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Hi all,   ThingWorx contains lots of useful functionality for your services (last count is 339 Snippets in ThingWorx 8.5.2). These snippets are an important part of the platform application building capabilities, and most of them are simple enough to understand based on their name and the description that appears when hovering on them.   I have witnessed that however, in some cases, the platform users are not aware of their full capabilities. With this in mind, I started creating some time ago a Snippet Guide for my personal use that I'm sharing now with the community. It contains additional explanations, documentation links and sample source code tested by me.   Please bear in mind that it was done for an earlier ThingWorx version and I did not have enough time to update it for 8.5.x, but it should work the same here as well.   This enhanced documentation is not supported by PTC, so please 1. do not open a Tech Support ticket based on the content of this document and, instead 2. Comment on this thread if there are things I can improve on it.   Happy New Year!
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Analytics projects typically involve using the Analytics API rather than the Analytics Builder to accomplish different tasks. The attached documentation provides examples of code snippets that can be used to automate the most common analytics tasks on a project such as: Creating a dataset Training a Model Real time scoring predictive and prescriptive Retrieving the validation metrics for a model Appending additional data to a dataset Retraining the model The documentation also provides examples that are specific to time series datasets. The attached .zip file contains both the document as well as some entities that you need to import in ThingWorx to access the services provided in the examples. 
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I have created a mashup which allows you to easily use and test the Prescriptions functionality in Thingworx Analytics (TWA). This is where you choose 1 or more fields for optimization, and TWA tells you how to adjust those fields to get an optimal outcome.   The functionality is based on a public sample dataset for concrete mixtures, full details are included in the attached documentation.  
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Recently I needed to be able to parse and handle XML data natively inside of a ThingWorx script, and this XML file happened to have a SOAP namespace as well. I learned a few things along the way that I couldn’t find a lot of documentation on, so am sharing here.   Lessons Learned The biggest lesson I learned is that ThingWorx uses “E4X” XML handling. This is a language that Mozilla created as a way for JavaScript to handle XML (the full name is “ECMAscript for XML”). While Mozilla deprecated the language in 2014, Rhino, the JavaScript engine that ThingWorx uses on the server, still supports it, so ThingWorx does too. Here’s a tutorial on E4X - https://developer.mozilla.org/en-US/docs/Archive/Web/E4X_tutorial The built-in linter in ThingWorx will complain about E4X syntax, but it still works. I learned how to get to the data I wanted and loop through to create an InfoTable. Hopefully this is what you want to do as well.   Selecting an Element and Iterating My data came inside of a SOAP envelope, which was meaningless information to me. I wanted to get down a few layers. Here’s a sample of my data that has made-up information in place of the customer's original data:                <SOAP-ENV : Envelope   xmlns : SOAP-ENV = "http://schemas.xmlsoap.org/soap/envelope/"   headers = "" >     <SOAP-ENV : Body >         < get_part_schResponse   xmlns = "urn:schemas-iwaysoftware-com:iwse" >             < get_part_schResult >                 < get_part_schRow >                     < PART_NO > 123456 </ PART_NO >                     < ORD_PROC_DIV_CD > E </ ORD_PROC_DIV_CD >                     < MFG_DIV_CD > E </ MFG_DIV_CD >                     < SCHED_DT > 2020-01-01 </ SCHED_DT >                 </ get_part_schRow >                 < get_part_schRow >                     < PART_NO > 789456 </ PART_NO >                     < ORD_PROC_DIV_CD > E </ ORD_PROC_DIV_CD >                     < MFG_DIV_CD > E </ MFG_DIV_CD >                     < SCHED_DT > 2020-01-01 </ SCHED_DT >                 </ get_part_schRow >             </ get_part_schResult >         </ get_part_schResponse >     </SOAP-ENV : Body > </SOAP-ENV : Envelope > To get to the schRow data, I need to get past SOAP and into a few layers of XML. To do that, I make a new variable and use the E4X selections to get there: var   data  =  resultXML .*:: Body .*:: get_part_schResponse .*:: get_part_schResult .*; Note a few things: resultXML is a variable in the service that contains the XML data. I skipped the Envelope tag since that’s the root. The .* syntax does not mean “all the following”, it means “all namespaces”. You can define and specify the namespaces instead of using .*, but I didn’t find value in that. I found some sample code that theoretically should work on a VMware forum: https://communities.vmware.com/thread/592000. This gives me schRow as an XML List that I can iterate through. You can see what you have at this point by converting the data to a String and outputting it: var   result  =  String ( data ); Now that I am to the schRow data, I can use a for loop to add to an InfoTable: for   each  ( var   row   in   data ) {      result . AddRow ({         PartNumber:   row .*:: PART_NO ,         OrderProcessingDivCD:   row .*:: ORD_PROC_DIV_CD ,         ManufacturingDivCD:   row .*:: MFG_DIV_CD ,         ScheduledDate:   row .*:: SCHED_DT     }); } Shoo! That’s it! Data into an InfoTable! Next time, I'll ask for a JSON API. 😊
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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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Here is a tutorial to explain the process of uploading a PMML file from an external system to Thingworx Analytics. The tutorial steps are explained in the attached PDF and all referenced files can be found in the attached ZIP.  
