CODEWITHSUNDEEP
pythonpython-3.xnumpyjitnumba
mathguy
mathguy

Reputation: 1518

Creating a numpy array decorated by njit from numba

The code is here:

import numba as nb
import numpy as np

@nb.njit
def func(size):
    ary = np.array([np.arange(size),np.arange(size)+1,np.arange(size)-1]).T
    X = np.array([ary[1:,0] - ary[:-1,2],
                  ary[1:,1] - ary[:-1,2],
                  ary[1:,0] - ary[1:,1]
                  ])
    return X

Z = func(10**9)

When I run the code, it gives me an error message and I don't really understand what's going on here. Do functions decorated by njit not support creating new arrays inside the functions? Error message is the following:

TypingError: Invalid use of Function(<built-in function array>) with argument(s) of type(s): (list(array(int64, 1d, C)))
 * parameterized
In definition 0:
    TypingError: array(int64, 1d, C) not allowed in a homogeneous sequence
    raised from C:\Users\User\Anaconda3\lib\site-packages\numba\typing\npydecl.py:459
In definition 1:
    TypingError: array(int64, 1d, C) not allowed in a homogeneous sequence
    raised from C:\Users\User\Anaconda3\lib\site-packages\numba\typing\npydecl.py:459
This error is usually caused by passing an argument of a type that is unsupported by the named function.
[1] During: resolving callee type: Function(<built-in function array>)
[2] During: typing of call at C:/Users/User/Desktop/all python file/3.2.4/nb_datatype.py (65)

EDIT: I forgot to transpose the array before edit, it should be a 10^9 by 3 array.

Upvotes: 6

Views: 5202

Answers (2)

v0rtex20k
v0rtex20k

Reputation: 1121

I followed @jpp's advice and switched to np.float64 rather than float, but I also had to switch np.empty([1,2,3], np.float64) to np.empty((1,2,3), np.float64).

Upvotes: 0

jpp
jpp

Reputation: 164683

Instantiating NumPy arrays via a list of NumPy arrays, or even a list of lists, is not supported by numba.njit. Instead, use np.empty and then assign values via NumPy indexing:

@nb.njit
def func(size):
    row_count = 3
    ary = np.empty((row_count, size))
    ranger = np.arange(size)
    ary[0] = ranger
    ary[1] = ranger + 1
    ary[2] = ranger - 1

    X = np.empty((row_count, row_count - 1))
    X[0] = ary[1:,0] - ary[:-1,2]
    X[1] = ary[1:,1] - ary[:-1,2]
    X[2] = ary[1:,0] - ary[1:,1]

    return X

Z = func(10**2)

print(Z)

array([[-1., -4.],
       [ 0., -3.],
       [-1., -1.]])

Upvotes: 5

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