 minutes before adding mascarpone, fig jam mixture, and the nuts. Continue to process for +/- 10 minutes. Pour semi-frozen mixture into a pyrex dish or glass tupperware. Freeze for at least two hours before serving.


This website is the Senpy Playground for Trivalent, which allows you to test the Senpy plugins developed inside the project. It provides a user-friendly interface to the functions exposed by the senpy API.
Senpy is a framework to build semantic sentiment and emotion analysis services. It does so by using a mix of web and semantic technologies, such as JSON-LD, RDFlib and Flask.
Senpy makes it easy to develop and publish your own analysis algorithms (plugins in senpy terms).
Once you get comfortable with the parameters and results, you are encouraged to issue your own requests to the API endpoint. You can find examples of API URL's when you try out a plugin with the "Analyse!" button on the "Test it" tab.
These are some of the things you can do with the API:


Thanks for becoming a user of the lux system at UCSC. Below, we highlight the user policies for the system. Anyone using the lux cluster assents to these policies as a condition of access to the lux cluster. For questions, please email<EMAIL_ADDRESS>The usage of lux is shared among many disciplines, and this breadth makes the detailed management of the system challenging. Software from many different disciplines requires a large range of libraries and packages, and the maintenance of these
The lux system frontend servers (lux-0, lux-1, lux-2) are designed to support users of the cluster in its intended usage mode (i.e., distributed jobs running on the nodes). Any computationally or memory intensive job should not be run on the frontend, and instead should be executed on a node allocated via the Slurm scheduler. This policy especially includes the execution of python scripts, including simulation post processing and plotting – these are tasks that should be run on the nodes. Any job running on the frontend can be terminated without notice to the user.
Computational tasks explicitly permitted on the frontend system are text/code editing, compilation, data transfer, and job monitoring. If you have a question about how to use the lux frontend systems, please email mailto:brant@ucsc.edu.


public void static main(String args[])
{
System.out.println("RITESH BISHT IS A DEVELOPER");
}
var k=function()
{
var a="hello";
}
Hey, I'm Ritesh Bisht 3rd year undergraduate,Currently Pursuing my B.tech in Information Technology(I.T) from Guru Gobind Singh Indraprastha Univeristy , Delhi,India.
I'm a developer and have keen interest in web development and java programming also I Like to Develop Logic For Different Problems.


I'm writing this in the midst of my first ever betel experience. Today I went to pick up a package from my friend's house containing, amongst other things, 25mL of a 5X betel nut extract, ordered from an online supplier. I was waiting to catch the bus home when impatience overcame me, and I dropped 0.5mL of the liquid (equivalent to 2.5g of nuts, by my calculation) under my tongue.
I decided to take this rather moderate dose, rather than a full 1mL dose, due to the fact that I had never used betel before, and didn't know what to expect. Anyway, I held the not unpleasant-tasting liquid in my mouth for about 10 minutes, by which time I was already feeling the effects - at first, a slightly dizzy feeling and mild euphoria. The bus came soon after I swallowed, and I felt quite stimulated, both mentally and physically. I had been in a pretty good mood before taking it, and it just seemed to boost that good mood into a fantastic one.
As I walked down to the back of the bus, I had the feeling that everyone was watching me and knew about the state I was in, sort of like the paranoia I get when really stoned in public, but it didn't worry me at all (actually I found it quite funny). I talked a bit to the guy sitting in front of me, and noticed that I was speaking much faster and louder than I usually do. Another effect was that my sense of time was altered - the 20 min bus ride felt like no more than 5 minutes.
Walking home from the bus stop (about 10 minute's walk) I felt lots of energy, and was in an extremely good mood. I got home and talked to my girlfriend on the phone, again noticing my fast and loud speach (she didn't notice though). I felt extremely happy that this stuff works so well, and decided to celebrate with a couple of Nitrous bulbs. I hadn't one these for quite a while, and I was very pleased with the effects. More pleasing still, however, is the fact that now, a good 15 minutes after taking the second bulb, I still feel moderate nitrous effects .  Needless to say, I will definitely be using this extract again, probably in the very near future.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


DragImage()
语法
Result = DragImage(ImageID [, Actions])概要
Starts a Drag & Drop operation with image data.
参数
ImageID The image to drag. ImageID() can be used to get this ID for an image. Actions (optional) A combination of the Drag & Drop actions that should be allowed for the image. If the parameter is not specified, #PB_Drag_Copy will be the only allowed action. Possible actions are: (they can be combined with '|') #PB_Drag_Copy: The image can be copied #PB_Drag_Move: The image can be moved #PB_Drag_Link: The image can be linkedThe user can decide which of these actions to take by pressing modifier keys like Ctrl or Shift. The actions that can really be taken also depend on the actions allowed by the drop target. (On MacOSX, the actions are only treated as a suggestion. The drop target can still choose another action.)
