def create_songdet(h5, sngidxfle):
'''
Collects song details for all unique songs heard by 100 raters.
Format of dictionary: { SongID : [ Att_0, Att_1, Att_2 ] }
'''
import hdf5_getters
sngdetfle = open("songdet.txt", "wb")
sngdetdic = dict()
sngidxfle = open(sngidxfle, "rb")
sngidxdic = pickle.load(sngidxfle)
for elem in sngidxdic:
songidx = sngidxdic[elem]
tempo = hdf5_getters.get_tempo(h5, songidx)
loud = hdf5_getters.get_loudness(h5, songidx)
year = hdf5_getters.get_year(h5, songidx)
tmsig = hdf5_getters.get_time_signature(h5, songidx)
key = hdf5_getters.get_key(h5, songidx)
mode = hdf5_getters.get_mode(h5, songidx)
duration = hdf5_getters.get_duration(h5, songidx)
fadein = hdf5_getters.get_end_of_fade_in(h5, songidx)
fadeout = hdf5_getters.get_start_of_fade_out(h5, songidx)
artfam = hdf5_getters.get_artist_familiarity(h5, songidx)
sngdetdic[elem] = [
duration, tmsig, tempo, key, mode, fadein, fadeout, year, loud,
artfam
]
pickle.dump(sngdetdic, sngdetfle)
sngdetfle.close()
def create_songdet(h5, sngidxfle):
'''
Collects song details for all unique songs heard by 100 raters.
Format of dictionary: { SongID : [ Att_0, Att_1, Att_2 ] }
'''
import hdf5_getters
sngdetfle = open("songdet.txt", "wb")
sngdetdic = dict()
sngidxfle = open(sngidxfle, "rb")
sngidxdic = pickle.load(sngidxfle)
for elem in sngidxdic:
songidx = sngidxdic[elem]
tempo = hdf5_getters.get_tempo(h5, songidx)
loud = hdf5_getters.get_loudness(h5, songidx)
year = hdf5_getters.get_year(h5, songidx)
tmsig = hdf5_getters.get_time_signature(h5, songidx)
key = hdf5_getters.get_key(h5, songidx)
mode = hdf5_getters.get_mode(h5, songidx)
duration = hdf5_getters.get_duration(h5, songidx)
fadein = hdf5_getters.get_end_of_fade_in(h5, songidx)
fadeout = hdf5_getters.get_start_of_fade_out(h5, songidx)
artfam = hdf5_getters.get_artist_familiarity(h5, songidx)
sngdetdic[elem] = [duration, tmsig, tempo,
key, mode, fadein, fadeout, year, loud, artfam]
pickle.dump(sngdetdic, sngdetfle)
sngdetfle.close()
def get_all_examples(basedir, genre_dict, ext='.h5'):
"""
From a base directory, goes through all subdirectories,
and grabs all songs and their features and puts them into a pandas dataframe
INPUT
basedir - base directory of the dataset
genre_dict - a dictionary mapping track id to genre based tagraum dataset
ext - extension, .h5 by default
RETURN
dataframe containing all song examples
"""
features_vs_genre = pd.DataFrame()
# iterate over all files in all subdirectories
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
# # count files
# count += len(files)
# apply function to all files
for f in files:
h5 = GETTERS.open_h5_file_read(f)
song_id = GETTERS.get_track_id(h5).decode('utf-8')
if (song_id in genre_dict):
genre = genre_dict[song_id]
year = GETTERS.get_year(h5)
duration = GETTERS.get_duration(h5)
end_of_fade_in = GETTERS.get_end_of_fade_in(h5)
loudness = GETTERS.get_loudness(h5)
song_hotttnesss = GETTERS.get_song_hotttnesss(h5)
tempo = GETTERS.get_tempo(h5)
key = GETTERS.get_key(h5)
key_confidence = GETTERS.get_key_confidence(h5)
mode = GETTERS.get_mode(h5)
mode_confidence = GETTERS.get_mode_confidence(h5)
time_signature = GETTERS.get_time_signature(h5)
time_signature_confidence = GETTERS.get_time_signature_confidence(
h5)
artist_name = GETTERS.get_artist_name(h5)
title = GETTERS.get_title(h5)
# length of sections_start array gives us number of start
num_sections = len(GETTERS.get_sections_start(h5))
num_segments = len(GETTERS.get_segments_confidence(h5))
example = pd.DataFrame(
data=[
(artist_name, title, song_id, genre, year, key,
key_confidence, mode, mode_confidence, time_signature,
time_signature_confidence, duration, end_of_fade_in,
loudness, song_hotttnesss, tempo, num_sections)
],
columns=[
'artist_name', 'title', 'song_id', 'genre', 'year',
'key', 'key_confidence', 'mode', 'mode_confidence',
'time_signature', 'time_signature_confidence',
'duration', 'end_of_fade_in', 'loudness',
'song_hotttnesss', 'tempo', 'num_segments'
])
features_vs_genre = features_vs_genre.append(example)
h5.close()
return features_vs_genre
def plots(track):
f, axarr = plt.subplots(2, sharex=True)
path = "../../msd_dense_subset/dense/"+track[2]+"/"+track[3]+"/"+track[4]+"/"+track+".h5"
h5 = GETTERS.open_h5_file_read(path)
segments = (GETTERS.get_segments_start(h5))
sections = (GETTERS.get_sections_start(h5))
max_loudness = (GETTERS.get_segments_loudness_max(h5))
loudness = (GETTERS.get_segments_loudness_start(h5))
average_loudness = (max_loudness + loudness) / 2
average_loudness_song = GETTERS.get_loudness(h5)
start_fade_out = GETTERS.get_start_of_fade_out(h5)
end_fade_in = GETTERS.get_end_of_fade_in(h5)
pitches = GETTERS.get_segments_pitches(h5)
h5.close()
axarr[0].set_title('loudness curve for ' + ut.get_track_info(track))
axarr[0].plot(segments, average_loudness, label='Filtered')
axarr[0].axhline(average_loudness_song, color='green')
for section in enumerate(sections):
axarr[0].axvline(section[1], color='red')
axarr[0].axvline(start_fade_out, color='green')
axarr[0].axvline(end_fade_in, color='green')
idx = list()
for section in sections:
dif = segments - section
posdif = np.where(dif >=0)
idx.append(posdif[0][0])
axarr[1].set_title('Chroma values for ' + ut.get_track_info(track))
#axarr[1].set_xticks(idx,sections.astype(int))
extent =[0,segments.shape[0],0,12]
axarr[1].imshow(pitches.transpose(),extent = extent,aspect = 'auto',interpolation='nearest',origin='lower')
plt.show()
def get_loudness(track,h5=None):
#returns
#0: the average loudness of the track
#1: the variance of the loudness over the track
#2: the difference between the highest and lowest dB value between the end of fade in and start of fade out
close = (h5== None)
if h5 == None:
path = "../../msd_dense_subset/dense/"+track[2]+"/"+track[3]+"/"+track[4]+"/"+track+".h5"
h5 = GETTERS.open_h5_file_read(path)
loudness_avg = GETTERS.get_loudness(h5)
loudnesses_interval = GETTERS.get_segments_loudness_max(h5)
start_segments = GETTERS.get_segments_start(h5)
start_fade_out = GETTERS.get_start_of_fade_out(h5)
end_fade_in = GETTERS.get_end_of_fade_in(h5)
idx = np.where((start_segments > end_fade_in) & (start_segments < start_fade_out))
if close:
h5.close()
#return (loudness_avg, np.var(loudnesses_interval[idx]), min(loudnesses_interval[idx]), max(loudnesses_interval[idx]))
return (loudness_avg, np.var(loudnesses_interval[idx]), abs(max(loudnesses_interval[idx]) - abs(min(loudnesses_interval[idx]))))
def plot_loudness_curve(track):
path = "../../msd_dense_subset/mood/"+track[2]+"/"+track[3]+"/"+track[4]+"/"+track+".h5"
h5 = GETTERS.open_h5_file_read(path)
segments = (GETTERS.get_segments_start(h5))
sections = (GETTERS.get_sections_start(h5))
max_loudness = (GETTERS.get_segments_loudness_max(h5))
loudness = (GETTERS.get_segments_loudness_start(h5))
average_loudness = (max_loudness + loudness) / 2
average_loudness_song = GETTERS.get_loudness(h5)
start_fade_out = GETTERS.get_start_of_fade_out(h5)
end_fade_in = GETTERS.get_end_of_fade_in(h5)
plt.title('loudness curve for ' + ut.get_track_info(track))
plt.ylabel('Loudness(dB)')
plt.xlabel('Time(s)')
plt.plot(segments, average_loudness, label='Filtered')
plt.axhline(average_loudness_song, color='green',label='average')
for section in enumerate(sections):
plt.axvline(section[1], color='red')
plt.axvline(start_fade_out, color='green')
plt.axvline(end_fade_in, color='green')
#plt.legend(('average'))
h5.close()
plt.show()
def get_feats(h5):
f = []
f.append(hdf5_getters.get_artist_name(h5).decode('utf8').replace(',', ''))
f.append(hdf5_getters.get_title(h5).decode('utf8').replace(',', ''))
f.append(str(hdf5_getters.get_loudness(h5)))
f.append(str(hdf5_getters.get_tempo(h5)))
f.append(str(hdf5_getters.get_time_signature(h5)))
f.append(str(hdf5_getters.get_key(h5)))
f.append(str(hdf5_getters.get_mode(h5)))
f.append(str(hdf5_getters.get_duration(h5)))
f.extend(get_statistical_feats(hdf5_getters.get_segments_timbre(h5)))
f.extend(get_statistical_feats(hdf5_getters.get_segments_pitches(h5)))
f.extend(get_statistical_feats(hdf5_getters.get_segments_loudness_max(h5)))
f.extend(
get_statistical_feats(hdf5_getters.get_segments_loudness_max_time(h5)))
f.extend(
get_statistical_feats(hdf5_getters.get_segments_loudness_start(h5)))
f.append(str(hdf5_getters.get_song_hotttnesss(h5)))
f.append(str(hdf5_getters.get_danceability(h5)))
f.append(str(hdf5_getters.get_end_of_fade_in(h5)))
f.append(str(hdf5_getters.get_energy(h5)))
f.append(str(hdf5_getters.get_start_of_fade_out(h5)))
f.append(str(hdf5_getters.get_year(h5)))
return f
def get_loudness(track, h5=None):
#returns
#0: the average loudness of the track
#1: the variance of the loudness over the track
#2: the difference between the highest and lowest dB value between the end of fade in and start of fade out
close = (h5 == None)
if h5 == None:
path = "../../msd_dense_subset/dense/" + track[2] + "/" + track[
3] + "/" + track[4] + "/" + track + ".h5"
h5 = GETTERS.open_h5_file_read(path)
loudness_avg = GETTERS.get_loudness(h5)
loudnesses_interval = GETTERS.get_segments_loudness_max(h5)
start_segments = GETTERS.get_segments_start(h5)
start_fade_out = GETTERS.get_start_of_fade_out(h5)
end_fade_in = GETTERS.get_end_of_fade_in(h5)
idx = np.where((start_segments > end_fade_in)
& (start_segments < start_fade_out))
if close:
h5.close()
#return (loudness_avg, np.var(loudnesses_interval[idx]), min(loudnesses_interval[idx]), max(loudnesses_interval[idx]))
return (loudness_avg, np.var(loudnesses_interval[idx]),
abs(
max(loudnesses_interval[idx]) -
abs(min(loudnesses_interval[idx]))))
def parse_aggregate_songs(file_name,file_name2,artist_map):
"""
Given an aggregate filename and artist_map in the format
{artist_name: {data pertaining to artist}}
"""
"""
TODO:
-this function goes through each song, if artist not in there,
add all data necesary and add first song info.
else update any specific song info
-song info is a map from attributename:[values]
"""
#artist_map = {}
h5 = hdf5_getters.open_h5_file_read(file_name)
numSongs = hdf5_getters.get_num_songs(h5)
print 'Parsing song file...'
for i in range(numSongs):
artist_name = hdf5_getters.get_artist_name(h5,i)
#Filter location
longi = hdf5_getters.get_artist_longitude(h5,i)
lat = hdf5_getters.get_artist_latitude(h5,i)
loc = hdf5_getters.get_artist_location(h5,i)
if math.isnan(lat) or math.isnan(longi):
#skip if no location
continue
#filter year
yr = hdf5_getters.get_year(h5,i)
if yr == 0:
#skip if no year
continue
#filter hotttness and familiarity
familiarity = hdf5_getters.get_artist_familiarity(h5,i)
hotttness = hdf5_getters.get_artist_hotttnesss(h5,i)
if familiarity<=0.0 or hotttness<=0.0:
#skip if no hotttness or familiarity computations
continue
#TODO:MAYBE filter on dance and energy
timbre = hdf5_getters.get_segments_timbre(h5,i)
#timbre[#] gives len 12 array so for each arr in timbre, add up to get segment and add to corresponding 12 features and avg across each
if not artist_name in artist_map:
#have not encountered the artist yet, so populate new map
sub_map = {}
sub_map['artist_familiarity'] = familiarity
sub_map['artist_hotttnesss'] = hotttness
sub_map['artist_id'] = hdf5_getters.get_artist_id(h5,i)
#longi = hdf5_getters.get_artist_longitude(h5,i)
#lat = hdf5_getters.get_artist_latitude(h5,i)
#longi = None if math.isnan(longi) else longi
#lat = None if math.isnan(lat) else lat
sub_map['artist_latitude'] = lat
sub_map['artist_longitude'] = longi
sub_map['artist_location'] = loc
sub_map['artist_terms'] = hdf5_getters.get_artist_terms(h5,i)
#TODO:see if should weight by freq or weight for if the term matches one of the feature terms
sub_map['artist_terms_freq'] = list(hdf5_getters.get_artist_terms_freq(h5,i))
sub_map['artist_terms_weight'] = list(hdf5_getters.get_artist_terms_weight(h5,i))
#song-sepcific data
#TODO COMPUTE AN AVG TIMBRE FOR A SONG BY IDEA:
#SUMMING DOWN EACH 12 VECTOR FOR EACH PT IN SONG AND AVG THIS ACROSS SONG
dance = hdf5_getters.get_danceability(h5,i)
dance = None if dance == 0.0 else dance
energy = hdf5_getters.get_energy(h5,i)
energy = None if energy == 0.0 else energy
sub_map['danceability'] = [dance]
sub_map['duration'] = [hdf5_getters.get_duration(h5,i)]
sub_map['end_of_fade_in'] = [hdf5_getters.get_end_of_fade_in(h5,i)]
sub_map['energy'] = [energy]
#since each song has a key, ask if feature for keys should be num of songs that appear in that key or
#just binary if any of their songs has that key or just be avg of songs with that key
#same for mode, since its either major or minor...should it be count or avg.?
sub_map['key'] = [hdf5_getters.get_key(h5,i)]
sub_map['loudness'] = [hdf5_getters.get_loudness(h5,i)]
sub_map['mode'] = [hdf5_getters.get_mode(h5,i)] #major or minor 0/1
s_hot = hdf5_getters.get_song_hotttnesss(h5,i)
s_hot = None if math.isnan(s_hot) else s_hot
sub_map['song_hotttnesss'] = [s_hot]
sub_map['start_of_fade_out'] = [hdf5_getters.get_start_of_fade_out(h5,i)]
sub_map['tempo'] = [hdf5_getters.get_tempo(h5,i)]
#should time signature be count as well? binary?
sub_map['time_signature'] = [hdf5_getters.get_time_signature(h5,i)]
sub_map['track_id'] = [hdf5_getters.get_track_id(h5,i)]
#should year be binary since they can have many songs across years and should it be year:count
sub_map['year'] = [yr]
artist_map[artist_name] = sub_map
else:
#artist already exists, so get its map and update song fields
dance = hdf5_getters.get_danceability(h5,i)
dance = None if dance == 0.0 else dance
energy = hdf5_getters.get_energy(h5,i)
energy = None if energy == 0.0 else energy
artist_map[artist_name]['danceability'].append(dance)
artist_map[artist_name]['duration'].append(hdf5_getters.get_duration(h5,i))
artist_map[artist_name]['end_of_fade_in'].append(hdf5_getters.get_end_of_fade_in(h5,i))
artist_map[artist_name]['energy'].append(energy)
artist_map[artist_name]['key'].append(hdf5_getters.get_key(h5,i))
artist_map[artist_name]['loudness'].append(hdf5_getters.get_loudness(h5,i))
artist_map[artist_name]['mode'].append(hdf5_getters.get_mode(h5,i)) #major or minor 0/1
s_hot = hdf5_getters.get_song_hotttnesss(h5,i)
s_hot = None if math.isnan(s_hot) else s_hot
artist_map[artist_name]['song_hotttnesss'].append(s_hot)
artist_map[artist_name]['start_of_fade_out'].append(hdf5_getters.get_start_of_fade_out(h5,i))
artist_map[artist_name]['tempo'].append(hdf5_getters.get_tempo(h5,i))
#should time signature be count as well? binary?
artist_map[artist_name]['time_signature'].append(hdf5_getters.get_time_signature(h5,i))
artist_map[artist_name]['track_id'].append(hdf5_getters.get_track_id(h5,i))
#should year be binary since they can have many songs across years and should it be year:count
artist_map[artist_name]['year'].append(yr)
h5 = hdf5_getters.open_h5_file_read(file_name2)
numSongs = hdf5_getters.get_num_songs(h5)
print 'Parsing song file2...'
for i in range(numSongs):
song_id = hdf5_getters.get_track_id(h5,i)
artist_name = hdf5_getters.get_artist_name(h5,i)
if artist_name in artist_map and song_id in artist_map[artist_name]['track_id']:
continue
#Filter location
longi = hdf5_getters.get_artist_longitude(h5,i)
lat = hdf5_getters.get_artist_latitude(h5,i)
loc = hdf5_getters.get_artist_location(h5,i)
if math.isnan(lat) or math.isnan(longi):
#skip if no location
continue
#filter year
yr = hdf5_getters.get_year(h5,i)
if yr == 0:
#skip if no year
continue
#filter hotttness and familiarity
familiarity = hdf5_getters.get_artist_familiarity(h5,i)
hotttness = hdf5_getters.get_artist_hotttnesss(h5,i)
if familiarity<=0.0 or hotttness<=0.0:
#skip if no hotttness or familiarity computations
continue
#TODO:MAYBE filter on dance and energy
timbre = hdf5_getters.get_segments_timbre(h5,i)
#timbre[#] gives len 12 array so for each arr in timbre, add up to get segment and add to corresponding 12 features and avg across each
if not artist_name in artist_map:
#have not encountered the artist yet, so populate new map
sub_map = {}
sub_map['artist_familiarity'] = familiarity
sub_map['artist_hotttnesss'] = hotttness
sub_map['artist_id'] = hdf5_getters.get_artist_id(h5,i)
#longi = hdf5_getters.get_artist_longitude(h5,i)
#lat = hdf5_getters.get_artist_latitude(h5,i)
#longi = None if math.isnan(longi) else longi
#lat = None if math.isnan(lat) else lat
sub_map['artist_latitude'] = lat
sub_map['artist_longitude'] = longi
sub_map['artist_location'] = loc
sub_map['artist_terms'] = hdf5_getters.get_artist_terms(h5,i)
#TODO:see if should weight by freq or weight for if the term matches one of the feature terms
sub_map['artist_terms_freq'] = list(hdf5_getters.get_artist_terms_freq(h5,i))
sub_map['artist_terms_weight'] = list(hdf5_getters.get_artist_terms_weight(h5,i))
#song-sepcific data
#TODO COMPUTE AN AVG TIMBRE FOR A SONG BY IDEA:
#SUMMING DOWN EACH 12 VECTOR FOR EACH PT IN SONG AND AVG THIS ACROSS SONG
dance = hdf5_getters.get_danceability(h5,i)
dance = None if dance == 0.0 else dance
energy = hdf5_getters.get_energy(h5,i)
energy = None if energy == 0.0 else energy
sub_map['danceability'] = [dance]
sub_map['duration'] = [hdf5_getters.get_duration(h5,i)]
sub_map['end_of_fade_in'] = [hdf5_getters.get_end_of_fade_in(h5,i)]
sub_map['energy'] = [energy]
#since each song has a key, ask if feature for keys should be num of songs that appear in that key or
#just binary if any of their songs has that key or just be avg of songs with that key
#same for mode, since its either major or minor...should it be count or avg.?
sub_map['key'] = [hdf5_getters.get_key(h5,i)]
sub_map['loudness'] = [hdf5_getters.get_loudness(h5,i)]
sub_map['mode'] = [hdf5_getters.get_mode(h5,i)] #major or minor 0/1
s_hot = hdf5_getters.get_song_hotttnesss(h5,i)
s_hot = None if math.isnan(s_hot) else s_hot
sub_map['song_hotttnesss'] = [s_hot]
sub_map['start_of_fade_out'] = [hdf5_getters.get_start_of_fade_out(h5,i)]
sub_map['tempo'] = [hdf5_getters.get_tempo(h5,i)]
#should time signature be count as well? binary?
sub_map['time_signature'] = [hdf5_getters.get_time_signature(h5,i)]
sub_map['track_id'] = [hdf5_getters.get_track_id(h5,i)]
#should year be binary since they can have many songs across years and should it be year:count
sub_map['year'] = [yr]
artist_map[artist_name] = sub_map
else:
#artist already exists, so get its map and update song fields
dance = hdf5_getters.get_danceability(h5,i)
dance = None if dance == 0.0 else dance
energy = hdf5_getters.get_energy(h5,i)
energy = None if energy == 0.0 else energy
artist_map[artist_name]['danceability'].append(dance)
artist_map[artist_name]['duration'].append(hdf5_getters.get_duration(h5,i))
artist_map[artist_name]['end_of_fade_in'].append(hdf5_getters.get_end_of_fade_in(h5,i))
artist_map[artist_name]['energy'].append(energy)
artist_map[artist_name]['key'].append(hdf5_getters.get_key(h5,i))
artist_map[artist_name]['loudness'].append(hdf5_getters.get_loudness(h5,i))
artist_map[artist_name]['mode'].append(hdf5_getters.get_mode(h5,i)) #major or minor 0/1
s_hot = hdf5_getters.get_song_hotttnesss(h5,i)
s_hot = None if math.isnan(s_hot) else s_hot
artist_map[artist_name]['song_hotttnesss'].append(s_hot)
artist_map[artist_name]['start_of_fade_out'].append(hdf5_getters.get_start_of_fade_out(h5,i))
artist_map[artist_name]['tempo'].append(hdf5_getters.get_tempo(h5,i))
#should time signature be count as well? binary?
artist_map[artist_name]['time_signature'].append(hdf5_getters.get_time_signature(h5,i))
artist_map[artist_name]['track_id'].append(hdf5_getters.get_track_id(h5,i))
#should year be binary since they can have many songs across years and should it be year:count
artist_map[artist_name]['year'].append(yr)
str(e)
for e in GETTERS.get_artist_mbtags_count(h5, i)) # array
artist_name = GETTERS.get_artist_name(h5, i)
artist_playmeid = GETTERS.get_artist_playmeid(h5, i)
artist_terms = ','.join(
str(e) for e in GETTERS.get_artist_terms(h5, i)) # array
#artist_terms_freq = ','.join(str(e) for e in GETTERS.get_artist_terms_freq(h5, i)) # array
#artist_terms_weight = ','.join(str(e) for e in GETTERS.get_artist_terms_weight(h5, i)) # array
#audio_md5 = GETTERS.get_audio_md5(h5, i)
#bars_confidence = ','.join(str(e) for e in GETTERS.get_bars_confidence(h5, i)) # array
#bars_start = ','.join(str(e) for e in GETTERS.get_bars_start(h5, i)) # array
#beats_confidence = ','.join(str(e) for e in GETTERS.get_beats_confidence(h5, i)) # array
#beats_start = ','.join(str(e) for e in GETTERS.get_beats_start(h5, i)) # array
danceability = GETTERS.get_danceability(h5, i)
duration = GETTERS.get_duration(h5, i)
end_of_fade_in = GETTERS.get_end_of_fade_in(h5, i)
energy = GETTERS.get_energy(h5, i)
key = GETTERS.get_key(h5, i)
key_confidence = GETTERS.get_key_confidence(h5, i)
loudness = GETTERS.get_loudness(h5, i)
mode = GETTERS.get_mode(h5, i)
mode_confidence = GETTERS.get_mode_confidence(h5, i)
release = GETTERS.get_release(h5, i)
release_7digitalid = GETTERS.get_release_7digitalid(h5, i)
#sections_confidence = ','.join(str(e) for e in GETTERS.get_sections_confidence(h5, i)) # array
#sections_start = ','.join(str(e) for e in GETTERS.get_sections_start(h5, i)) # array
#segments_confidence = ','.join(str(e) for e in GETTERS.get_segments_confidence(h5, i)) # array
#segments_loudness_max = ','.join(str(e) for e in GETTERS.get_segments_loudness_max(h5, i)) # array
#segments_loudness_max_time = ','.join(str(e) for e in GETTERS.get_segments_loudness_max_time(h5, i)) # array
#segments_loudness_start = ','.join(str(e) for e in GETTERS.get_segments_loudness_start(h5, i)) # array
#segments_pitches = ','.join(str(e) for e in GETTERS.get_segments_pitches(h5, i)) # array
def main():
outputFile1 = open('SongCSV.csv', 'w')
csvRowString = ""
#################################################
#if you want to prompt the user for the order of attributes in the csv,
#leave the prompt boolean set to True
#else, set 'prompt' to False and set the order of attributes in the 'else'
#clause
prompt = False
#################################################
if prompt == True:
while prompt:
prompt = False
csvAttributeString = raw_input("\n\nIn what order would you like the colums of the CSV file?\n" +
"Please delineate with commas. The options are: " +
"AlbumName, AlbumID, ArtistID, ArtistLatitude, ArtistLocation, ArtistLongitude,"+
" ArtistName, Danceability, Duration, KeySignature, KeySignatureConfidence, Tempo," +
" SongID, TimeSignature, TimeSignatureConfidence, Title, and Year.\n\n" +
"For example, you may write \"Title, Tempo, Duration\"...\n\n" +
"...or exit by typing 'exit'.\n\n")
csvAttributeList = re.split('\W+', csvAttributeString)
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
for attribute in csvAttributeList:
# print "Here is the attribute: " + attribute + " \n"
if attribute == 'AlbumID'.lower():
csvRowString += 'AlbumID'
elif attribute == 'AlbumName'.lower():
csvRowString += 'AlbumName'
elif attribute == 'ArtistID'.lower():
csvRowString += 'ArtistID'
elif attribute == 'ArtistLatitude'.lower():
csvRowString += 'ArtistLatitude'
elif attribute == 'ArtistLocation'.lower():
csvRowString += 'ArtistLocation'
elif attribute == 'ArtistLongitude'.lower():
csvRowString += 'ArtistLongitude'
elif attribute == 'ArtistName'.lower():
csvRowString += 'ArtistName'
elif attribute == 'Danceability'.lower():
csvRowString += 'Danceability'
elif attribute == 'Duration'.lower():
csvRowString += 'Duration'
elif attribute == 'KeySignature'.lower():
csvRowString += 'KeySignature'
elif attribute == 'KeySignatureConfidence'.lower():
csvRowString += 'KeySignatureConfidence'
elif attribute == 'SongID'.lower():
csvRowString += "SongID"
elif attribute == 'Tempo'.lower():
csvRowString += 'Tempo'
elif attribute == 'TimeSignature'.lower():
csvRowString += 'TimeSignature'
elif attribute == 'TimeSignatureConfidence'.lower():
csvRowString += 'TimeSignatureConfidence'
elif attribute == 'Title'.lower():
csvRowString += 'Title'
elif attribute == 'Year'.lower():
csvRowString += 'Year'
elif attribute == 'track_id'.lower():
csvRowString += 'track_id'
elif attribute == 'artist_familiarity'.lower():
csvRowString += 'artist_familiarity'
elif attribute == 'artist_hotttnesss'.lower():
csvRowString += 'artist_hotttnesss'
elif attribute == 'artist_mbid'.lower():
csvRowString += 'artist_mbid'
elif attribute == 'artist_playmeid'.lower():
csvRowString += 'artist_playmeid'
elif attribute == 'artist_7digitalid'.lower():
csvRowString += 'artist_7digitalid'
elif attribute == 'release'.lower():
csvRowString += 'release'
elif attribute == 'release_7digitalid'.lower():
csvRowString += 'release_7digitalid'
elif attribute == 'song_hotttnesss'.lower():
csvRowString += 'song_hotttnesss'
elif attribute == 'track_7digitalid'.lower():
csvRowString += 'track_7digitalid'
elif attribute == 'analysis_sample_rate'.lower():
csvRowString += 'analysis_sample_rate'
elif attribute == 'audio_md5'.lower():
csvRowString += 'audio_md5'
elif attribute == 'end_of_fade_in'.lower():
csvRowString += 'end_of_fade_in'
elif attribute == 'energy'.lower():
csvRowString += 'energy'
elif attribute == 'key'.lower():
csvRowString += 'key'
elif attribute == 'key_confidence'.lower():
csvRowString += 'key_confidence'
elif attribute == 'loudness'.lower():
csvRowString += 'loudness'
elif attribute == 'mode'.lower():
csvRowString += 'mode'
elif attribute == 'mode_confidence'.lower():
csvRowString += 'mode_confidence'
elif attribute == 'start_of_fade_out'.lower():
csvRowString += 'start_of_fade_out'
elif attribute == 'Exit'.lower():
sys.exit()
else:
prompt = True
print "=============="
print "I believe there has been an error with the input."
print "=============="
break
csvRowString += ","
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex-1]
csvRowString += "\n"
outputFile1.write(csvRowString);
csvRowString = ""
#else, if you want to hard code the order of the csv file and not prompt
#the user,
else:
#################################################
#change the order of the csv file here
#Default is to list all available attributes (in alphabetical order)
csvRowString = ("SongID,AlbumID,AlbumName,ArtistID,ArtistLatitude,ArtistLocation,"+
"ArtistLongitude,ArtistName,Danceability,Duration,KeySignature,"+
"KeySignatureConfidence,Tempo,TimeSignature,TimeSignatureConfidence,"+
"Title,Year,track_id,artist_hotttnesss,artist_mbid,artist_playmeid,artist_7digitalid,"+
"release,release_7digitalid,song_hotttnesss,track_7digitalid,analysis_sample_rate,audio_md5,"+
"end_of_fade_in,energy,key,key_confidence,loudness,mode,mode_confidence,start_of_fade_out")
#################################################
csvAttributeList = re.split('\W+', csvRowString)
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
outputFile1.write("SongNumber,");
outputFile1.write(csvRowString + "\n");
csvRowString = ""
#################################################
#Set the basedir here, the root directory from which the search
#for files stored in a (hierarchical data structure) will originate
basedir = "/vagrant/genrepython/MillionSongSubset" # "." As the default means the current directory
ext = ".h5" #Set the extension here. H5 is the extension for HDF5 files.
