Falkonry Java Client to access Falkonry Condition Prediction APIs

Releases

Installation

Maven install

<dependency>
  <groupId>com.falkonry</groupId>
  <artifactId>client</artifactId>
  <version>1.1.0</version>
</dependency>

Features

* Create Datastream for narrow/historian style data from a single entity
* Create Batch Datastream for narrow/historian style data from a single entity
* Create Datastream for narrow/historian style data from a multiple entities
* Create Datastream for wide style data from a single entity
* Create Datastream for wide style data from a multiple entities
* Create Datastream with microseconds precision
* Retrieve Datastreams
* Retrieve Datastream by id
* Delete Datastream by id
* Add EntityMeta to a Datastream
* Get EntityMeta of a Datastream
* Create Assessment
* Retrieve Assessments
* Retrieve Assessment by Id
* Delete Assessment by id
* Get Condition List Of Assessment
* Add historian narrow input data (json format) to multi entity Datastream (User for model revision)
* Add historian narrow input data (csv format) single entity to Datastream (User for model revision)
* Add historian wide input data (json format) to single entity Datastream (User for model revision)
* Add historian wide input data (csv format) to multi entity Datastream (User for model revision)
* Add historian narrow input data (json format) to single entity Batch Datastream (User for model revision)
* Add historian narrow input data (csv format) multi entity to Batch Datastream (User for model revision)
* Add historian wide input data (json format) to multi entity Batch Datastream (User for model revision)
* Add historian wide input data (csv format) to single entity Batch Datastream (User for model revision)
* Add live input data (json format) to Datastream (Used for live monitoring) 
* Add live input data (csv format) to Datastream (Used for live monitoring) 
* Add live input data (json format) from a stream to Datastream (Used for live monitoring) 
* Add live input data (csv format) from a stream to Datastream (Used for live monitoring) 
* Add facts data (json format) to Assessment of single entity datastream
* Add facts data (json format) to Assessment of single entity Batch datastream
* Add facts data (json format) with addition tag to Assessment of multi entity datastream
* Add facts data (csv format) to Assessment of single entity datastream
* Add facts data (csv format) with tags Assessment of single entity datastream
* Add facts data (json format) from a stream to Assessment of multi entity datastream
* Add facts data (csv format) from a stream to  Assessment of multi entity datastream
* Get Historian Output from Assessment
* Get Streaming Output
* Get Facts Data
* Get Input Data of Datastream
* Datastream On (Start live monitoring of datastream)
* Datastream Off (Stop live monitoring of datastream)

Quick Start

* Get auth token from the Falkonry Service UI.
* Read below examples for integration with various data formats.

Examples

Create Datastream for narrow/historian style data from a single entity

Usage:

    import com.falkonry.client.Falkonry;
    import com.falkonry.helper.models.Datasource;
    import com.falkonry.helper.models.Datastream;
    import com.falkonry.helper.models.Field;
    import com.falkonry.helper.models.TimeObject;
    import com.falkonry.helper.models.Signal;

    //instantiate Falkonry
    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");
    
    Datastream ds = new Datastream();
	ds.setName("Test-DS-" + Math.random());
	TimeObject time = new TimeObject();
	time.setIdentifier("time");
	time.setFormat("iso_8601");
	time.setZone("GMT");
	Signal signal = new Signal();

	signal.setValueIdentifier("value");
	signal.setSignalIdentifier("signal");
	Field field = new Field();
	field.setSignal(signal);
	field.setTime(time);
	ds.setField(field);
	Datasource dataSource = new Datasource();
	dataSource.setType("STANDALONE");
	ds.setDatasource(dataSource);
	Datastream datastream = falkonry.createDatastream(ds);

Create Batch Datastream for narrow/historian style data from a single entity

Usage:

    import com.falkonry.client.Falkonry;
    import com.falkonry.helper.models.Datasource;
    import com.falkonry.helper.models.Datastream;
    import com.falkonry.helper.models.Field;
    import com.falkonry.helper.models.TimeObject;
    import com.falkonry.helper.models.Signal;

    //instantiate Falkonry
    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");
    
    Datastream ds = new Datastream();
	ds.setName("Test-DS-" + Math.random());
	TimeObject time = new TimeObject();
	time.setIdentifier("time");
	time.setFormat("iso_8601");
	time.setZone("GMT");
	Signal signal = new Signal();

	signal.setValueIdentifier("value");
	signal.setSignalIdentifier("signal");
	Field field = new Field();
	field.setSignal(signal);
	field.setBatchIdentifier("batch");
	field.setTime(time);
	ds.setField(field);
	Datasource dataSource = new Datasource();
	dataSource.setType("STANDALONE");
	ds.setDatasource(dataSource);
	Datastream datastream = falkonry.createDatastream(ds);

Create Datastream for narrow/historian style data from a multiple entities

Usage:

    import com.falkonry.client.Falkonry;
    import com.falkonry.helper.models.Datasource;
    import com.falkonry.helper.models.Datastream;
    import com.falkonry.helper.models.Field;
    import com.falkonry.helper.models.TimeObject;
    import com.falkonry.helper.models.Signal;

    //instantiate Falkonry
    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");
    
    Datastream ds = new Datastream();
	ds.setName("Test-DS-" + Math.random());
	TimeObject time = new TimeObject();
	time.setIdentifier("time");
	time.setFormat("iso_8601");
	time.setZone("GMT");
	Signal signal = new Signal();

	signal.setValueIdentifier("value");
	signal.setSignalIdentifier("signal");
	Field field = new Field();
	field.setSignal(signal);
	field.setEntityIdentifiers("entity");
	field.setTime(time);
	ds.setField(field);
	Datasource dataSource = new Datasource();
	dataSource.setType("STANDALONE");
	ds.setDatasource(dataSource);

