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_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 validate_song(h5_file,song): '''Returns true/false if song is valid or not. This is essentially cleanup and only lets through songs which have 'good' data (have a non-negligible duration, and have segments being most important)''' try: assert gt.get_year(h5_file, song)!='0' assert gt.get_duration(h5_file, song)>60.0 assert gt.get_mode_confidence(h5_file, song)>0.2 assert gt.get_key_confidence(h5_file, song)>0.2 assert gt.get_time_signature_confidence(h5_file, song)>0.2 terms=np.array(gt.get_artist_terms(h5_file, song)) assert terms.size>0 segments=np.array(gt.get_segments_start) assert segments.size>0 sections=np.array(gt.get_sections_start) assert sections.size>0 except: return False return True
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 data_to_flat_file(basedir,ext='.h5') :
"""This function extract the information from the tables and creates the flat file."""
count = 0; #song counter
list_to_write= []
row_to_write = ""
writer = csv.writer(open("metadata_wholeA.csv", "wb"))
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root,'*'+ext))
for f in files:
print f #the name of the file
h5 = hdf5_getters.open_h5_file_read(f)
title = hdf5_getters.get_title(h5)
title= title.replace('"','')
comma=title.find(',') #eliminating commas in the title
if comma != -1:
print title
time.sleep(1)
album = hdf5_getters.get_release(h5)
album= album.replace('"','') #eliminating commas in the album
comma=album.find(',')
if comma != -1:
print album
time.sleep(1)
artist_name = hdf5_getters.get_artist_name(h5)
comma=artist_name.find(',')
if comma != -1:
print artist_name
time.sleep(1)
artist_name= artist_name.replace('"','') #eliminating double quotes
duration = hdf5_getters.get_duration(h5)
samp_rt = hdf5_getters.get_analysis_sample_rate(h5)
artist_7digitalid = hdf5_getters.get_artist_7digitalid(h5)
artist_fam = hdf5_getters.get_artist_familiarity(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_fam) == True:
artist_fam=-1
artist_hotness= hdf5_getters.get_artist_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_hotness) == True:
artist_hotness=-1
artist_id = hdf5_getters.get_artist_id(h5)
artist_lat = hdf5_getters.get_artist_latitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lat) == True:
artist_lat=-1
artist_loc = hdf5_getters.get_artist_location(h5)
#checks artist_loc to see if it is a hyperlink if it is set as empty string
artist_loc = artist_loc.replace(",", "\,");
if artist_loc.startswith("<a"):
artist_loc = ""
if len(artist_loc) > 100:
artist_loc = ""
artist_lon = hdf5_getters.get_artist_longitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lon) == True:
artist_lon=-1
artist_mbid = hdf5_getters.get_artist_mbid(h5)
artist_pmid = hdf5_getters.get_artist_playmeid(h5)
audio_md5 = hdf5_getters.get_audio_md5(h5)
danceability = hdf5_getters.get_danceability(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(danceability) == True:
danceability=-1
end_fade_in =hdf5_getters.get_end_of_fade_in(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(end_fade_in) == True:
end_fade_in=-1
energy = hdf5_getters.get_energy(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(energy) == True:
energy=-1
song_key = hdf5_getters.get_key(h5)
key_c = hdf5_getters.get_key_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(key_c) == True:
key_c=-1
loudness = hdf5_getters.get_loudness(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(loudness) == True:
loudness=-1
mode = hdf5_getters.get_mode(h5)
mode_conf = hdf5_getters.get_mode_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(mode_conf) == True:
mode_conf=-1
release_7digitalid = hdf5_getters.get_release_7digitalid(h5)
song_hot = hdf5_getters.get_song_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(song_hot) == True:
song_hot=-1
song_id = hdf5_getters.get_song_id(h5)
start_fade_out = hdf5_getters.get_start_of_fade_out(h5)
tempo = hdf5_getters.get_tempo(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(tempo) == True:
tempo=-1
time_sig = hdf5_getters.get_time_signature(h5)
time_sig_c = hdf5_getters.get_time_signature_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(time_sig_c) == True:
time_sig_c=-1
track_id = hdf5_getters.get_track_id(h5)
track_7digitalid = hdf5_getters.get_track_7digitalid(h5)
year = hdf5_getters.get_year(h5)
bars_c = hdf5_getters.get_bars_confidence(h5)
bars_c_avg= get_avg(bars_c)
bars_c_max= get_max(bars_c)
bars_c_min = get_min(bars_c)
bars_c_stddev= get_stddev(bars_c)
bars_c_count = get_count(bars_c)
bars_c_sum = get_sum(bars_c)
bars_start = hdf5_getters.get_bars_start(h5)
bars_start_avg = get_avg(bars_start)
bars_start_max= get_max(bars_start)
bars_start_min = get_min(bars_start)
bars_start_stddev= get_stddev(bars_start)
bars_start_count = get_count(bars_start)
bars_start_sum = get_sum(bars_start)
beats_c = hdf5_getters.get_beats_confidence(h5)
beats_c_avg= get_avg(beats_c)
beats_c_max= get_max(beats_c)
beats_c_min = get_min(beats_c)
beats_c_stddev= get_stddev(beats_c)
beats_c_count = get_count(beats_c)
beats_c_sum = get_sum(beats_c)
beats_start = hdf5_getters.get_beats_start(h5)
beats_start_avg = get_avg(beats_start)
beats_start_max= get_max(beats_start)
beats_start_min = get_min(beats_start)
beats_start_stddev= get_stddev(beats_start)
beats_start_count = get_count(beats_start)
beats_start_sum = get_sum(beats_start)
sec_c = hdf5_getters.get_sections_confidence(h5)
sec_c_avg= get_avg(sec_c)
sec_c_max= get_max(sec_c)
sec_c_min = get_min(sec_c)
sec_c_stddev= get_stddev(sec_c)
sec_c_count = get_count(sec_c)
sec_c_sum = get_sum(sec_c)
sec_start = hdf5_getters.get_sections_start(h5)
sec_start_avg = get_avg(sec_start)
sec_start_max= get_max(sec_start)
sec_start_min = get_min(sec_start)
sec_start_stddev= get_stddev(sec_start)
sec_start_count = get_count(sec_start)
sec_start_sum = get_sum(sec_start)
seg_c = hdf5_getters.get_segments_confidence(h5)
seg_c_avg= get_avg(seg_c)
seg_c_max= get_max(seg_c)
seg_c_min = get_min(seg_c)
seg_c_stddev= get_stddev(seg_c)
seg_c_count = get_count(seg_c)
seg_c_sum = get_sum(seg_c)
seg_loud_max = hdf5_getters.get_segments_loudness_max(h5)
seg_loud_max_avg= get_avg(seg_loud_max)
seg_loud_max_max= get_max(seg_loud_max)
seg_loud_max_min = get_min(seg_loud_max)
seg_loud_max_stddev= get_stddev(seg_loud_max)
seg_loud_max_count = get_count(seg_loud_max)
seg_loud_max_sum = get_sum(seg_loud_max)
seg_loud_max_time = hdf5_getters.get_segments_loudness_max_time(h5)
seg_loud_max_time_avg= get_avg(seg_loud_max_time)
seg_loud_max_time_max= get_max(seg_loud_max_time)
seg_loud_max_time_min = get_min(seg_loud_max_time)
seg_loud_max_time_stddev= get_stddev(seg_loud_max_time)
seg_loud_max_time_count = get_count(seg_loud_max_time)
seg_loud_max_time_sum = get_sum(seg_loud_max_time)
seg_loud_start = hdf5_getters.get_segments_loudness_start(h5)
seg_loud_start_avg= get_avg(seg_loud_start)
seg_loud_start_max= get_max(seg_loud_start)
seg_loud_start_min = get_min(seg_loud_start)
seg_loud_start_stddev= get_stddev(seg_loud_start)
seg_loud_start_count = get_count(seg_loud_start)
seg_loud_start_sum = get_sum(seg_loud_start)
seg_pitch = hdf5_getters.get_segments_pitches(h5)
pitch_size = len(seg_pitch)
seg_start = hdf5_getters.get_segments_start(h5)
seg_start_avg= get_avg(seg_start)
seg_start_max= get_max(seg_start)
seg_start_min = get_min(seg_start)
seg_start_stddev= get_stddev(seg_start)
seg_start_count = get_count(seg_start)
seg_start_sum = get_sum(seg_start)
seg_timbre = hdf5_getters.get_segments_timbre(h5)
tatms_c = hdf5_getters.get_tatums_confidence(h5)
tatms_c_avg= get_avg(tatms_c)
tatms_c_max= get_max(tatms_c)
tatms_c_min = get_min(tatms_c)
tatms_c_stddev= get_stddev(tatms_c)
tatms_c_count = get_count(tatms_c)
tatms_c_sum = get_sum(tatms_c)
tatms_start = hdf5_getters.get_tatums_start(h5)
tatms_start_avg= get_avg(tatms_start)
tatms_start_max= get_max(tatms_start)
tatms_start_min = get_min(tatms_start)
tatms_start_stddev= get_stddev(tatms_start)
tatms_start_count = get_count(tatms_start)
tatms_start_sum = get_sum(tatms_start)
#Getting the genres
genre_set = 0 #flag to see if the genre has been set or not
art_trm = hdf5_getters.get_artist_terms(h5)
trm_freq = hdf5_getters.get_artist_terms_freq(h5)
trn_wght = hdf5_getters.get_artist_terms_weight(h5)
a_mb_tags = hdf5_getters.get_artist_mbtags(h5)
genre_indexes=get_genre_indexes(trm_freq) #index of the highest freq
final_genre=[]
genres_so_far=[]
for i in range(len(genre_indexes)):
genre_tmp=get_genre(art_trm,genre_indexes[i]) #genre that corresponds to the highest freq
genres_so_far=genre_dict.get_genre_in_dict(genre_tmp) #getting the genre from the dictionary
if len(genres_so_far) != 0:
for i in genres_so_far:
final_genre.append(i)
genre_set=1 #genre was found in dictionary
if genre_set == 1:
col_num=[]
for genre in final_genre:
column=int(genre) #getting the column number of the genre
col_num.append(column)
genre_array=genre_columns(col_num) #genre array
else:
genre_array=genre_columns(-1) #the genre was not found in the dictionary
transpose_pitch= seg_pitch.transpose() #this is to tranpose the matrix,so we can have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_pitch_avg=[]
seg_pitch_max=[]
seg_pitch_min=[]
seg_pitch_stddev=[]
seg_pitch_count=[]
seg_pitch_sum=[]
i=0
#Getting the aggregate values in the pitches array
for row in transpose_pitch:
seg_pitch_avg.append(get_avg(row))
seg_pitch_max.append(get_max(row))
seg_pitch_min.append(get_min(row))
seg_pitch_stddev.append(get_stddev(row))
seg_pitch_count.append(get_count(row))
seg_pitch_sum.append(get_sum(row))
i=i+1
#extracting information from the timbre array
transpose_timbre = seg_pitch.transpose() #tranposing matrix, to have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_timbre_avg=[]
seg_timbre_max=[]
seg_timbre_min=[]
seg_timbre_stddev=[]
seg_timbre_count=[]
seg_timbre_sum=[]
i=0
for row in transpose_timbre:
seg_timbre_avg.append(get_avg(row))
seg_timbre_max.append(get_max(row))
seg_timbre_min.append(get_min(row))
seg_timbre_stddev.append(get_stddev(row))
seg_timbre_count.append(get_count(row))
seg_timbre_sum.append(get_sum(row))
i=i+1
#Writing to the flat file
writer.writerow([title,album,artist_name,year,duration,seg_start_count, tempo])
h5.close()
count=count+1;
print count;
def main(argv):
if len(argv) != 1:
print "Specify data directory"
return
basedir = argv[0]
outputFile1 = open('SongCSV.csv', 'w')
outputFile2 = open('TagsCSV.csv', 'w')
csvRowString = ""
csvLabelString = ""
#################################################
#if you want to prompt the user for the order of attributes in the csv,
#leave the prompt boolean set to True
#else, set 'prompt' to False and set the order of attributes in the 'else'
#clause
prompt = False
#################################################
if prompt == True:
while prompt:
prompt = False
csvAttributeString = raw_input("\n\nIn what order would you like the colums of the CSV file?\n" +
"Please delineate with commas. The options are: " +
"AlbumName, AlbumID, ArtistID, ArtistLatitude, ArtistLocation, ArtistLongitude,"+
" ArtistName, Danceability, Duration, KeySignature, KeySignatureConfidence, Tempo," +
" SongID, TimeSignature, TimeSignatureConfidence, Title, and Year.\n\n" +
"For example, you may write \"Title, Tempo, Duration\"...\n\n" +
"...or exit by typing 'exit'.\n\n")
csvAttributeList = re.split('\W+', csvAttributeString)
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
for attribute in csvAttributeList:
# print "Here is the attribute: " + attribute + " \n"
if attribute == 'AlbumID'.lower():
csvRowString += 'AlbumID'
elif attribute == 'AlbumName'.lower():
csvRowString += 'AlbumName'
elif attribute == 'ArtistID'.lower():
csvRowString += 'ArtistID'
elif attribute == 'ArtistLatitude'.lower():
csvRowString += 'ArtistLatitude'
elif attribute == 'ArtistLocation'.lower():
csvRowString += 'ArtistLocation'
elif attribute == 'ArtistLongitude'.lower():
csvRowString += 'ArtistLongitude'
elif attribute == 'ArtistName'.lower():
csvRowString += 'ArtistName'
elif attribute == 'Danceability'.lower():
csvRowString += 'Danceability'
elif attribute == 'Duration'.lower():
csvRowString += 'Duration'
elif attribute == 'KeySignature'.lower():
csvRowString += 'KeySignature'
elif attribute == 'KeySignatureConfidence'.lower():
csvRowString += 'KeySignatureConfidence'
elif attribute == 'SongID'.lower():
csvRowString += "SongID"
elif attribute == 'Tempo'.lower():
csvRowString += 'Tempo'
elif attribute == 'TimeSignature'.lower():
csvRowString += 'TimeSignature'
elif attribute == 'TimeSignatureConfidence'.lower():
csvRowString += 'TimeSignatureConfidence'
elif attribute == 'Title'.lower():
csvRowString += 'Title'
elif attribute == 'Year'.lower():
csvRowString += 'Year'
elif attribute == 'Exit'.lower():
sys.exit()
else:
prompt = True
print "=============="
print "I believe there has been an error with the input."
