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  Connect Devices and Equipment Using Industry-Standard Protocol Drivers in ThingWorx   GUIDE CONCEPT   This guide has step-by-step instructions for connecting ThingWorx Kepware Server to ThingWorx Foundation.   This guide will demonstrate the ease of connecting edge industrial equipment to ThingWorx Foundation without installing any software on production equipment.   We will also show how connecting different systems and devices improves operations through the creation of business intelligence.     YOU'LL LEARN HOW TO   Connect ThingWorx Kepware Server to ThingWorx Foundation Secure the connection with an Application Key Create an IndustrialGateway Thing Map ThingWorx Kepware Server Tags to ThingWorx Foundation Thing Properties Visualize Data from connected digital assets   Note: The estimated time to complete this guide is 30 minutes     Step 1: Installation   Download the ThingWorx Kepware Server executable application from MyKepware. If you desire installation instructions, you may find them in the attached guide: install-thingworx-kepware-server.zip . Navigate to START -> PTC. Click ThingWorx Kepware Server 6 Configuration.                           For additional information on ThingWorx Kepware Server, click Help -> Server Help on the Menu Bar.         Step 2: Create Gateway   To make a connection between ThingWorx Kepware Server and Foundation Server, you must first create a Thing.   WARNING: To avoid a timeout error, create a Thing in ThingWorx Foundation BEFORE attempting to make the connection in ThingWorx Kepware Server.   In ThingWorx Foundation Composer, click Browse > Modeling -> Things.   Click + NEW. In the Name field, type IndConn_Server, including matching capitalization. In the Description field, enter an appropriate description, such as Industrial Gateway Thing to connect to ThingWorx Kepware Server. If the Project field is not already set, search for and select PTCDefaultProject. In the Base Thing Template field, search for and select IndustrialGateway.   Click Save.     Step 3: Create an AppKey   To secure the connection between ThingWorx Foundation and ThingWorx Kepware Server, you need to utilize an Application Key.   In ThingWorx Foundation, click Browse > Security -> Applications Keys.   Click + New. In the Name field, type IndConn_AppKey. If Project is not already set, search for and select PTCDefaultProject. Set User Name Reference to the Administrator User. Click Yes on the warning pop-up.   Assign an Expiration Date that is far enough in the future to not interfere with your trial.   Click Save.   Under Key ID, click the page icon to the right of the Application Key string to copy it.     Step 4: Connect to Foundation   Now that you’ve created an IndustrialGateway Thing and an Application Key, you can configure ThingWorx Kepware Server to connect to ThingWorx Foundation.   Return to the ThingWorx Kepware Server Windows application. Right-click Project. Select Properties….   In the Property Editor pop-up, click ThingWorx. In the Enable field, select Yes from the drop-down. In the Host field, enter the IP address or URL of your ThingWorx Foundation server. Enter the Port number. If you are using the "hosted" Developer Portal trial, enter 443.     In the Application Key field, copy and paste the Application Key you just created. In the Trust self-signed certificates field, select Yes from the drop-down. In the Trust all certificates field, select Yes from the drop-down. In the Disable encryption field, select No from the drop-down if you are using a secure port. Select Yes if you are using an http port. Type IndConn_Server in the Thing name field, including matching capitalization. If you are connecting with a remote instance of ThingWorx Foundation and you expect any breaks or latency in your connection, enable Store and Forward. Click Apply in the pop-up. Click Ok.   In the ThingWorx Kepware Server Event window at the bottom, you should see a message indicating Connected to ThingWorx.     NOTE: If you do not see the "Connected" message, repeat the steps above, ensuring that all information is correct. In particular, check the Host, Port, and Thing name fields for errors.     Click here to view Part 2 of this guide.
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  Step 5: Mashup Builder API   The following APIs can be accessed by a widget in the context of the Mashup Builder:    API                                                                                                                Description this.jqElementId This is the DOM element ID of your object after renderHtml(). this.jqElement This is the jquery element. this.getProperty(name) / this.setProperty(name,value) Note that every call to this function will call afterSetProperty() if it’s defined in the widget. this.updatedProperties() This function should be called anytime properties are changed in the widget so that the Mashup Builder can update the widget properties window, the connections window, and so on. this.getInfotableMetadataForProperty(propertyName) If you need the infotable metadata for a property that you bound, you can get it by calling this API; it returns undefined if it is not bound. this.resetPropertyToDefaultValue(propertyName) This call resets the named property to its default value. this.removeBindingsFromPropertyAsTarget(propertyName) This call removes target data bindings from the propertyName. Use it only when the user has initiated an action that invalidates the property. this.removeBindingsFromPropertyAsSource(propertyName) This call removes source data bindings from the propertyName. Use this only when the user has initiated an action that invalidates the property. this.isPropertyBoundAsTarget(propertyName) This call returns a result that indicates if the property has been bound as a target. You can use it to determine if a property has been set or bound. this.isPropertyBoundAsSource(propertyName) This call returns a result that indicates if the property has been bound as a source. You can use it to determine if a property has been bound to a target.   Example of the Checkbox Widget’s validate() function: this.validate = function () { var result = []; if (!this.isPropertyBoundAsSource('State') && !this.isPropertyBoundAsTarget('State')) { result.push({ severity: 'warning', message: 'State for {target-id} is not bound' }); } return result; }   Example of the Blog Widgets validate() function: this.validate = function () { var result = []; var blogNameConfigured = this.getProperty('Blog'); if (blogNameConfigured === '' || blogNameConfigured === undefined) { if (!this.isPropertyBoundAsTarget('Blog')) { result.push({ severity: 'warning', message: 'Blog is not bound for {target-id}' }); } } return result; }     Step 6: Mashup Builder Callbacks   The following widget functions are called by the Mashup Builder to control the widget’s behavior. widgetProperties() - Returns a JSON structure that defines the properties of the widget. Listed are the possible properties of the widget:   Required Property   The only required property is:   name - The user-friendly widget name, as shown in the widget toolbar   Optional Properties   There are a number of optional properties that can be contained in the returned JSON structure.    Property                                                                 Description description A description of the widget, used for its tooltip. iconImage - File name of the widget icon/image category An array of strings for specifying one or more categories to which the widget belongs (such as “Common”, “Charts”, “Data”, “Containers”, and “Components”), enabling widgets to be filtered by type/category. isResizable true (default) or false defaultBindingTargetProperty Name of the property to use as the data/event binding target borderWidth If the widget has a border, set this property to the width of the border. This property ensures pixel-perfect WYSIWG between design and runtime. If you set a border of one pixel on the widget-content element at design time, you are making the widget two pixels taller and two pixels wider (one pixel on each side). To account for this discrepancy, set the borderWidth property to make the design-time widget the same number of pixels smaller. This property places the border inside the widget that you created and makes the width and height in the widget properties accurate. isContainer true or false (default). Controls whether an instance of the widget can be a container for other widget instances. customEditor The name of the custom editor dialog for entering and editing the widget configuration. The system assumes there is a dialog you created named TW.IDE.Dialogs.