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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 7 years ago
parent
commit
c9de0395a4
  1. 209
      spotifyvis/utils.py
  2. 198
      spotifyvis/views.py

209
spotifyvis/utils.py

@ -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 #

198
spotifyvis/views.py

@ -8,6 +8,7 @@ import urllib
import json import json
import pprint import pprint
from datetime import datetime from datetime import datetime
from .utils import *
TIME_FORMAT = '%Y-%m-%d-%H-%M-%S' TIME_FORMAT = '%Y-%m-%d-%H-%M-%S'
library_stats = {"audio_features":{}, "genres":{}, "year_released":{}, "artists":{}, "num_songs":0, "popularity":[], "total_runtime":0} library_stats = {"audio_features":{}, "genres":{}, "year_released":{}, "artists":{}, "num_songs":0, "popularity":[], "total_runtime":0}
@ -138,201 +139,8 @@ def user_data(request):
} }
tracks_to_query = 5 tracks_to_query = 5
parse_library(headers, tracks_to_query)
library_stats = {"audio_features":{}, "genres":{}, "year_released":{}, "artists":{}, "num_songs":0, "popularity":[], "total_runtime":0}
parse_library(headers, tracks_to_query, library_stats)
return render(request, 'spotifyvis/user_data.html', context) return render(request, 'spotifyvis/user_data.html', context)
# }}} user_data # # }}} user_data #
# parse_library {{{ #
def parse_library(headers, tracks):
"""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.
: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'])
# 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)
for artist_dict in track_dict['track']['artists']:
increase_artist_count(headers, artist_dict['name'], artist_dict['id'])
# calculates num_songs with offset + songs retrieved
library_stats['num_songs'] = offset + len(saved_tracks_response['items'])
offset += limit
calculate_genres_from_artists(headers)
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):
"""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
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, 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.
: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):
"""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.
: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):
"""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.
: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)
# 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):
"""Tallies up genre counts based on artists in library_stats.
:headers: For making the API call.
: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, artist_entry['count'])
# }}} calculate_genres_from_artists #
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