	Datastream datastream = falkonry.createDatastream(ds);

Create Datastream for wide style data from a single entity

Data:

    {"time":1467729675422, "signal1":41.11, "signal2":82.34, "signal3":74.63, "signal4":4.8}
    {"time":1467729668919, "signal1":78.11, "signal2":2.33, "signal3":4.6, "signal4":9.8}

    or

    time,signal1,signal2,signal3,signal4
    1467729675422,41.11,62.34,77.63,4.8
    1467729675445,43.91,82.64,73.63,3.8

Usage:

    import com.falkonry.client.Falkonry;
    import com.falkonry.helper.models.Datasource;
    import com.falkonry.helper.models.Datastream;
    import com.falkonry.helper.models.Field;
    import com.falkonry.helper.models.TimeObject;
    import com.falkonry.helper.models.Signal;

    //instantiate Falkonry
    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");

    Datastream ds = new Datastream();
    ds.setName("Test-DS-" + Math.random());
    TimeObject time = new TimeObject();
    time.setIdentifier("time");
    time.setFormat("millis");
    time.setZone("GMT");

    Field field = new Field();
    field.setTime(time);
    ds.setField(field);
    Datasource dataSource = new Datasource();
    dataSource.setType("PI");
    dataSource.sethost("https://test.piserver.com/piwebapi");
    dataSource.setElementTemplateName("SampleElementTempalte");
    ds.setDatasource(dataSource);

    // Input List
    List<Input> inputList = new ArrayList<Input>();
    Input currents = new Input();
    ValueType valueType = new ValueType();
    EventType eventType = new EventType();
    currents.setName("current");
    valueType.setType("Numeric");
    eventType.setType("Samples");
    currents.setValueType(valueType);
    currents.setEventType(eventType);
    inputList.add(currents);

    Input vibration = new Input();
    vibration.setName("vibration");
    valueType.setType("Numeric");
    eventType.setType("Samples");
    vibration.setValueType(valueType);
    vibration.setEventType(eventType);
    inputList.add(vibration);

    Input state = new Input();
    state.setName("state");
    valueType.setType("Categorical");
    eventType.setType("Samples");
    state.setValueType(valueType);
    state.setEventType(eventType);
    inputList.add(state);

    ds.setInputList(inputList);

    Datastream datastream = falkonry.createDatastream(ds);

Create Datastream for wide style data from multiple entities

Data:

    {"time":1467729675422, "entities": "entity1", "signal1":41.11, "signal2":82.34, "signal3":74.63, "signal4":4.8}
    {"time":1467729668919, "entities": "entity2", "signal1":78.11, "signal2":2.33, "signal3":4.6, "signal4":9.8}

    or

    time,entities,signal1,signal2,signal3,signal4
    1467729675422,entity1,41.11,62.34,77.63,4.8
    1467729675445,entity1,43.91,82.64,73.63,3.8

Usage:

    import com.falkonry.client.Falkonry;
    import com.falkonry.helper.models.Datasource;
    import com.falkonry.helper.models.Datastream;
    import com.falkonry.helper.models.Field;
    import com.falkonry.helper.models.TimeObject;
    import com.falkonry.helper.models.Signal;

    //instantiate Falkonry
    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");

    Datastream ds = new Datastream();
    ds.setName("Test-DS-" + Math.random());

    TimeObject time = new TimeObject();
    time.setIdentifier("time");
    time.setFormat("millis");
    time.setZone("GMT");

    Signal signal = new Signal();
    
    signal.setValueIdentifier("value");
    
    

    Datasource dataSource = new Datasource();
    dataSource.setType("STANDALONE");

    Field field = new Field();
    field.setSignal(signal);
    field.setTime(time);
    field.setEntityIdentifier("entities");

    ds.setDatasource(dataSource);
    ds.setField(field);

    Datastream datastream = falkonry.createDatastream(ds);


    //Add data to datastream
    String data = "time,entities,signal1,signal2,signal3,signal4" + "\n"
        + "1467729675422,entity1,41.11,62.34,77.63,4.8" + "\n"
        + "1467729675445,entity1,43.91,82.64,73.63,3.8";
    Map<String, String> options = new HashMap<String, String>();
    options.put("timeIdentifier", "time");
    options.put("timeFormat", "millis");
    options.put("fileFormat", "csv");
    falkonry.addInput(datastream.getId(), data, options);

Create Datastream with microseconds precision

Usage:

    import com.falkonry.client.Falkonry;
    import com.falkonry.helper.models.Datasource;
    import com.falkonry.helper.models.Datastream;
    import com.falkonry.helper.models.Field;
    import com.falkonry.helper.models.TimeObject;
    import com.falkonry.helper.models.Signal;

    //instantiate Falkonry
    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");
    Datastream ds = new Datastream();
	ds.setName("Test-DS-" + Math.random());
	ds.setTimePrecision("micro"); // "timePrecision" is use to store your data in different date time format. You can store your data in milliseconds("millis") or microseconds("micro"). Default will be "millis"

	TimeObject time = new TimeObject();
	time.setIdentifier("time");
	time.setFormat("iso_8601");
	time.setZone("GMT");

	Signal signal = new Signal();
	
	signal.setValueIdentifier("value");
	

	Datasource dataSource = new Datasource();
	dataSource.setType("STANDALONE");

	Field field = new Field();
	field.setSignal(signal);
	field.setTime(time);

	ds.setDatasource(dataSource);
	ds.setField(field);

