GitHub - chandresh-gurbani/falkonry-csharp-client: Falkonry C# Client to access Condition Prediction APIs

Falkonry C# Client to access Falkonry Condition Prediction APIs

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
* 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 facts data
* Get Datastream Input data
* Get Historian Output from Assessment
* Get Streaming Output
* 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

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;
    
    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
    
	var time = new Time();
	time.Zone = "GMT";
	time.Identifier = "time";
	time.Format = "iso_8601";

	var datasource = new Datasource();
	datasource.Type = "PI";
	var ds = new DatastreamRequest();
	var Field = new Field();
	var Signal = new Signal();
	Signal.ValueIdentifier = "value";
	Signal.TagIdentifier = "tag";
	Signal.IsSignalPrefix = true;
	Field.Signal = Signal;
	Field.Time = time;
	ds.Field = Field;
	ds.DataSource = datasource;
	var rnd = new System.Random();
	var randomNumber = System.Convert.ToString(rnd.Next(1, 10000));
	ds.Name = "TestDS" + randomNumber;
	ds.Field.Time = time;
	ds.DataSource = datasource;
	var datastream = _falkonry.CreateDatastream(ds);

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

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;
    
    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
    
	var time = new Time();
	time.Zone = "GMT";
	time.Identifier = "time";
	time.Format = "iso_8601";

	var datasource = new Datasource();
	datasource.Type = "PI";
	var ds = new DatastreamRequest();
	var Field = new Field();
	var Signal = new Signal();
	Signal.ValueIdentifier = "value";
	Signal.TagIdentifier = "tag";
	Signal.IsSignalPrefix = true;
	Signal.Delimiter = "_";
	Field.Signal = Signal;
	Field.Time = time;
	ds.Field = Field;
	ds.DataSource = datasource;
	var rnd = new System.Random();
	var randomNumber = System.Convert.ToString(rnd.Next(1, 10000));
	ds.Name = "TestDS" + randomNumber;
	ds.Field.Time = time;
	ds.DataSource = datasource;
	var datastream = _falkonry.CreateDatastream(ds);

Create Datastream for wide style data from a single entity

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;
    
    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
    
	var time = new Time();
	time.Zone = "GMT";
	time.Identifier = "time";
	time.Format = "iso_8601";

	var datasource = new Datasource();
	datasource.Type = "PI";
	var ds = new DatastreamRequest();
	var Field = new Field();
	var Signal = new Signal();
	using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;
    
    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
    
	var time = new Time();
	time.Zone = "GMT";
	time.Identifier = "time";
	time.Format = "iso_8601";

	var datasource = new Datasource();
	datasource.Type = "PI";
	var ds = new DatastreamRequest();
	// Input List
    var inputList = new List<Input>();
    var currents = new Input();
    currents.Name = "current";
    currents.ValueType = new ValueType();
    currents.EventType = new EventType();
    currents.ValueType.Type = "Numeric";
    currents.EventType.Type = "Samples";
    inputList.Add(currents);

    var vibration = new Input();
    vibration.Name = "vibration";
    vibration.ValueType = new ValueType();
    vibration.EventType = new EventType();
    vibration.ValueType.Type = "Numeric";
    vibration.EventType.Type = "Samples";
    inputList.Add(vibration);

    var state = new Input();
    state.Name = "state";
    state.ValueType = new ValueType();
    state.EventType = new EventType();
    state.ValueType.Type = "Categorical";
    state.EventType.Type = "Samples";
    inputList.Add(state);

    ds.InputList = inputList;
	var Field = new Field();
	var Signal = new Signal();
	Field.Signal = Signal;
	Field.Time = time;
	ds.Field = Field;
	ds.DataSource = datasource;
	var rnd = new System.Random();
	var randomNumber = System.Convert.ToString(rnd.Next(1, 10000));
	ds.Name = "TestDS" + randomNumber;
	ds.Field.Time = time;
	ds.DataSource = datasource;
	var datastream = _falkonry.CreateDatastream(ds);

