def process_song(h5_song_file):
song = {}
song['artist_familiarity'] = hdf5_getters.get_artist_familiarity(h5)
song['artist_id'] = hdf5_getters.get_artist_id(h5)
song['artist_name'] = hdf5_getters.get_artist_name(h5)
song['artist_hotttnesss'] = hdf5_getters.get_artist_hotttnesss(h5);
song['title'] = hdf5_getters.get_title(h5)
terms = hdf5_getters.get_artist_terms(h5)
terms_freq = hdf5_getters.get_artist_terms_freq(h5)
terms_weight = hdf5_getters.get_artist_terms_weight(h5)
terms_array = []
# Creating a array of [term, its frequency, its weight]. Doing this for all terms associated
# with the artist
for i in range(len(terms)):
terms_array.append([terms[i], terms_freq[i], terms_weight[i]])
song['artist_terms'] = terms_array
beats_start = hdf5_getters.get_beats_start(h5)
song['beats_start_variance'] = variance(beats_start) #beats variance in yocto seconds(10^-24s)
song['number_of_beats'] = len(beats_start)
song['duration'] = hdf5_getters.get_duration(h5)
song['loudness'] = hdf5_getters.get_loudness(h5)
sections_start = hdf5_getters.get_sections_start(h5)
song['sections_start_variance'] = variance(sections_start)
song['number_of_sections'] = len(sections_start)
segments_pitches = hdf5_getters.get_segments_pitches(h5)
(a0, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11) = split_segments(segments_pitches)
song['segments_pitches_variance'] = [variance(a0), variance(a1), variance(a2),
variance(a3), variance(a4), variance(a5), variance(a6), variance(a7),
variance(a8), variance(a9), variance(a10), variance(a11)]
song['segments_pitches_mean'] = [mean(a0), mean(a1), mean(a2), mean(a3), mean(a4),
mean(a5), mean(a6), mean(a7), mean(a8), mean(a9), mean(a10), mean(a11)]
segments_timbre = hdf5_getters.get_segments_timbre(h5)
(a0, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11) = split_segments(segments_timbre)
song['segments_timbre_variance'] = [variance(a0), variance(a1), variance(a2),
variance(a3), variance(a4), variance(a5), variance(a6), variance(a7),
variance(a8), variance(a9), variance(a10), variance(a11)]
song['segments_timbre_mean'] = [mean(a0), mean(a1), mean(a2), mean(a3), mean(a4),
mean(a5), mean(a6), mean(a7), mean(a8), mean(a9), mean(a10), mean(a11)]
song['tempo'] = hdf5_getters.get_tempo(h5)
song['_id'] = hdf5_getters.get_song_id(h5)
song['year'] = hdf5_getters.get_year(h5)
return song
def h5_to_csv_fields(h5,song): '''Converts h5 format to text Inputs: h5, an h5 file object, usable with the wrapper code MSongsDB song, an integer, representing which song in the h5 file to take the info out of (h5 files contain many songs) Output: a string representing all the information of this song, as a single line of a csv file ''' rv=[] ##All these are regular getter functions from wrapper code rv.append(gt.get_artist_name(h5,song)) rv.append(gt.get_title(h5, song)) rv.append(gt.get_release(h5, song)) rv.append(gt.get_year(h5,song)) rv.append(gt.get_duration(h5,song)) rv.append(gt.get_artist_familiarity(h5,song)) rv.append(gt.get_artist_hotttnesss(h5,song)) rv.append(gt.get_song_hotttnesss(h5, song)) ##artist_terms, artist_terms_freq, and artist_terms_weight getter functions ##are all arrays, so we need to turn them into strings first. We used '_' as a separator rv.append(array_to_csv_field(list(gt.get_artist_terms(h5,song)))) rv.append(array_to_csv_field(list(gt.get_artist_terms_freq(h5,song)))) rv.append(array_to_csv_field(list(gt.get_artist_terms_weight(h5,song)))) rv.append(gt.get_mode(h5,song)) rv.append(gt.get_key(h5,song)) rv.append(gt.get_tempo(h5,song)) rv.append(gt.get_loudness(h5,song)) rv.append(gt.get_danceability(h5,song)) rv.append(gt.get_energy(h5,song)) rv.append(gt.get_time_signature(h5,song)) rv.append(array_to_csv_field(list(gt.get_segments_start(h5,song)))) ##These arrays have vectors (Arrays) as items, 12 dimensional each ##An array like [[1,2,3],[4,5,6]] will be written to csv as '1;2;3_4;5;6', i.e. there's two types of separators rv.append(double_Array_to_csv_field(list(gt.get_segments_timbre(h5,song)),'_',';')) rv.append(double_Array_to_csv_field(list(gt.get_segments_pitches(h5,song)),'_',';')) rv.append(array_to_csv_field(list(gt.get_segments_loudness_start(h5,song)))) rv.append(array_to_csv_field(list(gt.get_segments_loudness_max(h5,song)))) rv.append(array_to_csv_field(list(gt.get_segments_loudness_max_time(h5,song)))) rv.append(array_to_csv_field(list(gt.get_sections_start(h5,song)))) ##turn this list into a string with comma separators (i.e. a csv line) rv_string=array_to_csv_field(rv, ",") rv_string+="\n" return rv_string
def 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 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 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 parse_aggregate_songs(file_name,file_name2,artist_map):
"""
Given an aggregate filename and artist_map in the format
{artist_name: {data pertaining to artist}}
"""
"""
TODO:
-this function goes through each song, if artist not in there,
add all data necesary and add first song info.
else update any specific song info
-song info is a map from attributename:[values]
"""
#artist_map = {}
h5 = hdf5_getters.open_h5_file_read(file_name)
numSongs = hdf5_getters.get_num_songs(h5)
print 'Parsing song file...'