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This post covers how to build and operationalize a time series model using Thingworx Analytics. A lookback window is used to read multiple previous rows before the current one, and base the prediction on those lookback rows.   In this example we use time series data to predict water flow for different water pumps in a system.   There is a full explanation of the method attached, also all necessary resources are included in the attached files.
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Fresh look at getting started with ThingWorx in a relevant context that outlines the DEVOPS needed to kick-start your programming.     For full-sized viewing, click on the YouTube link in the player controls. Visit the Online Success Guide to access our Expert Session videos at any time as well as additional information about ThingWorx training and services.
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I'm getting up to speed on all the great new stuff in 8.5, and have found that since the JavaScript engine was upgraded to Rhino 1.7.11, there's some awesome new JavaScript ES6 functionality available. I have tested arrow functions, filter, map, and reduce. Compose does not look like it is supported.   If you're not familiar with this functionality, I highly recommend reading up on them. Filter, map, and reduce are incredibly useful for working with arrays. They can save you a lot of annoying logic.   Here's some resources that I've found helpful for learning: JavaScript Functional Programming - map, filter and reduce Arrow Functions: Fat and Concise Syntax in Javascript If you really want to dive into ES6, Wes Bos has incredible tutorial sessions that are worth every penny: Wes Bos: ES6 for Everyone!   Have you played around with ES6 functionality in ThingWorx 8.5 yet?
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To help explain some of the different ways in which a prediction can be triggered from a Thingworx Analytics Model, I've built a mashup which allows you to easily trigger these types of prediction:   - API Realtime Prediction - Analytics Manager: Event - API Batch Prediction   For information on setting up this environment to use the mashup with some sample data, please see the attached instructions document: Prediction-Methods-Mashup.pdf. The referenced resource files can be found inside resources.zip   For more information on prediction scoring please see this related post: How to score new data with ThingWorx Analytics 8.3.x
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      Thingworx extensions are a great place to explore UI ideas and get that special feature you want.   Here is a quick primer on Widgets (Note: there is comprehensive documentation here which explores the complete development process ). The intention is not to explain every detail but just the most important points to get you started. I will explore more in additional posts. I also like images rather than lost of words to read. I have attached the simple Hello Word example as a start point and  I'm using Visual Code as my editor of choice.   The attached zip when unzipped will contain a folder called ui and metadata xml file. Within the ui folder there needs to be a folder that has the same name as the widget name. In this case its helloworld.   Metadata file - The 3 callouts are the most import. Package version: is the current version and each time a change is made the value needs to be updated. name: a unique name used through out the widget definition UIResources: The source locations for the widget definition. The UIResources files are used to define the widget in the ide (Composer) and runtime (Mashup). These 2 environments ide and runtime have matching pairs of css (cascading style sheets)  and a js (javascript) files.   The js files are where most of the work is done. There a number of functions used inside the javascript file but just to get things going we will focus on the renderHtml function. This is the function that will generate the HTML to be inserted in the widget location.   renderHtml (helloWorld.ide.js) In this very simple case the renderHtml in the runtime is the same as in the ide renderHtml (helloWorld.runtime.js)   Hopefully you can see that the HTML is pretty easy just some div and span tags with some code to get the Property called Salutation.   So we have the very basics and we are not worried to much about all the other things not mentioned. So to get the simple extension into Thingworx we use the Import -> Extensions menu option. The UI and metadata.xml file needs to be zipped up (as per attachment).  Below is a animated gif that shows how to import and use the widget   Very Quick Steps to import and use in mashup. Video Link : 2147   The next blog will explore functions and allow a user to click the label and display a random message. This will show how to use events   Widget Extensions Click Event