Create Datastream for wide style data from a multiple entities

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;
    
    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
    
	var time = new Time();
	time.Zone = "GMT";
	time.Identifier = "time";
	time.Format = "iso_8601";

	var datasource = new Datasource();
	datasource.Type = "PI";
	var ds = new DatastreamRequest();
	var Field = new Field();
	var Signal = new Signal();
	using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;
    
    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
    
	var time = new Time();
	time.Zone = "GMT";
	time.Identifier = "time";
	time.Format = "iso_8601";

	var datasource = new Datasource();
	datasource.Type = "PI";
	var ds = new DatastreamRequest();
	// Input List
    var inputList = new List<Input>();
    var currents = new Input();
    currents.Name = "current";
    currents.ValueType = new ValueType();
    currents.EventType = new EventType();
    currents.ValueType.Type = "Numeric";
    currents.EventType.Type = "Samples";
    inputList.Add(currents);

    var vibration = new Input();
    vibration.Name = "vibration";
    vibration.ValueType = new ValueType();
    vibration.EventType = new EventType();
    vibration.ValueType.Type = "Numeric";
    vibration.EventType.Type = "Samples";
    inputList.Add(vibration);

    var state = new Input();
    state.Name = "state";
    state.ValueType = new ValueType();
    state.EventType = new EventType();
    state.ValueType.Type = "Categorical";
    state.EventType.Type = "Samples";
    inputList.Add(state);

    ds.InputList = inputList;
	var Field = new Field();
	Field.EntityIdentifier = "Unit";
	var Signal = new Signal();
	Field.Signal = Signal;
	Field.Time = time;
	ds.Field = Field;
	ds.DataSource = datasource;
	var rnd = new System.Random();
	var randomNumber = System.Convert.ToString(rnd.Next(1, 10000));
	ds.Name = "TestDS" + randomNumber;
	ds.Field.Time = time;
	ds.DataSource = datasource;
	var datastream = _falkonry.CreateDatastream(ds);

Create Datastream with microseconds precision

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;
    
    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
    
    var time = new Time();
    time.Zone = "GMT";
    time.Identifier = "time";
    time.Format = "iso_8601";

    var datasource = new Datasource();
    datasource.Type = "PI";
    var ds = new DatastreamRequest();
    var Field = new Field();
    var Signal = new Signal();
    Signal.ValueIdentifier = "value";
    Signal.TagIdentifier = "tag";
    Signal.IsSignalPrefix = true;
    Field.Signal = Signal;
    Field.Time = time;
    ds.Field = Field;
    ds.DataSource = datasource;
    var rnd = new System.Random();
    var randomNumber = System.Convert.ToString(rnd.Next(1, 10000));
    ds.Name = "TestDS" + randomNumber;
    ds.TimePrecision = "micro"; # this is use to store your data in different date time format. If input data precision is in micorseconds then set "micro" else "millis". If not sent then it will be "millis"
    ds.Field.Time = time;
    ds.DataSource = datasource;
    var datastream = _falkonry.CreateDatastream(ds);

Retrieve Datastreams

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
    List<Datastream> datastreams = _falkonry.GetDatastreams();

Retrieve Datastream by id

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
	Datastream datastream = _falkonry.GetDatastream('datastreamId')

Delete Datastream by id

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
	_falkonry.DeleteDatastream('datastreamId')

Add EntityMeta to a Datastream

	using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
	
	// Create Datastream
	datastream = falkonry.getDatastream(datastream.id);

	// Create EntityMetaRequest
	List<EntityMetaRequest> entityMetaRequestList = new List<EntityMetaRequest>();
	EntityMetaRequest entityMetaRequest1 = new EntityMetaRequest();
	entityMetaRequest1.label = "User readbale label";
	entityMetaRequest1.sourceId = "1234-21342134";
	entityMetaRequest1.path = "//root/branch1/";

