def create_songdet(h5, sngidxfle):
    '''
    Collects song details for all unique songs heard by 100 raters.
    Format of dictionary: { SongID : [ Att_0, Att_1, Att_2 ] }
    '''
    import hdf5_getters
    sngdetfle = open("songdet.txt", "wb")
    sngdetdic = dict()
    sngidxfle = open(sngidxfle, "rb")
    sngidxdic = pickle.load(sngidxfle)
    for elem in sngidxdic:
        songidx = sngidxdic[elem]
        tempo = hdf5_getters.get_tempo(h5, songidx)
        loud = hdf5_getters.get_loudness(h5, songidx)
        year = hdf5_getters.get_year(h5, songidx)
        tmsig = hdf5_getters.get_time_signature(h5, songidx)
        key = hdf5_getters.get_key(h5, songidx)
        mode = hdf5_getters.get_mode(h5, songidx)
        duration = hdf5_getters.get_duration(h5, songidx)
        fadein = hdf5_getters.get_end_of_fade_in(h5, songidx)
        fadeout = hdf5_getters.get_start_of_fade_out(h5, songidx)
        artfam = hdf5_getters.get_artist_familiarity(h5, songidx)
        sngdetdic[elem] = [duration, tmsig, tempo,
                           key, mode, fadein, fadeout, year, loud, artfam]
    pickle.dump(sngdetdic, sngdetfle)
    sngdetfle.close()
def load_non_time_data():
    years = []
    ten_features=[]
    num = 0
    for root, dirs, files in os.walk(basedir):
        files = glob.glob(os.path.join(root,'*'+ext))
        for f in files:
            h5 = getter.open_h5_file_read(f)
            num += 1
            print(num)
            try:
                year = getter.get_year(h5)
                if year!=0:
                    years.append(year)
                    title_length = len(getter.get_title(h5))
                    terms_length = len(getter.get_artist_terms(h5))
                    tags_length = len(getter.get_artist_mbtags(h5))
                    hotness = getter.get_artist_hotttnesss(h5)
                    duration = getter.get_duration(h5)
                    loudness = getter.get_loudness(h5)
                    mode = getter.get_mode(h5)
                    release_length = len(getter.get_release(h5))
                    tempo = getter.get_tempo(h5)
                    name_length = len(getter.get_artist_name(h5))
                    ten_feature = np.hstack([title_length,tags_length, hotness, duration,
                                             terms_length, loudness, mode, release_length, tempo, name_length])
                    ten_features.append(ten_feature) 
            except:
                print(1)
            h5.close()
    return years,ten_features
示例#3
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def func_to_desired_song_data(filename):
    h5 = GETTERS.open_h5_file_read(filename)
    track_id = GETTERS.get_track_id(h5)
    for song in random_songs:
        if song[0] == track_id:
            print("FOUND ONE!")
            title = replace_characters(GETTERS.get_title(h5))
            artist = replace_characters(GETTERS.get_artist_name(h5))
            year = GETTERS.get_year(h5)
            tempo = GETTERS.get_tempo(h5)
            key = GETTERS.get_key(h5)
            loudness = GETTERS.get_loudness(h5)
            energy = GETTERS.get_energy(h5)
            danceability = GETTERS.get_danceability(h5)
            time_signature = GETTERS.get_time_signature(h5)
            mode = GETTERS.get_mode(h5)
            hotttness = GETTERS.get_song_hotttnesss(h5)

            song_data = {
                'title': title,
                'artist': artist,
                'year': year,
                'tempo': tempo,
                'key': key,
                'loudness': loudness,
                'energy': energy,
                'danceability': danceability,
                'time_signature': time_signature,
                'mode': mode,
                'hotttness': hotttness
            }

            all_the_data.append(song_data)

    h5.close()
示例#4
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def get_attribute(files):
    array = []
    count = 0
    for f in files:
        temp = []
        count += 1
        print(f)
        h5 = hdf5_getters.open_h5_file_read(f)
        temp.append(hdf5_getters.get_num_songs(h5))
        temp.append(hdf5_getters.get_artist_familiarity(h5))
        temp.append(hdf5_getters.get_artist_hotttnesss(h5))
        temp.append(hdf5_getters.get_danceability(h5))
        temp.append(hdf5_getters.get_energy(h5))
        temp.append(hdf5_getters.get_key(h5))
        temp.append(hdf5_getters.get_key_confidence(h5))
        temp.append(hdf5_getters.get_loudness(h5))
        temp.append(hdf5_getters.get_mode(h5))
        temp.append(hdf5_getters.get_mode_confidence(h5))
        temp.append(hdf5_getters.get_tempo(h5))
        temp.append(hdf5_getters.get_time_signature(h5))
        temp.append(hdf5_getters.get_time_signature_confidence(h5))
        temp.append(hdf5_getters.get_title(h5))
        temp.append(hdf5_getters.get_artist_name(h5))
        temp = np.nan_to_num(temp)
        array.append(temp)
        # if count%100 ==0:
        # print(array[count-100:count-1])
        # kmean.fit(array[count-100:count-1])
        h5.close()
    return array
示例#5
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def feat_from_file(path):
    
    feats = []
    h5 = GETTERS.open_h5_file_read(path)
    
    feats.append( GETTERS.get_track_id(h5) )
    feats.append( GETTERS.get_title(h5) )
    feats.append( GETTERS.get_artist_name(h5) )
    feats.append( GETTERS.get_year(h5) )
    feats.append( GETTERS.get_loudness(h5) )
    feats.append( GETTERS.get_tempo(h5) )
    feats.append( GETTERS.get_time_signature(h5) )
    feats.append( GETTERS.get_key(h5) )
    feats.append( GETTERS.get_mode(h5) )
    feats.append( GETTERS.get_duration(h5) )
    
    #timbre
    timbre = GETTERS.get_segments_timbre(h5)
    avg_timbre = np.average(timbre, axis=0)
    for k in avg_timbre:
        feats.append(k)
    var_timbre = np.var(timbre, axis=0)
    for k in var_timbre:
        feats.append(k)

    h5.close()
    
    return feats
示例#6
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def get_info(basedir,ext='.h5') :
    # Create new text file for storing the result of JSON objects
    resultFile = open("result.txt", "w")
    # Going through all sub-directories under the base directory
    for root, dirs, files in os.walk(basedir):
        files = glob.glob(os.path.join(root,'*'+ext))
        for f in files:
            # Open the HDF5 for reading the content
            h5 = hdf5_getters.open_h5_file_read(f)
            # Creating dictionary to convert to JSON object
            dictionary = {} 
            # Storing all fields 
            dictionary["song_title"] = hdf5_getters.get_title(h5).decode('Latin-1')
            dictionary["artist_name"] = hdf5_getters.get_artist_name(h5).decode('Latin-1')
            dictionary["key"] = float(hdf5_getters.get_key(h5))
            dictionary["minor-major"] = float(hdf5_getters.get_mode(h5))
            dictionary["hotness"] = hdf5_getters.get_song_hotttnesss(h5)
            dictionary["artist_location"] = hdf5_getters.get_artist_location(h5).decode('Latin-1')
            dictionary["longitude"] = float(hdf5_getters.get_artist_longitude(h5))
            dictionary["latitude"] = float(hdf5_getters.get_artist_latitude(h5))
            print(dictionary)
            # Write the created JSON object to the text file
            resultFile.write(str(json.dumps(dictionary)) + "\n")
            h5.close()
    resultFile.close()
示例#7
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def feat_from_file(path):
    """
    Extract a list of features in an array, already converted to string
    """
    feats = []
    h5 = GETTERS.open_h5_file_read(path)
    # basic info
    feats.append( GETTERS.get_track_id(h5) )
    feats.append( GETTERS.get_artist_name(h5).replace(',','') )
    feats.append( GETTERS.get_title(h5).replace(',','') )
    feats.append( GETTERS.get_loudness(h5) )
    feats.append( GETTERS.get_tempo(h5) )
    feats.append( GETTERS.get_time_signature(h5) )
    feats.append( GETTERS.get_key(h5) )
    feats.append( GETTERS.get_mode(h5) )
    feats.append( GETTERS.get_duration(h5) )
    # timbre
    timbre = GETTERS.get_segments_timbre(h5)
    avg_timbre = np.average(timbre,axis=0)
    for k in avg_timbre:
        feats.append(k)
    var_timbre = np.var(timbre,axis=0)
    for k in var_timbre:
        feats.append(k)
    # done with h5 file
    h5.close()
    # makes sure we return strings
    feats = map(lambda x: str(x), feats)
    return feats
示例#8
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def get_all_examples(basedir, genre_dict, ext='.h5'):
    """
    From a base directory, goes through all subdirectories,
    and grabs all songs and their features and puts them into a pandas dataframe 
    INPUT
       basedir    - base directory of the dataset
       genre_dict - a dictionary mapping track id to genre based tagraum dataset
       ext        - extension, .h5 by default
    RETURN
       dataframe containing all song examples
    """
    features_vs_genre = pd.DataFrame()

    # iterate over all files in all subdirectories
    for root, dirs, files in os.walk(basedir):
        files = glob.glob(os.path.join(root, '*' + ext))
        # # count files
        # count += len(files)
        # apply function to all files
        for f in files:
            h5 = GETTERS.open_h5_file_read(f)
            song_id = GETTERS.get_track_id(h5).decode('utf-8')
            if (song_id in genre_dict):
                genre = genre_dict[song_id]
                year = GETTERS.get_year(h5)
                duration = GETTERS.get_duration(h5)
                end_of_fade_in = GETTERS.get_end_of_fade_in(h5)
                loudness = GETTERS.get_loudness(h5)
                song_hotttnesss = GETTERS.get_song_hotttnesss(h5)
                tempo = GETTERS.get_tempo(h5)
                key = GETTERS.get_key(h5)
                key_confidence = GETTERS.get_key_confidence(h5)
                mode = GETTERS.get_mode(h5)
                mode_confidence = GETTERS.get_mode_confidence(h5)
                time_signature = GETTERS.get_time_signature(h5)
                time_signature_confidence = GETTERS.get_time_signature_confidence(
                    h5)
                artist_name = GETTERS.get_artist_name(h5)
                title = GETTERS.get_title(h5)
                # length of sections_start array gives us number of start
                num_sections = len(GETTERS.get_sections_start(h5))
                num_segments = len(GETTERS.get_segments_confidence(h5))
                example = pd.DataFrame(
                    data=[
                        (artist_name, title, song_id, genre, year, key,
                         key_confidence, mode, mode_confidence, time_signature,
                         time_signature_confidence, duration, end_of_fade_in,
                         loudness, song_hotttnesss, tempo, num_sections)
                    ],
                    columns=[
                        'artist_name', 'title', 'song_id', 'genre', 'year',
                        'key', 'key_confidence', 'mode', 'mode_confidence',
                        'time_signature', 'time_signature_confidence',
                        'duration', 'end_of_fade_in', 'loudness',
                        'song_hotttnesss', 'tempo', 'num_segments'
                    ])
                features_vs_genre = features_vs_genre.append(example)
            h5.close()

    return features_vs_genre
def create_songdet(h5, sngidxfle):
    '''
    Collects song details for all unique songs heard by 100 raters.
    Format of dictionary: { SongID : [ Att_0, Att_1, Att_2 ] }
    '''
    import hdf5_getters
    sngdetfle = open("songdet.txt", "wb")
    sngdetdic = dict()
    sngidxfle = open(sngidxfle, "rb")
    sngidxdic = pickle.load(sngidxfle)
    for elem in sngidxdic:
        songidx = sngidxdic[elem]
        tempo = hdf5_getters.get_tempo(h5, songidx)
        loud = hdf5_getters.get_loudness(h5, songidx)
        year = hdf5_getters.get_year(h5, songidx)
        tmsig = hdf5_getters.get_time_signature(h5, songidx)
        key = hdf5_getters.get_key(h5, songidx)
        mode = hdf5_getters.get_mode(h5, songidx)
        duration = hdf5_getters.get_duration(h5, songidx)
        fadein = hdf5_getters.get_end_of_fade_in(h5, songidx)
        fadeout = hdf5_getters.get_start_of_fade_out(h5, songidx)
        artfam = hdf5_getters.get_artist_familiarity(h5, songidx)
        sngdetdic[elem] = [
            duration, tmsig, tempo, key, mode, fadein, fadeout, year, loud,
            artfam
        ]
    pickle.dump(sngdetdic, sngdetfle)
    sngdetfle.close()
def feat_from_file(path):
    """
    Extract a list of features in an array, already converted to string
    """
    feats = []
    h5 = GETTERS.open_h5_file_read(path)
    # basic info
    feats.append(GETTERS.get_track_id(h5))
    feats.append(GETTERS.get_artist_name(h5).decode().replace(',', ''))
    feats.append(GETTERS.get_title(h5).decode().replace(',', ''))
    feats.append(GETTERS.get_loudness(h5))
    feats.append(GETTERS.get_tempo(h5))
    feats.append(GETTERS.get_time_signature(h5))
    feats.append(GETTERS.get_key(h5))
    feats.append(GETTERS.get_mode(h5))
    feats.append(GETTERS.get_duration(h5))
    # timbre
    timbre = GETTERS.get_segments_timbre(h5)
    avg_timbre = np.average(timbre, axis=0)
    for k in avg_timbre:
        feats.append(k)
    var_timbre = np.var(timbre, axis=0)
    for k in var_timbre:
        feats.append(k)
    # done with h5 file
    h5.close()
    # makes sure we return strings
    feats = map(lambda x: str(x), feats)
    return feats
def getMode(h5):
    """
    Returns whether the song is in major or minor key
    #Categorical Feature
    #0 --> [0 1]
    #1 --> [1 0]
    ::return: 2 dimensional list
    """
    mode = hdf5_getters.get_mode(h5)
    if mode == 0:
        return [0,1]
    elif mode == 1:
        return [1,0]

    #Mode info not available
    return ["nan"]
示例#12
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def get_key_feature(track, h5=None):
    #return
    #0: get key of the track
    #1:get mode (minor = 0, major = 1close = (h5== None)
    close = (h5 == None)
    if h5 == None:
        # build path
        path = "../../msd_dense_subset/dense/"+track[2]+"/"+track[3]+"/"+track[4]+"/"+track+".h5"
        h5 = GETTERS.open_h5_file_read(path)
    mode = GETTERS.get_mode(h5)
    key = GETTERS.get_key(h5)
    confidence_mode = GETTERS.get_mode_confidence(h5)
    confidence_key = GETTERS.get_key_confidence(h5)
    if close:
        h5.close()
    return (key,mode)
示例#13
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def get_key_feature(track, h5=None):
    #return
    #0: get key of the track
    #1:get mode (minor = 0, major = 1close = (h5== None)
    close = (h5 == None)
    if h5 == None:
        # build path
        path = "../../msd_dense_subset/dense/" + track[2] + "/" + track[
            3] + "/" + track[4] + "/" + track + ".h5"
        h5 = GETTERS.open_h5_file_read(path)
    mode = GETTERS.get_mode(h5)
    key = GETTERS.get_key(h5)
    confidence_mode = GETTERS.get_mode_confidence(h5)
    confidence_key = GETTERS.get_key_confidence(h5)
    if close:
        h5.close()
    return (key, mode)
def load_raw_data():
    years = []
    ten_features=[]
    timbres = []
    pitches = []
    min_length = 10000
    num = 0
    for root, dirs, files in os.walk(basedir):
        files = glob.glob(os.path.join(root,'*'+ext))
        for f in files:
            h5 = getter.open_h5_file_read(f)
            num += 1
            print(num)
            try:
                year = getter.get_year(h5)
                if year!=0:
                    timbre = getter.get_segments_timbre(h5)
                    s = np.size(timbre,0)
                    if s>=100:
                        if s<min_length:
                            min_length = s
                        pitch = getter.get_segments_pitches(h5)
                        years.append(year)
                        timbres.append(timbre)
                        pitches.append(pitch)
                        title_length = len(getter.get_title(h5))
                        terms_length = len(getter.get_artist_terms(h5))
                        tags_length = len(getter.get_artist_mbtags(h5))
                        hotness = getter.get_artist_hotttnesss(h5)
                        duration = getter.get_duration(h5)
                        loudness = getter.get_loudness(h5)
                        mode = getter.get_mode(h5)
                        release_length = len(getter.get_release(h5))
                        tempo = getter.get_tempo(h5)
                        name_length = len(getter.get_artist_name(h5))
                        ten_feature = np.hstack([title_length, hotness, duration, tags_length,
                                                 terms_length,loudness, mode, release_length, tempo, name_length])

                        ten_features.append(ten_feature) 
            except:
                print(1)
            h5.close()
    return years, timbres, pitches,min_length,ten_features
示例#15
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def h5_to_csv_fields(h5,song):
	'''Converts h5 format to text
		Inputs: h5, an h5 file object, usable with the wrapper code MSongsDB
			song, an integer, representing which song in the h5 file to take the info out of (h5 files contain many songs)
		Output: a string representing all the information of this song, as a single line of a csv file
	'''
	rv=[]
	##All these are regular getter functions from wrapper code
	rv.append(gt.get_artist_name(h5,song))
	rv.append(gt.get_title(h5, song))
	rv.append(gt.get_release(h5, song))
	rv.append(gt.get_year(h5,song))
	rv.append(gt.get_duration(h5,song))
	rv.append(gt.get_artist_familiarity(h5,song))
	rv.append(gt.get_artist_hotttnesss(h5,song))
	rv.append(gt.get_song_hotttnesss(h5, song))
	
