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The Finch HRIS API provides a unified way to connect to a multitide of HRIS systems. The API requires an access token issued by Finch. By default, Organization and Payroll requests use Finch's [Data Syncs](/developer-resources/Data-Syncs). If a request is made before the initial sync has completed, Finch will request data live from the provider. The latency on live requests may range from seconds to minutes depending on the provider and batch size. For automated integrations, Deductions requests (both read and write) are always made live to the provider. Latencies may range from seconds to minutes depending on the provider and batch size. Employer products are specified by the product parameter, a space-separated list of products that your application requests from an employer authenticating through Finch Connect. Valid product names are— - `company`: Read basic company data - `directory`: Read company directory and organization structure - `individual`: Read individual data, excluding income and employment data - `employment`: Read individual employment and income data - `payment`: Read payroll and contractor related payments by the company - `pay_statement`: Read detailed pay statements for each individual - `benefits`: Create and manage deductions and contributions and enrollment for an employer [](https://god.gw.postman.com/run-collection/21027137-08db0929-883d-4094-a9ce-dbf5a9bee4a4?action=collection%2Ffork&collection-url=entityId%3D21027137-08db0929-883d-4094-a9ce-dbf5a9bee4a4%26entityType%3Dcollection%26workspaceId%3D1edf19bc-e0a8-41e9-ac55-481a4b50790b)

The Finch HRIS API provides a unified way to connect to a multitide of HRIS systems. The API requires an access token issued by Finch. By default, Organization and Payroll requests use Finch's [Data Syncs](/developer-resources/Data-Syncs). If a request is made before the initial sync has completed, Finch will request data live from the provider. The latency on live requests may range from seconds to minutes depending on the provider and batch size. For automated integrations, Deductions requests (both read and write) are always made live to the provider. Latencies may range from seconds to minutes depending on the provider and batch size. Employer products are specified by the product parameter, a space-separated list of products that your application requests from an employer authenticating through Finch Connect. Valid product names are— - `company`: Read basic company data - `directory`: Read company directory and organization structure - `individual`: Read individual data, excluding income and employment data - `employment`: Read individual employment and income data - `payment`: Read payroll and contractor related payments by the company - `pay_statement`: Read detailed pay statements for each individual - `benefits`: Create and manage deductions and contributions and enrollment for an employer [](https://god.gw.postman.com/run-collection/21027137-08db0929-883d-4094-a9ce-dbf5a9bee4a4?action=collection%2Ffork&collection-url=entityId%3D21027137-08db0929-883d-4094-a9ce-dbf5a9bee4a4%26entityType%3Dcollection%26workspaceId%3D1edf19bc-e0a8-41e9-ac55-481a4b50790b)

OpenIMAJ (Open Intelligent Multimedia in Java) is a collection of libraries and tools for multimedia analysis written in the Java programming language. OpenIMAJ intends to be the first truly complete multimedia analysis library and contains modules for analysing images, videos, text, audio and even webpages. The OpenIMAJ image and video analysis and feature extraction modules contain methods for processing visual content and extracting state-of-the-art features, including SIFT. The OpenIMAJ clustering and nearest-neighbour libraries contain efficient, multi-threaded implementations of clustering algorithms including Hierarchical K-Means and Approximate K-Means. The clustering library makes it possible to easily create visual-bag-of-words representations for images and video with very large vocabularies. The text-analysis modules contain implementations of a statistical language classifier and low-level processing pipeline. A number of modules deal with content creation, including interactive slideshows and animations. The hardware integration modules allow cross-platform integration with devices including webcams, the Microsoft Kinect, and even devices such as GPS's. OpenIMAJ also incorporates a number of tools to enable extremely-large-scale multimedia analysis using a distributed computing approach based on Apache Hadoop.

EasyConfig provides simple way to overview and apply settings to file or folder based collections of files. Synonyms to "setting" are property, attribute, value while throughout application "setting" is used. The settings are groupped in "configuration" that is collection of settings from various sources. Main design concepts are: * minimalistic way to describe configuration * pluggable support for data types (validation), setting sources, source handlers Sample use case: An application is deployed in multiple locations. We need to quickly check key settings/parameters and optionally modify some of them. These values are located in different places: - in files directly in file structure - in files inside archive files (optionally nested archives) - values in DB tables - values accessible via URLs - other sources (just guessed: SSH/telnet connection+some command(s), UPnP devices, proprietary protocols, etc) We gather info from any supported (extendable) source and can modify and apply changes if supported by source (e.g. we can't update value that is count of rows in DB table, but we can read that value).

