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preprocessing.py
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preprocessing.py
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from datetime import timedelta, datetime
from math import exp, pow
import numpy as np
import pandas as pd
from helpers import convert_datestrings_df, date_range, construct_colorscale, convert_datestring
from helpers import round_datetime, get_deltas
from variables import target_range, initial_number_of_days
basal_profile_name = 'FirstOne'
basal_profile_name = '/' + basal_profile_name + '/'
def initiate_table(table, data_names, flip=True):
if 'created_at' in table.columns:
table = table.rename(columns={'created_at': 'timestamp'})
table = convert_datestrings_df(table.dropna(), data_names) # convert date strings to datetime
if flip:
table = table[::-1]
table = table.drop_duplicates().reset_index(drop=True) # drop duplicates
return table
# for aaps data
def create_logs_from_aaps_events(events, data_names):
logs = events[['timestamp', data_names]].dropna()
logs = logs[::-1]
return logs
entries = pd.read_csv('datasets/xdrip3.csv', sep=';', parse_dates=[['DAY', 'TIME']], dayfirst=True).iloc[:, :2]
entries.columns = ['timestamp', 'sgv']
events = pd.read_csv('datasets/aaps3.csv', sep=';')
events = events[['Date', 'g', 'U', '%', 'h', 'm']]
events = events.dropna(thresh=2) # drop rows with all nan
events.loc[:, 'Date'] = events['Date'].apply(convert_datestring) # convert to datetime
events['m'].fillna(0, inplace=True)
events['Date'] = events['Date'] + events['m'].apply(lambda x: timedelta(minutes=x))
events.columns = ['timestamp', 'carbs', 'bolus', '%', 'h', 'm']
logs_insulin = create_logs_from_aaps_events(events, 'bolus')
logs_carbs = create_logs_from_aaps_events(events, 'carbs')
profile = pd.read_csv('datasets/br.csv')
# initiate tables
logs_sgv = initiate_table(entries, ['sgv'], flip=False)
date_min = min(logs_sgv.timestamp.min(), logs_insulin.timestamp.min(), logs_carbs.timestamp.min())
date_max = max([logs_sgv.timestamp.max(), logs_insulin.timestamp.max(), logs_carbs.timestamp.max()])
start_date = date_max - timedelta(days=initial_number_of_days)
start_date_insights = date_max - timedelta(days=45)
end_date = date_max
days = date_range(date_min, date_max)
# get default basal rate
basal_columns = [i for i in profile.columns if (basal_profile_name + 'basal' in i) and ('timeAsSeconds' not in i)]
timestamps = []
br = []
for i in range(0, len(basal_columns), 2):
timestamps.append(profile[basal_columns[i]].values[0])
br.append(profile[basal_columns[i+1]].values[0])
br_default_timestamps = []
br_default_values = []
for day in days:
for i, timestamp in enumerate(timestamps):
br_default_timestamps.append(day.replace(hour=int(timestamp[:2]), minute=int(timestamp[3:])))
br_default_values.append(br[i])
logs_br_default = pd.DataFrame({
'timestamp': br_default_timestamps + [date_max],
'br_default': br_default_values + [br_default_values[-1]]
})
# get modified basal rate
df_tbr = events[['timestamp', '%', 'h']]
df_tbr = df_tbr.dropna(thresh=2)
df_tbr.columns = ['timestamp', 'value', 'duration']
df_tbr = initiate_table(df_tbr, ['duration', 'value'])
df_tbr['end_date'] = df_tbr.timestamp + pd.to_timedelta(df_tbr.duration, unit='hours')
df_tbr['end_date_value'] = [logs_br_default.br_default[logs_br_default.timestamp.searchsorted(date)-1] for date in df_tbr.end_date]
logs_br = pd.concat([logs_br_default.copy().rename(columns={'br_default': 'br'}),
