{% extends "../../layout.html" %} {% block sectionClasses %}plugins hasBreadCrumb plugins-spincast-http-client{% endblock %} {% block meta_title %}Plugins - Spincast HTTP Client{% endblock %} {% block meta_description %}Client to easily create and send HTTP requests.{% endblock %} {% block scripts %} {% endblock %} {% block body %}
Provides an easy way of creating and sending HTTP requests. The interface to inject to get a factory to create requests is HttpClient
Please note that this is the light version of the Spincast HTTP client,
without support for WebSockets
. If you want your client to be able
to establish WebSocket connections, use the
Spincast HTTP Client With WebSocket version!
This client is built around
Apache HttpClient. Note that
no extra dependencies are added by this plugin because it uses the
shaded version of Apache HttpClient, which is already included in the spincast-core
artifact.
All the integration tests of Spincast use this HTTP client so have a look at them for a lot of examples!
To use Spincast's HTTP Client, you can inject HttpClient
where you need it.
This object is a factory : it is thread safe and can be injected in multiple
classes.
public class AppController { private final HttpClient httpClient; @Inject public AppController(HttpClient httpClient) { this.httpClient = httpClient; } protected HttpClient httpClient() { return this.httpClient; } public void myRouteHandler(AppRequestContext context) { HttpResponse response = httpClient().GET("https://www.google.com").send(); if(response.getStatus() == HttpStatus.SC_OK) { //... } } }
Some examples :
GET
request. We get the response as a string and print it :
HttpResponse response = httpClient.GET("https://www.google.com").send(); System.out.println(response.getContentAsString());
POST
request. We send a Json
object representing
a user, with some extra HTTP headers :
User user = getUser(); HttpResponse response = httpClient.POST("https://example.com/api/users") .setJsonStringBody(user) .setCookie("cookieKey", "cookieValue") .setHeader("headerKey", "headerValue") .send(); if(response.getStatus() == 200) { // or use : HttpStatus.SC_OK String content = response.getContentAsString(); Cookie cookie = response.getCookie("someCookie"); String header = response.getHeaderFirst("someHeader"); } else { //... }
Json
and
use a JsonObject to represent the response :
String url = "http://example.com/some-api?format=json"; JsonObject apiResponse = httpClient.GET(url).send().getContentAsJsonObject();
There are a lot of features provided by this client, and the best way to discover all of them is to read the javadoc of the various components involved.
Here are some interesting ones:
HttpRequestBuilder<T> disableSslCertificateErrors()
The disableSslCertificateErrors()
method lets you easily disable any SSL certificates
validation. For example, this can be used to
test a local API served using a self-signed certificate
.
HttpRequestBuilder<T> disableRedirectHandling()
The disableRedirectHandling() method prevents the client to automatically follow redirects (which it does by default).
HttpRequestBuilder<T> setHttpAuthCredentials()
The setHttpAuthCredentials()
method lets you easily set HTTP basic authentication
credentials.
1. Add this Maven artifact to your project:
<dependency> <groupId>org.spincast</groupId> <artifactId>spincast-plugins-http-client</artifactId> <version>{{spincast.spincastCurrrentVersion}}</version> </dependency>
2. Add an instance of the SpincastHttpClientPlugin plugin to your Spincast Bootstrapper: {% verbatim %}
Spincast.configure() .plugin(new SpincastHttpClientPlugin()) // ...
The class implementing the SpincastPlugin interface is SpincastHttpClientPlugin.
httpClient()
Route Handlers
.
Example :
public void myRouteHandler(DefaultRequestContext context) { // Make to request to an API and get the response as // a JsonObject String url = "https://example.com/api/test"; JsonObject response = context.httpClient().GET(url).send().getContentAsJsonObject(); //... }
Learn how to install an add-on in the dedicated section of the documentation.


'The Mighty' (1998)
A movie review by Walter Frith
Member of the 'Internet Movie Critics Association' http://www.geocities.com/Hollywood/Studio/5713/index.html
and
Member of the 'Online Film Critics Society' http://ofcs.org/ofcs/
The after school specials that ABC television has produced in the past are a good stepping stone for producing 'The Mighty'. ABC has nothing to do with this film but the makers of this one certainly look like they watched a lot of them. An intelligent, if somewhat overly sentimental film, 'The Mighty' has some good performances from two young actors who steal the show away from their adult colleagues but some of the writing is a cross between corn ball and a lot of the dialogue and situations are flippant.
Set in Cincinnati, Ohio, 'The Mighty' is the story of two boys and their dysfunctional lives and how they become the best of friends. Elden Henson is an over sized seventh grader who refers to himself as Godzilla and draws stares from the other children at school because of his size and his homely features. He is taunted by a gang of punks at school who tease and harass him about his past and his father's imprisonment. Henson's father (James Gandolfini) killed his mother in a domestic dispute and is now serving time. Henson lives with his maternal grandparents (Gena Rowlands and Harry Dean Stanton) who care deeply for the boy.
Henson gets a tutor at school (Kieran Culkin), a boy who is highly intelligent at his level in life but he suffers from a disease that eats away at his body slowly and he is supported by arm braces when walking. The two meet awkwardly when Henson is blamed for an act of cruelty against Culkin in the school gymnasium and has to serve detention for it. Later, the two of them become close friends and Henson serves as Culkin's protector and is brought out of his shy shell after being inspired from the stories of brave knights brought on by the fact that Culkin also likes to draw references to medieval times and the sanctity of knights when describing a situation or involving himself in it.
'The Mighty' is a good family film that has a good marketing strategy by casting Sharon Stone as Culkin's mother. The ordinary style the film projects of everyday life in the surroundings of ordinary people are quite admirable. What I really thought was strange was how all the adult characters in the film were under written. I think it's a rather cheap marketing trick to have popular stars like Sharon Stone and Gillian Anderson in very under written roles.  Her character only appears in a couple of key scenes but Anderson is an excellent actress capable of the most impressive diversity.
Elden Henson and Kieran Culkin are terrific young actors but Henson clearly steals the show as his character goes through a transformation of soul searching and his life is clearly influenced for the better at the teachings of a tutor who has much to teach him and makes his life wholesome and determined to stay on the right track. A lesson much of today's youth needs badly. Parents are encouraged to take their children to see this film for a memorable experience that they will remember fondly as they grow older
OUT OF 5 > * * * 1/2
Visit FILM FOLLOW-UP by Walter Frith http://www.cgocable.net/~wfrith/movies.htm
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


