MC/markov_chain.py

import os,glob
from matplotlib.pyplot import pie
from mido import MidiFile
import datetime
import numpy as np
import pandas as pd
import subprocess
from music21 import *
from music21 import converter
from mido import Message, MidiFile, MidiTrack, MetaMessage

#number of notes to be used for prediction
window = 3

#num of notes to generate
#TODO: change this to accept values according to user
num_notes = 100

#midi ticks per quarter note, indicates tempo of track
quarter_note_ticks = 480

#accepted note durations: ranges from 16th note to whole dotted notes
accepeted_lengths = [0.25,0.375,0.5,0.75,1,1.5,2.0,3.0,4.0]
#Finds all absolute paths in directory
#https://stackoverflow.com/questions/9816816/get-absolute-paths-of-all-files-in-a-directory
def abs_paths(dir):
    for dir_path,_,filenames in os.walk(dir):
        for f in filenames:
            yield os.path.abspath(os.path.join(dir_path, f))
def pitch_to_int(nameWithOctave):
    # letter names with corresponding values
    letter_dict = {'C':0,'D':2,'E':4,'F':5,'G':7,'A':9,'B':11}
    # parse characters from strings
    chars = list(nameWithOctave)
    # convert octave number to corresponding midi value
    octave = 12*(int(chars[-1])+1)
    # select value from letter_dict using first character
    note = letter_dict[chars[0]]
    # set accidental value
    accidental = 0
    # does accidental exist?
    if not len(chars)==2:
        # increase (sharp) or decrease (flat) value by one
        accidental = 1 if chars[1]=='#' else -1
    # return sum of these numbers, middle C(4) == 60
    return octave + note + accidental
def get_pngs(path):
	filelist=os.listdir(path)
	for fichier in filelist[:]: # filelist[:] makes a copy of filelist.
		if not(fichier.endswith(".png")):
			filelist.remove(fichier)
	newlist = [path+'/'+x for x in filelist] #making it cwd
	return newlist
def generate_notes(csv_file):
    df_notes = pd.read_csv(csv_file)
    print(df_notes.shape)
    # define arrays for generated notes and durations
    gen_notes = []
    gen_durations = []
    # define note and duration feature columns based on names
    features = df_notes.columns[:-2]
    note_features = [s for s in features if "note" in s]
    duration_features = [s for s in features if "duration" in s]
    # define target columns
    note_target = df_notes.columns[-2]
    duration_target = df_notes.columns[-1]

    # sample random row from dataframe and define start notes and durations
    initial_sample = df_notes.sample()
    start_notes = list(initial_sample[note_features].values[0])
    start_durations = list(initial_sample[duration_features].values[0])
    # append starting notes and durations to gen arrays
    for note in start_notes:
        gen_notes.append(int(note))
    for duration in start_durations:
        gen_durations.append(duration)

    for i in range(num_notes) :
        rows = df_notes
        for i in range(window-1):
            rows = rows.loc[df_notes[note_features[i]] == start_notes[i]]
            rows = rows.loc[df_notes[duration_features[i]]== start_durations[i]]
    
        #This gives the same effect as probability.
        # We effectively sample from a list which might have more than 1 C note, Hence increasing its probability
        #Sometime, The start_notes and durations could be selected in such a way that we cannot generate any further notes uptill num_notes,
        #This means there maybe some combinations of notes such as 76,68 which are not there in the dataset and hence cannot be sampled.
        #In such cases, the only way about it would be to reset the start notes, because we cannot sample from an empty row 
        #So here we check if any rows which we ta
        if len(rows):
            next_sample = rows.sample()
            next_note = next_sample[note_target].values[0]
            next_duration = next_sample[duration_target].values[0]
            gen_notes.append(int(next_note))
            gen_durations.append(next_duration)

            start_notes.pop()
            start_durations.pop()

            start_notes.append(next_note)
            start_durations.append(next_duration)
        else:
            #Received empty row
            # print("Exiting!!!!!!")
            #restarting again to get new start notes
            return [],[]
           
        # print(rows[note_target].value_counts(normalize=True))
        # print(rows[duration_target].value_counts(normalize=True))

    return gen_notes, gen_durations

#MAIN FUNCTION
def main_markov(time_sign):
    command = "rm -r MC/gen_songs_midi/*"
    subprocess.Popen(command,shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE).communicate()
    # https://stackoverflow.com/questions/49462107/how-can-i-get-all-piano-parts-from-a-music21-score
    if not os.path.exists('tracks'):
        os.mkdir('tracks')
        os.mkdir('tracks/3_4')
        os.mkdir('tracks/4_4')
        os.mkdir('tracks/6_8')
        os.mkdir('tracks/2_2')
        os.mkdir('tracks/2_4')
        i = 0
        #Parse midi files into tracks folder

        
        for path in abs_paths('data'):
            print(path)
            piece = converter.parse(path)
            #print(list(piece.parts))
            # for part in piece.parts:
            part_notes = []
            l = piece.getTimeSignatures()
            
