def main():
if len(sys.argv) > 1:
in_file = sys.argv[1]
else:
in_file = "../input/input.txt"
token = sptfy.authSpotipy()
for f in glob.glob("../output/*.txt"):
os.remove(f)
config = hlpr.loadFile("../config", "config.csv", True)
token = sptfy.authSpotipy()
for line in fileinput.input(in_file):
track_id = line.split("/")[-1].strip()
track_info = sptfy.pullSpotifyTrack(track_id, token=token)
artist_id = track_info['spotify_artist_id']
artist_name = track_info['artist']
track_name = track_info['title']
secondary_artist = track_info['secondary_artist']
hlpr.sortGenres(artist_name,
artist_id,
track_name,
track_id,
secondary_artist,
token,
makePlaylists=True)
def main():
if len(sys.argv) > 1:
in_file = sys.argv[1]
else:
in_file = "../input/input.txt"
track_list = []
local_tracks = []
config = loadFile("../config", "config.csv", True)
token = sptfy.authSpotipy()
for line in fileinput.input(in_file):
if "local" in line:
local_tracks.append(line)
continue
track_id = sptfy.stripSpotifyLink(line)
track_data = sptfy.pullSpotifyTrack(track_id, token = token)
track_list.append(track_data)
sorted_tracks = sorted(
track_list,
key = lambda x: float(x['popularity']),
reverse = True
)
print "\n"
for item in sorted_tracks:
print str(item['popularity']) + " :: " + item['artist'] + \
" - " + item['title']
print "\n"
for item in sorted_tracks:
print "spotify:track:{}".format(item['spotify_id'].strip())
for item in local_tracks:
print item
print "\n"
def main():
if len(sys.argv) > 1:
in_file = sys.argv[1]
else:
in_file = "../input/input.txt"
token = sptfy.authSpotipy()
for f in glob.glob("../output/*.txt"):
os.remove(f)
config = hlpr.loadFile("../config", "config.csv", True)
token = sptfy.authSpotipy()
for line in fileinput.input(in_file):
track_id = line.split("/")[-1].strip()
track_info = sptfy.pullSpotifyTrack(track_id, token = token)
artist_id = track_info['spotify_artist_id']
artist_name = track_info['artist']
track_name = track_info['title']
secondary_artist = track_info['secondary_artist']
hlpr.sortGenres(artist_name, artist_id, track_name, track_id,
secondary_artist, token, makePlaylists = True)
def main():
# ## load tracks in playlist
fives = loadFile("input", "fives.txt")
fours = loadFile("input", "fours.txt")
threes = loadFile("input", "threes.txt")
twos = loadFile("input", "twos.txt")
ones = loadFile("input", "ones.txt")
## load database of song ratings
db = loadFile("../Databases", "song_ratings_db.csv")
album_ratings = loadFile("../Databases", "album_ratings_db.csv")
config = loadFile("../config", "config.csv", True)
token = sptfy.authSpotipy()
rating = 0
for ls in [ones, twos, threes, fours, fives]:
## playlists are looped in this order such that if a song is in multiple lists it's rating will end up being the highest one
rating = rating + 1
for song in ls:
track_id, spotify_uri = sptfy.getSpotifyTrackIDs(song)
if not lookupSongBySpotifyID(track_id, db):
track = sptfy.pullSpotifyTrack(track_id, token=token)
db = db.append([{
'spotify_id': track_id,
'artist': track['artist'],
'album': track['album'],
'spotify_album_id': track['spotify_album_id'],
'song': track['title'],
'rating': rating
}])
db = db.sort(['artist', 'album', 'rating'])
saveDataFrame(db, "../Databases", "song_ratings_db.csv")
for album in pd.unique(zip(db.artist, db.album)):
artist = album[0]
album = album[1]
if not lookupAlbumBySpotifyID(album, album_ratings):
album_id = pd.unique(db[db.artist == artist][db.album == album]
['spotify_album_id'])[0]
album_data = sptfy.pullSpotifyAlbum(album_id, token=token)
