Code Reference

This part of the project documentation focuses on an information-oriented approach. Use it as a reference for the technical implementation of the BabelBetes project code.

babelbetes.run_functions

run_functions.py

This script performs data normalization on raw study data found in the data/raw directory.

Execution

python run_functions.py

Process Overview: 1. Identifies the appropriate handler class (subclass of studydataset) for each folder in the data/raw directory (see supported studies). 2. Loads the study data into memory. 3. Extracts bolus, basal, CGM event histories, and age data into a standardized format (see Output Format). 4. Saves the extracted data as CSV files.

Output format:

The outptut format is standardized across all studies and follows the definitions of the studydataset base class.

Boluses

bolus_history.csv: Event stream of all bolus delivery events. Standard boluses are assumed to be delivered immediately.

Column Name	Type	Description
patient_id	str	Patient ID
datetime	pd.Timestamp	Datetime of the bolus event
bolus	float	Actual delivered bolus amount in units
delivery_duration	pd.Timedelta	Duration of the bolus delivery

Basal Rates

basal_history.csv:Event stream of basal rates, accounting for temporary basal adjustments, pump suspends, and closed-loop modes. The basal rates are active until the next rate is reported.

Column Name	Type	Description
patient_id	str	Patient ID
datetime	pd.Timestamp	Datetime of the basal rate start event
basal_rate	float	Basal rate in units per hour

CGM (Continuous Glucose Monitor)

cgm_history.csv: Event stream of CGM values.

Column Name	Type	Description
patient_id	str	Patient ID
datetime	pd.Timestamp	Datetime of the CGM measurement
cgm	float	CGM value in mg/dL

Age Data

age_data.csv: Patient age at study enrollment/start.

Column Name	Type	Description
patient_id	str	Patient ID
age	float	Patient age at study enrollment/start

Output Files:

For each study, the dataframes are saved in the data/out/<study-name>/ folder: - To reduce file size, the data is saved in a compressed format using the gzip - datetimes and timedeltas are saved as unix timestamps (seconds) and integers (seconds) respectively. - boluses and basals are rounded to 4 decimal places - cgm values are converted to integers

main(load_subset=False, remove_repetitive=True, input_dir=None, output_dir=None, studies=None, data_types=None)

Main function to process study data folders.

Parameters:

Name	Type	Description	Default
load_subset	bool	If True, runs the script on a limited amount of data (e.g. skipping rows).	False
input_dir	str	Custom input directory path. Defaults to 'data/raw'.	None
output_dir	str	Custom output directory path. Defaults to 'data/out'.	None
studies	list	List of study names to process. If None, all available studies will be processed. Available studies: IOBP2, Flair, PEDAP, DCLP3, DCLP5, ReplaceBG, Loop, T1DEXI, T1DEXIP	None
data_types	list	List of data types to extract ['cgm', 'bolus', 'basal', 'age']. If None, all types are extracted.	None

Logs

• Information about the current working directory and paths being used.
• Warnings for folders that do not match any known study patterns.
• Errors if no supported studies are found.
• Progress of processing each matched study folder.

process_folder(study, store, progress, remove_repetitive, data_types)

Processes the data for a given study by loading, extracting, and saving bolus, basal, CGM, and age events.

Parameters:

Name	Type	Description	Default
study	StudyDataset	Study instance to extract data from.	required
store	ParquetStore	Store to write data to.	required
progress	tqdm	Progress bar to update.	required
remove_repetitive	bool	Whether to drop repetitive basal values.	required
data_types	list	Data types to extract ['cgm', 'bolus', 'basal', 'age'].	required

babelbetes.studies.studydataset

StudyDataset

Abstract base class for clinical diabetes datasets with CGM, bolus, basal, and age data.

Subclasses implement four abstract methods: - _extract_bolus_event_history: Return bolus events as a DataFrame. - _extract_basal_event_history: Return basal rate events as a DataFrame. - _extract_cgm_history: Return CGM measurements as a DataFrame. - _extract_age_data: Return patient age at enrollment as a DataFrame.

Public properties (bolus, basal, cgm, age) validate output against pandera schemas and cache results via cached_property. Do not override them; override the private _extract_* methods instead.

For memory management when processing multiple studies, declare raw file cache attributes in _raw_attrs and call unload_raw() after extraction is complete.

age property

Patient age at enrollment as a validated, cached DataFrame.

