# TREETS
> Time Restricted Eating ExperimenTS.
## Install
`pip install treets`
## Example for a quick data analysis on phased studies.
```python
import treets.core as treets
import pandas as pd
```
Take a brief look on the food logging dataset and the reference information sheet
```python
treets.file_loader('data/col_test_data/yrt*').head(2)
```
<div>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Unnamed: 0</th>
<th>original_logtime</th>
<th>desc_text</th>
<th>food_type</th>
<th>PID</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0</td>
<td>2021-05-12 02:30:00 +0000</td>
<td>Milk</td>
<td>b</td>
<td>yrt1999</td>
</tr>
<tr>
<th>1</th>
<td>1</td>
<td>2021-05-12 02:45:00 +0000</td>
<td>Some Medication</td>
<td>m</td>
<td>yrt1999</td>
</tr>
</tbody>
</table>
</div>
```python
pd.read_excel('data/col_test_data/toy_data_17May2021.xlsx').head(2)
```
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>mCC_ID</th>
<th>Participant_Study_ID</th>
<th>Study Phase</th>
<th>Intervention group (TRE or HABIT)</th>
<th>Start_Day</th>
<th>End_day</th>
<th>Eating_Window_Start</th>
<th>Eating_Window_End</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>yrt1999</td>
<td>2</td>
<td>S-REM</td>
<td>TRE</td>
<td>2021-05-12</td>
<td>2021-05-14</td>
<td>00:00:00</td>
<td>23:59:00</td>
</tr>
<tr>
<th>1</th>
<td>yrt1999</td>
<td>2</td>
<td>T3-INT</td>
<td>TRE</td>
<td>2021-05-15</td>
<td>2021-05-18</td>
<td>08:00:00</td>
<td>18:00:00</td>
</tr>
</tbody>
</table>
</div>
Call summarize_data_with_experiment_phases() function to make the table that contains analytic information that we want.
```python
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
```python
df
```
<div>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>mCC_ID</th>
<th>Participant_Study_ID</th>
<th>Study Phase</th>
<th>Intervention group (TRE or HABIT)</th>
<th>Start_Day</th>
<th>End_day</th>
<th>Eating_Window_Start</th>
<th>Eating_Window_End</th>
<th>phase_duration</th>
<th>caloric_entries_num</th>
<th>...</th>
<th>logging_day_counts</th>
<th>%_logging_day_counts</th>
<th>good_logging_days</th>
<th>%_good_logging_days</th>
<th>good_window_days</th>
<th>%_good_window_days</th>
<th>outside_window_days</th>
<th>%_outside_window_days</th>
<th>adherent_days</th>
<th>%_adherent_days</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>yrt1999</td>
<td>2</td>
<td>S-REM</td>
<td>TRE</td>
<td>2021-05-12</td>
<td>2021-05-14</td>
<td>00:00:00</td>
<td>23:59:00</td>
<td>3 days</td>
<td>7</td>
<td>...</td>
<td>3</td>
<td>100.0%</td>
<td>2.0</td>
<td>66.67%</td>
<td>3.0</td>
<td>100.0%</td>
<td>0.0</td>
<td>0.0%</td>
<td>2.0</td>
<td>66.67%</td>
</tr>
<tr>
<th>1</th>
<td>yrt1999</td>
<td>2</td>
<td>T3-INT</td>
<td>TRE</td>
<td>2021-05-15</td>
<td>2021-05-18</td>
<td>08:00:00</td>
<td>18:00:00</td>
<td>4 days</td>
<td>8</td>
<td>...</td>
<td>3</td>
<td>75.0%</td>
<td>2.0</td>
<td>50.0%</td>
<td>1.0</td>
<td>25.0%</td>
<td>2.0</td>
<td>50.0%</td>
<td>1.0</td>
<td>25.0%</td>
</tr>
<tr>
<th>2</th>
<td>yrt2000</td>
<td>3</td>
<td>T3-INT</td>
<td>TRE</td>
<td>2021-05-12</td>
<td>2021-05-14</td>
<td>08:00:00</td>
<td>16:00:00</td>
<td>3 days</td>
<td>0</td>
<td>...</td>
