def debug_from_song_file(connect, h5path, verbose=0):
"""
Slow debugging function that takes a h5 file, reads the info,
check the match with musicbrainz db, prints out the result.
Only prints when we dont get exact match!
RETURN counts of how many files we filled for years, tags
"""
import hdf5_utils as HDF5
import hdf5_getters as GETTERS
h5 = HDF5.open_h5_file_read(h5path)
title = GETTERS.get_title(h5)
release = GETTERS.get_release(h5)
artist = GETTERS.get_artist_name(h5)
ambid = GETTERS.get_artist_mbid(h5)
h5.close()
# mbid
gotmbid = 1
if ambid == '':
gotmbid = 0
if verbose > 0: print('no mb id for:', artist)
# year
year = find_year_safemode(connect, ambid, title, release, artist)
gotyear = 1 if year > 0 else 0
if verbose > 0: print('no years for:', artist, '|', release, '|', title)
# tags
tags, counts = get_artist_tags(connect, ambid)
gottags = 1 if len(tags) > 0 else 0
if gottags == 0 and verbose > 0: print('no tags for:', artist)
# return indicator for mbid, year, tag
return gotmbid, gotyear, gottags
def debug_from_song_file(connect,h5path,verbose=0):
"""
Slow debugging function that takes a h5 file, reads the info,
check the match with musicbrainz db, prints out the result.
Only prints when we dont get exact match!
RETURN counts of how many files we filled for years, tags
"""
import hdf5_utils as HDF5
import hdf5_getters as GETTERS
h5 = HDF5.open_h5_file_read(h5path)
title = GETTERS.get_title(h5)
release = GETTERS.get_release(h5)
artist = GETTERS.get_artist_name(h5)
ambid = GETTERS.get_artist_mbid(h5)
h5.close()
# mbid
gotmbid=1
if ambid=='':
gotmbid = 0
if verbose>0: print('no mb id for:',artist)
# year
year = find_year_safemode(connect,ambid,title,release,artist)
gotyear = 1 if year > 0 else 0
if verbose>0: print('no years for:',artist,'|',release,'|',title)
# tags
tags,counts = get_artist_tags(connect,ambid)
gottags = 1 if len(tags) > 0 else 0
if gottags == 0 and verbose>0: print('no tags for:',artist)
# return indicator for mbid, year, tag
return gotmbid,gotyear,gottags
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
def _extractSongData(file_path, filename):
# song_id, title, release, artist_name, year
h5 = hdf5_getters.open_h5_file_read(file_path)
track_id = filename[:-3]
song_id = hdf5_getters.get_song_id(h5).decode('UTF-8')
dig7_id = hdf5_getters.get_track_7digitalid(h5)
title = hdf5_getters.get_title(h5).decode('UTF-8')
release = hdf5_getters.get_release(h5).decode('UTF-8')
artist_name = hdf5_getters.get_artist_name(h5).decode('UTF-8')
year = hdf5_getters.get_year(h5)
h5.close()
# print(song_id, track_id, dig7_id, title, release, artist_name, year)
return track_id, song_id, dig7_id, title, release, artist_name, year
def func_to_get_desired_values(filename, returnValue = False):
"""
This function does 3 simple things:
- open the song file
- get the elements we want and put them in
- close the file
INPUT :
filename - The name of the h5 file to be loaded
"""
global all_desired_data
# Open file
h5 = GETTERS.open_h5_file_read(filename)
# Create and fill a record
record = []
for element in elementsRequested:
result = getattr(GETTERS, element)(h5)
try:
if result == '':
result = 'Adlen - void'
except:
pass
try:
if isinstance(result, np.ndarray):
if len(result) > 1:
result = float(np.mean(result))
else:
result = ''
except:
try:
result = float(result)
except:
pass
record.append(result)
song_id = GETTERS.get_track_id(h5)
artist_name = GETTERS.get_artist_name(h5)
title = GETTERS.get_title(h5)
artist_mbtags = GETTERS.get_artist_mbtags(h5)
release = GETTERS.get_release(h5)
song_id = unicode(song_id.decode('utf-8'))
title = unicode(title.decode('utf-8'))
artist_name = unicode(artist_name.decode('utf-8'))
if not returnValue:
all_desired_data.append([[[song_id, title, artist_name, elementsRequested], artist_name, title, artist_mbtags, release], record])
h5.close()
if returnValue:
return [[[song_id, title, artist_name, elementsRequested], artist_name, title, artist_mbtags, release], record]
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
def h5_to_csv_fields(h5,song): '''Converts h5 format to text Inputs: h5, an h5 file object, usable with the wrapper code MSongsDB song, an integer, representing which song in the h5 file to take the info out of (h5 files contain many songs) Output: a string representing all the information of this song, as a single line of a csv file ''' rv=[] ##All these are regular getter functions from wrapper code rv.append(gt.get_artist_name(h5,song)) rv.append(gt.get_title(h5, song)) rv.append(gt.get_release(h5, song)) rv.append(gt.get_year(h5,song)) rv.append(gt.get_duration(h5,song)) rv.append(gt.get_artist_familiarity(h5,song)) rv.append(gt.get_artist_hotttnesss(h5,song)) rv.append(gt.get_song_hotttnesss(h5, song)) ##artist_terms, artist_terms_freq, and artist_terms_weight getter functions ##are all arrays, so we need to turn them into strings first. We used '_' as a separator rv.append(array_to_csv_field(list(gt.get_artist_terms(h5,song)))) rv.append(array_to_csv_field(list(gt.get_artist_terms_freq(h5,song)))) rv.append(array_to_csv_field(list(gt.get_artist_terms_weight(h5,song)))) rv.append(gt.get_mode(h5,song)) rv.append(gt.get_key(h5,song)) rv.append(gt.get_tempo(h5,song)) rv.append(gt.get_loudness(h5,song)) rv.append(gt.get_danceability(h5,song)) rv.append(gt.get_energy(h5,song)) rv.append(gt.get_time_signature(h5,song)) rv.append(array_to_csv_field(list(gt.get_segments_start(h5,song)))) ##These arrays have vectors (Arrays) as items, 12 dimensional each ##An array like [[1,2,3],[4,5,6]] will be written to csv as '1;2;3_4;5;6', i.e. there's two types of separators rv.append(double_Array_to_csv_field(list(gt.get_segments_timbre(h5,song)),'_',';')) rv.append(double_Array_to_csv_field(list(gt.get_segments_pitches(h5,song)),'_',';')) rv.append(array_to_csv_field(list(gt.get_segments_loudness_start(h5,song)))) rv.append(array_to_csv_field(list(gt.get_segments_loudness_max(h5,song)))) rv.append(array_to_csv_field(list(gt.get_segments_loudness_max_time(h5,song)))) rv.append(array_to_csv_field(list(gt.get_sections_start(h5,song)))) ##turn this list into a string with comma separators (i.e. a csv line) rv_string=array_to_csv_field(rv, ",") rv_string+="\n" return rv_string
def get_all_data(target, basedir, ext='.h5') :
# header
target.write("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n" % (
"track_id", "song_id", "title", "artist_name", "artist_location",
"artist_hotttnesss", "release", "year", "song_hotttnesss",
"danceability", "duration", "loudness", "sample_rate", "tempo"
))
count = 0
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root,'*'+ext))
for f in files:
for line in f:
new_file = open("tmp.txt", 'w')
new_file.write(line)
h5 = hdf5_getters.open_h5_file_read(new_file)
target.write("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n" % (
hdf5_getters.get_track_id(h5),
hdf5_getters.get_song_id(h5),
hdf5_getters.get_title(h5),
hdf5_getters.get_artist_name(h5),
hdf5_getters.get_artist_location(h5),
hdf5_getters.get_artist_hotttnesss(h5),
hdf5_getters.get_release(h5),
hdf5_getters.get_year(h5),
hdf5_getters.get_song_hotttnesss(h5),
hdf5_getters.get_danceability(h5),
hdf5_getters.get_duration(h5),
hdf5_getters.get_loudness(h5),
hdf5_getters.get_analysis_sample_rate(h5),
hdf5_getters.get_tempo(h5)
))
# show progress
count += 1
print "%d/10000" % (count)
h5.close()
def getURLFromH5(h5path):
if not os.path.isfile(h5path):
print 'invalid path (not a file):', h5path
sys.exit(0)
h5 = hdf5_utils.open_h5_file_read(h5path)
track_7digitalid = GETTERS.get_track_7digitalid(h5)
release_7digitalid = GETTERS.get_release_7digitalid(h5)
artist_7digitalid = GETTERS.get_artist_7digitalid(h5)
artist_name = GETTERS.get_artist_name(h5)
release_name = GETTERS.get_release(h5)
track_name = GETTERS.get_title(h5)
h5.close()
# we already have the 7digital track id? way too easy!
if track_7digitalid >= 0:
preview = get_preview_from_trackid(track_7digitalid)
if preview == '':
print 'something went wrong when looking by track id'
else:
print preview
return preview
sys.exit(0)
def get_all_attributes(filename):
"""
This function does 3 simple things:
- open the song file
- get all required attributes
- write it to a csv file
- close the files
"""
with open('attributes.csv', 'a') as csvfile:
try:
# let's apply the previous function to all files
csvwriter = csv.writer(csvfile, delimiter='\t')
h5 = GETTERS.open_h5_file_read(filename)
RESULTS = []
RESULTS.append(GETTERS.get_year(h5))
RESULTS.append(GETTERS.get_artist_id(h5))
RESULTS.append(GETTERS.get_artist_name(h5))
RESULTS.append(GETTERS.get_artist_mbid(h5))
RESULTS.append(convert_terms(GETTERS.get_artist_terms(h5)))
RESULTS.append(GETTERS.get_artist_hotttnesss(h5))
RESULTS.append(GETTERS.get_artist_latitude(h5))
RESULTS.append(GETTERS.get_artist_longitude(h5))
RESULTS.append(GETTERS.get_artist_familiarity(h5))
RESULTS.append(GETTERS.get_danceability(h5))
RESULTS.append(GETTERS.get_duration(h5))
RESULTS.append(GETTERS.get_energy(h5))
RESULTS.append(GETTERS.get_loudness(h5))
RESULTS.append(GETTERS.get_song_hotttnesss(h5))
RESULTS.append(GETTERS.get_song_id(h5))
RESULTS.append(GETTERS.get_tempo(h5))
RESULTS.append(GETTERS.get_time_signature(h5))
RESULTS.append(GETTERS.get_title(h5))
RESULTS.append(GETTERS.get_track_id(h5))
RESULTS.append(GETTERS.get_release(h5))
csvwriter.writerow(RESULTS)
h5.close()
except AttributeError:
pass
def getURLFromH5(h5path):
if not os.path.isfile(h5path):
print 'invalid path (not a file):',h5path
sys.exit(0)
h5 = hdf5_utils.open_h5_file_read(h5path)
track_7digitalid = GETTERS.get_track_7digitalid(h5)
release_7digitalid = GETTERS.get_release_7digitalid(h5)
artist_7digitalid = GETTERS.get_artist_7digitalid(h5)
artist_name = GETTERS.get_artist_name(h5)
release_name = GETTERS.get_release(h5)
track_name = GETTERS.get_title(h5)
h5.close()
# we already have the 7digital track id? way too easy!
print "Suggested Song URLs For you"
print "==========================="
if track_7digitalid >= 0:
preview = get_preview_from_trackid(track_7digitalid)
if preview == '':
print 'something went wrong when looking by track id'
else:
print preview
return preview
sys.exit(0)
def hd5_single_random_file_parser():
# Open an h5 file in read mode
h5 = hdf5_getters.open_h5_file_read(
'/home/skalogerakis/Documents/MillionSong/MillionSongSubset/A/M/G/TRAMGDX12903CEF79F.h5'
)
function_tracker = filter(
lambda x: x.startswith('get'),
hdf5_getters.__dict__.keys()) # Detects all the getter functions
for f in function_tracker: # Print everything in function tracker
print(f)
# First effort to check what each field contains.
print() # 55 available fields (exluding number of songs fields)
print("Num of songs -- ",
hdf5_getters.get_num_songs(h5)) # One song per file
print("Title -- ",
hdf5_getters.get_title(h5)) # Print the title of a specific h5 file
print("Artist familiarity -- ", hdf5_getters.get_artist_familiarity(h5))
print("Artist hotness -- ", hdf5_getters.get_artist_hotttnesss(h5))
print("Artist ID -- ", hdf5_getters.get_artist_id(h5))
print("Artist mbID -- ", hdf5_getters.get_artist_mbid(h5))
print("Artist playmeid -- ", hdf5_getters.get_artist_playmeid(h5))
print("Artist 7DigitalID -- ", hdf5_getters.get_artist_7digitalid(h5))
print("Artist latitude -- ", hdf5_getters.get_artist_latitude(h5))
print("Artist longitude -- ", hdf5_getters.get_artist_longitude(h5))
print("Artist location -- ", hdf5_getters.get_artist_location(h5))
print("Artist Name -- ", hdf5_getters.get_artist_name(h5))
print("Release -- ", hdf5_getters.get_release(h5))
print("Release 7DigitalID -- ", hdf5_getters.get_release_7digitalid(h5))
print("Song ID -- ", hdf5_getters.get_song_id(h5))
print("Song Hotness -- ", hdf5_getters.get_song_hotttnesss(h5))
print("Track 7Digital -- ", hdf5_getters.get_track_7digitalid(h5))
print("Similar artists -- ", hdf5_getters.get_similar_artists(h5))
print("Artist terms -- ", hdf5_getters.get_artist_terms(h5))
print("Artist terms freq -- ", hdf5_getters.get_artist_terms_freq(h5))
print("Artist terms weight -- ", hdf5_getters.get_artist_terms_weight(h5))
print("Analysis sample rate -- ",
hdf5_getters.get_analysis_sample_rate(h5))
print("Audio md5 -- ", hdf5_getters.get_audio_md5(h5))
print("Danceability -- ", hdf5_getters.get_danceability(h5))
print("Duration -- ", hdf5_getters.get_duration(h5))
print("End of Fade -- ", hdf5_getters.get_end_of_fade_in(h5))
print("Energy -- ", hdf5_getters.get_energy(h5))
print("Key -- ", hdf5_getters.get_key(h5))
print("Key Confidence -- ", hdf5_getters.get_key_confidence(h5))
print("Loudness -- ", hdf5_getters.get_loudness(h5))
print("Mode -- ", hdf5_getters.get_mode(h5))
print("Mode Confidence -- ", hdf5_getters.get_mode_confidence(h5))
print("Start of fade out -- ", hdf5_getters.get_start_of_fade_out(h5))
print("Tempo -- ", hdf5_getters.get_tempo(h5))
print("Time signature -- ", hdf5_getters.get_time_signature(h5))
print("Time signature confidence -- ",
hdf5_getters.get_time_signature_confidence(h5))
print("Track ID -- ", hdf5_getters.get_track_id(h5))
print("Segments Start -- ", hdf5_getters.get_segments_start(h5))
print("Segments Confidence -- ", hdf5_getters.get_segments_confidence(h5))
print("Segments Pitches -- ", hdf5_getters.get_segments_pitches(h5))
print("Segments Timbre -- ", hdf5_getters.get_segments_timbre(h5))
print("Segments Loudness max -- ",
hdf5_getters.get_segments_loudness_max(h5))
print("Segments Loudness max time-- ",
hdf5_getters.get_segments_loudness_max_time(h5))
print("Segments Loudness start -- ",
hdf5_getters.get_segments_loudness_start(h5))
print("Sections start -- ", hdf5_getters.get_sections_start(h5))
print("Sections Confidence -- ", hdf5_getters.get_sections_confidence(h5))
print("Beats start -- ", hdf5_getters.get_beats_start(h5))
print("Beats confidence -- ", hdf5_getters.get_beats_confidence(h5))
print("Bars start -- ", hdf5_getters.get_bars_start(h5))
print("Bars confidence -- ", hdf5_getters.get_bars_confidence(h5))
print("Tatums start -- ", hdf5_getters.get_tatums_start(h5))
print("Tatums confidence -- ", hdf5_getters.get_tatums_confidence(h5))
print("Artist mbtags -- ", hdf5_getters.get_artist_mbtags(h5))
print("Artist mbtags count -- ", hdf5_getters.get_artist_mbtags_count(h5))
print("Year -- ", hdf5_getters.get_year(h5))
fields = ['Title', 'Artist ID']
with open('Tester2.csv', 'w', newline='') as csvfile:
csv_writer = csv.writer(csvfile, delimiter=';')
# writing the fields
csv_writer.writerow(fields)
# writing the data rows
csv_writer.writerow(
[hdf5_getters.get_title(h5),
hdf5_getters.get_artist_id(h5)])
h5.close() # close h5 when completed in the end
#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)
#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) start_of_fade_out = GETTERS.get_start_of_fade_out(h5, i) #tatums_confidence = ','.join(str(e) for e in GETTERS.get_tatums_confidence(h5, i)) # array
def getInfo(files):
data = []
build_str = ''
with open(sys.argv[1], 'r') as f:
contents = f.read()
c = contents.split()
f.close()
print("creating csv with following fields:" + contents)
for i in c:
build_str = build_str + i + ','
build_str = build_str[:-1]
build_str = build_str + '\n'
for fil in files:
curFile = getters.open_h5_file_read(fil)
d2 = {}
get_table = {'track_id': getters.get_track_id(curFile), 'segments_pitches': getters.get_segments_pitches(curFile), 'time_signature_confidence': getters.get_time_signature_confidence(curFile), 'song_hotttnesss': getters.get_song_hotttnesss(curFile), 'artist_longitude': getters.get_artist_longitude(curFile), 'tatums_confidence': getters.get_tatums_confidence(curFile), 'num_songs': getters.get_num_songs(curFile), 'duration': getters.get_duration(curFile), 'start_of_fade_out': getters.get_start_of_fade_out(curFile), 'artist_name': getters.get_artist_name(curFile), 'similar_artists': getters.get_similar_artists(curFile), 'artist_mbtags': getters.get_artist_mbtags(curFile), 'artist_terms_freq': getters.get_artist_terms_freq(curFile), 'release': getters.get_release(curFile), 'song_id': getters.get_song_id(curFile), 'track_7digitalid': getters.get_track_7digitalid(curFile), 'title': getters.get_title(curFile), 'artist_latitude': getters.get_artist_latitude(curFile), 'energy': getters.get_energy(curFile), 'key': getters.get_key(curFile), 'release_7digitalid': getters.get_release_7digitalid(curFile), 'artist_mbid': getters.get_artist_mbid(curFile), 'segments_confidence': getters.get_segments_confidence(curFile), 'artist_hotttnesss': getters.get_artist_hotttnesss(curFile), 'time_signature': getters.get_time_signature(curFile), 'segments_loudness_max_time': getters.get_segments_loudness_max_time(curFile), 'mode': getters.get_mode(curFile), 'segments_loudness_start': getters.get_segments_loudness_start(curFile), 'tempo': getters.get_tempo(curFile), 'key_confidence': getters.get_key_confidence(curFile), 'analysis_sample_rate': getters.get_analysis_sample_rate(curFile), 'bars_confidence': getters.get_bars_confidence(curFile), 'artist_playmeid': getters.get_artist_playmeid(curFile), 'artist_terms_weight': getters.get_artist_terms_weight(curFile), 'segments_start': getters.get_segments_start(curFile), 'artist_location': getters.get_artist_location(curFile), 'loudness': getters.get_loudness(curFile), 'year': getters.get_year(curFile), 'artist_7digitalid': getters.get_artist_7digitalid(curFile), 'audio_md5': getters.get_audio_md5(curFile), 'segments_timbre': getters.get_segments_timbre(curFile), 'mode_confidence': getters.get_mode_confidence(curFile), 'end_of_fade_in': getters.get_end_of_fade_in(curFile), 'danceability': getters.get_danceability(curFile), 'artist_familiarity': getters.get_artist_familiarity(curFile), 'artist_mbtags_count': getters.get_artist_mbtags_count(curFile), 'tatums_start': getters.get_tatums_start(curFile), 'artist_id': getters.get_artist_id(curFile), 'segments_loudness_max': getters.get_segments_loudness_max(curFile), 'bars_start': getters.get_bars_start(curFile), 'beats_start': getters.get_beats_start(curFile), 'artist_terms': getters.get_artist_terms(curFile), 'sections_start': getters.get_sections_start(curFile), 'beats_confidence': getters.get_beats_confidence(curFile), 'sections_confidence': getters.get_sections_confidence(curFile)}
tid = fil.split('/')[-1].split('.')[0]
# print(c)
for i in c:
if i in get_table:
d2[i] = get_table[i]
d2[i] = str(d2[i]).replace('\n','')
build_str = build_str + d2[i] + ','
else:
print('error: unspecified field')
exit(0)
build_str = build_str[:-1]
# print(build_str[:-1])
build_str = build_str + '\n'
curFile.close()
build_str = build_str.replace('b','').replace("'",'').replace('"','')
return (build_str)
def data_to_flat_file(basedir,ext='.h5') :
"""This function extract the information from the tables and creates the flat file."""
