def get_learns_and_test(ref_audio_dir, data_path, t_index = 100, filter_key= True, n_learn_max = 99):
# load the test data
t_index, h5files, test_key, t_feats, t_seg_starts, t_seg_duration = get_test_params(t_index, data_path)
# Now load all the others
learn_feats_list = []
learn_segs_list = []
n_learn = 0
for fileIdx in range(t_index):
if filter_key and n_learn < n_learn_max:
if hdf5_getters.get_key(hdf5_getters.open_h5_file_read(os.path.join(data_path, h5files[fileIdx]))) == test_key:
get_ten_features_from_file(learn_feats_list, learn_segs_list, [], os.path.join(data_path, h5files[fileIdx]))
n_learn += 1
elif n_learn < n_learn_max:
get_ten_features_from_file(learn_feats_list, learn_segs_list, [], os.path.join(data_path, h5files[fileIdx]))
n_learn += 1
l_feats = np.concatenate(learn_feats_list, axis=0)
l_segments = np.vstack(learn_segs_list)
for h5file in h5files:
h5 = hdf5_getters.open_h5_file_read(os.path.join(data_path, h5file))
print h5file, hdf5_getters.get_tempo(h5), hdf5_getters.get_key(h5)
return l_feats, t_feats, t_seg_starts, t_seg_duration, l_segments, h5files, n_learn
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 feat_from_file(path):
"""
Extract a list of features in an array, already converted to string
"""
feats = []
h5 = GETTERS.open_h5_file_read(path)
# basic info
feats.append(GETTERS.get_track_id(h5))
feats.append(GETTERS.get_artist_name(h5).decode().replace(',', ''))
feats.append(GETTERS.get_title(h5).decode().replace(',', ''))
feats.append(GETTERS.get_loudness(h5))
feats.append(GETTERS.get_tempo(h5))
feats.append(GETTERS.get_time_signature(h5))
feats.append(GETTERS.get_key(h5))
feats.append(GETTERS.get_mode(h5))
feats.append(GETTERS.get_duration(h5))
# timbre
timbre = GETTERS.get_segments_timbre(h5)
avg_timbre = np.average(timbre, axis=0)
for k in avg_timbre:
feats.append(k)
var_timbre = np.var(timbre, axis=0)
for k in var_timbre:
feats.append(k)
# done with h5 file
h5.close()
# makes sure we return strings
feats = map(lambda x: str(x), feats)
return feats
def normalize_pitches(h5):
key = int(hdf5_getters.get_key(h5))
segments_pitches = hdf5_getters.get_segments_pitches(h5)
segments_pitches_new = [
transpose_by_key(pitch_seg, key) for pitch_seg in segments_pitches
]
return segments_pitches_new
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 get_info(basedir,ext='.h5') :
# Create new text file for storing the result of JSON objects
resultFile = open("result.txt", "w")
# Going through all sub-directories under the base directory
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root,'*'+ext))
for f in files:
# Open the HDF5 for reading the content
h5 = hdf5_getters.open_h5_file_read(f)
# Creating dictionary to convert to JSON object
dictionary = {}
# Storing all fields
dictionary["song_title"] = hdf5_getters.get_title(h5).decode('Latin-1')
dictionary["artist_name"] = hdf5_getters.get_artist_name(h5).decode('Latin-1')
dictionary["key"] = float(hdf5_getters.get_key(h5))
dictionary["minor-major"] = float(hdf5_getters.get_mode(h5))
dictionary["hotness"] = hdf5_getters.get_song_hotttnesss(h5)
dictionary["artist_location"] = hdf5_getters.get_artist_location(h5).decode('Latin-1')
dictionary["longitude"] = float(hdf5_getters.get_artist_longitude(h5))
dictionary["latitude"] = float(hdf5_getters.get_artist_latitude(h5))
print(dictionary)
# Write the created JSON object to the text file
resultFile.write(str(json.dumps(dictionary)) + "\n")
h5.close()
resultFile.close()
def extract_features(filename):
h5 = hdf5_getters.open_h5_file_read(filename)
f = [None] * len(features)
f[features.index('track_id')] = hdf5_getters.get_track_id(h5, 0).item()
f[features.index('song_id')] = hdf5_getters.get_song_id(h5, 0).item()
f[features.index('hotttnesss')] = hdf5_getters.get_artist_hotttnesss(
h5, 0).item()
f[features.index('danceability')] = hdf5_getters.get_danceability(
h5, 0).item()
f[features.index('duration')] = hdf5_getters.get_duration(h5, 0).item()
f[features.index('key')] = hdf5_getters.get_key(h5, 0).item()
f[features.index('energy')] = hdf5_getters.get_energy(h5, 0).item()
f[features.index('loudness')] = hdf5_getters.get_loudness(h5, 0).item()
f[features.index('year')] = hdf5_getters.get_year(h5, 0).item()
f[features.index('time_signature')] = hdf5_getters.get_time_signature(
h5, 0).item()
f[features.index('tempo')] = hdf5_getters.get_tempo(h5, 0).item()
tags = ''
for tag in hdf5_getters.get_artist_terms(h5):
tags += ('%s|' % tag)
# Remove trailing pipe.
tags = tags[:len(tags) - 1]
f[features.index('tags')] = tags
h5.close()
return f
def func_to_desired_song_data(filename):
h5 = GETTERS.open_h5_file_read(filename)
track_id = GETTERS.get_track_id(h5)
for song in random_songs:
if song[0] == track_id:
print("FOUND ONE!")
