class_docstr: Anomalies are those data points that are above a pre-specified threshold value.
  This algorithm does not take baseline time series.
class_docstr_tok: ["Anomalies","are","those","data","points","that","are","above","a","pre-specified","threshold","value",".","This","algorithm","does","not","take","baseline","time","series","."]
docstr: Compute anomaly scores for the time series
  This algorithm just takes the diff of threshold with current value as anomaly score
docstr_tok: ["Compute","anomaly","scores","for","the","time","series","This","algorithm","just","takes","the","diff","of","threshold","with","current","value","as","anomaly","score"]
code: def _set_scores(self):
      """
      Compute anomaly scores for the time series
      This algorithm just takes the diff of threshold with current value as anomaly score
      """
      anom_scores = {}
      for timestamp, value in self.time_series.items():
          anom_scores[timestamp] = 0.0
          if (
              self.absolute_threshold_value_upper
              and value > self.absolute_threshold_value_upper
          ):
              anom_scores[timestamp] = (
                  value - self.absolute_threshold_value_upper
              )
          if (
              self.absolute_threshold_value_lower
              and value < self.absolute_threshold_value_lower
          ):
              anom_scores[timestamp] = (
                  self.absolute_threshold_value_lower - value
              )
      self.anom_scores = TimeSeries(self._denoise_scores(anom_scores))
  
imports: from luminol import exceptions
  from luminol.algorithms.anomaly_detector_algorithms import AnomalyDetectorAlgorithm
  from luminol.modules.time_series import TimeSeries
label_desc: Use image node_id 2 for calling the AbsoluteThreshold obj's underlying member method code with example usage: obj._set_scores() without return types