Perlin noise in Python's noise library

MS103 :

I have a problem with generating Perlin noise for my project. As I wanted to understand how to use library properly, I tried to follow step-by-step this page: https://medium.com/@yvanscher/playing-with-perlin-noise-generating-realistic-archipelagos-b59f004d8401 In first part, there is code:

import noise
import numpy as np
from scipy.misc import toimage

shape = (1024,1024)
scale = 100.0
octaves = 6
persistence = 0.5
lacunarity = 2.0

world = np.zeros(shape)
for i in range(shape[0]):
    for j in range(shape[1]):
        world[i][j] = noise.pnoise2(i/scale, 
                                    j/scale, 
                                    octaves=octaves, 
                                    persistence=persistence, 
                                    lacunarity=lacunarity, 
                                    repeatx=1024, 
                                    repeaty=1024, 
                                    base=0)

toimage(world).show()

I copy-paste it with small change at the end (toimage is obsolete) so I have:

import noise
import numpy as np
from PIL import Image

shape = (1024,1024)
scale = 100
octaves = 6
persistence = 0.5
lacunarity = 2.0
seed = np.random.randint(0,100)

world = np.zeros(shape)
for i in range(shape[0]):
    for j in range(shape[1]):
        world[i][j] = noise.pnoise2(i/scale,
                                    j/scale,
                                    octaves=octaves,
                                    persistence=persistence,
                                    lacunarity=lacunarity,
                                    repeatx=1024,
                                    repeaty=1024,
                                    base=seed)

Image.fromarray(world, mode='L').show()

I tried a lot of diffrient modes, but this noise is not even close to coherent noise. My result is something like this (mode='L'). Could someone explain me, what am I doing wrong?

AlexNe :

Here is the working code. I took the liberty of cleaning it up a little. See comments for details. As a final advice: When testing code, use matplotlib for visualization. Its imshow() function is way more robust than PIL.

import noise
import numpy as np
from PIL import Image

shape = (1024,1024)
scale = .5
octaves = 6
persistence = 0.5
lacunarity = 2.0
seed = np.random.randint(0,100)

world = np.zeros(shape)

# make coordinate grid on [0,1]^2
x_idx = np.linspace(0, 1, shape[0])
y_idx = np.linspace(0, 1, shape[1])
world_x, world_y = np.meshgrid(x_idx, y_idx)

# apply perlin noise, instead of np.vectorize, cosider using itertools.starmap()
world = np.vectorize(noise.pnoise2)(world_x/scale,
                        world_y/scale,
                        octaves=octaves,
                        persistence=persistence,
                        lacunarity=lacunarity,
                        repeatx=1024,
                        repeaty=1024,
                        base=seed)

# here was the error: one needs to normalize the image first. Could be done without copying the array, though
img = np.floor((world + .5) * 255).astype(np.uint8) # <- Normalize world first
Image.fromarray(img, mode='L').show()