            # s = l.show('text') #prints piece time signature
            
            time_sig_num = piece.recurse().getElementsByClass(meter.TimeSignature)[0].numerator
            time_sig_denum = piece.recurse().getElementsByClass(meter.TimeSignature)[0].denominator #gets time signature for piece
            #print(piece[meter.TimeSignature][0])
            #print(piece['Measure'][0].timeSignature)
            
            for el in piece.recurse().notes:
                # print(el.offset, el, el.activeSite)
                # print(el.beatDuration)
                # print(el.quarterLength)
                # print(el._getTimeSignatureForBeat)
                # print(el.beat)
                
                # if getattr(el, 'isNote', None):
                #     print("this method works")
                #     print(el.nameWithOctave)
                #get all note messages from all tracks
                # for event in el:
                    # for y in event.contextSites():
                    #     if y[0] is part:
                    #         offset = y[1]
                
                if getattr(el, 'isNote', None) and el.isNote:
                    # print('note in {}'.format(el))
                    #check if note is in accepted length
                    #convert string to numerical value 
                    if el.quarterLength in accepeted_lengths:
                        part_notes.append([pitch_to_int(el.nameWithOctave), el.quarterLength])
            if not len(part_notes) == 0:
                np.save('tracks/'+str(time_sig_num)+'_'+str(time_sig_denum)+'/'+'{}.npy'.format(i), np.array(part_notes))
                i+=1
        print('Number of tracks parsed: {}'.format(i))
    if not glob.glob('MC/prepared*.csv'):
        sigs = ['3_4','4_4','6_8','2_2','2_4']
        columns = []
        for i in range(window):
            columns.append('note' + str(i))
            columns.append('duration' + str(i))
        for sig in sigs:
            df_notes = pd.DataFrame(columns=columns)
            # append segments from each track as rows to dataframe
            for path in abs_paths('tracks/'+sig):
                notes = np.load(path)
                for i in range(len(notes)-window):
                    # take every x notes and durations
                    segment = notes[i:i+window].flatten()
                    # make into pd.Series row
                    row = pd.Series(segment, index=df_notes.columns)
                    # append row to dataframe
                    df_notes = df_notes.append(row, ignore_index=True)
            # export
            df_notes.to_csv('prepared'+sig+'.csv', index=False)
    time_signature = str(time_sign).split('/')

    success = False
    gen_notes =[]
    gen_durations =[]

    #Retry mechanism
    csv_path = 'MC/prepared'+time_signature[0]+'_'+time_signature[1]+'.csv'
    while len(gen_notes)<num_notes:
        gen_notes,gen_durations = generate_notes(csv_path)


    print('Generated  notes/durations'.format(num_notes))
    print(gen_notes)
    print(gen_durations)

    #for unique file name
    dob = datetime.datetime.now().strftime('%H%M%S')
    modifier = format(dob)

    path = "MC/gen_songs_midi/song_"+modifier
    song_path = path + "/gen_song_"+modifier
    if not os.path.exists(path):
        os.makedirs(path)

    mid = MidiFile()
    track = MidiTrack()
       
    mid.tracks.append(track)
    print(mid)
    track.append((MetaMessage('time_signature', numerator=6, denominator=8)))
    # https://mido.readthedocs.io/en/latest/midi_files.html
    for i in range(num_notes):
        track.append(Message('note_on', channel=0, note=gen_notes[i], velocity=60, time=0))
        track.append(Message('note_on', channel=0, note=gen_notes[i], velocity=0,time=int(gen_durations[i]*quarter_note_ticks)))
    mid.save(song_path+".mid")

    subprocess.Popen(['midi2ly','-o',song_path+'.ly',song_path+'.mid']).communicate()
    subprocess.Popen(['lilypond','-fpng','-o',path,song_path+".ly"]).communicate()
    subprocess.Popen(['timidity',song_path+'.midi','-Ow','-o',song_path+'.wav']).communicate()
    png_list = get_pngs(path)

    return png_list,song_path+".wav"




# LILYPOND COMMANDS : To be used for generating music scores
# Installation : sudo apt-get install -y lilypond
# !midi2ly "new_song.ly"
# !lilypond -fpng "new_song-midi.ly"