for i in range(numSongs):
artist_name = hdf5_getters.get_artist_name(h5,i)
#Filter location
longi = hdf5_getters.get_artist_longitude(h5,i)
lat = hdf5_getters.get_artist_latitude(h5,i)
loc = hdf5_getters.get_artist_location(h5,i)
if math.isnan(lat) or math.isnan(longi):
#skip if no location
continue
#filter year
yr = hdf5_getters.get_year(h5,i)
if yr == 0:
#skip if no year
continue
#filter hotttness and familiarity
familiarity = hdf5_getters.get_artist_familiarity(h5,i)
hotttness = hdf5_getters.get_artist_hotttnesss(h5,i)
if familiarity<=0.0 or hotttness<=0.0:
#skip if no hotttness or familiarity computations
continue
#TODO:MAYBE filter on dance and energy
timbre = hdf5_getters.get_segments_timbre(h5,i)
#timbre[#] gives len 12 array so for each arr in timbre, add up to get segment and add to corresponding 12 features and avg across each
if not artist_name in artist_map:
#have not encountered the artist yet, so populate new map
sub_map = {}
sub_map['artist_familiarity'] = familiarity
sub_map['artist_hotttnesss'] = hotttness
sub_map['artist_id'] = hdf5_getters.get_artist_id(h5,i)
#longi = hdf5_getters.get_artist_longitude(h5,i)
#lat = hdf5_getters.get_artist_latitude(h5,i)
#longi = None if math.isnan(longi) else longi
#lat = None if math.isnan(lat) else lat
sub_map['artist_latitude'] = lat
sub_map['artist_longitude'] = longi
sub_map['artist_location'] = loc
sub_map['artist_terms'] = hdf5_getters.get_artist_terms(h5,i)
#TODO:see if should weight by freq or weight for if the term matches one of the feature terms
sub_map['artist_terms_freq'] = list(hdf5_getters.get_artist_terms_freq(h5,i))
sub_map['artist_terms_weight'] = list(hdf5_getters.get_artist_terms_weight(h5,i))
#song-sepcific data
#TODO COMPUTE AN AVG TIMBRE FOR A SONG BY IDEA:
#SUMMING DOWN EACH 12 VECTOR FOR EACH PT IN SONG AND AVG THIS ACROSS SONG
dance = hdf5_getters.get_danceability(h5,i)
dance = None if dance == 0.0 else dance
energy = hdf5_getters.get_energy(h5,i)
energy = None if energy == 0.0 else energy
sub_map['danceability'] = [dance]
sub_map['duration'] = [hdf5_getters.get_duration(h5,i)]
sub_map['end_of_fade_in'] = [hdf5_getters.get_end_of_fade_in(h5,i)]
sub_map['energy'] = [energy]
#since each song has a key, ask if feature for keys should be num of songs that appear in that key or
#just binary if any of their songs has that key or just be avg of songs with that key
#same for mode, since its either major or minor...should it be count or avg.?
sub_map['key'] = [hdf5_getters.get_key(h5,i)]
sub_map['loudness'] = [hdf5_getters.get_loudness(h5,i)]
sub_map['mode'] = [hdf5_getters.get_mode(h5,i)] #major or minor 0/1
s_hot = hdf5_getters.get_song_hotttnesss(h5,i)
s_hot = None if math.isnan(s_hot) else s_hot
sub_map['song_hotttnesss'] = [s_hot]
sub_map['start_of_fade_out'] = [hdf5_getters.get_start_of_fade_out(h5,i)]
sub_map['tempo'] = [hdf5_getters.get_tempo(h5,i)]
#should time signature be count as well? binary?
sub_map['time_signature'] = [hdf5_getters.get_time_signature(h5,i)]
sub_map['track_id'] = [hdf5_getters.get_track_id(h5,i)]
#should year be binary since they can have many songs across years and should it be year:count
sub_map['year'] = [yr]
artist_map[artist_name] = sub_map
else:
#artist already exists, so get its map and update song fields
dance = hdf5_getters.get_danceability(h5,i)
dance = None if dance == 0.0 else dance
energy = hdf5_getters.get_energy(h5,i)
energy = None if energy == 0.0 else energy
artist_map[artist_name]['danceability'].append(dance)
artist_map[artist_name]['duration'].append(hdf5_getters.get_duration(h5,i))
artist_map[artist_name]['end_of_fade_in'].append(hdf5_getters.get_end_of_fade_in(h5,i))
artist_map[artist_name]['energy'].append(energy)
artist_map[artist_name]['key'].append(hdf5_getters.get_key(h5,i))
artist_map[artist_name]['loudness'].append(hdf5_getters.get_loudness(h5,i))
artist_map[artist_name]['mode'].append(hdf5_getters.get_mode(h5,i)) #major or minor 0/1
s_hot = hdf5_getters.get_song_hotttnesss(h5,i)
s_hot = None if math.isnan(s_hot) else s_hot
artist_map[artist_name]['song_hotttnesss'].append(s_hot)
artist_map[artist_name]['start_of_fade_out'].append(hdf5_getters.get_start_of_fade_out(h5,i))
artist_map[artist_name]['tempo'].append(hdf5_getters.get_tempo(h5,i))
#should time signature be count as well? binary?
artist_map[artist_name]['time_signature'].append(hdf5_getters.get_time_signature(h5,i))
artist_map[artist_name]['track_id'].append(hdf5_getters.get_track_id(h5,i))
#should year be binary since they can have many songs across years and should it be year:count
artist_map[artist_name]['year'].append(yr)
h5 = hdf5_getters.open_h5_file_read(file_name2)
numSongs = hdf5_getters.get_num_songs(h5)
print 'Parsing song file2...'
for i in range(numSongs):
song_id = hdf5_getters.get_track_id(h5,i)
artist_name = hdf5_getters.get_artist_name(h5,i)
if artist_name in artist_map and song_id in artist_map[artist_name]['track_id']:
continue
#Filter location
longi = hdf5_getters.get_artist_longitude(h5,i)
lat = hdf5_getters.get_artist_latitude(h5,i)
loc = hdf5_getters.get_artist_location(h5,i)
if math.isnan(lat) or math.isnan(longi):
#skip if no location
continue
#filter year
yr = hdf5_getters.get_year(h5,i)
if yr == 0:
#skip if no year
continue
#filter hotttness and familiarity
familiarity = hdf5_getters.get_artist_familiarity(h5,i)
hotttness = hdf5_getters.get_artist_hotttnesss(h5,i)
if familiarity<=0.0 or hotttness<=0.0:
#skip if no hotttness or familiarity computations
continue
#TODO:MAYBE filter on dance and energy
timbre = hdf5_getters.get_segments_timbre(h5,i)
#timbre[#] gives len 12 array so for each arr in timbre, add up to get segment and add to corresponding 12 features and avg across each
if not artist_name in artist_map:
#have not encountered the artist yet, so populate new map
sub_map = {}
sub_map['artist_familiarity'] = familiarity
sub_map['artist_hotttnesss'] = hotttness
sub_map['artist_id'] = hdf5_getters.get_artist_id(h5,i)
#longi = hdf5_getters.get_artist_longitude(h5,i)
#lat = hdf5_getters.get_artist_latitude(h5,i)
#longi = None if math.isnan(longi) else longi
#lat = None if math.isnan(lat) else lat
sub_map['artist_latitude'] = lat
sub_map['artist_longitude'] = longi
sub_map['artist_location'] = loc
sub_map['artist_terms'] = hdf5_getters.get_artist_terms(h5,i)
#TODO:see if should weight by freq or weight for if the term matches one of the feature terms
sub_map['artist_terms_freq'] = list(hdf5_getters.get_artist_terms_freq(h5,i))
sub_map['artist_terms_weight'] = list(hdf5_getters.get_artist_terms_weight(h5,i))
#song-sepcific data
#TODO COMPUTE AN AVG TIMBRE FOR A SONG BY IDEA:
#SUMMING DOWN EACH 12 VECTOR FOR EACH PT IN SONG AND AVG THIS ACROSS SONG
dance = hdf5_getters.get_danceability(h5,i)
dance = None if dance == 0.0 else dance
energy = hdf5_getters.get_energy(h5,i)
energy = None if energy == 0.0 else energy
sub_map['danceability'] = [dance]
sub_map['duration'] = [hdf5_getters.get_duration(h5,i)]
sub_map['end_of_fade_in'] = [hdf5_getters.get_end_of_fade_in(h5,i)]
sub_map['energy'] = [energy]
#since each song has a key, ask if feature for keys should be num of songs that appear in that key or
#just binary if any of their songs has that key or just be avg of songs with that key
#same for mode, since its either major or minor...should it be count or avg.?