	Datastream datastream = falkonry.createDatastream(ds);

Retrieve Datastreams

    import com.falkonry.client.Falkonry;

    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");

    datastream = falkonry.getDatastreams();

Retrieve Datastream by Id

    import com.falkonry.client.Falkonry;

    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");

    datastream = falkonry.getDatastream("datastream_id"); //datastream's id

Delete Datastream by id

    import com.falkonry.client.Falkonry;

    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");

    datastream = falkonry.deleteDatastream("datastream_id"); //datastream's id

Add EntityMeta to a Datastream

	import com.falkonry.client.Falkonry;
    
    Falkonry falkonry   = new Falkonry("http://localhost:8080", "auth-token");
	String datastreamId = "hk7cgt56r3yln0";
    datastream = falkonry.getDatastream(datastreamId);
    EntityMetaRequest entityMetaRequest = new EntityMetaRequest();
    entityMetaRequest.setSourceId("entity1"); // Entity name which you have to change
    entityMetaRequest.setLabel("UNIT1"); // New Entity name
    entityMetaRequest.setPath(""); // For future use
    entityMetaRequests.add(entityMetaRequest);
    List<EntityMeta> entityMetas = falkonry.postEntityMeta(entityMetaRequests, datastream.getId());

Get EntityMeta of a Datastream

	import com.falkonry.client.Falkonry;
    
    Falkonry falkonry   = new Falkonry("http://localhost:8080", "auth-token");
    List<EntityMeta> entityMetas = falkonry.getEntityMeta("datastream_id"); //datastream's id

Create Assessment

    import com.falkonry.client.Falkonry;
    import com.falkonry.helper.models.*;

    //instantiate Falkonry
    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");

    Datastream ds = new Datastream();
    ds.setName("Test-DS-" + Math.random());
    TimeObject time = new TimeObject();
    time.setIdentifier("time");
    time.setFormat("iso_8601");
    time.setZone("GMT");

    Field field = new Field();
    field.setTime(time);
    ds.setField(field);
    Datasource dataSource = new Datasource();
    dataSource.setType("PI");
    dataSource.sethost("https://test.piserver.com/piwebapi");
    dataSource.setElementTemplateName("SampleElementTempalte");
    ds.setDatasource(dataSource);

    // Input List
    List<Input> inputList = new ArrayList<Input>();
    Input currents = new Input();
    ValueType valueType = new ValueType();
    EventType eventType = new EventType();
    currents.setName("current");
    valueType.setType("Numeric");
    eventType.setType("Samples");
    currents.setValueType(valueType);
    currents.setEventType(eventType);
    inputList.add(currents);

    Input vibration = new Input();
    vibration.setName("vibration");
    valueType.setType("Numeric");
    eventType.setType("Samples");
    vibration.setValueType(valueType);
    vibration.setEventType(eventType);
    inputList.add(vibration);

    Input state = new Input();
    state.setName("state");
    valueType.setType("Categorical");
    eventType.setType("Samples");
    state.setValueType(valueType);
    state.setEventType(eventType);
    inputList.add(state);

    ds.setInputList(inputList);

    Datastream datastream = falkonry.createDatastream(ds);

    AssessmentRequest assessmentRequest = new AssessmentRequest();
    assessmentRequest.setName("Health");
    assessmentRequest.setDatastream(datastream.getId());
    assessmentRequest.setAssessmentRate("PT1S");
    Assessment assessment = falkonry.createAssessment(assessmentRequest);
    

Retrieve Assessments

    import com.falkonry.client.Falkonry;

    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");

    List<Assessment> datastreams = falkonry.getAssessments();

Retrieve Assessment by Id

    import com.falkonry.client.Falkonry;

    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");

    Assessment assesssment = falkonry.getAssessment('assessment_id');

Delete Assessment by Id

    import com.falkonry.client.Falkonry;

    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");

    Assessment assesssment = falkonry.deleteAssessment('assessment_id');

Get Condition List Of Assessment

    import com.falkonry.client.Falkonry;

    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");

    Assessment assesssment = falkonry.getAssessment('assessment_id');
    // aprioriConditionList
	List<String> aprioriConditionList = assessment1.getAprioriConditionList();
	Assert.assertEquals(aprioriConditionList.size(), 0);
    

Add historian narrow input data (json format) to multi entity datastream (used for model revision)

Data:

    {"time" :"2016-03-01 01:01:01", "signal" : "current", "value" : 12.4, "car" : "car1"}
    {"time" :"2016-03-01 01:01:01", "signal" : "vibration", "value" : 3.4, "car" : "car1"}

Usage:

    import com.falkonry.client.Falkonry;
    import com.falkonry.helper.models.Datasource;
    import com.falkonry.helper.models.Datastream;
    import com.falkonry.helper.models.Field;
    import com.falkonry.helper.models.TimeObject;
    import com.falkonry.helper.models.Signal;

    //instantiate Falkonry
    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");
    //Add data to datastream
    String data = "{\"time\" :\"2016-03-01T01:01:01.000Z\",\"unit\":\"Unit1\", \"signal\" : \"current\", \"value\" : 12.5}\n" +
					"{\"time\" :\"2016-03-01T01:01:01.000Z\",\"unit\":\"Unit2\", \"signal\" : \"vibration\", \"value\" : 3.4}";

		Map<String, String> options = new HashMap<String, String>();
		options.put("timeIdentifier", "time");
		options.put("timeFormat", "iso_8601");
		options.put("timeZone", time.getZone());
		options.put("signalIdentifier", "signal");
		options.put("entityIdentifier", "unit");
		options.put("valueIdentifier", "value");
		options.put("fileFormat", "json");
		options.put("streaming", "false");
		options.put("hasMoreData", "false");
    falkonry.addInput('datastream-Id', data, options);

