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_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 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 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