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383 lines
14 KiB
383 lines
14 KiB
# imports {{{ #
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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
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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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def parse_library(headers, tracks, library_stats, user):
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"""Scans user's library for certain number of tracks to update library_stats with.
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:headers: For API call.
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:tracks: Number of tracks to get from user's library.
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:library_stats: Dictionary containing the data mined from user's library
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:user: a User object representing the user whose library we are parsing
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:returns: None
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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 = 5
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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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# use two separate variables to track, because the average popularity also requires num_samples
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num_samples = 0 # number of actual track samples
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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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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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# 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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# 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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track_artists.append(artist_obj)
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track_obj, track_created = save_track_obj(track_dict['track'], track_artists, 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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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 login 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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# calculates num_songs with offset + songs retrieved
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offset += limit
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# calculate_genres_from_artists(headers, library_stats)
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# pprint.pprint(library_stats)
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# }}} parse_library #
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# save_track_obj {{{ #
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def save_track_obj(track_dict, artists, user):
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"""Make an entry in the database for this track if it doesn't exist already.
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:track_dict: dictionary from the API call containing track information.
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:artists: artists of the song, passed in as a list of Artist objects.
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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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new_track, created = Track.objects.get_or_create(
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track_id=track_dict['id'],
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year=track_dict['album']['release_date'].split('-')[0],
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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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)
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# have to add artists and user after saving object since track needs to
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# have ID before filling in m2m field
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if created:
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for artist in artists:
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new_track.artists.add(artist)
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new_track.users.add(user)
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new_track.save()
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return new_track, created
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# }}} save_track_obj #
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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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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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"""
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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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features_dict = {}
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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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if key not in useless_keys:
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features_dict[key] = val
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setattr(audio_features_entry, key, val)
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audio_features_entry.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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: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_genres_from_artists {{{ #
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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.id, headers=headers).json()
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# update genre for artist in database with top genre
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artist_obj.update(genre=artist_response['genres'][0])
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# }}} calculate_genres_from_artists #
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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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def get_genre_data(user):
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"""Return genre data needed to create the graph user.
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:user: User object for which to return the data for.
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:returns: List of dicts containing counts for each genre.
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"""
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pass
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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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# print(track.name)
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