title = replace_characters(GETTERS.get_title(h5))
artist = replace_characters(GETTERS.get_artist_name(h5))
year = GETTERS.get_year(h5)
tempo = GETTERS.get_tempo(h5)
key = GETTERS.get_key(h5)
loudness = GETTERS.get_loudness(h5)
energy = GETTERS.get_energy(h5)
danceability = GETTERS.get_danceability(h5)
time_signature = GETTERS.get_time_signature(h5)
mode = GETTERS.get_mode(h5)
hotttness = GETTERS.get_song_hotttnesss(h5)
song_data = {
'title': title,
'artist': artist,
'year': year,
'tempo': tempo,
'key': key,
'loudness': loudness,
'energy': energy,
'danceability': danceability,
'time_signature': time_signature,
'mode': mode,
'hotttness': hotttness
}
all_the_data.append(song_data)
h5.close()
def get_attribute(files):
array = []
count = 0
for f in files:
temp = []
count += 1
print(f)
h5 = hdf5_getters.open_h5_file_read(f)
temp.append(hdf5_getters.get_num_songs(h5))
temp.append(hdf5_getters.get_artist_familiarity(h5))
temp.append(hdf5_getters.get_artist_hotttnesss(h5))
temp.append(hdf5_getters.get_danceability(h5))
temp.append(hdf5_getters.get_energy(h5))
temp.append(hdf5_getters.get_key(h5))
temp.append(hdf5_getters.get_key_confidence(h5))
temp.append(hdf5_getters.get_loudness(h5))
temp.append(hdf5_getters.get_mode(h5))
temp.append(hdf5_getters.get_mode_confidence(h5))
temp.append(hdf5_getters.get_tempo(h5))
temp.append(hdf5_getters.get_time_signature(h5))
temp.append(hdf5_getters.get_time_signature_confidence(h5))
temp.append(hdf5_getters.get_title(h5))
temp.append(hdf5_getters.get_artist_name(h5))
temp = np.nan_to_num(temp)
array.append(temp)
# if count%100 ==0:
# print(array[count-100:count-1])
# kmean.fit(array[count-100:count-1])
h5.close()
return array
def feat_from_file(path):
"""
Extract a list of features in an array, already converted to string
"""
feats = []
h5 = GETTERS.open_h5_file_read(path)
# basic info
feats.append( GETTERS.get_track_id(h5) )
feats.append( GETTERS.get_artist_name(h5).replace(',','') )
feats.append( GETTERS.get_title(h5).replace(',','') )
feats.append( GETTERS.get_loudness(h5) )
feats.append( GETTERS.get_tempo(h5) )
feats.append( GETTERS.get_time_signature(h5) )
feats.append( GETTERS.get_key(h5) )
feats.append( GETTERS.get_mode(h5) )
feats.append( GETTERS.get_duration(h5) )
# timbre
timbre = GETTERS.get_segments_timbre(h5)
avg_timbre = np.average(timbre,axis=0)
for k in avg_timbre:
feats.append(k)
var_timbre = np.var(timbre,axis=0)
for k in var_timbre:
feats.append(k)
# done with h5 file
h5.close()
# makes sure we return strings
feats = map(lambda x: str(x), feats)
return feats
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 getSongProperties(songCount = 3000, splitData = True):
songDict = {}
songIdDict = {}
songIdCount = 0
for root, dirs, files in os.walk(msd_subset_data_path):
files = glob.glob(os.path.join(root,'*.h5'))
for f in files:
h5 = GETTERS.open_h5_file_read(f)
tempo = GETTERS.get_tempo(h5)
danceability = GETTERS.get_danceability(h5)
energy = GETTERS.get_energy(h5)
loudness = GETTERS.get_loudness(h5)
#print GETTERS.get_artist_terms(h5)
timbre = GETTERS.get_segments_timbre(h5)
artist_hotness = GETTERS.get_artist_hotttnesss(h5)
song_key = GETTERS.get_key(h5)
songIdDict[GETTERS.get_song_id(h5)] = songIdCount
songDict[songIdCount] = [tempo,danceability,energy,loudness,artist_hotness,song_key]
songIdCount += 1
h5.close()
#if len(songDict) >2:
# break
#if len(songDict) >2:
# break
if songIdCount > songCount and splitData:
break
return songIdDict,songDict
def get_learns_multidir(ref_audio_dirs, filter_key= None, t_name=None,
n_learn_max = 99):
""" Load the features for a whole lotta directories """
# Now load all the others
learn_feats_list = []
learn_segs_list = []
n_learn = 0
for dir in ref_audio_dirs:
target_path = os.path.join(dir,'hdf5')
print "loading from %s"% target_path
h5files = [name for name in os.listdir(target_path) if 'h5' in name]
for fileIdx in range(len(h5files)):
if t_name in h5files[fileIdx]:
print "Excluding %s from learn"%t_name
continue
if filter_key is not None and n_learn < n_learn_max:
h5 = hdf5_getters.open_h5_file_read(os.path.join(target_path, h5files[fileIdx]))
target_key = hdf5_getters.get_key(h5)
h5.close()
if target_key == filter_key:
get_ten_feats_fullpath(learn_feats_list, learn_segs_list, os.path.join(target_path, h5files[fileIdx]))
n_learn += 1
elif n_learn < n_learn_max:
get_ten_feats_fullpath(learn_feats_list, learn_segs_list, os.path.join(target_path, h5files[fileIdx]))
n_learn += 1
print "Reached %d",n_learn
l_feats = np.concatenate(learn_feats_list, axis=0)
l_segments = np.vstack(learn_segs_list)
return l_feats,l_segments, n_learn
def feat_from_file(path):
feats = []
h5 = GETTERS.open_h5_file_read(path)
feats.append( GETTERS.get_track_id(h5) )
feats.append( GETTERS.get_title(h5) )
feats.append( GETTERS.get_artist_name(h5) )
feats.append( GETTERS.get_year(h5) )
feats.append( GETTERS.get_loudness(h5) )
feats.append( GETTERS.get_tempo(h5) )
feats.append( GETTERS.get_time_signature(h5) )
feats.append( GETTERS.get_key(h5) )
feats.append( GETTERS.get_mode(h5) )
feats.append( GETTERS.get_duration(h5) )
#timbre
timbre = GETTERS.get_segments_timbre(h5)
avg_timbre = np.average(timbre, axis=0)
for k in avg_timbre:
feats.append(k)
var_timbre = np.var(timbre, axis=0)
for k in var_timbre:
feats.append(k)
h5.close()
return feats
def get_key_tempo(filename):
h5 = GETTERS.open_h5_file_read(filename)
tempo = GETTERS.get_tempo(h5)
key = GETTERS.get_key(h5)
ar = GETTERS.get_artist_name(h5)
title = GETTERS.get_title(h5)
st = ""
terms = None
try:
a = artist.Artist(str(ar))
terms = a.get_terms()
time.sleep(.12)
except EchoNestIOError as e:
print "echonestIOerror"
except EchoNestAPIError as e:
if e.code == 3:
time.sleep(1)
elif e.code == 5:
print "code is 5"
else:
print "error.."