ratings = db[db.artist == artist][db.album ==
album]['rating'].tolist()
if len(ratings) < 3:
## don't make album ratings for singles
continue
score = 0
countNot3 = len(np.where(r != 3))
countMoreThan2 = len(np.where(r > 2))
countMoreThan3 = len(np.where(r > 3))
for r in ratings:
if r == 5:
score = score + 100 * 1.0
scoreNot3 = score + 100 * 1.0
elif r == 4:
score = score + 80 * 1.2
scoreNot3 = score + 80 * 1.2
elif r == 3:
score = score + 60 * 1.0
elif r == 2:
score = score + 40 * 1.2
scoreNot3 = score + 40 * 1.2
elif r == 1:
score = score + 20 * 1.0
scoreNot3 = score + 20 * 1.0
std = np.std(ratings)
if std == 0.0:
std = 0.25
if countNot3 == 0:
adjMean = 3
else:
adjMean = scoreNot3 / countNot3
prop4or5 = countMoreThan3 / len(r)
adj1 = (adjMean - 3) * prop4or5
adj2 = adj1 + countMoreThan2 * 0.03
score = np.mean(ratings) + adj2
if prop4or5 == 0:
adjSD = std * 0.05
else:
adjSD = std * prop4or5 / len(r)
score = score - adjSD
## min possible score: (mean of 1-star)
min1 = 1.0
## max possible score: Radiohead "OK Computer"
max1 = 5.662521
min2 = -1.0
max2 = -0.125
scaledScore = (score - min1) / (max1 - min1)
## transform (curves the linear scores to inflate higher scores and reduce lower)
transformedScore = -1 * (8 ^ (-1 * scaledScore))
scaledScore = (transformedScore - min2) / (max2 - min2)
album_score = (round(scaledScore * 1000)) / 1.0
if album_score > 1000:
album_score = 1000
if album_score >= 965:
album_rating = 5.0
elif album_score >= 890:
album_rating = 4.5
elif album_score >= 750:
album_rating = 4.0
elif album_score >= 690:
album_rating = 3.5
elif album_score >= 625:
album_rating = 3.0
elif album_score >= 420:
album_rating = 2.5
elif album_score > 325:
album_rating = 2.0
elif album_score > 235:
album_rating = 1.5
elif album_score >= 100:
album_rating = 1.0
elif album_score < 100:
album_rating = 0.5
album_ratings = album_ratings.append([{
'spotify_album_id': album_id,
'artist': artist,
'album': album,
'year': album_data['year'],
'album_rating': album_rating,
'album_score': album_score
}])
saveDataFrame(album_ratings, "../Databases", "album_ratings_db.csv")
def main(ags, explicit=False):
if args.ultimatechart is not None:
with open(args.ultimatechart, 'r') as f:
ultimatechart = f.readlines()
else:
ultimatechart = None
if args.billboardchart is not None:
with open(args.billboardchart, 'r') as f:
billboardchart = [line.rstrip() for line in f]
else:
billboardchart = None
cleans = []
explicits = []
local_tracks = []
config = loadFile("../config", "config.csv", True)
token = sptfy.authSpotipy()
cleans, local_tracks = sptfy.pullSpotifyTracks('../input',
'cleans.txt',
token=token)
explicits, local_tracks = sptfy.pullSpotifyTracks(
'../input',
'explicits.txt',
tracks=explicits,
local_tracks=local_tracks,
token=token)
if explicit:
# sort explicit list
sorted_tracks = sorted(explicits,
key=lambda x: float(x['popularity']),
reverse=True)
else:
# sort explicit list
sorted_tracks = sorted(explicits,
key=lambda x: float(x['popularity']),
reverse=True)
# now replace explicits with cleans
for e in explicits: # replace explicit track href by the clean version's href
for c in cleans:
if e['artist'] in c['artist'] and e['title'][:3] in c[
'title'][:3]: # compare artist name and first 4 characters of track name
e['spotify_id'] = c['spotify_id']
print "\n"
for item in sorted_tracks:
print str(item['popularity']
) + " :: " + item['artist'] + " - " + item['title']
print "\n"
for item in sorted_tracks:
print "spotify:track:{}".format(item['spotify_id'].strip())
