pwtools.num.match_mask

pwtools.num.match_mask(arr, values, fullout=False, eps=None)[source]

Bool array of len(arr) which is True if arr[i] == values[j], j=0..len(values).

This is the same as creating bool arrays like arr == some_value just that some_value can be an array (numpy can only do some_value = scalar). By default we assume integer arrays, unless eps is used.

With eps=None and fullout=False, it behaves like numpy.in1d.

Parameters:

  • arr (1d array)

  • values (1d array) – Values to be found in arr.

  • fullout (bool)

  • eps (float) – Use this threshold to compare array values.

Returns:

  • ret (fullout = False)

  • ret, idx_lst (fullout = True)

  • ret (1d array, bool, len(arr)) – Bool mask array for indexing arr. arr[ret] pulls out all values which are also in values.

  • idx_lst (1d array) – Indices for which arr[idx_lst[i]] equals some value in values.

Examples

>>> arr=array([1,2,3,4,5]); values=array([1,3])
>>> num.match_mask(arr, values)
array([ True, False,  True, False, False], dtype=bool)
>>> num.match_mask(arr, values, fullout=True)
(array([ True, False,  True, False, False], dtype=bool), array([0, 2]))
>>> arr[num.match_mask(arr, values)]
array([1, 3])
>>> # handle cases where len(values) > len(arr) and values not contained in arr
>>> arr=array([1,2,3,4,5]); values=array([1,3,3,3,7,9,-3,-4,-5])
>>> num.match_mask(arr, values, fullout=True)
(array([ True, False,  True, False, False], dtype=bool), array([0, 2]))
>>> # float values: use eps
>>> num.match_mask(arr+0.1, values, fullout=True, eps=0.2)
(array([ True, False,  True, False, False], dtype=bool), array([0, 2]))

See also

numpy.in1d