<name>. customEditorMenuText The text that appears on the flyout menu of the widget and the tooltip text for the Configure Widget Properties button. For example, “Configure Grid Columns.” allowPositioning true (default) or false supportsLabel true or false (default). If true, the widget exposes a label property used to create a text label that appears next to the widget in the Composer and at runtime. supportsAutoResize true or false (default). If true, the widget can be placed in responsive containers (such as columns, rows, responsive tabs, responsive mashups). properties A collection of JSON objects for the widget that describe the properties of the widget that can be modified when the widget is added to a mashup. These properties are displayed in the Properties window of the Mashup Builder - the name of each object is used as the property name and the corresponding attributes control how the property value is set. afterLoad() Called after the object is loaded and properties have been restored from the file, but before the object has been rendered renderHtml() [required] Returns HTML fragment that the Composer will place in the screen; the widget’s content container (e.g. div) must have a ‘widget-content’ class specified. After this container element is appended to the DOM, it becomes accessible via jqElement and its DOM element ID will be available in jqElementId widgetEvents() A collection of events. warnIfNotBound true or false; If true, the property will be checked by Composer to determine whether it is bound, then generate a to-do item if/when it is not. widgetServices() A collection of services. warnIfNotBound true or false; If true, the property will be checked by the Composer to determine whether it is bound, then generate a to-do item if/when it is not. afterRender() Called after the HTML fragment is inserted into the DOM. beforeDestroy() Called right before the widget’s DOM element gets removed and the widget is detached from its parent widget and delocated; this is the place in which to perform any clean-up of resources (e.g. plugins, event handlers) acquired throughout the lifetime of the widget. beforeSetProperty(name,value) [Mashup Builder only - not at runtime] Called before any property is updated within Composer; this is a good place to perform validation on the new property value before it is committed. If a message string is returned, then the message will be displayed to the user, and the new property value will not be committed. If nothing is returned, then the value is assumed to be valid. afterSetProperty(name,value) [Mashup Builder only - not at runtime] Called after any property is updated within Composer. Return true to have the widget re-rendered in Composer. afterAddBindingSource(bindingInfo) Whenever data is bound to the widget, you will call back with this (if you implemented it … it’s optional). The only field in bindingInfo is targetProperty which is the propertyName that was just bound. validate() Called when Composer refreshes its to-do list. The call must return an array of result objects with severity (optional and not implemented) and message (required) properties. The message text may contain one or more pre-defined tokens, such as {target-id}, which will contain a hyperlink that allows the user to navigate to or select the specific widget that generated the message.   Properties Section Breakdown   The following attributes can be specified for each property object:    Aspect                                           Description description A description of the widget, which is used for its tooltip. baseType The system base type name - if the baseType value is FIELDNAME the widget property window displays a dropdown list of fields available in the INFOTABLE bound to the sourcePropertyName value based on the baseTypeRestriction. mustImplement If the baseType is THINGNAME and you specify “mustImplement”, the Mashup Builder will restrict popups to those implementing the specified EntityType and EntityName [by calling QueryImplementingThings against said EntityType and EntityName]. baseTypeInfotableProperty If baseType is RENDERERWITHFORMAT, baseTypeInfotableProperty specifies which property’s infotable is used for configuration. sourcePropertyName When the property’s baseType is FIELDNAME, this attribute is used to determine which INFOTABLE’s fields are to be used to populate the FIELDNAME dropdown list. baseTypeRestriction When specified, this value is used to restrict the fields available in the FIELDNAME dropdown list. tagType If the baseType is TAGS this can be ‘DataTags’ (default) or ‘ModelTags.’ defaultValue Default undefined; used only for ‘property’ type. isBindingSource true or false; Allows the property to be a data binding source, default to false. isBindingTarget true or false; Allows the property to be a data binding target, default to false. isEditable true or false; Controls whether the property can be edited in Composer, default to true. isVisible true or false; Controls whether the property is visible in the properties window, default to true. isLocalizable true or false; Only important if baseType is STRING - controls whether the property can be localized or not. selectOptions An array of value / (display) text structures. warnIfNotBoundAsSource true or false; If true, the property will be checked by Composer to determine whether it is bound and generate a to-do item if/when it is not. warnIfNotBoundAsTarget true or false; If true, the property will be checked by Composer to determine whether it is bound and generate a to-do item if/when it is not.   Other Special BaseType   Additional special baseTypes:   STATEDEFINITION - Picks a StateDefinition STYLEDEFINITION - Picks a StyleDefinition RENDERERWITHSTATE - Displays a dialog and allows you to select a renderer and formatting. Note: You can set a default style by entering the string with the default style name in the defaultValue. When your binding changes, you should reset it to the default value, as shown in the code below:   this.afterAddBindingSource = function (bindingInfo) { if(bindingInfo['targetProperty'] === 'Data') { this.resetPropertyToDefaultValue('ValueFormat'); } }; STATEFORMATTING - Displays a dialog and allows you to pick a fixed style or state-based style. Note: You can set a default style by entering the string with the default style name in the defaultValue. When your binding changes, you should reset it to the default value as shown in the code above for RENDERERWITHSTATE. VOCABULARYNAME - Will pick a DataTag vocabulary   Some Examples   An example of the properties property: properties: { Prompt: { defaultValue: 'Search for...', baseType: STRING, isLocalizable: true }, Width: { defaultValue: 120 }, Height: { defaultValue: 20, isEditable: false }, }   An example of mustImplement: 'baseType': 'THINGNAME', 'mustImplement': { 'EntityType': 'ThingShapes', 'EntityName': 'Blog' }   An example of selectOptions:[ {value: ‘optionValue1’, text: ‘optionText1’}, {value: ‘optionValue2’, text: ‘optionText2’} ]   An example of validate function that allows you to navigate/select the specific widget that generated the message: this.validate = function () { var result = []; var srcUrl = this.getProperty('SourceURL'); if (srcUrl === '' || srcUrl === undefined) { result.push({ severity: 'warning', message: 'SourceURL is not defined for {target-id}'}); } return result; }     Click here to view Part 3 of this guide.
