#! /usr/bin/env python

import sys

from aubio import source, pitch

import os

from mido.midifiles import MidiTrack, MidiFile

import mido

from abjad import *

# if len(sys.argv) < 2:

# print("Usage: %s <filename> [samplerate]" % sys.argv[0])

# sys.exit(1)

# filename = sys.argv[1]

file_index = "137"

filename = "./Content/Minsky/clips/" + file_index + ".wav"

downsample = 1

samplerate = 44100 // downsample

if len( sys.argv ) > 2: samplerate = int(sys.argv[2])

win_s = 4096 // downsample # fft size

hop_s = 512 // downsample # hop size

s = source(filename, samplerate, hop_s)

samplerate = s.samplerate

tolerance = 0.00 # tolerance = 0.8

pitch_o = pitch("yin", win_s, hop_s, samplerate)

pitch_o.set_unit("midi")

pitch_o.set_tolerance(tolerance)

pitches = []

confidences = []

timez = []

# Open file

output_file_name = "./Content/Minsky/pitches/" + file_index + ".txt"

output_file = open(output_file_name, "w")

# total number of frames read

total_frames = 0

while True:

samples, read = s()

pitch = pitch_o(samples)[0]

#pitch = int(round(pitch))

confidence = pitch_o.get_confidence()

#if confidence < 0.8: pitch = 0.

print("%f %f %f" % (total_frames / float(samplerate), pitch, confidence))

# Write times and cleaned_pitches to a file (to use feed to MIDI synthesizer)

output_file.write("%f\t%f\t%f\n" % (total_frames / float(samplerate), pitch, confidence))

timez += [total_frames / float(samplerate)]

pitches += [pitch]

confidences += [confidence]

total_frames += read

if read < hop_s: break

if 0: sys.exit(0)

#print pitches

import os.path

from numpy import array, ma

import matplotlib.pyplot as plt

from demo_waveform_plot import get_waveform_plot, set_xlabels_sample2time

skip = 1

pitches = array(pitches[skip:])

confidences = array(confidences[skip:])

times = [t * hop_s for t in range(len(pitches))]

time = -1

with MidiFile() as mid:

track = MidiTrack()

mid.tracks.append(track)

track.append(mido.Message('program_change', program=12, time=0))

for t in range(len(pitches)):

# # Write times and cleaned_pitches to a file (to use feed to MIDI synthesizer)

# output_file.write("%f\t%f\t%f\n" % (total_frames / float(samplerate), pitch, confidence))

# Note

if time == -1:

time = total_frames / float(samplerate)

else:

time = (total_frames / float(samplerate)) - time

if pitches[t] < 0:

pitches[t] = 0

if pitches[t] > 127:

pitches[t] = 127

if timez[t] < 1:

timez[t] = 1

# track.append(mido.Message('note_on', note=int(pitch), velocity=int(confidence * 127.0), time=int(time)))

# track.append(mido.Message('note_off', note=int(pitch), velocity=int(127 - (confidence * 127.0)), time=32))

# track.append(mido.Message('note_on', note=int(pitch), velocity=int(confidence * 127.0), time=int(time)))

# track.append(mido.Message('note_off', note=int(pitch), velocity=int(127 - (confidence * 127.0)), time=32))

track.append(mido.Message('note_on', note=int(pitches[t]), velocity=int(confidences[t] * 127.0), time=int(timez[t])))

# track.append(mido.Message('note_off', note=int(pitches[t]), velocity=int(127 - (confidences[t] * 127.0)), time=0))

mid.save('./Content/Minsky/midi/' + file_index + '.mid')

fig = plt.figure()

ax1 = fig.add_subplot(311)

ax1 = get_waveform_plot(filename, samplerate = samplerate, block_size = hop_s, ax = ax1)

plt.setp(ax1.get_xticklabels(), visible = False)

ax1.set_xlabel('')

def array_from_text_file(filename, dtype = 'float'):

filename = os.path.join(os.path.dirname(__file__), filename)

return array([line.split() for line in open(filename).readlines()],

dtype = dtype)

ax2 = fig.add_subplot(312, sharex = ax1)

ground_truth = os.path.splitext(filename)[0] + '.f0.Corrected'

if os.path.isfile(ground_truth):

ground_truth = array_from_text_file(ground_truth)

true_freqs = ground_truth[:,2]

true_freqs = ma.masked_where(true_freqs < 2, true_freqs)

true_times = float(samplerate) * ground_truth[:,0]

ax2.plot(true_times, true_freqs, 'r')

ax2.axis( ymin = 0.9 * true_freqs.min(), ymax = 1.1 * true_freqs.max() )

# plot raw pitches

ax2.plot(times, pitches, '.g')

# plot cleaned up pitches

cleaned_pitches = pitches

#cleaned_pitches = ma.masked_where(cleaned_pitches < 0, cleaned_pitches)

#cleaned_pitches = ma.masked_where(cleaned_pitches > 120, cleaned_pitches)

cleaned_pitches = ma.masked_where(confidences < tolerance, cleaned_pitches)

ax2.plot(times, cleaned_pitches, '.-')

#ax2.axis( ymin = 0.9 * cleaned_pitches.min(), ymax = 1.1 * cleaned_pitches.max() )

#ax2.axis( ymin = 55, ymax = 70 )

plt.setp(ax2.get_xticklabels(), visible = False)

ax2.set_ylabel('f0 (midi)')

# Close output file

output_file.close()

# plot confidence

ax3 = fig.add_subplot(313, sharex = ax1)

# plot the confidence

ax3.plot(times, confidences)

# draw a line at tolerance

ax3.plot(times, [tolerance]*len(confidences))

ax3.axis( xmin = times[0], xmax = times[-1])

ax3.set_ylabel('condidence')

set_xlabels_sample2time(ax3, times[-1], samplerate)

# plt.show()

plt.savefig('./Content/Minsky/plots/' + file_index + '.svg')