Numba has support for fast indexing and understands NumPy arrays and infers types for various calls of the NumPy API.
Unfortunately, there are a few pitfalls. We hope to resolve these in the future, and to document them in the meantime:
| Operation | Example |
|---|---|
| Boundschecking | array[N], with N < 0 or N > array.shape[0] |
| Wraparound | array[-1] |
| Calls to imported functions | Importing things from numpy from numpy import zeros
@autojit
def func():
array = zeros(...)
|
| Calling without a dtype | Calling zeros, ones or empty without a dtype or with lists np.zeros((M, N)) # No dtype!
np.zeros([M, N], dtype=np.double) # Not a tuple or int!
|