sub_map['key'] = [hdf5_getters.get_key(h5,i)]
sub_map['loudness'] = [hdf5_getters.get_loudness(h5,i)]
sub_map['mode'] = [hdf5_getters.get_mode(h5,i)] #major or minor 0/1
s_hot = hdf5_getters.get_song_hotttnesss(h5,i)
s_hot = None if math.isnan(s_hot) else s_hot
sub_map['song_hotttnesss'] = [s_hot]
sub_map['start_of_fade_out'] = [hdf5_getters.get_start_of_fade_out(h5,i)]
sub_map['tempo'] = [hdf5_getters.get_tempo(h5,i)]
#should time signature be count as well? binary?
sub_map['time_signature'] = [hdf5_getters.get_time_signature(h5,i)]
sub_map['track_id'] = [hdf5_getters.get_track_id(h5,i)]
#should year be binary since they can have many songs across years and should it be year:count
sub_map['year'] = [yr]
artist_map[artist_name] = sub_map
else:
#artist already exists, so get its map and update song fields
dance = hdf5_getters.get_danceability(h5,i)
dance = None if dance == 0.0 else dance
energy = hdf5_getters.get_energy(h5,i)
energy = None if energy == 0.0 else energy
artist_map[artist_name]['danceability'].append(dance)
artist_map[artist_name]['duration'].append(hdf5_getters.get_duration(h5,i))
artist_map[artist_name]['end_of_fade_in'].append(hdf5_getters.get_end_of_fade_in(h5,i))
artist_map[artist_name]['energy'].append(energy)
artist_map[artist_name]['key'].append(hdf5_getters.get_key(h5,i))
artist_map[artist_name]['loudness'].append(hdf5_getters.get_loudness(h5,i))
artist_map[artist_name]['mode'].append(hdf5_getters.get_mode(h5,i)) #major or minor 0/1
s_hot = hdf5_getters.get_song_hotttnesss(h5,i)
s_hot = None if math.isnan(s_hot) else s_hot
artist_map[artist_name]['song_hotttnesss'].append(s_hot)
artist_map[artist_name]['start_of_fade_out'].append(hdf5_getters.get_start_of_fade_out(h5,i))
artist_map[artist_name]['tempo'].append(hdf5_getters.get_tempo(h5,i))
#should time signature be count as well? binary?
artist_map[artist_name]['time_signature'].append(hdf5_getters.get_time_signature(h5,i))
artist_map[artist_name]['track_id'].append(hdf5_getters.get_track_id(h5,i))
#should year be binary since they can have many songs across years and should it be year:count
artist_map[artist_name]['year'].append(yr)
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 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
process_completion += 1
tempPath = os.path.abspath(os.path.join(root,name))
h5file = hdf5_getters.open_h5_file_read(tempPath)
#meta info
track_id_str = hdf5_getters.get_track_id(h5file)
song_id_str = hdf5_getters.get_song_id(h5file)
year = hdf5_getters.get_year(h5file)
#genre info
artist_terms = hdf5_getters.get_artist_terms(h5file)
artist_mbtags = hdf5_getters.get_artist_mbtags(h5file)
artist_mbtags_count = hdf5_getters.get_artist_mbtags_count(h5file)
artist_terms_freq = hdf5_getters.get_artist_terms_freq(h5file)
artist_terms_weight = hdf5_getters.get_artist_terms_weight(h5file)
#factoring in mbtags counts & thresholding artist_terms
idx = 0
for countVal in artist_mbtags_count:
for rep in range(0,countVal-1):
artist_mbtags = numpy.append(artist_mbtags,artist_mbtags[idx])
idx += 1
for i in range(0,len(artist_terms)):
if artist_terms_freq[i]*artist_terms_weight[i] < 0.16: #0.4*0.4 = 0.16
artist_terms[i]=""
# finding genre
tags = " ".join(artist_mbtags)+" "+ " ".join(artist_terms)
tags = tags.replace(" "," ").replace(" "," ").replace(" & ","&")
loudness = hdf5_getters.get_loudness(hdf)
mode = hdf5_getters.get_mode(hdf)
mode_confidence = hdf5_getters.get_mode_confidence(hdf)
start_of_fade_out = hdf5_getters.get_start_of_fade_out(hdf)
tempo = hdf5_getters.get_tempo(hdf)
time_signature = hdf5_getters.get_time_signature(hdf)
time_signature_confidence = hdf5_getters.get_time_signature_confidence(
hdf)
track_id = hdf5_getters.get_track_id(hdf)
year = hdf5_getters.get_year(hdf)
similar_artists = hdf5_getters.get_similar_artists(hdf)
artist_terms = hdf5_getters.get_artist_terms(hdf)
artist_terms_freq = hdf5_getters.get_artist_terms_freq(hdf)
artist_terms_weight = hdf5_getters.get_artist_terms_weight(hdf)
segments_start = hdf5_getters.get_segments_start(hdf)
segments_confidence = hdf5_getters.get_segments_confidence(hdf)
segments_pitches = hdf5_getters.get_segments_pitches(hdf)
segments_timbre = hdf5_getters.get_segments_timbre(hdf)
segments_loudness_max = hdf5_getters.get_segments_loudness_max(
hdf)
segments_loudness_max_time = hdf5_getters.get_segments_loudness_max_time(
hdf)
segments_loudness_start = hdf5_getters.get_segments_loudness_start(
hdf)
sections_start = hdf5_getters.get_sections_start(hdf)
song.artistHotttnesss = remove_trap_characters(
str(hdf5_getters.get_artist_hotttnesss(songH5File)))
song.artistmbid = remove_trap_characters(
str(hdf5_getters.get_artist_mbid(songH5File)))
song.artistPlaymeid = remove_trap_characters(
str(hdf5_getters.get_artist_playmeid(songH5File)))
song.artist7digitalid = remove_trap_characters(
str(hdf5_getters.get_artist_7digitalid(songH5File)))
temp = hdf5_getters.get_artist_terms(songH5File)
song.artistTerms = remove_trap_characters(str(list(temp)))
song.artistTermsCount = get_list_length(temp)
song.artistTermsFreq = remove_trap_characters(
str(list(hdf5_getters.get_artist_terms_freq(songH5File))))
song.artistTermsWeight = remove_trap_characters(
str(list(hdf5_getters.get_artist_terms_weight(songH5File))))
temp = hdf5_getters.get_artist_mbtags(songH5File)
song.artistMBTags = remove_trap_characters(str(list(temp)))
song.artistMBTagsOuterCount = get_list_length(temp)
song.artistMBTagsCount = remove_trap_characters(
str(list(hdf5_getters.get_artist_mbtags_count(songH5File))))
song.analysisSampleRate = remove_trap_characters(
str(hdf5_getters.get_analysis_sample_rate(songH5File)))
song.audioMD5 = remove_trap_characters(
str(hdf5_getters.get_audio_md5(songH5File)))
song.endOfFadeIn = remove_trap_characters(
str(hdf5_getters.get_end_of_fade_in(songH5File)))
song.startOfFadeOut = remove_trap_characters(
str(hdf5_getters.get_start_of_fade_out(songH5File)))
song.energy = remove_trap_characters(
for parent_folder in os.listdir(path):
for sub_folder in os.listdir(path + '/' + parent_folder):
for child_folder in os.listdir(path + '/' + parent_folder +
'/' + sub_folder):
for file in os.listdir(path + '/' + parent_folder + '/' +
sub_folder + '/' + child_folder):
with h5.open_h5_file_read(path + '/' + parent_folder +
'/' + sub_folder + '/' +
child_folder + '/' +
file) as ds:
row = []
row += [h5.get_artist_terms(ds)]
row += [h5.get_artist_terms_freq(ds)]
row += [h5.get_artist_terms_weight(ds)]
row += [h5.get_danceability(ds)]
row += [h5.get_energy(ds)]
row += [h5.get_key(ds)]
row += [h5.get_mode(ds)]
row += [h5.get_loudness(ds)]
row += [
parent_folder + '/' + sub_folder + '/' +
child_folder + '/'
]
row += [file]
row += [h5.get_duration(ds)]
row += [h5.get_artist_familiarity(ds)]
row += [h5.get_similar_artists(ds)]
row += [h5.get_artist_id(ds)]
def data_to_flat_file(basedir, ext='.h5'):
""" This function extracts the information from the tables and creates the flat file. """