print "=============="
break
csvRowString += ","
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex-1]
csvRowString += "\n"
outputFile1.write(csvRowString);
csvRowString = ""
#else, if you want to hard code the order of the csv file and not prompt
#the user,
else:
#################################################
#change the order of the csv file here
#Default is to list all available attributes (in alphabetical order)
#csvRowString = ("SongID,AlbumID,AlbumName,ArtistID,ArtistLatitude,ArtistLocation,"+
# "ArtistLongitude,ArtistName,Danceability,Duration,KeySignature,"+
# "KeySignatureConfidence,Tempo,TimeSignature,TimeSignatureConfidence,"+
# "Title,Year")
csvRowString = ("ArtistFamiliarity,ArtistHotttnesss,"+
"BarsConfidence,BarsStart,BeatsConfidence,BeatsStart,Duration,"+
"EndOfFadeIn,Key,KeyConfidence,Loudness,Mode,ModeConfidence,"+
"SectionsConfidence,SectionsStart,SegmentsConfidence,SegmentsLoudnessMax,"+
"SegmentsLoudnessMaxTime,SegmentsLoudnessStart,SegmentsStart,"+
"SongHotttnesss,StartOfFadeOut,TatumsConfidence,TatumsStart,Tempo,TimeSignature,TimeSignatureConfidence,"+
"SegmentsPitches,SegmentsTimbre,Title,Year,Decade,ArtistMbtags")
#################################################
header = str()
csvAttributeList = re.split('\W+', csvRowString)
arrayAttributes = ["BarsConfidence","BarsStart","BeatsConfidence","BeatsStart",
"SectionsConfidence","SectionsStart","SegmentsConfidence","SegmentsLoudnessMax",
"SegmentsLoudnessMaxTime","SegmentsLoudnessStart","SegmentsStart",
"TatumsConfidence","TatumsStart"]
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
if(v=="SegmentsPitches"):
for i in range(90):
header = header + "SegmentsPitches" + str(i) + ","
elif(v=="SegmentsTimbre"):
for i in range(90):
header = header + "SegmentsTimbre" + str(i) + ","
elif(v in arrayAttributes):
header = header + v + str(0) + ","
header = header + v + str(1) + ","
else:
header = header + v + ","
outputFile1.write("SongNumber,");
#outputFile1.write(csvRowString + "\n");
outputFile1.write(header + "\n");
csvRowString = ""
#################################################
#Set the basedir here, the root directory from which the search
#for files stored in a (hierarchical data structure) will originate
#basedir = "MillionSongSubset/data/A/A/" # "." As the default means the current directory
ext = ".h5" #Set the extension here. H5 is the extension for HDF5 files.
#################################################
#FOR LOOP
all = sorted(os.walk(basedir))
for root, dirs, files in all:
files = sorted(glob.glob(os.path.join(root,'*'+ext)))
for f in files:
print f
songH5File = hdf5_getters.open_h5_file_read(f)
song = Song(str(hdf5_getters.get_song_id(songH5File)))
#testDanceability = hdf5_getters.get_danceability(songH5File)
# print type(testDanceability)
# print ("Here is the danceability: ") + str(testDanceability)
song.analysisSampleRate = str(hdf5_getters.get_analysis_sample_rate(songH5File))
song.artistFamiliarity = str(hdf5_getters.get_artist_familiarity(songH5File))
song.artistHotttnesss = str(hdf5_getters.get_artist_hotttnesss(songH5File))
song.artistLatitude = str(hdf5_getters.get_artist_latitude(songH5File))
song.artistLongitude = str(hdf5_getters.get_artist_longitude(songH5File))
song.artistMbid = str(hdf5_getters.get_artist_mbid(songH5File))
song.barsConfidence = np.array(hdf5_getters.get_bars_confidence(songH5File))
song.barsStart = np.array(hdf5_getters.get_bars_start(songH5File))
song.beatsConfidence = np.array(hdf5_getters.get_beats_confidence(songH5File))
song.beatsStart = np.array(hdf5_getters.get_beats_start(songH5File))
song.danceability = str(hdf5_getters.get_danceability(songH5File))
song.duration = str(hdf5_getters.get_duration(songH5File))
song.endOfFadeIn = str(hdf5_getters.get_end_of_fade_in(songH5File))
song.energy = str(hdf5_getters.get_energy(songH5File))
song.key = str(hdf5_getters.get_key(songH5File))
song.keyConfidence = str(hdf5_getters.get_key_confidence(songH5File))
song.loudness = str(hdf5_getters.get_loudness(songH5File))
song.mode = str(hdf5_getters.get_mode(songH5File))
song.modeConfidence = str(hdf5_getters.get_mode_confidence(songH5File))
song.sectionsConfidence = np.array(hdf5_getters.get_sections_confidence(songH5File))
song.sectionsStart = np.array(hdf5_getters.get_sections_start(songH5File))
song.segmentsConfidence = np.array(hdf5_getters.get_segments_confidence(songH5File))
song.segmentsLoudnessMax = np.array(hdf5_getters.get_segments_loudness_max(songH5File))
song.segmentsLoudnessMaxTime = np.array(hdf5_getters.get_segments_loudness_max_time(songH5File))
song.segmentsLoudnessStart = np.array(hdf5_getters.get_segments_loudness_start(songH5File))
song.segmentsPitches = np.array(hdf5_getters.get_segments_pitches(songH5File))
song.segmentsStart = np.array(hdf5_getters.get_segments_start(songH5File))
song.segmentsTimbre = np.array(hdf5_getters.get_segments_timbre(songH5File))
song.songHotttnesss = str(hdf5_getters.get_song_hotttnesss(songH5File))
song.startOfFadeOut = str(hdf5_getters.get_start_of_fade_out(songH5File))
song.tatumsConfidence = np.array(hdf5_getters.get_tatums_confidence(songH5File))
song.tatumsStart = np.array(hdf5_getters.get_tatums_start(songH5File))
song.tempo = str(hdf5_getters.get_tempo(songH5File))
song.timeSignature = str(hdf5_getters.get_time_signature(songH5File))
song.timeSignatureConfidence = str(hdf5_getters.get_time_signature_confidence(songH5File))
song.songid = str(hdf5_getters.get_song_id(songH5File))
song.title = str(hdf5_getters.get_title(songH5File))
song.year = str(hdf5_getters.get_year(songH5File))
song.artistMbtags = str(hdf5_getters.get_artist_mbtags(songH5File))
#print song count
csvRowString += str(song.songCount) + ","
csvLabelString += str(song.songCount) + ","
for attribute in csvAttributeList:
# print "Here is the attribute: " + attribute + " \n"
if attribute == 'AnalysisSampleRate'.lower():
csvRowString += song.analysisSampleRate
elif attribute == 'ArtistFamiliarity'.lower():
csvRowString += song.artistFamiliarity
elif attribute == 'ArtistHotttnesss'.lower():
csvRowString += song.artistHotttnesss
elif attribute == 'ArtistLatitude'.lower():
latitude = song.artistLatitude
if latitude == 'nan':
latitude = ''
csvRowString += latitude
elif attribute == 'ArtistLongitude'.lower():
longitude = song.artistLongitude
if longitude == 'nan':
longitude = ''
csvRowString += longitude
elif attribute == 'ArtistMbid'.lower():
csvRowString += song.artistMbid
elif attribute == 'BarsConfidence'.lower():
arr = song.barsConfidence
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'BarsStart'.lower():
arr = song.barsStart
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'BeatsConfidence'.lower():
arr = song.beatsConfidence
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'BeatsStart'.lower():
arr = song.beatsStart
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'Danceability'.lower():
csvRowString += song.danceability
elif attribute == 'Duration'.lower():
csvRowString += song.duration
elif attribute == 'EndOfFadeIn'.lower():
csvRowString += song.endOfFadeIn
elif attribute == 'Energy'.lower():
csvRowString += song.energy
elif attribute == 'Key'.lower():
csvRowString += song.key
elif attribute == 'KeyConfidence'.lower():
csvRowString += song.keyConfidence
elif attribute == 'Loudness'.lower():
csvRowString += song.loudness
elif attribute == 'Mode'.lower():
csvRowString += song.mode
elif attribute == 'ModeConfidence'.lower():
csvRowString += song.modeConfidence
elif attribute == 'SectionsConfidence'.lower():
arr = song.sectionsConfidence
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'SectionsStart'.lower():
arr = song.sectionsStart
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'SegmentsConfidence'.lower():
arr = song.segmentsConfidence
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'SegmentsLoudnessMax'.lower():
arr = song.segmentsLoudnessMax
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'SegmentsLoudnessMaxTime'.lower():
arr = song.segmentsLoudnessMaxTime
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'SegmentsLoudnessStart'.lower():
arr = song.segmentsLoudnessStart
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'SegmentsStart'.lower():
arr = song.segmentsStart
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'SongHotttnesss'.lower():
hotttnesss = song.songHotttnesss
if hotttnesss == 'nan':
hotttnesss = 'NaN'
csvRowString += hotttnesss
elif attribute == 'StartOfFadeOut'.lower():
csvRowString += song.startOfFadeOut
elif attribute == 'TatumsConfidence'.lower():
arr = song.tatumsConfidence
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'TatumsStart'.lower():
arr = song.tatumsStart
if arr.shape[0] == 0:
arrmean = ''
arrnorm = ''
else:
arrmean = np.mean(arr)
arrnorm = np.linalg.norm(arr)
csvRowString += str(arrmean) + ',' + str(arrnorm)
elif attribute == 'Tempo'.lower():
# print "Tempo: " + song.tempo
csvRowString += song.tempo
elif attribute == 'TimeSignature'.lower():
csvRowString += song.timeSignature
elif attribute == 'TimeSignatureConfidence'.lower():
# print "time sig conf: " + song.timeSignatureConfidence
csvRowString += song.timeSignatureConfidence
elif attribute == 'SegmentsPitches'.lower():
colmean = np.mean(song.segmentsPitches,axis=0)
for m in colmean:
csvRowString += str(m) + ","
cov = np.dot(song.segmentsPitches.T,song.segmentsPitches)
utriind = np.triu_indices(cov.shape[0])
feats = cov[utriind]
for feat in feats:
csvRowString += str(feat) + ","
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex-1]
elif attribute == 'SegmentsTimbre'.lower():
colmean = np.mean(song.segmentsTimbre,axis=0)
for m in colmean:
csvRowString += str(m) + ","
cov = np.dot(song.segmentsTimbre.T,song.segmentsTimbre)
utriind = np.triu_indices(cov.shape[0])
feats = cov[utriind]
for feat in feats:
csvRowString += str(feat) + ","
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex-1]
elif attribute == 'SongID'.lower():
csvRowString += "\"" + song.id + "\""
elif attribute == 'Title'.lower():
csvRowString += "\"" + song.title + "\""
elif attribute == 'Year'.lower():
csvRowString += song.year
elif attribute == 'Decade'.lower():
yr = song.year
if yr > 0:
decade = song.year[:-1] + '0'
else:
decade = '0'
csvRowString += decade
elif attribute == 'ArtistMbtags'.lower():
tags = song.artistMbtags[1:-1]
tags = "\"" + tags + "\""
tags = tags.replace("\n",'')
csvRowString += tags
tagsarray = shlex.split(tags)
for t in tagsarray:
csvLabelString += t + ","
else:
csvRowString += "Erm. This didn't work. Error. :( :(\n"
csvRowString += ","
'''
if attribute == 'AlbumID'.lower():
csvRowString += song.albumID
elif attribute == 'AlbumName'.lower():
albumName = song.albumName
albumName = albumName.replace(',',"")
csvRowString += "\"" + albumName + "\""
elif attribute == 'ArtistID'.lower():
csvRowString += "\"" + song.artistID + "\""
elif attribute == 'ArtistLatitude'.lower():
latitude = song.artistLatitude
if latitude == 'nan':
latitude = ''
csvRowString += latitude
elif attribute == 'ArtistLocation'.lower():
location = song.artistLocation
location = location.replace(',','')
csvRowString += "\"" + location + "\""
elif attribute == 'ArtistLongitude'.lower():
longitude = song.artistLongitude
if longitude == 'nan':
longitude = ''
csvRowString += longitude
elif attribute == 'ArtistName'.lower():
csvRowString += "\"" + song.artistName + "\""
elif attribute == 'Danceability'.lower():
csvRowString += song.danceability
elif attribute == 'Duration'.lower():
csvRowString += song.duration
elif attribute == 'KeySignature'.lower():
csvRowString += song.keySignature
elif attribute == 'KeySignatureConfidence'.lower():
# print "key sig conf: " + song.timeSignatureConfidence
csvRowString += song.keySignatureConfidence
elif attribute == 'SongID'.lower():
csvRowString += "\"" + song.id + "\""
elif attribute == 'Tempo'.lower():
# print "Tempo: " + song.tempo
csvRowString += song.tempo
elif attribute == 'TimeSignature'.lower():
csvRowString += song.timeSignature
elif attribute == 'TimeSignatureConfidence'.lower():
# print "time sig conf: " + song.timeSignatureConfidence
csvRowString += song.timeSignatureConfidence
elif attribute == 'Title'.lower():
csvRowString += "\"" + song.title + "\""
elif attribute == 'Year'.lower():
csvRowString += song.year
else:
csvRowString += "Erm. This didn't work. Error. :( :(\n"
csvRowString += ","
'''
#Remove the final comma from each row in the csv
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex-1]
csvRowString += "\n"
outputFile1.write(csvRowString)
csvRowString = ""
lastIndex = len(csvLabelString)
csvLabelString = csvLabelString[0:lastIndex-1]
csvLabelString += "\n"
outputFile2.write(csvLabelString)
csvLabelString = ""
songH5File.close()
outputFile1.close()
outputFile2.close()
def get_time_signature_confidence(self):
if self.h5 == None: self.open()
return hdf5_getters.get_time_signature_confidence(self.h5)
#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
similar_artists = ','.join(
str(e)
for e in GETTERS.get_similar_artists(h5, i)) # array
song_hotttnesss = GETTERS.get_song_hotttnesss(h5, i)
song_id = GETTERS.get_song_id(h5, i)