My name is Dan White and I love to write and code. I have ten years of experience as a Staff Technical Writer at Synopsys where I produce documentation for the flagship Fusion Compiler product. My validation utilities raise the quality of documentation for the company by catching syntax, style, and consistency errors before publication.
Documentation: Developed user guides, application notes, labs, man pages, and other materials for the company's flagship Fusion Compiler product.
Application Development: Developed validation utilities in Java, Go, and Python to enforce consistent formatting, command, and style guide adherence across user guides and man pages for Fusion Compiler. Some utilities caught the attention of writers in other divisions and were adopted to improve their documentation.
Leadership: Authored and conducted usability studies to better understand document effectiveness and to provide ideas for improvement. Hired, supervised, and mentored summer interns to develop a concordance builder, a man page example test framework, and a Markdown-to-FrameMaker XML document creation flow.
Led all Product Marketing activities for Synopsys' PrimeYield LCC and CMP design-for-manufacturing products.
Led all Product Marketing activities for HPL's YieldProjector and YieldDirector design-for-manufacturing products.
The following are some writing samples:
Connection Assessment: This writing sample represents a very heavily modified topic from the user guide I've worked on most recently. I develop this type of content by working closely with R&D, running the tool to reproduce and verify the results, and create screen captures and snippets for scripts. HTML PDF
Python Excels: This is a series of blog posts I first created in 2009 and revised in 2019. These posts demonstrate various techniques for automating common tasks in Microsoft Excel with Python. I originally hosted Python Excels on a custom Wordpress site, but recently migrated everything using a docs-as-code approach with Markdown files hosted on Github. pythonexcels.com Github
Other: I have many more writing samples, please contact me if you need to see more.
The following are some coding examples:
Java: The SSSCheck application performs a comprehensive document check for syntax, style guide, and spelling errors. This code represents a small fraction of an actual application I wrote for our team to identify hard-to-find errors and increase document quality. Wiki Code
Go: This is a simple API server I developed to learn and experiment with API documentation. The API server, written in the Go language, provides a few endpoints that access an SQL database. The database contains the Cars93 database with 93 records for different 1993 model cars. Swagger documentation Code
Python: The Python scripts for the Python Excels blog are available on GitHub at https://github.com/pythonexcels/examples.
Javascript: This is a companion front-end application for the Cars93 API server. I developed this application to learn more about how APIs are consumed and how API documentation could be used by developers. HTML Github.


The below graph is called the "type graph" or "class graph". It shows the graph hierarchy and has the
top 3 candidate classes highlighted in yellow in the graph below. The top suggested classes are listed below
the graph.
{%endif%}
The alpha balances the coverage and specificity. If the chosen annotation is too general decrease the value,
if it is specific but incorrect, consider increase it. It should be between 0 and 1.
If you don't know (or just want to start), choose specificity function 3 with alpha 0.01


Module: Auth0::Api::AuthenticationEndpoints
- Defined in:
- lib/auth0/api/authentication_endpoints.rb
Overview
https://auth0.com/docs/auth-api Describing functionality of auth0 authentication endpoints
Instance Method Summary (collapse)
- - (Object) change_password(email, password, connection_name = "Username-Password-Authentication")
- - (Object) delegation(id_token, target, scope = "open_id")
- - (Object) login(username, password, scope = "openid", id_token = nil, connection_name = "Username-Password-Authentication")
- - (Object) obtain_access_token
- - (Object) signup(email, password, connection_name = "Username-Password-Authentication")
- - (Object) token_info(id_token)
Instance Method Details
- (Object) change_password(email, password, connection_name = "Username-Password-Authentication")
54 55 56 57 58 59 60 61 62 |
# File 'lib/auth0/api/authentication_endpoints.rb', line 54 def change_password(email, password, connection_name = "Username-Password-Authentication") request_params = { client_id: @client_id, email: email, connection: connection_name, password: password } post("/dbconnections/change_password", request_params) end |
- (Object) delegation(id_token, target, scope = "open_id")
17 18 19 20 21 22 23 24 25 26 |
# File 'lib/auth0/api/authentication_endpoints.rb', line 17 def delegation(id_token, target, scope = "open_id") request_params = { client_id: @client_id, grant_type: "urn:ietf:params:oauth:grant-type:jwt-bearer", id_token: id_token, target: target, scope: scope } post("/delegation", request_params) end |
- (Object) login(username, password, scope = "openid", id_token = nil, connection_name = "Username-Password-Authentication")
29 30 31 32 33 34 35 36 37 38 39 40 |
# File 'lib/auth0/api/authentication_endpoints.rb', line 29 def login(username, password, scope = "openid", id_token=nil, connection_name="Username-Password-Authentication") request_params = { client_id: @client_id, username: username, password: password, scope: scope, connection: connection_name, grand_type: "password", id_token: id_token } post("/oauth/ro", request_params) end |
- (Object) obtain_access_token
7 8 9 10 11 12 13 14 |
# File 'lib/auth0/api/authentication_endpoints.rb', line 7 def obtain_access_token request_params = { client_id: @client_id, client_secret: @client_secret, grant_type: 'client_credentials' } post("/oauth/token", request_params)["access_token"] end |
- (Object) signup(email, password, connection_name = "Username-Password-Authentication")
43 44 45 46 47 48 49 50 51 |
# File 'lib/auth0/api/authentication_endpoints.rb', line 43 def signup(email, password, connection_name= "Username-Password-Authentication") request_params = { client_id: @client_id, email: email, connection: connection_name, password: password } post("/dbconnections/signup", request_params) end |
- (Object) token_info(id_token)
65 66 67 68 |
# File 'lib/auth0/api/authentication_endpoints.rb', line 65 def token_info(id_token) request_params = { id_token: id_token} post("/tokeninfo", request_params) end |


Contact (1997) ©1997 Matt Petras
Official site:
www.contact-themovie.com
Length: 153 min (2:33 hours) Rating: **** out of ****
This movie is one of the better ones that I have seen in a long time, although I have not read the book by Carl Sagan I am fairly sure that this movie does the book justice in every respect. The acting in this movie is just superb and casting for the parts could not have been done better I think. Although I think this movie is something along the lines of perfect I did find a few small flaws... There is a scene that kind of makes you think whoa this is not happening this is too cheesy.. well that scene makes sense if you think about it but it kind of struck me as being dumb at first. A few other small things would be that I noticed that in one scene they were panning in on a building and it was cloudy and foggy and really rainy.. but when the scenes shifted to inside the building it was already sunny through the windows. As mentioned above the acting in this movie is superb.. Jodie Foster produces another stellar performance and in my opinion it is her best to date. She shows emotion beautifully and fills the shoes of a very demanding role as if the role had been made for her. Not one moment in this movie did Jodie Foster seem like she was out of her element. In my humble opinion her performance in this movie should at least get her an Oscar nomination if not the prestigious award itself. Matthew McConaughey also delivers a grand performance in the movie.. although the movie centers around Jodie and her character all the other actors in the movie gave great performances. The sheer magnitude of some of the scenes was spectacular.. to bring together such large crowds for certain scenes and knowing when to use spectacular effects shows a certain genius from the point of the director Robert Zemeckis. Although there are parts in this movie that are predictable and the movie itself is rather lengthy neither of these things makes much of a disturbance. Mind you if your bladder can't take a large sized drink for more then 2.5 hours.. you might want to consider a medium. hehe... I highly recommend this movie and it is one of the better ones that have been released this year... it might not make the millions of JP2 but I would have to say that this movie should be one of the best of the year and maybe one of the better movies that have been made recently. Go see it if you have the chance.
July 1997
<EMAIL_ADDRESS>www.imactive.com/bootsector
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