Returns:

Type	Description
	pd.DataFrame: Columns: patient_id (str), age (int, 0–120).

basal property

Basal rate event history as a validated, cached DataFrame.

Notes

• Zero basal rates (pump suspends) must be included.
• Rates are active until the next event.

Returns:

Type	Description
	pd.DataFrame: Columns: patient_id (str), datetime (datetime64), basal_rate (float, units/hour).

bolus property

Bolus event history as a validated, cached DataFrame.

Returns:

Type	Description
	pd.DataFrame: Columns: patient_id (str), datetime (datetime64), bolus (float, units), delivery_duration (timedelta). Standard boluses have delivery_duration of 0 seconds.

cgm property

CGM measurements as a validated, cached DataFrame.

Returns:

Type	Description
	pd.DataFrame: Columns: patient_id (str), datetime (datetime64), cgm (float, mg/dL).

unload_raw()

Free raw file caches from memory.

Call this after all needed data types have been extracted to release the memory used by raw file DataFrames. Derived outputs (bolus, basal, cgm, age) are kept. Raw attributes to clear are declared by subclasses in _raw_attrs.

babelbetes.studies.iobp2.IOBP2

Bases: StudyDataset

babelbetes.studies.flair.Flair

Bases: StudyDataset

get_reported_tdds(method='max')

Retrieves reported total daily doses (TDDs) based on the specified method.

Parameters:

Name	Type	Description	Default
method	str	The method to use for retrieving the TDDs. - 'max': Returns the TDD with the maximum reported value for each patient and date. - 'sum': Returns the sum of all reported TDDs for each patient and date. - 'latest': Returns the TDD with the latest reported datetime for each patient and date. - 'all': Returns all TDDs without any grouping or filtering.	'max'

Returns:

Type	Description
DataFrame	The DataFrame containing the retrieved TDDs based on the specified method.

Raises:

Type	Description
ValueError	If the method is not one of: 'max', 'sum', 'latest', 'all'.

babelbetes.studies.pedap.PEDAP

Bases: StudyDataset

babelbetes.studies.dclp.DCLP3

Bases: StudyDataset

babelbetes.studies.dclp.DCLP5

Bases: DCLP3

babelbetes.studies.loop.Loop

Bases: StudyDataset

babelbetes.studies.t1dexi.T1DEXI

Bases: StudyDataset

babelbetes.studies.t1dexi.T1DEXIP

Bases: T1DEXI

babelbetes.studies.replacebg.ReplaceBG

Bases: StudyDataset

babelbetes.src

cdf

get_cdf(data, normalize=True)

Get the Cumulative Distribution Function (CDF) of a data array.

Parameters: data (array-like): The data array for which the CDF is to be calculated.

tuple: A tuple containing two elements: - data_sorted (array-like): The sorted data array. - cdf (array-like): The CDF values.

plot_cdf(data, title='CDF', xlabel='Value', ylabel='CDF', ax=None, log_scaled=False, percent_right_axis=False, **kwargs)

Plots the Cumulative Distribution Function (CDF) of a data array.

Parameters: data (array-like): The data array for which the CDF is to be plotted. title (str): The title of the plot. xlabel (str): The label for the x-axis. ylabel (str): The label for the y-axis. ax (matplotlib.axes._subplots.AxesSubplot): Optional axis to plot on. log_scaled (bool): Whether to apply log scale to the y-axis. percent_right_axis (bool): Whether to show the right axis in percent.

data_store

ParquetStore

Read/write interface for the partitioned Parquet output store.

Data is stored as Hive-partitioned Parquet files under base_path, with each data type in its own subdirectory to keep schemas homogeneous:

base_path/cgm/study_name=X/patient_id=Z/*.parquet
base_path/bolus/study_name=X/patient_id=Z/*.parquet
base_path/basal/study_name=X/patient_id=Z/*.parquet
base_path/age/study_name=X/patient_id=Z/*.parquet

Parameters:

Name	Type	Description	Default
base_path	str	Root directory of the store (e.g. "data/out").	required

cleanup(study_name, data_types=None)

Remove existing output for a study to ensure a clean write.

