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# geokey K Dimensional Z-Order curve utils. [](https://travis-ci.org/markrileybot/geokey) [](https://coveralls.io/github/markrileybot/geokey?branch=master) [](https://maven-badges.herokuapp.com/maven-central/io.github.markrileybot/geokey) ## Building ./gradlew build ## Gradle dependency See https://search.maven.org/artifact/io.github.markrileybot/geokey/ ## Using ### Use built in keys to make geohashes ```java import org.geokey.GeoKey; // Make a geo hash key String key = new GeoKey().setLatitude(48.669).setLongitude(-4.329).toString(); // "gbsuv7ztqzpts82uzfwq5e1bp" // parse a geo hash key GeoKey gk = new GeoKey("gbsuv7ztqzpts82uzfwq5e1bp"); ``` ### Make a special purpose K-Dimensional key ```java public class GeoTimeKey extends KDKey { private static final KDKeySpec spec = new KDKeySpec.Builder() .addDim(-180, 180, 1) .addDim(-90, 90, 1) .addDim(0, 1L << 62, 1) .setAlphabet(Alphabet.GEO_TIME_HASH) .build(); public GeoTimeKey() { super(spec); } public GeoTimeKey(String s) { super(spec, s); } public GeoTimeKey(byte[] s) { super(spec, s); } public GeoTimeKey setLatitude(double latitude) { set(1, latitude); return this; } public double getLatitude() { return super.get(1); } public GeoTimeKey setLongitude(double longitude) { set(0, longitude); return this; } public double getLongitude() { return super.get(0); } public GeoTimeKey setTime(long time) { set(2, time); return this; } public long getTime() { return (long) get(2); } } ```

[](https://drone.io/bitbucket.org/tidalwave/tidalwave-superpom-src/latest) The super POM for all Tidalwave projects. It is not designed for being used by others, as it contains some corporate-specific configurations, but its ancestor [TheseFooolishThings SuperPOM](http://bitbucket.org/tidalwave/thesefoolishthings-superpom-src) has been designed to be reusable. Please have a look at it. This super POM adds to its ancestor: + some Tidalwave variables that refers to the issue tracker, continuous integration system, etc...; + the definitions of versions of a number of commonly used libraries and their dependency management: * [AspectJ](https://www.eclipse.org/aspectj) * [Hamcrest Matchers](http://hamcrest.org/JavaHamcrest) * [JSR 330](https://github.com/google/guice/wiki/JSR330) * [Jakarta XML Binding (JAXB)](https://eclipse-ee4j.github.io/jaxb-ri/) * [Spotbugs annotations](https://spotbugs.readthedocs.io) * [JUnit](https://junit.org/junit5) * [Logback](http://logback.qos.ch) * [Lombok](https://projectlombok.org) * [SLF4J](slf4j.org) * [Spring 5](https://spring.io/projects/spring-framework) * [TestNG](https://testng.org) + the definition for Tidalwave 3rd party repository (stuff that is not available on Maven Central); + a profile for using the [TheseFoolishThings](http://tidalwave.it/projects/thesefoolishthings) Event Bus (```it.tidalwave-spring-messagebus-v1```); + profiles for the [Mycila License plugin](https://github.com/mycila/license-maven-plugin); + configuration of the UMLGraphDoc maven plugin; + the configuration for the TheseFoolishThings TestNG listener (which provides enhanced test logging); + definitions of some custom javadoc tags; + a blacklist for some old artifacts; + some other minor customisations.

[] (https://drone.io/bitbucket.org/tidalwave/thesefoolishthings-superpom-src/latest) A feature-rich SuperPOM for building Java projects. It features: * explicit version configuration for a number of plugins; * easy configurability by means of pre-defined properties to avoid cut & copy of plugin sections. A number of profiles, that can be easily activated, are available for: * Spring-AOP configuration; * different kinds of Continuous Integration tasks, including a full run of QA tools such as JaCoCo, FindBugs, PMD, etc... * deploying WARs and locally running them with Tomcat or Jetty; * creating a Mac OS X bundle for JavaFX applications; * creating .deb packages for both application and services; * a customized release cycle, including all requirements for the Maven Central such as signing, with a 'transactional' behaviour (all artifacts, both the DSCM and the Maven artifacts are prepared on the local disk, so they can be uploaded in a second moment); Remember to customise it ------------------------ If you use it, please remember to change the ```description```,```url```, ```organization```, ```developers```, ```license```, etc... to override those related to the development of this POM.