返回值
Returns one of the above Drag & Drop action values to indicate what action the user took, or #PB_Drag_None if the user aborted the Drag & Drop operation.
Note that if #PB_Drag_Move is returned, it is your responsibility to remove the dragged image from your application.
备注
Drag & Drop can basically be started any time, but the left mouse button should be currently pressed as otherwise the operation will end immediately without success. The usual time to start a Drag & Drop operation is when a Gadget reported an event with EventType() of #PB_EventType_DragStart.
示例
; Drag the image to an application that can accept images like an office or graphic program. ; If LoadImage(1, #PB_Compiler_Home + "examples/sources/data/PureBasicLogo.bmp") If OpenWindow(1, 200, 200, 400, 90, "Drag & Drop", #PB_Window_SystemMenu) ImageGadget(1, 10, 10, 380, 70, ImageID(1)) Repeat Event = WaitWindowEvent() If Event = #PB_Event_Gadget And EventGadget() = 1 And EventType() = #PB_EventType_DragStart DragImage(ImageID(1)) EndIf Until Event = #PB_Event_CloseWindow EndIf EndIf
参阅
DragFiles(), DragText(), DragPrivate(), DragOSFormats(), SetDragCallback()
已支持操作系统
所有


{% markdown %}
Five teams of the world’s best sailors are challenging for the right to take on Larry Ellison’s America’s Cup defenders Oracle Team USA in 50ft catamarans ‘flying’ over waters off Bermuda on hydrofoils in a qualifying contest that starts this weekend, following a [delay due to high winds](https://www.americascup.com/en/news/2577_Americas-Cup-Opening-Day-postponed-until-Saturday-27th-May.html).
All six teams will be racing boats that can reach around 46 knots (53 mph or 85km/h), powered by a fixed wing-sail the height of a seven-storey building.
{% endmarkdown %}
{% include "responsive-graphics/boat.html" %}
{% markdown %}
The boats lift their twin hulls out of the water using foils and daggerboards, bladelike J- or L-shaped carbon fibre boards which slot through hulls to stabilise the vessel in contact with the water.
Pushing a hull through water rather than over it creates drag, with resistance increasing with speed, so the foils skimming the surface allow the boat to move faster while using far less power. The boats can travel at three times the speed of the wind and in the right conditions can be sailed right around the course in 20-minute races entirely on their foils, manoeuvring through tacks and gybes without hulls making contact with the water.
“The difference is like riding a bicycle, and then riding it without the stabilisers on,” said Andy Claughton, head of technical and engineering at British team Land Rover BAR.
“We never see both hulls in contact with the water at any one time so little speed is lost in any manoeuvre, making the racing more like the monohull contests of old. The challenge is not so much trying to hit the top speed but maintaining and raising high average speeds throughout each race.”
In recent years, match sailing at this level has become [a high-tech enterprise](https://www.ft.com/content/aa3e7df0-de3b-11e2-9b47-00144feab7de) as teams with budgets of around £100m battle for engineering advantage by applying techniques developed in areas like aviation and [Formula One motor racing](https://www.ft.com/content/ecea90d4-e163-11e5-9217-6ae3733a2cd1).
The six boats are essentially identical, but can be tweaked within design rules so teams can gain advantages in their use of foils and daggerboards, rudder control systems, aerodynamics, and wing deployment.
Innovations this 35th regatta include Emirates Team New Zealand using a pedal cycling system instead of arm grinders to power its hydraulics while the British team has 400 sensors on board to help optimise tactics and decision-making, coupled with video and data feeds that allow the team to operate a ‘virtual chase boat’ at its Portsmouth base.
The [action off Bermuda](https://www.ft.com/content/66ac1828-3251-11e6-ad39-3fee5ffe5b5b) this summer represents the climax of a competition that started in Portsmouth two years ago.
The Louis Vuitton America’s Cup Qualifiers run to June 3. These races are followed by the Challenger Playoffs, which determine the one team which will meet defenders Oracle in the final match. In that duel, the first team to seven victories lifts the cup.
Land Rover BAR, [skippered by Sir Ben Ainslie](https://www.ft.com/content/0617fcac-4e12-11e6-8172-e39ecd3b86fc), is hoping to [win back the Cup for Britain](https://www.ft.com/content/ce53ec72-5af1-11e5-9846-de406ccb37f2) for the first time since the inaugural race of 1851.
Only four nations in the history of the oldest trophy in international sport have succeeded in winning in more than 30 events over 166 years. American teams have won 28 times, but Australia II’s victory in 1983 broke the New York Yacht Club’s grip on the cup. It has since been won twice by teams from Switzerland and twice by teams from New Zealand.