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  Based on real use cases and industry-leading solutions, this webinar looks at how developers can deliver valuable analytical outputs through experiences that ensure easy consumption of trusted analyses.   By taking a deep dive into a range of the analytics capabilities within ThingWorx, we will demonstrate how you can visualize complex analytic outputs to help your users understand what really matters in your data - and ultimately make quick, insightful business decisions.   Q&A   We didn’t have time to get to all of the questions during the live webcast, but we’ve answered them here on our blog. Have any additional questions? Please leave us a comment.   THIS SESSION TALKED ABOUT CONNECTIVITY WITH OTHER ANALYTICS PROGRAMS, SUCH AS R. HOW WOULD THINGWORX INTERACT WITH THE R PLATFORM? There are multiple reasons for using R and other analytics tools. Say you’re using R to build a predictive model, for instance—you can interact with data and load that information into our Analytics Server, even outside Analytics Builder. You can also interface in script within ThingWorx to run certain calculations driven by R. Moreover, even if you are building a model in R or any other tool, you can use Analytics Manager to operationalize data coming in from a Thing model that’s being scored against variables created elsewhere. Our ultimate goal is not to migrate you away from the tools you’re already using, but rather augment the development experience with ThingWorx integration.   HOW WOULD A DEVELOPER GO ABOUT CREATING & OBTAINING JSON AND CSV DATA FOR ANALYSIS? In terms of creating a historical data view, there are two separate methods. There’s creating a descriptive view for which you are using to create the model, and then there is operationalizing the model. On the operational side, it’s data coming from the Thing model being scored against the actual predictive model that’s created. For the descriptive view, the tool of choice ultimately boils down to organizational preference. How you load represented data into the Analytics Server is completely based on the tools with which you work.   CAN YOU EXPLAIN IN DETAIL THE STEPS FOR CONNECTING A MACHINE TO THE THINGWORX ANALYTICAL MODEL, INCLUDING HOW TO DEFINE THE DATA COMING FROM THE MACHINE TO CREATE THE MODEL? Absolutely. I’d encourage you to go check out our new Operationalize an Analytics Model developer guide available on the ThingWorx Developer Portal. In just 30 minutes, you’ll learn how to use Analytics Manager and ThingPredictor to automatically perform analytical calculations.   OUR ORGANIZATION SEES FEATURE ENGINEERING AS A KEY PART OF THE DATA ANALYSIS PROCESS. DO THINGWORX ALGORITHMS HANDLE FEATURE ENGINEERING INTERNALLY? Yes. There’s feature engineering in terms of getting a dataset ready for consumption. The technology ThingWorx provides is being able to automatically sift through the data and use various features to guide the selection of algorithms. Feature enrichment is what’s really powerful about our supervised machine learning capabilities.    HOW DO I INCORPORATE ADVANCED STYLING IN MY UI, LIKE ANIMATIONS AND RESPONSIVE BEHAVIORS? The standard way of achieving advanced styling in ThingWorx is to leverage the Media Entities, Style and State Definitions. Many widgets, such as the Value Display and the Shape Widget, have out-of-the-box ability to take a State Definition and apply advanced styling for things like severity of risks, etc. Watch our IoT Application Makeover webcast for more information about this topic.   DO YOU HAVE ANY RECOMMENDATIONS FOR GUARANTEEING DESIGN CONSISTENCY IN MY IOT APPLICATION? For non-designers: to keep your design clean and consistent, it is important to properly manage your Style Definitions. Define styles and stick to re-using them; don’t have five different styles for showing model accuracy, for instance. I would recommend creating 5-10 styles for text, and then from there choosing a color scheme for things like buttons, labels, charts, grid headers, and other elements where you need a vibrant color.
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  How to Display Data in Charts Guide Part 3   Step 6: Configure Charts   You now have a test Thing that has an Info Table Property and Time Series Property with values appropriate for display in various Chart Widgets, as well as a Mashup with those charts inside. However, in order for the Widget to display data, you need to bind it to the data source.   Bring Data into the Mashup   Select the Data tab in the top-right section of Composer.   Click the green + button under Data.   In the Entity Filter field, search for and select DDCThing. In the Services Filter field, type GetProperties. Click the right-arrow beside GetProperties The GetProperties Service will now appear on the right under Selected Services. Check the Execute on Load box under Selected Services. This will cause the GetProperties Service to execute as soon as the Mashup is loaded.   6. In the Services Filter field, type QueryPropertyHistory. 7. Click the right-arrow beside QueryPropertyHistory. 8. Check the Execute on Load box under Selected Services. 9. Click Done to close the Add Data pop-up window.   Drag-and-Drop Data Bindings   Under the Data tab, expand the Things_DDCThing > GetProperties service.   Drag GetProperties > InfoTableProperty to the Pie Chart Widget in the top-left section of the Canvas.           3. On the Select Binding Target pop-up, select Data. 4. Repeat steps 2-3 for the Label, Proportional, and Bubble charts. Note that the Bubble chart will be bound to DataSource1 rather than Data.     5. Drag-and-drop QueryPropertyHistory > Returned Data > All Data to the Line Chart Widget in the bottom-middle section of the Canvas. 6. On the Select Binding Target pop-up, select Data. 7. Repeat steps 5-6 for the Event Chart in the bottom-right of the Canvas.   Configure Chart Properties   Click the Pie Chart in the top-left of the Canvas to select it. In the bottom-left section of Composer, showing the Properties of the Pie Chart, type LabelField in the Filter Properties field. For the LabelField drop-down, select Label.   In the Filter Properties window, type ValueField. In the ValueField drop-down, select Value.       Repeat steps 1-5 for each of the other charts, making the following Widget Property changes: Chart Property Setting Label XAxisField XAxis Label DataField1 Data Proportional XAxisField XAxis Proportional DataField 1 ASensor Proportional DataField 2 BSensor Proportional DataField 3 CSensor Bubble XAxisField1 XValue Bubble YAxisField1 YValue Bubble BubbleValueField1 BubbleValue Line XAxisField timestamp Event XAxisField timestamp Event LabelField TimeSeriesProperty 7. At the top, click Save. 8. Click View Mashup.     Step 7: Next Steps   Congratulations!  In this guide, you learned how to:   Create a Data Shape to format an Info Table Create a Value Stream to record Property Value changes Create a Thing with an Info Table Property and a Time Series Property Create a Mashup with various Chart Widgets Link your Thing's Properties to the Chart Widgets   This is the last guide in the Customize UI and Display Options to Deploy Applications learning path.    Additional Resources   If you have questions, issues, or need additional information, refer to:   Resource Link Community Developer Community Forum Support Widget Help Center    
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Is your team operating an effective DevOps pipeline? DevOps is an important part of a mature, enterprise ready application, but the process isn’t simple.   This expert session will focus on how a full DevOps pipeline looks like and how PTC can help to build a seamless pipeline. Join us for our upcoming Expert Session to learn how to create a Docker image, integrate Azure with Docker and Git, and set up a seamless DevOps pipeline.   When? Thursday, September 30th 2021 | 11 AM EST Host: Tori FIrewind, Senior Engineer in PTC IOT Enterprise Deployment Center Registration link: https://www.ptc.com/en/resources/iiot/webcast/devops-pipeline-thingworx 
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  A series of training videos for ThingWorx Analytics   Guide Concept   This guide provides a series of training videos covering ThingWorx Analytics Server and Platform Analytics.   It is recommended that they be viewed in order.    Additionally, a downloadable .zip with additional training materials is provided.     You'll learn how to   Use ThingWorx Analytics Understand the Analytics Thought Process Basic Analytics concepts (data types, variables, modeling) Descriptive Analytics Predictive Analytics modeling techniques Familiarity with other topics: Prescriptive Analytics, Clustering, Time Series, Anomaly Detection Acquire basic knowledge of how to create an end-to-end Smart Application Deepen your Foundation and Analytics understanding NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete all parts of this guide is 12 hours       ThingWorx Analytics Overview   This guide will present a series of training videos for ThingWorx Analytics.It is recommended that you view each guide in-order, as future videos may build on the concepts learned in earlier ones.    Download CourseFiles.zip included in this guide, as it contains important materials for particular videos.   In this course, you will learn the basics of the ThingWorx Analytics Machine Learning process. You will understand how to perform Descriptive Analytics such as identifying Signals, Profiles, or building Clusters.   You will also understand how to train a Model and use Predictive and Prescriptive Scoring. Additional topics include Time Series, Anomaly Detection, and near-real-time Scoring. You will also learn about how to create a simple smart application using Analytics together with other pieces of the overall ThingWorx platform.     ThingWorx Analytics Video Guide   Module 1: ThingWorx Analytics Overview Module 2: Use Case Discussion Module 3: Data Profiling Module 4: Data Transformation and Feature Engineering Module 5: Descriptive Analytics Module 6: Predictive Models and Model Validation Module 7: Scoring Predictive Realtime Prescriptive Module 8: Time Series Modeling Module 9: Anomaly Detection Module 10: ThingWorx Foundation and Analytics Integration Module 11: Mini Project     Next Steps   Congratulations! You've successfully completed the Analytics Training Videos guide, and learned how to:   Use ThingWorx Analytics Understand the Analytics Thought Process Basic Analytics concepts (data types, variables, modeling) Descriptive Analytics Predictive Analytics modeling techniques Familiarity with other topics: Prescriptive Analytics, Clustering, Time Series, Anomaly Detection Acquire basic knowledge of how to create an end-to-end Smart Application Deepen your Foundation and Analytics understanding   Additional Resources If you have questions, issues, or need additional information, refer to:   Resource Link Community Developer Community Forum Analytics Support Help Center          
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This video is Module 10: ThingWorx Foundation & Analytics Integration of the ThingWorx Analytics Training videos. It gives a brief review of core ThingWorx Platform functionality, and how the Analytics server works on top of the platform. It also describes the process of creating a simple application, complete with a mashup to display the information from a predictive model.
