scipp.zeros#

scipp.zeros(*, dims=None, shape=None, sizes=None, unit=<automatically deduced unit>, dtype=DType('float64'), with_variances=False)#

Constructs a Variable with default initialized values with given dimension labels and shape.

The dims and shape can also be specified using a sizes dict. Optionally can add default initialized variances. Only keyword arguments accepted.

Parameters:

  • dims (Optional[Sequence[str]], default: None) – Optional (if sizes is specified), dimension labels.

  • shape (Optional[Sequence[int]], default: None) – Optional (if sizes is specified), dimension sizes.

  • sizes (Optional[dict[str, int]], default: None) – Optional, dimension label to size map.

  • unit (Unit | str | DefaultUnit | None, default: <automatically deduced unit>) – Unit of contents.

  • dtype (scipp.typing.DTypeLike) – Type of underlying data.

  • with_variances (bool, default: False) – If True includes variances initialized to 0.

Returns:

Variable – A variable filled with 0’s.

See also

scipp.array, scipp.empty, scipp.ones, scipp.scalar, scipp.zeros_like

Examples

>>> sc.zeros(dims=['x'], shape=[4])
<scipp.Variable> (x: 4)    float64  [dimensionless]  [0, 0, 0, 0]

>>> sc.zeros(sizes={'x': 4})
<scipp.Variable> (x: 4)    float64  [dimensionless]  [0, 0, 0, 0]

>>> sc.zeros(sizes={'y': 3}, with_variances=True)
<scipp.Variable> (y: 3)    float64  [dimensionless]  [0, 0, 0]  [0, 0, 0]

>>> sc.zeros(sizes={'z': 3}, unit='kg', dtype=int)
<scipp.Variable> (z: 3)      int64             [kg]  [0, 0, 0]