One objective of numba is having a seamless integration with NumPy. NumPy arrays provide an efficient storage method for homogeneous sets if data. NumPy dtypes provide type information useful when compiling, and the regular, structured storage of potentially large amounts of data in memory provides an ideal memory layout for code generation. numba excels at generating code that executes on top of NumPy arrays.
NumPy support in Numba comes in many forms: * numba understands NumPy ufuncs and is able to generate equivalent native code for many of them. * NumPy arrays are directly supported in numba. Access to Numpy arrays is very efficient, as indexing is lowered to memory accessing when possible. * numba is able to generate ufuncs/gufuncs. This means that it is possible to implement ufuncs/gufuncs within Python, getting speeds comparable to that of ufuncs/gufuncs implemented in C extension modules using the NumPy C API.
One objective on numba is having all the standard ufuncs in NumPy understood by numba. When a supported ufunc is found when compiling a function, numba maps the ufunc to equivalent native code. This allows the use of those ufuncs in numba code that gets compiled in no-python mode.
Right now support for ufuncs is quite limited in no-python mode. Meaning that only a selection of the ufuncs work in no-python mode.
Also, in its current implementation ufuncs working on arrays will only compile in no-python mode if the output is explicit (the output array is passed as second argument). Note that this limitation does not apply when applied to scalars
Following is a list of the different NumPy ufuncs that numba is aware of, sorted in the same way as in the NumPy documentation.
| UFUNC | MODE | |
|---|---|---|
| name | python-mode | no-python |
| add | Yes | Yes |
| subtract | Yes | Yes |
| multiply | Yes | Yes |
| divide | Yes | Yes |
| logaddexp | Yes | Yes |
| logaddexp2 | Yes | Yes |
| true_divide | Yes | Yes |
| floor_divide | Yes | Yes |
| negative | Yes | Yes |
| power | Yes | Yes |
| remainder | Yes | Yes |
| mod | Yes | Yes |
| fmod | Yes | Yes |
| abs | Yes | Yes |
| absolute | Yes | Yes |
| fabs | Yes | Yes |
| rint | Yes | Yes |
| sign | Yes | Yes |
| conj | Yes | Yes |
| exp | Yes | Yes |
| exp2 | Yes | Yes |
| log | Yes | Yes |
| log2 | Yes | Yes |
| log10 | Yes | Yes |
| expm1 | Yes | Yes |
| log1p | Yes | Yes |
| sqrt | Yes | Yes |
| square | Yes | Yes |
| reciprocal | Yes | Yes |
| conjugate | Yes | Yes |
| UFUNC | MODE | |
|---|---|---|
| name | python-mode | no-python |
| sin | Yes | Yes |
| cos | Yes | Yes |
| tan | Yes | Yes |
| arcsin | Yes | Yes |
| arccos | Yes | Yes |
| arctan | Yes | Yes |
| arctan2 | Yes | Yes |
| hypot | Yes | Yes |
| sinh | Yes | Yes |
| cosh | Yes | Yes |
| tanh | Yes | Yes |
| arcsinh | Yes | Yes |
| arccosh | Yes | Yes |
| arctanh | Yes | Yes |
| deg2rad | Yes | Yes |
| rad2deg | Yes | Yes |
| degrees | Yes | Yes |
| radians | Yes | Yes |
| UFUNC | MODE | |
|---|---|---|
| name | python-mode | no-python |
| bitwise_and | Yes | Yes |
| bitwise_or | Yes | Yes |
| bitwise_xor | Yes | Yes |
| bitwise_not | Yes | Yes |
| invert | Yes | Yes |
| left_shift | Yes | Yes |
| right_shift | Yes | Yes |
| UFUNC | MODE | |
|---|---|---|
| name | python-mode | no-python |
| greater | Yes | Yes |
| greater_equal | Yes | Yes |
| less | Yes | Yes |
| less_equal | Yes | Yes |
| not_equal | Yes | Yes |
| equal | Yes | Yes |
| logical_and | Yes | Yes |
| logical_or | Yes | Yes |
| logical_xor | Yes | Yes |
| logical_not | Yes | Yes |
| maximum | Yes | Yes |
| minimum | Yes | Yes |
| fmax | Yes | Yes |
| fmin | Yes | Yes |
| UFUNC | MODE | |
|---|---|---|
| name | python-mode | no-python |
| isfinite | Yes | Yes |
| isinf | Yes | Yes |
| isnan | Yes | Yes |
| signbit | Yes | Yes |
| copysign | Yes | Yes |
| nextafter | Yes | Yes |
| modf | Yes | No |
| ldexp | Yes* | Yes |
| frexp | Yes | No |
| floor | Yes | Yes |
| ceil | Yes | Yes |
| trunc | Yes | Yes |
| spacing | Yes | Yes |
* not supported on windows 32 bit