count = 0; #song counter
list_to_write= []
row_to_write = ""
writer = csv.writer(open("metadata_wholeA.csv", "wb"))
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root,'*'+ext))
for f in files:
print f #the name of the file
h5 = hdf5_getters.open_h5_file_read(f)
title = hdf5_getters.get_title(h5)
title= title.replace('"','')
comma=title.find(',') #eliminating commas in the title
if comma != -1:
print title
time.sleep(1)
album = hdf5_getters.get_release(h5)
album= album.replace('"','') #eliminating commas in the album
comma=album.find(',')
if comma != -1:
print album
time.sleep(1)
artist_name = hdf5_getters.get_artist_name(h5)
comma=artist_name.find(',')
if comma != -1:
print artist_name
time.sleep(1)
artist_name= artist_name.replace('"','') #eliminating double quotes
duration = hdf5_getters.get_duration(h5)
samp_rt = hdf5_getters.get_analysis_sample_rate(h5)
artist_7digitalid = hdf5_getters.get_artist_7digitalid(h5)
artist_fam = hdf5_getters.get_artist_familiarity(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_fam) == True:
artist_fam=-1
artist_hotness= hdf5_getters.get_artist_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_hotness) == True:
artist_hotness=-1
artist_id = hdf5_getters.get_artist_id(h5)
artist_lat = hdf5_getters.get_artist_latitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lat) == True:
artist_lat=-1
artist_loc = hdf5_getters.get_artist_location(h5)
#checks artist_loc to see if it is a hyperlink if it is set as empty string
artist_loc = artist_loc.replace(",", "\,");
if artist_loc.startswith("<a"):
artist_loc = ""
if len(artist_loc) > 100:
artist_loc = ""
artist_lon = hdf5_getters.get_artist_longitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lon) == True:
artist_lon=-1
artist_mbid = hdf5_getters.get_artist_mbid(h5)
artist_pmid = hdf5_getters.get_artist_playmeid(h5)
audio_md5 = hdf5_getters.get_audio_md5(h5)
danceability = hdf5_getters.get_danceability(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(danceability) == True:
danceability=-1
end_fade_in =hdf5_getters.get_end_of_fade_in(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(end_fade_in) == True:
end_fade_in=-1
energy = hdf5_getters.get_energy(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(energy) == True:
energy=-1
song_key = hdf5_getters.get_key(h5)
key_c = hdf5_getters.get_key_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(key_c) == True:
key_c=-1
loudness = hdf5_getters.get_loudness(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(loudness) == True:
loudness=-1
mode = hdf5_getters.get_mode(h5)
mode_conf = hdf5_getters.get_mode_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(mode_conf) == True:
mode_conf=-1
release_7digitalid = hdf5_getters.get_release_7digitalid(h5)
song_hot = hdf5_getters.get_song_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(song_hot) == True:
song_hot=-1
song_id = hdf5_getters.get_song_id(h5)
start_fade_out = hdf5_getters.get_start_of_fade_out(h5)
tempo = hdf5_getters.get_tempo(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(tempo) == True:
tempo=-1
time_sig = hdf5_getters.get_time_signature(h5)
time_sig_c = hdf5_getters.get_time_signature_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(time_sig_c) == True:
time_sig_c=-1
track_id = hdf5_getters.get_track_id(h5)
track_7digitalid = hdf5_getters.get_track_7digitalid(h5)
year = hdf5_getters.get_year(h5)
bars_c = hdf5_getters.get_bars_confidence(h5)
bars_c_avg= get_avg(bars_c)
bars_c_max= get_max(bars_c)
bars_c_min = get_min(bars_c)
bars_c_stddev= get_stddev(bars_c)
bars_c_count = get_count(bars_c)
bars_c_sum = get_sum(bars_c)
bars_start = hdf5_getters.get_bars_start(h5)
bars_start_avg = get_avg(bars_start)
bars_start_max= get_max(bars_start)
bars_start_min = get_min(bars_start)
bars_start_stddev= get_stddev(bars_start)
bars_start_count = get_count(bars_start)
bars_start_sum = get_sum(bars_start)
beats_c = hdf5_getters.get_beats_confidence(h5)
beats_c_avg= get_avg(beats_c)
beats_c_max= get_max(beats_c)
beats_c_min = get_min(beats_c)
beats_c_stddev= get_stddev(beats_c)
beats_c_count = get_count(beats_c)
beats_c_sum = get_sum(beats_c)
beats_start = hdf5_getters.get_beats_start(h5)
beats_start_avg = get_avg(beats_start)
beats_start_max= get_max(beats_start)
beats_start_min = get_min(beats_start)
beats_start_stddev= get_stddev(beats_start)
beats_start_count = get_count(beats_start)
beats_start_sum = get_sum(beats_start)
sec_c = hdf5_getters.get_sections_confidence(h5)
sec_c_avg= get_avg(sec_c)
sec_c_max= get_max(sec_c)
sec_c_min = get_min(sec_c)
sec_c_stddev= get_stddev(sec_c)
sec_c_count = get_count(sec_c)
sec_c_sum = get_sum(sec_c)
sec_start = hdf5_getters.get_sections_start(h5)
sec_start_avg = get_avg(sec_start)
sec_start_max= get_max(sec_start)
sec_start_min = get_min(sec_start)
sec_start_stddev= get_stddev(sec_start)
sec_start_count = get_count(sec_start)
sec_start_sum = get_sum(sec_start)
seg_c = hdf5_getters.get_segments_confidence(h5)
seg_c_avg= get_avg(seg_c)
seg_c_max= get_max(seg_c)
seg_c_min = get_min(seg_c)
seg_c_stddev= get_stddev(seg_c)
seg_c_count = get_count(seg_c)
seg_c_sum = get_sum(seg_c)
seg_loud_max = hdf5_getters.get_segments_loudness_max(h5)
seg_loud_max_avg= get_avg(seg_loud_max)
seg_loud_max_max= get_max(seg_loud_max)
seg_loud_max_min = get_min(seg_loud_max)
seg_loud_max_stddev= get_stddev(seg_loud_max)
seg_loud_max_count = get_count(seg_loud_max)
seg_loud_max_sum = get_sum(seg_loud_max)
seg_loud_max_time = hdf5_getters.get_segments_loudness_max_time(h5)
seg_loud_max_time_avg= get_avg(seg_loud_max_time)
seg_loud_max_time_max= get_max(seg_loud_max_time)
seg_loud_max_time_min = get_min(seg_loud_max_time)
seg_loud_max_time_stddev= get_stddev(seg_loud_max_time)
seg_loud_max_time_count = get_count(seg_loud_max_time)
seg_loud_max_time_sum = get_sum(seg_loud_max_time)
seg_loud_start = hdf5_getters.get_segments_loudness_start(h5)
seg_loud_start_avg= get_avg(seg_loud_start)
seg_loud_start_max= get_max(seg_loud_start)
seg_loud_start_min = get_min(seg_loud_start)
seg_loud_start_stddev= get_stddev(seg_loud_start)
seg_loud_start_count = get_count(seg_loud_start)
seg_loud_start_sum = get_sum(seg_loud_start)
seg_pitch = hdf5_getters.get_segments_pitches(h5)
pitch_size = len(seg_pitch)
seg_start = hdf5_getters.get_segments_start(h5)
seg_start_avg= get_avg(seg_start)
seg_start_max= get_max(seg_start)
seg_start_min = get_min(seg_start)
seg_start_stddev= get_stddev(seg_start)
seg_start_count = get_count(seg_start)
seg_start_sum = get_sum(seg_start)
seg_timbre = hdf5_getters.get_segments_timbre(h5)
tatms_c = hdf5_getters.get_tatums_confidence(h5)
tatms_c_avg= get_avg(tatms_c)
tatms_c_max= get_max(tatms_c)
tatms_c_min = get_min(tatms_c)
tatms_c_stddev= get_stddev(tatms_c)
tatms_c_count = get_count(tatms_c)
tatms_c_sum = get_sum(tatms_c)
tatms_start = hdf5_getters.get_tatums_start(h5)
tatms_start_avg= get_avg(tatms_start)
tatms_start_max= get_max(tatms_start)
tatms_start_min = get_min(tatms_start)
tatms_start_stddev= get_stddev(tatms_start)
tatms_start_count = get_count(tatms_start)
tatms_start_sum = get_sum(tatms_start)
#Getting the genres
genre_set = 0 #flag to see if the genre has been set or not
art_trm = hdf5_getters.get_artist_terms(h5)
trm_freq = hdf5_getters.get_artist_terms_freq(h5)
trn_wght = hdf5_getters.get_artist_terms_weight(h5)
a_mb_tags = hdf5_getters.get_artist_mbtags(h5)
genre_indexes=get_genre_indexes(trm_freq) #index of the highest freq
final_genre=[]
genres_so_far=[]
for i in range(len(genre_indexes)):
genre_tmp=get_genre(art_trm,genre_indexes[i]) #genre that corresponds to the highest freq
genres_so_far=genre_dict.get_genre_in_dict(genre_tmp) #getting the genre from the dictionary
if len(genres_so_far) != 0:
for i in genres_so_far:
final_genre.append(i)
genre_set=1 #genre was found in dictionary
if genre_set == 1:
col_num=[]
for genre in final_genre:
column=int(genre) #getting the column number of the genre
col_num.append(column)
genre_array=genre_columns(col_num) #genre array
else:
genre_array=genre_columns(-1) #the genre was not found in the dictionary
transpose_pitch= seg_pitch.transpose() #this is to tranpose the matrix,so we can have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_pitch_avg=[]
seg_pitch_max=[]
seg_pitch_min=[]
seg_pitch_stddev=[]
seg_pitch_count=[]
seg_pitch_sum=[]
i=0
#Getting the aggregate values in the pitches array
for row in transpose_pitch:
seg_pitch_avg.append(get_avg(row))
seg_pitch_max.append(get_max(row))
seg_pitch_min.append(get_min(row))
seg_pitch_stddev.append(get_stddev(row))
seg_pitch_count.append(get_count(row))
seg_pitch_sum.append(get_sum(row))
i=i+1
#extracting information from the timbre array
transpose_timbre = seg_pitch.transpose() #tranposing matrix, to have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_timbre_avg=[]
seg_timbre_max=[]
seg_timbre_min=[]
seg_timbre_stddev=[]
seg_timbre_count=[]
seg_timbre_sum=[]
i=0
for row in transpose_timbre:
seg_timbre_avg.append(get_avg(row))
seg_timbre_max.append(get_max(row))
seg_timbre_min.append(get_min(row))
seg_timbre_stddev.append(get_stddev(row))
seg_timbre_count.append(get_count(row))
seg_timbre_sum.append(get_sum(row))
i=i+1
#Writing to the flat file
writer.writerow([title,album,artist_name,year,duration,seg_start_count, tempo])
h5.close()
count=count+1;
print count;
def main():
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()
for root, dirs, files in os.walk(os.getcwd()):
for name in files:
if name.endswith(".h5"):
if process_completion % 10000 == 0:
print "done :", process_completion/10000.0, "%"
process_completion += 1
else:
process_completion += 1
tempPath = os.path.abspath(os.path.join(root,name))
h5file = hdf5_getters.open_h5_file_read(tempPath)
#data extract 1
track_id_str = hdf5_getters.get_track_id(h5file)
song_id_str = hdf5_getters.get_song_id(h5file)
album_name_str = unicodedata.normalize('NFKD', unicode(hdf5_getters.get_release(h5file),encoding='ASCII',errors='ignore'))
album_name_str = str(album_name_str).replace(","," ").replace("'","").replace("-"," ").replace("("," ").replace(")"," ").replace("/"," ").replace("\\"," ")
year_str = str(hdf5_getters.get_year(h5file))
counter += 1
file_io_counter += 1
if file_io_counter % 100 == 0:
if counter == 1:
my_array_data_extract_1 = numpy.array([track_id_str, song_id_str, album_name_str, year_str])
else :
my_array_data_extract_1 = numpy.vstack((my_array_data_extract_1,numpy.array([track_id_str, song_id_str, album_name_str, year_str])))
f_handle = file('msd_data_extract_1.bin','a')
numpy.savetxt(f_handle, my_array_data_extract_1, delimiter='|',fmt='%s')
f_handle.close()
def get_fields(files):
tracks = []
counts = {}
field_counts = []
for file in files:
h5 = hdf5_getters.open_h5_file_read(file)
t = {}
t['artist_familiarity'] = hdf5_getters.get_artist_familiarity(
h5) # estimation
t['artist_hotttnesss'] = hdf5_getters.get_artist_hotttnesss(
h5) # estimation
t['artist_name'] = hdf5_getters.get_artist_name(h5) # artist name
t['release'] = hdf5_getters.get_release(h5) # album name
t['title'] = hdf5_getters.get_title(h5) # title
t['len_similar_artists'] = len(
hdf5_getters.get_similar_artists(h5)) # number of similar artists
t['analysis_sample_rate'] = hdf5_getters.get_analysis_sample_rate(
h5) # sample rate of the audio used ?????????
t['duration'] = hdf5_getters.get_duration(h5) # seconds
t['key'] = hdf5_getters.get_key(h5) # key the song is in
t['key_confidence'] = hdf5_getters.get_key_confidence(
h5) # confidence measure
t['loudness'] = hdf5_getters.get_loudness(h5) # overall loudness in dB
t['mode_confidence'] = hdf5_getters.get_mode_confidence(
h5) # confidence measure
t['start_of_fade_out'] = hdf5_getters.get_start_of_fade_out(
h5) # time in sec
t['tempo'] = hdf5_getters.get_tempo(h5) # estimated tempo in BPM
t['time_signature'] = hdf5_getters.get_time_signature(
h5) # estimate of number of beats per bar, e.g. 4
t['year'] = hdf5_getters.get_year(
h5) # song release year from MusicBrainz or 0
timbre = hdf5_getters.get_segments_timbre(
h5) # 2D float array, texture features (MFCC+PCA-like)
t['segments_timbre'] = timbre
t['timbre_avg'] = timbre.mean(axis=0) # list of 12 averages
cov_mat_timbre = np.cov(timbre, rowvar=False)
cov_timbre = []
for i in range(len(cov_mat_timbre)):
for j in range(len(cov_mat_timbre) - i):
cov_timbre.append(cov_mat_timbre[i][j])
t['timbre_cov'] = cov_timbre # list of 78 covariances
pitch = hdf5_getters.get_segments_pitches(
h5) # 2D float array, chroma feature, one value per note
t['segments_pitch'] = pitch
t['pitch_avg'] = pitch.mean(axis=0) # list of 12 averages
cov_mat_pitch = np.cov(pitch, rowvar=False)
cov_pitch = []
for i in range(len(cov_mat_pitch)):
for j in range(len(cov_mat_pitch) - i):
cov_pitch.append(cov_mat_timbre[i][j])
t['pitch_cov'] = cov_pitch # list of 78 covariances
# seg_pitch = hdf5_getters.get_segments_pitches(h5) # 2D float array, chroma feature, one value per note
# print(seg_pitch.shape)
# t['artist_latitude'] = hdf5_getters.get_artist_latitude(h5) # float, ????????????????????????????????????????
# t['artist_longitude'] = hdf5_getters.get_artist_longitude(h5) # float, ??????????????????????????????????????
# t['artist_location'] = hdf5_getters.get_artist_location(h5) # location name
# t['song_hotttnesss'] = hdf5_getters.get_song_hotttnesss(h5) # estimation
# t['danceability'] = hdf5_getters.get_danceability(h5) # estimation
# t['end_of_fade_in'] = hdf5_getters.get_end_of_fade_in(h5) # seconds at the beginning of the song
# t['energy'] = hdf5_getters.get_energy(h5) # energy from listener point of view
# t['mode'] = hdf5_getters.get_mode(h5) # major or minor
# t['time_signature_confidence'] = hdf5_getters.get_time_signature_confidence(h5) # confidence measure
# t['artist_mbtags_count'] = len(hdf5_getters.get_artist_mbtags_count(h5)) # array int, tag counts for musicbrainz tags
# bad types or non arithmatic numbers
'''