	EntityMetaRequest entityMetaRequest2 = new EntityMetaRequest();
	entityMetaRequest2.label = "User readbale label2";
	entityMetaRequest2.sourceId = "1234-213421rawef";
	entityMetaRequest2.path = "//root/branch2/";

	entityMetaRequestList.Add(entityMetaRequest1);
	entityMetaRequestList.Add(entityMetaRequest2);

	List<EntityMeta> entityMetaResponseList = falkonry.postEntityMeta(entityMetaRequestList, datastream);

Get Entity Meta of datastream

	using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
	// Get entitymeta
	entityMetaResponseList = falkonry.getEntityMeta('datastream-id');

To create Assessment

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);

    AssessmentRequest asmt = new AssessmentRequest();
    asmt.name = "assessment name here";
	asmt.datastream= "datastream id";
    Assessment assessment = falkonry.createAssessment(asmt);

Retrieve Assessments

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
    List<Assessment> assessmentList = falkonry.getAssessments();

Retrieve Assessment by Id

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
    Assessment assessment = falkonry.getAssessment('assessment-id');

Delete assessment by id

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
    falkonry.DeleteAssessment('assessment-id');

Get Condition List Of Assessment

	using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
    Assessment assessment = falkonry.getAssessment('assessment-id');
	String data = "{\"time\" : \"2011-03-26T12:00:00Z\", \"entities\" : \"entity1\", \"end\" : \"2012-06-01T00:00:00Z\", \"Health\" : \"Normal\"}";
    string response = falkonry.addFacts('assessment-id',data, options);
	
	// aprioriConditionList 
	String [] conditionList = assessment.AprioriConditionList

	// Condition Listshould contain "Normal" as label

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

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);

    string data = "{\"time\" :\"2016-03-01 01:01:01\", \"current\" : 12.4, \"vibration\" : 3.4, \"state\" : \"On\"}";
    
    SortedDictionary<string, string> options = new SortedDictionary<string, string>();
    options.Add("timeIdentifier", "time");
    options.Add("timeFormat", "YYYY-MM-DD HH:mm:ss");
    options.Add("fileFormat", "json");
	options.Add("streaming", "false");
	options.Add("hasMoreData", "false");
    InputStatus inputstatus = falkonry.addInput('datastream-id', data, options);

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

	using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);

    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";
    
    SortedDictionary<string, string> options = new SortedDictionary<string, string>();
    options.Add("timeIdentifier", "time");
    options.Add("timeFormat", "millis");
    options.Add("fileFormat", "csv");
	options.Add("streaming", "false");
	options.Add("hasMoreData", "false");
    InputStatus inputstatus = falkonry.addInput('datastream-id', data, options);

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

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);

    /*This particular example will read data from a AddData.json file in debug folder in bin*/
    
    SortedDictionary<string, string> options = new SortedDictionary<string, string>();
    options.Add("timeIdentifier", "time");
    options.Add("timeFormat", "iso_8601");
	options.Add("streaming", "false");
	options.Add("hasMoreData", "false");
	options.Add("fileFormat", "json");
    
    string folder_path = Path.GetDirectoryName(System.Reflection.Assembly.GetExecutingAssembly().Location);

    string path = folder_path + "/AddData.json";
    //Alternatively, you can directly specify the folder path in the "folder_path" variable

    byte[] bytes = System.IO.File.ReadAllBytes(path);

    InputStatus inputstatus = falkonry.addInputStream('datastream-id', bytes, options);

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

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);

    /*This particular example will read data from a AddData.json file in debug folder in bin*/
    
    SortedDictionary<string, string> options = new SortedDictionary<string, string>();
    options.Add("timeIdentifier", "time");
    options.Add("timeFormat", "iso_8601");
	options.Add("streaming", "false");
	options.Add("hasMoreData", "false");
	options.Add("fileFormat", "csv");
    
    string folder_path = Path.GetDirectoryName(System.Reflection.Assembly.GetExecutingAssembly().Location);

    string path = folder_path + "/AddData.csv";
    //Alternatively, you can directly specify the folder path in the "folder_path" variable

    byte[] bytes = System.IO.File.ReadAllBytes(path);

