# Irregular sampling for PhysioNet-2012 dataset
#  & Train/test/val splits
# 
# Author: Theodoros Tsiligkaridis
# Last updated: May 4 2021
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:90% !important; }</style>"))

import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# ## Irregular sampling

P_list = np.load('../processed_data/P_list.npy', allow_pickle=True)
arr_outcomes = np.load('../processed_data/arr_outcomes.npy', allow_pickle=True)

ts_params = np.load('../processed_data/ts_params.npy', allow_pickle=True)
static_params = np.load('../processed_data/static_params.npy', allow_pickle=True)

print('number of samples: ', len(P_list))
print(len(ts_params), ts_params)
print(len(static_params), static_params)


# All patients were adults who were admitted for a wide variety of reasons to cardiac, medical, surgical, and trauma ICUs. ICU stays of less than 48 hours have been excluded.
# Up to 42 variables were recorded at least once during the first 48 hours after admission to the ICU. Not all variables are available in all cases, however. 
# Six of these variables are general descriptors (collected on admission), and the remainder are time series, for which multiple observations may be available.


# Estimate max_len across dataset
n = len(P_list)
max_tmins = 48*60
len_ts = []

for ind in range(n):  # for each patient
    ts = P_list[ind]['ts']
    unq_tmins = []
    for sample in ts: # for each instance (time point)
        current_tmin = sample[2]
        if (current_tmin not in unq_tmins) and (current_tmin < max_tmins):
            unq_tmins.append(current_tmin)
    len_ts.append( len(unq_tmins))

print('max unique time series length:', np.max(len_ts)) # np.max(len_ts) = 214


# # Histogram of time points
# _ = plt.hist(np.array(len_ts), bins='auto')
# plt.xlabel('Number of time points')
# plt.ylabel('Counts')
# plt.show()


extended_static_list = ['Age', 'Gender=0', 'Gender=1', 'Height', 'ICUType=1', 'ICUType=2', 'ICUType=3', 'ICUType=4', 'Weight']
np.save('../processed_data/extended_static_params.npy', extended_static_list)



"""Group all patient time series into arrays"""
n = len(P_list)
max_len = 215
F = len(ts_params)
PTdict_list = []
max_hr = 0
for ind in range(n):
    ID = P_list[ind]['id']
    static = P_list[ind]['static']
    ts = P_list[ind]['ts']
    
    # find unique times
    unq_tmins = []
    for sample in ts:
        current_tmin = sample[2]
        if (current_tmin not in unq_tmins) and (current_tmin < max_tmins):
            unq_tmins.append(current_tmin)
#     print('unique times (mins):', unq_tmins)
#     print('sequence length: ', len(unq_tmins))
    unq_tmins = np.array(unq_tmins)

    # one-hot encoding of categorical static variables
    extended_static = [static[0],0,0,static[2],0,0,0,0,static[4]]
    if static[1]==0:
        extended_static[1] = 1
    elif static[1]==1:
        extended_static[2] = 1
    if static[3]==1:
        extended_static[4] = 1
    elif static[3]==2:
        extended_static[5] = 1
    elif static[3]==3:
        extended_static[6] = 1
    elif static[3]==4:
        extended_static[7] = 1
    
    # construct array of maximal size
    Parr = np.zeros((max_len,F))
    Tarr = np.zeros((max_len,1))

    # for each time measurement find index and store
    for sample in ts:
        tmins = sample[2]
        param = sample[-2]
        value = sample[-1]
        if tmins < max_tmins:
            time_id  = np.where(tmins==unq_tmins)[0][0]
            param_id = np.where(ts_params==param)[0][0]
            Parr[time_id, param_id] = value
            Tarr[time_id, 0] = unq_tmins[time_id]
    
    length = len(unq_tmins)
    
    # construct dictionary
    my_dict = {'id':ID, 'static':static, 'extended_static':extended_static, 'arr':Parr, 'time':Tarr, 'length':length}
    
    # add array into list
    PTdict_list.append(my_dict)


print(len(PTdict_list))
np.save('../processed_data/PTdict_list.npy', PTdict_list)
print('PTdict_list.npy saved', PTdict_list[0].keys())
exit(-1)