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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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