count = 0
#song counter
list_to_write = []
group_index = 0
row_to_write = ""
writer = csv.writer(open("complete.csv", "wb"))
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
for f in files:
row = []
print f
h5 = hdf5_getters.open_h5_file_read(f)
title = hdf5_getters.get_title(h5)
title = title.replace('"', '')
row.append(title)
comma = title.find(',')
if comma != -1:
print title
time.sleep(1)
album = hdf5_getters.get_release(h5)
album = album.replace('"', '')
row.append(album)
comma = album.find(',')
if comma != -1:
print album
time.sleep(1)
artist_name = hdf5_getters.get_artist_name(h5)
comma = artist_name.find(',')
if comma != -1:
print artist_name
time.sleep(1)
artist_name = artist_name.replace('"', '')
row.append(artist_name)
duration = hdf5_getters.get_duration(h5)
row.append(duration)
samp_rt = hdf5_getters.get_analysis_sample_rate(h5)
row.append(samp_rt)
artist_7digitalid = hdf5_getters.get_artist_7digitalid(h5)
row.append(artist_7digitalid)
artist_fam = hdf5_getters.get_artist_familiarity(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_fam) == True:
artist_fam = -1
row.append(artist_fam)
artist_hotness = hdf5_getters.get_artist_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_hotness) == True:
artist_hotness = -1
row.append(artist_hotness)
artist_id = hdf5_getters.get_artist_id(h5)
row.append(artist_id)
artist_lat = hdf5_getters.get_artist_latitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lat) == True:
artist_lat = -1
row.append(artist_lat)
artist_loc = hdf5_getters.get_artist_location(h5)
row.append(artist_loc)
artist_lon = hdf5_getters.get_artist_longitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lon) == True:
artist_lon = -1
row.append(artist_lon)
artist_mbid = hdf5_getters.get_artist_mbid(h5)
row.append(artist_mbid)
#Getting the genre
art_trm = hdf5_getters.get_artist_terms(h5)
trm_freq = hdf5_getters.get_artist_terms_freq(h5)
trn_wght = hdf5_getters.get_artist_terms_weight(h5)
a_mb_tags = hdf5_getters.get_artist_mbtags(h5)
genre_indexes = get_genre_indexes(
trm_freq) #index of the highest freq
genre_set = 0 #flag to see if the genre has been set or not
final_genre = []
genres_so_far = []
for i in range(len(genre_indexes)):
genre_tmp = get_genre(
art_trm, genre_indexes[i]
) #genre that corresponds to the highest freq
genres_so_far = genre_dict.get_genre_in_dict(
genre_tmp) #getting the genre from the dictionary
if len(genres_so_far) != 0:
for i in genres_so_far:
final_genre.append(i)
genre_set = 1
if genre_set == 1:
col_num = []
for i in final_genre:
column = int(i) #getting the column number of the genre
col_num.append(column)
genre_array = genre_columns(col_num) #genre array
for i in range(len(
genre_array)): #appending the genre_array to the row
row.append(genre_array[i])
else:
genre_array = genre_columns(
-1
) #when there is no genre matched, return an array of [0...0]
for i in range(len(
genre_array)): #appending the genre_array to the row
row.append(genre_array[i])
artist_pmid = hdf5_getters.get_artist_playmeid(h5)
row.append(artist_pmid)
audio_md5 = hdf5_getters.get_audio_md5(h5)
row.append(audio_md5)
danceability = hdf5_getters.get_danceability(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(danceability) == True:
danceability = -1
row.append(danceability)
end_fade_in = hdf5_getters.get_end_of_fade_in(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(end_fade_in) == True:
end_fade_in = -1
row.append(end_fade_in)
energy = hdf5_getters.get_energy(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(energy) == True:
energy = -1
row.append(energy)
song_key = hdf5_getters.get_key(h5)
row.append(song_key)
key_c = hdf5_getters.get_key_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(key_c) == True:
key_c = -1
row.append(key_c)
loudness = hdf5_getters.get_loudness(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(loudness) == True:
loudness = -1
row.append(loudness)
mode = hdf5_getters.get_mode(h5)
row.append(mode)
mode_conf = hdf5_getters.get_mode_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(mode_conf) == True:
mode_conf = -1
row.append(mode_conf)
release_7digitalid = hdf5_getters.get_release_7digitalid(h5)
row.append(release_7digitalid)
song_hot = hdf5_getters.get_song_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(song_hot) == True:
song_hot = -1
row.append(song_hot)
song_id = hdf5_getters.get_song_id(h5)
row.append(song_id)
start_fade_out = hdf5_getters.get_start_of_fade_out(h5)
row.append(start_fade_out)
tempo = hdf5_getters.get_tempo(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(tempo) == True:
tempo = -1
row.append(tempo)
time_sig = hdf5_getters.get_time_signature(h5)
row.append(time_sig)
time_sig_c = hdf5_getters.get_time_signature_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(time_sig_c) == True:
time_sig_c = -1
row.append(time_sig_c)
track_id = hdf5_getters.get_track_id(h5)
row.append(track_id)
track_7digitalid = hdf5_getters.get_track_7digitalid(h5)
row.append(track_7digitalid)
year = hdf5_getters.get_year(h5)
row.append(year)
bars_c = hdf5_getters.get_bars_confidence(h5)
bars_start = hdf5_getters.get_bars_start(h5)
row_bars_padding = padding(
245
) #this is the array that will be attached at the end of th row
#--------------bars---------------"
gral_info = []
gral_info = row[:]
empty = []
for i, item in enumerate(bars_c):
row.append(group_index)
row.append(i)
row.append(bars_c[i])
bars_c_avg = get_avg(bars_c)
row.append(bars_c_avg)
bars_c_max = get_max(bars_c)
row.append(bars_c_max)
bars_c_min = get_min(bars_c)
row.append(bars_c_min)
bars_c_stddev = get_stddev(bars_c)
row.append(bars_c_stddev)
bars_c_count = get_count(bars_c)
row.append(bars_c_count)
bars_c_sum = get_sum(bars_c)
row.append(bars_c_sum)
row.append(bars_start[i])
bars_start_avg = get_avg(bars_start)
row.append(bars_start_avg)
bars_start_max = get_max(bars_start)
row.append(bars_start_max)
bars_start_min = get_min(bars_start)
row.append(bars_start_min)
bars_start_stddev = get_stddev(bars_start)
row.append(bars_start_stddev)
bars_start_count = get_count(bars_start)
row.append(bars_start_count)
bars_start_sum = get_sum(bars_start)
row.append(bars_start_sum)
for i in row_bars_padding:
row.append(i)
writer.writerow(row)
row = []
row = gral_info[:]
#--------beats---------------"
beats_c = hdf5_getters.get_beats_confidence(h5)
group_index = 1
row = []
row = gral_info[:]
row_front = padding(
14) #blanks left in front of the row(empty spaces for bars)
row_beats_padding = padding(231)
for i, item in enumerate(beats_c):
row.append(group_index)
row.append(i)
for index in row_front: #padding blanks in front of the beats
row.append(index)
row.append(beats_c[i])
beats_c_avg = get_avg(beats_c)
row.append(beats_c_avg)
beats_c_max = get_max(beats_c)
row.append(beats_c_max)
beats_c_min = get_min(beats_c)
row.append(beats_c_min)
beats_c_stddev = get_stddev(beats_c)
row.append(beats_c_stddev)
