Falkonry Java Client to access Falkonry Condition Prediction APIs

Installation

Maven install

<dependency>
  <groupId>com.falkonry</groupId>
  <artifactId>client</artifactId>
  <version>1.0.2</version>
</dependency>

Features

* Create 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 historical input data (json format) to Datastream (Used for model revision) 
* Add historical input data (csv format) to Datastream (Used for model revision) 
* Add historical input data (json format) from a stream to Datastream (Used for model revision) 
* Add historical input data (csv format) from a stream to Datastream (Used 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
* Add facts data (csv format) to Assessment
* Add facts data (json format) from a stream to Assessment
* Add facts data (csv format) from a stream to  Assessment
* 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.setTagIdentifier("tag");
	signal.setValueIdentifier("value");
	signal.setIsSignalPrefix(false);

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

	Field field = new Field();
	field.setSiganl(signal);
	field.setTime(time);
	// field.setEntityIdentifier("unit");

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

	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.setTagIdentifier("tag");
	signal.setValueIdentifier("value");
	signal.setDelimiter("_");
	signal.setIsSignalPrefix(false);

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

	Field field = new Field();
	field.setSiganl(signal);
	field.setTime(time);
	// field.setEntityIdentifier("unit");

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

	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.setTagIdentifier("tag");
    signal.setValueIdentifier("value");
    signal.setDelimiter("_");
    signal.setIsSignalPrefix(false);

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

    Field field = new Field();
    field.setSiganl(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.setTagIdentifier("tag");
	signal.setValueIdentifier("value");
	signal.setIsSignalPrefix(false);

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

	Field field = new Field();
	field.setSiganl(signal);
	field.setTime(time);
	// field.setEntityIdentifier("unit");

	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 historical input data (json format) to Datastream (Used for model revision)

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\", \"tag\" : \"signal1\", \"value\" : 3.4}" + "\n"
        + "{\"time\" : \"2016-03-01 01:01:02\", \"tag\" : \"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", "false");
	options.put("hasMoreData", "false");
    falkonry.addInput('datastream-Id', data, options);

Add historical input data (csv format) to Datastream (Used for model revision)

Data:

	time, tag, 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("fileFormat", "csv");
	    options.put("streaming", "false");
		options.put("hasMoreData", "false");
	    falkonry.addInput('datastream-Id', data, options);

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.setTagIdentifier("tag");
    signal.setValueIdentifier("value");
    signal.setDelimiter("_");
    signal.setIsSignalPrefix(false);

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

    Field field = new Field();
    field.setSiganl(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");
    options.put("streaming", "false");
	options.put("hasMoreData", "false");
    falkonry.addInput(datastream.getId(), data, options);

Add historical 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 historical 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>();
    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\", \"tag\" : \"signal1\", \"value\" : 3.4}" + "\n"
        + "{\"time\" : \"2016-03-01 01:01:02\", \"tag\" : \"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");
    falkonry.addInput('datastream-Id', data, options);

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

Data:

	time, tag, 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("fileFormat", "csv");
	    options.put("streaming", "true");
		options.put("hasMoreData", "false");
	    falkonry.addInput('datastream-Id', data, options);

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.setTagIdentifier("tag");
    signal.setValueIdentifier("value");
    signal.setDelimiter("_");
    signal.setIsSignalPrefix(false);

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

    Field field = new Field();
    field.setSiganl(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");
    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("timeIdentifier", "time");
    options.put("timeFormat", "millis");
    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>();
    Map<String, String> options = new HashMap<String, String>();
    options.put("timeIdentifier", "time");
    options.put("timeFormat", "millis");
    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

    import com.falkonry.client.Falkonry;

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

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

Add facts data (csv format) to Assessment

    import com.falkonry.client.Falkonry;

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

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

Add facts data (json format) from a stream to Assessment

    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");      
    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

    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");      
    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

Files

README.md

Latest commit

History

README.md

File metadata and controls

Installation

Features

Quick Start

Examples

Create 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 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 historical input data (json format) to Datastream (Used for model revision)

Add historical input data (csv format) to Datastream (Used for model revision)

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

Add historical input data (csv format) from a stream to Datastream (Used 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

Add facts data (csv format) to Assessment

Add facts data (json format) from a stream to Assessment

Add facts data (csv format) from a stream to Assessment

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)

Docs

Tests

License