diyaps-toolbox 0.0.2

Package for working with DIYAPs files

DIYAPS Toolbox

The DIYAPS (Do-it-yourself Artificial Pancreas) Toolbox provides a set of functions for working with the data which can be extracted from AndroidAPS, Loop (iOS) and OpenAPS systems. The following table provides an overview of all the data items which can be calculated using this module. Each of the functions is explained in detail below:

Data Item	Description	Function
Days of data donated	The period of time contained in the donated data based on the earliest and latest timestamp	calculate_days_donated(date, date_format = "%Y-%m-%d-%H:%M:%S")
Mean sensor glucose	Mean of all sensor glucose values in the Entries file	calculate_glucose_mean(glucose_values)
Standard deviation of sensor glucose	Standard deviation of all sensor glucose values in the Entries file	calculate_glucose_sd(glucose_values)
Coefficient of Variation of sensor glucose	Coefficient of variation of all sensor glucose values in the Entries file	calculate_glucose_cov(glucose_values)
Glucose Managment Indicator	Function for estimating the HbA1c value based on mean sensor glucose, as proposed by [1].	calculate_gmi(glucose_values)
% Time in Range	The time spent within the optimal range (by default set to 70 and 180 mg/dL) as a percentage	calculate_time_in_range(glucose_values, lower= 70, upper= 180)
% Time below Range	Calculates the time spent below a certain threshold value (default = 70 mg/dL)	calculate_time_below_range(glucose_values, lower = 70)
% Time above Range	Calculates the time spent above a certain threshold value (default= 180 mg/dL)	calculate_time_above_range(glucose_values, upper = 180)
Number of hypoglcemic episodes per day	The mean number of hypoglycemic episodes per day. This function considers how often the blood glucose level dropped below the threshold (default = 70 mg/dL)	calculate_hypos_per_day(glucose_values, date, lower= 70, date_format = "%Y-%m-%d-%H:%M:%S")
Number of hyperglycemic episodes per day	The mean number of hyperglycemic episodes per day. This function considers how often the blood glucose level rose above the threshold (default = 180 mg/dL)	calculate_hypers_per_day(glucose_values, date, upper= 180, date_format = "%Y-%m-%d-%H:%M:%S")
Percentage of persistent hypoglcemic episodes	The amount of hypoglycemic episodes that persisted for longer than 2 hours, as a percentage of all hypoglycemic episodes	calculate_persistent_hypo(glucose_values, lower= 70)
Percentage of persistent hyperglcemic episodes	The amount of hyperglycemic episodes that persisted for longer than 4 hours, as a percentage of all hypoglycemic episodes	calculate_persistent_hyper(glucose_values, upper = 180)
Percentage of overtreated hypoglcemic episodes	The percentage of hypoglycemic episodes that resulted in subsequent hyperglycemia within 2 hours	calculate_overtreated_hypo(glucose_values, lower= 70, upper = 180)
Percentage of overtreated hyperglcemic episodes	The percentage of hyperglycemic episodes that resulted in subsequent hypoglycemia within 4 hours	calculate_overtreated_hyper(glucose_values, upper= 180, lower = 70)
Number of meal boluses per day	The mean number of meal boluses per day	calculate_meal_bolus_per_day(treatment_df, date_format = "%Y-%m-%d-%H:%M:%S")
Number of correction boluses per day	The mean number of correction boluses per day	calculate_correction_bolus_per_day(treatment_df, date_format = "%Y-%m-%d-%H:%M:%S")
Grams of carbohydrates per day	The mean number of carbohydrates entered into the system per day	calculate_carbs_per_day(treatment_df, date_format = "%Y-%m-%d-%H:%M:%S")
Units of correction insulin per day	The mean number of insulin units administered by the system as a correction bolus per day	calculate_correction_units_per_day(treatment_df, date_format = "%Y-%m-%d-%H:%M:%S")
Grams of carbs per meal	The mean number of carbohydrates per meal bolus	calculate_carbs_per_bolus(treatment_df)
Units of insulin per correction dose	The mean number of insulin units per correction bolus	calculate_correction_units_per_day(treatment_df, date_format = "%Y-%m-%d-%H:%M:%S")

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Installation

pip install diyaps_toolbox

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[1]: Richard M. Bergenstal, Roy W. Beck, Kelly L. Close, George Grunberger, David B. Sacks, Aaron Kowalski, Adam S. Brown, Lutz Heinemann, Grazia Aleppo, Donna B. Ryan, Tonya D. Riddlesworth, and William T. Cefalu. “Glucose Management Indica- tor (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring”. In: Diabetes Care 41.11 (Sept. 2018), pages 2275–2280. doi: 10.2337/dc18-1581. url: https://doi.org/10.2337/dc18-1581.