beats_c_count = get_count(beats_c)
row.append(beats_c_count)
beats_c_sum = get_sum(beats_c)
row.append(beats_c_sum)
beats_start = hdf5_getters.get_beats_start(h5)
row.append(beats_start[i])
beats_start_avg = get_avg(beats_start)
row.append(beats_start_avg)
beats_start_max = get_max(beats_start)
row.append(beats_start_max)
beats_start_min = get_min(beats_start)
row.append(beats_start_min)
beats_start_stddev = get_stddev(beats_start)
row.append(beats_start_stddev)
beats_start_count = get_count(beats_start)
row.append(beats_start_count)
beats_start_sum = get_sum(beats_start)
row.append(beats_start_sum)
for i in row_beats_padding:
row.append(i)
writer.writerow(row)
row = []
row = gral_info[:]
# "--------sections---------------"
row_sec_padding = padding(
217) #blank spaces left at the end of the row
sec_c = hdf5_getters.get_sections_confidence(h5)
group_index = 2
row = []
row = gral_info[:]
row_front = padding(
28) #blank spaces left in front(empty spaces for bars,beats)
for i, item in enumerate(sec_c):
row.append(group_index)
row.append(i)
for index in row_front: #padding blanks in front of the sections
row.append(index)
row.append(sec_c[i])
sec_c_avg = get_avg(sec_c)
row.append(sec_c_avg)
sec_c_max = get_max(sec_c)
row.append(sec_c_max)
sec_c_min = get_min(sec_c)
row.append(sec_c_min)
sec_c_stddev = get_stddev(sec_c)
row.append(sec_c_stddev)
sec_c_count = get_count(sec_c)
row.append(sec_c_count)
sec_c_sum = get_sum(sec_c)
row.append(sec_c_sum)
sec_start = hdf5_getters.get_sections_start(h5)
row.append(sec_start[i])
sec_start_avg = get_avg(sec_start)
row.append(sec_start_avg)
sec_start_max = get_max(sec_start)
row.append(sec_start_max)
sec_start_min = get_min(sec_start)
row.append(sec_start_min)
sec_start_stddev = get_stddev(sec_start)
row.append(sec_start_stddev)
sec_start_count = get_count(sec_start)
row.append(sec_start_count)
sec_start_sum = get_sum(sec_start)
row.append(sec_start_sum)
for i in row_sec_padding: #appending the blank spaces at the end of the row
row.append(i)
writer.writerow(row)
row = []
row = gral_info[:]
#--------segments---------------"
row_seg_padding = padding(182) #blank spaces at the end of the row
row_front = padding(42) #blank spaces left in front of segments
seg_c = hdf5_getters.get_segments_confidence(h5)
group_index = 3
row = []
row = gral_info[:]
for i, item in enumerate(seg_c):
row.append(group_index)
row.append(i)
for index in row_front: #padding blanks in front of the segments
row.append(index)
row.append(seg_c[i])
seg_c_avg = get_avg(seg_c)
row.append(seg_c_avg)
seg_c_max = get_max(seg_c)
row.append(seg_c_max)
seg_c_min = get_min(seg_c)
row.append(seg_c_min)
seg_c_stddev = get_stddev(seg_c)
row.append(seg_c_stddev)
seg_c_count = get_count(seg_c)
row.append(seg_c_count)
seg_c_sum = get_sum(seg_c)
row.append(seg_c_sum)
seg_loud_max = hdf5_getters.get_segments_loudness_max(h5)
row.append(seg_loud_max[i])
seg_loud_max_avg = get_avg(seg_loud_max)
row.append(seg_loud_max_avg)
seg_loud_max_max = get_max(seg_loud_max)
row.append(seg_loud_max_max)
seg_loud_max_min = get_min(seg_loud_max)
row.append(seg_loud_max_min)
seg_loud_max_stddev = get_stddev(seg_loud_max)
row.append(seg_loud_max_stddev)
seg_loud_max_count = get_count(seg_loud_max)
row.append(seg_loud_max_count)
seg_loud_max_sum = get_sum(seg_loud_max)
row.append(seg_loud_max_sum)
seg_loud_max_time = hdf5_getters.get_segments_loudness_max_time(
h5)
row.append(seg_loud_max_time[i])
seg_loud_max_time_avg = get_avg(seg_loud_max_time)
row.append(seg_loud_max_time_avg)
seg_loud_max_time_max = get_max(seg_loud_max_time)
row.append(seg_loud_max_time_max)
seg_loud_max_time_min = get_min(seg_loud_max_time)
row.append(seg_loud_max_time_min)
seg_loud_max_time_stddev = get_stddev(seg_loud_max_time)
row.append(seg_loud_max_time_stddev)
seg_loud_max_time_count = get_count(seg_loud_max_time)
row.append(seg_loud_max_time_count)
seg_loud_max_time_sum = get_sum(seg_loud_max_time)
row.append(seg_loud_max_time_sum)
seg_loud_start = hdf5_getters.get_segments_loudness_start(h5)
row.append(seg_loud_start[i])
seg_loud_start_avg = get_avg(seg_loud_start)
row.append(seg_loud_start_avg)
seg_loud_start_max = get_max(seg_loud_start)
row.append(seg_loud_start_max)
seg_loud_start_min = get_min(seg_loud_start)
row.append(seg_loud_start_min)
seg_loud_start_stddev = get_stddev(seg_loud_start)
row.append(seg_loud_start_stddev)
seg_loud_start_count = get_count(seg_loud_start)
row.append(seg_loud_start_count)
seg_loud_start_sum = get_sum(seg_loud_start)
row.append(seg_loud_start_sum)
seg_start = hdf5_getters.get_segments_start(h5)
row.append(seg_start[i])
seg_start_avg = get_avg(seg_start)
row.append(seg_start_avg)
seg_start_max = get_max(seg_start)
row.append(seg_start_max)
seg_start_min = get_min(seg_start)
row.append(seg_start_min)
seg_start_stddev = get_stddev(seg_start)
row.append(seg_start_stddev)
seg_start_count = get_count(seg_start)
row.append(seg_start_count)
seg_start_sum = get_sum(seg_start)
row.append(seg_start_sum)
for i in row_seg_padding: #appending blank spaces at the end of the row
row.append(i)
writer.writerow(row)
row = []
row = gral_info[:]
#----------segments pitch and timbre---------------"
row_seg2_padding = padding(
14) #blank spaces left at the end of the row
row_front = padding(
77) #blank spaces left at the front of the segments and timbre
seg_pitch = hdf5_getters.get_segments_pitches(h5)
transpose_pitch = seg_pitch.transpose(
) #this is to tranpose the matrix,so we can have 12 rows
group_index = 4
row = []
row = gral_info[:]
for i, item in enumerate(transpose_pitch[0]):
row.append(group_index)
row.append(i)
for index in row_front: #padding blanks in front of segments and timbre
row.append(index)
row.append(transpose_pitch[0][i])
seg_pitch_avg = get_avg(transpose_pitch[0])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[0])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[0])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[0])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[0])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[0])
row.append(seg_pitch_sum)
row.append(transpose_pitch[1][i])
seg_pitch_avg = get_avg(transpose_pitch[1])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[1])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[1])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[1])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[1])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[1])
row.append(seg_pitch_sum)
row.append(transpose_pitch[2][i])
seg_pitch_avg = get_avg(transpose_pitch[2])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[2])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[2])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[2])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[2])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[2])
row.append(seg_pitch_sum)
row.append(transpose_pitch[3][i])