#################################################
#FOR LOOP
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root,'*'+ext))
for f in files:
print f
songH5File = hdf5_getters.open_h5_file_read(f)
song = Song(str(hdf5_getters.get_song_id(songH5File)))
testDanceability = hdf5_getters.get_danceability(songH5File)
# print type(testDanceability)
# print ("Here is the danceability: ") + str(testDanceability)
song.artistID = str(hdf5_getters.get_artist_id(songH5File))
song.albumID = str(hdf5_getters.get_release_7digitalid(songH5File))
song.albumName = str(hdf5_getters.get_release(songH5File))
song.artistLatitude = str(hdf5_getters.get_artist_latitude(songH5File))
song.artistLocation = str(hdf5_getters.get_artist_location(songH5File))
song.artistLongitude = str(hdf5_getters.get_artist_longitude(songH5File))
song.artistName = str(hdf5_getters.get_artist_name(songH5File))
song.danceability = str(hdf5_getters.get_danceability(songH5File))
song.duration = str(hdf5_getters.get_duration(songH5File))
# song.setGenreList()
song.keySignature = str(hdf5_getters.get_key(songH5File))
song.keySignatureConfidence = str(hdf5_getters.get_key_confidence(songH5File))
# song.lyrics = None
# song.popularity = None
song.tempo = str(hdf5_getters.get_tempo(songH5File))
song.timeSignature = str(hdf5_getters.get_time_signature(songH5File))
song.timeSignatureConfidence = str(hdf5_getters.get_time_signature_confidence(songH5File))
song.title = str(hdf5_getters.get_title(songH5File))
song.year = str(hdf5_getters.get_year(songH5File))
song.track_id = str(hdf5_getters.get_track_id(songH5File))
song.artist_familiarity = str(hdf5_getters.get_artist_familiarity(songH5File))
song.artist_hotttnesss = str(hdf5_getters.get_artist_hotttnesss(songH5File))
song.artist_mbid = str(hdf5_getters.get_artist_mbid(songH5File))
song.artist_playmeid = str(hdf5_getters.get_artist_playmeid(songH5File))
song.artist_7digitalid = str(hdf5_getters.get_artist_7digitalid(songH5File))
song.release = str(hdf5_getters.get_release(songH5File))
song.release_7digitalid = str(hdf5_getters.get_release_7digitalid(songH5File))
song.song_hotttnesss = str(hdf5_getters.get_song_hotttnesss(songH5File))
song.track_7digitalid = str(hdf5_getters.get_track_7digitalid(songH5File))
song.analysis_sample_rate = str(hdf5_getters.get_analysis_sample_rate(songH5File))
song.audio_md5 = str(hdf5_getters.get_audio_md5(songH5File))
song.end_of_fade_in = str(hdf5_getters.get_end_of_fade_in(songH5File))
song.energy = str(hdf5_getters.get_energy(songH5File))
song.key = str(hdf5_getters.get_key(songH5File))
song.key_confidence = str(hdf5_getters.get_key_confidence(songH5File))
song.loudness = str(hdf5_getters.get_loudness(songH5File))
song.mode = str(hdf5_getters.get_mode(songH5File))
song.mode_confidence = str(hdf5_getters.get_mode_confidence(songH5File))
song.start_of_fade_out = str(hdf5_getters.get_start_of_fade_out(songH5File))
#print song count
csvRowString += str(song.songCount) + ","
for attribute in csvAttributeList:
# print "Here is the attribute: " + attribute + " \n"
if attribute == 'AlbumID'.lower():
csvRowString += song.albumID
elif attribute == 'AlbumName'.lower():
albumName = song.albumName
albumName = albumName.replace(',',"")
csvRowString += "\"" + albumName + "\""
elif attribute == 'ArtistID'.lower():
csvRowString += "\"" + song.artistID + "\""
elif attribute == 'ArtistLatitude'.lower():
latitude = song.artistLatitude
if latitude == 'nan':
latitude = ''
csvRowString += latitude
elif attribute == 'ArtistLocation'.lower():
location = song.artistLocation
location = location.replace(',','')
csvRowString += "\"" + location + "\""
elif attribute == 'ArtistLongitude'.lower():
longitude = song.artistLongitude
if longitude == 'nan':
longitude = ''
csvRowString += longitude
elif attribute == 'ArtistName'.lower():
csvRowString += "\"" + song.artistName + "\""
elif attribute == 'Danceability'.lower():
csvRowString += song.danceability
elif attribute == 'Duration'.lower():
csvRowString += song.duration
elif attribute == 'KeySignature'.lower():
csvRowString += song.keySignature
elif attribute == 'KeySignatureConfidence'.lower():
# print "key sig conf: " + song.timeSignatureConfidence
csvRowString += song.keySignatureConfidence
elif attribute == 'SongID'.lower():
csvRowString += "\"" + song.id + "\""
elif attribute == 'Tempo'.lower():
# print "Tempo: " + song.tempo
csvRowString += song.tempo
elif attribute == 'TimeSignature'.lower():
csvRowString += song.timeSignature
elif attribute == 'TimeSignatureConfidence'.lower():
# print "time sig conf: " + song.timeSignatureConfidence
csvRowString += song.timeSignatureConfidence
elif attribute == 'Title'.lower():
csvRowString += "\"" + song.title + "\""
elif attribute == 'Year'.lower():
csvRowString += song.year
elif attribute == 'track_id'.lower():
csvRowString += song.track_id
elif attribute == 'artist_familiarity'.lower():
csvRowString += song.artist_familiarity
elif attribute == 'artist_hotttnesss'.lower():
csvRowString += song.artist_hotttnesss
elif attribute == 'artist_mbid'.lower():
csvRowString += song.artist_mbid
elif attribute == 'artist_playmeid'.lower():
csvRowString += song.artist_playmeid
elif attribute == 'artist_7digitalid'.lower():
csvRowString += song.artist_7digitalid
elif attribute == 'release'.lower():
csvRowString += song.release
elif attribute == 'release_7digitalid'.lower():
csvRowString += song.release_7digitalid
elif attribute == 'song_hotttnesss'.lower():
csvRowString += song.song_hotttnesss
elif attribute == 'track_7digitalid'.lower():
csvRowString += song.track_7digitalid
elif attribute == 'analysis_sample_rate'.lower():
csvRowString += song.analysis_sample_rate
elif attribute == 'audio_md5'.lower():
csvRowString += song.audio_md5
elif attribute == 'end_of_fade_in'.lower():
csvRowString += song.end_of_fade_in
elif attribute == 'energy'.lower():
csvRowString += song.energy
elif attribute == 'key'.lower():
csvRowString += song.key
elif attribute == 'key_confidence'.lower():
csvRowString += song.key_confidence
elif attribute == 'loudness'.lower():
csvRowString += song.loudness
elif attribute == 'mode'.lower():
csvRowString += song.mode
elif attribute == 'mode_confidence'.lower():
csvRowString += song.mode_confidence
elif attribute == 'start_of_fade_out'.lower():
csvRowString += song.start_of_fade_out
else:
csvRowString += "Erm. This didn't work. Error. :( :(\n"
csvRowString += ","
#Remove the final comma from each row in the csv
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex-1]
csvRowString += "\n"
outputFile1.write(csvRowString)
csvRowString = ""
songH5File.close()
outputFile1.close()
def main():
basedir = "./../songMetaInfo.txt"
ext = ".h5"
if len(sys.argv) > 1:
basedir = sys.argv[1]
outputfile = 'SongFileMetaData.csv'
if len(sys.argv) > 2:
outputfile = sys.argv[2]
csvWriter = open(outputfile, 'w')
csvWriter.write(
"title,songId,artistId,artistfamilarity,artistHotness,songHotness," +
"songEnfOfFadeIn,startFadeout,energy,loudness,albumID,albumName,artistName,danceability,duration,keySignatureConfidence,tempo,timeSignature,timeSignatureConfidence,year\n"
)
with open(basedir) as file:
for line in file.readlines():
f = line.strip()
#newf = f + "text"
print f
#print f
try:
songH5File = hdf5_getters.open_h5_file_read(f)
csvStr = ""
#0
title = str(hdf5_getters.get_title(songH5File))
csvStr += title + ","
#1
songId = str(hdf5_getters.get_song_id(songH5File))
csvStr += songId + ","
#2
artistId = str(hdf5_getters.get_artist_id(songH5File))
csvStr += artistId + ","
#3
artistfamilarity = str(
hdf5_getters.get_artist_familiarity(songH5File))
csvStr += artistfamilarity + ","
#4
artistHotness = str(
hdf5_getters.get_artist_hotttnesss(songH5File))
csvStr += artistHotness + ","
#5
songHotness = str(hdf5_getters.get_song_hotttnesss(songH5File))
csvStr += songHotness + ","
#6
songEnfOfFadeIn = str(
hdf5_getters.get_end_of_fade_in(songH5File))
csvStr += songEnfOfFadeIn + ","
#7
startFadeOut = str(
hdf5_getters.get_start_of_fade_out(songH5File))
csvStr += startFadeOut + ","
#8
energy = str(hdf5_getters.get_energy(songH5File))
csvStr += energy + ","
#9
loudness = str(hdf5_getters.get_loudness(songH5File))
csvStr += loudness + ","
#10
albumID = str(hdf5_getters.get_release_7digitalid(songH5File))
csvStr += albumID + ","
#11
albumName = str(hdf5_getters.get_release(songH5File))
csvStr += albumName + ","
#12
artistName = str(hdf5_getters.get_artist_name(songH5File))
csvStr += artistName + ","
#13
danceability = str(hdf5_getters.get_danceability(songH5File))
csvStr += danceability + ","
#14
duration = str(hdf5_getters.get_duration(songH5File))
csvStr += duration + ","
#15
keySignatureConfidence = str(
hdf5_getters.get_key_confidence(songH5File))
csvStr += keySignatureConfidence + ","
#16
tempo = str(hdf5_getters.get_tempo(songH5File))
csvStr += tempo + ","
## 17
timeSignature = str(
hdf5_getters.get_time_signature(songH5File))
csvStr += timeSignature + ","
#18
timeSignatureConfidence = str(
hdf5_getters.get_time_signature_confidence(songH5File))
csvStr += timeSignatureConfidence + ","
#19
year = str(hdf5_getters.get_year(songH5File))
csvStr += year + ","
#print song count
csvStr += "\n"
csvWriter.write(csvStr)
#print csvStr
songH5File.close()
except:
print "Error in processing file"
csvWriter.close()
def main():
outputFile1 = open('SongCSV.csv', 'w')
csvRowString = ""
#################################################
#if you want to prompt the user for the order of attributes in the csv,
#leave the prompt boolean set to True
#else, set 'prompt' to False and set the order of attributes in the 'else'
#clause
prompt = False
#################################################
if prompt == True:
while prompt:
prompt = False
csvAttributeString = raw_input(
"\n\nIn what order would you like the colums of the CSV file?\n"
+ "Please delineate with commas. The options are: " +
"AlbumName, AlbumID, ArtistID, ArtistLatitude, ArtistLocation, ArtistLongitude,"
+
" ArtistName, Danceability, Duration, KeySignature, KeySignatureConfidence, Tempo,"
+
" SongID, TimeSignature, TimeSignatureConfidence, Title, and Year.\n\n"
+
"For example, you may write \"Title, Tempo, Duration\"...\n\n"
+ "...or exit by typing 'exit'.\n\n")
csvAttributeList = re.split('\W+', csvAttributeString)
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
for attribute in csvAttributeList:
# print "Here is the attribute: " + attribute + " \n"
if attribute == 'AlbumID'.lower():
csvRowString += 'AlbumID'
elif attribute == 'AlbumName'.lower():
csvRowString += 'AlbumName'
elif attribute == 'ArtistID'.lower():
csvRowString += 'ArtistID'
elif attribute == 'ArtistLatitude'.lower():
csvRowString += 'ArtistLatitude'
elif attribute == 'ArtistLocation'.lower():
csvRowString += 'ArtistLocation'
elif attribute == 'ArtistLongitude'.lower():
csvRowString += 'ArtistLongitude'
elif attribute == 'ArtistName'.lower():
csvRowString += 'ArtistName'
elif attribute == 'Danceability'.lower():
csvRowString += 'Danceability'
elif attribute == 'Duration'.lower():
csvRowString += 'Duration'
elif attribute == 'KeySignature'.lower():
csvRowString += 'KeySignature'
elif attribute == 'KeySignatureConfidence'.lower():
csvRowString += 'KeySignatureConfidence'
elif attribute == 'SongID'.lower():
csvRowString += "SongID"
elif attribute == 'Tempo'.lower():
csvRowString += 'Tempo'
elif attribute == 'TimeSignature'.lower():
csvRowString += 'TimeSignature'
elif attribute == 'TimeSignatureConfidence'.lower():
csvRowString += 'TimeSignatureConfidence'
elif attribute == 'Title'.lower():
csvRowString += 'Title'
elif attribute == 'Year'.lower():
csvRowString += 'Year'
elif attribute == 'Familiarity'.lower(): ####Added by us!
csvRowString += song.familiarity
elif attribute == 'artist_mbid'.lower():
csvRowString += song.artist_mbid
elif attribute == 'artist_playmeid'.lower():
csvRowString += song.artist_playmeid
elif attribute == 'artist_7digid'.lower():
csvRowString += song.artist_7digid
elif attribute == 'hottness'.lower():
csvRowString += song.hottness
elif attribute == 'song_hottness'.lower():
csvRowString += song.song_hottness
elif attribute == 'digitalid7'.lower():
csvRowString += song.digitalid7
elif attribute == 'similar_artists'.lower():
csvRowString += song.similar_artists
elif attribute == 'artist_terms'.lower():
csvRowString += song.artist_terms
elif attribute == 'art_terms_freq'.lower():
csvRowString += song.art_terms_freq
elif attribute == 'art_terms_weight'.lower():
csvRowString += song.art_terms_weight
elif attribute == 'a_sample_rate'.lower():
csvRowString += song.a_sample_rate
elif attribute == 'audio_md5'.lower():
csvRowString += song.audio_md5
elif attribute == 'end_of_fade_in'.lower():
csvRowString += song.end_of_fade_in
elif attribute == 'energy'.lower():
csvRowString += song.energy
elif attribute == 'loudness'.lower():
csvRowString += song.loudness
elif attribute == 'mode'.lower():
csvRowString += song.mode
elif attribute == 'mode_conf'.lower():
csvRowString += song.mode_conf
elif attribute == 'start_of_fade_out'.lower():
csvRowString += song.start_of_fade_out
elif attribute == 'trackid'.lower():
csvRowString += song.trackid
elif attribute == 'segm_start'.lower():
csvRowString += song.segm_start
elif attribute == 'segm_conf'.lower():
csvRowString += song.segm_conf
elif attribute == 'segm_pitch'.lower():
csvRowString += song.segm_pitch
elif attribute == 'segm_timbre'.lower():
csvRowString += song.segm_timbre
elif attribute == 'segm_max_loud'.lower():
csvRowString += song.segm_max_loud
elif attribute == 'segm_max_loud_time'.lower():
csvRowString += song.segm_max_loud_time
elif attribute == 'segm_loud_start'.lower():
csvRowString += song.segm_loud_start
elif attribute == 'sect_start'.lower():
csvRowString += song.sect_start
elif attribute == 'sect_conf'.lower():
csvRowString += song.sect_conf
elif attribute == 'beats_start'.lower():
csvRowString += song.beats_start
elif attribute == 'beats_conf'.lower():
csvRowString += song.beats_conf
elif attribute == 'bars_start'.lower():
csvRowString += song.bars_start
elif attribute == 'bars_conf'.lower():
csvRowString += song.bars_conf
elif attribute == 'tatums_start'.lower():
csvRowString += song.tatums_start
elif attribute == 'tatums_conf'.lower():
csvRowString += song.tatums_conf
elif attribute == 'artist_mbtags'.lower():
csvRowString += song.artist_mbtags
elif attribute == 'artist_mbtags_count'.lower():
csvRowString += song.artist_mbtags_count
elif attribute == 'Exit'.lower():
sys.exit()
else:
prompt = True
print("==============")
print("I believe there has been an error with the input.")
print("==============")
break
csvRowString += ","
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex - 1]
csvRowString += "\n"
outputFile1.write(csvRowString)
csvRowString = ""
#else, if you want to hard code the order of the csv file and not prompt
#the user,
else:
#################################################
#change the order of the csv file here
#Default is to list all available attributes (in alphabetical order)
csvRowString = "SongID,AlbumID,AlbumName,ArtistID,ArtistLatitude,ArtistLocation,ArtistLongitude,ArtistName,Danceability,Duration,KeySignature,KeySignatureConfidence,Tempo,TimeSignature,TimeSignatureConfidence,Title,Year,Familiarity,Artist_Mbid,Artist_PlaymeId,Artist_7didId,Hottness,Song_Hottness,7digitalid,A_Sample_Rate,Audio_Md5,End_Of_Fade_In,Energy,Loudness,Mode,Mode_Conf,Start_Of_Fade_Out,TrackId"
#################################################
csvAttributeList = re.split(',', csvRowString)
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
csvRowString += "\n"
outputFile1.write(csvRowString)
csvRowString = ""
#################################################
#Set the basedir here, the root directory from which the search
#for files stored in a (hierarchical data structure) will originate
basedir = "/home/bigdata/smalltest/" # "." As the default means the current directory
ext = ".h5" #Set the extension here. H5 is the extension for HDF5 files.
#################################################
#FOR LOOP
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
for f in files:
print(f)
songH5File = hdf5_getters.open_h5_file_read(f)
song = Song(str(hdf5_getters.get_song_id(songH5File)))
# testDanceability = hdf5_getters.get_danceability(songH5File)
# print type(testDanceability)
# print ("Here is the danceability: ") + str(testDanceability)
song.artistID = str(hdf5_getters.get_artist_id(songH5File))
song.albumID = str(hdf5_getters.get_release_7digitalid(songH5File))
song.albumName = str(hdf5_getters.get_release(songH5File))
song.artistLatitude = str(
hdf5_getters.get_artist_latitude(songH5File))
song.artistLocation = str(
hdf5_getters.get_artist_location(songH5File))
song.artistLongitude = str(
hdf5_getters.get_artist_longitude(songH5File))
song.artistName = str(hdf5_getters.get_artist_name(songH5File))
song.danceability = str(hdf5_getters.get_danceability(songH5File))
song.duration = str(hdf5_getters.get_duration(songH5File))
# song.setGenreList()
song.keySignature = str(hdf5_getters.get_key(songH5File))
song.keySignatureConfidence = str(
hdf5_getters.get_key_confidence(songH5File))
# song.lyrics = None
# song.popularity = None
song.tempo = str(hdf5_getters.get_tempo(songH5File))
song.timeSignature = str(
hdf5_getters.get_time_signature(songH5File))
song.timeSignatureConfidence = str(
hdf5_getters.get_time_signature_confidence(songH5File))
song.title = str(hdf5_getters.get_title(songH5File))
song.year = str(hdf5_getters.get_year(songH5File))
#########Added by us!
song.familiarity = str(
hdf5_getters.get_artist_familiarity(songH5File))
song.artist_mbid = str(hdf5_getters.get_artist_mbid(songH5File))
song.artist_playmeid = str(
hdf5_getters.get_artist_playmeid(songH5File))
song.artist_7digid = str(
hdf5_getters.get_artist_7digitalid(songH5File))
song.hottness = str(hdf5_getters.get_artist_hotttnesss(songH5File))
song.song_hottness = str(
hdf5_getters.get_song_hotttnesss(songH5File))
song.digitalid7 = str(
hdf5_getters.get_track_7digitalid(songH5File))
#song.similar_artists = str(hdf5_getters.get_similar_artists(songH5File))
#song.artist_terms = str(hdf5_getters.get_artist_terms(songH5File))
#song.art_terms_freq = str(hdf5_getters.get_artist_terms_freq(songH5File))
#song.art_terms_weight = str(hdf5_getters.get_artist_terms_weight(songH5File))
song.a_sample_rate = str(
hdf5_getters.get_analysis_sample_rate(songH5File))
song.audio_md5 = str(hdf5_getters.get_audio_md5(songH5File))
song.end_of_fade_in = str(
hdf5_getters.get_end_of_fade_in(songH5File))
song.energy = str(hdf5_getters.get_energy(songH5File))
song.loudness = str(hdf5_getters.get_loudness(songH5File))
song.mode = str(hdf5_getters.get_mode(songH5File))
song.mode_conf = str(hdf5_getters.get_mode_confidence(songH5File))
song.start_of_fade_out = str(
hdf5_getters.get_start_of_fade_out(songH5File))
song.trackid = str(hdf5_getters.get_track_id(songH5File))
#song.segm_start = str(hdf5_getters.get_segments_start(songH5File))
#song.segm_conf = str(hdf5_getters.get_segments_confidence(songH5File))
#song.segm_pitch = str(hdf5_getters.get_segments_pitches(songH5File))
#song.segm_timbre = str(hdf5_getters.get_segments_timbre(songH5File))
#song.segm_max_loud = str(hdf5_getters.get_segments_loudness_max(songH5File))
#song.segm_max_loud_time = str(hdf5_getters.get_segments_loudness_max_time(songH5File))
#song.segm_loud_start = str(hdf5_getters.get_segments_loudness_start(songH5File))
#song.sect_start = str(hdf5_getters.get_sections_start(songH5File))
#song.sect_conf = str(hdf5_getters.get_sections_confidence(songH5File))
#song.beats_start = str(hdf5_getters.get_beats_start(songH5File))
#song.beats_conf = str(hdf5_getters.get_beats_confidence(songH5File))
#song.bars_start = str(hdf5_getters.get_bars_start(songH5File))
#song.bars_conf = str(hdf5_getters.get_bars_confidence(songH5File))
#song.tatums_start = str(hdf5_getters.get_tatums_start(songH5File))
#song.tatums_conf = str(hdf5_getters.get_tatums_confidence(songH5File))
#song.artist_mbtags = str(hdf5_getters.get_artist_mbtags(songH5File))
#song.artist_mbtags_count = str(hdf5_getters.get_artist_mbtags_count(songH5File))
#print song count
#csvRowString += str(song.songCount) + ","
for attribute in csvAttributeList:
# print "Here is the attribute: " + attribute + " \n"
if attribute == 'AlbumID'.lower():
csvRowString += song.albumID
elif attribute == 'AlbumName'.lower():
albumName = song.albumName
albumName = albumName.replace("b\"", "")
albumName = albumName.replace("\"", "")
albumName = albumName.replace(',', "")
csvRowString += "\"" + albumName + "\""
elif attribute == 'ArtistID'.lower():
csvRowString += "\"" + song.artistID + "\""
elif attribute == 'ArtistLatitude'.lower():
latitude = song.artistLatitude
if latitude == 'nan':
latitude = ''
csvRowString += latitude
elif attribute == 'ArtistLocation'.lower():
location = song.artistLocation
location = location.replace(',', '')
location = location.replace("b\"", "")
location = location.replace("\"", "")
csvRowString += "\"" + location + "\""
elif attribute == 'ArtistLongitude'.lower():
longitude = song.artistLongitude
if longitude == 'nan':
longitude = ''
csvRowString += longitude
elif attribute == 'ArtistName'.lower():
artistName = song.artistName
artistName = artistName.replace("b\"", "")
artistName = artistName.replace("\"", "")
csvRowString += "\"" + artistName + "\""
elif attribute == 'Danceability'.lower():
csvRowString += song.danceability
elif attribute == 'Duration'.lower():
csvRowString += song.duration
elif attribute == 'KeySignature'.lower():
csvRowString += song.keySignature
elif attribute == 'KeySignatureConfidence'.lower():
# print "key sig conf: " + song.timeSignatureConfidence
csvRowString += song.keySignatureConfidence
elif attribute == 'SongID'.lower():
csvRowString += "\"" + song.id + "\""
elif attribute == 'Tempo'.lower():
# print "Tempo: " + song.tempo
csvRowString += song.tempo
elif attribute == 'TimeSignature'.lower():
csvRowString += song.timeSignature
elif attribute == 'TimeSignatureConfidence'.lower():
# print "time sig conf: " + song.timeSignatureConfidence
csvRowString += song.timeSignatureConfidence
elif attribute == 'Title'.lower():
t = song.title
t = t.replace("b\"", "")
t = t.replace("\"", "")
csvRowString += "\"" + t + "\""
elif attribute == 'Year'.lower():
csvRowString += song.year
elif attribute == 'Familiarity'.lower(): ####Added by us!
csvRowString += song.familiarity
elif attribute == 'artist_mbid'.lower():
csvRowString += "\"" + song.artist_mbid + "\""
elif attribute == 'artist_playmeid'.lower():
csvRowString += song.artist_playmeid
elif attribute == 'artist_7digid'.lower():
csvRowString += song.artist_7digid
elif attribute == 'hottness'.lower():
csvRowString += song.hottness
elif attribute == 'song_hottness'.lower():
csvRowString += song.song_hottness
elif attribute == 'digitalid7'.lower():
csvRowString += song.digitalid7
elif attribute == 'similar_artists'.lower():
csvRowString += song.similar_artists
elif attribute == 'artist_terms'.lower():
csvRowString += song.artist_terms
elif attribute == 'art_terms_freq'.lower():
csvRowString += song.art_terms_freq
elif attribute == 'art_terms_weight'.lower():
csvRowString += song.art_terms_weight
elif attribute == 'a_sample_rate'.lower():
csvRowString += song.a_sample_rate
elif attribute == 'audio_md5'.lower():
csvRowString += "\"" + song.audio_md5 + "\""
elif attribute == 'end_of_fade_in'.lower():
csvRowString += song.end_of_fade_in
elif attribute == 'energy'.lower():
csvRowString += song.energy
elif attribute == 'loudness'.lower():
csvRowString += song.loudness
elif attribute == 'mode'.lower():
csvRowString += song.mode
elif attribute == 'mode_conf'.lower():
csvRowString += song.mode_conf
elif attribute == 'start_of_fade_out'.lower():
csvRowString += song.start_of_fade_out
elif attribute == 'trackid'.lower():
csvRowString += "\"" + song.trackid + "\""
elif attribute == 'segm_start'.lower():
csvRowString += song.segm_start
elif attribute == 'segm_conf'.lower():
csvRowString += song.segm_conf
elif attribute == 'segm_pitch'.lower():
csvRowString += song.segm_pitch
elif attribute == 'segm_timbre'.lower():
csvRowString += song.segm_timbre
elif attribute == 'segm_max_loud'.lower():
csvRowString += song.segm_max_loud
elif attribute == 'segm_max_loud_time'.lower():
csvRowString += song.segm_max_loud_time
elif attribute == 'segm_loud_start'.lower():
csvRowString += song.segm_loud_start
elif attribute == 'sect_start'.lower():
csvRowString += song.sect_start
elif attribute == 'sect_conf'.lower():
csvRowString += song.sect_conf
elif attribute == 'beats_start'.lower():
csvRowString += song.beats_start
elif attribute == 'beats_conf'.lower():
csvRowString += song.beats_conf
elif attribute == 'bars_start'.lower():
csvRowString += song.bars_start
elif attribute == 'bars_conf'.lower():
csvRowString += song.bars_conf
elif attribute == 'tatums_start'.lower():
csvRowString += song.tatums_start
elif attribute == 'tatums_conf'.lower():
csvRowString += song.tatums_conf
elif attribute == 'artist_mbtags'.lower():
csvRowString += song.artist_mbtags
elif attribute == 'artist_mbtags_count'.lower():
csvRowString += song.artist_mbtags_count
else:
csvRowString += "\"ERR\""
csvRowString += ","
#Remove the final comma from each row in the csv
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex - 1]
csvRowString += "\n"
outputFile1.write(csvRowString)
csvRowString = ""
songH5File.close()
outputFile1.close()
def getData(starting_point):
starting = starting_point * 10000
files = glob.glob('/mnt/snap/data/*/*/*/*.h5')
file_one_round = files[starting:starting + 10000]
artist_ids = []
song_beats_persecond = []
song_duration = []
song_end_fade_in = []
song_start_fade_out = []
song_key = []
song_loudness = []
song_segments_loudness_max = []
song_segments_loudness_min = []
song_segments_loudness_med = []
song_segments_loudness_time_max = []
song_segments_loudness_time_min = []
song_segments_loudness_time_med = []
song_mode = []
song_sections_start = []
song_pitches = []
song_timbre = []
song_tempo = []
song_time_signature = []
song_title = []
artist_name = []
year = []
idx = np.triu_indices(12)
#count = 1
for f in file_one_round:
h5 = HDF5.open_h5_file_read(f)
songYear = g.get_year(h5)
if songYear < 1990:
continue
artist_id = g.get_artist_id(h5)
song_beat = (g.get_beats_start(h5)).tolist()
songDuration = g.get_duration(h5)
song_beat_persecond = float(len(song_beat)) / songDuration
song_end_fadein = g.get_end_of_fade_in(h5)
song_start_fadeout = g.get_start_of_fade_out(h5)
songKey = g.get_key(h5)
songLoudness = g.get_loudness(h5)
song_loudness_max = (g.get_segments_loudness_max(h5)) // 10
song_loudness_antilog = np.power(10, song_loudness_max)
song_segmentsLoudness_max = np.amax(song_loudness_antilog)
song_segmentsLoudness_min = np.amin(song_loudness_antilog)
song_segmentsLoudness_med = np.median(song_loudness_antilog)
song_segmentsLoudness_max_time = (
g.get_segments_loudness_max_time(h5)).tolist()
song_loudness_time = np.multiply(song_loudness_antilog,
song_segmentsLoudness_max_time)
song_segmentsLoudnessTime_max = np.amax(song_loudness_time)
song_segmentsLoudnessTime_min = np.amin(song_loudness_time)
song_segmentsLoudnessTime_med = np.median(song_loudness_time)
songMode = g.get_mode(h5)
song_sectionsStart = (g.get_sections_start(h5)).tolist()
songPitches = g.get_segments_pitches(h5)
songPitches_cov = np.cov(songPitches, rowvar=False)
songPitches_mean = np.mean(songPitches, axis=0)
#print(songPitches_cov.shape)
songTimbre = g.get_segments_timbre(h5)
songTimbre_cov = np.cov(songTimbre, rowvar=False)
songTimbre_mean = np.mean(songTimbre, axis=0)
#print(songTimbre_cov.shape)
songTempo = g.get_tempo(h5)
songTime_signature = g.get_time_signature(h5)
songTitle = g.get_title(h5)
artistName = g.get_artist_name(h5)
artist_ids.append(artist_id)
song_beats_persecond.append(song_beat_persecond)
song_duration.append(songDuration)
song_end_fade_in.append(song_end_fadein)
song_start_fade_out.append(song_start_fadeout)
song_key.append(songKey)
song_loudness.append(songLoudness)
song_segments_loudness_max.append(song_segmentsLoudness_max)
song_segments_loudness_min.append(song_segmentsLoudness_min)
song_segments_loudness_med.append(song_segmentsLoudness_med)
song_segments_loudness_time_max.append(song_segmentsLoudnessTime_max)
song_segments_loudness_time_min.append(song_segmentsLoudnessTime_min)
song_segments_loudness_time_med.append(song_segmentsLoudnessTime_med)
song_mode.append(songMode)
song_sections_start.append(song_sectionsStart)
pitches_mean_cov = (songPitches_cov[idx]).tolist()
pitches_mean_cov.extend((songPitches_mean).tolist())
song_pitches.append(pitches_mean_cov)
timbre_mean_cov = (songTimbre_cov[idx]).tolist()
timbre_mean_cov.extend((songTimbre_mean).tolist())
song_timbre.append(timbre_mean_cov)
song_tempo.append(songTempo)
song_time_signature.append(songTime_signature)
song_title.append(songTitle)
artist_name.append(artistName)
year.append(songYear)
#print(count)
#count = count + 1
h5.close()
#def createDictsFrom2DArray(dictionary, colName, featureList):
# for i in range(0,12):
# dictionary[colName+str(i)] = featureList[i]
#i = 1
#for t in itertools.izip_longest(*featureList):
# dictionary[colName+str(i)] = t
# i = i + 1
# return dictionary
data = collections.OrderedDict()
data['year'] = year
data['artist_name'] = artist_name
data['artist_id'] = artist_ids
data['song_title'] = song_title
data['song_beats_persecond'] = song_beats_persecond
data['song_duration'] = song_duration
data['song_end_fade_in'] = song_end_fade_in
data['song_start_fade_out'] = song_start_fade_out
data['song_key'] = song_key
data['song_loudness'] = song_loudness
data['song_loudness_max'] = song_segments_loudness_max
data['song_loudness_min'] = song_segments_loudness_min
data['song_loudness_med'] = song_segments_loudness_med
data['song_loudness_time_max'] = song_segments_loudness_time_max
data['song_loudness_time_min'] = song_segments_loudness_time_min
data['song_loudness_time_med'] = song_segments_loudness_time_med
data['song_mode'] = song_mode
data['song_tempo'] = song_tempo
data['song_time_signature'] = song_time_signature
data = createDictsFrom1DArray(data, 'pitches', song_pitches)
data = createDictsFrom1DArray(data, 'timbre', song_timbre)
data = createDictsFrom1DArray(data, 'sections_start', song_sections_start)
df = pd.DataFrame(data)
print('before return ' + str(starting_point))
return df
def convert_to_csv():
i = 0
data = []
target = []
count = 0
with open('data_timbre.csv', 'w+') as f:
with open('target_timbre.csv', 'w+') as f2:
writer = csv.writer(f)
target_writer = csv.writer(f2)
for root, dirs, files in os.walk(msd_subset_data_path):
files = glob.glob(os.path.join(root, '*.h5'))
for f in sorted(files):
try: # Opening is very prone to causing exceptions, we'll just skip file if exception is thrown
h5 = getter.open_h5_file_read(f)
year = getter.get_year(h5)
if year:
count += 1
analysis_file = open('current_analysis_status.txt',
'a')
update = "Currently at file name: " + str(
f) + " and at number " + str(count) + "\n"
analysis_file.write(update)
print update
analysis_file.close()
target.append([year])
row = []
timbre = getter.get_segments_timbre(h5)
segstarts = getter.get_segments_start(h5)
btstarts = getter.get_beats_start(h5)
duration = getter.get_duration(h5)
end_of_fade_in = getter.get_end_of_fade_in(h5)
key = getter.get_key(h5)
key_confidence = getter.get_key_confidence(h5)
loudness = getter.get_loudness(h5)
start_of_fade_out = getter.get_start_of_fade_out(
h5)
tempo = getter.get_tempo(h5)
time_signature = getter.get_time_signature(h5)
time_signature_confidence = getter.get_time_signature_confidence(
h5)
h5.close() # VERY IMPORTANT
segstarts = np.array(segstarts).flatten()
btstarts = np.array(btstarts).flatten()
bttimbre = align_feats(
timbre.T, segstarts, btstarts, duration,
end_of_fade_in, key, key_confidence, loudness,
start_of_fade_out, tempo, time_signature,
time_signature_confidence)
if bttimbre is None:
continue # Skip this track, some features broken
npicks, winsize, finaldim = 12, 12, 144 # Calculated by 12 * 12. 12 is fixed as number of dimensions.
