import datetime
import os
import sys
import numpy as np
import pandas as pd
from pathlib import Path
from tqdm import tqdm
import importlib
import disease_cohort
importlib.reload(disease_cohort)
import disease_cohort
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + './../..')
if not os.path.exists("./data/cohort"):
    os.makedirs("./data/cohort")
if not os.path.exists("./data/summary"):
    os.makedirs("./data/summary")
    
def get_visit_pts(mimic4_path:str, group_col:str, visit_col:str, admit_col:str, disch_col:str, adm_visit_col:str, use_mort:bool, use_los:bool, los:int, use_admn:bool, disease_label:str,use_ICU:bool):
    """Combines the MIMIC-IV core/patients table information with either the icu/icustays or core/admissions data.

    Parameters:
    mimic4_path: path to mimic-iv folder containing MIMIC-IV data
    group_col: patient identifier to group patients (normally subject_id)
    visit_col: visit identifier for individual patient visits (normally hadm_id or stay_id)
    admit_col: column for visit start date information (normally admittime or intime)
    disch_col: column for visit end date information (normally dischtime or outtime)
    use_ICU: describes whether to speficially look at ICU visits in icu/icustays OR look at general admissions from core/admissions
    """

    visit = None # df containing visit information depending on using ICU or not
    if use_ICU:
        visit = pd.read_csv(mimic4_path + "icu/icustays.csv.gz", compression='gzip', header=0, index_col=None, parse_dates=[admit_col, disch_col])
        if use_admn:
            # icustays doesn't have a way to identify if patient died during visit; must
            # use core/patients to remove such stay_ids for readmission labels
            pts = pd.read_csv(mimic4_path + "hosp/patients.csv.gz", compression='gzip', header=0, index_col=None, usecols=['subject_id', 'dod'], parse_dates=['dod'])
            visit = visit.merge(pts, how='inner', left_on='subject_id', right_on='subject_id')
            visit = visit.loc[(visit.dod.isna()) | (visit.dod >= visit[disch_col])]
            if len(disease_label):
                hids=disease_cohort.extract_diag_cohort(visit['hadm_id'],disease_label,mimic4_path)
                visit=visit[visit['hadm_id'].isin(hids['hadm_id'])]
                print("[ READMISSION DUE TO "+disease_label+" ]")
        
    else:
        visit = pd.read_csv(mimic4_path + "hosp/admissions.csv.gz", compression='gzip', header=0, index_col=None, parse_dates=[admit_col, disch_col])
        visit['los']=visit[disch_col]-visit[admit_col]

        visit[admit_col] = pd.to_datetime(visit[admit_col])
        visit[disch_col] = pd.to_datetime(visit[disch_col])        
        visit['los']=pd.to_timedelta(visit[disch_col]-visit[admit_col],unit='h')
        visit['los']=visit['los'].astype(str)
        visit[['days', 'dummy','hours']] = visit['los'].str.split(' ', -1, expand=True)
        visit['los']=pd.to_numeric(visit['days'])
        visit=visit.drop(columns=['days', 'dummy','hours'])
        
        
        if use_admn:
            # remove hospitalizations with a death; impossible for readmission for such visits
            visit = visit.loc[visit.hospital_expire_flag == 0]
        if len(disease_label):
                hids=disease_cohort.extract_diag_cohort(visit['hadm_id'],disease_label,mimic4_path)
                visit=visit[visit['hadm_id'].isin(hids['hadm_id'])]
                print("[ READMISSION DUE TO "+disease_label+" ]")

    pts = pd.read_csv(
            mimic4_path + "hosp/patients.csv.gz", compression='gzip', header=0, index_col = None, usecols=[group_col, 'anchor_year', 'anchor_age', 'anchor_year_group', 'dod','gender']
        )
    pts['yob']= pts['anchor_year'] - pts['anchor_age']  # get yob to ensure a given visit is from an adult
    pts['min_valid_year'] = pts['anchor_year'] + (2019 - pts['anchor_year_group'].str.slice(start=-4).astype(int))
    
    # Define anchor_year corresponding to the anchor_year_group 2017-2019. This is later used to prevent consideration
    # of visits with prediction windows outside the dataset's time range (2008-2019)
    #[[group_col, visit_col, admit_col, disch_col]]
    if use_ICU:
        visit_pts = visit[[group_col, visit_col, adm_visit_col, admit_col, disch_col,'los']].merge(
            pts[[group_col, 'anchor_year', 'anchor_age', 'yob', 'min_valid_year', 'dod','gender']], how='inner', left_on=group_col, right_on=group_col
        )
    else:
        visit_pts = visit[[group_col, visit_col, admit_col, disch_col,'los']].merge(
                pts[[group_col, 'anchor_year', 'anchor_age', 'yob', 'min_valid_year', 'dod','gender']], how='inner', left_on=group_col, right_on=group_col
            )