<td>0</td>
<td>0.0%</td>
<td>0.0</td>
<td>0.0%</td>
<td>0.0</td>
<td>0.0%</td>
<td>0.0</td>
<td>0.0%</td>
<td>0.0</td>
<td>0.0%</td>
</tr>
<tr>
<th>3</th>
<td>yrt2000</td>
<td>3</td>
<td>T3-INT</td>
<td>TRE</td>
<td>2021-05-15</td>
<td>2021-05-18</td>
<td>08:00:00</td>
<td>16:00:00</td>
<td>4 days</td>
<td>0</td>
<td>...</td>
<td>0</td>
<td>0.0%</td>
<td>0.0</td>
<td>0.0%</td>
<td>0.0</td>
<td>0.0%</td>
<td>0.0</td>
<td>0.0%</td>
<td>0.0</td>
<td>0.0%</td>
</tr>
<tr>
<th>4</th>
<td>yrt2001</td>
<td>4</td>
<td>T12-A</td>
<td>TRE</td>
<td>NaT</td>
<td>NaT</td>
<td>NaN</td>
<td>NaN</td>
<td>NaT</td>
<td>0</td>
<td>...</td>
<td>0</td>
<td>nan%</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
</tr>
</tbody>
</table>
<p>5 rows × 32 columns</p>
</div>
Look at resulting statistical information for the first row in the resulting dataset.
```python
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
```python
df = treets.file_loader('data/test_food_details.csv')
df.head(2)
```
<div>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Unnamed: 0</th>
<th>ID</th>
<th>unique_code</th>
<th>research_info_id</th>
<th>desc_text</th>
<th>food_type</th>
<th>original_logtime</th>
<th>foodimage_file_name</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>1340147</td>
<td>7572733</td>
<td>alqt14018795225</td>
<td>150</td>
<td>Water</td>
<td>w</td>
<td>2017-12-08 17:30:00+00:00</td>
<td>NaN</td>
</tr>
<tr>
<th>1</th>
<td>1340148</td>
<td>411111</td>
<td>alqt14018795225</td>
<td>150</td>
<td>Coffee White</td>
<td>b</td>
<td>2017-12-09 00:01:00+00:00</td>
<td>NaN</td>
</tr>
</tbody>
</table>
</div>
preprocess the data to create features we might need in the furthur analysis such as float time, week count since the first week, etc.
```python
df = treets.load_food_data(df,'unique_code', 'original_logtime',4)
df.head(2)
```
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Unnamed: 0</th>
<th>ID</th>
<th>unique_code</th>
<th>research_info_id</th>
<th>desc_text</th>
<th>food_type</th>
<th>original_logtime</th>
<th>date</th>
<th>float_time</th>
<th>time</th>
<th>week_from_start</th>
<th>year</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>1340147</td>
<td>7572733</td>
<td>alqt14018795225</td>
<td>150</td>
<td>Water</td>
<td>w</td>
<td>2017-12-08 17:30:00+00:00</td>
<td>2017-12-08</td>
<td>17.500000</td>
<td>17:30:00</td>
<td>1</td>
<td>2017</td>
</tr>
<tr>
<th>1</th>
<td>1340148</td>
<td>411111</td>
<td>alqt14018795225</td>
<td>150</td>
<td>Coffee White</td>
<td>b</td>
<td>2017-12-09 00:01:00+00:00</td>
<td>2017-12-08</td>
<td>24.016667</td>
<td>00:01:00</td>
<td>1</td>
<td>2017</td>
</tr>
</tbody>
</table>
</div>
Call summarize_data() function to make the table that contains analytic information that we want.¶
```python
df = treets.summarize_data(df, 'unique_code', 'float_time', 'date')
df.head(2)
```
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>unique_code</th>
<th>num_days</th>
<th>num_total_items</th>
<th>num_f_n_b</th>
<th>num_medications</th>
<th>num_water</th>
<th>first_cal_avg</th>
<th>first_cal_std</th>
<th>last_cal_avg</th>
<th>last_cal_std</th>
<th>eating_win_avg</th>
<th>eating_win_std</th>