--- layout: post title: Lush Buffy tags: - Lush status: publish type: post published: true meta: _edit_last: '2' _aioseop_description: LUSH buffy body scrub and butter review and pictures _aioseop_title: LUSH Buffy Review _aioseop_keywords: LUSH buffy review, Lush Buffy, Body scrub, Lush body scrub, Lush Body butter, Body butter, Keratosis pilaris cure, Dry skin moisturizer _yoast_wpseo_linkdex: '0' _thumbnail_id: '4732' _yoast_wpseo_focuskw: '' _yoast_wpseo_title: '' _yoast_wpseo_metadesc: '' _yoast_wpseo_metakeywords: '' _yoast_wpseo_meta-robots-noindex: '0' _yoast_wpseo_meta-robots-nofollow: '0' _yoast_wpseo_meta-robots-adv: none _yoast_wpseo_sitemap-include: ! '-' _yoast_wpseo_sitemap-prio: ! '-' _yoast_wpseo_canonical: '' _yoast_wpseo_redirect: '' --- "You know what I got today ??" "What Di ?" "My Buffy.." "When did you love pets..?" "Oho, its not a pet.. its the new back layer from LUSH" Scrubbing always takes a back seat when I am rushing to office, but not anymore.. I have Lush buffy with me and its one of the most effect body scrub and body butter ever. I have never mentioned before on the blog that I suffer from Keratosis pilaris(red bumps on forearms), which can be a real embarrassment sometimes and I mostly refrain from flaunting my arms. Only solution to KP is lots of exfoliation and heavy moisturizer but who has that much time, and little efforts don't count here. I am sure people with KP will relate to me here. This product was made for me, a gift for my dry skin. It was love at first sniff...
Its a superb body scrub and a moisture bomb. After shower I step away from water and use it like a regular soap in circular motion. Initially it might feel a bit gritty, but gradually it smoothens out and is not harsh, but avoid using on thin skin like face, neck etc. then wash of with warm water. I use it daily, without a miss.
Its amazingly moisturizing, and I don't need any kind of moisturizer after this, skin stays soft and moisturized for more than 24 hours even for a dry skin like mine. Its a slayer for dead skin and bumps leaving behind smooth, soft and supple skin for hours. I could see change in the texture of my skin in 3-4 days.
No more KP bumps but the old red marks are still there (Fingers crossed.. they will be gone soon)
It has been almost 2 weeks since I started using this, but I still have more than 3/4th of the product left.
Even though its a bit steeply priced, but totally worth and has found a permanent place in my shelf.. I will totally recommend this to everyone who has dry skin, and specially to those who have red bumps, KP and require a thoroughly pampered and moisturized skin. Oily skin can give this a skip.. So do you think you are going to get your buffy ? *Product was sent for review, but that has not affected my opinion at all.


public final class CardIssuer
extends java.lang.Enum
| Modifier and Type | Field and Description |
|---|---|
static CardIssuer |
AmericanExpress
The American Express Company Card
|
static CardIssuer |
BmoAbm
The Bank of Montreal ABM Card
|
static CardIssuer |
ChinaTUnion
China T-Union Transportation Card
|
static CardIssuer |
ChinaUnionPay
China UnionPay Card
|
static CardIssuer |
CibcAdvantageDebit
Canadian Imperial Bank of Commerce Advantage Debit Card
|
static CardIssuer |
Ciss
CISS Card
|
static CardIssuer |
Dankort
Dankort Card
|
static CardIssuer |
DinersClubInternational
Diners Club International Card
|
static CardIssuer |
DinersClubUsCanada
Diners Club United States & Canada Card
|
static CardIssuer |
DiscoverCard
Discover Card
|
static CardIssuer |
Hsbc
HSBC Bank Canada Card
|
static CardIssuer |
InstaPayment
InstaPayment Card
|
static CardIssuer |
InterPayment
InterPayment Card
|
static CardIssuer |
Jcb
The JCB Company Card
|
static CardIssuer |
Laser
Laser Debit Card (deprecated)
|
static CardIssuer |
Maestro
Maestro Debit Card
|
static CardIssuer |
MasterCard
MasterCard Inc.
|
static CardIssuer |
Mir
MIR Card
|
static CardIssuer |
Other
Unidentified Card
|
static CardIssuer |
RbcClient
The Royal Bank of Canada Client Card
|
static CardIssuer |
RuPay
RuPay Card
|
static CardIssuer |
ScotiaBank
ScotiaBank Scotia Card
|
static CardIssuer |
TdCtAccess
TD Canada Trust Access Card
|
static CardIssuer |
Troy
Troy Card
|
static CardIssuer |
Uatp
Universal Air Travel Plan Inc.
|
static CardIssuer |
Verve
Interswitch Verve Card
|
static CardIssuer |
Visa
Visa Inc.
|
| Modifier and Type | Method and Description |
|---|---|
static CardIssuer |
valueOf(java.lang.String name) |
static CardIssuer[] |
values() |
public static final CardIssuer Other
public static final CardIssuer AmericanExpress
public static final CardIssuer BmoAbm
public static final CardIssuer ChinaTUnion
public static final CardIssuer ChinaUnionPay
public static final CardIssuer CibcAdvantageDebit
public static final CardIssuer Ciss
public static final CardIssuer DinersClubInternational
public static final CardIssuer DinersClubUsCanada
public static final CardIssuer DiscoverCard
public static final CardIssuer Hsbc
public static final CardIssuer RuPay
public static final CardIssuer InterPayment
public static final CardIssuer InstaPayment
public static final CardIssuer Jcb
public static final CardIssuer Laser
public static final CardIssuer Maestro
public static final CardIssuer Dankort
public static final CardIssuer Mir
public static final CardIssuer MasterCard
public static final CardIssuer RbcClient
public static final CardIssuer ScotiaBank
public static final CardIssuer TdCtAccess
public static final CardIssuer Troy
public static final CardIssuer Visa
public static final CardIssuer Uatp
public static final CardIssuer Verve
public static CardIssuer[] values()
public static CardIssuer valueOf(java.lang.String name)


F12 on your keybord will activate debugging.
Then select "Console" in the debugger menu.
Then click Run again.


Choose a different enforcement option
You should only apply for a bailiff to visit the defendant if you think they'll be able to get your money back.
You can choose a different enforcement option.
You should only apply for a bailiff to visit the defendant if you think they'll be able to get your money back.
You can choose a different enforcement option.