start_of_fade_out = GETTERS.get_start_of_fade_out(h5, i)
#tatums_confidence = ','.join(str(e) for e in GETTERS.get_tatums_confidence(h5, i)) # array
#tatums_start = ','.join(str(e) for e in GETTERS.get_tatums_start(h5, i)) # array
tempo = GETTERS.get_tempo(h5, i)
time_signature = GETTERS.get_time_signature(h5, i)
time_signature_confidence = GETTERS.get_time_signature_confidence(
h5, i)
title = GETTERS.get_title(h5, i)
track_7digitalid = GETTERS.get_track_7digitalid(h5, i)
track_id = GETTERS.get_track_id(h5, i)
year = GETTERS.get_year(h5, i)
loops += 1
#row = {'analysis_sample_rate':analysis_sample_rate,'artist_7digitalid':artist_7digitalid,'artist_familiarity':artist_familiarity,'artist_hotttnesss':artist_hotttnesss,'artist_id':artist_id,'artist_latitude':artist_latitude,'artist_location':artist_location,'artist_longitude':artist_longitude,'artist_mbid':artist_mbid,'artist_mbtags_count':artist_mbtags_count,'artist_mbtags':artist_mbtags,'artist_name':artist_name,'artist_terms_freq':artist_terms_freq,'artist_terms_weight':artist_terms_weight,'artist_terms':artist_terms,'audio_md5':audio_md5,'bars_confidence':bars_confidence,'bars_start':bars_start,'beats_confidence':beats_confidence,'beats_start':beats_start,'danceability':danceability,'duration':duration,'end_of_fade_in':end_of_fade_in,'energy':energy,'key_confidence':key_confidence,'key':key,'loudness':loudness,'mode_confidence':mode_confidence,'mode':mode,'release_7digitalid':release_7digitalid,'release':release,'sections_confidence':sections_confidence,'sections_start':sections_start,'segments_confidence':segments_confidence,'segments_loudness_max_time':segments_loudness_max_time,'segments_loudness_max':segments_loudness_max,'segments_loudness_start':segments_loudness_start,'segments_pitches':segments_pitches,'segments_start':segments_start,'segments_timbre':segments_timbre,'similar_artists':similar_artists,'song_hotttnesss':song_hotttnesss,'song_id':song_id,'start_of_fade_out':start_of_fade_out,'tatums_confidence':tatums_confidence,'tatums_start':tatums_start,'tempo':tempo,'time_signature_confidence':time_signature_confidence,'time_signature':time_signature,'title':title,'track_7digitalid':track_7digitalid,'track_id':track_id,'year':year,}
row = {
'analysis_sample_rate': analysis_sample_rate,
'artist_7digitalid': artist_7digitalid,
'artist_familiarity': artist_familiarity,
'artist_hotttnesss': artist_hotttnesss,
'artist_id': artist_id,
'artist_latitude': artist_latitude,
'artist_location': artist_location,
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)
time_signature = hdf5_getters.get_time_signature(h5)
time_signature_confidence = hdf5_getters.get_time_signature_confidence(h5)
found_tracks += 1
# Append all features to an array
features = []
features.append(track_id)
features.append(danceability)
features.append(duration)
features.append(time_of_fade_in)
features.append(energy)
for i in range(12):
if i == key:
features.append(key_confidence)
else:
features.append(0.0)
#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
similar_artists = ','.join(str(e) for e in GETTERS.get_similar_artists(h5, i)) # array
song_hotttnesss = GETTERS.get_song_hotttnesss(h5, i)
song_id = GETTERS.get_song_id(h5, i)
start_of_fade_out = GETTERS.get_start_of_fade_out(h5, i)
#tatums_confidence = ','.join(str(e) for e in GETTERS.get_tatums_confidence(h5, i)) # array
#tatums_start = ','.join(str(e) for e in GETTERS.get_tatums_start(h5, i)) # array
tempo = GETTERS.get_tempo(h5, i)
time_signature = GETTERS.get_time_signature(h5, i)
time_signature_confidence = GETTERS.get_time_signature_confidence(h5, i)
title = GETTERS.get_title(h5, i)
track_7digitalid = GETTERS.get_track_7digitalid(h5, i)
track_id = GETTERS.get_track_id(h5, i)
year = GETTERS.get_year(h5, i)
loops += 1
#row = {'analysis_sample_rate':analysis_sample_rate,'artist_7digitalid':artist_7digitalid,'artist_familiarity':artist_familiarity,'artist_hotttnesss':artist_hotttnesss,'artist_id':artist_id,'artist_latitude':artist_latitude,'artist_location':artist_location,'artist_longitude':artist_longitude,'artist_mbid':artist_mbid,'artist_mbtags_count':artist_mbtags_count,'artist_mbtags':artist_mbtags,'artist_name':artist_name,'artist_terms_freq':artist_terms_freq,'artist_terms_weight':artist_terms_weight,'artist_terms':artist_terms,'audio_md5':audio_md5,'bars_confidence':bars_confidence,'bars_start':bars_start,'beats_confidence':beats_confidence,'beats_start':beats_start,'danceability':danceability,'duration':duration,'end_of_fade_in':end_of_fade_in,'energy':energy,'key_confidence':key_confidence,'key':key,'loudness':loudness,'mode_confidence':mode_confidence,'mode':mode,'release_7digitalid':release_7digitalid,'release':release,'sections_confidence':sections_confidence,'sections_start':sections_start,'segments_confidence':segments_confidence,'segments_loudness_max_time':segments_loudness_max_time,'segments_loudness_max':segments_loudness_max,'segments_loudness_start':segments_loudness_start,'segments_pitches':segments_pitches,'segments_start':segments_start,'segments_timbre':segments_timbre,'similar_artists':similar_artists,'song_hotttnesss':song_hotttnesss,'song_id':song_id,'start_of_fade_out':start_of_fade_out,'tatums_confidence':tatums_confidence,'tatums_start':tatums_start,'tempo':tempo,'time_signature_confidence':time_signature_confidence,'time_signature':time_signature,'title':title,'track_7digitalid':track_7digitalid,'track_id':track_id,'year':year,}
row = {'analysis_sample_rate':analysis_sample_rate,'artist_7digitalid':artist_7digitalid,'artist_familiarity':artist_familiarity,'artist_hotttnesss':artist_hotttnesss,'artist_id':artist_id,'artist_latitude':artist_latitude,'artist_location':artist_location,'artist_longitude':artist_longitude,'artist_mbid':artist_mbid,'artist_mbtags_count':artist_mbtags_count,'artist_mbtags':artist_mbtags,'artist_name':artist_name,'artist_terms':artist_terms,'danceability':danceability,'duration':duration,'end_of_fade_in':end_of_fade_in,'energy':energy,'key_confidence':key_confidence,'key':key,'loudness':loudness,'mode_confidence':mode_confidence,'mode':mode,'release_7digitalid':release_7digitalid,'release':release,'similar_artists':similar_artists,'song_hotttnesss':song_hotttnesss,'song_id':song_id,'start_of_fade_out':start_of_fade_out,'tempo':tempo,'time_signature_confidence':time_signature_confidence,'time_signature':time_signature,'title':title,'track_7digitalid':track_7digitalid,'track_id':track_id,'year':year,
}
key = (row['track_id'])
#for k, v in row.items(): print k, type(v)
def main():
basedir = "D:/Master K"
ext = ".H5" # Set the extension here. H5 is the extension for HDF5 files.
songs = []
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
#songs = {}
#keys = list()
#values = list()
for f in files:
print(f)
songH5File = hdf5_getters.open_h5_file_read(f)
song = Song(str(hdf5_getters.get_song_id(songH5File)))
item = {"song_id": song.id.replace('b', '')}
song.artistID = str(hdf5_getters.get_artist_id(songH5File))
song.artistID = song.artistID.replace('b', '', 1)
item["song_artistID"] = song.artistID
song.albumID = str(hdf5_getters.get_release_7digitalid(songH5File))
song.albumID = song.albumID.replace('b', '', 1)
item["song_albumID"] = song.albumID
song.albumName = str(hdf5_getters.get_release(songH5File))
song.albumName = song.albumName.replace('b', '', 1)
item["song_albumName"] = song.albumName
song.artistLatitude = str(
hdf5_getters.get_artist_latitude(songH5File))
song.artistLatitude = song.artistLatitude.replace('b', '', 1)
item["song_artistLatitude"] = song.artistLatitude
song.artistLocation = str(
hdf5_getters.get_artist_location(songH5File))
song.artistLocation = song.artistLocation.replace('b', '', 1)
item["song_artistLocation"] = song.artistLocation
song.artistLongitude = str(
hdf5_getters.get_artist_longitude(songH5File))
song.artistLongitude = song.artistLongitude.replace('b', '', 1)
item["song_artistLongitude"] = song.artistLongitude
song.artistName = str(hdf5_getters.get_artist_name(songH5File))
song.artistName = song.artistName.replace('b', '', 1)
item["song_artistName"] = song.artistName
song.danceability = str(hdf5_getters.get_danceability(songH5File))
song.danceability = song.danceability.replace('b', '', 1)
item["song_danceability"] = song.danceability
song.duration = str(hdf5_getters.get_duration(songH5File))
song.duration = song.duration.replace('b', '', 1)
item["song_duration"] = song.duration
song.keySignature = str(hdf5_getters.get_key(songH5File))
song.keySignature = song.keySignature.replace('b', '', 1)
item["song_keySignature"] = song.keySignature
song.keySignatureConfidence = str(
hdf5_getters.get_key_confidence(songH5File))
song.keySignatureConfidence = song.keySignatureConfidence.replace(
'b', '', 1)
item["song_keySignatureConfidence"] = song.keySignatureConfidence
song.tempo = str(hdf5_getters.get_tempo(songH5File))
song.tempo = song.tempo.replace('b', '', 1)
item["song_tempo"] = song.tempo
song.timeSignature = str(
hdf5_getters.get_time_signature(songH5File))
song.timeSignature = song.timeSignature.replace('b', '', 1)
item["song_timeSignature"] = song.timeSignature
song.timeSignatureConfidence = str(
hdf5_getters.get_time_signature_confidence(songH5File))
song.timeSignatureConfidence = song.timeSignatureConfidence.replace(
'b', '', 1)
item["song_timeSignatureConfidence"] = song.timeSignatureConfidence
song.title = str(hdf5_getters.get_title(songH5File))
song.title = song.title.replace('b', '', 1)
item["song_title"] = song.title
song.year = str(hdf5_getters.get_year(songH5File))
song.year = song.year.replace('b', '', 1)
item["song_year"] = song.year
#song.mfcc = str(hdf5_getters.get_segments_timbre(songH5File))
#item["song_mfcc"] = song.mfcc
item["song_mfcc"] = list(
hdf5_getters.get_segments_timbre(songH5File))
song.hotness = str(hdf5_getters.get_artist_hotttnesss(songH5File))
item["song_hotness"] = song.hotness
songs.append(item)
songH5File.close()
#song_dict= dict(zip(keys, values))
with open("D:\data_file_k.json", "w") as write_file:
json.dump(songs, write_file, cls=NumpyEncoder)
def main():
basedir = "./../songMetaInfo.txt"
ext = ".h5"
if len(sys.argv) > 1:
basedir = sys.argv[1]
outputfile = 'SongFileMetaData.csv'
if len(sys.argv) > 2:
outputfile = sys.argv[2]
csvWriter = open(outputfile, 'w')
csvWriter.write(
"title,songId,artistId,artistfamilarity,artistHotness,songHotness," +
"songEnfOfFadeIn,startFadeout,energy,loudness,albumID,albumName,artistName,danceability,duration,keySignatureConfidence,tempo,timeSignature,timeSignatureConfidence,year\n"
)
with open(basedir) as file:
for line in file.readlines():
f = line.strip()
#newf = f + "text"
print f
#print f
try:
songH5File = hdf5_getters.open_h5_file_read(f)
csvStr = ""
#0
title = str(hdf5_getters.get_title(songH5File))
csvStr += title + ","
#1
songId = str(hdf5_getters.get_song_id(songH5File))
csvStr += songId + ","
#2
artistId = str(hdf5_getters.get_artist_id(songH5File))
csvStr += artistId + ","
#3
artistfamilarity = str(
hdf5_getters.get_artist_familiarity(songH5File))
csvStr += artistfamilarity + ","
#4
artistHotness = str(
hdf5_getters.get_artist_hotttnesss(songH5File))
csvStr += artistHotness + ","
#5
songHotness = str(hdf5_getters.get_song_hotttnesss(songH5File))
csvStr += songHotness + ","
#6
songEnfOfFadeIn = str(
hdf5_getters.get_end_of_fade_in(songH5File))
csvStr += songEnfOfFadeIn + ","
#7
startFadeOut = str(
hdf5_getters.get_start_of_fade_out(songH5File))
csvStr += startFadeOut + ","
#8
energy = str(hdf5_getters.get_energy(songH5File))
csvStr += energy + ","
#9
loudness = str(hdf5_getters.get_loudness(songH5File))
csvStr += loudness + ","
#10
albumID = str(hdf5_getters.get_release_7digitalid(songH5File))
csvStr += albumID + ","
#11
albumName = str(hdf5_getters.get_release(songH5File))
csvStr += albumName + ","
#12
artistName = str(hdf5_getters.get_artist_name(songH5File))
csvStr += artistName + ","
#13
danceability = str(hdf5_getters.get_danceability(songH5File))
csvStr += danceability + ","
#14
duration = str(hdf5_getters.get_duration(songH5File))
csvStr += duration + ","
#15
keySignatureConfidence = str(
hdf5_getters.get_key_confidence(songH5File))
csvStr += keySignatureConfidence + ","
#16
tempo = str(hdf5_getters.get_tempo(songH5File))
csvStr += tempo + ","
## 17
timeSignature = str(
hdf5_getters.get_time_signature(songH5File))
csvStr += timeSignature + ","
#18
timeSignatureConfidence = str(
hdf5_getters.get_time_signature_confidence(songH5File))
csvStr += timeSignatureConfidence + ","
#19
year = str(hdf5_getters.get_year(songH5File))
csvStr += year + ","
#print song count
csvStr += "\n"
csvWriter.write(csvStr)
#print csvStr
songH5File.close()
except:
print "Error in processing file"
csvWriter.close()
def hd5_single_random_file_parser():
# Open an h5 file in read mode
h5 = hdf5_getters.open_h5_file_read(
'/home/skalogerakis/Documents/MillionSong/MillionSongSubset/A/M/G/TRAMGDX12903CEF79F.h5'
)
function_tracker = filter(
lambda x: x.startswith('get'),
hdf5_getters.__dict__.keys()) # Detects all the getter functions
for f in function_tracker: # Print everything in function tracker
print(f)
