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copysign

Return element-wise values of x1 with the sign of x2. Inputs are broadcast together. 
function copysign(x1: ArrayLike, x2: ArrayLike | number): NDArray
NameTypeDefaultDescription
x1ArrayLike—Values whose magnitudes are used.
x2ArrayLike—Values whose signs are copied.
Returns: NDArray — Array with magnitudes from x1 and signs from x2. 
import * as np from 'numpy-ts';

np.copysign([1, -2, 3], [-1, 1, -1]);
// array([-1, 2, -3])

// Broadcasting with a scalar
np.copysign([1, 2, 3], -1);
// array([-1, -2, -3])

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signbit

Return element-wise true where the sign bit is set (i.e., the value is negative). This correctly detects -0.0. 
function signbit(x: ArrayLike): NDArray
NameTypeDefaultDescription
xArrayLike—Input array.
Returns: NDArray — Boolean array, true where the sign bit is set. 
import * as np from 'numpy-ts';

np.signbit([-1, 0, 1, -0.0]);
// array([true, false, false, true])

np.signbit([-Infinity, Infinity]);
// array([true, false])

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nextafter

Return the next representable floating-point value after x1 towards x2, element-wise. Inputs are broadcast together. 
function nextafter(x1: ArrayLike, x2: ArrayLike | number): NDArray
NameTypeDefaultDescription
x1ArrayLike—Starting values.
x2ArrayLike—Direction values.
Returns: NDArray — The next representable float after each x1[i] in the direction of x2[i]. 
import * as np from 'numpy-ts';

// Smallest float greater than 1.0
const eps = np.nextafter([1.0], [2.0]);
// array([1.0000000000000002])

// Largest float less than 1.0
const before = np.nextafter([1.0], [0.0]);
// array([0.9999999999999998])

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spacing

Return the distance between each element and the nearest adjacent representable floating-point number. This is the ULP (unit in the last place) of each value. 
function spacing(x: ArrayLike): NDArray
NameTypeDefaultDescription
xArrayLike—Input array.
Returns: NDArray — The spacing (ULP) for each element. 
import * as np from 'numpy-ts';

np.spacing([1.0]);
// array([2.220446049250313e-16])  -- machine epsilon

np.spacing([1e10]);
// array([0.0000019073486328125])  -- larger spacing for larger values

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promote_types

Return the smallest dtype to which both input dtypes can be safely cast. This follows NumPy’s type promotion rules. 
function promote_types(dtype1: DType, dtype2: DType): DType
NameTypeDefaultDescription
dtype1DType—First dtype.
dtype2DType—Second dtype.
Returns: DType — The promoted dtype that can represent values from both input dtypes without loss. 
import * as np from 'numpy-ts';

np.promote_types('int32', 'float32');
// 'float64'

np.promote_types('int8', 'int16');
// 'int16'

np.promote_types('float32', 'float64');
// 'float64'

np.promote_types('bool', 'int32');
// 'int32'