	##artist_terms, artist_terms_freq, and artist_terms_weight getter functions
	##are all arrays, so we need to turn them into strings first. We used '_' as a separator
	rv.append(array_to_csv_field(list(gt.get_artist_terms(h5,song))))
	rv.append(array_to_csv_field(list(gt.get_artist_terms_freq(h5,song))))
	rv.append(array_to_csv_field(list(gt.get_artist_terms_weight(h5,song))))
	rv.append(gt.get_mode(h5,song))
	rv.append(gt.get_key(h5,song))
	rv.append(gt.get_tempo(h5,song))
	rv.append(gt.get_loudness(h5,song))
	rv.append(gt.get_danceability(h5,song))
	rv.append(gt.get_energy(h5,song))
	rv.append(gt.get_time_signature(h5,song))
	rv.append(array_to_csv_field(list(gt.get_segments_start(h5,song))))
	##These arrays have vectors (Arrays) as items, 12 dimensional each
	##An array like [[1,2,3],[4,5,6]] will be written to csv as '1;2;3_4;5;6', i.e. there's two types of separators
	rv.append(double_Array_to_csv_field(list(gt.get_segments_timbre(h5,song)),'_',';'))
	rv.append(double_Array_to_csv_field(list(gt.get_segments_pitches(h5,song)),'_',';'))
	rv.append(array_to_csv_field(list(gt.get_segments_loudness_start(h5,song))))
	rv.append(array_to_csv_field(list(gt.get_segments_loudness_max(h5,song))))
	rv.append(array_to_csv_field(list(gt.get_segments_loudness_max_time(h5,song))))
	rv.append(array_to_csv_field(list(gt.get_sections_start(h5,song))))
	##turn this list into a string with comma separators (i.e. a csv line)
	rv_string=array_to_csv_field(rv, ",")
	rv_string+="\n"
	return rv_string
 def append_files(self, letter):
     path = '/Users/cole/eclipse-workspace/EC2 File Transfer/Data/' + letter + '/'
     files = os.listdir(path)
     import csv
     with open('library_csv.csv', 'a') as library_csv:
         writer = csv.writer(library_csv)
         for filename in files:
             hdf5path = path + filename
             h5 = hdf5_getters.open_h5_file_read(hdf5path)
             # get all getters
             loudness = hdf5.get_loudness(h5)
             key = hdf5.get_key(h5)
             mode = hdf5.get_mode(h5)
             tempo = hdf5.get_tempo(h5)
             ts = hdf5.get_time_signature(h5)
             title = hdf5.get_title(h5)
             writer.writerow([loudness, key, mode, tempo, ts, title])
             # print them
             h5.close()
         library_csv.close()
示例#17
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def get_attribute(f):
    temp = []
    count += 1
    print(f)
    h5 = hdf5_getters.open_h5_file_read(f)
    temp.append(hdf5_getters.get_num_songs(h5))
    temp.append(hdf5_getters.get_artist_familiarity(h5))
    temp.append(hdf5_getters.get_artist_hotttnesss(h5))
    temp.append(hdf5_getters.get_danceability(h5))
    temp.append(hdf5_getters.get_energy(h5))
    temp.append(hdf5_getters.get_key(h5))
    temp.append(hdf5_getters.get_key_confidence(h5))
    temp.append(hdf5_getters.get_loudness(h5))
    temp.append(hdf5_getters.get_mode(h5))
    temp.append(hdf5_getters.get_mode_confidence(h5))
    temp.append(hdf5_getters.get_tempo(h5))
    temp.append(hdf5_getters.get_time_signature(h5))
    temp.append(hdf5_getters.get_time_signature_confidence(h5))
    temp = np.nan_to_num(temp)
    array.append(temp)
    h5.close()
示例#18
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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
示例#19
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    def root_retrieve(self, path):
        #r=root, d=directory, f=file
        import csv
        with open('library_csv.csv', 'w') as library_csv:
            writer = csv.writer(library_csv)
            writer.writerow(
                ['Loudness', 'Key', 'Mode', 'Tempo', 'timeSignature', 'Title'])
            for r, d, f in os.walk(path):
                for song in f:
                    hdf5path = os.path.join(r, song)
                    h5 = hdf5_getters.open_h5_file_read(hdf5path)
                    # get all getters
                    loudness = hdf5.get_loudness(h5)
                    key = hdf5.get_key(h5)
                    mode = hdf5.get_mode(h5)
                    tempo = hdf5.get_tempo(h5)
                    ts = hdf5.get_time_signature(h5)
                    title = hdf5.get_title(h5)

                    writer.writerow([loudness, key, mode, tempo, ts, title])
                    # print them
                    h5.close()
            library_csv.close()
        print("done")
def 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
示例#21
0
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
示例#22
0
def get_info(basedir, ext='.h5'):
    resultFile = open("result.txt", "w")
    for root, dirs, files in os.walk(basedir):
        files = glob.glob(os.path.join(root, '*' + ext))
        for f in files:
            h5 = hdf5_getters.open_h5_file_read(f)
            dictionary = {}
            dictionary["song_title"] = hdf5_getters.get_title(h5).decode(
                'Latin-1')
            dictionary["artist_name"] = hdf5_getters.get_artist_name(
                h5).decode('Latin-1')
            dictionary["key"] = float(hdf5_getters.get_key(h5))
            dictionary["minor-major"] = float(hdf5_getters.get_mode(h5))
            dictionary["hotness"] = hdf5_getters.get_song_hotttnesss(h5)
            dictionary["artist_location"] = hdf5_getters.get_artist_location(
                h5).decode('Latin-1')
            dictionary["longitude"] = float(
                hdf5_getters.get_artist_longitude(h5))
            dictionary["latitude"] = float(
                hdf5_getters.get_artist_latitude(h5))
            print(dictionary)
            resultFile.write(str(json.dumps(dictionary)) + "\n")
            h5.close()
    resultFile.close()
示例#23
0
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
for i in range(0, numSongs):
	#Handle each one
	year = h5get.get_year(h5, i)
	if year < 1980 or year > 2010:
		continue;

	song = Song()

	song.year = year

	song.tempo = h5get.get_tempo(h5, i)
	song.duration = h5get.get_duration(h5, i) 
	song.key = h5get.get_key(h5, i)
	song.energy = h5get.get_energy(h5, i)
	song.time_sig = h5get.get_time_signature(h5,i)
	song.mode = h5get.get_mode(h5,i);

	song.hotness = h5get.get_song_hotttnesss(h5, i)
	#print "Hotness: ", song.hotness;
	if math.isnan(song.hotness):
		song.hotness = 0.1;

	song.artist = h5get.get_artist_name(h5, i)

	song.name = h5get.get_title(h5, i)

	if (song.artist.lower(), song.name.lower()) in all_chart_info:
		song.chart_score = float(all_chart_info[(song.artist.lower(), song.name.lower())]);
		print " Got us some data! ", song.artist, " -- ", song.name, ": ", song.chart_score
	else:
		#song.chart_score = float('nan');
示例#25
0
    h5 = hdf5_getters.open_h5_file_read(filepath)
    n = hdf5_getters.get_num_songs(h5)
    for row in range(n):
        song_id = hdf5_getters.get_song_id(h5, songidx=row).decode('UTF-8')
        #         artist = hdf5_getters.get_artist_name(h5,songidx=row).decode('UTF-8')
        #         title= hdf5_getters.get_title(h5,songidx=row)#.decode('UTF-8')
        #         artist = "".join(c for c in unicodedata.normalize('NFD', str(artist.decode("utf8"))) if unicodedata.category(c) != "Mn")
        #         title = "".join(c for c in unicodedata.normalize('NFD', str(title.decode("utf8"))) if unicodedata.category(c) != "Mn")

        #single number features
        danceability = hdf5_getters.get_danceability(h5, songidx=row)
        duration = hdf5_getters.get_duration(h5, songidx=row)
        energy = hdf5_getters.get_energy(h5, songidx=row)
        loudness = hdf5_getters.get_loudness(h5, songidx=row)
        musicalKey = hdf5_getters.get_key(h5, songidx=row)
        mode = hdf5_getters.get_mode(h5, songidx=row)
        tempo = hdf5_getters.get_tempo(h5, songidx=row)
        time_signature = hdf5_getters.get_time_signature(h5, songidx=row)
        year = hdf5_getters.get_year(h5, songidx=row)
        song_hottness = hdf5_getters.get_song_hotttnesss(h5, songidx=row)
        end_of_fade_in = hdf5_getters.get_end_of_fade_in(h5, songidx=row)
        start_of_fade_out = hdf5_getters.get_start_of_fade_out(h5, songidx=row)

        #timestamp features
        #take last element and divide by length to get beats/unit time, segments/unit_time
        bars_start = hdf5_getters.get_bars_start(h5, songidx=row)
        beats_start = hdf5_getters.get_beats_start(h5, songidx=row)
        sections_start = hdf5_getters.get_sections_start(h5, songidx=row)
        tatums_start = hdf5_getters.get_tatums_start(h5, songidx=row)
        segments_start = hdf5_getters.get_segments_start(h5, songidx=row)
        if len(bars_start) == 0: bars_start = 0.
示例#26
0
def main():
    outputFile1 = open('SongCSVFinal.csv', 'w')
    csvRowString = ""

    #################################################
    #if you want to prompt the user for the order of attributes in the csv,
    #leave the prompt boolean set to True
    #else, set 'prompt' to False and set the order of attributes in the 'else'
    #clause
    prompt = False
    #################################################
    if prompt == True:
        while prompt:

            prompt = False

            csvAttributeString = raw_input(
                "\n\nIn what order would you like the colums of the CSV file?\n"
                + "Please delineate with commas. The options are: " +
                "AlbumName, AlbumID, ArtistID, ArtistLatitude, ArtistLocation, ArtistLongitude,"
                +
                " ArtistName, Danceability, Duration, KeySignature, KeySignatureConfidence, Tempo,"
                +
                " SongID, TimeSignature, TimeSignatureConfidence, Title, and Year.\n\n"
                +
                "For example, you may write \"Title, Tempo, Duration\"...\n\n"
                + "...or exit by typing 'exit'.\n\n")

            csvAttributeList = re.split('\W+', csvAttributeString)
            for i, v in enumerate(csvAttributeList):
                csvAttributeList[i] = csvAttributeList[i].lower()

            for attribute in csvAttributeList:
                # print "Here is the attribute: " + attribute + " \n"

                if attribute == 'AlbumID'.lower():
                    csvRowString += 'AlbumID'
                elif attribute == 'AlbumName'.lower():
                    csvRowString += 'AlbumName'
                elif attribute == 'ArtistID'.lower():
                    csvRowString += 'ArtistID'
                elif attribute == 'ArtistLatitude'.lower():
                    csvRowString += 'ArtistLatitude'
                elif attribute == 'ArtistLocation'.lower():
                    csvRowString += 'ArtistLocation'
                elif attribute == 'ArtistLongitude'.lower():
                    csvRowString += 'ArtistLongitude'
                elif attribute == 'ArtistName'.lower():
                    csvRowString += 'ArtistName'
                elif attribute == 'Danceability'.lower():
                    csvRowString += 'Danceability'
                elif attribute == 'Duration'.lower():
                    csvRowString += 'Duration'
                elif attribute == 'KeySignature'.lower():
                    csvRowString += 'KeySignature'
                elif attribute == 'KeySignatureConfidence'.lower():
                    csvRowString += 'KeySignatureConfidence'
                elif attribute == 'SongID'.lower():
                    csvRowString += "SongID"
                elif attribute == 'Tempo'.lower():
                    csvRowString += 'Tempo'
                elif attribute == 'TimeSignature'.lower():
                    csvRowString += 'TimeSignature'
                elif attribute == 'TimeSignatureConfidence'.lower():
                    csvRowString += 'TimeSignatureConfidence'
                elif attribute == 'Title'.lower():
                    csvRowString += 'Title'
                elif attribute == 'Year'.lower():
                    csvRowString += 'Year'
                elif attribute == 'Exit'.lower():
                    sys.exit()
                else:
                    prompt = True
                    print "=============="
                    print "I believe there has been an error with the input."
                    print "=============="
                    break

                csvRowString += ","

            lastIndex = len(csvRowString)
            csvRowString = csvRowString[0:lastIndex - 1]
            csvRowString += "\n"
            outputFile1.write(csvRowString)
            csvRowString = ""
    #else, if you want to hard code the order of the csv file and not prompt
    #the user,
    else:
        #################################################
        #change the order of the csv file here
        #Default is to list all available attributes (in alphabetical order)
        csvRowString = (
            "SongID,AlbumID,AlbumName,ArtistID,ArtistLatitude,ArtistLocation,"
            +
            "ArtistLongitude,ArtistName,Danceability,Duration,KeySignature," +
            "KeySignatureConfidence,Tempo,TimeSignature,TimeSignatureConfidence,"
            +
            "Title,Year,mbID,Energy,ArtistFamiliarity,Hotness,end_of_fade_in,key,keyConfidence,Loudness,"
            + "mode,mode_confidence,start_of_fade_out")
        #################################################

        csvAttributeList = re.split('\W+', csvRowString)
        for i, v in enumerate(csvAttributeList):
            csvAttributeList[i] = csvAttributeList[i].lower()
        outputFile1.write("SongNumber,")
        outputFile1.write(csvRowString + "\n")
        csvRowString = ""

    #################################################

    #Set the basedir here, the root directory from which the search
    #for files stored in a (hierarchical data structure) will originate
    basedir = "."  # "." As the default means the current directory
    ext = ".h5"  #Set the extension here. H5 is the extension for HDF5 files.
    #################################################
    #files = glob.glob(os.path.join(root,'*'+ext))
    #FOR LOOP
    for root, dirs, files in os.walk(basedir):
        files = glob.glob(os.path.join(root, '*' + ext))
        print(files)
        for f in files:
            songH5File = hdf5_getters.open_h5_file_read(f)
            print('hello 1')
            print(songH5File)
            song = Song(str(hdf5_getters.get_song_id(songH5File)))

            testDanceability = hdf5_getters.get_danceability(songH5File)
            # print type(testDanceability)
            # print ("Here is the danceability: ") + str(testDanceability)

            song.artistID = str(hdf5_getters.get_artist_id(songH5File))
            song.albumID = str(hdf5_getters.get_release_7digitalid(songH5File))
            song.albumName = str(hdf5_getters.get_release(songH5File))
            song.artistLatitude = str(
                hdf5_getters.get_artist_latitude(songH5File))
            song.artistLocation = str(
                hdf5_getters.get_artist_location(songH5File))
            song.artistLongitude = str(
                hdf5_getters.get_artist_longitude(songH5File))
            song.artistName = str(hdf5_getters.get_artist_name(songH5File))
            song.danceability = str(hdf5_getters.get_danceability(songH5File))
            song.duration = str(hdf5_getters.get_duration(songH5File))
            # song.setGenreList()
            song.keySignature = str(hdf5_getters.get_key(songH5File))
            song.keySignatureConfidence = str(
                hdf5_getters.get_key_confidence(songH5File))
            # song.lyrics = None
            # song.popularity = None
            song.tempo = str(hdf5_getters.get_tempo(songH5File))
            song.timeSignature = str(
                hdf5_getters.get_time_signature(songH5File))
            song.timeSignatureConfidence = str(
                hdf5_getters.get_time_signature_confidence(songH5File))
            song.title = str(hdf5_getters.get_title(songH5File))
            song.year = str(hdf5_getters.get_year(songH5File))
            song.mbID = str(hdf5_getters.get_artist_mbid(songH5File))
            song.energy = str(hdf5_getters.get_energy(songH5File))
            #song.beatConfidence = str(hdf5_getters.get_beats_confidence(songH5File))
            song.tempo = str(hdf5_getters.get_tempo(songH5File))
            song.loudness = str(hdf5_getters.get_loudness(songH5File))
            song.key = str(hdf5_getters.get_key(songH5File))
            song.artistFamiliarity = str(
                hdf5_getters.get_artist_familiarity(songH5File))
            song.keyConfidence = str(
                hdf5_getters.get_key_confidence(songH5File))
            song.hotness = str(hdf5_getters.get_artist_hotttnesss(songH5File))
            #song.sampleRate= str(hdf5_getters.get_analysis_sample_rate(songH5File))
            song.end_of_fade_in = str(
                hdf5_getters.get_end_of_fade_in(songH5File))
            song.mode = str(hdf5_getters.get_mode(songH5File))
            song.mode_confidence = str(
                hdf5_getters.get_mode_confidence(songH5File))
            song.start_of_fade_out = str(
                hdf5_getters.get_start_of_fade_out(songH5File))

            #print song count
            csvRowString += str(song.songCount) + ","

            for attribute in csvAttributeList:
                # print "Here is the attribute: " + attribute + " \n"

                if attribute == 'AlbumID'.lower():
                    csvRowString += song.albumID
                elif attribute == 'AlbumName'.lower():
                    albumName = song.albumName
                    albumName = albumName.replace(',', "")
                    csvRowString += "\"" + albumName + "\""
                elif attribute == 'ArtistID'.lower():
                    csvRowString += "\"" + song.artistID + "\""
                elif attribute == 'ArtistLatitude'.lower():
                    latitude = song.artistLatitude
                    if latitude == 'nan':
                        latitude = ''
                    csvRowString += latitude
                elif attribute == 'ArtistLocation'.lower():
                    location = song.artistLocation
                    location = location.replace(',', '')
                    csvRowString += "\"" + location + "\""
                elif attribute == 'ArtistLongitude'.lower():
                    longitude = song.artistLongitude
                    if longitude == 'nan':
                        longitude = ''
                    csvRowString += longitude
                elif attribute == 'ArtistName'.lower():
                    csvRowString += "\"" + song.artistName + "\""
                elif attribute == 'Danceability'.lower():
                    csvRowString += song.danceability
                elif attribute == 'Duration'.lower():
                    csvRowString += song.duration
                elif attribute == 'KeySignature'.lower():
                    csvRowString += song.keySignature
                elif attribute == 'KeySignatureConfidence'.lower():
                    # print "key sig conf: " + song.timeSignatureConfidence
                    csvRowString += song.keySignatureConfidence
                elif attribute == 'SongID'.lower():
                    csvRowString += "\"" + song.id + "\""
                elif attribute == 'Tempo'.lower():
                    # print "Tempo: " + song.tempo
                    csvRowString += song.tempo
                elif attribute == 'TimeSignature'.lower():
                    csvRowString += song.timeSignature
                elif attribute == 'TimeSignatureConfidence'.lower():
                    # print "time sig conf: " + song.timeSignatureConfidence
                    csvRowString += song.timeSignatureConfidence
                elif attribute == 'Title'.lower():
                    csvRowString += "\"" + song.title + "\""
                elif attribute == 'Year'.lower():
                    csvRowString += song.year
                elif attribute == 'mbID'.lower():
                    csvRowString += song.mbID
                elif attribute == 'Energy'.lower():
                    csvRowString += song.energy
                elif attribute == 'ArtistFamiliarity'.lower():
                    csvRowString += song.artistFamiliarity
                elif attribute == 'Hotness'.lower():
                    csvRowString += song.hotness

#elif attribute == 'SampleRate'.lower():
#csvRowString += song.sampleRate
                elif attribute == 'end_of_fade_in'.lower():
                    csvRowString += song.end_of_fade_in
                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 == 'mode_confidence'.lower():
                    csvRowString += song.mode_confidence
                elif attribute == 'start_of_fade_out'.lower():
                    csvRowString += song.start_of_fade_out
                else:
                    csvRowString += "Erm. This didn't work. Error. :( :(\n"

                csvRowString += ","

            #Remove the final comma from each row in the csv
            lastIndex = len(csvRowString)
            csvRowString = csvRowString[0:lastIndex - 1]
            csvRowString += "\n"
            outputFile1.write(csvRowString)
            csvRowString = ""

            songH5File.close()

    outputFile1.close()
def main():
    outputFile1 = open('SongCSV.csv', 'w')
    csvRowString = ""