# First effort to check what each field contains.
print() # 55 available fields (exluding number of songs fields)
print("Num of songs -- ",
hdf5_getters.get_num_songs(h5)) # One song per file
print("Title -- ",
hdf5_getters.get_title(h5)) # Print the title of a specific h5 file
print("Artist familiarity -- ", hdf5_getters.get_artist_familiarity(h5))
print("Artist hotness -- ", hdf5_getters.get_artist_hotttnesss(h5))
print("Artist ID -- ", hdf5_getters.get_artist_id(h5))
print("Artist mbID -- ", hdf5_getters.get_artist_mbid(h5))
print("Artist playmeid -- ", hdf5_getters.get_artist_playmeid(h5))
print("Artist 7DigitalID -- ", hdf5_getters.get_artist_7digitalid(h5))
print("Artist latitude -- ", hdf5_getters.get_artist_latitude(h5))
print("Artist longitude -- ", hdf5_getters.get_artist_longitude(h5))
print("Artist location -- ", hdf5_getters.get_artist_location(h5))
print("Artist Name -- ", hdf5_getters.get_artist_name(h5))
print("Release -- ", hdf5_getters.get_release(h5))
print("Release 7DigitalID -- ", hdf5_getters.get_release_7digitalid(h5))
print("Song ID -- ", hdf5_getters.get_song_id(h5))
print("Song Hotness -- ", hdf5_getters.get_song_hotttnesss(h5))
print("Track 7Digital -- ", hdf5_getters.get_track_7digitalid(h5))
print("Similar artists -- ", hdf5_getters.get_similar_artists(h5))
print("Artist terms -- ", hdf5_getters.get_artist_terms(h5))
print("Artist terms freq -- ", hdf5_getters.get_artist_terms_freq(h5))
print("Artist terms weight -- ", hdf5_getters.get_artist_terms_weight(h5))
print("Analysis sample rate -- ",
hdf5_getters.get_analysis_sample_rate(h5))
print("Audio md5 -- ", hdf5_getters.get_audio_md5(h5))
print("Danceability -- ", hdf5_getters.get_danceability(h5))
print("Duration -- ", hdf5_getters.get_duration(h5))
print("End of Fade -- ", hdf5_getters.get_end_of_fade_in(h5))
print("Energy -- ", hdf5_getters.get_energy(h5))
print("Key -- ", hdf5_getters.get_key(h5))
print("Key Confidence -- ", hdf5_getters.get_key_confidence(h5))
print("Loudness -- ", hdf5_getters.get_loudness(h5))
print("Mode -- ", hdf5_getters.get_mode(h5))
print("Mode Confidence -- ", hdf5_getters.get_mode_confidence(h5))
print("Start of fade out -- ", hdf5_getters.get_start_of_fade_out(h5))
print("Tempo -- ", hdf5_getters.get_tempo(h5))
print("Time signature -- ", hdf5_getters.get_time_signature(h5))
print("Time signature confidence -- ",
hdf5_getters.get_time_signature_confidence(h5))
print("Track ID -- ", hdf5_getters.get_track_id(h5))
print("Segments Start -- ", hdf5_getters.get_segments_start(h5))
print("Segments Confidence -- ", hdf5_getters.get_segments_confidence(h5))
print("Segments Pitches -- ", hdf5_getters.get_segments_pitches(h5))
print("Segments Timbre -- ", hdf5_getters.get_segments_timbre(h5))
print("Segments Loudness max -- ",
hdf5_getters.get_segments_loudness_max(h5))
print("Segments Loudness max time-- ",
hdf5_getters.get_segments_loudness_max_time(h5))
print("Segments Loudness start -- ",
hdf5_getters.get_segments_loudness_start(h5))
print("Sections start -- ", hdf5_getters.get_sections_start(h5))
print("Sections Confidence -- ", hdf5_getters.get_sections_confidence(h5))
print("Beats start -- ", hdf5_getters.get_beats_start(h5))
print("Beats confidence -- ", hdf5_getters.get_beats_confidence(h5))
print("Bars start -- ", hdf5_getters.get_bars_start(h5))
print("Bars confidence -- ", hdf5_getters.get_bars_confidence(h5))
print("Tatums start -- ", hdf5_getters.get_tatums_start(h5))
print("Tatums confidence -- ", hdf5_getters.get_tatums_confidence(h5))
print("Artist mbtags -- ", hdf5_getters.get_artist_mbtags(h5))
print("Artist mbtags count -- ", hdf5_getters.get_artist_mbtags_count(h5))
print("Year -- ", hdf5_getters.get_year(h5))
fields = ['Title', 'Artist ID']
with open('Tester2.csv', 'w', newline='') as csvfile:
csv_writer = csv.writer(csvfile, delimiter=';')
# writing the fields
csv_writer.writerow(fields)
# writing the data rows
csv_writer.writerow(
[hdf5_getters.get_title(h5),
hdf5_getters.get_artist_id(h5)])
h5.close() # close h5 when completed in the end
def complete_hd5_to_csv(basedir):
ext = '.h5' # Get all files with extension .h5
# Header title. Essentially it is a schema for all the following songs
header = [
'Title', 'Artist familiarity', 'Artist hotness', 'Artist ID',
'Artist mbID', 'Artist playmeid', 'Artist 7DigitalID',
'Artist latitude', 'Artist longitude', 'Artist location',
'Artist Name', 'Release', 'Release 7DigitalID', 'Song ID',
'Song Hotness', 'Track 7Digital', 'Analysis sample rate', 'Audio md5',
'Danceability', 'Duration', 'End of Fade', 'Energy', 'Key',
'Key Confidence', 'Loudness', 'Mode', 'Mode Confidence',
'Start of fade out', 'Tempo', 'Time signature',
'Time signature confidence', 'Track ID', 'Year'
]
with open('Tester2.csv', 'w', newline='') as csvfile:
csv_writer = csv.writer(csvfile, delimiter=';')
# writing the header line. This line contains the schema of the data
csv_writer.writerow(header)
# Read all files from the given directories
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
print(files)
for f in files:
h5 = hdf5_getters.open_h5_file_read(f)
# Write as row all elements. NOTE: Only the serialized elements are parsed and not arrays
csv_writer.writerow([
hdf5_getters.get_title(h5),
hdf5_getters.get_artist_familiarity(h5),
hdf5_getters.get_artist_hotttnesss(h5),
hdf5_getters.get_artist_id(h5),
hdf5_getters.get_artist_mbid(h5),
hdf5_getters.get_artist_playmeid(h5),
hdf5_getters.get_artist_7digitalid(h5),
hdf5_getters.get_artist_latitude(h5),
hdf5_getters.get_artist_longitude(h5),
hdf5_getters.get_artist_location(h5),
hdf5_getters.get_artist_name(h5),
hdf5_getters.get_release(h5),
hdf5_getters.get_release_7digitalid(h5),
hdf5_getters.get_song_id(h5),
hdf5_getters.get_song_hotttnesss(h5),
hdf5_getters.get_track_7digitalid(h5),
hdf5_getters.get_analysis_sample_rate(h5),
hdf5_getters.get_audio_md5(h5),
hdf5_getters.get_danceability(h5),
hdf5_getters.get_duration(h5),
hdf5_getters.get_end_of_fade_in(h5),
hdf5_getters.get_energy(h5),
hdf5_getters.get_key(h5),
hdf5_getters.get_key_confidence(h5),
hdf5_getters.get_loudness(h5),
hdf5_getters.get_mode(h5),
hdf5_getters.get_mode_confidence(h5),
hdf5_getters.get_start_of_fade_out(h5),
hdf5_getters.get_tempo(h5),
hdf5_getters.get_time_signature(h5),
hdf5_getters.get_time_signature_confidence(h5),
hdf5_getters.get_track_id(h5),
hdf5_getters.get_year(h5)
])
# For debugging purposes. Everything as expected
# print()
# print("Num of songs -- ", hdf5_getters.get_num_songs(h5)) # One song per file
# print("Title -- ", hdf5_getters.get_title(h5)) # Print the title of a specific h5 file
# print("Artist familiarity -- ", hdf5_getters.get_artist_familiarity(h5))
# print("Artist hotness -- ", hdf5_getters.get_artist_hotttnesss(h5))
# print("Artist ID -- ", hdf5_getters.get_artist_id(h5))
# print("Artist mbID -- ", hdf5_getters.get_artist_mbid(h5))
# print("Artist playmeid -- ", hdf5_getters.get_artist_playmeid(h5))
# print("Artist 7DigitalID -- ", hdf5_getters.get_artist_7digitalid(h5))
# print("Artist latitude -- ", hdf5_getters.get_artist_latitude(h5))
# print("Artist longitude -- ", hdf5_getters.get_artist_longitude(h5))
# print("Artist location -- ", hdf5_getters.get_artist_location(h5))
# print("Artist Name -- ", hdf5_getters.get_artist_name(h5))
# print("Release -- ", hdf5_getters.get_release(h5))
# print("Release 7DigitalID -- ", hdf5_getters.get_release_7digitalid(h5))
# print("Song ID -- ", hdf5_getters.get_song_id(h5))
# print("Song Hotness -- ", hdf5_getters.get_song_hotttnesss(h5))
# print("Track 7Digital -- ", hdf5_getters.get_track_7digitalid(h5))
# print("Analysis sample rate -- ", hdf5_getters.get_analysis_sample_rate(h5))
# print("Audio md5 -- ", hdf5_getters.get_audio_md5(h5))
# print("Danceability -- ", hdf5_getters.get_danceability(h5))
# print("Duration -- ", hdf5_getters.get_duration(h5))
# print("End of Fade -- ", hdf5_getters.get_end_of_fade_in(h5))
# print("Energy -- ", hdf5_getters.get_energy(h5))
# print("Key -- ", hdf5_getters.get_key(h5))
# print("Key Confidence -- ", hdf5_getters.get_key_confidence(h5))
# print("Loudness -- ", hdf5_getters.get_loudness(h5))
# print("Mode -- ", hdf5_getters.get_mode(h5))
# print("Mode Confidence -- ", hdf5_getters.get_mode_confidence(h5))
# print("Start of fade out -- ", hdf5_getters.get_start_of_fade_out(h5))
# print("Tempo -- ", hdf5_getters.get_tempo(h5))
# print("Time signature -- ", hdf5_getters.get_time_signature(h5))
# print("Time signature confidence -- ", hdf5_getters.get_time_signature_confidence(h5))
# print("Track ID -- ", hdf5_getters.get_track_id(h5))
# # print("Artist mbtags -- ", hdf5_getters.get_artist_mbtags(h5))
# # print("Artist mbtags count -- ", hdf5_getters.get_artist_mbtags_count(h5))
# print("Year -- ", hdf5_getters.get_year(h5))
h5.close()
def main():
dataset_dir = sys.argv[1]
global feat
Create_BoW(dataset_dir)
Size_BoW = Index_BoW(Bag_Words)
count = Frequency(Size_BoW, dataset_dir)
Size_BoW = Prune(count)
Lablify()
print "Forming Dataset..."
