Supported NumPy Functions

NumPy QuadDType supports a comprehensive set of NumPy universal functions (ufuncs) and array functions.

Element-wise Arithmetic Operations

Unary Arithmetic

Function	Operator	Description
np.negative	-x	Numerical negative
np.positive	+x	Numerical positive
np.absolute	abs(x)	Absolute value

Binary Arithmetic

Function	Operator	Description
np.add	+	Addition
np.subtract	-	Subtraction
np.multiply	*	Multiplication
np.divide	/	Division
np.true_divide	/	True division
np.floor_divide	//	Floor division
np.mod	%	Modulo
np.power	**	Power

Element-wise Sign Functions

Function	Description
np.sign	Sign indicator
np.signbit	Test for negative sign bit (works with NaN)
np.copysign	Copy sign of second to first

Element-wise Trigonometric Functions

Function	Description
np.sin	Sine
np.cos	Cosine
np.tan	Tangent
np.arcsin	Inverse sine
np.arccos	Inverse cosine
np.arctan	Inverse tangent
np.arctan2	Two-argument inverse tangent

Element-wise Hyperbolic Functions

Function	Description
np.sinh	Hyperbolic sine
np.cosh	Hyperbolic cosine
np.tanh	Hyperbolic tangent
np.arcsinh	Inverse hyperbolic sine
np.arccosh	Inverse hyperbolic cosine
np.arctanh	Inverse hyperbolic tangent

Element-wise Exponential Functions

Function	Description
np.exp	Exponential (\(e^x\))
np.exp2	Base-2 exponential (\(2^x\))
np.expm1	exp(x) - 1 (accurate for small x)

Element-wise Logarithmic Functions

Function	Description
np.log	Natural logarithm
np.log2	Base-2 logarithm
np.log10	Base-10 logarithm
np.log1p	log(1 + x) (accurate for small x)

Element-wise Power and Root Functions

Function	Description
np.square	Square (\(x^2\))
np.sqrt	Square root
np.cbrt	Cube root
np.hypot	Hypotenuse (\(\sqrt{x^2 + y^2}\))

Element-wise Rounding Functions

Function	Description
np.floor	Floor (round down)
np.ceil	Ceiling (round up)
np.trunc	Truncate toward zero
np.rint	Round to nearest integer (ties to even)

Element-wise Classification Functions

Function	Description
np.isfinite	Test for finite values
np.isinf	Test for infinity
np.isnan	Test for NaN

Element-wise Comparison Functions

Function	Operator	Description
np.equal	==	Equal
np.not_equal	!=	Not equal
np.less	<	Less than
np.less_equal	<=	Less than or equal
np.greater	>	Greater than
np.greater_equal	>=	Greater than or equal

Element-wise Minimum/Maximum

Function	Description
np.minimum	Minimum
np.maximum	Maximum
np.fmin	Minimum (ignores NaN)
np.fmax	Maximum (ignores NaN)

Reduction Functions

Function	Description
np.sum	Sum of elements
np.prod	Product of elements
np.mean	Arithmetic mean
np.min / np.amin	Minimum value
np.max / np.amax	Maximum value

Array Creation and Manipulation

Creation

Function	Description
np.zeros	Array of zeros
np.ones	Array of ones
np.empty	Uninitialized array
np.full	Array filled with given value
np.arange	Range of values
np.linspace	Linearly spaced values

Manipulation

Function	Description
np.reshape	Reshape array
np.transpose	Transpose array
np.concatenate	Join arrays
np.stack	Stack arrays
np.split	Split array

Linear Algebra (via QuadBLAS)

When QuadBLAS is available (not on Windows):

Function	Description
np.matmul / @	Matrix multiplication

Type Conversion

Function	Description
np.astype	Convert array dtype
np.array	Create array from data

Usage Examples

Trigonometric

import numpy as np
from numpy_quaddtype import QuadPrecDType, pi

x = np.array([0, pi/6, pi/4, pi/3, pi/2, pi], dtype=QuadPrecDType())

print("sin(x):", np.sin(x))
print("cos(x):", np.cos(x))

Exponential and Logarithmic

import numpy as np
from numpy_quaddtype import QuadPrecDType

x = np.array([1, 2, 3], dtype=QuadPrecDType())

print("exp(x):", np.exp(x))
print("log(exp(x)):", np.log(np.exp(x)))  # Should return x

Reductions

import numpy as np
from numpy_quaddtype import QuadPrecDType

arr = np.arange(1, 11, dtype=QuadPrecDType())

print("Sum:", np.sum(arr))        # 55
print("Product:", np.prod(arr))   # 3628800 (10!)
print("Mean:", np.mean(arr))      # 5.5