{% endmarkdown %}
{% markdown %}
## The teams
* **Emirates Team New Zealand** skippered by Glenn Ashby were last team to reach Bermuda after three years’ training off Auckland and perhaps have most on the line
* **Artemis Racing** skippered by Nathan Outteridge, which recently won nine practice races in a week. The tragic loss of Bart Simpson in 2013 ended that cup attempt
* **SoftBank Team Japan** skippered by Dean Barker. The newest team has plenty of experience and Barker has had a rare opportunity to build a winning team from scratch
* **Land Rover BAR** skippered by Sir Ben Ainslie. Set on winning the ‘Auld Mug’ trophy back for Britain
* **Groupama Team France** skippered by Franck Cammas with a team having a wealth of experience from all sailing’s arenas
* **Oracle Team USA**, cup defenders. Skippered by Jimmy Spithill, they are looking to win the cup for a third time after wins in 2010 and 2013 in an epic comeback masterminded by Ainslie as tactician
{% include "responsive-graphics/table-scoreboard.html" %}
{% include "responsive-graphics/table-stats.html" %}


|
NetBeans UML Modeling |
org::galagosearch::core::eval::stat::Stat
public class Stat
| Properties | |
|---|---|
| Alias | Stat |
| Visibility | public |
| Final | |
| Transient | |
| Abstract | |
| Leaf |
| Attribute Summary | |
|---|---|
double |
FPMIN
A small number close to the smallest representable floating point number |
int |
lgfN
PRIVATE MEMBERS FOR USE IN GAMMA FUNCTION METHODS AND HISTOGRAM CONSTRUCTION METHODS GAMMA FUNCTIONS Lanczos Gamma Function approximation - N (number of coefficients -1) |
double |
lgfCoeff[0..*]
Lanczos Gamma Function approximation - Coefficients |
double |
lgfGamma
Lanczos Gamma Function approximation - small gamma |
int |
igfiter
Maximum number of iterations allowed in Incomplete Gamma Function calculations |
double |
igfeps
Tolerance used in terminating series in Incomplete Gamma Function calculations |
double |
histTol
HISTOGRAM CONSTRUCTION Tolerance used in including an upper point in last histogram bin when it is outside due to riunding erors |
| Operation Summary | |
|---|---|
double |
mean(double aa[0..*]) METHODS Arithmetic mean of a 1D array of doubles, aa |
double |
mean(double aa[0..*], double ww[0..*]) Weighted arithmetic mean of a 1D array of doubles, aa |
double |
mean(float aa[0..*], float ww[0..*]) Weighted arithmetic mean of a 1D array of floats, aa |
double |
geometricMean(double aa[0..*]) Geometric mean of a 1D array of doubles, aa |
float |
geometricMean(float aa[0..*]) Geometric mean of a 1D array of floats, aa |
double |
geometricMean(double aa[0..*], double ww[0..*]) Weighted geometric mean of a 1D array of doubles, aa |
float |
geometricMean(float aa[0..*], float ww[0..*]) Weighted geometric mean of a 1D array of floats, aa |
double |
harmonicMean(double aa[0..*]) Harmonic mean of a 1D array of doubles, aa |
float |
harmonicMean(float aa[0..*]) Harmonic mean of a 1D array of floats, aa |
double |
harmonicMean(double aa[0..*], double ww[0..*]) Weighted harmonic mean of a 1D array of doubles, aa |
float |
harmonicMean(float aa[0..*], float ww[0..*]) Weighted harmonic mean of a 1D array of floats, aa |
double |
generalisedMean(double aa[0..*], double m) Generalised mean of a 1D array of doubles, aa |
float |
generalisedMean(float aa[0..*], float m) Generalised mean of a 1D array of floats, aa |
double |
interQuartileMean(double aa[0..*]) Interquartile mean of a 1D array of doubles, aa |
float |
interQuartileMean(float aa[0..*]) Interquartile mean of a 1D array of floats, aa |
double |
rms(double aa[0..*]) Root mean square (rms) of a 1D array of doubles, aa |
float |
rms(float aa[0..*]) Root mean square (rms) of a 1D array of floats, aa |
float |
mean(float aa[0..*]) Arithmetic mean of a 1D array of floats, aa |
double |
mean(int aa[0..*]) Arithmetic mean of a 1D array of int, aa |
double |
mean(long aa[0..*]) Arithmetic mean of a 1D array of long, aa |
double |
median(double aa[0..*]) Median of a 1D array of doubles, aa |
float |
median(float aa[0..*]) Median of a 1D array of floats, aa |
double |
median(int aa[0..*]) Median of a 1D array of int, aa |
double |
median(long aa[0..*]) Median of a 1D array of long, aa |
double |
standardDeviation(double aa[0..*]) Standard deviation of a 1D array of doubles, aa |
float |
standardDeviation(float aa[0..*]) Standard deviation of a 1D array of floats, aa |
double |
standardDeviation(int aa[0..*]) Standard deviation of a 1D array of int, aa |
double |
standardDeviation(long aa[0..*]) Standard deviation of a 1D array of long, aa |
double |
standardDeviation(double aa[0..*], double ww[0..*]) Weighted standard deviation of a 1D array of doubles, aa |
float |
standardDeviation(float aa[0..*], float ww[0..*]) Weighted standard deviation of a 1D array of floats, aa |
double |
volatilityLogChange(double array[0..*]) volatility log (doubles) |
float |
volatilityLogChange(float array[0..*]) volatility log (floats) |
double |
volatilityPerCentChange(double array[0..*]) volatility percentage (double) |
double |
volatilityPerCentChange(float array[0..*]) volatility percentage (float) |
double |