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You can control the Tracking Indicator that is used to mark the ThingMark position. The Tracking Indicator is a green hexagon, in the screenshot below the red arrow points to it. You can control the display of this tracking indicator via the Display Tracking Indicator property of the ThingMark widget: But you can also get fancier. Here is an exmaple that shows the tracking indicator for 3 seconds when the tracking has started and then hides it automatically. To achieve such a behavior you'll have to use a bit of Javascript. We'll first create a function hideIn3Sec() in the javascript section of our view and then add it to the javascript handler of the Tracking Acquired event of the ThingMark widget. Step 1: Here is the code for copy/paste convenience: $scope.hideIn3Sec=function(){   // The $timeout function has two arguments: the function to execute (defined inline here)   // and the time in msec after which the function is invoked.   $timeout( function hide(){     // you may have to change 'thingMark-1' by the id of the ThingMark in your own experience     $scope.app.view['Home'].wdg['thingMark-1']['trackingIndicator']=false;   },   3000); } Step 2: That's it. Have fun!
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Use Case: You’ve published a model from Analytics Builder to Analytics Manager, and then used service CreateOrUpdateThingTemplateForModel on resource TW.AnalysisServices.ModelManagementServicesAPI. A thing created from the resulting template will have an infotable called “data” which needs to be populated in order to trigger an Analysis Event & Job. For example you might have been following the online documentation for Analytics Manager > Working with Thing Predictor > Demo: Using Thing Predictor, link here. This script makes it easy to create a line of test data into field "data" on your thing to trigger the analysis event & job. Also fields causalTechnique, goalName and importantFieldCount are set programmatically, these are needed for the analysis event & job. Also this script might be useful as a general example of how to write to an infotable property on a thing. The JavaScript code is shown here and also attached as a text file to this post: me.causalTechnique = 'FULL_RANGE' me.goalName = 'predict_Compressor_failure' me.importantFieldCount = 3 // ThingPredictor.test_3f1a6a31-e388-4232-9e47-284572658a4a.InputParamsdataDataShape entry object //var newEntry = new Object(); var params = { infoTableName : "InfoTable", dataShapeName : "ThingPredictor.test-integer_afebaef3-b2cf-4347-824c-a39c11ddbb4a.InputParamsdataDataShape" }; // CreateInfoTableFromDataShape(infoTableName:STRING("InfoTable"), dataShapeName:STRING):INFOTABLE(ThingPredictor.test_3f1a6a31-e388-4232-9e47-284572658a4a.InputParamsdataDataShape) var myInfoTable = Resources["InfoTableFunctions"].CreateInfoTableFromDataShape(params); // 2 - CREATE INFOTABLE ROW USING object var newEntry = new Object(); newEntry._Pressure = 10.5; // NUMBER newEntry._Temperature = 45.1; // NUMBER newEntry._VibrationX = 81; // NUMBER newEntry._VibrationY = 65; // NUMBER //newEntry.key = 4; // STRING - isPrimaryKey = true // 3 - ADD INFOTABLE ROW USING TO INFOTABLE myInfoTable.AddRow(newEntry); // 3 – PERSIST INFOTABLE TO THE THING PROPERTY ‘data’ me.data = myInfoTable;