Add historian narrow input data (csv format) to single entity to Datastream (used for model revision)

Data:

	 time,signal,value
    2016-03-01 01:01:01,L1DynVert,3.4
    2016-03-01 01:01:01,L1VertAvg,1.4

Usage:

    import com.falkonry.client.Falkonry;
    import com.falkonry.helper.models.Datasource;
    import com.falkonry.helper.models.Datastream;
    import com.falkonry.helper.models.Field;
    import com.falkonry.helper.models.TimeObject;
    import com.falkonry.helper.models.Signal;

    //instantiate Falkonry
    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");
	//Add data to datastream
    String data = "time,signal,value\n" + 
					"2012-01-03T18:16:00.000Z,L1DynVert,9.95\n" + 
					"2012-01-03T18:16:00.000Z,L1VertAvg,12.95\n" + 
					"2012-01-03T18:16:00.000Z,L1VertPk,19.95";

	Map<String, String> options = new HashMap<String, String>();
	options.put("fileFormat", "csv");
	options.put("streaming", "false");
	options.put("hasMoreData", "false");
    falkonry.addInput('datastream-Id', data, options);

Add historian wide input data (json format) to single entity Datastream (used for model revision)

Data:

   
	{"time" :"2016-03-01 01:01:01", "current" : 12.4, "vibration" : 3.4, "state" : "On"}
    

Usage:

    import com.falkonry.client.Falkonry;
    import com.falkonry.helper.models.Datasource;
    import com.falkonry.helper.models.Datastream;
    import com.falkonry.helper.models.Field;
    import com.falkonry.helper.models.TimeObject;
    import com.falkonry.helper.models.Signal;

    //instantiate Falkonry
    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");

    Datastream ds = new Datastream();
    ds.setName("Test-DS-" + Math.random());

    TimeObject time = new TimeObject();
    time.setIdentifier("time");
    time.setFormat("millis");
    time.setZone("GMT");

    Signal signal = new Signal();
    
    signal.setValueIdentifier("value");
    
    

    Datasource dataSource = new Datasource();
    dataSource.setType("STANDALONE");

    Field field = new Field();
    field.setSignal(signal);
    field.setTime(time);
    field.setEntityIdentifier("entities");

    ds.setDatasource(dataSource);
    ds.setField(field);

    Datastream datastream = falkonry.createDatastream(ds);


    //Add data to datastream
    String data = "{\"time\" :\"2016-03-01 01:01:01\", \"current\" : 12.4, \"vibration\" : 3.4, \"state\" : \"On\"}";

	Map<String, String> options = new HashMap<String, String>();
	options.put("fileFormat", "json");
	options.put("streaming", "false");
	options.put("hasMoreData", "false");
    falkonry.addInput(datastream.getId(), data, options);

Add historian wide input data (csv format) to multi entity Datastream (used for model revision)

Data:

	time,unit,L1DynVert,L1VertAvg,L1VertPk 
    2012-01-03T18:16:00.000Z,unit1,4.6,9.95,89.95 
    2012-01-03T18:16:00.000Z,unit1,5.2,12.95,5.85 
    2012-01-03T18:16:00.000Z,unit2,74.3,19.95,9.0

Usage:

    import com.falkonry.client.Falkonry;
    import com.falkonry.helper.models.Datasource;
    import com.falkonry.helper.models.Datastream;
    import com.falkonry.helper.models.Field;
    import com.falkonry.helper.models.TimeObject;
    import com.falkonry.helper.models.Signal;

    //instantiate Falkonry
    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");
	//Add data to datastream
    
	String data = "time,unit,L1DynVert,L1VertAvg,L1VertPk\n" + 
    "2012-01-03T18:16:00.000Z,unit1,4.6,9.95,89.95\n" + 
    "2012-01-03T18:16:00.000Z,unit1,5.2,12.95,5.85\n" + 
    "2012-01-03T18:16:00.000Z,unit2,74.3,19.95,9.0";

	Map<String, String> options = new HashMap<String, String>();
	options.put("timeIdentifier", "time");
	options.put("timeFormat", "iso_8601");
	options.put("timeZone", "GMT");
	options.put("fileFormat", "csv");
	options.put("streaming", "false");
	options.put("hasMoreData", "false");
	options.put("entityIdentifier", "unit");

    falkonry.addInput('datastream-Id', data, options);

Add historian narrow input data (json format) to single entity Batch datastream (used for model revision)

Data:

    {"time" :"2016-03-01 01:01:01", "batch": "batch-1", "signal" : "current", "value" : 12.4}
    {"time" :"2016-03-01 01:01:01", "batch": "batch-1", "signal" : "vibration", "value" : 3.4}

Usage:

    import com.falkonry.client.Falkonry;
    import com.falkonry.helper.models.Datasource;
    import com.falkonry.helper.models.Datastream;
    import com.falkonry.helper.models.Field;
    import com.falkonry.helper.models.TimeObject;
    import com.falkonry.helper.models.Signal;

    //instantiate Falkonry
    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");
    //Add data to datastream
    String data = "{\"time\" :\"2016-03-01T01:01:01.000Z\", \"batch\" : \"batch-1\", \"signal\" : \"current\", \"value\" : 12.5}\n" +
					"{\"time\" :\"2016-03-01T01:01:01.000Z\", \"batch\" : \"batch-1\", \"signal\" : \"vibration\", \"value\" : 3.4}";