if terms:
print terms[0]['name']
with open('points.csv', 'a') as fp:
a = csv.writer(fp, delimiter=',')
a.writerow([tempo, key, ar, title, terms[0]['name']])
h5.close()
def get_test_params(t_index, data_path):
h5files = [name for name in os.listdir(data_path) if 'h5' in name] # isolate one of them
t_index = len(h5files) - 1 # get the test
test_feats_list = []
test_segs_list = []
test_confidence_list = []
get_ten_features_from_file(test_feats_list, test_segs_list, test_confidence_list, os.path.join(data_path, h5files[t_index]))
t_feats = test_feats_list[0]
t_seg_starts = test_segs_list[0][0]
t_seg_duration = np.diff(t_seg_starts)
test_key = hdf5_getters.get_key(hdf5_getters.open_h5_file_read(os.path.join(data_path, h5files[t_index])))
return t_index, h5files, test_key, t_feats, t_seg_starts, t_seg_duration
def get_test(test_path):
test_feats_list = []
test_segs_list = []
test_confidence_list = []
get_ten_features_from_file(test_feats_list, test_segs_list, test_confidence_list, test_path)
t_feats = test_feats_list[0]
t_seg_starts = test_segs_list[0][0]
t_seg_duration = np.diff(t_seg_starts)
h5 = hdf5_getters.open_h5_file_read(test_path)
test_key = hdf5_getters.get_key(h5)
h5.close()
return test_key, t_feats, t_seg_starts, t_seg_duration
def get_key_feature(track, h5=None):
#return
#0: get key of the track
#1:get mode (minor = 0, major = 1close = (h5== None)
close = (h5 == None)
if h5 == None:
# build path
path = "../../msd_dense_subset/dense/"+track[2]+"/"+track[3]+"/"+track[4]+"/"+track+".h5"
h5 = GETTERS.open_h5_file_read(path)
mode = GETTERS.get_mode(h5)
key = GETTERS.get_key(h5)
confidence_mode = GETTERS.get_mode_confidence(h5)
confidence_key = GETTERS.get_key_confidence(h5)
if close:
h5.close()
return (key,mode)
def get_key_feature(track, h5=None):
#return
#0: get key of the track
#1:get mode (minor = 0, major = 1close = (h5== None)
close = (h5 == None)
if h5 == None:
# build path
path = "../../msd_dense_subset/dense/" + track[2] + "/" + track[
3] + "/" + track[4] + "/" + track + ".h5"
h5 = GETTERS.open_h5_file_read(path)
mode = GETTERS.get_mode(h5)
key = GETTERS.get_key(h5)
confidence_mode = GETTERS.get_mode_confidence(h5)
confidence_key = GETTERS.get_key_confidence(h5)
if close:
h5.close()
return (key, mode)
def h5_to_csv_fields(h5,song): '''Converts h5 format to text Inputs: h5, an h5 file object, usable with the wrapper code MSongsDB song, an integer, representing which song in the h5 file to take the info out of (h5 files contain many songs) Output: a string representing all the information of this song, as a single line of a csv file ''' rv=[] ##All these are regular getter functions from wrapper code rv.append(gt.get_artist_name(h5,song)) rv.append(gt.get_title(h5, song)) rv.append(gt.get_release(h5, song)) rv.append(gt.get_year(h5,song)) rv.append(gt.get_duration(h5,song)) rv.append(gt.get_artist_familiarity(h5,song)) rv.append(gt.get_artist_hotttnesss(h5,song)) rv.append(gt.get_song_hotttnesss(h5, song)) ##artist_terms, artist_terms_freq, and artist_terms_weight getter functions ##are all arrays, so we need to turn them into strings first. We used '_' as a separator rv.append(array_to_csv_field(list(gt.get_artist_terms(h5,song)))) rv.append(array_to_csv_field(list(gt.get_artist_terms_freq(h5,song)))) rv.append(array_to_csv_field(list(gt.get_artist_terms_weight(h5,song)))) rv.append(gt.get_mode(h5,song)) rv.append(gt.get_key(h5,song)) rv.append(gt.get_tempo(h5,song)) rv.append(gt.get_loudness(h5,song)) rv.append(gt.get_danceability(h5,song)) rv.append(gt.get_energy(h5,song)) rv.append(gt.get_time_signature(h5,song)) rv.append(array_to_csv_field(list(gt.get_segments_start(h5,song)))) ##These arrays have vectors (Arrays) as items, 12 dimensional each ##An array like [[1,2,3],[4,5,6]] will be written to csv as '1;2;3_4;5;6', i.e. there's two types of separators rv.append(double_Array_to_csv_field(list(gt.get_segments_timbre(h5,song)),'_',';')) rv.append(double_Array_to_csv_field(list(gt.get_segments_pitches(h5,song)),'_',';')) rv.append(array_to_csv_field(list(gt.get_segments_loudness_start(h5,song)))) rv.append(array_to_csv_field(list(gt.get_segments_loudness_max(h5,song)))) rv.append(array_to_csv_field(list(gt.get_segments_loudness_max_time(h5,song)))) rv.append(array_to_csv_field(list(gt.get_sections_start(h5,song)))) ##turn this list into a string with comma separators (i.e. a csv line) rv_string=array_to_csv_field(rv, ",") rv_string+="\n" return rv_string
def append_files(self, letter):
path = '/Users/cole/eclipse-workspace/EC2 File Transfer/Data/' + letter + '/'
files = os.listdir(path)
import csv
with open('library_csv.csv', 'a') as library_csv:
writer = csv.writer(library_csv)
for filename in files:
hdf5path = path + filename
h5 = hdf5_getters.open_h5_file_read(hdf5path)
# get all getters
loudness = hdf5.get_loudness(h5)
key = hdf5.get_key(h5)
mode = hdf5.get_mode(h5)
tempo = hdf5.get_tempo(h5)
ts = hdf5.get_time_signature(h5)
title = hdf5.get_title(h5)
writer.writerow([loudness, key, mode, tempo, ts, title])
# print them
h5.close()
library_csv.close()
def get_attribute(f):
temp = []
count += 1
print(f)
h5 = hdf5_getters.open_h5_file_read(f)
temp.append(hdf5_getters.get_num_songs(h5))
temp.append(hdf5_getters.get_artist_familiarity(h5))
temp.append(hdf5_getters.get_artist_hotttnesss(h5))
temp.append(hdf5_getters.get_danceability(h5))
temp.append(hdf5_getters.get_energy(h5))
temp.append(hdf5_getters.get_key(h5))
temp.append(hdf5_getters.get_key_confidence(h5))
temp.append(hdf5_getters.get_loudness(h5))
temp.append(hdf5_getters.get_mode(h5))
temp.append(hdf5_getters.get_mode_confidence(h5))
temp.append(hdf5_getters.get_tempo(h5))
temp.append(hdf5_getters.get_time_signature(h5))
temp.append(hdf5_getters.get_time_signature_confidence(h5))
temp = np.nan_to_num(temp)
array.append(temp)
h5.close()
def getKey(h5):
"""
Returns the key of the song as an expanded feature
As there are 12 keys, the output vector is going to be 12 dimensional
:param h5: input song file
:return: 12 dimensional vector (list)
Examples:
Key: 1 --> Output: [1 0 0 0 0 0 0 0 0 0 0 0]
Key 11 --> Output: [0 0 0 0 0 0 0 0 0 0 1 0]
Key 12 --> Output: [0 0 0 0 0 0 0 0 0 0 0 1]
"""
#initializing the 12 dimensional vector as a list of zeros
output_vector = [0] * 12
#get key
key = hdf5_getters.get_key(h5)
#Key info not available, return None, exit function
if key == -1:
return ["nan"]
#put the '1' into the appropriate index
output_vector[key] = 1
return output_vector
def get_song_data(results):
songs_data = []
for f in results:
h5 = getter.open_h5_file_read(f)
songs_data.append([
os.path.basename(f),
getter.get_artist_name(h5),
getter.get_title(h5),
getter.get_time_signature(h5),
getter.get_key(h5),
getter.get_segments_loudness_max(h5),
getter.get_mode(h5),
getter.get_beats_confidence(h5),
getter.get_duration(h5),
getter.get_tempo(h5),
getter.get_loudness(h5),
getter.get_segments_timbre(h5),
getter.get_segments_pitches(h5),
getter.get_key_confidence(h5),
])
h5.close()
return songs_data
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 feat_from_file(path):
"""
Extract a list of features in an array, already converted to string
"""
feats = []
h5 = GETTERS.open_h5_file_read(path)
# basic info
feats.append(GETTERS.get_track_id(h5))
#feats.append( GETTERS.get_artist_name(h5).replace(',','') )
#feats.append( GETTERS.get_title(h5).replace(',','') )
feats.append(GETTERS.get_loudness(h5))
feats.append(GETTERS.get_tempo(h5))
feats.append(GETTERS.get_time_signature(h5))
feats.append(GETTERS.get_key(h5))
feats.append(GETTERS.get_mode(h5))
feats.append(GETTERS.get_duration(h5))
feats.append(GETTERS.get_hotnesss(h5))
segments_loudness = np.asarray(GETTERS.get_segments_loudness_max(h5))
max_segment_indice = np.argmax(segments_loudness)
# timbre
timbre = GETTERS.get_segments_timbre(h5)
max_segment_timbre = timbre[max_segment_indice, :]
avg_timbre = np.average(timbre, axis=0)
for k in avg_timbre:
feats.append(k)
var_timbre = np.var(timbre, axis=0)
for k in var_timbre:
feats.append(k)
