# %%

"""
Matching SQI Dataframe Window to Clinical Time of "Event" for Multiple Records
==============================================================================
Matching SQI Dataframe Window to Clinical Time of "Event" for Multiple Records
"""

#%%
# Importing Libraries

from datetime import date
import pandas as pd
import numpy as np
from datetime import datetime
import matplotlib.pyplot as plt
import os 

#%%
# Loading SQI Data and clinical data

filename_SQIs = r'..\..\..\..\OUCRU\Outputs\Complete_SQIs.csv'
filename_Clinical = r'..\..\..\..\OUCRU\Clinical\v0.0.10\01nva_data_stacked_corrected.csv'
study_no_path = r'..\..\..\..\OUCRU\01NVa_Dengue\Adults'


#taking a list of records
study_no_list = os.listdir(study_no_path) # Need to take the last 8 characters i.e. study_no_list[i][-8:]

#reading .csv into dataframes
Clinical = pd.read_csv(filename_Clinical)
SQIs = pd.read_csv(filename_SQIs)

SQIs['PPG_w_s'] = pd.to_datetime(SQIs['PPG_w_s'])
SQIs['PPG_w_f'] = pd.to_datetime(SQIs['PPG_w_f'])


TERMINAL = True

#Showing Data
if TERMINAL:
    print("\n Clincial Data:")
    print(Clinical)
    print("\n SQI Data:")
    print(SQIs)

def match_clinical_to_SQIs(Clinical, SQIs, event, study_no_list):
    SQIs[event]=np.nan #forming an empty column for the event 
    print("\n EVENT: ", event)
    #iterating through the study_no
    for i in range(len(study_no_list)):
        print("\n STUDY-NO: ", study_no_list[i])
        #finding the rows for which the following logic is satisfied
        event_row = Clinical[(((Clinical.column == event) & (Clinical.result == 'TRUE')) | (Clinical.result == event)) & (Clinical.study_no == study_no_list[i][-8:])]
        #iterating through the event row for indexing
        event_row['date'] = pd.to_datetime(event_row['date'])
        if event == 'shock_admission' and not event_row.empty:
            SQIs['shock_admission'][SQIs.study_no == study_no_list[i][-8:]] = True #Showing whether a record indicates a shock on admission
        else:
            for j in range(len(event_row)):
                valid_SQI_rows = SQIs[(SQIs.PPG_w_s <= event_row.date[event_row.date.index[j]]) & (SQIs.PPG_w_f > event_row.date[event_row.date.index[j]]) & (SQIs.study_no == study_no_list[i][-8:])]
                SQIs[event][valid_SQI_rows.index] = True #Setting the value to True for the window corresponding to an event
                if valid_SQI_rows.empty:
                    ##If the date is equal to the date of an event, we can raise True for the whole day, in case no time is present (or it's out of the recording)
                    SQIs[event][(SQIs.PPG_w_s.dt.date == event_row.date[event_row.date.index[j]].date) & (SQIs.PPG_w_f.dt.date == event_row.date[event_row.date.index[j]].date) & (SQIs.study_no == study_no_list[i][-8:])]
    return SQIs

'''
NOTE: 
We previously had functions that fetched ppg start and 
calculated relative times but since we included the PPG Start times
in the SQI file, that is no longer needed.
'''

#Calling the matching function by first defining the event
event_lookup = 'event_shock'
SQIs_with_Clinical = match_clinical_to_SQIs(Clinical, SQIs, event_lookup, study_no_list)

event_lookup = 'reshock24'
SQIs_with_Clinical = match_clinical_to_SQIs(Clinical, SQIs_with_Clinical, event_lookup, study_no_list)

event_lookup = 'diagnosis_admission' #all diagnosis admission are for shock so we can use this as is 
SQIs_with_Clinical = match_clinical_to_SQIs(Clinical, SQIs_with_Clinical, event_lookup, study_no_list)

event_lookup = 'shock_admission'
SQIs_with_Clinical = match_clinical_to_SQIs(Clinical, SQIs_with_Clinical, event_lookup, study_no_list)
#In the second iteration we call the function with the output of the first one, so that we can form 
#multiple columns of events
event_lookup = 'ascites'
SQIs_with_Clinical = match_clinical_to_SQIs(Clinical, SQIs_with_Clinical, event_lookup, study_no_list)

event_lookup = 'respiratory_distress'
SQIs_with_Clinical = match_clinical_to_SQIs(Clinical, SQIs_with_Clinical, event_lookup, study_no_list)

event_lookup = 'ventilation_cannula'
SQIs_with_Clinical = match_clinical_to_SQIs(Clinical, SQIs_with_Clinical, event_lookup, study_no_list)

event_lookup = 'ventilation_mechanical'
SQIs_with_Clinical = match_clinical_to_SQIs(Clinical, SQIs_with_Clinical, event_lookup, study_no_list)

event_lookup = 'ventilation_ncpap'
SQIs_with_Clinical = match_clinical_to_SQIs(Clinical, SQIs_with_Clinical, event_lookup, study_no_list)

event_lookup = 'bleeding_severe'
SQIs_with_Clinical = match_clinical_to_SQIs(Clinical, SQIs_with_Clinical, event_lookup, study_no_list)

event_lookup = 'cns_abnormal'
SQIs_with_Clinical = match_clinical_to_SQIs(Clinical, SQIs_with_Clinical, event_lookup, study_no_list)

event_lookup = 'liver_mild'
SQIs_with_Clinical = match_clinical_to_SQIs(Clinical, SQIs_with_Clinical, event_lookup, study_no_list)

event_lookup = 'pleural_effusion'
SQIs_with_Clinical = match_clinical_to_SQIs(Clinical, SQIs_with_Clinical, event_lookup, study_no_list)

event_lookup = 'skidney'
SQIs_with_Clinical = match_clinical_to_SQIs(Clinical, SQIs_with_Clinical, event_lookup, study_no_list)



if TERMINAL:
    print('\n SQIs with Clinical Event Match:')
    print(SQIs_with_Clinical)


#Saving output to CSV
SQIs_with_Clinical.to_csv(r'..\..\..\..\OUCRU\Outputs\Complete_SQIs_with_Clinical.csv')


if TERMINAL:
    #example of the output
    fig, axs = plt.subplots(nrows=2, sharex=True, figsize=(8, 10))
    img = plt.imread(r'..\..\..\..\MISC\SQI_Clinical_Image_example_2.png')
    axs[0].set_title('Example a Event Alignment with SQIs in Excel')
    axs[0].imshow(img)
    img2 = plt.imread(r'..\..\..\..\MISC\SQI_Clinical_Image_example_3.png')
    axs[1].imshow(img2)
    plt.show()