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Check out this new KCS article which links to all known best practice documents available for ThingWorx. This article will get larger in time as more articles are published related to the Dos and Don'ts of building an IoT application! Do you know when to use timers, and where to implement their subscriptions? How about ensuring info tables are used at the proper time, and data tables at others? Pesky performance issues wherein ThingWorx runs slow for apparently no reason? All of these questions and more are addressed here!
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This script will get all contacts (optionally limited to a particular organization) and check whether there is a DeviceContact associated with it.  If there is no DeviceContact (meaning it is not associated with a device), it deletes the contact. Note - It is worthwhile to test this script by commenting out the contact.delete() line first and reviewing which contacts will be deleted.  Also, this script works by finding all contacts, therefore it is not recommended to run the script repeatedly within a short period of time. Parameter: organizationName  (OPTIONAL) - Str - the name of the organization import net.sf.json.JSONObject import com.axeda.drm.sdk.device.DeviceFinder import com.axeda.drm.sdk.device.Device import com.axeda.drm.sdk.device.ModelFinder import com.axeda.drm.sdk.device.Model import com.axeda.drm.sdk.data.CurrentDataFinder import com.axeda.drm.sdk.data.DataValue import net.sf.json.groovy.JsonSlurper import com.axeda.drm.sdk.contact.Contact import com.axeda.drm.sdk.contact.ContactFinder import com.axeda.drm.sdk.contact.Location import com.axeda.drm.sdk.contact.LocationFinder import com.axeda.drm.sdk.contact.OrganizationFinder import com.axeda.drm.sdk.Context import com.axeda.drm.sdk.contact.Organization import com.axeda.drm.sdk.contact.DeviceContact import com.axeda.drm.sdk.contact.ContactMethodType import com.axeda.drm.sdk.contact.DeviceContactFinder import groovy.json.* import com.axeda.drm.sdk.scripto.Request import com.axeda.common.sdk.id.Identifier /** * ContactDelete.groovy * ----------------------- * * Finds all contacts, then finds the device contact for each contact. If null, deletes the contact. * * @params * organizationName (OPTIONAL) Str - limit the contact deletion to an organization * * * @author Sara Streeter <sstreeter@axeda.com> */ /** * initialize our global variables * json = the contents of our response * infoString = a stringBuilder used to collect debug information during the script * contentType = the content type we will return * scriptname = The name of this Script, used in multiple places */ def json = new groovy.json.JsonBuilder() def infoString = new StringBuilder() def contentType = "application/json" def scriptName = "ContactDelete.groovy" def root = ["result":["deleted":[]]] def timings = [:] timings.contactFinding = 0 timings.contactIterating = 0 wholestart = System.currentTimeMillis() final Context CONTEXT = Context.getSDKContext() try {       def params = Request?.parameters?.size() > 0 ? Request?.parameters : parameters       ContactFinder cfinder = new ContactFinder(CONTEXT)       def start = System.currentTimeMillis()     def organization       if (params.organizationName != null && params.organizationName != ""){         OrganizationFinder oFinder = new OrganizationFinder(CONTEXT)         oFinder.setName(params.organizationName)         organization = oFinder.find()               if (organization){             cfinder.setOrganization(organization)         }     }       List<Contact> contacts = cfinder.findAll()     timings.contactFinding += System.currentTimeMillis()-start       root.result.contactSize = contacts.size()       start = System.currentTimeMillis()     contacts.each{  contact ->           DeviceContactFinder dcfinder = new DeviceContactFinder(CONTEXT)         dcfinder.setContactId(contact.id)         def dc = dcfinder.findAll()         if (dc.size() == 0){             root.result.deleted << [                 id: contact.id.value,                 firstName: contact.firstName,                 lastName: contact.lastName,                 organization: contact.organization?.name             ]             contact.delete()  // comment out this line to check which contacts will be deleted first.         }     }     timings.contactIterating += System.currentTimeMillis()-start     } catch (Exception e) {     processException(scriptName,json,e) } finally {     timings.wholescript = System.currentTimeMillis() - wholestart     root += [timings: timings] } return ['Content-Type': 'application/json', 'Content': JSONObject.fromObject(root).toString(2)] /*     Processes the contents of an Exception and add it to the Errors collection     @param json The markup builder */ private def processException(String scriptName, JsonBuilder json, Exception e) {     // catch the exception output     def logStringWriter = new StringWriter()     e.printStackTrace(new PrintWriter(logStringWriter))     logger.error("Exception occurred in ${scriptName}: ${logStringWriter.toString()}")     /*         Construct the error response         - errorCode Will be an element from an agreed upon enum         - errorMessage The text of the exception      */     json.errors  {         error {             message     "[${scriptName}]: " + e.getMessage()             timestamp   "${System.currentTimeMillis()}"         }     }     return json }
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How to enable ThingWorx Performance Advisor Applies To ThingWorx 7.2+ Description How to enable ThingWorx Performance Advisor Resolution In the ThingWorx Composer, go to Systems > Subsystems, select the PlatformSubsystem and choose Configuration In the Metrics Reporting Service Configuration Select the "Enable Metrics Reporting" checkbox to activate Performance Advisor reporting Enter your current PTC credentials (username and password) for either the customer support portal or the developer portal After providing those details, use the Request button to request an Authorization Key. Customer Number and Name will be filled automatically and an Authorization Key is generated which allows the server identifying itself to the PTC environment. Those fields are read-only. ThingWorx is now ready to send Performance Advisor data and metrics to PTC Related FAQ - Performance Advisor for ThingWorx | PTC https://community.thingworx.com/community/developers/blog/2017/05/22/performance-advisor-for-thingworx-explore-configure…
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The Metadata for the 8.1 release has been updated from that of the previous releases as part of the architecture update that is new in 8.1.  The goal of this blog post is to inform you of these changes to the metadata, and where you can find more information on both the new metadata format, as well as all the other changes that are new for the 8.1 release. New Structure The image below provides an excerpt of what the metadata file itself will look like in practice. The table below provides a definition for what each field in the metadata is and how it should be populated in your metadata file. Parameter Description Required/Optional fieldName The exact name of the field as it appears in the data file. Required values A list of the acceptable values for the field. Note: For Ordinal opTypes, the values must be presented in the correct order. Required if the opType is Ordinal Optional for Categorical opType Do not use for Boolean and Continuous range For a Continuous field, defines the minimum and maximum values the field can accept. For informational purposes only. Optional dataType Describes what type of data the field contains. Options include: Long, Integer, Short, Byte, Double, Boolean, String, Other. Note: Select the most accurate dataType. Selecting the String dataType for numeric data can lead to undesirable results. Required opType Describes how the data in the field can be used. Options include: Categorical, Boolean, Ordinal, Continuous, Informational, Temporal, Entity_ID Required timeSamplingInterval An integer representing the time between observations in a temporal field. Required if the opType is Temporal Do not use for other opTypes isStatic A flag indicating whether or not the value in a temporal field can change over time. Marking a field as static reduces training time by removing redundant data points for fields that do not change. Optional Things to Remember Remember that the Metadata file that you create will need to match the data file that you have; furthermore, all of the columns that you have in your dataset will need to be represented in the metadata file. The metadata file needs to be a JSON file. Setting the opType parameter incorrectly can have a severe impact on system performance.  For example, setting a numerical field that has thousands of different values as categorical instead of continuous will cause the system to handle each value as an independent category, instead of just a number, which will result in significantly longer processing time. Additional References For more information on all the other changes that are new in the 8.1 release please follow this link for the complete reference document. Feel free to use the blank example metadata file attached to this post to help you get started on your own.