# t['audio_md5'] = hdf5_getters.get_audio_md5(h5) # hash code of the audio used for the analysis by The Echo Nest
# t['artist_terms_weight'] = hdf5_getters.get_artist_terms_weight(h5) # array float, echonest tags weight ?????
# t['artist_terms_freq'] = hdf5_getters.get_artist_terms_freq(h5) # array float, echonest tags freqs ??????????
# t['artist_terms'] = hdf5_getters.get_artist_terms(h5) # array string, echonest tags ?????????????????????????
# t['artist_id'] = hdf5_getters.get_artist_id(h5) # echonest id
# t['artist_mbid'] = hdf5_getters.get_artist_mbid(h5) # musicbrainz id
# t['artist_playmeid'] = hdf5_getters.get_artist_playmeid(h5) # playme id
# t['artist_7digitalid'] = hdf5_getters.get_artist_7digitalid(h5) # 7digital id
# t['release_7digitalid'] = hdf5_getters.get_release_7digitalid(h5) # 7digital id
# t['song_id'] = hdf5_getters.get_song_id(h5) # echonest id
# t['track_7digitalid'] = hdf5_getters.get_track_7digitalid(h5) # 7digital id
# t['similar_artists'] = hdf5_getters.get_similar_artists(h5) # string array of sim artist ids
# t['track_id'] = hdf5_getters.get_track_id(h5) # echonest track id
# t['segments_start'] = hdf5_getters.get_segments_start(h5) # array floats, musical events, ~ note onsets
# t['segments_confidence'] = hdf5_getters.get_segments_confidence(h5) # array floats, confidence measure
# t['segments_pitches'] = hdf5_getters.get_segments_pitches(h5) # 2D float array, chroma feature, one value per note
# t['segments_timbre'] = hdf5_getters.get_segments_timbre(h5) # 2D float array, texture features (MFCC+PCA-like)
# t['segments_loudness_max'] = hdf5_getters.get_segments_loudness_max(h5) # float array, max dB value
# t['segments_loudness_max_time'] = hdf5_getters.get_segments_loudness_max_time(h5) # float array, time of max dB value, i.e. end of attack
# t['segments_loudness_start'] = hdf5_getters.get_segments_loudness_start(h5) # array float, dB value at onset
# t['sections_start'] = hdf5_getters.get_sections_start(h5) # array float, largest grouping in a song, e.g. verse
# t['sections_confidence'] = hdf5_getters.get_sections_confidence(h5) # array float, confidence measure
# t['beats_start'] = hdf5_getters.get_beats_start(h5) # array float, result of beat tracking
# t['beats_confidence'] = hdf5_getters.get_beats_confidence(h5) # array float, confidence measure
# t['bars_start'] = hdf5_getters.get_bars_start(h5) # array float, beginning of bars, usually on a beat
# t['bars_confidence'] = hdf5_getters.get_bars_confidence(h5) # array float, confidence measure
# t['tatums_start'] = hdf5_getters.get_tatums_start(h5) # array float, smallest rythmic element
# t['tatums_confidence'] = hdf5_getters.get_tatums_confidence(h5) # array float, confidence measure
# t['artist_mbtags'] = hdf5_getters.get_artist_mbtags(h5) # array string, tags from musicbrainz.org
'''
h5.close()
for key, value in t.items():
if isinstance(value, float) and math.isnan(value):
pass
if type(value) is np.ndarray:
if key in counts.keys():
counts[key] += 1
else:
counts[key] = 1
elif value:
if key in counts.keys():
counts[key] += 1
else:
counts[key] = 1
elif key not in counts.keys():
counts[key] = 0
count = 0
for key, value in t.items():
if isinstance(value, float) and math.isnan(value):
pass
elif type(value) is np.ndarray:
count += 1
elif value:
count += 1
field_counts.append(count)
# progress bar
if num_of_tracks >= 100:
i = files.index(file) + 1
scale = num_of_tracks / 100
if i % math.ceil(len(files) * .05) == 0:
sys.stdout.write('\r')
# the exact output you're looking for:
sys.stdout.write("Loading dataframe: [%-100s] %d%%" %
('=' * int(i // scale), 1 / scale * i))
sys.stdout.flush()
time.sleep(.01)
tracks.append(t)
print()
return tracks, counts, field_counts
def main():
outputFileName = sys.argv[2]
outputFile1 = open(outputFileName, 'w')
csvRowString = ""
#################################################
#if you want to prompt the user for the order of attributes in the csv,
#leave the prompt boolean set to True
#else, set 'prompt' to False and set the order of attributes in the 'else'
#clause
prompt = False
#################################################
if prompt == True:
while prompt:
prompt = False
csvAttributeString = raw_input(
"\n\nIn what order would you like the colums of the CSV file?\n"
+ "Please delineate with commas. The options are: " +
"AlbumName, AlbumID, ArtistID, ArtistLatitude, ArtistLocation, ArtistLongitude,"
+
" ArtistName, Danceability, Duration, KeySignature, KeySignatureConfidence, Tempo,"
+
" SongID, TimeSignature, TimeSignatureConfidence, Title, and Year.\n\n"
+
"For example, you may write \"Title, Tempo, Duration\"...\n\n"
+ "...or exit by typing 'exit'.\n\n")
csvAttributeList = re.split('\W+', csvAttributeString)
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
for attribute in csvAttributeList:
# print "Here is the attribute: " + attribute + " \n"
if attribute == 'AlbumID'.lower():
csvRowString += 'AlbumID'
elif attribute == 'AlbumName'.lower():
csvRowString += 'AlbumName'
elif attribute == 'ArtistID'.lower():
csvRowString += 'ArtistID'
elif attribute == 'ArtistLatitude'.lower():
csvRowString += 'ArtistLatitude'
elif attribute == 'ArtistLocation'.lower():
csvRowString += 'ArtistLocation'
elif attribute == 'ArtistLongitude'.lower():
csvRowString += 'ArtistLongitude'
elif attribute == 'ArtistName'.lower():
csvRowString += 'ArtistName'
elif attribute == 'Danceability'.lower():
csvRowString += 'Danceability'
elif attribute == 'Duration'.lower():
csvRowString += 'Duration'
elif attribute == 'KeySignature'.lower():
csvRowString += 'KeySignature'
elif attribute == 'KeySignatureConfidence'.lower():
csvRowString += 'KeySignatureConfidence'
elif attribute == 'SongID'.lower():
csvRowString += "SongID"
elif attribute == 'Tempo'.lower():
csvRowString += 'Tempo'
elif attribute == 'TimeSignature'.lower():
csvRowString += 'TimeSignature'
elif attribute == 'TimeSignatureConfidence'.lower():
csvRowString += 'TimeSignatureConfidence'
elif attribute == 'Title'.lower():
csvRowString += 'Title'
elif attribute == 'Year'.lower():
csvRowString += 'Year'
elif attribute == 'Exit'.lower():
sys.exit()
else:
prompt = True
print "=============="
print "I believe there has been an error with the input."
print "=============="
break
csvRowString += ","
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex - 1]
csvRowString += "\n"
# outputFile1.write(csvRowString);
csvRowString = ""
#else, if you want to hard code the order of the csv file and not prompt
#the user,
else:
#################################################
#change the order of the csv file here
#Default is to list all available attributes (in alphabetical order)
csvRowString = (
"SongID,AlbumID,AlbumName,ArtistID,ArtistLatitude,ArtistLocation,"
+
"ArtistLongitude,ArtistFamiliarity,ArtistHotttnesss,ArtistName," +
"ArtistMBTags,ArtistTerms," +
"Danceability,Energy,Duration,KeySignature," +
"KeySignatureConfidence,Loudness,Mode,Hotttnesss,Tempo,TimeSignature,TimeSignatureConfidence,"
+ "Title,Year")
#################################################
csvAttributeList = re.split('\W+', csvRowString)
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
# outputFile1.write("SongNumber,");
# outputFile1.write(csvRowString + "\n");
csvRowString = ""
#################################################
#Set the basedir here, the root directory from which the search
#for files stored in a (hierarchical data structure) will originate
basedir = sys.argv[1] # "." As the default means the current directory
ext = ".h5" #Set the extension here. H5 is the extension for HDF5 files.
#################################################
#FOR LOOP
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
for f in files:
print f
songH5File = hdf5_getters.open_h5_file_read(f)
song = Song(str(hdf5_getters.get_song_id(songH5File)))
testDanceability = hdf5_getters.get_danceability(songH5File)
# print type(testDanceability)
# print ("Here is the danceability: ") + str(testDanceability)
song.artistID = str(hdf5_getters.get_artist_id(songH5File))
song.albumID = str(hdf5_getters.get_release_7digitalid(songH5File))
song.albumName = str(hdf5_getters.get_release(songH5File))
song.artistLatitude = str(
hdf5_getters.get_artist_latitude(songH5File))
song.artistLocation = str(
hdf5_getters.get_artist_location(songH5File))
song.artistLongitude = str(
hdf5_getters.get_artist_longitude(songH5File))
song.artistFamiliarity = str(
hdf5_getters.get_artist_familiarity(songH5File))
song.artistHotttnesss = str(
hdf5_getters.get_artist_hotttnesss(songH5File))
song.artistName = str(hdf5_getters.get_artist_name(songH5File))
song.artistMBTags = ','.join(
hdf5_getters.get_artist_mbtags(songH5File))
# song.artistMBTagsCount = ','.join(hdf5_getters.get_artist_mbtags_count(songH5File))
song.artistTerms = ','.join(
hdf5_getters.get_artist_terms(songH5File))
song.danceability = str(hdf5_getters.get_danceability(songH5File))
song.energy = str(hdf5_getters.get_energy(songH5File))
song.duration = str(hdf5_getters.get_duration(songH5File))
# song.setGenreList()
song.keySignature = str(hdf5_getters.get_key(songH5File))
song.keySignatureConfidence = str(
hdf5_getters.get_key_confidence(songH5File))
song.loudness = str(hdf5_getters.get_loudness(songH5File))
song.mode = str(hdf5_getters.get_mode(songH5File))
# song.lyrics = None
# song.popularity = None
song.hotttnesss = str(hdf5_getters.get_song_hotttnesss(songH5File))
song.tempo = str(hdf5_getters.get_tempo(songH5File))
song.timeSignature = str(
hdf5_getters.get_time_signature(songH5File))
song.timeSignatureConfidence = str(
hdf5_getters.get_time_signature_confidence(songH5File))
song.title = str(hdf5_getters.get_title(songH5File))
song.year = str(hdf5_getters.get_year(songH5File))
#print song count
# csvRowString += str(song.songCount) + ","
rowString = json.dumps({
'AlbumID': song.albumID,
'AlbumName': song.albumName,
'ArtistID': song.artistID,
'ArtistLatitude': song.artistLatitude,
'ArtistLocation': song.artistLocation,
'ArtistLongitude': song.artistLongitude,
'ArtistFamiliarity': song.artistFamiliarity,
'ArtistHotttnesss': song.artistHotttnesss,
'ArtistName': song.artistName,
'ArtistMBTags': song.artistMBTags,
'ArtistTerms': song.artistTerms,
'Danceability': song.danceability,
'Energy': song.energy,
'Duration': song.duration,
'KeySignature': song.keySignature,
'KeySignatureConfidence': song.keySignatureConfidence,
'Loudness': song.loudness,
'Mode': song.mode,
'Hotttnesss': song.hotttnesss,
'Tempo': song.tempo,
'SongID': song.id,
'TimeSignature': song.timeSignature,
'TimeSignatureConfidence': song.timeSignatureConfidence,
'Title': song.title,
'Year': song.year,
})
#Remove the final comma from each row in the csv
rowString += "\n"
outputFile1.write(rowString)
songH5File.close()
outputFile1.close()
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
cursor.execute("INSERT INTO artist_genres VALUES ('" +
artist_id + "','" + term + "')")
for tag in mbtags:
tag = tag.replace("'", "")
cursor.execute(
"SELECT * FROM artist_genres WHERE artist_id='" +
artist_id + "' AND genre ='" + tag + "'")
if cursor.rowcount != 1:
cursor.execute("INSERT INTO artist_genres VALUES ('" +
artist_id + "','" + tag + "')")
''' Store track tuples '''
track_id = h.get_track_id(h5, 0)
track_title = h.get_title(h5, 0)
track_title = track_title.replace("'", "")
track_album = h.get_release(h5, 0)
track_album = track_album.replace("'", "")
track_duration = str(h.get_duration(h5, 0))
track_year = str(h.get_year(h5, 0))
cursor.execute("SELECT * FROM track WHERE track_id = '" +
track_id + "'")
rs = cursor.fetchall()
if cursor.rowcount != 1:
cursor.execute("INSERT INTO track VALUES ('" + track_id +
"','" + track_title + "','" + artist_id +
"','" + artist_name + "','" + track_album +
"'," + track_duration + "," + track_year + ");")
''' Store track_analysis tuples '''
print("Track ID: " + h.get_track_id(h5, 0))
track_tempo = str(h.get_tempo(h5, 0))
def get_all_rows(basedir, ext='.h5'):
rows = []
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
for f in files:
# print(os.path.join(root, f))
h5 = hdf5_getters.open_h5_file_read(f)
num_songs = hdf5_getters.get_num_songs(h5)
# print(num_songs)
for i in range(num_songs):
print(i)
obj = {}
obj['artist_name'] = hdf5_getters.get_artist_name(
h5, i).decode('UTF-8')
obj['artist_familiarity'] = hdf5_getters.get_artist_familiarity(
h5, i)
obj['artist_hotness'] = hdf5_getters.get_artist_hotttnesss(
h5, i)
obj['artist_id'] = hdf5_getters.get_artist_id(
h5, i).decode('UTF-8')
# obj['artist_mbid']=hdf5_getters.get_artist_mbid(h5,i).decode('UTF-8')
obj['artist_playmeid'] = hdf5_getters.get_artist_playmeid(
h5, i)
obj['artist_7digitalid'] = hdf5_getters.get_artist_7digitalid(
h5, i)
# obj['artist_latitude']=hdf5_getters.get_artist_latitude(h5,i)
# obj['artist_longitude']=hdf5_getters.get_artist_longitude(h5,i)
# obj['artist_location']=hdf5_getters.get_artist_location(h5,i).decode('UTF-8')
obj['artist_name'] = hdf5_getters.get_artist_name(
h5, i).decode('UTF-8')
obj['release'] = hdf5_getters.get_release(h5,
i).decode('UTF-8')
obj['song_hotttnesss'] = hdf5_getters.get_song_hotttnesss(
h5, i)
obj['title'] = hdf5_getters.get_title(h5, i).decode('UTF-8')
# obj['artist_terms']=hdf5_getters.get_artist_terms(h5)
# obj['artist_terms_freq']=hdf5_getters.get_artist_terms_freq(h5)
# obj['artist_terms_weight']=hdf5_getters.get_artist_terms_weight(h5)
# obj['audio_md5']=hdf5_getters.get_audio_md5(h5).decode('UTF-8')
obj['danceability'] = hdf5_getters.get_danceability(h5, i)
obj['duration'] = hdf5_getters.get_duration(h5, i)
obj['end_of_fade_in'] = hdf5_getters.get_end_of_fade_in(h5, i)
obj['energy'] = hdf5_getters.get_energy(h5, i)
obj['key'] = hdf5_getters.get_key(h5, i)
obj['key_confidence'] = hdf5_getters.get_key_confidence(h5, i)
obj['loudness'] = hdf5_getters.get_loudness(h5, i)
obj['mode'] = hdf5_getters.get_mode(h5, i)
# obj['start_of_fade_out']=hdf5_getters.get_start_of_fade_out(h5)
obj['tempo'] = hdf5_getters.get_tempo(h5, i)
obj['time_signature'] = hdf5_getters.get_time_signature(h5, i)
# obj['time_signature_confidence']=hdf5_getters.get_time_signature_confidence(h5)
obj['track_id'] = hdf5_getters.get_track_id(h5,
i).decode('UTF-8')
# obj['segments_start']=hdf5_getters.get_segments_start(h5)
# obj['segments_confidence']=hdf5_getters.get_segments_confidence(h5)
# obj['segments_pitches']=hdf5_getters.get_segments_pitches(h5)
# obj['segments_timbre']=hdf5_getters.get_segments_timbre(h5)
# obj['segments_loudness_max']=hdf5_getters.get_segments_loudness_max(h5)
# obj['segments_loudness_max_time']=hdf5_getters.get_segments_loudness_max_time(h5)
# obj['segments_confidence']=hdf5_getters.get_segments_confidence(h5)
# obj['segments_loudness_start']=hdf5_getters.get_segments_loudness_start(h5)
# obj['sections_start']=hdf5_getters.get_sections_start(h5)
# obj['sections_confidence']=hdf5_getters.get_sections_confidence(h5)
# obj['beats_start']=hdf5_getters.get_beats_start(h5)
# obj['beats_confidence']=hdf5_getters.get_beats_confidence(h5)
# obj['bars_start']=hdf5_getters.get_bars_start(h5)
# obj['bars_confidence']=hdf5_getters.get_bars_confidence(h5)
# obj['tatums_start']=hdf5_getters.get_tatums_start(h5)
# obj['artist_mbtags']=hdf5_getters.get_artist_mbtags(h5)
# obj['artist_mbtags_count']=hdf5_getters.get_artist_mbtags_count(h5)
obj['year'] = hdf5_getters.get_year(h5, i)
rows.append(obj)
h5.close()
return rows
def main():
outputFile1 = open('SongCSV.csv', 'w')
csvRowString = ""
#################################################
#if you want to prompt the user for the order of attributes in the csv,
#leave the prompt boolean set to True
#else, set 'prompt' to False and set the order of attributes in the 'else'
#clause
prompt = False
#################################################
if prompt == True:
while prompt:
prompt = False
csvAttributeString = raw_input(
"\n\nIn what order would you like the colums of the CSV file?\n"
+ "Please delineate with commas. The options are: " +
"AlbumName, AlbumID, ArtistID, ArtistLatitude, ArtistLocation, ArtistLongitude,"
+
" ArtistName, Danceability, Duration, KeySignature, KeySignatureConfidence, Tempo,"
+
" SongID, TimeSignature, TimeSignatureConfidence, Title, and Year.\n\n"
+
"For example, you may write \"Title, Tempo, Duration\"...\n\n"
+ "...or exit by typing 'exit'.\n\n")
csvAttributeList = re.split('\W+', csvAttributeString)
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
for attribute in csvAttributeList:
# print "Here is the attribute: " + attribute + " \n"
if attribute == 'AlbumID'.lower():
csvRowString += 'AlbumID'
elif attribute == 'AlbumName'.lower():
csvRowString += 'AlbumName'
elif attribute == 'ArtistID'.lower():
csvRowString += 'ArtistID'
elif attribute == 'ArtistLatitude'.lower():
csvRowString += 'ArtistLatitude'
elif attribute == 'ArtistLocation'.lower():
csvRowString += 'ArtistLocation'
elif attribute == 'ArtistLongitude'.lower():
csvRowString += 'ArtistLongitude'
elif attribute == 'ArtistName'.lower():
csvRowString += 'ArtistName'
elif attribute == 'Danceability'.lower():
csvRowString += 'Danceability'
elif attribute == 'Duration'.lower():
csvRowString += 'Duration'
elif attribute == 'KeySignature'.lower():
csvRowString += 'KeySignature'
elif attribute == 'KeySignatureConfidence'.lower():
csvRowString += 'KeySignatureConfidence'
elif attribute == 'SongID'.lower():
csvRowString += "SongID"
elif attribute == 'Tempo'.lower():
csvRowString += 'Tempo'
elif attribute == 'TimeSignature'.lower():
csvRowString += 'TimeSignature'
elif attribute == 'TimeSignatureConfidence'.lower():
csvRowString += 'TimeSignatureConfidence'
elif attribute == 'Title'.lower():
csvRowString += 'Title'
elif attribute == 'Year'.lower():
csvRowString += 'Year'
elif attribute == 'Familiarity'.lower(): ####Added by us!
