glucopy.Gframe.auc#

Gframe.auc(per_day: bool = False, time_unit='m', threshold: int | float = 0, above: bool = True)[source]#

Calculates the Area Under the Curve (AUC) using the trapezoidal rule.

\[AUC = \frac{1}{2} \sum_{i=1}^{N-1} (X_i + X_{i+1}) * (t_{i+1} - t_i)\]

\(X_i\) is the \(i\)-th measurement of the glucose concentration at time \(t_i\).
\(X_{i+1}\) is the \((i+1)\)-th measurement of the glucose concentration at time \(t_{i+1}\).
\(t_i\) is the \(i\)-th time associated with the \(X_i\) measurement.
\(t_{i+1}\) is the \((i+1)\)-th time associated with the \(X_{i+1}\) measurement.
\(N\) is the number of glucose readings.

Parameters:

per_day (bool, default False) – If True, returns a pandas.Series with the AUC for each day. If False, returns the AUC for the entire dataset.
time_unit (str, default 'm' (minutes)) – The time unit for the x-axis. Can be ‘s (seconds)’, ‘m (minutes)’, or ‘h (hours)’.
threshold (int | float, default 0) – The threshold value above which the AUC will be calculated.
above (bool, default True) – If True, the AUC will be calculated above the threshold. If False, the AUC will be calculated below the threshold.

Returns:

auc – Area Under the Curve (AUC).

Return type:

float | pandas.Series

Examples

Calculating the AUC for the entire dataset and minutes as the time unit (default):

In [1]: import glucopy as gp

In [2]: gf = gp.data('prueba_1')

In [3]: gf.auc()
Out[3]: 23075053.5

Calculating the AUC for each day and minutes as the time unit (default):

In [4]: gf.auc(per_day=True)
Out[4]: 
Day
2020-11-27     42030.0
2020-11-28    196732.0
2020-11-29    208903.5
2020-11-30    170577.5
2020-12-01    198432.0
                ...   
2021-03-14    203134.5
2021-03-15    175091.0
2021-03-16    230388.0
2021-03-17    163342.0
2021-03-18    158780.5
Name: AUC, Length: 112, dtype: float64

Calculating the AUC for the entire dataset, hours as the time unit, and below the threshold (100):

In [5]: gf.auc(time_unit='h', threshold=100, above=False)
Out[5]: 19509.93333333331