While Python has arbitrary-sized integers, integers in Numba-compiled functions get a fixed size through type inference (usually, the size of a machine integer). This means that arithmetic operations can wrapround or produce undefined results or overflow.
Type inference can be overriden by an explicit type specification, if fine-grained control of integer width is desired.
Calling the bitwise complement operator (the ~ operator) on a Python
boolean returns an integer, while the same operator on a Numpy boolean
returns another boolean:
>>> ~True
-2
>>> ~np.bool_(True)
False
Numba follows the Numpy semantics.
In nopython mode, global and closure variables are frozen by Numba: a Numba-compiled function sees the value of those variables at the time the function was compiled. Also, it is not possible to change their values from the function.
Numba may or may not copy global variables referenced inside a compiled function. Small global arrays are copied for potential compiler optimization with immutability assumption. However, large global arrays are not copied to conserve memory. The definition of “small” and “large” may change.
Todo
This document needs completing.