Gframe#

Constructor#

Gframe([data, unit, date_column, ...])

Class for the analysis of CGM data.

Utilities#

Gframe.convert_unit([new_unit])

Converts the unit of the CGM signal.

Summary#

Gframe.summary([auc_time_unit, ...])

Calculates a summary of the metrics for the entire dataset or for each day separately.

Metrics#

1. Joint data analysis metrics for glycaemia dynamics#

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

2. Analysis of distribution in the plane for glycaemia dynamics#

`Gframe.fd`([per_day, interval, decimals, count])	Calculates the Frequency Distribution (Fi) for a given target range of glucose.
`Gframe.auc`([per_day, time_unit, threshold, ...])	Calculates the Area Under the Curve (AUC) using the trapezoidal rule.

3. Amplitude and distribution of frequencies metrics for glycaemia dynamics#

`Gframe.mage`([per_day])	Calculates the Mean Amplitude of Glycaemic Excursions (MAGE).
`Gframe.dt`([per_day])	Calculates the Distance Travelled (DT).

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

`Gframe.bgi`([per_day, index_type, maximum])	Calculates the Low Blood Glucose Index (LBGI) or the High Blood Glucose Index (LBGI).
`Gframe.hbgi`([per_day, maximum])	This is an alias for `glucopy.Gframe.bgi()` with index_type='h'.
`Gframe.lbgi`([per_day, maximum])	This is an alias for `glucopy.Gframe.bgi()` with index_type='l'.
`Gframe.adrr`()	Calculates the Average Daily Risk Range (ADRR).
`Gframe.grade`([percentage])	Calculates the contributions of the Glycaemic Risk Assessment Diabetes Equation (GRADE) to Hypoglycaemia, Euglycaemia and Hyperglycaemia.
`Gframe.qscore`([slack])	Calculates the Q-Score.

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

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

6. Computational methods for the analysis of glycemic dynamics#

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