processed_feats = extract_and_compress(
bttimbre, npicks, winsize, finaldim)
n_p_feats = processed_feats.shape[0]
if processed_feats is None:
continue # Skip this track, some features broken
row = processed_feats.flatten()
if len(
row
) != 12 * 144: # 12 dimensions * 144 features per dimension
continue # Not enough features
year_row = np.array([year])
if row.any() and year_row.any():
writer.writerow(row)
target_writer.writerow(year_row)
i += 1
else:
h5.close()
except Exception:
pass
print 'Finished!'
analysis_file = open('current_analysis_status.txt', 'a')
analysis_file.write('Done!')
analysis_file.close()
return
def classify(h5):
output_array={}
# duration
duration=hdf5_getters.get_duration(h5)
output_array["duration"]=duration ### ADDED VALUE TO ARRAY
# number of bars
bars=hdf5_getters.get_bars_start(h5)
num_bars=len(bars)
output_array["num_bars"]=num_bars ### ADDED VALUE TO ARRAY
# mean and variance in bar length
bar_length=numpy.ediff1d(bars)
variance_bar_length=numpy.var(bar_length)
output_array["variance_bar_length"]=variance_bar_length ### ADDED VALUE TO ARRAY
# number of beats
beats=hdf5_getters.get_beats_start(h5)
num_beats=len(beats)
output_array["num_beats"]=num_beats ### ADDED VALUE TO ARRAY
# mean and variance in beats length
beats_length=numpy.ediff1d(beats)
variance_beats_length=numpy.var(bar_length)
output_array["variance_beats_length"]=variance_beats_length ### ADDED VALUE TO ARRAY
# danceability
danceability=hdf5_getters.get_danceability(h5)
output_array["danceability"]=danceability ### ADDED VALUE TO ARRAY
# end of fade in
end_of_fade_in=hdf5_getters.get_end_of_fade_in(h5)
output_array["end_of_fade_in"]=end_of_fade_in ### ADDED VALUE TO ARRAY
# energy
energy=hdf5_getters.get_energy(h5)
output_array["energy"]=energy ### ADDED VALUE TO ARRAY
# key
key=hdf5_getters.get_key(h5)
output_array["key"]=int(key) ### ADDED VALUE TO ARRAY
# loudness
loudness=hdf5_getters.get_loudness(h5)
output_array["loudness"]=loudness ### ADDED VALUE TO ARRAY
# mode
mode=hdf5_getters.get_mode(h5)
output_array["mode"]=int(mode) ### ADDED VALUE TO ARRAY
# number sections
sections=hdf5_getters.get_sections_start(h5)
num_sections=len(sections)
output_array["num_sections"]=num_sections ### ADDED VALUE TO ARRAY
# mean and variance in sections length
sections_length=numpy.ediff1d(sections)
variance_sections_length=numpy.var(sections)
output_array["variance_sections_length"]=variance_sections_length ### ADDED VALUE TO ARRAY
# number segments
segments=hdf5_getters.get_segments_start(h5)
num_segments=len(segments)
output_array["num_segments"]=num_segments ### ADDED VALUE TO ARRAY
# mean and variance in segments length
segments_length=numpy.ediff1d(segments)
variance_segments_length=numpy.var(segments)
output_array["variance_segments_length"]=variance_segments_length ### ADDED VALUE TO ARRAY
# segment loudness max
segment_loudness_max_array=hdf5_getters.get_segments_loudness_max(h5)
segment_loudness_max_time_array=hdf5_getters.get_segments_loudness_max_time(h5)
segment_loudness_max_index=0
for i in range(len(segment_loudness_max_array)):
if segment_loudness_max_array[i]>segment_loudness_max_array[segment_loudness_max_index]:
segment_loudness_max_index=i
segment_loudness_max=segment_loudness_max_array[segment_loudness_max_index]
segment_loudness_max_time=segment_loudness_max_time_array[segment_loudness_max_index]
output_array["segment_loudness_max"]=segment_loudness_max ### ADDED VALUE TO ARRAY
output_array["segment_loudness_time"]=segment_loudness_max_time ### ADDED VALUE TO ARRAY
# POSSIBLE TODO: use average function instead and weight by segment length
# segment loudness mean (start)
segment_loudness_array=hdf5_getters.get_segments_loudness_start(h5)
segment_loudness_mean=numpy.mean(segment_loudness_array)
output_array["segment_loudness_mean"]=segment_loudness_mean ### ADDED VALUE TO ARRAY
# segment loudness variance (start)
segment_loudness_variance=numpy.var(segment_loudness_array)
output_array["segment_loudness_variance"]=segment_loudness_variance ### ADDED VALUE TO ARRAY
# segment pitches
segment_pitches_array=hdf5_getters.get_segments_pitches(h5)
segment_pitches_mean=numpy.mean(segment_pitches_array,axis=0).tolist()
output_array["segment_pitches_mean"]=segment_pitches_mean
# segment pitches variance (start)
segment_pitches_variance=numpy.var(segment_pitches_array,axis=0).tolist()
output_array["segment_pitches_variance"]=segment_pitches_variance
# segment timbres
segment_timbres_array=hdf5_getters.get_segments_timbre(h5)
segment_timbres_mean=numpy.mean(segment_timbres_array,axis=0).tolist()
output_array["segment_timbres_mean"]=segment_timbres_mean
# segment timbres variance (start)
segment_timbres_variance=numpy.var(segment_timbres_array,axis=0).tolist()
output_array["segment_timbres_variance"]=segment_timbres_variance
# hotttnesss
hottness=hdf5_getters.get_song_hotttnesss(h5,0)
output_array["hottness"]=hottness ### ADDED VALUE TO ARRAY
# duration-start of fade out
start_of_fade_out=hdf5_getters.get_start_of_fade_out(h5)
fade_out=duration-start_of_fade_out
output_array["fade_out"]=fade_out ### ADDED VALUE TO ARRAY
# tatums
tatums=hdf5_getters.get_tatums_start(h5)
num_tatums=len(tatums)
output_array["num_tatums"]=num_tatums ### ADDED VALUE TO ARRAY
# mean and variance in tatums length
tatums_length=numpy.ediff1d(tatums)
variance_tatums_length=numpy.var(tatums_length)
output_array["variance_tatums_length"]=variance_tatums_length ### ADDED VALUE TO ARRAY
# tempo
tempo=hdf5_getters.get_tempo(h5)
output_array["tempo"]=tempo ### ADDED VALUE TO ARRAY
# time signature
time_signature=hdf5_getters.get_time_signature(h5)
output_array["time_signature"]=int(time_signature) ### ADDED VALUE TO ARRAY
# year
year=hdf5_getters.get_year(h5)
output_array["year"]=int(year) ### ADDED VALUE TO ARRAY
# artist terms
artist_terms=hdf5_getters.get_artist_terms(h5,0)
output_array["artist_terms"]=artist_terms.tolist()
artist_terms_freq=hdf5_getters.get_artist_terms_freq(h5,0)
output_array["artist_terms_freq"]=artist_terms_freq.tolist()
artist_name=hdf5_getters.get_artist_name(h5,0)
output_array["artist_name"]=artist_name
artist_id=hdf5_getters.get_artist_id(h5,0)
output_array["artist_id"]=artist_id
# title
title=hdf5_getters.get_title(h5,0)
output_array["title"]=title
return output_array
def main():
dataset_dir = sys.argv[1]
feat =[]
feat1=[]
feat2=[]
feat3=[]
feat4=[]
print "Forming Dataset..."
listing1 = os.listdir(dataset_dir)
for a in listing1:
listing2 = os.listdir(dataset_dir+a+'/')
for b in listing2:
listing3 = os.listdir(dataset_dir+a+'/'+b+'/')
for c in listing3:
listing4 = os.listdir(dataset_dir+a+'/'+b+'/'+c+'/')
for d in listing4:
h5 = hdf5_getters.open_h5_file_read(dataset_dir+a+'/'+b+'/'+c+'/'+d)
feat =[]
feat1=[]
feat2=[]
feat3=[]
feat4=[]
temp = hdf5_getters.get_artist_hotttnesss(h5)
if (math.isnan(temp) or temp==0.0):
h5.close()
continue
temp = hdf5_getters.get_artist_familiarity(h5)
if (math.isnan(temp) or temp==0.0):
h5.close()
continue
temp = hdf5_getters.get_end_of_fade_in(h5)
if (math.isnan(temp)):
h5.close()
continue
temp = hdf5_getters.get_key_confidence(h5)
if (math.isnan(temp)):
h5.close()
continue
temp = hdf5_getters.get_loudness(h5)
if (math.isnan(temp)):
h5.close()
continue
temp = hdf5_getters.get_mode_confidence(h5)
if (math.isnan(temp)):
h5.close()
continue
temp = hdf5_getters.get_sections_confidence(h5)
if temp.size == 0:
h5.close()
continue
temp = hdf5_getters.get_segments_confidence(h5)
if temp.size == 0:
h5.close()
continue
temp = hdf5_getters.get_segments_loudness_max(h5)
if temp.size == 0:
h5.close()
continue
temp = hdf5_getters.get_segments_loudness_max_time(h5)
if temp.size == 0:
h5.close()
continue
temp = hdf5_getters.get_segments_pitches(h5)
if temp.size == 0:
h5.close()
continue
temp = hdf5_getters.get_segments_timbre(h5)
if temp.size == 0:
h5.close()
continue
temp = hdf5_getters.get_start_of_fade_out(h5)
if (math.isnan(temp)):
h5.close()
continue
temp = hdf5_getters.get_tempo(h5)
if (math.isnan(temp)):
h5.close()
continue
temp = hdf5_getters.get_time_signature_confidence(h5)
if (math.isnan(temp)):
h5.close()
continue
temp = hdf5_getters.get_year(h5)
if temp == 0:
h5.close()
continue
temp = hdf5_getters.get_artist_terms(h5)
if temp.size == 0:
h5.close()
continue
temp_ = hdf5_getters.get_artist_terms_weight(h5)
if temp_.size == 0:
continue
temp = hdf5_getters.get_bars_confidence(h5)
sz = temp.size
if sz<50:
h5.close()
continue
temp = hdf5_getters.get_beats_confidence(h5)
sz = temp.size
if sz <50:
h5.close()
continue
mm = np.mean(temp)
vv = np.var(temp)
if mm==0.0 and vv==0.0:
h5.close()
continue
temp = hdf5_getters.get_segments_confidence(h5)
sz = temp.size
if sz <50:
h5.close()
continue
temp = hdf5_getters.get_tatums_confidence(h5)
sz = temp.size
if sz <50:
h5.close()
continue
temp = hdf5_getters.get_song_hotttnesss(h5)
if (math.isnan(temp) or temp==0.0):
h5.close()
continue
temp = hdf5_getters.get_bars_confidence(h5)
sz = temp.size
sz1 = sz/50
i=1
j=0
while i<=50:
if i == 50:
sz2 = sz
else:
sz2 = i*sz1
num=0.0
acc = 0
while j<sz2:
acc += temp[j]
j+=1
num+=1.0
mm = acc/num
feat1.append(mm)
i+=1
temp = hdf5_getters.get_beats_confidence(h5)
sz = temp.size
sz1 = sz/50
i=1
j=0
while i<=50:
if i == 50:
sz2 = sz
else:
sz2 = i*sz1
num=0.0
acc = 0
while j<sz2:
acc += temp[j]
j+=1
num+=1.0
mm = acc/num
feat2.append(mm)
i+=1
temp = hdf5_getters.get_segments_confidence(h5)
sz = temp.size
sz1 = sz/50
i=1
j=0
while i<=50:
if i == 50:
sz2 = sz
else:
sz2 = i*sz1
num=0.0
acc = 0
while j<sz2:
acc += temp[j]
j+=1
num+=1.0
mm = acc/num
feat3.append(mm)
i+=1
temp = hdf5_getters.get_tatums_confidence(h5)
sz = temp.size
sz1 = sz/50
i=1
j=0
while i<=50:
if i == 50:
sz2 = sz
else:
sz2 = i*sz1
num=0.0
acc = 0
while j<sz2:
acc += temp[j]
j+=1
num+=1.0
mm = acc/num
feat4.append(mm)
i+=1
i=0
avg = 0.0
while i<50:
avg = (feat1[i] + feat2[i] + feat3[i] + feat4[i])/4.0
feat.append(avg)
i++
temp = hdf5_getters.get_song_hotttnesss(h5)
hott = 0
if temp >=0.75:
hott = 1
elif temp >=0.40 and temp <0.75:
hott = 2
else:
hott = 3
feat.append(hott)
h5.close()
count = 1
f=open('MSD_DATASET_LSTM.txt', 'a')
outstring=''
cnt = 0
feat_size = len(feat)
for i in feat:
cnt+=1
outstring+=str(i)
if (cnt!=feat_size):
outstring+=','
outstring+='\n'
f.write(outstring)
f.close()
def data_to_flat_file(basedir,ext='.h5') :
"""This function extract the information from the tables and creates the flat file."""
count = 0; #song counter
list_to_write= []
row_to_write = ""
writer = csv.writer(open("metadata.csv", "wb"))
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root,'*'+ext))
for f in files:
print f #the name of the file
h5 = hdf5_getters.open_h5_file_read(f)
title = hdf5_getters.get_title(h5)
title= title.replace('"','')
comma=title.find(',') #eliminating commas in the title
if comma != -1:
print title
time.sleep(1)
album = hdf5_getters.get_release(h5)
album= album.replace('"','') #eliminating commas in the album
comma=album.find(',')
if comma != -1:
print album
time.sleep(1)
artist_name = hdf5_getters.get_artist_name(h5)
comma=artist_name.find(',')
if comma != -1:
print artist_name
time.sleep(1)
artist_name= artist_name.replace('"','') #eliminating double quotes
duration = hdf5_getters.get_duration(h5)
samp_rt = hdf5_getters.get_analysis_sample_rate(h5)
artist_7digitalid = hdf5_getters.get_artist_7digitalid(h5)
artist_fam = hdf5_getters.get_artist_familiarity(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_fam) == True:
artist_fam=-1
artist_hotness= hdf5_getters.get_artist_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_hotness) == True:
artist_hotness=-1
artist_id = hdf5_getters.get_artist_id(h5)
artist_lat = hdf5_getters.get_artist_latitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lat) == True:
artist_lat=-1
artist_loc = hdf5_getters.get_artist_location(h5)
#checks artist_loc to see if it is a hyperlink if it is set as empty string
artist_loc = artist_loc.replace(",", "\,");
if artist_loc.startswith("<a"):
artist_loc = ""
if len(artist_loc) > 100:
artist_loc = ""
artist_lon = hdf5_getters.get_artist_longitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lon) == True:
artist_lon=-1
artist_mbid = hdf5_getters.get_artist_mbid(h5)
artist_pmid = hdf5_getters.get_artist_playmeid(h5)
audio_md5 = hdf5_getters.get_audio_md5(h5)
danceability = hdf5_getters.get_danceability(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(danceability) == True:
danceability=-1
end_fade_in =hdf5_getters.get_end_of_fade_in(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(end_fade_in) == True:
end_fade_in=-1
energy = hdf5_getters.get_energy(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(energy) == True:
energy=-1
song_key = hdf5_getters.get_key(h5)
key_c = hdf5_getters.get_key_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(key_c) == True:
key_c=-1
loudness = hdf5_getters.get_loudness(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(loudness) == True:
loudness=-1
mode = hdf5_getters.get_mode(h5)
mode_conf = hdf5_getters.get_mode_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(mode_conf) == True:
mode_conf=-1
release_7digitalid = hdf5_getters.get_release_7digitalid(h5)
song_hot = hdf5_getters.get_song_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(song_hot) == True:
song_hot=-1
song_id = hdf5_getters.get_song_id(h5)
start_fade_out = hdf5_getters.get_start_of_fade_out(h5)
tempo = hdf5_getters.get_tempo(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(tempo) == True:
tempo=-1
time_sig = hdf5_getters.get_time_signature(h5)
time_sig_c = hdf5_getters.get_time_signature_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(time_sig_c) == True:
time_sig_c=-1
track_id = hdf5_getters.get_track_id(h5)
track_7digitalid = hdf5_getters.get_track_7digitalid(h5)
year = hdf5_getters.get_year(h5)
bars_c = hdf5_getters.get_bars_confidence(h5)
bars_c_avg= get_avg(bars_c)
bars_c_max= get_max(bars_c)
bars_c_min = get_min(bars_c)
bars_c_stddev= get_stddev(bars_c)
bars_c_count = get_count(bars_c)
bars_c_sum = get_sum(bars_c)
bars_start = hdf5_getters.get_bars_start(h5)
bars_start_avg = get_avg(bars_start)
bars_start_max= get_max(bars_start)
bars_start_min = get_min(bars_start)
bars_start_stddev= get_stddev(bars_start)
bars_start_count = get_count(bars_start)
bars_start_sum = get_sum(bars_start)
beats_c = hdf5_getters.get_beats_confidence(h5)
beats_c_avg= get_avg(beats_c)
beats_c_max= get_max(beats_c)
beats_c_min = get_min(beats_c)
beats_c_stddev= get_stddev(beats_c)
beats_c_count = get_count(beats_c)
beats_c_sum = get_sum(beats_c)
beats_start = hdf5_getters.get_beats_start(h5)
beats_start_avg = get_avg(beats_start)
beats_start_max= get_max(beats_start)
beats_start_min = get_min(beats_start)
beats_start_stddev= get_stddev(beats_start)
beats_start_count = get_count(beats_start)
beats_start_sum = get_sum(beats_start)
sec_c = hdf5_getters.get_sections_confidence(h5)
sec_c_avg= get_avg(sec_c)
sec_c_max= get_max(sec_c)
sec_c_min = get_min(sec_c)
sec_c_stddev= get_stddev(sec_c)
sec_c_count = get_count(sec_c)
sec_c_sum = get_sum(sec_c)
sec_start = hdf5_getters.get_sections_start(h5)
sec_start_avg = get_avg(sec_start)
sec_start_max= get_max(sec_start)
sec_start_min = get_min(sec_start)
sec_start_stddev= get_stddev(sec_start)
sec_start_count = get_count(sec_start)
sec_start_sum = get_sum(sec_start)
seg_c = hdf5_getters.get_segments_confidence(h5)
seg_c_avg= get_avg(seg_c)
seg_c_max= get_max(seg_c)
seg_c_min = get_min(seg_c)
seg_c_stddev= get_stddev(seg_c)
seg_c_count = get_count(seg_c)
seg_c_sum = get_sum(seg_c)
seg_loud_max = hdf5_getters.get_segments_loudness_max(h5)
seg_loud_max_avg= get_avg(seg_loud_max)
seg_loud_max_max= get_max(seg_loud_max)
seg_loud_max_min = get_min(seg_loud_max)
seg_loud_max_stddev= get_stddev(seg_loud_max)
seg_loud_max_count = get_count(seg_loud_max)
seg_loud_max_sum = get_sum(seg_loud_max)
seg_loud_max_time = hdf5_getters.get_segments_loudness_max_time(h5)
seg_loud_max_time_avg= get_avg(seg_loud_max_time)
seg_loud_max_time_max= get_max(seg_loud_max_time)
seg_loud_max_time_min = get_min(seg_loud_max_time)
seg_loud_max_time_stddev= get_stddev(seg_loud_max_time)
seg_loud_max_time_count = get_count(seg_loud_max_time)
seg_loud_max_time_sum = get_sum(seg_loud_max_time)
seg_loud_start = hdf5_getters.get_segments_loudness_start(h5)
seg_loud_start_avg= get_avg(seg_loud_start)
seg_loud_start_max= get_max(seg_loud_start)
seg_loud_start_min = get_min(seg_loud_start)
seg_loud_start_stddev= get_stddev(seg_loud_start)
seg_loud_start_count = get_count(seg_loud_start)
seg_loud_start_sum = get_sum(seg_loud_start)
seg_pitch = hdf5_getters.get_segments_pitches(h5)
pitch_size = len(seg_pitch)
seg_start = hdf5_getters.get_segments_start(h5)
seg_start_avg= get_avg(seg_start)
seg_start_max= get_max(seg_start)
seg_start_min = get_min(seg_start)
seg_start_stddev= get_stddev(seg_start)
seg_start_count = get_count(seg_start)
seg_start_sum = get_sum(seg_start)
seg_timbre = hdf5_getters.get_segments_timbre(h5)
tatms_c = hdf5_getters.get_tatums_confidence(h5)
tatms_c_avg= get_avg(tatms_c)
tatms_c_max= get_max(tatms_c)
tatms_c_min = get_min(tatms_c)
tatms_c_stddev= get_stddev(tatms_c)
tatms_c_count = get_count(tatms_c)
tatms_c_sum = get_sum(tatms_c)
tatms_start = hdf5_getters.get_tatums_start(h5)
tatms_start_avg= get_avg(tatms_start)
tatms_start_max= get_max(tatms_start)
tatms_start_min = get_min(tatms_start)
tatms_start_stddev= get_stddev(tatms_start)
tatms_start_count = get_count(tatms_start)
tatms_start_sum = get_sum(tatms_start)
#Getting the genres
genre_set = 0 #flag to see if the genre has been set or not
art_trm = hdf5_getters.get_artist_terms(h5)
trm_freq = hdf5_getters.get_artist_terms_freq(h5)
trn_wght = hdf5_getters.get_artist_terms_weight(h5)
a_mb_tags = hdf5_getters.get_artist_mbtags(h5)
genre_indexes=get_genre_indexes(trm_freq) #index of the highest freq
final_genre=[]
genres_so_far=[]
for i in range(len(genre_indexes)):
genre_tmp=get_genre(art_trm,genre_indexes[i]) #genre that corresponds to the highest freq
genres_so_far=genre_dict.get_genre_in_dict(genre_tmp) #getting the genre from the dictionary
if len(genres_so_far) != 0:
for i in genres_so_far:
final_genre.append(i)
genre_set=1 #genre was found in dictionary
if genre_set == 1:
col_num=[]
for genre in final_genre:
column=int(genre) #getting the column number of the genre
col_num.append(column)
genre_array=genre_columns(col_num) #genre array
else:
genre_array=genre_columns(-1) #the genre was not found in the dictionary
transpose_pitch= seg_pitch.transpose() #this is to tranpose the matrix,so we can have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_pitch_avg=[]
seg_pitch_max=[]
seg_pitch_min=[]
seg_pitch_stddev=[]
seg_pitch_count=[]
seg_pitch_sum=[]
i=0
#Getting the aggregate values in the pitches array
for row in transpose_pitch:
seg_pitch_avg.append(get_avg(row))
seg_pitch_max.append(get_max(row))
seg_pitch_min.append(get_min(row))
seg_pitch_stddev.append(get_stddev(row))
seg_pitch_count.append(get_count(row))
seg_pitch_sum.append(get_sum(row))
i=i+1
#extracting information from the timbre array
transpose_timbre = seg_pitch.transpose() #tranposing matrix, to have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_timbre_avg=[]
seg_timbre_max=[]
seg_timbre_min=[]
seg_timbre_stddev=[]
seg_timbre_count=[]
seg_timbre_sum=[]
i=0
for row in transpose_timbre:
seg_timbre_avg.append(get_avg(row))
seg_timbre_max.append(get_max(row))
seg_timbre_min.append(get_min(row))
seg_timbre_stddev.append(get_stddev(row))
seg_timbre_count.append(get_count(row))
seg_timbre_sum.append(get_sum(row))
i=i+1
#Writing to the flat file
writer.writerow([title,album,artist_name,duration,samp_rt,artist_7digitalid,artist_fam,artist_hotness,artist_id,artist_lat,artist_loc,artist_lon,artist_mbid,genre_array[0],genre_array[1],genre_array[2],
genre_array[3],genre_array[4],genre_array[5],genre_array[6],genre_array[7],genre_array[8],genre_array[9],genre_array[10],genre_array[11],genre_array[12],genre_array[13],genre_array[14],genre_array[15],
genre_array[16],genre_array[17],genre_array[18],genre_array[19],genre_array[20],genre_array[21],genre_array[22],genre_array[23],genre_array[24],genre_array[25],genre_array[26],
genre_array[27],genre_array[28],genre_array[29],genre_array[30],genre_array[31],genre_array[32],genre_array[33],genre_array[34],genre_array[35],genre_array[36],genre_array[37],genre_array[38],
genre_array[39],genre_array[40],genre_array[41],genre_array[42],genre_array[43],genre_array[44],genre_array[45],genre_array[46],genre_array[47],genre_array[48],genre_array[49],
genre_array[50],genre_array[51],genre_array[52],genre_array[53],genre_array[54],genre_array[55],genre_array[56],genre_array[57],genre_array[58],genre_array[59],
genre_array[60],genre_array[61],genre_array[62],genre_array[63],genre_array[64],genre_array[65],genre_array[66],genre_array[67],genre_array[68],genre_array[69],
genre_array[70],genre_array[71],genre_array[72],genre_array[73],genre_array[74],genre_array[75],genre_array[76],genre_array[77],genre_array[78],genre_array[79],
genre_array[80],genre_array[81],genre_array[82],genre_array[83],genre_array[84],genre_array[85],genre_array[86],genre_array[87],genre_array[88],genre_array[89],
genre_array[90],genre_array[91],genre_array[92],genre_array[93],genre_array[94],genre_array[95],genre_array[96],genre_array[97],genre_array[98],genre_array[99],genre_array[100],genre_array[101],
genre_array[102],genre_array[103],genre_array[104],genre_array[105],genre_array[106],genre_array[107],genre_array[108],genre_array[109],genre_array[110],genre_array[111],genre_array[112],
genre_array[113],genre_array[114],genre_array[115],genre_array[116],genre_array[117],genre_array[118],genre_array[119],genre_array[120],genre_array[121],genre_array[122],genre_array[123],
genre_array[124],genre_array[125],genre_array[126],genre_array[127],genre_array[128],genre_array[129],genre_array[130],genre_array[131],genre_array[132],
artist_pmid,audio_md5,danceability,end_fade_in,energy,song_key,key_c,loudness,mode,mode_conf,release_7digitalid,song_hot,song_id,start_fade_out,tempo,time_sig,time_sig_c,track_id,track_7digitalid,year,bars_c_avg,bars_c_max,bars_c_min,bars_c_stddev,bars_c_count,bars_c_sum,bars_start_avg,bars_start_max,bars_start_min,bars_start_stddev,bars_start_count,bars_start_sum,beats_c_avg,beats_c_max,beats_c_min,beats_c_stddev,beats_c_count,beats_c_sum,beats_start_avg,beats_start_max,beats_start_min, beats_start_stddev,beats_start_count,beats_start_sum, sec_c_avg,sec_c_max,sec_c_min,sec_c_stddev,sec_c_count,sec_c_sum,sec_start_avg,sec_start_max,sec_start_min,sec_start_stddev,sec_start_count,sec_start_sum,seg_c_avg,seg_c_max,seg_c_min,seg_c_stddev,seg_c_count,seg_c_sum,seg_loud_max_avg,seg_loud_max_max,seg_loud_max_min,seg_loud_max_stddev,seg_loud_max_count,seg_loud_max_sum,seg_loud_max_time_avg,seg_loud_max_time_max,seg_loud_max_time_min,seg_loud_max_time_stddev,seg_loud_max_time_count,seg_loud_max_time_sum,seg_loud_start_avg,seg_loud_start_max,seg_loud_start_min,seg_loud_start_stddev,seg_loud_start_count,seg_loud_start_sum,seg_pitch_avg[0],seg_pitch_max[0],seg_pitch_min[0],seg_pitch_stddev[0],seg_pitch_count[0],seg_pitch_sum[0],seg_pitch_avg[1],seg_pitch_max[1],seg_pitch_min[1],seg_pitch_stddev[1],seg_pitch_count[1],seg_pitch_sum[1],seg_pitch_avg[2],seg_pitch_max[2],seg_pitch_min[2],seg_pitch_stddev[2],seg_pitch_count[2],seg_pitch_sum[2],seg_pitch_avg[3],seg_pitch_max[3],seg_pitch_min[3],seg_pitch_stddev[3],seg_pitch_count[3],seg_pitch_sum[3],seg_pitch_avg[4],seg_pitch_max[4],seg_pitch_min[4],seg_pitch_stddev[4],seg_pitch_count[4],seg_pitch_sum[4],seg_pitch_avg[5],seg_pitch_max[5],seg_pitch_min[5],seg_pitch_stddev[5],seg_pitch_count[5],seg_pitch_sum[5],seg_pitch_avg[6],seg_pitch_max[6],seg_pitch_min[6],seg_pitch_stddev[6],seg_pitch_count[6],seg_pitch_sum[6],seg_pitch_avg[7],seg_pitch_max[7],seg_pitch_min[7],seg_pitch_stddev[7],seg_pitch_count[7],seg_pitch_sum[7],seg_pitch_avg[8],seg_pitch_max[8],seg_pitch_min[8],seg_pitch_stddev[8],seg_pitch_count[8],seg_pitch_sum[8],seg_pitch_avg[9],seg_pitch_max[9],seg_pitch_min[9],seg_pitch_stddev[9],seg_pitch_count[9],seg_pitch_sum[9],seg_pitch_avg[10],seg_pitch_max[10],seg_pitch_min[10],seg_pitch_stddev[10],seg_pitch_count[10],seg_pitch_sum[10],seg_pitch_avg[11],seg_pitch_max[11],seg_pitch_min[11],
seg_pitch_stddev[11],seg_pitch_count[11],seg_pitch_sum[11],seg_start_avg,seg_start_max,seg_start_min,seg_start_stddev,
seg_start_count,seg_start_sum,seg_timbre_avg[0],seg_timbre_max[0],seg_timbre_min[0],seg_timbre_stddev[0],seg_timbre_count[0],
seg_timbre_sum[0],seg_timbre_avg[1],seg_timbre_max[1],seg_timbre_min[1],seg_timbre_stddev[1],seg_timbre_count[1],
seg_timbre_sum[1],seg_timbre_avg[2],seg_timbre_max[2],seg_timbre_min[2],seg_timbre_stddev[2],seg_timbre_count[2],
seg_timbre_sum[2],seg_timbre_avg[3],seg_timbre_max[3],seg_timbre_min[3],seg_timbre_stddev[3],seg_timbre_count[3],
seg_timbre_sum[3],seg_timbre_avg[4],seg_timbre_max[4],seg_timbre_min[4],seg_timbre_stddev[4],seg_timbre_count[4],
seg_timbre_sum[4],seg_timbre_avg[5],seg_timbre_max[5],seg_timbre_min[5],seg_timbre_stddev[5],seg_timbre_count[5],
seg_timbre_sum[5],seg_timbre_avg[6],seg_timbre_max[6],seg_timbre_min[6],seg_timbre_stddev[6],seg_timbre_count[6],
seg_timbre_sum[6],seg_timbre_avg[7],seg_timbre_max[7],seg_timbre_min[7],seg_timbre_stddev[7],seg_timbre_count[7],
seg_timbre_sum[7],seg_timbre_avg[8],seg_timbre_max[8],seg_timbre_min[8],seg_timbre_stddev[8],seg_timbre_count[8],
seg_timbre_sum[8],seg_timbre_avg[9],seg_timbre_max[9],seg_timbre_min[9],seg_timbre_stddev[9],seg_timbre_count[9],
seg_timbre_sum[9],seg_timbre_avg[10],seg_timbre_max[10],seg_timbre_min[10],seg_timbre_stddev[10],seg_timbre_count[10],
seg_timbre_sum[10],seg_timbre_avg[11],seg_timbre_max[11],seg_timbre_min[11],seg_timbre_stddev[11],seg_timbre_count[11],
seg_timbre_sum[11],tatms_c_avg,tatms_c_max,tatms_c_min,tatms_c_stddev,tatms_c_count,tatms_c_sum,tatms_start_avg,tatms_start_max,tatms_start_min,tatms_start_stddev,tatms_start_count,tatms_start_sum])
h5.close()
count=count+1;
print count;
song.artistTermsFreq = remove_trap_characters(
str(list(hdf5_getters.get_artist_terms_freq(songH5File))))
song.artistTermsWeight = remove_trap_characters(
str(list(hdf5_getters.get_artist_terms_weight(songH5File))))
temp = hdf5_getters.get_artist_mbtags(songH5File)
song.artistMBTags = remove_trap_characters(str(list(temp)))
song.artistMBTagsOuterCount = get_list_length(temp)
song.artistMBTagsCount = remove_trap_characters(
str(list(hdf5_getters.get_artist_mbtags_count(songH5File))))
song.analysisSampleRate = remove_trap_characters(
str(hdf5_getters.get_analysis_sample_rate(songH5File)))
song.audioMD5 = remove_trap_characters(
str(hdf5_getters.get_audio_md5(songH5File)))
song.endOfFadeIn = remove_trap_characters(
str(hdf5_getters.get_end_of_fade_in(songH5File)))
song.startOfFadeOut = remove_trap_characters(
str(hdf5_getters.get_start_of_fade_out(songH5File)))
song.energy = remove_trap_characters(
str(hdf5_getters.get_energy(songH5File)))
song.release = remove_trap_characters(
str(hdf5_getters.get_release(songH5File)))
song.release7digitalid = remove_trap_characters(