csvRowString += song.familiarity
elif attribute == 'artist_mbid'.lower():
csvRowString += song.artist_mbid
elif attribute == 'artist_playmeid'.lower():
csvRowString += song.artist_playmeid
elif attribute == 'artist_7digid'.lower():
csvRowString += song.artist_7digid
elif attribute == 'hottness'.lower():
csvRowString += song.hottness
elif attribute == 'song_hottness'.lower():
csvRowString += song.song_hottness
elif attribute == 'digitalid7'.lower():
csvRowString += song.digitalid7
elif attribute == 'similar_artists'.lower():
csvRowString += song.similar_artists
elif attribute == 'artist_terms'.lower():
csvRowString += song.artist_terms
elif attribute == 'art_terms_freq'.lower():
csvRowString += song.art_terms_freq
elif attribute == 'art_terms_weight'.lower():
csvRowString += song.art_terms_weight
elif attribute == 'a_sample_rate'.lower():
csvRowString += song.a_sample_rate
elif attribute == 'audio_md5'.lower():
csvRowString += song.audio_md5
elif attribute == 'end_of_fade_in'.lower():
csvRowString += song.end_of_fade_in
elif attribute == 'energy'.lower():
csvRowString += song.energy
elif attribute == 'loudness'.lower():
csvRowString += song.loudness
elif attribute == 'mode'.lower():
csvRowString += song.mode
elif attribute == 'mode_conf'.lower():
csvRowString += song.mode_conf
elif attribute == 'start_of_fade_out'.lower():
csvRowString += song.start_of_fade_out
elif attribute == 'trackid'.lower():
csvRowString += song.trackid
elif attribute == 'segm_start'.lower():
csvRowString += song.segm_start
elif attribute == 'segm_conf'.lower():
csvRowString += song.segm_conf
elif attribute == 'segm_pitch'.lower():
csvRowString += song.segm_pitch
elif attribute == 'segm_timbre'.lower():
csvRowString += song.segm_timbre
elif attribute == 'segm_max_loud'.lower():
csvRowString += song.segm_max_loud
elif attribute == 'segm_max_loud_time'.lower():
csvRowString += song.segm_max_loud_time
elif attribute == 'segm_loud_start'.lower():
csvRowString += song.segm_loud_start
elif attribute == 'sect_start'.lower():
csvRowString += song.sect_start
elif attribute == 'sect_conf'.lower():
csvRowString += song.sect_conf
elif attribute == 'beats_start'.lower():
csvRowString += song.beats_start
elif attribute == 'beats_conf'.lower():
csvRowString += song.beats_conf
elif attribute == 'bars_start'.lower():
csvRowString += song.bars_start
elif attribute == 'bars_conf'.lower():
csvRowString += song.bars_conf
elif attribute == 'tatums_start'.lower():
csvRowString += song.tatums_start
elif attribute == 'tatums_conf'.lower():
csvRowString += song.tatums_conf
elif attribute == 'artist_mbtags'.lower():
csvRowString += song.artist_mbtags
elif attribute == 'artist_mbtags_count'.lower():
csvRowString += song.artist_mbtags_count
elif attribute == 'Exit'.lower():
sys.exit()
else:
prompt = True
print("==============")
print("I believe there has been an error with the input.")
print("==============")
break
csvRowString += ","
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex - 1]
csvRowString += "\n"
outputFile1.write(csvRowString)
csvRowString = ""
#else, if you want to hard code the order of the csv file and not prompt
#the user,
else:
#################################################
#change the order of the csv file here
#Default is to list all available attributes (in alphabetical order)
csvRowString = "SongID,AlbumID,AlbumName,ArtistID,ArtistLatitude,ArtistLocation,ArtistLongitude,ArtistName,Danceability,Duration,KeySignature,KeySignatureConfidence,Tempo,TimeSignature,TimeSignatureConfidence,Title,Year,Familiarity,Artist_Mbid,Artist_PlaymeId,Artist_7didId,Hottness,Song_Hottness,7digitalid,A_Sample_Rate,Audio_Md5,End_Of_Fade_In,Energy,Loudness,Mode,Mode_Conf,Start_Of_Fade_Out,TrackId"
#################################################
csvAttributeList = re.split(',', csvRowString)
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
csvRowString += "\n"
outputFile1.write(csvRowString)
csvRowString = ""
#################################################
#Set the basedir here, the root directory from which the search
#for files stored in a (hierarchical data structure) will originate
basedir = "/home/bigdata/smalltest/" # "." As the default means the current directory
ext = ".h5" #Set the extension here. H5 is the extension for HDF5 files.
#################################################
#FOR LOOP
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
for f in files:
print(f)
songH5File = hdf5_getters.open_h5_file_read(f)
song = Song(str(hdf5_getters.get_song_id(songH5File)))
# testDanceability = hdf5_getters.get_danceability(songH5File)
# print type(testDanceability)
# print ("Here is the danceability: ") + str(testDanceability)
song.artistID = str(hdf5_getters.get_artist_id(songH5File))
song.albumID = str(hdf5_getters.get_release_7digitalid(songH5File))
song.albumName = str(hdf5_getters.get_release(songH5File))
song.artistLatitude = str(
hdf5_getters.get_artist_latitude(songH5File))
song.artistLocation = str(
hdf5_getters.get_artist_location(songH5File))
song.artistLongitude = str(
hdf5_getters.get_artist_longitude(songH5File))
song.artistName = str(hdf5_getters.get_artist_name(songH5File))
song.danceability = str(hdf5_getters.get_danceability(songH5File))
song.duration = str(hdf5_getters.get_duration(songH5File))
# song.setGenreList()
song.keySignature = str(hdf5_getters.get_key(songH5File))
song.keySignatureConfidence = str(
hdf5_getters.get_key_confidence(songH5File))
# song.lyrics = None
# song.popularity = None
song.tempo = str(hdf5_getters.get_tempo(songH5File))
song.timeSignature = str(
hdf5_getters.get_time_signature(songH5File))
song.timeSignatureConfidence = str(
hdf5_getters.get_time_signature_confidence(songH5File))
song.title = str(hdf5_getters.get_title(songH5File))
song.year = str(hdf5_getters.get_year(songH5File))
#########Added by us!
song.familiarity = str(
hdf5_getters.get_artist_familiarity(songH5File))
song.artist_mbid = str(hdf5_getters.get_artist_mbid(songH5File))
song.artist_playmeid = str(
hdf5_getters.get_artist_playmeid(songH5File))
song.artist_7digid = str(
hdf5_getters.get_artist_7digitalid(songH5File))
song.hottness = str(hdf5_getters.get_artist_hotttnesss(songH5File))
song.song_hottness = str(
hdf5_getters.get_song_hotttnesss(songH5File))
song.digitalid7 = str(
hdf5_getters.get_track_7digitalid(songH5File))
#song.similar_artists = str(hdf5_getters.get_similar_artists(songH5File))
#song.artist_terms = str(hdf5_getters.get_artist_terms(songH5File))
#song.art_terms_freq = str(hdf5_getters.get_artist_terms_freq(songH5File))
#song.art_terms_weight = str(hdf5_getters.get_artist_terms_weight(songH5File))
song.a_sample_rate = str(
hdf5_getters.get_analysis_sample_rate(songH5File))
song.audio_md5 = str(hdf5_getters.get_audio_md5(songH5File))
song.end_of_fade_in = str(
hdf5_getters.get_end_of_fade_in(songH5File))
song.energy = str(hdf5_getters.get_energy(songH5File))
song.loudness = str(hdf5_getters.get_loudness(songH5File))
song.mode = str(hdf5_getters.get_mode(songH5File))
song.mode_conf = str(hdf5_getters.get_mode_confidence(songH5File))
song.start_of_fade_out = str(
hdf5_getters.get_start_of_fade_out(songH5File))
song.trackid = str(hdf5_getters.get_track_id(songH5File))
#song.segm_start = str(hdf5_getters.get_segments_start(songH5File))
#song.segm_conf = str(hdf5_getters.get_segments_confidence(songH5File))
#song.segm_pitch = str(hdf5_getters.get_segments_pitches(songH5File))
#song.segm_timbre = str(hdf5_getters.get_segments_timbre(songH5File))
#song.segm_max_loud = str(hdf5_getters.get_segments_loudness_max(songH5File))
#song.segm_max_loud_time = str(hdf5_getters.get_segments_loudness_max_time(songH5File))
#song.segm_loud_start = str(hdf5_getters.get_segments_loudness_start(songH5File))
#song.sect_start = str(hdf5_getters.get_sections_start(songH5File))
#song.sect_conf = str(hdf5_getters.get_sections_confidence(songH5File))
#song.beats_start = str(hdf5_getters.get_beats_start(songH5File))
#song.beats_conf = str(hdf5_getters.get_beats_confidence(songH5File))
#song.bars_start = str(hdf5_getters.get_bars_start(songH5File))
#song.bars_conf = str(hdf5_getters.get_bars_confidence(songH5File))
#song.tatums_start = str(hdf5_getters.get_tatums_start(songH5File))
#song.tatums_conf = str(hdf5_getters.get_tatums_confidence(songH5File))
#song.artist_mbtags = str(hdf5_getters.get_artist_mbtags(songH5File))
#song.artist_mbtags_count = str(hdf5_getters.get_artist_mbtags_count(songH5File))
#print song count
#csvRowString += str(song.songCount) + ","
for attribute in csvAttributeList:
# print "Here is the attribute: " + attribute + " \n"
if attribute == 'AlbumID'.lower():
csvRowString += song.albumID
elif attribute == 'AlbumName'.lower():
albumName = song.albumName
albumName = albumName.replace("b\"", "")
albumName = albumName.replace("\"", "")
albumName = albumName.replace(',', "")
csvRowString += "\"" + albumName + "\""
elif attribute == 'ArtistID'.lower():
csvRowString += "\"" + song.artistID + "\""
elif attribute == 'ArtistLatitude'.lower():
latitude = song.artistLatitude
if latitude == 'nan':
latitude = ''
csvRowString += latitude
elif attribute == 'ArtistLocation'.lower():
location = song.artistLocation
location = location.replace(',', '')
location = location.replace("b\"", "")
location = location.replace("\"", "")
csvRowString += "\"" + location + "\""
elif attribute == 'ArtistLongitude'.lower():
longitude = song.artistLongitude
if longitude == 'nan':
longitude = ''
csvRowString += longitude
elif attribute == 'ArtistName'.lower():
artistName = song.artistName
artistName = artistName.replace("b\"", "")
artistName = artistName.replace("\"", "")
csvRowString += "\"" + artistName + "\""
elif attribute == 'Danceability'.lower():
csvRowString += song.danceability
elif attribute == 'Duration'.lower():
csvRowString += song.duration
elif attribute == 'KeySignature'.lower():
csvRowString += song.keySignature
elif attribute == 'KeySignatureConfidence'.lower():
# print "key sig conf: " + song.timeSignatureConfidence
csvRowString += song.keySignatureConfidence
elif attribute == 'SongID'.lower():
csvRowString += "\"" + song.id + "\""
elif attribute == 'Tempo'.lower():
# print "Tempo: " + song.tempo
csvRowString += song.tempo
elif attribute == 'TimeSignature'.lower():
csvRowString += song.timeSignature
elif attribute == 'TimeSignatureConfidence'.lower():
# print "time sig conf: " + song.timeSignatureConfidence
csvRowString += song.timeSignatureConfidence
elif attribute == 'Title'.lower():
t = song.title
t = t.replace("b\"", "")
t = t.replace("\"", "")
csvRowString += "\"" + t + "\""
elif attribute == 'Year'.lower():
csvRowString += song.year
elif attribute == 'Familiarity'.lower(): ####Added by us!
csvRowString += song.familiarity
elif attribute == 'artist_mbid'.lower():
csvRowString += "\"" + song.artist_mbid + "\""
elif attribute == 'artist_playmeid'.lower():
csvRowString += song.artist_playmeid
elif attribute == 'artist_7digid'.lower():
csvRowString += song.artist_7digid
elif attribute == 'hottness'.lower():
csvRowString += song.hottness
elif attribute == 'song_hottness'.lower():
csvRowString += song.song_hottness
elif attribute == 'digitalid7'.lower():
csvRowString += song.digitalid7
elif attribute == 'similar_artists'.lower():
csvRowString += song.similar_artists
elif attribute == 'artist_terms'.lower():
csvRowString += song.artist_terms
elif attribute == 'art_terms_freq'.lower():
csvRowString += song.art_terms_freq
elif attribute == 'art_terms_weight'.lower():
csvRowString += song.art_terms_weight
elif attribute == 'a_sample_rate'.lower():
csvRowString += song.a_sample_rate
elif attribute == 'audio_md5'.lower():
csvRowString += "\"" + song.audio_md5 + "\""
elif attribute == 'end_of_fade_in'.lower():
csvRowString += song.end_of_fade_in
elif attribute == 'energy'.lower():
csvRowString += song.energy
elif attribute == 'loudness'.lower():
csvRowString += song.loudness
elif attribute == 'mode'.lower():
csvRowString += song.mode
elif attribute == 'mode_conf'.lower():
csvRowString += song.mode_conf
elif attribute == 'start_of_fade_out'.lower():
csvRowString += song.start_of_fade_out
elif attribute == 'trackid'.lower():
csvRowString += "\"" + song.trackid + "\""
elif attribute == 'segm_start'.lower():
csvRowString += song.segm_start
elif attribute == 'segm_conf'.lower():
csvRowString += song.segm_conf
elif attribute == 'segm_pitch'.lower():
csvRowString += song.segm_pitch
elif attribute == 'segm_timbre'.lower():
csvRowString += song.segm_timbre
elif attribute == 'segm_max_loud'.lower():
csvRowString += song.segm_max_loud
elif attribute == 'segm_max_loud_time'.lower():
csvRowString += song.segm_max_loud_time
elif attribute == 'segm_loud_start'.lower():
csvRowString += song.segm_loud_start
elif attribute == 'sect_start'.lower():
csvRowString += song.sect_start
elif attribute == 'sect_conf'.lower():
csvRowString += song.sect_conf
elif attribute == 'beats_start'.lower():
csvRowString += song.beats_start
elif attribute == 'beats_conf'.lower():
csvRowString += song.beats_conf
elif attribute == 'bars_start'.lower():
csvRowString += song.bars_start
elif attribute == 'bars_conf'.lower():
csvRowString += song.bars_conf
elif attribute == 'tatums_start'.lower():
csvRowString += song.tatums_start
elif attribute == 'tatums_conf'.lower():
csvRowString += song.tatums_conf
elif attribute == 'artist_mbtags'.lower():
csvRowString += song.artist_mbtags
elif attribute == 'artist_mbtags_count'.lower():
csvRowString += song.artist_mbtags_count
else:
csvRowString += "\"ERR\""
csvRowString += ","
#Remove the final comma from each row in the csv
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex - 1]
csvRowString += "\n"
outputFile1.write(csvRowString)
csvRowString = ""
songH5File.close()
outputFile1.close()
def 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;
print 'Get one at: http://developer.7digital.net/'
print 'Pass it as a flag: -7digitalkey KEY'
print 'or set it under environment variable: DIGITAL7_API_KEY'
sys.exit(0)
if not os.path.isfile(h5path):
print 'invalid path (not a file):',h5path
sys.exit(0)
# open h5 song, get all we know about the song
h5 = hdf5_utils.open_h5_file_read(h5path)
track_7digitalid = GETTERS.get_track_7digitalid(h5)
release_7digitalid = GETTERS.get_release_7digitalid(h5)
artist_7digitalid = GETTERS.get_artist_7digitalid(h5)
artist_name = GETTERS.get_artist_name(h5)
release_name = GETTERS.get_release(h5)
track_name = GETTERS.get_title(h5)
h5.close()