Parameters:

Name	Type	Description	Default
study_name	str	Study whose output should be removed.	required
data_types	list	Specific data types to remove. If None, removes the study from all data type directories.	None

Returns:

Name	Type	Description
list		Paths that were actually removed.

load(study=None, data_type=None, patient=None)

Load data from the store, optionally filtered by study, data type, or patient.

Parameters:

Name	Type	Description	Default
study	str | list[str]	Filter by study name(s) (e.g. "Flair" or ["Flair", "DCLP3"]).	None
data_type	str | list[str]	One or more of 'cgm', 'bolus', 'basal', 'age'. A single string returns a DataFrame; a list returns a dict[str, DataFrame].	None
patient	str | list[str]	Filter by patient ID(s).	None

Returns:

Type	Description
	pd.DataFrame: When data_type is a single string.
	dict[str, pd.DataFrame]: When data_type is a list or None (keyed by data type).

save(df, study_name, data_type)

Write a DataFrame to the store for a given study and data type.

Parameters:

Name	Type	Description	Default
df	DataFrame	Data to save.	required
study_name	str	Study identifier (e.g. "Flair").	required
data_type	str	One of 'cgm', 'bolus', 'basal', 'age'.	required

date_helper

convert_duration_to_timedelta(duration)

Parse a duration string in the format "hours:minutes:seconds" and return a timedelta object. Args: duration_str (str): The duration string to parse in the form of "hours:minutes:seconds". Returns: timedelta: A timedelta object representing the parsed duration.

parse_flair_dates(dates, format_date='%m/%d/%Y', format_time='%I:%M:%S %p')

Optimized parsing of date strings with or without time components.

drawing

create_axis()

Creates a new figure and axis for plotting.

Returns:

Name	Type	Description
figure	Figure	The created figure.
axes	Axes	The created axis.

drawAbsoluteBasalRates(ax, datetimes, rate, **kwargs)

Draws the absolute basal rates on the given axes.

Parameters:

Name	Type	Description	Default
ax	Axes	The axes on which to draw the basal rates.	required
datetimes	array - like	An array of datetime objects representing the time points.	required
rate	array - like	An array of basal rates corresponding to the time points.	required
**kwargs	dict	Additional keyword arguments to customize the plot. Possible keys include: 'hatch' (str): The hatch pattern for the plot. Default is '//'. 'label' (str): The label for the plot. Default is 'true basal rate'. 'edgecolor' (str): The edge color for the plot. Default is 'black'.	{}

drawBasal(ax, datetimes, rates, color=colors['Basal'], **kwargs)

Draws the basal rates on the given axes.

Parameters:

Name	Type	Description	Default
ax	Axes	The axes on which to draw the basal rates.	required
datetimes	list of datetime	List of datetime objects representing the time points.	required
rates	list of float	List of basal rates corresponding to the datetime points.	required
color	str	Color for the basal rates plot. Defaults to colors['Basal'].	colors['Basal']
**kwargs	dict	Additional keyword arguments to customize the plot.	{}

drawBoluses(ax, datetimes, boluses, **kwargs)

Draws insulin boluses events on a given matplotlib axis.

Parameters:

Name	Type	Description	Default
ax	Axes	The axis on which to draw the boluses.	required
datetimes	list of datetime.datetime	List of datetime objects representing the times of the boluses.	required
boluses	list of float	List of bolus values corresponding to the datetimes.	required
**kwargs	dict	Additional keyword arguments passed to the ax.bar() method.	{}

drawCGM(ax, datetimes, values, color=colors['CGM'], unit='mg/dL', target_range=True, **kwargs)

Draws CGM (Continuous Glucose Monitoring) data on the given axes.

Parameters:

Name	Type	Description	Default
ax	Axes	The axes on which to draw the CGM data.	required
datetimes	list of datetime	List of datetime objects representing the time points.	required
values	list of float	List of glucose values corresponding to the datetime points.	required
color	str	Color for the CGM plot. Defaults to colors['CGM'].	colors['CGM']
**kwargs	dict	Additional keyword arguments to customize the plot.	{}

drawExtendedBoluses(ax, datetimes, boluses_units, duration, color=colors['Bolus'], **kwargs)

Draws extended boluses on the given axes.

