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from google.colab import drive
drive.mount('/content/drive')

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import pandas as pd
df = pd.read_csv('drive/My Drive/my-cgm-exploration/Example_CGM_data.csv')
df.head(20)

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df = df.iloc[10:,:]
df.head()

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df = df[['Timestamp (YYYY-MM-DDThh:mm:ss)', 'Glucose Value (mg/dL)']]
df.rename(columns={
  "Timestamp (YYYY-MM-DDThh:mm:ss)": "time", 
  "Glucose Value (mg/dL)": "glucose"},
  inplace=True)
df.head()

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df = df.copy(deep=True)
df['time'] = pd.to_datetime(df['time'])
df['time'] = df['time']\
              .dt.tz_localize(tz='UTC')\
              .dt.tz_convert('US/Pacific')

df.set_index('time', inplace=True)
df.head()

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from datetime import datetime, date

z = df.copy(deep=True)
z['t1'] = z.index
z['t2'] = z['t1'].shift(1)
z['t_diff'] = z['t1'] - z['t2']
z['t_diff'].describe()  

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# Inspect samples closer than average
import numpy as np
import matplotlib.pyplot as plt

expected_diff = timedelta(minutes=4, seconds=30)
close_samples_idxs = np.where(z['t_diff'] < expected_diff)[0]

c_samples_times = z.iloc[close_samples_idxs, :]['t_diff']
c_samples_times = c_samples_times.apply(lambda x: x.seconds)

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expected_diff = timedelta(minutes=5, seconds=30)
missing_samples_idxs = np.where(z['t_diff'] > expected_diff)[0]

c_samples_times = z.iloc[missing_samples_idxs, :]['t_diff']
c_samples_times

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df['glucose'] = df['glucose'].astype(int)
_ = sns.distplot(df['glucose'])
df.describe()

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z = df.copy(deep=True)
z = z.resample('D').mean()
z['date'] = z.index.date
z.insert(z.shape[1], 'day', z.index.value_counts().sort_index().cumsum())

plt.figure(figsize=(16, 6))
fs = sns.regplot(
  data = z, 
  x ='day', 
  y ='glucose',