JSON Schema (Pre-v10)
This documentation only applies to Manatee.Json versions 9 and earlier. To work with JSON Schema in Manatee.Json version 10, please refer to the latest JSON Schema page.
The occasion may arise when you wish to validate that a JSON object is in the correct form (has the appropriate keys and the right types of values). Enter JSON Schema. Much like XML Schema with XML, JSON Schema defines a pattern for JSON data. A JSON Schema validator can verify that a given JSON object meets the requirements as defined by the JSON Schema. This validation can come in handy as a precursor step before deserializing.
More information about JSON Schema can be found at json-schema.org.
Building JSON Schema
In Manatee.Json, JSON Schema are defined using the JsonSchema
class, which can be found in the Manatee.Json.Schema
namespace. This object contains the properties you'll need to validate any JSON object. Let's take a look at this object in a bit more detail to understand all of the parts of a JSON Schema.
| Property | Validates | Supported by draft | Description |
|---|---|---|---|
Id |
Any | 4/6/7 | This is a string value which defines an id for this schema. Value must be a URI. When deserialized, a schema will automatically register itself with JsonSchemaRegistry. For draft 4, this is represented in JSON as the `id` property; for draft 6, this has been updated to `$id`. |
Schema |
Any | 4/6/7 | This will usually be the same value as the Id property, but may be different. Value must be a URI. It is used to identify a schema which governs the valid shape of this schema. If left null, the default to be used is specified in the `JsonSchemaFactory` static class. |
Title |
Any | 4/6/7 | Provides a title for this schema. This is informational only and does not affect validation. |
Description |
Any | 4/6/7 | Provides a description for this schema. This is informational only and does not affect validation. |
Default |
Any | 4/6/7 | Defines a default value. This may be any value. |
MultipleOf |
Numbers | 4/6/7 | Defines a required divisor for accepted values. |
Maximum |
Numbers | 4/6/7 | Defines a maximum for accepted values. |
ExclusiveMaximum |
Numbers | 4/6/7 | draft 4 (boolean): Defines whether the value in the Maximum property is acceptable.
draft 6/7 (number): Defines an exclusive maximum. |
Minimum |
Numbers | 4/6/7 | Defines a minimum for accepted values. |
ExclusiveMinimum |
Numbers | 4/6/7 | draft 4 (boolean): Defines whether the value in the Minimum property is acceptable.
draft 6/7 (number): Defines an exclusive minimum. |
MaxLength |
Strings | 4/6/7 | Defines a maximum number of characters. |
MinLength |
Strings | 4/6/7 | Defines a minimum number of characters. |
Pattern |
Strings | 4/6/7 | Defines a regular expression for which acceptable values must pass validation. |
AdditionalItems |
Arrays | 4/6/7 | Defines whether additional items are allowed in acceptable values. May be True , False , or a JSON schema. |
Items |
Arrays | 4/6/7 | Defines a JSON schema or sequence of JSON schemata to validate contained elements. |
MaxItems |
Arrays | 4/6/7 | Defines a maximum number of elements allowed. |
MinItems |
Arrays | 4/6/7 | Defines a minimum number of elements allowed. |
UniqueItems |
Arrays | 4/6/7 | Defines whether each element must be unique. |
MaxProperties |
Objects | 4/6/7 | Defines a maximum number of properties allowed. |
MinProperties |
Objects | 4/6/7 | Defines a minimum number of properties allowed. |
AdditionalProperties |
Objects | 4/6/7 | Defines whether any keys not listed in the Properties property may appear as well as the schema to validate any such properties. See AdditionalProperties and PatternProperties below for more information. |
Properties |
Objects | 4/6/7 | Defines a collection of keys which may appear in the object being validated as well as the schema to validate the value under that key. |
Definitions |
Any | 4/6/7 | Defines a collection of type definitions that may be referenced throughout this schema. |
PatternProperties |
Objects | 4/6/7 | Defines criteria which any additional keys must adhere as well as the schema to validate those properties. See AdditionalProperties and PatternProperties below for more information. |
Dependencies |
Objects | 4/6/7 | Defines any keys on which other keys depend. For instance, the JSON Schema draft 4 document defines that the exclusiveMaximum property requires that the maximum property be present in the object being validated. |
Enum |
Any | 4/6/7 | Defines a discrete collection of acceptable values. |
Type |
Any | 4/6/7 | Defines a type or a collection of simple JSON Schema types acceptable. |
AllOf |
Any | 4/6/7 | Defines a collection of JSON Schema, all of which acceptable values must pass. |
AnyOf |
Any | 4/6/7 | Defines a collection of JSON Schema, one or more of which acceptable values must pass. |
OneOf |
Any | 4/6/7 | Defines a collection of JSON Schema, exactly one of which acceptable values must pass. |
Not |
Any | 4/6/7 | Defines a JSON Schema for which acceptable values must fail. |
Format |
Strings | 4/6/7 | Allows special validation on strings. |
Required |
Objects | 4/6/7 | Lists names of required properties. |
ExtraneousDetails |
Any | 4/6/7 | Stores any properties not explicitly defined in the JSON Schema specifications to be added to the schema. There is no validation on these properties. |
PropertyNames |
Any | 6/7 | Defines a schema for any property names that aren't explicitly defined. Differs from AdditionalProperties in that this looks at the property name instead of the property value. |
If/Then/Else |
Any | 7 | Each property defines a schema. If the `If` schema passes validation, the `Then` schema is validated, otherwise the `Else` schema is validated. |
Type Definitions
Type definitions are typically used to extend the basic definitions: array, boolean, integer, null, number, object, and string. Each of these schema types have their own distinct properties. For instance, the string schema type can have a minimum length requirement. Type definitions provide a method for naming a string type with a minimum length so that it can be referenced anywhere within the JSON Schema document.
To reference a type definition from within the JSON Schema document, the relative path to the definition must be given using a slash-delimited property string with a pound sign specifying the root of the document. Typically a reference will appear in the following form:
#/definitions/[type name]
Ideally all type definitions will be contained in the root schema object's Definitions
collection, however they could technically appear in non-schema-standard properties (These will appear in the ExtraneousDetails
collection on the JsonSchema
object.). References to type definitions can even be made from other type definitions.
See the References section below for more information on references.
Properties
The most important part of any JSON Schema is the collection of properties it defines. This is what allows the schema to validate that a JSON object contains the right kind of data.
Let's start with an example. Suppose we have a schema which defines a single property:
{"properties":{"myProperty":{}}}
which can declared in Manatee.Json by
var schema = new JsonSchema07
{
Properties = new Dictionary<string, IJsonSchema>
{
["myProperty"] = new JsonSchema07()
