#!/usr/bin/env python

import numpy as np
import pandas as pd

def voss(nrows, ncols=16):
    """Generates pink noise using the Voss-McCartney algorithm.

    nrows: number of values to generate
    rcols: number of random sources to add

    returns: NumPy array
    """
    array = np.empty((nrows, ncols))
    array.fill(np.nan)
    array[0, :] = np.random.random(ncols)
    array[:, 0] = np.random.random(nrows)

    # the total number of changes is nrows
    n = nrows
    cols = np.random.geometric(0.5, n)
    cols[cols >= ncols] = 0
    rows = np.random.randint(nrows, size=n)
    array[rows, cols] = np.random.random(n)

    df = pd.DataFrame(array)
    df.fillna(method='ffill', axis=0, inplace=True)
    total = df.sum(axis=1)

    return total.values

def print_array(ys):
    print '{\n%s\n}' % (',\n'.join([ ', '.join([ '0x%02x' % (y,) for y in ys[i:(i+16)] ]) for i in range(0, N, 16) ]),)

N = 512
# Sine
xs = np.linspace(0, 2 * np.pi, N)
ys = np.uint8(np.clip((np.sin(xs) + 1) * 255. / 2, 0, 255))
print_array(ys)

# Square
xs = np.linspace(0, 1, N)
ys = np.uint8([ 0 if x <= 0.5 else 255 for x in xs ])
print_array(ys)

# Triangle
xs = np.linspace(0, 1, N)
ys = (np.array([ 2 * x if x <= 0.5 else 2 - 2 * x for x in xs ]) * 255).astype(np.uint8)
print_array(ys)

# Pink Noise
#fn = 64
#xs = np.linspace(0, 1, N)
#ys = (np.repeat(voss(fn) * 255, len(xs) / fn)).astype(np.uint8)

# White Noise
xs = np.linspace(0, 1, N)
ys = np.random.randint(0, N, len(xs)).astype(np.uint8)
print_array(ys)

import matplotlib.pyplot as plt
plt.plot(xs, ys)
plt.show()