for k in max_segment_timbre:
feats.append(k)
# done with h5 file
h5.close()
# makes sure we return strings
feats = [str(x) for x in feats]
return feats
def get_info(basedir, ext='.h5'):
resultFile = open("result.txt", "w")
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
for f in files:
h5 = hdf5_getters.open_h5_file_read(f)
dictionary = {}
dictionary["song_title"] = hdf5_getters.get_title(h5).decode(
'Latin-1')
dictionary["artist_name"] = hdf5_getters.get_artist_name(
h5).decode('Latin-1')
dictionary["key"] = float(hdf5_getters.get_key(h5))
dictionary["minor-major"] = float(hdf5_getters.get_mode(h5))
dictionary["hotness"] = hdf5_getters.get_song_hotttnesss(h5)
dictionary["artist_location"] = hdf5_getters.get_artist_location(
h5).decode('Latin-1')
dictionary["longitude"] = float(
hdf5_getters.get_artist_longitude(h5))
dictionary["latitude"] = float(
hdf5_getters.get_artist_latitude(h5))
print(dictionary)
resultFile.write(str(json.dumps(dictionary)) + "\n")
h5.close()
resultFile.close()
def root_retrieve(self, path):
#r=root, d=directory, f=file
import csv
with open('library_csv.csv', 'w') as library_csv:
writer = csv.writer(library_csv)
writer.writerow(
['Loudness', 'Key', 'Mode', 'Tempo', 'timeSignature', 'Title'])
for r, d, f in os.walk(path):
for song in f:
hdf5path = os.path.join(r, song)
h5 = hdf5_getters.open_h5_file_read(hdf5path)
# get all getters
loudness = hdf5.get_loudness(h5)
key = hdf5.get_key(h5)
mode = hdf5.get_mode(h5)
tempo = hdf5.get_tempo(h5)
ts = hdf5.get_time_signature(h5)
title = hdf5.get_title(h5)
writer.writerow([loudness, key, mode, tempo, ts, title])
# print them
h5.close()
library_csv.close()
print("done")
def func_to_extract_features(filename):
"""
This function does 3 simple things:
- open the song file
- get artist ID and put it
- close the file
"""
global cntnan
global cntdanceability
global listfeatures
cf = []
h5 = GETTERS.open_h5_file_read(filename)
nanfound = 0
#Get target feature: song hotness
song_hotness = GETTERS.get_song_hotttnesss(h5)
if math.isnan(song_hotness):
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(song_hotness)
#Get danceablity
# song_danceability = GETTERS.get_danceability(h5)
# if song_danceability == 0:
# nanfound = 1
# cntnan = cntnan + 1
# else:
# cf.append(song_danceability)
#Get song loudness
song_loudness = GETTERS.get_loudness(h5)
if math.isnan(song_loudness):
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(song_loudness)
#Get song energy
# song_energy = GETTERS.get_energy(h5)
# if song_energy == 0:
# nanfound = 1
# cntnan = cntnan + 1
# else:
# cf.append(song_energy)
#Get key of the song
song_key = GETTERS.get_key(h5)
if math.isnan(song_key):
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(song_key)
#Get mode of the song
song_mode = GETTERS.get_mode(h5)
if math.isnan(song_mode):
nanfound = 1
cntnan = cntnan + 1
elif song_mode == 0:
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(song_mode)
#Get duration of the song
song_duration = GETTERS.get_duration(h5)
if math.isnan(song_duration):
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(song_duration)
#Get Average Pitch Class across all segments
#Get the pitches (12 pitches histogram for each segment)
pitches = GETTERS.get_segments_pitches(h5)
M = np.mat(pitches)
meanpitches = M.mean(axis=0)
pitches_arr = np.asarray(meanpitches)
pitches_list = []
for i in range(0,12):
pitches_list.append(pitches_arr[0][i])
cf.append(pitches_list)
#Get Average Timbre Class across all segments
timbres = GETTERS.get_segments_timbre(h5)
M = np.mat(timbres)
meantimbres = M.mean(axis=0)
timbre_arr = np.asarray(meantimbres)
timbre_list = []
for i in range(0,12):
timbre_list.append(timbre_arr[0][i])
cf.append(timbre_list)
#Get song year
song_year = GETTERS.get_year(h5)
if song_year == 0:
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(song_year)
if nanfound == 0:
strlist = list_to_csv(cf)
print strlist
listfeatures.append(strlist)
h5.close()
def func_to_extract_features(filename):
"""
This function does 3 simple things:
- open the song file
- get artist ID and put it
- close the file
"""
global cntnan
global cntdanceability
global listfeatures
global listhotness
global listyear
global listloudness
global listkey
global listmode
global listduration
cf = []
h5 = GETTERS.open_h5_file_read(filename)
nanfound = 0
#Get target feature: song hotness
#FEATURE 0
song_hotness = GETTERS.get_song_hotttnesss(h5)
if math.isnan(song_hotness):
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(song_hotness)
#FEATURE 1
#Get song loudness
song_loudness = GETTERS.get_loudness(h5)
if math.isnan(song_loudness):
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(song_loudness)
#FEATURE 2
#Get key of the song
song_key = GETTERS.get_key(h5)
if math.isnan(song_key):
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(song_key)
#FEATURE 3
#Get duration of the song
song_duration = GETTERS.get_duration(h5)
if math.isnan(song_duration):
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(song_duration)
#FEATURE 4-15
#Get Average Pitch Class across all segments
#Get the pitches (12 pitches histogram for each segment)
pitches = GETTERS.get_segments_pitches(h5)
M = np.mat(pitches)
meanpitches = M.mean(axis=0)
pitches_arr = np.asarray(meanpitches)
pitches_list = []
for i in range(0,12):
pitches_list.append(pitches_arr[0][i])
cf.append(pitches_list)
#FEATURE 16, 27
#Get Average Timbre Class across all segments
timbres = GETTERS.get_segments_timbre(h5)
M = np.mat(timbres)
meantimbres = M.mean(axis=0)
timbre_arr = np.asarray(meantimbres)
timbre_list = []
for i in range(0,12):
timbre_list.append(timbre_arr[0][i])
cf.append(timbre_list)
#FEATURE 28
#Get song year
song_year = GETTERS.get_year(h5)
if song_year == 0:
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(song_year)
#FEATURE 29
#Get song tempo
song_tempo = GETTERS.get_tempo(h5)
cf.append(song_tempo)
#Feature 30
#Get max loudness for each segment
max_loudness_arr = GETTERS.get_segments_loudness_max(h5)
start_loudness_arr = GETTERS.get_segments_loudness_start(h5)
if nanfound == 0:
cf.append(max(max_loudness_arr)-min(start_loudness_arr))
#Feature 31
artist_familiarity = GETTERS.get_artist_familiarity(h5)
cf.append(artist_familiarity)
#Feature 32
song_title = GETTERS.get_title(h5)
cf.append(song_title)
#Featture 33
artist_name = GETTERS.get_artist_name(h5)
cf.append(artist_name)
#Feature 34
#location = GETTERS.get_artist_location(h5)
#cf.append(location)
#Tags
artist_mbtags = GETTERS.get_artist_mbtags(h5)
if not artist_mbtags.size:
genre = "Unknown"
else:
artist_mbcount = np.array(GETTERS.get_artist_mbtags_count(h5))
index_max = artist_mbcount.argmax(axis=0)
genre = artist_mbtags[index_max]
if genre == 'espa\xc3\xb1ol':
genre = "Unknown"
cf.append(genre)
if nanfound == 0:
strlist = list_to_csv(cf)
listfeatures.append(strlist)
mydict.setdefault(artist_name,[]).append(song_hotness)
h5.close()
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()
# 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)
# Get time signature
time_signature = hdf5_getters.get_time_signature(h5)
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 normalize_pitches(h5): key = int(hdf5_getters.get_key(h5)) segments_pitches = hdf5_getters.get_segments_pitches(h5) segments_pitches_new = [transpose_by_key(pitch_seg,key) for pitch_seg in segments_pitches] return segments_pitches_new
def get_fields(files):
tracks = []
counts = {}
field_counts = []
for file in files:
h5 = hdf5_getters.open_h5_file_read(file)
t = {}
t['artist_familiarity'] = hdf5_getters.get_artist_familiarity(
h5) # estimation
t['artist_hotttnesss'] = hdf5_getters.get_artist_hotttnesss(
h5) # estimation
t['artist_name'] = hdf5_getters.get_artist_name(h5) # artist name
t['release'] = hdf5_getters.get_release(h5) # album name
t['title'] = hdf5_getters.get_title(h5) # title
t['len_similar_artists'] = len(
hdf5_getters.get_similar_artists(h5)) # number of similar artists
t['analysis_sample_rate'] = hdf5_getters.get_analysis_sample_rate(
h5) # sample rate of the audio used ?????????