seg_pitch_avg = get_avg(transpose_pitch[3])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[3])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[3])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[3])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[3])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[3])
row.append(seg_pitch_sum)
row.append(transpose_pitch[4][i])
seg_pitch_avg = get_avg(transpose_pitch[4])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[4])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[4])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[4])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[4])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[4])
row.append(seg_pitch_sum)
row.append(transpose_pitch[5][i])
seg_pitch_avg = get_avg(transpose_pitch[5])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[5])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[5])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[5])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[5])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[5])
row.append(seg_pitch_sum)
row.append(transpose_pitch[6][i])
seg_pitch_avg = get_avg(transpose_pitch[6])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[6])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[6])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[6])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[6])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[6])
row.append(seg_pitch_sum)
row.append(transpose_pitch[7][i])
seg_pitch_avg = get_avg(transpose_pitch[7])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[7])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[7])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[7])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[7])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[7])
row.append(seg_pitch_sum)
row.append(transpose_pitch[8][i])
seg_pitch_avg = get_avg(transpose_pitch[8])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[8])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[8])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[8])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[8])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[8])
row.append(seg_pitch_sum)
row.append(transpose_pitch[9][i])
seg_pitch_avg = get_avg(transpose_pitch[9])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[9])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[9])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[9])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[9])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[9])
row.append(seg_pitch_sum)
row.append(transpose_pitch[10][i])
seg_pitch_avg = get_avg(transpose_pitch[10])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[10])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[10])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[10])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[10])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[10])
row.append(seg_pitch_sum)
row.append(transpose_pitch[11][i])
seg_pitch_avg = get_avg(transpose_pitch[11])
row.append(seg_pitch_avg)
seg_pitch_max = get_max(transpose_pitch[11])
row.append(seg_pitch_max)
seg_pitch_min = get_min(transpose_pitch[11])
row.append(seg_pitch_min)
seg_pitch_stddev = get_stddev(transpose_pitch[11])
row.append(seg_pitch_stddev)
seg_pitch_count = get_count(transpose_pitch[11])
row.append(seg_pitch_count)
seg_pitch_sum = get_sum(transpose_pitch[11])
row.append(seg_pitch_sum)
#timbre arrays
seg_timbre = hdf5_getters.get_segments_timbre(h5)
transpose_timbre = seg_pitch.transpose(
) #tranposing matrix, to have 12 rows
row.append(transpose_timbre[0][i])
seg_timbre_avg = get_avg(transpose_timbre[0])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[0])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[0])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[0])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[0])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[0])
row.append(seg_timbre_sum)
row.append(transpose_timbre[1][i])
seg_timbre_avg = get_avg(transpose_timbre[1])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[1])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[1])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[1])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[1])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[1])
row.append(seg_timbre_sum)
row.append(transpose_timbre[2][i])
seg_timbre_avg = get_avg(transpose_timbre[2])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[2])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[2])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[2])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[2])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[2])
row.append(seg_timbre_sum)
row.append(transpose_timbre[3][i])
seg_timbre_avg = get_avg(transpose_timbre[3])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[3])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[3])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[3])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[3])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[3])
row.append(seg_timbre_sum)
row.append(transpose_timbre[4][i])
seg_timbre_avg = get_avg(transpose_timbre[4])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[4])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[4])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[4])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[4])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[4])
row.append(seg_timbre_sum)
row.append(transpose_timbre[5][i])
seg_timbre_avg = get_avg(transpose_timbre[5])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[5])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[5])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[5])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[5])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[5])
row.append(seg_timbre_sum)
row.append(transpose_timbre[6][i])
seg_timbre_avg = get_avg(transpose_timbre[6])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[6])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[6])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[6])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[6])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[6])
row.append(seg_timbre_sum)
row.append(transpose_timbre[7][i])
seg_timbre_avg = get_avg(transpose_timbre[7])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[7])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[7])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[7])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[7])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[7])
row.append(seg_timbre_sum)
row.append(transpose_timbre[8][i])
