Error when using `numpy.random.normal()` with Numba-CodePudding

I'm exploring a bit Numba to optimize some signal processing codes. According to Numba's documentation, the function from the numpy.random package is well supported by the just-in-time compiler. However, when I run

import numpy as np
from numba import jit

@jit(nopython=True)
def numba():
    noise = np.random.normal(size=100)

# ...

if __name__ == "__main__":
    numba()

I get the following error:

Traceback (most recent call last):
  File ".../test.py", line 89, in <module>
    numba()
  File ".../venv/lib/python3.9/site-packages/numba/core/dispatcher.py", line 468, in _compile_for_args
    error_rewrite(e, 'typing')
  File ".../venv/lib/python3.9/site-packages/numba/core/dispatcher.py", line 409, in error_rewrite
    raise e.with_traceback(None)
numba.core.errors.TypingError: Failed in nopython mode pipeline (step: nopython frontend)
No implementation of function Function(<built-in method normal of numpy.random.mtrand.RandomState object at 0x104355740>) found for signature:
 
 >>> normal(size=Literal[int](100000))
 
There are 4 candidate implementations:
  - Of which 4 did not match due to:
  Overload in function '_OverloadWrapper._build.<locals>.ol_generated': File: numba/core/overload_glue.py: Line 129.
    With argument(s): '(size=int64)':
   Rejected as the implementation raised a specific error:
     TypingError: unsupported call signature
  raised from .../venv/lib/python3.9/site-packages/numba/core/typing/templates.py:439

During: resolving callee type: Function(<built-in method normal of numpy.random.mtrand.RandomState object at 0x104355740>)
During: typing of call at .../test.py (65)


File "test.py", line 65:
def numba():
    noise = np.random.normal(size=SIZE)
    ^

Am I doing something obviously silly?

Thanks a lot!

CodePudding user response：

if you check the current state of documentation, the size argument is not supported yet.

since numba compiles this to machine code, this is equivalent in terms of speed.

@jit(nopython=True)
def numba():
    noise = np.empty(100,dtype=np.float64)
    for i in range(100):
        noise[i] = np.random.normal()
    return noise

Edit: the numba version is actually twice as fast ... probably because it doesn't parse inputs.