Medium - Get started analysing your CGM data

apply-range.py

def applyThreshold(val, urgentLow=54, low=70, high=180):
  if val > high:
      return 'high'
  elif val <= high and val >= low:
    return 'in_range'
  elif val < low and val > urgentLow:
    return 'low'
  else:
    return 'urgent_low';

df['Status'] = df['glucose'].apply(lambda x: applyThreshold(x))

prep-range-ratio.py

z = df.copy(deep=True)
z = z[z['Status'] == 'in_range'][['Status']]
z = z.resample('D').count()
z = z.apply(lambda x: x/(((24*60)/5)+1))
z = z.drop(z.head(1).index)
z = z.drop(z.tail(1).index)
z.insert(z.shape[1], 'day', z.index.value_counts().sort_index().cumsum())

range-trend-plt.py

plt.figure(figsize=(16, 6))
ax = sns.regplot(
  data = z, 
  x ='day', 
  y ='Status', 
  ci = 95, 
  color = '#13ac5f')
_ = ax.set(xlabel='Day', ylabel='Daily in-range-ratio')