str(hdf5_getters.get_release_7digitalid(songH5File)))
song.songHotness = remove_trap_characters(
str(hdf5_getters.get_song_hotttnesss(songH5File)))
song.track7digitalid = remove_trap_characters(
str(hdf5_getters.get_track_7digitalid(songH5File)))
temp = hdf5_getters.get_similar_artists(songH5File)
song.similarartists = remove_trap_characters(str(list(list(temp))))
def data_to_flat_file(basedir, ext='.h5'):
""" This function extracts the information from the tables and creates the flat file. """
count = 0
#song counter
list_to_write = []
group_index = 0
row_to_write = ""
writer = csv.writer(open("complete.csv", "wb"))
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
for f in files:
row = []
print f
h5 = hdf5_getters.open_h5_file_read(f)
title = hdf5_getters.get_title(h5)
title = title.replace('"', '')
row.append(title)
comma = title.find(',')
if comma != -1:
print title
time.sleep(1)
album = hdf5_getters.get_release(h5)
album = album.replace('"', '')
row.append(album)
comma = album.find(',')
if comma != -1:
print album
time.sleep(1)
artist_name = hdf5_getters.get_artist_name(h5)
comma = artist_name.find(',')
if comma != -1:
print artist_name
time.sleep(1)
artist_name = artist_name.replace('"', '')
row.append(artist_name)
duration = hdf5_getters.get_duration(h5)
row.append(duration)
samp_rt = hdf5_getters.get_analysis_sample_rate(h5)
row.append(samp_rt)
artist_7digitalid = hdf5_getters.get_artist_7digitalid(h5)
row.append(artist_7digitalid)
artist_fam = hdf5_getters.get_artist_familiarity(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_fam) == True:
artist_fam = -1
row.append(artist_fam)
artist_hotness = hdf5_getters.get_artist_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_hotness) == True:
artist_hotness = -1
row.append(artist_hotness)
artist_id = hdf5_getters.get_artist_id(h5)
row.append(artist_id)
artist_lat = hdf5_getters.get_artist_latitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lat) == True:
artist_lat = -1
row.append(artist_lat)
artist_loc = hdf5_getters.get_artist_location(h5)
row.append(artist_loc)
artist_lon = hdf5_getters.get_artist_longitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lon) == True:
artist_lon = -1
row.append(artist_lon)
artist_mbid = hdf5_getters.get_artist_mbid(h5)
row.append(artist_mbid)
#Getting the genre
art_trm = hdf5_getters.get_artist_terms(h5)
trm_freq = hdf5_getters.get_artist_terms_freq(h5)
trn_wght = hdf5_getters.get_artist_terms_weight(h5)
a_mb_tags = hdf5_getters.get_artist_mbtags(h5)
genre_indexes = get_genre_indexes(
trm_freq) #index of the highest freq
genre_set = 0 #flag to see if the genre has been set or not
final_genre = []
genres_so_far = []
for i in range(len(genre_indexes)):
genre_tmp = get_genre(
art_trm, genre_indexes[i]
) #genre that corresponds to the highest freq
genres_so_far = genre_dict.get_genre_in_dict(
genre_tmp) #getting the genre from the dictionary
if len(genres_so_far) != 0:
for i in genres_so_far:
final_genre.append(i)
genre_set = 1
if genre_set == 1:
col_num = []
for i in final_genre:
column = int(i) #getting the column number of the genre
col_num.append(column)
genre_array = genre_columns(col_num) #genre array
for i in range(len(
genre_array)): #appending the genre_array to the row
row.append(genre_array[i])
else:
genre_array = genre_columns(
-1
) #when there is no genre matched, return an array of [0...0]
for i in range(len(
genre_array)): #appending the genre_array to the row
row.append(genre_array[i])
artist_pmid = hdf5_getters.get_artist_playmeid(h5)
row.append(artist_pmid)
audio_md5 = hdf5_getters.get_audio_md5(h5)
row.append(audio_md5)
danceability = hdf5_getters.get_danceability(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(danceability) == True:
danceability = -1
row.append(danceability)
end_fade_in = hdf5_getters.get_end_of_fade_in(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(end_fade_in) == True:
end_fade_in = -1
row.append(end_fade_in)
energy = hdf5_getters.get_energy(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(energy) == True:
energy = -1
row.append(energy)
song_key = hdf5_getters.get_key(h5)
row.append(song_key)
key_c = hdf5_getters.get_key_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(key_c) == True:
key_c = -1
row.append(key_c)
loudness = hdf5_getters.get_loudness(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(loudness) == True:
loudness = -1
row.append(loudness)
mode = hdf5_getters.get_mode(h5)
row.append(mode)
mode_conf = hdf5_getters.get_mode_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(mode_conf) == True:
mode_conf = -1
row.append(mode_conf)
release_7digitalid = hdf5_getters.get_release_7digitalid(h5)
row.append(release_7digitalid)
song_hot = hdf5_getters.get_song_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(song_hot) == True:
song_hot = -1
row.append(song_hot)
song_id = hdf5_getters.get_song_id(h5)
row.append(song_id)
start_fade_out = hdf5_getters.get_start_of_fade_out(h5)
row.append(start_fade_out)
tempo = hdf5_getters.get_tempo(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(tempo) == True:
tempo = -1
row.append(tempo)
time_sig = hdf5_getters.get_time_signature(h5)
row.append(time_sig)
time_sig_c = hdf5_getters.get_time_signature_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(time_sig_c) == True:
time_sig_c = -1
row.append(time_sig_c)
track_id = hdf5_getters.get_track_id(h5)
row.append(track_id)
track_7digitalid = hdf5_getters.get_track_7digitalid(h5)
row.append(track_7digitalid)
year = hdf5_getters.get_year(h5)
row.append(year)
bars_c = hdf5_getters.get_bars_confidence(h5)
bars_start = hdf5_getters.get_bars_start(h5)
row_bars_padding = padding(
245
) #this is the array that will be attached at the end of th row
#--------------bars---------------"
gral_info = []
gral_info = row[:]
empty = []
for i, item in enumerate(bars_c):
row.append(group_index)
row.append(i)
row.append(bars_c[i])
bars_c_avg = get_avg(bars_c)
row.append(bars_c_avg)
bars_c_max = get_max(bars_c)
row.append(bars_c_max)
bars_c_min = get_min(bars_c)
row.append(bars_c_min)
bars_c_stddev = get_stddev(bars_c)
row.append(bars_c_stddev)
bars_c_count = get_count(bars_c)
row.append(bars_c_count)
bars_c_sum = get_sum(bars_c)
row.append(bars_c_sum)
row.append(bars_start[i])
bars_start_avg = get_avg(bars_start)
row.append(bars_start_avg)
bars_start_max = get_max(bars_start)
row.append(bars_start_max)
bars_start_min = get_min(bars_start)
row.append(bars_start_min)
bars_start_stddev = get_stddev(bars_start)
row.append(bars_start_stddev)
bars_start_count = get_count(bars_start)
row.append(bars_start_count)
bars_start_sum = get_sum(bars_start)
row.append(bars_start_sum)
for i in row_bars_padding:
row.append(i)
writer.writerow(row)
row = []
row = gral_info[:]
#--------beats---------------"
beats_c = hdf5_getters.get_beats_confidence(h5)
group_index = 1
row = []
row = gral_info[:]
row_front = padding(
14) #blanks left in front of the row(empty spaces for bars)
row_beats_padding = padding(231)
for i, item in enumerate(beats_c):
row.append(group_index)
row.append(i)
for index in row_front: #padding blanks in front of the beats
row.append(index)
row.append(beats_c[i])
beats_c_avg = get_avg(beats_c)
row.append(beats_c_avg)
beats_c_max = get_max(beats_c)
row.append(beats_c_max)
beats_c_min = get_min(beats_c)
row.append(beats_c_min)
beats_c_stddev = get_stddev(beats_c)
row.append(beats_c_stddev)
beats_c_count = get_count(beats_c)
row.append(beats_c_count)
beats_c_sum = get_sum(beats_c)
row.append(beats_c_sum)
beats_start = hdf5_getters.get_beats_start(h5)
row.append(beats_start[i])
beats_start_avg = get_avg(beats_start)
row.append(beats_start_avg)
beats_start_max = get_max(beats_start)
row.append(beats_start_max)
beats_start_min = get_min(beats_start)
row.append(beats_start_min)
beats_start_stddev = get_stddev(beats_start)
row.append(beats_start_stddev)
beats_start_count = get_count(beats_start)
row.append(beats_start_count)
beats_start_sum = get_sum(beats_start)
row.append(beats_start_sum)
for i in row_beats_padding:
row.append(i)
writer.writerow(row)
row = []
row = gral_info[:]
# "--------sections---------------"
row_sec_padding = padding(
217) #blank spaces left at the end of the row
sec_c = hdf5_getters.get_sections_confidence(h5)
group_index = 2
row = []
row = gral_info[:]
row_front = padding(
28) #blank spaces left in front(empty spaces for bars,beats)
for i, item in enumerate(sec_c):
row.append(group_index)
row.append(i)
for index in row_front: #padding blanks in front of the sections
row.append(index)
row.append(sec_c[i])
sec_c_avg = get_avg(sec_c)
row.append(sec_c_avg)
sec_c_max = get_max(sec_c)
row.append(sec_c_max)
sec_c_min = get_min(sec_c)
row.append(sec_c_min)
sec_c_stddev = get_stddev(sec_c)
row.append(sec_c_stddev)
sec_c_count = get_count(sec_c)
row.append(sec_c_count)
sec_c_sum = get_sum(sec_c)
row.append(sec_c_sum)
sec_start = hdf5_getters.get_sections_start(h5)
row.append(sec_start[i])
sec_start_avg = get_avg(sec_start)
row.append(sec_start_avg)
sec_start_max = get_max(sec_start)
row.append(sec_start_max)
sec_start_min = get_min(sec_start)
row.append(sec_start_min)
sec_start_stddev = get_stddev(sec_start)
row.append(sec_start_stddev)
sec_start_count = get_count(sec_start)
row.append(sec_start_count)
sec_start_sum = get_sum(sec_start)
row.append(sec_start_sum)
for i in row_sec_padding: #appending the blank spaces at the end of the row
row.append(i)
writer.writerow(row)
row = []
row = gral_info[:]
#--------segments---------------"
row_seg_padding = padding(182) #blank spaces at the end of the row
row_front = padding(42) #blank spaces left in front of segments
seg_c = hdf5_getters.get_segments_confidence(h5)
group_index = 3
row = []
row = gral_info[:]
for i, item in enumerate(seg_c):
row.append(group_index)
row.append(i)
for index in row_front: #padding blanks in front of the segments
row.append(index)
row.append(seg_c[i])
seg_c_avg = get_avg(seg_c)
row.append(seg_c_avg)
seg_c_max = get_max(seg_c)
row.append(seg_c_max)
seg_c_min = get_min(seg_c)
row.append(seg_c_min)
seg_c_stddev = get_stddev(seg_c)
row.append(seg_c_stddev)
seg_c_count = get_count(seg_c)
row.append(seg_c_count)
seg_c_sum = get_sum(seg_c)
row.append(seg_c_sum)
seg_loud_max = hdf5_getters.get_segments_loudness_max(h5)
row.append(seg_loud_max[i])
seg_loud_max_avg = get_avg(seg_loud_max)
row.append(seg_loud_max_avg)
seg_loud_max_max = get_max(seg_loud_max)
row.append(seg_loud_max_max)
seg_loud_max_min = get_min(seg_loud_max)
row.append(seg_loud_max_min)
seg_loud_max_stddev = get_stddev(seg_loud_max)
row.append(seg_loud_max_stddev)
seg_loud_max_count = get_count(seg_loud_max)
row.append(seg_loud_max_count)
seg_loud_max_sum = get_sum(seg_loud_max)
row.append(seg_loud_max_sum)
seg_loud_max_time = hdf5_getters.get_segments_loudness_max_time(
h5)
row.append(seg_loud_max_time[i])
seg_loud_max_time_avg = get_avg(seg_loud_max_time)
row.append(seg_loud_max_time_avg)
seg_loud_max_time_max = get_max(seg_loud_max_time)
row.append(seg_loud_max_time_max)
seg_loud_max_time_min = get_min(seg_loud_max_time)
row.append(seg_loud_max_time_min)
seg_loud_max_time_stddev = get_stddev(seg_loud_max_time)
row.append(seg_loud_max_time_stddev)
seg_loud_max_time_count = get_count(seg_loud_max_time)
row.append(seg_loud_max_time_count)
seg_loud_max_time_sum = get_sum(seg_loud_max_time)
row.append(seg_loud_max_time_sum)
seg_loud_start = hdf5_getters.get_segments_loudness_start(h5)
row.append(seg_loud_start[i])
seg_loud_start_avg = get_avg(seg_loud_start)
row.append(seg_loud_start_avg)
seg_loud_start_max = get_max(seg_loud_start)
row.append(seg_loud_start_max)
seg_loud_start_min = get_min(seg_loud_start)
row.append(seg_loud_start_min)
seg_loud_start_stddev = get_stddev(seg_loud_start)
row.append(seg_loud_start_stddev)
seg_loud_start_count = get_count(seg_loud_start)
row.append(seg_loud_start_count)
seg_loud_start_sum = get_sum(seg_loud_start)
row.append(seg_loud_start_sum)
seg_start = hdf5_getters.get_segments_start(h5)
row.append(seg_start[i])
seg_start_avg = get_avg(seg_start)
row.append(seg_start_avg)
seg_start_max = get_max(seg_start)
row.append(seg_start_max)
seg_start_min = get_min(seg_start)
row.append(seg_start_min)
seg_start_stddev = get_stddev(seg_start)
row.append(seg_start_stddev)
seg_start_count = get_count(seg_start)
row.append(seg_start_count)
seg_start_sum = get_sum(seg_start)
row.append(seg_start_sum)
for i in row_seg_padding: #appending blank spaces at the end of the row
row.append(i)
writer.writerow(row)
row = []
row = gral_info[:]
#----------segments pitch and timbre---------------"
row_seg2_padding = padding(
14) #blank spaces left at the end of the row
row_front = padding(
77) #blank spaces left at the front of the segments and timbre
seg_pitch = hdf5_getters.get_segments_pitches(h5)
transpose_pitch = seg_pitch.transpose(
) #this is to tranpose the matrix,so we can have 12 rows
group_index = 4
row = []
row = gral_info[:]
for i, item in enumerate(transpose_pitch[0]):
row.append(group_index)
row.append(i)
for index in row_front: #padding blanks in front of segments and timbre
row.append(index)
row.append(transpose_pitch[0][i])
seg_pitch_avg = get_avg(transpose_pitch[0])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[0])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[0])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[0])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[0])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[0])
row.append(seg_pitch_sum)
row.append(transpose_pitch[1][i])
seg_pitch_avg = get_avg(transpose_pitch[1])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[1])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[1])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[1])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[1])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[1])
row.append(seg_pitch_sum)
row.append(transpose_pitch[2][i])
seg_pitch_avg = get_avg(transpose_pitch[2])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[2])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[2])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[2])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[2])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[2])
row.append(seg_pitch_sum)
row.append(transpose_pitch[3][i])
seg_pitch_avg = get_avg(transpose_pitch[3])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[3])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[3])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[3])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[3])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[3])
row.append(seg_pitch_sum)
row.append(transpose_pitch[4][i])
seg_pitch_avg = get_avg(transpose_pitch[4])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[4])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[4])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[4])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[4])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[4])
row.append(seg_pitch_sum)
row.append(transpose_pitch[5][i])
seg_pitch_avg = get_avg(transpose_pitch[5])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[5])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[5])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[5])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[5])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[5])
row.append(seg_pitch_sum)
row.append(transpose_pitch[6][i])
seg_pitch_avg = get_avg(transpose_pitch[6])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[6])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[6])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[6])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[6])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[6])
row.append(seg_pitch_sum)
row.append(transpose_pitch[7][i])
seg_pitch_avg = get_avg(transpose_pitch[7])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[7])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[7])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[7])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[7])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[7])
row.append(seg_pitch_sum)
row.append(transpose_pitch[8][i])
seg_pitch_avg = get_avg(transpose_pitch[8])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[8])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[8])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[8])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[8])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[8])
row.append(seg_pitch_sum)
row.append(transpose_pitch[9][i])
seg_pitch_avg = get_avg(transpose_pitch[9])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[9])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[9])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[9])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[9])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[9])
row.append(seg_pitch_sum)
row.append(transpose_pitch[10][i])
seg_pitch_avg = get_avg(transpose_pitch[10])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[10])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[10])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[10])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[10])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[10])
row.append(seg_pitch_sum)
row.append(transpose_pitch[11][i])
seg_pitch_avg = get_avg(transpose_pitch[11])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[11])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[11])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[11])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[11])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[11])
row.append(seg_pitch_sum)
#timbre arrays
seg_timbre = hdf5_getters.get_segments_timbre(h5)
transpose_timbre = seg_pitch.transpose(
) #tranposing matrix, to have 12 rows
row.append(transpose_timbre[0][i])
seg_timbre_avg = get_avg(transpose_timbre[0])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[0])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[0])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[0])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[0])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[0])
row.append(seg_timbre_sum)
row.append(transpose_timbre[1][i])
seg_timbre_avg = get_avg(transpose_timbre[1])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[1])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[1])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[1])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[1])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[1])
row.append(seg_timbre_sum)
row.append(transpose_timbre[2][i])
seg_timbre_avg = get_avg(transpose_timbre[2])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[2])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[2])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[2])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[2])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[2])
row.append(seg_timbre_sum)
row.append(transpose_timbre[3][i])
seg_timbre_avg = get_avg(transpose_timbre[3])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[3])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[3])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[3])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[3])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[3])
row.append(seg_timbre_sum)
row.append(transpose_timbre[4][i])
seg_timbre_avg = get_avg(transpose_timbre[4])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[4])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[4])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[4])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[4])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[4])
row.append(seg_timbre_sum)
row.append(transpose_timbre[5][i])
seg_timbre_avg = get_avg(transpose_timbre[5])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[5])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[5])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[5])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[5])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[5])
row.append(seg_timbre_sum)
row.append(transpose_timbre[6][i])
seg_timbre_avg = get_avg(transpose_timbre[6])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[6])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[6])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[6])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[6])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[6])
row.append(seg_timbre_sum)
row.append(transpose_timbre[7][i])
seg_timbre_avg = get_avg(transpose_timbre[7])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[7])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[7])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[7])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[7])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[7])
row.append(seg_timbre_sum)
row.append(transpose_timbre[8][i])
seg_timbre_avg = get_avg(transpose_timbre[8])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[8])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[8])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[8])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[8])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[8])
row.append(seg_timbre_sum)
row.append(transpose_timbre[9][i])
seg_timbre_avg = get_avg(transpose_timbre[9])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[9])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[9])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[9])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[9])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[9])
row.append(seg_timbre_sum)
row.append(transpose_timbre[10][i])
seg_timbre_avg = get_avg(transpose_timbre[10])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[10])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[10])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[10])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[10])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[10])
row.append(seg_timbre_sum)
row.append(transpose_timbre[11][i])
seg_timbre_avg = get_avg(transpose_timbre[11])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[11])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[11])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[11])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[11])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[11])
row.append(seg_timbre_sum)
for item in row_seg2_padding:
row.append(item)
writer.writerow(row)
row = []
row = gral_info[:]
# "--------tatums---------------"
tatms_c = hdf5_getters.get_tatums_confidence(h5)
group_index = 5
row_front = padding(245) #blank spaces left in front of tatums
row = []
row = gral_info[:]
for i, item in enumerate(tatms_c):
row.append(group_index)
row.append(i)
for item in row_front: #appending blank spaces at the front of the row
row.append(item)
row.append(tatms_c[i])
tatms_c_avg = get_avg(tatms_c)
row.append(tatms_c_avg)
tatms_c_max = get_max(tatms_c)
row.append(tatms_c_max)
tatms_c_min = get_min(tatms_c)
row.append(tatms_c_min)
tatms_c_stddev = get_stddev(tatms_c)
row.append(tatms_c_stddev)
tatms_c_count = get_count(tatms_c)
row.append(tatms_c_count)
tatms_c_sum = get_sum(tatms_c)
row.append(tatms_c_sum)
tatms_start = hdf5_getters.get_tatums_start(h5)
row.append(tatms_start[i])
tatms_start_avg = get_avg(tatms_start)
row.append(tatms_start_avg)
tatms_start_max = get_max(tatms_start)
row.append(tatms_start_max)
tatms_start_min = get_min(tatms_start)
row.append(tatms_start_min)
tatms_start_stddev = get_stddev(tatms_start)
row.append(tatms_start_stddev)
tatms_start_count = get_count(tatms_start)
row.append(tatms_start_count)
tatms_start_sum = get_sum(tatms_start)
row.append(tatms_start_sum)
writer.writerow(row)
row = []
row = gral_info[:]
transpose_pitch = seg_pitch.transpose(
) #this is to tranpose the matrix,so we can have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_pitch_avg = []
seg_pitch_max = []
seg_pitch_min = []
seg_pitch_stddev = []
seg_pitch_count = []
seg_pitch_sum = []
i = 0
#Getting the aggregate values in the pitches array
for row in transpose_pitch:
seg_pitch_avg.append(get_avg(row))
seg_pitch_max.append(get_max(row))
seg_pitch_min.append(get_min(row))
seg_pitch_stddev.append(get_stddev(row))
seg_pitch_count.append(get_count(row))
seg_pitch_sum.append(get_sum(row))
i = i + 1
#extracting information from the timbre array
transpose_timbre = seg_pitch.transpose(
) #tranposing matrix, to have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_timbre_avg = []
seg_timbre_max = []
seg_timbre_min = []
seg_timbre_stddev = []
seg_timbre_count = []
seg_timbre_sum = []
i = 0
for row in transpose_timbre:
seg_timbre_avg.append(get_avg(row))
seg_timbre_max.append(get_max(row))
seg_timbre_min.append(get_min(row))
seg_timbre_stddev.append(get_stddev(row))
seg_timbre_count.append(get_count(row))
seg_timbre_sum.append(get_sum(row))
i = i + 1
h5.close()
count = count + 1
print count
def get_all_rows(basedir, ext='.h5'):
rows = []
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
for f in files:
# print(os.path.join(root, f))
h5 = hdf5_getters.open_h5_file_read(f)
num_songs = hdf5_getters.get_num_songs(h5)
# print(num_songs)
for i in range(num_songs):
print(i)
obj = {}
obj['artist_name'] = hdf5_getters.get_artist_name(
h5, i).decode('UTF-8')
obj['artist_familiarity'] = hdf5_getters.get_artist_familiarity(
h5, i)
obj['artist_hotness'] = hdf5_getters.get_artist_hotttnesss(
h5, i)
obj['artist_id'] = hdf5_getters.get_artist_id(
h5, i).decode('UTF-8')
# obj['artist_mbid']=hdf5_getters.get_artist_mbid(h5,i).decode('UTF-8')
obj['artist_playmeid'] = hdf5_getters.get_artist_playmeid(
h5, i)
obj['artist_7digitalid'] = hdf5_getters.get_artist_7digitalid(
h5, i)
# obj['artist_latitude']=hdf5_getters.get_artist_latitude(h5,i)
# obj['artist_longitude']=hdf5_getters.get_artist_longitude(h5,i)
# obj['artist_location']=hdf5_getters.get_artist_location(h5,i).decode('UTF-8')
obj['artist_name'] = hdf5_getters.get_artist_name(
h5, i).decode('UTF-8')
obj['release'] = hdf5_getters.get_release(h5,
i).decode('UTF-8')
obj['song_hotttnesss'] = hdf5_getters.get_song_hotttnesss(
h5, i)
obj['title'] = hdf5_getters.get_title(h5, i).decode('UTF-8')
# obj['artist_terms']=hdf5_getters.get_artist_terms(h5)
# obj['artist_terms_freq']=hdf5_getters.get_artist_terms_freq(h5)
# obj['artist_terms_weight']=hdf5_getters.get_artist_terms_weight(h5)
# obj['audio_md5']=hdf5_getters.get_audio_md5(h5).decode('UTF-8')
obj['danceability'] = hdf5_getters.get_danceability(h5, i)
obj['duration'] = hdf5_getters.get_duration(h5, i)
obj['end_of_fade_in'] = hdf5_getters.get_end_of_fade_in(h5, i)
obj['energy'] = hdf5_getters.get_energy(h5, i)
obj['key'] = hdf5_getters.get_key(h5, i)
obj['key_confidence'] = hdf5_getters.get_key_confidence(h5, i)