df_tbr[['timestamp', 'value']].copy().rename(columns={'value': 'br'}),
df_tbr[['end_date', 'end_date_value']].copy().rename(columns={'end_date': 'timestamp', 'end_date_value': 'br'})
]).reset_index(drop=True)
logs_br = logs_br.sort_values(by='timestamp')
for i in range(len(df_tbr)):
logs_br = logs_br.drop(logs_br[(logs_br['timestamp'] > df_tbr['timestamp'].iloc[i]) & (logs_br['timestamp'] < df_tbr['end_date'].iloc[i])].index)
def get_insulin_activity(logs, tp=55, td=360):
timestamp = logs.timestamp
insulin = logs.bolus
def scalable_exp_ia(t, tp, td):
tp = float(tp)
td = float(td)
tau = tp * (1 - tp / td) / (1 - 2 * tp / td)
a = 2 * (tau / td)
S = 1 / (1 - a + (1 + a) * exp(-td / tau))
return (S / pow(tau, 2)) * t * (1 - t / td) * exp(-t / tau)
number_of_days = len(days)
insulin_activity = np.zeros((number_of_days + 2) * 1440) # TODO: find bug
date_start = datetime.combine(date_min, datetime.min.time())
x = np.linspace(0, td, num=td)
y = np.array([scalable_exp_ia(t, tp, td) for t in x])
for d, i in zip(timestamp, insulin):
start = int((round_datetime(d) - date_start).total_seconds() / 60)
insulin_activity[start:start + td] += i * y
return insulin_activity, date_start
insulin_activity, date_start = get_insulin_activity(logs_insulin)
insulin_activity = insulin_activity[[int((d - date_start).total_seconds() / 60) for d in logs_sgv.timestamp]]
logs_insulin_activity = pd.DataFrame({
'timestamp': logs_sgv.timestamp,
'insulin_activity': insulin_activity
})
sgv_delta = get_deltas(logs_sgv['sgv'])
insulin_activity_delta = get_deltas(pd.Series(insulin_activity))
# sgv array for time boxes
tmp = logs_sgv.copy()
tmp['minute'] = tmp.timestamp.dt.hour * 60 + tmp.timestamp.dt.minute
tmp['date'] = tmp.timestamp.dt.date
dates = tmp['date'].unique()
date_dict = dict(enumerate(dates))
inv_date_dict = {v: k for k, v in date_dict.items()}
tmp['line'] = [inv_date_dict[item] for item in tmp['date']]
sgv_array_for_agp = np.full([len(dates), 1440], np.nan)
for i in range(len(tmp)):
element = tmp.iloc[i]
sgv_array_for_agp[element['line'], element['minute']] = element['sgv']
# sgv for plots
logs_sgv['low'] = logs_sgv['sgv'] <= target_range[0]
logs_sgv['high'] = logs_sgv['sgv'] >= target_range[1]
threshold_crossings_high = np.diff(logs_sgv.sgv > target_range[1], prepend=False)
logs_sgv_plot = logs_sgv.copy()
logs_sgv_plot.loc[threshold_crossings_high, 'sgv'] = target_range[1]
threshold_crossings_low = np.diff(logs_sgv.sgv < target_range[0], prepend=False)
logs_sgv_plot.loc[threshold_crossings_low, 'sgv'] = target_range[0]
logs_sgv_plot['low'] = logs_sgv_plot['sgv'] <= target_range[0]
logs_sgv_plot['high'] = logs_sgv_plot['sgv'] >= target_range[1]
# targets
targets = [1, 54, 70, 180, 250, max(logs_sgv.sgv)]
colors = [
'rgb(251,90,82)',
'rgb(255,140,126)',
'rgb(120,211,168)',
'rgb(188,155,233)',
'rgb(139,99,213)'
]
domain = [target/targets[-1] for target in targets]
colors_insulin = [
'rgb(201,231,246)',
'rgb(173,225,246)',
'rgb(146,220,245)',
'rgb(118,214,245)',
'rgb(90,209,245)'
]
colors_carbs = [
'rgb(234,231,220)',
'rgb(239,227,193)',
'rgb(245,222,167)',
'rgb(250,218,140)',
'rgb(255,213,114)'
]
colorscales = [construct_colorscale(colors_insulin),
construct_colorscale(colors_carbs),
construct_colorscale(colors, domain)]
colorscale = construct_colorscale(colors, domain)
colors_categorical = [
# 'rgb(229,236,246)',
'rgb(251,90,82)',
'rgb(120,211,168)',
'rgb(139,99,213)',
# 'rgb(139,99,213)'
]
colorscale_categorical = construct_colorscale(colors_categorical)