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Video Author:                     Polina Osipova Original Post Date:            June 10, 2016   Description: This is a video tutorial on adding State Formatting in a Mashup using State Definitions.      
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Back in 2018 an interesting capability was added to ThingWorx Foundation allowing you to enable statistical calculation of service and subscription execution.   We typically advise customers to approach this with caution for production systems as the additional overhead can be more than you want to add to the work the platform needs to handle.  This said, these statistics is used consciously can be extremely helpful during development, testing, and troubleshooting to help ascertain which entities are executing what services and where potential system bottlenecks or areas deserving performance optimization may lie.   Although I've used the Utilization Subsystem services for statistics for some time now, I've always found that the Composer table view is not sufficient for a deeper multi-dimensional analysis.  Today I took a first step in remedying this by getting these metrics into Excel and I wanted to share it with the community as it can be quite helpful in giving developers and architects another view into their ThingWorx applications and to take and compare benchmarks to ensure that the operational and scaling is happening as was expected when the application was put into production.   Utilization Subsystem Statistics You can enable and configure statistics calculation from the Subsystem Configuration tab.  The help documentation does a good job of explaining this so I won't mention it here.  Base guidance is not to use Persisted statistics, nor percentile calculation as both have significant performance impacts.  Aggregate statistics are less resource intensive as there are less counters so this would be more appropriate for a production environment.  Specific entity statistics require greater resources and this will scale up as well with the number of provisioned entities that you have (ie: 1,000 machines versus 10,000 machines) whereas aggregate statistics will remain more constant as you scale up your deployment and its load.   Utilization Subsystem Services In the subsystem Services tab, you can select "UtilizationSubsystem" from the filter drop down and you will see all of the relevant services to retrieve and reset the statistics.     Here I'm using the GetEntityStatistics service to get entity statistics for Services and Subscriptions.     Giving us something like this.      Using Postman to Save the Results to File I have used Postman to do the same REST API call and to format the results as HTML and to save these results to file so that they can be imported into Excel.   You need to call '/Thingworx/Subsystems/UtilizationSubsystem/Services/GetEntityStatistics' as a POST request with the Content-Type and Accept headers set to 'application/xml'.  Of course you also need to add an appropriately permissioned and secured AppKey to the headers in order to authenticate and get service execution authorization.     You'll note the Export Results > Save to a file menu over on the right to get your results saved.   Importing the HTML Results into Excel As simple as I would like to hope that getting a standard web formatted file into Excel should be, it didn't turn out to be as easy as I would have hoped and so I have to switch over to Windows to take advantage of Power Query.   From the Data ribbon, select Get Data > From File > From XML.  Then find and select the HTML file saved in the previous step.     Once it has loaded the file and done some preparation, you'll need to select the GetEntityStatistics table in the results on the left.  This should display all of the statistics in a preview table on the right.     Once the query completed, you should have a table showing your statistical data ready for... well... slicing and dicing.     The good news is that I did the hard part for you, so you can just download the attached spreadsheet and update the dataset with your fresh data to have everything parsed out into separate columns for you.     Now you can use the column filters to search for entity or service patterns or to select specific entities or attributes that you want to analyze.  You'll need to later clear the column filters to get your whole dataset back.     Updating the Spreadsheet with Fresh Data In order to make this data and its analysis more relevant, I went back and reset all of the statistics and took a new sample which was exactly one hour long.  This way I would get correct recent min/max execution time values as well as having a better understanding of just how many executions / triggers are happening in a one hour period for my benchmark.   Once I got the new HTML file save, I went into Excel's Data ribbon, selected a cell in the data table area, and clicked "Queries & Connections" which brought up the pane on the right which shows my original query.     Hovering over this query, I'm prompted with some stuff and I chose "Edit".     Then I clicked on the tiny little gear to the right of "Source" over on the pane on the right side.     Finally I was able to select the new file and Power Query opened it up for me.     I just needed to click "Close & Load" to save and refresh the query providing data to the table.     The only thing at this point is that I didn't have my nice little sparklines as my regional decimal character is not a period - so I selected the time columns and did a "Replace All" from '.' to ',' to turn them into numbers instead of text.     Et Voila!   There you have it - ready to sort, filter, search and review to help you better understand which parts of your application may be overly resource hungry, or even to spot faulty equipment that may be communicating and triggering workflows far more often than it should.   Specific vs General Depending on the type of analysis that you're doing you might find that the aggregate statistics are a better option.  As they'll be far, far less that the entity specific statistics they'll do a better job of giving you a holistic view of the types of things that are happening with your ThingWorx applications execution.   The entity specific data set that I'm showing here would be a better choice for troubleshooting and diagnostics to try to understand why certain customers/assets/machines are behaving strangely as we can specifically drill into these stats.  Keep in mind however that you should then compare these findings with the general baseline to see how this particular asset is behaving compared to the whole fleet.   As a size guideline - I did an entity specific version of this file for a customer with 1,000 machines and the Excel spreadsheet was 7Mb compared to the 30kb of the one attached here and just opening it and saving it was tough for Excel (likely due to all of my nested formulas).  Just keep this in mind as you use this feature as there is memory overhead meaning also garbage collection and associated CPU usage for such.
