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Merge pull request #8 from chrisshyi/master

Get soundtrack musical features
master
Kevin Mok 7 years ago
committed by GitHub
parent
commit
569c83732b
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  1. 83
      spotifyvis/views.py

83
spotifyvis/views.py

@ -159,12 +159,19 @@ def parse_library(headers, tracks):
# keeps track of point to get songs from
offset = 0
payload = {'limit': str(limit)}
for i in range(0, tracks, 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['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
@ -175,6 +182,80 @@ def parse_library(headers, tracks):
# }}} 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 implementationof 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):

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