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The System user is pivotal in securing your application and the simplest approach is to assign the System user to ALL Collections and give it Runtime Service Execute. These Collection Permissions ONLY Export to ThingworxStorage vs. the File Export, it becomes quite painful to manage this and then roll this out to a new machine. Best and fastest solution? Script the Assignment, you can take this script which does it for the System user and extend it to include any other Collection Level permissions you might need to set, like adding Entity Create Design Time for the System user. --------------------------------------------------------- //@ThingworxExtensionApiMethod(since={6,6}) //public void AddCollectionRunTimePermission(java.lang.String collectionName, //       java.lang.String type, //       java.lang.String resource, //       java.lang.String principal, //       java.lang.String principalType, //       java.lang.Boolean allow) //    throws java.lang.Exception // //Service Category: //    Permissions // //Service Description: //    Add a run time permission. // //Parameters: //    collectionName - Collection name (Things, Users, ThingShapes, etc.) - STRING //    type - Permission type (PropertyRead PropertyWrite ServiceInvoke EventInvoke EventSubscribe) - STRING //    resource - Resource name (* = all or enter a specific resource to override) - STRING //    principal - Principal name (name of user or group) - STRING //    principalType - Principal type (User or Group) - STRING //    allow - Permission (true = allow, false = deny) - BOOLEAN //Throws: //    java.lang.Exception - If an error occurs //   var params = {     modelTags: undefined /* TAGS */,     type: undefined /* STRING */ }; // result: INFOTABLE dataShape: EntityCount var EntityTypeList = Subsystems["PlatformSubsystem"].GetEntityCount(params); for each (var row in EntityTypeList.rows) {     try {         var params = {             principal: "System" /* STRING */,             allow: true /* BOOLEAN */,             resource: "*" /* STRING */,             type: "ServiceInvoke" /* STRING */,             principalType: "User" /* STRING */,             collectionName: row.name /* STRING */         };         // no return         Resources["CollectionFunctions"].AddCollectionRunTimePermission(params);     }     catch(err) {     } }
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The following script takes a parameter of a model name, a device serial number and a data item name, finds the asset location and uses that longitude to determine the current TimeZone.  It then converts the Timezone of the data item timestamp to an Eastern Standard Timezone timestamp. import groovy.xml.MarkupBuilder import com.axeda.drm.sdk.Context import java.util.TimeZone import com.axeda.drm.sdk.data.* import com.axeda.drm.sdk.device.* import com.axeda.common.sdk.jdbc.*; import net.sf.json.JSONObject import net.sf.json.JSONArray import com.axeda.drm.sdk.mobilelocation.MobileLocationFinder import com.axeda.drm.sdk.mobilelocation.MobileLocation import com.axeda.drm.sdk.mobilelocation.CurrentMobileLocationFinder def response try {     Context ctx = Context.getUserContext()     ModelFinder mfinder = new ModelFinder(ctx)     mfinder.setName(parameters.model_name)     Model m = mfinder.find()     DeviceFinder dfinder = new DeviceFinder(ctx)     dfinder.setModel(m);     dfinder.setSerialNumber(parameters.device)     Device d = dfinder.find()     CurrentMobileLocationFinder cmlFinder = new CurrentMobileLocationFinder(ctx);     cmlFinder.setDeviceId(d.id.getValue());     MobileLocation ml = cmlFinder.find();     def lng = -72.158203125     if (ml?.lng){         lng = ml?.lng     }     // set boundaries for timezones - longitudes     def est = setUSTimeZone(-157.95415000000003)     def tz = setUSTimeZone(lng)     CurrentDataFinder cdfinder = new CurrentDataFinder(ctx, d)     DataValue dvalue = cdfinder.find(parameters.data_item_name)     def adjtime = convertToNewTimeZone(dvalue.getTimestamp(),tz,est)     def results = JSONObject.fromObject(lat: ml?.lat, lng: ml?.lng, current: [name: dvalue.dataItem.name, time: adjtime.format("MM/dd/yyyy HH:mm"), value: dvalue.asString()]).toString(2)     response = results } catch (Exception e) {     response = [                 message: "Error: " + e.message             ]     response =  JSONObject.fromObject(response).toString(2) } return ['Content-Type': 'application/json', 'Cache-Control':'no-cache', 'Content': response] def setUSTimeZone(lng){     TimeZone tz     // set boundaries for US timezones by longitude     if (lng <= -67.1484375 && lng > -85.517578125){         tz = TimeZone.getTimeZone("EST");     }     else if (lng <= -85.517578125 && lng > -96.591796875){         tz = TimeZone.getTimeZone("CST");     }     else