Refactor helper functions in views.py
Refactored helper functions in views.py into utils.py. Instead of having a global dictionary object for user library data, helper functions now mutate a dictionary passed in as a parameter.
This commit is contained in:
Chris Shyi
209
spotifyvis/utils.py
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209
spotifyvis/utils.py
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@@ -0,0 +1,209 @@
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import requests
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import math
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import pprint
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# parse_library {{{ #
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def parse_library(headers, tracks, library_stats):
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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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: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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for _ in range(0, tracks, limit):
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payload['offset'] = str(offset)
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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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num_samples = offset
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for track_dict in saved_tracks_response['items']:
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# Track the number of samples for calculating
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# audio feature averages and standard deviations on the fly
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num_samples += 1
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get_track_info(track_dict['track'], library_stats)
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# get_genre(headers, track_dict['track']['album']['id'])
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audio_features_dict = get_audio_features(headers, track_dict['track']['id'])
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for feature, feature_data in audio_features_dict.items():
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update_audio_feature_stats(feature, feature_data, num_samples, library_stats)
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for artist_dict in track_dict['track']['artists']:
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increase_artist_count(headers, artist_dict['name'], artist_dict['id'], library_stats)
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# calculates num_songs with offset + songs retrieved
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library_stats['num_songs'] = offset + len(saved_tracks_response['items'])
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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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def get_audio_features(headers, track_id):
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"""Returns the audio features of a soundtrack
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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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Returns:
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A dictionary with the features as its keys
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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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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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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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return features_dict
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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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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]['average'] = new_mean
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library_stats['audio_features'][feature]['std_dev'] = new_std_dev
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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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# get_track_info {{{ #
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def get_track_info(track_dict, library_stats):
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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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:returns: None
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"""
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# popularity
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library_stats['popularity'].append(track_dict['popularity'])
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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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# artist
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# artist_names = [artist['name'] for artist in track_dict['artists']]
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# for artist_name in artist_names:
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# increase_nested_key('artists', artist_name)
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# runtime
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library_stats['total_runtime'] += float(track_dict['duration_ms']) / 60
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# }}} get_track_info #
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# calculate_genres_from_artists {{{ #
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def calculate_genres_from_artists(headers, library_stats):
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"""Tallies up genre counts based on artists in library_stats.
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:headers: For making the API call.
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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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for artist_entry in library_stats['artists'].values():
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artist_response = requests.get('https://api.spotify.com/v1/artists/' + artist_entry['id'], headers=headers).json()
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# increase each genre count by artist count
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for genre in artist_response['genres']:
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increase_nested_key('genres', genre, library_stats, artist_entry['count'])
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# }}} calculate_genres_from_artists #
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@@ -8,6 +8,7 @@ import urllib
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import json
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import pprint
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from datetime import datetime
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from .utils import *
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TIME_FORMAT = '%Y-%m-%d-%H-%M-%S'
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library_stats = {"audio_features":{}, "genres":{}, "year_released":{}, "artists":{}, "num_songs":0, "popularity":[], "total_runtime":0}
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@@ -138,201 +139,8 @@ def user_data(request):
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}
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tracks_to_query = 5
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parse_library(headers, tracks_to_query)
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library_stats = {"audio_features":{}, "genres":{}, "year_released":{}, "artists":{}, "num_songs":0, "popularity":[], "total_runtime":0}
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parse_library(headers, tracks_to_query, library_stats)
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return render(request, 'spotifyvis/user_data.html', context)
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# }}} user_data #
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# parse_library {{{ #
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def parse_library(headers, tracks):
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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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: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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for _ in range(0, tracks, limit):
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payload['offset'] = str(offset)
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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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num_samples = offset
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for track_dict in saved_tracks_response['items']:
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# Track the number of samples for calculating
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# audio feature averages and standard deviations on the fly
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num_samples += 1
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get_track_info(track_dict['track'])
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# get_genre(headers, track_dict['track']['album']['id'])
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audio_features_dict = get_audio_features(headers, track_dict['track']['id'])
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for feature, feature_data in audio_features_dict.items():
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update_audio_feature_stats(feature, feature_data, num_samples)
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for artist_dict in track_dict['track']['artists']:
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increase_artist_count(headers, artist_dict['name'], artist_dict['id'])
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# calculates num_songs with offset + songs retrieved
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library_stats['num_songs'] = offset + len(saved_tracks_response['items'])
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offset += limit
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calculate_genres_from_artists(headers)
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pprint.pprint(library_stats)
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# }}} parse_library #
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def get_audio_features(headers, track_id):
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"""Returns the audio features of a soundtrack
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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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Returns:
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A dictionary with the features as its keys
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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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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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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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return features_dict
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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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def update_audio_feature_stats(feature, new_data_point, sample_size):
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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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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]['average'] = new_mean
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library_stats['audio_features'][feature]['std_dev'] = new_std_dev
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# increase_nested_key {{{ #
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def increase_nested_key(top_key, nested_key, 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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: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):
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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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: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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# get_track_info {{{ #
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def get_track_info(track_dict):
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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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:returns: None
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"""
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# popularity
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library_stats['popularity'].append(track_dict['popularity'])
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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)
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# artist
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# artist_names = [artist['name'] for artist in track_dict['artists']]
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# for artist_name in artist_names:
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# increase_nested_key('artists', artist_name)
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# runtime
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library_stats['total_runtime'] += float(track_dict['duration_ms']) / 60
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# }}} get_track_info #
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# calculate_genres_from_artists {{{ #
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def calculate_genres_from_artists(headers):
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"""Tallies up genre counts based on artists in library_stats.
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:headers: For making the API call.
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:returns: None
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"""
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for artist_entry in library_stats['artists'].values():
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artist_response = requests.get('https://api.spotify.com/v1/artists/' + artist_entry['id'], headers=headers).json()
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# increase each genre count by artist count
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for genre in artist_response['genres']:
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increase_nested_key('genres', genre, artist_entry['count'])
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# }}} calculate_genres_from_artists #
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# }}} user_data #
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