    InputStatus inputstatus = falkonry.addInputStream('datastream-id', bytes, options);

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

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);

    string data = "{\"time\" :\"2016-03-01 01:01:01\", \"current\" : 12.4, \"vibration\" : 3.4, \"state\" : \"On\"}";
    
    SortedDictionary<string, string> options = new SortedDictionary<string, string>();
    options.Add("timeIdentifier", "time");
    options.Add("timeFormat", "YYYY-MM-DD HH:mm:ss");
    options.Add("fileFormat", "json");
	options.Add("streaming", "true");
	options.Add("hasMoreData", "false");
    InputStatus inputstatus = falkonry.addInput('datastream-id', data, options);

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

	using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);

    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";
    
    SortedDictionary<string, string> options = new SortedDictionary<string, string>();
    options.Add("timeIdentifier", "time");
    options.Add("timeFormat", "millis");
    options.Add("fileFormat", "csv");
	options.Add("streaming", "true");
	options.Add("hasMoreData", "false");
    InputStatus inputstatus = falkonry.addInput('datastream-id', data, options);

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

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);

    /*This particular example will read data from a AddData.json file in debug folder in bin*/
    
    SortedDictionary<string, string> options = new SortedDictionary<string, string>();
    options.Add("timeIdentifier", "time");
    options.Add("timeFormat", "iso_8601");
	options.Add("streaming", "true");
	options.Add("hasMoreData", "false");
	options.Add("fileFormat", "json");
    
    string folder_path = Path.GetDirectoryName(System.Reflection.Assembly.GetExecutingAssembly().Location);

    string path = folder_path + "/AddData.json";
    //Alternatively, you can directly specify the folder path in the "folder_path" variable

    byte[] bytes = System.IO.File.ReadAllBytes(path);

    InputStatus inputstatus = falkonry.addInputStream('datastream-id', bytes, options);

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

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);

    /*This particular example will read data from a AddData.json file in debug folder in bin*/
    
    SortedDictionary<string, string> options = new SortedDictionary<string, string>();
    options.Add("timeIdentifier", "time");
    options.Add("timeFormat", "iso_8601");
	options.Add("streaming", "false");
	options.Add("hasMoreData", "false");
	options.Add("fileFormat", "csv");
    
    string folder_path = Path.GetDirectoryName(System.Reflection.Assembly.GetExecutingAssembly().Location);

    string path = folder_path + "/AddData.csv";
    //Alternatively, you can directly specify the folder path in the "folder_path" variable

    byte[] bytes = System.IO.File.ReadAllBytes(path);

    InputStatus inputstatus = falkonry.addInputStream('datastream-id', bytes, options);

Add facts data (json format) to Assessment

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token = "Add your token here";   
    SortedDictionary<string, string> options = new SortedDictionary<string, string>();
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
    String data = "{\"time\" : \"2011-03-26T12:00:00Z\", \"entities\" : \"entity1\", \"end\" : \"2012-06-01T00:00:00Z\", \"Health\" : \"Normal\"}";
    string response = falkonry.addFacts('assessment-id',data, options);

Add facts data (csv format) to Assessment

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token = "Add your token here";   
    SortedDictionary<string, string> options = new SortedDictionary<string, string>();
    Falkonry falkonry = new Falkonry("http://localhost:8080", 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-id',data, options);

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

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
    string path = "Insert the path to your file here";
    byte[] bytes = System.IO.File.ReadAllBytes(path);
    string response = falkonry.AddFactsStream('assessment-id',bytes, options);

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

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
    string path = "Insert the path to your file here";
    byte[] bytes = System.IO.File.ReadAllBytes(path);
    string response = falkonry.AddFactsStream('assessment-id',bytes, options);

Get facts data of Assessment

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
    string assessment = "id of the assessment here";
    string response = falkonry.getFacts('assessment',options);

Get Datastream Input data

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
    string datastream = "id of the datastream here";
    string response = falkonry.GetDatastreamData('datastream',options);

Get Historian Output from Assessment

	using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
	
	// Create Datastream

	// create Assessment

	// Add Data To dataStream

	// From Falkonry UI, run a model revision.

