def get_btloudnessmax(h5): """ Get beat-aligned loudness max from a song file of the Million Song Dataset INPUT: h5 - filename or open h5 file RETURN: btloudnessmax - beat-aligned loudness max, one beat per column or None if something went wrong (e.g. no beats) """ # if string, open and get max loudness, if h5, get max loudness if type(h5).__name__ == 'str': h5 = GETTERS.open_h5_file_read(h5) loudnessmax = GETTERS.get_segments_loudness_max(h5) segstarts = GETTERS.get_segments_start(h5) btstarts = GETTERS.get_beats_start(h5) duration = GETTERS.get_duration(h5) h5.close() else: loudnessmax = GETTERS.get_segments_loudness_max(h5) segstarts = GETTERS.get_segments_start(h5) btstarts = GETTERS.get_beats_start(h5) duration = GETTERS.get_duration(h5) # get the series of starts for segments and beats # NOTE: MAYBE USELESS? # result for track: 'TR0002Q11C3FA8332D' # segstarts.shape = (708,) # btstarts.shape = (304,) segstarts = np.array(segstarts).flatten() btstarts = np.array(btstarts).flatten() # aligned features btloudnessmax = align_feats(loudnessmax.reshape(1,loudnessmax.shape[0]),segstarts,btstarts,duration) if btloudnessmax is None: return None # done (no renormalization) return btloudnessmax
def get_btchromas_loudness(h5): """ Similar to btchroma, but adds the loudness back. We use the segments_loudness_max There is no max value constraint, simply no negative values. """ # if string, open and get chromas, if h5, get chromas if type(h5).__name__ == "str": h5 = GETTERS.open_h5_file_read(h5) chromas = GETTERS.get_segments_pitches(h5) segstarts = GETTERS.get_segments_start(h5) btstarts = GETTERS.get_beats_start(h5) duration = GETTERS.get_duration(h5) loudnessmax = GETTERS.get_segments_loudness_max(h5) h5.close() else: chromas = GETTERS.get_segments_pitches(h5) segstarts = GETTERS.get_segments_start(h5) btstarts = GETTERS.get_beats_start(h5) duration = GETTERS.get_duration(h5) loudnessmax = GETTERS.get_segments_loudness_max(h5) # get the series of starts for segments and beats segstarts = np.array(segstarts).flatten() btstarts = np.array(btstarts).flatten() # add back loudness chromas = chromas.T * idB(loudnessmax) # aligned features btchroma = align_feats(chromas, segstarts, btstarts, duration) if btchroma is None: return None # done (no renormalization) return btchroma
def get_btchromas_loudness(h5): """ Similar to btchroma, but adds the loudness back. We use the segments_loudness_max There is no max value constraint, simply no negative values. """ # if string, open and get chromas, if h5, get chromas if type(h5).__name__ == 'str': h5 = GETTERS.open_h5_file_read(h5) chromas = GETTERS.get_segments_pitches(h5) segstarts = GETTERS.get_segments_start(h5) btstarts = GETTERS.get_beats_start(h5) duration = GETTERS.get_duration(h5) loudnessmax = GETTERS.get_segments_loudness_max(h5) h5.close() else: chromas = GETTERS.get_segments_pitches(h5) segstarts = GETTERS.get_segments_start(h5) btstarts = GETTERS.get_beats_start(h5) duration = GETTERS.get_duration(h5) loudnessmax = GETTERS.get_segments_loudness_max(h5) # get the series of starts for segments and beats segstarts = np.array(segstarts).flatten() btstarts = np.array(btstarts).flatten() # add back loudness chromas = chromas.T * idB(loudnessmax) # aligned features btchroma = align_feats(chromas, segstarts, btstarts, duration) if btchroma is None: return None # done (no renormalization) return btchroma
def h5_files_to_np_array(dir, filename): list = get_h5_files(dir) num_done = 0 seg_array = [] #Go through every file and get the desired information. for file in list: song = getters.open_h5_file_read(file) seg_append = np.array(getters.get_segments_pitches(song)) seg_append = np.c_[seg_append, np.array(getters.get_segments_timbre(song))] seg_append = np.c_[seg_append, np.array(getters.get_segments_loudness_max(song))] seg_append = np.c_[seg_append, np.array(getters.get_segments_loudness_start(song))] start = np.array(getters.get_segments_start(song)) for i in range(0, len(start) - 1): if i != (len(start) - 1): start[i] = start[i + 1] - start[i] start[len(start) - 1] = getters.get_duration(song) - start[len(start) - 1] seg_append = np.c_[seg_append, start] #Add the arrays to the bottom of the list seg_array.extend(seg_append.tolist()) song.close() num_done = num_done + 1 #Gives a count for every 500 files completed if num_done % 500 == 0: print num_done, " of ", len(list) #Convert the list to a Numpy array seg_array = np.array(seg_array) #Save the array in a file seg_array.dump(filename) print len(seg_array), " number of segments in the set." return seg_array
def get_all_titles(basedir, ext='.h5'): titles = [] artist_names = [] terms = [] loudness = [] segments_loudness_max = [] for root, dirs, files in os.walk(basedir): files = glob.glob(os.path.join(root, '*' + ext)) for f in files: h5 = hdf5_getters.open_h5_file_read(f) titles.append(hdf5_getters.get_title(h5)) artist_names.append(hdf5_getters.get_artist_name(h5)) try: terms.append(hdf5_getters.get_artist_terms(h5)) except: pass loudness.append(hdf5_getters.get_loudness(h5)) try: segments_loudness_max.append( hdf5_getters.get_segments_loudness_max(h5)) except: pass h5.close() return titles, artist_names, terms, loudness, segments_loudness_max
def get_all_titles(basedir,ext='.h5') : titles = [] artist_names = [] terms = [] loudness = [] segments_loudness_max = [] for root, dirs, files in os.walk(basedir): files = glob.glob(os.path.join(root,'*'+ext)) for f in files: h5 = hdf5_getters.open_h5_file_read(f) titles.append(hdf5_getters.get_title(h5)) artist_names.append(hdf5_getters.get_artist_name(h5)) try: terms.append(hdf5_getters.get_artist_terms(h5)) except: pass loudness.append(hdf5_getters.get_loudness(h5)) try: segments_loudness_max.append(hdf5_getters.get_segments_loudness_max(h5)) except: pass h5.close() return titles, artist_names, terms, loudness, segments_loudness_max
def plots(track): f, axarr = plt.subplots(2, sharex=True) path = "../../msd_dense_subset/dense/"+track[2]+"/"+track[3]+"/"+track[4]+"/"+track+".h5" h5 = GETTERS.open_h5_file_read(path) segments = (GETTERS.get_segments_start(h5)) sections = (GETTERS.get_sections_start(h5)) max_loudness = (GETTERS.get_segments_loudness_max(h5)) loudness = (GETTERS.get_segments_loudness_start(h5)) average_loudness = (max_loudness + loudness) / 2 average_loudness_song = GETTERS.get_loudness(h5) start_fade_out = GETTERS.get_start_of_fade_out(h5) end_fade_in = GETTERS.get_end_of_fade_in(h5) pitches = GETTERS.get_segments_pitches(h5) h5.close() axarr[0].set_title('loudness curve for ' + ut.get_track_info(track)) axarr[0].plot(segments, average_loudness, label='Filtered') axarr[0].axhline(average_loudness_song, color='green') for section in enumerate(sections): axarr[0].axvline(section[1], color='red') axarr[0].axvline(start_fade_out, color='green') axarr[0].axvline(end_fade_in, color='green') idx = list() for section in sections: dif = segments - section posdif = np.where(dif >=0) idx.append(posdif[0][0]) axarr[1].set_title('Chroma values for ' + ut.get_track_info(track)) #axarr[1].set_xticks(idx,sections.astype(int)) extent =[0,segments.shape[0],0,12] axarr[1].imshow(pitches.transpose(),extent = extent,aspect = 'auto',interpolation='nearest',origin='lower') plt.show()
def hdf5_to_features(file_name): """ Receives path to HDF5 file, returns 2 lists of identification for the song as well as the features for the algorithm. Parameters ---------- file_name : str Absolute path to the HDF5 file. Returns ------- list1 : list List consisting of ID, song title and artist name. list2 : list 34 features to represent the song. """ with hdf5_getters.open_h5_file_read(file_name) as reader: # ID ID = hdf5_getters.get_song_id(reader) title = hdf5_getters.get_title(reader) artist = hdf5_getters.get_artist_name(reader) # Features 1-4 beat_starts = hdf5_getters.get_beats_start(reader) beat_durations = np.diff(beat_starts, axis=0) # try: tempo_10, tempo_90 = np.quantile(beat_durations, [0.1, 0.9]) # except: # print(beat_durations) # exit() temp_var = np.var(beat_durations) temp_mean = np.mean(beat_durations) # Features 5-8 segment_loudness = hdf5_getters.get_segments_loudness_max(reader) loud_10, loud_90 = np.quantile(segment_loudness, [0.1, 0.9]) loud_var = np.var(segment_loudness) loud_mean = np.mean(segment_loudness) # Features 9-21 pitch_dominance = hdf5_getters.get_segments_pitches(reader) pitch_means = pitch_dominance.mean(axis=0) pitch_var = pitch_means.var() # Features 22-34 timbre = hdf5_getters.get_segments_timbre(reader) timbre_means = timbre.mean(axis=0) timbre_var = timbre_means.var() return [ID, title, artist], [ tempo_10, tempo_90, temp_var, temp_mean, loud_10, loud_90, loud_var, loud_mean ] + list(pitch_means) + [pitch_var] + list(timbre_means) + [timbre_var]
def get_btloudnessmax(h5): """ Get beat-aligned loudness max from a song file of the Million Song Dataset INPUT: h5 - filename or open h5 file RETURN: btloudnessmax - beat-aligned loudness max, one beat per column or None if something went wrong (e.g. no beats) """ # if string, open and get max loudness, if h5, get max loudness if type(h5).__name__ == 'str': h5 = GETTERS.open_h5_file_read(h5) loudnessmax = GETTERS.get_segments_loudness_max(h5) segstarts = GETTERS.get_segments_start(h5) btstarts = GETTERS.get_beats_start(h5) duration = GETTERS.get_duration(h5) h5.close() else: loudnessmax = GETTERS.get_segments_loudness_max(h5) segstarts = GETTERS.get_segments_start(h5) btstarts = GETTERS.get_beats_start(h5) duration = GETTERS.get_duration(h5) # get the series of starts for segments and beats # NOTE: MAYBE USELESS? # result for track: 'TR0002Q11C3FA8332D' # segstarts.shape = (708,) # btstarts.shape = (304,) segstarts = np.array(segstarts).flatten() btstarts = np.array(btstarts).flatten() # reverse dB loudnessmax = idB(loudnessmax) # aligned features btloudnessmax = align_feats(loudnessmax.reshape(1, loudnessmax.shape[0]), segstarts, btstarts, duration) if btloudnessmax is None: return None # set it back to dB btloudnessmax = dB(btloudnessmax + 1e-10) # done (no renormalization) return btloudnessmax