}
}
This schema is not very interesting (or useful) as it only defines that the property should exist (although it's not required), and it doesn't define a type for it. Suppose we pass the following JSON objects through the validator:
{}
{"myProperty":false}
{"myProperty":"some string"}
{"otherProperty":35.4}
The schema will validate all of them as compliant because it has no type restrictions for the property. Let's specify it as string.
{"properties":{"myProperty":{"type":"string"}}}
which is declared in Manatee.Json by
var schema = new JsonSchema07
{
Properties = new Dictionary<string, IJsonSchema>
{
["myProperty"] = new JsonSchema07 {Type = JsonSchemaType.String}
}
}
Now, the JSON object with a value of false
will pass validation; the others will pass. (The ones missing the property pass because it's not listed as required.)
The JsonSchemaType
enumeration is marked with a Flags
attribute, so that they can be combined. For instance, the JSON Schema draft 6 metaschema declares a type of both boolean
and object
. This is done in an array
{"type": ["object", "boolean"]}
This is declared in code using the |
operator.
var schema = new JsonSchema07 { Type = JsonSchemaType.Object | JsonSchemaType.Boolean };
AdditionalProperties
and PatternProperties
According to the JSON Schema documentation, these two properties have a somewhat unique interaction. In short, the rules governing the validation of these properties are as follows:
PatternProperties
defines a collection of Regular Expression/JSON Schema pairs. If a property name in an object matches a Regular Expression, it must also pass the defined JSON Schema.AdditionalProperties
may be true, false, or a valid schema.- For
JsonSchema04
, this is implemented via two static fields on theAdditionalProperties
type (True
andFalse
) and a general constructor which allows the injection of anIJsonSchema
implementation. - For
JsonSchema06
andJsonSchema07
, the property is merelyIJsonSchema
since drafts 6 and 7 support booleans as valid schemas.
- For
Now here's the tricky part. The behavior of these properties depends on the value of AdditionalProperties
.
- If
AdditionalProperties
is true- The properties listed in
Properties
are evaluated per normal. PatternProperties
applies to all properties, even those inProperties
.
- The properties listed in
- If
AdditionalProperties
is a valid schema- The properties listed in
Properties
are evaluated per normal. PatternProperties
applies to all properties, even those inProperties
.- The
AdditionalProperties
schema is used to validate all properties not handled byProperties
orPatternProperties
.
- The properties listed in
- If
AdditionalProperties
is false- The properties listed in
Properties
are evaluated per normal. PatternProperties
applies only to properties not defined inProperties
.- No other properties are allowed.
- The properties listed in
The default value for AdditionalProperties
is the empty schema, which validates all additional properties (like True
).
References
Suppose we want to impose a minimum string length of 10 on the myProperty
value from the above schema. We can define the schema in two ways:
By explicitly defining the type in the property definition
{
"properties":{
"myProperty":{
"type":"string",
"minLength":10
}
}
}
or by defining the type and creating a reference to it:
{
"definitions":{
"stringWithMinLength10":{
"type":"string",
"minLength":10
}
},
"properties":{
"myProperty":{"$ref":"#/definitions/stringWithMinLength10"}
}
}
As you can see, the reference is defined by an object with a special key: "$ref"
. The value is the path to the definition relative to the root of the schema. The value may also be a URI where the schema can be downloaded, such as http://json-schema.org/geo
. Now that you understand how type definitions and references work, let's take a look at the above schema declared with Manatee.Json.
var schema = new JsonSchema07
{
Definitions = new Dictionary<string, object>
{
["stringWithMinLenth10"] = new JsonSchema06
{
Type = JsonSchemaTypeDefinition.String},
MinLength = 10
}
},
Properties = new Dictionary<string, object>
{
["myProperty"] = new JsonSchemaReference(
"#/definitions/stringWithMinLength10",
typeof(JsonSchema06))
}
}
NOTE We need to tell the JsonSchemaReference
class what version of schema we'd like constructed if the schema doesn't itself declare a version.
The declaration is very similar to the schema itself. Defining a new type is typically done when that type is used for several properties. In the above example, it is somewhat unnecessary.
External references are a useful way to organize complex type hierarchies; they allow a type defined in one schema to be referenced in another schema. For example, the following are all valid syntax:
{"$ref": "http://example.com/otherSchema.json"}
{"$ref": "http://example.com/otherSchema.json#/definitions/exampleType"}
{"$ref": "./otherSchema.json"}
{"$ref": "../otherSchema.json#/definitions/exampleType"}
In other words, references can use either absolute or relative URIs to indicate external schemas, in addition to an optional JSON pointer to the part of the file being referenced. External references do not need to comply to the same meta-schema as the file doing the referencing; a schema complying to draft 4 can reference an external schema complying to draft 7, and vice versa. Manatee.Json can automatically access external references using the JsonSchemaFactory.FromJson function, which has an optional parameter for the document's URI — necessary information for resolving relative URIs.
As a final note on properties, let's take a look at how the latest version of JSON Schema (draft 4) defines properties which are required by making the myProperty
property required. The schema itself would look like this:
{
"properties":{
"myProperty":{
"type":"string",
"minLength":10
}
},
"required":["myProperty"]
}
The schema object has a property which declares which properties are required. Manatee.Json is very similar:
var schema = new JsonSchema06
{
Properties = new Dictionary<string, IJsonSchema>
{
["myProperty")] = new JsonSchema06
{
Type = JsonSchemaTypeDefinition.String,
MinLength = 10
}
},
Required = new [] {"myProperty"}
};
One Schema to Rule Them All
It may be appropriate for a schema to accept multiple kinds of data. For instance, if you're reading from an API, and your DTOs (Data Transfer Objects) can sometimes be represented as the full object and sometimes by only a string (like an ID), it's useful to have a schema for that object that can validate both cases.
Let's consider the following DTO:
public class SampleDto
{
public string Id { get; set; }
public double Score { get; set; }
public string Username { get; set; }
}
A schema could be defined that could accept the complete object
{
"Id" : "b5e72f0a-cf1b-4732-bb83-3bb756c77ee1",
"Score" : 9.5,
"Username" : "gregsdennis"
}
or simply a string (interpreted to be the ID)
"b5e72f0a-cf1b-4732-bb83-3bb756c77ee1"
Such a schema could look like
{
"type" : [ "object", "string" ],
"properties" : {
"Id" : {
"type" : "string",
"pattern" : "^[a-fA-F0-9]{8}-([a-fA-F0-9]{4}-){3}[a-fA-F0-9]{12}$"
},
"Score" : {
"type" : "number",
"minimum" : 0
}
"Username" : { "type" : "string" }
},
"pattern" : "^[a-fA-F0-9]{8}-([a-fA-F0-9]{4}-){3}[a-fA-F0-9]{12}$"
}
This schema defines:
- that the JSON could be a string or an object