# we already have the 7digital track id? way too easy!
if track_7digitalid >= 0:
preview = get_preview_from_trackid(track_7digitalid)
if preview == '':
print 'something went wrong when looking by track id'
else:
print preview
sys.exit(0)
# we have the release id? get all tracks, find the closest match
if release_7digitalid >= 0:
tracks_name_ids = get_tracks_from_releaseid(release_7digitalid)
def main():
basedir = "./../songMetaInfo.txt"
ext = ".h5"
if len(sys.argv) > 1:
basedir = sys.argv[1]
outputfile = 'SongFileMetaData.csv'
if len(sys.argv) > 2:
outputfile = sys.argv[2]
csvWriter = open(outputfile, 'w')
csvWriter.write(
"title,songId,artistId,artistfamilarity,artistHotness,songHotness," +
"songEnfOfFadeIn,startFadeout,energy,loudness,albumID,albumName,artistName,danceability,duration,keySignatureConfidence,tempo,timeSignature,timeSignatureConfidence,year\n"
)
with open(basedir) as file:
for line in file.readlines():
f = line.strip()
#newf = f + "text"
print f
#print f
try:
songH5File = hdf5_getters.open_h5_file_read(f)
csvStr = ""
#0
title = str(hdf5_getters.get_title(songH5File))
csvStr += title + ","
#1
songId = str(hdf5_getters.get_song_id(songH5File))
csvStr += songId + ","
#2
artistId = str(hdf5_getters.get_artist_id(songH5File))
csvStr += artistId + ","
#3
artistfamilarity = str(
hdf5_getters.get_artist_familiarity(songH5File))
csvStr += artistfamilarity + ","
#4
artistHotness = str(
hdf5_getters.get_artist_hotttnesss(songH5File))
csvStr += artistHotness + ","
#5
songHotness = str(hdf5_getters.get_song_hotttnesss(songH5File))
csvStr += songHotness + ","
#6
songEnfOfFadeIn = str(
hdf5_getters.get_end_of_fade_in(songH5File))
csvStr += songEnfOfFadeIn + ","
#7
startFadeOut = str(
hdf5_getters.get_start_of_fade_out(songH5File))
csvStr += startFadeOut + ","
#8
energy = str(hdf5_getters.get_energy(songH5File))
csvStr += energy + ","
#9
loudness = str(hdf5_getters.get_loudness(songH5File))
csvStr += loudness + ","
#10
albumID = str(hdf5_getters.get_release_7digitalid(songH5File))
csvStr += albumID + ","
#11
albumName = str(hdf5_getters.get_release(songH5File))
csvStr += albumName + ","
#12
artistName = str(hdf5_getters.get_artist_name(songH5File))
csvStr += artistName + ","
#13
danceability = str(hdf5_getters.get_danceability(songH5File))
csvStr += danceability + ","
#14
duration = str(hdf5_getters.get_duration(songH5File))
csvStr += duration + ","
#15
keySignatureConfidence = str(
hdf5_getters.get_key_confidence(songH5File))
csvStr += keySignatureConfidence + ","
#16
tempo = str(hdf5_getters.get_tempo(songH5File))
csvStr += tempo + ","
## 17
timeSignature = str(
hdf5_getters.get_time_signature(songH5File))
csvStr += timeSignature + ","
#18
timeSignatureConfidence = str(
hdf5_getters.get_time_signature_confidence(songH5File))
csvStr += timeSignatureConfidence + ","
#19
year = str(hdf5_getters.get_year(songH5File))
csvStr += year + ","
#print song count
csvStr += "\n"
csvWriter.write(csvStr)
#print csvStr
songH5File.close()
except:
print "Error in processing file"
csvWriter.close()
def complete_hd5_to_csv(basedir):
ext = '.h5' # Get all files with extension .h5
# Header title. Essentially it is a schema for all the following songs
header = [
'Title', 'Artist familiarity', 'Artist hotness', 'Artist ID',
'Artist mbID', 'Artist playmeid', 'Artist 7DigitalID',
'Artist latitude', 'Artist longitude', 'Artist location',
'Artist Name', 'Release', 'Release 7DigitalID', 'Song ID',
'Song Hotness', 'Track 7Digital', 'Analysis sample rate', 'Audio md5',
'Danceability', 'Duration', 'End of Fade', 'Energy', 'Key',
'Key Confidence', 'Loudness', 'Mode', 'Mode Confidence',
'Start of fade out', 'Tempo', 'Time signature',
'Time signature confidence', 'Track ID', 'Year'
]
with open('Tester2.csv', 'w', newline='') as csvfile:
csv_writer = csv.writer(csvfile, delimiter=';')
# writing the header line. This line contains the schema of the data
csv_writer.writerow(header)
# Read all files from the given directories
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
print(files)
for f in files:
h5 = hdf5_getters.open_h5_file_read(f)
# Write as row all elements. NOTE: Only the serialized elements are parsed and not arrays
csv_writer.writerow([
hdf5_getters.get_title(h5),
hdf5_getters.get_artist_familiarity(h5),
hdf5_getters.get_artist_hotttnesss(h5),
hdf5_getters.get_artist_id(h5),
hdf5_getters.get_artist_mbid(h5),
hdf5_getters.get_artist_playmeid(h5),
hdf5_getters.get_artist_7digitalid(h5),
hdf5_getters.get_artist_latitude(h5),
hdf5_getters.get_artist_longitude(h5),
hdf5_getters.get_artist_location(h5),
hdf5_getters.get_artist_name(h5),
hdf5_getters.get_release(h5),
hdf5_getters.get_release_7digitalid(h5),
hdf5_getters.get_song_id(h5),
hdf5_getters.get_song_hotttnesss(h5),
hdf5_getters.get_track_7digitalid(h5),
hdf5_getters.get_analysis_sample_rate(h5),
hdf5_getters.get_audio_md5(h5),
hdf5_getters.get_danceability(h5),
hdf5_getters.get_duration(h5),
hdf5_getters.get_end_of_fade_in(h5),
hdf5_getters.get_energy(h5),
hdf5_getters.get_key(h5),
hdf5_getters.get_key_confidence(h5),
hdf5_getters.get_loudness(h5),
hdf5_getters.get_mode(h5),
hdf5_getters.get_mode_confidence(h5),
hdf5_getters.get_start_of_fade_out(h5),
hdf5_getters.get_tempo(h5),
hdf5_getters.get_time_signature(h5),
hdf5_getters.get_time_signature_confidence(h5),
hdf5_getters.get_track_id(h5),
hdf5_getters.get_year(h5)
])
# For debugging purposes. Everything as expected
# print()
# print("Num of songs -- ", hdf5_getters.get_num_songs(h5)) # One song per file
# print("Title -- ", hdf5_getters.get_title(h5)) # Print the title of a specific h5 file
# print("Artist familiarity -- ", hdf5_getters.get_artist_familiarity(h5))
# print("Artist hotness -- ", hdf5_getters.get_artist_hotttnesss(h5))
# print("Artist ID -- ", hdf5_getters.get_artist_id(h5))
# print("Artist mbID -- ", hdf5_getters.get_artist_mbid(h5))
# print("Artist playmeid -- ", hdf5_getters.get_artist_playmeid(h5))
# print("Artist 7DigitalID -- ", hdf5_getters.get_artist_7digitalid(h5))
# print("Artist latitude -- ", hdf5_getters.get_artist_latitude(h5))
# print("Artist longitude -- ", hdf5_getters.get_artist_longitude(h5))
# print("Artist location -- ", hdf5_getters.get_artist_location(h5))
# print("Artist Name -- ", hdf5_getters.get_artist_name(h5))
# print("Release -- ", hdf5_getters.get_release(h5))
# print("Release 7DigitalID -- ", hdf5_getters.get_release_7digitalid(h5))
# print("Song ID -- ", hdf5_getters.get_song_id(h5))
# print("Song Hotness -- ", hdf5_getters.get_song_hotttnesss(h5))
# print("Track 7Digital -- ", hdf5_getters.get_track_7digitalid(h5))
# print("Analysis sample rate -- ", hdf5_getters.get_analysis_sample_rate(h5))
# print("Audio md5 -- ", hdf5_getters.get_audio_md5(h5))
# print("Danceability -- ", hdf5_getters.get_danceability(h5))
# print("Duration -- ", hdf5_getters.get_duration(h5))
# print("End of Fade -- ", hdf5_getters.get_end_of_fade_in(h5))
# print("Energy -- ", hdf5_getters.get_energy(h5))
# print("Key -- ", hdf5_getters.get_key(h5))
# print("Key Confidence -- ", hdf5_getters.get_key_confidence(h5))
# print("Loudness -- ", hdf5_getters.get_loudness(h5))
# print("Mode -- ", hdf5_getters.get_mode(h5))
# print("Mode Confidence -- ", hdf5_getters.get_mode_confidence(h5))
# print("Start of fade out -- ", hdf5_getters.get_start_of_fade_out(h5))
# print("Tempo -- ", hdf5_getters.get_tempo(h5))
# print("Time signature -- ", hdf5_getters.get_time_signature(h5))
# print("Time signature confidence -- ", hdf5_getters.get_time_signature_confidence(h5))
# print("Track ID -- ", hdf5_getters.get_track_id(h5))
# # print("Artist mbtags -- ", hdf5_getters.get_artist_mbtags(h5))
# # print("Artist mbtags count -- ", hdf5_getters.get_artist_mbtags_count(h5))
# print("Year -- ", hdf5_getters.get_year(h5))
h5.close()
term = term.replace("'","")
cursor.execute("SELECT * FROM artist_genres WHERE artist_id='" + artist_id + "' AND genre ='" + term + "'")
if cursor.rowcount != 1:
cursor.execute("INSERT INTO artist_genres VALUES ('" + artist_id + "','" + term + "')")
for tag in mbtags:
tag = tag.replace("'","")
cursor.execute("SELECT * FROM artist_genres WHERE artist_id='" + artist_id + "' AND genre ='" + tag + "'")
if cursor.rowcount != 1:
cursor.execute("INSERT INTO artist_genres VALUES ('" + artist_id + "','" + tag + "')")
''' Store track tuples '''
track_id = h.get_track_id(h5,0)
track_title = h.get_title(h5,0)
track_title = track_title.replace("'","")
track_album = h.get_release(h5,0)
track_album = track_album.replace("'","")
track_duration = str(h.get_duration(h5,0))
track_year = str(h.get_year(h5,0))
cursor.execute("SELECT * FROM track WHERE track_id = '" + track_id + "'")
rs = cursor.fetchall()
if cursor.rowcount != 1:
cursor.execute("INSERT INTO track VALUES ('" + track_id + "','" + track_title + "','" + artist_id + "','" + artist_name + "','" + track_album + "'," + track_duration + "," + track_year + ");")
''' Store track_analysis tuples '''
print ("Track ID: " + h.get_track_id(h5,0))
track_tempo = str(h.get_tempo(h5,0))
track_key = str(h.get_key(h5,0))
track_danceability = str(h.get_danceability(h5,0))
if track_danceability == "nan":
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()
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
song.artistMBTags = remove_trap_characters(str(list(temp)))
song.artistMBTagsOuterCount = get_list_length(temp)
song.artistMBTagsCount = remove_trap_characters(
str(list(hdf5_getters.get_artist_mbtags_count(songH5File))))
song.analysisSampleRate = remove_trap_characters(
str(hdf5_getters.get_analysis_sample_rate(songH5File)))
song.audioMD5 = remove_trap_characters(
str(hdf5_getters.get_audio_md5(songH5File)))
song.endOfFadeIn = remove_trap_characters(
str(hdf5_getters.get_end_of_fade_in(songH5File)))
song.startOfFadeOut = remove_trap_characters(
str(hdf5_getters.get_start_of_fade_out(songH5File)))
song.energy = remove_trap_characters(
str(hdf5_getters.get_energy(songH5File)))
song.release = remove_trap_characters(
str(hdf5_getters.get_release(songH5File)))
song.release7digitalid = remove_trap_characters(
str(hdf5_getters.get_release_7digitalid(songH5File)))
song.songHotness = remove_trap_characters(
str(hdf5_getters.get_song_hotttnesss(songH5File)))
song.track7digitalid = remove_trap_characters(
str(hdf5_getters.get_track_7digitalid(songH5File)))
temp = hdf5_getters.get_similar_artists(songH5File)
song.similarartists = remove_trap_characters(str(list(list(temp))))
song.similarArtistsCount = get_list_length(temp)
song.loudness = remove_trap_characters(
str(hdf5_getters.get_loudness(songH5File)))
song.mode = remove_trap_characters(
str(hdf5_getters.get_mode(songH5File)))
song.modeConfidence = remove_trap_characters(
def main():
basedir = "D:/Master K"
ext = ".H5" # Set the extension here. H5 is the extension for HDF5 files.
songs = []
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
#songs = {}
#keys = list()
#values = list()
for f in files:
print(f)
songH5File = hdf5_getters.open_h5_file_read(f)
song = Song(str(hdf5_getters.get_song_id(songH5File)))
item = {"song_id": song.id.replace('b', '')}
song.artistID = str(hdf5_getters.get_artist_id(songH5File))
song.artistID = song.artistID.replace('b', '', 1)
item["song_artistID"] = song.artistID
song.albumID = str(hdf5_getters.get_release_7digitalid(songH5File))
song.albumID = song.albumID.replace('b', '', 1)
item["song_albumID"] = song.albumID
song.albumName = str(hdf5_getters.get_release(songH5File))
song.albumName = song.albumName.replace('b', '', 1)
item["song_albumName"] = song.albumName
song.artistLatitude = str(
hdf5_getters.get_artist_latitude(songH5File))
song.artistLatitude = song.artistLatitude.replace('b', '', 1)
item["song_artistLatitude"] = song.artistLatitude
song.artistLocation = str(
hdf5_getters.get_artist_location(songH5File))
song.artistLocation = song.artistLocation.replace('b', '', 1)
item["song_artistLocation"] = song.artistLocation
song.artistLongitude = str(
hdf5_getters.get_artist_longitude(songH5File))
song.artistLongitude = song.artistLongitude.replace('b', '', 1)
item["song_artistLongitude"] = song.artistLongitude
song.artistName = str(hdf5_getters.get_artist_name(songH5File))
song.artistName = song.artistName.replace('b', '', 1)
item["song_artistName"] = song.artistName
song.danceability = str(hdf5_getters.get_danceability(songH5File))
song.danceability = song.danceability.replace('b', '', 1)
item["song_danceability"] = song.danceability
song.duration = str(hdf5_getters.get_duration(songH5File))
song.duration = song.duration.replace('b', '', 1)
item["song_duration"] = song.duration
song.keySignature = str(hdf5_getters.get_key(songH5File))
song.keySignature = song.keySignature.replace('b', '', 1)
item["song_keySignature"] = song.keySignature
song.keySignatureConfidence = str(
hdf5_getters.get_key_confidence(songH5File))
song.keySignatureConfidence = song.keySignatureConfidence.replace(
'b', '', 1)
item["song_keySignatureConfidence"] = song.keySignatureConfidence
song.tempo = str(hdf5_getters.get_tempo(songH5File))
song.tempo = song.tempo.replace('b', '', 1)
item["song_tempo"] = song.tempo
song.timeSignature = str(
hdf5_getters.get_time_signature(songH5File))
song.timeSignature = song.timeSignature.replace('b', '', 1)
item["song_timeSignature"] = song.timeSignature
song.timeSignatureConfidence = str(
hdf5_getters.get_time_signature_confidence(songH5File))
song.timeSignatureConfidence = song.timeSignatureConfidence.replace(
'b', '', 1)
item["song_timeSignatureConfidence"] = song.timeSignatureConfidence
song.title = str(hdf5_getters.get_title(songH5File))
song.title = song.title.replace('b', '', 1)
item["song_title"] = song.title
song.year = str(hdf5_getters.get_year(songH5File))
song.year = song.year.replace('b', '', 1)
item["song_year"] = song.year
#song.mfcc = str(hdf5_getters.get_segments_timbre(songH5File))
#item["song_mfcc"] = song.mfcc
item["song_mfcc"] = list(
hdf5_getters.get_segments_timbre(songH5File))
song.hotness = str(hdf5_getters.get_artist_hotttnesss(songH5File))
item["song_hotness"] = song.hotness
songs.append(item)
songH5File.close()
#song_dict= dict(zip(keys, values))
with open("D:\data_file_k.json", "w") as write_file:
json.dump(songs, write_file, cls=NumpyEncoder)
writer_tatums_file = csv.writer(outputTatumsFile, lineterminator='\n')
writer_MBTags_File = csv.writer(outputMBTagsFile, lineterminator='\n')
for dirname, dirs, files in os.walk(files):
for filename in files:
filename_without_extension, extension = os.path.splitext(filename)
if extension == '.h5':
hdf = hdf5_getters.open_h5_file_read(dirname + '/' + filename)
artist_id = hdf5_getters.get_artist_id(hdf)
artists_mb_id = hdf5_getters.get_artist_mbid(hdf)
artist_playmeid = hdf5_getters.get_artist_playmeid(hdf)
artist_7digitalid = hdf5_getters.get_artist_7digitalid(hdf)
artist_name = hdf5_getters.get_artist_name(hdf)
artist_familarity = hdf5_getters.get_artist_familiarity(hdf)
artist_hotttnesss = hdf5_getters.get_artist_hotttnesss(hdf)
artist_location = hdf5_getters.get_artist_location(hdf)
release = hdf5_getters.get_release(hdf)
release_7digitalid = hdf5_getters.get_release_7digitalid(hdf)
song_id = hdf5_getters.get_song_id(hdf)
title = hdf5_getters.get_title(hdf)
song_hotttnesss = hdf5_getters.get_song_hotttnesss(hdf)
track_7digitalid = hdf5_getters.get_track_7digitalid(hdf)
analysis_sample_rate = hdf5_getters.get_analysis_sample_rate(
hdf)
audio_md5 = hdf5_getters.get_audio_md5(hdf)
duration = hdf5_getters.get_duration(hdf)
end_of_fade_in = hdf5_getters.get_end_of_fade_in(hdf)
energy = hdf5_getters.get_energy(hdf)
key = hdf5_getters.get_key(hdf)
key_confidence = hdf5_getters.get_key_confidence(hdf)
loudness = hdf5_getters.get_loudness(hdf)
mode = hdf5_getters.get_mode(hdf)
print 'You need to set a 7digital API key!'
print 'Get one at: http://developer.7digital.net/'
print 'Pass it as a flag: -7digitalkey KEY'
print 'or set it under environment variable: DIGITAL7_API_KEY'
sys.exit(0)
if not os.path.isfile(h5path):
print 'invalid path (not a file):', h5path
sys.exit(0)
# open h5 song, get all we know about the song
h5 = hdf5_utils.open_h5_file_read(h5path)
track_7digitalid = GETTERS.get_track_7digitalid(h5)
release_7digitalid = GETTERS.get_release_7digitalid(h5)
artist_7digitalid = GETTERS.get_artist_7digitalid(h5)
artist_name = GETTERS.get_artist_name(h5)
release_name = GETTERS.get_release(h5)
track_name = GETTERS.get_title(h5)
h5.close()
# we already have the 7digital track id? way too easy!
if track_7digitalid >= 0:
preview = get_preview_from_trackid(track_7digitalid)
if preview == '':
print 'something went wrong when looking by track id'
else:
print preview
sys.exit(0)
# we have the release id? get all tracks, find the closest match
if release_7digitalid >= 0:
tracks_name_ids = get_tracks_from_releaseid(release_7digitalid)
def data_to_flat_file(basedir, ext='.h5'):
"""This function extract the information from the tables and creates the flat file."""