    # only take adult patients
#     visit_pts['Age']=visit_pts[admit_col].dt.year - visit_pts['yob']
#     visit_pts = visit_pts.loc[visit_pts['Age'] >= 18]
    visit_pts['Age']=visit_pts['anchor_age']
    visit_pts = visit_pts.loc[visit_pts['Age'] >= 18]
    
    ##Add Demo data
    eth = pd.read_csv(mimic4_path + "hosp/admissions.csv.gz", compression='gzip', header=0, usecols=['hadm_id', 'insurance','race'], index_col=None)
    visit_pts= visit_pts.merge(eth, how='inner', left_on='hadm_id', right_on='hadm_id')
    
    if use_ICU:
        return visit_pts[[group_col, visit_col, adm_visit_col, admit_col, disch_col,'los', 'min_valid_year', 'dod','Age','gender','race', 'insurance']]
    else:
        return visit_pts.dropna(subset=['min_valid_year'])[[group_col, visit_col, admit_col, disch_col,'los', 'min_valid_year', 'dod','Age','gender','race', 'insurance']]


def validate_row(row, ctrl, invalid, max_year, disch_col, valid_col, gap):
    """Checks if visit's prediction window potentially extends beyond the dataset range (2008-2019).
    An 'invalid row' is NOT guaranteed to be outside the range, only potentially outside due to
    de-identification of MIMIC-IV being done through 3-year time ranges.
    
    To be invalid, the end of the prediction window's year must both extend beyond the maximum seen year
    for a patient AND beyond the year that corresponds to the 2017-2019 anchor year range for a patient"""
    print("disch_col",row[disch_col])
    print(gap)
    pred_year = (row[disch_col] + gap).year
    if max_year < pred_year and pred_year > row[valid_col]:
        invalid = invalid.append(row)
    else:
        ctrl = ctrl.append(row)
    return ctrl, invalid


def partition_by_los(df:pd.DataFrame, los:int, group_col:str, visit_col:str, admit_col:str, disch_col:str, valid_col:str):
    
    invalid = df.loc[(df[admit_col].isna()) | (df[disch_col].isna()) | (df['los'].isna())]
    cohort = df.loc[(~df[admit_col].isna()) & (~df[disch_col].isna()) & (~df['los'].isna())]
    
    
    #cohort=cohort.fillna(0)
    pos_cohort=cohort[cohort['los']>los]
    neg_cohort=cohort[cohort['los']<=los]
    neg_cohort=neg_cohort.fillna(0)
    pos_cohort=pos_cohort.fillna(0)
    
    pos_cohort['label']=1
    neg_cohort['label']=0
    
    cohort=pd.concat([pos_cohort,neg_cohort], axis=0)
    cohort=cohort.sort_values(by=[group_col,admit_col])
    #print("cohort",cohort.shape)
    print("[ LOS LABELS FINISHED ]")
    return cohort, invalid
        
        
def partition_by_readmit(df:pd.DataFrame, gap:datetime.timedelta, group_col:str, visit_col:str, admit_col:str, disch_col:str, valid_col:str):
    """Applies labels to individual visits according to whether or not a readmission has occurred within the specified `gap` days.
    For a given visit, another visit must occur within the gap window for a positive readmission label.
    The gap window starts from the disch_col time and the admit_col of subsequent visits are considered."""
    
    case = pd.DataFrame()   # hadm_ids with readmission within the gap period
    ctrl = pd.DataFrame()   # hadm_ids without readmission within the gap period
    invalid = pd.DataFrame()    # hadm_ids that are not considered in the cohort

    # Iterate through groupbys based on group_col (subject_id). Data is sorted by subject_id and admit_col (admittime)
    # to ensure that the most current hadm_id is last in a group.
    #grouped= df[[group_col, visit_col, admit_col, disch_col, valid_col]].sort_values(by=[group_col, admit_col]).groupby(group_col)
    grouped= df.sort_values(by=[group_col, admit_col]).groupby(group_col)
    for subject, group in tqdm(grouped):
        max_year = group.max()[disch_col].year

        if group.shape[0] <= 1:
            #ctrl, invalid = validate_row(group.iloc[0], ctrl, invalid, max_year, disch_col, valid_col, gap)   # A group with 1 row has no readmission; goes to ctrl
            ctrl = ctrl.append(group.iloc[0])
        else:
            for idx in range(group.shape[0]-1):
                visit_time = group.iloc[idx][disch_col]  # For each index (a unique hadm_id), get its timestamp
                if group.loc[
                    (group[admit_col] > visit_time) &    # Readmissions must come AFTER the current timestamp
                    (group[admit_col] - visit_time <= gap)   # Distance between a timestamp and readmission must be within gap
                    ].shape[0] >= 1:                # If ANY rows meet above requirements, a readmission has occurred after that visit

                    case = case.append(group.iloc[idx])
                else:
                    # If no readmission is found, only add to ctrl if prediction window is guaranteed to be within the
                    # time range of the dataset (2008-2019). Visits with prediction windows existing in potentially out-of-range
                    # dates (like 2018-2020) are excluded UNLESS the prediction window takes place the same year as the visit,
                    # in which case it is guaranteed to be within 2008-2019

                    ctrl = ctrl.append(group.iloc[idx])

