class_docstr: Abstract class for business days since a previous event.
  Returns the number of **business days** (not trading days!) since
  the most recent event date for each asset.
  
  This doesn't use trading days for symmetry with
  BusinessDaysUntilNextEarnings.
  
  Assets which announced or will announce the event today will produce a
  value of 0.0. Assets that announced the event on the previous business
  day will produce a value of 1.0.
  
  Assets for which the event date is `NaT` will produce a value of `NaN`.
code: def _compute(self, arrays, dates, assets, mask):
      # Coerce from [ns] to [D] for numpy busday_count.
      announce_dates = arrays[0].astype(datetime64D_dtype)
  
      # Set masked values to NaT.
      announce_dates[~mask] = NaTD
  
      # Convert row labels into a column vector for broadcasted comparison.
      reference_dates = dates.values.astype(datetime64D_dtype)[
          :, newaxis
      ]
      return busday_count_mask_NaT(announce_dates, reference_dates)
  
file_path: catalyst/catalyst/pipeline/factors/events.py
imports: from numpy import newaxis
  from catalyst.utils.numpy_utils import NaTD, busday_count_mask_NaT, datetime64D_dtype, float64_dtype
  from .factor import Factor
label_desc: Use image node_id 1 for calling the BusinessDaysSincePreviousEvent obj's underlying member method code with example usage: obj._compute(arrays, dates, assets, mask) and returns: busday_count_mask_NaT