<th>good_logging_count</th>
<th>first_cal variation (90%-10%)</th>
<th>last_cal variation (90%-10%)</th>
<th>2.5%</th>
<th>95%</th>
<th>duration mid 95%</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>alqt1148284857</td>
<td>13</td>
<td>149</td>
<td>96</td>
<td>19</td>
<td>34</td>
<td>7.821795</td>
<td>6.710717</td>
<td>23.485897</td>
<td>4.869082</td>
<td>15.664103</td>
<td>8.231201</td>
<td>146</td>
<td>2.966667</td>
<td>9.666667</td>
<td>4.535000</td>
<td>26.813333</td>
<td>22.636667</td>
</tr>
<tr>
<th>1</th>
<td>alqt14018795225</td>
<td>64</td>
<td>488</td>
<td>484</td>
<td>3</td>
<td>1</td>
<td>7.525781</td>
<td>5.434563</td>
<td>25.858594</td>
<td>3.374839</td>
<td>18.332813</td>
<td>6.603913</td>
<td>484</td>
<td>13.450000</td>
<td>3.100000</td>
<td>4.183333</td>
<td>27.438333</td>
<td>23.416667</td>
</tr>
</tbody>
</table>
</div>
Look at resulting statistical information for the first row in the resulting dataset.
```python
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
```python
# import the dataset
df = treets.file_loader('data/col_test_data/yrt*')
df.head(3)
```
<div>
<style scoped>
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}
.dataframe tbody tr th {
vertical-align: top;
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.dataframe thead th {
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}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Unnamed: 0</th>
<th>original_logtime</th>
<th>desc_text</th>
<th>food_type</th>
<th>PID</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0</td>
<td>2021-05-12 02:30:00 +0000</td>
<td>Milk</td>
<td>b</td>
<td>yrt1999</td>
</tr>
<tr>
<th>1</th>
<td>1</td>
<td>2021-05-12 02:45:00 +0000</td>
<td>Some Medication</td>
<td>m</td>
<td>yrt1999</td>
</tr>
<tr>
<th>2</th>
<td>2</td>
<td>2021-05-12 04:45:00 +0000</td>
<td>bacon egg</td>
<td>f</td>
<td>yrt1999</td>
</tr>
</tbody>
</table>
</div>
```python
treets.clean_loggings(df, 'desc_text', 'PID').head(3)
```
<div>
<style scoped>
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.dataframe tbody tr th {
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>PID</th>
<th>desc_text</th>
<th>cleaned</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>yrt1999</td>
<td>Milk</td>
<td>[milk]</td>
</tr>
<tr>
<th>1</th>
<td>yrt1999</td>
<td>Some Medication</td>
<td>[medication]</td>
</tr>
<tr>
<th>2</th>
<td>yrt1999</td>
<td>bacon egg</td>
<td>[bacon, egg]</td>
</tr>
</tbody>
</table>
</div>
We can see that words are lower cased, modifiers are removed(2nd row) and items are split into individual items(third row).
## Visualizations
```python
# import the dataset
df = treets.file_loader('data/test_food_details.csv')
df.head(2)
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
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.dataframe thead th {
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}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Unnamed: 0</th>
<th>ID</th>
<th>unique_code</th>
<th>research_info_id</th>
<th>desc_text</th>
<th>food_type</th>
<th>original_logtime</th>
<th>foodimage_file_name</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>1340147</td>