		Map<String, String> options = new HashMap<String, String>();
		options.put("timeIdentifier", "time");
		options.put("timeFormat", "iso_8601");
		options.put("timeZone", time.getZone());
		options.put("signalIdentifier", "signal");
		options.put("valueIdentifier", "value");
		options.put("batchIdentifier", "batch");
		options.put("fileFormat", "json");
		options.put("streaming", "false");
		options.put("hasMoreData", "false");
    falkonry.addInput('datastream-Id', data, options);

Add historian narrow input data (csv format) to multi entity to Batch Datastream (used for model revision)

Data:

	 time,signal,value,unit,batch
    2016-03-01 01:01:01,L1DynVert,3.4,unit1,batch1
    2016-03-01 01:01:01,L1VertAvg,1.4,unit2,batch2

Usage:

    import com.falkonry.client.Falkonry;
    import com.falkonry.helper.models.Datasource;
    import com.falkonry.helper.models.Datastream;
    import com.falkonry.helper.models.Field;
    import com.falkonry.helper.models.TimeObject;
    import com.falkonry.helper.models.Signal;

    //instantiate Falkonry
    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");
	//Add data to datastream
    String data = "time,signal,value,unit,batch\n" + 
					"2012-01-03T18:16:00.000Z,L1DynVert,9.95,unit1,batch1\n" + 
					"2012-01-03T18:16:00.000Z,L1VertAvg,12.95,unit2,batch2\n" + 
					"2012-01-03T18:16:00.000Z,L1VertPk,19.95,unit3,batch3";

	Map<String, String> options = new HashMap<String, String>();
	options.put("fileFormat", "csv");
	options.put("streaming", "false");
	options.put("hasMoreData", "false");
    falkonry.addInput('datastream-Id', data, options);

Add historian wide input data (json format) to multi entity Batch Datastream (used for model revision)

Data:

   
	{"time" :"2016-03-01 01:01:01", "current" : 12.4, "vibration" : 3.4, "state" : "On", "batch" : "batch1", "unit" : "unit1"}
    

Usage:

    import com.falkonry.client.Falkonry;
    import com.falkonry.helper.models.Datasource;
    import com.falkonry.helper.models.Datastream;
    import com.falkonry.helper.models.Field;
    import com.falkonry.helper.models.TimeObject;
    import com.falkonry.helper.models.Signal;

    //instantiate Falkonry
    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");

    Datastream ds = new Datastream();
    ds.setName("Test-DS-" + Math.random());

    TimeObject time = new TimeObject();
    time.setIdentifier("time");
    time.setFormat("millis");
    time.setZone("GMT");

    Signal signal = new Signal();
    
    signal.setValueIdentifier("value");
    
    

    Datasource dataSource = new Datasource();
    dataSource.setType("STANDALONE");

    Field field = new Field();
    field.setSignal(signal);
    field.setTime(time);
    field.setEntityIdentifier("unit");
    field.setBatchIdentifier("batch");

    ds.setDatasource(dataSource);
    ds.setField(field);

    Datastream datastream = falkonry.createDatastream(ds);


    //Add data to datastream
    String data = "{\"time\" :\"2016-03-01 01:01:01\", \"current\" : 12.4, \"vibration\" : 3.4, \"state\" : \"On\", \"batch\" : \"batch1\", \"unit\" : \"unit1\"}";

	Map<String, String> options = new HashMap<String, String>();
	options.put("fileFormat", "json");
	options.put("streaming", "false");
	options.put("hasMoreData", "false");
    falkonry.addInput(datastream.getId(), data, options);

Add historian wide input data (csv format) to single entity Batch Datastream (used for model revision)

Data:

	time,L1DynVert,L1VertAvg,L1VertPk,batch
    2012-01-03T18:16:00.000Z,4.6,9.95,89.95,batch1
    2012-01-03T18:16:00.000Z,5.2,12.95,5.85,batch2
    2012-01-03T18:16:00.000Z,74.3,19.95,9.0,batch3

Usage:

    import com.falkonry.client.Falkonry;
    import com.falkonry.helper.models.Datasource;
    import com.falkonry.helper.models.Datastream;
    import com.falkonry.helper.models.Field;
    import com.falkonry.helper.models.TimeObject;
    import com.falkonry.helper.models.Signal;

    //instantiate Falkonry
    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");
	//Add data to datastream
    
	String data = "time,L1DynVert,L1VertAvg,L1VertPk,batch\n" + 
    "2012-01-03T18:16:00.000Z,4.6,9.95,89.95,batch1\n" + 
    "2012-01-03T18:16:00.000Z,5.2,12.95,5.85,batch2\n" + 
    "2012-01-03T18:16:00.000Z,74.3,19.95,9.0,batch3";

	Map<String, String> options = new HashMap<String, String>();
	options.put("timeIdentifier", "time");
	options.put("timeFormat", "iso_8601");
	options.put("timeZone", "GMT");
	options.put("fileFormat", "csv");
	options.put("streaming", "false");
	options.put("hasMoreData", "false");
	options.put("entityIdentifier", "unit");
	options.put("batchIdentifier", "batch");

    falkonry.addInput('datastream-Id', data, options);

Add historian input data (json format) from a stream to Datastream (Used for model revision)

    import com.falkonry.client.Falkonry
    import org.apache.commons.io.FileUtils;

    Falkonry falkonry   = new Falkonry("http://localhost:8080", "auth-token");
    Map<String, String> options = new HashMap<String, String>();
    options.put("timeIdentifier", "time");
    options.put("timeFormat", "millis");
    options.put("fileFormat", "csv");
    options.put("streaming", "false");
	options.put("hasMoreData", "false");