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Behavior of ThingWorx Analytics Anomaly Detection with Data Gaps In ThingWorx Analytics, Anomaly detection is performed through the ThingWatcher API framework. This is done by observing the Data from an Edge device, learning what the data stream should look like and then monitoring for any unexpected sequences of Data within the incoming Data stream. Ideally, for this process to work properly, there should be no Data Gaps. However, Data Gaps do occur, this blog describes how ThingWatcher deals with them in order to achieve high performance in anomaly detection. Data Gaps and phases affected: In anomaly detection, ThingWatcher goes through three consecutive phases which are Initializing, Calibrating and then Monitoring. Both the Initializing and Monitoring phases involve either collecting or monitoring streamed Data, so these two phases are sensitive to Data streaming Gaps. The Calibrating phase involves the use of already collected data to create the Anomaly Detection Model. Thus this phase is not directly affected by Data gaps. Dealing with Long and Short Data Gaps: Initializing Phase: During this phase, Data is collected and as part of the collection process, the sampling rate is imputed. So when short data gaps occur these are interpolated so that there are no missing values. However long Gaps might also occur. A Data gap is considered to be long if there is more than three missing Data points which should amount to three times the sampling rate. Basically, if the Timestamp on a data point is greater than the previous timestamp by more than three times the sampling rate that is considered to be a long gap. If a long gap occurs, ThingWatcher would restart the Data Collection process since long Data gaps are not acceptable. The data recollection process could be initiated three times when there are long gaps before failing if the gaps persist. The Data source would then no longer be considered reliable Monitoring Phase: In this phase, the Data stream is monitored to detect any unexpected behavior. In that case, if a short time gap occurs between the previous and the current TimedValue data points, the lookback buffer would be cleared. ThingWatcher will re-enter the Buffering state and will remain in this state until the lookback window buffer is completely filled. For more information on The functionalities of ThingWatcher, Please refer to the ThingWatcher Deployment Guide https://support.ptc.com/WCMS/files/173109/en/ThingWatcher-Deployment-Guide-8.0.pdf However, if the gaps are long and exceed three times the sampling rate, data filling could no longer be a valid solution and Data collection restarts. It is important to note that these imputed values decrease the accuracy of the Anomaly Detection Model. Therefore data monitored by ThingWatcher should be incremented in regular intervals. In general, persistent data gaps should be avoided by ensuring that data is streamed such that the timestamps increase in regular increments and any gaps that exist are generally incidental and small.
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  Some call him JB. Some call him Joe. Others call him @bironology. I call him an awesome guy—he’s our CTO of IoT here at PTC—Joe Biron!   Joe’s an architect at heart, a developer, an avid gamer and a technologist (check him out on Twitter @bironology).   Watch as he guest stars in the Microsoft Channel 9 “IoT Deep Dive Live” show! Listen to him explain how you can build end-to-end industrial solutions with ThingWorx and Azure. ­­ And, guess who’s running the demo behind the scenes: our Global ThingWorx COE Lead, Neal Hagermoser, who you may recognize from a previous Ask Kaya post where I interviewed him on why we chose to partner with Azure.   Enjoy the show and stay connected! Kaya
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This video will walk you through the first steps of how to set-up Analytics Manager for Real-Time Scoring. More specifically this video demonstrates how to create an Analysis Provider and start ThingPredictor Agent. NOTE: For version 8.1 the startup command for the Agent has changed view the command in PTC Help center.   Updated Link for access to this video:  ThingWorx Analytics Manager: Create an Analysis Provider & Start the ThingPredictor Agent                                  
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ThingWorx Manufacturing Apps Setup and Configuration Guide 8.2 ThingWorx Manufacturing and Service Apps Customization Guide 8.2
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Data Model Implementation Guide Part 2   Step 4: SystemConnector Thing Template   After grouping our second set of common functionality and information, we came up with the list below for the second Thing Template to create, SystemConnector with 3 Properties. The breakdown for the SystemConnector Thing Template is as follows:   Follow the below instruction to create this Entity and get the implementation phase of your development cycle going.   System Connector Properties   Let's jump right in. In the ThingWorx Composer, click the + New at the top of the screen.        2. Select Thing Template in the dropdown. 3. In the name field, enter SystemConnector and select a Project (ie, PTCDefaultProject). 4. For the Base Thing Template field, select GenericThing. 5. Click Save. 6. Switch to the Properties and Alerts tab. 7. Click the plus button to add a new Property.   The Properties for the SystemConnector Thing Template are as follows: Name Base Type Aspects Data Change Type EndPointConfig String Persistent and Logged VALUE OperatorCredentials PASSWORD Persistent VALUE ProdManagerCredentials PASSWORD Persistent VALUE Follow the next steps for all the Properties shown in our template property table. Click Add. Enter the name of the property (ie, EndPointConfig). Select the Base Type of the proprty from the dropdown. Check the checkboxes for the property Aspects. Select the Data Change Type from the dropdown.   Click Done when finished creating the property. Your Properties should match the below configurations.            Step 5: HazardousAsset Thing Template     After another round of prioritizing and grouping common functionality and information, we came up with the third Thing Template to create, HazardousAsset. It is a child of the LineAsset Thing Template with one added Service. The breakdown for the HazardousAsset Thing Template is as follows:   Hazardous Asset Service   In the ThingWorx Composer, click the + New at the top of the screen. 2. Select Thing Template in the dropdown. 3. For the Base Thing Template field, select LineAsset and select a Project (PTCDefaultProject). 4. In the name field, enter HazardousAsset. 5.  Click Save then edit to store all changes now. 6.  Switch to the Services tab. 7.  Click Add. 8.  Enter EmergencyShutdown as the name of the service. 9. Switch to the Me/Entities tab. 10. Expand Properties. 11. Click the arrow next to the State property. 12. Modify the generated code to match the following:       me.State = "Danger!! Emergency Shutdown";       Your first Service is complete! 13. Click Done. 14. Click Save to save your changes. Your Service should match the below configurations.     Step 6: InventoryManager Thing Shape   This time around, we will create our first ThingShape, InventoryManager with 1 Property. The breakdown for the InventoryManager Thing Shape is as follows:   Follow the below instruction to create this Entity and get the implementation phase of your development cycle going. System Connector Properties The properties for the InventoryManager Thing Shape are as follows: Name Base Type Aspects Data Change Type ProductCount INTEGER Min Value:0 Persistent and Logged ALWAYS In the ThingWorx Composer, click the + New at the top of the screen. Select Thing Shape in the dropdown. In the name field, enter InventoryManager and select a Project (ie, PTCDefaultProject).       4. Click Save then Edit to store all changes now.         5. Switch to the Properties tab.        6. Click Add.       7. Enter ProductCount as the name of the property.       8. Select the Base Type of the proprty from the dropdown (ie, INTEGER).       9. Check the checkboxes for the property Aspects.      10. Select the Data Change Type from the dropdown.            11. Click Done when finished creating the property. Your Properties should match the below configurations.   Add Thing Shape to Template   We can see that there is some overlap in the components of our HazardousAsset and LineAsset ThingTemplates. In particular, both want information about the product count. Because HazardousAsset inherits from LineAsset, would only need to change LineAsset. Follow the steps below to perform this change: Open the LineAsset Thing Template. In the Implemented Shapes field, enter and select InventoryManager. Save changes.         Click here to view Part 3 of this guide.   
