Made minor changes to variable names

spotifyvis/views.py

@@ -143,80 +143,6 @@ def user_data(request):
 
 #  }}} user_data  # 
 
-def get_audio_features(track_id, headers):
-    """Returns the audio features of a soundtrack
-
-    Args:
-        track_id: the id of the soundtrack, needed to query the Spotify API
-        headers: headers containing the API token
- 
-    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:
-        (updated_mean, 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:
-        current_mean = library_stats['audio_features'][feature]['average']
-        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]['std_dev'] = new_std_dev
-
-
 #  parse_library {{{ # 
 
 def parse_library(headers, tracks):
@@ -256,6 +182,80 @@ def parse_library(headers, tracks):
 
 #  }}} parse_library # 
 
+def get_audio_features(track_id, headers):
+    """Returns the audio features of a soundtrack
+
+    Args:
+        track_id: the id of the soundtrack, needed to query the Spotify API
+        headers: headers containing the API token
+ 
+    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):