Metrics

This module contains functions for calculating metrics of a glucose time series. The functions are meant to be used by the glucopy.Gframe class, but can be used independently as well as long as the input has the next columns:

• ‘Timestamp’ : datetime64[ns] # pandas datetime

• ‘Day’ : datetime.date

• ‘Time’ : datetime.time

• ‘CGM’ : number

1. Joint data analysis metrics for glycaemia dynamics

metrics.mean(df[, per_day])	Calculates the mean of the CGM values.
metrics.std(df[, per_day, ddof])	Calculates the standard deviation of the CGM values.
metrics.cv(df[, per_day, ddof])	Calculates the Coefficient of Variation (CV) of the CGM values.
metrics.quantile(df[, per_day, q, interpolation])	Calculates the quantile of the CGM values.
metrics.iqr(df[, per_day, interpolation])	Calculates the Interquartile Range (IQR) of the CGM values.
metrics.modd(df[, target_time, slack, ignore_na])	Calculates the Mean of Daily Differences (MODD).
metrics.tir(df[, interval, percentage, decimals])	Calculates the Time in Range (TIR) for a given target range of glucose.

2. Analysis of distribution in the plane for glycaemia dynamics

metrics.fd(df[, interval, decimals, count])	Calculates the Frequency Distribution (FD) for a given target range of glucose.
metrics.auc(df[, time_unit, threshold, above])	Calculates the Area Under the Curve (AUC) using the trapezoidal rule.

3. Amplitude and distribution of frequencies metrics for glycaemia dynamics

metrics.mage(df)	Calculates the Mean Amplitude of Glycaemic Excursions (MAGE).
metrics.dt(df)	Calculates the Distance Travelled (DT).

4. Metrics for the analysis of glycaemic dynamics using scores of glucose values

metrics.bgi(df[, unit, index_type, maximum])	Calculates the Low Blood Glucose Index (LBGI) or the High Blood Glucose Index (LBGI).
metrics.grade(df[, percentage, unit])	Calculates the contributions of the Glycaemic Risk Assessment Diabetes Equation (GRADE) to Hypoglycaemia, Euglycaemia and Hyperglycaemia.

5. Metrics for the analysis of glycaemic dynamics using variability estimation

metrics.mard(cgm_df, smbg_df[, slack, ...])	Calculates the Mean Absolute Relative Difference (MARD).
metrics.conga(df[, m, slack, ignore_na, ddof])	Calculates the Continuous Overall Net Glycaemic Action (CONGA).
metrics.gvp(df)	Calculates the Glucose Variability Percentage (GVP), with time in minutes.
metrics.mag(df[, time_unit])	Calculates the Mean Absolute Glucose Change per unit of time (MAG).

6. Computational methods for the analysis of glycemic dynamics

metrics.dfa(df[, scale, overlap, integrate, ...])	Calculates the Detrended Fluctuation Analysis (DFA) using neurokit2.fractal_dfa().
metrics.samp_en(df[, delay, dimension, ...])	Calculates the Sample Entropy using neurokit2.entropy_sample()
metrics.mse(df[, scale, dimension, ...])	Calculates the Multiscale Sample Entropy using neurokit2.entropy_multiscale()