def get_ten_feats_fullpath(feats_all, segments_all, h5file): h5 = hdf5_getters.open_h5_file_read(h5file) timbre = hdf5_getters.get_segments_timbre(h5) loudness_start = hdf5_getters.get_segments_loudness_start(h5) loudness_max = hdf5_getters.get_segments_loudness_max(h5) loudness_max_time = hdf5_getters.get_segments_loudness_max_time(h5) C = hdf5_getters.get_segments_pitches(h5) (hdf5path, filename) = os.path.split(h5file) target_audio_dir = os.path.split(hdf5path)[0] target_audio_path = os.path.join(target_audio_dir, os.path.splitext(filename)[0]) segments_all.append(np.array([hdf5_getters.get_segments_start(h5), target_audio_path])) feats_all.append(np.hstack((timbre, loudness_start.reshape((loudness_start.shape[0], 1)), loudness_max.reshape((loudness_max.shape[0], 1)), loudness_max_time.reshape((loudness_max_time.shape[0], 1)), C))) h5.close()
def extractFeatures(self): with hdf5_getters.open_h5_file_read(self.h5) as h5: self.tempo = hdf5_getters.get_tempo(h5, 0) ## Select the first 20 segments of the song self.segments_start = hdf5_getters.get_segments_start(h5)[0:20] ## Each segment has 12 timbre coeffs self.segments_timbre = hdf5_getters.get_segments_timbre( h5)[0:20].flatten() ## Each segment contains info on 12 pitch classes (C, C#, D to B) self.segments_pitches = hdf5_getters.get_segments_timbre( h5)[0:20].flatten() ## Segment intensity points self.segments_loudness_max = hdf5_getters.get_segments_loudness_max( h5)[0:20].flatten() ## Beats self.beats_start = hdf5_getters.get_beats_start(h5) self.beats_duration = np.diff(self.beats_start)[0:20] self.beats_average_period = np.mean(self.beats_duration)
def get_loudness(track,h5=None): #returns #0: the average loudness of the track #1: the variance of the loudness over the track #2: the difference between the highest and lowest dB value between the end of fade in and start of fade out close = (h5== None) if h5 == None: path = "../../msd_dense_subset/dense/"+track[2]+"/"+track[3]+"/"+track[4]+"/"+track+".h5" h5 = GETTERS.open_h5_file_read(path) loudness_avg = GETTERS.get_loudness(h5) loudnesses_interval = GETTERS.get_segments_loudness_max(h5) start_segments = GETTERS.get_segments_start(h5) start_fade_out = GETTERS.get_start_of_fade_out(h5) end_fade_in = GETTERS.get_end_of_fade_in(h5) idx = np.where((start_segments > end_fade_in) & (start_segments < start_fade_out)) if close: h5.close() #return (loudness_avg, np.var(loudnesses_interval[idx]), min(loudnesses_interval[idx]), max(loudnesses_interval[idx])) return (loudness_avg, np.var(loudnesses_interval[idx]), abs(max(loudnesses_interval[idx]) - abs(min(loudnesses_interval[idx]))))
def 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 get_song_data(results): songs_data = [] for f in results: h5 = getter.open_h5_file_read(f) songs_data.append([ os.path.basename(f), getter.get_artist_name(h5), getter.get_title(h5), getter.get_time_signature(h5), getter.get_key(h5), getter.get_segments_loudness_max(h5), getter.get_mode(h5), getter.get_beats_confidence(h5), getter.get_duration(h5), getter.get_tempo(h5), getter.get_loudness(h5), getter.get_segments_timbre(h5), getter.get_segments_pitches(h5), getter.get_key_confidence(h5), ]) h5.close() return songs_data
def plot_loudness_curve(track): path = "../../msd_dense_subset/mood/"+track[2]+"/"+track[3]+"/"+track[4]+"/"+track+".h5" h5 = GETTERS.open_h5_file_read(path) segments = (GETTERS.get_segments_start(h5)) sections = (GETTERS.get_sections_start(h5)) max_loudness = (GETTERS.get_segments_loudness_max(h5)) loudness = (GETTERS.get_segments_loudness_start(h5)) average_loudness = (max_loudness + loudness) / 2 average_loudness_song = GETTERS.get_loudness(h5) start_fade_out = GETTERS.get_start_of_fade_out(h5) end_fade_in = GETTERS.get_end_of_fade_in(h5) plt.title('loudness curve for ' + ut.get_track_info(track)) plt.ylabel('Loudness(dB)') plt.xlabel('Time(s)') plt.plot(segments, average_loudness, label='Filtered') plt.axhline(average_loudness_song, color='green',label='average') for section in enumerate(sections): plt.axvline(section[1], color='red') plt.axvline(start_fade_out, color='green') plt.axvline(end_fade_in, color='green') #plt.legend(('average')) h5.close() plt.show()
def get_loudness(track, h5=None): #returns #0: the average loudness of the track #1: the variance of the loudness over the track #2: the difference between the highest and lowest dB value between the end of fade in and start of fade out close = (h5 == None) if h5 == None: path = "../../msd_dense_subset/dense/" + track[2] + "/" + track[ 3] + "/" + track[4] + "/" + track + ".h5" h5 = GETTERS.open_h5_file_read(path) loudness_avg = GETTERS.get_loudness(h5) loudnesses_interval = GETTERS.get_segments_loudness_max(h5) start_segments = GETTERS.get_segments_start(h5) start_fade_out = GETTERS.get_start_of_fade_out(h5) end_fade_in = GETTERS.get_end_of_fade_in(h5) idx = np.where((start_segments > end_fade_in) & (start_segments < start_fade_out)) if close: h5.close() #return (loudness_avg, np.var(loudnesses_interval[idx]), min(loudnesses_interval[idx]), max(loudnesses_interval[idx])) return (loudness_avg, np.var(loudnesses_interval[idx]), abs( max(loudnesses_interval[idx]) - abs(min(loudnesses_interval[idx]))))
def get_feats(h5): f = [] f.append(hdf5_getters.get_artist_name(h5).decode('utf8').replace(',', '')) f.append(hdf5_getters.get_title(h5).decode('utf8').replace(',', '')) f.append(str(hdf5_getters.get_loudness(h5))) f.append(str(hdf5_getters.get_tempo(h5))) f.append(str(hdf5_getters.get_time_signature(h5))) f.append(str(hdf5_getters.get_key(h5))) f.append(str(hdf5_getters.get_mode(h5))) f.append(str(hdf5_getters.get_duration(h5))) f.extend(get_statistical_feats(hdf5_getters.get_segments_timbre(h5))) f.extend(get_statistical_feats(hdf5_getters.get_segments_pitches(h5))) f.extend(get_statistical_feats(hdf5_getters.get_segments_loudness_max(h5))) f.extend( get_statistical_feats(hdf5_getters.get_segments_loudness_max_time(h5))) f.extend( get_statistical_feats(hdf5_getters.get_segments_loudness_start(h5))) f.append(str(hdf5_getters.get_song_hotttnesss(h5))) f.append(str(hdf5_getters.get_danceability(h5))) f.append(str(hdf5_getters.get_end_of_fade_in(h5))) f.append(str(hdf5_getters.get_energy(h5))) f.append(str(hdf5_getters.get_start_of_fade_out(h5))) f.append(str(hdf5_getters.get_year(h5))) return f
def feat_from_file(path): """ Extract a list of features in an array, already converted to string """ feats = [] h5 = GETTERS.open_h5_file_read(path) # basic info feats.append(GETTERS.get_track_id(h5)) #feats.append( GETTERS.get_artist_name(h5).replace(',','') ) #feats.append( GETTERS.get_title(h5).replace(',','') ) feats.append(GETTERS.get_loudness(h5)) feats.append(GETTERS.get_tempo(h5)) feats.append(GETTERS.get_time_signature(h5)) feats.append(GETTERS.get_key(h5)) feats.append(GETTERS.get_mode(h5)) feats.append(GETTERS.get_duration(h5)) feats.append(GETTERS.get_hotnesss(h5)) segments_loudness = np.asarray(GETTERS.get_segments_loudness_max(h5)) max_segment_indice = np.argmax(segments_loudness) # timbre timbre = GETTERS.get_segments_timbre(h5) max_segment_timbre = timbre[max_segment_indice, :] avg_timbre = np.average(timbre, axis=0) for k in avg_timbre: feats.append(k) var_timbre = np.var(timbre, axis=0) for k in var_timbre: feats.append(k) for k in max_segment_timbre: feats.append(k) # done with h5 file h5.close() # makes sure we return strings feats = [str(x) for x in feats] return feats
def func_to_extract_features(filename): """ This function does 3 simple things: - open the song file - get artist ID and put it - close the file """ global cntnan global listfeatures cf = [] h5 = GETTERS.open_h5_file_read(filename) nanfound = 0 # Get target feature: song hotness # FEATURE 0 song_hotness = GETTERS.get_song_hotttnesss(h5) if math.isnan(song_hotness): nanfound = 1 cntnan = cntnan + 1 h5.close() return 0 elif song_hotness > 0.3 and song_hotness < 0.6: h5.close() return 0 else: cf.append(song_hotness) # FEATURE 1 # Get song loudness song_loudness = GETTERS.get_loudness(h5) if math.isnan(song_loudness): nanfound = 1 cntnan = cntnan + 1 else: cf.append(song_loudness) # FEATURE 2 # Get key of the song song_key = GETTERS.get_key(h5) if math.isnan(song_key): nanfound = 1 cntnan = cntnan + 1 else: cf.append(song_key) # FEATURE 3 # Get duration of the song song_duration = GETTERS.get_duration(h5) if math.isnan(song_duration): nanfound = 1 cntnan = cntnan + 1 else: cf.append(song_duration) # Feature 4 # Get song tempo song_tempo = GETTERS.get_tempo(h5) if math.isnan(song_tempo): nanfound = 1 cntnan = cntnan + 1 else: cf.append(song_tempo) # Feature 5: artist familarity artist_familiarity = GETTERS.get_artist_familiarity(h5) if math.isnan(artist_familiarity): nanfound = 1 cntnan = cntnan + 1 else: cf.append(artist_familiarity) # Feature 6: artist_hotness artist_hotness = GETTERS.get_artist_hotttnesss(h5) if math.isnan(artist_hotness): nanfound = 1 cntnan = cntnan + 1 else: cf.append(artist_hotness) # Feature 7 time signature time_signature = GETTERS.get_time_signature(h5) cf.append(time_signature) # Feature 8 # Loudness COV loudness_segments = np.array(GETTERS.get_segments_loudness_max(h5)) loudness_cov = abs(variation(loudness_segments)) if math.isnan(loudness_cov): nanfound = 1 cntnan = cntnan + 1 else: cf.append(loudness_cov) # Feature 9 # Beat COV beat_segments = np.array(GETTERS.get_beats_start(h5)) beat_cov = abs(variation(beat_segments)) if math.isnan(beat_cov): nanfound = 1 cntnan = cntnan + 1 else: cf.append(beat_cov) # Feature 10 # Year song_year = GETTERS.get_year(h5) if song_year == 0: nanfound = 1 cntnan = cntnan + 1 else: cf.append(song_year) if nanfound == 0: strlist = list_to_csv(cf) listfeatures.append(strlist) strtitle = GETTERS.get_title(h5) listtitle.append(strtitle) h5.close()