count = 0
#song counter
list_to_write = []
row_to_write = ""
writer = csv.writer(open("metadata_wholeA.csv", "wb"))
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
for f in files:
print f #the name of the file
h5 = hdf5_getters.open_h5_file_read(f)
title = hdf5_getters.get_title(h5)
title = title.replace('"', '')
comma = title.find(',') #eliminating commas in the title
if comma != -1:
print title
time.sleep(1)
album = hdf5_getters.get_release(h5)
album = album.replace('"', '') #eliminating commas in the album
comma = album.find(',')
if comma != -1:
print album
time.sleep(1)
artist_name = hdf5_getters.get_artist_name(h5)
comma = artist_name.find(',')
if comma != -1:
print artist_name
time.sleep(1)
artist_name = artist_name.replace('"',
'') #eliminating double quotes
duration = hdf5_getters.get_duration(h5)
samp_rt = hdf5_getters.get_analysis_sample_rate(h5)
artist_7digitalid = hdf5_getters.get_artist_7digitalid(h5)
artist_fam = hdf5_getters.get_artist_familiarity(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_fam) == True:
artist_fam = -1
artist_hotness = hdf5_getters.get_artist_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_hotness) == True:
artist_hotness = -1
artist_id = hdf5_getters.get_artist_id(h5)
artist_lat = hdf5_getters.get_artist_latitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lat) == True:
artist_lat = -1
artist_loc = hdf5_getters.get_artist_location(h5)
#checks artist_loc to see if it is a hyperlink if it is set as empty string
artist_loc = artist_loc.replace(",", "\,")
if artist_loc.startswith("<a"):
artist_loc = ""
if len(artist_loc) > 100:
artist_loc = ""
artist_lon = hdf5_getters.get_artist_longitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lon) == True:
artist_lon = -1
artist_mbid = hdf5_getters.get_artist_mbid(h5)
artist_pmid = hdf5_getters.get_artist_playmeid(h5)
audio_md5 = hdf5_getters.get_audio_md5(h5)
danceability = hdf5_getters.get_danceability(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(danceability) == True:
danceability = -1
end_fade_in = hdf5_getters.get_end_of_fade_in(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(end_fade_in) == True:
end_fade_in = -1
energy = hdf5_getters.get_energy(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(energy) == True:
energy = -1
song_key = hdf5_getters.get_key(h5)
key_c = hdf5_getters.get_key_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(key_c) == True:
key_c = -1
loudness = hdf5_getters.get_loudness(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(loudness) == True:
loudness = -1
mode = hdf5_getters.get_mode(h5)
mode_conf = hdf5_getters.get_mode_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(mode_conf) == True:
mode_conf = -1
release_7digitalid = hdf5_getters.get_release_7digitalid(h5)
song_hot = hdf5_getters.get_song_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(song_hot) == True:
song_hot = -1
song_id = hdf5_getters.get_song_id(h5)
start_fade_out = hdf5_getters.get_start_of_fade_out(h5)
tempo = hdf5_getters.get_tempo(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(tempo) == True:
tempo = -1
time_sig = hdf5_getters.get_time_signature(h5)
time_sig_c = hdf5_getters.get_time_signature_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(time_sig_c) == True:
time_sig_c = -1
track_id = hdf5_getters.get_track_id(h5)
track_7digitalid = hdf5_getters.get_track_7digitalid(h5)
year = hdf5_getters.get_year(h5)
bars_c = hdf5_getters.get_bars_confidence(h5)
bars_c_avg = get_avg(bars_c)
bars_c_max = get_max(bars_c)
bars_c_min = get_min(bars_c)
bars_c_stddev = get_stddev(bars_c)
bars_c_count = get_count(bars_c)
bars_c_sum = get_sum(bars_c)
bars_start = hdf5_getters.get_bars_start(h5)
bars_start_avg = get_avg(bars_start)
bars_start_max = get_max(bars_start)
bars_start_min = get_min(bars_start)
bars_start_stddev = get_stddev(bars_start)
bars_start_count = get_count(bars_start)
bars_start_sum = get_sum(bars_start)
beats_c = hdf5_getters.get_beats_confidence(h5)
beats_c_avg = get_avg(beats_c)
beats_c_max = get_max(beats_c)
beats_c_min = get_min(beats_c)
beats_c_stddev = get_stddev(beats_c)
beats_c_count = get_count(beats_c)
beats_c_sum = get_sum(beats_c)
beats_start = hdf5_getters.get_beats_start(h5)
beats_start_avg = get_avg(beats_start)
beats_start_max = get_max(beats_start)
beats_start_min = get_min(beats_start)
beats_start_stddev = get_stddev(beats_start)
beats_start_count = get_count(beats_start)
beats_start_sum = get_sum(beats_start)
sec_c = hdf5_getters.get_sections_confidence(h5)
sec_c_avg = get_avg(sec_c)
sec_c_max = get_max(sec_c)
sec_c_min = get_min(sec_c)
sec_c_stddev = get_stddev(sec_c)
sec_c_count = get_count(sec_c)
sec_c_sum = get_sum(sec_c)
sec_start = hdf5_getters.get_sections_start(h5)
sec_start_avg = get_avg(sec_start)
sec_start_max = get_max(sec_start)
sec_start_min = get_min(sec_start)
sec_start_stddev = get_stddev(sec_start)
sec_start_count = get_count(sec_start)
sec_start_sum = get_sum(sec_start)
seg_c = hdf5_getters.get_segments_confidence(h5)
seg_c_avg = get_avg(seg_c)
seg_c_max = get_max(seg_c)
seg_c_min = get_min(seg_c)
seg_c_stddev = get_stddev(seg_c)
seg_c_count = get_count(seg_c)
seg_c_sum = get_sum(seg_c)
seg_loud_max = hdf5_getters.get_segments_loudness_max(h5)
seg_loud_max_avg = get_avg(seg_loud_max)
seg_loud_max_max = get_max(seg_loud_max)
seg_loud_max_min = get_min(seg_loud_max)
seg_loud_max_stddev = get_stddev(seg_loud_max)
seg_loud_max_count = get_count(seg_loud_max)
seg_loud_max_sum = get_sum(seg_loud_max)
seg_loud_max_time = hdf5_getters.get_segments_loudness_max_time(h5)
seg_loud_max_time_avg = get_avg(seg_loud_max_time)
seg_loud_max_time_max = get_max(seg_loud_max_time)
seg_loud_max_time_min = get_min(seg_loud_max_time)
seg_loud_max_time_stddev = get_stddev(seg_loud_max_time)
seg_loud_max_time_count = get_count(seg_loud_max_time)
seg_loud_max_time_sum = get_sum(seg_loud_max_time)
seg_loud_start = hdf5_getters.get_segments_loudness_start(h5)
seg_loud_start_avg = get_avg(seg_loud_start)
seg_loud_start_max = get_max(seg_loud_start)
seg_loud_start_min = get_min(seg_loud_start)
seg_loud_start_stddev = get_stddev(seg_loud_start)
seg_loud_start_count = get_count(seg_loud_start)
seg_loud_start_sum = get_sum(seg_loud_start)
seg_pitch = hdf5_getters.get_segments_pitches(h5)
pitch_size = len(seg_pitch)
seg_start = hdf5_getters.get_segments_start(h5)
seg_start_avg = get_avg(seg_start)
seg_start_max = get_max(seg_start)
seg_start_min = get_min(seg_start)
seg_start_stddev = get_stddev(seg_start)
seg_start_count = get_count(seg_start)
seg_start_sum = get_sum(seg_start)
seg_timbre = hdf5_getters.get_segments_timbre(h5)
tatms_c = hdf5_getters.get_tatums_confidence(h5)
tatms_c_avg = get_avg(tatms_c)
tatms_c_max = get_max(tatms_c)
tatms_c_min = get_min(tatms_c)
tatms_c_stddev = get_stddev(tatms_c)
tatms_c_count = get_count(tatms_c)
tatms_c_sum = get_sum(tatms_c)
tatms_start = hdf5_getters.get_tatums_start(h5)
tatms_start_avg = get_avg(tatms_start)
tatms_start_max = get_max(tatms_start)
tatms_start_min = get_min(tatms_start)
tatms_start_stddev = get_stddev(tatms_start)
tatms_start_count = get_count(tatms_start)
tatms_start_sum = get_sum(tatms_start)
#Getting the genres
genre_set = 0 #flag to see if the genre has been set or not
art_trm = hdf5_getters.get_artist_terms(h5)
trm_freq = hdf5_getters.get_artist_terms_freq(h5)
trn_wght = hdf5_getters.get_artist_terms_weight(h5)
a_mb_tags = hdf5_getters.get_artist_mbtags(h5)
genre_indexes = get_genre_indexes(
trm_freq) #index of the highest freq
final_genre = []
genres_so_far = []
for i in range(len(genre_indexes)):
genre_tmp = get_genre(
art_trm, genre_indexes[i]
) #genre that corresponds to the highest freq
genres_so_far = genre_dict.get_genre_in_dict(
genre_tmp) #getting the genre from the dictionary
if len(genres_so_far) != 0:
for i in genres_so_far:
final_genre.append(i)
genre_set = 1 #genre was found in dictionary
if genre_set == 1:
col_num = []
for genre in final_genre:
column = int(
genre) #getting the column number of the genre
col_num.append(column)
genre_array = genre_columns(col_num) #genre array
else:
genre_array = genre_columns(
-1) #the genre was not found in the dictionary
transpose_pitch = seg_pitch.transpose(
) #this is to tranpose the matrix,so we can have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_pitch_avg = []
seg_pitch_max = []
seg_pitch_min = []
seg_pitch_stddev = []
seg_pitch_count = []
seg_pitch_sum = []
i = 0
#Getting the aggregate values in the pitches array
for row in transpose_pitch:
seg_pitch_avg.append(get_avg(row))
seg_pitch_max.append(get_max(row))
seg_pitch_min.append(get_min(row))
seg_pitch_stddev.append(get_stddev(row))
seg_pitch_count.append(get_count(row))
seg_pitch_sum.append(get_sum(row))
i = i + 1
#extracting information from the timbre array
transpose_timbre = seg_pitch.transpose(
) #tranposing matrix, to have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_timbre_avg = []
seg_timbre_max = []
seg_timbre_min = []
seg_timbre_stddev = []
seg_timbre_count = []
seg_timbre_sum = []
i = 0
for row in transpose_timbre:
seg_timbre_avg.append(get_avg(row))
seg_timbre_max.append(get_max(row))
seg_timbre_min.append(get_min(row))
seg_timbre_stddev.append(get_stddev(row))
seg_timbre_count.append(get_count(row))
seg_timbre_sum.append(get_sum(row))
i = i + 1
#Writing to the flat file
writer.writerow([
title, album, artist_name, year, duration, seg_start_count,
tempo
])
h5.close()
count = count + 1
print count
def main():
outputFile1 = open('SongCSV.csv', 'w')
csvRowString = ""
#################################################
#if you want to prompt the user for the order of attributes in the csv,
#leave the prompt boolean set to True
#else, set 'prompt' to False and set the order of attributes in the 'else'
#clause
prompt = False
#################################################
if prompt == True:
while prompt:
prompt = False
csvAttributeString = raw_input("\n\nIn what order would you like the colums of the CSV file?\n" +
"Please delineate with commas. The options are: " +
"AlbumName, AlbumID, ArtistID, ArtistLatitude, ArtistLocation, ArtistLongitude,"+
" ArtistName, Danceability, Duration, KeySignature, KeySignatureConfidence, Tempo," +
" SongID, TimeSignature, TimeSignatureConfidence, Title, and Year.\n\n" +
"For example, you may write \"Title, Tempo, Duration\"...\n\n" +
"...or exit by typing 'exit'.\n\n")
csvAttributeList = re.split('\W+', csvAttributeString)
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
for attribute in csvAttributeList:
# print "Here is the attribute: " + attribute + " \n"
if attribute == 'AlbumID'.lower():
csvRowString += 'AlbumID'
elif attribute == 'AlbumName'.lower():
csvRowString += 'AlbumName'
elif attribute == 'ArtistID'.lower():
csvRowString += 'ArtistID'
elif attribute == 'ArtistLatitude'.lower():
csvRowString += 'ArtistLatitude'
elif attribute == 'ArtistLocation'.lower():
csvRowString += 'ArtistLocation'
elif attribute == 'ArtistLongitude'.lower():
csvRowString += 'ArtistLongitude'
elif attribute == 'ArtistName'.lower():
csvRowString += 'ArtistName'
elif attribute == 'Danceability'.lower():
csvRowString += 'Danceability'
elif attribute == 'Duration'.lower():
csvRowString += 'Duration'
elif attribute == 'KeySignature'.lower():
csvRowString += 'KeySignature'
elif attribute == 'KeySignatureConfidence'.lower():
csvRowString += 'KeySignatureConfidence'
elif attribute == 'SongID'.lower():
csvRowString += "SongID"
elif attribute == 'Tempo'.lower():
csvRowString += 'Tempo'
elif attribute == 'TimeSignature'.lower():
csvRowString += 'TimeSignature'
elif attribute == 'TimeSignatureConfidence'.lower():
csvRowString += 'TimeSignatureConfidence'
elif attribute == 'Title'.lower():
csvRowString += 'Title'
elif attribute == 'Year'.lower():
csvRowString += 'Year'
elif attribute == 'Exit'.lower():
sys.exit()
else:
prompt = True
print "=============="
print "I believe there has been an error with the input."
print "=============="
break
csvRowString += ","
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex-1]
csvRowString += "\n"
outputFile1.write(csvRowString);
csvRowString = ""
#else, if you want to hard code the order of the csv file and not prompt
#the user,
else:
#################################################
#change the order of the csv file here
#Default is to list all available attributes (in alphabetical order)
csvRowString = ("SongID,AlbumID,AlbumName,ArtistID,ArtistLatitude,ArtistLocation,"+
"ArtistLongitude,ArtistName,Danceability,Duration,KeySignature,"+
"KeySignatureConfidence,Tempo,TimeSignature,TimeSignatureConfidence,"+
"Title,Year")
#################################################
csvAttributeList = re.split('\W+', csvRowString)
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
outputFile1.write("SongNumber,");
outputFile1.write(csvRowString + "\n");
csvRowString = ""
#################################################
#Set the basedir here, the root directory from which the search
#for files stored in a (hierarchical data structure) will originate
basedir = "." # "." As the default means the current directory
ext = ".H5" #Set the extension here. H5 is the extension for HDF5 files.
#################################################
#FOR LOOP
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root,'*'+ext))
for f in files:
print f
songH5File = hdf5_getters.open_h5_file_read(f)
song = Song(str(hdf5_getters.get_song_id(songH5File)))
testDanceability = hdf5_getters.get_danceability(songH5File)
# print type(testDanceability)
# print ("Here is the danceability: ") + str(testDanceability)
song.artistID = str(hdf5_getters.get_artist_id(songH5File))
song.albumID = str(hdf5_getters.get_release_7digitalid(songH5File))
song.albumName = str(hdf5_getters.get_release(songH5File))
song.artistLatitude = str(hdf5_getters.get_artist_latitude(songH5File))
song.artistLocation = str(hdf5_getters.get_artist_location(songH5File))
song.artistLongitude = str(hdf5_getters.get_artist_longitude(songH5File))
song.artistName = str(hdf5_getters.get_artist_name(songH5File))
song.danceability = str(hdf5_getters.get_danceability(songH5File))
song.duration = str(hdf5_getters.get_duration(songH5File))
# song.setGenreList()
song.keySignature = str(hdf5_getters.get_key(songH5File))
song.keySignatureConfidence = str(hdf5_getters.get_key_confidence(songH5File))
# song.lyrics = None
# song.popularity = None
song.tempo = str(hdf5_getters.get_tempo(songH5File))
song.timeSignature = str(hdf5_getters.get_time_signature(songH5File))
song.timeSignatureConfidence = str(hdf5_getters.get_time_signature_confidence(songH5File))
song.title = str(hdf5_getters.get_title(songH5File))
song.year = str(hdf5_getters.get_year(songH5File))
#print song count
csvRowString += str(song.songCount) + ","
for attribute in csvAttributeList:
# print "Here is the attribute: " + attribute + " \n"
if attribute == 'AlbumID'.lower():
csvRowString += song.albumID
elif attribute == 'AlbumName'.lower():
albumName = song.albumName
albumName = albumName.replace(',',"")
csvRowString += "\"" + albumName + "\""
elif attribute == 'ArtistID'.lower():
csvRowString += "\"" + song.artistID + "\""
elif attribute == 'ArtistLatitude'.lower():
latitude = song.artistLatitude
if latitude == 'nan':
latitude = ''
csvRowString += latitude
elif attribute == 'ArtistLocation'.lower():
location = song.artistLocation
location = location.replace(',','')
csvRowString += "\"" + location + "\""
elif attribute == 'ArtistLongitude'.lower():
longitude = song.artistLongitude
if longitude == 'nan':
longitude = ''
csvRowString += longitude
elif attribute == 'ArtistName'.lower():
csvRowString += "\"" + song.artistName + "\""
elif attribute == 'Danceability'.lower():
csvRowString += song.danceability
elif attribute == 'Duration'.lower():
csvRowString += song.duration
elif attribute == 'KeySignature'.lower():
csvRowString += song.keySignature
elif attribute == 'KeySignatureConfidence'.lower():
# print "key sig conf: " + song.timeSignatureConfidence
csvRowString += song.keySignatureConfidence
elif attribute == 'SongID'.lower():
csvRowString += "\"" + song.id + "\""
elif attribute == 'Tempo'.lower():
# print "Tempo: " + song.tempo
csvRowString += song.tempo
elif attribute == 'TimeSignature'.lower():
csvRowString += song.timeSignature
elif attribute == 'TimeSignatureConfidence'.lower():
# print "time sig conf: " + song.timeSignatureConfidence
csvRowString += song.timeSignatureConfidence
elif attribute == 'Title'.lower():
csvRowString += "\"" + song.title + "\""
elif attribute == 'Year'.lower():
csvRowString += song.year
else:
csvRowString += "Erm. This didn't work. Error. :( :(\n"
csvRowString += ","
#Remove the final comma from each row in the csv
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex-1]
csvRowString += "\n"
outputFile1.write(csvRowString)
csvRowString = ""
songH5File.close()
outputFile1.close()
continue
# Get the artist name
artist_name = hdf5_getters.get_artist_name(h5)
artist = artist_name.translate(None, string.punctuation)
# Get the title of the song
title_song = hdf5_getters.get_title(h5)
title = title_song.translate(None, string.punctuation)
# Get artist location
artist_location = hdf5_getters.get_artist_location(h5)
artist_loc = artist_location.translate(None, string.punctuation)
# Get release
release_song = hdf5_getters.get_release(h5)
release = release_song.translate(None, string.punctuation)
# Get artist HOTTTNESSSSSS
hotttness = hdf5_getters.get_artist_hotttnesss(h5)
# Get artist familiarity
familiarity = hdf5_getters.get_artist_familiarity(h5)
# Get danceability
danceability = hdf5_getters.get_danceability(h5)
# Get duration
duration = hdf5_getters.get_duration(h5)
# Get energy
def data_to_flat_file(basedir,ext='.h5') :
"""This function extract the information from the tables and creates the flat file."""
