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@ -3,16 +3,21 @@ import requests |
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import math |
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import pprint |
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from .models import Artist, User, Track, AudioFeatures |
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from django.db.models import Count, Q |
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from .models import * |
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from django.db.models import Count, Q, F |
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from django.http import JsonResponse |
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from django.core import serializers |
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import json |
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# }}} imports # |
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# parse_library {{{ # |
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USER_TRACKS_LIMIT = 50 |
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ARTIST_LIMIT = 50 |
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FEATURES_LIMIT = 100 |
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# ARTIST_LIMIT = 25 |
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# FEATURES_LIMIT = 25 |
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# parse_library {{{ # |
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def parse_library(headers, tracks, user): |
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"""Scans user's library for certain number of tracks to update library_stats with. |
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@ -25,59 +30,108 @@ def parse_library(headers, tracks, user): |
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""" |
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# TODO: implement importing entire library with 0 as tracks param |
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# number of tracks to get with each call |
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limit = 50 |
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# keeps track of point to get songs from |
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offset = 0 |
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payload = {'limit': str(limit)} |
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payload = {'limit': str(USER_TRACKS_LIMIT)} |
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artist_genre_queue = [] |
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features_queue = [] |
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# iterate until hit requested num of tracks |
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for _ in range(0, tracks, limit): |
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for i in range(0, tracks, USER_TRACKS_LIMIT): |
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payload['offset'] = str(offset) |
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# get current set of tracks |
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saved_tracks_response = requests.get('https://api.spotify.com/v1/me/tracks', headers=headers, params=payload).json() |
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saved_tracks_response = requests.get('https://api.spotify.com/v1/me/tracks', |
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headers=headers, |
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params=payload).json() |
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# TODO: refactor the for loop body into helper function |
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# iterate through each track |
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for track_dict in saved_tracks_response['items']: |
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# add artists {{{ # |
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# update artist info before track so that Track object can reference |
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# Artist object |
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track_artists = [] |
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for artist_dict in track_dict['track']['artists']: |
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artist_obj, artist_created = Artist.objects.get_or_create( |
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artist_id=artist_dict['id'], |
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name=artist_dict['name'], |
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) |
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# update_artist_genre(headers, artist_obj) |
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# get_or_create() returns a tuple (obj, created) |
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name=artist_dict['name'],) |
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# only add/tally up artist genres if new |
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if artist_created: |
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artist_genre_queue.append(artist_obj) |
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if len(artist_genre_queue) == ARTIST_LIMIT: |
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add_artist_genres(headers, artist_genre_queue) |
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artist_genre_queue = [] |
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track_artists.append(artist_obj) |
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top_genre = get_top_genre(headers, |
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track_dict['track']['artists'][0]['id']) |
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# }}} add artists # |
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# WIP: get most common genre |
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top_genre = "" |
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track_obj, track_created = save_track_obj(track_dict['track'], |
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track_artists, top_genre, user) |
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# if a new track is not created, the associated audio feature does not need to be created again |
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# add audio features {{{ # |
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# if a new track is not created, the associated audio feature does |
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# not need to be created again |
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if track_created: |
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save_audio_features(headers, track_dict['track']['id'], track_obj) |
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""" |
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TODO: Put this logic in another function |
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# Audio analysis could be empty if not present in Spotify database |
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if len(audio_features_dict) != 0: |
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# Track the number of audio analyses for calculating |
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# audio feature averages and standard deviations on the fly |
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feature_data_points += 1 |
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for feature, feature_data in audio_features_dict.items(): |
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update_audio_feature_stats(feature, feature_data, |
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feature_data_points, library_stats) |
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""" |
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features_queue.append(track_obj) |
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if len(features_queue) == FEATURES_LIMIT: |
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get_audio_features(headers, features_queue) |
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features_queue = [] |
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# }}} add audio features # |
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# temporary console logging |
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print("#{}-{}: {} - {}".format(offset + 1, |
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offset + USER_TRACKS_LIMIT, |
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track_obj.artists.first(), |
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track_obj.name)) |
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# calculates num_songs with offset + songs retrieved |
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offset += limit |
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# pprint.pprint(library_stats) |
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offset += USER_TRACKS_LIMIT |
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# clean-up {{{ # |
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# update remaining artists without genres and songs without features if |
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# there are any |
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if len(artist_genre_queue) > 0: |
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add_artist_genres(headers, artist_genre_queue) |
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if len(features_queue) > 0: |
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get_audio_features(headers, features_queue) |
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# }}} clean-up # |
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update_track_genres(user) |
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# }}} parse_library # |