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 getData(starting_point): starting = starting_point * 10000 files = glob.glob('/mnt/snap/data/*/*/*/*.h5') file_one_round = files[starting:starting + 10000] artist_ids = [] song_beats_persecond = [] song_duration = [] song_end_fade_in = [] song_start_fade_out = [] song_key = [] song_loudness = [] song_segments_loudness_max = [] song_segments_loudness_min = [] song_segments_loudness_med = [] song_segments_loudness_time_max = [] song_segments_loudness_time_min = [] song_segments_loudness_time_med = [] song_mode = [] song_sections_start = [] song_pitches = [] song_timbre = [] song_tempo = [] song_time_signature = [] song_title = [] artist_name = [] year = [] idx = np.triu_indices(12) #count = 1 for f in file_one_round: h5 = HDF5.open_h5_file_read(f) songYear = g.get_year(h5) if songYear < 1990: continue artist_id = g.get_artist_id(h5) song_beat = (g.get_beats_start(h5)).tolist() songDuration = g.get_duration(h5) song_beat_persecond = float(len(song_beat)) / songDuration song_end_fadein = g.get_end_of_fade_in(h5) song_start_fadeout = g.get_start_of_fade_out(h5) songKey = g.get_key(h5) songLoudness = g.get_loudness(h5) song_loudness_max = (g.get_segments_loudness_max(h5)) // 10 song_loudness_antilog = np.power(10, song_loudness_max) song_segmentsLoudness_max = np.amax(song_loudness_antilog) song_segmentsLoudness_min = np.amin(song_loudness_antilog) song_segmentsLoudness_med = np.median(song_loudness_antilog) song_segmentsLoudness_max_time = ( g.get_segments_loudness_max_time(h5)).tolist() song_loudness_time = np.multiply(song_loudness_antilog, song_segmentsLoudness_max_time) song_segmentsLoudnessTime_max = np.amax(song_loudness_time) song_segmentsLoudnessTime_min = np.amin(song_loudness_time) song_segmentsLoudnessTime_med = np.median(song_loudness_time) songMode = g.get_mode(h5) song_sectionsStart = (g.get_sections_start(h5)).tolist() songPitches = g.get_segments_pitches(h5) songPitches_cov = np.cov(songPitches, rowvar=False) songPitches_mean = np.mean(songPitches, axis=0) #print(songPitches_cov.shape) songTimbre = g.get_segments_timbre(h5) songTimbre_cov = np.cov(songTimbre, rowvar=False) songTimbre_mean = np.mean(songTimbre, axis=0) #print(songTimbre_cov.shape) songTempo = g.get_tempo(h5) songTime_signature = g.get_time_signature(h5) songTitle = g.get_title(h5) artistName = g.get_artist_name(h5) artist_ids.append(artist_id) song_beats_persecond.append(song_beat_persecond) song_duration.append(songDuration) song_end_fade_in.append(song_end_fadein) song_start_fade_out.append(song_start_fadeout) song_key.append(songKey) song_loudness.append(songLoudness) song_segments_loudness_max.append(song_segmentsLoudness_max) song_segments_loudness_min.append(song_segmentsLoudness_min) song_segments_loudness_med.append(song_segmentsLoudness_med) song_segments_loudness_time_max.append(song_segmentsLoudnessTime_max) song_segments_loudness_time_min.append(song_segmentsLoudnessTime_min) song_segments_loudness_time_med.append(song_segmentsLoudnessTime_med) song_mode.append(songMode) song_sections_start.append(song_sectionsStart) pitches_mean_cov = (songPitches_cov[idx]).tolist() pitches_mean_cov.extend((songPitches_mean).tolist()) song_pitches.append(pitches_mean_cov) timbre_mean_cov = (songTimbre_cov[idx]).tolist() timbre_mean_cov.extend((songTimbre_mean).tolist()) song_timbre.append(timbre_mean_cov) song_tempo.append(songTempo) song_time_signature.append(songTime_signature) song_title.append(songTitle) artist_name.append(artistName) year.append(songYear) #print(count) #count = count + 1 h5.close() #def createDictsFrom2DArray(dictionary, colName, featureList): # for i in range(0,12): # dictionary[colName+str(i)] = featureList[i] #i = 1 #for t in itertools.izip_longest(*featureList): # dictionary[colName+str(i)] = t # i = i + 1 # return dictionary data = collections.OrderedDict() data['year'] = year data['artist_name'] = artist_name data['artist_id'] = artist_ids data['song_title'] = song_title data['song_beats_persecond'] = song_beats_persecond data['song_duration'] = song_duration data['song_end_fade_in'] = song_end_fade_in data['song_start_fade_out'] = song_start_fade_out data['song_key'] = song_key data['song_loudness'] = song_loudness data['song_loudness_max'] = song_segments_loudness_max data['song_loudness_min'] = song_segments_loudness_min data['song_loudness_med'] = song_segments_loudness_med data['song_loudness_time_max'] = song_segments_loudness_time_max data['song_loudness_time_min'] = song_segments_loudness_time_min data['song_loudness_time_med'] = song_segments_loudness_time_med data['song_mode'] = song_mode data['song_tempo'] = song_tempo data['song_time_signature'] = song_time_signature data = createDictsFrom1DArray(data, 'pitches', song_pitches) data = createDictsFrom1DArray(data, 'timbre', song_timbre) data = createDictsFrom1DArray(data, 'sections_start', song_sections_start) df = pd.DataFrame(data) print('before return ' + str(starting_point)) return df
def func_to_extract_features(filename): """ This function does 3 simple things: - open the song file - get artist ID and put it - close the file """ global cntnan global listfeatures cf = [] h5 = GETTERS.open_h5_file_read(filename) nanfound = 0 #Get target feature: song hotness #FEATURE 0 song_hotness = GETTERS.get_song_hotttnesss(h5) if math.isnan(song_hotness): nanfound = 1 cntnan = cntnan + 1 h5.close() return 0 elif song_hotness > 0.3 and song_hotness < 0.6: h5.close() return 0 else: if song_hotness <= 0.3: hotness_class = 0 elif song_hotness >= 0.6: hotness_class = 1 cf.append(hotness_class) #FEATURE 1 #Get song loudness song_loudness = GETTERS.get_loudness(h5) if math.isnan(song_loudness): nanfound = 1 cntnan = cntnan + 1 else: #cf.append(song_loudness) pass #FEATURE 2 #Get key of the song song_key = GETTERS.get_key(h5) if math.isnan(song_key): nanfound = 1 cntnan = cntnan + 1 else: # cf.append(song_key) pass #FEATURE 3 song_duration = GETTERS.get_duration(h5) if math.isnan(song_duration): nanfound = 1 cntnan = cntnan + 1 else: # cf.append(song_duration) pass #Feature 4 #Get song tempo song_tempo = GETTERS.get_tempo(h5) if math.isnan(song_tempo): nanfound = 1 cntnan = cntnan + 1 else: # cf.append(song_tempo) pass #Feature 5: artist familarity artist_familiarity = GETTERS.get_artist_familiarity(h5) if math.isnan(artist_familiarity): nanfound = 1 cntnan = cntnan + 1 else: # cf.append(artist_familiarity) pass #Feature 6: artist_hotness artist_hotness = GETTERS.get_artist_hotttnesss(h5) if math.isnan(artist_hotness): nanfound = 1 cntnan = cntnan + 1 else: # cf.append(artist_hotness) pass #Feature 7 time signature time_signature = GETTERS.get_time_signature(h5) # cf.append(time_signature) #Feature 8 #Loudness COV loudness_segments = np.array(GETTERS.get_segments_loudness_max(h5)) loudness_cov = abs(variation(loudness_segments)) if math.isnan(loudness_cov): nanfound = 1 cntnan = cntnan + 1 else: # cf.append(loudness_cov) pass #Feature 9 #Beat COV beat_segments = np.array(GETTERS.get_beats_start(h5)) beat_cov = abs(variation(beat_segments)) if math.isnan(beat_cov): nanfound = 1 cntnan = cntnan + 1 else: # cf.append(beat_cov) pass #Feature 10 #Year song_year = GETTERS.get_year(h5) if song_year == 0: nanfound = 1 cntnan = cntnan + 1 else: # cf.append(song_year) pass title = GETTERS.get_title(h5) if title in energydict: audio_summary = energydict[title] energy = audio_summary['energy'] danceability = audio_summary['danceability'] speechiness = audio_summary['speechiness'] liveness = audio_summary['liveness'] else: stitle = re.sub(r'\([^)]*\)','', title) if stitle in energydict: audio_summary = energydict[stitle] energy = audio_summary['energy'] danceability = audio_summary['danceability'] speechiness = audio_summary['speechiness'] liveness = audio_summary['liveness'] else: energy = 0.0 danceability = 0.0 speechiness = 0.0 liveness = 0.0 # Feature 11 cf.append(energy) # Feature 12 # cf.append(danceability) # Feature 13 # cf.append(speechiness) # Feature 14 # cf.append(liveness) if nanfound == 0: strlist = list_to_csv(cf) listfeatures.append(strlist) h5.close()