for item in local_tracks:
print item
print "\n"
if ultimatechart is not None or billboardchart is not None:
print "Look into keeping the following songs in Airplay this week...\n"
if ultimatechart is not None:
nums = ('01', '02', '03', '04', '05', '06', '07', '08', '09') + tuple(
str(x) for x in range(10, 101))
uc = [item for item in ultimatechart if item.startswith(nums)]
for item in sorted_tracks:
if item['popularity'] < 75:
artist = item['artist']
title = item['title']
artist = re.sub("&", "and",
re.sub(r'([^\s\w]|_)+', '', artist).lower())
title = re.sub("&", "and",
re.sub(r'([^\s\w]|_)+', '', title).lower())
for u in uc:
x = re.sub(r'([^\s\w]|_)+', '', re.sub("&", "and",
u)).lower()
if artist + " " in x:
artist_match = True
else:
artist_match = False
if title + " by" in x:
title_match = True
else:
title_match = False
xs = [i for i in x.split(" ")]
if title_match or (artist_match and any(
[True for t in title.split(" ") if t in xs])):
print str(
item['popularity']
) + " :: " + item['artist'] + " - " + item['title']
break
else:
print "\nIf you want to compare to Ultimate Chart, please provide a txt version as an arg to --ultimatechart"
if billboardchart is not None:
for item in sorted_tracks:
if item['popularity'] < 75:
artist = item['artist']
title = item['title']
artist = re.sub("&", "and",
re.sub(r'([^\s\w]|_)+', '', artist).lower())
title = re.sub("&", "and",
re.sub(r'([^\s\w]|_)+', '', title).lower())
for u in billboardchart:
x = re.sub(r'([^\s\w]|_)+', '', re.sub("&", "and",
u)).lower()
if artist in x:
artist_match = True
else:
artist_match = False
if title in x:
title_match = True
else:
title_match = False
xs = [i for i in x.split(" ")]
if title_match or (artist_match and any(
[True for t in title.split(" ") if t in xs])):
print str(
item['popularity']
) + " :: " + item['artist'] + " - " + item['title']
break
else:
print "\nIf you want to compare to Billboard Chart, please provide a txt version as an arg to --billboardchart\n"
print "\n"
def main():
track_list = hlpr.loadFile("../input", "input.txt")
desired_len = int(
input("\nHow many tracks would you like the playlist to be? "))
num_to_drop = len(track_list) - desired_len
sort_col1 = input("\nEnter one of the following features to sort on: \
\nacousticness\ndanceability\nenergy\nvalence\npopularity \
\nrelease_date\nyear\n\n")
ascending1 = int(
input("\nChoose sort order for this feature: \
\n(0) descending\n(1) ascending\n\n"))
sort_col2 = input(
"\nEnter another feature to sort on (blank to sort on one feature only): \
\nacousticness\ndanceability\nenergy\nvalence\npopularity \
\nrelease_date\nyear\n\n")
if sort_col2 != '':
ascending2 = int(
input("\nChoose sort order for this feature: \
\n(0) descending\n(1) ascending\n\n"))
else:
sort_col2 = None
ascending2 = 1
config = hlpr.loadFile("../config", "config.csv", True)
token = sptfy.authSpotipy()
if "popularity" in [sort_col1, sort_col2]:
db, unfound_tracks = sptfy.pullSpotifyTracks('../input',
'input.txt',
token=token)
df = pd.DataFrame.from_dict(db)
db, unfound_tracks = hlpr.processInput(input_playlist="input.txt")
db = db.merge(df[["spotify_id", "popularity"]], on="spotify_id")
else:
## get subset of db based on input.txt
db, unfound_tracks = hlpr.processInput(input_playlist="input.txt")
if sort_col1 not in db.columns:
print("\nSort column '{}' not in database!").format(sort_col1)
sys.exit()
if sort_col2 is not None and sort_col2 not in db.columns:
print("\nSort column '{}' not in database!").format(sort_col2)
