Features for sample

Features for sample

Runs the DSP block against a sample. This will move the sliding window (dependent on the sliding window length and the sliding window increase parameters in the impulse) over the complete file, and run the DSP function for every window that is extracted.

GEThttps://studio.edgeimpulse.com/v1/api/{projectId}/dsp/{dspId}/features/get-graph/classification/{sampleId}

Path parameters

projectId*integer

Project ID

dspId*integer

DSP Block ID, use the impulse functions to retrieve the ID

sampleId*integer

Sample ID

Response

200

OK

Body

application/json

success*boolean

Whether the operation succeeded

errorstring

Optional error description (set if 'success' was false)

totalSampleCount*integer

Total number of windows in the data set

data*array of object

skipFirstFeatures*integer

When showing the processed features, skip the first X features. This is used in dimensionality reduction where artificial features are introduced in the response (on the first few positions).

Request

JavaScriptCurlPython

const response = await fetch('https://studio.edgeimpulse.com/v1/api/{projectId}/dsp/{dspId}/features/get-graph/classification/{sampleId}', {
    method: 'GET',
    headers: {},
});
const data = await response.json();

Response

200

{
  "success": false,
  "error": "text",
  "totalSampleCount": 0,
  "data": [
    {
      "X": "`{ 9.81, 0.32, 0.79 }`",
      "y": 0,
      "yLabel": "text",
      "sample": {
        "id": 0,
        "name": "text",
        "startMs": 0,
        "endMs": 0
      }
    }
  ],
  "skipFirstFeatures": 0
}