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# update_track_genres {{{ # |
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def update_track_genres(user): |
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"""Updates user's tracks with the most common genre associated with the |
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songs' artist(s). |
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:user: User object who's tracks are being updated. |
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:returns: None |
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""" |
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user_tracks = Track.objects.filter(users__exact=user) |
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for track in user_tracks: |
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# just using this variable to save another call to db |
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track_artists = track.artists.all() |
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# set genres to first artist's genres then find intersection with others |
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shared_genres = track_artists.first().genres.all() |
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for artist in track_artists: |
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shared_genres.intersection(artist.genres.all()) |
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most_common_genre = shared_genres.order_by('-num_songs').first() |
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track.genre = most_common_genre if most_common_genre is not None \ |
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else "undefined" |
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track.save() |
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# print(track.name, track.genre) |
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# }}} update_track_genres # |
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# save_track_obj {{{ # |
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def save_track_obj(track_dict, artists, top_genre, user): |
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@ -87,6 +141,7 @@ def save_track_obj(track_dict, artists, top_genre, user): |
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:artists: artists of the song, passed in as a list of Artist objects. |
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:top_genre: top genre associated with this track (see get_top_genre). |
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:user: User object for which this Track is to be associated with. |
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:returns: (The created/retrieved Track object, created) |
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""" |
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@ -100,7 +155,7 @@ def save_track_obj(track_dict, artists, top_genre, user): |
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popularity=int(track_dict['popularity']), |
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runtime=int(float(track_dict['duration_ms']) / 1000), |
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name=track_dict['name'], |
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genre=top_genre, |
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# genre=top_genre, |
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) |
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# have to add artists and user after saving object since track needs to |
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@ -115,282 +170,78 @@ def save_track_obj(track_dict, artists, top_genre, user): |
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# get_audio_features {{{ # |
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def save_audio_features(headers, track_id, track): |
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"""Creates and saves a new AudioFeatures object |
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def get_audio_features(headers, track_objs): |
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"""Creates and saves a new AudioFeatures objects for the respective |
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track_objs. track_objs should contain the API limit for a single call |
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(FEATURES_LIMIT) for maximum efficiency. |
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Args: |
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headers: headers containing the API token |
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track_id: the id of the soundtrack, needed to query the Spotify API |
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track: Track object to associate with the new AudioFeatures object |
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:headers: headers containing the API token |
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:track_objs: Track objects to associate with the new AudioFeatures object |
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:returns: None |
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""" |
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response = requests.get("https://api.spotify.com/v1/audio-features/{}".format(track_id), headers = headers).json() |
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if 'error' in response: |
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return {} |
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track_ids = str.join(",", [track_obj.track_id for track_obj in track_objs]) |
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params = {'ids': track_ids} |
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features_response = requests.get("https://api.spotify.com/v1/audio-features", |
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headers=headers,params=params).json()['audio_features'] |
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# pprint.pprint(features_response) |
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useless_keys = [ "key", "mode", "type", "liveness", "id", "uri", "track_href", "analysis_url", "time_signature", ] |
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for i in range(len(track_objs)): |
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if features_response[i] is not None: |
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# Data that we don't need |
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useless_keys = [ |
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"key", "mode", "type", "liveness", "id", "uri", "track_href", "analysis_url", "time_signature", |
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] |
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audio_features_entry = AudioFeatures() |
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audio_features_entry.track = track |
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for key, val in response.items(): |
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cur_features_obj = AudioFeatures() |
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cur_features_obj.track = track_objs[i] |
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for key, val in features_response[i].items(): |
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if key not in useless_keys: |
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setattr(audio_features_entry, key, val) |
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audio_features_entry.save() |
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setattr(cur_features_obj, key, val) |
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cur_features_obj.save() |
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# }}} get_audio_features # |
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# update_std_dev {{{ # |
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def update_std_dev(cur_mean, cur_std_dev, new_data_point, sample_size): |
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"""Calculates the standard deviation for a sample without storing all data points |
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Args: |
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cur_mean: the current mean for N = (sample_size - 1) |
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cur_std_dev: the current standard deviation for N = (sample_size - 1) |
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new_data_point: a new data point |
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sample_size: sample size including the new data point |
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Returns: |
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(new_mean, new_std_dev) |
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""" |
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# This is an implementation of Welford's method |
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# http://jonisalonen.com/2013/deriving-welfords-method-for-computing-variance/ |
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new_mean = ((sample_size - 1) * cur_mean + new_data_point) / sample_size |
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delta_variance = (new_data_point - new_mean) * (new_data_point - cur_mean) |
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new_std_dev = math.sqrt( |
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(math.pow(cur_std_dev, 2) * (sample_size - 2) + delta_variance) / ( |
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sample_size - 1 |
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)) |
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return new_mean, new_std_dev |
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# }}} update_std_dev # |