obj['loudness'] = hdf5_getters.get_loudness(h5, i)
obj['mode'] = hdf5_getters.get_mode(h5, i)
# obj['start_of_fade_out']=hdf5_getters.get_start_of_fade_out(h5)
obj['tempo'] = hdf5_getters.get_tempo(h5, i)
obj['time_signature'] = hdf5_getters.get_time_signature(h5, i)
# obj['time_signature_confidence']=hdf5_getters.get_time_signature_confidence(h5)
obj['track_id'] = hdf5_getters.get_track_id(h5,
i).decode('UTF-8')
# obj['segments_start']=hdf5_getters.get_segments_start(h5)
# obj['segments_confidence']=hdf5_getters.get_segments_confidence(h5)
# obj['segments_pitches']=hdf5_getters.get_segments_pitches(h5)
# obj['segments_timbre']=hdf5_getters.get_segments_timbre(h5)
# obj['segments_loudness_max']=hdf5_getters.get_segments_loudness_max(h5)
# obj['segments_loudness_max_time']=hdf5_getters.get_segments_loudness_max_time(h5)
# obj['segments_confidence']=hdf5_getters.get_segments_confidence(h5)
# obj['segments_loudness_start']=hdf5_getters.get_segments_loudness_start(h5)
# obj['sections_start']=hdf5_getters.get_sections_start(h5)
# obj['sections_confidence']=hdf5_getters.get_sections_confidence(h5)
# obj['beats_start']=hdf5_getters.get_beats_start(h5)
# obj['beats_confidence']=hdf5_getters.get_beats_confidence(h5)
# obj['bars_start']=hdf5_getters.get_bars_start(h5)
# obj['bars_confidence']=hdf5_getters.get_bars_confidence(h5)
# obj['tatums_start']=hdf5_getters.get_tatums_start(h5)
# obj['artist_mbtags']=hdf5_getters.get_artist_mbtags(h5)
# obj['artist_mbtags_count']=hdf5_getters.get_artist_mbtags_count(h5)
obj['year'] = hdf5_getters.get_year(h5, i)
rows.append(obj)
h5.close()
return rows
def data_to_flat_file(basedir,ext='.h5') :
""" This function extracts the information from the tables and creates the flat file. """
count = 0; #song counter
list_to_write= []
group_index=0
row_to_write = ""
writer = csv.writer(open("complete.csv", "wb"))
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root,'*'+ext))
for f in files:
row=[]
print f
h5 = hdf5_getters.open_h5_file_read(f)
title = hdf5_getters.get_title(h5)
title= title.replace('"','')
row.append(title)
comma=title.find(',')
if comma != -1:
print title
time.sleep(1)
album = hdf5_getters.get_release(h5)
album= album.replace('"','')
row.append(album)
comma=album.find(',')
if comma != -1:
print album
time.sleep(1)
artist_name = hdf5_getters.get_artist_name(h5)
comma=artist_name.find(',')
if comma != -1:
print artist_name
time.sleep(1)
artist_name= artist_name.replace('"','')
row.append(artist_name)
duration = hdf5_getters.get_duration(h5)
row.append(duration)
samp_rt = hdf5_getters.get_analysis_sample_rate(h5)
row.append(samp_rt)
artist_7digitalid = hdf5_getters.get_artist_7digitalid(h5)
row.append(artist_7digitalid)
artist_fam = hdf5_getters.get_artist_familiarity(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_fam) == True:
artist_fam=-1
row.append(artist_fam)
artist_hotness= hdf5_getters.get_artist_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_hotness) == True:
artist_hotness=-1
row.append(artist_hotness)
artist_id = hdf5_getters.get_artist_id(h5)
row.append(artist_id)
artist_lat = hdf5_getters.get_artist_latitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lat) == True:
artist_lat=-1
row.append(artist_lat)
artist_loc = hdf5_getters.get_artist_location(h5)
row.append(artist_loc)
artist_lon = hdf5_getters.get_artist_longitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lon) == True:
artist_lon=-1
row.append(artist_lon)
artist_mbid = hdf5_getters.get_artist_mbid(h5)
row.append(artist_mbid)
#Getting the genre
art_trm = hdf5_getters.get_artist_terms(h5)
trm_freq = hdf5_getters.get_artist_terms_freq(h5)
trn_wght = hdf5_getters.get_artist_terms_weight(h5)
a_mb_tags = hdf5_getters.get_artist_mbtags(h5)
genre_indexes=get_genre_indexes(trm_freq) #index of the highest freq
genre_set=0 #flag to see if the genre has been set or not
final_genre=[]
genres_so_far=[]
for i in range(len(genre_indexes)):
genre_tmp=get_genre(art_trm,genre_indexes[i]) #genre that corresponds to the highest freq
genres_so_far=genre_dict.get_genre_in_dict(genre_tmp) #getting the genre from the dictionary
if len(genres_so_far) != 0:
for i in genres_so_far:
final_genre.append(i)
genre_set=1
if genre_set == 1:
col_num=[]
for i in final_genre:
column=int(i) #getting the column number of the genre
col_num.append(column)
genre_array=genre_columns(col_num) #genre array
for i in range(len(genre_array)): #appending the genre_array to the row
row.append(genre_array[i])
else:
genre_array=genre_columns(-1) #when there is no genre matched, return an array of [0...0]
for i in range(len(genre_array)): #appending the genre_array to the row
row.append(genre_array[i])
artist_pmid = hdf5_getters.get_artist_playmeid(h5)
row.append(artist_pmid)
audio_md5 = hdf5_getters.get_audio_md5(h5)
row.append(audio_md5)
danceability = hdf5_getters.get_danceability(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(danceability) == True:
danceability=-1
row.append(danceability)
end_fade_in =hdf5_getters.get_end_of_fade_in(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(end_fade_in) == True:
end_fade_in=-1
row.append(end_fade_in)
energy = hdf5_getters.get_energy(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(energy) == True:
energy=-1
row.append(energy)
song_key = hdf5_getters.get_key(h5)
row.append(song_key)
key_c = hdf5_getters.get_key_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(key_c) == True:
key_c=-1
row.append(key_c)
loudness = hdf5_getters.get_loudness(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(loudness) == True:
loudness=-1
row.append(loudness)
mode = hdf5_getters.get_mode(h5)
row.append(mode)
mode_conf = hdf5_getters.get_mode_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(mode_conf) == True:
mode_conf=-1
row.append(mode_conf)
release_7digitalid = hdf5_getters.get_release_7digitalid(h5)
row.append(release_7digitalid)
song_hot = hdf5_getters.get_song_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(song_hot) == True:
song_hot=-1
row.append(song_hot)
song_id = hdf5_getters.get_song_id(h5)
row.append(song_id)
start_fade_out = hdf5_getters.get_start_of_fade_out(h5)
row.append(start_fade_out)
tempo = hdf5_getters.get_tempo(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(tempo) == True:
tempo=-1
row.append(tempo)
time_sig = hdf5_getters.get_time_signature(h5)
row.append(time_sig)
time_sig_c = hdf5_getters.get_time_signature_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(time_sig_c) == True:
time_sig_c=-1
row.append(time_sig_c)
track_id = hdf5_getters.get_track_id(h5)
row.append(track_id)
track_7digitalid = hdf5_getters.get_track_7digitalid(h5)
row.append(track_7digitalid)
year = hdf5_getters.get_year(h5)
row.append(year)
bars_c = hdf5_getters.get_bars_confidence(h5)
bars_start = hdf5_getters.get_bars_start(h5)
row_bars_padding=padding(245) #this is the array that will be attached at the end of th row
#--------------bars---------------"
gral_info=[]
gral_info=row[:]
empty=[]
for i,item in enumerate(bars_c):
row.append(group_index)
row.append(i)
row.append(bars_c[i])
bars_c_avg= get_avg(bars_c)
row.append(bars_c_avg)
bars_c_max= get_max(bars_c)
row.append(bars_c_max)
bars_c_min = get_min(bars_c)
row.append(bars_c_min)
bars_c_stddev= get_stddev(bars_c)
row.append(bars_c_stddev)
bars_c_count = get_count(bars_c)
row.append(bars_c_count)
bars_c_sum = get_sum(bars_c)
row.append(bars_c_sum)
row.append(bars_start[i])
bars_start_avg = get_avg(bars_start)
row.append(bars_start_avg)
bars_start_max= get_max(bars_start)
row.append(bars_start_max)
bars_start_min = get_min(bars_start)
row.append(bars_start_min)
bars_start_stddev= get_stddev(bars_start)
row.append(bars_start_stddev)
bars_start_count = get_count(bars_start)
row.append(bars_start_count)
bars_start_sum = get_sum(bars_start)
row.append(bars_start_sum)
for i in row_bars_padding:
row.append(i)
writer.writerow(row)
row=[]
row=gral_info[:]
#--------beats---------------"
beats_c = hdf5_getters.get_beats_confidence(h5)
group_index=1
row=[]
row=gral_info[:]
row_front=padding(14) #blanks left in front of the row(empty spaces for bars)
row_beats_padding=padding(231)
for i,item in enumerate(beats_c):
row.append(group_index)
row.append(i)
for index in row_front: #padding blanks in front of the beats
row.append(index)
row.append(beats_c[i])
beats_c_avg= get_avg(beats_c)
row.append(beats_c_avg)
beats_c_max= get_max(beats_c)
row.append(beats_c_max)
beats_c_min = get_min(beats_c)
row.append(beats_c_min)
beats_c_stddev= get_stddev(beats_c)
row.append(beats_c_stddev)
beats_c_count = get_count(beats_c)
row.append(beats_c_count)
beats_c_sum = get_sum(beats_c)
row.append(beats_c_sum)
beats_start = hdf5_getters.get_beats_start(h5)
row.append(beats_start[i])
beats_start_avg = get_avg(beats_start)
row.append(beats_start_avg)
beats_start_max= get_max(beats_start)
row.append(beats_start_max)
beats_start_min = get_min(beats_start)
row.append(beats_start_min)
beats_start_stddev= get_stddev(beats_start)
row.append(beats_start_stddev)
beats_start_count = get_count(beats_start)
row.append(beats_start_count)
beats_start_sum = get_sum(beats_start)
row.append(beats_start_sum)
for i in row_beats_padding:
row.append(i)
writer.writerow(row)
row=[]
row=gral_info[:]
# "--------sections---------------"
row_sec_padding=padding(217) #blank spaces left at the end of the row
sec_c = hdf5_getters.get_sections_confidence(h5)
group_index=2
row=[]
row=gral_info[:]
row_front=padding(28) #blank spaces left in front(empty spaces for bars,beats)
for i,item in enumerate(sec_c):
row.append(group_index)
row.append(i)
for index in row_front: #padding blanks in front of the sections
row.append(index)
row.append(sec_c[i])
sec_c_avg= get_avg(sec_c)
row.append(sec_c_avg)
sec_c_max= get_max(sec_c)
row.append(sec_c_max)
sec_c_min = get_min(sec_c)
row.append(sec_c_min)
sec_c_stddev= get_stddev(sec_c)
row.append(sec_c_stddev)
sec_c_count = get_count(sec_c)
row.append(sec_c_count)
sec_c_sum = get_sum(sec_c)
row.append(sec_c_sum)
sec_start = hdf5_getters.get_sections_start(h5)
row.append(sec_start[i])
sec_start_avg = get_avg(sec_start)
row.append(sec_start_avg)
sec_start_max= get_max(sec_start)
row.append(sec_start_max)
sec_start_min = get_min(sec_start)
row.append(sec_start_min)
sec_start_stddev= get_stddev(sec_start)
row.append(sec_start_stddev)
sec_start_count = get_count(sec_start)
row.append(sec_start_count)
sec_start_sum = get_sum(sec_start)
row.append(sec_start_sum)
for i in row_sec_padding: #appending the blank spaces at the end of the row
row.append(i)
writer.writerow(row)
row=[]
row=gral_info[:]
#--------segments---------------"
row_seg_padding=padding(182) #blank spaces at the end of the row
row_front=padding(42) #blank spaces left in front of segments
seg_c = hdf5_getters.get_segments_confidence(h5)
group_index=3
row=[]
row=gral_info[:]
for i,item in enumerate(seg_c):
row.append(group_index)
row.append(i)
for index in row_front: #padding blanks in front of the segments
row.append(index)
row.append(seg_c[i])
seg_c_avg= get_avg(seg_c)
row.append(seg_c_avg)
seg_c_max= get_max(seg_c)
row.append(seg_c_max)
seg_c_min = get_min(seg_c)
row.append(seg_c_min)
seg_c_stddev= get_stddev(seg_c)
row.append(seg_c_stddev)
seg_c_count = get_count(seg_c)
row.append(seg_c_count)
seg_c_sum = get_sum(seg_c)
row.append(seg_c_sum)
seg_loud_max = hdf5_getters.get_segments_loudness_max(h5)
row.append(seg_loud_max[i])
seg_loud_max_avg= get_avg(seg_loud_max)
row.append(seg_loud_max_avg)
seg_loud_max_max= get_max(seg_loud_max)
row.append(seg_loud_max_max)
seg_loud_max_min = get_min(seg_loud_max)
row.append(seg_loud_max_min)
seg_loud_max_stddev= get_stddev(seg_loud_max)
row.append(seg_loud_max_stddev)
seg_loud_max_count = get_count(seg_loud_max)
row.append(seg_loud_max_count)
seg_loud_max_sum = get_sum(seg_loud_max)
row.append(seg_loud_max_sum)
seg_loud_max_time = hdf5_getters.get_segments_loudness_max_time(h5)
row.append(seg_loud_max_time[i])
seg_loud_max_time_avg= get_avg(seg_loud_max_time)
row.append(seg_loud_max_time_avg)
seg_loud_max_time_max= get_max(seg_loud_max_time)
row.append(seg_loud_max_time_max)
seg_loud_max_time_min = get_min(seg_loud_max_time)
row.append(seg_loud_max_time_min)
seg_loud_max_time_stddev= get_stddev(seg_loud_max_time)
row.append(seg_loud_max_time_stddev)
seg_loud_max_time_count = get_count(seg_loud_max_time)
row.append(seg_loud_max_time_count)
seg_loud_max_time_sum = get_sum(seg_loud_max_time)
row.append(seg_loud_max_time_sum)
seg_loud_start = hdf5_getters.get_segments_loudness_start(h5)
row.append(seg_loud_start[i])
seg_loud_start_avg= get_avg(seg_loud_start)
row.append(seg_loud_start_avg)
seg_loud_start_max= get_max(seg_loud_start)
row.append(seg_loud_start_max)
seg_loud_start_min = get_min(seg_loud_start)
row.append(seg_loud_start_min)
seg_loud_start_stddev= get_stddev(seg_loud_start)
row.append(seg_loud_start_stddev)
seg_loud_start_count = get_count(seg_loud_start)
row.append(seg_loud_start_count)
seg_loud_start_sum = get_sum(seg_loud_start)
row.append(seg_loud_start_sum)
seg_start = hdf5_getters.get_segments_start(h5)
row.append(seg_start[i])
seg_start_avg= get_avg(seg_start)
row.append(seg_start_avg)
seg_start_max= get_max(seg_start)
row.append(seg_start_max)
seg_start_min = get_min(seg_start)
row.append(seg_start_min)
seg_start_stddev= get_stddev(seg_start)
row.append(seg_start_stddev)
seg_start_count = get_count(seg_start)
row.append(seg_start_count)
seg_start_sum = get_sum(seg_start)
row.append(seg_start_sum)
for i in row_seg_padding: #appending blank spaces at the end of the row
row.append(i)
writer.writerow(row)
row=[]
row=gral_info[:]
#----------segments pitch and timbre---------------"
row_seg2_padding=padding(14) #blank spaces left at the end of the row
row_front=padding(77) #blank spaces left at the front of the segments and timbre
seg_pitch = hdf5_getters.get_segments_pitches(h5)
transpose_pitch= seg_pitch.transpose() #this is to tranpose the matrix,so we can have 12 rows
group_index=4
row=[]
row=gral_info[:]
for i,item in enumerate(transpose_pitch[0]):
row.append(group_index)
row.append(i)
for index in row_front: #padding blanks in front of segments and timbre
row.append(index)
row.append(transpose_pitch[0][i])
seg_pitch_avg= get_avg(transpose_pitch[0])
row.append(seg_pitch_avg)
seg_pitch_max= get_max(transpose_pitch[0])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[0])
row.append(seg_pitch_min)
seg_pitch_stddev= get_stddev(transpose_pitch[0])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[0])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[0])
row.append(seg_pitch_sum)
row.append(transpose_pitch[1][i])
seg_pitch_avg= get_avg(transpose_pitch[1])
row.append(seg_pitch_avg)
seg_pitch_max= get_max(transpose_pitch[1])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[1])
row.append(seg_pitch_min)
seg_pitch_stddev= get_stddev(transpose_pitch[1])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[1])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[1])
row.append(seg_pitch_sum)
row.append(transpose_pitch[2][i])
seg_pitch_avg= get_avg(transpose_pitch[2])
row.append(seg_pitch_avg)
seg_pitch_max= get_max(transpose_pitch[2])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[2])
row.append(seg_pitch_min)
seg_pitch_stddev= get_stddev(transpose_pitch[2])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[2])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[2])
row.append(seg_pitch_sum)
row.append(transpose_pitch[3][i])
seg_pitch_avg= get_avg(transpose_pitch[3])
row.append(seg_pitch_avg)
seg_pitch_max= get_max(transpose_pitch[3])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[3])
row.append(seg_pitch_min)
seg_pitch_stddev= get_stddev(transpose_pitch[3])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[3])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[3])
row.append(seg_pitch_sum)
row.append(transpose_pitch[4][i])
seg_pitch_avg= get_avg(transpose_pitch[4])
row.append(seg_pitch_avg)
seg_pitch_max= get_max(transpose_pitch[4])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[4])
row.append(seg_pitch_min)
seg_pitch_stddev= get_stddev(transpose_pitch[4])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[4])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[4])
row.append(seg_pitch_sum)
row.append(transpose_pitch[5][i])
seg_pitch_avg= get_avg(transpose_pitch[5])
row.append(seg_pitch_avg)
seg_pitch_max= get_max(transpose_pitch[5])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[5])
row.append(seg_pitch_min)
seg_pitch_stddev= get_stddev(transpose_pitch[5])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[5])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[5])
row.append(seg_pitch_sum)
row.append(transpose_pitch[6][i])
seg_pitch_avg= get_avg(transpose_pitch[6])
row.append(seg_pitch_avg)
seg_pitch_max= get_max(transpose_pitch[6])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[6])
row.append(seg_pitch_min)
seg_pitch_stddev= get_stddev(transpose_pitch[6])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[6])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[6])
row.append(seg_pitch_sum)
row.append(transpose_pitch[7][i])
seg_pitch_avg= get_avg(transpose_pitch[7])
row.append(seg_pitch_avg)
seg_pitch_max= get_max(transpose_pitch[7])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[7])
row.append(seg_pitch_min)
seg_pitch_stddev= get_stddev(transpose_pitch[7])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[7])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[7])
row.append(seg_pitch_sum)
row.append(transpose_pitch[8][i])
seg_pitch_avg= get_avg(transpose_pitch[8])
row.append(seg_pitch_avg)
seg_pitch_max= get_max(transpose_pitch[8])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[8])
row.append(seg_pitch_min)
seg_pitch_stddev= get_stddev(transpose_pitch[8])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[8])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[8])
row.append(seg_pitch_sum)
row.append(transpose_pitch[9][i])
seg_pitch_avg= get_avg(transpose_pitch[9])
row.append(seg_pitch_avg)
seg_pitch_max= get_max(transpose_pitch[9])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[9])
row.append(seg_pitch_min)
seg_pitch_stddev= get_stddev(transpose_pitch[9])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[9])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[9])
row.append(seg_pitch_sum)
row.append(transpose_pitch[10][i])
seg_pitch_avg= get_avg(transpose_pitch[10])
row.append(seg_pitch_avg)
seg_pitch_max= get_max(transpose_pitch[10])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[10])
row.append(seg_pitch_min)
seg_pitch_stddev= get_stddev(transpose_pitch[10])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[10])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[10])
row.append(seg_pitch_sum)
row.append(transpose_pitch[11][i])
seg_pitch_avg= get_avg(transpose_pitch[11])
row.append(seg_pitch_avg)
seg_pitch_max= get_max(transpose_pitch[11])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[11])
row.append(seg_pitch_min)
seg_pitch_stddev= get_stddev(transpose_pitch[11])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[11])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[11])
row.append(seg_pitch_sum)
#timbre arrays
seg_timbre = hdf5_getters.get_segments_timbre(h5)
transpose_timbre = seg_pitch.transpose() #tranposing matrix, to have 12 rows
row.append(transpose_timbre[0][i])
seg_timbre_avg= get_avg(transpose_timbre[0])
row.append(seg_timbre_avg)
seg_timbre_max= get_max(transpose_timbre[0])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[0])
row.append(seg_timbre_min)
seg_timbre_stddev=get_stddev(transpose_timbre[0])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[0])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[0])
row.append(seg_timbre_sum)
row.append(transpose_timbre[1][i])
seg_timbre_avg= get_avg(transpose_timbre[1])
row.append(seg_timbre_avg)
seg_timbre_max= get_max(transpose_timbre[1])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[1])
row.append(seg_timbre_min)
seg_timbre_stddev= get_stddev(transpose_timbre[1])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[1])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[1])
row.append(seg_timbre_sum)
row.append(transpose_timbre[2][i])
seg_timbre_avg= get_avg(transpose_timbre[2])
row.append(seg_timbre_avg)
seg_timbre_max= get_max(transpose_timbre[2])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[2])
row.append(seg_timbre_min)
seg_timbre_stddev= get_stddev(transpose_timbre[2])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[2])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[2])
row.append(seg_timbre_sum)
row.append(transpose_timbre[3][i])
seg_timbre_avg= get_avg(transpose_timbre[3])
row.append(seg_timbre_avg)
seg_timbre_max= get_max(transpose_timbre[3])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[3])
row.append(seg_timbre_min)
seg_timbre_stddev= get_stddev(transpose_timbre[3])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[3])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[3])
row.append(seg_timbre_sum)
row.append(transpose_timbre[4][i])
seg_timbre_avg= get_avg(transpose_timbre[4])
row.append(seg_timbre_avg)
seg_timbre_max= get_max(transpose_timbre[4])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[4])
row.append(seg_timbre_min)
seg_timbre_stddev= get_stddev(transpose_timbre[4])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[4])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[4])
row.append(seg_timbre_sum)
row.append(transpose_timbre[5][i])
seg_timbre_avg= get_avg(transpose_timbre[5])
row.append(seg_timbre_avg)
seg_timbre_max= get_max(transpose_timbre[5])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[5])
row.append(seg_timbre_min)
seg_timbre_stddev= get_stddev(transpose_timbre[5])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[5])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[5])
row.append(seg_timbre_sum)
row.append(transpose_timbre[6][i])
seg_timbre_avg= get_avg(transpose_timbre[6])
row.append(seg_timbre_avg)
seg_timbre_max= get_max(transpose_timbre[6])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[6])
row.append(seg_timbre_min)
seg_timbre_stddev= get_stddev(transpose_timbre[6])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[6])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[6])
row.append(seg_timbre_sum)
row.append(transpose_timbre[7][i])
seg_timbre_avg= get_avg(transpose_timbre[7])
row.append(seg_timbre_avg)
seg_timbre_max= get_max(transpose_timbre[7])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[7])
row.append(seg_timbre_min)
seg_timbre_stddev= get_stddev(transpose_timbre[7])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[7])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[7])
row.append(seg_timbre_sum)
row.append(transpose_timbre[8][i])
seg_timbre_avg= get_avg(transpose_timbre[8])
row.append(seg_timbre_avg)
seg_timbre_max= get_max(transpose_timbre[8])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[8])
row.append(seg_timbre_min)
seg_timbre_stddev= get_stddev(transpose_timbre[8])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[8])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[8])
row.append(seg_timbre_sum)
row.append(transpose_timbre[9][i])
seg_timbre_avg= get_avg(transpose_timbre[9])
row.append(seg_timbre_avg)
seg_timbre_max= get_max(transpose_timbre[9])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[9])
row.append(seg_timbre_min)
seg_timbre_stddev= get_stddev(transpose_timbre[9])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[9])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[9])
row.append(seg_timbre_sum)
row.append(transpose_timbre[10][i])
seg_timbre_avg= get_avg(transpose_timbre[10])
row.append(seg_timbre_avg)
seg_timbre_max= get_max(transpose_timbre[10])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[10])
row.append(seg_timbre_min)
seg_timbre_stddev= get_stddev(transpose_timbre[10])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[10])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[10])
row.append(seg_timbre_sum)
row.append(transpose_timbre[11][i])
seg_timbre_avg= get_avg(transpose_timbre[11])
row.append(seg_timbre_avg)
seg_timbre_max= get_max(transpose_timbre[11])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[11])
row.append(seg_timbre_min)
seg_timbre_stddev= get_stddev(transpose_timbre[11])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[11])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[11])
row.append(seg_timbre_sum)
for item in row_seg2_padding:
row.append(item)
writer.writerow(row)
row=[]
row=gral_info[:]
# "--------tatums---------------"
tatms_c = hdf5_getters.get_tatums_confidence(h5)
group_index=5
row_front=padding(245) #blank spaces left in front of tatums
row=[]
row=gral_info[:]
for i,item in enumerate(tatms_c):
row.append(group_index)
row.append(i)
for item in row_front: #appending blank spaces at the front of the row
row.append(item)
row.append(tatms_c[i])
tatms_c_avg= get_avg(tatms_c)
row.append(tatms_c_avg)
tatms_c_max= get_max(tatms_c)
row.append(tatms_c_max)
tatms_c_min = get_min(tatms_c)
row.append(tatms_c_min)
tatms_c_stddev= get_stddev(tatms_c)
row.append(tatms_c_stddev)
tatms_c_count = get_count(tatms_c)
row.append(tatms_c_count)
tatms_c_sum = get_sum(tatms_c)
row.append(tatms_c_sum)
tatms_start = hdf5_getters.get_tatums_start(h5)
row.append(tatms_start[i])
tatms_start_avg= get_avg(tatms_start)
row.append(tatms_start_avg)
tatms_start_max= get_max(tatms_start)
row.append(tatms_start_max)
tatms_start_min = get_min(tatms_start)
row.append(tatms_start_min)
tatms_start_stddev= get_stddev(tatms_start)
row.append(tatms_start_stddev)
tatms_start_count = get_count(tatms_start)
row.append(tatms_start_count)
tatms_start_sum = get_sum(tatms_start)
row.append(tatms_start_sum)
writer.writerow(row)
row=[]
row=gral_info[:]
transpose_pitch= seg_pitch.transpose() #this is to tranpose the matrix,so we can have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_pitch_avg=[]
seg_pitch_max=[]
seg_pitch_min=[]
seg_pitch_stddev=[]
seg_pitch_count=[]
seg_pitch_sum=[]
i=0
#Getting the aggregate values in the pitches array
for row in transpose_pitch:
seg_pitch_avg.append(get_avg(row))
seg_pitch_max.append(get_max(row))
seg_pitch_min.append(get_min(row))
seg_pitch_stddev.append(get_stddev(row))
seg_pitch_count.append(get_count(row))
seg_pitch_sum.append(get_sum(row))
i=i+1
#extracting information from the timbre array
transpose_timbre = seg_pitch.transpose() #tranposing matrix, to have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_timbre_avg=[]
seg_timbre_max=[]
seg_timbre_min=[]
seg_timbre_stddev=[]
seg_timbre_count=[]
seg_timbre_sum=[]
i=0
for row in transpose_timbre:
seg_timbre_avg.append(get_avg(row))
seg_timbre_max.append(get_max(row))
seg_timbre_min.append(get_min(row))
seg_timbre_stddev.append(get_stddev(row))
seg_timbre_count.append(get_count(row))
seg_timbre_sum.append(get_sum(row))
i=i+1
h5.close()
count=count+1;
print count;
def data_to_flat_file(basedir,ext='.h5') :
"""This function extract the information from the tables and creates the flat file."""