<td>7572733</td>
<td>alqt14018795225</td>
<td>150</td>
<td>Water</td>
<td>w</td>
<td>2017-12-08 17:30:00+00:00</td>
<td>NaN</td>
</tr>
<tr>
<th>1</th>
<td>1340148</td>
<td>411111</td>
<td>alqt14018795225</td>
<td>150</td>
<td>Coffee White</td>
<td>b</td>
<td>2017-12-09 00:01:00+00:00</td>
<td>NaN</td>
</tr>
</tbody>
</table>
</div>
make a scatter plot for people's breakfast time
```python
# 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.
```python
treets.swarmplot(df, 50, 'unique_code', 'date', 'local_time')
```
Raw data
{
"_id": null,
"home_page": "https://github.com/FleischerResearchLab/treets/tree/master/",
"name": "treets",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.6",
"maintainer_email": "",
"keywords": "circadian ryhthm",
"author": "Qiwen Zhang, Jason Fleischer",
"author_email": "owenzhang1999@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/e3/f9/e32d6b0f18c5f96af95bf15376c43dc4aa782f192223812daf587fbf0f48/treets-1.0.4.tar.gz",
"platform": null,
"description": "# TREETS\n> Time Restricted Eating ExperimenTS.\n\n\n## Install\n\n`pip install treets`\n\n## Example for a quick data analysis on phased studies.\n\n```python\nimport treets.core as treets\nimport pandas as pd\n```\n\nTake a brief look on the food logging dataset and the reference information sheet\n\n```python\ntreets.file_loader('data/col_test_data/yrt*').head(2)\n```\n\n\n\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Unnamed: 0</th>\n <th>original_logtime</th>\n <th>desc_text</th>\n <th>food_type</th>\n <th>PID</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>2021-05-12 02:30:00 +0000</td>\n <td>Milk</td>\n <td>b</td>\n <td>yrt1999</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1</td>\n <td>2021-05-12 02:45:00 +0000</td>\n <td>Some Medication</td>\n <td>m</td>\n <td>yrt1999</td>\n </tr>\n </tbody>\n</table>\n</div>\n\n\n\n```python\npd.read_excel('data/col_test_data/toy_data_17May2021.xlsx').head(2)\n```\n\n\n\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>mCC_ID</th>\n <th>Participant_Study_ID</th>\n <th>Study Phase</th>\n <th>Intervention group (TRE or HABIT)</th>\n <th>Start_Day</th>\n <th>End_day</th>\n <th>Eating_Window_Start</th>\n <th>Eating_Window_End</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>yrt1999</td>\n <td>2</td>\n <td>S-REM</td>\n <td>TRE</td>\n <td>2021-05-12</td>\n <td>2021-05-14</td>\n <td>00:00:00</td>\n <td>23:59:00</td>\n </tr>\n <tr>\n <th>1</th>\n <td>yrt1999</td>\n <td>2</td>\n <td>T3-INT</td>\n <td>TRE</td>\n <td>2021-05-15</td>\n <td>2021-05-18</td>\n <td>08:00:00</td>\n <td>18:00:00</td>\n </tr>\n </tbody>\n</table>\n</div>\n\n\n\nCall summarize_data_with_experiment_phases() function to make the table that contains analytic information that we want.\n\n```python\ndf = treets.summarize_data_with_experiment_phases(treets.file_loader('data/col_test_data/yrt*')\\\n , pd.read_excel('data/col_test_data/toy_data_17May2021.xlsx'))\n```\n\n Participant yrt1999 didn't log any food items in the following day(s):\n 2021-05-18\n Participant yrt2000 didn't log any food items in the following day(s):\n 2021-05-12\n 2021-05-13\n 2021-05-14\n 2021-05-15\n 2021-05-16\n 2021-05-17\n 2021-05-18\n Participant yrt1999 have bad logging day(s) in the following day(s):\n 2021-05-12\n 2021-05-15\n Participant yrt1999 have bad window day(s) in the following day(s):\n 2021-05-15\n 2021-05-17\n Participant yrt1999 have non adherent day(s) in the following day(s):\n 2021-05-12\n 2021-05-15\n 2021-05-17\n\n\n```python\ndf\n```\n\n\n\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>mCC_ID</th>\n <th>Participant_Study_ID</th>\n <th>Study Phase</th>\n <th>Intervention group (TRE or HABIT)</th>\n <th>Start_Day</th>\n <th>End_day</th>\n <th>Eating_Window_Start</th>\n <th>Eating_Window_End</th>\n <th>phase_duration</th>\n <th>caloric_entries_num</th>\n <th>...