t['duration'] = hdf5_getters.get_duration(h5) # seconds
t['key'] = hdf5_getters.get_key(h5) # key the song is in
t['key_confidence'] = hdf5_getters.get_key_confidence(
h5) # confidence measure
t['loudness'] = hdf5_getters.get_loudness(h5) # overall loudness in dB
t['mode_confidence'] = hdf5_getters.get_mode_confidence(
h5) # confidence measure
t['start_of_fade_out'] = hdf5_getters.get_start_of_fade_out(
h5) # time in sec
t['tempo'] = hdf5_getters.get_tempo(h5) # estimated tempo in BPM
t['time_signature'] = hdf5_getters.get_time_signature(
h5) # estimate of number of beats per bar, e.g. 4
t['year'] = hdf5_getters.get_year(
h5) # song release year from MusicBrainz or 0
timbre = hdf5_getters.get_segments_timbre(
h5) # 2D float array, texture features (MFCC+PCA-like)
t['segments_timbre'] = timbre
t['timbre_avg'] = timbre.mean(axis=0) # list of 12 averages
cov_mat_timbre = np.cov(timbre, rowvar=False)
cov_timbre = []
for i in range(len(cov_mat_timbre)):
for j in range(len(cov_mat_timbre) - i):
cov_timbre.append(cov_mat_timbre[i][j])
t['timbre_cov'] = cov_timbre # list of 78 covariances
pitch = hdf5_getters.get_segments_pitches(
h5) # 2D float array, chroma feature, one value per note
t['segments_pitch'] = pitch
t['pitch_avg'] = pitch.mean(axis=0) # list of 12 averages
cov_mat_pitch = np.cov(pitch, rowvar=False)
cov_pitch = []
for i in range(len(cov_mat_pitch)):
for j in range(len(cov_mat_pitch) - i):
cov_pitch.append(cov_mat_timbre[i][j])
t['pitch_cov'] = cov_pitch # list of 78 covariances
# seg_pitch = hdf5_getters.get_segments_pitches(h5) # 2D float array, chroma feature, one value per note
# print(seg_pitch.shape)
# t['artist_latitude'] = hdf5_getters.get_artist_latitude(h5) # float, ????????????????????????????????????????
# t['artist_longitude'] = hdf5_getters.get_artist_longitude(h5) # float, ??????????????????????????????????????
# t['artist_location'] = hdf5_getters.get_artist_location(h5) # location name
# t['song_hotttnesss'] = hdf5_getters.get_song_hotttnesss(h5) # estimation
# t['danceability'] = hdf5_getters.get_danceability(h5) # estimation
# t['end_of_fade_in'] = hdf5_getters.get_end_of_fade_in(h5) # seconds at the beginning of the song
# t['energy'] = hdf5_getters.get_energy(h5) # energy from listener point of view
# t['mode'] = hdf5_getters.get_mode(h5) # major or minor
# t['time_signature_confidence'] = hdf5_getters.get_time_signature_confidence(h5) # confidence measure
# t['artist_mbtags_count'] = len(hdf5_getters.get_artist_mbtags_count(h5)) # array int, tag counts for musicbrainz tags
# bad types or non arithmatic numbers
'''