count = 0; #song counter
list_to_write= []
row_to_write = ""
writer = csv.writer(open("metadata_wholeA.csv", "wb"))
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root,'*'+ext))
for f in files:
print f #the name of the file
h5 = hdf5_getters.open_h5_file_read(f)
title = hdf5_getters.get_title(h5)
title= title.replace('"','')
comma=title.find(',') #eliminating commas in the title
if comma != -1:
print title
time.sleep(1)
album = hdf5_getters.get_release(h5)
album= album.replace('"','') #eliminating commas in the album
comma=album.find(',')
if comma != -1:
print album
time.sleep(1)
artist_name = hdf5_getters.get_artist_name(h5)
comma=artist_name.find(',')
if comma != -1:
print artist_name
time.sleep(1)
artist_name= artist_name.replace('"','') #eliminating double quotes
duration = hdf5_getters.get_duration(h5)
samp_rt = hdf5_getters.get_analysis_sample_rate(h5)
artist_7digitalid = hdf5_getters.get_artist_7digitalid(h5)
artist_fam = hdf5_getters.get_artist_familiarity(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_fam) == True:
artist_fam=-1
artist_hotness= hdf5_getters.get_artist_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_hotness) == True:
artist_hotness=-1
artist_id = hdf5_getters.get_artist_id(h5)
artist_lat = hdf5_getters.get_artist_latitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lat) == True:
artist_lat=-1
artist_loc = hdf5_getters.get_artist_location(h5)
#checks artist_loc to see if it is a hyperlink if it is set as empty string
artist_loc = artist_loc.replace(",", "\,");
if artist_loc.startswith("<a"):
artist_loc = ""
if len(artist_loc) > 100:
artist_loc = ""
artist_lon = hdf5_getters.get_artist_longitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lon) == True:
artist_lon=-1
artist_mbid = hdf5_getters.get_artist_mbid(h5)
artist_pmid = hdf5_getters.get_artist_playmeid(h5)
audio_md5 = hdf5_getters.get_audio_md5(h5)
danceability = hdf5_getters.get_danceability(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(danceability) == True:
danceability=-1
end_fade_in =hdf5_getters.get_end_of_fade_in(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(end_fade_in) == True:
end_fade_in=-1
energy = hdf5_getters.get_energy(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(energy) == True:
energy=-1
song_key = hdf5_getters.get_key(h5)
key_c = hdf5_getters.get_key_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(key_c) == True:
key_c=-1
loudness = hdf5_getters.get_loudness(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(loudness) == True:
loudness=-1
mode = hdf5_getters.get_mode(h5)
mode_conf = hdf5_getters.get_mode_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(mode_conf) == True:
mode_conf=-1
release_7digitalid = hdf5_getters.get_release_7digitalid(h5)
song_hot = hdf5_getters.get_song_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(song_hot) == True:
song_hot=-1
song_id = hdf5_getters.get_song_id(h5)
start_fade_out = hdf5_getters.get_start_of_fade_out(h5)
tempo = hdf5_getters.get_tempo(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(tempo) == True:
tempo=-1
time_sig = hdf5_getters.get_time_signature(h5)
time_sig_c = hdf5_getters.get_time_signature_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(time_sig_c) == True:
time_sig_c=-1
track_id = hdf5_getters.get_track_id(h5)
track_7digitalid = hdf5_getters.get_track_7digitalid(h5)
year = hdf5_getters.get_year(h5)
bars_c = hdf5_getters.get_bars_confidence(h5)
bars_c_avg= get_avg(bars_c)
bars_c_max= get_max(bars_c)
bars_c_min = get_min(bars_c)
bars_c_stddev= get_stddev(bars_c)
bars_c_count = get_count(bars_c)
bars_c_sum = get_sum(bars_c)
bars_start = hdf5_getters.get_bars_start(h5)
bars_start_avg = get_avg(bars_start)
bars_start_max= get_max(bars_start)
bars_start_min = get_min(bars_start)
bars_start_stddev= get_stddev(bars_start)
bars_start_count = get_count(bars_start)
bars_start_sum = get_sum(bars_start)
beats_c = hdf5_getters.get_beats_confidence(h5)
beats_c_avg= get_avg(beats_c)
beats_c_max= get_max(beats_c)
beats_c_min = get_min(beats_c)
beats_c_stddev= get_stddev(beats_c)
beats_c_count = get_count(beats_c)
beats_c_sum = get_sum(beats_c)
beats_start = hdf5_getters.get_beats_start(h5)
beats_start_avg = get_avg(beats_start)
beats_start_max= get_max(beats_start)
beats_start_min = get_min(beats_start)
beats_start_stddev= get_stddev(beats_start)
beats_start_count = get_count(beats_start)
beats_start_sum = get_sum(beats_start)
sec_c = hdf5_getters.get_sections_confidence(h5)
sec_c_avg= get_avg(sec_c)
sec_c_max= get_max(sec_c)
sec_c_min = get_min(sec_c)
sec_c_stddev= get_stddev(sec_c)
sec_c_count = get_count(sec_c)
sec_c_sum = get_sum(sec_c)
sec_start = hdf5_getters.get_sections_start(h5)
sec_start_avg = get_avg(sec_start)
sec_start_max= get_max(sec_start)
sec_start_min = get_min(sec_start)
sec_start_stddev= get_stddev(sec_start)
sec_start_count = get_count(sec_start)
sec_start_sum = get_sum(sec_start)
seg_c = hdf5_getters.get_segments_confidence(h5)
seg_c_avg= get_avg(seg_c)
seg_c_max= get_max(seg_c)
seg_c_min = get_min(seg_c)
seg_c_stddev= get_stddev(seg_c)
seg_c_count = get_count(seg_c)
seg_c_sum = get_sum(seg_c)
seg_loud_max = hdf5_getters.get_segments_loudness_max(h5)
seg_loud_max_avg= get_avg(seg_loud_max)
seg_loud_max_max= get_max(seg_loud_max)
seg_loud_max_min = get_min(seg_loud_max)
seg_loud_max_stddev= get_stddev(seg_loud_max)
seg_loud_max_count = get_count(seg_loud_max)
seg_loud_max_sum = get_sum(seg_loud_max)
seg_loud_max_time = hdf5_getters.get_segments_loudness_max_time(h5)
seg_loud_max_time_avg= get_avg(seg_loud_max_time)
seg_loud_max_time_max= get_max(seg_loud_max_time)
seg_loud_max_time_min = get_min(seg_loud_max_time)
seg_loud_max_time_stddev= get_stddev(seg_loud_max_time)
seg_loud_max_time_count = get_count(seg_loud_max_time)
seg_loud_max_time_sum = get_sum(seg_loud_max_time)
seg_loud_start = hdf5_getters.get_segments_loudness_start(h5)
seg_loud_start_avg= get_avg(seg_loud_start)
seg_loud_start_max= get_max(seg_loud_start)
seg_loud_start_min = get_min(seg_loud_start)
seg_loud_start_stddev= get_stddev(seg_loud_start)
seg_loud_start_count = get_count(seg_loud_start)
seg_loud_start_sum = get_sum(seg_loud_start)
seg_pitch = hdf5_getters.get_segments_pitches(h5)
pitch_size = len(seg_pitch)
seg_start = hdf5_getters.get_segments_start(h5)
seg_start_avg= get_avg(seg_start)
seg_start_max= get_max(seg_start)
seg_start_min = get_min(seg_start)
seg_start_stddev= get_stddev(seg_start)
seg_start_count = get_count(seg_start)
seg_start_sum = get_sum(seg_start)
seg_timbre = hdf5_getters.get_segments_timbre(h5)
tatms_c = hdf5_getters.get_tatums_confidence(h5)
tatms_c_avg= get_avg(tatms_c)
tatms_c_max= get_max(tatms_c)
tatms_c_min = get_min(tatms_c)
tatms_c_stddev= get_stddev(tatms_c)
tatms_c_count = get_count(tatms_c)
tatms_c_sum = get_sum(tatms_c)
tatms_start = hdf5_getters.get_tatums_start(h5)
tatms_start_avg= get_avg(tatms_start)
tatms_start_max= get_max(tatms_start)
tatms_start_min = get_min(tatms_start)
tatms_start_stddev= get_stddev(tatms_start)
tatms_start_count = get_count(tatms_start)
tatms_start_sum = get_sum(tatms_start)
#Getting the genres
genre_set = 0 #flag to see if the genre has been set or not
art_trm = hdf5_getters.get_artist_terms(h5)
trm_freq = hdf5_getters.get_artist_terms_freq(h5)
trn_wght = hdf5_getters.get_artist_terms_weight(h5)
a_mb_tags = hdf5_getters.get_artist_mbtags(h5)
genre_indexes=get_genre_indexes(trm_freq) #index of the highest freq
final_genre=[]
genres_so_far=[]
for i in range(len(genre_indexes)):
genre_tmp=get_genre(art_trm,genre_indexes[i]) #genre that corresponds to the highest freq
genres_so_far=genre_dict.get_genre_in_dict(genre_tmp) #getting the genre from the dictionary
if len(genres_so_far) != 0:
for i in genres_so_far:
final_genre.append(i)
genre_set=1 #genre was found in dictionary
if genre_set == 1:
col_num=[]
for genre in final_genre:
column=int(genre) #getting the column number of the genre
col_num.append(column)
genre_array=genre_columns(col_num) #genre array
else:
genre_array=genre_columns(-1) #the genre was not found in the dictionary
transpose_pitch= seg_pitch.transpose() #this is to tranpose the matrix,so we can have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_pitch_avg=[]
seg_pitch_max=[]
seg_pitch_min=[]
seg_pitch_stddev=[]
seg_pitch_count=[]
seg_pitch_sum=[]
i=0
#Getting the aggregate values in the pitches array
for row in transpose_pitch:
seg_pitch_avg.append(get_avg(row))
seg_pitch_max.append(get_max(row))
seg_pitch_min.append(get_min(row))
seg_pitch_stddev.append(get_stddev(row))
seg_pitch_count.append(get_count(row))
seg_pitch_sum.append(get_sum(row))
i=i+1
#extracting information from the timbre array
transpose_timbre = seg_pitch.transpose() #tranposing matrix, to have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_timbre_avg=[]
seg_timbre_max=[]
seg_timbre_min=[]
seg_timbre_stddev=[]
seg_timbre_count=[]
seg_timbre_sum=[]
i=0
for row in transpose_timbre:
seg_timbre_avg.append(get_avg(row))
seg_timbre_max.append(get_max(row))
seg_timbre_min.append(get_min(row))
seg_timbre_stddev.append(get_stddev(row))
seg_timbre_count.append(get_count(row))
seg_timbre_sum.append(get_sum(row))
i=i+1
#Writing to the flat file
writer.writerow([title,album,artist_name,year,duration,seg_start_count, tempo])
h5.close()
count=count+1;
print count;
def main(argv):
if len(argv) != 1:
print "Specify data directory"
return
basedir = argv[0]
outputFile1 = open('SongCSV.csv', 'w')
outputFile2 = open('TagsCSV.csv', 'w')
csvRowString = ""
csvLabelString = ""
#################################################
#if you want to prompt the user for the order of attributes in the csv,
#leave the prompt boolean set to True
#else, set 'prompt' to False and set the order of attributes in the 'else'
#clause
prompt = False
#################################################
if prompt == True:
while prompt:
prompt = False
csvAttributeString = raw_input("\n\nIn what order would you like the colums of the CSV file?\n" +
"Please delineate with commas. The options are: " +
"AlbumName, AlbumID, ArtistID, ArtistLatitude, ArtistLocation, ArtistLongitude,"+
" ArtistName, Danceability, Duration, KeySignature, KeySignatureConfidence, Tempo," +
" SongID, TimeSignature, TimeSignatureConfidence, Title, and Year.\n\n" +
"For example, you may write \"Title, Tempo, Duration\"...\n\n" +
"...or exit by typing 'exit'.\n\n")
csvAttributeList = re.split('\W+', csvAttributeString)
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
for attribute in csvAttributeList:
# print "Here is the attribute: " + attribute + " \n"
if attribute == 'AlbumID'.lower():
csvRowString += 'AlbumID'
elif attribute == 'AlbumName'.lower():
csvRowString += 'AlbumName'
elif attribute == 'ArtistID'.lower():
csvRowString += 'ArtistID'
elif attribute == 'ArtistLatitude'.lower():
csvRowString += 'ArtistLatitude'
elif attribute == 'ArtistLocation'.lower():
csvRowString += 'ArtistLocation'
elif attribute == 'ArtistLongitude'.lower():
csvRowString += 'ArtistLongitude'
elif attribute == 'ArtistName'.lower():
csvRowString += 'ArtistName'
elif attribute == 'Danceability'.lower():
csvRowString += 'Danceability'
elif attribute == 'Duration'.lower():
csvRowString += 'Duration'
elif attribute == 'KeySignature'.lower():
csvRowString += 'KeySignature'
elif attribute == 'KeySignatureConfidence'.lower():
csvRowString += 'KeySignatureConfidence'
elif attribute == 'SongID'.lower():
csvRowString += "SongID"
elif attribute == 'Tempo'.lower():
csvRowString += 'Tempo'
elif attribute == 'TimeSignature'.lower():
csvRowString += 'TimeSignature'
elif attribute == 'TimeSignatureConfidence'.lower():
csvRowString += 'TimeSignatureConfidence'
elif attribute == 'Title'.lower():
csvRowString += 'Title'
elif attribute == 'Year'.lower():
csvRowString += 'Year'
elif attribute == 'Exit'.lower():
sys.exit()
else:
prompt = True
print "=============="
print "I believe there has been an error with the input."
print "=============="
break
csvRowString += ","
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex-1]
csvRowString += "\n"
outputFile1.write(csvRowString);
csvRowString = ""
#else, if you want to hard code the order of the csv file and not prompt
#the user,
else:
#################################################
#change the order of the csv file here
#Default is to list all available attributes (in alphabetical order)
#csvRowString = ("SongID,AlbumID,AlbumName,ArtistID,ArtistLatitude,ArtistLocation,"+
# "ArtistLongitude,ArtistName,Danceability,Duration,KeySignature,"+
# "KeySignatureConfidence,Tempo,TimeSignature,TimeSignatureConfidence,"+
# "Title,Year")
csvRowString = ("ArtistFamiliarity,ArtistHotttnesss,"+
"BarsConfidence,BarsStart,BeatsConfidence,BeatsStart,Duration,"+
"EndOfFadeIn,Key,KeyConfidence,Loudness,Mode,ModeConfidence,"+
"SectionsConfidence,SectionsStart,SegmentsConfidence,SegmentsLoudnessMax,"+
"SegmentsLoudnessMaxTime,SegmentsLoudnessStart,SegmentsStart,"+
"SongHotttnesss,StartOfFadeOut,TatumsConfidence,TatumsStart,Tempo,TimeSignature,TimeSignatureConfidence,"+
"SegmentsPitches,SegmentsTimbre,Title,Year,Decade,ArtistMbtags")
#################################################
header = str()
csvAttributeList = re.split('\W+', csvRowString)
arrayAttributes = ["BarsConfidence","BarsStart","BeatsConfidence","BeatsStart",
"SectionsConfidence","SectionsStart","SegmentsConfidence","SegmentsLoudnessMax",
"SegmentsLoudnessMaxTime","SegmentsLoudnessStart","SegmentsStart",
"TatumsConfidence","TatumsStart"]
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
if(v=="SegmentsPitches"):
for i in range(90):
header = header + "SegmentsPitches" + str(i) + ","
elif(v=="SegmentsTimbre"):
for i in range(90):
header = header + "SegmentsTimbre" + str(i) + ","
elif(v in arrayAttributes):
header = header + v + str(0) + ","
header = header + v + str(1) + ","
else:
header = header + v + ","
outputFile1.write("SongNumber,");
#outputFile1.write(csvRowString + "\n");
outputFile1.write(header + "\n");
csvRowString = ""
#################################################
#Set the basedir here, the root directory from which the search
#for files stored in a (hierarchical data structure) will originate
#basedir = "MillionSongSubset/data/A/A/" # "." As the default means the current directory
ext = ".h5" #Set the extension here. H5 is the extension for HDF5 files.
#################################################
#FOR LOOP
all = sorted(os.walk(basedir))
for root, dirs, files in all:
files = sorted(glob.glob(os.path.join(root,'*'+ext)))
for f in files:
print f
songH5File = hdf5_getters.open_h5_file_read(f)
song = Song(str(hdf5_getters.get_song_id(songH5File)))
#testDanceability = hdf5_getters.get_danceability(songH5File)
# print type(testDanceability)
# print ("Here is the danceability: ") + str(testDanceability)
song.analysisSampleRate = str(hdf5_getters.get_analysis_sample_rate(songH5File))
song.artistFamiliarity = str(hdf5_getters.get_artist_familiarity(songH5File))
song.artistHotttnesss = str(hdf5_getters.get_artist_hotttnesss(songH5File))
song.artistLatitude = str(hdf5_getters.get_artist_latitude(songH5File))
song.artistLongitude = str(hdf5_getters.get_artist_longitude(songH5File))
song.artistMbid = str(hdf5_getters.get_artist_mbid(songH5File))
song.barsConfidence = np.array(hdf5_getters.get_bars_confidence(songH5File))
song.barsStart = np.array(hdf5_getters.get_bars_start(songH5File))
song.beatsConfidence = np.array(hdf5_getters.get_beats_confidence(songH5File))
song.beatsStart = np.array(hdf5_getters.get_beats_start(songH5File))
song.danceability = str(hdf5_getters.get_danceability(songH5File))
song.duration = str(hdf5_getters.get_duration(songH5File))
song.endOfFadeIn = str(hdf5_getters.get_end_of_fade_in(songH5File))
song.energy = str(hdf5_getters.get_energy(songH5File))
song.key = str(hdf5_getters.get_key(songH5File))
song.keyConfidence = str(hdf5_getters.get_key_confidence(songH5File))
song.loudness = str(hdf5_getters.get_loudness(songH5File))
song.mode = str(hdf5_getters.get_mode(songH5File))
song.modeConfidence = str(hdf5_getters.get_mode_confidence(songH5File))
song.sectionsConfidence = np.array(hdf5_getters.get_sections_confidence(songH5File))
song.sectionsStart = np.array(hdf5_getters.get_sections_start(songH5File))
song.segmentsConfidence = np.array(hdf5_getters.get_segments_confidence(songH5File))
song.segmentsLoudnessMax = np.array(hdf5_getters.get_segments_loudness_max(songH5File))
song.segmentsLoudnessMaxTime = np.array(hdf5_getters.get_segments_loudness_max_time(songH5File))
song.segmentsLoudnessStart = np.array(hdf5_getters.get_segments_loudness_start(songH5File))
song.segmentsPitches = np.array(hdf5_getters.get_segments_pitches(songH5File))
song.segmentsStart = np.array(hdf5_getters.get_segments_start(songH5File))
song.segmentsTimbre = np.array(hdf5_getters.get_segments_timbre(songH5File))
song.songHotttnesss = str(hdf5_getters.get_song_hotttnesss(songH5File))
song.startOfFadeOut = str(hdf5_getters.get_start_of_fade_out(songH5File))
song.tatumsConfidence = np.array(hdf5_getters.get_tatums_confidence(songH5File))
song.tatumsStart = np.array(hdf5_getters.get_tatums_start(songH5File))
song.tempo = str(hdf5_getters.get_tempo(songH5File))
song.timeSignature = str(hdf5_getters.get_time_signature(songH5File))
song.timeSignatureConfidence = str(hdf5_getters.get_time_signature_confidence(songH5File))
song.songid = str(hdf5_getters.get_song_id(songH5File))
song.title = str(hdf5_getters.get_title(songH5File))
song.year = str(hdf5_getters.get_year(songH5File))
song.artistMbtags = str(hdf5_getters.get_artist_mbtags(songH5File))
#print song count
csvRowString += str(song.songCount) + ","
csvLabelString += str(song.songCount) + ","
for attribute in csvAttributeList:
# print "Here is the attribute: " + attribute + " \n"
if attribute == 'AnalysisSampleRate'.lower():
csvRowString += song.analysisSampleRate
elif attribute == 'ArtistFamiliarity'.lower():
csvRowString += song.artistFamiliarity
elif attribute == 'ArtistHotttnesss'.lower():
csvRowString += song.artistHotttnesss
elif attribute == 'ArtistLatitude'.lower():
latitude = song.artistLatitude
if latitude == 'nan':
latitude = ''
csvRowString += latitude
elif attribute == 'ArtistLongitude'.lower():
longitude = song.artistLongitude
if longitude == 'nan':
longitude = ''
csvRowString += longitude
elif attribute == 'ArtistMbid'.lower():
csvRowString += song.artistMbid
elif attribute == 'BarsConfidence'.lower():
arr = song.barsConfidence
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'BarsStart'.lower():
arr = song.barsStart
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'BeatsConfidence'.lower():
arr = song.beatsConfidence
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'BeatsStart'.lower():
arr = song.beatsStart
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'Danceability'.lower():
csvRowString += song.danceability
elif attribute == 'Duration'.lower():
csvRowString += song.duration
elif attribute == 'EndOfFadeIn'.lower():
csvRowString += song.endOfFadeIn
elif attribute == 'Energy'.lower():
csvRowString += song.energy
elif attribute == 'Key'.lower():
csvRowString += song.key
elif attribute == 'KeyConfidence'.lower():
csvRowString += song.keyConfidence
elif attribute == 'Loudness'.lower():
csvRowString += song.loudness
elif attribute == 'Mode'.lower():
csvRowString += song.mode
elif attribute == 'ModeConfidence'.lower():
csvRowString += song.modeConfidence
elif attribute == 'SectionsConfidence'.lower():
arr = song.sectionsConfidence
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'SectionsStart'.lower():
arr = song.sectionsStart
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'SegmentsConfidence'.lower():
arr = song.segmentsConfidence
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'SegmentsLoudnessMax'.lower():
arr = song.segmentsLoudnessMax
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'SegmentsLoudnessMaxTime'.lower():
arr = song.segmentsLoudnessMaxTime
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'SegmentsLoudnessStart'.lower():
arr = song.segmentsLoudnessStart
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'SegmentsStart'.lower():
arr = song.segmentsStart
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'SongHotttnesss'.lower():
hotttnesss = song.songHotttnesss
if hotttnesss == 'nan':
hotttnesss = 'NaN'
csvRowString += hotttnesss
elif attribute == 'StartOfFadeOut'.lower():
csvRowString += song.startOfFadeOut
elif attribute == 'TatumsConfidence'.lower():
arr = song.tatumsConfidence
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'TatumsStart'.lower():
arr = song.tatumsStart
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'Tempo'.lower():
# print "Tempo: " + song.tempo
csvRowString += song.tempo
elif attribute == 'TimeSignature'.lower():
csvRowString += song.timeSignature
elif attribute == 'TimeSignatureConfidence'.lower():
# print "time sig conf: " + song.timeSignatureConfidence
csvRowString += song.timeSignatureConfidence
elif attribute == 'SegmentsPitches'.lower():
colmean = np.mean(song.segmentsPitches,axis=0)
for m in colmean:
csvRowString += str(m) + ","
cov = np.dot(song.segmentsPitches.T,song.segmentsPitches)
utriind = np.triu_indices(cov.shape[0])
feats = cov[utriind]
for feat in feats:
csvRowString += str(feat) + ","
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex-1]
elif attribute == 'SegmentsTimbre'.lower():
colmean = np.mean(song.segmentsTimbre,axis=0)
for m in colmean:
csvRowString += str(m) + ","
cov = np.dot(song.segmentsTimbre.T,song.segmentsTimbre)
utriind = np.triu_indices(cov.shape[0])
feats = cov[utriind]
for feat in feats:
csvRowString += str(feat) + ","
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex-1]
elif attribute == 'SongID'.lower():
csvRowString += "\"" + song.id + "\""
elif attribute == 'Title'.lower():
csvRowString += "\"" + song.title + "\""
elif attribute == 'Year'.lower():
csvRowString += song.year
elif attribute == 'Decade'.lower():
yr = song.year
if yr > 0:
decade = song.year[:-1] + '0'
else:
decade = '0'
csvRowString += decade
elif attribute == 'ArtistMbtags'.lower():
tags = song.artistMbtags[1:-1]
tags = "\"" + tags + "\""
tags = tags.replace("\n",'')
csvRowString += tags
tagsarray = shlex.split(tags)
for t in tagsarray:
csvLabelString += t + ","
else:
csvRowString += "Erm. This didn't work. Error. :( :(\n"
csvRowString += ","
'''
if attribute == 'AlbumID'.lower():
csvRowString += song.albumID
elif attribute == 'AlbumName'.lower():
albumName = song.albumName
albumName = albumName.replace(',',"")
csvRowString += "\"" + albumName + "\""
elif attribute == 'ArtistID'.lower():
csvRowString += "\"" + song.artistID + "\""
elif attribute == 'ArtistLatitude'.lower():
latitude = song.artistLatitude
if latitude == 'nan':
latitude = ''
csvRowString += latitude
elif attribute == 'ArtistLocation'.lower():
location = song.artistLocation
location = location.replace(',','')
csvRowString += "\"" + location + "\""
elif attribute == 'ArtistLongitude'.lower():
longitude = song.artistLongitude
if longitude == 'nan':
longitude = ''
csvRowString += longitude
elif attribute == 'ArtistName'.lower():
csvRowString += "\"" + song.artistName + "\""
elif attribute == 'Danceability'.lower():
csvRowString += song.danceability
elif attribute == 'Duration'.lower():
csvRowString += song.duration
elif attribute == 'KeySignature'.lower():
csvRowString += song.keySignature
elif attribute == 'KeySignatureConfidence'.lower():
# print "key sig conf: " + song.timeSignatureConfidence
csvRowString += song.keySignatureConfidence
elif attribute == 'SongID'.lower():
csvRowString += "\"" + song.id + "\""
elif attribute == 'Tempo'.lower():
# print "Tempo: " + song.tempo
csvRowString += song.tempo
elif attribute == 'TimeSignature'.lower():
csvRowString += song.timeSignature
elif attribute == 'TimeSignatureConfidence'.lower():
# print "time sig conf: " + song.timeSignatureConfidence
csvRowString += song.timeSignatureConfidence
elif attribute == 'Title'.lower():
csvRowString += "\"" + song.title + "\""
elif attribute == 'Year'.lower():
csvRowString += song.year
else:
csvRowString += "Erm. This didn't work. Error. :( :(\n"
csvRowString += ","
'''
#Remove the final comma from each row in the csv
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex-1]
csvRowString += "\n"
outputFile1.write(csvRowString)
csvRowString = ""
lastIndex = len(csvLabelString)
csvLabelString = csvLabelString[0:lastIndex-1]
csvLabelString += "\n"
outputFile2.write(csvLabelString)
csvLabelString = ""
songH5File.close()
outputFile1.close()
outputFile2.close()
# title= hdf5_getters.get_title(h5,songidx=row)#.decode('UTF-8')
# artist = "".join(c for c in unicodedata.normalize('NFD', str(artist.decode("utf8"))) if unicodedata.category(c) != "Mn")
# title = "".join(c for c in unicodedata.normalize('NFD', str(title.decode("utf8"))) if unicodedata.category(c) != "Mn")
#single number features
danceability = hdf5_getters.get_danceability(h5, songidx=row)
duration = hdf5_getters.get_duration(h5, songidx=row)
energy = hdf5_getters.get_energy(h5, songidx=row)
loudness = hdf5_getters.get_loudness(h5, songidx=row)
musicalKey = hdf5_getters.get_key(h5, songidx=row)
mode = hdf5_getters.get_mode(h5, songidx=row)
tempo = hdf5_getters.get_tempo(h5, songidx=row)
time_signature = hdf5_getters.get_time_signature(h5, songidx=row)
year = hdf5_getters.get_year(h5, songidx=row)
song_hottness = hdf5_getters.get_song_hotttnesss(h5, songidx=row)
end_of_fade_in = hdf5_getters.get_end_of_fade_in(h5, songidx=row)
start_of_fade_out = hdf5_getters.get_start_of_fade_out(h5, songidx=row)
#timestamp features
#take last element and divide by length to get beats/unit time, segments/unit_time
bars_start = hdf5_getters.get_bars_start(h5, songidx=row)
beats_start = hdf5_getters.get_beats_start(h5, songidx=row)
sections_start = hdf5_getters.get_sections_start(h5, songidx=row)
tatums_start = hdf5_getters.get_tatums_start(h5, songidx=row)
segments_start = hdf5_getters.get_segments_start(h5, songidx=row)
if len(bars_start) == 0: bars_start = 0.
else: bars_start = bars_start[-1] / len(bars_start)
if len(beats_start) == 0: beats_start = 0.
else: beats_start = beats_start[-1] / len(beats_start)
if len(sections_start) == 0: sections_start = 0.
else: sections_start = sections_start[-1] / len(sections_start)
def main():
outputFile = open('songs.csv', 'w')
writer = csv.writer(outputFile)
csvRowString = "song_number,artist_familiarity,artist_hotttnesss,artist_id,artist_mbid,artist_playmeid,artist_7digitalid,artist_latitude,artist_longitude,artist_location,artist_name,release,release_7digitalid,song_id,song_hotttnesss,title,track_7digitalid,analysis_sample_rate,audio_md5,danceability,duration,end_of_fade_in,energy,key,key_confidence,loudness,mode,mode_confidence,start_of_fade_out,tempo,time_signature,time_signature_confidence,track_id,year"
outputFile.write(csvRowString + "\n")
csvRowString = ""
#################################################
#Set the basedir here, the root directory from which the search
#for files stored in a (hierarchical data structure) will originate
basedir = "." # "." As the default means the current directory
ext = ".H5" #Set the extension here. H5 is the extension for HDF5 files.