    #################################################
    #if you want to prompt the user for the order of attributes in the csv,
    #leave the prompt boolean set to True
    #else, set 'prompt' to False and set the order of attributes in the 'else'
    #clause
    prompt = False
    #################################################
    if prompt == True:
        while prompt:

            prompt = False

            csvAttributeString = raw_input("\n\nIn what order would you like the colums of the CSV file?\n" +
                "Please delineate with commas. The options are: " +
                "AlbumName, AlbumID, ArtistID, ArtistLatitude, ArtistLocation, ArtistLongitude,"+
                " ArtistName, Danceability, Duration, KeySignature, KeySignatureConfidence, Tempo," +
                " SongID, TimeSignature, TimeSignatureConfidence, Title, and Year.\n\n" +
                "For example, you may write \"Title, Tempo, Duration\"...\n\n" +
                "...or exit by typing 'exit'.\n\n")

            csvAttributeList = re.split('\W+', csvAttributeString)
            for i, v in enumerate(csvAttributeList):
                csvAttributeList[i] = csvAttributeList[i].lower()

            for attribute in csvAttributeList:
                # print "Here is the attribute: " + attribute + " \n"


                if attribute == 'AlbumID'.lower():
                    csvRowString += 'AlbumID'
                elif attribute == 'AlbumName'.lower():
                    csvRowString += 'AlbumName'
                elif attribute == 'ArtistID'.lower():
                    csvRowString += 'ArtistID'
                elif attribute == 'ArtistLatitude'.lower():
                    csvRowString += 'ArtistLatitude'
                elif attribute == 'ArtistLocation'.lower():
                    csvRowString += 'ArtistLocation'
                elif attribute == 'ArtistLongitude'.lower():
                    csvRowString += 'ArtistLongitude'
                elif attribute == 'ArtistName'.lower():
                    csvRowString += 'ArtistName'
                elif attribute == 'Danceability'.lower():
                    csvRowString += 'Danceability'
                elif attribute == 'Duration'.lower():
                    csvRowString += 'Duration'
                elif attribute == 'KeySignature'.lower():
                    csvRowString += 'KeySignature'
                elif attribute == 'KeySignatureConfidence'.lower():
                    csvRowString += 'KeySignatureConfidence'
                elif attribute == 'SongID'.lower():
                    csvRowString += "SongID"
                elif attribute == 'Tempo'.lower():
                    csvRowString += 'Tempo'
                elif attribute == 'TimeSignature'.lower():
                    csvRowString += 'TimeSignature'
                elif attribute == 'TimeSignatureConfidence'.lower():
                    csvRowString += 'TimeSignatureConfidence'
                elif attribute == 'Title'.lower():
                    csvRowString += 'Title'
                elif attribute == 'Year'.lower():
                    csvRowString += 'Year'
                elif attribute == 'track_id'.lower():
                    csvRowString += 'track_id' 
                elif attribute == 'artist_familiarity'.lower():
                    csvRowString += 'artist_familiarity' 
                elif attribute == 'artist_hotttnesss'.lower():
                    csvRowString += 'artist_hotttnesss' 
                elif attribute == 'artist_mbid'.lower():
                    csvRowString += 'artist_mbid'
                elif attribute == 'artist_playmeid'.lower():
                    csvRowString += 'artist_playmeid'
                elif attribute == 'artist_7digitalid'.lower():
                    csvRowString += 'artist_7digitalid'
                elif attribute == 'release'.lower():
                    csvRowString += 'release' 
                elif attribute == 'release_7digitalid'.lower():
                    csvRowString += 'release_7digitalid' 
                elif attribute == 'song_hotttnesss'.lower():
                    csvRowString += 'song_hotttnesss'
                elif attribute == 'track_7digitalid'.lower():
                    csvRowString += 'track_7digitalid' 
                elif attribute == 'analysis_sample_rate'.lower():
                    csvRowString += 'analysis_sample_rate' 
                elif attribute == 'audio_md5'.lower():
                    csvRowString += 'audio_md5' 
                elif attribute == 'end_of_fade_in'.lower():
                    csvRowString += 'end_of_fade_in'
                elif attribute == 'energy'.lower():
                    csvRowString += 'energy'  
                elif attribute == 'key'.lower():
                    csvRowString += 'key'  
                elif attribute == 'key_confidence'.lower():
                    csvRowString += 'key_confidence' 
                elif attribute == 'loudness'.lower():
                    csvRowString += 'loudness' 
                elif attribute == 'mode'.lower():
                    csvRowString += 'mode'  
                elif attribute == 'mode_confidence'.lower():
                    csvRowString += 'mode_confidence'   
                elif attribute == 'start_of_fade_out'.lower():
                    csvRowString += 'start_of_fade_out'                                                                                
                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,track_id,artist_hotttnesss,artist_mbid,artist_playmeid,artist_7digitalid,"+
            "release,release_7digitalid,song_hotttnesss,track_7digitalid,analysis_sample_rate,audio_md5,"+
            "end_of_fade_in,energy,key,key_confidence,loudness,mode,mode_confidence,start_of_fade_out")
        #################################################

        csvAttributeList = re.split('\W+', csvRowString)
        for i, v in enumerate(csvAttributeList):
            csvAttributeList[i] = csvAttributeList[i].lower()
        outputFile1.write("SongNumber,");
        outputFile1.write(csvRowString + "\n");
        csvRowString = ""  

    #################################################


    #Set the basedir here, the root directory from which the search
    #for files stored in a (hierarchical data structure) will originate
    basedir = "/vagrant/genrepython/MillionSongSubset" # "." As the default means the current directory
    ext = ".h5" #Set the extension here. H5 is the extension for HDF5 files.
    #################################################

    #FOR LOOP
    for root, dirs, files in os.walk(basedir):        
        files = glob.glob(os.path.join(root,'*'+ext))
        for f in files:
            print f

            songH5File = hdf5_getters.open_h5_file_read(f)
            song = Song(str(hdf5_getters.get_song_id(songH5File)))

            testDanceability = hdf5_getters.get_danceability(songH5File)
            # print type(testDanceability)
            # print ("Here is the danceability: ") + str(testDanceability)

            song.artistID = str(hdf5_getters.get_artist_id(songH5File))
            song.albumID = str(hdf5_getters.get_release_7digitalid(songH5File))
            song.albumName = str(hdf5_getters.get_release(songH5File))
            song.artistLatitude = str(hdf5_getters.get_artist_latitude(songH5File))
            song.artistLocation = str(hdf5_getters.get_artist_location(songH5File))
            song.artistLongitude = str(hdf5_getters.get_artist_longitude(songH5File))
            song.artistName = str(hdf5_getters.get_artist_name(songH5File))
            song.danceability = str(hdf5_getters.get_danceability(songH5File))
            song.duration = str(hdf5_getters.get_duration(songH5File))
            # song.setGenreList()
            song.keySignature = str(hdf5_getters.get_key(songH5File))
            song.keySignatureConfidence = str(hdf5_getters.get_key_confidence(songH5File))
            # song.lyrics = None
            # song.popularity = None
            song.tempo = str(hdf5_getters.get_tempo(songH5File))
            song.timeSignature = str(hdf5_getters.get_time_signature(songH5File))
            song.timeSignatureConfidence = str(hdf5_getters.get_time_signature_confidence(songH5File))
            song.title = str(hdf5_getters.get_title(songH5File))
            song.year = str(hdf5_getters.get_year(songH5File))
            song.track_id = str(hdf5_getters.get_track_id(songH5File))
            song.artist_familiarity = str(hdf5_getters.get_artist_familiarity(songH5File))
            song.artist_hotttnesss = str(hdf5_getters.get_artist_hotttnesss(songH5File))
            song.artist_mbid = str(hdf5_getters.get_artist_mbid(songH5File))
            song.artist_playmeid = str(hdf5_getters.get_artist_playmeid(songH5File))
            song.artist_7digitalid = str(hdf5_getters.get_artist_7digitalid(songH5File))
            song.release = str(hdf5_getters.get_release(songH5File))
            song.release_7digitalid = str(hdf5_getters.get_release_7digitalid(songH5File))
            song.song_hotttnesss = str(hdf5_getters.get_song_hotttnesss(songH5File))
            song.track_7digitalid = str(hdf5_getters.get_track_7digitalid(songH5File))
            song.analysis_sample_rate = str(hdf5_getters.get_analysis_sample_rate(songH5File))
            song.audio_md5 = str(hdf5_getters.get_audio_md5(songH5File))
            song.end_of_fade_in = str(hdf5_getters.get_end_of_fade_in(songH5File))
            song.energy = str(hdf5_getters.get_energy(songH5File))
            song.key = str(hdf5_getters.get_key(songH5File))
            song.key_confidence = str(hdf5_getters.get_key_confidence(songH5File))
            song.loudness = str(hdf5_getters.get_loudness(songH5File))
            song.mode = str(hdf5_getters.get_mode(songH5File))
            song.mode_confidence = str(hdf5_getters.get_mode_confidence(songH5File))
            song.start_of_fade_out = str(hdf5_getters.get_start_of_fade_out(songH5File))

            #print song count
            csvRowString += str(song.songCount) + ","

            for attribute in csvAttributeList:
                # print "Here is the attribute: " + attribute + " \n"

                if attribute == 'AlbumID'.lower():
                    csvRowString += song.albumID
                elif attribute == 'AlbumName'.lower():
                    albumName = song.albumName
                    albumName = albumName.replace(',',"")
                    csvRowString += "\"" + albumName + "\""
                elif attribute == 'ArtistID'.lower():
                    csvRowString += "\"" + song.artistID + "\""
                elif attribute == 'ArtistLatitude'.lower():
                    latitude = song.artistLatitude
                    if latitude == 'nan':
                        latitude = ''
                    csvRowString += latitude
                elif attribute == 'ArtistLocation'.lower():
                    location = song.artistLocation
                    location = location.replace(',','')
                    csvRowString += "\"" + location + "\""
                elif attribute == 'ArtistLongitude'.lower():
                    longitude = song.artistLongitude
                    if longitude == 'nan':
                        longitude = ''
                    csvRowString += longitude                
                elif attribute == 'ArtistName'.lower():
                    csvRowString += "\"" + song.artistName + "\""                
                elif attribute == 'Danceability'.lower():
                    csvRowString += song.danceability
                elif attribute == 'Duration'.lower():
                    csvRowString += song.duration
                elif attribute == 'KeySignature'.lower():
                    csvRowString += song.keySignature
                elif attribute == 'KeySignatureConfidence'.lower():
                    # print "key sig conf: " + song.timeSignatureConfidence                                 
                    csvRowString += song.keySignatureConfidence
                elif attribute == 'SongID'.lower():
                    csvRowString += "\"" + song.id + "\""
                elif attribute == 'Tempo'.lower():
                    # print "Tempo: " + song.tempo
                    csvRowString += song.tempo
                elif attribute == 'TimeSignature'.lower():
                    csvRowString += song.timeSignature
                elif attribute == 'TimeSignatureConfidence'.lower():
                    # print "time sig conf: " + song.timeSignatureConfidence                                   
                    csvRowString += song.timeSignatureConfidence
                elif attribute == 'Title'.lower():
                    csvRowString += "\"" + song.title + "\""
                elif attribute == 'Year'.lower():
                    csvRowString += song.year
                elif attribute == 'track_id'.lower():
                    csvRowString += song.track_id 
                elif attribute == 'artist_familiarity'.lower():
                    csvRowString += song.artist_familiarity  
                elif attribute == 'artist_hotttnesss'.lower():
                    csvRowString += song.artist_hotttnesss 
                elif attribute == 'artist_mbid'.lower():
                    csvRowString += song.artist_mbid
                elif attribute == 'artist_playmeid'.lower():
                    csvRowString += song.artist_playmeid 
                elif attribute == 'artist_7digitalid'.lower():
                    csvRowString += song.artist_7digitalid 
                elif attribute == 'release'.lower():
                    csvRowString += song.release
                elif attribute == 'release_7digitalid'.lower():
                    csvRowString += song.release_7digitalid 
                elif attribute == 'song_hotttnesss'.lower():
                    csvRowString += song.song_hotttnesss  
                elif attribute == 'track_7digitalid'.lower():
                    csvRowString += song.track_7digitalid 
                elif attribute == 'analysis_sample_rate'.lower():
                    csvRowString += song.analysis_sample_rate  
                elif attribute == 'audio_md5'.lower():
                    csvRowString += song.audio_md5 
                elif attribute == 'end_of_fade_in'.lower():
                    csvRowString += song.end_of_fade_in  
                elif attribute == 'energy'.lower():
                    csvRowString += song.energy 
                elif attribute == 'key'.lower():
                    csvRowString += song.key 
                elif attribute == 'key_confidence'.lower():
                    csvRowString += song.key_confidence 
                elif attribute == 'loudness'.lower():
                    csvRowString += song.loudness
                elif attribute == 'mode'.lower():
                    csvRowString += song.mode   
                elif attribute == 'mode_confidence'.lower():
                    csvRowString += song.mode_confidence    
                elif attribute == 'start_of_fade_out'.lower():
                    csvRowString += song.start_of_fade_out                                                                              
                else:
                    csvRowString += "Erm. This didn't work. Error. :( :(\n"

                csvRowString += ","

            #Remove the final comma from each row in the csv
            lastIndex = len(csvRowString)
            csvRowString = csvRowString[0:lastIndex-1]
            csvRowString += "\n"
            outputFile1.write(csvRowString)
            csvRowString = ""

            songH5File.close()

    outputFile1.close()
def writeSingleHDF5FileToTxtFile(songHDF5FileName):
    global maximumArtistNameLen
    global maximumArtistTagLen
    global maximumSongNameLen
    global maximumAlbumNameLen
    """
    This function does 3 simple things:
    - open the song file
    - get artist ID and put it
    - close the file
    """
    songHDF5File = GETTERS.open_h5_file_read(songHDF5FileName)

    songID = GETTERS.get_song_id(songHDF5File)
    songName = GETTERS.get_title(songHDF5File)
    artistID = GETTERS.get_artist_id(songHDF5File)
    songAlbum = GETTERS.get_release(songHDF5File)
    songYear = GETTERS.get_year(songHDF5File)
    songTempo = GETTERS.get_tempo(songHDF5File)
    songDanceability = GETTERS.get_danceability(songHDF5File)
    songDuration = GETTERS.get_duration(songHDF5File)
    songEnergy = GETTERS.get_energy(songHDF5File)
    songKey = GETTERS.get_key(songHDF5File)
    songLoudness = GETTERS.get_loudness(songHDF5File)
    songMode = GETTERS.get_mode(songHDF5File)
    songTimeSignature = GETTERS.get_time_signature(songHDF5File)

    songsTableFile.write(songID + "\t" + songName + "\t" + artistID + "\t" +
                         songAlbum + "\t" + str(songYear) + "\t" +
                         str(songTempo) + "\t" + str(songDanceability) + "\t" +
                         str(songDuration) + "\t" + str(songEnergy) + "\t" +
                         str(songKey) + "\t" + str(songLoudness) + "\t" +
                         str(songMode) + "\t" + str(songTimeSignature) +
                         "\t\n")

    artistName = GETTERS.get_artist_name(songHDF5File)
    artistFamiliarity = GETTERS.get_artist_familiarity(songHDF5File)
    artistTagsArray = GETTERS.get_artist_mbtags(songHDF5File)

    artistsTableFile.write(artistID + "\t" + artistName + "\t" +
                           str(artistFamiliarity) + "\t\n")

    if len(songName) > maximumSongNameLen:
        maximumSongNameLen = len(songName)

    if len(songAlbum) > maximumAlbumNameLen:
        maximumAlbumNameLen = len(songAlbum)

    if len(artistName) > maximumArtistNameLen:
        maximumArtistNameLen = len(artistName)

    for artistTag in artistTagsArray:
        if artistTag in allowedTagsSet:

            artistsTagsTableFile.write(artistID + "\t" + artistTag + "\t\n")
            if artistTag not in tagsSet:
                tagsTableFile.write(artistTag + "\t\n")
                tagsSet.add(artistTag)
            if len(artistTag) > maximumArtistTagLen:
                maximumArtistTagLen = len(artistTag)

    similarArtists = GETTERS.get_similar_artists(songHDF5File)

    for similarArtist in similarArtists:
        similarArtistsPairsList.add((artistID, similarArtist))

    artistsIDsSet.add(artistID)
    artistsNamesSet.add(artistName)

    songHDF5File.close()
示例#29
0
def main():
    outputFile = open('songs.csv', 'w')
    writer = csv.writer(outputFile)

    csvRowString = "song_number,artist_familiarity,artist_hotttnesss,artist_id,artist_mbid,artist_playmeid,artist_7digitalid,artist_latitude,artist_longitude,artist_location,artist_name,release,release_7digitalid,song_id,song_hotttnesss,title,track_7digitalid,analysis_sample_rate,audio_md5,danceability,duration,end_of_fade_in,energy,key,key_confidence,loudness,mode,mode_confidence,start_of_fade_out,tempo,time_signature,time_signature_confidence,track_id,year"

    outputFile.write(csvRowString + "\n")
    csvRowString = ""

    #################################################
    #Set the basedir here, the root directory from which the search
    #for files stored in a (hierarchical data structure) will originate
    basedir = "."  # "." As the default means the current directory
    ext = ".H5"  #Set the extension here. H5 is the extension for HDF5 files.
    #################################################

    #FOR LOOP
    songCount = 0
    for root, dirs, files in os.walk(basedir):
        files = glob.glob(os.path.join(root, '*' + ext))
        for f in files:
            print(f)

            songH5File = hdf5_getters.open_h5_file_read(f)

            values = [
                songCount,
                hdf5_getters.get_artist_familiarity(songH5File),
                hdf5_getters.get_artist_hotttnesss(songH5File),
                hdf5_getters.get_artist_id(songH5File),
                hdf5_getters.get_artist_mbid(songH5File),
                hdf5_getters.get_artist_playmeid(songH5File),
                hdf5_getters.get_artist_7digitalid(songH5File),
                hdf5_getters.get_artist_latitude(songH5File),
                hdf5_getters.get_artist_longitude(songH5File),
                hdf5_getters.get_artist_location(songH5File),
                hdf5_getters.get_artist_name(songH5File),
                hdf5_getters.get_release(songH5File),
                hdf5_getters.get_release_7digitalid(songH5File),
                hdf5_getters.get_song_id(songH5File),
                hdf5_getters.get_song_hotttnesss(songH5File),
                hdf5_getters.get_title(songH5File),
                hdf5_getters.get_track_7digitalid(songH5File),
                hdf5_getters.get_analysis_sample_rate(songH5File),
                hdf5_getters.get_audio_md5(songH5File),
                hdf5_getters.get_danceability(songH5File),
                hdf5_getters.get_duration(songH5File),
                hdf5_getters.get_end_of_fade_in(songH5File),
                hdf5_getters.get_energy(songH5File),
                hdf5_getters.get_key(songH5File),
                hdf5_getters.get_key_confidence(songH5File),
                hdf5_getters.get_loudness(songH5File),
                hdf5_getters.get_mode(songH5File),
                hdf5_getters.get_mode_confidence(songH5File),
                hdf5_getters.get_start_of_fade_out(songH5File),
                hdf5_getters.get_tempo(songH5File),
                hdf5_getters.get_time_signature(songH5File),
                hdf5_getters.get_time_signature_confidence(songH5File),
                hdf5_getters.get_track_id(songH5File),
                hdf5_getters.get_year(songH5File)
            ]
            songH5File.close()
            songCount = songCount + 1

            writer.writerow(values)