listing1 = os.listdir(dataset_dir)
for a in listing1:
listing2 = os.listdir(dataset_dir+a+'/')
for b in listing2:
listing3 = os.listdir(dataset_dir+a+'/'+b+'/')
for c in listing3:
listing4 = os.listdir(dataset_dir+a+'/'+b+'/'+c+'/')
for d in listing4:
h5 = hdf5_getters.open_h5_file_read(dataset_dir+a+'/'+b+'/'+c+'/'+d)
feat = []
temp = hdf5_getters.get_artist_hotttnesss(h5)
if (math.isnan(temp) or temp==0.0):
h5.close()
continue
feat.append(temp)
temp = hdf5_getters.get_artist_familiarity(h5)
if (math.isnan(temp) or temp==0.0):
h5.close()
continue
feat.append(temp)
temp = hdf5_getters.get_bars_confidence(h5)
if temp.size == 0:
h5.close()
continue
MeanVar(temp)
temp = hdf5_getters.get_beats_confidence(h5)
if temp.size == 0:
h5.close()
continue
mm = np.mean(temp)
vv = np.var(temp)
if mm==0.0 and vv==0.0:
h5.close()
continue
feat.append(mm)
feat.append(vv)
feat.append(hdf5_getters.get_duration(h5))
temp = hdf5_getters.get_end_of_fade_in(h5)
if (math.isnan(temp)):
h5.close()
continue
feat.append(temp)
feat.append(hdf5_getters.get_key(h5))
temp = hdf5_getters.get_key_confidence(h5)
if (math.isnan(temp)):
h5.close()
continue
feat.append(temp)
temp = hdf5_getters.get_loudness(h5)
if (math.isnan(temp)):
h5.close()
continue
feat.append(temp)
feat.append(hdf5_getters.get_mode(h5))
temp = hdf5_getters.get_mode_confidence(h5)
if (math.isnan(temp)):
h5.close()
continue
feat.append(temp)
temp = hdf5_getters.get_sections_confidence(h5)
if temp.size == 0:
h5.close()
continue
MeanVar(temp)
temp = hdf5_getters.get_segments_confidence(h5)
if temp.size == 0:
h5.close()
continue
MeanVar(temp)
temp = hdf5_getters.get_segments_loudness_max(h5)
if temp.size == 0:
h5.close()
continue
MeanVar(temp)
temp = hdf5_getters.get_segments_loudness_max_time(h5)
if temp.size == 0:
h5.close()
continue
MeanVar(temp)
temp = hdf5_getters.get_segments_pitches(h5)
if temp.size == 0:
h5.close()
continue
MeanVar(temp)
temp = hdf5_getters.get_segments_timbre(h5)
if temp.size == 0:
h5.close()
continue
MeanVar(temp)
temp = hdf5_getters.get_start_of_fade_out(h5)
if (math.isnan(temp)):
h5.close()
continue
feat.append(temp)
temp = hdf5_getters.get_tatums_confidence(h5)
if temp.size == 0:
h5.close()
continue
MeanVar(temp)
temp = hdf5_getters.get_tempo(h5)
if (math.isnan(temp)):
h5.close()
continue
feat.append(temp)
feat.append(hdf5_getters.get_time_signature(h5))
temp = hdf5_getters.get_time_signature_confidence(h5)
if (math.isnan(temp)):
h5.close()
continue
feat.append(temp)
temp = hdf5_getters.get_year(h5)
if temp == 0:
h5.close()
continue
feat.append(temp)
temp = hdf5_getters.get_artist_terms(h5)
if temp.size == 0:
h5.close()
continue
temp_ = hdf5_getters.get_artist_terms_weight(h5)
if temp_.size == 0:
continue
for j in Final_BoW:
if j in temp:
x = np.where(temp==j)
x = x[0][0]
feat.append(temp_[x])
else:
x = 0.0
feat.append(x)
temp = hdf5_getters.get_song_hotttnesss(h5)
if (math.isnan(temp) or temp==0.0):
h5.close()
continue
hott = 0
if temp >=0.75:
hott = 1
elif temp >=0.40 and temp <0.75:
hott = 2
else:
hott = 3
feat.append(hott)
h5.close()
count = 1
f=open('MSD_DATASET.txt', 'a')
outstring=''
cnt = 0
feat_size = len(feat)
for i in feat:
cnt+=1
outstring+=str(i)
if (cnt!=feat_size):
outstring+=','
outstring+='\n'
f.write(outstring)
f.close()
def getInfo(files):
data = []
build_str = ''
with open(sys.argv[1], 'r') as f:
contents = f.read()
c = contents.split()
f.close()
print("creating csv with following fields:" + contents)
for i in c:
build_str = build_str + i + ','
build_str = build_str[:-1]
build_str = build_str + '\n'
for fil in files:
curFile = getters.open_h5_file_read(fil)
d2 = {}
get_table = {'track_id': getters.get_track_id(curFile), 'segments_pitches': getters.get_segments_pitches(curFile), 'time_signature_confidence': getters.get_time_signature_confidence(curFile), 'song_hotttnesss': getters.get_song_hotttnesss(curFile), 'artist_longitude': getters.get_artist_longitude(curFile), 'tatums_confidence': getters.get_tatums_confidence(curFile), 'num_songs': getters.get_num_songs(curFile), 'duration': getters.get_duration(curFile), 'start_of_fade_out': getters.get_start_of_fade_out(curFile), 'artist_name': getters.get_artist_name(curFile), 'similar_artists': getters.get_similar_artists(curFile), 'artist_mbtags': getters.get_artist_mbtags(curFile), 'artist_terms_freq': getters.get_artist_terms_freq(curFile), 'release': getters.get_release(curFile), 'song_id': getters.get_song_id(curFile), 'track_7digitalid': getters.get_track_7digitalid(curFile), 'title': getters.get_title(curFile), 'artist_latitude': getters.get_artist_latitude(curFile), 'energy': getters.get_energy(curFile), 'key': getters.get_key(curFile), 'release_7digitalid': getters.get_release_7digitalid(curFile), 'artist_mbid': getters.get_artist_mbid(curFile), 'segments_confidence': getters.get_segments_confidence(curFile), 'artist_hotttnesss': getters.get_artist_hotttnesss(curFile), 'time_signature': getters.get_time_signature(curFile), 'segments_loudness_max_time': getters.get_segments_loudness_max_time(curFile), 'mode': getters.get_mode(curFile), 'segments_loudness_start': getters.get_segments_loudness_start(curFile), 'tempo': getters.get_tempo(curFile), 'key_confidence': getters.get_key_confidence(curFile), 'analysis_sample_rate': getters.get_analysis_sample_rate(curFile), 'bars_confidence': getters.get_bars_confidence(curFile), 'artist_playmeid': getters.get_artist_playmeid(curFile), 'artist_terms_weight': getters.get_artist_terms_weight(curFile), 'segments_start': getters.get_segments_start(curFile), 'artist_location': getters.get_artist_location(curFile), 'loudness': getters.get_loudness(curFile), 'year': getters.get_year(curFile), 'artist_7digitalid': getters.get_artist_7digitalid(curFile), 'audio_md5': getters.get_audio_md5(curFile), 'segments_timbre': getters.get_segments_timbre(curFile), 'mode_confidence': getters.get_mode_confidence(curFile), 'end_of_fade_in': getters.get_end_of_fade_in(curFile), 'danceability': getters.get_danceability(curFile), 'artist_familiarity': getters.get_artist_familiarity(curFile), 'artist_mbtags_count': getters.get_artist_mbtags_count(curFile), 'tatums_start': getters.get_tatums_start(curFile), 'artist_id': getters.get_artist_id(curFile), 'segments_loudness_max': getters.get_segments_loudness_max(curFile), 'bars_start': getters.get_bars_start(curFile), 'beats_start': getters.get_beats_start(curFile), 'artist_terms': getters.get_artist_terms(curFile), 'sections_start': getters.get_sections_start(curFile), 'beats_confidence': getters.get_beats_confidence(curFile), 'sections_confidence': getters.get_sections_confidence(curFile)}
tid = fil.split('/')[-1].split('.')[0]
# print(c)
for i in c:
if i in get_table:
d2[i] = get_table[i]
d2[i] = str(d2[i]).replace('\n','')
build_str = build_str + d2[i] + ','
else:
print('error: unspecified field')
exit(0)
build_str = build_str[:-1]
# print(build_str[:-1])
build_str = build_str + '\n'
curFile.close()
build_str = build_str.replace('b','').replace("'",'').replace('"','')
return (build_str)
def main():
outputFile1 = open('SongCSV.csv', 'w')
csvRowString = ""
#################################################
#if you want to prompt the user for the order of attributes in the csv,
#leave the prompt boolean set to True
#else, set 'prompt' to False and set the order of attributes in the 'else'
#clause
prompt = False
#################################################
if prompt == True:
while prompt:
prompt = False
csvAttributeString = raw_input(
"\n\nIn what order would you like the colums of the CSV file?\n"
+ "Please delineate with commas. The options are: " +
"AlbumName, AlbumID, ArtistID, ArtistLatitude, ArtistLocation, ArtistLongitude,"
+
" ArtistName, Danceability, Duration, KeySignature, KeySignatureConfidence, Tempo,"
+
" SongID, TimeSignature, TimeSignatureConfidence, Title, and Year.\n\n"
+
"For example, you may write \"Title, Tempo, Duration\"...\n\n"
+ "...or exit by typing 'exit'.\n\n")
csvAttributeList = re.split('\W+', csvAttributeString)
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
for attribute in csvAttributeList:
# print "Here is the attribute: " + attribute + " \n"
if attribute == 'AlbumID'.lower():
csvRowString += 'AlbumID'
elif attribute == 'AlbumName'.lower():
csvRowString += 'AlbumName'
elif attribute == 'ArtistID'.lower():
csvRowString += 'ArtistID'
elif attribute == 'ArtistLatitude'.lower():
csvRowString += 'ArtistLatitude'
elif attribute == 'ArtistLocation'.lower():
csvRowString += 'ArtistLocation'
elif attribute == 'ArtistLongitude'.lower():
csvRowString += 'ArtistLongitude'
elif attribute == 'ArtistName'.lower():
csvRowString += 'ArtistName'
elif attribute == 'Danceability'.lower():
csvRowString += 'Danceability'
elif attribute == 'Duration'.lower():
csvRowString += 'Duration'
elif attribute == 'KeySignature'.lower():
csvRowString += 'KeySignature'
elif attribute == 'KeySignatureConfidence'.lower():
csvRowString += 'KeySignatureConfidence'
elif attribute == 'SongID'.lower():
csvRowString += "SongID"
elif attribute == 'Tempo'.lower():
csvRowString += 'Tempo'
elif attribute == 'TimeSignature'.lower():
csvRowString += 'TimeSignature'
elif attribute == 'TimeSignatureConfidence'.lower():
csvRowString += 'TimeSignatureConfidence'
elif attribute == 'Title'.lower():
csvRowString += 'Title'
elif attribute == 'Year'.lower():
csvRowString += 'Year'
elif attribute == 'Familiarity'.lower(): ####Added by us!
csvRowString += song.familiarity
elif attribute == 'artist_mbid'.lower():
csvRowString += song.artist_mbid
elif attribute == 'artist_playmeid'.lower():
csvRowString += song.artist_playmeid
elif attribute == 'artist_7digid'.lower():
csvRowString += song.artist_7digid
elif attribute == 'hottness'.lower():
csvRowString += song.hottness
elif attribute == 'song_hottness'.lower():
csvRowString += song.song_hottness
elif attribute == 'digitalid7'.lower():
csvRowString += song.digitalid7
elif attribute == 'similar_artists'.lower():
csvRowString += song.similar_artists
elif attribute == 'artist_terms'.lower():
csvRowString += song.artist_terms
elif attribute == 'art_terms_freq'.lower():
csvRowString += song.art_terms_freq
elif attribute == 'art_terms_weight'.lower():
csvRowString += song.art_terms_weight
elif attribute == 'a_sample_rate'.lower():
csvRowString += song.a_sample_rate
elif attribute == 'audio_md5'.lower():
csvRowString += song.audio_md5
elif attribute == 'end_of_fade_in'.lower():
csvRowString += song.end_of_fade_in
elif attribute == 'energy'.lower():
csvRowString += song.energy
elif attribute == 'loudness'.lower():
csvRowString += song.loudness
elif attribute == 'mode'.lower():
csvRowString += song.mode
elif attribute == 'mode_conf'.lower():
csvRowString += song.mode_conf
elif attribute == 'start_of_fade_out'.lower():
csvRowString += song.start_of_fade_out
elif attribute == 'trackid'.lower():
csvRowString += song.trackid
elif attribute == 'segm_start'.lower():
csvRowString += song.segm_start
elif attribute == 'segm_conf'.lower():
csvRowString += song.segm_conf
elif attribute == 'segm_pitch'.lower():
csvRowString += song.segm_pitch
elif attribute == 'segm_timbre'.lower():
csvRowString += song.segm_timbre
elif attribute == 'segm_max_loud'.lower():
csvRowString += song.segm_max_loud
elif attribute == 'segm_max_loud_time'.lower():
csvRowString += song.segm_max_loud_time
elif attribute == 'segm_loud_start'.lower():
csvRowString += song.segm_loud_start
elif attribute == 'sect_start'.lower():
csvRowString += song.sect_start
elif attribute == 'sect_conf'.lower():
csvRowString += song.sect_conf
elif attribute == 'beats_start'.lower():
csvRowString += song.beats_start
elif attribute == 'beats_conf'.lower():
csvRowString += song.beats_conf
elif attribute == 'bars_start'.lower():
csvRowString += song.bars_start
elif attribute == 'bars_conf'.lower():
csvRowString += song.bars_conf
elif attribute == 'tatums_start'.lower():
csvRowString += song.tatums_start
elif attribute == 'tatums_conf'.lower():
csvRowString += song.tatums_conf
elif attribute == 'artist_mbtags'.lower():
csvRowString += song.artist_mbtags
elif attribute == 'artist_mbtags_count'.lower():
csvRowString += song.artist_mbtags_count
elif attribute == 'Exit'.lower():
sys.exit()
else:
prompt = True
print("==============")
print("I believe there has been an error with the input.")
print("==============")
break
csvRowString += ","
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex - 1]
csvRowString += "\n"
outputFile1.write(csvRowString)
csvRowString = ""
#else, if you want to hard code the order of the csv file and not prompt
#the user,
else:
#################################################
#change the order of the csv file here
#Default is to list all available attributes (in alphabetical order)
csvRowString = "SongID,AlbumID,AlbumName,ArtistID,ArtistLatitude,ArtistLocation,ArtistLongitude,ArtistName,Danceability,Duration,KeySignature,KeySignatureConfidence,Tempo,TimeSignature,TimeSignatureConfidence,Title,Year,Familiarity,Artist_Mbid,Artist_PlaymeId,Artist_7didId,Hottness,Song_Hottness,7digitalid,A_Sample_Rate,Audio_Md5,End_Of_Fade_In,Energy,Loudness,Mode,Mode_Conf,Start_Of_Fade_Out,TrackId"
#################################################
csvAttributeList = re.split(',', csvRowString)
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
csvRowString += "\n"
outputFile1.write(csvRowString)
csvRowString = ""
#################################################
#Set the basedir here, the root directory from which the search
#for files stored in a (hierarchical data structure) will originate
basedir = "/home/bigdata/smalltest/" # "." As the default means the current directory
ext = ".h5" #Set the extension here. H5 is the extension for HDF5 files.