if (lng <= -96.591796875 && lng > -113.90625){         tz = TimeZone.getTimeZone("MST");     }     else if (lng <= -113.90625){         tz = TimeZone.getTimeZone("PST");     }     logger.info(tz)     return tz } public Date convertToNewTimeZone(Date date, TimeZone oldTimeZone, TimeZone newTimeZone){     long oldDateinMilliSeconds=date.time - oldTimeZone.rawOffset     // oldtimeZone.rawOffset returns the difference(in milliSeconds) of time in that timezone with the time in GMT     // date.time returns the milliseconds of the date     Date dateInGMT=new Date(oldDateinMilliSeconds)     long convertedDateInMilliSeconds = dateInGMT.time + newTimeZone.rawOffset     Date convertedDate = new Date(convertedDateInMilliSeconds)     return convertedDate }
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For a recent project, I was needing to find all of the children in a Network Hierarchy of a particular template type... so I put together a little script that I thought I'd share. Maybe this will be useful to others as well.   In my situation, this script lived in the Location template. This was useful so that I could find all the Sensor Things under any particular node, no matter how deep they are.   For example, given a network like this: Location 1 Sensor 1 Location 1A Sensor 2 Sensor 3 Location 1AA Sensor 4 Location 1B Sensor 5 If you run this service in Location 1, you'll get an InfoTable with these Things: Sensor 1 Sensor 2 Sensor 3 Sensor 4 Sensor 5 From Location 1A: Sensor 2 Sensor 3 Sensor 4 From Location 1AA: Sensor 4 From Location 1B: Sensor 5   For this service, these are the inputs/outputs: Inputs: none Output: InfoTable of type NetworkConnection   // CreateInfoTableFromDataShape(infoTableName:STRING("InfoTable"), dataShapeName:STRING):INFOTABLE(AlertSummary) let result = Resources["InfoTableFunctions"].CreateInfoTableFromDataShape({ infoTableName : "InfoTable", dataShapeName : "NetworkConnection" }); // since the hierarchy could contain locations or sensors, need to recursively loop down to get all the sensors function findChildrenSensors(thingName) { let childrenThings = Networks["Hierarchy_NW"].GetChildConnections({ name: thingName /* STRING */ }); for each (var row in childrenThings.rows) { // row.to has the name of the child Thing if (Things[row.to].IsDerivedFromTemplate({thingTemplateName: "Location_TT"})) { findChildrenSensors(row.to); } else if (Things[row.to].IsDerivedFromTemplate({thingTemplateName: "Sensor_TT"})) { result.AddRow(row); } } } findChildrenSensors(me.name);    
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I have put together a small sample of how to get property values from a Windows Powershell command into Thingworx through an agent using the Java SDK. In order to use this you need to import entities from ExampleExport.xml and then run SteamSensorClient.java passing in the parameters shown in run-configuration.txt (URL, port and AppKey must be adapted for your system). ExampleExport.xml is a sample file distributed with the Java SDK which I’ve also included in the zipfile attached to this post. You need to go in Thingworx Composer to Import/Export … Import from File … Entities … Single File … Choose File … Import. Further instructions / details are given in this short video: Video Link : 2181
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The following code snippet will retrieve a months worth of data from the system and return it as a CSV document suitable for import into your spreadsheet or reporting tool of choice. import static com.axeda.sdk.v2.dsl.Bridges.* import com.axeda.drm.sdk.Context import com.axeda.common.sdk.id.Identifier import com.axeda.services.v2.* import com.axeda.sdk.v2.exception.* def ac = new AuditCriteria() ac.fromDate = Date.parse('yyyy-MM-dd', '2017-03-01') ac.toDate   = Date.parse('yyyy-MM-dd', '2017-03-31') def retString = '' tcount = 0 while ( (results = auditBridge.find(ac)) != null  && tcount < results .totalCount) {   results.audits.each { res ->     retString += "${res?.user?.id},${res?.asset?.serialNumber},${res?.category},${res.message},${res.date}\n"     tcount++   }   ac.pageNumber = ac.pageNumber + 1 } return retString
