class_docstr: A parameter group that specifies a set of filters to apply to tabular data.
      
class_docstr_tok: ["A","parameter","group","that","specifies","a","set","of","filters","to","apply","to","tabular","data","."]
docstr: Return a boolean mask indicating whether each item in *data* passes
  the filter critera.
docstr_tok: ["Return","a","boolean","mask","indicating","whether","each","item","in","*","data","*","passes","the","filter","critera","."]
code: def generateMask(self, data):
      """Return a boolean mask indicating whether each item in *data* passes
      the filter critera.
      """
      mask = np.ones(len(data), dtype=bool)
      if len(data) == 0:
          return mask
      for fp in self:
          if fp.value() is False:
              continue
          mask &= fp.generateMask(data, mask.copy())
          # key, mn, mx = fp.fieldName, fp['Min'], fp['Max']
  
          # vals = data[key]
          # mask &= (vals >= mn)
          # mask &= (vals < mx)  ## Use inclusive minimum and non-inclusive maximum. This makes it easier to create non-overlapping selections
      return mask
  
imports: from collections import OrderedDict
  import numpy
  from ..None import functions
  from ..None import parametertree
  from ..Qt import QtCore
label_desc: Use image node_id 7 for calling the DataFilterParameter obj's underlying member method code with example usage: obj.generateMask(data) and returns: mask, mask