The Source Analyst library is a powerful tool designed to streamline and expedite the tracking of traffic sources for mobile applications. This versatile library is aptly named "Source Analyst" and is an invaluable asset for app developers and marketers seeking to gain deeper insights into the performance of their advertising campaigns. With just one simple function call, Source Analyst empowers you to efficiently investigate the effectiveness of various advertising sources. Key Features: Effortless Tracking: Source Analyst simplifies the complex task of tracking the origins of traffic for your mobile app. No need for convoluted setups or extensive coding – one function is all it takes. Comprehensive Insights: Gain a comprehensive understanding of where your app's users are coming from. Whether it's through social media, search engines, referral links, or other channels, Source Analyst provides you with clear data on traffic sources. Performance Evaluation: Evaluate the performance of your advertising campaigns with precision. Discover which sources are driving the most valuable users to your app, helping you optimize your marketing efforts effectively. Time-Saving: Say goodbye to hours spent on manual data collection and analysis. Source Analyst automates the tracking process, freeing up your time to focus on making data-driven decisions. Customization: Tailor Source Analyst to your specific needs. Customize the library to track the metrics that matter most to your app's success. Real-time Data: Access real-time data, ensuring that you always have up-to-date insights into your traffic sources. Integration-Friendly: Seamlessly integrate Source Analyst into your existing mobile app infrastructure, whether you're developing for Android or iOS. User-Friendly: Source Analyst is designed with user-friendliness in mind. Its intuitive interface and straightforward documentation make it accessible to developers of all levels of expertise. How It Works: Using Source Analyst is as easy as calling a single function within your code. Simply integrate the library into your app, and you can begin tracking traffic sources immediately. From there, Source Analyst compiles and presents the data in a clear and organized manner, allowing you to make data-driven decisions with ease. In a world where understanding the origins of your app's traffic is essential for marketing success, Source Analyst is the go-to solution. Say goodbye to the complexity of tracking sources and embrace the simplicity and effectiveness of Source Analyst for your mobile app. Harness the power of Source Analyst and unlock a new level of insight into your app's performance today!

Introducing "PerScope" Library: Simplifying Privacy Policy Event Handling for Android Apps "PerScope" is a cutting-edge library designed to streamline the processing of privacy policy events within regions where compliance with local legislation is crucial. Specifically crafted for Android applications, this library addresses the intricate task of managing privacy policy-related events while adhering to the legal requirements of the country in which the app is deployed. In today's digital landscape, ensuring user privacy and data protection is of paramount importance. Different countries have varying legal frameworks dictating how user data should be handled, necessitating robust mechanisms to accommodate these differences seamlessly. This is where the "PerScope" library shines. The key feature that sets "PerScope" apart is its incredible simplicity. With just a single function call, developers can integrate the library into their Android applications and gain immediate access to a comprehensive suite of tools for managing privacy policy events. Whether it's presenting privacy-related notifications, tracking user consents, or adapting the app's behavior based on regional requirements, "PerScope" handles it all efficiently and effectively. Here's a glimpse of what "PerScope" brings to the table: Localized Compliance: "PerScope" empowers developers to align their apps with the privacy laws of each region. By intelligently detecting the user's location, the library ensures that the app's behavior remains compliant with the specific privacy regulations of that area. Event Handling Made Easy: Instead of grappling with complex event management code, developers can integrate the "PerScope" function, drastically reducing development time and effort. The library takes care of the intricate event handling process seamlessly. Dynamic Adaptation: With the ability to dynamically adapt the app's features based on the user's consent and the local legal requirements, "PerScope" ensures a personalized and compliant user experience. Notification Presentation: "PerScope" assists in presenting privacy-related notifications to users, making it easier to inform them about data collection practices and obtain necessary consents. Smooth Integration: The library is designed to be easily integrated into existing Android applications, minimizing disruptions to the development process. In a nutshell, "PerScope" is a developer's go-to solution for managing privacy policy events within Android apps. Its single-function approach, combined with its capacity to handle a complex and critical aspect of app development, makes it an indispensable tool for app creators aiming to provide a user-centric, privacy-respecting experience while complying with regional legislation. Stay on the right side of the law and prioritize user privacy with the power of "PerScope."