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Axeda Enterprise has long provided a feature to run custom code on the server side in response to end user requests or events triggered by data sent in by remote agents.  Version 6.6 introduced Axeda Artisan - an Apache Maven based tool to add modern best practices to developing Axeda-based solutions, using modern code editors such as Eclipse and IntelliJ, and allowing for the use of source code control tools like Git or Clearcase.  One downside to Artisan, however, is that it has no export tool - no way to take currently existing entities in the Axeda instance, and save them. The attached Groovy script, GetCustomObjects.groovy, solves that problem for custom objects.  It will iterate an Axeda instance and save any found CustomObjects to disk for backup, or to use to bootstrap an Artisan project from an existing instance. { / }  » groovy GetCustomObjects.groovy usage: getCustomObjects -acceptBadSSL          Ignore any TLS validation issues -h                     help -instance <instance>   instance name - directory to store results -password <password>   password -url <url>             url of Axeda Machine Cloud -username <username>   username An example call might look like: { / } groovy GetCustomObjects.groovy -instance prod-instance -url https://prod-instance.example.com -username <uname> -password <pwd> This will save all custom objects in a directory called prod-instance.
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Thingworx actually provides some services for this, but it exports them to an XML file. I'm pretty sure that there are people who will be able to turn this into something easily legible in a mashup. There are two services in CollectionFunctions ExportUserPermissions ImportUserPermissions
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Objective Learn how the Scripto Web Service helps you to present platform information in your HTML with JavaScript and dynamic page updating.  After this tutorial, you will know how to create Axeda Custom Objects that return formatted results to JavaScript using XmlHttpResponse, and how a very simple page can incorporate platform data into your browser-based user interface. Part 1 - Simple Scripto In Using Scripto, you learned how Scripto can be called from very simple clients, even the most basic HTTP tools. This tutorial builds on the examples in that tutorial. The following HelloWorld script accepts a parameter named "foo". This means that the caller of the script may supply a value for this parameter, and the script simple returns a message that includes the value supplied. import static com.axeda.sdk.v2.dsl.Bridges.* import com.axeda.services.v2.* import com.axeda.sdk.v2.exception.* return "Hello world, ${parameters.foo}" In the first part of this tutorial, we'll be creating an HTML page with some JavaScript that simply calls the HelloWorld script and puts the result on the page. Create an HTML File Open up your favorite text editor and create a blank document. Paste in this simple scaffold, which includes a very simple FORM with fields for your developer platform email and password, and the "foo" parameter. < html > < head > < title > Axeda Developer Connection Simple Ajax HelloWorld Example </ title > </ head > < body > < form name = "f1" >         Platform email (login): < input name = "username" type = "text" > < br />         Password: < input name = "password" type = "password" > < br />         foo: < input name = "foo" type = "text" > < br /> < input value = "Go" type = "button" onclick = 'JavaScript: callScripto()' /> </ p > < div id = "result" > </ div > </ form > </ body > </ html > Pretty basic HTML that you've seen lots of times. Notice the form onclick refers to a JavaScript function. We'll be adding that next. Add the JavaScript Directly under the <title> tag, add the following < script language = "Javascript" > var scriptoURL = " http://dev6.axeda.com/services/v1/rest/Scripto/execute/ " ; var scriptName = "HelloWorld2" ; < / script > This defines our JavaScript block, and a couple of constants to tell our script where the server's Scripto REST endpoint is, and the name of the script we will be running. Let's add in our callScripto() function. Paste the following directly under the scriptName variable declaration: function callScripto ( ) { try {                 netscape . security . PrivilegeManager . enablePrivilege ( "UniversalBrowserRead" ) ; } catch ( e ) { // must be IE }    var xmlHttpReq = false ;    var self = this ;    // Mozilla/Safari    if ( window . XMLHttpRequest ) {                 self . xmlHttpReq = new XMLHttpRequest ( ) ;    } // IE else if ( window . ActiveXObject ) {                 self . xmlHttpReq = new ActiveXObject ( "Microsoft.XMLHTTP" ) ; }    var form = document . forms [ 'f1' ] ;    var username = form . username . value ;    var password = form . password . value ;    var url = scriptoURL + scriptName + "?username=" + username + "&password=" + password ;             self . xmlHttpReq . open ( 'POST' , url , true ) ;             self . xmlHttpReq . setRequestHeader ( 'Content-Type' , 'application/x-www-form-urlencoded' ) ;             self . xmlHttpReq . onreadystatechange = function ( ) {       if ( self . xmlHttpReq . readyState == 4 ) {                     updatepage ( self . xmlHttpReq . responseText ) ;       }    }    var foo = form . foo . value ;    var qstr = 'foo=' + escape ( foo ) ;    self . xmlHttpReq . send ( qstr ) ; } That was a lot to process in one chunk, so let's examine each piece. This piece just tells the browser that we'll be wanting to make some Ajax calls to a remote server. We'll be running the example right off a local file system (at first), so this is necessary to ask for permission. try {                 netscape . security . PrivilegeManager . enablePrivilege ( "UniversalBrowserRead" ) ; } catch ( e ) { // must be IE } This part creates an XmlHttpRequest object, which is a standard object available in browsers via JavaScript. Because of slight browser differences, this code creates the correct object based on the browser type. var xmlHttpReq = false ; var self = this ; // Mozilla/Safari if ( window . XMLHttpRequest ) {                 self . xmlHttpReq = new XMLHttpRequest ( ) ; } // IE else if ( window . ActiveXObject ) {                 self . xmlHttpReq = new ActiveXObject ( "Microsoft.XMLHTTP" ) ; } Next we create the URL that will be used to make the HTTP call. This simply combines our scriptoURL, scriptName, and platform credentials. var form = document . forms [ 'f1' ] ; var username = form . username . value ; var password = form . password . value ; var url = scriptoURL + scriptName + "?username=" + username + "&password=" + password ; Now let's tell the xmlHttpReq object what we want from it. we'll also reference the name of another JavaScript function which will be invoked when the operation completes. self . xmlHttpReq . open ( 'POST' , url , true ) ;     self . xmlHttpReq . setRequestHeader ( 'Content-Type' , 'application/x-www-form-urlencoded' ) ;     self . xmlHttpReq . onreadystatechange = function ( ) { if ( self . xmlHttpReq . readyState == 4 ) {             updatepage ( self . xmlHttpReq . responseText ) ; } } Finally, for this function, we'll grab the "foo" parameter from the form and tell the prepped xmlHttpReq object to post it. var qstr = 'foo=' + escape ( foo ) ;     self . xmlHttpReq . send ( qstr ) ; almost done. We just need to supply the updatepage function that we referenced above. Add this code directly before the </script> close tag: function updatepage ( str ) {             document . getElementById ( "result" ) . innerHTML = str ; } Try it out Save your file as helloworld.html and open it in a browser by starting your browser and choosing "Open File". You can also download a zip with the file prepared for you at the end of this page. If you are using Internet Explorer, IE will pop a bar asking you if it is OK for the script inside this page to execute a script. Choose "Allow Blocked Content". Type in your platform email address (the address you registered for the developer connection with) and your password. Enter any text that you like for "foo". When you click "Go", the area below the button will display the result of the Scripto call. Note that if you are using Mozilla Firefox, you will be warned about the script wanting to access a remote server. Click "Allow". Congratulations! You have learned how to call a Custom Object-backed Scripto service to display dynamic platform content inside a very simple HTML page. Next Steps Be sure to check out the tutorial on Hosting Custom Applications to learn how you can make this page get directly served from your platform account, with its very own URL. Also explore code samples that show more sophisticated HTML+AJAX examples using Google Charts and other presentation tools.
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