    outputFile.close()
 artist_terms = ','.join(str(e) for e in GETTERS.get_artist_terms(h5, i)) # array
 #artist_terms_freq = ','.join(str(e) for e in GETTERS.get_artist_terms_freq(h5, i)) # array
 #artist_terms_weight = ','.join(str(e) for e in GETTERS.get_artist_terms_weight(h5, i)) # array
 #audio_md5 = GETTERS.get_audio_md5(h5, i)
 #bars_confidence = ','.join(str(e) for e in GETTERS.get_bars_confidence(h5, i)) # array
 #bars_start = ','.join(str(e) for e in GETTERS.get_bars_start(h5, i)) # array
 #beats_confidence = ','.join(str(e) for e in GETTERS.get_beats_confidence(h5, i)) # array
 #beats_start = ','.join(str(e) for e in GETTERS.get_beats_start(h5, i)) # array
 danceability = GETTERS.get_danceability(h5, i)
 duration = GETTERS.get_duration(h5, i)
 end_of_fade_in = GETTERS.get_end_of_fade_in(h5, i)
 energy = GETTERS.get_energy(h5, i)
 key = GETTERS.get_key(h5, i)
 key_confidence = GETTERS.get_key_confidence(h5, i)
 loudness = GETTERS.get_loudness(h5, i)
 mode = GETTERS.get_mode(h5, i)
 mode_confidence = GETTERS.get_mode_confidence(h5, i)
 release = GETTERS.get_release(h5, i)
 release_7digitalid = GETTERS.get_release_7digitalid(h5, i)
 #sections_confidence = ','.join(str(e) for e in GETTERS.get_sections_confidence(h5, i)) # array
 #sections_start = ','.join(str(e) for e in GETTERS.get_sections_start(h5, i)) # array
 #segments_confidence = ','.join(str(e) for e in GETTERS.get_segments_confidence(h5, i)) # array
 #segments_loudness_max = ','.join(str(e) for e in GETTERS.get_segments_loudness_max(h5, i)) # array
 #segments_loudness_max_time = ','.join(str(e) for e in GETTERS.get_segments_loudness_max_time(h5, i)) # array
 #segments_loudness_start = ','.join(str(e) for e in GETTERS.get_segments_loudness_start(h5, i)) # array
 #segments_pitches = ','.join(str(e) for e in GETTERS.get_segments_pitches(h5, i)) # array
 #segments_start = ','.join(str(e) for e in GETTERS.get_segments_start(h5, i)) # array
 #segments_timbre = ','.join(str(e) for e in GETTERS.get_segments_timbre(h5, i)) # array
 similar_artists = ','.join(str(e) for e in GETTERS.get_similar_artists(h5, i)) # array
 song_hotttnesss = GETTERS.get_song_hotttnesss(h5, i)
 song_id = GETTERS.get_song_id(h5, i)
示例#31
0
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()
示例#32
0
def classify(h5):
	output_array={}
	# duration
	duration=hdf5_getters.get_duration(h5)
	output_array["duration"]=duration	### ADDED VALUE TO ARRAY
	# number of bars
	bars=hdf5_getters.get_bars_start(h5)
	num_bars=len(bars)
	output_array["num_bars"]=num_bars	### ADDED VALUE TO ARRAY
	# mean and variance in bar length
	bar_length=numpy.ediff1d(bars)
	variance_bar_length=numpy.var(bar_length)
	output_array["variance_bar_length"]=variance_bar_length	### ADDED VALUE TO ARRAY
	# number of beats
	beats=hdf5_getters.get_beats_start(h5)
	num_beats=len(beats)
	output_array["num_beats"]=num_beats	### ADDED VALUE TO ARRAY
	# mean and variance in beats length
	beats_length=numpy.ediff1d(beats)
	variance_beats_length=numpy.var(bar_length)
	output_array["variance_beats_length"]=variance_beats_length	### ADDED VALUE TO ARRAY
	# danceability
	danceability=hdf5_getters.get_danceability(h5)
	output_array["danceability"]=danceability	### ADDED VALUE TO ARRAY
	# end of fade in
	end_of_fade_in=hdf5_getters.get_end_of_fade_in(h5)
	output_array["end_of_fade_in"]=end_of_fade_in	### ADDED VALUE TO ARRAY
	# energy
	energy=hdf5_getters.get_energy(h5)
	output_array["energy"]=energy	### ADDED VALUE TO ARRAY
	# key
	key=hdf5_getters.get_key(h5)
	output_array["key"]=int(key)	### ADDED VALUE TO ARRAY
	# loudness
	loudness=hdf5_getters.get_loudness(h5)
	output_array["loudness"]=loudness	### ADDED VALUE TO ARRAY
	# mode
	mode=hdf5_getters.get_mode(h5)
	output_array["mode"]=int(mode)	### ADDED VALUE TO ARRAY
	# number sections
	sections=hdf5_getters.get_sections_start(h5)
	num_sections=len(sections)
	output_array["num_sections"]=num_sections	### ADDED VALUE TO ARRAY
	# mean and variance in sections length
	sections_length=numpy.ediff1d(sections)
	variance_sections_length=numpy.var(sections)
	output_array["variance_sections_length"]=variance_sections_length	### ADDED VALUE TO ARRAY
	# number segments
	segments=hdf5_getters.get_segments_start(h5)
	num_segments=len(segments)
	output_array["num_segments"]=num_segments	### ADDED VALUE TO ARRAY
	# mean and variance in segments length
	segments_length=numpy.ediff1d(segments)
	variance_segments_length=numpy.var(segments)
	output_array["variance_segments_length"]=variance_segments_length	### ADDED VALUE TO ARRAY
	# segment loudness max
	segment_loudness_max_array=hdf5_getters.get_segments_loudness_max(h5)
	segment_loudness_max_time_array=hdf5_getters.get_segments_loudness_max_time(h5)
	segment_loudness_max_index=0
	for i in range(len(segment_loudness_max_array)):
		if segment_loudness_max_array[i]>segment_loudness_max_array[segment_loudness_max_index]:
			segment_loudness_max_index=i
	segment_loudness_max=segment_loudness_max_array[segment_loudness_max_index]
	segment_loudness_max_time=segment_loudness_max_time_array[segment_loudness_max_index]
	output_array["segment_loudness_max"]=segment_loudness_max	### ADDED VALUE TO ARRAY
	output_array["segment_loudness_time"]=segment_loudness_max_time	### ADDED VALUE TO ARRAY
			
	# POSSIBLE TODO: use average function instead and weight by segment length
	# segment loudness mean (start)
	segment_loudness_array=hdf5_getters.get_segments_loudness_start(h5)
	segment_loudness_mean=numpy.mean(segment_loudness_array)
	output_array["segment_loudness_mean"]=segment_loudness_mean	### ADDED VALUE TO ARRAY
	# segment loudness variance (start)
	segment_loudness_variance=numpy.var(segment_loudness_array)
	output_array["segment_loudness_variance"]=segment_loudness_variance	### ADDED VALUE TO ARRAY
	# segment pitches
	segment_pitches_array=hdf5_getters.get_segments_pitches(h5)
	segment_pitches_mean=numpy.mean(segment_pitches_array,axis=0).tolist()
	output_array["segment_pitches_mean"]=segment_pitches_mean
	# segment pitches variance (start)
	segment_pitches_variance=numpy.var(segment_pitches_array,axis=0).tolist()
	output_array["segment_pitches_variance"]=segment_pitches_variance
	# segment timbres
	segment_timbres_array=hdf5_getters.get_segments_timbre(h5)
	segment_timbres_mean=numpy.mean(segment_timbres_array,axis=0).tolist()
	output_array["segment_timbres_mean"]=segment_timbres_mean
	# segment timbres variance (start)
	segment_timbres_variance=numpy.var(segment_timbres_array,axis=0).tolist()
	output_array["segment_timbres_variance"]=segment_timbres_variance
	# hotttnesss
	hottness=hdf5_getters.get_song_hotttnesss(h5,0)
	output_array["hottness"]=hottness	### ADDED VALUE TO ARRAY
	# duration-start of fade out
	start_of_fade_out=hdf5_getters.get_start_of_fade_out(h5)
	fade_out=duration-start_of_fade_out
	output_array["fade_out"]=fade_out	### ADDED VALUE TO ARRAY
	# tatums
	tatums=hdf5_getters.get_tatums_start(h5)
	num_tatums=len(tatums)
	output_array["num_tatums"]=num_tatums	### ADDED VALUE TO ARRAY
	# mean and variance in tatums length
	tatums_length=numpy.ediff1d(tatums)
	variance_tatums_length=numpy.var(tatums_length)
	output_array["variance_tatums_length"]=variance_tatums_length	### ADDED VALUE TO ARRAY
	# tempo
	tempo=hdf5_getters.get_tempo(h5)
	output_array["tempo"]=tempo	### ADDED VALUE TO ARRAY
	# time signature
	time_signature=hdf5_getters.get_time_signature(h5)
	output_array["time_signature"]=int(time_signature)	### ADDED VALUE TO ARRAY
	# year
	year=hdf5_getters.get_year(h5)
	output_array["year"]=int(year)	### ADDED VALUE TO ARRAY
	# artist terms
	artist_terms=hdf5_getters.get_artist_terms(h5,0)
	output_array["artist_terms"]=artist_terms.tolist()
	artist_terms_freq=hdf5_getters.get_artist_terms_freq(h5,0)
	output_array["artist_terms_freq"]=artist_terms_freq.tolist()
	artist_name=hdf5_getters.get_artist_name(h5,0)
	output_array["artist_name"]=artist_name
	artist_id=hdf5_getters.get_artist_id(h5,0)
	output_array["artist_id"]=artist_id
	# title
	title=hdf5_getters.get_title(h5,0)
	output_array["title"]=title

	return output_array
def data_to_flat_file(basedir,ext='.h5') :
    """ This function extracts the information from the tables and creates the flat file. """
    count = 0; #song counter
    list_to_write= []
    group_index=0
    row_to_write = ""
    writer = csv.writer(open("complete.csv", "wb"))
    for root, dirs, files in os.walk(basedir):
	files = glob.glob(os.path.join(root,'*'+ext))
        for f in files:
	    row=[]
	    print f
            h5 = hdf5_getters.open_h5_file_read(f)
	    title = hdf5_getters.get_title(h5) 
	    title= title.replace('"','') 
            row.append(title)
	    comma=title.find(',')
	    if	comma != -1:
		    print title
		    time.sleep(1)
	    album = hdf5_getters.get_release(h5)
	    album= album.replace('"','')
            row.append(album)
	    comma=album.find(',')
	    if	comma != -1:
		    print album
		    time.sleep(1)
	    artist_name = hdf5_getters.get_artist_name(h5)
	    comma=artist_name.find(',')
	    if	comma != -1:
		    print artist_name
		    time.sleep(1)
	    artist_name= artist_name.replace('"','')
            row.append(artist_name)
	    duration = hdf5_getters.get_duration(h5)
            row.append(duration)
	    samp_rt = hdf5_getters.get_analysis_sample_rate(h5)
            row.append(samp_rt)
	    artist_7digitalid = hdf5_getters.get_artist_7digitalid(h5)
            row.append(artist_7digitalid)
	    artist_fam = hdf5_getters.get_artist_familiarity(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(artist_fam) == True:
	            artist_fam=-1
            row.append(artist_fam)
	    artist_hotness= hdf5_getters.get_artist_hotttnesss(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(artist_hotness) == True:
	             artist_hotness=-1
            row.append(artist_hotness)
	    artist_id = hdf5_getters.get_artist_id(h5)
            row.append(artist_id)           
	    artist_lat = hdf5_getters.get_artist_latitude(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(artist_lat) == True:
	            artist_lat=-1
            row.append(artist_lat)
	    artist_loc = hdf5_getters.get_artist_location(h5)
            row.append(artist_loc)
	    artist_lon = hdf5_getters.get_artist_longitude(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(artist_lon) == True:
	            artist_lon=-1
            row.append(artist_lon)
	    artist_mbid = hdf5_getters.get_artist_mbid(h5)
            row.append(artist_mbid)

	    #Getting the genre				       
            art_trm = hdf5_getters.get_artist_terms(h5)
            trm_freq = hdf5_getters.get_artist_terms_freq(h5)
	    trn_wght = hdf5_getters.get_artist_terms_weight(h5)
	    a_mb_tags = hdf5_getters.get_artist_mbtags(h5)
	    genre_indexes=get_genre_indexes(trm_freq) 		    #index of the highest freq
	    genre_set=0					            #flag to see if the genre has been set or not
	    final_genre=[]
	    genres_so_far=[]
	    for i in range(len(genre_indexes)):
		    genre_tmp=get_genre(art_trm,genre_indexes[i])   #genre that corresponds to the highest freq
		    genres_so_far=genre_dict.get_genre_in_dict(genre_tmp) #getting the genre from the dictionary
		    if len(genres_so_far) != 0:
			for i in genres_so_far:
				final_genre.append(i)
			    	genre_set=1
			
			
	    if genre_set == 1:
		col_num=[]
		for i in final_genre:
			column=int(i)				#getting the column number of the genre
			col_num.append(column)
	
		genre_array=genre_columns(col_num)	                #genre array 
	        for i in range(len(genre_array)):                   	#appending the genre_array to the row 
			row.append(genre_array[i])
	    else:
		genre_array=genre_columns(-1)				#when there is no genre matched, return an array of [0...0]
	        for i in range(len(genre_array)):                   	#appending the genre_array to the row 
			row.append(genre_array[i])
					

	    artist_pmid = hdf5_getters.get_artist_playmeid(h5)
            row.append(artist_pmid)
	    audio_md5 = hdf5_getters.get_audio_md5(h5)
            row.append(audio_md5)
	    danceability = hdf5_getters.get_danceability(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(danceability) == True:
	            danceability=-1
            row.append(danceability)
	    end_fade_in =hdf5_getters.get_end_of_fade_in(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(end_fade_in) == True:
	            end_fade_in=-1
            row.append(end_fade_in)
	    energy = hdf5_getters.get_energy(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(energy) == True:
	            energy=-1
            row.append(energy)
            song_key = hdf5_getters.get_key(h5)
            row.append(song_key)
	    key_c = hdf5_getters.get_key_confidence(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(key_c) == True:
	            key_c=-1
            row.append(key_c)
	    loudness = hdf5_getters.get_loudness(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(loudness) == True:
	            loudness=-1
            row.append(loudness)
	    mode = hdf5_getters.get_mode(h5)
            row.append(mode)
	    mode_conf = hdf5_getters.get_mode_confidence(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(mode_conf) == True:
	            mode_conf=-1
            row.append(mode_conf)
	    release_7digitalid = hdf5_getters.get_release_7digitalid(h5)
            row.append(release_7digitalid)
	    song_hot = hdf5_getters.get_song_hotttnesss(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(song_hot) == True:
	            song_hot=-1
            row.append(song_hot)
	    song_id = hdf5_getters.get_song_id(h5)
            row.append(song_id)
	    start_fade_out = hdf5_getters.get_start_of_fade_out(h5)
            row.append(start_fade_out)
	    tempo = hdf5_getters.get_tempo(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(tempo) == True:
	            tempo=-1
            row.append(tempo)
	    time_sig = hdf5_getters.get_time_signature(h5)
            row.append(time_sig)
	    time_sig_c = hdf5_getters.get_time_signature_confidence(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(time_sig_c) == True:
	            time_sig_c=-1
            row.append(time_sig_c)
	    track_id = hdf5_getters.get_track_id(h5)
            row.append(track_id)
	    track_7digitalid = hdf5_getters.get_track_7digitalid(h5)
            row.append(track_7digitalid)
	    year = hdf5_getters.get_year(h5)
            row.append(year)
	    bars_c = hdf5_getters.get_bars_confidence(h5)
            bars_start = hdf5_getters.get_bars_start(h5)
	    row_bars_padding=padding(245)   #this is the array that will be attached at the end of th row

	    #--------------bars---------------"
	    gral_info=[]
	    gral_info=row[:]
	    empty=[]
	    for i,item in enumerate(bars_c):
                row.append(group_index)
                row.append(i)
                row.append(bars_c[i])
	        bars_c_avg= get_avg(bars_c)
                row.append(bars_c_avg)
	        bars_c_max= get_max(bars_c)	
                row.append(bars_c_max)
	        bars_c_min = get_min(bars_c)
                row.append(bars_c_min)
	        bars_c_stddev= get_stddev(bars_c)
                row.append(bars_c_stddev)
	        bars_c_count = get_count(bars_c)
                row.append(bars_c_count)
	        bars_c_sum = get_sum(bars_c)
                row.append(bars_c_sum)
                row.append(bars_start[i])	         
	        bars_start_avg = get_avg(bars_start)
                row.append(bars_start_avg)	         
	        bars_start_max= get_max(bars_start)
                row.append(bars_start_max)	         
	        bars_start_min = get_min(bars_start)
                row.append(bars_start_min)	         
	        bars_start_stddev= get_stddev(bars_start)
                row.append(bars_start_stddev)	         
	        bars_start_count = get_count(bars_start)
                row.append(bars_start_count)	         
	        bars_start_sum = get_sum(bars_start)
                row.append(bars_start_sum)	         
		for i in row_bars_padding:
			row.append(i)

                writer.writerow(row)
		row=[]
		row=gral_info[:]
	 

            #--------beats---------------"
	    beats_c = hdf5_getters.get_beats_confidence(h5)
	    group_index=1
	    row=[]
	    row=gral_info[:]
	    row_front=padding(14)  	#blanks left in front of the row(empty spaces for bars)
	    row_beats_padding=padding(231)
	    for i,item in enumerate(beats_c):
	   	row.append(group_index)
		row.append(i)
		for index in row_front:  #padding blanks in front of the beats
			row.append(index)
		
		row.append(beats_c[i])
	        beats_c_avg= get_avg(beats_c)
		row.append(beats_c_avg)
	        beats_c_max= get_max(beats_c)
		row.append(beats_c_max)
                beats_c_min = get_min(beats_c)
		row.append(beats_c_min)
	        beats_c_stddev= get_stddev(beats_c)
		row.append(beats_c_stddev)
	        beats_c_count = get_count(beats_c)
		row.append(beats_c_count)
	        beats_c_sum = get_sum(beats_c)
		row.append(beats_c_sum)
                beats_start = hdf5_getters.get_beats_start(h5)
		row.append(beats_start[i])
 	        beats_start_avg = get_avg(beats_start)
		row.append(beats_start_avg)
	        beats_start_max= get_max(beats_start)
		row.append(beats_start_max)
	        beats_start_min = get_min(beats_start)
		row.append(beats_start_min)
	        beats_start_stddev= get_stddev(beats_start)
		row.append(beats_start_stddev)
	        beats_start_count = get_count(beats_start)
		row.append(beats_start_count)
	        beats_start_sum = get_sum(beats_start)
		row.append(beats_start_sum)
		for i in row_beats_padding:
			row.append(i)
                
		writer.writerow(row)
		row=[]
		row=gral_info[:]

            # "--------sections---------------"
	    row_sec_padding=padding(217)	#blank spaces left at the end of the row
	    sec_c = hdf5_getters.get_sections_confidence(h5)
	    group_index=2
	    row=[]
	    row=gral_info[:]
	    row_front=padding(28)		#blank spaces left in front(empty spaces for bars,beats)
	    for i,item in enumerate(sec_c):
		row.append(group_index)
		row.append(i)
		for index in row_front:  	#padding blanks in front of the sections
			row.append(index)

		row.append(sec_c[i])
                sec_c_avg= get_avg(sec_c)
		row.append(sec_c_avg)
	        sec_c_max= get_max(sec_c)
		row.append(sec_c_max)
	        sec_c_min = get_min(sec_c)
		row.append(sec_c_min)
	        sec_c_stddev= get_stddev(sec_c)
		row.append(sec_c_stddev)
	        sec_c_count = get_count(sec_c)
		row.append(sec_c_count)
	        sec_c_sum = get_sum(sec_c)
		row.append(sec_c_sum)
	        sec_start = hdf5_getters.get_sections_start(h5)
		row.append(sec_start[i])	   
                sec_start_avg = get_avg(sec_start)
		row.append(sec_start_avg)
	        sec_start_max= get_max(sec_start)
		row.append(sec_start_max)
	        sec_start_min = get_min(sec_start)
		row.append(sec_start_min)
	        sec_start_stddev= get_stddev(sec_start)
		row.append(sec_start_stddev)
	        sec_start_count = get_count(sec_start)
		row.append(sec_start_count)
	        sec_start_sum = get_sum(sec_start)
		row.append(sec_start_sum)
		for i in row_sec_padding:	#appending the blank spaces at the end of the row
			row.append(i)
                

		writer.writerow(row)
		row=[]
		row=gral_info[:]