    File file = new File("tmp/data.json");      
    ByteArrayInputStream istream = new ByteArrayInputStream(FileUtils.readFileToByteArray(file));

    InputStatus inputStatus = falkonry.addInputStream('datastream-Id',byteArrayInputStream,options);

Add historian input data (csv format) from a stream to Datastream (Used for model revision)

    import com.falkonry.client.Falkonry
    import org.apache.commons.io.FileUtils;

    Falkonry falkonry   = new Falkonry("http://localhost:8080", "auth-token");
    Map<String, String> options = new HashMap<String, String>();
    options.put("timeIdentifier", "time");
    options.put("timeFormat", "millis");
    options.put("fileFormat", "csv");
    options.put("streaming", "false");
	options.put("hasMoreData", "false");

    File file = new File("tmp/data.csv");     
    ByteArrayInputStream istream = new ByteArrayInputStream(FileUtils.readFileToByteArray(file));

    InputStatus inputStatus = falkonry.addInputStream('datastream-Id',byteArrayInputStream,options);

Add live input data (json format) to Datastream (Used for live monitoring)

Data:

    {"time" :"2016-03-01 01:01:01", "tag" : "signal1", "value" : 3.4}
    {"time" :"2016-03-01 01:01:02", "tag" : "signal2", "value" : 9.3}

Usage:

    import com.falkonry.client.Falkonry;
    import com.falkonry.helper.models.Datasource;
    import com.falkonry.helper.models.Datastream;
    import com.falkonry.helper.models.Field;
    import com.falkonry.helper.models.TimeObject;
    import com.falkonry.helper.models.Signal;

    //instantiate Falkonry
    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");
    //Add data to datastream
    String data = "{\"time\" : \"2016-03-01 01:01:01\", \"signal\" : \"signal1\", \"value\" : 3.4}" + "\n"
        + "{\"time\" : \"2016-03-01 01:01:02\", \"signal\" : \"signal2\", \"value\" : 9.3}";

    Map<String, String> options = new HashMap<String, String>();
    options.put("timeIdentifier", "time");
    options.put("timeFormat", "iso_8601");
    options.put("fileFormat", "csv");
    options.put("streaming", "true");
	options.put("hasMoreData", "false");
	options.put("signalIdentifier", "signal");
	options.put("valueIdentifier", "value");
    falkonry.addInput('datastream-Id', data, options);

Add live input data (csv format) to Datastream (Used for live monitoring)

Data:

	time,signal,entity,value
    2016-03-01 01:01:01,signal1,entity1,3.4
    2016-03-01 01:01:01,signal2,entity1,1.4
    2016-03-01 01:01:02,signal1,entity2,9.3
    2016-03-01 01:01:02,signal2,entity2,4.3

Usage:

    import com.falkonry.client.Falkonry;
    import com.falkonry.helper.models.Datasource;
    import com.falkonry.helper.models.Datastream;
    import com.falkonry.helper.models.Field;
    import com.falkonry.helper.models.TimeObject;
    import com.falkonry.helper.models.Signal;

    //instantiate Falkonry
    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");
	 //Add data to datastream
	    String data = "time,entities,signal1,signal2,signal3,signal4" + "\n"
	        + "1467729675422,entity1,41.11,62.34,77.63,4.8" + "\n"
	        + "1467729675445,entity1,43.91,82.64,73.63,3.8";
	    Map<String, String> options = new HashMap<String, String>();
	    options.put("timeIdentifier", "time");
	    options.put("timeFormat", "millis");
	    options.put("timeZone", "GMT");
	    options.put("fileFormat", "csv");
	    options.put("streaming", "true");
		options.put("hasMoreData", "false");
	    falkonry.addInput('datastream-Id', data, options);

Add live input data (json format) to Datastream (Used for live monitoring)

Data:

    {"time":1467729675422, "entities": "entity1", "signal1":41.11, "signal2":82.34, "signal3":74.63, "signal4":4.8}
    {"time":1467729668919, "entities": "entity2", "signal1":78.11, "signal2":2.33, "signal3":4.6, "signal4":9.8}

    or

    time,entities,signal1,signal2,signal3,signal4
    1467729675422,entity1,41.11,62.34,77.63,4.8
    1467729675445,entity1,43.91,82.64,73.63,3.8

Usage:

    import com.falkonry.client.Falkonry;
    import com.falkonry.helper.models.Datasource;
    import com.falkonry.helper.models.Datastream;
    import com.falkonry.helper.models.Field;
    import com.falkonry.helper.models.TimeObject;
    import com.falkonry.helper.models.Signal;

    //instantiate Falkonry
    Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");

    Datastream ds = new Datastream();
    ds.setName("Test-DS-" + Math.random());

    TimeObject time = new TimeObject();
    time.setIdentifier("time");
    time.setFormat("millis");
    time.setZone("GMT");

    Signal signal = new Signal();
    
    signal.setValueIdentifier("value");
    
    

    Datasource dataSource = new Datasource();
    dataSource.setType("STANDALONE");

    Field field = new Field();
    field.setSignal(signal);
    field.setTime(time);
    field.setEntityIdentifier("entities");

    ds.setDatasource(dataSource);
    ds.setField(field);

    Datastream datastream = falkonry.createDatastream(ds);


    //Add data to datastream
    String data = "time,entities,signal1,signal2,signal3,signal4" + "\n"
        + "1467729675422,entity1,41.11,62.34,77.63,4.8" + "\n"
        + "1467729675445,entity1,43.91,82.64,73.63,3.8";
    Map<String, String> options = new HashMap<String, String>();
    options.put("timeIdentifier", "time");
    options.put("timeFormat", "millis");
    options.put("timeZone", "GMT");
    options.put("fileFormat", "csv");
    options.put("streaming", "false");
	options.put("hasMoreData", "false");
    falkonry.addInput(datastream.getId(), data, options);