Enable full data quality and data lineage for BigData Projects. Huemul BigDataGovernance, es una librería que trabaja sobre Spark, Hive y HDFS. Permite la implementación de una **estrategia corporativa de dato único**, basada en buenas prácticas de Gobierno de Datos. Permite implementar tablas con control de Primary Key y Foreing Key al insertar y actualizar datos utilizando la librería, Validación de nulos, largos de textos, máximos/mínimos de números y fechas, valores únicos y valores por default. También permite clasificar los campos en aplicabilidad de derechos ARCO para facilitar la implementación de leyes de protección de datos tipo GDPR, identificar los niveles de seguridad y si se está aplicando algún tipo de encriptación. Adicionalmente permite agregar reglas de validación más complejas sobre la misma tabla. Facilita la configuración y lectura de las interfaces de entrada, permitiendo ajustar los parámetros de lectura en esquemas altamente cambientes, crea trazabilidad de las interfaces con las tablas en forma automática, y almacena los diccionarios de datos en un repositorio central. Finalmente, también automatiza la generación de código a partir de las definiciones de las interfaces de entrada, y la creación del código inicial de lógica de negocio.

Enable full data quality and data lineage for BigData Projects. Huemul BigDataGovernance, es una librería que trabaja sobre Spark, Hive y HDFS. Permite la implementación de una **estrategia corporativa de dato único**, basada en buenas prácticas de Gobierno de Datos. Permite implementar tablas con control de Primary Key y Foreing Key al insertar y actualizar datos utilizando la librería, Validación de nulos, largos de textos, máximos/mínimos de números y fechas, valores únicos y valores por default. También permite clasificar los campos en aplicabilidad de derechos ARCO para facilitar la implementación de leyes de protección de datos tipo GDPR, identificar los niveles de seguridad y si se está aplicando algún tipo de encriptación. Adicionalmente permite agregar reglas de validación más complejas sobre la misma tabla. Facilita la configuración y lectura de las interfaces de entrada, permitiendo ajustar los parámetros de lectura en esquemas altamente cambientes, crea trazabilidad de las interfaces con las tablas en forma automática, y almacena los diccionarios de datos en un repositorio central. Finalmente, también automatiza la generación de código a partir de las definiciones de las interfaces de entrada, y la creación del código inicial de lógica de negocio.

This is an implementation of the Neural Gas algorithm on distance data (Relational Neural Gas) for unsupervised clustering. We recommend that you use the functions provided by the RelationalNeuralGas class for your purposes. All other classes and functions are utilities which are used by this central class. In particular, you can use RelationalNeuralGas.train() to obtain a RNGModel (i.e. a clustering of your data), and subsequently you can use RelationalNeuralGas.getAssignments() to obtain the resulting cluster assignments, and RelationalNeuralGas.classify() to cluster new points which are not part of the training data set. The underlying scientific work is summarized nicely in the dissertation "Topographic Mapping of Dissimilarity Datasets" by Alexander Hasenfuss (2009). The basic properties of an Relational Neural Gas algorithm are the following: 1.) It is relational: The data is represented only in terms of a pairwise distance matrix. 2.) It is a clustering method: The algorithm provides a clustering model, that is: After calculation, each data point should be assigned to a cluster (for this package here we only consider hard clustering, that is: each data point is assigned to exactly one cluster). 3.) It is a vector quantization method: Each cluster corresponds to a prototype, which is in the center of the cluster and data points are assigned to the cluster if and only if they are closest to this particular prototype. 4.) It is rank-based: The updates of the prototypes depend only on the distance ranking, not on the absolute value of the distances.