sys.exit()
sorted_db1 = db.sort_values(by=sort_col1, ascending=ascending1)
if sort_col2 is not None:
sorted_db2 = db.sort_values(by=sort_col2, ascending=ascending2)
lst1 = sorted_db1.index.values
lst2 = sorted_db2.index.values
i = len(track_list) - num_to_drop
remove = np.intersect1d(lst1[i:], lst2[i:])
while len(remove) < num_to_drop:
i -= 1
remove = np.intersect1d(lst1[i:], lst2[i:])
keep = [i for i in db.index if i not in remove]
remove_db = db.loc[remove, ]
keep_tracks = [
"spotify:track:{}".format(x[1]['spotify_id']).rstrip() \
if len(x[1]['spotify_id']) < 36 else x[1]['spotify_id'].rstrip() \
for x in db.loc[keep, ].iterrows()
]
remove_db = remove_db.sort_values(by=[sort_col1, sort_col2],
ascending=[ascending1, ascending2])
remove_tracks = [
"spotify:track:{}".format(x[1]['spotify_id']).rstrip() \
if len(x[1]['spotify_id']) < 36 else x[1]['spotify_id'].rstrip() \
for x in remove_db.iterrows()
]
print("\nThese songs will be removed:\n")
print(remove_db[['artist', 'title', sort_col1, sort_col2]])
print("")
print("\n".join(remove_tracks))
print("\n\nThese songs remain:\n")
print("\n".join(keep_tracks))
if len(unfound_tracks) > 0:
print("\nAdd these local tracks wherever you want in the playlist:\n")
print("\n".join(unfound_tracks))
def main():
## which feature to sort on
sort_col1 = raw_input(
"\nEnter one of the following features to sort on: \
\ntitle\nartist\nalbum\nduration\ntempo\ntime_signature\nkey\nmode \
\nloudness\nacousticness\ndanceability\nenergy\ninstrumentalness \
\nliveness\nspeechiness\nvalence\npopularity\nrelease_date\nyear\n\n"
)
## ascending or descending (1 = ascending, 0 = descending)
ascending1 = int(raw_input(
"\nChoose sort order for this feature: \
\n(0) descending\n(1) ascending\n\n"
))
while ascending1 != 0 and ascending1 != 1:
ascending1 = int(raw_input(
"\nEnter either 0 or 1: \
\n(0) descending\n(1) ascending\n\n"
))
if sort_col1 in ["album", "artist", "tempo", "time_signature", "key",
"mode", "popularity", "release_date", "year"]:
sort_col2 = raw_input(
"\nIn case of ties what second feature would you like to sort on: \
\ntitle\nartist\nalbum\nduration\ntempo\ntime_signature\nkey\nmode \
\nloudness\nacousticness\ndanceability\nenergy\ninstrumentalness \
\nliveness\nspeechiness\nvalence\npopularity\nrelease_date\nyear\n\n"
)
## ascending or descending (1 = ascending, 0 = descending)
ascending2 = int(raw_input(
"\nChoose sort order for this feature: \
\n(0) descending\n(1) ascending\n\n"
))
while ascending2 != 0 and ascending2 != 1:
ascending2 = int(raw_input(
"\nEnter either 0 or 1: \
\n(0) descending\n(1) ascending\n\n"
))
else:
sort_col2 = None
ascending2 = 1
print("")
config = hlpr.loadFile("../config", "config.csv", True)
token = sptfy.authSpotipy()
if "popularity" in [sort_col1, sort_col2]:
db, unfound_tracks = sptfy.pullSpotifyTracks('../input',
'input.txt',
token = token)
df = pd.DataFrame.from_dict(db)
db, unfound_tracks = hlpr.processInput(input_playlist = "input.txt")
db = db.merge(df[["spotify_id", "popularity"]], on = "spotify_id")
else:
## get subset of db based on input.txt
db, unfound_tracks = hlpr.processInput(input_playlist = "input.txt")
if sort_col1 not in db.columns:
print("\nSort column '{}' not in database!").format(sort_col1)
sys.exit()
if sort_col2 is not None and sort_col2 not in db.columns:
print("\nSort column '{}' not in database!").format(sort_col2)
sys.exit()
if sort_col2 is None:
sorted_db = db.sort_values(by = sort_col1, ascending = ascending1)
else:
