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Refactor helper functions in views.py
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.master
Chris Shyi
6 years ago
2 changed files with 213 additions and 196 deletions
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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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