            #--------segments---------------"
	    row_seg_padding=padding(182)	#blank spaces at the end of the row
 	    row_front=padding(42)		#blank spaces left in front of segments
	    seg_c = hdf5_getters.get_segments_confidence(h5)
	    group_index=3
	    row=[]
	    row=gral_info[:]
	    for i,item in enumerate(seg_c):
		row.append(group_index)
		row.append(i)
		for index in row_front:  	#padding blanks in front of the segments
			row.append(index)

		row.append(seg_c[i])
                seg_c_avg= get_avg(seg_c)
		row.append(seg_c_avg)
	        seg_c_max= get_max(seg_c)
		row.append(seg_c_max)
	        seg_c_min = get_min(seg_c)
		row.append(seg_c_min)
	        seg_c_stddev= get_stddev(seg_c)
		row.append(seg_c_stddev)
	        seg_c_count = get_count(seg_c)
		row.append(seg_c_count)
	        seg_c_sum = get_sum(seg_c)
		row.append(seg_c_sum)
                seg_loud_max = hdf5_getters.get_segments_loudness_max(h5)
		row.append(seg_loud_max[i])
                seg_loud_max_avg= get_avg(seg_loud_max)
		row.append(seg_loud_max_avg)
	        seg_loud_max_max= get_max(seg_loud_max)
		row.append(seg_loud_max_max)
	        seg_loud_max_min = get_min(seg_loud_max)
		row.append(seg_loud_max_min)
	        seg_loud_max_stddev= get_stddev(seg_loud_max)
		row.append(seg_loud_max_stddev)
	        seg_loud_max_count = get_count(seg_loud_max)
		row.append(seg_loud_max_count)
	        seg_loud_max_sum = get_sum(seg_loud_max)
		row.append(seg_loud_max_sum)
	        seg_loud_max_time = hdf5_getters.get_segments_loudness_max_time(h5)
		row.append(seg_loud_max_time[i])
	        seg_loud_max_time_avg= get_avg(seg_loud_max_time)
		row.append(seg_loud_max_time_avg)
	        seg_loud_max_time_max= get_max(seg_loud_max_time)
		row.append(seg_loud_max_time_max)
	        seg_loud_max_time_min = get_min(seg_loud_max_time)
		row.append(seg_loud_max_time_min)
	        seg_loud_max_time_stddev= get_stddev(seg_loud_max_time)
		row.append(seg_loud_max_time_stddev)
	        seg_loud_max_time_count = get_count(seg_loud_max_time)
		row.append(seg_loud_max_time_count)
	        seg_loud_max_time_sum = get_sum(seg_loud_max_time)
		row.append(seg_loud_max_time_sum)
	        seg_loud_start = hdf5_getters.get_segments_loudness_start(h5)
		row.append(seg_loud_start[i])
	        seg_loud_start_avg= get_avg(seg_loud_start)
		row.append(seg_loud_start_avg)
	        seg_loud_start_max= get_max(seg_loud_start)
		row.append(seg_loud_start_max)
	        seg_loud_start_min = get_min(seg_loud_start)
		row.append(seg_loud_start_min)
	        seg_loud_start_stddev= get_stddev(seg_loud_start)
		row.append(seg_loud_start_stddev)
	        seg_loud_start_count = get_count(seg_loud_start)
		row.append(seg_loud_start_count)
	        seg_loud_start_sum = get_sum(seg_loud_start)					      
		row.append(seg_loud_start_sum)
	        seg_start = hdf5_getters.get_segments_start(h5)
		row.append(seg_start[i])
	        seg_start_avg= get_avg(seg_start)
		row.append(seg_start_avg)
	        seg_start_max= get_max(seg_start)
		row.append(seg_start_max)
	        seg_start_min = get_min(seg_start)
		row.append(seg_start_min)
	        seg_start_stddev= get_stddev(seg_start)
		row.append(seg_start_stddev)
	        seg_start_count = get_count(seg_start)
		row.append(seg_start_count)
	        seg_start_sum = get_sum(seg_start)
		row.append(seg_start_sum)
		for i in row_seg_padding:	#appending blank spaces at the end of the row
			row.append(i)
                
		writer.writerow(row)
		row=[]
		row=gral_info[:]

	    #----------segments pitch and timbre---------------"
	    row_seg2_padding=padding(14)	#blank spaces left at the end of the row
	    row_front=padding(77)		#blank spaces left at the front of the segments and timbre
	    seg_pitch = hdf5_getters.get_segments_pitches(h5)
	    transpose_pitch= seg_pitch.transpose()          #this is to tranpose the matrix,so we can have 12 rows
	    group_index=4
	    row=[]
	    row=gral_info[:]
	    for i,item in enumerate(transpose_pitch[0]):
		row.append(group_index)
		row.append(i)
		for index in row_front:  	#padding blanks in front of segments and timbre
			row.append(index)
	   
		row.append(transpose_pitch[0][i])
  		seg_pitch_avg= get_avg(transpose_pitch[0])
		row.append(seg_pitch_avg)
		seg_pitch_max= get_max(transpose_pitch[0])	
		row.append(seg_pitch_max)
		seg_pitch_min = get_min(transpose_pitch[0])
		row.append(seg_pitch_min)
		seg_pitch_stddev= get_stddev(transpose_pitch[0])
		row.append(seg_pitch_stddev)
		seg_pitch_count = get_count(transpose_pitch[0])
		row.append(seg_pitch_count)
		seg_pitch_sum = get_sum(transpose_pitch[0])
		row.append(seg_pitch_sum)   
 		row.append(transpose_pitch[1][i])
 		seg_pitch_avg= get_avg(transpose_pitch[1])
		row.append(seg_pitch_avg)
		seg_pitch_max= get_max(transpose_pitch[1])	
		row.append(seg_pitch_max)
	        seg_pitch_min = get_min(transpose_pitch[1])
		row.append(seg_pitch_min)
	        seg_pitch_stddev= get_stddev(transpose_pitch[1])
		row.append(seg_pitch_stddev)
	        seg_pitch_count = get_count(transpose_pitch[1])
		row.append(seg_pitch_count)
	        seg_pitch_sum = get_sum(transpose_pitch[1])
		row.append(seg_pitch_sum)   
		row.append(transpose_pitch[2][i])
 		seg_pitch_avg= get_avg(transpose_pitch[2])
		row.append(seg_pitch_avg)
		seg_pitch_max= get_max(transpose_pitch[2])	
		row.append(seg_pitch_max)
	        seg_pitch_min = get_min(transpose_pitch[2])
		row.append(seg_pitch_min)
	        seg_pitch_stddev= get_stddev(transpose_pitch[2])
		row.append(seg_pitch_stddev)
	        seg_pitch_count = get_count(transpose_pitch[2])
		row.append(seg_pitch_count)
	        seg_pitch_sum = get_sum(transpose_pitch[2])
		row.append(seg_pitch_sum)   
		row.append(transpose_pitch[3][i])
 		seg_pitch_avg= get_avg(transpose_pitch[3])
		row.append(seg_pitch_avg)
		seg_pitch_max= get_max(transpose_pitch[3])	
		row.append(seg_pitch_max)
	        seg_pitch_min = get_min(transpose_pitch[3])
		row.append(seg_pitch_min)
	        seg_pitch_stddev= get_stddev(transpose_pitch[3])
		row.append(seg_pitch_stddev)
	        seg_pitch_count = get_count(transpose_pitch[3])
		row.append(seg_pitch_count)
	        seg_pitch_sum = get_sum(transpose_pitch[3])
		row.append(seg_pitch_sum)   
		row.append(transpose_pitch[4][i])
 		seg_pitch_avg= get_avg(transpose_pitch[4])
		row.append(seg_pitch_avg)
		seg_pitch_max= get_max(transpose_pitch[4])	
		row.append(seg_pitch_max)
	        seg_pitch_min = get_min(transpose_pitch[4])
		row.append(seg_pitch_min)
	        seg_pitch_stddev= get_stddev(transpose_pitch[4])
		row.append(seg_pitch_stddev)
	        seg_pitch_count = get_count(transpose_pitch[4])
		row.append(seg_pitch_count)
	        seg_pitch_sum = get_sum(transpose_pitch[4])
		row.append(seg_pitch_sum)   
		row.append(transpose_pitch[5][i])
 		seg_pitch_avg= get_avg(transpose_pitch[5])
		row.append(seg_pitch_avg)
		seg_pitch_max= get_max(transpose_pitch[5])	
		row.append(seg_pitch_max)
	        seg_pitch_min = get_min(transpose_pitch[5])
		row.append(seg_pitch_min)
	        seg_pitch_stddev= get_stddev(transpose_pitch[5])
		row.append(seg_pitch_stddev)
	        seg_pitch_count = get_count(transpose_pitch[5])
		row.append(seg_pitch_count)
	        seg_pitch_sum = get_sum(transpose_pitch[5])
		row.append(seg_pitch_sum)   
		row.append(transpose_pitch[6][i])
 		seg_pitch_avg= get_avg(transpose_pitch[6])
		row.append(seg_pitch_avg)
		seg_pitch_max= get_max(transpose_pitch[6])	
		row.append(seg_pitch_max)
	        seg_pitch_min = get_min(transpose_pitch[6])
		row.append(seg_pitch_min)
	        seg_pitch_stddev= get_stddev(transpose_pitch[6])
		row.append(seg_pitch_stddev)
	        seg_pitch_count = get_count(transpose_pitch[6])
		row.append(seg_pitch_count)
	        seg_pitch_sum = get_sum(transpose_pitch[6])
		row.append(seg_pitch_sum)   
		row.append(transpose_pitch[7][i])
 		seg_pitch_avg= get_avg(transpose_pitch[7])
		row.append(seg_pitch_avg)
		seg_pitch_max= get_max(transpose_pitch[7])	
		row.append(seg_pitch_max)
	        seg_pitch_min = get_min(transpose_pitch[7])
		row.append(seg_pitch_min)
	        seg_pitch_stddev= get_stddev(transpose_pitch[7])
		row.append(seg_pitch_stddev)
	        seg_pitch_count = get_count(transpose_pitch[7])
		row.append(seg_pitch_count)
	        seg_pitch_sum = get_sum(transpose_pitch[7])
		row.append(seg_pitch_sum)   
		row.append(transpose_pitch[8][i])
 		seg_pitch_avg= get_avg(transpose_pitch[8])
		row.append(seg_pitch_avg)
		seg_pitch_max= get_max(transpose_pitch[8])	
		row.append(seg_pitch_max)
	        seg_pitch_min = get_min(transpose_pitch[8])
		row.append(seg_pitch_min)
	        seg_pitch_stddev= get_stddev(transpose_pitch[8])
		row.append(seg_pitch_stddev)
	        seg_pitch_count = get_count(transpose_pitch[8])
		row.append(seg_pitch_count)
	        seg_pitch_sum = get_sum(transpose_pitch[8])
		row.append(seg_pitch_sum)   
		row.append(transpose_pitch[9][i])
 		seg_pitch_avg= get_avg(transpose_pitch[9])
		row.append(seg_pitch_avg)
		seg_pitch_max= get_max(transpose_pitch[9])	
		row.append(seg_pitch_max)
	        seg_pitch_min = get_min(transpose_pitch[9])
		row.append(seg_pitch_min)
	        seg_pitch_stddev= get_stddev(transpose_pitch[9])
		row.append(seg_pitch_stddev)
	        seg_pitch_count = get_count(transpose_pitch[9])
		row.append(seg_pitch_count)
	        seg_pitch_sum = get_sum(transpose_pitch[9])
		row.append(seg_pitch_sum)   
		row.append(transpose_pitch[10][i])
 		seg_pitch_avg= get_avg(transpose_pitch[10])
		row.append(seg_pitch_avg)
		seg_pitch_max= get_max(transpose_pitch[10])	
		row.append(seg_pitch_max)
	        seg_pitch_min = get_min(transpose_pitch[10])
		row.append(seg_pitch_min)
	        seg_pitch_stddev= get_stddev(transpose_pitch[10])
		row.append(seg_pitch_stddev)
	        seg_pitch_count = get_count(transpose_pitch[10])
		row.append(seg_pitch_count)
	        seg_pitch_sum = get_sum(transpose_pitch[10])
		row.append(seg_pitch_sum)   
		row.append(transpose_pitch[11][i])
 		seg_pitch_avg= get_avg(transpose_pitch[11])
		row.append(seg_pitch_avg)
		seg_pitch_max= get_max(transpose_pitch[11])	
		row.append(seg_pitch_max)
	        seg_pitch_min = get_min(transpose_pitch[11])
		row.append(seg_pitch_min)
	        seg_pitch_stddev= get_stddev(transpose_pitch[11])
		row.append(seg_pitch_stddev)
	        seg_pitch_count = get_count(transpose_pitch[11])
		row.append(seg_pitch_count)
	        seg_pitch_sum = get_sum(transpose_pitch[11])
		row.append(seg_pitch_sum)   
		#timbre arrays
	        seg_timbre = hdf5_getters.get_segments_timbre(h5)
                transpose_timbre = seg_pitch.transpose() #tranposing matrix, to have 12 rows
		row.append(transpose_timbre[0][i])
  		seg_timbre_avg= get_avg(transpose_timbre[0])
		row.append(seg_timbre_avg)
		seg_timbre_max= get_max(transpose_timbre[0])	
		row.append(seg_timbre_max)
		seg_timbre_min = get_min(transpose_timbre[0])
		row.append(seg_timbre_min)
		seg_timbre_stddev=get_stddev(transpose_timbre[0])
		row.append(seg_timbre_stddev)
		seg_timbre_count = get_count(transpose_timbre[0])
		row.append(seg_timbre_count)
		seg_timbre_sum = get_sum(transpose_timbre[0])
		row.append(seg_timbre_sum)   
 		row.append(transpose_timbre[1][i])
 		seg_timbre_avg= get_avg(transpose_timbre[1])
		row.append(seg_timbre_avg)
		seg_timbre_max= get_max(transpose_timbre[1])	
		row.append(seg_timbre_max)
	        seg_timbre_min = get_min(transpose_timbre[1])
		row.append(seg_timbre_min)
	        seg_timbre_stddev= get_stddev(transpose_timbre[1])
		row.append(seg_timbre_stddev)
	        seg_timbre_count = get_count(transpose_timbre[1])
		row.append(seg_timbre_count)
	        seg_timbre_sum = get_sum(transpose_timbre[1])
		row.append(seg_timbre_sum)   
		row.append(transpose_timbre[2][i])
 		seg_timbre_avg= get_avg(transpose_timbre[2])
		row.append(seg_timbre_avg)
		seg_timbre_max= get_max(transpose_timbre[2])	
		row.append(seg_timbre_max)
	        seg_timbre_min = get_min(transpose_timbre[2])
		row.append(seg_timbre_min)
	        seg_timbre_stddev= get_stddev(transpose_timbre[2])
		row.append(seg_timbre_stddev)
	        seg_timbre_count = get_count(transpose_timbre[2])
		row.append(seg_timbre_count)
	        seg_timbre_sum = get_sum(transpose_timbre[2])
		row.append(seg_timbre_sum)   
		
		row.append(transpose_timbre[3][i])
 		seg_timbre_avg= get_avg(transpose_timbre[3])
		row.append(seg_timbre_avg)
		seg_timbre_max= get_max(transpose_timbre[3])	
		row.append(seg_timbre_max)
	        seg_timbre_min = get_min(transpose_timbre[3])
		row.append(seg_timbre_min)
	        seg_timbre_stddev= get_stddev(transpose_timbre[3])
		row.append(seg_timbre_stddev)
	        seg_timbre_count = get_count(transpose_timbre[3])
		row.append(seg_timbre_count)
	        seg_timbre_sum = get_sum(transpose_timbre[3])
		row.append(seg_timbre_sum)   
		
		row.append(transpose_timbre[4][i])
 		seg_timbre_avg= get_avg(transpose_timbre[4])
		row.append(seg_timbre_avg)
		seg_timbre_max= get_max(transpose_timbre[4])	
		row.append(seg_timbre_max)
	        seg_timbre_min = get_min(transpose_timbre[4])
		row.append(seg_timbre_min)
	        seg_timbre_stddev= get_stddev(transpose_timbre[4])
		row.append(seg_timbre_stddev)
	        seg_timbre_count = get_count(transpose_timbre[4])
		row.append(seg_timbre_count)
	        seg_timbre_sum = get_sum(transpose_timbre[4])
		row.append(seg_timbre_sum)   
		
		row.append(transpose_timbre[5][i])
 		seg_timbre_avg= get_avg(transpose_timbre[5])
		row.append(seg_timbre_avg)
		seg_timbre_max= get_max(transpose_timbre[5])	
		row.append(seg_timbre_max)
	        seg_timbre_min = get_min(transpose_timbre[5])
		row.append(seg_timbre_min)
	        seg_timbre_stddev= get_stddev(transpose_timbre[5])
		row.append(seg_timbre_stddev)
	        seg_timbre_count = get_count(transpose_timbre[5])
		row.append(seg_timbre_count)
	        seg_timbre_sum = get_sum(transpose_timbre[5])
		row.append(seg_timbre_sum)   
		
		row.append(transpose_timbre[6][i])
 		seg_timbre_avg= get_avg(transpose_timbre[6])
		row.append(seg_timbre_avg)
		seg_timbre_max= get_max(transpose_timbre[6])	
		row.append(seg_timbre_max)
	        seg_timbre_min = get_min(transpose_timbre[6])
		row.append(seg_timbre_min)
	        seg_timbre_stddev= get_stddev(transpose_timbre[6])
		row.append(seg_timbre_stddev)
	        seg_timbre_count = get_count(transpose_timbre[6])
		row.append(seg_timbre_count)
	        seg_timbre_sum = get_sum(transpose_timbre[6])
		row.append(seg_timbre_sum)   
		
		row.append(transpose_timbre[7][i])
 		seg_timbre_avg= get_avg(transpose_timbre[7])
		row.append(seg_timbre_avg)
		seg_timbre_max= get_max(transpose_timbre[7])	
		row.append(seg_timbre_max)
	        seg_timbre_min = get_min(transpose_timbre[7])
		row.append(seg_timbre_min)
	        seg_timbre_stddev= get_stddev(transpose_timbre[7])
		row.append(seg_timbre_stddev)
	        seg_timbre_count = get_count(transpose_timbre[7])
		row.append(seg_timbre_count)
	        seg_timbre_sum = get_sum(transpose_timbre[7])
		row.append(seg_timbre_sum)   
		
		row.append(transpose_timbre[8][i])
 		seg_timbre_avg= get_avg(transpose_timbre[8])
		row.append(seg_timbre_avg)
		seg_timbre_max= get_max(transpose_timbre[8])	
		row.append(seg_timbre_max)
	        seg_timbre_min = get_min(transpose_timbre[8])
		row.append(seg_timbre_min)
	        seg_timbre_stddev= get_stddev(transpose_timbre[8])
		row.append(seg_timbre_stddev)
	        seg_timbre_count = get_count(transpose_timbre[8])
		row.append(seg_timbre_count)
	        seg_timbre_sum = get_sum(transpose_timbre[8])
		row.append(seg_timbre_sum)   
		
		row.append(transpose_timbre[9][i])
 		seg_timbre_avg= get_avg(transpose_timbre[9])
		row.append(seg_timbre_avg)
		seg_timbre_max= get_max(transpose_timbre[9])	
		row.append(seg_timbre_max)
	        seg_timbre_min = get_min(transpose_timbre[9])
		row.append(seg_timbre_min)
	        seg_timbre_stddev= get_stddev(transpose_timbre[9])
		row.append(seg_timbre_stddev)
	        seg_timbre_count = get_count(transpose_timbre[9])
		row.append(seg_timbre_count)
	        seg_timbre_sum = get_sum(transpose_timbre[9])
		row.append(seg_timbre_sum)   
		
		row.append(transpose_timbre[10][i])
 		seg_timbre_avg= get_avg(transpose_timbre[10])
		row.append(seg_timbre_avg)
		seg_timbre_max= get_max(transpose_timbre[10])	
		row.append(seg_timbre_max)
	        seg_timbre_min = get_min(transpose_timbre[10])
		row.append(seg_timbre_min)
	        seg_timbre_stddev= get_stddev(transpose_timbre[10])
		row.append(seg_timbre_stddev)
	        seg_timbre_count = get_count(transpose_timbre[10])
		row.append(seg_timbre_count)
	        seg_timbre_sum = get_sum(transpose_timbre[10])
		row.append(seg_timbre_sum)   
		
		row.append(transpose_timbre[11][i])
 		seg_timbre_avg= get_avg(transpose_timbre[11])
		row.append(seg_timbre_avg)
		seg_timbre_max= get_max(transpose_timbre[11])	
		row.append(seg_timbre_max)
	        seg_timbre_min = get_min(transpose_timbre[11])
		row.append(seg_timbre_min)
	        seg_timbre_stddev= get_stddev(transpose_timbre[11])
		row.append(seg_timbre_stddev)
	        seg_timbre_count = get_count(transpose_timbre[11])
		row.append(seg_timbre_count)
	        seg_timbre_sum = get_sum(transpose_timbre[11])
		row.append(seg_timbre_sum)
	        for item in row_seg2_padding:
			row.append(item)
		writer.writerow(row)
		row=[]
		row=gral_info[:]