</th>\n <th>logging_day_counts</th>\n <th>%_logging_day_counts</th>\n <th>good_logging_days</th>\n <th>%_good_logging_days</th>\n <th>good_window_days</th>\n <th>%_good_window_days</th>\n <th>outside_window_days</th>\n <th>%_outside_window_days</th>\n <th>adherent_days</th>\n <th>%_adherent_days</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>yrt1999</td>\n <td>2</td>\n <td>S-REM</td>\n <td>TRE</td>\n <td>2021-05-12</td>\n <td>2021-05-14</td>\n <td>00:00:00</td>\n <td>23:59:00</td>\n <td>3 days</td>\n <td>7</td>\n <td>...</td>\n <td>3</td>\n <td>100.0%</td>\n <td>2.0</td>\n <td>66.67%</td>\n <td>3.0</td>\n <td>100.0%</td>\n <td>0.0</td>\n <td>0.0%</td>\n <td>2.0</td>\n <td>66.67%</td>\n </tr>\n <tr>\n <th>1</th>\n <td>yrt1999</td>\n <td>2</td>\n <td>T3-INT</td>\n <td>TRE</td>\n <td>2021-05-15</td>\n <td>2021-05-18</td>\n <td>08:00:00</td>\n <td>18:00:00</td>\n <td>4 days</td>\n <td>8</td>\n <td>...</td>\n <td>3</td>\n <td>75.0%</td>\n <td>2.0</td>\n <td>50.0%</td>\n <td>1.0</td>\n <td>25.0%</td>\n <td>2.0</td>\n <td>50.0%</td>\n <td>1.0</td>\n <td>25.0%</td>\n </tr>\n <tr>\n <th>2</th>\n <td>yrt2000</td>\n <td>3</td>\n <td>T3-INT</td>\n <td>TRE</td>\n <td>2021-05-12</td>\n <td>2021-05-14</td>\n <td>08:00:00</td>\n <td>16:00:00</td>\n <td>3 days</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0.0%</td>\n <td>0.0</td>\n <td>0.0%</td>\n <td>0.0</td>\n <td>0.0%</td>\n <td>0.0</td>\n <td>0.0%</td>\n <td>0.0</td>\n <td>0.0%</td>\n </tr>\n <tr>\n <th>3</th>\n <td>yrt2000</td>\n <td>3</td>\n <td>T3-INT</td>\n <td>TRE</td>\n <td>2021-05-15</td>\n <td>2021-05-18</td>\n <td>08:00:00</td>\n <td>16:00:00</td>\n <td>4 days</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0.0%</td>\n <td>0.0</td>\n <td>0.0%</td>\n <td>0.0</td>\n <td>0.0%</td>\n <td>0.0</td>\n <td>0.0%</td>\n <td>0.0</td>\n <td>0.0%</td>\n </tr>\n <tr>\n <th>4</th>\n <td>yrt2001</td>\n <td>4</td>\n <td>T12-A</td>\n <td>TRE</td>\n <td>NaT</td>\n <td>NaT</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaT</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>nan%</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows \u00d7 32 columns</p>\n</div>\n\n\n\nLook at resulting statistical information for the first row in the resulting dataset.\n\n```python\ndf.iloc[0]\n```\n\n\n\n\n mCC_ID yrt1999\n Participant_Study_ID 2\n Study Phase S-REM\n Intervention group (TRE or HABIT) TRE\n Start_Day 2021-05-12 00:00:00\n End_day 2021-05-14 00:00:00\n Eating_Window_Start 00:00:00\n Eating_Window_End 23:59:00\n phase_duration 3 days 00:00:00\n caloric_entries_num 7\n medication_num 0\n water_num 0\n first_cal_avg 5.916667\n first_cal_std 2.240722\n last_cal_avg 19.666667\n last_cal_std 12.933323\n mean_daily_eating_window 13.75\n std_daily_eating_window 11.986972\n earliest_entry 4.5\n 2.5% 4.5375\n 97.5% 27.5625\n duration mid 95% 23.025\n logging_day_counts 3\n %_logging_day_counts 100.0%\n good_logging_days 2.0\n %_good_logging_days 66.67%\n good_window_days 3.0\n %_good_window_days 100.0%\n outside_window_days 0.0\n %_outside_window_days 0.0%\n adherent_days 2.0\n %_adherent_days 66.67%\n Name: 0, dtype: object\n\n\n\n## Example for a quick data analysis on non-phased studies.