Python gas pedal numba use details

python a gas pedal package, here do not talk about the principle, only about the application process, as well as give a few small examples, directly write on the line; also gives numba how to define the use of List

1. The simplest use

When the output return value is an integer or floating point number:

from numba import jit
@jit(nopython=True)
def f(x, y):
    return x+y

if __name__ == '__main__':
    a = 1
    b = 1
    print(f(a,b))

Explanation:

Use the decorator, also, nopython=True is to prevent numba from automatically changing the acceleration mode, i.e. using nopython.

The above code is equivalent:

from numba import njit
@njit  # aka jit(nopython=True)
def f(x, y):
    return x+y

if __name__ == '__main__':
    a = 1
    b = 1
    print(f(a,b))

2、Advancement

When the output return value is "value of different type":

Decorator usage: generated_jit

from numba import generated_jit, typed

@generated_jit(nopython=True)
def f2(x):
    if x==1:
        return lambda x: x+1
    else:
        return lambda x: [1,2]

if __name__ == '__main__':
    a = 1
    print(f2(a))

When performing matrix or vector operations:

Using the decorator: vectorize

from numba import vectorize, float64
import numpy as np

@vectorize([float64(float64, float64)])  # Note the brackets
def f(x, y):
    return x + y

if __name__ == '__main__':
    a = ([1,2])
    b = ([2,2])
    print(f(a,b))

replenishment

Define lists within numba:

import numpy as np
import time
NUM = 160
from numba import jit
from  import List

a = List()
(1) # Also need to specify the data type, stuff a 1, the data type is int

@jit(nopython=True)
def f(a):
    for i in range(NUM):
        (i)

if __name__ == '__main__':
   for i in range(5):
    start = ()
    f(a)
    print(()-start)

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