def classify(h5): output_array={} # duration duration=hdf5_getters.get_duration(h5) output_array["duration"]=duration ### ADDED VALUE TO ARRAY # number of bars bars=hdf5_getters.get_bars_start(h5) num_bars=len(bars) output_array["num_bars"]=num_bars ### ADDED VALUE TO ARRAY # mean and variance in bar length bar_length=numpy.ediff1d(bars) variance_bar_length=numpy.var(bar_length) output_array["variance_bar_length"]=variance_bar_length ### ADDED VALUE TO ARRAY # number of beats beats=hdf5_getters.get_beats_start(h5) num_beats=len(beats) output_array["num_beats"]=num_beats ### ADDED VALUE TO ARRAY # mean and variance in beats length beats_length=numpy.ediff1d(beats) variance_beats_length=numpy.var(bar_length) output_array["variance_beats_length"]=variance_beats_length ### ADDED VALUE TO ARRAY # danceability danceability=hdf5_getters.get_danceability(h5) output_array["danceability"]=danceability ### ADDED VALUE TO ARRAY # end of fade in end_of_fade_in=hdf5_getters.get_end_of_fade_in(h5) output_array["end_of_fade_in"]=end_of_fade_in ### ADDED VALUE TO ARRAY # energy energy=hdf5_getters.get_energy(h5) output_array["energy"]=energy ### ADDED VALUE TO ARRAY # key key=hdf5_getters.get_key(h5) output_array["key"]=int(key) ### ADDED VALUE TO ARRAY # loudness loudness=hdf5_getters.get_loudness(h5) output_array["loudness"]=loudness ### ADDED VALUE TO ARRAY # mode mode=hdf5_getters.get_mode(h5) output_array["mode"]=int(mode) ### ADDED VALUE TO ARRAY # number sections sections=hdf5_getters.get_sections_start(h5) num_sections=len(sections) output_array["num_sections"]=num_sections ### ADDED VALUE TO ARRAY # mean and variance in sections length sections_length=numpy.ediff1d(sections) variance_sections_length=numpy.var(sections) output_array["variance_sections_length"]=variance_sections_length ### ADDED VALUE TO ARRAY # number segments segments=hdf5_getters.get_segments_start(h5) num_segments=len(segments) output_array["num_segments"]=num_segments ### ADDED VALUE TO ARRAY # mean and variance in segments length segments_length=numpy.ediff1d(segments) variance_segments_length=numpy.var(segments) output_array["variance_segments_length"]=variance_segments_length ### ADDED VALUE TO ARRAY # segment loudness max segment_loudness_max_array=hdf5_getters.get_segments_loudness_max(h5) segment_loudness_max_time_array=hdf5_getters.get_segments_loudness_max_time(h5) segment_loudness_max_index=0 for i in range(len(segment_loudness_max_array)): if segment_loudness_max_array[i]>segment_loudness_max_array[segment_loudness_max_index]: segment_loudness_max_index=i segment_loudness_max=segment_loudness_max_array[segment_loudness_max_index] segment_loudness_max_time=segment_loudness_max_time_array[segment_loudness_max_index] output_array["segment_loudness_max"]=segment_loudness_max ### ADDED VALUE TO ARRAY output_array["segment_loudness_time"]=segment_loudness_max_time ### ADDED VALUE TO ARRAY # POSSIBLE TODO: use average function instead and weight by segment length # segment loudness mean (start) segment_loudness_array=hdf5_getters.get_segments_loudness_start(h5) segment_loudness_mean=numpy.mean(segment_loudness_array) output_array["segment_loudness_mean"]=segment_loudness_mean ### ADDED VALUE TO ARRAY # segment loudness variance (start) segment_loudness_variance=numpy.var(segment_loudness_array) output_array["segment_loudness_variance"]=segment_loudness_variance ### ADDED VALUE TO ARRAY # segment pitches segment_pitches_array=hdf5_getters.get_segments_pitches(h5) segment_pitches_mean=numpy.mean(segment_pitches_array,axis=0).tolist() output_array["segment_pitches_mean"]=segment_pitches_mean # segment pitches variance (start) segment_pitches_variance=numpy.var(segment_pitches_array,axis=0).tolist() output_array["segment_pitches_variance"]=segment_pitches_variance # segment timbres segment_timbres_array=hdf5_getters.get_segments_timbre(h5) segment_timbres_mean=numpy.mean(segment_timbres_array,axis=0).tolist() output_array["segment_timbres_mean"]=segment_timbres_mean # segment timbres variance (start) segment_timbres_variance=numpy.var(segment_timbres_array,axis=0).tolist() output_array["segment_timbres_variance"]=segment_timbres_variance # hotttnesss hottness=hdf5_getters.get_song_hotttnesss(h5,0) output_array["hottness"]=hottness ### ADDED VALUE TO ARRAY # duration-start of fade out start_of_fade_out=hdf5_getters.get_start_of_fade_out(h5) fade_out=duration-start_of_fade_out output_array["fade_out"]=fade_out ### ADDED VALUE TO ARRAY # tatums tatums=hdf5_getters.get_tatums_start(h5) num_tatums=len(tatums) output_array["num_tatums"]=num_tatums ### ADDED VALUE TO ARRAY # mean and variance in tatums length tatums_length=numpy.ediff1d(tatums) variance_tatums_length=numpy.var(tatums_length) output_array["variance_tatums_length"]=variance_tatums_length ### ADDED VALUE TO ARRAY # tempo tempo=hdf5_getters.get_tempo(h5) output_array["tempo"]=tempo ### ADDED VALUE TO ARRAY # time signature time_signature=hdf5_getters.get_time_signature(h5) output_array["time_signature"]=int(time_signature) ### ADDED VALUE TO ARRAY # year year=hdf5_getters.get_year(h5) output_array["year"]=int(year) ### ADDED VALUE TO ARRAY # artist terms artist_terms=hdf5_getters.get_artist_terms(h5,0) output_array["artist_terms"]=artist_terms.tolist() artist_terms_freq=hdf5_getters.get_artist_terms_freq(h5,0) output_array["artist_terms_freq"]=artist_terms_freq.tolist() artist_name=hdf5_getters.get_artist_name(h5,0) output_array["artist_name"]=artist_name artist_id=hdf5_getters.get_artist_id(h5,0) output_array["artist_id"]=artist_id # title title=hdf5_getters.get_title(h5,0) output_array["title"]=title return output_array
def 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
# Retrieve features from HDF5 danceability = hdf5_getters.get_danceability(h5) duration = hdf5_getters.get_duration(h5) time_of_fade_in = hdf5_getters.get_end_of_fade_in(h5) energy = hdf5_getters.get_energy(h5) key = hdf5_getters.get_key(h5) key_confidence = hdf5_getters.get_key_confidence(h5) loudness = hdf5_getters.get_loudness(h5) mode = hdf5_getters.get_mode(h5) mode_confidence = hdf5_getters.get_mode_confidence(h5) sections_start = hdf5_getters.get_sections_start(h5) num_sections = len(sections_start) if num_sections == 0: h5.close() continue segments_loudness_max = hdf5_getters.get_segments_loudness_max(h5) segments_loudness_start = hdf5_getters.get_segments_loudness_start(h5) num_segments = len(hdf5_getters.get_segments_start(h5)) num_tatums = len(hdf5_getters.get_tatums_start(h5)) time_of_fade_out = duration - hdf5_getters.get_start_of_fade_out(h5) tempo = hdf5_getters.get_tempo(h5) time_signature = hdf5_getters.get_time_signature(h5) time_signature_confidence = hdf5_getters.get_time_signature_confidence(h5) found_tracks += 1 # Append all features to an array features = [] features.append(track_id) features.append(danceability) features.append(duration)
song.trackID = remove_trap_characters( str(hdf5_getters.get_track_id(songH5File))) temp = hdf5_getters.get_segments_start(songH5File) song.segmentsStart = remove_trap_characters(str(list(temp))) song.segmentsCount = get_list_length(temp) song.segmentsConfidence = \ remove_trap_characters(str(list(hdf5_getters.get_segments_confidence(songH5File)))) # song.segmentsPitches = remove_trap_characters(str(list(hdf5_getters.get_segments_pitches(songH5File)))) song.segmentsPitches = \ remove_trap_characters(str(parse_nested_list(hdf5_getters.get_segments_pitches(songH5File)))) # song.segmentsTimbre = remove_trap_characters(str(list(hdf5_getters.get_segments_timbre(songH5File)))) song.segmentsTimbre = \ remove_trap_characters(str(parse_nested_list(hdf5_getters.get_segments_timbre(songH5File)))) song.segmentsLoudnessMax = \ remove_trap_characters(str(list(hdf5_getters.get_segments_loudness_max(songH5File)))) song.segmentsLoudnessMaxTime = \ remove_trap_characters(str(list(hdf5_getters.get_segments_loudness_max_time(songH5File)))) song.segmentsLoudnessStart = \ remove_trap_characters(str(list(hdf5_getters.get_segments_loudness_start(songH5File)))) temp = hdf5_getters.get_sections_start(songH5File) song.sectionStarts = remove_trap_characters(str(list(temp))) song.sectionCount = get_list_length(temp) song.sectionsConfidence = remove_trap_characters( str(list(hdf5_getters.get_sections_confidence(songH5File)))) temp = hdf5_getters.get_beats_start(songH5File) song.beatsStart = remove_trap_characters(str(list(temp))) song.beatsCount = get_list_length(temp) song.beatsConfidence = remove_trap_characters(
def main(): dataset_dir = sys.argv[1] feat =[] feat1=[] feat2=[] feat3=[] feat4=[] print "Forming Dataset..." listing1 = os.listdir(dataset_dir) for a in listing1: listing2 = os.listdir(dataset_dir+a+'/') for b in listing2: listing3 = os.listdir(dataset_dir+a+'/'+b+'/') for c in listing3: listing4 = os.listdir(dataset_dir+a+'/'+b+'/'+c+'/') for d in listing4: h5 = hdf5_getters.open_h5_file_read(dataset_dir+a+'/'+b+'/'+c+'/'+d) feat =[] feat1=[] feat2=[] feat3=[] feat4=[] temp = hdf5_getters.get_artist_hotttnesss(h5) if (math.isnan(temp) or temp==0.0): h5.close() continue temp = hdf5_getters.get_artist_familiarity(h5) if (math.isnan(temp) or temp==0.0): h5.close() continue temp = hdf5_getters.get_end_of_fade_in(h5) if (math.isnan(temp)): h5.close() continue temp = hdf5_getters.get_key_confidence(h5) if (math.isnan(temp)): h5.close() continue temp = hdf5_getters.get_loudness(h5) if (math.isnan(temp)): h5.close() continue temp = hdf5_getters.get_mode_confidence(h5) if (math.isnan(temp)): h5.close() continue temp = hdf5_getters.get_sections_confidence(h5) if temp.size == 0: h5.close() continue temp = hdf5_getters.get_segments_confidence(h5) if temp.size == 0: h5.close() continue temp = hdf5_getters.get_segments_loudness_max(h5) if temp.size == 0: h5.close() continue temp = hdf5_getters.get_segments_loudness_max_time(h5) if temp.size == 0: h5.close() continue temp = hdf5_getters.get_segments_pitches(h5) if temp.size == 0: h5.close() continue temp = hdf5_getters.get_segments_timbre(h5) if temp.size == 0: h5.close() continue temp = hdf5_getters.get_start_of_fade_out(h5) if (math.isnan(temp)): h5.close() continue temp = hdf5_getters.get_tempo(h5) if (math.isnan(temp)): h5.close() continue temp = hdf5_getters.get_time_signature_confidence(h5) if (math.isnan(temp)): h5.close() continue temp = hdf5_getters.get_year(h5) if temp == 0: h5.close() continue temp = hdf5_getters.get_artist_terms(h5) if temp.size == 0: h5.close() continue temp_ = hdf5_getters.get_artist_terms_weight(h5) if temp_.size == 0: continue temp = hdf5_getters.get_bars_confidence(h5) sz = temp.size if sz<50: h5.close() continue temp = hdf5_getters.get_beats_confidence(h5) sz = temp.size if sz <50: h5.close() continue mm = np.mean(temp) vv = np.var(temp) if mm==0.0 and vv==0.0: h5.close() continue temp = hdf5_getters.get_segments_confidence(h5) sz = temp.size if sz <50: h5.close() continue temp = hdf5_getters.get_tatums_confidence(h5) sz = temp.size if sz <50: h5.close() continue temp = hdf5_getters.get_song_hotttnesss(h5) if (math.isnan(temp) or temp==0.0): h5.close() continue temp = hdf5_getters.get_bars_confidence(h5) sz = temp.size sz1 = sz/50 i=1 j=0 while i<=50: if i == 50: sz2 = sz else: sz2 = i*sz1 num=0.0 acc = 0 while j<sz2: acc += temp[j] j+=1 num+=1.0 mm = acc/num feat1.append(mm) i+=1 temp = hdf5_getters.get_beats_confidence(h5) sz = temp.size sz1 = sz/50 i=1 j=0 while i<=50: if i == 50: sz2 = sz else: sz2 = i*sz1 num=0.0 acc = 0 while j<sz2: acc += temp[j] j+=1 num+=1.0 mm = acc/num feat2.append(mm) i+=1 temp = hdf5_getters.get_segments_confidence(h5) sz = temp.size sz1 = sz/50 i=1 j=0 while i<=50: if i == 50: sz2 = sz else: sz2 = i*sz1 num=0.0 acc = 0 while j<sz2: acc += temp[j] j+=1 num+=1.0 mm = acc/num feat3.append(mm) i+=1 temp = hdf5_getters.get_tatums_confidence(h5) sz = temp.size sz1 = sz/50 i=1 j=0 while i<=50: if i == 50: sz2 = sz else: sz2 = i*sz1 num=0.0 acc = 0 while j<sz2: acc += temp[j] j+=1 num+=1.0 mm = acc/num feat4.append(mm) i+=1 i=0 avg = 0.0 while i<50: avg = (feat1[i] + feat2[i] + feat3[i] + feat4[i])/4.0 feat.append(avg) i++ temp = hdf5_getters.get_song_hotttnesss(h5) hott = 0 if temp >=0.75: hott = 1 elif temp >=0.40 and temp <0.75: hott = 2 else: hott = 3 feat.append(hott) h5.close() count = 1 f=open('MSD_DATASET_LSTM.txt', 'a') outstring='' cnt = 0 feat_size = len(feat) for i in feat: cnt+=1 outstring+=str(i) if (cnt!=feat_size): outstring+=',' outstring+='\n' f.write(outstring) f.close()
def main(argv): if len(argv) != 1: print "Specify data directory" return basedir = argv[0] outputFile1 = open('SongCSV.csv', 'w') outputFile2 = open('TagsCSV.csv', 'w') csvRowString = "" csvLabelString = "" ################################################# #if you want to prompt the user for the order of attributes in the csv, #leave the prompt boolean set to True #else, set 'prompt' to False and set the order of attributes in the 'else' #clause prompt = False ################################################# if prompt == True: while prompt: prompt = False csvAttributeString = raw_input("\n\nIn what order would you like the colums of the CSV file?\n" + "Please delineate with commas. The options are: " + "AlbumName, AlbumID, ArtistID, ArtistLatitude, ArtistLocation, ArtistLongitude,"+ " ArtistName, Danceability, Duration, KeySignature, KeySignatureConfidence, Tempo," + " SongID, TimeSignature, TimeSignatureConfidence, Title, and Year.\n\n" + "For example, you may write \"Title, Tempo, Duration\"...\n\n" + "...or exit by typing 'exit'.\n\n") csvAttributeList = re.split('\W+', csvAttributeString) for i, v in enumerate(csvAttributeList): csvAttributeList[i] = csvAttributeList[i].lower() for attribute in csvAttributeList: # print "Here is the attribute: " + attribute + " \n" if attribute == 'AlbumID'.lower(): csvRowString += 'AlbumID' elif attribute == 'AlbumName'.lower(): csvRowString += 'AlbumName' elif attribute == 'ArtistID'.lower(): csvRowString += 'ArtistID' elif attribute == 'ArtistLatitude'.lower(): csvRowString += 'ArtistLatitude' elif attribute == 'ArtistLocation'.lower(): csvRowString += 'ArtistLocation' elif attribute == 'ArtistLongitude'.lower(): csvRowString += 'ArtistLongitude' elif attribute == 'ArtistName'.lower(): csvRowString += 'ArtistName' elif attribute == 'Danceability'.lower(): csvRowString += 'Danceability' elif attribute == 'Duration'.lower(): csvRowString += 'Duration' elif attribute == 'KeySignature'.lower(): csvRowString += 'KeySignature' elif attribute == 'KeySignatureConfidence'.lower(): csvRowString += 'KeySignatureConfidence' elif attribute == 'SongID'.lower(): csvRowString += "SongID" elif attribute == 'Tempo'.lower(): csvRowString += 'Tempo' elif attribute == 'TimeSignature'.lower(): csvRowString += 'TimeSignature' elif attribute == 'TimeSignatureConfidence'.lower(): csvRowString += 'TimeSignatureConfidence' elif attribute == 'Title'.lower(): csvRowString += 'Title' elif attribute == 'Year'.lower(): csvRowString += 'Year' elif attribute == 'Exit'.lower(): sys.exit() else: prompt = True print "==============" print "I believe there has been an error with the input." print "==============" break csvRowString += "," lastIndex = len(csvRowString) csvRowString = csvRowString[0:lastIndex-1] csvRowString += "\n" outputFile1.write(csvRowString); csvRowString = "" #else, if you want to hard code the order of the csv file and not prompt #the user, else: ################################################# #change the order of the csv file here #Default is to list all available attributes (in alphabetical order) #csvRowString = ("SongID,AlbumID,AlbumName,ArtistID,ArtistLatitude,ArtistLocation,"+ # "ArtistLongitude,ArtistName,Danceability,Duration,KeySignature,"+ # "KeySignatureConfidence,Tempo,TimeSignature,TimeSignatureConfidence,"+ # "Title,Year") csvRowString = ("ArtistFamiliarity,ArtistHotttnesss,"+ "BarsConfidence,BarsStart,BeatsConfidence,BeatsStart,Duration,"+ "EndOfFadeIn,Key,KeyConfidence,Loudness,Mode,ModeConfidence,"+ "SectionsConfidence,SectionsStart,SegmentsConfidence,SegmentsLoudnessMax,"+ "SegmentsLoudnessMaxTime,SegmentsLoudnessStart,SegmentsStart,"+ "SongHotttnesss,StartOfFadeOut,TatumsConfidence,TatumsStart,Tempo,TimeSignature,TimeSignatureConfidence,"+ "SegmentsPitches,SegmentsTimbre,Title,Year,Decade,ArtistMbtags") ################################################# header = str() csvAttributeList = re.split('\W+', csvRowString) arrayAttributes = ["BarsConfidence","BarsStart","BeatsConfidence","BeatsStart", "SectionsConfidence","SectionsStart","SegmentsConfidence","SegmentsLoudnessMax", "SegmentsLoudnessMaxTime","SegmentsLoudnessStart","SegmentsStart", "TatumsConfidence","TatumsStart"] for i, v in enumerate(csvAttributeList): csvAttributeList[i] = csvAttributeList[i].lower() if(v=="SegmentsPitches"): for i in range(90): header = header + "SegmentsPitches" + str(i) + "," elif(v=="SegmentsTimbre"): for i in range(90): header = header + "SegmentsTimbre" + str(i) + "," elif(v in arrayAttributes): header = header + v + str(0) + "," header = header + v + str(1) + "," else: header = header + v + "," outputFile1.write("SongNumber,"); #outputFile1.write(csvRowString + "\n"); outputFile1.write(header + "\n"); csvRowString = "" ################################################# #Set the basedir here, the root directory from which the search #for files stored in a (hierarchical data structure) will originate #basedir = "MillionSongSubset/data/A/A/" # "." As the default means the current directory ext = ".h5" #Set the extension here. H5 is the extension for HDF5 files. ################################################# #FOR LOOP all = sorted(os.walk(basedir)) for root, dirs, files in all: files = sorted(glob.glob(os.path.join(root,'*'+ext))) for f in files: print f songH5File = hdf5_getters.open_h5_file_read(f) song = Song(str(hdf5_getters.get_song_id(songH5File))) #testDanceability = hdf5_getters.get_danceability(songH5File) # print type(testDanceability) # print ("Here is the danceability: ") + str(testDanceability) song.analysisSampleRate = str(hdf5_getters.get_analysis_sample_rate(songH5File)) song.artistFamiliarity = str(hdf5_getters.get_artist_familiarity(songH5File)) song.artistHotttnesss = str(hdf5_getters.get_artist_hotttnesss(songH5File)) song.artistLatitude = str(hdf5_getters.get_artist_latitude(songH5File)) song.artistLongitude = str(hdf5_getters.get_artist_longitude(songH5File)) song.artistMbid = str(hdf5_getters.get_artist_mbid(songH5File)) song.barsConfidence = np.array(hdf5_getters.get_bars_confidence(songH5File)) song.barsStart = np.array(hdf5_getters.get_bars_start(songH5File)) song.beatsConfidence = np.array(hdf5_getters.get_beats_confidence(songH5File)) song.beatsStart = np.array(hdf5_getters.get_beats_start(songH5File)) song.danceability = str(hdf5_getters.get_danceability(songH5File)) song.duration = str(hdf5_getters.get_duration(songH5File)) song.endOfFadeIn = str(hdf5_getters.get_end_of_fade_in(songH5File)) song.energy = str(hdf5_getters.get_energy(songH5File)) song.key = str(hdf5_getters.get_key(songH5File)) song.keyConfidence = str(hdf5_getters.get_key_confidence(songH5File)) song.loudness = str(hdf5_getters.get_loudness(songH5File)) song.mode = str(hdf5_getters.get_mode(songH5File)) song.modeConfidence = str(hdf5_getters.get_mode_confidence(songH5File)) song.sectionsConfidence = np.array(hdf5_getters.get_sections_confidence(songH5File)) song.sectionsStart = np.array(hdf5_getters.get_sections_start(songH5File)) song.segmentsConfidence = np.array(hdf5_getters.get_segments_confidence(songH5File)) song.segmentsLoudnessMax = np.array(hdf5_getters.get_segments_loudness_max(songH5File)) song.segmentsLoudnessMaxTime = np.array(hdf5_getters.get_segments_loudness_max_time(songH5File)) song.segmentsLoudnessStart = np.array(hdf5_getters.get_segments_loudness_start(songH5File)) song.segmentsPitches = np.array(hdf5_getters.get_segments_pitches(songH5File)) song.segmentsStart = np.array(hdf5_getters.get_segments_start(songH5File)) song.segmentsTimbre = np.array(hdf5_getters.get_segments_timbre(songH5File)) song.songHotttnesss = str(hdf5_getters.get_song_hotttnesss(songH5File)) song.startOfFadeOut = str(hdf5_getters.get_start_of_fade_out(songH5File)) song.tatumsConfidence = np.array(hdf5_getters.get_tatums_confidence(songH5File)) song.tatumsStart = np.array(hdf5_getters.get_tatums_start(songH5File)) song.tempo = str(hdf5_getters.get_tempo(songH5File)) song.timeSignature = str(hdf5_getters.get_time_signature(songH5File)) song.timeSignatureConfidence = str(hdf5_getters.get_time_signature_confidence(songH5File)) song.songid = str(hdf5_getters.get_song_id(songH5File)) song.title = str(hdf5_getters.get_title(songH5File)) song.year = str(hdf5_getters.get_year(songH5File)) song.artistMbtags = str(hdf5_getters.get_artist_mbtags(songH5File)) #print song count csvRowString += str(song.songCount) + "," csvLabelString += str(song.songCount) + "," for attribute in csvAttributeList: # print "Here is the attribute: " + attribute + " \n" if attribute == 'AnalysisSampleRate'.lower(): csvRowString += song.analysisSampleRate elif attribute == 'ArtistFamiliarity'.lower(): csvRowString += song.artistFamiliarity elif attribute == 'ArtistHotttnesss'.lower(): csvRowString += song.artistHotttnesss elif attribute == 'ArtistLatitude'.lower(): latitude = song.artistLatitude if latitude == 'nan': latitude = '' csvRowString += latitude elif attribute == 'ArtistLongitude'.lower(): longitude = song.artistLongitude if longitude == 'nan': longitude = '' csvRowString += longitude elif attribute == 'ArtistMbid'.lower(): csvRowString += song.artistMbid elif attribute == 'BarsConfidence'.lower(): arr = song.barsConfidence if arr.shape[0] == 0: arrmean = '' arrnorm = '' else: arrmean = np.mean(arr) arrnorm = np.linalg.norm(arr) csvRowString += str(arrmean) + ',' + str(arrnorm) elif attribute == 'BarsStart'.lower(): arr = song.barsStart if arr.shape[0] == 0: arrmean = '' arrnorm = '' else: arrmean = np.mean(arr) arrnorm = np.linalg.norm(arr) csvRowString += str(arrmean) + ',' + str(arrnorm) elif attribute == 'BeatsConfidence'.lower(): arr = song.beatsConfidence if arr.shape[0] == 0: arrmean = '' arrnorm = '' else: arrmean = np.mean(arr) arrnorm = np.linalg.norm(arr) csvRowString += str(arrmean) + ',' + str(arrnorm) elif attribute == 'BeatsStart'.lower(): arr = song.beatsStart if arr.shape[0] == 0: arrmean = '' arrnorm = '' else: arrmean = np.mean(arr) arrnorm = np.linalg.norm(arr) csvRowString += str(arrmean) + ',' + str(arrnorm) elif attribute == 'Danceability'.lower(): csvRowString += song.danceability elif attribute == 'Duration'.lower(): csvRowString += song.duration elif attribute == 'EndOfFadeIn'.lower(): csvRowString += song.endOfFadeIn elif attribute == 'Energy'.lower(): csvRowString += song.energy elif attribute == 'Key'.lower(): csvRowString += song.key elif attribute == 'KeyConfidence'.lower(): csvRowString += song.keyConfidence elif attribute == 'Loudness'.lower(): csvRowString += song.loudness elif attribute == 'Mode'.lower(): csvRowString += song.mode elif attribute == 'ModeConfidence'.lower(): csvRowString += song.modeConfidence elif attribute == 'SectionsConfidence'.lower(): arr = song.sectionsConfidence if arr.shape[0] == 0: arrmean = '' arrnorm = '' else: arrmean = np.mean(arr) arrnorm = np.linalg.norm(arr) csvRowString += str(arrmean) + ',' + str(arrnorm) elif attribute == 'SectionsStart'.lower(): arr = song.sectionsStart if arr.shape[0] == 0: arrmean = '' arrnorm = '' else: arrmean = np.mean(arr) arrnorm = np.linalg.norm(arr) csvRowString += str(arrmean) + ',' + str(arrnorm) elif attribute == 'SegmentsConfidence'.lower(): arr = song.segmentsConfidence if arr.shape[0] == 0: arrmean = '' arrnorm = '' else: arrmean = np.mean(arr) arrnorm = np.linalg.norm(arr) csvRowString += str(arrmean) + ',' + str(arrnorm) elif attribute == 'SegmentsLoudnessMax'.lower(): arr = song.segmentsLoudnessMax if arr.shape[0] == 0: arrmean = '' arrnorm = '' else: arrmean = np.mean(arr) arrnorm = np.linalg.norm(arr) csvRowString += str(arrmean) + ',' + str(arrnorm) elif attribute == 'SegmentsLoudnessMaxTime'.lower(): arr = song.segmentsLoudnessMaxTime if arr.shape[0] == 0: arrmean = '' arrnorm = '' else: arrmean = np.mean(arr) arrnorm = np.linalg.norm(arr) csvRowString += str(arrmean) + ',' + str(arrnorm) elif attribute == 'SegmentsLoudnessStart'.lower(): arr = song.segmentsLoudnessStart if arr.shape[0] == 0: arrmean = '' arrnorm = '' else: arrmean = np.mean(arr) arrnorm = np.linalg.norm(arr) csvRowString += str(arrmean) + ',' + str(arrnorm) elif attribute == 'SegmentsStart'.lower(): arr = song.segmentsStart if arr.shape[0] == 0: arrmean = '' arrnorm = '' else: arrmean = np.mean(arr) arrnorm = np.linalg.norm(arr) csvRowString += str(arrmean) + ',' + str(arrnorm) elif attribute == 'SongHotttnesss'.lower(): hotttnesss = song.songHotttnesss if hotttnesss == 'nan': hotttnesss = 'NaN' csvRowString += hotttnesss elif attribute == 'StartOfFadeOut'.lower(): csvRowString += song.startOfFadeOut elif attribute == 'TatumsConfidence'.lower(): arr = song.tatumsConfidence if arr.shape[0] == 0: arrmean = '' arrnorm = '' else: arrmean = np.mean(arr) arrnorm = np.linalg.norm(arr) csvRowString += str(arrmean) + ',' + str(arrnorm) elif attribute == 'TatumsStart'.lower(): arr = song.tatumsStart if arr.shape[0] == 0: arrmean = '' arrnorm = '' else: arrmean = np.mean(arr) arrnorm = np.linalg.norm(arr) csvRowString += str(arrmean) + ',' + str(arrnorm) elif attribute == 'Tempo'.lower(): # print "Tempo: " + song.tempo csvRowString += song.tempo elif attribute == 'TimeSignature'.lower(): csvRowString += song.timeSignature elif attribute == 'TimeSignatureConfidence'.lower(): # print "time sig conf: " + song.timeSignatureConfidence csvRowString += song.timeSignatureConfidence elif attribute == 'SegmentsPitches'.lower(): colmean = np.mean(song.segmentsPitches,axis=0) for m in colmean: csvRowString += str(m) + "," cov = np.dot(song.segmentsPitches.T,song.segmentsPitches) utriind = np.triu_indices(cov.shape[0]) feats = cov[utriind] for feat in feats: csvRowString += str(feat) + "," lastIndex = len(csvRowString) csvRowString = csvRowString[0:lastIndex-1] elif attribute == 'SegmentsTimbre'.lower(): colmean = np.mean(song.segmentsTimbre,axis=0) for m in colmean: csvRowString += str(m) + "," cov = np.dot(song.segmentsTimbre.T,song.segmentsTimbre) utriind = np.triu_indices(cov.shape[0]) feats = cov[utriind] for feat in feats: csvRowString += str(feat) + "," lastIndex = len(csvRowString) csvRowString = csvRowString[0:lastIndex-1] elif attribute == 'SongID'.lower(): csvRowString += "\"" + song.id + "\"" elif attribute == 'Title'.lower(): csvRowString += "\"" + song.title + "\"" elif attribute == 'Year'.lower(): csvRowString += song.year elif attribute == 'Decade'.lower(): yr = song.year if yr > 0: decade = song.year[:-1] + '0' else: decade = '0' csvRowString += decade elif attribute == 'ArtistMbtags'.lower(): tags = song.artistMbtags[1:-1] tags = "\"" + tags + "\"" tags = tags.replace("\n",'') csvRowString += tags tagsarray = shlex.split(tags) for t in tagsarray: csvLabelString += t + "," else: csvRowString += "Erm. This didn't work. Error. :( :(\n" csvRowString += "," ''' if attribute == 'AlbumID'.lower(): csvRowString += song.albumID elif attribute == 'AlbumName'.lower(): albumName = song.albumName albumName = albumName.replace(',',"") csvRowString += "\"" + albumName + "\"" elif attribute == 'ArtistID'.lower(): csvRowString += "\"" + song.artistID + "\"" elif attribute == 'ArtistLatitude'.lower(): latitude = song.artistLatitude if latitude == 'nan': latitude = '' csvRowString += latitude elif attribute == 'ArtistLocation'.lower(): location = song.artistLocation location = location.replace(',','') csvRowString += "\"" + location + "\"" elif attribute == 'ArtistLongitude'.lower(): longitude = song.artistLongitude if longitude == 'nan': longitude = '' csvRowString += longitude elif attribute == 'ArtistName'.lower(): csvRowString += "\"" + song.artistName + "\"" elif attribute == 'Danceability'.lower(): csvRowString += song.danceability elif attribute == 'Duration'.lower(): csvRowString += song.duration elif attribute == 'KeySignature'.lower(): csvRowString += song.keySignature elif attribute == 'KeySignatureConfidence'.lower(): # print "key sig conf: " + song.timeSignatureConfidence csvRowString += song.keySignatureConfidence elif attribute == 'SongID'.lower(): csvRowString += "\"" + song.id + "\"" elif attribute == 'Tempo'.lower(): # print "Tempo: " + song.tempo csvRowString += song.tempo elif attribute == 'TimeSignature'.lower(): csvRowString += song.timeSignature elif attribute == 'TimeSignatureConfidence'.lower(): # print "time sig conf: " + song.timeSignatureConfidence csvRowString += song.timeSignatureConfidence elif attribute == 'Title'.lower(): csvRowString += "\"" + song.title + "\"" elif attribute == 'Year'.lower(): csvRowString += song.year else: csvRowString += "Erm. This didn't work. Error. :( :(\n" csvRowString += "," ''' #Remove the final comma from each row in the csv lastIndex = len(csvRowString) csvRowString = csvRowString[0:lastIndex-1] csvRowString += "\n" outputFile1.write(csvRowString) csvRowString = "" lastIndex = len(csvLabelString) csvLabelString = csvLabelString[0:lastIndex-1] csvLabelString += "\n" outputFile2.write(csvLabelString) csvLabelString = "" songH5File.close() outputFile1.close() outputFile2.close()