coefficientOfVariation(double array[0..*]) Coefficient of variation of an array of doubles |
float |
coefficientOfVariation(float array[0..*]) Coefficient of variation of an array of float |
double[0..*] |
subtractMean(double array[0..*]) Subtract mean of an array from data array elements |
float[0..*] |
subtractMean(float array[0..*]) Subtract mean of an array from data array elements |
double |
variance(double aa[0..*]) Variance of a 1D array of doubles, aa |
float |
variance(float aa[0..*]) Variance of a 1D array of floats, aa |
double |
variance(int aa[0..*]) Variance of a 1D array of int, aa |
double |
variance(long aa[0..*]) Variance of a 1D array of ilong, aa |
double |
variance(double aa[0..*], double ww[0..*]) Weighted variance of a 1D array of doubles, aa |
float |
variance(float aa[0..*], float ww[0..*]) Weighted variance of a 1D array of floats, aa |
double |
covariance(double xx[0..*], double yy[0..*]) Covariance of two 1D arrays of doubles, xx and yy |
float |
covariance(float xx[0..*], float yy[0..*]) Covariance of two 1D arrays of floats, xx and yy |
double |
covariance(int xx[0..*], int yy[0..*]) Covariance of two 1D arrays of ints, xx and yy |
double |
covariance(long xx[0..*], long yy[0..*]) Covariance of two 1D arrays of ints, xx and yy |
double |
covariance(double xx[0..*], double yy[0..*], double ww[0..*]) Weighted covariance of two 1D arrays of doubles, xx and yy with weights ww |
double |
gamma(double x) Gamma function Lanczos approximation (6 terms) |
double |
getLanczosGamma() Return the Lanczos constant gamma |
int |
getLanczosN() Return the Lanczos constant N (number of coeeficients + 1) |
double[0..*] |
getLanczosCoeff() Return the Lanczos coeeficients |
double |
getFpmin() Return the nearest smallest representable floating point number to zero with mantissa rounded to 1. |
double |
logGamma(double x) log to base e of the Gamma function Lanczos approximation (6 terms) |
double |
incompleteGamma(double a, double x) Regularised Incomplete Gamma Function P(a,x) = integral from zero to x of (exp(-t)t^(a-1))dt |
double |
incompleteGammaComplementary(double a, double x) Complementary Incomplete Gamma Function Q(a,x) = 1 - P(a,x) = 1 - integral from zero to x of (exp(-t)t^(a-1))dt Also known as the Incomplete Gamma Function |
double |
incompleteGammaSer(double a, double x) Regularised Incomplete Gamma Function P(a,x) = integral from zero to x of (exp(-t)t^(a-1))dt Series representation of the function - valid for x < a + 1 > |
double |
incompleteGammaFract(double a, double x) Regularised Incomplete Gamma Function P(a,x) = integral from zero to x of (exp(-t)t^(a-1))dt Continued Fraction representation of the function - valid for x >= a + 1 This method follows the general procedure used in Numerical Recipes for C, The Art of Scientific Computing by W H Press, S A Teukolsky, W T Vetterling & B P Flannery Cambridge University Press, http://www. |
void |
setIncGammaMaxIter(int igfiter) Reset the maximum number of iterations allowed in the calculation of the incomplete gamma functions |
int |
getIncGammaMaxIter() Return the maximum number of iterations allowed in the calculation of the incomplete gamma functions |
void |
setIncGammaTol(double igfeps) Reset the tolerance used in the calculation of the incomplete gamma functions |
double |
getIncGammaTol() Return the tolerance used in the calculation of the incomplete gamm functions |
double |
beta(double z, double w) Beta function |
double |
incompleteBeta(double z, double w, double x) Regularised Incomplete Beta function Continued Fraction approximation (see Numerical recipies for details of method) |
double |
contFract(double a, double b, double x) Incomplete fraction summation used in the method incompleteBeta modified Lentz's method |
double |
erf(double x) Error Function |
double |
erfc(double x) Complementary Error Function |
double |
normalProb(double mean, double sd, double upperlimit) Gaussian (normal) cumulative distribution function probability that a variate will assume a value less than the upperlimit mean = the mean, sd = standard deviation |
double |
gaussianProb(double mean, double sd, double upperlimit) Gaussian (normal) cumulative distribution function probability that a variate will assume a value less than the upperlimit mean = the mean, sd = standard deviation |
double |
normalProb(double mean, double sd, double lowerlimit, double upperlimit) Gaussian (normal) cumulative distribution function probability that a variate will assume a value between the lower and the upper limits mean = the mean, sd = standard deviation |
double |
gaussianProb(double mean, double sd, double lowerlimit, double upperlimit) Gaussian (normal) cumulative distribution function probability that a variate will assume a value between the lower and the upper limits mean = the mean, sd = standard deviation |
double |
normal(double mean, double sd, double x) Gaussian (normal) probability mean = the mean, sd = standard deviation |
double |
gaussian(double mean, double sd, double x) Gaussian (normal) probability mean = the mean, sd = standard deviation |
double[0..*] |
normalRand(double mean, double sd, int n) Returns an array of Gaussian (normal) random deviates - clock seed mean = the mean, sd = standard deviation, length of array |
