treets 1.0.5

This library provides functions to analyzes food logging data.

TREETS

#hide
from treets import *

Install

pip install treets

Example for a quick data analysis on phased studies.

import treets.core as treets
import pandas as pd

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

treets.file_loader('data/col_test_data/yrt*').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

Call summarize_data_with_experiment_phases() function to make the table that contains analytic information that we want.

df = treets.summarize_data_with_experiment_phases(treets.file_loader('data/col_test_data/yrt*')\
                      , 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_num	...	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	...	3	100.0%	2.0	66.67%	3.0	100.0%	0.0	0.0%	2.0	66.67%
1	yrt1999	2	T3-INT	TRE	2021-05-15	2021-05-18	08:00:00	18:00:00	4 days	8	...	3	75.0%	2.0	50.0%	1.0	25.0%	2.0	50.0%	1.0	25.0%
2	yrt2000	3	T3-INT	TRE	2021-05-12	2021-05-14	08:00:00	16:00:00	3 days	0	...	0	0.0%	0.0	0.0%	0.0	0.0%	0.0	0.0%	0.0	0.0%
3	yrt2000	3	T3-INT	TRE	2021-05-15	2021-05-18	08:00:00	16:00:00	4 days	0	...	0	0.0%	0.0	0.0%	0.0	0.0%	0.0	0.0%	0.0	0.0%
4	yrt2001	4	T12-A	TRE	NaT	NaT	NaN	NaN	NaT	0	...	0	nan%	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 32 columns

Look at resulting statistical information for the first row in the resulting dataset.

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_num                                    7
medication_num                                         0
water_num                                              0
first_cal_avg                                   5.916667
first_cal_std                                   2.240722
last_cal_avg                                   19.666667
last_cal_std                                   12.933323
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
%_logging_day_counts                              100.0%
good_logging_days                                    2.0
%_good_logging_days                               66.67%
good_window_days                                     3.0
%_good_window_days                                100.0%
outside_window_days                                  0.0
%_outside_window_days                               0.0%
adherent_days                                        2.0
%_adherent_days                                   66.67%
Name: 0, dtype: object

Example for a quick data analysis on non-phased studies.

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 create features we might need in the furthur analysis such as float time, week count since the first week, etc.

df = treets.load_food_data(df,'unique_code', 'original_logtime',4)
df.head(2)

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

Call summarize_data() function to make the table that contains analytic information that we want.¶

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

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	unique_code	num_days	num_total_items	num_f_n_b	num_medications	num_water	first_cal_avg	first_cal_std	last_cal_avg	last_cal_std	eating_win_avg	eating_win_std	good_logging_count	first_cal variation (90%-10%)	last_cal 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	8.231201	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	6.603913	484	13.450000	3.100000	4.183333	27.438333	23.416667

Look at resulting statistical information for the first row in the resulting dataset.

df.iloc[0]

unique_code                      alqt1148284857
num_days                                     13
num_total_items                             149
num_f_n_b                                    96
num_medications                              19
num_water                                    34
first_cal_avg                          7.821795
first_cal_std                          6.710717
last_cal_avg                          23.485897
last_cal_std                           4.869082
eating_win_avg                        15.664103
eating_win_std                         8.231201
good_logging_count                          146
first_cal variation (90%-10%)          2.966667
last_cal variation (90%-10%)           9.666667
2.5%                                      4.535
95%                                   26.813333
duration mid 95%                      22.636667
Name: 0, dtype: object

Clean text in food loggings

# import the dataset
df = treets.file_loader('data/col_test_data/yrt*')
df.head(3)

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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
2	2	2021-05-12 04:45:00 +0000	bacon egg	f	yrt1999

treets.clean_loggings(df, 'desc_text', 'PID').head(3)

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	PID	desc_text	cleaned
0	yrt1999	Milk	[milk]
1	yrt1999	Some Medication	[medication]
2	yrt1999	bacon egg	[bacon, egg]

We can see that words are lower cased, modifiers are removed(2nd row) and items are split into individual items(third row).

Visualizations

# import the 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

make a scatter plot for people’s breakfast time

# create required features for function first_cal_mean_with_error_bar()
df['original_logtime'] = pd.to_datetime(df['original_logtime'])
df['local_time'] = treets.find_float_time(df, 'original_logtime')
df['date'] = treets.find_date(df, 'original_logtime')

# call the function
treets.first_cal_mean_with_error_bar(df,'unique_code', 'date', 'local_time')

Use swarmplot to visualize each person’s eating time distribution.

treets.swarmplot(df, 50, 'unique_code', 'date', 'local_time')