# t['audio_md5'] = hdf5_getters.get_audio_md5(h5) # hash code of the audio used for the analysis by The Echo Nest
# t['artist_terms_weight'] = hdf5_getters.get_artist_terms_weight(h5) # array float, echonest tags weight ?????
# t['artist_terms_freq'] = hdf5_getters.get_artist_terms_freq(h5) # array float, echonest tags freqs ??????????
# t['artist_terms'] = hdf5_getters.get_artist_terms(h5) # array string, echonest tags ?????????????????????????
# t['artist_id'] = hdf5_getters.get_artist_id(h5) # echonest id
# t['artist_mbid'] = hdf5_getters.get_artist_mbid(h5) # musicbrainz id
# t['artist_playmeid'] = hdf5_getters.get_artist_playmeid(h5) # playme id
# t['artist_7digitalid'] = hdf5_getters.get_artist_7digitalid(h5) # 7digital id
# t['release_7digitalid'] = hdf5_getters.get_release_7digitalid(h5) # 7digital id
# t['song_id'] = hdf5_getters.get_song_id(h5) # echonest id
# t['track_7digitalid'] = hdf5_getters.get_track_7digitalid(h5) # 7digital id
# t['similar_artists'] = hdf5_getters.get_similar_artists(h5) # string array of sim artist ids
# t['track_id'] = hdf5_getters.get_track_id(h5) # echonest track id
# t['segments_start'] = hdf5_getters.get_segments_start(h5) # array floats, musical events, ~ note onsets
# t['segments_confidence'] = hdf5_getters.get_segments_confidence(h5) # array floats, confidence measure
# t['segments_pitches'] = hdf5_getters.get_segments_pitches(h5) # 2D float array, chroma feature, one value per note
# t['segments_timbre'] = hdf5_getters.get_segments_timbre(h5) # 2D float array, texture features (MFCC+PCA-like)
# t['segments_loudness_max'] = hdf5_getters.get_segments_loudness_max(h5) # float array, max dB value
# t['segments_loudness_max_time'] = hdf5_getters.get_segments_loudness_max_time(h5) # float array, time of max dB value, i.e. end of attack
# t['segments_loudness_start'] = hdf5_getters.get_segments_loudness_start(h5) # array float, dB value at onset
# t['sections_start'] = hdf5_getters.get_sections_start(h5) # array float, largest grouping in a song, e.g. verse
# t['sections_confidence'] = hdf5_getters.get_sections_confidence(h5) # array float, confidence measure
# t['beats_start'] = hdf5_getters.get_beats_start(h5) # array float, result of beat tracking
# t['beats_confidence'] = hdf5_getters.get_beats_confidence(h5) # array float, confidence measure
# t['bars_start'] = hdf5_getters.get_bars_start(h5) # array float, beginning of bars, usually on a beat
# t['bars_confidence'] = hdf5_getters.get_bars_confidence(h5) # array float, confidence measure
# t['tatums_start'] = hdf5_getters.get_tatums_start(h5) # array float, smallest rythmic element
# t['tatums_confidence'] = hdf5_getters.get_tatums_confidence(h5) # array float, confidence measure
# t['artist_mbtags'] = hdf5_getters.get_artist_mbtags(h5) # array string, tags from musicbrainz.org
'''
h5.close()
for key, value in t.items():
if isinstance(value, float) and math.isnan(value):
pass
if type(value) is np.ndarray:
if key in counts.keys():
counts[key] += 1
else:
counts[key] = 1
elif value:
if key in counts.keys():
counts[key] += 1
else:
counts[key] = 1
elif key not in counts.keys():
counts[key] = 0
count = 0
for key, value in t.items():
if isinstance(value, float) and math.isnan(value):
pass
elif type(value) is np.ndarray:
count += 1
elif value:
count += 1
field_counts.append(count)
# progress bar
if num_of_tracks >= 100:
i = files.index(file) + 1
scale = num_of_tracks / 100
if i % math.ceil(len(files) * .05) == 0:
sys.stdout.write('\r')
# the exact output you're looking for:
sys.stdout.write("Loading dataframe: [%-100s] %d%%" %
('=' * int(i // scale), 1 / scale * i))
sys.stdout.flush()
time.sleep(.01)
tracks.append(t)
print()
return tracks, counts, field_counts
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 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 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_key(self):
if self.h5 == None: self.open()
return hdf5_getters.get_key(self.h5)
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;
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)
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))
time_of_fade_out = duration - hdf5_getters.get_start_of_fade_out(h5)
tempo = hdf5_getters.get_tempo(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
else:
process_completion += 1
tempPath = os.path.abspath(os.path.join(root,name))
h5file = hdf5_getters.open_h5_file_read(tempPath)
#meta info
track_id_str = hdf5_getters.get_track_id(h5file).replace(",","")
song_id_str = hdf5_getters.get_song_id(h5file).replace(",","")
title_str = hdf5_getters.get_title(h5file).replace(","," ")
artist_name_str = hdf5_getters.get_artist_name(h5file).replace(","," ")
artist_location_str = hdf5_getters.get_artist_location(h5file).replace(","," ")
# song-level data info
duration = hdf5_getters.get_duration(h5file)
key = hdf5_getters.get_key(h5file)
mode = hdf5_getters.get_mode(h5file)
tempo = hdf5_getters.get_tempo(h5file)
time_signature = hdf5_getters.get_time_signature(h5file)
h5file.close()
counter += 1
file_io_counter += 1
if file_io_counter % 100 == 0:
if counter == 1:
my_array_metadata_extract_1 = numpy.array([track_id_str, song_id_str, title_str, artist_name_str, artist_location_str])
my_array_metadata_extract_2 = numpy.array([track_id_str, song_id_str, duration, key, mode, tempo, time_signature])
else :
my_array_metadata_extract_1 = numpy.vstack((my_array_metadata_extract_1,numpy.array([track_id_str, song_id_str, title_str, artist_name_str, artist_location_str])))
my_array_metadata_extract_2 = numpy.vstack((my_array_metadata_extract_2,numpy.array([track_id_str, song_id_str, duration, key, mode, tempo, time_signature])))
if __name__ == "__main__":
with open("fields.csv", "wb") as f:
writer = csv.writer(f) # initialize the csv writer
# for each track in the summary file, get the 11 fields and output to csv
h5_file = hdf5_getters.open_h5_file_read("msd_summary_file.h5")
for k in range(1000000):
print "index!!!: ", k
id = hdf5_getters.get_track_id(h5_file, k) # get track_id TRA13e39..
title = hdf5_getters.get_title(h5_file, k) # get song title
artist_name = hdf5_getters.get_artist_name(h5_file, k)
year = int(hdf5_getters.get_year(h5_file, k))
hotness = float(hdf5_getters.get_song_hotttnesss(h5_file, k))
artist_familiarity = float(hdf5_getters.get_artist_familiarity(h5_file, k))
f5 = int(hdf5_getters.get_key(h5_file, k)) # get key
f2 = float(hdf5_getters.get_loudness(h5_file, k)) # get loudness
f1 = float(hdf5_getters.get_tempo(h5_file, k)) # get tempo
f4 = int(hdf5_getters.get_duration(h5_file, k)) # get duration
f3 = float(hdf5_getters.get_time_signature(h5_file, k)) # get time signature
# Get rid of missing info and change invalid numbers for meta data
if not artist_name:
artist_name = "unknown"
if not artist_familiarity:
artist_familiarity = 0.0
if not hotness:
hotness = 0.0
def func_to_extract_features(filename):
"""
This function does 3 simple things:
- open the song file
- get artist ID and put it
- close the file
"""
global cntnan
global listfeatures
cf = []
h5 = GETTERS.open_h5_file_read(filename)
nanfound = 0
#Get target feature: song hotness
#FEATURE 0
song_hotness = GETTERS.get_song_hotttnesss(h5)
if math.isnan(song_hotness):
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(song_hotness)
#FEATURE 1
#Get song loudness
song_loudness = GETTERS.get_loudness(h5)
if math.isnan(song_loudness):
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(song_loudness)