double[0..*] |
gaussianRand(double mean, double sd, int n) Returns an array of Gaussian (normal) random deviates - clock seed mean = the mean, sd = standard deviation, length of array |
double[0..*] |
normalRand(double mean, double sd, int n, long seed) Returns an array of Gaussian (normal) random deviates - user provided seed mean = the mean, sd = standard deviation, length of array |
double[0..*] |
gaussianRand(double mean, double sd, int n, long seed) Returns an array of Gaussian (normal) random deviates - user provided seed mean = the mean, sd = standard deviation, length of array |
double |
logisticProb(double mu, double beta, double upperlimit) Logistic cumulative distribution function probability that a variate will assume a value less than the upperlimit mu = location parameter, beta = scale parameter |
double |
logisticProb(double mu, double beta, double lowerlimit, double upperlimit) Logistic cumulative distribution function probability that a variate will assume a value between the lower and the upper limits mu = location parameter, beta = scale parameter |
double |
logistic(double mu, double beta, double x) Logistic probability density function mu = location parameter, beta = scale parameter |
double[0..*] |
logisticRand(double mu, double beta, int n) Returns an array of logistic distribution random deviates - clock seed mu = location parameter, beta = scale parameter |
double[0..*] |
logisticRand(double mu, double beta, int n, long seed) Returns an array of Gaussian (normal) random deviates - user provided seed mean = the mean, sd = standard deviation, length of array |
double |
logisticMean(double mu) Logistic distribution mean |
double |
logisticStandDev(double beta) Logistic distribution standard deviation |
double |
logisticMode(double mu) Logistic distribution mode |
double |
logisticMedian(double mu) Logistic distribution median |
double |
lorentzianProb(double mu, double gamma, double upperlimit) Lorentzian cumulative distribution function probability that a variate will assume a value less than the upperlimit |
double |
lorentzianProb(double mu, double gamma, double lowerlimit, double upperlimit) Lorentzian cumulative distribution function probability that a variate will assume a value between the lower and the upper limits |
double |
poissonProb(int k, double mean) Cumulative Poisson Probability Function probability that a number of Poisson random events will occur between 0 and k (inclusive) k is an integer greater than equal to 1 mean = mean of the Poisson distribution |
double |
poisson(int k, double mean) Poisson Probability Function k is an integer greater than or equal to zero mean = mean of the Poisson distribution |
double[0..*] |
poissonRand(double mean, int n) Returns an array of Poisson random deviates - clock seed mean = the mean, n = length of array follows the ideas of Numerical Recipes |
double[0..*] |
poissonRand(double mean, int n, long seed) Returns an array of Poisson random deviates - user provided seed mean = the mean, n = length of array follows the ideas of Numerical Recipes |
double[0..*] |
poissonRandCalc(Random rr, double mean, int n) Calculates and returns an array of Poisson random deviates |
double |
chiSquareProb(double chiSquare, int nu) Chi-Square Probability Function probability that an observed chi-square value for a correct model should be less than chiSquare nu = the degrees of freedom |
double |
chiSquare(double observed[0..*], double expected[0..*], double variance[0..*]) Chi-Square Statistic for Poisson distribution |
double |
chiSquareFreq(double observedFreq[0..*], double expectedFreq[0..*]) Chi-Square Statistic for Poisson distribution for frequency data and Poisson distribution for each bin double arguments |
double |
chiSquareFreq(int observedFreq[0..*], int expectedFreq[0..*]) Chi-Square Statistic for Poisson distribution for frequency data and Poisson distribution for each bin int arguments |
double |
binomialProb(double p, int n, int k) Returns the binomial cumulative probabilty |
double |
binomial(double p, int n, int k) Returns a binomial mass probabilty function |
double |
binomialCoeff(int n, int k) Returns a binomial Coefficient as a double |
double[0..*] |
binomialRand(double prob, int nTrials, int n) Returns an array of n Binomial pseudorandom deviates from a binomial distribution of nTrial trials each of probablity, prob, after bndlev Numerical Recipes in C - W. |
double |
fTestProb(double fValue, int df1, int df2) Returns the F-distribution probabilty for degrees of freedom df1, df2 F ratio provided |
double |
fTestProb(double var1, int df1, double var2, int df2) Returns the F-distribution probabilty for degrees of freedom df1, df2 numerator and denominator variances provided |
double |
fTestValueGivenFprob(double fProb, int df1, int df2) Returns the F-test value corresponding to a F-distribution probabilty, fProb, for degrees of freedom df1, df2 |
double |
fTestBisect(double fProb, double fTestLow, double fTestHigh, int df1, int df2, int endTest) Bisection procedure for calculating and F-test value corresponding to a given F-test probability |
double |