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ThingWorx Service Apps Setup and Configuration Guide 8.2 ThingWorx Manufacturing and Service Apps Customization Guide 8.2
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Data Model Implementation Guide Part 3   Step 7: Unique Components Thing Templates   All of the shared component groups have been created. The next stage is creating the unique component group of ThingTemplates. Each of the below sections will cover one ThingTemplate, how the final property configuration should look, and any other aspects that should be added. The breakdown for the unique component group ThingTemplates is as follows:   Robotic Arm Properties   The properties for the RoboticArm ThingTemplate are as follows: Name Base Type Aspects Data Change Type TimeSincePickup NUMBER, Min Value: 0 Persistent and Logged ALWAYS Axis1 String Persistent and Logged VALUE Axis2 String Persistent and Logged VALUE Axis3 String Persistent and Logged VALUE ClampPressure NUMBER, Min Value: 0 Persistent and Logged ALWAYS ClampStatus String Persistent and Logged ALWAYS   Your properties should match the below configurations.   Pneumatic Gate Properties   The properties for the PneumaticGate ThingTemplate are as follows: Name Base Type Aspects Data Change Type GateStatus String Persistent and Logged ALWAYS   Your properties should match the below configurations.   Conveyor Belt Properties   The properties for the ConveyorBelt ThingTemplate are as follows: Name Base Type Aspects Data Change Type BeltSpeed INTEGER, Min Value: 0 Persistent and Logged ALWAYS BeltTemp INTEGER, Min Value: 0 Persistent and Logged ALWAYS BeltRPM INTEGER, Min Value: 0 Persistent and Logged ALWAYS   Your properties should match the below configurations.   Quality Control Camera   Properties   The properties for the QualityControlCamera ThingTemplate are as follows: Name Base Type Aspects Data Change Type QualityReading INTEGER, Min Value: 0 Persistent and Logged ALWAYS QualitySettings String Persistent and Logged ALWAYS CurrentImage IMAGE Persistent and Logged ALWAYS   Your properties should match the below configurations.   Event   Create a new Event named BadQuality. Select AlertStatus as the Data Shape. Your Event should match the below configurations:     Step 8: Data Tables and Data Shapes   For the Data Model we created, an individual DataTable would be best utilized for each products, production orders, and maintenance requests. Utilizing DataTables will allow us to store and track all of these items within our application. In order to have DataTables, we will need DataShapes to create the schema that each DataTable will follow. This database creation aspect can be considered a part of the Data Model design or a part of the Database Design. Nevertheless, the question of whether to create DataTables is based on the question of needed real time information or needed static information. Products, production orders, and maintenance requests can be considered static data. Tracking the location of a moving truck can be considered a need for real time data. This scenario calls for using DataTables, but a real time application will often have places where Streams and ValueStreams are utilized (DataShapes will also be needed for Streams and ValueStreams). NOTE: The DataShapes (schemas) shown below are for a simplified example. There are different ways you can create your database setup based on your own needs and wants. DataTable Name DataShape Purpose MaintenanceRequestDataTable MaintenanceRequest Store information about all maintenanced requests created ProductDataTable ProductDataShape Store information about the product line ProductionOrderDataTable ProductionOrderDataShape Store all information about production orders that have been placed   Maintenance Requests DataShape   The maintenance requests DataShape needs to be trackable (unique) and contain helpful information to complete the request. The DataShape fields are as follows: Name Base Type Additional Info Primary Key ID String NONE YES Title String NONE NO Request String NONE NO CompletionDate DATETIME NONE NO   Unless you’ve decided to change things around, your DataShape fields should match the below configurations.   Products DataShape   The product DataShape needs to be trackable (unique) and contain information about the product. The DataShape fields are as follows: Name Base Type Additional Info Primary Key ProductId String NONE YES Product String NONE NO Description String NONE NO Cost NUMBER Minimum: 0 NO   Unless you’ve decided to change things around, your DataShape fields should match the below configurations.   Production Order DataShape   The production order DataShape needs to be trackable (unique), contain information that would allow the operator and manager to know where it is in production, and information to help make decisions. The DataShape fields are as follows: Name Base Type Additional Info Primary Key OrderId String NONE YES Product InfoTable: DataShape: ProductDataShape NONE NO ProductionStage String NONE NO OrderDate DATETIME NONE NO DueDate DATETIME NONE NO   Unless you’ve decided to change things around, your DataShape fields should match the below configurations.     Step 9: SystemConnections Implementation   We have created the ThingTemplates and ThingShapes that will be utilized within our Data Model for creating instances (Things). Before we finish the build out of our Data Model, let's create the Services that will be necessary for the MaintenanceSystem and ProductionOrderSystem Things.    This guide will not cover the JavaScript and business logic aspect of creating an application. When complete with the below sections, see the Summary page for how to create that level of definition.       Maintenance System   This is the system managed by the maintenance user and geared towards their needs.   Properties   The properties for the MaintenanceSystem Thing are as follows:     Name Base Type Aspects Data Change Type  MaintEngineerCredentials  PASSWORD  Persistent  VALUE    Your properties should match the below configurations.         Services    The Services for the MaintenanceSystem Thing are as follows:    Service Name  Parameters  Output Base Type Purpose   GetAllMaintenanceRequests  NONE  InfoTable: MaintenanceRequest  Get all of the maintenance requests filed for the maintenance user.  GetFilteredMaintenanceRequests  String: TitleFilter  InfoTable: MaintenanceRequest  Get a filtered version of all maintenance requests filed for the maintenance user.  UpdateMaintenanceRequests  String: RequestTitle  NOTHING  Update a maintenance request already in the system.    Use the same method for creating Services that were provided in earlier sections. Your Services list should match the below configurations.     Production Order System   This is the system utilized by the operator and product manager users and geared towards their needs.   Services   The Services for the ProductionOrderSystem Thing are as follows:      Service Name  Parameters Output Base Type   AssignProductionOrders String: Operator, String: ProductOrder  NOTHING   CreateProductionOrders  String: OrderNumber, String: Product, DATETIME: DueDate  NOTHING  DeleteProductionOrders  String: ProductOrder  NOTHING  GetFilteredProductionOrders  String: ProductOrder  InfoTable: ProductionOrder  GetProductionLineList  NONE  InfoTable: ProductDataShape  GetUnfilteredProductionOrders  NONE  InfoTable: ProductionOrder  MarkSelfOperator  NONE  BOOLEAN  UpdateProductionOrdersOP  String: ProductOrder, String: UpdatedInformation  NOTHING  UpdateProductionOrdersPM  String: ProductOrder, String: UpdatedInformation  NOTHING   Use the same method for creating Services that were provided in earlier sections. Your Services list should match the below configurations.       Challenge Yourself     Complete the implementation of the Data Model shown below by creating the Thing instances of the ThingTemplates we have created. When finish, add more to the Data Model. Some ideas are below.         Ideas for what can be added to this Data Model: #  Idea  1 Add users and permissions   2  Add Mashups to view maintenance requests, products, and production orders  3  Add business logic to the Data Model   Step 10: Next Steps     Congratulations! You've successfully completed the Data Model Implementation Guide. This guide has given you the basic tools to: Create Things, Thing Templates, and Thing Shapes Add Events and Subscriptions   The next guide in the Design and Implement Data Models to Enable Predictive Analytics learning path is Create Custom Business Logic.  
