time-restricted-eating-experiments 0.3.0

This library provides functions to analyzes food logging data.

Time Restricted Eating ExperimenTS

Install

pip install time_restricted_eating_experiments

Example for data analysis on the Columbia study

import time_restricted_eating_experiments.columbia as treetsc
import pandas as pd

Take a brief look on the food logging dataset and the reference information sheet

treetsc.read_logging_data('data/col_test_data').head(2)

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	Unnamed: 0	original_logtime	desc_text	food_type	PID
0	0	2021-05-12 02:30:00 +0000	milk	b	yrt1999
1	1	2021-05-12 02:45:00 +0000	some medication	m	yrt1999

pd.read_excel('data/col_test_data/toy_data_17May2021.xlsx').head(2)

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	mCC_ID	Participant_Study_ID	Study Phase	Intervention group (TRE or HABIT)	Start_Day	End_day	Eating_Window_Start	Eating_Window_End
0	yrt1999	2	S-REM	TRE	2021-05-12	2021-05-14	00:00:00	23:59:00
1	yrt1999	2	T3-INT	TRE	2021-05-15	2021-05-18	08:00:00	18:00:00

make the table that contains extra analytic information that we want

df = treetsc.make_table(treetsc.read_logging_data('data/col_test_data')\
                      , pd.read_excel('data/col_test_data/toy_data_17May2021.xlsx'))

Participant yrt1999 didn't log any food items in the following day(s):
2021-05-18
Participant yrt2000 didn't log any food items in the following day(s):
2021-05-12
2021-05-13
2021-05-14
2021-05-15
2021-05-16
2021-05-17
2021-05-18
Participant yrt1999 have bad logging day(s) in the following day(s):
2021-05-12
2021-05-15
Participant yrt1999 have bad window day(s) in the following day(s):
2021-05-15
2021-05-17
Participant yrt1999 have non adherent day(s) in the following day(s):
2021-05-12
2021-05-15
2021-05-17

df

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	mCC_ID	Participant_Study_ID	Study Phase	Intervention group (TRE or HABIT)	Start_Day	End_day	Eating_Window_Start	Eating_Window_End	phase_duration	caloric_entries	...	logging_day_counts	%_logging_day_counts	good_logging_days	%_good_logging_days	good_window_days	%_good_window_days	outside_window_days	%_outside_window_days	adherent_days	%_adherent_days
0	yrt1999	2	S-REM	TRE	2021-05-12	2021-05-14	00:00:00	23:59:00	3 days	7.0	...	3.0	1.00	2.0	0.666667	3.0	1.00	0.0	0.0	2.0	0.666667
1	yrt1999	2	T3-INT	TRE	2021-05-15	2021-05-18	08:00:00	18:00:00	4 days	8.0	...	3.0	0.75	2.0	0.500000	1.0	0.25	2.0	0.5	1.0	0.250000
2	yrt2000	3	T3-INT	TRE	2021-05-12	2021-05-14	08:00:00	16:00:00	3 days	0.0	...	0.0	0.00	0.0	0.000000	0.0	0.00	0.0	0.0	0.0	0.000000
3	yrt2000	3	T3-INT	TRE	2021-05-15	2021-05-18	08:00:00	16:00:00	4 days	0.0	...	0.0	0.00	0.0	0.000000	0.0	0.00	0.0	0.0	0.0	0.000000
4	yrt2001	4	T12-A	TRE	NaT	NaT	NaN	NaN	NaT	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 26 columns

df.iloc[0]

mCC_ID                                           yrt1999
Participant_Study_ID                                   2
Study Phase                                        S-REM
Intervention group (TRE or HABIT)                    TRE
Start_Day                            2021-05-12 00:00:00
End_day                              2021-05-14 00:00:00
Eating_Window_Start                             00:00:00
Eating_Window_End                               23:59:00
phase_duration                           3 days 00:00:00
caloric_entries                                      7.0
mean_daily_eating_window                           13.75
std_daily_eating_window                        11.986972
earliest_entry                                       4.5
2.5%                                              4.5375
97.5%                                            27.5625
duration mid 95%                                  23.025
logging_day_counts                                   3.0
%_logging_day_counts                                 1.0
good_logging_days                                    2.0
%_good_logging_days                             0.666667
good_window_days                                     3.0
%_good_window_days                                   1.0
outside_window_days                                  0.0
%_outside_window_days                                0.0
adherent_days                                        2.0
%_adherent_days                                 0.666667
Name: 0, dtype: object