            # "--------tatums---------------"
	    tatms_c = hdf5_getters.get_tatums_confidence(h5)
	    group_index=5
	    row_front=padding(245)	#blank spaces left in front of tatums
	    row=[]
	    row=gral_info[:]
	    for i,item in enumerate(tatms_c):
		row.append(group_index)
		row.append(i)
		for item in row_front:	#appending blank spaces at the front of the row
			row.append(item)

		row.append(tatms_c[i])
		tatms_c_avg= get_avg(tatms_c)
		row.append(tatms_c_avg)
	 	tatms_c_max= get_max(tatms_c)
		row.append(tatms_c_max)
	        tatms_c_min = get_min(tatms_c)
		row.append(tatms_c_min)
	        tatms_c_stddev= get_stddev(tatms_c)
		row.append(tatms_c_stddev)
                tatms_c_count = get_count(tatms_c)
		row.append(tatms_c_count)
                tatms_c_sum = get_sum(tatms_c)
		row.append(tatms_c_sum)
                tatms_start = hdf5_getters.get_tatums_start(h5)
		row.append(tatms_start[i])
	        tatms_start_avg= get_avg(tatms_start)
		row.append(tatms_start_avg)
	        tatms_start_max= get_max(tatms_start)
		row.append(tatms_start_max)
	        tatms_start_min = get_min(tatms_start)
		row.append(tatms_start_min)
	        tatms_start_stddev= get_stddev(tatms_start)
		row.append(tatms_start_stddev)
	        tatms_start_count = get_count(tatms_start)
		row.append(tatms_start_count)
	        tatms_start_sum = get_sum(tatms_start)				   
		row.append(tatms_start_sum)
		writer.writerow(row)
		row=[]
		row=gral_info[:]


 
	    transpose_pitch= seg_pitch.transpose() #this is to tranpose the matrix,so we can have 12 rows
	    #arrays containing the aggregate values of the 12 rows
	    seg_pitch_avg=[]
	    seg_pitch_max=[]
	    seg_pitch_min=[]
            seg_pitch_stddev=[]
            seg_pitch_count=[]
	    seg_pitch_sum=[]
            i=0
	    #Getting the aggregate values in the pitches array
	    for row in transpose_pitch:
		   seg_pitch_avg.append(get_avg(row))
		   seg_pitch_max.append(get_max(row))
	           seg_pitch_min.append(get_min(row))
		   seg_pitch_stddev.append(get_stddev(row))
		   seg_pitch_count.append(get_count(row))
                   seg_pitch_sum.append(get_sum(row))
		   i=i+1

	    #extracting information from the timbre array 
            transpose_timbre = seg_pitch.transpose() #tranposing matrix, to have 12 rows
	    #arrays containing the aggregate values of the 12 rows
	    seg_timbre_avg=[]
	    seg_timbre_max=[]
	    seg_timbre_min=[]
            seg_timbre_stddev=[]
            seg_timbre_count=[]
	    seg_timbre_sum=[]
            i=0
	    for row in transpose_timbre:
		   seg_timbre_avg.append(get_avg(row))
		   seg_timbre_max.append(get_max(row))
	           seg_timbre_min.append(get_min(row))
		   seg_timbre_stddev.append(get_stddev(row))
		   seg_timbre_count.append(get_count(row))
                   seg_timbre_sum.append(get_sum(row))
		   i=i+1








	    h5.close()
	    count=count+1;
	    print count;
 def get_mode(self):
     if self.h5 == None: self.open()
     return hdf5_getters.get_mode(self.h5)
    if not os.path.isfile(hdf5path):
        continue
        #print('ERROR: file',hdf5path,'does not exist.')
        #sys.exit(0)

    h5 = hdf5_getters.open_h5_file_read(hdf5path)

    # Retrieve features from HDF5
    danceability = hdf5_getters.get_danceability(h5)
    duration = hdf5_getters.get_duration(h5)
    time_of_fade_in = hdf5_getters.get_end_of_fade_in(h5)
    energy = hdf5_getters.get_energy(h5)
    key = hdf5_getters.get_key(h5)
    key_confidence = hdf5_getters.get_key_confidence(h5)
    loudness = hdf5_getters.get_loudness(h5)
    mode = hdf5_getters.get_mode(h5)
    mode_confidence = hdf5_getters.get_mode_confidence(h5)
    sections_start = hdf5_getters.get_sections_start(h5)
    num_sections = len(sections_start)
    if num_sections == 0:
        h5.close()
        continue
    segments_loudness_max = hdf5_getters.get_segments_loudness_max(h5)
    segments_loudness_start = hdf5_getters.get_segments_loudness_start(h5)
    num_segments = len(hdf5_getters.get_segments_start(h5))
    num_tatums = len(hdf5_getters.get_tatums_start(h5))
    time_of_fade_out = duration - hdf5_getters.get_start_of_fade_out(h5)
    tempo = hdf5_getters.get_tempo(h5)
    time_signature = hdf5_getters.get_time_signature(h5)
    time_signature_confidence = hdf5_getters.get_time_signature_confidence(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 cntdanceability
    global listfeatures
    cf = []
    h5 = GETTERS.open_h5_file_read(filename)
    nanfound = 0

    #Get target feature: song hotness
    song_hotness = GETTERS.get_song_hotttnesss(h5)
    if math.isnan(song_hotness):
       nanfound = 1
       cntnan = cntnan + 1
    else:
       cf.append(song_hotness)

    #Get danceablity
#    song_danceability = GETTERS.get_danceability(h5)
    
#    if song_danceability == 0:
#       nanfound = 1
#       cntnan = cntnan + 1
#    else:
#       cf.append(song_danceability)

    #Get song loudness
    song_loudness = GETTERS.get_loudness(h5)
    
    if math.isnan(song_loudness):
       nanfound = 1
       cntnan = cntnan + 1
    else:
       cf.append(song_loudness)

    #Get song energy 
#    song_energy = GETTERS.get_energy(h5)
    
#    if song_energy == 0:
#       nanfound = 1
#       cntnan = cntnan + 1
#    else:
#       cf.append(song_energy)

    #Get key of the song
    song_key = GETTERS.get_key(h5)
    if math.isnan(song_key):
       nanfound = 1
       cntnan = cntnan + 1
    else:
       cf.append(song_key)

    #Get mode of the song
    song_mode = GETTERS.get_mode(h5)
    if math.isnan(song_mode):
       nanfound = 1
       cntnan = cntnan + 1
    elif song_mode == 0:
       nanfound = 1
       cntnan = cntnan + 1
    else:
       cf.append(song_mode)

    #Get duration of the song
    song_duration = GETTERS.get_duration(h5)
    if math.isnan(song_duration):
       nanfound = 1
       cntnan = cntnan + 1
    else:
       cf.append(song_duration)

    #Get Average Pitch Class across all segments
    #Get the pitches (12 pitches histogram for each segment)
    pitches = GETTERS.get_segments_pitches(h5)
    M = np.mat(pitches)
    meanpitches = M.mean(axis=0)
    pitches_arr = np.asarray(meanpitches)
    pitches_list = []
    for i in range(0,12):
	pitches_list.append(pitches_arr[0][i])

    cf.append(pitches_list)

    #Get Average Timbre Class across all segments
    timbres = GETTERS.get_segments_timbre(h5)
    M = np.mat(timbres)
    meantimbres = M.mean(axis=0)
    timbre_arr = np.asarray(meantimbres)
    timbre_list = []
    for i in range(0,12):
	timbre_list.append(timbre_arr[0][i])

    cf.append(timbre_list)

    #Get song year
    song_year = GETTERS.get_year(h5)
    if song_year == 0:
       nanfound = 1
       cntnan = cntnan + 1
    else:
      cf.append(song_year)

    if nanfound == 0:
       strlist = list_to_csv(cf)
       print strlist
       listfeatures.append(strlist)

    h5.close()
def data_to_flat_file(basedir,ext='.h5') :
    """This function extract the information from the tables and creates the flat file."""	
    count = 0;	#song counter
    list_to_write= []
    row_to_write = ""
    writer = csv.writer(open("metadata_wholeA.csv", "wb"))
    for root, dirs, files in os.walk(basedir):
	files = glob.glob(os.path.join(root,'*'+ext))
        for f in files:
	    print f	#the name of the file
            h5 = hdf5_getters.open_h5_file_read(f)
	    title = hdf5_getters.get_title(h5) 
	    title= title.replace('"','') 
	    comma=title.find(',')	#eliminating commas in the title
	    if	comma != -1:
		    print title
		    time.sleep(1)
	    album = hdf5_getters.get_release(h5)
	    album= album.replace('"','')	#eliminating commas in the album	
	    comma=album.find(',')
	    if	comma != -1:
		    print album
		    time.sleep(1)
	    artist_name = hdf5_getters.get_artist_name(h5)
	    comma=artist_name.find(',')
	    if	comma != -1:
		    print artist_name
		    time.sleep(1)
	    artist_name= artist_name.replace('"','')	#eliminating double quotes
	    duration = hdf5_getters.get_duration(h5)
	    samp_rt = hdf5_getters.get_analysis_sample_rate(h5)
	    artist_7digitalid = hdf5_getters.get_artist_7digitalid(h5)
	    artist_fam = hdf5_getters.get_artist_familiarity(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(artist_fam) == True:
	            artist_fam=-1
	    artist_hotness= hdf5_getters.get_artist_hotttnesss(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(artist_hotness) == True:
	            artist_hotness=-1
	    artist_id = hdf5_getters.get_artist_id(h5)
	    artist_lat = hdf5_getters.get_artist_latitude(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(artist_lat) == True:
	            artist_lat=-1
	    artist_loc = hdf5_getters.get_artist_location(h5)
		#checks artist_loc to see if it is a hyperlink if it is set as empty string
	    artist_loc = artist_loc.replace(",", "\,");
	    if artist_loc.startswith("<a"):
                artist_loc = ""
	    if len(artist_loc) > 100:
                artist_loc = ""
	    artist_lon = hdf5_getters.get_artist_longitude(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(artist_lon) == True:
	            artist_lon=-1
	    artist_mbid = hdf5_getters.get_artist_mbid(h5)
	    artist_pmid = hdf5_getters.get_artist_playmeid(h5)
	    audio_md5 = hdf5_getters.get_audio_md5(h5)
	    danceability = hdf5_getters.get_danceability(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(danceability) == True:
	            danceability=-1
	    end_fade_in =hdf5_getters.get_end_of_fade_in(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(end_fade_in) == True:
	            end_fade_in=-1
	    energy = hdf5_getters.get_energy(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(energy) == True:
	            energy=-1
            song_key = hdf5_getters.get_key(h5)
	    key_c = hdf5_getters.get_key_confidence(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(key_c) == True:
	            key_c=-1
	    loudness = hdf5_getters.get_loudness(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(loudness) == True:
	            loudness=-1
	    mode = hdf5_getters.get_mode(h5)
	    mode_conf = hdf5_getters.get_mode_confidence(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(mode_conf) == True:
	            mode_conf=-1
	    release_7digitalid = hdf5_getters.get_release_7digitalid(h5)
	    song_hot = hdf5_getters.get_song_hotttnesss(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(song_hot) == True:
	            song_hot=-1
	    song_id = hdf5_getters.get_song_id(h5)
	    start_fade_out = hdf5_getters.get_start_of_fade_out(h5)
	    tempo = hdf5_getters.get_tempo(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(tempo) == True:
	            tempo=-1
	    time_sig = hdf5_getters.get_time_signature(h5)
	    time_sig_c = hdf5_getters.get_time_signature_confidence(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(time_sig_c) == True:
	            time_sig_c=-1
	    track_id = hdf5_getters.get_track_id(h5)
	    track_7digitalid = hdf5_getters.get_track_7digitalid(h5)
	    year = hdf5_getters.get_year(h5)
	    bars_c = hdf5_getters.get_bars_confidence(h5)
	    bars_c_avg= get_avg(bars_c)
	    bars_c_max= get_max(bars_c)
	    bars_c_min = get_min(bars_c)
	    bars_c_stddev= get_stddev(bars_c)
	    bars_c_count = get_count(bars_c)
	    bars_c_sum = get_sum(bars_c)
	    bars_start = hdf5_getters.get_bars_start(h5)
	    bars_start_avg = get_avg(bars_start)
	    bars_start_max= get_max(bars_start)
	    bars_start_min = get_min(bars_start)
	    bars_start_stddev= get_stddev(bars_start)
	    bars_start_count = get_count(bars_start)
	    bars_start_sum = get_sum(bars_start)
            beats_c = hdf5_getters.get_beats_confidence(h5)
            beats_c_avg= get_avg(beats_c)
	    beats_c_max= get_max(beats_c)
	    beats_c_min = get_min(beats_c)
	    beats_c_stddev= get_stddev(beats_c)
	    beats_c_count = get_count(beats_c)
	    beats_c_sum = get_sum(beats_c)
            beats_start = hdf5_getters.get_beats_start(h5)
 	    beats_start_avg = get_avg(beats_start)
	    beats_start_max= get_max(beats_start)
	    beats_start_min = get_min(beats_start)
	    beats_start_stddev= get_stddev(beats_start)
	    beats_start_count = get_count(beats_start)
	    beats_start_sum = get_sum(beats_start)
	    sec_c = hdf5_getters.get_sections_confidence(h5)
            sec_c_avg= get_avg(sec_c)
	    sec_c_max= get_max(sec_c)
	    sec_c_min = get_min(sec_c)
	    sec_c_stddev= get_stddev(sec_c)
	    sec_c_count = get_count(sec_c)
	    sec_c_sum = get_sum(sec_c)
	    sec_start = hdf5_getters.get_sections_start(h5)
            sec_start_avg = get_avg(sec_start)
	    sec_start_max= get_max(sec_start)
	    sec_start_min = get_min(sec_start)
	    sec_start_stddev= get_stddev(sec_start)
	    sec_start_count = get_count(sec_start)
	    sec_start_sum = get_sum(sec_start)
	    seg_c = hdf5_getters.get_segments_confidence(h5)
	    seg_c_avg= get_avg(seg_c)
	    seg_c_max= get_max(seg_c)
	    seg_c_min = get_min(seg_c)
	    seg_c_stddev= get_stddev(seg_c)
	    seg_c_count = get_count(seg_c)
	    seg_c_sum = get_sum(seg_c)
            seg_loud_max = hdf5_getters.get_segments_loudness_max(h5)
            seg_loud_max_avg= get_avg(seg_loud_max)
	    seg_loud_max_max= get_max(seg_loud_max)
	    seg_loud_max_min = get_min(seg_loud_max)
	    seg_loud_max_stddev= get_stddev(seg_loud_max)
	    seg_loud_max_count = get_count(seg_loud_max)
	    seg_loud_max_sum = get_sum(seg_loud_max)
	    seg_loud_max_time = hdf5_getters.get_segments_loudness_max_time(h5)
	    seg_loud_max_time_avg= get_avg(seg_loud_max_time)
	    seg_loud_max_time_max= get_max(seg_loud_max_time)
	    seg_loud_max_time_min = get_min(seg_loud_max_time)
	    seg_loud_max_time_stddev= get_stddev(seg_loud_max_time)
	    seg_loud_max_time_count = get_count(seg_loud_max_time)
	    seg_loud_max_time_sum = get_sum(seg_loud_max_time)
	    seg_loud_start = hdf5_getters.get_segments_loudness_start(h5)
	    seg_loud_start_avg= get_avg(seg_loud_start)
	    seg_loud_start_max= get_max(seg_loud_start)
	    seg_loud_start_min = get_min(seg_loud_start)
	    seg_loud_start_stddev= get_stddev(seg_loud_start)
	    seg_loud_start_count = get_count(seg_loud_start)
	    seg_loud_start_sum = get_sum(seg_loud_start)					      
	    seg_pitch = hdf5_getters.get_segments_pitches(h5)
	    pitch_size = len(seg_pitch)
	    seg_start = hdf5_getters.get_segments_start(h5)
	    seg_start_avg= get_avg(seg_start)
	    seg_start_max= get_max(seg_start)
	    seg_start_min = get_min(seg_start)
	    seg_start_stddev= get_stddev(seg_start)
	    seg_start_count = get_count(seg_start)
	    seg_start_sum = get_sum(seg_start)
	    seg_timbre = hdf5_getters.get_segments_timbre(h5)
	    tatms_c = hdf5_getters.get_tatums_confidence(h5)
	    tatms_c_avg= get_avg(tatms_c)
	    tatms_c_max= get_max(tatms_c)
	    tatms_c_min = get_min(tatms_c)
	    tatms_c_stddev= get_stddev(tatms_c)
	    tatms_c_count = get_count(tatms_c)
	    tatms_c_sum = get_sum(tatms_c)
	    tatms_start = hdf5_getters.get_tatums_start(h5)
	    tatms_start_avg= get_avg(tatms_start)
	    tatms_start_max= get_max(tatms_start)
	    tatms_start_min = get_min(tatms_start)
	    tatms_start_stddev= get_stddev(tatms_start)
	    tatms_start_count = get_count(tatms_start)
	    tatms_start_sum = get_sum(tatms_start)
	
	    #Getting the genres
	    genre_set = 0    #flag to see if the genre has been set or not
	    art_trm = hdf5_getters.get_artist_terms(h5)
	    trm_freq = hdf5_getters.get_artist_terms_freq(h5)
	    trn_wght = hdf5_getters.get_artist_terms_weight(h5)
	    a_mb_tags = hdf5_getters.get_artist_mbtags(h5)
	    genre_indexes=get_genre_indexes(trm_freq) #index of the highest freq
	    final_genre=[]
	    genres_so_far=[]
	    for i in range(len(genre_indexes)):
		    genre_tmp=get_genre(art_trm,genre_indexes[i])   #genre that corresponds to the highest freq
		    genres_so_far=genre_dict.get_genre_in_dict(genre_tmp) #getting the genre from the dictionary
		    if len(genres_so_far) != 0:
			    for i in genres_so_far:
				final_genre.append(i)
				genre_set=1				#genre was found in dictionary
				  
		
	    
	    if genre_set == 1:
		    col_num=[]
		   
		    for genre in final_genre:
			    column=int(genre)				#getting the column number of the genre
			    col_num.append(column)

		    genre_array=genre_columns(col_num)	         #genre array
 	    else:
		    genre_array=genre_columns(-1)		#the genre was not found in the dictionary

	    transpose_pitch= seg_pitch.transpose() #this is to tranpose the matrix,so we can have 12 rows
	    #arrays containing the aggregate values of the 12 rows
	    seg_pitch_avg=[]
	    seg_pitch_max=[]
	    seg_pitch_min=[]
            seg_pitch_stddev=[]
            seg_pitch_count=[]
	    seg_pitch_sum=[]
            i=0
	    #Getting the aggregate values in the pitches array
	    for row in transpose_pitch:
		   seg_pitch_avg.append(get_avg(row))
		   seg_pitch_max.append(get_max(row))
	           seg_pitch_min.append(get_min(row))
		   seg_pitch_stddev.append(get_stddev(row))
		   seg_pitch_count.append(get_count(row))
                   seg_pitch_sum.append(get_sum(row))
		   i=i+1