#FEATURE 2
#Get key of the song
song_key = GETTERS.get_key(h5)
if math.isnan(song_key):
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(song_key)
#FEATURE 3
#Get duration of the song
song_duration = GETTERS.get_duration(h5)
if math.isnan(song_duration):
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(song_duration)
#FEATURE 4-15
#Get Average Pitch Class across all segments
#Get the pitches (12 pitches histogram for each segment)
pitches = GETTERS.get_segments_pitches(h5)
M = np.mat(pitches)
meanpitches = M.mean(axis=0)
pitches_arr = np.asarray(meanpitches)
pitches_list = []
for i in range(0,12):
pitches_list.append(pitches_arr[0][i])
cf.append(pitches_list)
#FEATURE 16, 27
#Get Average Timbre Class across all segments
timbres = GETTERS.get_segments_timbre(h5)
M = np.mat(timbres)
meantimbres = M.mean(axis=0)
timbre_arr = np.asarray(meantimbres)
timbre_list = []
for i in range(0,12):
timbre_list.append(timbre_arr[0][i])
cf.append(timbre_list)
#FEATURE 28
#Get song year
song_year = GETTERS.get_year(h5)
if song_year == 0:
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(song_year)
#FEATURE 29
#Get song tempo
song_tempo = GETTERS.get_tempo(h5)
cf.append(song_tempo)
#Feature 30
#Get max loudness for each segment
max_loudness_arr = GETTERS.get_segments_loudness_max(h5)
start_loudness_arr = GETTERS.get_segments_loudness_start(h5)
if nanfound == 0:
cf.append(max(max_loudness_arr)-min(start_loudness_arr))
#Feature 31
artist_familiarity = GETTERS.get_artist_familiarity(h5)
cf.append(artist_familiarity)
#Feature 32
artist_hotness = GETTERS.get_artist_hotttnesss(h5)
if math.isnan(artist_hotness):
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(artist_hotness)
if nanfound == 0:
strlist = list_to_csv(cf)
listfeatures.append(strlist)
h5.close()
def func_to_extract_features(filename):
"""
This function does 3 simple things:
- open the song file
- get artist ID and put it
- close the file
"""
global cntnan
global listfeatures
cf = []
h5 = GETTERS.open_h5_file_read(filename)
nanfound = 0
#Get target feature: song hotness
#FEATURE 0
song_hotness = GETTERS.get_song_hotttnesss(h5)
if math.isnan(song_hotness):
nanfound = 1
cntnan = cntnan + 1
h5.close()
return 0
elif song_hotness > 0.3 and song_hotness < 0.6:
h5.close()
return 0
else:
if song_hotness <= 0.3:
hotness_class = 0
elif song_hotness >= 0.6:
hotness_class = 1
cf.append(hotness_class)
#FEATURE 1
#Get song loudness
song_loudness = GETTERS.get_loudness(h5)
if math.isnan(song_loudness):
nanfound = 1
cntnan = cntnan + 1
else:
#cf.append(song_loudness)
pass
#FEATURE 2
#Get key of the song
song_key = GETTERS.get_key(h5)
if math.isnan(song_key):
nanfound = 1
cntnan = cntnan + 1
else:
# cf.append(song_key)
pass
#FEATURE 3
song_duration = GETTERS.get_duration(h5)
if math.isnan(song_duration):
nanfound = 1
cntnan = cntnan + 1
else:
# cf.append(song_duration)
pass
#Feature 4
#Get song tempo
song_tempo = GETTERS.get_tempo(h5)
if math.isnan(song_tempo):
nanfound = 1
cntnan = cntnan + 1
else:
# cf.append(song_tempo)
pass
#Feature 5: artist familarity
artist_familiarity = GETTERS.get_artist_familiarity(h5)
if math.isnan(artist_familiarity):
nanfound = 1
cntnan = cntnan + 1
else:
# cf.append(artist_familiarity)
pass
#Feature 6: artist_hotness
artist_hotness = GETTERS.get_artist_hotttnesss(h5)
if math.isnan(artist_hotness):
nanfound = 1
cntnan = cntnan + 1
else:
# cf.append(artist_hotness)
pass
#Feature 7 time signature
time_signature = GETTERS.get_time_signature(h5)
# cf.append(time_signature)
#Feature 8
#Loudness COV
loudness_segments = np.array(GETTERS.get_segments_loudness_max(h5))
loudness_cov = abs(variation(loudness_segments))
if math.isnan(loudness_cov):
nanfound = 1
cntnan = cntnan + 1
else:
# cf.append(loudness_cov)
pass
#Feature 9
#Beat COV
beat_segments = np.array(GETTERS.get_beats_start(h5))
beat_cov = abs(variation(beat_segments))
if math.isnan(beat_cov):
nanfound = 1
cntnan = cntnan + 1
else:
# cf.append(beat_cov)
pass
#Feature 10
#Year
song_year = GETTERS.get_year(h5)
if song_year == 0:
nanfound = 1
cntnan = cntnan + 1
else:
# cf.append(song_year)
pass
title = GETTERS.get_title(h5)
if title in energydict:
audio_summary = energydict[title]
energy = audio_summary['energy']
danceability = audio_summary['danceability']
speechiness = audio_summary['speechiness']
liveness = audio_summary['liveness']
else:
stitle = re.sub(r'\([^)]*\)','', title)
if stitle in energydict:
audio_summary = energydict[stitle]
energy = audio_summary['energy']
danceability = audio_summary['danceability']
speechiness = audio_summary['speechiness']
liveness = audio_summary['liveness']
else:
energy = 0.0
danceability = 0.0
speechiness = 0.0
liveness = 0.0
# Feature 11
cf.append(energy)
# Feature 12
# cf.append(danceability)
# Feature 13
# cf.append(speechiness)
# Feature 14
# cf.append(liveness)
if nanfound == 0:
strlist = list_to_csv(cf)
listfeatures.append(strlist)
h5.close()
def writeSingleHDF5FileToTxtFile(songHDF5FileName):
global maximumArtistNameLen
global maximumArtistTagLen
global maximumSongNameLen
global maximumAlbumNameLen
"""
This function does 3 simple things:
- open the song file
- get artist ID and put it
- close the file
"""
songHDF5File = GETTERS.open_h5_file_read(songHDF5FileName)
songID = GETTERS.get_song_id(songHDF5File)
songName = GETTERS.get_title(songHDF5File)
artistID = GETTERS.get_artist_id(songHDF5File)
songAlbum = GETTERS.get_release(songHDF5File)
songYear = GETTERS.get_year(songHDF5File)
songTempo = GETTERS.get_tempo(songHDF5File)
songDanceability = GETTERS.get_danceability(songHDF5File)
songDuration = GETTERS.get_duration(songHDF5File)
songEnergy = GETTERS.get_energy(songHDF5File)
songKey = GETTERS.get_key(songHDF5File)
songLoudness = GETTERS.get_loudness(songHDF5File)
songMode = GETTERS.get_mode(songHDF5File)
songTimeSignature = GETTERS.get_time_signature(songHDF5File)
songsTableFile.write(songID + "\t" + songName + "\t" + artistID + "\t" +
songAlbum + "\t" + str(songYear) + "\t" +
str(songTempo) + "\t" + str(songDanceability) + "\t" +
str(songDuration) + "\t" + str(songEnergy) + "\t" +
str(songKey) + "\t" + str(songLoudness) + "\t" +
str(songMode) + "\t" + str(songTimeSignature) +
"\t\n")
artistName = GETTERS.get_artist_name(songHDF5File)
artistFamiliarity = GETTERS.get_artist_familiarity(songHDF5File)
artistTagsArray = GETTERS.get_artist_mbtags(songHDF5File)
artistsTableFile.write(artistID + "\t" + artistName + "\t" +
str(artistFamiliarity) + "\t\n")
if len(songName) > maximumSongNameLen:
maximumSongNameLen = len(songName)
if len(songAlbum) > maximumAlbumNameLen:
maximumAlbumNameLen = len(songAlbum)
if len(artistName) > maximumArtistNameLen:
maximumArtistNameLen = len(artistName)
for artistTag in artistTagsArray:
if artistTag in allowedTagsSet:
artistsTagsTableFile.write(artistID + "\t" + artistTag + "\t\n")
if artistTag not in tagsSet:
tagsTableFile.write(artistTag + "\t\n")
tagsSet.add(artistTag)
if len(artistTag) > maximumArtistTagLen:
maximumArtistTagLen = len(artistTag)
similarArtists = GETTERS.get_similar_artists(songHDF5File)
for similarArtist in similarArtists:
similarArtistsPairsList.add((artistID, similarArtist))
artistsIDsSet.add(artistID)
artistsNamesSet.add(artistName)
songHDF5File.close()
for filepath in tqdm(filepaths):
h5 = hdf5_getters.open_h5_file_read(filepath)
n = hdf5_getters.get_num_songs(h5)
for row in range(n):
song_id = hdf5_getters.get_song_id(h5, songidx=row).decode('UTF-8')
