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import requestsimport mathimport pprint
# parse_library {{{ #
def parse_library(headers, tracks, library_stats): """Scans user's library for certain number of tracks to update library_stats with.
:headers: For API call. :tracks: Number of tracks to get from user's library. :library_stats: Dictionary containing the data mined from user's library
:returns: None
"""
# TODO: implement importing entire library with 0 as tracks param # number of tracks to get with each call limit = 5 # keeps track of point to get songs from offset = 0 payload = {'limit': str(limit)} for _ in range(0, tracks, limit): payload['offset'] = str(offset) saved_tracks_response = requests.get('https://api.spotify.com/v1/me/tracks', headers=headers, params=payload).json() num_samples = offset for track_dict in saved_tracks_response['items']: # Track the number of samples for calculating # audio feature averages and standard deviations on the fly num_samples += 1 get_track_info(track_dict['track'], library_stats, num_samples) # get_genre(headers, track_dict['track']['album']['id']) audio_features_dict = get_audio_features(headers, track_dict['track']['id']) for feature, feature_data in audio_features_dict.items(): update_audio_feature_stats(feature, feature_data, num_samples, library_stats) for artist_dict in track_dict['track']['artists']: increase_artist_count(headers, artist_dict['name'], artist_dict['id'], library_stats) # calculates num_songs with offset + songs retrieved library_stats['num_songs'] = offset + len(saved_tracks_response['items']) offset += limit calculate_genres_from_artists(headers, library_stats) pprint.pprint(library_stats)
# }}} parse_library #
def get_audio_features(headers, track_id): """Returns the audio features of a soundtrack
Args: headers: headers containing the API token track_id: the id of the soundtrack, needed to query the Spotify API Returns: A dictionary with the features as its keys """
response = requests.get("https://api.spotify.com/v1/audio-features/{}".format(track_id), headers = headers).json() features_dict = {}
# Data that we don't need useless_keys = [ "key", "mode", "type", "liveness", "id", "uri", "track_href", "analysis_url", "time_signature", ] for key, val in response.items(): if key not in useless_keys: features_dict[key] = val
return features_dict
def update_std_dev(cur_mean, cur_std_dev, new_data_point, sample_size): """Calculates the standard deviation for a sample without storing all data points
Args: cur_mean: the current mean for N = (sample_size - 1) cur_std_dev: the current standard deviation for N = (sample_size - 1) new_data_point: a new data point sample_size: sample size including the new data point Returns: (new_mean, new_std_dev) """
# This is an implementation of Welford's method # http://jonisalonen.com/2013/deriving-welfords-method-for-computing-variance/ new_mean = ((sample_size - 1) * cur_mean + new_data_point) / sample_size delta_variance = (new_data_point - new_mean) * (new_data_point - cur_mean) new_std_dev = math.sqrt( (math.pow(cur_std_dev, 2) * (sample_size - 2) + delta_variance) / ( sample_size - 1 )) return new_mean, new_std_dev
def update_audio_feature_stats(feature, new_data_point, sample_size, library_stats): """Updates the audio feature statistics in library_stats
Args: feature: the audio feature to be updated (string) new_data_point: new data to update the stats with sample_size: sample size including the new data point library_stats Dictionary containing the data mined from user's Spotify library
Returns: None """
# first time the feature is considered if sample_size < 2: library_stats['audio_features'][feature] = { "average": new_data_point, "std_dev": 0, } else: cur_mean = library_stats['audio_features'][feature]['average'] cur_std_dev = library_stats['audio_features'][feature]['std_dev'] new_mean, new_std_dev = update_std_dev(cur_mean, cur_std_dev, new_data_point, sample_size)
library_stats['audio_features'][feature] = { "average": new_mean, "std_dev": new_std_dev }
# increase_nested_key {{{ #
def increase_nested_key(top_key, nested_key, library_stats, amount=1): """Increases count for the value of library_stats[top_key][nested_key]. Checks if nested_key exists already and takes
appropriate action.
:top_key: First key of library_stats. :nested_key: Key in top_key's dict for which we want to increase value of. :library_stats: Dictionary containing the data mined from user's Spotify library
:returns: None
"""
if nested_key not in library_stats[top_key]: library_stats[top_key][nested_key] = amount else: library_stats[top_key][nested_key] += amount
# }}} increase_nested_key #
# increase_artist_count {{{ #
def increase_artist_count(headers, artist_name, artist_id, library_stats): """Increases count for artist in library_stats and stores the artist_id.
