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@ -159,12 +159,19 @@ def parse_library(headers, tracks): |
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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 i in range(0, tracks, 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']) |
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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['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) |
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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']) |
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# calculates num_songs with offset + songs retrieved |
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@ -175,6 +182,80 @@ def parse_library(headers, tracks): |
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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 implementationof 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): |
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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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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, amount=1): |
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