seg_timbre_avg = get_avg(transpose_timbre[8])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[8])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[8])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[8])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[8])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[8])
row.append(seg_timbre_sum)
row.append(transpose_timbre[9][i])
seg_timbre_avg = get_avg(transpose_timbre[9])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[9])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[9])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[9])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[9])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[9])
row.append(seg_timbre_sum)
row.append(transpose_timbre[10][i])
seg_timbre_avg = get_avg(transpose_timbre[10])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[10])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[10])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[10])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[10])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[10])
row.append(seg_timbre_sum)
row.append(transpose_timbre[11][i])
seg_timbre_avg = get_avg(transpose_timbre[11])
row.append(seg_timbre_avg)
seg_timbre_max = get_max(transpose_timbre[11])
row.append(seg_timbre_max)
seg_timbre_min = get_min(transpose_timbre[11])
row.append(seg_timbre_min)
seg_timbre_stddev = get_stddev(transpose_timbre[11])
row.append(seg_timbre_stddev)
seg_timbre_count = get_count(transpose_timbre[11])
row.append(seg_timbre_count)
seg_timbre_sum = get_sum(transpose_timbre[11])
row.append(seg_timbre_sum)
for item in row_seg2_padding:
row.append(item)
writer.writerow(row)
row = []
row = gral_info[:]
# "--------tatums---------------"
tatms_c = hdf5_getters.get_tatums_confidence(h5)
group_index = 5
row_front = padding(245) #blank spaces left in front of tatums
row = []
row = gral_info[:]
for i, item in enumerate(tatms_c):
row.append(group_index)
row.append(i)
for item in row_front: #appending blank spaces at the front of the row
row.append(item)
row.append(tatms_c[i])
tatms_c_avg = get_avg(tatms_c)
row.append(tatms_c_avg)
tatms_c_max = get_max(tatms_c)
row.append(tatms_c_max)
tatms_c_min = get_min(tatms_c)
row.append(tatms_c_min)
tatms_c_stddev = get_stddev(tatms_c)
row.append(tatms_c_stddev)
tatms_c_count = get_count(tatms_c)
row.append(tatms_c_count)
tatms_c_sum = get_sum(tatms_c)
row.append(tatms_c_sum)
tatms_start = hdf5_getters.get_tatums_start(h5)
row.append(tatms_start[i])
tatms_start_avg = get_avg(tatms_start)
row.append(tatms_start_avg)
tatms_start_max = get_max(tatms_start)
row.append(tatms_start_max)
tatms_start_min = get_min(tatms_start)
row.append(tatms_start_min)
tatms_start_stddev = get_stddev(tatms_start)
row.append(tatms_start_stddev)
tatms_start_count = get_count(tatms_start)
row.append(tatms_start_count)
tatms_start_sum = get_sum(tatms_start)
row.append(tatms_start_sum)
writer.writerow(row)
row = []
row = gral_info[:]
transpose_pitch = seg_pitch.transpose(
) #this is to tranpose the matrix,so we can have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_pitch_avg = []
seg_pitch_max = []
seg_pitch_min = []
seg_pitch_stddev = []
seg_pitch_count = []
seg_pitch_sum = []
i = 0
#Getting the aggregate values in the pitches array
for row in transpose_pitch:
seg_pitch_avg.append(get_avg(row))
seg_pitch_max.append(get_max(row))
seg_pitch_min.append(get_min(row))
seg_pitch_stddev.append(get_stddev(row))
seg_pitch_count.append(get_count(row))
seg_pitch_sum.append(get_sum(row))
i = i + 1
#extracting information from the timbre array
transpose_timbre = seg_pitch.transpose(
) #tranposing matrix, to have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_timbre_avg = []
seg_timbre_max = []
seg_timbre_min = []
seg_timbre_stddev = []
seg_timbre_count = []
seg_timbre_sum = []
i = 0
for row in transpose_timbre:
seg_timbre_avg.append(get_avg(row))
seg_timbre_max.append(get_max(row))
seg_timbre_min.append(get_min(row))
seg_timbre_stddev.append(get_stddev(row))
seg_timbre_count.append(get_count(row))
seg_timbre_sum.append(get_sum(row))
i = i + 1
h5.close()
count = count + 1
print count
def data_to_flat_file(basedir, ext='.h5'):
"""This function extract the information from the tables and creates the flat file."""
count = 0
#song counter
list_to_write = []
row_to_write = ""
writer = csv.writer(open("metadata_wholeA.csv", "wb"))
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
for f in files:
print f #the name of the file
h5 = hdf5_getters.open_h5_file_read(f)
title = hdf5_getters.get_title(h5)
title = title.replace('"', '')
comma = title.find(',') #eliminating commas in the title
if comma != -1:
print title
time.sleep(1)
album = hdf5_getters.get_release(h5)
album = album.replace('"', '') #eliminating commas in the album
comma = album.find(',')
if comma != -1:
print album
time.sleep(1)
artist_name = hdf5_getters.get_artist_name(h5)
comma = artist_name.find(',')
if comma != -1:
print artist_name
time.sleep(1)
artist_name = artist_name.replace('"',
'') #eliminating double quotes
duration = hdf5_getters.get_duration(h5)
samp_rt = hdf5_getters.get_analysis_sample_rate(h5)
artist_7digitalid = hdf5_getters.get_artist_7digitalid(h5)
artist_fam = hdf5_getters.get_artist_familiarity(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_fam) == True:
artist_fam = -1
artist_hotness = hdf5_getters.get_artist_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_hotness) == True:
artist_hotness = -1
artist_id = hdf5_getters.get_artist_id(h5)
artist_lat = hdf5_getters.get_artist_latitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lat) == True:
artist_lat = -1
artist_loc = hdf5_getters.get_artist_location(h5)
#checks artist_loc to see if it is a hyperlink if it is set as empty string
artist_loc = artist_loc.replace(",", "\,")
if artist_loc.startswith("<a"):
artist_loc = ""
if len(artist_loc) > 100:
artist_loc = ""
artist_lon = hdf5_getters.get_artist_longitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lon) == True:
artist_lon = -1
artist_mbid = hdf5_getters.get_artist_mbid(h5)
artist_pmid = hdf5_getters.get_artist_playmeid(h5)
audio_md5 = hdf5_getters.get_audio_md5(h5)
danceability = hdf5_getters.get_danceability(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(danceability) == True:
danceability = -1
end_fade_in = hdf5_getters.get_end_of_fade_in(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(end_fade_in) == True:
end_fade_in = -1
energy = hdf5_getters.get_energy(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(energy) == True:
energy = -1
song_key = hdf5_getters.get_key(h5)
key_c = hdf5_getters.get_key_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(key_c) == True:
key_c = -1
loudness = hdf5_getters.get_loudness(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(loudness) == True:
loudness = -1
mode = hdf5_getters.get_mode(h5)