count = 0; #song counter
list_to_write= []
row_to_write = ""
writer = csv.writer(open("metadata.csv", "wb"))
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root,'*'+ext))
for f in files:
print f #the name of the file
h5 = hdf5_getters.open_h5_file_read(f)
title = hdf5_getters.get_title(h5)
title= title.replace('"','')
comma=title.find(',') #eliminating commas in the title
if comma != -1:
print title
time.sleep(1)
album = hdf5_getters.get_release(h5)
album= album.replace('"','') #eliminating commas in the album
comma=album.find(',')
if comma != -1:
print album
time.sleep(1)
artist_name = hdf5_getters.get_artist_name(h5)
comma=artist_name.find(',')
if comma != -1:
print artist_name
time.sleep(1)
artist_name= artist_name.replace('"','') #eliminating double quotes
duration = hdf5_getters.get_duration(h5)
samp_rt = hdf5_getters.get_analysis_sample_rate(h5)
artist_7digitalid = hdf5_getters.get_artist_7digitalid(h5)
artist_fam = hdf5_getters.get_artist_familiarity(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_fam) == True:
artist_fam=-1
artist_hotness= hdf5_getters.get_artist_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_hotness) == True:
artist_hotness=-1
artist_id = hdf5_getters.get_artist_id(h5)
artist_lat = hdf5_getters.get_artist_latitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lat) == True:
artist_lat=-1
artist_loc = hdf5_getters.get_artist_location(h5)
#checks artist_loc to see if it is a hyperlink if it is set as empty string
artist_loc = artist_loc.replace(",", "\,");
if artist_loc.startswith("<a"):
artist_loc = ""
if len(artist_loc) > 100:
artist_loc = ""
artist_lon = hdf5_getters.get_artist_longitude(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(artist_lon) == True:
artist_lon=-1
artist_mbid = hdf5_getters.get_artist_mbid(h5)
artist_pmid = hdf5_getters.get_artist_playmeid(h5)
audio_md5 = hdf5_getters.get_audio_md5(h5)
danceability = hdf5_getters.get_danceability(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(danceability) == True:
danceability=-1
end_fade_in =hdf5_getters.get_end_of_fade_in(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(end_fade_in) == True:
end_fade_in=-1
energy = hdf5_getters.get_energy(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(energy) == True:
energy=-1
song_key = hdf5_getters.get_key(h5)
key_c = hdf5_getters.get_key_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(key_c) == True:
key_c=-1
loudness = hdf5_getters.get_loudness(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(loudness) == True:
loudness=-1
mode = hdf5_getters.get_mode(h5)
mode_conf = hdf5_getters.get_mode_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(mode_conf) == True:
mode_conf=-1
release_7digitalid = hdf5_getters.get_release_7digitalid(h5)
song_hot = hdf5_getters.get_song_hotttnesss(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(song_hot) == True:
song_hot=-1
song_id = hdf5_getters.get_song_id(h5)
start_fade_out = hdf5_getters.get_start_of_fade_out(h5)
tempo = hdf5_getters.get_tempo(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(tempo) == True:
tempo=-1
time_sig = hdf5_getters.get_time_signature(h5)
time_sig_c = hdf5_getters.get_time_signature_confidence(h5)
#checking if we get a "nan" if we do we change it to -1
if numpy.isnan(time_sig_c) == True:
time_sig_c=-1
track_id = hdf5_getters.get_track_id(h5)
track_7digitalid = hdf5_getters.get_track_7digitalid(h5)
year = hdf5_getters.get_year(h5)
bars_c = hdf5_getters.get_bars_confidence(h5)
bars_c_avg= get_avg(bars_c)
bars_c_max= get_max(bars_c)
bars_c_min = get_min(bars_c)
bars_c_stddev= get_stddev(bars_c)
bars_c_count = get_count(bars_c)
bars_c_sum = get_sum(bars_c)
bars_start = hdf5_getters.get_bars_start(h5)
bars_start_avg = get_avg(bars_start)
bars_start_max= get_max(bars_start)
bars_start_min = get_min(bars_start)
bars_start_stddev= get_stddev(bars_start)
bars_start_count = get_count(bars_start)
bars_start_sum = get_sum(bars_start)
beats_c = hdf5_getters.get_beats_confidence(h5)
beats_c_avg= get_avg(beats_c)
beats_c_max= get_max(beats_c)
beats_c_min = get_min(beats_c)
beats_c_stddev= get_stddev(beats_c)
beats_c_count = get_count(beats_c)
beats_c_sum = get_sum(beats_c)
beats_start = hdf5_getters.get_beats_start(h5)
beats_start_avg = get_avg(beats_start)
beats_start_max= get_max(beats_start)
beats_start_min = get_min(beats_start)
beats_start_stddev= get_stddev(beats_start)
beats_start_count = get_count(beats_start)
beats_start_sum = get_sum(beats_start)
sec_c = hdf5_getters.get_sections_confidence(h5)
sec_c_avg= get_avg(sec_c)
sec_c_max= get_max(sec_c)
sec_c_min = get_min(sec_c)
sec_c_stddev= get_stddev(sec_c)
sec_c_count = get_count(sec_c)
sec_c_sum = get_sum(sec_c)
sec_start = hdf5_getters.get_sections_start(h5)
sec_start_avg = get_avg(sec_start)
sec_start_max= get_max(sec_start)
sec_start_min = get_min(sec_start)
sec_start_stddev= get_stddev(sec_start)
sec_start_count = get_count(sec_start)
sec_start_sum = get_sum(sec_start)
seg_c = hdf5_getters.get_segments_confidence(h5)
seg_c_avg= get_avg(seg_c)
seg_c_max= get_max(seg_c)
seg_c_min = get_min(seg_c)
seg_c_stddev= get_stddev(seg_c)
seg_c_count = get_count(seg_c)
seg_c_sum = get_sum(seg_c)
seg_loud_max = hdf5_getters.get_segments_loudness_max(h5)
seg_loud_max_avg= get_avg(seg_loud_max)
seg_loud_max_max= get_max(seg_loud_max)
seg_loud_max_min = get_min(seg_loud_max)
seg_loud_max_stddev= get_stddev(seg_loud_max)
seg_loud_max_count = get_count(seg_loud_max)
seg_loud_max_sum = get_sum(seg_loud_max)
seg_loud_max_time = hdf5_getters.get_segments_loudness_max_time(h5)
seg_loud_max_time_avg= get_avg(seg_loud_max_time)
seg_loud_max_time_max= get_max(seg_loud_max_time)
seg_loud_max_time_min = get_min(seg_loud_max_time)
seg_loud_max_time_stddev= get_stddev(seg_loud_max_time)
seg_loud_max_time_count = get_count(seg_loud_max_time)
seg_loud_max_time_sum = get_sum(seg_loud_max_time)
seg_loud_start = hdf5_getters.get_segments_loudness_start(h5)
seg_loud_start_avg= get_avg(seg_loud_start)
seg_loud_start_max= get_max(seg_loud_start)
seg_loud_start_min = get_min(seg_loud_start)
seg_loud_start_stddev= get_stddev(seg_loud_start)
seg_loud_start_count = get_count(seg_loud_start)
seg_loud_start_sum = get_sum(seg_loud_start)
seg_pitch = hdf5_getters.get_segments_pitches(h5)
pitch_size = len(seg_pitch)
seg_start = hdf5_getters.get_segments_start(h5)
seg_start_avg= get_avg(seg_start)
seg_start_max= get_max(seg_start)
seg_start_min = get_min(seg_start)
seg_start_stddev= get_stddev(seg_start)
seg_start_count = get_count(seg_start)
seg_start_sum = get_sum(seg_start)
seg_timbre = hdf5_getters.get_segments_timbre(h5)
tatms_c = hdf5_getters.get_tatums_confidence(h5)
tatms_c_avg= get_avg(tatms_c)
tatms_c_max= get_max(tatms_c)
tatms_c_min = get_min(tatms_c)
tatms_c_stddev= get_stddev(tatms_c)
tatms_c_count = get_count(tatms_c)
tatms_c_sum = get_sum(tatms_c)
tatms_start = hdf5_getters.get_tatums_start(h5)
tatms_start_avg= get_avg(tatms_start)
tatms_start_max= get_max(tatms_start)
tatms_start_min = get_min(tatms_start)
tatms_start_stddev= get_stddev(tatms_start)
tatms_start_count = get_count(tatms_start)
tatms_start_sum = get_sum(tatms_start)
#Getting the genres
genre_set = 0 #flag to see if the genre has been set or not
art_trm = hdf5_getters.get_artist_terms(h5)
trm_freq = hdf5_getters.get_artist_terms_freq(h5)
trn_wght = hdf5_getters.get_artist_terms_weight(h5)
a_mb_tags = hdf5_getters.get_artist_mbtags(h5)
genre_indexes=get_genre_indexes(trm_freq) #index of the highest freq
final_genre=[]
genres_so_far=[]
for i in range(len(genre_indexes)):
genre_tmp=get_genre(art_trm,genre_indexes[i]) #genre that corresponds to the highest freq
genres_so_far=genre_dict.get_genre_in_dict(genre_tmp) #getting the genre from the dictionary
if len(genres_so_far) != 0:
for i in genres_so_far:
final_genre.append(i)
genre_set=1 #genre was found in dictionary
if genre_set == 1:
col_num=[]
for genre in final_genre:
column=int(genre) #getting the column number of the genre
col_num.append(column)
genre_array=genre_columns(col_num) #genre array
else:
genre_array=genre_columns(-1) #the genre was not found in the dictionary
transpose_pitch= seg_pitch.transpose() #this is to tranpose the matrix,so we can have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_pitch_avg=[]
seg_pitch_max=[]
seg_pitch_min=[]
seg_pitch_stddev=[]
seg_pitch_count=[]
seg_pitch_sum=[]
i=0
#Getting the aggregate values in the pitches array
for row in transpose_pitch:
seg_pitch_avg.append(get_avg(row))
seg_pitch_max.append(get_max(row))
seg_pitch_min.append(get_min(row))
seg_pitch_stddev.append(get_stddev(row))
seg_pitch_count.append(get_count(row))
seg_pitch_sum.append(get_sum(row))
i=i+1
#extracting information from the timbre array
transpose_timbre = seg_pitch.transpose() #tranposing matrix, to have 12 rows
#arrays containing the aggregate values of the 12 rows
seg_timbre_avg=[]
seg_timbre_max=[]
seg_timbre_min=[]
seg_timbre_stddev=[]
seg_timbre_count=[]
seg_timbre_sum=[]
i=0
for row in transpose_timbre:
seg_timbre_avg.append(get_avg(row))
seg_timbre_max.append(get_max(row))
seg_timbre_min.append(get_min(row))
seg_timbre_stddev.append(get_stddev(row))
seg_timbre_count.append(get_count(row))
seg_timbre_sum.append(get_sum(row))
i=i+1
#Writing to the flat file
writer.writerow([title,album,artist_name,duration,samp_rt,artist_7digitalid,artist_fam,artist_hotness,artist_id,artist_lat,artist_loc,artist_lon,artist_mbid,genre_array[0],genre_array[1],genre_array[2],
genre_array[3],genre_array[4],genre_array[5],genre_array[6],genre_array[7],genre_array[8],genre_array[9],genre_array[10],genre_array[11],genre_array[12],genre_array[13],genre_array[14],genre_array[15],
genre_array[16],genre_array[17],genre_array[18],genre_array[19],genre_array[20],genre_array[21],genre_array[22],genre_array[23],genre_array[24],genre_array[25],genre_array[26],
genre_array[27],genre_array[28],genre_array[29],genre_array[30],genre_array[31],genre_array[32],genre_array[33],genre_array[34],genre_array[35],genre_array[36],genre_array[37],genre_array[38],
genre_array[39],genre_array[40],genre_array[41],genre_array[42],genre_array[43],genre_array[44],genre_array[45],genre_array[46],genre_array[47],genre_array[48],genre_array[49],
genre_array[50],genre_array[51],genre_array[52],genre_array[53],genre_array[54],genre_array[55],genre_array[56],genre_array[57],genre_array[58],genre_array[59],
genre_array[60],genre_array[61],genre_array[62],genre_array[63],genre_array[64],genre_array[65],genre_array[66],genre_array[67],genre_array[68],genre_array[69],
genre_array[70],genre_array[71],genre_array[72],genre_array[73],genre_array[74],genre_array[75],genre_array[76],genre_array[77],genre_array[78],genre_array[79],
genre_array[80],genre_array[81],genre_array[82],genre_array[83],genre_array[84],genre_array[85],genre_array[86],genre_array[87],genre_array[88],genre_array[89],
genre_array[90],genre_array[91],genre_array[92],genre_array[93],genre_array[94],genre_array[95],genre_array[96],genre_array[97],genre_array[98],genre_array[99],genre_array[100],genre_array[101],
genre_array[102],genre_array[103],genre_array[104],genre_array[105],genre_array[106],genre_array[107],genre_array[108],genre_array[109],genre_array[110],genre_array[111],genre_array[112],
genre_array[113],genre_array[114],genre_array[115],genre_array[116],genre_array[117],genre_array[118],genre_array[119],genre_array[120],genre_array[121],genre_array[122],genre_array[123],
genre_array[124],genre_array[125],genre_array[126],genre_array[127],genre_array[128],genre_array[129],genre_array[130],genre_array[131],genre_array[132],
artist_pmid,audio_md5,danceability,end_fade_in,energy,song_key,key_c,loudness,mode,mode_conf,release_7digitalid,song_hot,song_id,start_fade_out,tempo,time_sig,time_sig_c,track_id,track_7digitalid,year,bars_c_avg,bars_c_max,bars_c_min,bars_c_stddev,bars_c_count,bars_c_sum,bars_start_avg,bars_start_max,bars_start_min,bars_start_stddev,bars_start_count,bars_start_sum,beats_c_avg,beats_c_max,beats_c_min,beats_c_stddev,beats_c_count,beats_c_sum,beats_start_avg,beats_start_max,beats_start_min, beats_start_stddev,beats_start_count,beats_start_sum, sec_c_avg,sec_c_max,sec_c_min,sec_c_stddev,sec_c_count,sec_c_sum,sec_start_avg,sec_start_max,sec_start_min,sec_start_stddev,sec_start_count,sec_start_sum,seg_c_avg,seg_c_max,seg_c_min,seg_c_stddev,seg_c_count,seg_c_sum,seg_loud_max_avg,seg_loud_max_max,seg_loud_max_min,seg_loud_max_stddev,seg_loud_max_count,seg_loud_max_sum,seg_loud_max_time_avg,seg_loud_max_time_max,seg_loud_max_time_min,seg_loud_max_time_stddev,seg_loud_max_time_count,seg_loud_max_time_sum,seg_loud_start_avg,seg_loud_start_max,seg_loud_start_min,seg_loud_start_stddev,seg_loud_start_count,seg_loud_start_sum,seg_pitch_avg[0],seg_pitch_max[0],seg_pitch_min[0],seg_pitch_stddev[0],seg_pitch_count[0],seg_pitch_sum[0],seg_pitch_avg[1],seg_pitch_max[1],seg_pitch_min[1],seg_pitch_stddev[1],seg_pitch_count[1],seg_pitch_sum[1],seg_pitch_avg[2],seg_pitch_max[2],seg_pitch_min[2],seg_pitch_stddev[2],seg_pitch_count[2],seg_pitch_sum[2],seg_pitch_avg[3],seg_pitch_max[3],seg_pitch_min[3],seg_pitch_stddev[3],seg_pitch_count[3],seg_pitch_sum[3],seg_pitch_avg[4],seg_pitch_max[4],seg_pitch_min[4],seg_pitch_stddev[4],seg_pitch_count[4],seg_pitch_sum[4],seg_pitch_avg[5],seg_pitch_max[5],seg_pitch_min[5],seg_pitch_stddev[5],seg_pitch_count[5],seg_pitch_sum[5],seg_pitch_avg[6],seg_pitch_max[6],seg_pitch_min[6],seg_pitch_stddev[6],seg_pitch_count[6],seg_pitch_sum[6],seg_pitch_avg[7],seg_pitch_max[7],seg_pitch_min[7],seg_pitch_stddev[7],seg_pitch_count[7],seg_pitch_sum[7],seg_pitch_avg[8],seg_pitch_max[8],seg_pitch_min[8],seg_pitch_stddev[8],seg_pitch_count[8],seg_pitch_sum[8],seg_pitch_avg[9],seg_pitch_max[9],seg_pitch_min[9],seg_pitch_stddev[9],seg_pitch_count[9],seg_pitch_sum[9],seg_pitch_avg[10],seg_pitch_max[10],seg_pitch_min[10],seg_pitch_stddev[10],seg_pitch_count[10],seg_pitch_sum[10],seg_pitch_avg[11],seg_pitch_max[11],seg_pitch_min[11],
seg_pitch_stddev[11],seg_pitch_count[11],seg_pitch_sum[11],seg_start_avg,seg_start_max,seg_start_min,seg_start_stddev,
seg_start_count,seg_start_sum,seg_timbre_avg[0],seg_timbre_max[0],seg_timbre_min[0],seg_timbre_stddev[0],seg_timbre_count[0],
seg_timbre_sum[0],seg_timbre_avg[1],seg_timbre_max[1],seg_timbre_min[1],seg_timbre_stddev[1],seg_timbre_count[1],
seg_timbre_sum[1],seg_timbre_avg[2],seg_timbre_max[2],seg_timbre_min[2],seg_timbre_stddev[2],seg_timbre_count[2],
seg_timbre_sum[2],seg_timbre_avg[3],seg_timbre_max[3],seg_timbre_min[3],seg_timbre_stddev[3],seg_timbre_count[3],
seg_timbre_sum[3],seg_timbre_avg[4],seg_timbre_max[4],seg_timbre_min[4],seg_timbre_stddev[4],seg_timbre_count[4],
seg_timbre_sum[4],seg_timbre_avg[5],seg_timbre_max[5],seg_timbre_min[5],seg_timbre_stddev[5],seg_timbre_count[5],
seg_timbre_sum[5],seg_timbre_avg[6],seg_timbre_max[6],seg_timbre_min[6],seg_timbre_stddev[6],seg_timbre_count[6],
seg_timbre_sum[6],seg_timbre_avg[7],seg_timbre_max[7],seg_timbre_min[7],seg_timbre_stddev[7],seg_timbre_count[7],
seg_timbre_sum[7],seg_timbre_avg[8],seg_timbre_max[8],seg_timbre_min[8],seg_timbre_stddev[8],seg_timbre_count[8],
seg_timbre_sum[8],seg_timbre_avg[9],seg_timbre_max[9],seg_timbre_min[9],seg_timbre_stddev[9],seg_timbre_count[9],
seg_timbre_sum[9],seg_timbre_avg[10],seg_timbre_max[10],seg_timbre_min[10],seg_timbre_stddev[10],seg_timbre_count[10],
seg_timbre_sum[10],seg_timbre_avg[11],seg_timbre_max[11],seg_timbre_min[11],seg_timbre_stddev[11],seg_timbre_count[11],
seg_timbre_sum[11],tatms_c_avg,tatms_c_max,tatms_c_min,tatms_c_stddev,tatms_c_count,tatms_c_sum,tatms_start_avg,tatms_start_max,tatms_start_min,tatms_start_stddev,tatms_start_count,tatms_start_sum])
h5.close()
count=count+1;
print count;
def main():
outputFile1 = open('SongCSV.csv', 'w')
csvRowString = ""
#################################################
#if you want to prompt the user for the order of attributes in the csv,
#leave the prompt boolean set to True
#else, set 'prompt' to False and set the order of attributes in the 'else'
#clause
prompt = False
#################################################
if prompt == True:
while prompt:
prompt = False
csvAttributeString = raw_input(
"\n\nIn what order would you like the colums of the CSV file?\n"
+ "Please delineate with commas. The options are: " +
"AlbumName, AlbumID, ArtistID, ArtistLatitude, ArtistLocation, ArtistLongitude,"
+
" ArtistName, Danceability, Duration, KeySignature, KeySignatureConfidence, Tempo,"
+
" SongID, TimeSignature, TimeSignatureConfidence, Title, and Year.\n\n"
+
"For example, you may write \"Title, Tempo, Duration\"...\n\n"
+ "...or exit by typing 'exit'.\n\n")
csvAttributeList = re.split('\W+', csvAttributeString)
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
for attribute in csvAttributeList:
# print "Here is the attribute: " + attribute + " \n"
if attribute == 'AlbumID'.lower():
csvRowString += 'AlbumID'
elif attribute == 'AlbumName'.lower():
csvRowString += 'AlbumName'
elif attribute == 'ArtistID'.lower():
csvRowString += 'ArtistID'
elif attribute == 'ArtistLatitude'.lower():
csvRowString += 'ArtistLatitude'
elif attribute == 'ArtistLocation'.lower():
csvRowString += 'ArtistLocation'
elif attribute == 'ArtistLongitude'.lower():
csvRowString += 'ArtistLongitude'
elif attribute == 'ArtistName'.lower():
csvRowString += 'ArtistName'
elif attribute == 'Danceability'.lower():
csvRowString += 'Danceability'
elif attribute == 'Duration'.lower():
csvRowString += 'Duration'
elif attribute == 'KeySignature'.lower():
csvRowString += 'KeySignature'
elif attribute == 'KeySignatureConfidence'.lower():
csvRowString += 'KeySignatureConfidence'
elif attribute == 'SongID'.lower():
csvRowString += "SongID"
elif attribute == 'Tempo'.lower():
csvRowString += 'Tempo'
elif attribute == 'TimeSignature'.lower():
csvRowString += 'TimeSignature'
elif attribute == 'TimeSignatureConfidence'.lower():
csvRowString += 'TimeSignatureConfidence'
elif attribute == 'Title'.lower():
csvRowString += 'Title'
elif attribute == 'Year'.lower():
csvRowString += 'Year'
elif attribute == 'Exit'.lower():
sys.exit()
else:
prompt = True
print "=============="
print "I believe there has been an error with the input."
print "=============="
break
csvRowString += ","
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex - 1]
csvRowString += "\n"
outputFile1.write(csvRowString)
csvRowString = ""
#else, if you want to hard code the order of the csv file and not prompt
#the user,
else:
#################################################
#change the order of the csv file here
#Default is to list all available attributes (in alphabetical order)
csvRowString = (
"SongID,AlbumID,AlbumName,TrackId,ArtistID,ArtistLatitude,ArtistLocation,"
+
"ArtistLongitude,ArtistName,Danceability,Duration,KeySignature," +
"KeySignatureConfidence,Tempo,TimeSignature,TimeSignatureConfidence,"
+ "Title,Year")
#################################################
csvAttributeList = re.split('\W+', csvRowString)
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
outputFile1.write("SongNumber,")
outputFile1.write(csvRowString + "\n")
csvRowString = ""
#################################################
#Set the basedir here, the root directory from which the search
#for files stored in a (hierarchical data structure) will originate
basedir = "/home/umwangye/millonsong/MillionSongSubset/data/" # "." As the default means the current directory
ext = ".h5" #Set the extension here. H5 is the extension for HDF5 files.