Pact provider ============= sub project of https://github.com/DiUS/pact-jvm The pact provider is responsible for verifying that an API provider adheres to a number of pacts authored by its clients This library provides the basic tools required to automate the process, and should be usable on its own in many instances. Framework and build tool specific bindings will be provided in separate libraries that build on top of this core functionality. ### Running Pacts Main takes 2 arguments: The first is the root folder of your pact files (all .json files in root and subfolders are assumed to be pacts) The second is the location of your pact config json file. ### Pact config The pact config is a simple mapping of provider names to endpoint url's paths will be appended to endpoint url's when interactions are attempted for an example see: https://github.com/DiUS/pact-jvm/blob/master/pact-jvm-provider/src/test/resources/pact-config.json ### Provider State Before each interaction is executed, the provider under test will have the opportunity to enter a state. Generally the state maps to a set of fixture data for mocking out services that the provider is a consumer of (they will have their own pacts) The pact framework will instruct the test server to enter that state by sending: POST "${config.stateChangeUrl.url}/setup" { "state" : "${interaction.stateName}" } ### An example of running provider verification with junit This example uses java, junit and hamcrest matchers to run the provider verification. As the provider service is a DropWizard application, it uses the DropwizardAppRule to startup the service before running any test. Warning: It only grabs the first interaction from the pact file with the consumer, where there could be many. (This could possibly be solved with a parameterized test) ```java public class PactJVMProviderJUnitTest { @ClassRule public static TestRule startServiceRule = new DropwizardAppRule<DropwizardAppConfig>(DropwizardApp.class, "config.yml"); private static ProviderInfo serviceProvider; private static Pact testConsumerPact; @BeforeClass public static void setupProvider() { serviceProvider = new ProviderInfo("Dropwizard App"); serviceProvider.setProtocol("http"); serviceProvider.setHost("localhost"); serviceProvider.setPort(8080); serviceProvider.setPath("/"); ConsumerInfo consumer = new ConsumerInfo(); consumer.setName("test_consumer"); consumer.setPactFile(new File("target/pacts/ping_client-ping_service.json")); // serviceProvider.getConsumers().add(consumer); testConsumerPact = (Pact) new PactReader().loadPact(consumer.getPactFile()); } @Test @SuppressWarnings("unchecked") public void runConsumerPacts() { //grab the first interaction from the pact with consumer List<Interaction> interactions = scala.collection.JavaConversions.seqAsJavaList(testConsumerPact.interactions()); Interaction interaction1 = interactions.get(0); //setup any provider state //setup the client and interaction to fire against the provider ProviderClient client = new ProviderClient(); client.setProvider(serviceProvider); client.setRequest(interaction1.request()); Map<String, Object> clientResponse = (Map<String, Object>) client.makeRequest(); Map<String, Object> result = (Map<String, Object>) ResponseComparison.compareResponse(interaction1.response(), clientResponse, (int) clientResponse.get("statusCode"), (Map) clientResponse.get("headers"), (String) clientResponse.get("data")); //assert all good assertThat(result.get("method"), is(true)); // method type matches Map headers = (Map) result.get("headers"); //headers match headers.forEach( (k, v) -> assertThat(format("Header: [%s] does not match", k), v, org.hamcrest.Matchers.equalTo(true)) ); assertThat((Collection<Object>)((Map)result.get("body")).values(), org.hamcrest.Matchers.hasSize(0)); // empty list of body mismatches } } ``` ### An example of running provider verification with spock This example uses groovy and spock to run the provider verification. Again the provider service is a DropWizard application, and is using the DropwizardAppRule to startup the service. This example runs all interactions using spocks Unroll feature ```groovy class PactJVMProviderSpockSpec extends Specification { @ClassRule @Shared TestRule startServiceRule = new DropwizardAppRule<DropwizardAppConfig>(DropwizardApp.class, "config.yml"); @Shared ProviderInfo serviceProvider @Shared Pact testConsumerPact def setupSpec() { serviceProvider = new ProviderInfo("Dropwizard App") serviceProvider.protocol = "http" serviceProvider.host = "localhost" serviceProvider.port = 8080; serviceProvider.path = "/" def consumer = serviceProvider.hasPactWith("ping_consumer", { pactFile = new File('target/pacts/ping_client-ping_service.json') }) testConsumerPact = (Pact) new PactReader().loadPact(consumer.getPactFile()); } def cleanup() { //cleanup provider state //ie. db.truncateAllTables() } def cleanupSpec() { //cleanup provider } @Unroll def "Provider Pact - With Consumer"() { given: //setup provider state // ie. db.setupRecords() // serviceProvider.requestFilter = { req -> // req.addHeader('Authorization', token) // } when: ProviderClient client = new ProviderClient(provider: serviceProvider, request: interaction.request()) Map clientResponse = (Map) client.makeRequest() Map result = (Map) ResponseComparison.compareResponse(interaction.response(), clientResponse, clientResponse.statusCode, clientResponse.headers, clientResponse.data) then: // method matches result.method == true // headers all match, spock needs the size checked before // asserting each result if (result.headers.size() > 0) { result.headers.each() { k, v -> assert v == true } } // empty list of body mismatches result.body.size() == 0 where: interaction << scala.collection.JavaConversions.seqAsJavaList(testConsumerPact.interactions()) } } ```