- that, if an object, it should have properties
Id
as a string matching a GUID RegExScore
as a number greater than or equal to 0Username
as any string
- that, if a string, it should match a GUID RegEx
This would be built in Manatee.Json as
var guidPattern = "^[a-fA-F0-9]{8}-([a-fA-F0-9]{4}-){3}[a-fA-F0-9]{12}$";
var schema = new JsonSchema
{
Type = JsonSchemaType.String | JsonSchemaType.Object,
Properties = new Dictionary<string, object>
{
["Id"] = new JsonSchema06
{
Type = JsonSchemaType.String,
Pattern = guidPattern
},
["Score"] = new JsonSchema06
{
Type = JsonSchemaType.Number,
Minimum = 0
},
["Username"] = new JsonSchema06
{
Type = JsonSchemaType.String
},
},
Pattern = guidPattern
}
The key here to remember is that Properties
only validates when the JSON is an object, and Pattern
only validates when the JSON is a string. Otherwise, these are ignored. The Type
property ensures that it fails for anything except strings or objects.
That covers all of the basics for defining and building schema directly. As expected, schema can also be downloaded and deserialized. This will be covered in the Schema Serialization section below.
NOTE On occasion, an object may have a property which is of the same type. In order to declare this kind of property, you should use the static property Root
defined on the appropriate schema class. This will instruct the reference to validate the property against the root schema.
Validation
The purpose of JSON Schema is to validate that a JSON value meets certain criteria. Let's take a look at how Manatee.Json schema objects provide this service.
Every schema object is required to implement the IJsonSchema
interface. This interface defines a single method:
SchemaValidationResults Validate(JsonValue json, JsonValue root = null)
It is this method which we will use to validate incoming JSON values. Let's begin with our aforementioned schema and JSON objects:
{
"properties":{
"myProperty":{
"type":"string",
"minLength":10
}
},
"required":["myProperty"]
}
{}
{"myProperty":false}
{"myProperty":"some string"}
{"otherProperty":35.4}
"nonObject"
To validate these, all we have to do is pass these into our schema's Validate()
method.
var schema = GetSchema(); // defines the schema we have listed above.
var emptyJson = new JsonObject();
var booleanJson = new JsonObject { {"myProperty", false} };
var stringJson = new JsonObject { {"myProperty", "some string"} };
var numberJson = new JsonObject { {"otherProperty", 35.4} };
var nonObject = (JsonValue)"nonObject";
var emptyResults = schema.Validate(emptyJson);
var booleanResults = schema.Validate(booleanJson);
var stringResults = schema.Validate(stringJson);
var numberResults = schema.Validate(numberJson);
var nonObjectResults = schame.Validate(nonObject);
IMPORTANT You may have noticed that the second parameter is not used. This parameter is used internally for validating subschema and resolving references and should not be used explicitly.
The various results objects are of type SchemaValidationResults
, which has two properties:
- IsValid - Indicates if the tested JSON value is valid,
- Errors - A collection of errors found while validating. Each error lists a JSONPath to the problem element and a message which describes the issue.
In the above example, the following would be reported:
emptyJson
andnumberJson
failed because"myProperty"
was not found.booleanJson
failed because"myProperty"
is of the wrong type.stringJson
passed validation.nonObject
also passed validation becauseproperties
andrequired
ignore non-object JSON.
Schema Serialization
Of what use would JSON Schema objects be to a developer if they were not serializable to and from JSON? Not a whole lot. Therefore, Manatee.Json's schema objects are designed to be completely serializable to and from JsonObject
s (and subsequently to and from strings).
var schema = GetSchema();
var json = schema.ToJson(null);
This can also be done through a JsonSerializer
instance, of course. The serializer will yield the same results.
NOTE The null
passed into the ToJson()
method indicates that a serializer instance is not required to serialize the schema classes.
For deserialization, you will need to go through the JsonSchemaFactory.FromJson()
static method. Simply pass your JSON in, and you get the appropriate JSON schema out.
var json = GetSchemaJson();
var schema = JsonSchemaFactory.FromJson(json);
You may prefer to use deserialization combined with JSON object construction to generate your schema.
var json = new JsonObject
{
{"properties", new JsonObject
{
{"myProperty", new JsonObject
{
{"type", "string"},
{"minLength", 10}
}
}
}
}
{"required", new JsonArray {"myProperty"}}
};
var serializer = new JsonSerializer();
var schema = serializer.Deserialize<IJsonSchema>(json);
As you can see, the schema definition is easy to read. The downside to this approach is that you don't get the compile-time checking of the JsonSchema0x
classes.
This is also useful when your schema is defined at a URI. Simply download the content, parse it, and run it through the schema factory.
var json = JsonValue.Parse(new WebClient().DownloadString("http://json-schema.org/geo"));
var schema = JsonSchemaFactory.FromJson(json);
Schema Version Selection
JsonSchemaFactory
will make a best guess when deserializing schemata, but many times, both drafts may work for a given schema. In these cases a default implementation must be used.
To determine the schema version, use JsonSchemaFactory.SetDefaultSchemaVersion<T>()
and pass in either JsonSchema04
, JsonSchema06
, or JsonSchema07
. This will instruct the system to create instances of this type when deserializing and dereferencing schemata.
NOTE This method will throw an exception if any type outside of these two are used.
Somewhat related to schema serialization is the ability to download schema directly. When you need to do this, it is suggested that you use the JsonSchemaRegistry.Get()
static method. Just pass in a URI, and it will download and deserialize the schema automatically. The benefit to using this method is that the class will also cache the schema for future use. If there are any references to that URI, it will simply return this schema rather than downloading and deserializing again.
This static class also exposes additional methods which allow you to manage the cache of schemata.
Register(JsonSchema)
- Adds the schema to the cache.Unregister(JsonSchema)
- Removes the schema from the cache.Unregister(string)
- Removes a schema registered to the specified URI from the cache.
NOTE To register a schema, it must have a valid id
.
Final Notes
Manatee.Json's schema implementation has been designed using the latest two drafts of JSON Schema: draft 4 and draft 6. Included with these schemas are self-validating schema (metaschema) which serve as a basis for all other schema. This means that any schema you create must pass validation by this base schema. Furthermore, both schema classes define their metaschema and the empty schema as static properties.
As drafts 6 and 7 support booleans as valid schema, implicit casts have been created from bool
to JsonSchema06
and JsonSchema07
. These are translated to the static properties True
and False
on their respective types. These casts will not work implicitly when assigning to a property of type IJsonSchema
; an explicit cast will be required.