	    #extracting information from the timbre array 
            transpose_timbre = seg_pitch.transpose() #tranposing matrix, to have 12 rows
	    #arrays containing the aggregate values of the 12 rows
	    seg_timbre_avg=[]
	    seg_timbre_max=[]
	    seg_timbre_min=[]
            seg_timbre_stddev=[]
            seg_timbre_count=[]
	    seg_timbre_sum=[]
            i=0
	    for row in transpose_timbre:
		   seg_timbre_avg.append(get_avg(row))
		   seg_timbre_max.append(get_max(row))
	           seg_timbre_min.append(get_min(row))
		   seg_timbre_stddev.append(get_stddev(row))
		   seg_timbre_count.append(get_count(row))
                   seg_timbre_sum.append(get_sum(row))
		   i=i+1
		


		#Writing to the flat file
            writer.writerow([title,album,artist_name,year,duration,seg_start_count, tempo])

	    h5.close()
	    count=count+1;
	    print count;
def data_to_flat_file(basedir,ext='.h5') :
    """This function extract the information from the tables and creates the flat file."""	
    count = 0;	#song counter
    list_to_write= []
    row_to_write = ""
    writer = csv.writer(open("metadata.csv", "wb"))
    for root, dirs, files in os.walk(basedir):
	files = glob.glob(os.path.join(root,'*'+ext))
        for f in files:
	    print f	#the name of the file
            h5 = hdf5_getters.open_h5_file_read(f)
	    title = hdf5_getters.get_title(h5) 
	    title= title.replace('"','') 
	    comma=title.find(',')	#eliminating commas in the title
	    if	comma != -1:
		    print title
		    time.sleep(1)
	    album = hdf5_getters.get_release(h5)
	    album= album.replace('"','')	#eliminating commas in the album	
	    comma=album.find(',')
	    if	comma != -1:
		    print album
		    time.sleep(1)
	    artist_name = hdf5_getters.get_artist_name(h5)
	    comma=artist_name.find(',')
	    if	comma != -1:
		    print artist_name
		    time.sleep(1)
	    artist_name= artist_name.replace('"','')	#eliminating double quotes
	    duration = hdf5_getters.get_duration(h5)
	    samp_rt = hdf5_getters.get_analysis_sample_rate(h5)
	    artist_7digitalid = hdf5_getters.get_artist_7digitalid(h5)
	    artist_fam = hdf5_getters.get_artist_familiarity(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(artist_fam) == True:
	            artist_fam=-1
	    artist_hotness= hdf5_getters.get_artist_hotttnesss(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(artist_hotness) == True:
	            artist_hotness=-1
	    artist_id = hdf5_getters.get_artist_id(h5)
	    artist_lat = hdf5_getters.get_artist_latitude(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(artist_lat) == True:
	            artist_lat=-1
	    artist_loc = hdf5_getters.get_artist_location(h5)
		#checks artist_loc to see if it is a hyperlink if it is set as empty string
	    artist_loc = artist_loc.replace(",", "\,");
	    if artist_loc.startswith("<a"):
                artist_loc = ""
	    if len(artist_loc) > 100:
                artist_loc = ""
	    artist_lon = hdf5_getters.get_artist_longitude(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(artist_lon) == True:
	            artist_lon=-1
	    artist_mbid = hdf5_getters.get_artist_mbid(h5)
	    artist_pmid = hdf5_getters.get_artist_playmeid(h5)
	    audio_md5 = hdf5_getters.get_audio_md5(h5)
	    danceability = hdf5_getters.get_danceability(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(danceability) == True:
	            danceability=-1
	    end_fade_in =hdf5_getters.get_end_of_fade_in(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(end_fade_in) == True:
	            end_fade_in=-1
	    energy = hdf5_getters.get_energy(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(energy) == True:
	            energy=-1
            song_key = hdf5_getters.get_key(h5)
	    key_c = hdf5_getters.get_key_confidence(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(key_c) == True:
	            key_c=-1
	    loudness = hdf5_getters.get_loudness(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(loudness) == True:
	            loudness=-1
	    mode = hdf5_getters.get_mode(h5)
	    mode_conf = hdf5_getters.get_mode_confidence(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(mode_conf) == True:
	            mode_conf=-1
	    release_7digitalid = hdf5_getters.get_release_7digitalid(h5)
	    song_hot = hdf5_getters.get_song_hotttnesss(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(song_hot) == True:
	            song_hot=-1
	    song_id = hdf5_getters.get_song_id(h5)
	    start_fade_out = hdf5_getters.get_start_of_fade_out(h5)
	    tempo = hdf5_getters.get_tempo(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(tempo) == True:
	            tempo=-1
	    time_sig = hdf5_getters.get_time_signature(h5)
	    time_sig_c = hdf5_getters.get_time_signature_confidence(h5)
	    #checking if we get a "nan" if we do we change it to -1
	    if numpy.isnan(time_sig_c) == True:
	            time_sig_c=-1
	    track_id = hdf5_getters.get_track_id(h5)
	    track_7digitalid = hdf5_getters.get_track_7digitalid(h5)
	    year = hdf5_getters.get_year(h5)
	    bars_c = hdf5_getters.get_bars_confidence(h5)
	    bars_c_avg= get_avg(bars_c)
	    bars_c_max= get_max(bars_c)
	    bars_c_min = get_min(bars_c)
	    bars_c_stddev= get_stddev(bars_c)
	    bars_c_count = get_count(bars_c)
	    bars_c_sum = get_sum(bars_c)
	    bars_start = hdf5_getters.get_bars_start(h5)
	    bars_start_avg = get_avg(bars_start)
	    bars_start_max= get_max(bars_start)
	    bars_start_min = get_min(bars_start)
	    bars_start_stddev= get_stddev(bars_start)
	    bars_start_count = get_count(bars_start)
	    bars_start_sum = get_sum(bars_start)
            beats_c = hdf5_getters.get_beats_confidence(h5)
            beats_c_avg= get_avg(beats_c)
	    beats_c_max= get_max(beats_c)
	    beats_c_min = get_min(beats_c)
	    beats_c_stddev= get_stddev(beats_c)
	    beats_c_count = get_count(beats_c)
	    beats_c_sum = get_sum(beats_c)
            beats_start = hdf5_getters.get_beats_start(h5)
 	    beats_start_avg = get_avg(beats_start)
	    beats_start_max= get_max(beats_start)
	    beats_start_min = get_min(beats_start)
	    beats_start_stddev= get_stddev(beats_start)
	    beats_start_count = get_count(beats_start)
	    beats_start_sum = get_sum(beats_start)
	    sec_c = hdf5_getters.get_sections_confidence(h5)
            sec_c_avg= get_avg(sec_c)
	    sec_c_max= get_max(sec_c)
	    sec_c_min = get_min(sec_c)
	    sec_c_stddev= get_stddev(sec_c)
	    sec_c_count = get_count(sec_c)
	    sec_c_sum = get_sum(sec_c)
	    sec_start = hdf5_getters.get_sections_start(h5)
            sec_start_avg = get_avg(sec_start)
	    sec_start_max= get_max(sec_start)
	    sec_start_min = get_min(sec_start)
	    sec_start_stddev= get_stddev(sec_start)
	    sec_start_count = get_count(sec_start)
	    sec_start_sum = get_sum(sec_start)
	    seg_c = hdf5_getters.get_segments_confidence(h5)
	    seg_c_avg= get_avg(seg_c)
	    seg_c_max= get_max(seg_c)
	    seg_c_min = get_min(seg_c)
	    seg_c_stddev= get_stddev(seg_c)
	    seg_c_count = get_count(seg_c)
	    seg_c_sum = get_sum(seg_c)
            seg_loud_max = hdf5_getters.get_segments_loudness_max(h5)
            seg_loud_max_avg= get_avg(seg_loud_max)
	    seg_loud_max_max= get_max(seg_loud_max)
	    seg_loud_max_min = get_min(seg_loud_max)
	    seg_loud_max_stddev= get_stddev(seg_loud_max)
	    seg_loud_max_count = get_count(seg_loud_max)
	    seg_loud_max_sum = get_sum(seg_loud_max)
	    seg_loud_max_time = hdf5_getters.get_segments_loudness_max_time(h5)
	    seg_loud_max_time_avg= get_avg(seg_loud_max_time)
	    seg_loud_max_time_max= get_max(seg_loud_max_time)
	    seg_loud_max_time_min = get_min(seg_loud_max_time)
	    seg_loud_max_time_stddev= get_stddev(seg_loud_max_time)
	    seg_loud_max_time_count = get_count(seg_loud_max_time)
	    seg_loud_max_time_sum = get_sum(seg_loud_max_time)
	    seg_loud_start = hdf5_getters.get_segments_loudness_start(h5)
	    seg_loud_start_avg= get_avg(seg_loud_start)
	    seg_loud_start_max= get_max(seg_loud_start)
	    seg_loud_start_min = get_min(seg_loud_start)
	    seg_loud_start_stddev= get_stddev(seg_loud_start)
	    seg_loud_start_count = get_count(seg_loud_start)
	    seg_loud_start_sum = get_sum(seg_loud_start)					      
	    seg_pitch = hdf5_getters.get_segments_pitches(h5)
	    pitch_size = len(seg_pitch)
	    seg_start = hdf5_getters.get_segments_start(h5)
	    seg_start_avg= get_avg(seg_start)
	    seg_start_max= get_max(seg_start)
	    seg_start_min = get_min(seg_start)
	    seg_start_stddev= get_stddev(seg_start)
	    seg_start_count = get_count(seg_start)
	    seg_start_sum = get_sum(seg_start)
	    seg_timbre = hdf5_getters.get_segments_timbre(h5)
	    tatms_c = hdf5_getters.get_tatums_confidence(h5)
	    tatms_c_avg= get_avg(tatms_c)
	    tatms_c_max= get_max(tatms_c)
	    tatms_c_min = get_min(tatms_c)
	    tatms_c_stddev= get_stddev(tatms_c)
	    tatms_c_count = get_count(tatms_c)
	    tatms_c_sum = get_sum(tatms_c)
	    tatms_start = hdf5_getters.get_tatums_start(h5)
	    tatms_start_avg= get_avg(tatms_start)
	    tatms_start_max= get_max(tatms_start)
	    tatms_start_min = get_min(tatms_start)
	    tatms_start_stddev= get_stddev(tatms_start)
	    tatms_start_count = get_count(tatms_start)
	    tatms_start_sum = get_sum(tatms_start)
	
	    #Getting the genres
	    genre_set = 0    #flag to see if the genre has been set or not
	    art_trm = hdf5_getters.get_artist_terms(h5)
	    trm_freq = hdf5_getters.get_artist_terms_freq(h5)
	    trn_wght = hdf5_getters.get_artist_terms_weight(h5)
	    a_mb_tags = hdf5_getters.get_artist_mbtags(h5)
	    genre_indexes=get_genre_indexes(trm_freq) #index of the highest freq
	    final_genre=[]
	    genres_so_far=[]
	    for i in range(len(genre_indexes)):
		    genre_tmp=get_genre(art_trm,genre_indexes[i])   #genre that corresponds to the highest freq
		    genres_so_far=genre_dict.get_genre_in_dict(genre_tmp) #getting the genre from the dictionary
		    if len(genres_so_far) != 0:
			    for i in genres_so_far:
				final_genre.append(i)
				genre_set=1				#genre was found in dictionary
				  
		
	    
	    if genre_set == 1:
		    col_num=[]
		   
		    for genre in final_genre:
			    column=int(genre)				#getting the column number of the genre
			    col_num.append(column)

		    genre_array=genre_columns(col_num)	         #genre array
 	    else:
		    genre_array=genre_columns(-1)		#the genre was not found in the dictionary

	    transpose_pitch= seg_pitch.transpose() #this is to tranpose the matrix,so we can have 12 rows
	    #arrays containing the aggregate values of the 12 rows
	    seg_pitch_avg=[]
	    seg_pitch_max=[]
	    seg_pitch_min=[]
            seg_pitch_stddev=[]
            seg_pitch_count=[]
	    seg_pitch_sum=[]
            i=0
	    #Getting the aggregate values in the pitches array
	    for row in transpose_pitch:
		   seg_pitch_avg.append(get_avg(row))
		   seg_pitch_max.append(get_max(row))
	           seg_pitch_min.append(get_min(row))
		   seg_pitch_stddev.append(get_stddev(row))
		   seg_pitch_count.append(get_count(row))
                   seg_pitch_sum.append(get_sum(row))
		   i=i+1

	    #extracting information from the timbre array 
            transpose_timbre = seg_pitch.transpose() #tranposing matrix, to have 12 rows
	    #arrays containing the aggregate values of the 12 rows
	    seg_timbre_avg=[]
	    seg_timbre_max=[]
	    seg_timbre_min=[]
            seg_timbre_stddev=[]
            seg_timbre_count=[]
	    seg_timbre_sum=[]
            i=0
	    for row in transpose_timbre:
		   seg_timbre_avg.append(get_avg(row))
		   seg_timbre_max.append(get_max(row))
	           seg_timbre_min.append(get_min(row))
		   seg_timbre_stddev.append(get_stddev(row))
		   seg_timbre_count.append(get_count(row))
                   seg_timbre_sum.append(get_sum(row))
		   i=i+1
		


		#Writing to the flat file

            writer.writerow([title,album,artist_name,duration,samp_rt,artist_7digitalid,artist_fam,artist_hotness,artist_id,artist_lat,artist_loc,artist_lon,artist_mbid,genre_array[0],genre_array[1],genre_array[2],
genre_array[3],genre_array[4],genre_array[5],genre_array[6],genre_array[7],genre_array[8],genre_array[9],genre_array[10],genre_array[11],genre_array[12],genre_array[13],genre_array[14],genre_array[15],
genre_array[16],genre_array[17],genre_array[18],genre_array[19],genre_array[20],genre_array[21],genre_array[22],genre_array[23],genre_array[24],genre_array[25],genre_array[26],
genre_array[27],genre_array[28],genre_array[29],genre_array[30],genre_array[31],genre_array[32],genre_array[33],genre_array[34],genre_array[35],genre_array[36],genre_array[37],genre_array[38],
genre_array[39],genre_array[40],genre_array[41],genre_array[42],genre_array[43],genre_array[44],genre_array[45],genre_array[46],genre_array[47],genre_array[48],genre_array[49],
genre_array[50],genre_array[51],genre_array[52],genre_array[53],genre_array[54],genre_array[55],genre_array[56],genre_array[57],genre_array[58],genre_array[59],
genre_array[60],genre_array[61],genre_array[62],genre_array[63],genre_array[64],genre_array[65],genre_array[66],genre_array[67],genre_array[68],genre_array[69],
genre_array[70],genre_array[71],genre_array[72],genre_array[73],genre_array[74],genre_array[75],genre_array[76],genre_array[77],genre_array[78],genre_array[79],
genre_array[80],genre_array[81],genre_array[82],genre_array[83],genre_array[84],genre_array[85],genre_array[86],genre_array[87],genre_array[88],genre_array[89],
genre_array[90],genre_array[91],genre_array[92],genre_array[93],genre_array[94],genre_array[95],genre_array[96],genre_array[97],genre_array[98],genre_array[99],genre_array[100],genre_array[101],
genre_array[102],genre_array[103],genre_array[104],genre_array[105],genre_array[106],genre_array[107],genre_array[108],genre_array[109],genre_array[110],genre_array[111],genre_array[112],
genre_array[113],genre_array[114],genre_array[115],genre_array[116],genre_array[117],genre_array[118],genre_array[119],genre_array[120],genre_array[121],genre_array[122],genre_array[123],
genre_array[124],genre_array[125],genre_array[126],genre_array[127],genre_array[128],genre_array[129],genre_array[130],genre_array[131],genre_array[132],
artist_pmid,audio_md5,danceability,end_fade_in,energy,song_key,key_c,loudness,mode,mode_conf,release_7digitalid,song_hot,song_id,start_fade_out,tempo,time_sig,time_sig_c,track_id,track_7digitalid,year,bars_c_avg,bars_c_max,bars_c_min,bars_c_stddev,bars_c_count,bars_c_sum,bars_start_avg,bars_start_max,bars_start_min,bars_start_stddev,bars_start_count,bars_start_sum,beats_c_avg,beats_c_max,beats_c_min,beats_c_stddev,beats_c_count,beats_c_sum,beats_start_avg,beats_start_max,beats_start_min, beats_start_stddev,beats_start_count,beats_start_sum, sec_c_avg,sec_c_max,sec_c_min,sec_c_stddev,sec_c_count,sec_c_sum,sec_start_avg,sec_start_max,sec_start_min,sec_start_stddev,sec_start_count,sec_start_sum,seg_c_avg,seg_c_max,seg_c_min,seg_c_stddev,seg_c_count,seg_c_sum,seg_loud_max_avg,seg_loud_max_max,seg_loud_max_min,seg_loud_max_stddev,seg_loud_max_count,seg_loud_max_sum,seg_loud_max_time_avg,seg_loud_max_time_max,seg_loud_max_time_min,seg_loud_max_time_stddev,seg_loud_max_time_count,seg_loud_max_time_sum,seg_loud_start_avg,seg_loud_start_max,seg_loud_start_min,seg_loud_start_stddev,seg_loud_start_count,seg_loud_start_sum,seg_pitch_avg[0],seg_pitch_max[0],seg_pitch_min[0],seg_pitch_stddev[0],seg_pitch_count[0],seg_pitch_sum[0],seg_pitch_avg[1],seg_pitch_max[1],seg_pitch_min[1],seg_pitch_stddev[1],seg_pitch_count[1],seg_pitch_sum[1],seg_pitch_avg[2],seg_pitch_max[2],seg_pitch_min[2],seg_pitch_stddev[2],seg_pitch_count[2],seg_pitch_sum[2],seg_pitch_avg[3],seg_pitch_max[3],seg_pitch_min[3],seg_pitch_stddev[3],seg_pitch_count[3],seg_pitch_sum[3],seg_pitch_avg[4],seg_pitch_max[4],seg_pitch_min[4],seg_pitch_stddev[4],seg_pitch_count[4],seg_pitch_sum[4],seg_pitch_avg[5],seg_pitch_max[5],seg_pitch_min[5],seg_pitch_stddev[5],seg_pitch_count[5],seg_pitch_sum[5],seg_pitch_avg[6],seg_pitch_max[6],seg_pitch_min[6],seg_pitch_stddev[6],seg_pitch_count[6],seg_pitch_sum[6],seg_pitch_avg[7],seg_pitch_max[7],seg_pitch_min[7],seg_pitch_stddev[7],seg_pitch_count[7],seg_pitch_sum[7],seg_pitch_avg[8],seg_pitch_max[8],seg_pitch_min[8],seg_pitch_stddev[8],seg_pitch_count[8],seg_pitch_sum[8],seg_pitch_avg[9],seg_pitch_max[9],seg_pitch_min[9],seg_pitch_stddev[9],seg_pitch_count[9],seg_pitch_sum[9],seg_pitch_avg[10],seg_pitch_max[10],seg_pitch_min[10],seg_pitch_stddev[10],seg_pitch_count[10],seg_pitch_sum[10],seg_pitch_avg[11],seg_pitch_max[11],seg_pitch_min[11],
seg_pitch_stddev[11],seg_pitch_count[11],seg_pitch_sum[11],seg_start_avg,seg_start_max,seg_start_min,seg_start_stddev, 
seg_start_count,seg_start_sum,seg_timbre_avg[0],seg_timbre_max[0],seg_timbre_min[0],seg_timbre_stddev[0],seg_timbre_count[0],
seg_timbre_sum[0],seg_timbre_avg[1],seg_timbre_max[1],seg_timbre_min[1],seg_timbre_stddev[1],seg_timbre_count[1],
seg_timbre_sum[1],seg_timbre_avg[2],seg_timbre_max[2],seg_timbre_min[2],seg_timbre_stddev[2],seg_timbre_count[2],
seg_timbre_sum[2],seg_timbre_avg[3],seg_timbre_max[3],seg_timbre_min[3],seg_timbre_stddev[3],seg_timbre_count[3],
seg_timbre_sum[3],seg_timbre_avg[4],seg_timbre_max[4],seg_timbre_min[4],seg_timbre_stddev[4],seg_timbre_count[4],
seg_timbre_sum[4],seg_timbre_avg[5],seg_timbre_max[5],seg_timbre_min[5],seg_timbre_stddev[5],seg_timbre_count[5],
seg_timbre_sum[5],seg_timbre_avg[6],seg_timbre_max[6],seg_timbre_min[6],seg_timbre_stddev[6],seg_timbre_count[6],
seg_timbre_sum[6],seg_timbre_avg[7],seg_timbre_max[7],seg_timbre_min[7],seg_timbre_stddev[7],seg_timbre_count[7],
seg_timbre_sum[7],seg_timbre_avg[8],seg_timbre_max[8],seg_timbre_min[8],seg_timbre_stddev[8],seg_timbre_count[8],
seg_timbre_sum[8],seg_timbre_avg[9],seg_timbre_max[9],seg_timbre_min[9],seg_timbre_stddev[9],seg_timbre_count[9],
seg_timbre_sum[9],seg_timbre_avg[10],seg_timbre_max[10],seg_timbre_min[10],seg_timbre_stddev[10],seg_timbre_count[10],
seg_timbre_sum[10],seg_timbre_avg[11],seg_timbre_max[11],seg_timbre_min[11],seg_timbre_stddev[11],seg_timbre_count[11],
seg_timbre_sum[11],tatms_c_avg,tatms_c_max,tatms_c_min,tatms_c_stddev,tatms_c_count,tatms_c_sum,tatms_start_avg,tatms_start_max,tatms_start_min,tatms_start_stddev,tatms_start_count,tatms_start_sum])






