Numba jit unknown error during python function - numpy

Question

I made this function, but numba always give me error. Both chr_pos and pos are 1D arrays. What can be the problem?
#nb.njit
def create_needed_pos(chr_pos, pos):
needed_pos=[]
needed_pos=np.array(needed_pos,dtype=np.float64)
for i in range(len(chr_pos)):
for k in range(len(pos)):
if chr_pos[i] == pos[k]:
if i==1 and k==1:
needed_pos=pos[k]
else:
a=pos[k]
needed_pos=np.append(needed_pos,[a])
return needed_pos
needed_pos=create_needed_pos(chr_pos, pos)
The errors:
warnings.warn(errors.NumbaDeprecationWarning(msg,
<input>:1: NumbaWarning:
Compilation is falling back to object mode WITHOUT looplifting enabled because Function "create_needed_pos" failed type inference due to: Cannot unify array(float64, 1d, C) and int32 for 'needed_pos.1', defined at <input> (5)
File "<input>", line 5:
<source missing, REPL/exec in use?>
During: typing of intrinsic-call at <input> (9)
File "<input>", line 9:
<source missing, REPL/exec in use?>

Answer 1

The message
Cannot unify array(float64, 1d, C) and int32 for 'needed_pos.1'
is telling you that you are trying to assign an integer variable to an array. That happens in this line:
needed_pos=pos[k]
You can do that in normal Python, but Numba requires static types. You must assign an array of floats to an array of floats. For example, replacing the line by
needed_pos = pos[k:k+1]
The same error message says you are trying to assign an int, and this indicates that pos receives an array of ints. You must pass an array of floats instead.
After those changes, Numba still complains here:
needed_pos = []
needed_pos = np.array(needed_pos, dtype=np.float64)
with the message
numba.core.errors.TypingError: Failed in nopython mode pipeline (step: nopython frontend)
Cannot infer the type of variable 'needed_pos', have imprecise type: list(undefined)<iv=None>.
because it doesn't know the type of the elements that needed_pos will contain.
You can replace those two lines with one that creates an array of size zero with a known type:
needed_pos = np.array((0,), dtype=np.float64)
Now the program compiles and produces the same result with or without Numba.
But a problem remains. Numpy arrays work best when they have a fixed size. If you are continuously adding elements you'd better use lists (Numba lists in this case). This way for example:
#nb.njit
def create_needed_pos(chr_pos, pos):
needed_pos = nb.typed.List.empty_list(nb.float64)
for i in range(len(chr_pos)):
for k in range(len(pos)):
if chr_pos[i] == pos[k]:
if i == k == 1:
needed_pos = nb.typed.List([pos[k]])
else:
needed_pos.append(pos[k])
return needed_pos