studentT(double tValue, int df) Returns the Student t probability density function |
double |
studentTProb(double tValue, int df) Returns the Student t cumulative distribution function probability |
double |
probAtn(double tValue, int df) Returns the A(t|n) distribution probabilty |
double[0..*,0..*] |
histogramBins(double data[0..*], double binWidth, double binZero, double binUpper) Distribute data into bins to obtain histogram zero bin position and upper limit provided |
double[0..*,0..*] |
histogramBins(double data[0..*], double binWidth, double binZero) Distribute data into bins to obtain histogram zero bin position provided |
double[0..*,0..*] |
histogramBins(double data[0..*], double binWidth) Distribute data into bins to obtain histogram zero bin position calculated |
int |
factorial(int n) factorial of n argument and return are integer, therefore limited to 0<=n<=12 see below for long and double arguments =n<=12 see> |
long |
factorial(long n) factorial of n argument and return are long, therefore limited to 0<=n<=20 see below for double argument =n<=20 see> |
double |
factorial(double n) factorial of n Argument is of type double but must be, numerically, an integer factorial returned as double but is, numerically, should be an integer numerical rounding may makes this an approximation after n = 21 |
double |
logFactorial(int n) log to base e of the factorial of n log[e](factorial) returned as double numerical rounding may makes this an approximation |
double |
logFactorial(double n) log to base e of the factorial of n Argument is of type double but must be, numerically, an integer log[e](factorial) returned as double numerical rounding may makes this an approximation |
double |
corrCoeff(double xx[0..*], double yy[0..*]) Calculate correlation coefficient x y data as double |
float |
corrCoeff(float x[0..*], float y[0..*]) Calculate correlation coefficient x y data as float |
double |
corrCoeff(int x[0..*], int y[0..*]) Calculate correlation coefficient x y data as int |
double |
corrCoeff(double x[0..*], double y[0..*], double w[0..*]) Calculate weighted correlation coefficient x y data and weights w as double |
double |
corrCoeff(int element00, int element01, int element10, int element11) Calculate correlation coefficient Binary data x and y Input is the frequency matrix, F, elements, f(i,j) f(0,0) - element00 - frequency of x and y both = 1 f(0,1) - element01 - frequency of x = 0 and y = 1 f(1,0) - element10 - frequency of x = 1 and y = 0 f(1,1) - element11 - frequency of x and y both = 0 |
double |
corrCoeff(int freqMatrix[0..*,0..*]) Calculate correlation coefficient Binary data x and y Input is the frequency matrix, F F(0,0) - frequency of x and y both = 1 F(0,1) - frequency of x = 0 and y = 1 F(1,0) - frequency of x = 1 and y = 0 F(1,1) - frequency of x and y both = 0 |
double |
linearCorrCoeff(double rCoeff, int nu) Linear correlation coefficient single probablity old name calls renamed method |
double |
corrCoeffPdf(double rCoeff, int nu) Linear correlation coefficient single probablity |
double |
weibullProb(double mu, double sigma, double gamma, double upperlimit) Weibull cumulative distribution function probability that a variate will assume a value less than the upperlimit |
double |
weibullProb(double mu, double sigma, double gamma, double lowerlimit, double upperlimit) Weibull cumulative distribution function probability that a variate will assume a value between the lower and the upper limits |
double |
weibull(double mu, double sigma, double gamma, double x) Weibull probability |
double |
weibullMean(double mu, double sigma, double gamma) Weibull mean |
double |
weibullStandDev(double sigma, double gamma) Weibull standard deviation |
double |
weibullMode(double mu, double sigma, double gamma) Weibull mode |
double |
weibullMedian(double mu, double sigma, double gamma) Weibull median |
double[0..*] |
weibullRand(double mu, double sigma, double gamma, int n) Returns an array of Weibull (Type III EVD) random deviates - clock seed mu = location parameter, sigma = cale parameter, gamma = shape parametern = length of array |
double[0..*] |
weibullRand(double mu, double sigma, double gamma, int n, long seed) Returns an array of Weibull (Type III EVD) random deviates - user supplied seed mu = location parameter, sigma = cale parameter, gamma = shape parametern = length of array |
double |
frechetProb(double mu, double sigma, double gamma, double upperlimit) Frechet cumulative distribution function probability that a variate will assume a value less than the upperlimit |
double |
frechetProb(double mu, double sigma, double gamma, double lowerlimit, double upperlimit) Frechet cumulative distribution function probability that a variate will assume a value between the lower and the upper limits |
double |
exponentialProb(double mu, double sigma, double upperlimit) Exponential cumulative distribution function probability that a variate will assume a value less than the upperlimit |
double |
exponentialProb(double mu, double sigma, double lowerlimit, double upperlimit) Exponential cumulative distribution function probability that a variate will assume a value between the lower and the upper limits |