:headers: For making the API call. :artist_name: Artist to increase count for. :artist_id: The Spotify ID for the artist. :library_stats: Dictionary containing the data mined from user's Spotify library
:returns: None
"""
if artist_name not in library_stats['artists']: library_stats['artists'][artist_name] = {} library_stats['artists'][artist_name]['count'] = 1 library_stats['artists'][artist_name]['id'] = artist_id else: library_stats['artists'][artist_name]['count'] += 1
# }}} increase_artist_count #
def update_popularity_stats(new_data_point, library_stats, sample_size): """Updates the popularity statistics in library_stats
Args: new_data_point: new data to update the popularity stats with library_stats: Dictionary containing data mined from user's Spotify library sample_size: The sample size including the new data Returns: None """
if sample_size < 2: library_stats['popularity'] = { "average": new_data_point, "std_dev": 0, } else : cur_mean_popularity = library_stats['popularity']['average'] cur_popularity_stdev = library_stats['popularity']['std_dev'] new_mean, new_std_dev = update_std_dev( cur_mean_popularity, cur_popularity_stdev, new_data_point, sample_size) library_stats['popularity'] = { "average": new_mean, "std_dev": new_std_dev, }
# get_track_info {{{ #
def get_track_info(track_dict, library_stats, sample_size): """Get all the info from the track_dict directly returned by the API call in parse_library.
:track_dict: Dict returned from the API call containing the track info. :library_stats: Dictionary containing the data mined from user's Spotify library :sample_size: The sample size so far including this track
:returns: None
"""
# popularity update_popularity_stats(track_dict['popularity'], library_stats, sample_size) # year year_released = track_dict['album']['release_date'].split('-')[0] increase_nested_key('year_released', year_released, library_stats) # artist # artist_names = [artist['name'] for artist in track_dict['artists']] # for artist_name in artist_names: # increase_nested_key('artists', artist_name)
# runtime library_stats['total_runtime'] += float(track_dict['duration_ms']) / (1000 * 60)
# }}} get_track_info #
# calculate_genres_from_artists {{{ #
def calculate_genres_from_artists(headers, library_stats): """Tallies up genre counts based on artists in library_stats.
:headers: For making the API call. :library_stats: Dictionary containing the data mined from user's Spotify library
:returns: None
"""
for artist_entry in library_stats['artists'].values(): artist_response = requests.get('https://api.spotify.com/v1/artists/' + artist_entry['id'], headers=headers).json() # increase each genre count by artist count for genre in artist_response['genres']: increase_nested_key('genres', genre, library_stats, artist_entry['count'])
# }}} calculate_genres_from_artists #
def process_library_stats(library_stats): """Processes library_stats into format more suitable for D3 consumption
Args: library_stats: Dictionary containing the data mined from user's Spotify library Returns: A new dictionary that contains the data in library_stats, in a format more suitable for D3 consumption """
processed_library_stats = {} for key in library_stats: if key == 'artists' or key == 'genres' or key == 'year_released': for inner_key in library_stats[key]: if key not in processed_library_stats: processed_library_stats[key] = [] processed_item_key = '' # identifier key for each dict in the list count = 0 if 'artist' in key: processed_item_key = 'name' count = library_stats[key][inner_key]['count'] elif 'genre' in key: processed_item_key = 'genre' count = library_stats[key][inner_key] else: processed_item_key = 'year' count = library_stats[key][inner_key]
processed_library_stats[key].append({ processed_item_key: inner_key, "count": count }) elif key == 'audio_features': for audio_feature in library_stats[key]: if 'audio_features' not in processed_library_stats: processed_library_stats['audio_features'] = [] processed_library_stats['audio_features'].append({ 'feature': audio_feature, 'average': library_stats[key][audio_feature]['average'], 'std_dev': library_stats[key][audio_feature]['std_dev'] }) # TODO: Not sure about final form for 'popularity' # elif key == 'popularity': # processed_library_stats[key] = [] # processed_library_stats[key].append({
# }) elif key == 'num_songs' or key == 'total_runtime' or key == 'popularity': processed_library_stats[key] = library_stats[key] return processed_library_stats
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