def expe_1_synth_from_same_sample(): input_dir = '/sons/rwc/Learn/' output_dir = '/sons/rwc/Learn/hdf5/' audiofile = input_dir + 'rwc-g-m01_1.wav' h5file = output_dir + 'rwc-g-m01_1.h5' # load the Echo Nest features h5 = hdf5_getters.open_h5_file_read(h5file) timbre = hdf5_getters.get_segments_timbre(h5) loudness_start = hdf5_getters.get_segments_loudness_start(h5) loudness_max = hdf5_getters.get_segments_loudness_max(h5) loudness_max_time = hdf5_getters.get_segments_loudness_max_time(h5) C = hdf5_getters.get_segments_pitches(h5) segments_all = hdf5_getters.get_segments_start(h5) learn_feats_all = np.hstack((timbre, loudness_start.reshape((loudness_start.shape[0],1)), C)) # Ok That was the best possible case, now let us try to find the nearest neighbors, # get the segment back and resynthesize! learn_duration = 200 # in seconds test_start = 200 test_duration = 5 # Get learning data learning = Signal(audiofile, mono=True) learning.crop(0, learn_duration*learning.fs) wsize = 1024 tstep = 512 # Get the magnitude spectrum for the given audio file learn_specs = features.get_stft(learning.data, wsize, tstep) learn_specs = learn_specs.T max_l_seg_idx = np.where(segments_all < learn_duration)[0][-1] l_segments = segments_all[:max_l_seg_idx] l_segment_lengths = (l_segments[1:] - l_segments[0:-1])*learning.fs learn_feats = learn_feats_all[:max_l_seg_idx,:] # we must keep in mind for each segment index, the corresponding indices in the learn_spec mat l_seg_bounds = [] ref_time = np.arange(0., float(learning.length)/float(learning.fs), float(tstep)/float(learning.fs)) for segI in range(len(l_segments)-1): startIdx = np.where(ref_time > l_segments[segI])[0][0] endIdx = np.where(ref_time > l_segments[segI+1])[0][0] l_seg_bounds.append((startIdx,endIdx)) l_seg_bounds.append((endIdx, ref_time.shape[0])) # Get testing data testing = Signal(audiofile, mono=True) testing.crop(test_start*testing.fs, (test_start+test_duration)*learning.fs) # get the testing features min_t_seg_idx = np.where(segments_all < test_start)[0][-1] max_t_seg_idx = np.where(segments_all < test_start + test_duration)[0][-1] t_segments = segments_all[min_t_seg_idx:max_t_seg_idx] t_segment_lengths = (t_segments[1:] - t_segments[0:-1])*testing.fs test_feats = learn_feats_all[min_t_seg_idx:max_t_seg_idx,:] # find the nearest neighbors from sklearn.neighbors import NearestNeighbors neigh = NearestNeighbors(1) # fit on the learning data neigh.fit(learn_feats) neighb_segments_idx = neigh.kneighbors(test_feats, return_distance=False) # kneighs is a set of segment indices, we need to get the spectrogram back from the learning data # then fit the new segment lengths target_length = int(test_duration*testing.fs) neighb_segments = zip(neighb_segments_idx[:,0], t_segment_lengths.astype(int)) morphed_spectro = spec_morph(np.abs(learn_specs), target_length, neighb_segments, l_seg_bounds) # retrieve true stft for comparison test_specs = features.get_stft(testing.data, wsize, tstep) plt.figure() plt.subplot(121) plt.imshow(np.log(np.abs(test_specs)), origin='lower') plt.colorbar() plt.subplot(122) plt.imshow(np.log(morphed_spectro.T), origin='lower') plt.colorbar() plt.show() init_vec = np.random.randn(morphed_spectro.shape[0]*tstep) rec_method2 = transforms.gl_recons(morphed_spectro.T, init_vec, 10, wsize, tstep, display=False) rec_sig_2 = Signal(rec_method2, testing.fs, mono=True, normalize=True) rec_sig_2.write('/sons/tests/rec_sig2.wav')
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 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 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
if len(sections_start) == 0: sections_start = 0. else: sections_start = sections_start[-1] / len(sections_start) if len(tatums_start) == 0: tatums_start = 0. else: tatums_start = tatums_start[-1] / len(tatums_start) if len(segments_start) == 0: segments_start = 0. else: segments_start = segments_start[-1] / len(segments_start) #time series features #take mean max_loudness_time = hdf5_getters.get_segments_loudness_max_time( h5, songidx=row) segments_loudness_start = hdf5_getters.get_segments_loudness_start( h5, songidx=row) segments_pitches = hdf5_getters.get_segments_pitches(h5, songidx=row) segments_timbre = hdf5_getters.get_segments_timbre(h5, songidx=row) max_loudness = hdf5_getters.get_segments_loudness_max(h5, songidx=row) segments_pitches = np.mean(segments_pitches) segments_timbre = np.mean(segments_timbre) max_loudness = np.mean(max_loudness) max_loudness_time = np.mean(max_loudness_time) segments_loudness_start = np.mean(segments_loudness_start) l = [ song_id, danceability, duration, energy, loudness, musicalKey, mode, tempo, time_signature, year, song_hottness, max_loudness, end_of_fade_in, start_of_fade_out, bars_start, beats_start, sections_start, tatums_start, segments_start, max_loudness_time, segments_loudness_start, segments_pitches, segments_timbre ] for idx, val in enumerate(l):
best_tag = t if best_tag == '': best_tag = 'other' if best_tag != 'other': h5_dict = dict() h5_dict['title'] = hdf5_getters.get_title(h5) h5_dict['artist_name'] = hdf5_getters.get_artist_name(h5) h5_dict['year'] = hdf5_getters.get_year(h5) h5_dict['beats_confidence'] = hdf5_getters.get_beats_confidence(h5).tolist() h5_dict['beats_start'] = hdf5_getters.get_beats_start(h5).tolist() h5_dict['tempo'] = hdf5_getters.get_tempo(h5) h5_dict['time_signature'] = hdf5_getters.get_time_signature(h5) h5_dict['segments_timbre'] = hdf5_getters.get_segments_timbre(h5).tolist() h5_dict['segments_loudness_max'] = hdf5_getters.get_segments_loudness_max(h5).tolist() h5_dict['segments_loudness_max_time'] = hdf5_getters.get_segments_loudness_max_time(h5).tolist() h5_dict['segments_loudness_start'] = hdf5_getters.get_segments_loudness_start(h5).tolist() h5_dict['segments_pitches'] = hdf5_getters.get_segments_pitches(h5).tolist() h5_dict['best_tag'] = best_tag h5_dict['duration'] = hdf5_getters.get_duration(h5) song_dict[track_id] = h5_dict song_count += 1 print 'song {0}: {1} by {2}, year of {3}'.format(str(song_count),h5_dict['title'],h5_dict['artist_name'],h5_dict['year']) h5.close() with open(json_flat_file_electronic_metadata_name, 'w') as text_file: text_file.write('{}'.format(song_dict))
def get_segments_loudness_max(self): if self.h5 == None: self.open() return hdf5_getters.get_segments_loudness_max(self.h5)
def func_to_extract_features(filename): """ This function does 3 simple things: - open the song file - get artist ID and put it - close the file """ global cntnan global listfeatures cf = [] h5 = GETTERS.open_h5_file_read(filename) nanfound = 0 #Get target feature: song hotness #FEATURE 0 song_hotness = GETTERS.get_song_hotttnesss(h5) if math.isnan(song_hotness): nanfound = 1 cntnan = cntnan + 1 else: cf.append(song_hotness) #FEATURE 1 #Get song loudness song_loudness = GETTERS.get_loudness(h5) if math.isnan(song_loudness): nanfound = 1 cntnan = cntnan + 1 else: cf.append(song_loudness) #FEATURE 2 #Get key of the song song_key = GETTERS.get_key(h5) if math.isnan(song_key): nanfound = 1 cntnan = cntnan + 1 else: cf.append(song_key) #FEATURE 3 #Get duration of the song song_duration = GETTERS.get_duration(h5) if math.isnan(song_duration): nanfound = 1 cntnan = cntnan + 1 else: cf.append(song_duration) #FEATURE 4-15 #Get Average Pitch Class across all segments #Get the pitches (12 pitches histogram for each segment) pitches = GETTERS.get_segments_pitches(h5) M = np.mat(pitches) meanpitches = M.mean(axis=0) pitches_arr = np.asarray(meanpitches) pitches_list = [] for i in range(0,12): pitches_list.append(pitches_arr[0][i]) cf.append(pitches_list) #FEATURE 16, 27 #Get Average Timbre Class across all segments timbres = GETTERS.get_segments_timbre(h5) M = np.mat(timbres) meantimbres = M.mean(axis=0) timbre_arr = np.asarray(meantimbres) timbre_list = [] for i in range(0,12): timbre_list.append(timbre_arr[0][i]) cf.append(timbre_list) #FEATURE 28 #Get song year song_year = GETTERS.get_year(h5) if song_year == 0: nanfound = 1 cntnan = cntnan + 1 else: cf.append(song_year) #FEATURE 29 #Get song tempo song_tempo = GETTERS.get_tempo(h5) cf.append(song_tempo) #Feature 30 #Get max loudness for each segment max_loudness_arr = GETTERS.get_segments_loudness_max(h5) start_loudness_arr = GETTERS.get_segments_loudness_start(h5) if nanfound == 0: cf.append(max(max_loudness_arr)-min(start_loudness_arr)) #Feature 31 artist_familiarity = GETTERS.get_artist_familiarity(h5) cf.append(artist_familiarity) #Feature 32 artist_hotness = GETTERS.get_artist_hotttnesss(h5) if math.isnan(artist_hotness): nanfound = 1 cntnan = cntnan + 1 else: cf.append(artist_hotness) if nanfound == 0: strlist = list_to_csv(cf) listfeatures.append(strlist) h5.close()
if best_tag == '': best_tag = 'other' # print best_tag h5_dict = dict() h5_dict['title'] = hdf5_getters.get_title(h5) h5_dict['artist_name'] = hdf5_getters.get_artist_name(h5) h5_dict['year'] = hdf5_getters.get_year(h5) h5_dict['beats_confidence'] = hdf5_getters.get_beats_confidence(h5) h5_dict['beats_start'] = hdf5_getters.get_beats_start(h5) h5_dict['tempo'] = hdf5_getters.get_tempo(h5) h5_dict['time_signature'] = hdf5_getters.get_time_signature(h5) h5_dict['segments_timbre'] = hdf5_getters.get_segments_timbre(h5) h5_dict['segments_loudness_max'] = hdf5_getters.get_segments_loudness_max(h5) h5_dict['segments_loudness_max_time'] = hdf5_getters.get_segments_loudness_max_time(h5) h5_dict['segments_loudness_start'] = hdf5_getters.get_segments_loudness_start(h5) h5_dict['best_tag'] = best_tag song_dict[track_id] = h5_dict h5.close() with open(json_flat_file_metadata_name, 'w') as text_file: text_file.write('{}'.format(song_dict)) print 'Time elapsed: {0} seconds'.format(str(time.time() - start_time))
h5_dict['title'] = hdf5_getters.get_title(h5) h5_dict['artist_name'] = hdf5_getters.get_artist_name(h5) h5_dict['year'] = hdf5_getters.get_year(h5) h5_dict[ 'beats_confidence'] = hdf5_getters.get_beats_confidence( h5).tolist() h5_dict['beats_start'] = hdf5_getters.get_beats_start( h5).tolist() h5_dict['tempo'] = hdf5_getters.get_tempo(h5) h5_dict[ 'time_signature'] = hdf5_getters.get_time_signature(h5) h5_dict[ 'segments_timbre'] = hdf5_getters.get_segments_timbre( h5).tolist() h5_dict[ 'segments_loudness_max'] = hdf5_getters.get_segments_loudness_max( h5).tolist() h5_dict[ 'segments_loudness_max_time'] = hdf5_getters.get_segments_loudness_max_time( h5).tolist() h5_dict[ 'segments_loudness_start'] = hdf5_getters.get_segments_loudness_start( h5).tolist() h5_dict[ 'segments_pitches'] = hdf5_getters.get_segments_pitches( h5).tolist() h5_dict['best_tag'] = best_tag h5_dict['duration'] = hdf5_getters.get_duration(h5) song_dict[track_id] = h5_dict song_count += 1 print 'song {0}: {1} by {2}, year of {3}'.format(