pact-jvm-consumer-junit5 ======================== JUnit 5 support for Pact consumer tests ## Dependency The library is available on maven central using: * group-id = `au.com.dius` * artifact-id = `pact-jvm-consumer-junit5_2.12` * version-id = `3.5.x` ## Usage ### 1. Add the Pact consumer test extension to the test class. To write Pact consumer tests with JUnit 5, you need to add `@ExtendWith(PactConsumerTestExt)` to your test class. This replaces the `PactRunner` used for JUnit 4 tests. The rest of the test follows a similar pattern as for JUnit 4 tests. ```java @ExtendWith(PactConsumerTestExt.class) class ExampleJavaConsumerPactTest { ``` ### 2. create a method annotated with `@Pact` that returns the interactions for the test For each test (as with JUnit 4), you need to define a method annotated with the `@Pact` annotation that returns the interactions for the test. ```java @Pact(provider=&quot;test_provider&quot;, consumer=&quot;test_consumer&quot;) public RequestResponsePact createPact(PactDslWithProvider builder) { return builder .given(&quot;test state&quot;) .uponReceiving(&quot;ExampleJavaConsumerPactTest test interaction&quot;) .path(&quot;/&quot;) .method(&quot;GET&quot;) .willRespondWith() .status(200) .body(&quot;{\&quot;responsetest\&quot;: true}&quot;) .toPact(); } ``` ### 3. Link the mock server with the interactions for the test with `@PactTestFor` Then the final step is to use the `@PactTestFor` annotation to tell the Pact extension how to setup the Pact test. You can either put this annotation on the test class, or on the test method. For examples see [ArticlesTest](src/test/java/au/com/dius/pact/consumer/junit5/ArticlesTest.java) and [MultiTest](src/test/groovy/au/com/dius/pact/consumer/junit5/MultiTest.groovy). The `@PactTestFor` annotation allows you to control the mock server in the same way as the JUnit 4 `PactProviderRule`. It allows you to set the hostname to bind to (default is `localhost`) and the port (default is to use a random port). You can also set the Pact specification version to use (default is V3). ```java @ExtendWith(PactConsumerTestExt.class) @PactTestFor(providerName = &quot;ArticlesProvider&quot;, port = &quot;1234&quot;) public class ExampleJavaConsumerPactTest { ``` **NOTE on the hostname**: The mock server runs in the same JVM as the test, so the only valid values for hostname are: | hostname | result | | -------- | ------ | | `localhost` | binds to the address that localhost points to (normally the loopback adapter) | | `127.0.0.1` or `::1` | binds to the loopback adapter | | host name | binds to the default interface that the host machines DNS name resolves to | | `0.0.0.0` or `::` | binds to the all interfaces on the host machine | #### Matching the interactions by provider name If you set the `providerName` on the `@PactTestFor` annotation, then the first method with a `@Pact` annotation with the same provider name will be used. See [ArticlesTest](src/test/java/au/com/dius/pact/consumer/junit5/ArticlesTest.java) for an example. #### Matching the interactions by method name If you set the `pactMethod` on the `@PactTestFor` annotation, then the method with the provided name will be used (it still needs a `@Pact` annotation). See [MultiTest](src/test/groovy/au/com/dius/pact/consumer/junit5/MultiTest.groovy) for an example. ### Injecting the mock server into the test You can get the mock server injected into the test method by adding a `MockServer` parameter to the test method. ```java @Test void test(MockServer mockServer) { HttpResponse httpResponse = Request.Get(mockServer.getUrl() + &quot;/articles.json&quot;).execute().returnResponse(); assertThat(httpResponse.getStatusLine().getStatusCode(), is(equalTo(200))); } ``` This helps with getting the base URL of the mock server, especially when a random port is used. ## Unsupported The current implementation does not support tests with multiple providers. This will be added in a later release.

The OODT grid services (product and profile services) use CORBA or RMI as their underlying network transport. However, limitations of CORBA and RMI make them inappropriate for large-scale deployments. For one, both are procedural mechanisms, providing a remote interface that resembles a method call. This makes streaming of data from a service impossible, because there are limitations to the sizes of data structures that can be passed over a remote method call. Instead, repeated calls must be made to retrieve each block of a product, making transfer speeds horribly slow compared to HTTP or FTP. (Block-based retrieval of profiles was never implemented, resulting in out of memory conditions for large profile results, which is another problem.) Second, both CORBA and RMI rely on a central name registry. The registry makes an object independent of its network location, enabling a client to call it by name (looking up its last known location in the registry). However, this requires that server objects be able to make outbound network calls to the registry (through any outbound firewall), and that the registry accept those registrations (through any inbound firewall). This required administrative action at institutions hosting server objects and at the institution hosting the registry. Often, these firewall exceptions would change without notice as system adminstrators changed at each location (apparently firewall exceptions are poorly documented everywhere). Further, in the two major deployments of OODT (PDS and EDRN), server objects have almost never moved, nullifying any benefit of the registry. This project, OODT Web Grid Services, avoids the prolems of CORBA and RMI by using HTTP as the transport mechanism for products and profiles. Further, it provides a password-protected mechanism to add new sets of product and profile query handlers, enabling seamless activation of additional capabilities.

