# ported from https://github.com/pvigier/perlin-numpy

import os, sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
  
import numpy as np 
 

def interpolant(t):
    return t*t*t*(t*(t*6 - 15) + 10)


def generate_perlin_noise_2d(
        shape, res, tileable=(False, False), interpolant=interpolant, percentile=None,
):
    """Generate a 2D numpy array of perlin noise.

    Args:
        shape: The shape of the generated array (tuple of two ints).
            This must be a multple of res.
        res: The number of periods of noise to generate along each
            axis (tuple of two ints). Note shape must be a multiple of
            res.
        tileable: If the noise should be tileable along each axis
            (tuple of two bools). Defaults to (False, False).
        interpolant: The interpolation function, defaults to
            t*t*t*(t*(t*6 - 15) + 10).

    Returns:
        A numpy array of shape shape with the generated noise.

    Raises:
        ValueError: If shape is not a multiple of res.
    """
    delta = (res[0] / shape[0], res[1] / shape[1])
    d = (shape[0] // res[0], shape[1] // res[1])
    grid = np.mgrid[0:res[0]:delta[0], 0:res[1]:delta[1]]\
             .transpose(1, 2, 0) % 1
    # Gradients
    angles = 2*np.pi*np.random.rand(res[0]+1, res[1]+1)
    gradients = np.dstack((np.cos(angles), np.sin(angles)))
    if tileable[0]:
        gradients[-1,:] = gradients[0,:]
    if tileable[1]:
        gradients[:,-1] = gradients[:,0]
    gradients = gradients.repeat(d[0], 0).repeat(d[1], 1)
    g00 = gradients[    :-d[0],    :-d[1]]
    g10 = gradients[d[0]:     ,    :-d[1]]
    g01 = gradients[    :-d[0],d[1]:     ]
    g11 = gradients[d[0]:     ,d[1]:     ]
    # Ramps
    n00 = np.sum(np.dstack((grid[:,:,0]  , grid[:,:,1]  )) * g00, 2)
    n10 = np.sum(np.dstack((grid[:,:,0]-1, grid[:,:,1]  )) * g10, 2)
    n01 = np.sum(np.dstack((grid[:,:,0]  , grid[:,:,1]-1)) * g01, 2)
    n11 = np.sum(np.dstack((grid[:,:,0]-1, grid[:,:,1]-1)) * g11, 2)
    # Interpolation
    t = interpolant(grid)
    n0 = n00*(1-t[:,:,0]) + t[:,:,0]*n10
    n1 = n01*(1-t[:,:,0]) + t[:,:,0]*n11

    noise = np.sqrt(2)*((1-t[:,:,1])*n0 + t[:,:,1]*n1)
    if percentile is None:
        return noise
    shres = np.percentile(noise, percentile) 
    mask = np.zeros_like(noise) 
    mask[noise >= shres] = 1.
    noise *= mask
    return noise, mask


def generate_fractal_noise_2d(
        shape, res, octaves=1, persistence=0.5,
        lacunarity=2, tileable=(False, False),
        interpolant=interpolant, percentile=None
):
    """Generate a 2D numpy array of fractal noise.

    Args:
        shape: The shape of the generated array (tuple of two ints).
            This must be a multiple of lacunarity**(octaves-1)*res.
        res: The number of periods of noise to generate along each
            axis (tuple of two ints). Note shape must be a multiple of
            (lacunarity**(octaves-1)*res).
        octaves: The number of octaves in the noise. Defaults to 1.
        persistence: The scaling factor between two octaves.
        lacunarity: The frequency factor between two octaves.
        tileable: If the noise should be tileable along each axis
            (tuple of two bools). Defaults to (False, False).
        interpolant: The, interpolation function, defaults to
            t*t*t*(t*(t*6 - 15) + 10).

    Returns:
        A numpy array of fractal noise and of shape shape generated by
        combining several octaves of perlin noise.

    Raises:
        ValueError: If shape is not a multiple of
            (lacunarity**(octaves-1)*res).
    """
    noise = np.zeros(shape)
    frequency = 1
    amplitude = 1
    for _ in range(octaves):
        noise += amplitude * generate_perlin_noise_2d(
            shape, (frequency*res[0], frequency*res[1]), tileable, interpolant
        )
        frequency *= lacunarity
        amplitude *= persistence
    if percentile is None:
        return noise
    shres = np.percentile(noise, percentile) 
    mask = np.zeros_like(noise) 
    mask[noise >= shres] = 1.
    noise *= mask
    return noise, mask



# generate multiple noises, assign argmax as label 

def generate_fractal2d_batch(num, shape, res, octaves=5):
    noises = []
    for i in range(num):
        noise, _ = generate_fractal_noise_2d(shape, res, octaves)
        noises.append(noise)
    idx = np.argmax(np.array(noises), axis = 0) 
    return idx
    

def generate_perlin2d_batch(num, shape, res):
    noises = []
    for i in range(num):
        noise, _ = generate_perlin_noise_2d(shape, res) 
        noises.append(noise)
    idx = np.argmax(np.array(noises), axis = 0) 
    return idx