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glucopy.Gframe#

class glucopy.Gframe(data=None, unit: str = 'mg/dL', date_column: list[str] | str | int = 0, cgm_column: str | int = 1, date_format: str | None = None, dropna: bool = True)[source]#

Class for the analysis of CGM data. it uses a pandas DataFrame as the main data structure.

To create a Gframe object from a csv file or an excel file, check Input/Output glucopy.read_csv() and glucopy.read_excel().

Parameters:

  • data (pandas DataFrame) – DataFrame containing the CGM signal information, it will be saved into a DataFrame with the columns [‘Timestamp’,’Day’,’Time’,’CGM’]

  • unit (str, default 'mg/dL') – CGM signal measurement unit.

  • date_column (str or str array, default None) – The name or names of the column(s) containing the date information If it’s a str, it will be the name of the single column containing the date information If it’s a str array, it will be the 2 names of the columns containing the date information, eg. [‘Date’,’Time’] If it’s None, it will be assumed that the date information is in the first column

  • cgm_column (str, default None) – The name of the column containing the CGM signal information If it’s None, it will be assumed that the CGM signal information is in the second column

  • date_format (str, default None) – Format of the date information, if None, it will be assumed that the date information is in a consistent format

  • dropna (bool, default True) – If True, removes all rows with NaN values

data#

DataFrame containing the CGM signal information, it will be saved into a DataFrame with the columns:

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

  • ‘Day’ : datetime.date

  • ‘Time’ : datetime.time

  • ‘CGM’ : number

Type:

pandas DataFrame

unit#

CGM signal measurement unit. Can be ‘mg/dL’ or ‘mmol/L’.

Type:

str

n_samples#

Number of samples in the data.

Type:

int

n_days#

Number of days in the data.

Type:

int

max#

Maximum value of the CGM signal.

Type:

float

min#

Minimum value of the CGM signal.

Type:

float

Examples

Creating a Gframe object from a pandas DataFrame:

In [1]: import glucopy as gp

In [2]: import pandas as pd

In [3]: df = pd.DataFrame({'Timestamp':['2020-01-01 12:00:00','2020-01-01 12:05:00','2020-01-01 12:10:00'],
   ...:                    'CGM':[100,110,120]})
   ...: 

In [4]: gf = gp.Gframe(df)

In [5]: gf
Out[5]: 
            Timestamp         Day      Time  CGM
0 2020-01-01 12:00:00  2020-01-01  12:00:00  100
1 2020-01-01 12:05:00  2020-01-01  12:05:00  110
2 2020-01-01 12:10:00  2020-01-01  12:10:00  120

Creating a Gframe object from a pandas DataFrame with extra columns:

In [6]: df = pd.DataFrame({'Timestamp':['2020-01-01 12:00:00','2020-01-01 12:05:00','2020-01-01 12:10:00'],
   ...:                     'Extra':[1,2,3],
   ...:                     'CGM':[100,110,120]})
   ...: 

In [7]: gf = gp.Gframe(df, cgm_column='CGM', date_column='Timestamp')

In [8]: gf
Out[8]: 
            Timestamp         Day      Time  CGM
0 2020-01-01 12:00:00  2020-01-01  12:00:00  100
1 2020-01-01 12:05:00  2020-01-01  12:05:00  110
2 2020-01-01 12:10:00  2020-01-01  12:10:00  120
__init__(data=None, unit: str = 'mg/dL', date_column: list[str] | str | int = 0, cgm_column: str | int = 1, date_format: str | None = None, dropna: bool = True)[source]#

Methods

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

adrr()

Calculates the Average Daily Risk Range (ADRR).

auc([per_day, time_unit, threshold, above])

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

bgi([per_day, index_type, maximum])

Calculates the Low Blood Glucose Index (LBGI) or the High Blood Glucose Index (LBGI).

conga([per_day, m, slack, ignore_na, ddof])

Calculates the Continuous Overall Net Glycaemic Action (CONGA).

convert_unit([new_unit])

Converts the unit of the CGM signal.

cv([per_day, ddof])

Calculates the Coefficient of Variation (CV) of the CGM values.

dfa([per_day, scale, overlap, integrate, ...])

Calculates the Detrended Fluctuation Analysis (DFA) using neurokit2.fractal_dfa().

dt([per_day])

Calculates the Distance Travelled (DT).

fd([per_day, interval, decimals, count])

Calculates the Frequency Distribution (Fi) for a given target range of glucose.

grade([percentage])

Calculates the contributions of the Glycaemic Risk Assessment Diabetes Equation (GRADE) to Hypoglycaemia, Euglycaemia and Hyperglycaemia.

gvp()

Calculates the Glucose Variability Percentage (GVP), with time in minutes.

hbgi([per_day, maximum])

This is an alias for glucopy.Gframe.bgi() with index_type='h'.

iqr([per_day, interpolation])

Calculates the Interquartile Range (IQR) of the CGM values.

lbgi([per_day, maximum])

This is an alias for glucopy.Gframe.bgi() with index_type='l'.

mag([per_day, time_unit])

Calculates the Mean Absolute Glucose Change per unit of time (MAG).

mage([per_day])

Calculates the Mean Amplitude of Glycaemic Excursions (MAGE).

mard(smbg_df[, slack, interpolate])

Calculates the Mean Absolute Relative Difference (MARD).

mean([per_day])

Calculates the mean of the CGM values.

modd([target_time, slack, ignore_na])

Calculates the Mean of Daily Differences (MODD).

mse([per_day, scale, dimension, tolerance, ...])

Calculates the Multiscale Sample Entropy using neurokit2.entropy_multiscale()

pcv([per_day, ddof])

Calculates the Percentage Coefficient of Variation (%CV) of the CGM values.

qscore([slack])

Calculates the Q-Score.

quantile([per_day, q, interpolation])

Calculates the quantile of the CGM values.

samp_en([per_day, delay, dimension, tolerance])

Calculates the Sample Entropy using neurokit2.entropy_sample()

std([per_day, ddof])

Calculates the standard deviation of the CGM values.

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

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

tir([per_day, interval, percentage, decimals])

Calculates the Time in Range (TIR) for a given target range of glucose.
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