Parameters:

Name	Type	Description	Default
ax	Axes	The axes on which to draw the boluses.	required
datetimes	list of datetime	List of datetime objects representing the times of the boluses.	required
boluses_units	list of float	List of bolus units corresponding to each datetime.	required
duration	list of numpy.timedelta	List of delivery duration for each bolus.	required
color	str	Color of the boluses. Default is colors['Bolus'].	colors['Bolus']
**kwargs	dict	Additional keyword arguments to pass to the bar function.	{}

drawSuspendTimes(ax, start_date, duration)

Draws a bar on the given axis to represent suspend times.

Parameters:

Name	Type	Description	Default
ax	Axes	The axis on which to draw the bar.	required
start_date	datetime - like	The starting date and time for the bar.	required
duration	timedelta	The duration for which the bar extends.	required

drawTempBasal(ax, datetimes, temp_basal_rates, temp_basal_durations, temp_basal_types, color=colors['Basal'], **kwargs)

Draws temporary basal rates on the given axes.

Parameters:

Name	Type	Description	Default
ax	Axes	The axes on which to draw the temporary basal rates.	required
datetimes	list of datetime	List of datetime objects representing the times of the temporary basal rates.	required
temp_basal_rates	list of float	List of temporary basal rates corresponding to the datetimes.	required
temp_basal_durations	list of numpy.timedelta	List of temporary basal durations corresponding to the datetimes.	required
color	str	Color of the temporary basal rates. Default is colors['Basal'].	colors['Basal']
**kwargs	dict	Additional keyword arguments passed to the ax.bar() method.	{}

draw_presence_matrix(ax, df, x_col, y_col, offset=0, **kwargs)

Scatter plots unique available x_values for y_col groups in a DataFrame. Args: ax (matplotlib.axes.Axes): The matplotlib Axes object to plot on. df (pd.DataFrame): The input DataFrame containing the data to plot. x_col (str): The column name in the DataFrame representing the x-axis values (e.g., datetime). y_col (str): The column name in the DataFrame representing the y-axis values used for grouping (e.g., patient IDs). offset (int, optional): An offset to apply to the y-axis values. Defaults to 0. **kwargs: Additional keyword arguments to pass to the ax.scatter method. Returns: None

format_time_axis(ax, major_interval_days=1, minor_interval_hours=2, major_format='%-d/%-m/%Y', minor_format='%H:%M:%S')

Formats the x-axis of the given matplotlib axis for time series plots. Sets major ticks to days and minor ticks to hours, with appropriate formatting.

Parameters:

Name	Type	Description	Default
ax	Axes	The axis to format.	required

parse_duration(duration_str)

Parses a duration string in the format "HH:MM:SS" and returns a timedelta object.

Parameters:

Name	Type	Description	Default
duration_str	str	A string representing the duration in the format "HH:MM:SS".	required

Returns: timedelta: A timedelta object representing the parsed duration.

find_periods

find_periods(df, value_col, time_col, start_trigger_fun, stop_trigger_fun, use_last_start_occurence=False)

Find periods in a DataFrame based on start and stop triggers.

Parameters:

Name	Type	Description	Default
df	DataFrame	The DataFrame to search for periods.	required
value_col	str	The name of the column containing the trigger values.	required
time_col	str	The name of the column containing the time values.	required
start_trigger_fun	callable	The value that indicates the start of a period.	required
stop_trigger_fun	callable	The value that indicates the end of a period.	required
use_last_start_occurence	bool	If True, the last occurrence of the start trigger will be used.	False

Returns:

Name	Type	Description
list	list	A list of named tuples representing the periods found. Each namedtuple contains the following attributes: - start_index (int): The index of the start trigger in the DataFrame. - end_index (int): The index of the stop trigger in the DataFrame. - start_time: The time value of the start trigger. - end_time: The time value of the stop trigger.

logger

Logger

get_logger(name, level=logging.DEBUG) staticmethod

Returns a configured logger instance with the specified name and log level.

Parameters:

Name	Type	Description	Default
name	str	The name of the logger.	required
level	int	The logging level (e.g., logging.INFO, logging.DEBUG).	DEBUG

Returns:

Type	Description
Logger	logging.Logger: Configured logger.

pandas_helper

count_differences_in_duplicates(df, subset)

Counts the number of differences between duplicated rows for all columns.

Parameters:

Name	Type	Description	Default
df	DataFrame	The input DataFrame.	required

Returns:

Name	Type	Description
series	Series	A series where the index represents column names and values represent the count of differences.

extract_surrounding_rows(df, index, n, sort_by)

Extracts rows surrounding a given index after sorting the DataFrame by a subset of columns.