            #ctrl, invalid = validate_row(group.iloc[-1], ctrl, invalid, max_year, disch_col, valid_col, gap)  # The last hadm_id datewise is guaranteed to have no readmission logically
            ctrl = ctrl.append(group.iloc[-1])
            #print(f"[ {gap.days} DAYS ] {case.shape[0] + ctrl.shape[0]}/{df.shape[0]} {visit_col}s processed")

    print("[ READMISSION LABELS FINISHED ]")
    return case, ctrl, invalid


def partition_by_mort(df:pd.DataFrame, group_col:str, visit_col:str, admit_col:str, disch_col:str, death_col:str):
    """Applies labels to individual visits according to whether or not a death has occurred within
    the times of the specified admit_col and disch_col"""

    invalid = df.loc[(df[admit_col].isna()) | (df[disch_col].isna())]

    cohort = df.loc[(~df[admit_col].isna()) & (~df[disch_col].isna())]
    
    cohort['label']=0
    pos_cohort=cohort[~cohort[death_col].isna()]
    neg_cohort=cohort[cohort[death_col].isna()]
    neg_cohort=neg_cohort.fillna(0)
    pos_cohort=pos_cohort.fillna(0)
    pos_cohort[death_col] = pd.to_datetime(pos_cohort[death_col])

    pos_cohort['label'] = np.where((pos_cohort[death_col] >= pos_cohort[admit_col]) & (pos_cohort[death_col] <= pos_cohort[disch_col]),1,0)
    
    pos_cohort['label'] = pos_cohort['label'].astype("Int32")
    cohort=pd.concat([pos_cohort,neg_cohort], axis=0)
    cohort=cohort.sort_values(by=[group_col,admit_col])
    #print("cohort",cohort.shape)
    print("[ MORTALITY LABELS FINISHED ]")
    return cohort, invalid


def get_case_ctrls(df:pd.DataFrame, gap:int, group_col:str, visit_col:str, admit_col:str, disch_col:str, valid_col:str, death_col:str, use_mort=False,use_admn=False,use_los=False) -> pd.DataFrame:
    """Handles logic for creating the labelled cohort based on arguments passed to extract().

    Parameters:
    df: dataframe with patient data
    gap: specified time interval gap for readmissions
    group_col: patient identifier to group patients (normally subject_id)
    visit_col: visit identifier for individual patient visits (normally hadm_id or stay_id)
    admit_col: column for visit start date information (normally admittime or intime)
    disch_col: column for visit end date information (normally dischtime or outtime)
    valid_col: generated column containing a patient's year that corresponds to the 2017-2019 anchor time range
    dod_col: Date of death column
    """

    case = None  # hadm_ids with readmission within the gap period
    ctrl = None   # hadm_ids without readmission within the gap period
    invalid = None    # hadm_ids that are not considered in the cohort

    if use_mort:
        return partition_by_mort(df, group_col, visit_col, admit_col, disch_col, death_col)
    elif use_admn:
        gap = datetime.timedelta(days=gap)
        # transform gap into a timedelta to compare with datetime columns
        case, ctrl, invalid = partition_by_readmit(df, gap, group_col, visit_col, admit_col, disch_col, valid_col)

        # case hadm_ids are labelled 1 for readmission, ctrls have a 0 label
        case['label'] = np.ones(case.shape[0]).astype(int)
        ctrl['label'] = np.zeros(ctrl.shape[0]).astype(int)

        return pd.concat([case, ctrl], axis=0), invalid
    elif use_los:
        return partition_by_los(df, gap, group_col, visit_col, admit_col, disch_col, death_col)

    # print(f"[ {gap.days} DAYS ] {invalid.shape[0]} hadm_ids are invalid")


def extract_data(use_ICU:str, label:str, time:int, icd_code:str, root_dir,mimic_path, disease_label, cohort_output=None, summary_output=None):
    """Extracts cohort data and summary from MIMIC-IV data based on provided parameters.