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# update_audio_feature_stats {{{ # |
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def update_audio_feature_stats(feature, new_data_point, sample_size, library_stats): |
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"""Updates the audio feature statistics in library_stats |
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Args: |
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feature: the audio feature to be updated (string) |
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new_data_point: new data to update the stats with |
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sample_size: sample size including the new data point |
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library_stats Dictionary containing the data mined from user's Spotify library |
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Returns: |
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None |
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""" |
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# first time the feature is considered |
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if sample_size < 2: |
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library_stats['audio_features'][feature] = { |
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"average": new_data_point, |
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"std_dev": 0, |
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} |
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else: |
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cur_mean = library_stats['audio_features'][feature]['average'] |
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cur_std_dev = library_stats['audio_features'][feature]['std_dev'] |
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new_mean, new_std_dev = update_std_dev(cur_mean, cur_std_dev, new_data_point, sample_size) |
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library_stats['audio_features'][feature] = { |
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"average": new_mean, |
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"std_dev": new_std_dev |
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} |
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# }}} update_audio_feature_stats # |
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# increase_nested_key {{{ # |
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def increase_nested_key(top_key, nested_key, library_stats, amount=1): |
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"""Increases count for the value of library_stats[top_key][nested_key]. Checks if nested_key exists already and takes |
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appropriate action. |
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:top_key: First key of library_stats. |
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:nested_key: Key in top_key's dict for which we want to increase value of. |
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:library_stats: Dictionary containing the data mined from user's Spotify library |
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def process_artist_genre(genre_name, artist_obj): |
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"""Increase count for correspoding Genre object to genre_name and add that |
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Genre to artist_obj. |
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:genre_name: Name of genre. |
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:artist_obj: Artist object to add Genre object to. |
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:returns: None |
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""" |
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if nested_key not in library_stats[top_key]: |
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library_stats[top_key][nested_key] = amount |
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else: |
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library_stats[top_key][nested_key] += amount |
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# }}} increase_nested_key # |
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# increase_artist_count {{{ # |
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def increase_artist_count(headers, artist_name, artist_id, library_stats): |
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"""Increases count for artist in library_stats and stores the artist_id. |
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:headers: For making the API call. |
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:artist_name: Artist to increase count for. |
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:artist_id: The Spotify ID for the artist. |
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:library_stats: Dictionary containing the data mined from user's Spotify library |
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:returns: None |
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""" |
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if artist_name not in library_stats['artists']: |
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library_stats['artists'][artist_name] = {} |
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library_stats['artists'][artist_name]['count'] = 1 |
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library_stats['artists'][artist_name]['id'] = artist_id |
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else: |
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library_stats['artists'][artist_name]['count'] += 1 |
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# }}} increase_artist_count # |
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# update_popularity_stats {{{ # |
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def update_popularity_stats(new_data_point, library_stats, sample_size): |
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"""Updates the popularity statistics in library_stats |
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Args: |
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new_data_point: new data to update the popularity stats with |
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library_stats: Dictionary containing data mined from user's Spotify library |
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sample_size: The sample size including the new data |
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Returns: |
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None |
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""" |
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if sample_size < 2: |
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library_stats['popularity'] = { |
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"average": new_data_point, |
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"std_dev": 0, |
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} |
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else : |
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cur_mean_popularity = library_stats['popularity']['average'] |
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cur_popularity_stdev = library_stats['popularity']['std_dev'] |
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new_mean, new_std_dev = update_std_dev( |
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cur_mean_popularity, cur_popularity_stdev, new_data_point, sample_size) |
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library_stats['popularity'] = { |
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"average": new_mean, |
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"std_dev": new_std_dev, |
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} |
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# }}} update_popularity_stats # |
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# get_track_info {{{ # |
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def get_track_info(track_dict, library_stats, sample_size): |
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"""Get all the info from the track_dict directly returned by the API call in parse_library. |
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:track_dict: Dict returned from the API call containing the track info. |
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:library_stats: Dictionary containing the data mined from user's Spotify library |
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:sample_size: The sample size so far including this track |
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:returns: None |
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""" |
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# popularity |
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update_popularity_stats(track_dict['popularity'], library_stats, sample_size) |
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# year |
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year_released = track_dict['album']['release_date'].split('-')[0] |
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increase_nested_key('year_released', year_released, library_stats) |
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# runtime |
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library_stats['total_runtime'] += float(track_dict['duration_ms']) / (1000 * 60) |
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# }}} get_track_info # |
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# update_artist_genre {{{ # |
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def update_artist_genre(headers, artist_obj): |
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"""Updates the top genre for an artist by querying the Spotify API |