Parameters:

Name	Type	Description	Default
df	DataFrame	The input DataFrame.	required
index	int	The row index to center on.	required
n	int	The number of rows before and after the given index to extract (using logical indexing).	required
sort_by	list	List of column names to sort the DataFrame by.	required

Returns: pd.DataFrame: A DataFrame containing the extracted rows.

get_df(path, usecols=None, subset=False, dtype=None, encoding=None)

Reads a data file from a given path, handling both standard file formats and files within ZIP archives.

Parameters:

Name	Type	Description	Default
path	str	The file path or a path to a file inside a ZIP archive.	required
usecols	list	List of column names to include in the df.	None
subset	bool	If True, read only a subset of the data for lightweight testing.	False
dtype	dict	Data types to enforce for specific columns.	None
encoding	str	Encoding to use when reading text files (csv/txt). If None, uses pandas default (utf-8).	None

Returns:

Name	Type	Description
dataframe	DataFrame	The loaded data as a Pandas DataFrame.

get_duplicated_max_indexes(df, check_cols, max_col)

Find duplicate indexes, maximum indexes, and indexes to drop in a dataframe.

Args: df (pd.DataFrame): The dataframe to check for duplicates. check_cols (list): The columns to check for duplicates. max_col (str): The column to use for keeping the maximum value.

tuple: A tuple containing three elements: - duplicated_indexes (np.array): Indexes of duplicated rows. - max_indexes (np.array): Indexes of rows with the maximum value in the max_col. - drop_indexes (np.array): Indexes of rows to drop.

Example

Example usage get duplicated max indexes

df = pd.DataFrame({ 'PtID': [1, 1, 1, 2, 2, 2, 3, 3, 3, 1], 'DataDtTm': [1, 2, 3, 1, 2, 2, 1, 1, 1, 2], 'CGMValue': [1, 2, 3, 1, 2, 3, 4, 2, 3, 3] }) dup_indexes, max_indexes, drop_indexes = get_duplicated_max_indexes(df, ['PtID', 'DataDtTm'], 'CGMValue') print(df.drop(drop_indexes))

grouped_value_counts(df, group_cols, value_cols)

Count the number of NaN, Non-NaN, and Zero values in each group of a DataFrame.

Parameters:

Name	Type	Description	Default
df	DataFrame	The input DataFrame.	required
group_cols	str or list	The column(s) to group by.	required
value_cols	str or list	The column(s) to count values for.	required

Returns:

Name	Type	Description
dataframe	DataFrame	A DataFrame containing the count of NaN, Non-NaN, and Zero values for each group.

head_tail(df, n=2)

Returns the first n rows and the last n rows of a DataFrame.

Parameters:

Name	Type	Description	Default
df	DataFrame	The DataFrame to get the head and tail of.	required
n	int	The number of rows to return from the head and tail of the DataFrame.	2

Returns:

Name	Type	Description
dataframe	DataFrame	A new pandas dataframe containing The first n rows of the DataFrame. The last n rows of the DataFrame.

overlaps(df, datetime_col, duration_col)

Check for overlapping intervals in a DataFrame.

Parameters:

Name	Type	Description	Default
df	DataFrame	A DataFrame containing at least two columns: - 'datetime_col': Start times of the intervals. - 'duration_col': Durations of the intervals.	required

Returns: pd.Series: A boolean Series indicating whether each interval overlaps with the next interval

repetitive(df, datetime_col, value_col, max_duration)

Get the indexes of repetitive values in a DataFrame based on a datetime column and a value column. Args: df (pd.DataFrame): The DataFrame to process. datetime_col (str): The name of the datetime column. value_col (str): The name of the value column. max_duration (timedelta, optional): To prevent long gaps between values, this parameter is used define the max duration for which consecutive values are dropped. At least one value will be kept whenever duration exceeds tha map_duration.

Returns:

Name	Type	Description
tuple	tuple	A tuple containing three elements: - i_all_rep (np.array): Indexes of all repetitive values. - i_keep (np.array): Indexes of the first occurrence of repetitive values. - i_drop (np.array): Indexes of values to drop (to remove repetitive values after the first occurrence).

split_groups(x, threshold)

Assigns unique group IDs based on the distance between consecutive values.