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D Jordan
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What did you learn from your 'mindfulness conversation'? How did the other person find it?
From both perspectives, having the listener remain stoic / indifferent was really strange, and I felt less inclined to speak
with emotion -- and it would be something to get used to.
I think the idea of a "mindulness" conversation is great / practical in many other terms, but it's
not often seen as such. For example, generally just talking to family, or your partner could be seen
as a "mindfulness" conversation, but the most consistent difference is for the listener to be more
pro-active in "listening". This can affect the way you act / say things as the speaker; possibly
more emotionally-attached to what you are saying.
Which of the three suggested techniques will you use to sustain your practice?
Definitely having a buddy makes things easier, and a lot more sustainable; as having a partner for something unfamiliar generally makes things more comfortable


{% if module_enabled %}
| Document Type | Number present |
| {{ type }} | {{ index_info['doc_types'][type] }} |
| Total | {{ index_info['num_indexed_docs'] }} |
If gcb_can_index_automatically is enabled on the Admin > Settings page, then Index Course will be run daily to keep your courses' indices up to date.
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{% else %}


Telephone: 01276 400 333
You can call us from 8am to 6pm, Monday to Friday, excluding Bank Holidays.
Email: FastStream@@Parity.net
@()(implicit request: Request[_], flash: Flash, user: Option[CachedData], feedbackUrl: String, messages: Messages) @main_template(title = "Contact us") {
Telephone: 01276 400 333
You can call us from 8am to 6pm, Monday to Friday, excluding Bank Holidays.
Email: FastStream@@Parity.net


{% extends 'layout.html' %} {% block title %} BlockPy: About {% endblock %} {% block statusbar %} {% endblock %} {% block extrahead %} {% endblock %} {% block body %}
BlockPy is a web-based Python environment that lets you work with blocks, text, or both. Designed for Data Science and equipped with powerful tools like the State Explorer and Guided Feedback, the goal of BlockPy is to let you solve authentic, real-world problems.
The goal of BlockPy is to give you a gentle introduction to Python but eventually mature you into a more serious programming environment (such as Spyder or PyCharm). Long-term, we may support some game/animation design stuff that Scratch/Snap does, but that's not the real goal.
The BlockPy project is aimed at solving some hard technical problems: having a block-based environment for a dynamic language can be tricky - are a given pair of square brackets representing list indexing or dictionary indexing? Our goal is to use advanced program analysis techniques to provide excellent support to learners.
| Austin Cory Bart |
Primary Developer
|
| Javier Tibau |
Developer
|
| Eli Tilevich |
Professor
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| Clifford A. Shaffer |
Professor
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| Dennis Kafura |
Professor
|


PICS | Man ends up in tree after being flung from bakkie
by Riaan GroblerA man was flung from his bakkie and landed up in a tree when he lost control of the vehicle on the M7 Durban-bound near the Winston Churchill off-ramp on Sunday at around 14:30.
According to Rescue Care spokesperson Garrith Jamieson, paramedics arrived on the scene "to find chaos".
PICS | 9 dead as Toyota bakkie, Avanza collide in Limpopo
A bakkie had somehow lost control before rolling down a side embankment.
"As the bakkie was rolling, the three occupants were all ejected from the vehicle," Jamieson said.
"The driver was found stuck up a tree with moderate injuries."
The Durban Fire Department, assisted by the community and Rescue Care Paramedics, had to use a ladder to get to the patient before bringing him down to safety.
"One passenger had sustained critical injuries and was found lying in the slow lane. Advanced life support paramedics placed the man on a manual ventilator on the scene. The three occupants, once stabilised, were transported to the Lenmed Ethekwini Hospital and Heart Centre for the urgent care that they required."
The events leading up to the collision is unknown; however, the SAPS were on the scene and will be investigating further.


Minecraft Chatlog Stats
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The processing may take some minutes, also the screen may freeze for a short while :)
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[[formatTime(server.time)]] Playtime