Add live input data (json format) from a stream to Datastream (Used for live monitoring)

    import com.falkonry.client.Falkonry
    import org.apache.commons.io.FileUtils;

    Falkonry falkonry   = new Falkonry("http://localhost:8080", "auth-token");
    Map<String, String> options = new HashMap<String, String>();
    options.put("fileFormat", "csv");
    options.put("streaming", "true");
	options.put("hasMoreData", "false");

    File file = new File("tmp/data.json");      
    ByteArrayInputStream istream = new ByteArrayInputStream(FileUtils.readFileToByteArray(file));

    InputStatus inputStatus = falkonry.addInputStream('datastream-Id',byteArrayInputStream,options);

Add live input data (csv format) from a stream to Datastream (Used for live monitoring)

    import com.falkonry.client.Falkonry
    import org.apache.commons.io.FileUtils;

    Falkonry falkonry   = new Falkonry("http://localhost:8080", "auth-token");
    Map<String, String> options = new HashMap<String, String>();
    options.put("fileFormat", "csv");
    options.put("streaming", "true");
	options.put("hasMoreData", "false");

    File file = new File("tmp/data.csv");     
    ByteArrayInputStream istream = new ByteArrayInputStream(FileUtils.readFileToByteArray(file));

    InputStatus inputStatus = falkonry.addInputStream('datastream-Id',byteArrayInputStream,options);

Add facts data (json format) to Assessment of single entity datastream

Data Sample

{"time" : "2011-03-26T12:00:00.000entitythienentity"thientity "end" : "2012-06-01T00:00:00.000Z", "Health" : "Normal"}
{"time" : "2014-02-10T23:00:00.0entity "thientity "thientity "end" : "2014-03-20T12:00:00.000Z", "Health" : "Spalling"}

```java
  import com.falkonry.client.Falkonry;

  Falkonry falkonry = new Falkonry("https://sandbox.falkonry.ai", "auth-token");
  Map<String, String> options = new HashMap<String, String>();
  options.put("startTimeIdentifier", "time");
  options.put("endTimeIdentifier", "end");
  options.put("timeFormat", "iso_8601");
  options.put("timeZone", "GMT");
  options.put("valueIdentifier", "Health"); 
  String data = "{\"time\" : \"2011-03-26T12:00:00.000Z\", \"end\" : \"2012-06-01T00:00:00.000Z\", \"Health\" : \"Normal\"}";
  String response = falkonry.addfacts(assessment.getId(),data, options);

Add facts data (json format) to Assessment of single entity batch datastream

Data Sample

{ "batch": "batch1" , "Health" : "Normal"}
{ "batch": "batch2", "Health" : "Spalling"}

```java
  import com.falkonry.client.Falkonry;

  Falkonry falkonry = new Falkonry("https://sandbox.falkonry.ai", "auth-token");
  options.put("batchIdentifier", "batch");
  options.put("valueIdentifier", "Health"); 
  String data = "{ \"Health\" : \"Normal\", \"batch\" : \"batch1\"}";
  String response = falkonry.addfacts(assessment.getId(),data, options);

Add facts data (json format) with addition tag to Assessment of multi entity datastream

Data Sample

time,end,car,Health
2011-03-31T00:00:00.000Z,2011-04-01T00:00:00.000Z,IL9753,Normal
2011-03-31T00:00:00.000Z,2011-04-01T00:00:00.000Z,HI3821,Normal

```java
  import com.falkonry.client.Falkonry;

  Falkonry falkonry = new Falkonry("https://sandbox.falkonry.ai", "auth-token");
  Map<String, String> options = new HashMap<String, String>();
  options.put("startTimeIdentifier", "time");
  options.put("endTimeIdentifier", "end");
  options.put("timeFormat", "iso_8601");
  options.put("timeZone", "GMT");
  options.put("valueIdentifier", "Health"); 
  options.put("entityIdentifier", "entities");
  options.put("additionalTag", "testTag");

  String data = "{\"time\" : \"2011-03-26T12:00:00.000Z\", \"entities\" : \"entity1\", \"end\" : \"2012-06-01T00:00:00.000Z\", \"Health\" : \"Normal\"}";
  String response = falkonry.addfacts(assessment.getId(),data, options);

Add facts data (csv format) to Assessment of single entity datastream

  import com.falkonry.client.Falkonry;

  Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");
  Map<String, String> options = new HashMap<String, String>();
  options.put("startTimeIdentifier", "time");
  options.put("endTimeIdentifier", "end");
  options.put("timeFormat", "iso_8601");
  options.put("timeZone", "GMT");
  options.put("valueIdentifier", "Health");
  String data = "time,end,Health\n2011-03-31T00:00:00.000Z,2011-04-01T00:00:00.000Z,Normal\n2011-03-31T00:00:00.000Z,2011-04-01T00:00:00.000Z,Normal";
  String response = falkonry.addFacts(assessment.getId(),data, options);

Add facts data (csv format) with tags Assessment of multi entity datastream

  import com.falkonry.client.Falkonry;

  Falkonry falkonry = new Falkonry("http://localhost:8080", "auth-token");
  Map<String, String> options = new HashMap<String, String>();
  options.put("startTimeIdentifier", "time");
  options.put("endTimeIdentifier", "end");
  options.put("timeFormat", "iso_8601");
  options.put("timeZone", "GMT");
  options.put("entityIdentifier", "car");
  options.put("valueIdentifier", "Health");
  options.put("tagIdentifier", "Tag");

  String data = "time,end,car,Health,Tag\n2011-03-31T00:00:00.000Z,2011-04-01T00:00:00.000Z,IL9753,Normal,testTag1\n2011-03-31T00:00:00.000Z,2011-04-01T00:00:00.000Z,HI3821,Normal,testTag2";
  String response = falkonry.addFacts(assessment.getId(),data, options);

Add facts data (json format) from a stream to Assessment of multi entity datastream

File Sample:

    time,car,end,Health
    2011-03-26T12:00:00.000Z,car1,2012-06-01T00:00:00.000Z,Normal
    2014-02-10T23:00:00.000Z,car2,2014-03-20T12:00:00.000Z,Spalling

