def snoise2dz(size, z, scale=0.05, octaves=1, persistence=0.25, lacunarity=2.0):
    """
    z as seeds
    scale이 작을 수록 패턴이 커지는 효과
    """
    import noise
    z_l = len(z)

    data = np.empty((z_l, size[0], size[1]), dtype='float32')
    for iz in range(z_l):
        zvalue = z[iz]
        for y in range(size[0]):
            for x in range(size[1]):
                v = noise.snoise3(x * scale, y * scale, zvalue,
                                  octaves=octaves, persistence=persistence, lacunarity=lacunarity)
                data[iz, y, x] = v
    data = data * 0.5 + 0.5
    if __debug__:
        assert data.min() >= 0. and data.max() <= 1.0
    return data
def snoise2dz(size, z, scale=0.05, octaves=1, persistence=0.25, lacunarity=2.0):
    """
    z as seeds
    scale이 작을 수록 패턴이 커지는 효과
    """
    import noise
    z_l = len(z)

    data = np.empty((z_l, size[0], size[1]), dtype='float32')
    for iz in range(z_l):
        zvalue = z[iz]
        for y in range(size[0]):
            for x in range(size[1]):
                v = noise.snoise3(x * scale, y * scale, zvalue,
                                  octaves=octaves, persistence=persistence, lacunarity=lacunarity)
                data[iz, y, x] = v
    data = data * 0.5 + 0.5
    if __debug__:
        assert data.min() >= 0. and data.max() <= 1.0
    return data
def snoise2dz ( size , z , scale = 0.05 , octaves = 1 , persistence = 0.25 , lacunarity = 2.0 ) : import noise z_l = len ( z ) data = np . empty ( ( z_l , size [ 0 ] , size [ 1 ] ) , dtype = 'float32' ) for iz in range ( z_l ) : zvalue = z [ iz ] for y in range ( size [ 0 ] ) : for x in range ( size [ 1 ] ) : v = noise . snoise3 ( x * scale , y * scale , zvalue , octaves = octaves , persistence = persistence , lacunarity = lacunarity ) data [ iz , y , x ] = v data = data * 0.5 + 0.5 if __debug__ : assert data . min ( ) >= 0. and data . max ( ) <= 1.0 return data
z as seeds
    scale이 작을 수록 패턴이 커지는 효과
z as seeds scale이 작을 수록 패턴이 커지는 효과