#################################################
#FOR LOOP
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, '*' + ext))
for f in files:
print f
songH5File = hdf5_getters.open_h5_file_read(f)
#song = Song(str(hdf5_getters.get_song_id(songH5File)))
#testDanceability = hdf5_getters.get_danceability(songH5File)
# print type(testDanceability)
# print ("Here is the danceability: ") + str(testDanceability)
numPerH5 = hdf5_getters.get_num_songs(songH5File)
for cnt in range(numPerH5):
song = Song(str(hdf5_getters.get_song_id(songH5File, cnt)))
song.trackId = str(hdf5_getters.get_track_id(songH5File, cnt))
song.artistID = str(hdf5_getters.get_artist_id(
songH5File, cnt))
song.albumID = str(
hdf5_getters.get_release_7digitalid(songH5File, cnt))
song.albumName = str(hdf5_getters.get_release(songH5File, cnt))
song.artistLatitude = str(
hdf5_getters.get_artist_latitude(songH5File, cnt))
song.artistLocation = str(
hdf5_getters.get_artist_location(songH5File, cnt))
song.artistLongitude = str(
hdf5_getters.get_artist_longitude(songH5File, cnt))
song.artistName = str(
hdf5_getters.get_artist_name(songH5File, cnt))
song.danceability = str(
hdf5_getters.get_danceability(songH5File, cnt))
song.duration = str(hdf5_getters.get_duration(songH5File, cnt))
# song.setGenreList()
song.keySignature = str(hdf5_getters.get_key(songH5File, cnt))
song.keySignatureConfidence = str(
hdf5_getters.get_key_confidence(songH5File, cnt))
# song.lyrics = None
# song.popularity = None
song.tempo = str(hdf5_getters.get_tempo(songH5File, cnt))
song.timeSignature = str(
hdf5_getters.get_time_signature(songH5File, cnt))
song.timeSignatureConfidence = str(
hdf5_getters.get_time_signature_confidence(
songH5File, cnt))
song.title = str(hdf5_getters.get_title(songH5File, cnt))
song.year = str(hdf5_getters.get_year(songH5File, cnt))
#print song count
csvRowString += str(song.songCount) + ","
for attribute in csvAttributeList:
# print "Here is the attribute: " + attribute + " \n"
if attribute == 'AlbumID'.lower():
csvRowString += song.albumID
elif attribute == 'AlbumName'.lower():
albumName = song.albumName
albumName = albumName.replace(',', "")
csvRowString += "\"" + albumName + "\""
elif attribute == 'TrackId'.lower():
csvRowString += song.trackId
elif attribute == 'ArtistID'.lower():
csvRowString += "\"" + song.artistID + "\""
elif attribute == 'ArtistLatitude'.lower():
latitude = song.artistLatitude
if latitude == 'nan':
latitude = ''
csvRowString += latitude
elif attribute == 'ArtistLocation'.lower():
location = song.artistLocation
location = location.replace(',', '')
csvRowString += "\"" + location + "\""
elif attribute == 'ArtistLongitude'.lower():
longitude = song.artistLongitude
if longitude == 'nan':
longitude = ''
csvRowString += longitude
elif attribute == 'ArtistName'.lower():
csvRowString += "\"" + song.artistName + "\""
elif attribute == 'Danceability'.lower():
csvRowString += song.danceability
elif attribute == 'Duration'.lower():
csvRowString += song.duration
elif attribute == 'KeySignature'.lower():
csvRowString += song.keySignature
elif attribute == 'KeySignatureConfidence'.lower():
# print "key sig conf: " + song.timeSignatureConfidence
csvRowString += song.keySignatureConfidence
elif attribute == 'SongID'.lower():
csvRowString += "\"" + song.id + "\""
elif attribute == 'Tempo'.lower():
# print "Tempo: " + song.tempo
csvRowString += song.tempo
elif attribute == 'TimeSignature'.lower():
csvRowString += song.timeSignature
elif attribute == 'TimeSignatureConfidence'.lower():
# print "time sig conf: " + song.timeSignatureConfidence
csvRowString += song.timeSignatureConfidence
elif attribute == 'Title'.lower():
csvRowString += "\"" + song.title + "\""
elif attribute == 'Year'.lower():
csvRowString += song.year
else:
csvRowString += "Erm. This didn't work. Error. :( :(\n"
csvRowString += ","
#Remove the final comma from each row in the csv
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex - 1]
csvRowString += "\n"
outputFile1.write(csvRowString)
csvRowString = ""
songH5File.close()
outputFile1.close()
def main():
outputFile1 = open('SongCSV.csv', 'w')
csvRowString = ""
#################################################
#if you want to prompt the user for the order of attributes in the csv,
#leave the prompt boolean set to True
#else, set 'prompt' to False and set the order of attributes in the 'else'
#clause
prompt = False
#################################################
if prompt == True:
while prompt:
prompt = False
csvAttributeString = raw_input(
"\n\nIn what order would you like the colums of the CSV file?\n"
+ "Please delineate with commas. The options are: " +
"AlbumName, AlbumID, ArtistID, ArtistLatitude, ArtistLocation, ArtistLongitude,"
+
" ArtistName, Danceability, Duration, KeySignature, KeySignatureConfidence, Tempo,"
+
" SongID, TimeSignature, TimeSignatureConfidence, Title, Year and Hotttnesss.\n\n"
+
"For example, you may write \"Title, Tempo, Duration\"...\n\n"
+ "...or exit by typing 'exit'.\n\n")
csvAttributeList = re.split('\W+', csvAttributeString)
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
for attribute in csvAttributeList:
# print "Here is the attribute: " + attribute + " \n"
if attribute == 'AlbumID'.lower():
csvRowString += 'AlbumID'
elif attribute == 'AlbumName'.lower():
csvRowString += 'AlbumName'
elif attribute == 'ArtistID'.lower():
csvRowString += 'ArtistID'
elif attribute == 'ArtistLatitude'.lower():
csvRowString += 'ArtistLatitude'
elif attribute == 'ArtistLocation'.lower():
csvRowString += 'ArtistLocation'
elif attribute == 'ArtistLongitude'.lower():
csvRowString += 'ArtistLongitude'
elif attribute == 'ArtistName'.lower():
csvRowString += 'ArtistName'
elif attribute == 'Danceability'.lower():
csvRowString += 'Danceability'
elif attribute == 'Duration'.lower():
csvRowString += 'Duration'
elif attribute == 'KeySignature'.lower():
csvRowString += 'KeySignature'
elif attribute == 'KeySignatureConfidence'.lower():
csvRowString += 'KeySignatureConfidence'
elif attribute == 'SongID'.lower():
csvRowString += "SongID"
elif attribute == 'Tempo'.lower():
csvRowString += 'Tempo'
elif attribute == 'TimeSignature'.lower():
csvRowString += 'TimeSignature'
elif attribute == 'TimeSignatureConfidence'.lower():
csvRowString += 'TimeSignatureConfidence'
elif attribute == 'Title'.lower():
csvRowString += 'Title'
elif attribute == 'Year'.lower():
csvRowString += 'Year'
elif attribute == 'SongHotttnesss'.lower():
csvRowString += 'SongHotttnesss'
elif attribute == 'Exit'.lower():
sys.exit()
else:
prompt = True
print("==============")
print("I believe there has been an error with the input.")
print("==============")
break
csvRowString += ","
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex - 1]
csvRowString += "\n"
outputFile1.write(csvRowString)
csvRowString = ""
#else, if you want to hard code the order of the csv file and not prompt
#the user,
else:
#################################################
#change the order of the csv file here
#Default is to list all available attributes (in alphabetical order)
csvRowString = (
"SongID,AlbumID,AlbumName,ArtistFamiliarity,ArtistHotttnesss,ArtistID,"
+ "ArtistLatitude,ArtistLocation," +
"ArtistLongitude,ArtistName,BarsConfidence,BarsStart,BeatsConfidence,"
+
"BeatsStart,Danceability,Duration,EndOfFadeIn,Energy,KeySignature,"
+
"KeySignatureConfidence,Loudness,Mode,ModeConfidence,SectionsConfidence,"
+ "SectionsStart,SegmentsConfidence,SegmentsLoudnessMax," +
"SegmentsLoudnessMaxTime,SegmentsLoudnessMaxStart,SegmentsPitches,"
+ "SegmentsStart,SegmentsTimbre,SongHotttnesss,TatumsConfidence," +
"TatumsStart,Tempo,TimeSignature," +
"TimeSignatureConfidence,Title,Year")
'''
csvRowString = ("SongID,AlbumID,AlbumName,ArtistFamiliarity,"+
"ArtistHotttnesss,ArtistID,"+
"ArtistLatitude,ArtistLocation,"+
"ArtistLongitude,ArtistName,"+
"BarsConfidence,BarsStart,"+
"Danceability,Duration,EndOfFadeIn,Energy,KeySignature,"+
"KeySignatureConfidence,Loudness,Mode,ModeConfidence,"+
"SegmentsPitches,"+
"SegmentsTimbre,SongHotttnesss,"+
"Tempo,TimeSignature,"+
"TimeSignatureConfidence,Title,Year")
'''
#################################################
csvAttributeList = re.split('\W+', csvRowString)
for i, v in enumerate(csvAttributeList):
csvAttributeList[i] = csvAttributeList[i].lower()
outputFile1.write("SongNumber,")
outputFile1.write(csvRowString + "\n")
csvRowString = ""
#################################################
#Set the basedir here, the root directory from which the search
#for files stored in a (hierarchical data structure) will originate
#basedir = "./I/"
basedir = "./MillionSongSubset/data/" # "." As the default means the current directory
#basedir = "/Users/dafirebanks/Downloads/MillionSongSubset/data/" # "." As the default means the current directory
ext = "*.h5" #Set the extension here. H5 is the extension for HDF5 files.
#################################################
#FOR LOOP
for root, dirs, files in os.walk(basedir):
files = glob.glob(os.path.join(root, ext))
for f in files:
print(f)
songH5File = hdf5_getters.open_h5_file_read(f)
song = Song(str(hdf5_getters.get_song_id(songH5File)))
#testDanceability = hdf5_getters.get_danceability(songH5File)
# print type(testDanceability)
# print ("Here is the danceability: ") + str(testDanceability)
song.artistFamiliarity = str(
hdf5_getters.get_artist_familiarity(songH5File))
song.artistHotttnesss = str(
hdf5_getters.get_artist_hotttnesss(songH5File))
song.artistID = str(hdf5_getters.get_artist_id(songH5File))
song.albumID = str(hdf5_getters.get_release_7digitalid(songH5File))
song.albumName = str(hdf5_getters.get_release(songH5File))
song.artistLatitude = str(
hdf5_getters.get_artist_latitude(songH5File))
song.artistLocation = str(
hdf5_getters.get_artist_location(songH5File))
song.artistLongitude = str(
hdf5_getters.get_artist_longitude(songH5File))
song.artistName = str(hdf5_getters.get_artist_name(songH5File))
song.barsConfidence = str(
hdf5_getters.get_bars_confidence(songH5File)).replace(
",", "").replace("\n", "")
song.barsStart = str(
hdf5_getters.get_bars_start(songH5File)).replace(",",
"").replace(
"\n", "")
song.beatsConfidence = str(
hdf5_getters.get_beats_confidence(songH5File)).replace(
",", "").replace("\n", "")
song.beatsStart = str(
hdf5_getters.get_beats_start(songH5File)).replace(",",
"").replace(
"\n", "")
song.danceability = str(hdf5_getters.get_danceability(songH5File))
song.duration = str(hdf5_getters.get_duration(songH5File))
song.endOfFadeIn = str(hdf5_getters.get_end_of_fade_in(songH5File))
song.energy = str(hdf5_getters.get_energy(songH5File))
# song.setGenreList()
song.hotttnesss = str(hdf5_getters.get_song_hotttnesss(songH5File))
song.keySignature = str(hdf5_getters.get_key(songH5File))
song.keySignatureConfidence = str(
hdf5_getters.get_key_confidence(songH5File))
song.loudness = str(hdf5_getters.get_loudness(songH5File))
song.mode = str(hdf5_getters.get_mode(songH5File))
song.modeConfidence = str(
hdf5_getters.get_mode_confidence(songH5File))
# song.lyrics = None
# song.popularity = None
song.sectionsConfidence = str(
hdf5_getters.get_sections_confidence(songH5File)).replace(
",", "").replace("\n", "")
song.sectionsStart = str(
hdf5_getters.get_segments_start(songH5File)).replace(
",", "").replace("\n", "")
song.segmentsConfidence = str(
hdf5_getters.get_segments_confidence(songH5File)).replace(
",", "").replace("\n", "")
song.segmentsLoudnessMax = str(
hdf5_getters.get_segments_loudness_max(songH5File)).replace(
",", "").replace("\n", "")
song.segmentsLoudnessMaxTime = str(
hdf5_getters.get_segments_loudness_max_time(
songH5File)).replace(",", "").replace("\n", "")
song.segmentsLoudnessMaxStart = str(
hdf5_getters.get_segments_loudness_start(songH5File)).replace(
",", "").replace("\n", "")
song.segmentsPitches = str(
hdf5_getters.get_segments_pitches(songH5File)).replace(
",", "").replace("\n", "")
song.segmentsStart = str(
hdf5_getters.get_segments_start(songH5File)).replace(
",", "").replace("\n", "")
song.segmentsTimbre = str(
hdf5_getters.get_segments_timbre(songH5File)).replace(
",", "").replace("\n", "")
song.startOfFadeOut = str(
hdf5_getters.get_start_of_fade_out(songH5File))
song.tatumsConfidence = str(
hdf5_getters.get_tatums_confidence(songH5File)).replace(
",", "").replace("\n", "")
song.tatumsStart = str(
hdf5_getters.get_tatums_start(songH5File)).replace(
",", "").replace("\n", "")
song.tempo = str(hdf5_getters.get_tempo(songH5File))
song.timeSignature = str(
hdf5_getters.get_time_signature(songH5File))
song.timeSignatureConfidence = str(
hdf5_getters.get_time_signature_confidence(songH5File))
song.title = str(hdf5_getters.get_title(songH5File))
song.year = str(hdf5_getters.get_year(songH5File))
#print song count
csvRowString += str(song.songCount) + ","
for attribute in csvAttributeList:
# print "Here is the attribute: " + attribute + " \n"
if attribute == 'AlbumID'.lower():
csvRowString += song.albumID
elif attribute == 'AlbumName'.lower():
albumName = song.albumName
albumName = albumName.replace(',', "")
csvRowString += "\"" + albumName + "\""
elif attribute == 'ArtistFamiliarity'.lower():
csvRowString += song.artistFamiliarity
elif attribute == 'ArtistHotttnesss'.lower():
csvRowString += song.artistHotttnesss
elif attribute == 'ArtistID'.lower():
csvRowString += "\"" + song.artistID + "\""
elif attribute == 'ArtistLatitude'.lower():
latitude = song.artistLatitude
if latitude == 'nan':
latitude = ''
csvRowString += latitude
elif attribute == 'ArtistLocation'.lower():
location = song.artistLocation
location = location.replace(',', '')
csvRowString += "\"" + location + "\""
elif attribute == 'ArtistLongitude'.lower():
longitude = song.artistLongitude
if longitude == 'nan':
longitude = ''
csvRowString += longitude
elif attribute == 'ArtistName'.lower():
csvRowString += "\"" + song.artistName + "\""
elif attribute == 'BarsConfidence'.lower():
csvRowString += song.barsConfidence
elif attribute == 'BarsStart'.lower():
csvRowString += song.barsStart
elif attribute == 'BeatsConfidence'.lower():
csvRowString += song.beatsConfidence
elif attribute == 'BeatsStart'.lower():
csvRowString += song.beatsStart
elif attribute == 'Danceability'.lower():
csvRowString += song.danceability
elif attribute == 'Duration'.lower():
csvRowString += song.duration
elif attribute == 'EndOfFadeIn'.lower():
csvRowString += song.endOfFadeIn
elif attribute == 'Energy'.lower():
csvRowString += song.energy
elif attribute == 'KeySignature'.lower():
csvRowString += song.keySignature
elif attribute == 'KeySignatureConfidence'.lower():
# print "key sig conf: " + song.timeSignatureConfidence
csvRowString += song.keySignatureConfidence
elif attribute == 'Loudness'.lower():
csvRowString += song.loudness
elif attribute == 'Mode'.lower():
csvRowString += song.mode
elif attribute == 'ModeConfidence'.lower():
csvRowString += song.modeConfidence
elif attribute == 'SectionsConfidence'.lower():
csvRowString += song.sectionsConfidence
elif attribute == 'SectionsStart'.lower():
csvRowString += song.sectionsStart
elif attribute == 'SegmentsConfidence'.lower():
csvRowString += song.segmentsConfidence
elif attribute == 'SegmentsLoudnessMax'.lower():
csvRowString += song.segmentsLoudnessMax
elif attribute == 'SegmentsLoudnessMaxTime'.lower():
csvRowString += song.segmentsLoudnessMaxTime
elif attribute == 'SegmentsLoudnessMaxStart'.lower():
csvRowString += song.segmentsLoudnessMaxStart
elif attribute == 'SegmentsPitches'.lower():
csvRowString += song.segmentsPitches
elif attribute == 'SegmentsStart'.lower():
csvRowString += song.segmentsStart
elif attribute == 'SegmentsTimbre'.lower():
csvRowString += song.segmentsTimbre
elif attribute == 'SongHotttnesss'.lower():
csvRowString += song.hotttnesss
elif attribute == 'SongID'.lower():
csvRowString += "\"" + song.id + "\""
elif attribute == 'StartOfFadeOut'.lower():
csvRowString += song.startOfFadeOut
elif attribute == 'TatumsConfidence'.lower():
csvRowString += song.tatumsConfidence
elif attribute == 'TatumsStart'.lower():
csvRowString += song.tatumsStart
elif attribute == 'Tempo'.lower():
# print "Tempo: " + song.tempo
csvRowString += song.tempo
elif attribute == 'TimeSignature'.lower():
csvRowString += song.timeSignature
elif attribute == 'TimeSignatureConfidence'.lower():
# print "time sig conf: " + song.timeSignatureConfidence
csvRowString += song.timeSignatureConfidence
elif attribute == 'Title'.lower():
csvRowString += "\"" + song.title + "\""
elif attribute == 'Year'.lower():
csvRowString += song.year
else:
csvRowString += "Erm. This didn't work. Error. :( :(\n"
csvRowString += ","
#Remove the final comma from each row in the csv
lastIndex = len(csvRowString)
csvRowString = csvRowString[0:lastIndex - 1]
csvRowString += "\n"
outputFile1.write(csvRowString)
csvRowString = ""
songH5File.close()
outputFile1.close()