```java
    import com.falkonry.client.Falkonry;
    import org.apache.commons.io.FileUtils;

    Falkonry falkonry   = new Falkonry("http://localhost:8080", "auth-token");
    File file = new File("res/factsData.json"); 
    Map<String, String> options = new HashMap<String, String>();
    options.put("startTimeIdentifier", "time");
    options.put("endTimeIdentifier", "end");
    options.put("timeFormat", "iso_8601");
    options.put("timeZone", "GMT");
    options.put("entityIdentifier", "car");
    options.put("valueIdentifier", "Health");       
    ByteArrayInputStream byteArrayInputStream = new ByteArrayInputStream(FileUtils.readFileToByteArray(file));
    String response = falkonry.addFactsStream(assessment.getId(),byteArrayInputStream, options)

Add facts data (csv format) from a stream to Assessment of multi entity datastream

File Sample:

    
    time,car,end,Health
    2011-03-26T12:00:00Z,HI3821,2012-06-01T00:00:00Z,Normal
    2014-02-10T23:00:00Z,HI3821,2014-03-20T12:00:00Z,Spalling

  import com.falkonry.client.Falkonry;
  import org.apache.commons.io.FileUtils;

  Falkonry falkonry   = new Falkonry("http://localhost:8080", "auth-token");
  File file = new File("res/factsData.csv"); 
  Map<String, String> options = new HashMap<String, String>();
  options.put("startTimeIdentifier", "time");
  options.put("endTimeIdentifier", "end");
  options.put("timeFormat", "iso_8601");
  options.put("timeZone", "GMT");
  options.put("entityIdentifier", "car");
  options.put("valueIdentifier", "Health");     
  ByteArrayInputStream byteArrayInputStream = new ByteArrayInputStream(FileUtils.readFileToByteArray(file));
  String response = falkonry.addFactsStream(assessment.getId(),byteArrayInputStream, options);

Get Historian Output from Assessment

    Map<String, String> options = new HashMap<String, String>();
    options.put("startTime", "2011-07-17T01:00:00.000Z"); // in the format YYYY-MM-DDTHH:mm:ss.SSSZ
    options.put("endTime", "2011-08-18T01:00:00.000Z");  // in the format YYYY-MM-DDTHH:mm:ss.SSSZ
    options.put("responseFormat", "application/json");  // also avaibale options 1. text/csv 2. application/json

    assessment.setId("wpyred1glh6c5r");
    HttpResponseFormat httpResponse = falkonry.getHistoricalOutput(assessment_id, options);

Get Streaming Output

	import com.falkonry.client.Falkonry;

    Falkonry falkonry   = new Falkonry("http://localhost:8080", "auth-token");
    String assessment = "wpyred1glh6c5r";
    BufferedReader outputBuffer;
    outputBuffer = falkonry.getOutput(assessment);

Get Facts Data

	import com.falkonry.client.Falkonry;

	Falkonry falkonry   = new Falkonry("http://localhost:8080", "auth-token");
	String assessment = "wpyred1glh6c5r";
	Map<String, String> options = new HashMap<String, String>();
    options.put("startTime", "2011-07-17T01:00:00.000Z"); // in the format YYYY-MM-DDTHH:mm:ss.SSSZ
    options.put("endTime", "2011-08-18T01:00:00.000Z");  // in the format YYYY-MM-DDTHH:mm:ss.SSSZ
    options.put("responseFormat", "application/json");  // also avaibale options 1. text/csv 2. application/json
	// Get Facts
	HttpResponseFormat factsResponse = falkonry.getFactsData(assessment, options);

Get Input Data of Datastream

	import com.falkonry.client.Falkonry;

	Falkonry falkonry   = new Falkonry("http://localhost:8080", "auth-token");
	String datastream = "wpyred1glh6c5r";
	Map<String, String> options = new HashMap<String, String>();
    options.put("responseFormat", "application/json");  // also avaibale options 1. text/csv 2. application/json
	// Get Facts
	HttpResponseFormat factsResponse = falkonry.getInputData(datastream, options);

Datastream On (Start live monitoring of datastream)

	import com.falkonry.client.Falkonry;

    Falkonry falkonry   = new Falkonry("http://localhost:8080", "auth-token");
	String datastreamId = "hk7cgt56r3yln0";
    List<Assessment> liveAssessments = falkonry.onDatastream(datastreamId);
    

Datastream Off (Stop live monitoring of datastream)

	import com.falkonry.client.Falkonry;

    Falkonry falkonry   = new Falkonry("http://localhost:8080", "auth-token");
	String datastreamId = "hk7cgt56r3yln0";
    List<Assessment> assessments = falkonry.offDatastream(datastreamId);
    

Docs

[Falkonry APIs](https://app.falkonry.ai/api)

Tests

To run the test suite, first install the dependencies, then run tests from the test folder.

License

Available under MIT License