\n\ntake a look at the original dataset\n\n```python\ndf = treets.file_loader('data/test_food_details.csv')\ndf.head(2)\n```\n\n\n\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Unnamed: 0</th>\n <th>ID</th>\n <th>unique_code</th>\n <th>research_info_id</th>\n <th>desc_text</th>\n <th>food_type</th>\n <th>original_logtime</th>\n <th>foodimage_file_name</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1340147</td>\n <td>7572733</td>\n <td>alqt14018795225</td>\n <td>150</td>\n <td>Water</td>\n <td>w</td>\n <td>2017-12-08 17:30:00+00:00</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1340148</td>\n <td>411111</td>\n <td>alqt14018795225</td>\n <td>150</td>\n <td>Coffee White</td>\n <td>b</td>\n <td>2017-12-09 00:01:00+00:00</td>\n <td>NaN</td>\n </tr>\n </tbody>\n</table>\n</div>\n\n\n\npreprocess the data to create features we might need in the furthur analysis such as float time, week count since the first week, etc.\n\n```python\ndf = treets.load_food_data(df,'unique_code', 'original_logtime',4)\ndf.head(2)\n```\n\n\n\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Unnamed: 0</th>\n <th>ID</th>\n <th>unique_code</th>\n <th>research_info_id</th>\n <th>desc_text</th>\n <th>food_type</th>\n <th>original_logtime</th>\n <th>date</th>\n <th>float_time</th>\n <th>time</th>\n <th>week_from_start</th>\n <th>year</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1340147</td>\n <td>7572733</td>\n <td>alqt14018795225</td>\n <td>150</td>\n <td>Water</td>\n <td>w</td>\n <td>2017-12-08 17:30:00+00:00</td>\n <td>2017-12-08</td>\n <td>17.500000</td>\n <td>17:30:00</td>\n <td>1</td>\n <td>2017</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1340148</td>\n <td>411111</td>\n <td>alqt14018795225</td>\n <td>150</td>\n <td>Coffee White</td>\n <td>b</td>\n <td>2017-12-09 00:01:00+00:00</td>\n <td>2017-12-08</td>\n <td>24.016667</td>\n <td>00:01:00</td>\n <td>1</td>\n <td>2017</td>\n </tr>\n </tbody>\n</table>\n</div>\n\n\n\nCall summarize_data() function to make the table that contains analytic information that we want.\u00b6\n\n```python\ndf = treets.summarize_data(df, 'unique_code', 'float_time', 'date')\ndf.head(2)\n```\n\n\n\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>unique_code</th>\n <th>num_days</th>\n <th>num_total_items</th>\n <th>num_f_n_b</th>\n <th>num_medications</th>\n <th>num_water</th>\n <th>first_cal_avg</th>\n <th>first_cal_std</th>\n <th>last_cal_avg</th>\n <th>last_cal_std</th>\n <th>eating_win_avg</th>\n <th>eating_win_std</th>\n <th>good_logging_count</th>\n <th>first_cal variation (90%-10%)</th>\n <th>last_cal variation (90%-10%)</th>\n <th>2.5%</th>\n <th>95%</th>\n <th>duration mid 95%</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>alqt1148284857</td>\n <td>13</td>\n <td>149</td>\n <td>96</td>\n <td>19</td>\n <td>34</td>\n <td>7.821795</td>\n <td>6.710717</td>\n <td>23.485897</td>\n <td>4.869082</td>\n <td>15.664103</td>\n <td>8.231201</td>\n <td>146</td>\n <td>2.966667</td>\n <td>9.666667</td>\n <td>4.535000</td>\n <td>26.813333</td>\n <td>22.636667</td>\n </tr>\n <tr>\n <th>1</th>\n <td>alqt14018795225</td>\n <td>64</td>\n <td>488</td>\n <td>484</td>\n <td>3</td>\n <td>1</td>\n <td>7.525781</td>\n <td>5.434563</td>\n <td>25.858594</td>\n <td>3.374839</td>\n <td>18.332813</td>\n <td>6.603913</td>\n <td>484</td>\n <td>13.450000</td>\n <td>3.100000</td>\n <td>4.183333</td>\n <td>27.438333</td>\n <td>23.416667</td>\n </tr>\n </tbody>\n</table>\n</div>\n\n\n\nLook at resulting statistical information for the first row in the resulting dataset.