sorted_db = db.sort_values(by = [sort_col1, sort_col2],
ascending = [ascending1, ascending2])
sorted_tracks = [
"spotify:track:{}".format(x[1]['spotify_id']).rstrip() \
if len(x[1]['spotify_id']) < 36 else x[1]['spotify_id'].rstrip() \
for x in sorted_db.iterrows()
]
print("\n")
print(sorted_db[["artist", "title"]])
hlpr.writeTextFile(sorted_tracks, "../output", "sorted_playlist.txt")
print("\n")
print("\n".join(sorted_tracks))
if len(unfound_tracks) > 0:
print("\nAdd these local tracks wherever you want in the playlist:\n")
print("\n".join(unfound_tracks))
def processInput(terms = False, genres = False, input_playlist = None):
## set spotify auth
config = loadFile("../config", "config.csv", True)
try:
token = sptfy.authSpotipy()
except:
token = None
## load tracks in playlist
if input_playlist is not None:
in_tracks = loadFile("../input", input_playlist)
else:
in_tracks = loadFile("../input", "input.txt")
## load database of metadata
song_db = loadFile("../Databases", "song_db.csv")
shutil.copyfile("../Databases/song_db.csv",
"../Databases/_Backup/song_db.csv")
artist_db = loadFile("../Databases", "artist_db.csv")
shutil.copyfile("../Databases/artist_db.csv",
"../Databases/_Backup/artist_db.csv")
unfound_tracks = []
for track in in_tracks:
if 'local' in track:
artist, title, album = sptfy.formatLocalTrack(track)
song_uri = sptfy.searchSpotifyTrack(
artist,
title,
album,
first = True,
token = token
)
if song_uri is not None:
if not dbm.lookupSongBySpotifyID(song_uri, song_db):
song = sptfy.getAudioFeatures(song_uri, token = token)
if song is not None:
song_db = song_db.append(song, ignore_index = True)
else:
print "{} not found.".format(track)
unfound_tracks.append(track)
else:
if not dbm.lookupSongBySpotifyID(track, song_db):
song = sptfy.getAudioFeatures(track, token = token)
if song is not None:
song_db = song_db.append(song, ignore_index = True)
else:
print "{} not found.".format(track)
unfound_tracks.append(track)
song_db = song_db.drop_duplicates('spotify_id')
dbm.saveDataFrame(song_db, "../Databases", "song_db.csv")
## subset song database on tracks in playlist
db_subset = dbm.subsetDataFrame(song_db, in_tracks)
if genres:
## build dict of artists with genres
artist_db = dbm.buildArtistDataFrame(
db_subset,
artist_db,
token = token
)
dbm.saveDataFrame(artist_db, "../Databases", "artist_db.csv")
## add artist genres to songs subset db
## and because capitalization might be different
## drop 'artist' from one of the dataframes before merging
artist_db = artist_db.drop('artist', 1)
db_out = dbm.addArtistDataToSongs(db_subset, artist_db)
else:
db_out = db_subset
return db_out, unfound_tracks
def processInput(terms=False, genres=False, input_playlist=None):
## set spotify auth
config = loadFile("../config", "config.csv", True)
try:
token = sptfy.authSpotipy()
except:
token = None
## load tracks in playlist
if input_playlist is not None:
in_tracks = loadFile("../input", input_playlist)
else:
in_tracks = loadFile("../input", "input.txt")
## load database of metadata
song_db = loadFile("../Databases", "song_db.csv")
shutil.copyfile("../Databases/song_db.csv",
"../Databases/_Backup/song_db.csv")
artist_db = loadFile("../Databases", "artist_db.csv")
shutil.copyfile("../Databases/artist_db.csv",
"../Databases/_Backup/artist_db.csv")
unfound_tracks = []
for track in in_tracks:
if 'local' in track:
artist, title, album = sptfy.formatLocalTrack(track)
song_uri = sptfy.searchSpotifyTrack(artist,
title,
album,
first=True,
token=token)
if song_uri is not None:
if not dbm.lookupSongBySpotifyID(song_uri, song_db):
song = sptfy.getAudioFeatures(song_uri, token=token)
if song is not None:
song_db = song_db.append(song, ignore_index=True)
else:
print("{} not found.".format(track))
unfound_tracks.append(track)
else:
if not dbm.lookupSongBySpotifyID(track, song_db):
song = sptfy.getAudioFeatures(track, token=token)
if song is not None:
song_db = song_db.append(song, ignore_index=True)
else:
print("{} not found.".format(track))
unfound_tracks.append(track)
song_db = song_db.drop_duplicates('spotify_id')
dbm.saveDataFrame(song_db, "../Databases", "song_db.csv")
## subset song database on tracks in playlist
db_subset = dbm.subsetDataFrame(song_db, in_tracks)
if genres:
## build dict of artists with genres
artist_db = dbm.buildArtistDataFrame(db_subset, artist_db, token=token)
dbm.saveDataFrame(artist_db, "../Databases", "artist_db.csv")
## add artist genres to songs subset db
## and because capitalization might be different
## drop 'artist' from one of the dataframes before merging
artist_db = artist_db.drop('artist', 1)
db_out = dbm.addArtistDataToSongs(db_subset, artist_db)
else:
db_out = db_subset
return db_out, unfound_tracks
def main():
# ## load tracks in playlist
fives = loadFile("input", "fives.txt")
fours = loadFile("input", "fours.txt")
threes = loadFile("input", "threes.txt")
twos = loadFile("input", "twos.txt")
ones = loadFile("input", "ones.txt")
## load database of song ratings
db = loadFile("../Databases", "song_ratings_db.csv")
album_ratings = loadFile("../Databases", "album_ratings_db.csv")
config = loadFile("../config", "config.csv", True)
token = sptfy.authSpotipy()
rating = 0
for ls in [ones, twos, threes, fours, fives]:
## playlists are looped in this order such that if a song is in multiple lists it's rating will end up being the highest one
rating = rating + 1
for song in ls:
track_id, spotify_uri = sptfy.getSpotifyTrackIDs(song)
if not lookupSongBySpotifyID(track_id, db):
track = sptfy.pullSpotifyTrack(track_id, token = token)
db = db.append([{'spotify_id' : track_id, 'artist' : track['artist'], 'album' : track['album'], 'spotify_album_id' : track['spotify_album_id'], 'song' : track['title'], 'rating' : rating}])
db = db.sort(['artist', 'album', 'rating'])
saveDataFrame(db, "../Databases", "song_ratings_db.csv")
for album in pd.unique(zip(db.artist, db.album)):
artist = album[0]
album = album[1]
if not lookupAlbumBySpotifyID(album, album_ratings):
album_id = pd.unique(db[db.artist == artist][db.album == album]['spotify_album_id'])[0]
album_data = sptfy.pullSpotifyAlbum(album_id, token = token)
ratings = db[db.artist == artist][db.album == album]['rating'].tolist()
if len(ratings) < 3:
## don't make album ratings for singles
continue
score = 0
countNot3 = len(np.where(r != 3))
countMoreThan2 = len(np.where(r > 2))
countMoreThan3 = len(np.where(r > 3))
for r in ratings:
if r == 5:
score = score + 100 * 1.0
scoreNot3 = score + 100 * 1.0
elif r == 4:
score = score + 80 * 1.2
scoreNot3 = score + 80 * 1.2
elif r == 3:
score = score + 60 * 1.0
elif r == 2:
score = score + 40 * 1.2
scoreNot3 = score + 40 * 1.2
elif r == 1:
score = score + 20 * 1.0
scoreNot3 = score + 20 * 1.0
std = np.std(ratings)
if std == 0.0:
std = 0.25
if countNot3 == 0:
adjMean = 3
else:
adjMean = scoreNot3 / countNot3
prop4or5 = countMoreThan3 / len(r)
adj1 = (adjMean - 3) * prop4or5
adj2 = adj1 + countMoreThan2 * 0.03
score = np.mean(ratings) + adj2
if prop4or5 == 0:
adjSD = std * 0.05
else:
