Kevin-Mok/spotify-lib-vis
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Implement audio feature statistics calculation

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Implemented the calculation average and standard deviation of audio features.
This commit is contained in:
Chris Shyi
2018-05-20 16:28:46 -04:00
parent 9878e7954d
commit d7002f7571
1 changed files with 19 additions and 7 deletions
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26
spotifyvis/views.py
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@@ -168,11 +168,12 @@ def get_audio_features(track_id, headers):
return features_dict return features_dict
def update_std_dev(cur_mean, new_data_point, sample_size): 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 """Calculates the standard deviation for a sample without storing all data points
Args: Args:
cur_mean: the current mean for N = (sample_size - 1) 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 new_data_point: a new data point
sample_size: sample size including the new data point sample_size: sample size including the new data point
@@ -182,8 +183,12 @@ def update_std_dev(cur_mean, new_data_point, sample_size):
# This is an implementationof Welford's method # This is an implementationof Welford's method
# http://jonisalonen.com/2013/deriving-welfords-method-for-computing-variance/ # http://jonisalonen.com/2013/deriving-welfords-method-for-computing-variance/
new_mean = ((sample_size - 1) * cur_mean + new_data_point) / sample_size new_mean = ((sample_size - 1) * cur_mean + new_data_point) / sample_size
std_dev = (new_data_point - new_mean) * (new_data_point - cur_mean) delta_variance = (new_data_point - new_mean) * (new_data_point - cur_mean)
return new_mean, std_dev 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): def update_audio_feature_stats(feature, new_data_point, sample_size):
@@ -203,13 +208,13 @@ def update_audio_feature_stats(feature, new_data_point, sample_size):
"average": new_data_point, "average": new_data_point,
"std_dev": 0, "std_dev": 0,
} }
else: else:
current_mean = library_stats['audio_features'][feature]['average'] current_mean = library_stats['audio_features'][feature]['average']
updated_mean, std_dev = update_std_dev(current_mean, new_data_point, sample_size) cur_std_dev = library_stats['audio_features'][feature]['std_dev']
updated_mean, new_std_dev = update_std_dev(current_mean, cur_std_dev, new_data_point, sample_size)
library_stats['audio_features'][feature]['average'] = updated_mean library_stats['audio_features'][feature]['average'] = updated_mean
library_stats['audio_features'][feature]['std_dev'] = std_dev library_stats['audio_features'][feature]['std_dev'] = new_std_dev
# parse_library {{{ # # parse_library {{{ #
@@ -228,12 +233,19 @@ def parse_library(headers, tracks):
# keeps track of point to get songs from # keeps track of point to get songs from
offset = 0 offset = 0
payload = {'limit': str(limit)} payload = {'limit': str(limit)}
for i in range(0, tracks, limit): for _ in range(0, tracks, limit):
payload['offset'] = str(offset) payload['offset'] = str(offset)
saved_tracks_response = requests.get('https://api.spotify.com/v1/me/tracks', headers=headers, params=payload).json() 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']: 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_track_info(track_dict['track'])
# get_genre(headers, track_dict['track']['album']['id']) # get_genre(headers, track_dict['track']['album']['id'])
audio_features_dict = get_audio_features(track_dict['id'], headers)
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']: for artist_dict in track_dict['track']['artists']:
increase_artist_count(headers, artist_dict['name'], artist_dict['id']) increase_artist_count(headers, artist_dict['name'], artist_dict['id'])
# calculates num_songs with offset + songs retrieved # calculates num_songs with offset + songs retrieved