tidy3d.ScalarFieldTimeDataArray

class ScalarFieldTimeDataArray

Bases: AbstractSpatialDataArray

Spatial distribution in the time-domain.

Example

>>> x = [1,2]
>>> y = [2,3,4]
>>> z = [3,4,5,6]
>>> t = [0, 1e-12, 2e-12]
>>> coords = dict(x=x, y=y, z=z, t=t)
>>> fd = ScalarFieldTimeDataArray(np.random.random((2,3,4,3)), coords=coords)

Attributes

Methods

item(*args)	Copy an element of an array to a standard Python scalar and return it.
searchsorted(v[, side, sorter])	Find indices where elements of v should be inserted in a to maintain order.

Inherited Common Usage

item(*args)

Copy an element of an array to a standard Python scalar and return it.

Parameters:

*args (Arguments (variable number and type)) –

• none: in this case, the method only works for arrays with one element (a.size == 1), which element is copied into a standard Python scalar object and returned.

• int_type: this argument is interpreted as a flat index into the array, specifying which element to copy and return.

• tuple of int_types: functions as does a single int_type argument, except that the argument is interpreted as an nd-index into the array.

Returns:

z – A copy of the specified element of the array as a suitable Python scalar

Return type:

Standard Python scalar object

Notes

When the data type of a is longdouble or clongdouble, item() returns a scalar array object because there is no available Python scalar that would not lose information. Void arrays return a buffer object for item(), unless fields are defined, in which case a tuple is returned.

item is very similar to a[args], except, instead of an array scalar, a standard Python scalar is returned. This can be useful for speeding up access to elements of the array and doing arithmetic on elements of the array using Python’s optimized math.

Examples

>>> import numpy as np
>>> np.random.seed(123)
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[2, 2, 6],
       [1, 3, 6],
       [1, 0, 1]])
>>> x.item(3)
1
>>> x.item(7)
0
>>> x.item((0, 1))
2
>>> x.item((2, 2))
1

For an array with object dtype, elements are returned as-is.

>>> a = np.array([np.int64(1)], dtype=object)
>>> a.item() #return np.int64
np.int64(1)

searchsorted(v, side='left', sorter=None)

Find indices where elements of v should be inserted in a to maintain order.

For full documentation, see numpy.searchsorted

See also

numpy.searchsorted

equivalent function