	// Fetch Historical output data for given assessment, startTime , endtime
	SortedDictionary<string, string> options = new SortedDictionary<string, string>();
	options.Add("startTime", "2011-01-01T01:00:00.000Z"); // in the format YYYY-MM-DDTHH:mm:ss.SSSZ
	options.Add("endTime", "2011-06-01T01:00:00.000Z");  // in the format YYYY-MM-DDTHH:mm:ss.SSSZ
	options.Add("responseFormat", "application/json");  // also avaibale options 1. text/csv 2. application/json

	HttpResponse httpResponse = falkonry.getHistoricalOutput(assessment, options);
	// If data is not readily avaiable then, a tracker id will be sent with 202 status code. While falkonry will genrate ouptut data
	// Client should do timely pooling on the using same method, sending tracker id (__id) in the query params
	// Once data is avaiable server will response with 200 status code and data in json/csv format.

	if (httpResponse.statusCode == 202)
	{
		Tracker trackerResponse = javascript.Deserialize<Tracker>(httpResponse.response);
		// get id from the tracker
		string __id = trackerResponse.__id;

		// use this tracker for checking the status of the process.
		options = new SortedDictionary<string, string>();
		options.Add("tarckerId", __id);
		options.Add("responseFormat", "application/json");

		httpResponse = falkonry.getHistoricalOutput(assessment, options);

		// if status is 202 call the same request again

		// if statsu is 200, output data will be present in httpResponse.response field
	}
	if (httpResponse.statusCode > 400)
	{
		// Some Error has occured. Please httpResponse.response for detail message
	}

Get Streaming Output

    using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);

    string assessment_id ="assessment ID here";
    System.IO.Stream streamrecieved = falkonry.getOutput(assessment_id, null, null);
    //The folder path below by default is debug in bin.
    string folder_path = System.IO.Path.GetDirectoryName(System.Reflection.Assembly.GetExecutingAssembly().Location);
    //Alternatively, you can also specify the path of the folder in thr folder_path variable
    //The outflow will get saved in an outflow.txt file there
    string path = folder_path + "/outflow.txt";

    System.IO.StreamReader streamreader = new System.IO.StreamReader(streamrecieved);
    System.IO.StreamWriter streamwriter = new System.IO.StreamWriter(path);
    string line;
    using (streamwriter)
    {
        while ((line = streamreader.ReadLine()) != null)
        {
            streamwriter.WriteLine(line);
        }
    }

Datastream On (Start live monitoring of datastream)

	using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
	string datastream_id = "Your datastream id";
	falkonry.onDatastream(datastream_id);

Datastream Off (Stop live monitoring of datastream)

	using falkonry_csharp_client;
    using falkonry_csharp_client.helper.models;

    string token="Add your token here";   
    Falkonry falkonry = new Falkonry("http://localhost:8080", token);
	string datastream_id = "Your datastream id";
	falkonry.offDatastream(datastream_id);

Docs

[Falkonry APIs](https://service.falkonry.io/api)

License

Available under MIT License

Name		Name	Last commit message	Last commit date
Latest commit History 135 Commits
bin		bin
packages		packages
release		release
src		src
test		test
.gitattributes		.gitattributes
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
app.config		app.config
falkonry-csharp-client.csproj		falkonry-csharp-client.csproj
falkonry-csharp-client.csproj.user		falkonry-csharp-client.csproj.user
packages.config		packages.config

License

chandresh-gurbani/falkonry-csharp-client

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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 a multiple entities

Create Datastream with microseconds precision

Retrieve Datastreams

Retrieve Datastream by id

Delete Datastream by id

Add EntityMeta to a Datastream

Get Entity Meta of datastream

To 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 (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 facts data of Assessment

Get Datastream Input data

Get Historian Output from Assessment

Get Streaming Output

Datastream On (Start live monitoring of datastream)

Datastream Off (Stop live monitoring of datastream)

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