double |
exponential(double mu, double sigma, double x) Exponential probability |
double |
exponentialMean(double mu, double sigma) Exponential mean |
double |
exponentialStandDev(double sigma) Exponential standard deviation |
double |
exponentialMode(double mu) Exponential mode |
double |
exponentialMedian(double mu, double sigma) Exponential median |
double[0..*] |
exponentialRand(double mu, double sigma, int n) Returns an array of Exponential random deviates - clock seed mu = location parameter, sigma = cale parameter, gamma = shape parametern = length of array |
double[0..*] |
exponentialRand(double mu, double sigma, int n, long seed) Returns an array of Exponential random deviates - user supplied seed mu = location parameter, sigma = cale parameter, gamma = shape parametern = length of array |
| Attribute Detail |
|---|
public double FPMIN
private int lgfN
private double lgfCoeff[0..*]
private double lgfGamma
private int igfiter
private double igfeps
private double histTol
| Operation Detail |
|---|
public static double mean(double aa[0..*])
public static double mean(double aa[0..*], double ww[0..*])
public static double mean(float aa[0..*], float ww[0..*])
public static double geometricMean(double aa[0..*])
public static float geometricMean(float aa[0..*])
public static double geometricMean(double aa[0..*], double ww[0..*])
public static float geometricMean(float aa[0..*], float ww[0..*])
public static double harmonicMean(double aa[0..*])
public static float harmonicMean(float aa[0..*])
public static double harmonicMean(double aa[0..*], double ww[0..*])
public static float harmonicMean(float aa[0..*], float ww[0..*])
public static double generalisedMean(double aa[0..*], double m)
public static float generalisedMean(float aa[0..*], float m)
public static double interQuartileMean(double aa[0..*])
public static float interQuartileMean(float aa[0..*])
public static double rms(double aa[0..*])
public static float rms(float aa[0..*])
public static float mean(float aa[0..*])
public static double mean(int aa[0..*])
public static double mean(long aa[0..*])
public static double median(double aa[0..*])
public static float median(float aa[0..*])
public static double median(int aa[0..*])
public static double median(long aa[0..*])
public static double standardDeviation(double aa[0..*])
public static float standardDeviation(float aa[0..*])
public static double standardDeviation(int aa[0..*])
public static double standardDeviation(long aa[0..*])
public static double standardDeviation(double aa[0..*], double ww[0..*])
public static float standardDeviation(float aa[0..*], float ww[0..*])
public static double volatilityLogChange(double array[0..*])
public static float volatilityLogChange(float array[0..*])
public static double volatilityPerCentChange(double array[0..*])
public static double volatilityPerCentChange(float array[0..*])
public static double coefficientOfVariation(double array[0..*])
public static float coefficientOfVariation(float array[0..*])
public static double[0..*] subtractMean(double array[0..*])
public static float[0..*] subtractMean(float array[0..*])
public static double variance(double aa[0..*])
public static float variance(float aa[0..*])
public static double variance(int aa[0..*])
public static double variance(long aa[0..*])
public static double variance(double aa[0..*], double ww[0..*])
public static float variance(float aa[0..*], float ww[0..*])
public static double covariance(double xx[0..*], double yy[0..*])
public static float covariance(float xx[0..*], float yy[0..*])
public static double covariance(int xx[0..*], int yy[0..*])
public static double covariance(long xx[0..*], long yy[0..*])
public static double covariance(double xx[0..*], double yy[0..*], double ww[0..*])
public static double gamma(double x)
public static double getLanczosGamma()
public static int getLanczosN()
public static double[0..*] getLanczosCoeff()
public static double getFpmin()
public static double logGamma(double x)
public static double incompleteGamma(double a, double x)
public static double incompleteGammaComplementary(double a, double x)
public static double incompleteGammaSer(double a, double x)
public static double incompleteGammaFract(double a, double x)
public static void setIncGammaMaxIter(int igfiter)
public static int getIncGammaMaxIter()
public static void setIncGammaTol(double igfeps)
public static double getIncGammaTol()
public static double beta(double z, double w)
public static double incompleteBeta(double z, double w, double x)
public static double contFract(double a, double b, double x)
public static double erf(double x)
public static double erfc(double x)
public static double normalProb(double mean, double sd, double upperlimit)
public static double gaussianProb(double mean, double sd, double upperlimit)
public static double normalProb(double mean, double sd, double lowerlimit, double upperlimit)
public static double gaussianProb(double mean, double sd, double lowerlimit, double upperlimit)
public static double normal(double mean, double sd, double x)
public static double gaussian(double mean, double sd, double x)
public static double[0..*] normalRand(double mean, double sd, int n)
