from sklearn.cluster import KMeans
from mingus.containers.Bar import Bar
from mingus.midi import fluidsynth
import numpy as np
import pandas as pd
import re
fluidsynth.init('/usr/share/sounds/sf2/FluidR3_GM.sf2',"alsa")

# Import and order the original notes.
oscar2 = pd.read_csv('oscar2notes.txt', skiprows=2)[:].sort("Offset")
oscar2.index = xrange(1, len(oscar2) + 1)
oscar2 = oscar2[oscar2.Octave >= 4] # threshold >= octave 4 for melodies
with open('oscar2notes.txt', 'rb') as f:
    metmark = float(f.readline())
    tsig_num, tsig_den = [i for i in f.readline().replace(' /', '').split()]
print "# of notes: %s" % len(oscar2)
oscar2.head()

# Convert music21 note to mingus note.
# This version (different from that in 3. Play Notes)
# doesn't return a Note object: returns a string.
def mingifytext(note):
    accidental = re.compile("[A-Z](-|#)[0-9]")
    if accidental.match(note):
        if '-' not in note: note = "%s%s-%s" % (note[0], note[1], note[2])
        else: note = note.replace('-', 'b-')
    else: note = "%s-%s" % (note[0], note[1])
    return note

allnotes = [mingifytext("%s%s" % (n, o)) for n, o in zip(oscar2["Note/Rest"], oscar2["Octave"])]

# Given a duration (say 2 for 2 quarter notes), get
# the duration in mingus form. This means that you
# can feed in the duration into mingus like
# fluidsynth.place_note(note, duration)
# Stands for toMingusBeat.
def toMB(duration):
    return (1. / duration)

# Test your to-mingus-beat function.
note1 = "C-5"
note2 = "D-5"
note3 = "E-5"
dur1 = 0.25
dur2 = 0.5
dur3 = 0.25

b = Bar()
b.set_meter((4, 4))
b.place_notes(note1, toMB(dur1))
b.place_notes(note2, toMB(dur2))
b.place_notes(note3, toMB(dur3))

fluidsynth.play_Bar(b)

# test with oscar's playing
b = Bar()
b.set_meter((sum([toMB(length) for length in oscar2["Len"]]), 4))
for note, length in zip(allnotes, oscar2["Len"]):
    b.place_notes(note, toMB(length))

fluidsynth.play_Bar(b, 1, 1000)

# need to work more on mingus note values.
b