inRange¶

utilipy.data_utils.select.inRange(*args: Union[numpy.array, Sequence], rng: Union[Sequence, ellipsis] = Ellipsis, lbi: bool = True, ubi: bool = False, as_ind: bool = False) → numpy.array[source]¶

Multidimensional box selection.

Parameters

argsSequence

list of values along each dimension must be the same length can be same-shaped ND arrays, each treated as a series of rows.

rngSequence or Ellipsis, optional

(default Ellipsis) the domain for each argument in args:

args = [[x1], [x2], ...]
rng =   [1st [lower, upper],
         2nd [lower, upper],
         ...]

if each ‘xn’ is multidimensional

lbibool, optional

(default True) Lower Bound Inclusive, whether to be inclusive on the lower bound

ubibool, optional

(default False) Upper Bound Inclusive, whether to be inclusive on the upper bound

as_indbool, optional

(default False) whether to return bool array or the indices (where(bool array == True)) sets the default behavior for the wrapped fnction func

Returns

inrangebool ndarray: boolean array to select values in box selection

Other Parameters

as_indbool or “flatten”, optional: (default False) whether to return a boolean array, or array of indices.

Raises

ValueError: if as_ind is “flatten” and cannot be flattened (len > 1)

See also

outRange: multidimensional box exclusion
ioRange: inRange and outRange combined

Examples

list of args:

>>> x = np.arange(5)
>>> y = np.arange(5) + 10
>>> inRange(x, y, rng=[[0, 3], [10, 15]]) 
array([ True,  True,  True, False, False])

multidimensional arg:

>>> x = np.array([[ 0,  1], [10, 11]])
>>> rng = [[0, 3], [9, 11]]
>>> inRange(x, rng=rng) 
array([[ True,  True],
       [ True, False]])

Todo

allow lbi & rbi to be lists, matching args, for individual adjustment

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inRange¶