public static double[0..*] gaussianRand(double mean, double sd, int n)
public static double[0..*] normalRand(double mean, double sd, int n, long seed)
public static double[0..*] gaussianRand(double mean, double sd, int n, long seed)
public static double logisticProb(double mu, double beta, double upperlimit)
public static double logisticProb(double mu, double beta, double lowerlimit, double upperlimit)
public static double logistic(double mu, double beta, double x)
public static double[0..*] logisticRand(double mu, double beta, int n)
public static double[0..*] logisticRand(double mu, double beta, int n, long seed)
public static double logisticMean(double mu)
public static double logisticStandDev(double beta)
public static double logisticMode(double mu)
public static double logisticMedian(double mu)
public static double lorentzianProb(double mu, double gamma, double upperlimit)
public static double lorentzianProb(double mu, double gamma, double lowerlimit, double upperlimit)
public static double poissonProb(int k, double mean)
public static double poisson(int k, double mean)
public static double[0..*] poissonRand(double mean, int n)
public static double[0..*] poissonRand(double mean, int n, long seed)
private static double[0..*] poissonRandCalc(Random rr, double mean, int n)
public static double chiSquareProb(double chiSquare, int nu)
public static double chiSquare(double observed[0..*], double expected[0..*], double variance[0..*])
public static double chiSquareFreq(double observedFreq[0..*], double expectedFreq[0..*])
public static double chiSquareFreq(int observedFreq[0..*], int expectedFreq[0..*])
public static double binomialProb(double p, int n, int k)
public static double binomial(double p, int n, int k)
public static double binomialCoeff(int n, int k)
public double[0..*] binomialRand(double prob, int nTrials, int n)
public static double fTestProb(double fValue, int df1, int df2)
public static double fTestProb(double var1, int df1, double var2, int df2)
public static double fTestValueGivenFprob(double fProb, int df1, int df2)
private static double fTestBisect(double fProb, double fTestLow, double fTestHigh, int df1, int df2, int endTest)
public static double studentT(double tValue, int df)
public static double studentTProb(double tValue, int df)
public static double probAtn(double tValue, int df)
public static double[0..*,0..*] histogramBins(double data[0..*], double binWidth, double binZero, double binUpper)
public static double[0..*,0..*] histogramBins(double data[0..*], double binWidth, double binZero)
public static double[0..*,0..*] histogramBins(double data[0..*], double binWidth)
public static int factorial(int n)
public static long factorial(long n)
public static double factorial(double n)
public static double logFactorial(int n)
public static double logFactorial(double n)
public static double corrCoeff(double xx[0..*], double yy[0..*])
public static float corrCoeff(float x[0..*], float y[0..*])
public static double corrCoeff(int x[0..*], int y[0..*])
public static double corrCoeff(double x[0..*], double y[0..*], double w[0..*])
public static double corrCoeff(int element00, int element01, int element10, int element11)
public static double corrCoeff(int freqMatrix[0..*,0..*])
public static double linearCorrCoeff(double rCoeff, int nu)
public static double corrCoeffPdf(double rCoeff, int nu)
public static double weibullProb(double mu, double sigma, double gamma, double upperlimit)
public static double weibullProb(double mu, double sigma, double gamma, double lowerlimit, double upperlimit)
public static double weibull(double mu, double sigma, double gamma, double x)
public static double weibullMean(double mu, double sigma, double gamma)
public static double weibullStandDev(double sigma, double gamma)
public static double weibullMode(double mu, double sigma, double gamma)
public static double weibullMedian(double mu, double sigma, double gamma)
public static double[0..*] weibullRand(double mu, double sigma, double gamma, int n)
public static double[0..*] weibullRand(double mu, double sigma, double gamma, int n, long seed)
public static double frechetProb(double mu, double sigma, double gamma, double upperlimit)
public static double frechetProb(double mu, double sigma, double gamma, double lowerlimit, double upperlimit)
public static double exponentialProb(double mu, double sigma, double upperlimit)
public static double exponentialProb(double mu, double sigma, double lowerlimit, double upperlimit)
public static double exponential(double mu, double sigma, double x)
public static double exponentialMean(double mu, double sigma)
public static double exponentialStandDev(double sigma)
public static double exponentialMode(double mu)
public static double exponentialMedian(double mu, double sigma)
public static double[0..*] exponentialRand(double mu, double sigma, int n)
public static double[0..*] exponentialRand(double mu, double sigma, int n, long seed)
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NetBeans UML Modeling |


{{detailsEdge.mFrom.mName}}
{{detailsEdge.mTo.mPlayerTown ? detailsEdge.mTo.mName : 'Place to build a town'}}
Cost: 1 wood and 1 clay