pact-jvm-consumer-groovy-v3 =========================== Groovy DSL for Pact JVM implementing V3 specification changes. ##Dependency The library is available on maven central using: * group-id = `au.com.dius` * artifact-id = `pact-jvm-consumer-groovy-v3_2.11` * version-id = `2.2.x` or `3.0.x` ##Usage Add the `pact-jvm-consumer-groovy-v3` library to your test class path. This provides a `PactMessageBuilder` class for you to use to define your pacts. If you are using gradle for your build, add it to your `build.gradle`: dependencies { testCompile 'au.com.dius:pact-jvm-consumer-groovy-v3_2.11:2.2.12' } ## Consumer test for a message consumer The `PactMessageBuilder` class provides a DSL for defining your message expectations. It works in much the same way as the `PactBuilder` class for Request-Response interactions. ### Step 1 - define the message expectations Create a test that uses the `PactMessageBuilder` to define a message expectation, and then call `run`. This will invoke the given closure with a message for each one defined in the pact. ```groovy def eventStream = new PactMessageBuilder().call { serviceConsumer 'messageConsumer' hasPactWith 'messageProducer' given 'order with id 10000004 exists' expectsToReceive 'an order confirmation message' withMetaData(type: 'OrderConfirmed') // Can define any key-value pairs here withContent(contentType: 'application/json') { type 'OrderConfirmed' audit { userCode 'messageService' } origin 'message-service' referenceId '10000004-2' timeSent: '2015-07-22T10:14:28+00:00' value { orderId '10000004' value '10.000000' fee '10.00' gst '15.00' } } } ``` ### Step 2 - call your message handler with the generated messages This example tests a message handler that gets messages from a Kafka topic. In this case the Pact message is wrapped as a Kafka `MessageAndMetadata`. ```groovy eventStream.run { Message message -> messageHandler.handleMessage(new MessageAndMetadata('topic', 1, new kafka.message.Message(message.contentsAsBytes()), 0, null, valueDecoder)) } ``` ### Step 3 - validate that the message was handled correctly ```groovy def order = orderRepository.getOrder('10000004') assert order.status == 'confirmed' assert order.value == 10.0 ``` ### Step 4 - Publish the pact file If the test was successful, a pact file would have been produced with the message from step 1.

pact-jvm-consumer-groovy-v3 =========================== Groovy DSL for Pact JVM implementing V3 specification changes. ##Dependency The library is available on maven central using: * group-id = `au.com.dius` * artifact-id = `pact-jvm-consumer-groovy-v3_2.11` * version-id = `2.2.x` or `3.0.x` ##Usage Add the `pact-jvm-consumer-groovy-v3` library to your test class path. This provides a `PactMessageBuilder` class for you to use to define your pacts. If you are using gradle for your build, add it to your `build.gradle`: dependencies { testCompile 'au.com.dius:pact-jvm-consumer-groovy-v3_2.11:2.2.12' } ## Consumer test for a message consumer The `PactMessageBuilder` class provides a DSL for defining your message expectations. It works in much the same way as the `PactBuilder` class for Request-Response interactions. ### Step 1 - define the message expectations Create a test that uses the `PactMessageBuilder` to define a message expectation, and then call `run`. This will invoke the given closure with a message for each one defined in the pact. ```groovy def eventStream = new PactMessageBuilder().call { serviceConsumer 'messageConsumer' hasPactWith 'messageProducer' given 'order with id 10000004 exists' expectsToReceive 'an order confirmation message' withMetaData(type: 'OrderConfirmed') // Can define any key-value pairs here withContent(contentType: 'application/json') { type 'OrderConfirmed' audit { userCode 'messageService' } origin 'message-service' referenceId '10000004-2' timeSent: '2015-07-22T10:14:28+00:00' value { orderId '10000004' value '10.000000' fee '10.00' gst '15.00' } } } ``` ### Step 2 - call your message handler with the generated messages This example tests a message handler that gets messages from a Kafka topic. In this case the Pact message is wrapped as a Kafka `MessageAndMetadata`. ```groovy eventStream.run { Message message -> messageHandler.handleMessage(new MessageAndMetadata('topic', 1, new kafka.message.Message(message.contentsAsBytes()), 0, null, valueDecoder)) } ``` ### Step 3 - validate that the message was handled correctly ```groovy def order = orderRepository.getOrder('10000004') assert order.status == 'confirmed' assert order.value == 10.0 ``` ### Step 4 - Publish the pact file If the test was successful, a pact file would have been produced with the message from step 1.