adjSD = std * prop4or5 / len(r)
score = score - adjSD
## min possible score: (mean of 1-star)
min1 = 1.0
## max possible score: Radiohead "OK Computer"
max1 = 5.662521
min2 = -1.0
max2 = -0.125
scaledScore = (score - min1) / (max1 - min1)
## transform (curves the linear scores to inflate higher scores and reduce lower)
transformedScore = -1 * (8 ^ (-1 * scaledScore))
scaledScore = (transformedScore - min2) / (max2 - min2)
album_score = (round (scaledScore * 1000)) / 1.0
if album_score > 1000:
album_score = 1000
if album_score >= 965:
album_rating = 5.0
elif album_score >= 890:
album_rating = 4.5
elif album_score >= 750:
album_rating = 4.0
elif album_score >= 690:
album_rating = 3.5
elif album_score >= 625:
album_rating = 3.0
elif album_score >= 420:
album_rating = 2.5
elif album_score > 325:
album_rating = 2.0
elif album_score > 235:
album_rating = 1.5
elif album_score >= 100:
album_rating = 1.0
elif album_score < 100:
album_rating = 0.5
album_ratings = album_ratings.append([{'spotify_album_id' : album_id, 'artist' : artist, 'album' : album, 'year' : album_data['year'], 'album_rating' : album_rating, 'album_score' : album_score}])
saveDataFrame(album_ratings, "../Databases", "album_ratings_db.csv")
def main(ags, explicit=False):
if args.billboardchart is not None:
with open(args.billboardchart, 'r') as f:
billboardchart = [line.rstrip() for line in f]
else:
billboardchart = None
cleans = []
explicits = []
local_tracks = []
config = loadFile("../config", "config.csv", True)
try:
token = sptfy.authSpotipy()
except:
token = None
sptpy = sptfy.getSpotifyCred()
cleans, local_tracks = sptfy.pullSpotifyTracks('../input',
'cleans.txt',
sptpy=sptpy)
explicits, local_tracks = sptfy.pullSpotifyTracks(
'../input',
'explicits.txt',
tracks=explicits,
local_tracks=local_tracks,
sptpy=sptpy)
if explicit:
# sort explicit list
sorted_tracks = sorted(explicits,
key=lambda x: float(x['popularity']),
reverse=True)
else:
# sort explicit list
sorted_tracks = sorted(explicits,
key=lambda x: float(x['popularity']),
reverse=True)
# now replace explicits with cleans
for e in explicits: # replace explicit track href by the clean version's href
for c in cleans:
if e['artist'] in c['artist'] and e['title'][:3] in c[
'title'][:3]: # compare artist name and first 4 characters of track name
e['spotify_id'] = c['spotify_id']
print("\n")
for item in sorted_tracks:
print(
str(item['popularity']) + " :: " + item['artist'] + " - " +
item['title'])
print("\n")
for item in sorted_tracks:
print("spotify:track:{}".format(item['spotify_id'].strip()))
for item in local_tracks:
print(item)
print("\n")
if billboardchart is not None:
print(
"\nLook into keeping the following songs in Airplay this week...\n"
)
for item in sorted_tracks:
if item['popularity'] < 75:
artist = item['artist']
title = item['title']
artist = re.sub("&", "and",
re.sub(r'([^\s\w]|_)+', '', artist).lower())
title = re.sub("&", "and",
re.sub(r'([^\s\w]|_)+', '', title).lower())
for u in billboardchart:
x = re.sub(r'([^\s\w]|_)+', '', re.sub("&", "and",
u)).lower()
if artist in x:
artist_match = True
else:
artist_match = False
if title in x:
title_match = True
else:
title_match = False
xs = [i for i in x.split(" ")]
if title_match or (artist_match and any(
[True for t in title.split(" ") if t in xs])):
print(
str(item['popularity']) + " :: " + item['artist'] +
" - " + item['title'])
break
else:
print(
"\nIf you want to compare to Billboard Chart, please provide a txt version as an arg to --billboardchart\n"
)
print("\n")