CGMPlot (cgm_df:pandas.core.frame.DataFrame, diet_df:Optional[pandas.core.frame.DataFrame]=None, cgm_date_col:str='collection_timestamp', gluc_col:str='glucose', diet_date_col:str='collection_timestamp', diet_text_col:str='short_food_name', ax:Optional[matplotlib.axes._axes.Axes]=None, smooth:bool=False, sleep_tuples:Optional[List[Tuple[pandas._libs.tslibs.timestamps. Timestamp,pandas._libs.tslibs.timestamps.Timestamp]]]=None)
*Initialize a CGMPlot object.
Args: cgm_df (pd.DataFrame): DataFrame containing the glucose measurements. diet_df (Optional[pd.DataFrame], optional): DataFrame containing the diet data. Defaults to None. cgm_date_col (str, optional): Name of the date column in cgm_df. Defaults to “collection_timestamp”. gluc_col (str, optional): Name of the glucose column in cgm_df. Defaults to “glucose”. diet_date_col (str, optional): Name of the date column in diet_df. Defaults to “collection_timestamp”. diet_text_col (str, optional): Name of the text column in diet_df. Defaults to “short_food_name”. ax (Optional[plt.Axes], optional): Matplotlib Axes object to plot on. Defaults to None. smooth (bool, optional): Apply smoothing to the glucose curve. Defaults to False. sleep_tuples (Optional[List[Tuple[pd.Timestamp, pd.Timestamp]]], optional): List of sleep start and end times. Defaults to None.*
cgm_df= pd.read_parquet("./examples/cgm/cgm.parquet")
cgm_df.head()| glucose | ||
|---|---|---|
| participant_id | collection_timestamp | |
| 0 | 2020-06-22 00:14:00+03:00 | 106.2 |
| 2020-06-22 00:29:00+03:00 | 100.8 | |
| 2020-06-22 00:44:00+03:00 | 97.2 | |
| 2020-06-22 00:59:00+03:00 | 95.4 | |
| 2020-06-22 01:14:00+03:00 | 93.6 |
diet_df = pd.read_parquet("./examples/diet_logging/diet_logging.parquet")
diet_df.head()| short_food_name | food_category | weight_g | calories_kcal | carbohydrate_g | lipid_g | protein_g | sodium_mg | alcohol_g | dietary_fiber_g | ||
|---|---|---|---|---|---|---|---|---|---|---|---|
| participant_id | collection_timestamp | ||||||||||
| 0 | 2020-06-21 16:06:00+03:00 | Quinoa | Pasta, Grains and Side dishes_wholewheat | 56.0 | 78.3104 | 12.1744 | 1.2992 | 2.8000 | 37.1504 | 0.0 | 1.4896 |
| 2020-06-21 16:06:00+03:00 | Hummus Salad | Pulses and products | 80.0 | 215.2000 | 9.4400 | 16.8000 | 6.5600 | 377.6000 | NaN | 0.0000 | |
| 2020-06-21 16:06:00+03:00 | Meatballs | Beef, veal, lamb, and other meat products | 180.0 | 311.0940 | 16.1280 | 15.4440 | 24.7320 | 1164.5460 | 0.0 | 2.3904 | |
| 2020-06-21 19:28:00+03:00 | Banana | Fruits | 128.0 | 113.9200 | 25.9072 | 0.4224 | 1.3952 | 1.2800 | 0.0 | 3.3280 | |
| 2020-06-21 21:07:00+03:00 | Bread | Bread | 60.0 | 162.6000 | 28.3800 | 2.1000 | 5.2800 | 367.8000 | 0.0 | 1.6200 |
start_date = pd.to_datetime('2020-06-22', utc=True).tz_convert('Asia/Jerusalem')
end_date = pd.to_datetime('2020-06-24',utc=True).tz_convert('Asia/Jerusalem')
sample_cgm = cgm_df[(cgm_df.index.get_level_values('collection_timestamp') >= start_date) \
& (cgm_df.index.get_level_values('collection_timestamp') <= end_date)]
sample_diet = diet_df[(diet_df.index.get_level_values('collection_timestamp') >= start_date) \
& (diet_df.index.get_level_values('collection_timestamp') <= end_date)]cgmplt = CGMPlot(cgm_df=sample_cgm,
gluc_col="glucose",
diet_df=sample_diet,
smooth=True)
cgmplt.plot()
AGP (cgm_df:pandas.core.frame.DataFrame, cgm_date_col:str='collection_timestamp', gluc_col:str='glucose', ax:Optional[matplotlib.axes._axes.Axes]=None)
*Initialize an AGP object.
Args: cgm_df (pd.DataFrame): DataFrame containing the glucose measurements. cgm_date_col (str, optional): Name of the date column in cgm_df. Defaults to “collection_timestamp”. gluc_col (str, optional): Name of the glucose column in cgm_df. Defaults to “glucose”. ax (Optional[plt.Axes], optional): Matplotlib Axes object to plot on. Defaults to None.*
agp = AGP(cgm_df=cgm_df.reset_index(), cgm_date_col="collection_timestamp", gluc_col="glucose")
agp.plot()