df.iloc[1]

mCC_ID                                           yrt1999
Participant_Study_ID                                   2
Study Phase                                       T3-INT
Intervention group (TRE or HABIT)                    TRE
Start_Day                            2021-05-15 00:00:00
End_day                              2021-05-18 00:00:00
Eating_Window_Start                             08:00:00
Eating_Window_End                               18:00:00
phase_duration                           4 days 00:00:00
caloric_entries                                      8.0
mean_daily_eating_window                        8.666667
std_daily_eating_window                         8.504901
earliest_entry                                       7.5
2.5%                                                 7.7
97.5%                                               23.9
duration mid 95%                                    16.2
logging_day_counts                                   3.0
%_logging_day_counts                                0.75
good_logging_days                                    2.0
%_good_logging_days                                  0.5
good_window_days                                     1.0
%_good_window_days                                  0.25
outside_window_days                                  2.0
%_outside_window_days                                0.5
adherent_days                                        1.0
%_adherent_days                                     0.25
Name: 1, dtype: object

Example for data analysis using time restricted eating experiments core module

import time_restricted_eating_experiments.core as treets
import pandas as pd

take a look at the original dataset

df = treets.file_loader('data/test_food_details.csv')
df.head(2)

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	Unnamed: 0	ID	unique_code	research_info_id	desc_text	food_type	original_logtime	foodimage_file_name
0	1340147	7572733	alqt14018795225	150	Water	w	2017-12-08 17:30:00+00:00	NaN
1	1340148	411111	alqt14018795225	150	Coffee White	b	2017-12-09 00:01:00+00:00	NaN

preprocess the data to have extra basic features

df = treets.load_public_data(df,4)
df.head(2)

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	Unnamed: 0	ID	unique_code	research_info_id	desc_text	food_type	original_logtime	original_logtime_notz	date	local_time	time	week_from_start	year
0	1340147	7572733	alqt14018795225	150	Water	w	2017-12-08 17:30:00+00:00	2017-12-08 17:30:00+00:00	2017-12-08	17.500000	17:30:00	1	2017
1	1340148	411111	alqt14018795225	150	Coffee White	b	2017-12-09 00:01:00+00:00	2017-12-09 00:01:00+00:00	2017-12-08	24.016667	00:01:00	1	2017

do a brief annalysis

df = treets.summarize_data(df, 'unique_code', 'local_time', 'date')
df.head(2)

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	unique_code	num_days	num_total_items	num_f_n_b	num_medications	num_water	breakfast_avg	breakfast_std	dinner_avg	dinner_std	eating_win_avg	eating_win_std	good_logging_count	breakfast variation (90%-10%)	dinner variation (90%-10%)	2.5%	95%	duration mid 95%
0	alqt1148284857	13	149	96	19	34	7.821795	6.710717	23.485897	4.869082	15.664103	-1.841635	146	2.966667	9.666667	4.535000	26.813333	22.636667
1	alqt14018795225	64	488	484	3	1	7.525781	5.434563	25.858594	3.374839	18.332813	-2.059723	484	13.450000	3.100000	4.183333	27.438333	23.416667

df.iloc[0]

unique_code                      alqt1148284857
num_days                                     13
num_total_items                             149
num_f_n_b                                    96
num_medications                              19
num_water                                    34
breakfast_avg                          7.821795
breakfast_std                          6.710717
dinner_avg                            23.485897
dinner_std                             4.869082
eating_win_avg                        15.664103
eating_win_std                        -1.841635
good_logging_count                          146
breakfast variation (90%-10%)          2.966667
dinner variation (90%-10%)             9.666667
2.5%                                      4.535
95%                                   26.813333
duration mid 95%                      22.636667
Name: 0, dtype: object

df.iloc[1]