Made minor changes to variable names
This commit is contained in:
Chris Shyi
@@ -143,80 +143,6 @@ def user_data(request):
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# }}} user_data #
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def get_audio_features(track_id, headers):
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"""Returns the audio features of a soundtrack
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Args:
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track_id: the id of the soundtrack, needed to query the Spotify API
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headers: headers containing the API token
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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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(updated_mean, 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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current_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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updated_mean, new_std_dev = update_std_dev(current_mean, cur_std_dev, new_data_point, sample_size)
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library_stats['audio_features'][feature]['average'] = updated_mean
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library_stats['audio_features'][feature]['std_dev'] = new_std_dev
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# parse_library {{{ #
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def parse_library(headers, tracks):
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@@ -256,6 +182,80 @@ def parse_library(headers, tracks):
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# }}} parse_library #
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def get_audio_features(track_id, headers):
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"""Returns the audio features of a soundtrack
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Args:
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track_id: the id of the soundtrack, needed to query the Spotify API
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headers: headers containing the API token
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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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