@coroutine
def broadcast(targets):
while True:
message = yield
for it in targets:
try:
it.send(message)
except StopIteration:
targets.remove(it)
continue
if message == STOP:
break
# songs_to_print = set()
for dataset_path in ['music']:
dp = HardDriveProvider(dataset_path, True)
# print(len(dp.get_all()))
# print(sum(1 if song['id3'] else 0
# for song in map(dp.get_by_id, dp.get_all())))
# for song in dp.get_all():
# song_data = dp.get_by_id(song)
# if song_data['id3']:
# pass
# exit()
all_parsers = broadcast([id3_v2_update(),
last_fm_update(),
echo_nest_update()
])
# , ])
# , librosa_update()])
for i, song in enumerate(dp.get_all(), 1):
from dataproviders import HardDriveProvider
def id3_tabbed(j):
return '\t'.join(())
def echonest_tabbed(j):
return '\t'.join(())
def lastfm_tabbed(j):
return '\t'.join(())
NAME = 'music'
data = HardDriveProvider(NAME)
with open(NAME + '.tsv', 'w') as w:
for d in data.get_all():
w.write('{}\n'.format('\t'.join((id3_tabbed(d),
lastfm_tabbed(d),
echonest_tabbed(d)))))
import matplotlib.pyplot as plt
from collections import defaultdict
from dataproviders import HardDriveProvider
from scipy import cluster
dp = HardDriveProvider('music')
artist_tags = defaultdict(lambda: [])
song_tags = defaultdict(lambda: [])
artist_plays = defaultdict(lambda: 0)
track_plays = defaultdict(lambda: 0)
track_listeners = defaultdict(lambda: 0)
album_listeners = defaultdict(lambda: 0)
album_plays = defaultdict(lambda: 0)
song_to_album = defaultdict(lambda: '')
for song_path in dp.get_all():
song = dp.get_by_id(song_path)
song_id3 = song['id3']
if song_id3:
artist_title = song_id3['artist']
s_tags = [tag_str.strip() for tag_str in
song_id3['genre'].split(';')]
song_tags[song_path].extend(s_tags)
artist_tags[artist_title].extend(s_tags)
song_fm = song['lastfm']
if song_fm:
album_title = song_fm['album']
if album_title:
song_to_album[song_path] = album_title
album_listeners[album_title] += int(song_fm['albumlisteners'])
def broadcast(targets):
while True:
message = yield
for it in targets:
try:
it.send(message)
except StopIteration:
targets.remove(it)
continue
if message == STOP:
break
# songs_to_print = set()
for dataset_path in ['VkDataset #5']:
dp = HardDriveProvider(dataset_path)
# print(len(dp.get_all()))
# print(sum(1 if song['id3'] else 0
# for song in map(dp.get_by_id, dp.get_all())))
# for song in dp.get_all():
# song_data = dp.get_by_id(song)
# if song_data['id3']:
# pass
# exit()
all_parsers = broadcast(
[id3_v2_update(),
last_fm_update(),
echo_nest_update()])
# , ])
# , librosa_update()])
for i, song in enumerate(dp.get_all(), 1):
import matplotlib.pyplot as plt
from collections import defaultdict, Counter
from dataproviders import HardDriveProvider
from gensim import corpora, models
from itertools import chain
from scipy import cluster
dp = HardDriveProvider('music')
artist_tags = defaultdict(lambda: Counter())
song_tags = defaultdict(lambda: Counter())
artist_plays = defaultdict(lambda: 0)
track_plays = defaultdict(lambda: 0)
track_listeners = defaultdict(lambda: 0)
album_listeners = defaultdict(lambda: 0)
album_plays = defaultdict(lambda: 0)
song_to_album = defaultdict(lambda: '')
for song_path in dp.get_all():
song = dp.get_by_id(song_path)
song_id3 = song['id3']
if song_id3:
artist_title = song_id3['artist']
s_tags = [tag_str.strip() for tag_str in
song_id3['genre'].split(';')]
song_tags[song_path].update(s_tags)
artist_tags[artist_title].update(s_tags)
song_fm = song['lastfm']
if song_fm:
album_title = song_fm['album']