#################################################
#FOR LOOP
songCount = 0
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
for f in files:
print(f)
songH5File = hdf5_getters.open_h5_file_read(f)
values = [
songCount,
hdf5_getters.get_artist_familiarity(songH5File),
hdf5_getters.get_artist_hotttnesss(songH5File),
hdf5_getters.get_artist_id(songH5File),
hdf5_getters.get_artist_mbid(songH5File),
hdf5_getters.get_artist_playmeid(songH5File),
hdf5_getters.get_artist_7digitalid(songH5File),
hdf5_getters.get_artist_latitude(songH5File),
hdf5_getters.get_artist_longitude(songH5File),
hdf5_getters.get_artist_location(songH5File),
hdf5_getters.get_artist_name(songH5File),
hdf5_getters.get_release(songH5File),
hdf5_getters.get_release_7digitalid(songH5File),
hdf5_getters.get_song_id(songH5File),
hdf5_getters.get_song_hotttnesss(songH5File),
hdf5_getters.get_title(songH5File),
hdf5_getters.get_track_7digitalid(songH5File),
hdf5_getters.get_analysis_sample_rate(songH5File),
hdf5_getters.get_audio_md5(songH5File),
hdf5_getters.get_danceability(songH5File),
hdf5_getters.get_duration(songH5File),
hdf5_getters.get_end_of_fade_in(songH5File),
hdf5_getters.get_energy(songH5File),
hdf5_getters.get_key(songH5File),
hdf5_getters.get_key_confidence(songH5File),
hdf5_getters.get_loudness(songH5File),
hdf5_getters.get_mode(songH5File),
hdf5_getters.get_mode_confidence(songH5File),
hdf5_getters.get_start_of_fade_out(songH5File),
hdf5_getters.get_tempo(songH5File),
hdf5_getters.get_time_signature(songH5File),
hdf5_getters.get_time_signature_confidence(songH5File),
hdf5_getters.get_track_id(songH5File),
hdf5_getters.get_year(songH5File)
]
songH5File.close()
songCount = songCount + 1
writer.writerow(values)
outputFile.close()
artist_mbid = GETTERS.get_artist_mbid(h5, i) artist_mbtags = ','.join(str(e) for e in GETTERS.get_artist_mbtags(h5, i)) # array artist_mbtags_count = ','.join(str(e) for e in GETTERS.get_artist_mbtags_count(h5, i)) # array artist_name = GETTERS.get_artist_name(h5, i) artist_playmeid = GETTERS.get_artist_playmeid(h5, i) artist_terms = ','.join(str(e) for e in GETTERS.get_artist_terms(h5, i)) # array #artist_terms_freq = ','.join(str(e) for e in GETTERS.get_artist_terms_freq(h5, i)) # array #artist_terms_weight = ','.join(str(e) for e in GETTERS.get_artist_terms_weight(h5, i)) # array #audio_md5 = GETTERS.get_audio_md5(h5, i) #bars_confidence = ','.join(str(e) for e in GETTERS.get_bars_confidence(h5, i)) # array #bars_start = ','.join(str(e) for e in GETTERS.get_bars_start(h5, i)) # array #beats_confidence = ','.join(str(e) for e in GETTERS.get_beats_confidence(h5, i)) # array #beats_start = ','.join(str(e) for e in GETTERS.get_beats_start(h5, i)) # array danceability = GETTERS.get_danceability(h5, i) duration = GETTERS.get_duration(h5, i) end_of_fade_in = GETTERS.get_end_of_fade_in(h5, i) energy = GETTERS.get_energy(h5, i) key = GETTERS.get_key(h5, i) key_confidence = GETTERS.get_key_confidence(h5, i) loudness = GETTERS.get_loudness(h5, i) mode = GETTERS.get_mode(h5, i) mode_confidence = GETTERS.get_mode_confidence(h5, i) release = GETTERS.get_release(h5, i) release_7digitalid = GETTERS.get_release_7digitalid(h5, i) #sections_confidence = ','.join(str(e) for e in GETTERS.get_sections_confidence(h5, i)) # array #sections_start = ','.join(str(e) for e in GETTERS.get_sections_start(h5, i)) # array #segments_confidence = ','.join(str(e) for e in GETTERS.get_segments_confidence(h5, i)) # array #segments_loudness_max = ','.join(str(e) for e in GETTERS.get_segments_loudness_max(h5, i)) # array #segments_loudness_max_time = ','.join(str(e) for e in GETTERS.get_segments_loudness_max_time(h5, i)) # array #segments_loudness_start = ','.join(str(e) for e in GETTERS.get_segments_loudness_start(h5, i)) # array #segments_pitches = ','.join(str(e) for e in GETTERS.get_segments_pitches(h5, i)) # array
time_signature=[]
end_of_fade_in=[]
mode=[]
start_of_fade_out=[]
song_hotttnesss=[]
for i in range(0,len(file)):
h5 = yay.open_h5_file_read('F:\sem4\ml\project\MillionSongSubset\data\A\{}'.format(file[i]))
duration.append(yay.get_duration(h5))
artist_familiarity.append(yay.get_artist_familiarity(h5))
artist_hotttnesss.append(yay.get_artist_hotttnesss(h5))
tempo.append(yay.get_tempo(h5))
loudness.append(yay.get_loudness(h5))
key.append(yay.get_key(h5))
time_signature.append(yay.get_time_signature(h5))
end_of_fade_in.append(yay.get_end_of_fade_in(h5))
mode.append(yay.get_mode(h5))
start_of_fade_out.append(yay.get_start_of_fade_out(h5))
song_hotttnesss.append(yay.get_song_hotttnesss(h5))
rows = zip(duration,artist_familiarity,artist_hotttnesss,tempo,loudness,key,time_signature,
end_of_fade_in,mode,start_of_fade_out,song_hotttnesss)
import csv
with open('training_data.csv', "w", encoding="ISO-8859-1", newline='') as f:
fieldnames = ['duration','artist_familiarity','artist_hotttnesss','tempo','loudness','key','time_signature',
'end_of_fade_in','mode','start_of_fade_out','song_hotttnesss']
writer = csv.DictWriter(f, fieldnames=fieldnames)
writer.writeheader()
writer = csv.writer(f)
def hd5_single_random_file_parser():
# Open an h5 file in read mode
h5 = hdf5_getters.open_h5_file_read(
'/home/skalogerakis/Documents/MillionSong/MillionSongSubset/A/M/G/TRAMGDX12903CEF79F.h5'
)
function_tracker = filter(
lambda x: x.startswith('get'),
hdf5_getters.__dict__.keys()) # Detects all the getter functions
for f in function_tracker: # Print everything in function tracker
print(f)
# First effort to check what each field contains.
print() # 55 available fields (exluding number of songs fields)
print("Num of songs -- ",
hdf5_getters.get_num_songs(h5)) # One song per file
print("Title -- ",
hdf5_getters.get_title(h5)) # Print the title of a specific h5 file
print("Artist familiarity -- ", hdf5_getters.get_artist_familiarity(h5))
print("Artist hotness -- ", hdf5_getters.get_artist_hotttnesss(h5))
print("Artist ID -- ", hdf5_getters.get_artist_id(h5))
print("Artist mbID -- ", hdf5_getters.get_artist_mbid(h5))
print("Artist playmeid -- ", hdf5_getters.get_artist_playmeid(h5))
print("Artist 7DigitalID -- ", hdf5_getters.get_artist_7digitalid(h5))
print("Artist latitude -- ", hdf5_getters.get_artist_latitude(h5))
print("Artist longitude -- ", hdf5_getters.get_artist_longitude(h5))
print("Artist location -- ", hdf5_getters.get_artist_location(h5))
print("Artist Name -- ", hdf5_getters.get_artist_name(h5))
print("Release -- ", hdf5_getters.get_release(h5))
print("Release 7DigitalID -- ", hdf5_getters.get_release_7digitalid(h5))
print("Song ID -- ", hdf5_getters.get_song_id(h5))
print("Song Hotness -- ", hdf5_getters.get_song_hotttnesss(h5))
print("Track 7Digital -- ", hdf5_getters.get_track_7digitalid(h5))
print("Similar artists -- ", hdf5_getters.get_similar_artists(h5))
print("Artist terms -- ", hdf5_getters.get_artist_terms(h5))
print("Artist terms freq -- ", hdf5_getters.get_artist_terms_freq(h5))
print("Artist terms weight -- ", hdf5_getters.get_artist_terms_weight(h5))
print("Analysis sample rate -- ",
hdf5_getters.get_analysis_sample_rate(h5))
print("Audio md5 -- ", hdf5_getters.get_audio_md5(h5))
print("Danceability -- ", hdf5_getters.get_danceability(h5))
print("Duration -- ", hdf5_getters.get_duration(h5))
print("End of Fade -- ", hdf5_getters.get_end_of_fade_in(h5))
print("Energy -- ", hdf5_getters.get_energy(h5))
print("Key -- ", hdf5_getters.get_key(h5))
print("Key Confidence -- ", hdf5_getters.get_key_confidence(h5))
print("Loudness -- ", hdf5_getters.get_loudness(h5))
print("Mode -- ", hdf5_getters.get_mode(h5))
print("Mode Confidence -- ", hdf5_getters.get_mode_confidence(h5))
print("Start of fade out -- ", hdf5_getters.get_start_of_fade_out(h5))
print("Tempo -- ", hdf5_getters.get_tempo(h5))
print("Time signature -- ", hdf5_getters.get_time_signature(h5))
print("Time signature confidence -- ",
hdf5_getters.get_time_signature_confidence(h5))
print("Track ID -- ", hdf5_getters.get_track_id(h5))
print("Segments Start -- ", hdf5_getters.get_segments_start(h5))
print("Segments Confidence -- ", hdf5_getters.get_segments_confidence(h5))
print("Segments Pitches -- ", hdf5_getters.get_segments_pitches(h5))
print("Segments Timbre -- ", hdf5_getters.get_segments_timbre(h5))
print("Segments Loudness max -- ",
hdf5_getters.get_segments_loudness_max(h5))
print("Segments Loudness max time-- ",
hdf5_getters.get_segments_loudness_max_time(h5))
print("Segments Loudness start -- ",
hdf5_getters.get_segments_loudness_start(h5))
print("Sections start -- ", hdf5_getters.get_sections_start(h5))
print("Sections Confidence -- ", hdf5_getters.get_sections_confidence(h5))
print("Beats start -- ", hdf5_getters.get_beats_start(h5))
print("Beats confidence -- ", hdf5_getters.get_beats_confidence(h5))
print("Bars start -- ", hdf5_getters.get_bars_start(h5))
print("Bars confidence -- ", hdf5_getters.get_bars_confidence(h5))
print("Tatums start -- ", hdf5_getters.get_tatums_start(h5))
print("Tatums confidence -- ", hdf5_getters.get_tatums_confidence(h5))
print("Artist mbtags -- ", hdf5_getters.get_artist_mbtags(h5))
print("Artist mbtags count -- ", hdf5_getters.get_artist_mbtags_count(h5))
print("Year -- ", hdf5_getters.get_year(h5))
fields = ['Title', 'Artist ID']
with open('Tester2.csv', 'w', newline='') as csvfile:
csv_writer = csv.writer(csvfile, delimiter=';')
# writing the fields
csv_writer.writerow(fields)
# writing the data rows
csv_writer.writerow(
[hdf5_getters.get_title(h5),
hdf5_getters.get_artist_id(h5)])
h5.close() # close h5 when completed in the end
def main():
outputFile1 = open('SongCSVFinal.csv', 'w')
csvRowString = ""
#################################################
#if you want to prompt the user for the order of attributes in the csv,
#leave the prompt boolean set to True
#else, set 'prompt' to False and set the order of attributes in the 'else'
#clause
prompt = False
#################################################
if prompt == True:
while prompt:
prompt = False
csvAttributeString = raw_input(
"\n\nIn what order would you like the colums of the CSV file?\n"
+ "Please delineate with commas. The options are: " +
"AlbumName, AlbumID, ArtistID, ArtistLatitude, ArtistLocation, ArtistLongitude,"
+
" ArtistName, Danceability, Duration, KeySignature, KeySignatureConfidence, Tempo,"
+
" SongID, TimeSignature, TimeSignatureConfidence, Title, and Year.\n\n"
+
"For example, you may write \"Title, Tempo, Duration\"...\n\n"
+ "...or exit by typing 'exit'.\n\n")
csvAttributeList = re.split('\W+', csvAttributeString)
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
for attribute in csvAttributeList:
# print "Here is the attribute: " + attribute + " \n"
if attribute == 'AlbumID'.lower():
csvRowString += 'AlbumID'
elif attribute == 'AlbumName'.lower():
csvRowString += 'AlbumName'
elif attribute == 'ArtistID'.lower():
csvRowString += 'ArtistID'
elif attribute == 'ArtistLatitude'.lower():
csvRowString += 'ArtistLatitude'
elif attribute == 'ArtistLocation'.lower():
csvRowString += 'ArtistLocation'
elif attribute == 'ArtistLongitude'.lower():
csvRowString += 'ArtistLongitude'
elif attribute == 'ArtistName'.lower():
csvRowString += 'ArtistName'
elif attribute == 'Danceability'.lower():
csvRowString += 'Danceability'
elif attribute == 'Duration'.lower():
csvRowString += 'Duration'
elif attribute == 'KeySignature'.lower():
csvRowString += 'KeySignature'
elif attribute == 'KeySignatureConfidence'.lower():
csvRowString += 'KeySignatureConfidence'
elif attribute == 'SongID'.lower():
csvRowString += "SongID"
elif attribute == 'Tempo'.lower():
csvRowString += 'Tempo'
elif attribute == 'TimeSignature'.lower():
csvRowString += 'TimeSignature'
elif attribute == 'TimeSignatureConfidence'.lower():
csvRowString += 'TimeSignatureConfidence'
elif attribute == 'Title'.lower():
csvRowString += 'Title'
elif attribute == 'Year'.lower():
csvRowString += 'Year'
elif attribute == 'Exit'.lower():
sys.exit()
else:
prompt = True
print "=============="
print "I believe there has been an error with the input."
print "=============="
break
csvRowString += ","
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex - 1]
csvRowString += "\n"
outputFile1.write(csvRowString)
csvRowString = ""
#else, if you want to hard code the order of the csv file and not prompt
#the user,
else:
#################################################
#change the order of the csv file here
#Default is to list all available attributes (in alphabetical order)
csvRowString = (
"SongID,AlbumID,AlbumName,ArtistID,ArtistLatitude,ArtistLocation,"
+
"ArtistLongitude,ArtistName,Danceability,Duration,KeySignature," +
"KeySignatureConfidence,Tempo,TimeSignature,TimeSignatureConfidence,"
+
"Title,Year,mbID,Energy,ArtistFamiliarity,Hotness,end_of_fade_in,key,keyConfidence,Loudness,"
+ "mode,mode_confidence,start_of_fade_out")
#################################################
csvAttributeList = re.split('\W+', csvRowString)
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
outputFile1.write("SongNumber,")
outputFile1.write(csvRowString + "\n")
csvRowString = ""
#################################################
#Set the basedir here, the root directory from which the search
#for files stored in a (hierarchical data structure) will originate
basedir = "." # "." As the default means the current directory
ext = ".h5" #Set the extension here. H5 is the extension for HDF5 files.
#################################################
#files = glob.glob(os.path.join(root,'*'+ext))
#FOR LOOP
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
print(files)
for f in files:
songH5File = hdf5_getters.open_h5_file_read(f)
print('hello 1')
print(songH5File)
song = Song(str(hdf5_getters.get_song_id(songH5File)))
testDanceability = hdf5_getters.get_danceability(songH5File)
# print type(testDanceability)
# print ("Here is the danceability: ") + str(testDanceability)
song.artistID = str(hdf5_getters.get_artist_id(songH5File))
song.albumID = str(hdf5_getters.get_release_7digitalid(songH5File))
song.albumName = str(hdf5_getters.get_release(songH5File))
song.artistLatitude = str(
hdf5_getters.get_artist_latitude(songH5File))
song.artistLocation = str(
hdf5_getters.get_artist_location(songH5File))
song.artistLongitude = str(
hdf5_getters.get_artist_longitude(songH5File))
song.artistName = str(hdf5_getters.get_artist_name(songH5File))
song.danceability = str(hdf5_getters.get_danceability(songH5File))
song.duration = str(hdf5_getters.get_duration(songH5File))
# song.setGenreList()
song.keySignature = str(hdf5_getters.get_key(songH5File))
song.keySignatureConfidence = str(
hdf5_getters.get_key_confidence(songH5File))
# song.lyrics = None
# song.popularity = None
song.tempo = str(hdf5_getters.get_tempo(songH5File))
song.timeSignature = str(
hdf5_getters.get_time_signature(songH5File))
song.timeSignatureConfidence = str(
hdf5_getters.get_time_signature_confidence(songH5File))
song.title = str(hdf5_getters.get_title(songH5File))
song.year = str(hdf5_getters.get_year(songH5File))
song.mbID = str(hdf5_getters.get_artist_mbid(songH5File))
song.energy = str(hdf5_getters.get_energy(songH5File))
#song.beatConfidence = str(hdf5_getters.get_beats_confidence(songH5File))
song.tempo = str(hdf5_getters.get_tempo(songH5File))
song.loudness = str(hdf5_getters.get_loudness(songH5File))
song.key = str(hdf5_getters.get_key(songH5File))
song.artistFamiliarity = str(
hdf5_getters.get_artist_familiarity(songH5File))
song.keyConfidence = str(
hdf5_getters.get_key_confidence(songH5File))
song.hotness = str(hdf5_getters.get_artist_hotttnesss(songH5File))
#song.sampleRate= str(hdf5_getters.get_analysis_sample_rate(songH5File))
song.end_of_fade_in = str(
hdf5_getters.get_end_of_fade_in(songH5File))
song.mode = str(hdf5_getters.get_mode(songH5File))
song.mode_confidence = str(
hdf5_getters.get_mode_confidence(songH5File))
song.start_of_fade_out = str(
hdf5_getters.get_start_of_fade_out(songH5File))
#print song count
csvRowString += str(song.songCount) + ","
for attribute in csvAttributeList:
# print "Here is the attribute: " + attribute + " \n"
if attribute == 'AlbumID'.lower():
csvRowString += song.albumID
elif attribute == 'AlbumName'.lower():
albumName = song.albumName
albumName = albumName.replace(',', "")
csvRowString += "\"" + albumName + "\""
elif attribute == 'ArtistID'.lower():
csvRowString += "\"" + song.artistID + "\""
elif attribute == 'ArtistLatitude'.lower():
latitude = song.artistLatitude
if latitude == 'nan':
latitude = ''
csvRowString += latitude
elif attribute == 'ArtistLocation'.lower():
location = song.artistLocation
location = location.replace(',', '')
csvRowString += "\"" + location + "\""
elif attribute == 'ArtistLongitude'.lower():
longitude = song.artistLongitude
if longitude == 'nan':
longitude = ''
csvRowString += longitude
elif attribute == 'ArtistName'.lower():
csvRowString += "\"" + song.artistName + "\""
elif attribute == 'Danceability'.lower():
csvRowString += song.danceability
elif attribute == 'Duration'.lower():
csvRowString += song.duration
elif attribute == 'KeySignature'.lower():
csvRowString += song.keySignature
elif attribute == 'KeySignatureConfidence'.lower():
# print "key sig conf: " + song.timeSignatureConfidence
csvRowString += song.keySignatureConfidence
elif attribute == 'SongID'.lower():
csvRowString += "\"" + song.id + "\""
elif attribute == 'Tempo'.lower():
# print "Tempo: " + song.tempo
csvRowString += song.tempo
elif attribute == 'TimeSignature'.lower():
csvRowString += song.timeSignature
elif attribute == 'TimeSignatureConfidence'.lower():
# print "time sig conf: " + song.timeSignatureConfidence
csvRowString += song.timeSignatureConfidence
elif attribute == 'Title'.lower():
csvRowString += "\"" + song.title + "\""
elif attribute == 'Year'.lower():
csvRowString += song.year
elif attribute == 'mbID'.lower():
csvRowString += song.mbID
elif attribute == 'Energy'.lower():
csvRowString += song.energy
elif attribute == 'ArtistFamiliarity'.lower():
csvRowString += song.artistFamiliarity
elif attribute == 'Hotness'.lower():
csvRowString += song.hotness
#elif attribute == 'SampleRate'.lower():
#csvRowString += song.sampleRate
elif attribute == 'end_of_fade_in'.lower():
csvRowString += song.end_of_fade_in
elif attribute == 'key'.lower():
csvRowString += song.key
elif attribute == 'keyConfidence'.lower():
csvRowString += song.keyConfidence
elif attribute == 'Loudness'.lower():
csvRowString += song.loudness
elif attribute == 'mode'.lower():
csvRowString += song.mode
elif attribute == 'mode_confidence'.lower():
csvRowString += song.mode_confidence
elif attribute == 'start_of_fade_out'.lower():
csvRowString += song.start_of_fade_out
else:
csvRowString += "Erm. This didn't work. Error. :( :(\n"
csvRowString += ","
#Remove the final comma from each row in the csv
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex - 1]
csvRowString += "\n"
outputFile1.write(csvRowString)
csvRowString = ""
songH5File.close()
outputFile1.close()
# Get loudness
loudness = hdf5_getters.get_loudness(h5)
# Get year
year = hdf5_getters.get_year(h5)
# Get tempo
tempo = hdf5_getters.get_tempo(h5)
#########################################################
# Get analysis sample rate
analysis_rate = hdf5_getters.get_analysis_sample_rate(h5)
# Get end of fade in
end_of_fade_in = hdf5_getters.get_end_of_fade_in(h5)
# Get key
key = hdf5_getters.get_key(h5)
# Get key confidence
key_confidence = hdf5_getters.get_key_confidence(h5)
# Get mode
mode = hdf5_getters.get_mode(h5)
# Get mode confidence
mode_confidence = hdf5_getters.get_mode_confidence(h5)
# Get start of fade-out
start_of_fade_out = hdf5_getters.get_start_of_fade_out(h5)
def convert_to_csv():
i = 0
data = []
target = []
count = 0
with open('data_timbre.csv', 'w+') as f:
with open('target_timbre.csv', 'w+') as f2:
writer = csv.writer(f)
target_writer = csv.writer(f2)
for root, dirs, files in os.walk(msd_subset_data_path):
files = glob.glob(os.path.join(root,'*.h5'))
for f in sorted(files):
try: # Opening is very prone to causing exceptions, we'll just skip file if exception is thrown
h5 = getter.open_h5_file_read(f)
year = getter.get_year(h5)
if year:
count +=1
analysis_file = open('current_analysis_status.txt','a')
update = "Currently at file name: " + str(f) + " and at number " + str(count) + "\n"
analysis_file.write(update)
print update
analysis_file.close()
target.append([year])
row = []
timbre = getter.get_segments_timbre(h5)
segstarts = getter.get_segments_start(h5)
btstarts = getter.get_beats_start(h5)
duration = getter.get_duration(h5)
end_of_fade_in = getter.get_end_of_fade_in(h5)
key = getter.get_key(h5)
key_confidence = getter.get_key_confidence(h5)
loudness = getter.get_loudness(h5)
start_of_fade_out = getter.get_start_of_fade_out(h5)
tempo = getter.get_tempo(h5)
time_signature = getter.get_time_signature(h5)
time_signature_confidence = getter.get_time_signature_confidence(h5)
h5.close() # VERY IMPORTANT
segstarts = np.array(segstarts).flatten()
btstarts = np.array(btstarts).flatten()
bttimbre = align_feats(timbre.T, segstarts, btstarts, duration, end_of_fade_in, key, key_confidence, loudness, start_of_fade_out, tempo, time_signature, time_signature_confidence)
if bttimbre is None:
continue # Skip this track, some features broken
npicks, winsize, finaldim = 12, 12, 144 # Calculated by 12 * 12. 12 is fixed as number of dimensions.
processed_feats = extract_and_compress(bttimbre, npicks, winsize, finaldim)
n_p_feats = processed_feats.shape[0]
if processed_feats is None:
continue # Skip this track, some features broken
row = processed_feats.flatten()
if len(row) != 12*144: # 12 dimensions * 144 features per dimension
continue # Not enough features
year_row = np.array([year])
if row.any() and year_row.any():
writer.writerow(row)
target_writer.writerow(year_row)
i+=1
else:
h5.close()
except Exception:
pass
print 'Finished!'
analysis_file = open('current_analysis_status.txt','a')
analysis_file.write('Done!')
analysis_file.close()
return
def complete_hd5_to_csv(basedir):
ext = '.h5' # Get all files with extension .h5
# Header title. Essentially it is a schema for all the following songs
header = [
'Title', 'Artist familiarity', 'Artist hotness', 'Artist ID',
'Artist mbID', 'Artist playmeid', 'Artist 7DigitalID',
'Artist latitude', 'Artist longitude', 'Artist location',
'Artist Name', 'Release', 'Release 7DigitalID', 'Song ID',
'Song Hotness', 'Track 7Digital', 'Analysis sample rate', 'Audio md5',
'Danceability', 'Duration', 'End of Fade', 'Energy', 'Key',
'Key Confidence', 'Loudness', 'Mode', 'Mode Confidence',
'Start of fade out', 'Tempo', 'Time signature',
'Time signature confidence', 'Track ID', 'Year'
]
with open('Tester2.csv', 'w', newline='') as csvfile:
csv_writer = csv.writer(csvfile, delimiter=';')
# writing the header line. This line contains the schema of the data
csv_writer.writerow(header)
# Read all files from the given directories
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
print(files)
for f in files:
h5 = hdf5_getters.open_h5_file_read(f)
# Write as row all elements. NOTE: Only the serialized elements are parsed and not arrays
csv_writer.writerow([
hdf5_getters.get_title(h5),
hdf5_getters.get_artist_familiarity(h5),
hdf5_getters.get_artist_hotttnesss(h5),
hdf5_getters.get_artist_id(h5),
hdf5_getters.get_artist_mbid(h5),
hdf5_getters.get_artist_playmeid(h5),
hdf5_getters.get_artist_7digitalid(h5),
hdf5_getters.get_artist_latitude(h5),
hdf5_getters.get_artist_longitude(h5),
hdf5_getters.get_artist_location(h5),
hdf5_getters.get_artist_name(h5),
hdf5_getters.get_release(h5),
hdf5_getters.get_release_7digitalid(h5),
hdf5_getters.get_song_id(h5),
hdf5_getters.get_song_hotttnesss(h5),
hdf5_getters.get_track_7digitalid(h5),
hdf5_getters.get_analysis_sample_rate(h5),
hdf5_getters.get_audio_md5(h5),
hdf5_getters.get_danceability(h5),
hdf5_getters.get_duration(h5),
hdf5_getters.get_end_of_fade_in(h5),
hdf5_getters.get_energy(h5),
hdf5_getters.get_key(h5),
hdf5_getters.get_key_confidence(h5),
hdf5_getters.get_loudness(h5),
hdf5_getters.get_mode(h5),
hdf5_getters.get_mode_confidence(h5),
hdf5_getters.get_start_of_fade_out(h5),
hdf5_getters.get_tempo(h5),
hdf5_getters.get_time_signature(h5),
hdf5_getters.get_time_signature_confidence(h5),
hdf5_getters.get_track_id(h5),
hdf5_getters.get_year(h5)
])
# For debugging purposes. Everything as expected
# print()
# print("Num of songs -- ", hdf5_getters.get_num_songs(h5)) # One song per file
# print("Title -- ", hdf5_getters.get_title(h5)) # Print the title of a specific h5 file
# print("Artist familiarity -- ", hdf5_getters.get_artist_familiarity(h5))
# print("Artist hotness -- ", hdf5_getters.get_artist_hotttnesss(h5))
# print("Artist ID -- ", hdf5_getters.get_artist_id(h5))
# print("Artist mbID -- ", hdf5_getters.get_artist_mbid(h5))
# print("Artist playmeid -- ", hdf5_getters.get_artist_playmeid(h5))
# print("Artist 7DigitalID -- ", hdf5_getters.get_artist_7digitalid(h5))
# print("Artist latitude -- ", hdf5_getters.get_artist_latitude(h5))
# print("Artist longitude -- ", hdf5_getters.get_artist_longitude(h5))
# print("Artist location -- ", hdf5_getters.get_artist_location(h5))
# print("Artist Name -- ", hdf5_getters.get_artist_name(h5))
# print("Release -- ", hdf5_getters.get_release(h5))
# print("Release 7DigitalID -- ", hdf5_getters.get_release_7digitalid(h5))
# print("Song ID -- ", hdf5_getters.get_song_id(h5))
# print("Song Hotness -- ", hdf5_getters.get_song_hotttnesss(h5))
# print("Track 7Digital -- ", hdf5_getters.get_track_7digitalid(h5))
# print("Analysis sample rate -- ", hdf5_getters.get_analysis_sample_rate(h5))
# print("Audio md5 -- ", hdf5_getters.get_audio_md5(h5))
# print("Danceability -- ", hdf5_getters.get_danceability(h5))
# print("Duration -- ", hdf5_getters.get_duration(h5))
# print("End of Fade -- ", hdf5_getters.get_end_of_fade_in(h5))
# print("Energy -- ", hdf5_getters.get_energy(h5))
# print("Key -- ", hdf5_getters.get_key(h5))
# print("Key Confidence -- ", hdf5_getters.get_key_confidence(h5))
# print("Loudness -- ", hdf5_getters.get_loudness(h5))
# print("Mode -- ", hdf5_getters.get_mode(h5))
# print("Mode Confidence -- ", hdf5_getters.get_mode_confidence(h5))
# print("Start of fade out -- ", hdf5_getters.get_start_of_fade_out(h5))
# print("Tempo -- ", hdf5_getters.get_tempo(h5))
# print("Time signature -- ", hdf5_getters.get_time_signature(h5))
# print("Time signature confidence -- ", hdf5_getters.get_time_signature_confidence(h5))
# print("Track ID -- ", hdf5_getters.get_track_id(h5))
# # print("Artist mbtags -- ", hdf5_getters.get_artist_mbtags(h5))
# # print("Artist mbtags count -- ", hdf5_getters.get_artist_mbtags_count(h5))
# print("Year -- ", hdf5_getters.get_year(h5))
h5.close()
hdf5path = "MillionSongSubset/data/"+f1+"/"+f2+"/"+f3+"/"+track_id+".h5"
total_tracks += 1
# Check existence of the HDF5 file
if not os.path.isfile(hdf5path):
continue
#print('ERROR: file',hdf5path,'does not exist.')
