<gh_stars>0
#! /usr/bin/env python

import numpy as np
from aubio import pitch
import pylab as plt

buf_size = 2048 * 1
hop_size = buf_size // 4

samplerate = 44100
minfreq = 40
maxfreq = 6000

def sinewave(freq, duration, samplerate = samplerate):
    """ generate a sinewave """
    length = hop_size
    while length < duration * samplerate:
        length += hop_size
    return np.sin( 2. * np.pi * np.arange(length) * freq / samplerate ).astype("float32")

def get_stats_for_pitch_method(method, freqs, samplerate = samplerate):
    """ for a given pitch method and a list of frequency, generate a sinewave
    and get mean deviation """
    means = np.zeros(len(freqs))
    medians = np.zeros(len(freqs))
    for freq, fn in zip(freqs, range(len(freqs))):
        s = sinewave(freq, .50).reshape(-1, hop_size)
        #s = (sinewave(freq, .50) + .0*sinewave(freq/2., .50)).reshape(-1, hop_size)
        p = pitch(method, buf_size, hop_size, samplerate = samplerate)
        candidates = np.zeros(len(s))
        #samples = np.zeros(buf_size)
        for frame, i in zip(s, range(len(s))):
            candidates[i] = p(frame)[0]
        # skip first few candidates
        candidates = candidates[4:]
        means[fn] = np.mean(candidates[candidates != 0] - freq)
        medians[fn] = np.median(candidates[candidates != 0] - freq)
        print (freq, means[fn], medians[fn])
    return means, medians

if __name__ == '__main__':
    freqs = np.arange(minfreq, maxfreq, 1.)
    modes = ["yin", "yinfft"]
    for mode in modes:
        means, medians = get_stats_for_pitch_method(mode, freqs)
        plt.figure()
        plt.plot(freqs, means, 'g-')
        plt.plot(freqs, medians, 'r--')
        #plt.savefig(mode + '_deviations_test.png', dpi=300)
        plt.show()

#! /usr/bin/env python

import numpy as np
from aubio import pitch
import pylab as plt

buf_size = 2048 * 1
hop_size = buf_size // 4

samplerate = 44100
minfreq = 40
maxfreq = 6000

def sinewave(freq, duration, samplerate = samplerate):
    """ generate a sinewave """
    length = hop_size
    while length < duration * samplerate:
        length += hop_size
    return np.sin( 2. * np.pi * np.arange(length) * freq / samplerate ).astype("float32")

def get_stats_for_pitch_method(method, freqs, samplerate = samplerate):
    """ for a given pitch method and a list of frequency, generate a sinewave
    and get mean deviation """
    means = np.zeros(len(freqs))
    medians = np.zeros(len(freqs))
    for freq, fn in zip(freqs, range(len(freqs))):
        s = sinewave(freq, .50).reshape(-1, hop_size)
        #s = (sinewave(freq, .50) + .0*sinewave(freq/2., .50)).reshape(-1, hop_size)
        p = pitch(method, buf_size, hop_size, samplerate = samplerate)
        candidates = np.zeros(len(s))
        #samples = np.zeros(buf_size)
        for frame, i in zip(s, range(len(s))):
            candidates[i] = p(frame)[0]
        # skip first few candidates
        candidates = candidates[4:]
        means[fn] = np.mean(candidates[candidates != 0] - freq)
        medians[fn] = np.median(candidates[candidates != 0] - freq)
        print (freq, means[fn], medians[fn])
    return means, medians

if __name__ == '__main__':
    freqs = np.arange(minfreq, maxfreq, 1.)
    modes = ["yin", "yinfft"]
    for mode in modes:
        means, medians = get_stats_for_pitch_method(mode, freqs)
        plt.figure()
        plt.plot(freqs, means, 'g-')
        plt.plot(freqs, medians, 'r--')
        #plt.savefig(mode + '_deviations_test.png', dpi=300)
        plt.show()
#! /usr/bin/env python generate a sinewave for a given pitch method and a list of frequency, generate a sinewave     and get mean deviation #s = (sinewave(freq, .50) + .0*sinewave(freq/2., .50)).reshape(-1, hop_size) #samples = np.zeros(buf_size) # skip first few candidates #plt.savefig(mode + '_deviations_test.png', dpi=300)