	    h5.close()
	    count=count+1;
	    print count;
end_of_fade_in=[]
mode=[]
start_of_fade_out=[]
song_hotttnesss=[]

for i in range(0,len(file)):
    h5 = yay.open_h5_file_read('F:\sem4\ml\project\MillionSongSubset\data\A\{}'.format(file[i]))
    duration.append(yay.get_duration(h5))
    artist_familiarity.append(yay.get_artist_familiarity(h5))
    artist_hotttnesss.append(yay.get_artist_hotttnesss(h5))
    tempo.append(yay.get_tempo(h5))
    loudness.append(yay.get_loudness(h5))
    key.append(yay.get_key(h5))
    time_signature.append(yay.get_time_signature(h5))
    end_of_fade_in.append(yay.get_end_of_fade_in(h5))
    mode.append(yay.get_mode(h5))
    start_of_fade_out.append(yay.get_start_of_fade_out(h5))
    song_hotttnesss.append(yay.get_song_hotttnesss(h5))

rows = zip(duration,artist_familiarity,artist_hotttnesss,tempo,loudness,key,time_signature,
           end_of_fade_in,mode,start_of_fade_out,song_hotttnesss)

import csv

with open('training_data.csv', "w", encoding="ISO-8859-1", newline='') as f:
    fieldnames = ['duration','artist_familiarity','artist_hotttnesss','tempo','loudness','key','time_signature',
                  'end_of_fade_in','mode','start_of_fade_out','song_hotttnesss']
    writer = csv.DictWriter(f, fieldnames=fieldnames)
    writer.writeheader()
    writer = csv.writer(f)
    for row in rows:
示例#40
0
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
示例#41
0
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
示例#43
0
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
示例#44
0
            #########################################################

            # Get analysis sample rate
            analysis_rate = hdf5_getters.get_analysis_sample_rate(h5)

            # Get end of fade in
            end_of_fade_in = hdf5_getters.get_end_of_fade_in(h5)

            # Get key
            key = hdf5_getters.get_key(h5)

            # Get key confidence
            key_confidence = hdf5_getters.get_key_confidence(h5)

            # Get mode
            mode = hdf5_getters.get_mode(h5)

            # Get mode confidence
            mode_confidence = hdf5_getters.get_mode_confidence(h5)

            # Get start of fade-out
            start_of_fade_out = hdf5_getters.get_start_of_fade_out(h5)

            # Get time signature
            time_signature = hdf5_getters.get_time_signature(h5)

            # Get time signature confidence
            time_signature_conf = hdf5_getters.get_time_signature_confidence(
                h5)

            # Get track_id
示例#45
0
def parse_aggregate_songs(file_name,file_name2,artist_map):
    """
    Given an aggregate filename and artist_map in the format
    {artist_name: {data pertaining to artist}}
    """
    """
    TODO: 
    -this function goes through each song, if artist not in there,
    add all data necesary and add first song info.
    else update any specific song info

    -song info is a map from attributename:[values]
    """
    #artist_map = {}
    h5 = hdf5_getters.open_h5_file_read(file_name)
    numSongs = hdf5_getters.get_num_songs(h5)
    print 'Parsing song file...'
    for i in range(numSongs):
        artist_name = hdf5_getters.get_artist_name(h5,i)

        #Filter location
        longi = hdf5_getters.get_artist_longitude(h5,i)
        lat = hdf5_getters.get_artist_latitude(h5,i)
        loc = hdf5_getters.get_artist_location(h5,i)
        if math.isnan(lat) or math.isnan(longi):
            #skip if no location
            continue

        #filter year
        yr = hdf5_getters.get_year(h5,i)
        if yr == 0:
            #skip if no year
            continue

        #filter hotttness and familiarity
        familiarity = hdf5_getters.get_artist_familiarity(h5,i)
        hotttness = hdf5_getters.get_artist_hotttnesss(h5,i)
        if familiarity<=0.0 or hotttness<=0.0:
            #skip if no hotttness or familiarity computations
            continue

        #TODO:MAYBE filter on dance and energy
        timbre = hdf5_getters.get_segments_timbre(h5,i)
        #timbre[#] gives len 12 array so for each arr in timbre, add up to get segment and add to corresponding 12 features and avg across each
        if not artist_name in artist_map:
            #have not encountered the artist yet, so populate new map
            sub_map = {}
            sub_map['artist_familiarity'] = familiarity
            sub_map['artist_hotttnesss'] = hotttness
            sub_map['artist_id'] = hdf5_getters.get_artist_id(h5,i)
            #longi = hdf5_getters.get_artist_longitude(h5,i)
            #lat = hdf5_getters.get_artist_latitude(h5,i)
            #longi = None if math.isnan(longi) else longi
            #lat = None if math.isnan(lat) else lat
            sub_map['artist_latitude'] = lat
            sub_map['artist_longitude'] = longi
            sub_map['artist_location'] = loc
            sub_map['artist_terms'] = hdf5_getters.get_artist_terms(h5,i)
            #TODO:see if should weight by freq or weight for if the term matches one of the feature terms
            sub_map['artist_terms_freq'] = list(hdf5_getters.get_artist_terms_freq(h5,i))
            sub_map['artist_terms_weight'] = list(hdf5_getters.get_artist_terms_weight(h5,i))

            #song-sepcific data
            #TODO COMPUTE AN AVG TIMBRE FOR A SONG BY IDEA:
            #SUMMING DOWN EACH 12 VECTOR FOR EACH PT IN SONG AND AVG THIS ACROSS SONG
            dance = hdf5_getters.get_danceability(h5,i)
            dance = None if dance == 0.0 else dance
            energy = hdf5_getters.get_energy(h5,i)
            energy = None if energy == 0.0 else energy
            sub_map['danceability'] = [dance]
            sub_map['duration'] = [hdf5_getters.get_duration(h5,i)]
            sub_map['end_of_fade_in'] = [hdf5_getters.get_end_of_fade_in(h5,i)]
            sub_map['energy'] = [energy]
            #since each song has a key, ask if feature for keys should be num of songs that appear in that key or
            #just binary if any of their songs has that key or just be avg of songs with that key
            #same for mode, since its either major or minor...should it be count or avg.?
            sub_map['key'] = [hdf5_getters.get_key(h5,i)]
            sub_map['loudness'] = [hdf5_getters.get_loudness(h5,i)]
            sub_map['mode'] = [hdf5_getters.get_mode(h5,i)] #major or minor 0/1
            s_hot = hdf5_getters.get_song_hotttnesss(h5,i)
            s_hot = None if math.isnan(s_hot) else s_hot
            sub_map['song_hotttnesss'] = [s_hot]
            sub_map['start_of_fade_out'] = [hdf5_getters.get_start_of_fade_out(h5,i)]
            sub_map['tempo'] = [hdf5_getters.get_tempo(h5,i)]
            #should time signature be count as well? binary?
            sub_map['time_signature'] = [hdf5_getters.get_time_signature(h5,i)]
            sub_map['track_id'] = [hdf5_getters.get_track_id(h5,i)]
            #should year be binary since they can have many songs across years and should it be year:count
            sub_map['year'] = [yr]

            artist_map[artist_name] = sub_map
        else:
            #artist already exists, so get its map and update song fields
            dance = hdf5_getters.get_danceability(h5,i)
            dance = None if dance == 0.0 else dance
            energy = hdf5_getters.get_energy(h5,i)
            energy = None if energy == 0.0 else energy
            artist_map[artist_name]['danceability'].append(dance)
            artist_map[artist_name]['duration'].append(hdf5_getters.get_duration(h5,i))
            artist_map[artist_name]['end_of_fade_in'].append(hdf5_getters.get_end_of_fade_in(h5,i))
            artist_map[artist_name]['energy'].append(energy)
            artist_map[artist_name]['key'].append(hdf5_getters.get_key(h5,i))
            artist_map[artist_name]['loudness'].append(hdf5_getters.get_loudness(h5,i))
            artist_map[artist_name]['mode'].append(hdf5_getters.get_mode(h5,i)) #major or minor 0/1
            s_hot = hdf5_getters.get_song_hotttnesss(h5,i)
            s_hot = None if math.isnan(s_hot) else s_hot
            artist_map[artist_name]['song_hotttnesss'].append(s_hot)
            artist_map[artist_name]['start_of_fade_out'].append(hdf5_getters.get_start_of_fade_out(h5,i))
            artist_map[artist_name]['tempo'].append(hdf5_getters.get_tempo(h5,i))
            #should time signature be count as well? binary?
            artist_map[artist_name]['time_signature'].append(hdf5_getters.get_time_signature(h5,i))
            artist_map[artist_name]['track_id'].append(hdf5_getters.get_track_id(h5,i))
            #should year be binary since they can have many songs across years and should it be year:count
            artist_map[artist_name]['year'].append(yr)

    h5 = hdf5_getters.open_h5_file_read(file_name2)
    numSongs = hdf5_getters.get_num_songs(h5)
    print 'Parsing song file2...'
    for i in range(numSongs):
        song_id = hdf5_getters.get_track_id(h5,i)
        artist_name = hdf5_getters.get_artist_name(h5,i)
        if artist_name in artist_map and song_id in artist_map[artist_name]['track_id']:
            continue

        #Filter location
        longi = hdf5_getters.get_artist_longitude(h5,i)
        lat = hdf5_getters.get_artist_latitude(h5,i)
        loc = hdf5_getters.get_artist_location(h5,i)
        if math.isnan(lat) or math.isnan(longi):
            #skip if no location
            continue

        #filter year
        yr = hdf5_getters.get_year(h5,i)
        if yr == 0:
            #skip if no year
            continue

        #filter hotttness and familiarity
        familiarity = hdf5_getters.get_artist_familiarity(h5,i)
        hotttness = hdf5_getters.get_artist_hotttnesss(h5,i)
        if familiarity<=0.0 or hotttness<=0.0:
            #skip if no hotttness or familiarity computations
            continue

        #TODO:MAYBE filter on dance and energy
        timbre = hdf5_getters.get_segments_timbre(h5,i)
        #timbre[#] gives len 12 array so for each arr in timbre, add up to get segment and add to corresponding 12 features and avg across each
        if not artist_name in artist_map:
            #have not encountered the artist yet, so populate new map
            sub_map = {}
            sub_map['artist_familiarity'] = familiarity
            sub_map['artist_hotttnesss'] = hotttness
            sub_map['artist_id'] = hdf5_getters.get_artist_id(h5,i)
            #longi = hdf5_getters.get_artist_longitude(h5,i)
            #lat = hdf5_getters.get_artist_latitude(h5,i)
            #longi = None if math.isnan(longi) else longi
            #lat = None if math.isnan(lat) else lat
            sub_map['artist_latitude'] = lat
            sub_map['artist_longitude'] = longi
            sub_map['artist_location'] = loc
            sub_map['artist_terms'] = hdf5_getters.get_artist_terms(h5,i)
            #TODO:see if should weight by freq or weight for if the term matches one of the feature terms
            sub_map['artist_terms_freq'] = list(hdf5_getters.get_artist_terms_freq(h5,i))
            sub_map['artist_terms_weight'] = list(hdf5_getters.get_artist_terms_weight(h5,i))

            #song-sepcific data
            #TODO COMPUTE AN AVG TIMBRE FOR A SONG BY IDEA:
            #SUMMING DOWN EACH 12 VECTOR FOR EACH PT IN SONG AND AVG THIS ACROSS SONG
            dance = hdf5_getters.get_danceability(h5,i)
            dance = None if dance == 0.0 else dance
            energy = hdf5_getters.get_energy(h5,i)
            energy = None if energy == 0.0 else energy
            sub_map['danceability'] = [dance]
            sub_map['duration'] = [hdf5_getters.get_duration(h5,i)]
            sub_map['end_of_fade_in'] = [hdf5_getters.get_end_of_fade_in(h5,i)]
            sub_map['energy'] = [energy]
            #since each song has a key, ask if feature for keys should be num of songs that appear in that key or
            #just binary if any of their songs has that key or just be avg of songs with that key
            #same for mode, since its either major or minor...should it be count or avg.?
            sub_map['key'] = [hdf5_getters.get_key(h5,i)]
            sub_map['loudness'] = [hdf5_getters.get_loudness(h5,i)]
            sub_map['mode'] = [hdf5_getters.get_mode(h5,i)] #major or minor 0/1
            s_hot = hdf5_getters.get_song_hotttnesss(h5,i)
            s_hot = None if math.isnan(s_hot) else s_hot
            sub_map['song_hotttnesss'] = [s_hot]
            sub_map['start_of_fade_out'] = [hdf5_getters.get_start_of_fade_out(h5,i)]
            sub_map['tempo'] = [hdf5_getters.get_tempo(h5,i)]
            #should time signature be count as well? binary?
            sub_map['time_signature'] = [hdf5_getters.get_time_signature(h5,i)]
            sub_map['track_id'] = [hdf5_getters.get_track_id(h5,i)]
            #should year be binary since they can have many songs across years and should it be year:count
            sub_map['year'] = [yr]

            artist_map[artist_name] = sub_map
        else:
            #artist already exists, so get its map and update song fields
            dance = hdf5_getters.get_danceability(h5,i)
            dance = None if dance == 0.0 else dance
            energy = hdf5_getters.get_energy(h5,i)
            energy = None if energy == 0.0 else energy
            artist_map[artist_name]['danceability'].append(dance)
            artist_map[artist_name]['duration'].append(hdf5_getters.get_duration(h5,i))
            artist_map[artist_name]['end_of_fade_in'].append(hdf5_getters.get_end_of_fade_in(h5,i))
            artist_map[artist_name]['energy'].append(energy)
            artist_map[artist_name]['key'].append(hdf5_getters.get_key(h5,i))
            artist_map[artist_name]['loudness'].append(hdf5_getters.get_loudness(h5,i))
            artist_map[artist_name]['mode'].append(hdf5_getters.get_mode(h5,i)) #major or minor 0/1
            s_hot = hdf5_getters.get_song_hotttnesss(h5,i)
            s_hot = None if math.isnan(s_hot) else s_hot
            artist_map[artist_name]['song_hotttnesss'].append(s_hot)
            artist_map[artist_name]['start_of_fade_out'].append(hdf5_getters.get_start_of_fade_out(h5,i))
            artist_map[artist_name]['tempo'].append(hdf5_getters.get_tempo(h5,i))
            #should time signature be count as well? binary?
            artist_map[artist_name]['time_signature'].append(hdf5_getters.get_time_signature(h5,i))
            artist_map[artist_name]['track_id'].append(hdf5_getters.get_track_id(h5,i))
            #should year be binary since they can have many songs across years and should it be year:count
            artist_map[artist_name]['year'].append(yr)
示例#46
0
     str(e) for e in GETTERS.get_artist_terms(h5, i))  # array
 #artist_terms_freq = ','.join(str(e) for e in GETTERS.get_artist_terms_freq(h5, i)) # array
 #artist_terms_weight = ','.join(str(e) for e in GETTERS.get_artist_terms_weight(h5, i)) # array
 #audio_md5 = GETTERS.get_audio_md5(h5, i)
 #bars_confidence = ','.join(str(e) for e in GETTERS.get_bars_confidence(h5, i)) # array
 #bars_start = ','.join(str(e) for e in GETTERS.get_bars_start(h5, i)) # array
 #beats_confidence = ','.join(str(e) for e in GETTERS.get_beats_confidence(h5, i)) # array
 #beats_start = ','.join(str(e) for e in GETTERS.get_beats_start(h5, i)) # array
 danceability = GETTERS.get_danceability(h5, i)
 duration = GETTERS.get_duration(h5, i)
 end_of_fade_in = GETTERS.get_end_of_fade_in(h5, i)
 energy = GETTERS.get_energy(h5, i)
 key = GETTERS.get_key(h5, i)
 key_confidence = GETTERS.get_key_confidence(h5, i)
 loudness = GETTERS.get_loudness(h5, i)
 mode = GETTERS.get_mode(h5, i)
 mode_confidence = GETTERS.get_mode_confidence(h5, i)
 release = GETTERS.get_release(h5, i)
 release_7digitalid = GETTERS.get_release_7digitalid(h5, i)
 #sections_confidence = ','.join(str(e) for e in GETTERS.get_sections_confidence(h5, i)) # array
 #sections_start = ','.join(str(e) for e in GETTERS.get_sections_start(h5, i)) # array
 #segments_confidence = ','.join(str(e) for e in GETTERS.get_segments_confidence(h5, i)) # array
 #segments_loudness_max = ','.join(str(e) for e in GETTERS.get_segments_loudness_max(h5, i)) # array
 #segments_loudness_max_time = ','.join(str(e) for e in GETTERS.get_segments_loudness_max_time(h5, i)) # array
 #segments_loudness_start = ','.join(str(e) for e in GETTERS.get_segments_loudness_start(h5, i)) # array
 #segments_pitches = ','.join(str(e) for e in GETTERS.get_segments_pitches(h5, i)) # array
 #segments_start = ','.join(str(e) for e in GETTERS.get_segments_start(h5, i)) # array
 #segments_timbre = ','.join(str(e) for e in GETTERS.get_segments_timbre(h5, i)) # array
 similar_artists = ','.join(
     str(e)
     for e in GETTERS.get_similar_artists(h5, i))  # array
示例#47
0
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)