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© Copyright 1986-2008 by the University of Washington. Written by Joseph Felsenstein. Permission is granted to copy this document provided that no fee is charged for it and that this copyright notice is not removed.
This program estimates phylogenies by the restricted maximum likelihood method based on the Brownian motion model. It is based on the model of Edwards and Cavalli-Sforza (1964; Cavalli-Sforza and Edwards, 1967). Gomberg (1966), Felsenstein (1973b, 1981c) and Thompson (1975) have done extensive further work leading to efficient algorithms. Contml uses restricted maximum likelihood estimation (REML), which is the criterion used by Felsenstein (1973b). The actual algorithm is an iterative EM Algorithm (Dempster, Laird, and Rubin, 1977) which is guaranteed to always give increasing likelihoods. The algorithm is described in detail in a paper of mine (Felsenstein, 1981c), which you should definitely consult if you are going to use this program. Some simulation tests of it are given by Rohlf and Wooten (1988) and Kim and Burgman (1988).
The default (gene frequency) mode treats the input as gene frequencies at a series of loci, and square-root-transforms the allele frequencies (constructing the frequency of the missing allele at each locus first). This enables us to use the Brownian motion model on the resulting coordinates, in an approximation equivalent to using Cavalli-Sforza and Edwards's (1967) chord measure of genetic distance and taking that to give distance between particles undergoing pure Brownian motion. It assumes that each locus evolves independently by pure genetic drift.
The alternative continuous characters mode (menu option C) treats the input as a series of coordinates of each species in N dimensions. It assumes that we have transformed the characters to remove correlations and to standardize their variances.
Many current users of Contml use it to analyze microsatellite data. There are three ways to do this:
http://evolution.gs.washington.edu/phylip/software.html
.
Those distance measures allow for mutation during the divergence of the populations. But even they are not perfect -- they do not allow us to use all the information contained in the gene frequency differences within a copy number allele. There is a need for a more complete statistical treatment of inference of phylogenies from microsatellite models, ones that take both mutation and genetic drift fully into account.
0.10 X 18 + 0.24 X 19 + 0.60 X 20 + 0.06 X 21 = 19.62
copies. These values can, I believe, be calculated by a spreadsheet program. Each microsatellite is represented by one character, and the continuous character mode of Contml is used (not the gene frequencies mode). This coding allows for mutation that changes copy number. It does not make complete use of all data, but neither does the treatment of microsatellite gene frequencies as changing only by genetic drift.
The input file is as described in the continuous characters documentation file above. Options are selected using a menu:
Continuous character Maximum Likelihood method version 3.69 Settings for this run: U Search for best tree? Yes C Gene frequencies or continuous characters? Gene frequencies A Input file has all alleles at each locus? No, one allele missing at each O Outgroup root? No, use as outgroup species 1 G Global rearrangements? No J Randomize input order of species? No. Use input order M Analyze multiple data sets? No 0 Terminal type (IBM PC, ANSI, none)? ANSI 1 Print out the data at start of run No 2 Print indications of progress of run Yes 3 Print out tree Yes 4 Write out trees onto tree file? Yes Y to accept these or type the letter for one to change |
Option U is the usual User Tree option. Options C (Continuous Characters) and A (All alleles present) have been described in the Gene Frequencies and Continuous Characters Programs documentation file. The options G, J, O and M are the usual Global Rearrangements, Jumble order of species, Outgroup root, and Multiple Data Sets options.
The M (Multiple data sets) option does not allow multiple sets of weights instead of multiple data sets, as there are no weights in this program.
The G and J options have no effect if the User Tree option is selected. User trees are given with a trifurcation (three-way split) at the base. They can start from any interior node. Thus the tree:
A ! *--B ! *-----C ! *--D ! E
can be represented by any of the following:
(A,B,(C,(D,E))); ((A,B),C,(D,E)); (((A,B),C),D,E);
(there are of course 69 other representations as well obtained from these by swapping the order of branches at an interior node).
The output has a standard appearance. The topology of the tree is given by an unrooted tree diagram. The lengths (in time or in expected amounts of variance) are given in a table below the topology, and a rough confidence interval given for each length. Negative lower bounds on length indicate that rearrangements may be acceptable.
The units of length are amounts of expected accumulated variance (not time). The log likelihood (natural log) of each tree is also given, and it is indicated how many topologies have been tried. The tree does not necessarily have all tips contemporary, and the log likelihood may be either positive or negative (this simply corresponds to whether the density function does or does not exceed 1) and a negative log likelihood does not indicate any error. The log likelihood allows various formal likelihood ratio hypothesis tests. The description of the tree includes approximate standard errors on the lengths of segments of the tree. These are calculated by considering only the curvature of the likelihood surface as the length of the segment is varied, holding all other lengths constant. As such they are most probably underestimates of the variance, and hence may give too much confidence in the given tree.
One should use caution in interpreting the likelihoods that are printed out. If the model is wrong, it will not be possible to use the likelihoods to make formal statistical statements. Thus, if gene frequencies are being analyzed, but the gene frequencies change not only by genetic drift, but also by mutation, the model is not correct. It would be as well-justified in this case to use Gendist to compute the Nei (1972) genetic distance and then use Fitch, Kitsch or Neighbor to make a tree. If continuous characters are being analyzed, but if the characters have not been transformed to new coordinates that evolve independently and at equal rates, then the model is also violated and no statistical analysis is possible.
If the U (User Tree) option is used and more than one tree is supplied, the program also performs a statistical test of each of these trees against the one with highest likelihood. If there are two user trees, the test done is one which is due to Kishino and Hasegawa (1989), a version of a test originally introduced by Templeton (1983). In this implementation it uses the mean and variance of log-likelihood differences between trees, taken across loci. If the two trees' means are more than 1.96 standard deviations different then the trees are declared significantly different. This use of the empirical variance of log-likelihood differences is more robust and nonparametric than the classical likelihood ratio test, and may to some extent compensate for any lack of realism in the model underlying this program.
If there are more than two trees, the test done is an extension of the KHT test, due to Shimodaira and Hasegawa (1999). They pointed out that a correction for the number of trees was necessary, and they introduced a resampling method to make this correction. The version used here is a multivariate normal approximation to their test; it is due to Shimodaira (1998). The variances and covariances of the sum of log likelihoods across loci are computed for all pairs of trees. To test whether the difference between each tree and the best one is larger than could have been expected if they all had the same expected log-likelihood, log-likelihoods for all trees are sampled with these covariances and equal means (Shimodaira and Hasegawa's "least favorable hypothesis"), and a P value is computed from the fraction of times the difference between the tree's value and the highest log-likelihood exceeds that actually observed. Note that this sampling needs random numbers, and so the program will prompt the user for a random number seed if one has not already been supplied. With the two-tree KHT test no random numbers are used.
In either the KHT or the SH test the program prints out a table of the log-likelihoods of each tree, the differences of each from the highest one, the variance of that quantity as determined by the log-likelihood differences at individual sites, and a conclusion as to whether that tree is or is not significantly worse than the best one.
One problem which sometimes arises is that the program is fed two species (or populations) with identical transformed gene frequencies: this can happen if sample sizes are small and/or many loci are monomorphic. In this case the program "gets its knickers in a twist" and can divide by zero, usually causing a crash. If you suspect that this has happened, check for two species with identical coordinates. If you find them, eliminate one from the problem: the two must always show up as being at the same point on the tree anyway.
The constants available for modification at the beginning of the program include "epsilon1", a small quantity used in the iterations of branch lengths, "epsilon2", another not quite so small quantity used to check whether gene frequencies that were fed in for all alleles do not add up to 1, "smoothings", the number of passes through a given tree in the iterative likelihood maximization for a given topology, "maxtrees", the maximum number of user trees that will be used for the Kishino-Hasegawa-Templeton test, and "namelength", the length of species names. There is no provision in this program for saving multiple trees that are tied for having the highest likelihood, mostly because an exact tie is unlikely anyway.
The algorithm does not run as quickly as the discrete character methods but is not enormously slower. Like them, its execution time should rise as the cube of the number of species.
This data set was compiled by me from the compilation of human gene frequencies by Mourant (1976). It appeared in a paper of mine (Felsenstein, 1981c) on maximum likelihood phylogenies from gene frequencies. The names of the loci and alleles are given in that paper.
5 10 2 2 2 2 2 2 2 2 2 2 European 0.2868 0.5684 0.4422 0.4286 0.3828 0.7285 0.6386 0.0205 0.8055 0.5043 African 0.1356 0.4840 0.0602 0.0397 0.5977 0.9675 0.9511 0.0600 0.7582 0.6207 Chinese 0.1628 0.5958 0.7298 1.0000 0.3811 0.7986 0.7782 0.0726 0.7482 0.7334 American 0.0144 0.6990 0.3280 0.7421 0.6606 0.8603 0.7924 0.0000 0.8086 0.8636 Australian 0.1211 0.2274 0.5821 1.0000 0.2018 0.9000 0.9837 0.0396 0.9097 0.2976 |
Continuous character Maximum Likelihood method version 3.69 5 Populations, 10 Loci Numbers of alleles at the loci: ------- -- ------- -- --- ----- 2 2 2 2 2 2 2 2 2 2 Name Gene Frequencies ---- ---- ----------- locus: 1 2 3 4 5 6 7 8 9 10 European 0.28680 0.56840 0.44220 0.42860 0.38280 0.72850 0.63860 0.02050 0.80550 0.50430 African 0.13560 0.48400 0.06020 0.03970 0.59770 0.96750 0.95110 0.06000 0.75820 0.62070 Chinese 0.16280 0.59580 0.72980 1.00000 0.38110 0.79860 0.77820 0.07260 0.74820 0.73340 American 0.01440 0.69900 0.32800 0.74210 0.66060 0.86030 0.79240 0.00000 0.80860 0.86360 Australian 0.12110 0.22740 0.58210 1.00000 0.20180 0.90000 0.98370 0.03960 0.90970 0.29760 +-----------------------------------------------------------African ! ! +-------------------------------Australian 1-------------3 ! ! +-----------------------American ! +-----2 ! +Chinese ! +European remember: this is an unrooted tree! Ln Likelihood = 38.71914 Between And Length Approx. Confidence Limits ------- --- ------ ------- ---------- ------ 1 African 0.09693444 ( 0.03123910, 0.19853605) 1 3 0.02252816 ( 0.00089799, 0.05598045) 3 Australian 0.05247406 ( 0.01177094, 0.11542376) 3 2 0.00945315 ( -0.00897717, 0.03795670) 2 American 0.03806240 ( 0.01095938, 0.07997877) 2 Chinese 0.00208822 ( -0.00960622, 0.02017434) 1 European 0.00000000 ( -0.01627246, 0.02516630) |