#################################################
#FOR LOOP
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
for f in files:
print(f)
songH5File = hdf5_getters.open_h5_file_read(f)
song = Song(str(hdf5_getters.get_song_id(songH5File)))
# testDanceability = hdf5_getters.get_danceability(songH5File)
# print type(testDanceability)
# print ("Here is the danceability: ") + str(testDanceability)
song.artistID = str(hdf5_getters.get_artist_id(songH5File))
song.albumID = str(hdf5_getters.get_release_7digitalid(songH5File))
song.albumName = str(hdf5_getters.get_release(songH5File))
song.artistLatitude = str(
hdf5_getters.get_artist_latitude(songH5File))
song.artistLocation = str(
hdf5_getters.get_artist_location(songH5File))
song.artistLongitude = str(
hdf5_getters.get_artist_longitude(songH5File))
song.artistName = str(hdf5_getters.get_artist_name(songH5File))
song.danceability = str(hdf5_getters.get_danceability(songH5File))
song.duration = str(hdf5_getters.get_duration(songH5File))
# song.setGenreList()
song.keySignature = str(hdf5_getters.get_key(songH5File))
song.keySignatureConfidence = str(
hdf5_getters.get_key_confidence(songH5File))
# song.lyrics = None
# song.popularity = None
song.tempo = str(hdf5_getters.get_tempo(songH5File))
song.timeSignature = str(
hdf5_getters.get_time_signature(songH5File))
song.timeSignatureConfidence = str(
hdf5_getters.get_time_signature_confidence(songH5File))
song.title = str(hdf5_getters.get_title(songH5File))
song.year = str(hdf5_getters.get_year(songH5File))
#########Added by us!
song.familiarity = str(
hdf5_getters.get_artist_familiarity(songH5File))
song.artist_mbid = str(hdf5_getters.get_artist_mbid(songH5File))
song.artist_playmeid = str(
hdf5_getters.get_artist_playmeid(songH5File))
song.artist_7digid = str(
hdf5_getters.get_artist_7digitalid(songH5File))
song.hottness = str(hdf5_getters.get_artist_hotttnesss(songH5File))
song.song_hottness = str(
hdf5_getters.get_song_hotttnesss(songH5File))
song.digitalid7 = str(
hdf5_getters.get_track_7digitalid(songH5File))
#song.similar_artists = str(hdf5_getters.get_similar_artists(songH5File))
#song.artist_terms = str(hdf5_getters.get_artist_terms(songH5File))
#song.art_terms_freq = str(hdf5_getters.get_artist_terms_freq(songH5File))
#song.art_terms_weight = str(hdf5_getters.get_artist_terms_weight(songH5File))
song.a_sample_rate = str(
hdf5_getters.get_analysis_sample_rate(songH5File))
song.audio_md5 = str(hdf5_getters.get_audio_md5(songH5File))
song.end_of_fade_in = str(
hdf5_getters.get_end_of_fade_in(songH5File))
song.energy = str(hdf5_getters.get_energy(songH5File))
song.loudness = str(hdf5_getters.get_loudness(songH5File))
song.mode = str(hdf5_getters.get_mode(songH5File))
song.mode_conf = str(hdf5_getters.get_mode_confidence(songH5File))
song.start_of_fade_out = str(
hdf5_getters.get_start_of_fade_out(songH5File))
song.trackid = str(hdf5_getters.get_track_id(songH5File))
#song.segm_start = str(hdf5_getters.get_segments_start(songH5File))
#song.segm_conf = str(hdf5_getters.get_segments_confidence(songH5File))
#song.segm_pitch = str(hdf5_getters.get_segments_pitches(songH5File))
#song.segm_timbre = str(hdf5_getters.get_segments_timbre(songH5File))
#song.segm_max_loud = str(hdf5_getters.get_segments_loudness_max(songH5File))
#song.segm_max_loud_time = str(hdf5_getters.get_segments_loudness_max_time(songH5File))
#song.segm_loud_start = str(hdf5_getters.get_segments_loudness_start(songH5File))
#song.sect_start = str(hdf5_getters.get_sections_start(songH5File))
#song.sect_conf = str(hdf5_getters.get_sections_confidence(songH5File))
#song.beats_start = str(hdf5_getters.get_beats_start(songH5File))
#song.beats_conf = str(hdf5_getters.get_beats_confidence(songH5File))
#song.bars_start = str(hdf5_getters.get_bars_start(songH5File))
#song.bars_conf = str(hdf5_getters.get_bars_confidence(songH5File))
#song.tatums_start = str(hdf5_getters.get_tatums_start(songH5File))
#song.tatums_conf = str(hdf5_getters.get_tatums_confidence(songH5File))
#song.artist_mbtags = str(hdf5_getters.get_artist_mbtags(songH5File))
#song.artist_mbtags_count = str(hdf5_getters.get_artist_mbtags_count(songH5File))
#print song count
#csvRowString += str(song.songCount) + ","
for attribute in csvAttributeList:
# print "Here is the attribute: " + attribute + " \n"
if attribute == 'AlbumID'.lower():
csvRowString += song.albumID
elif attribute == 'AlbumName'.lower():
albumName = song.albumName
albumName = albumName.replace("b\"", "")
albumName = albumName.replace("\"", "")
albumName = albumName.replace(',', "")
csvRowString += "\"" + albumName + "\""
elif attribute == 'ArtistID'.lower():
csvRowString += "\"" + song.artistID + "\""
elif attribute == 'ArtistLatitude'.lower():
latitude = song.artistLatitude
if latitude == 'nan':
latitude = ''
csvRowString += latitude
elif attribute == 'ArtistLocation'.lower():
location = song.artistLocation
location = location.replace(',', '')
location = location.replace("b\"", "")
location = location.replace("\"", "")
csvRowString += "\"" + location + "\""
elif attribute == 'ArtistLongitude'.lower():
longitude = song.artistLongitude
if longitude == 'nan':
longitude = ''
csvRowString += longitude
elif attribute == 'ArtistName'.lower():
artistName = song.artistName
artistName = artistName.replace("b\"", "")
artistName = artistName.replace("\"", "")
csvRowString += "\"" + artistName + "\""
elif attribute == 'Danceability'.lower():
csvRowString += song.danceability
elif attribute == 'Duration'.lower():
csvRowString += song.duration
elif attribute == 'KeySignature'.lower():
csvRowString += song.keySignature
elif attribute == 'KeySignatureConfidence'.lower():
# print "key sig conf: " + song.timeSignatureConfidence
csvRowString += song.keySignatureConfidence
elif attribute == 'SongID'.lower():
csvRowString += "\"" + song.id + "\""
elif attribute == 'Tempo'.lower():
# print "Tempo: " + song.tempo
csvRowString += song.tempo
elif attribute == 'TimeSignature'.lower():
csvRowString += song.timeSignature
elif attribute == 'TimeSignatureConfidence'.lower():
# print "time sig conf: " + song.timeSignatureConfidence
csvRowString += song.timeSignatureConfidence
elif attribute == 'Title'.lower():
t = song.title
t = t.replace("b\"", "")
t = t.replace("\"", "")
csvRowString += "\"" + t + "\""
elif attribute == 'Year'.lower():
csvRowString += song.year
elif attribute == 'Familiarity'.lower(): ####Added by us!
csvRowString += song.familiarity
elif attribute == 'artist_mbid'.lower():
csvRowString += "\"" + song.artist_mbid + "\""
elif attribute == 'artist_playmeid'.lower():
csvRowString += song.artist_playmeid
elif attribute == 'artist_7digid'.lower():
csvRowString += song.artist_7digid
elif attribute == 'hottness'.lower():
csvRowString += song.hottness
elif attribute == 'song_hottness'.lower():
csvRowString += song.song_hottness
elif attribute == 'digitalid7'.lower():
csvRowString += song.digitalid7
elif attribute == 'similar_artists'.lower():
csvRowString += song.similar_artists
elif attribute == 'artist_terms'.lower():
csvRowString += song.artist_terms
elif attribute == 'art_terms_freq'.lower():
csvRowString += song.art_terms_freq
elif attribute == 'art_terms_weight'.lower():
csvRowString += song.art_terms_weight
elif attribute == 'a_sample_rate'.lower():
csvRowString += song.a_sample_rate
elif attribute == 'audio_md5'.lower():
csvRowString += "\"" + song.audio_md5 + "\""
elif attribute == 'end_of_fade_in'.lower():
csvRowString += song.end_of_fade_in
elif attribute == 'energy'.lower():
csvRowString += song.energy
elif attribute == 'loudness'.lower():
csvRowString += song.loudness
elif attribute == 'mode'.lower():
csvRowString += song.mode
elif attribute == 'mode_conf'.lower():
csvRowString += song.mode_conf
elif attribute == 'start_of_fade_out'.lower():
csvRowString += song.start_of_fade_out
elif attribute == 'trackid'.lower():
csvRowString += "\"" + song.trackid + "\""
elif attribute == 'segm_start'.lower():
csvRowString += song.segm_start
elif attribute == 'segm_conf'.lower():
csvRowString += song.segm_conf
elif attribute == 'segm_pitch'.lower():
csvRowString += song.segm_pitch
elif attribute == 'segm_timbre'.lower():
csvRowString += song.segm_timbre
elif attribute == 'segm_max_loud'.lower():
csvRowString += song.segm_max_loud
elif attribute == 'segm_max_loud_time'.lower():
csvRowString += song.segm_max_loud_time
elif attribute == 'segm_loud_start'.lower():
csvRowString += song.segm_loud_start
elif attribute == 'sect_start'.lower():
csvRowString += song.sect_start
elif attribute == 'sect_conf'.lower():
csvRowString += song.sect_conf
elif attribute == 'beats_start'.lower():
csvRowString += song.beats_start
elif attribute == 'beats_conf'.lower():
csvRowString += song.beats_conf
elif attribute == 'bars_start'.lower():
csvRowString += song.bars_start
elif attribute == 'bars_conf'.lower():
csvRowString += song.bars_conf
elif attribute == 'tatums_start'.lower():
csvRowString += song.tatums_start
elif attribute == 'tatums_conf'.lower():
csvRowString += song.tatums_conf
elif attribute == 'artist_mbtags'.lower():
csvRowString += song.artist_mbtags
elif attribute == 'artist_mbtags_count'.lower():
csvRowString += song.artist_mbtags_count
else:
csvRowString += "\"ERR\""
csvRowString += ","
#Remove the final comma from each row in the csv
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex - 1]
csvRowString += "\n"
outputFile1.write(csvRowString)
csvRowString = ""
songH5File.close()
outputFile1.close()
def fill_attributes(song, songH5File):
#----------------------------non array attributes-------------------------------
song.analysisSampleRate = str(
hdf5_getters.get_analysis_sample_rate(songH5File))
song.artistDigitalID = str(hdf5_getters.get_artist_7digitalid(songH5File))
song.artistFamiliarity = str(
hdf5_getters.get_artist_familiarity(songH5File))
song.artistHotness = str(hdf5_getters.get_artist_hottness(songH5File))
song.artistID = str(hdf5_getters.get_artist_id(songH5File))
song.artistLatitude = str(hdf5_getters.get_artist_latitude(songH5File))
song.artistLocation = str(hdf5_getters.get_artist_location(songH5File))
song.artistLongitude = str(hdf5_getters.get_artist_longitude(songH5File))
song.artistmbID = str(hdf5_getters.get_artist_mbid(songH5File))
song.artistName = str(hdf5_getters.get_artist_name(songH5File))
song.artistPlayMeID = str(hdf5_getters.get_artist_playmeid(songH5File))
song.audioMD5 = str(hdf5_getters.get_audio_md5(songH5File))
song.danceability = str(hdf5_getters.get_danceability(songH5File))
song.duration = str(hdf5_getters.get_duration(songH5File))
song.endOfFadeIn = str(hdf5_getters.get_end_of_fade_in(songH5File))
song.energy = str(hdf5_getters.get_energy(songH5File))
song.key = str(hdf5_getters.get_key(songH5File))
song.keyConfidence = str(hdf5_getters.get_key_confidence(songH5File))
song.segementsConfidence = str(
hdf5_getters.get_segments_confidence(songH5File))
song.segementsConfidence = str(
hdf5_getters.get_sections_confidence(songH5File))
song.loudness = str(hdf5_getters.get_loudness(songH5File))
song.mode = str(hdf5_getters.get_mode(songH5File))
song.modeConfidence = str(hdf5_getters.get_mode_confidence(songH5File))
song.release = str(hdf5_getters.get_release(songH5File))
song.releaseDigitalID = str(
hdf5_getters.get_release_7digitalid(songH5File))
song.songHotttnesss = str(hdf5_getters.get_song_hotttnesss(songH5File))
song.startOfFadeOut = str(hdf5_getters.get_start_of_fade_out(songH5File))
song.tempo = str(hdf5_getters.get_tempo(songH5File))
song.timeSignature = str(hdf5_getters.get_time_signature(songH5File))
song.timeSignatureConfidence = str(
hdf5_getters.get_time_signature_confidence(songH5File))
song.title = str(hdf5_getters.get_title(songH5File))
song.trackID = str(hdf5_getters.get_track_id(songH5File))
song.trackDigitalID = str(hdf5_getters.get_track_7digitalid(songH5File))
song.year = str(hdf5_getters.get_year(songH5File))
#-------------------------------array attributes--------------------------------------
#array float
song.beatsStart_mean, song.beatsStart_var = convert_array_to_meanvar(
hdf5_getters.get_beats_start(songH5File))
#array float
song.artistTermsFreq_mean, song.artistTermsFreq_var = convert_array_to_meanvar(
hdf5_getters.get_artist_terms_freq(songH5File))
#array float
song.artistTermsWeight_mean, song.artistTermsWeight_var = convert_array_to_meanvar(
hdf5_getters.get_artist_terms_weight(songH5File))
#array int
song.artistmbTagsCount_mean, song.artistmbTagsCount_var = convert_array_to_meanvar(
hdf5_getters.get_artist_mbtags_count(songH5File))
#array float
song.barsConfidence_mean, song.barsConfidence_var = convert_array_to_meanvar(
hdf5_getters.get_bars_confidence(songH5File))
#array float
song.barsStart_mean, song.barsStart_var = convert_array_to_meanvar(
hdf5_getters.get_bars_start(songH5File))
#array float
song.beatsConfidence_mean, song.beatsConfidence_var = convert_array_to_meanvar(
hdf5_getters.get_beats_confidence(songH5File))
#array float
song.sectionsConfidence_mean, song.sectionsConfidence_var = convert_array_to_meanvar(
hdf5_getters.get_sections_confidence(songH5File))
#array float
song.sectionsStart_mean, song.sectionsStart_var = convert_array_to_meanvar(
hdf5_getters.get_sections_start(songH5File))
#array float
song.segmentsConfidence_mean, song.segmentsConfidence_var = convert_array_to_meanvar(
hdf5_getters.get_segments_confidence(songH5File))
#array float
song.segmentsLoudness_mean, song.segmentsLoudness_var = convert_array_to_meanvar(
hdf5_getters.get_segments_loudness_max(songH5File))
#array float
song.segmentsLoudnessMaxTime_mean, song.segmentsLoudnessMaxTime_var = convert_array_to_meanvar(
hdf5_getters.get_segments_loudness_max_time(songH5File))
#array float
song.segmentsLoudnessMaxStart_mean, song.segmentsLoudnessMaxStart_var = convert_array_to_meanvar(
hdf5_getters.get_segments_loudness_start(songH5File))
#array float
song.segmentsStart_mean, song.segmentsStart_var = convert_array_to_meanvar(
hdf5_getters.get_segments_start(songH5File))
#array float
song.tatumsConfidence_mean, song.tatumsConfidence_var = convert_array_to_meanvar(
hdf5_getters.get_tatums_confidence(songH5File))
#array float
song.tatumsStart_mean, song.tatumsStart_var = convert_array_to_meanvar(
hdf5_getters.get_tatums_start(songH5File))
#array2d float
song.segmentsTimbre_mean, song.segmentsTimbre_var = covert_2darray_to_meanvar(
hdf5_getters.get_segments_timbre(songH5File))
#array2d float
song.segmentsPitches_mean, song.segmentsPitches_var = covert_2darray_to_meanvar(