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:headers: For making the API call. |
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:artist_obj: the Artist object whose genre field will be updated |
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:returns: None |
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""" |
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artist_response = requests.get('https://api.spotify.com/v1/artists/' + artist_obj.artist_id, headers=headers).json() |
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# update genre for artist in database with top genre |
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if len(artist_response['genres']) > 0: |
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artist_obj.genre = artist_response['genres'][0] |
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genre_obj, created = Genre.objects.get_or_create(name=genre_name, |
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defaults={'num_songs':1}) |
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if not created: |
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genre_obj.num_songs = F('num_songs') + 1 |
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genre_obj.save() |
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artist_obj.genres.add(genre_obj) |
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artist_obj.save() |
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# }}} # |
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# add_artist_genres {{{ # |
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# get_top_genre {{{ # |
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def get_top_genre(headers, top_artist_id): |
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"""Updates the top genre for a track by querying the Spotify API |
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def add_artist_genres(headers, artist_objs): |
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"""Adds genres to artist_objs and increases the count the respective Genre |
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object. artist_objs should contain the API limit for a single call |
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(ARTIST_LIMIT) for maximum efficiency. |
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:headers: For making the API call. |
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:top_artist: The first artist's (listed in the track) Spotify ID. |
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:artist_objs: List of Artist objects for which to add/tally up genres for. |
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:returns: The first genre listed for the top_artist. |
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:returns: None |
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""" |
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artist_response = requests.get('https://api.spotify.com/v1/artists/' + |
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top_artist_id, headers=headers).json() |
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if len(artist_response['genres']) > 0: |
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return artist_response['genres'][0] |
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artist_ids = str.join(",", [artist_obj.artist_id for artist_obj in artist_objs]) |
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params = {'ids': artist_ids} |
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artists_response = requests.get('https://api.spotify.com/v1/artists/', |
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headers=headers, params=params).json()['artists'] |
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# pprint.pprint(artists_response) |
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for i in range(len(artist_objs)): |
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if len(artists_response[i]['genres']) == 0: |
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process_artist_genre("undefined", artist_objs[i]) |
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else: |
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return "undefined" |
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# }}} # |
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for genre in artists_response[i]['genres']: |
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process_artist_genre(genre, artist_objs[i]) |
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# process_library_stats {{{ # |
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def process_library_stats(library_stats): |
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"""Processes library_stats into format more suitable for D3 consumption |
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Args: |
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library_stats: Dictionary containing the data mined from user's Spotify library |
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Returns: |
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A new dictionary that contains the data in library_stats, in a format more suitable for D3 consumption |
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""" |
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processed_library_stats = {} |
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for key in library_stats: |
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if key == 'artists' or key == 'genres' or key == 'year_released': |
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for inner_key in library_stats[key]: |
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if key not in processed_library_stats: |
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processed_library_stats[key] = [] |
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processed_item_key = '' # identifier key for each dict in the list |
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count = 0 |
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if 'artist' in key: |
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processed_item_key = 'name' |
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count = library_stats[key][inner_key]['count'] |
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elif 'genre' in key: |
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processed_item_key = 'genre' |
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count = library_stats[key][inner_key] |
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else: |
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processed_item_key = 'year' |
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count = library_stats[key][inner_key] |
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processed_library_stats[key].append({ |
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processed_item_key: inner_key, |
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"count": count |
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}) |
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elif key == 'audio_features': |
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for audio_feature in library_stats[key]: |
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if 'audio_features' not in processed_library_stats: |
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processed_library_stats['audio_features'] = [] |
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processed_library_stats['audio_features'].append({ |
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'feature': audio_feature, |
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'average': library_stats[key][audio_feature]['average'], |
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'std_dev': library_stats[key][audio_feature]['std_dev'] |
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}) |
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# TODO: Not sure about final form for 'popularity' |
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# elif key == 'popularity': |
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# processed_library_stats[key] = [] |
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# processed_library_stats[key].append({ |
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# }) |
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elif key == 'num_songs' or key == 'total_runtime' or key == 'popularity': |
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processed_library_stats[key] = library_stats[key] |
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return processed_library_stats |
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# }}} process_library_stats # |
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# }}} add_artist_genres # |
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# get_artists_in_genre {{{ # |
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|
@ -413,6 +264,8 @@ def get_artists_in_genre(user, genre, max_songs): |
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|
processed_artist_counts = {} |
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|
songs_added = 0 |
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|
|
for artist in artist_counts: |
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|
# hacky way to not have total count overflow due to there being multiple |
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|
|
# artists on a track |
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|
|
if songs_added + artist.num_songs <= max_songs: |
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|
processed_artist_counts[artist.name] = artist.num_songs |
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|
|
songs_added += artist.num_songs |
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