Browse Source
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
8 years ago
2 changed files with 213 additions and 196 deletions
@ -0,0 +1,209 @@ |
|||
import requests |
|||
import math |
|||
import pprint |
|||
|
|||
# parse_library {{{ # |
|||
|
|||
def parse_library(headers, tracks, library_stats): |
|||
"""Scans user's library for certain number of tracks to update library_stats with. |
|||
|
|||
:headers: For API call. |
|||
:tracks: Number of tracks to get from user's library. |
|||
:library_stats: Dictionary containing the data mined from user's library |
|||
|
|||
:returns: None |
|||
|
|||
""" |
|||
# TODO: implement importing entire library with 0 as tracks param |
|||
# number of tracks to get with each call |
|||
limit = 5 |
|||
# keeps track of point to get songs from |
|||
offset = 0 |
|||
payload = {'limit': str(limit)} |
|||
for _ in range(0, tracks, limit): |
|||
payload['offset'] = str(offset) |
|||
saved_tracks_response = requests.get('https://api.spotify.com/v1/me/tracks', headers=headers, params=payload).json() |
|||
num_samples = offset |
|||
for track_dict in saved_tracks_response['items']: |
|||
# Track the number of samples for calculating |
|||
# audio feature averages and standard deviations on the fly |
|||
num_samples += 1 |
|||
get_track_info(track_dict['track'], library_stats) |
|||
# get_genre(headers, track_dict['track']['album']['id']) |
|||
audio_features_dict = get_audio_features(headers, track_dict['track']['id']) |
|||
for feature, feature_data in audio_features_dict.items(): |
|||
update_audio_feature_stats(feature, feature_data, num_samples, library_stats) |
|||
for artist_dict in track_dict['track']['artists']: |
|||
increase_artist_count(headers, artist_dict['name'], artist_dict['id'], library_stats) |
|||
# calculates num_songs with offset + songs retrieved |
|||
library_stats['num_songs'] = offset + len(saved_tracks_response['items']) |
|||
offset += limit |
|||
calculate_genres_from_artists(headers, library_stats) |
|||
pprint.pprint(library_stats) |
|||
|
|||
# }}} parse_library # |
|||
|
|||
def get_audio_features(headers, track_id): |
|||
"""Returns the audio features of a soundtrack |
|||
|
|||
Args: |
|||
headers: headers containing the API token |
|||
track_id: the id of the soundtrack, needed to query the Spotify API |
|||
|
|||
Returns: |
|||
A dictionary with the features as its keys |
|||
""" |
|||
|
|||
response = requests.get("https://api.spotify.com/v1/audio-features/{}".format(track_id), headers = headers).json() |
|||
features_dict = {} |
|||
|
|||
# Data that we don't need |
|||
useless_keys = [ |
|||
"key", "mode", "type", "liveness", "id", "uri", "track_href", "analysis_url", "time_signature", |
|||
] |
|||
for key, val in response.items(): |
|||
if key not in useless_keys: |
|||
features_dict[key] = val |
|||
|
|||
return features_dict |
|||
|
|||
|
|||
def update_std_dev(cur_mean, cur_std_dev, new_data_point, sample_size): |
|||
"""Calculates the standard deviation for a sample without storing all data points |
|||
|
|||
Args: |
|||
cur_mean: the current mean for N = (sample_size - 1) |
|||
cur_std_dev: the current standard deviation for N = (sample_size - 1) |
|||
new_data_point: a new data point |
|||
sample_size: sample size including the new data point |
|||
|
|||
Returns: |
|||
(new_mean, new_std_dev) |
|||
""" |
|||
# This is an implementation of Welford's method |
|||
# http://jonisalonen.com/2013/deriving-welfords-method-for-computing-variance/ |
|||
new_mean = ((sample_size - 1) * cur_mean + new_data_point) / sample_size |
|||
delta_variance = (new_data_point - new_mean) * (new_data_point - cur_mean) |
|||
new_std_dev = math.sqrt( |
|||
(math.pow(cur_std_dev, 2) * (sample_size - 2) + delta_variance) / ( |
|||
sample_size - 1 |
|||
)) |
|||
return new_mean, new_std_dev |
|||
|
|||
|
|||
def update_audio_feature_stats(feature, new_data_point, sample_size, library_stats): |
|||
"""Updates the audio feature statistics in library_stats |
|||
|
|||
Args: |
|||
feature: the audio feature to be updated (string) |
|||
new_data_point: new data to update the stats with |
|||