mode_conf = hdf5_getters.get_mode_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(mode_conf) == True:
mode_conf = -1
release_7digitalid = hdf5_getters.get_release_7digitalid(h5)
song_hot = hdf5_getters.get_song_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(song_hot) == True:
song_hot = -1
song_id = hdf5_getters.get_song_id(h5)
start_fade_out = hdf5_getters.get_start_of_fade_out(h5)
tempo = hdf5_getters.get_tempo(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(tempo) == True:
tempo = -1
time_sig = hdf5_getters.get_time_signature(h5)
time_sig_c = hdf5_getters.get_time_signature_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(time_sig_c) == True:
time_sig_c = -1
track_id = hdf5_getters.get_track_id(h5)
track_7digitalid = hdf5_getters.get_track_7digitalid(h5)
year = hdf5_getters.get_year(h5)
bars_c = hdf5_getters.get_bars_confidence(h5)
bars_c_avg = get_avg(bars_c)
bars_c_max = get_max(bars_c)
bars_c_min = get_min(bars_c)
bars_c_stddev = get_stddev(bars_c)
bars_c_count = get_count(bars_c)
bars_c_sum = get_sum(bars_c)
bars_start = hdf5_getters.get_bars_start(h5)
bars_start_avg = get_avg(bars_start)
bars_start_max = get_max(bars_start)
bars_start_min = get_min(bars_start)
bars_start_stddev = get_stddev(bars_start)
bars_start_count = get_count(bars_start)
bars_start_sum = get_sum(bars_start)
beats_c = hdf5_getters.get_beats_confidence(h5)
beats_c_avg = get_avg(beats_c)
beats_c_max = get_max(beats_c)
beats_c_min = get_min(beats_c)
beats_c_stddev = get_stddev(beats_c)
beats_c_count = get_count(beats_c)
beats_c_sum = get_sum(beats_c)
beats_start = hdf5_getters.get_beats_start(h5)
beats_start_avg = get_avg(beats_start)
beats_start_max = get_max(beats_start)
beats_start_min = get_min(beats_start)
beats_start_stddev = get_stddev(beats_start)
beats_start_count = get_count(beats_start)
beats_start_sum = get_sum(beats_start)
sec_c = hdf5_getters.get_sections_confidence(h5)
sec_c_avg = get_avg(sec_c)
sec_c_max = get_max(sec_c)
sec_c_min = get_min(sec_c)
sec_c_stddev = get_stddev(sec_c)
sec_c_count = get_count(sec_c)
sec_c_sum = get_sum(sec_c)
sec_start = hdf5_getters.get_sections_start(h5)
sec_start_avg = get_avg(sec_start)
sec_start_max = get_max(sec_start)
sec_start_min = get_min(sec_start)
sec_start_stddev = get_stddev(sec_start)
sec_start_count = get_count(sec_start)
sec_start_sum = get_sum(sec_start)
seg_c = hdf5_getters.get_segments_confidence(h5)
seg_c_avg = get_avg(seg_c)
seg_c_max = get_max(seg_c)
seg_c_min = get_min(seg_c)
seg_c_stddev = get_stddev(seg_c)
seg_c_count = get_count(seg_c)
seg_c_sum = get_sum(seg_c)
seg_loud_max = hdf5_getters.get_segments_loudness_max(h5)
seg_loud_max_avg = get_avg(seg_loud_max)
seg_loud_max_max = get_max(seg_loud_max)
seg_loud_max_min = get_min(seg_loud_max)
seg_loud_max_stddev = get_stddev(seg_loud_max)
seg_loud_max_count = get_count(seg_loud_max)
seg_loud_max_sum = get_sum(seg_loud_max)
seg_loud_max_time = hdf5_getters.get_segments_loudness_max_time(h5)
seg_loud_max_time_avg = get_avg(seg_loud_max_time)
seg_loud_max_time_max = get_max(seg_loud_max_time)
seg_loud_max_time_min = get_min(seg_loud_max_time)
seg_loud_max_time_stddev = get_stddev(seg_loud_max_time)
seg_loud_max_time_count = get_count(seg_loud_max_time)
seg_loud_max_time_sum = get_sum(seg_loud_max_time)
seg_loud_start = hdf5_getters.get_segments_loudness_start(h5)
seg_loud_start_avg = get_avg(seg_loud_start)
seg_loud_start_max = get_max(seg_loud_start)
seg_loud_start_min = get_min(seg_loud_start)
seg_loud_start_stddev = get_stddev(seg_loud_start)
seg_loud_start_count = get_count(seg_loud_start)
seg_loud_start_sum = get_sum(seg_loud_start)
seg_pitch = hdf5_getters.get_segments_pitches(h5)
pitch_size = len(seg_pitch)
seg_start = hdf5_getters.get_segments_start(h5)
seg_start_avg = get_avg(seg_start)
seg_start_max = get_max(seg_start)
seg_start_min = get_min(seg_start)
seg_start_stddev = get_stddev(seg_start)
seg_start_count = get_count(seg_start)
seg_start_sum = get_sum(seg_start)
seg_timbre = hdf5_getters.get_segments_timbre(h5)
tatms_c = hdf5_getters.get_tatums_confidence(h5)
tatms_c_avg = get_avg(tatms_c)
tatms_c_max = get_max(tatms_c)
tatms_c_min = get_min(tatms_c)
tatms_c_stddev = get_stddev(tatms_c)
tatms_c_count = get_count(tatms_c)
tatms_c_sum = get_sum(tatms_c)
tatms_start = hdf5_getters.get_tatums_start(h5)
tatms_start_avg = get_avg(tatms_start)
tatms_start_max = get_max(tatms_start)
tatms_start_min = get_min(tatms_start)
tatms_start_stddev = get_stddev(tatms_start)
tatms_start_count = get_count(tatms_start)
tatms_start_sum = get_sum(tatms_start)
#Getting the genres
genre_set = 0 #flag to see if the genre has been set or not
art_trm = hdf5_getters.get_artist_terms(h5)
trm_freq = hdf5_getters.get_artist_terms_freq(h5)
trn_wght = hdf5_getters.get_artist_terms_weight(h5)
a_mb_tags = hdf5_getters.get_artist_mbtags(h5)
genre_indexes = get_genre_indexes(
trm_freq) #index of the highest freq
final_genre = []
genres_so_far = []
for i in range(len(genre_indexes)):
genre_tmp = get_genre(
art_trm, genre_indexes[i]
) #genre that corresponds to the highest freq
genres_so_far = genre_dict.get_genre_in_dict(
genre_tmp) #getting the genre from the dictionary
if len(genres_so_far) != 0:
for i in genres_so_far:
final_genre.append(i)
genre_set = 1 #genre was found in dictionary
if genre_set == 1:
col_num = []
for genre in final_genre:
column = int(
genre) #getting the column number of the genre
col_num.append(column)
genre_array = genre_columns(col_num) #genre array
else:
genre_array = genre_columns(
-1) #the genre was not found in the dictionary
transpose_pitch = seg_pitch.transpose(
) #this is to tranpose the matrix,so we can have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_pitch_avg = []
seg_pitch_max = []
seg_pitch_min = []
seg_pitch_stddev = []
seg_pitch_count = []
seg_pitch_sum = []
i = 0
#Getting the aggregate values in the pitches array
for row in transpose_pitch:
seg_pitch_avg.append(get_avg(row))
seg_pitch_max.append(get_max(row))
seg_pitch_min.append(get_min(row))
seg_pitch_stddev.append(get_stddev(row))
seg_pitch_count.append(get_count(row))
seg_pitch_sum.append(get_sum(row))
i = i + 1
#extracting information from the timbre array
transpose_timbre = seg_pitch.transpose(
) #tranposing matrix, to have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_timbre_avg = []
seg_timbre_max = []
seg_timbre_min = []
seg_timbre_stddev = []
seg_timbre_count = []
seg_timbre_sum = []
i = 0
for row in transpose_timbre:
seg_timbre_avg.append(get_avg(row))
seg_timbre_max.append(get_max(row))
seg_timbre_min.append(get_min(row))
seg_timbre_stddev.append(get_stddev(row))
seg_timbre_count.append(get_count(row))
seg_timbre_sum.append(get_sum(row))
i = i + 1
#Writing to the flat file
writer.writerow([
title, album, artist_name, year, duration, seg_start_count,
tempo
])
h5.close()
count = count + 1
print count
def 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 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)