import math, random

W = 255
H = 255
background=(100,100,100)
gradtable = [ (0,0) for i in range(0,W*H) ]

def precalc_gradtable():
    rnd = random.Random()
    for i in range(0,H):
        for j in range(0, W):
            x = float((rnd.randint(1,2*W))-W)/W
            y = float((rnd.randint(1,2*H))-H)/H
            s = math.sqrt(x * x + y * y)
            if s!=0:
                x = x / s
                y = y / s
            else:
                x = 0
                y = 0
            gradtable[i*H+j] = (x,y)

#calculate dot product for v1 and v2
def dot(v1,v2):
    return ( (v1[0]*v2[0]) + (v1[1]*v2[1]) )

# get a pseudorandom gradient vector
def gradient(x,y):

    # normalize!
    return gradtable[y*H+x]

def s_curve(x):
    return ( 3*x*x - 2*x*x*x )

def noise2d(x,y):

    x0 = math.floor(x)
    y0 = math.floor(y)
    x1 = x0 + 1.0
    y1 = y0 + 1.0

    i_x0 = int(x0)
    i_x1 = int(x1)
    i_y0 = int(y0)
    i_y1 = int(y1)

    s = dot(gradient(i_x0, i_y0),(x-x0, y-y0))
    t = dot(gradient(i_x1, i_y0),(x-x1, y-y0))
    u = dot(gradient(i_x0, i_y1),(x-x0, y-y1))
    v = dot(gradient(i_x1, i_y1),(x-x1, y-y1))

    s_x = s_curve( x - x0 )
    a = s + s_x*t - s_x*s
    b = u + s_x*v - s_x*u

    s_y = s_curve( y - y0 )
    z = a + s_y*b - s_y*a

    return z

def col( a ):
    return int(round((128-(128*a))))

precalc_gradtable()