# artist = hdf5_getters.get_artist_name(h5,songidx=row).decode('UTF-8')
# 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)
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 #segments_start = ','.join(str(e) for e in GETTERS.get_segments_start(h5, i)) # array #segments_timbre = ','.join(str(e) for e in GETTERS.get_segments_timbre(h5, i)) # array
def func_to_extract_features(filename):
"""
This function does 3 simple things:
- open the song file
- get artist ID and put it
- close the file
"""
global cntnan
global listfeatures
cf = []
h5 = GETTERS.open_h5_file_read(filename)
nanfound = 0
# Get target feature: song hotness
# FEATURE 0
song_hotness = GETTERS.get_song_hotttnesss(h5)
if math.isnan(song_hotness):
nanfound = 1
cntnan = cntnan + 1
h5.close()
return 0
elif song_hotness > 0.3 and song_hotness < 0.6:
h5.close()
return 0
else:
cf.append(song_hotness)
# FEATURE 1
# Get song loudness
song_loudness = GETTERS.get_loudness(h5)
if math.isnan(song_loudness):
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(song_loudness)
# FEATURE 2
# Get key of the song
song_key = GETTERS.get_key(h5)
if math.isnan(song_key):
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(song_key)
# FEATURE 3
# Get duration of the song
song_duration = GETTERS.get_duration(h5)
if math.isnan(song_duration):
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(song_duration)
# Feature 4
# Get song tempo
song_tempo = GETTERS.get_tempo(h5)
if math.isnan(song_tempo):
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(song_tempo)
# Feature 5: artist familarity
artist_familiarity = GETTERS.get_artist_familiarity(h5)
if math.isnan(artist_familiarity):
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(artist_familiarity)
# Feature 6: artist_hotness
artist_hotness = GETTERS.get_artist_hotttnesss(h5)
if math.isnan(artist_hotness):
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(artist_hotness)
# Feature 7 time signature
time_signature = GETTERS.get_time_signature(h5)
cf.append(time_signature)
# Feature 8
# Loudness COV
loudness_segments = np.array(GETTERS.get_segments_loudness_max(h5))
loudness_cov = abs(variation(loudness_segments))
if math.isnan(loudness_cov):
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(loudness_cov)
# Feature 9
# Beat COV
beat_segments = np.array(GETTERS.get_beats_start(h5))
beat_cov = abs(variation(beat_segments))
if math.isnan(beat_cov):
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(beat_cov)
# Feature 10
# Year
song_year = GETTERS.get_year(h5)
if song_year == 0:
nanfound = 1
cntnan = cntnan + 1
else:
cf.append(song_year)
if nanfound == 0:
strlist = list_to_csv(cf)
listfeatures.append(strlist)
strtitle = GETTERS.get_title(h5)
listtitle.append(strtitle)
h5.close()
track_title = h.get_title(h5,0)
track_title = track_title.replace("'","")
track_album = h.get_release(h5,0)
track_album = track_album.replace("'","")
track_duration = str(h.get_duration(h5,0))
track_year = str(h.get_year(h5,0))
cursor.execute("SELECT * FROM track WHERE track_id = '" + track_id + "'")
rs = cursor.fetchall()
if cursor.rowcount != 1:
cursor.execute("INSERT INTO track VALUES ('" + track_id + "','" + track_title + "','" + artist_id + "','" + artist_name + "','" + track_album + "'," + track_duration + "," + track_year + ");")
''' Store track_analysis tuples '''
print ("Track ID: " + h.get_track_id(h5,0))
track_tempo = str(h.get_tempo(h5,0))
track_key = str(h.get_key(h5,0))
track_danceability = str(h.get_danceability(h5,0))
if track_danceability == "nan":
track_danceability = "0.0"
track_hottness = str(h.get_song_hotttnesss(h5,0))
if track_hottness == "nan":
track_hottness = "0.0"
cursor.execute("SELECT * FROM track_analysis WHERE track_id = '" + track_id + "'")
rs = cursor.fetchall()
if cursor.rowcount != 1:
cursor.execute("INSERT INTO track_analysis VALUES ('" + track_id + "'," + track_tempo + "," + track_key + "," + track_danceability + "," + track_hottness + ");")
h5.close()
db.commit()
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 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 == '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,TrackId,ArtistID,ArtistLatitude,ArtistLocation,"
+
"ArtistLongitude,ArtistName,Danceability,Duration,KeySignature," +
"KeySignatureConfidence,Tempo,TimeSignature,TimeSignatureConfidence,"
+ "Title,Year")
#################################################
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 = "/home/umwangye/millonsong/MillionSongSubset/data/" # "." 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)
numPerH5 = hdf5_getters.get_num_songs(songH5File)
for cnt in range(numPerH5):
song = Song(str(hdf5_getters.get_song_id(songH5File, cnt)))
song.trackId = str(hdf5_getters.get_track_id(songH5File, cnt))
song.artistID = str(hdf5_getters.get_artist_id(
songH5File, cnt))
song.albumID = str(
hdf5_getters.get_release_7digitalid(songH5File, cnt))
song.albumName = str(hdf5_getters.get_release(songH5File, cnt))
song.artistLatitude = str(
hdf5_getters.get_artist_latitude(songH5File, cnt))
song.artistLocation = str(
hdf5_getters.get_artist_location(songH5File, cnt))
song.artistLongitude = str(
hdf5_getters.get_artist_longitude(songH5File, cnt))
song.artistName = str(
hdf5_getters.get_artist_name(songH5File, cnt))
song.danceability = str(
hdf5_getters.get_danceability(songH5File, cnt))
song.duration = str(hdf5_getters.get_duration(songH5File, cnt))
# song.setGenreList()
song.keySignature = str(hdf5_getters.get_key(songH5File, cnt))
song.keySignatureConfidence = str(
hdf5_getters.get_key_confidence(songH5File, cnt))
# song.lyrics = None
# song.popularity = None
song.tempo = str(hdf5_getters.get_tempo(songH5File, cnt))
song.timeSignature = str(
hdf5_getters.get_time_signature(songH5File, cnt))
song.timeSignatureConfidence = str(
hdf5_getters.get_time_signature_confidence(
songH5File, cnt))
song.title = str(hdf5_getters.get_title(songH5File, cnt))
song.year = str(hdf5_getters.get_year(songH5File, cnt))
#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 == 'TrackId'.lower():
csvRowString += song.trackId
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 = ""
songH5File.close()
outputFile1.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 == '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")
#################################################
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.
#################################################
#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))
#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
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 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)
min_score = 20.0; max_score = -1.0; print numSongs for i in range(0, numSongs): #Handle each one year = h5get.get_year(h5, i) if year < 1980 or year > 2010: continue; song = Song() song.year = year song.tempo = h5get.get_tempo(h5, i) song.duration = h5get.get_duration(h5, i) song.key = h5get.get_key(h5, i) song.energy = h5get.get_energy(h5, i) song.time_sig = h5get.get_time_signature(h5,i) song.mode = h5get.get_mode(h5,i); song.hotness = h5get.get_song_hotttnesss(h5, i) #print "Hotness: ", song.hotness; if math.isnan(song.hotness): song.hotness = 0.1; song.artist = h5get.get_artist_name(h5, i) song.name = h5get.get_title(h5, i) if (song.artist.lower(), song.name.lower()) in all_chart_info: song.chart_score = float(all_chart_info[(song.artist.lower(), song.name.lower())]);