#sys.exit(0)
h5 = hdf5_getters.open_h5_file_read(hdf5path)
# Retrieve features from HDF5
danceability = hdf5_getters.get_danceability(h5)
duration = hdf5_getters.get_duration(h5)
time_of_fade_in = hdf5_getters.get_end_of_fade_in(h5)
energy = hdf5_getters.get_energy(h5)
key = hdf5_getters.get_key(h5)
key_confidence = hdf5_getters.get_key_confidence(h5)
loudness = hdf5_getters.get_loudness(h5)
mode = hdf5_getters.get_mode(h5)
mode_confidence = hdf5_getters.get_mode_confidence(h5)
sections_start = hdf5_getters.get_sections_start(h5)
num_sections = len(sections_start)
if num_sections == 0:
h5.close()
continue
segments_loudness_max = hdf5_getters.get_segments_loudness_max(h5)
segments_loudness_start = hdf5_getters.get_segments_loudness_start(h5)
num_segments = len(hdf5_getters.get_segments_start(h5))
num_tatums = len(hdf5_getters.get_tatums_start(h5))
def classify(h5):
output_array = {}
# duration
duration = hdf5_getters.get_duration(h5)
output_array["duration"] = duration ### ADDED VALUE TO ARRAY
# number of bars
bars = hdf5_getters.get_bars_start(h5)
num_bars = len(bars)
output_array["num_bars"] = num_bars ### ADDED VALUE TO ARRAY
# mean and variance in bar length
bar_length = numpy.ediff1d(bars)
variance_bar_length = numpy.var(bar_length)
output_array[
"variance_bar_length"] = variance_bar_length ### ADDED VALUE TO ARRAY
# number of beats
beats = hdf5_getters.get_beats_start(h5)
num_beats = len(beats)
output_array["num_beats"] = num_beats ### ADDED VALUE TO ARRAY
# mean and variance in beats length
beats_length = numpy.ediff1d(beats)
variance_beats_length = numpy.var(bar_length)
output_array[
"variance_beats_length"] = variance_beats_length ### ADDED VALUE TO ARRAY
# danceability
danceability = hdf5_getters.get_danceability(h5)
output_array["danceability"] = danceability ### ADDED VALUE TO ARRAY
# end of fade in
end_of_fade_in = hdf5_getters.get_end_of_fade_in(h5)
output_array["end_of_fade_in"] = end_of_fade_in ### ADDED VALUE TO ARRAY
# energy
energy = hdf5_getters.get_energy(h5)
output_array["energy"] = energy ### ADDED VALUE TO ARRAY
# key
key = hdf5_getters.get_key(h5)
output_array["key"] = int(key) ### ADDED VALUE TO ARRAY
# loudness
loudness = hdf5_getters.get_loudness(h5)
output_array["loudness"] = loudness ### ADDED VALUE TO ARRAY
# mode
mode = hdf5_getters.get_mode(h5)
output_array["mode"] = int(mode) ### ADDED VALUE TO ARRAY
# number sections
sections = hdf5_getters.get_sections_start(h5)
num_sections = len(sections)
output_array["num_sections"] = num_sections ### ADDED VALUE TO ARRAY
# mean and variance in sections length
sections_length = numpy.ediff1d(sections)
variance_sections_length = numpy.var(sections)
output_array[
"variance_sections_length"] = variance_sections_length ### ADDED VALUE TO ARRAY
# number segments
segments = hdf5_getters.get_segments_start(h5)
num_segments = len(segments)
output_array["num_segments"] = num_segments ### ADDED VALUE TO ARRAY
# mean and variance in segments length
segments_length = numpy.ediff1d(segments)
variance_segments_length = numpy.var(segments)
output_array[
"variance_segments_length"] = variance_segments_length ### ADDED VALUE TO ARRAY
# segment loudness max
segment_loudness_max_array = hdf5_getters.get_segments_loudness_max(h5)
segment_loudness_max_time_array = hdf5_getters.get_segments_loudness_max_time(
h5)
segment_loudness_max_index = 0
for i in range(len(segment_loudness_max_array)):
if segment_loudness_max_array[i] > segment_loudness_max_array[
segment_loudness_max_index]:
segment_loudness_max_index = i
segment_loudness_max = segment_loudness_max_array[
segment_loudness_max_index]
segment_loudness_max_time = segment_loudness_max_time_array[
segment_loudness_max_index]
output_array[
"segment_loudness_max"] = segment_loudness_max ### ADDED VALUE TO ARRAY
output_array[
"segment_loudness_time"] = segment_loudness_max_time ### ADDED VALUE TO ARRAY
# POSSIBLE TODO: use average function instead and weight by segment length
# segment loudness mean (start)
segment_loudness_array = hdf5_getters.get_segments_loudness_start(h5)
segment_loudness_mean = numpy.mean(segment_loudness_array)
output_array[
"segment_loudness_mean"] = segment_loudness_mean ### ADDED VALUE TO ARRAY
# segment loudness variance (start)
segment_loudness_variance = numpy.var(segment_loudness_array)
output_array[
"segment_loudness_variance"] = segment_loudness_variance ### ADDED VALUE TO ARRAY
# segment pitches
segment_pitches_array = hdf5_getters.get_segments_pitches(h5)
segment_pitches_mean = numpy.mean(segment_pitches_array, axis=0).tolist()
output_array["segment_pitches_mean"] = segment_pitches_mean
# segment pitches variance (start)
segment_pitches_variance = numpy.var(segment_pitches_array,
axis=0).tolist()
output_array["segment_pitches_variance"] = segment_pitches_variance
# segment timbres
segment_timbres_array = hdf5_getters.get_segments_timbre(h5)
segment_timbres_mean = numpy.mean(segment_timbres_array, axis=0).tolist()
output_array["segment_timbres_mean"] = segment_timbres_mean
# segment timbres variance (start)
segment_timbres_variance = numpy.var(segment_timbres_array,
axis=0).tolist()
output_array["segment_timbres_variance"] = segment_timbres_variance
# hotttnesss
hottness = hdf5_getters.get_song_hotttnesss(h5, 0)
output_array["hottness"] = hottness ### ADDED VALUE TO ARRAY
# duration-start of fade out
start_of_fade_out = hdf5_getters.get_start_of_fade_out(h5)
fade_out = duration - start_of_fade_out
output_array["fade_out"] = fade_out ### ADDED VALUE TO ARRAY
# tatums
tatums = hdf5_getters.get_tatums_start(h5)
num_tatums = len(tatums)
output_array["num_tatums"] = num_tatums ### ADDED VALUE TO ARRAY
# mean and variance in tatums length
tatums_length = numpy.ediff1d(tatums)
variance_tatums_length = numpy.var(tatums_length)
output_array[
"variance_tatums_length"] = variance_tatums_length ### ADDED VALUE TO ARRAY
# tempo
tempo = hdf5_getters.get_tempo(h5)
output_array["tempo"] = tempo ### ADDED VALUE TO ARRAY
# time signature
time_signature = hdf5_getters.get_time_signature(h5)
output_array["time_signature"] = int(
time_signature) ### ADDED VALUE TO ARRAY
# year
year = hdf5_getters.get_year(h5)
output_array["year"] = int(year) ### ADDED VALUE TO ARRAY
# artist terms
artist_terms = hdf5_getters.get_artist_terms(h5, 0)
output_array["artist_terms"] = artist_terms.tolist()
artist_terms_freq = hdf5_getters.get_artist_terms_freq(h5, 0)
output_array["artist_terms_freq"] = artist_terms_freq.tolist()
artist_name = hdf5_getters.get_artist_name(h5, 0)
output_array["artist_name"] = artist_name
artist_id = hdf5_getters.get_artist_id(h5, 0)
output_array["artist_id"] = artist_id
# title
title = hdf5_getters.get_title(h5, 0)
output_array["title"] = title
return output_array
def data_to_flat_file(basedir, ext='.h5'):
"""This function extract the information from the tables and creates the flat file."""
count = 0
#song counter
list_to_write = []
row_to_write = ""
writer = csv.writer(open("metadata_wholeA.csv", "wb"))
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
for f in files:
print f #the name of the file
h5 = hdf5_getters.open_h5_file_read(f)
title = hdf5_getters.get_title(h5)
title = title.replace('"', '')
comma = title.find(',') #eliminating commas in the title
if comma != -1:
print title
time.sleep(1)
album = hdf5_getters.get_release(h5)
album = album.replace('"', '') #eliminating commas in the album
comma = album.find(',')
if comma != -1:
print album
time.sleep(1)
artist_name = hdf5_getters.get_artist_name(h5)
comma = artist_name.find(',')
if comma != -1:
print artist_name
time.sleep(1)
artist_name = artist_name.replace('"',
'') #eliminating double quotes
duration = hdf5_getters.get_duration(h5)
samp_rt = hdf5_getters.get_analysis_sample_rate(h5)
artist_7digitalid = hdf5_getters.get_artist_7digitalid(h5)
artist_fam = hdf5_getters.get_artist_familiarity(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_fam) == True:
artist_fam = -1
artist_hotness = hdf5_getters.get_artist_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_hotness) == True:
artist_hotness = -1
artist_id = hdf5_getters.get_artist_id(h5)
artist_lat = hdf5_getters.get_artist_latitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lat) == True:
artist_lat = -1
artist_loc = hdf5_getters.get_artist_location(h5)
#checks artist_loc to see if it is a hyperlink if it is set as empty string
artist_loc = artist_loc.replace(",", "\,")
if artist_loc.startswith("<a"):
artist_loc = ""
if len(artist_loc) > 100:
artist_loc = ""
artist_lon = hdf5_getters.get_artist_longitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lon) == True:
artist_lon = -1
artist_mbid = hdf5_getters.get_artist_mbid(h5)
artist_pmid = hdf5_getters.get_artist_playmeid(h5)
audio_md5 = hdf5_getters.get_audio_md5(h5)
danceability = hdf5_getters.get_danceability(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(danceability) == True:
danceability = -1
end_fade_in = hdf5_getters.get_end_of_fade_in(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(end_fade_in) == True:
end_fade_in = -1
energy = hdf5_getters.get_energy(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(energy) == True:
energy = -1
song_key = hdf5_getters.get_key(h5)
key_c = hdf5_getters.get_key_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(key_c) == True:
key_c = -1
loudness = hdf5_getters.get_loudness(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(loudness) == True:
loudness = -1
mode = hdf5_getters.get_mode(h5)
mode_conf = hdf5_getters.get_mode_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(mode_conf) == True:
mode_conf = -1
release_7digitalid = hdf5_getters.get_release_7digitalid(h5)
song_hot = hdf5_getters.get_song_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(song_hot) == True:
song_hot = -1
song_id = hdf5_getters.get_song_id(h5)
start_fade_out = hdf5_getters.get_start_of_fade_out(h5)
tempo = hdf5_getters.get_tempo(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(tempo) == True:
tempo = -1
time_sig = hdf5_getters.get_time_signature(h5)
time_sig_c = hdf5_getters.get_time_signature_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(time_sig_c) == True:
time_sig_c = -1
track_id = hdf5_getters.get_track_id(h5)
track_7digitalid = hdf5_getters.get_track_7digitalid(h5)
year = hdf5_getters.get_year(h5)
bars_c = hdf5_getters.get_bars_confidence(h5)
bars_c_avg = get_avg(bars_c)
bars_c_max = get_max(bars_c)
bars_c_min = get_min(bars_c)
bars_c_stddev = get_stddev(bars_c)
bars_c_count = get_count(bars_c)
bars_c_sum = get_sum(bars_c)
bars_start = hdf5_getters.get_bars_start(h5)
bars_start_avg = get_avg(bars_start)
bars_start_max = get_max(bars_start)
bars_start_min = get_min(bars_start)
bars_start_stddev = get_stddev(bars_start)
bars_start_count = get_count(bars_start)
bars_start_sum = get_sum(bars_start)
beats_c = hdf5_getters.get_beats_confidence(h5)
beats_c_avg = get_avg(beats_c)
beats_c_max = get_max(beats_c)
beats_c_min = get_min(beats_c)
beats_c_stddev = get_stddev(beats_c)
beats_c_count = get_count(beats_c)
beats_c_sum = get_sum(beats_c)
beats_start = hdf5_getters.get_beats_start(h5)
beats_start_avg = get_avg(beats_start)
beats_start_max = get_max(beats_start)
beats_start_min = get_min(beats_start)
beats_start_stddev = get_stddev(beats_start)
beats_start_count = get_count(beats_start)
beats_start_sum = get_sum(beats_start)
sec_c = hdf5_getters.get_sections_confidence(h5)
sec_c_avg = get_avg(sec_c)
sec_c_max = get_max(sec_c)
sec_c_min = get_min(sec_c)
sec_c_stddev = get_stddev(sec_c)
sec_c_count = get_count(sec_c)
sec_c_sum = get_sum(sec_c)
sec_start = hdf5_getters.get_sections_start(h5)
sec_start_avg = get_avg(sec_start)
sec_start_max = get_max(sec_start)
sec_start_min = get_min(sec_start)
sec_start_stddev = get_stddev(sec_start)
sec_start_count = get_count(sec_start)
sec_start_sum = get_sum(sec_start)
seg_c = hdf5_getters.get_segments_confidence(h5)
seg_c_avg = get_avg(seg_c)
seg_c_max = get_max(seg_c)
seg_c_min = get_min(seg_c)
seg_c_stddev = get_stddev(seg_c)
seg_c_count = get_count(seg_c)
seg_c_sum = get_sum(seg_c)
seg_loud_max = hdf5_getters.get_segments_loudness_max(h5)
seg_loud_max_avg = get_avg(seg_loud_max)
seg_loud_max_max = get_max(seg_loud_max)
seg_loud_max_min = get_min(seg_loud_max)
seg_loud_max_stddev = get_stddev(seg_loud_max)
seg_loud_max_count = get_count(seg_loud_max)
seg_loud_max_sum = get_sum(seg_loud_max)
seg_loud_max_time = hdf5_getters.get_segments_loudness_max_time(h5)
seg_loud_max_time_avg = get_avg(seg_loud_max_time)
seg_loud_max_time_max = get_max(seg_loud_max_time)
seg_loud_max_time_min = get_min(seg_loud_max_time)
seg_loud_max_time_stddev = get_stddev(seg_loud_max_time)
seg_loud_max_time_count = get_count(seg_loud_max_time)
seg_loud_max_time_sum = get_sum(seg_loud_max_time)
seg_loud_start = hdf5_getters.get_segments_loudness_start(h5)
seg_loud_start_avg = get_avg(seg_loud_start)
seg_loud_start_max = get_max(seg_loud_start)
seg_loud_start_min = get_min(seg_loud_start)
seg_loud_start_stddev = get_stddev(seg_loud_start)
seg_loud_start_count = get_count(seg_loud_start)
seg_loud_start_sum = get_sum(seg_loud_start)
seg_pitch = hdf5_getters.get_segments_pitches(h5)
pitch_size = len(seg_pitch)
seg_start = hdf5_getters.get_segments_start(h5)
seg_start_avg = get_avg(seg_start)
seg_start_max = get_max(seg_start)
seg_start_min = get_min(seg_start)
seg_start_stddev = get_stddev(seg_start)
seg_start_count = get_count(seg_start)
seg_start_sum = get_sum(seg_start)
seg_timbre = hdf5_getters.get_segments_timbre(h5)
tatms_c = hdf5_getters.get_tatums_confidence(h5)
tatms_c_avg = get_avg(tatms_c)
tatms_c_max = get_max(tatms_c)
tatms_c_min = get_min(tatms_c)
tatms_c_stddev = get_stddev(tatms_c)
tatms_c_count = get_count(tatms_c)
tatms_c_sum = get_sum(tatms_c)
tatms_start = hdf5_getters.get_tatums_start(h5)
tatms_start_avg = get_avg(tatms_start)
tatms_start_max = get_max(tatms_start)
tatms_start_min = get_min(tatms_start)
tatms_start_stddev = get_stddev(tatms_start)
tatms_start_count = get_count(tatms_start)
tatms_start_sum = get_sum(tatms_start)
#Getting the genres
genre_set = 0 #flag to see if the genre has been set or not
art_trm = hdf5_getters.get_artist_terms(h5)
trm_freq = hdf5_getters.get_artist_terms_freq(h5)
trn_wght = hdf5_getters.get_artist_terms_weight(h5)
a_mb_tags = hdf5_getters.get_artist_mbtags(h5)
genre_indexes = get_genre_indexes(
trm_freq) #index of the highest freq
final_genre = []
genres_so_far = []
for i in range(len(genre_indexes)):
genre_tmp = get_genre(
art_trm, genre_indexes[i]
) #genre that corresponds to the highest freq
genres_so_far = genre_dict.get_genre_in_dict(
genre_tmp) #getting the genre from the dictionary
if len(genres_so_far) != 0:
for i in genres_so_far:
final_genre.append(i)
genre_set = 1 #genre was found in dictionary
if genre_set == 1:
col_num = []
for genre in final_genre:
column = int(
genre) #getting the column number of the genre
col_num.append(column)
genre_array = genre_columns(col_num) #genre array
else:
genre_array = genre_columns(
-1) #the genre was not found in the dictionary
transpose_pitch = seg_pitch.transpose(
) #this is to tranpose the matrix,so we can have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_pitch_avg = []
seg_pitch_max = []
seg_pitch_min = []
seg_pitch_stddev = []
seg_pitch_count = []
seg_pitch_sum = []
i = 0
#Getting the aggregate values in the pitches array
for row in transpose_pitch:
seg_pitch_avg.append(get_avg(row))
seg_pitch_max.append(get_max(row))
seg_pitch_min.append(get_min(row))
seg_pitch_stddev.append(get_stddev(row))
seg_pitch_count.append(get_count(row))
seg_pitch_sum.append(get_sum(row))
i = i + 1
#extracting information from the timbre array
transpose_timbre = seg_pitch.transpose(
) #tranposing matrix, to have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_timbre_avg = []
seg_timbre_max = []
seg_timbre_min = []
seg_timbre_stddev = []
seg_timbre_count = []
seg_timbre_sum = []
i = 0
for row in transpose_timbre:
seg_timbre_avg.append(get_avg(row))
seg_timbre_max.append(get_max(row))
seg_timbre_min.append(get_min(row))
seg_timbre_stddev.append(get_stddev(row))
seg_timbre_count.append(get_count(row))
seg_timbre_sum.append(get_sum(row))
i = i + 1
#Writing to the flat file
writer.writerow([
title, album, artist_name, year, duration, seg_start_count,
tempo
])
h5.close()
count = count + 1
print count
def fill_attributes(song, songH5File):
#----------------------------non array attributes-------------------------------
song.analysisSampleRate = str(
hdf5_getters.get_analysis_sample_rate(songH5File))
song.artistDigitalID = str(hdf5_getters.get_artist_7digitalid(songH5File))
song.artistFamiliarity = str(
hdf5_getters.get_artist_familiarity(songH5File))
song.artistHotness = str(hdf5_getters.get_artist_hottness(songH5File))
song.artistID = str(hdf5_getters.get_artist_id(songH5File))
song.artistLatitude = str(hdf5_getters.get_artist_latitude(songH5File))
song.artistLocation = str(hdf5_getters.get_artist_location(songH5File))
song.artistLongitude = str(hdf5_getters.get_artist_longitude(songH5File))
song.artistmbID = str(hdf5_getters.get_artist_mbid(songH5File))
song.artistName = str(hdf5_getters.get_artist_name(songH5File))
song.artistPlayMeID = str(hdf5_getters.get_artist_playmeid(songH5File))
song.audioMD5 = str(hdf5_getters.get_audio_md5(songH5File))
song.danceability = str(hdf5_getters.get_danceability(songH5File))
song.duration = str(hdf5_getters.get_duration(songH5File))
song.endOfFadeIn = str(hdf5_getters.get_end_of_fade_in(songH5File))
song.energy = str(hdf5_getters.get_energy(songH5File))
song.key = str(hdf5_getters.get_key(songH5File))
song.keyConfidence = str(hdf5_getters.get_key_confidence(songH5File))
song.segementsConfidence = str(
hdf5_getters.get_segments_confidence(songH5File))
song.segementsConfidence = str(
hdf5_getters.get_sections_confidence(songH5File))
song.loudness = str(hdf5_getters.get_loudness(songH5File))
song.mode = str(hdf5_getters.get_mode(songH5File))
song.modeConfidence = str(hdf5_getters.get_mode_confidence(songH5File))
song.release = str(hdf5_getters.get_release(songH5File))
song.releaseDigitalID = str(
hdf5_getters.get_release_7digitalid(songH5File))
song.songHotttnesss = str(hdf5_getters.get_song_hotttnesss(songH5File))
song.startOfFadeOut = str(hdf5_getters.get_start_of_fade_out(songH5File))
song.tempo = str(hdf5_getters.get_tempo(songH5File))
song.timeSignature = str(hdf5_getters.get_time_signature(songH5File))
song.timeSignatureConfidence = str(
hdf5_getters.get_time_signature_confidence(songH5File))
song.title = str(hdf5_getters.get_title(songH5File))
song.trackID = str(hdf5_getters.get_track_id(songH5File))
song.trackDigitalID = str(hdf5_getters.get_track_7digitalid(songH5File))
song.year = str(hdf5_getters.get_year(songH5File))
#-------------------------------array attributes--------------------------------------
#array float
song.beatsStart_mean, song.beatsStart_var = convert_array_to_meanvar(
hdf5_getters.get_beats_start(songH5File))
#array float
song.artistTermsFreq_mean, song.artistTermsFreq_var = convert_array_to_meanvar(
hdf5_getters.get_artist_terms_freq(songH5File))
#array float
song.artistTermsWeight_mean, song.artistTermsWeight_var = convert_array_to_meanvar(
hdf5_getters.get_artist_terms_weight(songH5File))
#array int
song.artistmbTagsCount_mean, song.artistmbTagsCount_var = convert_array_to_meanvar(
hdf5_getters.get_artist_mbtags_count(songH5File))
#array float
song.barsConfidence_mean, song.barsConfidence_var = convert_array_to_meanvar(
hdf5_getters.get_bars_confidence(songH5File))
#array float
song.barsStart_mean, song.barsStart_var = convert_array_to_meanvar(
hdf5_getters.get_bars_start(songH5File))
#array float
song.beatsConfidence_mean, song.beatsConfidence_var = convert_array_to_meanvar(
hdf5_getters.get_beats_confidence(songH5File))
#array float
song.sectionsConfidence_mean, song.sectionsConfidence_var = convert_array_to_meanvar(
hdf5_getters.get_sections_confidence(songH5File))
#array float
song.sectionsStart_mean, song.sectionsStart_var = convert_array_to_meanvar(
hdf5_getters.get_sections_start(songH5File))
#array float
song.segmentsConfidence_mean, song.segmentsConfidence_var = convert_array_to_meanvar(
hdf5_getters.get_segments_confidence(songH5File))
#array float
song.segmentsLoudness_mean, song.segmentsLoudness_var = convert_array_to_meanvar(
hdf5_getters.get_segments_loudness_max(songH5File))
#array float
song.segmentsLoudnessMaxTime_mean, song.segmentsLoudnessMaxTime_var = convert_array_to_meanvar(
hdf5_getters.get_segments_loudness_max_time(songH5File))
#array float
song.segmentsLoudnessMaxStart_mean, song.segmentsLoudnessMaxStart_var = convert_array_to_meanvar(
hdf5_getters.get_segments_loudness_start(songH5File))
#array float
song.segmentsStart_mean, song.segmentsStart_var = convert_array_to_meanvar(
hdf5_getters.get_segments_start(songH5File))
#array float
song.tatumsConfidence_mean, song.tatumsConfidence_var = convert_array_to_meanvar(
hdf5_getters.get_tatums_confidence(songH5File))
#array float
song.tatumsStart_mean, song.tatumsStart_var = convert_array_to_meanvar(
hdf5_getters.get_tatums_start(songH5File))
#array2d float
song.segmentsTimbre_mean, song.segmentsTimbre_var = covert_2darray_to_meanvar(
hdf5_getters.get_segments_timbre(songH5File))
#array2d float
song.segmentsPitches_mean, song.segmentsPitches_var = covert_2darray_to_meanvar(
hdf5_getters.get_segments_pitches(songH5File))
#------------------------array string attributes------------------------
song.similarArtists = convert_array_to_string(
hdf5_getters.get_similar_artists(songH5File)) #array string
song.artistTerms = convert_array_to_string(
hdf5_getters.get_artist_terms(songH5File)) #array string
song.artistmbTags = convert_array_to_string(
hdf5_getters.get_artist_mbtags(songH5File)) #array string
return song
def main():
dataset_dir = sys.argv[1]
global feat
Create_BoW(dataset_dir)
Size_BoW = Index_BoW(Bag_Words)
count = Frequency(Size_BoW, dataset_dir)
Size_BoW = Prune(count)
Lablify()
print "Forming Dataset..."
listing1 = os.listdir(dataset_dir)
for a in listing1:
listing2 = os.listdir(dataset_dir+a+'/')
for b in listing2:
listing3 = os.listdir(dataset_dir+a+'/'+b+'/')
for c in listing3:
listing4 = os.listdir(dataset_dir+a+'/'+b+'/'+c+'/')
for d in listing4:
h5 = hdf5_getters.open_h5_file_read(dataset_dir+a+'/'+b+'/'+c+'/'+d)
feat = []
temp = hdf5_getters.get_artist_hotttnesss(h5)
if (math.isnan(temp) or temp==0.0):
h5.close()
continue
feat.append(temp)
temp = hdf5_getters.get_artist_familiarity(h5)
if (math.isnan(temp) or temp==0.0):
h5.close()
continue
feat.append(temp)
temp = hdf5_getters.get_bars_confidence(h5)
if temp.size == 0:
h5.close()
continue
MeanVar(temp)
temp = hdf5_getters.get_beats_confidence(h5)
if temp.size == 0:
h5.close()
continue
mm = np.mean(temp)
vv = np.var(temp)
if mm==0.0 and vv==0.0:
h5.close()
continue
feat.append(mm)
feat.append(vv)
feat.append(hdf5_getters.get_duration(h5))
temp = hdf5_getters.get_end_of_fade_in(h5)
if (math.isnan(temp)):
h5.close()
continue
feat.append(temp)
feat.append(hdf5_getters.get_key(h5))
temp = hdf5_getters.get_key_confidence(h5)
if (math.isnan(temp)):
h5.close()
continue
feat.append(temp)
temp = hdf5_getters.get_loudness(h5)
if (math.isnan(temp)):
h5.close()
continue
feat.append(temp)
feat.append(hdf5_getters.get_mode(h5))
temp = hdf5_getters.get_mode_confidence(h5)
if (math.isnan(temp)):
h5.close()
continue
feat.append(temp)
temp = hdf5_getters.get_sections_confidence(h5)
if temp.size == 0:
h5.close()
continue
MeanVar(temp)
temp = hdf5_getters.get_segments_confidence(h5)
if temp.size == 0:
h5.close()
continue
MeanVar(temp)
temp = hdf5_getters.get_segments_loudness_max(h5)
if temp.size == 0:
h5.close()
continue
MeanVar(temp)
temp = hdf5_getters.get_segments_loudness_max_time(h5)
if temp.size == 0:
h5.close()
continue
MeanVar(temp)
temp = hdf5_getters.get_segments_pitches(h5)
if temp.size == 0:
h5.close()
continue
MeanVar(temp)
temp = hdf5_getters.get_segments_timbre(h5)
if temp.size == 0:
h5.close()
continue
MeanVar(temp)
temp = hdf5_getters.get_start_of_fade_out(h5)
if (math.isnan(temp)):
h5.close()
continue
feat.append(temp)
temp = hdf5_getters.get_tatums_confidence(h5)
if temp.size == 0:
h5.close()
continue
MeanVar(temp)
temp = hdf5_getters.get_tempo(h5)
if (math.isnan(temp)):
h5.close()
continue
feat.append(temp)
feat.append(hdf5_getters.get_time_signature(h5))
temp = hdf5_getters.get_time_signature_confidence(h5)
if (math.isnan(temp)):
h5.close()
continue
feat.append(temp)
temp = hdf5_getters.get_year(h5)
if temp == 0:
h5.close()
continue
feat.append(temp)
temp = hdf5_getters.get_artist_terms(h5)
if temp.size == 0:
h5.close()
continue
temp_ = hdf5_getters.get_artist_terms_weight(h5)
if temp_.size == 0:
continue
for j in Final_BoW:
if j in temp:
x = np.where(temp==j)
x = x[0][0]
feat.append(temp_[x])
else:
x = 0.0
feat.append(x)
temp = hdf5_getters.get_song_hotttnesss(h5)
if (math.isnan(temp) or temp==0.0):
h5.close()
continue
hott = 0
if temp >=0.75:
hott = 1
elif temp >=0.40 and temp <0.75:
hott = 2
else:
hott = 3
feat.append(hott)
h5.close()
count = 1
f=open('MSD_DATASET.txt', 'a')
outstring=''
cnt = 0
feat_size = len(feat)
for i in feat:
cnt+=1
outstring+=str(i)
if (cnt!=feat_size):
outstring+=','
outstring+='\n'
f.write(outstring)
f.close()
def getInfo(files):
data = []
build_str = ''
with open(sys.argv[1], 'r') as f:
contents = f.read()
c = contents.split()
f.close()
print("creating csv with following fields:" + contents)
for i in c:
build_str = build_str + i + ','
build_str = build_str[:-1]
build_str = build_str + '\n'
for fil in files:
curFile = getters.open_h5_file_read(fil)
d2 = {}
get_table = {'track_id': getters.get_track_id(curFile), 'segments_pitches': getters.get_segments_pitches(curFile), 'time_signature_confidence': getters.get_time_signature_confidence(curFile), 'song_hotttnesss': getters.get_song_hotttnesss(curFile), 'artist_longitude': getters.get_artist_longitude(curFile), 'tatums_confidence': getters.get_tatums_confidence(curFile), 'num_songs': getters.get_num_songs(curFile), 'duration': getters.get_duration(curFile), 'start_of_fade_out': getters.get_start_of_fade_out(curFile), 'artist_name': getters.get_artist_name(curFile), 'similar_artists': getters.get_similar_artists(curFile), 'artist_mbtags': getters.get_artist_mbtags(curFile), 'artist_terms_freq': getters.get_artist_terms_freq(curFile), 'release': getters.get_release(curFile), 'song_id': getters.get_song_id(curFile), 'track_7digitalid': getters.get_track_7digitalid(curFile), 'title': getters.get_title(curFile), 'artist_latitude': getters.get_artist_latitude(curFile), 'energy': getters.get_energy(curFile), 'key': getters.get_key(curFile), 'release_7digitalid': getters.get_release_7digitalid(curFile), 'artist_mbid': getters.get_artist_mbid(curFile), 'segments_confidence': getters.get_segments_confidence(curFile), 'artist_hotttnesss': getters.get_artist_hotttnesss(curFile), 'time_signature': getters.get_time_signature(curFile), 'segments_loudness_max_time': getters.get_segments_loudness_max_time(curFile), 'mode': getters.get_mode(curFile), 'segments_loudness_start': getters.get_segments_loudness_start(curFile), 'tempo': getters.get_tempo(curFile), 'key_confidence': getters.get_key_confidence(curFile), 'analysis_sample_rate': getters.get_analysis_sample_rate(curFile), 'bars_confidence': getters.get_bars_confidence(curFile), 'artist_playmeid': getters.get_artist_playmeid(curFile), 'artist_terms_weight': getters.get_artist_terms_weight(curFile), 'segments_start': getters.get_segments_start(curFile), 'artist_location': getters.get_artist_location(curFile), 'loudness': getters.get_loudness(curFile), 'year': getters.get_year(curFile), 'artist_7digitalid': getters.get_artist_7digitalid(curFile), 'audio_md5': getters.get_audio_md5(curFile), 'segments_timbre': getters.get_segments_timbre(curFile), 'mode_confidence': getters.get_mode_confidence(curFile), 'end_of_fade_in': getters.get_end_of_fade_in(curFile), 'danceability': getters.get_danceability(curFile), 'artist_familiarity': getters.get_artist_familiarity(curFile), 'artist_mbtags_count': getters.get_artist_mbtags_count(curFile), 'tatums_start': getters.get_tatums_start(curFile), 'artist_id': getters.get_artist_id(curFile), 'segments_loudness_max': getters.get_segments_loudness_max(curFile), 'bars_start': getters.get_bars_start(curFile), 'beats_start': getters.get_beats_start(curFile), 'artist_terms': getters.get_artist_terms(curFile), 'sections_start': getters.get_sections_start(curFile), 'beats_confidence': getters.get_beats_confidence(curFile), 'sections_confidence': getters.get_sections_confidence(curFile)}
tid = fil.split('/')[-1].split('.')[0]
# print(c)
for i in c:
if i in get_table:
d2[i] = get_table[i]
d2[i] = str(d2[i]).replace('\n','')
build_str = build_str + d2[i] + ','
else:
print('error: unspecified field')
exit(0)
build_str = build_str[:-1]
# print(build_str[:-1])
build_str = build_str + '\n'
curFile.close()
build_str = build_str.replace('b','').replace("'",'').replace('"','')
return (build_str)
