sherpa.utils.filter_bins(mins, maxes, axislist, integrated=False)[source] [edit on github]

What mask represents the given set of filters?

The ranges are treated as inclusive at both ends if integrated is False, the default, otherwise the lower limit is inclusive but the upper limit is exclusive.

  • mins (sequence of values) – The minimum value of the valid range (elements may be None).

  • maxes (sequence of values) – The maximum value of the valid range (elements may be None).

  • axislist (sequence of arrays) – The axis to apply the range to. There must be the same number of elements in mins, maxes, and axislist. The number of elements of each element of axislist must also agree (the cell values do not need to match).

  • integrated (bool, optional) – Is the data integrated (we have low and high bin edges)? The default is False. When True it is expected that axislist contains a even number of rows, where the odd values are the low edges and the even values the upper edges, and that the mins and maxes only ever contain a single value, given in (None, hi) and (lo, None) ordering.


mask – A mask indicating whether the values are included (True) or excluded (False). If any of the input sequences are empty then None will be returned.

Return type:

ndarray or None


Calculate those points in xs which are in the range 1.5 <= x <= 4.

>>> xs = [1, 2, 3, 4, 5]
>>> filter_bins([1.5], [4], [xs])
array([False,  True,  True,  True, False])

Repeat the above calculation by combining filters for x >= 1.5 and x <= 4 (note that the grid must be repeated for each filter):

>>> filter_bins([1.5, None], [None, 4], [xs, xs])
array([False,  True,  True,  True, False])

For integrated data sets the lower and upper edges should be sent separately with the max and min limits, along with setting the integrated flag. The following selects the bins that cover the range 2 to 4 and 1.5 to 3.5:

>>> xlo = [1, 2, 3, 4, 5]
>>> xhi = [2, 3, 4, 5, 6]
>>> filter_bins([None, 2], [4, None], [xlo, xhi], integrated=True)
array([False,  True,  True,  False, False])
>>> filter_bins([None, 1.5], [3.5, None], [xlo, xhi], integrated=True)
array([True,  True,  True,  False, False])