class_docstr: Applies basic post-filtering to a time series of anomaly scores
  
  1. Determine which points are anomalies by comparing the absolute value of
     their anomaly score to ``alm_threshold``
  2. Only fire an alarm when ``min_alm_in_window`` of points (within a window
     of ``alarm_window_minutes`` minutes) are labeled as anomalies.
  3. If there is an alarm, then all alarms for the next ``alm_suppress_minutes``
     minutes will be suppressed.
  
  Return a time series of filtered anomaly scores, where the only non-zero
  values are the anomaly scores which were marked as alarms (and not
  suppressed).
code: def alm_threshold(self, x):
      self.threshold.alm_threshold = x
  
imports: import bisect
  import logging
  import numpy
  from merlion.evaluate.anomaly import TSADMetric
  from merlion.post_process.base import PostRuleBase
  from merlion.utils import UnivariateTimeSeries, TimeSeries
label_desc: Use image node_id 3 for calling the AggregateAlarms obj's underlying member method code with example usage: obj.alm_threshold(x) without return types