    Parameters:
    cohort_output: name of labelled cohort output file
    summary_output: name of summary output file
    use_ICU: state whether to use ICU patient data or not
    label: Can either be '{day} day Readmission' or 'Mortality', decides what binary data label signifies"""
    print("===========MIMIC-IV v2============")
    if not cohort_output:
        cohort_output="cohort_" + use_ICU.lower() + "_" + label.lower().replace(" ", "_") + "_" + str(time) + "_" + disease_label
    if not summary_output:
        summary_output="summary_" + use_ICU.lower() + "_" + label.lower().replace(" ", "_")  + "_" + str(time) + "_" + disease_label
    
    if icd_code=="No Disease Filter":
        if len(disease_label):
            print(f"EXTRACTING FOR: | {use_ICU.upper()} | {label.upper()} DUE TO {disease_label.upper()} | {str(time)} | ")
        else:
            print(f"EXTRACTING FOR: | {use_ICU.upper()} | {label.upper()} | {str(time)} |")
    else:
        if len(disease_label):
            print(f"EXTRACTING FOR: | {use_ICU.upper()} | {label.upper()} DUE TO {disease_label.upper()} | ADMITTED DUE TO {icd_code.upper()} | {str(time)} |")
        else:
            print(f"EXTRACTING FOR: | {use_ICU.upper()} | {label.upper()} | ADMITTED DUE TO {icd_code.upper()} | {str(time)} |")
    #print(label)
    cohort, invalid = None, None # final labelled output and df of invalid records, respectively
    pts = None  # valid patients generated by get_visit_pts based on use_ICU and label
    ICU=use_ICU
    group_col, visit_col, admit_col, disch_col, death_col, adm_visit_col = "", "", "", "", "", ""
    #print(label)
    use_mort = label == "Mortality" # change to boolean value
    use_admn=label=='Readmission'
    los=0
    use_los= label=='Length of Stay'
    
    #print(use_mort)
    #print(use_admn)
    #print(use_los)
    if use_los:
        los=time
    use_ICU = use_ICU == "ICU" # change to boolean value
    use_disease=icd_code!="No Disease Filter"
    
    if use_ICU:
        group_col='subject_id'
        visit_col='stay_id'
        admit_col='intime'
        disch_col='outtime'
        death_col='dod'
        adm_visit_col='hadm_id'
    else:
        group_col='subject_id'
        visit_col='hadm_id'
        admit_col='admittime'
        disch_col='dischtime'
        death_col='dod'

    pts = get_visit_pts(
        mimic4_path=mimic_path,
        group_col=group_col,
        visit_col=visit_col,
        admit_col=admit_col,
        disch_col=disch_col,
        adm_visit_col=adm_visit_col,
        use_mort=use_mort,
        use_los=use_los,
        los=los,
        use_admn=use_admn,
        disease_label=disease_label,
        use_ICU=use_ICU
    )

    
    # cols to be extracted from get_case_ctrls
    cols = [group_col, visit_col, admit_col, disch_col, 'Age','gender','ethnicity','insurance','label']

    if use_mort:
        cols.append(death_col)
        cohort, invalid = get_case_ctrls(pts, None, group_col, visit_col, admit_col, disch_col,'min_valid_year', death_col, use_mort=True,use_admn=False,use_los=False)
    elif use_admn:
        interval = time
        cohort, invalid = get_case_ctrls(pts, interval, group_col, visit_col, admit_col, disch_col,'min_valid_year', death_col, use_mort=False,use_admn=True,use_los=False)
    elif use_los:
        cohort, invalid = get_case_ctrls(pts, los, group_col, visit_col, admit_col, disch_col,'min_valid_year', death_col, use_mort=False,use_admn=False,use_los=True)
    #print(cohort.head())
    
    if use_ICU:
        cols.append(adm_visit_col)

    
    if use_disease:
        hids=disease_cohort.extract_diag_cohort(cohort['hadm_id'],icd_code,mimic_path)
        cohort=cohort[cohort['hadm_id'].isin(hids['hadm_id'])]
        cohort_output=cohort_output+"_"+icd_code
        summary_output=summary_output+"_"+icd_code
    #print(cohort[cols].head())
    # save output
    cohort=cohort.rename(columns={"race":"ethnicity"})
    cohort[cols].to_csv("./data/cohort/"+cohort_output+".csv.gz", index=False, compression='gzip')
    print("[ COHORT SUCCESSFULLY SAVED ]")

    summary = "\n".join([
        f"{label} FOR {ICU} DATA",
        f"# Admission Records: {cohort.shape[0]}",
        f"# Patients: {cohort[group_col].nunique()}",
        f"# Positive cases: {cohort[cohort['label']==1].shape[0]}",
        f"# Negative cases: {cohort[cohort['label']==0].shape[0]}"
    ])

    # save basic summary of data
    with open(f"./data/cohort/{summary_output}.txt", "w") as f:
        f.write(summary)

    print("[ SUMMARY SUCCESSFULLY SAVED ]")
    print(summary)

    return cohort_output