2.1. Types and signatures¶
As an optimizing compiler, Numba needs to decide on the type of each variable to generate efficient machine code. Python’s standard types are not precise enough for that, so we had to develop our own fine-grained type system.
You will encounter Numba types mainly when trying to inspect the results of Numba’s type inference, for debugging or educational purposes. However, you need to use types explicitly if compiling code ahead-of-time.
A signature specifies the type of a function. Exactly which kind of signature is allowed depends on the context (AOT or JIT compilation), but signatures always involve some representation of Numba types to specifiy the concrete types for the function’s arguments and, if required, the function’s return type.
An example function signature would be the string
(or the equivalent
"float64(int32, int32)") which specifies a
function taking two 32-bit integers and returning a double-precision float.
2.1.3. Basic types¶
The most basic types can be expressed through simple expressions. The
symbols below refer to attributes of the main
numba module (so if
you read “boolean”, it means that symbol can be accessed as
Many types are available both as a canonical name and a shorthand alias,
following Numpy’s conventions.
The following table contains the elementary numeric types currently defined by Numba and their aliases.
|boolean||b1||represented as a byte|
|uint8, byte||u1||8-bit unsigned byte|
|uint16||u2||16-bit unsigned integer|
|uint32||u4||32-bit unsigned integer|
|uint64||u8||64-bit unsigned integer|
|int8, char||i1||8-bit signed byte|
|int16||i2||16-bit signed integer|
|int32||i4||32-bit signed integer|
|int64||i8||64-bit signed integer|
|intc||–||C int-sized integer|
|uintc||–||C int-sized unsigned integer|
|uintp||–||pointer-sized unsigned integer|
|float32||f4||single-precision floating-point number|
|float64, double||f8||double-precision floating-point number|
|complex64||c8||single-precision complex number|
|complex128||c16||double-precision complex number|
The easy way to declare array types is to subscript an elementary type according to the number of dimensions. For example a 1-dimension single-precision array:
>>> numba.float32[:] array(float32, 1d, A)
or a 3-dimension array of the same underlying type:
>>> numba.float32[:, :, :] array(float32, 3d, A)
This syntax defines array types with no particular layout (producing code
that accepts both non-contiguous and contiguous arrays), but you can
specify a particular contiguity by using the
::1 index either at
the beginning or the end of the index specification:
>>> numba.float32[::1] array(float32, 1d, C) >>> numba.float32[:, :, ::1] array(float32, 3d, C) >>> numba.float32[::1, :, :] array(float32, 3d, F)
2.1.4. Advanced types¶
For more advanced declarations, you have to explicitly call helper functions or classes provided by Numba.
The APIs documented here are not guaranteed to be stable. Unless necessary, it is recommended to let Numba infer argument types by using the signature-less variant of @jit.
184.108.40.206. Numpy scalars¶
Instead of using
typeof(), non-trivial scalars such as
structured types can also be constructed programmatically.
Create a Numba type corresponding to the given Numpy dtype:
>>> struct_dtype = np.dtype([('row', np.float64), ('col', np.float64)]) >>> ty = numba.from_dtype(struct_dtype) >>> ty Record([('row', '<f8'), ('col', '<f8')]) >>> ty[:, :] unaligned array(Record([('row', '<f8'), ('col', '<f8')]), 2d, A)
Create a Numba type for Numpy datetimes of the given unit. unit should be a string amongst the codes recognized by Numpy (e.g.
Array(dtype, ndim, layout)¶
Create an array type. dtype should be a Numba type. ndim is the number of dimensions of the array (a positive integer). layout is a string giving the layout of the array:
Ameans any layout,
Cmeans C-contiguous and