\n\n```python\ndf.iloc[0]\n```\n\n\n\n\n unique_code alqt1148284857\n num_days 13\n num_total_items 149\n num_f_n_b 96\n num_medications 19\n num_water 34\n first_cal_avg 7.821795\n first_cal_std 6.710717\n last_cal_avg 23.485897\n last_cal_std 4.869082\n eating_win_avg 15.664103\n eating_win_std 8.231201\n good_logging_count 146\n first_cal variation (90%-10%) 2.966667\n last_cal variation (90%-10%) 9.666667\n 2.5% 4.535\n 95% 26.813333\n duration mid 95% 22.636667\n Name: 0, dtype: object\n\n\n\n## Clean text in food loggings\n\n```python\n# import the dataset\ndf = treets.file_loader('data/col_test_data/yrt*')\ndf.head(3)\n```\n\n\n\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Unnamed: 0</th>\n <th>original_logtime</th>\n <th>desc_text</th>\n <th>food_type</th>\n <th>PID</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>2021-05-12 02:30:00 +0000</td>\n <td>Milk</td>\n <td>b</td>\n <td>yrt1999</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1</td>\n <td>2021-05-12 02:45:00 +0000</td>\n <td>Some Medication</td>\n <td>m</td>\n <td>yrt1999</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2</td>\n <td>2021-05-12 04:45:00 +0000</td>\n <td>bacon egg</td>\n <td>f</td>\n <td>yrt1999</td>\n </tr>\n </tbody>\n</table>\n</div>\n\n\n\n```python\ntreets.clean_loggings(df, 'desc_text', 'PID').head(3)\n```\n\n\n\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PID</th>\n <th>desc_text</th>\n <th>cleaned</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>yrt1999</td>\n <td>Milk</td>\n <td>[milk]</td>\n </tr>\n <tr>\n <th>1</th>\n <td>yrt1999</td>\n <td>Some Medication</td>\n <td>[medication]</td>\n </tr>\n <tr>\n <th>2</th>\n <td>yrt1999</td>\n <td>bacon egg</td>\n <td>[bacon, egg]</td>\n </tr>\n </tbody>\n</table>\n</div>\n\n\n\nWe can see that words are lower cased, modifiers are removed(2nd row) and items are split into individual items(third row).\n\n## Visualizations\n\n```python\n# import the dataset\ndf = treets.file_loader('data/test_food_details.csv')\ndf.head(2)\n```\n\n\n\n\n<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Unnamed: 0</th>\n <th>ID</th>\n <th>unique_code</th>\n <th>research_info_id</th>\n <th>desc_text</th>\n <th>food_type</th>\n <th>original_logtime</th>\n <th>foodimage_file_name</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1340147</td>\n <td>7572733</td>\n <td>alqt14018795225</td>\n <td>150</td>\n <td>Water</td>\n <td>w</td>\n <td>2017-12-08 17:30:00+00:00</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1340148</td>\n <td>411111</td>\n <td>alqt14018795225</td>\n <td>150</td>\n <td>Coffee White</td>\n <td>b</td>\n <td>2017-12-09 00:01:00+00:00</td>\n <td>NaN</td>\n </tr>\n </tbody>\n</table>\n</div>\n\n\n\nmake a scatter plot for people's breakfast time\n\n```python\n# create required features for function first_cal_mean_with_error_bar()\ndf['original_logtime'] = pd.to_datetime(df['original_logtime'])\ndf['local_time'] = treets.find_float_time(df, 'original_logtime')\ndf['date'] = treets.find_date(df, 'original_logtime')\n\n# call the function\ntreets.first_cal_mean_with_error_bar(df,'unique_code', 'date', 'local_time')\n```\n\n\n\n\n\nUse swarmplot to visualize each person's eating time distribution.\n\n```python\ntreets.swarmplot(df, 50, 'unique_code', 'date', 'local_time')\n```\n\n\n\n\n\n\n",
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