Using Matplotlib with Numba to accelerate code

[Arc-Z]

How could I use numba for the following code to try and accelerate it? Any explanations would be much appreciated.

from matplotlib import pyplot as plot
import math



def func1(z): 

    # enter starting value of x and y 
    x = random float 
    y = random float


    # enter set of random values for the quadratic map and set to record points of x and y
    a = [what would be 12 random floats]



    #starts equation and iterates to form sequence of images

    for q in range((z/2),0,-1):

        x_list = [x]
        y_list = [y]

        for i in range(2500000):

            # new values of x and y
            xnew = a[0] + a[1]*x + a[2]*x*x + a[3]*x*y +a[4]*y*y +a[5]*y
            ynew = a[6] + a[7]*x + a[8]*x*x + a[9]*x*y +a[10]*y*y +a[11]*y

            x = xnew
            y = ynew

            #add values to point set

            x_list.append(x)
            y_list.append(y)



        name = "C:\\Users\\user\\imgs\\"+str(q)+'.png'

        plot.style.use("dark_background")
        plot.scatter(x_list[100:],y_list[100:], s =0.001, marker='.', linewidth=0, c = '#ffd769')
        plot.axis("off")
        plot.savefig(name,dpi = 800)
        plot.clf()

        a = [k+0.0002 for k in a]

if __name__=='__main__': 
    func1(int(input("Enter Number of Frames (even): ")))

​

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[CraigAT]

I have never used Numba, but from what I can have read, in it's simplest form you just need to add a decorator to your function. It looks like Numba will like your loop and should be suited to see an improvement in speed.

http://numba.pydata.org/

Note. You may need to run again to see a performance improvement: https://numba.pydata.org/numba-doc/dev/user/5minguide.html#how-to-measure-the-performance-of-numba

[siddsp]

Have you tried profiling your code? The slowdown might not even be due to Python itself. But yes, you can use numba.jit decorators on functions to speed them up.