sample_size: sample size including the new data point |
|||
library_stats Dictionary containing the data mined from user's Spotify library |
|||
|
|||
|
|||
Returns: |
|||
None |
|||
""" |
|||
# first time the feature is considered |
|||
if sample_size < 2: |
|||
library_stats['audio_features'][feature] = { |
|||
"average": new_data_point, |
|||
"std_dev": 0, |
|||
} |
|||
else: |
|||
cur_mean = library_stats['audio_features'][feature]['average'] |
|||
cur_std_dev = library_stats['audio_features'][feature]['std_dev'] |
|||
new_mean, new_std_dev = update_std_dev(cur_mean, cur_std_dev, new_data_point, sample_size) |
|||
|
|||
library_stats['audio_features'][feature]['average'] = new_mean |
|||
library_stats['audio_features'][feature]['std_dev'] = new_std_dev |
|||
|
|||
|
|||
# increase_nested_key {{{ # |
|||
|
|||
def increase_nested_key(top_key, nested_key, library_stats, amount=1): |
|||
"""Increases count for the value of library_stats[top_key][nested_key]. Checks if nested_key exists already and takes |
|||
appropriate action. |
|||
|
|||
:top_key: First key of library_stats. |
|||
:nested_key: Key in top_key's dict for which we want to increase value of. |
|||
:library_stats: Dictionary containing the data mined from user's Spotify library |
|||
|
|||
:returns: None |
|||
|
|||
""" |
|||
if nested_key not in library_stats[top_key]: |
|||
library_stats[top_key][nested_key] = amount |
|||
else: |
|||
library_stats[top_key][nested_key] += amount |
|||
|
|||
# }}} increase_nested_key # |
|||
|
|||
# increase_artist_count {{{ # |
|||
|
|||
def increase_artist_count(headers, artist_name, artist_id, library_stats): |
|||
"""Increases count for artist in library_stats and stores the artist_id. |
|||
|
|||
:headers: For making the API call. |
|||
:artist_name: Artist to increase count for. |
|||
:artist_id: The Spotify ID for the artist. |
|||
:library_stats: Dictionary containing the data mined from user's Spotify library |
|||
|
|||
:returns: None |
|||
|
|||
""" |
|||
if artist_name not in library_stats['artists']: |
|||
library_stats['artists'][artist_name] = {} |
|||
library_stats['artists'][artist_name]['count'] = 1 |
|||
library_stats['artists'][artist_name]['id'] = artist_id |
|||
else: |
|||
library_stats['artists'][artist_name]['count'] += 1 |
|||
|
|||
# }}} increase_artist_count # |
|||
|
|||
# get_track_info {{{ # |
|||
|
|||
def get_track_info(track_dict, library_stats): |
|||
"""Get all the info from the track_dict directly returned by the API call in parse_library. |
|||
|
|||
:track_dict: Dict returned from the API call containing the track info. |
|||
:library_stats: Dictionary containing the data mined from user's Spotify library |
|||
|
|||
:returns: None |
|||
|
|||
""" |
|||
# popularity |
|||
library_stats['popularity'].append(track_dict['popularity']) |
|||
|
|||
# year |
|||
year_released = track_dict['album']['release_date'].split('-')[0] |
|||
increase_nested_key('year_released', year_released, library_stats) |
|||
|
|||
# artist |
|||
# artist_names = [artist['name'] for artist in track_dict['artists']] |
|||
# for artist_name in artist_names: |
|||
# increase_nested_key('artists', artist_name) |
|||
|
|||
# runtime |
|||
library_stats['total_runtime'] += float(track_dict['duration_ms']) / 60 |
|||
|
|||
# }}} get_track_info # |
|||
|
|||
# calculate_genres_from_artists {{{ # |
|||
|
|||
def calculate_genres_from_artists(headers, library_stats): |
|||
"""Tallies up genre counts based on artists in library_stats. |
|||
|
|||
:headers: For making the API call. |
|||
:library_stats: Dictionary containing the data mined from user's Spotify library |
|||
|
|||
:returns: None |
|||
|
|||
""" |
|||
for artist_entry in library_stats['artists'].values(): |
|||
artist_response = requests.get('https://api.spotify.com/v1/artists/' + artist_entry['id'], headers=headers).json() |
|||
# increase each genre count by artist count |
|||
for genre in artist_response['genres']: |
|||
increase_nested_key('genres', genre, library_stats, artist_entry['count']) |
|||
|
|||
# }}} calculate_genres_from_artists # |
|||
Write
Preview
Loading…
Cancel
Save
Reference in new issue