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In ThingWorx Analytics, you have the possibility to use an external model for scoring. In this written tutorial, I would like to provide an overview of how you can use a model developed in Python, using the scikit-learn library in ThingWorx Analytics. The provided attachment contains an archive with the following files: iris_data.csv: A dataset for pattern recognition that has a categorical goal. You can click here to read more about this dataset TestRFToPmml.ipynb: A Jupyter notebook file with the source code for the Python model as well as the steps to export it to PMML RF_Iris.pmml: The PMML file with the model that you can directly upload in Analytics without going through the steps of training the model in Python The tutorial assumes you already have some knowledge of ThingWorx and ThingWorx Analytics. Also, if you plan to run the Python code and train the model yourself, you need to have Jupyter notebook installed (I used the one from the Anaconda distribution). For demonstration purposes, I have created a very simple random forest model in Python. To convert the model to PMML, I have used the sklearn2pmml library. Because ThingWorx Analytics supports PMML format 4.3, you need to install sklearn2pmml version 0.56.2 (the highest version that supports PMML 4.3). To read more about this library, please click here Furthermore, to use your model with the older version of the sklearn2pmml, I have installed scikit-learn version 0.23.2.  You will find the commands to install the two libraries in the first two cells of the notebook.   Code Walkthrough The first step is to import the required libraries (please note that pandas library is also required to transform the .csv to a Dataframe object):   import pandas from sklearn.ensemble import RandomForestClassifier from sklearn2pmml import sklearn2pmml from sklearn.model_selection import GridSearchCV from sklearn2pmml.pipeline import PMMLPipeline   After importing the required libraries, we convert the iris_data.csv to a pandas dataframe and then create the features (X) as well as the goal (Y) vectors:   iris_df = pandas.read_csv("iris_data.csv") iris_X = iris_df[iris_df.columns.difference(["class"])] iris_y = iris_df["class"]   To best tune the random forest, we will use the GridSearchCSV and cross-validation. We want to test what parameters have the best validation metrics and for this, we will use a utility function that will print the results:   def print_results(results): print('BEST PARAMS: {}\n'.format(results.best_params_)) means = results.cv_results_['mean_test_score'] stds = results.cv_results_['std_test_score'] for mean, std, params in zip(means, stds, results.cv_results_['params']): print('{} (+/-{}) for {}'.format(round(mean, 3), round(std * 2, 3), params))   We create the random forest model and train it with different numbers of estimators and maximum depth. We will then call the previous function to compare the results for the different parameters:   rf = RandomForestClassifier() parameters = { 'n_estimators': [5, 50, 250], 'max_depth': [2, 4, 8, 16, 32, None] } cv = GridSearchCV(rf, parameters, cv=5) cv.fit(iris_X, iris_y) print_results(cv)   To convert the model to a PMML file, we need to create a PMMLPipeline object, in which we pass the RandomForestClassifier with the tuning parameters we identified in the previous step (please note that in your case, the parameters can be different than in my example). You can check the sklearn2pmml  documentation  to see other examples for creating this PMMLPipeline object :   pipeline = PMMLPipeline([ ("classifier", RandomForestClassifier(max_depth=4,n_estimators=5)) ]) pipeline.fit(iris_X, iris_y)   Then we perform the export:   sklearn2pmml(pipeline, "RF_Iris.pmml", with_repr = True)   The model has now been exported as a PMML file in the same folder as the Jupyter Notebook file and we can upload it to ThingWorx Analytics.   Uploading and Exploring the PMML in Analytics To upload and use the model for scoring, there are two steps that you need to do: First, the PMML file needs to be uploaded to a ThingWorx File Repository Then, go to your Analytics Results thing (the name should be YourAnalyticsGateway_ResultsThing) and execute the service UploadModelFromRepository. Here you will need to specify the repository name and path for your PMML file, as well as a name for your model (and optionally a description)   If everything goes well, the result of the service will be an id. You can save this id to a separate file because you will use it later on. You can verify the status of this model and if it’s ready to use by executing the service GetDetails:   Assuming you want to use the PMML for scoring, but you were not the one to develop the model, maybe you don’t know what the expected inputs and the output of the model are. There are two services that can help you with this: QueryInputFields – to verify the fields expected as input parameters for a scoring job   QueryOutputFields – to verify the expected output of the model The resultType input parameter can be either MODELS or CLUSTERS, depending on the type of model,    Using the PMML for Scoring With all this information at hand, we are now ready to use this PMML for real-time scoring. In a Thing of your choice, define a service to test out the scoring for the PMML we have just uploaded. Create a new service with an infotable as the output (don’t add a datashape). The input data for scoring will be hardcoded in the service, but you can also add it as service input parameters and pass them via a Mashup or from another source. The script will be as follows:   // Values: INFOTABLE dataShape: "" let datasetRef = DataShapes["AnalyticsDatasetRef"].CreateValues(); // Values: INFOTABLE dataShape: "" let data = DataShapes["IrisData"].CreateValues(); data.AddRow({ sepal_length: 2.7, sepal_width: 3.1, petal_length: 2.1, petal_width: 0.4 }); datasetRef.AddRow({ data: data}); // predictiveScores: INFOTABLE dataShape: "" let result = Things["AnalyticsServer_PredictionThing"].RealtimeScore({ modelUri: "results:/models/" + "97471e07-137a-41bb-9f29-f43f107bf9ca", //replace with your own id datasetRef: datasetRef /* INFOTABLE */, });   Once you execute the service, the output should look like this (as we would have expected, according to the output fields in the PMML model):   As you have seen, it is easy to use a model built in Python in ThingWorx Analytics. Please note that you may use it only for scoring, and the model will not appear in Analytics Builder since you have created it on a different platform. If you have any questions about this brief written tutorial, let me know.
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