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 classify(h5): output_array = {} # duration duration = hdf5_getters.get_duration(h5) output_array["duration"] = duration ### ADDED VALUE TO ARRAY # number of bars bars = hdf5_getters.get_bars_start(h5) num_bars = len(bars) output_array["num_bars"] = num_bars ### ADDED VALUE TO ARRAY # mean and variance in bar length bar_length = numpy.ediff1d(bars) variance_bar_length = numpy.var(bar_length) output_array[ "variance_bar_length"] = variance_bar_length ### ADDED VALUE TO ARRAY # number of beats beats = hdf5_getters.get_beats_start(h5) num_beats = len(beats) output_array["num_beats"] = num_beats ### ADDED VALUE TO ARRAY # mean and variance in beats length beats_length = numpy.ediff1d(beats) variance_beats_length = numpy.var(bar_length) output_array[ "variance_beats_length"] = variance_beats_length ### ADDED VALUE TO ARRAY # danceability danceability = hdf5_getters.get_danceability(h5) output_array["danceability"] = danceability ### ADDED VALUE TO ARRAY # end of fade in end_of_fade_in = hdf5_getters.get_end_of_fade_in(h5) output_array["end_of_fade_in"] = end_of_fade_in ### ADDED VALUE TO ARRAY # energy energy = hdf5_getters.get_energy(h5) output_array["energy"] = energy ### ADDED VALUE TO ARRAY # key key = hdf5_getters.get_key(h5) output_array["key"] = int(key) ### ADDED VALUE TO ARRAY # loudness loudness = hdf5_getters.get_loudness(h5) output_array["loudness"] = loudness ### ADDED VALUE TO ARRAY # mode mode = hdf5_getters.get_mode(h5) output_array["mode"] = int(mode) ### ADDED VALUE TO ARRAY # number sections sections = hdf5_getters.get_sections_start(h5) num_sections = len(sections) output_array["num_sections"] = num_sections ### ADDED VALUE TO ARRAY # mean and variance in sections length sections_length = numpy.ediff1d(sections) variance_sections_length = numpy.var(sections) output_array[ "variance_sections_length"] = variance_sections_length ### ADDED VALUE TO ARRAY # number segments segments = hdf5_getters.get_segments_start(h5) num_segments = len(segments) output_array["num_segments"] = num_segments ### ADDED VALUE TO ARRAY # mean and variance in segments length segments_length = numpy.ediff1d(segments) variance_segments_length = numpy.var(segments) output_array[ "variance_segments_length"] = variance_segments_length ### ADDED VALUE TO ARRAY # segment loudness max segment_loudness_max_array = hdf5_getters.get_segments_loudness_max(h5) segment_loudness_max_time_array = hdf5_getters.get_segments_loudness_max_time( h5) segment_loudness_max_index = 0 for i in range(len(segment_loudness_max_array)): if segment_loudness_max_array[i] > segment_loudness_max_array[ segment_loudness_max_index]: segment_loudness_max_index = i segment_loudness_max = segment_loudness_max_array[ segment_loudness_max_index] segment_loudness_max_time = segment_loudness_max_time_array[ segment_loudness_max_index] output_array[ "segment_loudness_max"] = segment_loudness_max ### ADDED VALUE TO ARRAY output_array[ "segment_loudness_time"] = segment_loudness_max_time ### ADDED VALUE TO ARRAY # POSSIBLE TODO: use average function instead and weight by segment length # segment loudness mean (start) segment_loudness_array = hdf5_getters.get_segments_loudness_start(h5) segment_loudness_mean = numpy.mean(segment_loudness_array) output_array[ "segment_loudness_mean"] = segment_loudness_mean ### ADDED VALUE TO ARRAY # segment loudness variance (start) segment_loudness_variance = numpy.var(segment_loudness_array) output_array[ "segment_loudness_variance"] = segment_loudness_variance ### ADDED VALUE TO ARRAY # segment pitches segment_pitches_array = hdf5_getters.get_segments_pitches(h5) segment_pitches_mean = numpy.mean(segment_pitches_array, axis=0).tolist() output_array["segment_pitches_mean"] = segment_pitches_mean # segment pitches variance (start) segment_pitches_variance = numpy.var(segment_pitches_array, axis=0).tolist() output_array["segment_pitches_variance"] = segment_pitches_variance # segment timbres segment_timbres_array = hdf5_getters.get_segments_timbre(h5) segment_timbres_mean = numpy.mean(segment_timbres_array, axis=0).tolist() output_array["segment_timbres_mean"] = segment_timbres_mean # segment timbres variance (start) segment_timbres_variance = numpy.var(segment_timbres_array, axis=0).tolist() output_array["segment_timbres_variance"] = segment_timbres_variance # hotttnesss hottness = hdf5_getters.get_song_hotttnesss(h5, 0) output_array["hottness"] = hottness ### ADDED VALUE TO ARRAY # duration-start of fade out start_of_fade_out = hdf5_getters.get_start_of_fade_out(h5) fade_out = duration - start_of_fade_out output_array["fade_out"] = fade_out ### ADDED VALUE TO ARRAY # tatums tatums = hdf5_getters.get_tatums_start(h5) num_tatums = len(tatums) output_array["num_tatums"] = num_tatums ### ADDED VALUE TO ARRAY # mean and variance in tatums length tatums_length = numpy.ediff1d(tatums) variance_tatums_length = numpy.var(tatums_length) output_array[ "variance_tatums_length"] = variance_tatums_length ### ADDED VALUE TO ARRAY # tempo tempo = hdf5_getters.get_tempo(h5) output_array["tempo"] = tempo ### ADDED VALUE TO ARRAY # time signature time_signature = hdf5_getters.get_time_signature(h5) output_array["time_signature"] = int( time_signature) ### ADDED VALUE TO ARRAY # year year = hdf5_getters.get_year(h5) output_array["year"] = int(year) ### ADDED VALUE TO ARRAY # artist terms artist_terms = hdf5_getters.get_artist_terms(h5, 0) output_array["artist_terms"] = artist_terms.tolist() artist_terms_freq = hdf5_getters.get_artist_terms_freq(h5, 0) output_array["artist_terms_freq"] = artist_terms_freq.tolist() artist_name = hdf5_getters.get_artist_name(h5, 0) output_array["artist_name"] = artist_name artist_id = hdf5_getters.get_artist_id(h5, 0) output_array["artist_id"] = artist_id # title title = hdf5_getters.get_title(h5, 0) output_array["title"] = title return output_array
def data_to_flat_file(basedir,ext='.h5') : """This function extract the information from the tables and creates the flat file.""" count = 0; #song counter list_to_write= [] row_to_write = "" writer = csv.writer(open("metadata.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 func_to_extract_features(filename): """ This function does 3 simple things: - open the song file - get artist ID and put it - close the file """ global cntnan global cntdanceability global listfeatures global listhotness global listyear global listloudness global listkey global listmode global listduration cf = [] h5 = GETTERS.open_h5_file_read(filename) nanfound = 0 #Get target feature: song hotness #FEATURE 0 song_hotness = GETTERS.get_song_hotttnesss(h5) if math.isnan(song_hotness): nanfound = 1 cntnan = cntnan + 1 else: cf.append(song_hotness) #FEATURE 1 #Get song loudness song_loudness = GETTERS.get_loudness(h5) if math.isnan(song_loudness): nanfound = 1 cntnan = cntnan + 1 else: cf.append(song_loudness) #FEATURE 2 #Get key of the song song_key = GETTERS.get_key(h5) if math.isnan(song_key): nanfound = 1 cntnan = cntnan + 1 else: cf.append(song_key) #FEATURE 3 #Get duration of the song song_duration = GETTERS.get_duration(h5) if math.isnan(song_duration): nanfound = 1 cntnan = cntnan + 1 else: cf.append(song_duration) #FEATURE 4-15 #Get Average Pitch Class across all segments #Get the pitches (12 pitches histogram for each segment) pitches = GETTERS.get_segments_pitches(h5) M = np.mat(pitches) meanpitches = M.mean(axis=0) pitches_arr = np.asarray(meanpitches) pitches_list = [] for i in range(0,12): pitches_list.append(pitches_arr[0][i]) cf.append(pitches_list) #FEATURE 16, 27 #Get Average Timbre Class across all segments timbres = GETTERS.get_segments_timbre(h5) M = np.mat(timbres) meantimbres = M.mean(axis=0) timbre_arr = np.asarray(meantimbres) timbre_list = [] for i in range(0,12): timbre_list.append(timbre_arr[0][i]) cf.append(timbre_list) #FEATURE 28 #Get song year song_year = GETTERS.get_year(h5) if song_year == 0: nanfound = 1 cntnan = cntnan + 1 else: cf.append(song_year) #FEATURE 29 #Get song tempo song_tempo = GETTERS.get_tempo(h5) cf.append(song_tempo) #Feature 30 #Get max loudness for each segment max_loudness_arr = GETTERS.get_segments_loudness_max(h5) start_loudness_arr = GETTERS.get_segments_loudness_start(h5) if nanfound == 0: cf.append(max(max_loudness_arr)-min(start_loudness_arr)) #Feature 31 artist_familiarity = GETTERS.get_artist_familiarity(h5) cf.append(artist_familiarity) #Feature 32 song_title = GETTERS.get_title(h5) cf.append(song_title) #Featture 33 artist_name = GETTERS.get_artist_name(h5) cf.append(artist_name) #Feature 34 #location = GETTERS.get_artist_location(h5) #cf.append(location) #Tags artist_mbtags = GETTERS.get_artist_mbtags(h5) if not artist_mbtags.size: genre = "Unknown" else: artist_mbcount = np.array(GETTERS.get_artist_mbtags_count(h5)) index_max = artist_mbcount.argmax(axis=0) genre = artist_mbtags[index_max] if genre == 'espa\xc3\xb1ol': genre = "Unknown" cf.append(genre) if nanfound == 0: strlist = list_to_csv(cf) listfeatures.append(strlist) mydict.setdefault(artist_name,[]).append(song_hotness) h5.close()
def 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)