hdf5_getters.get_segments_pitches(songH5File))
#------------------------array string attributes------------------------
song.similarArtists = convert_array_to_string(
hdf5_getters.get_similar_artists(songH5File)) #array string
song.artistTerms = convert_array_to_string(
hdf5_getters.get_artist_terms(songH5File)) #array string
song.artistmbTags = convert_array_to_string(
hdf5_getters.get_artist_mbtags(songH5File)) #array string
return song
def 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 main():
outputFileName = sys.argv[2]
outputFile1 = open(outputFileName, '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,ArtistFamiliarity,ArtistHotttnesss,ArtistName," +
"ArtistMBTags,ArtistTerms," +
"Danceability,Energy,Duration,KeySignature," +
"KeySignatureConfidence,Loudness,Mode,Hotttnesss,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 = sys.argv[1] # "." 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.artistFamiliarity = str(
hdf5_getters.get_artist_familiarity(songH5File))
song.artistHotttnesss = str(
hdf5_getters.get_artist_hotttnesss(songH5File))
song.artistName = str(hdf5_getters.get_artist_name(songH5File))
song.artistMBTags = ','.join(
hdf5_getters.get_artist_mbtags(songH5File))
# song.artistMBTagsCount = ','.join(hdf5_getters.get_artist_mbtags_count(songH5File))
song.artistTerms = ','.join(
hdf5_getters.get_artist_terms(songH5File))
song.danceability = str(hdf5_getters.get_danceability(songH5File))
song.energy = str(hdf5_getters.get_energy(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.loudness = str(hdf5_getters.get_loudness(songH5File))
song.mode = str(hdf5_getters.get_mode(songH5File))
# song.lyrics = None
# song.popularity = None
song.hotttnesss = str(hdf5_getters.get_song_hotttnesss(songH5File))
song.tempo = str(hdf5_getters.get_tempo(songH5File))
song.timeSignature = str(
hdf5_getters.get_time_signature(songH5File))
song.timeSignatureConfidence = str(
hdf5_getters.get_time_signature_confidence(songH5File))
song.title = str(hdf5_getters.get_title(songH5File))
song.year = str(hdf5_getters.get_year(songH5File))
#print song count
# csvRowString += str(song.songCount) + ","
rowString = json.dumps({
'AlbumID': song.albumID,
'AlbumName': song.albumName,
'ArtistID': song.artistID,
'ArtistLatitude': song.artistLatitude,
'ArtistLocation': song.artistLocation,
'ArtistLongitude': song.artistLongitude,
'ArtistFamiliarity': song.artistFamiliarity,
'ArtistHotttnesss': song.artistHotttnesss,
'ArtistName': song.artistName,
'ArtistMBTags': song.artistMBTags,
'ArtistTerms': song.artistTerms,
'Danceability': song.danceability,
'Energy': song.energy,
'Duration': song.duration,
'KeySignature': song.keySignature,
'KeySignatureConfidence': song.keySignatureConfidence,
'Loudness': song.loudness,
'Mode': song.mode,
'Hotttnesss': song.hotttnesss,
'Tempo': song.tempo,
'SongID': song.id,
'TimeSignature': song.timeSignature,
'TimeSignatureConfidence': song.timeSignatureConfidence,
'Title': song.title,
'Year': song.year,
})
#Remove the final comma from each row in the csv
rowString += "\n"
outputFile1.write(rowString)
songH5File.close()
outputFile1.close()
song.artistName = remove_trap_characters(
str(hdf5_getters.get_artist_name(songH5File)))
song.danceability = remove_trap_characters(
str(hdf5_getters.get_danceability(songH5File)))
song.duration = remove_trap_characters(
str(hdf5_getters.get_duration(songH5File)))
song.keySignature = remove_trap_characters(
str(hdf5_getters.get_key(songH5File)))
song.keySignatureConfidence = remove_trap_characters(
str(hdf5_getters.get_key_confidence(songH5File)))
song.tempo = remove_trap_characters(
str(hdf5_getters.get_tempo(songH5File)))
song.timeSignature = remove_trap_characters(
str(hdf5_getters.get_time_signature(songH5File)))
song.timeSignatureConfidence = remove_trap_characters(
str(hdf5_getters.get_time_signature_confidence(songH5File)))
song.title = remove_trap_characters(
str(hdf5_getters.get_title(songH5File)))
song.year = remove_trap_characters(
str(hdf5_getters.get_year(songH5File)))
song.trackID = remove_trap_characters(
str(hdf5_getters.get_track_id(songH5File)))
temp = hdf5_getters.get_segments_start(songH5File)
song.segmentsStart = remove_trap_characters(str(list(temp)))
song.segmentsCount = get_list_length(temp)
song.segmentsConfidence = \
remove_trap_characters(str(list(hdf5_getters.get_segments_confidence(songH5File))))
# song.segmentsPitches = remove_trap_characters(str(list(hdf5_getters.get_segments_pitches(songH5File))))
song.segmentsPitches = \
remove_trap_characters(str(parse_nested_list(hdf5_getters.get_segments_pitches(songH5File))))
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 main():
dataset_dir = sys.argv[1]
feat =[]
feat1=[]
feat2=[]
feat3=[]
feat4=[]
print "Forming Dataset..."
listing1 = os.listdir(dataset_dir)
for a in listing1:
listing2 = os.listdir(dataset_dir+a+'/')
for b in listing2:
listing3 = os.listdir(dataset_dir+a+'/'+b+'/')
for c in listing3:
listing4 = os.listdir(dataset_dir+a+'/'+b+'/'+c+'/')
for d in listing4:
h5 = hdf5_getters.open_h5_file_read(dataset_dir+a+'/'+b+'/'+c+'/'+d)
feat =[]
feat1=[]
feat2=[]
feat3=[]
feat4=[]
temp = hdf5_getters.get_artist_hotttnesss(h5)
if (math.isnan(temp) or temp==0.0):
h5.close()
continue
temp = hdf5_getters.get_artist_familiarity(h5)
if (math.isnan(temp) or temp==0.0):
h5.close()
continue
temp = hdf5_getters.get_end_of_fade_in(h5)
if (math.isnan(temp)):
h5.close()
continue
temp = hdf5_getters.get_key_confidence(h5)
if (math.isnan(temp)):
h5.close()
continue
temp = hdf5_getters.get_loudness(h5)
if (math.isnan(temp)):
h5.close()
continue
temp = hdf5_getters.get_mode_confidence(h5)
if (math.isnan(temp)):
h5.close()
continue
temp = hdf5_getters.get_sections_confidence(h5)
if temp.size == 0:
h5.close()
continue
temp = hdf5_getters.get_segments_confidence(h5)
if temp.size == 0:
h5.close()
continue
temp = hdf5_getters.get_segments_loudness_max(h5)
if temp.size == 0:
h5.close()
continue
temp = hdf5_getters.get_segments_loudness_max_time(h5)
if temp.size == 0:
h5.close()
continue
temp = hdf5_getters.get_segments_pitches(h5)
if temp.size == 0:
h5.close()
continue
temp = hdf5_getters.get_segments_timbre(h5)
if temp.size == 0:
h5.close()
continue
temp = hdf5_getters.get_start_of_fade_out(h5)
if (math.isnan(temp)):
h5.close()
continue
temp = hdf5_getters.get_tempo(h5)
if (math.isnan(temp)):
h5.close()
continue
temp = hdf5_getters.get_time_signature_confidence(h5)
if (math.isnan(temp)):
h5.close()
continue
temp = hdf5_getters.get_year(h5)
if temp == 0:
h5.close()
continue
temp = hdf5_getters.get_artist_terms(h5)
if temp.size == 0:
h5.close()
continue
temp_ = hdf5_getters.get_artist_terms_weight(h5)
if temp_.size == 0:
continue
temp = hdf5_getters.get_bars_confidence(h5)
sz = temp.size
if sz<50:
h5.close()
continue
temp = hdf5_getters.get_beats_confidence(h5)
sz = temp.size
if sz <50:
h5.close()
continue
mm = np.mean(temp)
vv = np.var(temp)
if mm==0.0 and vv==0.0:
h5.close()
continue
temp = hdf5_getters.get_segments_confidence(h5)
sz = temp.size
if sz <50:
h5.close()
continue
temp = hdf5_getters.get_tatums_confidence(h5)
sz = temp.size
if sz <50:
h5.close()
continue
temp = hdf5_getters.get_song_hotttnesss(h5)
if (math.isnan(temp) or temp==0.0):
h5.close()
continue
temp = hdf5_getters.get_bars_confidence(h5)
sz = temp.size
sz1 = sz/50
i=1
j=0
while i<=50:
if i == 50:
sz2 = sz
else:
sz2 = i*sz1
num=0.0
acc = 0
while j<sz2:
acc += temp[j]
j+=1
num+=1.0
mm = acc/num
feat1.append(mm)
i+=1
temp = hdf5_getters.get_beats_confidence(h5)
sz = temp.size
sz1 = sz/50
i=1
j=0
while i<=50:
if i == 50:
sz2 = sz
else:
sz2 = i*sz1
num=0.0
acc = 0
while j<sz2:
acc += temp[j]
j+=1
num+=1.0
mm = acc/num
feat2.append(mm)
i+=1
temp = hdf5_getters.get_segments_confidence(h5)
sz = temp.size
sz1 = sz/50
i=1
j=0
while i<=50:
if i == 50:
sz2 = sz
else:
sz2 = i*sz1
num=0.0
acc = 0
while j<sz2:
acc += temp[j]
j+=1
num+=1.0
mm = acc/num
feat3.append(mm)
i+=1
temp = hdf5_getters.get_tatums_confidence(h5)
sz = temp.size
sz1 = sz/50
i=1
j=0
while i<=50:
if i == 50:
sz2 = sz
else:
sz2 = i*sz1
num=0.0
acc = 0
while j<sz2:
acc += temp[j]
j+=1
num+=1.0
mm = acc/num
feat4.append(mm)
i+=1
i=0
avg = 0.0
while i<50:
avg = (feat1[i] + feat2[i] + feat3[i] + feat4[i])/4.0
feat.append(avg)
i++
temp = hdf5_getters.get_song_hotttnesss(h5)
hott = 0
if temp >=0.75:
hott = 1
elif temp >=0.40 and temp <0.75:
hott = 2
else:
hott = 3
feat.append(hott)
h5.close()
count = 1
f=open('MSD_DATASET_LSTM.txt', 'a')
outstring=''
cnt = 0
feat_size = len(feat)
for i in feat:
cnt+=1
outstring+=str(i)
if (cnt!=feat_size):
outstring+=','
outstring+='\n'
f.write(outstring)
f.close()
def main():
outputFile = open('songs.csv', 'w')
writer = csv.writer(outputFile)
csvRowString = "song_number,artist_familiarity,artist_hotttnesss,artist_id,artist_mbid,artist_playmeid,artist_7digitalid,artist_latitude,artist_longitude,artist_location,artist_name,release,release_7digitalid,song_id,song_hotttnesss,title,track_7digitalid,analysis_sample_rate,audio_md5,danceability,duration,end_of_fade_in,energy,key,key_confidence,loudness,mode,mode_confidence,start_of_fade_out,tempo,time_signature,time_signature_confidence,track_id,year"
outputFile.write(csvRowString + "\n")
csvRowString = ""
#################################################
#Set the basedir here, the root directory from which the search
#for files stored in a (hierarchical data structure) will originate
basedir = "." # "." As the default means the current directory
ext = ".H5" #Set the extension here. H5 is the extension for HDF5 files.
#################################################
#FOR LOOP
songCount = 0
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
for f in files:
print(f)
songH5File = hdf5_getters.open_h5_file_read(f)
values = [
songCount,
hdf5_getters.get_artist_familiarity(songH5File),
hdf5_getters.get_artist_hotttnesss(songH5File),
hdf5_getters.get_artist_id(songH5File),
hdf5_getters.get_artist_mbid(songH5File),
hdf5_getters.get_artist_playmeid(songH5File),
hdf5_getters.get_artist_7digitalid(songH5File),
hdf5_getters.get_artist_latitude(songH5File),
hdf5_getters.get_artist_longitude(songH5File),
hdf5_getters.get_artist_location(songH5File),
hdf5_getters.get_artist_name(songH5File),
hdf5_getters.get_release(songH5File),
hdf5_getters.get_release_7digitalid(songH5File),
hdf5_getters.get_song_id(songH5File),
hdf5_getters.get_song_hotttnesss(songH5File),
hdf5_getters.get_title(songH5File),
hdf5_getters.get_track_7digitalid(songH5File),
hdf5_getters.get_analysis_sample_rate(songH5File),
hdf5_getters.get_audio_md5(songH5File),
hdf5_getters.get_danceability(songH5File),
hdf5_getters.get_duration(songH5File),
hdf5_getters.get_end_of_fade_in(songH5File),
hdf5_getters.get_energy(songH5File),
hdf5_getters.get_key(songH5File),
hdf5_getters.get_key_confidence(songH5File),
hdf5_getters.get_loudness(songH5File),
hdf5_getters.get_mode(songH5File),
hdf5_getters.get_mode_confidence(songH5File),
hdf5_getters.get_start_of_fade_out(songH5File),
hdf5_getters.get_tempo(songH5File),
hdf5_getters.get_time_signature(songH5File),
hdf5_getters.get_time_signature_confidence(songH5File),
hdf5_getters.get_track_id(songH5File),
hdf5_getters.get_year(songH5File)
]
songH5File.close()
songCount = songCount + 1
writer.writerow(values)
outputFile.close()
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 convert_to_csv():
i = 0
data = []
target = []
count = 0
with open('data_timbre.csv', 'w+') as f:
with open('target_timbre.csv', 'w+') as f2:
writer = csv.writer(f)
target_writer = csv.writer(f2)
for root, dirs, files in os.walk(msd_subset_data_path):
files = glob.glob(os.path.join(root,'*.h5'))
for f in sorted(files):
try: # Opening is very prone to causing exceptions, we'll just skip file if exception is thrown
h5 = getter.open_h5_file_read(f)
year = getter.get_year(h5)
if year:
count +=1
analysis_file = open('current_analysis_status.txt','a')
update = "Currently at file name: " + str(f) + " and at number " + str(count) + "\n"
analysis_file.write(update)
print update
analysis_file.close()
target.append([year])
row = []
timbre = getter.get_segments_timbre(h5)
segstarts = getter.get_segments_start(h5)
btstarts = getter.get_beats_start(h5)
duration = getter.get_duration(h5)
end_of_fade_in = getter.get_end_of_fade_in(h5)
key = getter.get_key(h5)
key_confidence = getter.get_key_confidence(h5)
loudness = getter.get_loudness(h5)
start_of_fade_out = getter.get_start_of_fade_out(h5)
tempo = getter.get_tempo(h5)
time_signature = getter.get_time_signature(h5)
time_signature_confidence = getter.get_time_signature_confidence(h5)
h5.close() # VERY IMPORTANT
segstarts = np.array(segstarts).flatten()
btstarts = np.array(btstarts).flatten()
bttimbre = align_feats(timbre.T, segstarts, btstarts, duration, end_of_fade_in, key, key_confidence, loudness, start_of_fade_out, tempo, time_signature, time_signature_confidence)
if bttimbre is None:
continue # Skip this track, some features broken
npicks, winsize, finaldim = 12, 12, 144 # Calculated by 12 * 12. 12 is fixed as number of dimensions.
processed_feats = extract_and_compress(bttimbre, npicks, winsize, finaldim)
n_p_feats = processed_feats.shape[0]
if processed_feats is None:
continue # Skip this track, some features broken
row = processed_feats.flatten()
if len(row) != 12*144: # 12 dimensions * 144 features per dimension
continue # Not enough features
year_row = np.array([year])
if row.any() and year_row.any():
writer.writerow(row)
target_writer.writerow(year_row)
i+=1
else:
h5.close()
except Exception:
pass
print 'Finished!'
analysis_file = open('current_analysis_status.txt','a')
analysis_file.write('Done!')
analysis_file.close()
return
def 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 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 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
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)
# Get time signature confidence
time_signature_conf = hdf5_getters.get_time_signature_confidence(
h5)
# Get track_id
#track_id = hdf5_getters.get_track_id(h5)
num_songs = hdf5_getters.get_num_songs(h5)
#flush the table
h5.flush()
# Close the h5 file
h5.close()
tables.file._open_files.close_all()
# Write to the CSV file
csvfile.write(artist + "," + title + "," + artist_loc + "," +
release + "," + str(hotttness) + "," +
def get_all_files(basedir,ext='.h5') :
"""
From a root directory, go through all subdirectories
and find all files with the given extension.