Parameters:

Name	Type	Description	Default
x	Series	Series of numerical values.	required
threshold	same as x.diff()	The maximum distance between two consecutive values to consider them in the same group. Must be same type as x.diff() values (e.g. int, float, Timedelta)	required

Returns:

Type	Description
Series	The Series containing the data.

Example

df = pd.DataFrame({'sensor': ['a', 'a', 'b', 'b', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'b'], 'y': [0, 1, 2, 3, 10, 11, 12, 13, 50, 51, 70, 71]}) df['sensor_session'] = df.groupby('sensor').y.transform(lambda x: split_groups(x, 5)) start_ends = df.groupby(['sensor', 'sensor_session']).y.agg(['idxmin','idxmax']).reset_index()

split_sequences(df, label_col)

Assigns a unique group ID to each sequence of consecutive labels.

Parameters:

Name	Type	Description	Default
df	DataFrame	The DataFrame containing the data.	required
label_col	str	The column name for the labels.	required

Returns:

Name	Type	Description
group_ids	Series	The group IDs.

Example

df = pd.DataFrame({'label': ['A', 'A', 'B', 'B', 'B', 'A', 'A', 'C', 'C', 'A']}) df['sequence'] = split_sequences(df, 'label') print(df) start_ends = df.groupby(['label', 'sequence']).apply(lambda group: pd.Series({ 'idxmin': group.index.min(), 'idxmax': group.index.max() }),include_groups=False).reset_index() print(start_ends)

postprocessing

basal_transform(basal_data)

Transform the basal data by aligning timestamps and handling duplicates.

Parameters:

Name	Type	Description	Default
basal_data	DataFrame	The input is a basal data dataframe containing columns 'datetime', and 'basal_rate'.	required

Returns:

Name	Type	Description
basal_data	DataFrame	The transformed basal equivalent deliveries with aligned timestamps and duplicates removed.

bolus_transform(df)

Transform the bolus data by aligning timestamps, handling duplicates, and extending boluses based on durations.

Parameters:

Name	Type	Description	Default
df	DataFrame	The input is a bolus data dataframe containing columns 'datetime', 'bolus', and 'delivery_duration'.	required

Returns:

Name	Type	Description
bolus_data	DataFrame	5 Minute resampled and time aligned at midnight bolus data with columns: datetime, delivery

cgm_transform(cgm_data)

Time aligns the cgm data to midnight with a 5 minute sampling rate.

Parameters:

Name	Type	Description	Default
cgm_data	DataFrame	The input is a cgm data dataframe containing columns 'datetime', and 'cgm'.	required

Returns:

Name	Type	Description
cgm_data	DataFrame	The transformed cgm data with aligned timestamps.

tdd

calculate_daily_basal_dose(df)

Calculate the Total Daily Dose (TDD) of basal insulin for each day in the given DataFrame.

Parameters:

Name	Type	Description	Default
df	DataFrame	The DataFrame containing the insulin data.	required

Returns:

Name	Type	Description
tdds	DataFrame	dataframe with two columns: date and dose golding the daily total basal dose.

Required Column Names

• datetime: The timestamp of each basal insulin rate event.
• basal_rate: The basal insulin rate event [U/hr].

calculate_daily_bolus_dose(df)

Calculate the daily bolus dose for each patient. Parameters: df (pandas.DataFrame): The input DataFrame containing the following columns: - datetime (datetime): The date and time of the bolus dose. - bolus (float): The amount of bolus dose. Returns: pandas.DataFrame: A DataFrame with the daily bolus dose for each patient, grouped by patient_id and date.

calculate_tdd(df_bolus, df_basal)

Calculates the total daily dose (TDD) by merging the daily basal dose and daily bolus dose. Parameters: df_bolus (DataFrame): DataFrame containing the bolus dose data. - patient_id (int): The ID of the patient. - datetime (datetime): The date and time of the bolus dose. - bolus (float): The amount of bolus dose. df_basal (DataFrame): DataFrame containing the basal dose data. - patient_id (int): The ID of the patient. - datetime (datetime): The date and time of the basal dose. - basal_rate (float): The basal insulin rate event [U/hr]. Returns: tdd (DataFrame): DataFrame containing both the bolus and basal tdd data.

total_delivered(df, datetime_col, rate_col)

Calculate the total delivered insulin over the time intervals in the given DataFrame.