Return all absolute paths in a list.
"""
c=0
title=[]#get_title(h5)
name=[]#get_artist_name`
familiarity=[]#get_artist_familiarity()
artist_hotness=[]#get_artist_hotttnesss
song_hotness=[]# get_song_hotttnesss
danceability=[]#get_danceability
energy=[]#get_energy
loudness=[]#get_loudness
tempo=[]#get_tempo
mode_confidence=[]#get_mode_confidence()
time_sig_confidence=[]#get_time_signature_confidence()
no_segments=[]#len(get_segments_start())
avg_segment_confidence=[]#np.mean(hdf5_getters.get_segments_confidence(h5))
avg_segment_pitches=[]#np.mean(hdf5_getters.get_segments_pitches(h51))
no_sections=[]#len(hdf5_getters.get_sections_start())
avg_sections_confidence=[]#np.mean(hdf5_getters.get_sections_confidence(h51))
no_beats_start=[]#len(hdf5_getters.get_beats_start(h51))
avg_beats_confidence=[]#np.mean(hdf5_getters.get_beats_confidence(h51))
no_bars=[]#len(hdf5_getters.get_bars_start(h5))
avg_bar_confidence=[]#np.mean(hdf5_getters.get_bars_confidence((h51))
no_tatums_start=[]#len(hdf5_getters.get_tatums_start(h5))
avg_tatums_start=[]#np.mean(get_tatums_confidence())
billboard_presence=[]#returned value from web scraper
key=[]
duration=[]
mode=[]
target=pd.read_csv('Billboard.csv')
j=0
if(not(os.path.isfile('./data.csv'))):
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)
songnme=hdf5_getters.get_title(h5)
artst=hdf5_getters.get_artist_name(h5)
for i in range(len(target)):
if(target.Title[i]==songnme and target.Name[i]==artst):
billboard_presence.append(target.Presence[i])
title.append(songnme)
name.append(artst)
familiarity.append(hdf5_getters.get_artist_familiarity(h5))
artist_hotness.append(hdf5_getters.get_artist_hotttnesss(h5))
song_hotness.append(hdf5_getters. get_song_hotttnesss(h5))
danceability.append(hdf5_getters.get_danceability(h5))
key.append(hdf5_getters.get_key(h5))
duration.append(hdf5_getters.get_duration(h5))
mode.append(hdf5_getters.get_mode(h5))
energy.append(hdf5_getters.get_energy(h5))
loudness.append(hdf5_getters.get_loudness(h5))
tempo.append(hdf5_getters.get_tempo(h5))
mode_confidence.append(hdf5_getters.get_mode_confidence(h5))
time_sig_confidence.append(hdf5_getters.get_time_signature_confidence(h5))
no_segments.append(len(hdf5_getters.get_segments_start(h5)))
avg_segment_confidence.append(np.mean(hdf5_getters.get_segments_confidence(h5)))
avg_segment_pitches.append(np.mean(hdf5_getters.get_segments_pitches(h5)))
no_sections.append(len(hdf5_getters.get_sections_start(h5)))
avg_sections_confidence.append(np.mean(hdf5_getters.get_sections_confidence(h5)))
no_beats_start.append(len(hdf5_getters.get_beats_start(h5)))
avg_beats_confidence.append(np.mean(hdf5_getters.get_beats_confidence(h5)))
no_bars.append(len(hdf5_getters.get_bars_start(h5)))
avg_bar_confidence.append(np.mean(hdf5_getters.get_bars_confidence(h5)))
no_tatums_start.append(len(hdf5_getters.get_tatums_start(h5)))
avg_tatums_start.append(np.mean(hdf5_getters.get_tatums_confidence(h5)))
j+=1
print j #prints the index number of each song, to keep track of the song being saved to the database, and to identify errors.
break;
h5.close()
print "Created Arrays"
df=pd.DataFrame(title,columns=['Title'])
df['Artist_Name']=name
df['Familiarity']=familiarity
df['Hotness']=artist_hotness
df['Song_hotness']=song_hotness
df['Danceability']=danceability
df['energy']=energy
df['loudness']=loudness
df['tempo']=tempo
df['mode_confidence']=mode_confidence
df['time_sig_confidence']=time_sig_confidence
df['no_segments']=no_segments
df['avg_segment_confidence']=avg_segment_confidence
df['avg_segment_pitches']=avg_segment_pitches
df['no_sections']=no_sections
df['avg_sections_confidence']=avg_sections_confidence
df['no_beats_start']=no_beats_start
df['avg_beats_confidence']=avg_beats_confidence
df['no_bars']=no_bars
df['avg_bar_confidence']=avg_bar_confidence
df['no_tatums_start']=no_tatums_start
df['avg_tatums_start']=avg_tatums_start
df['key']=key
df['Mode']=mode
df['duration']=duration
df['Presence']=billboard_presence
print df.head()
print billboard_presence
df.to_csv("data.csv")
else:
df=pd.read_csv('data.csv',index_col=0)
print "Number of features in the created dataset:",
print len(df.keys())
print
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, Year and Hotttnesss.\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 == 'SongHotttnesss'.lower():
csvRowString += 'SongHotttnesss'
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,ArtistFamiliarity,ArtistHotttnesss,ArtistID,"
+ "ArtistLatitude,ArtistLocation," +
"ArtistLongitude,ArtistName,BarsConfidence,BarsStart,BeatsConfidence,"
+
"BeatsStart,Danceability,Duration,EndOfFadeIn,Energy,KeySignature,"
+
"KeySignatureConfidence,Loudness,Mode,ModeConfidence,SectionsConfidence,"
+ "SectionsStart,SegmentsConfidence,SegmentsLoudnessMax," +
"SegmentsLoudnessMaxTime,SegmentsLoudnessMaxStart,SegmentsPitches,"
+ "SegmentsStart,SegmentsTimbre,SongHotttnesss,TatumsConfidence," +
"TatumsStart,Tempo,TimeSignature," +
"TimeSignatureConfidence,Title,Year")
'''
csvRowString = ("SongID,AlbumID,AlbumName,ArtistFamiliarity,"+
"ArtistHotttnesss,ArtistID,"+
"ArtistLatitude,ArtistLocation,"+
"ArtistLongitude,ArtistName,"+
"BarsConfidence,BarsStart,"+
"Danceability,Duration,EndOfFadeIn,Energy,KeySignature,"+
"KeySignatureConfidence,Loudness,Mode,ModeConfidence,"+
"SegmentsPitches,"+
"SegmentsTimbre,SongHotttnesss,"+
"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 = "./I/"
basedir = "./MillionSongSubset/data/" # "." As the default means the current directory
#basedir = "/Users/dafirebanks/Downloads/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)
song.artistFamiliarity = str(
hdf5_getters.get_artist_familiarity(songH5File))
song.artistHotttnesss = str(
hdf5_getters.get_artist_hotttnesss(songH5File))
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.barsConfidence = str(
hdf5_getters.get_bars_confidence(songH5File)).replace(
",", "").replace("\n", "")
song.barsStart = str(
hdf5_getters.get_bars_start(songH5File)).replace(",",
"").replace(
"\n", "")
song.beatsConfidence = str(
hdf5_getters.get_beats_confidence(songH5File)).replace(
",", "").replace("\n", "")
song.beatsStart = str(
hdf5_getters.get_beats_start(songH5File)).replace(",",
"").replace(
"\n", "")
song.danceability = str(hdf5_getters.get_danceability(songH5File))
song.duration = str(hdf5_getters.get_duration(songH5File))
song.endOfFadeIn = str(hdf5_getters.get_end_of_fade_in(songH5File))
song.energy = str(hdf5_getters.get_energy(songH5File))
# song.setGenreList()
song.hotttnesss = str(hdf5_getters.get_song_hotttnesss(songH5File))
song.keySignature = str(hdf5_getters.get_key(songH5File))
song.keySignatureConfidence = str(
hdf5_getters.get_key_confidence(songH5File))
song.loudness = str(hdf5_getters.get_loudness(songH5File))
song.mode = str(hdf5_getters.get_mode(songH5File))
song.modeConfidence = str(
hdf5_getters.get_mode_confidence(songH5File))
# song.lyrics = None
# song.popularity = None
song.sectionsConfidence = str(
hdf5_getters.get_sections_confidence(songH5File)).replace(
",", "").replace("\n", "")
song.sectionsStart = str(
hdf5_getters.get_segments_start(songH5File)).replace(
",", "").replace("\n", "")
song.segmentsConfidence = str(
hdf5_getters.get_segments_confidence(songH5File)).replace(
",", "").replace("\n", "")
song.segmentsLoudnessMax = str(
hdf5_getters.get_segments_loudness_max(songH5File)).replace(
",", "").replace("\n", "")
song.segmentsLoudnessMaxTime = str(
hdf5_getters.get_segments_loudness_max_time(
songH5File)).replace(",", "").replace("\n", "")
song.segmentsLoudnessMaxStart = str(
hdf5_getters.get_segments_loudness_start(songH5File)).replace(
",", "").replace("\n", "")
song.segmentsPitches = str(
hdf5_getters.get_segments_pitches(songH5File)).replace(
",", "").replace("\n", "")
song.segmentsStart = str(
hdf5_getters.get_segments_start(songH5File)).replace(
",", "").replace("\n", "")
song.segmentsTimbre = str(
hdf5_getters.get_segments_timbre(songH5File)).replace(
",", "").replace("\n", "")
song.startOfFadeOut = str(
hdf5_getters.get_start_of_fade_out(songH5File))
song.tatumsConfidence = str(
hdf5_getters.get_tatums_confidence(songH5File)).replace(
",", "").replace("\n", "")
song.tatumsStart = str(
hdf5_getters.get_tatums_start(songH5File)).replace(
",", "").replace("\n", "")
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 == 'ArtistFamiliarity'.lower():
csvRowString += song.artistFamiliarity
elif attribute == 'ArtistHotttnesss'.lower():
csvRowString += song.artistHotttnesss
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 == 'BarsConfidence'.lower():
csvRowString += song.barsConfidence
elif attribute == 'BarsStart'.lower():
csvRowString += song.barsStart
elif attribute == 'BeatsConfidence'.lower():
csvRowString += song.beatsConfidence
elif attribute == 'BeatsStart'.lower():
csvRowString += song.beatsStart
elif attribute == 'Danceability'.lower():
csvRowString += song.danceability
elif attribute == 'Duration'.lower():
csvRowString += song.duration
elif attribute == 'EndOfFadeIn'.lower():
csvRowString += song.endOfFadeIn
elif attribute == 'Energy'.lower():
csvRowString += song.energy
elif attribute == 'KeySignature'.lower():
csvRowString += song.keySignature
elif attribute == 'KeySignatureConfidence'.lower():
# print "key sig conf: " + song.timeSignatureConfidence
csvRowString += song.keySignatureConfidence
elif attribute == 'Loudness'.lower():
csvRowString += song.loudness
elif attribute == 'Mode'.lower():
csvRowString += song.mode
elif attribute == 'ModeConfidence'.lower():
csvRowString += song.modeConfidence
elif attribute == 'SectionsConfidence'.lower():
csvRowString += song.sectionsConfidence
elif attribute == 'SectionsStart'.lower():
csvRowString += song.sectionsStart
elif attribute == 'SegmentsConfidence'.lower():
csvRowString += song.segmentsConfidence
elif attribute == 'SegmentsLoudnessMax'.lower():
csvRowString += song.segmentsLoudnessMax
elif attribute == 'SegmentsLoudnessMaxTime'.lower():
csvRowString += song.segmentsLoudnessMaxTime
elif attribute == 'SegmentsLoudnessMaxStart'.lower():
csvRowString += song.segmentsLoudnessMaxStart
elif attribute == 'SegmentsPitches'.lower():
csvRowString += song.segmentsPitches
elif attribute == 'SegmentsStart'.lower():
csvRowString += song.segmentsStart
elif attribute == 'SegmentsTimbre'.lower():
csvRowString += song.segmentsTimbre
elif attribute == 'SongHotttnesss'.lower():
csvRowString += song.hotttnesss
elif attribute == 'SongID'.lower():
csvRowString += "\"" + song.id + "\""
elif attribute == 'StartOfFadeOut'.lower():
csvRowString += song.startOfFadeOut
elif attribute == 'TatumsConfidence'.lower():
csvRowString += song.tatumsConfidence
elif attribute == 'TatumsStart'.lower():
csvRowString += song.tatumsStart
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()
