import pandas as pd
import numpy as np
import datetime

import math, sys, yaml

from esm_preprocess import clean_up_esm
from esm import classify_sessions_by_completion_time, preprocess_esm

input_data_files = dict(snakemake.input)

def format_timestamp(x):
    tstring=""
    space = False
    if x//3600 > 0:
        tstring += f"{x//3600}H"
        space = True
    if x % 3600 // 60 > 0:
        tstring += f" {x % 3600 // 60}M" if "H" in tstring else f"{x % 3600 // 60}M"
    if x % 60 > 0:  
        tstring += f" {x % 60}S" if "M" in tstring or "H" in tstring else f"{x % 60}S"
    
    return tstring


def extract_ers_from_file(esm_df, device_id): 

    pd.set_option("display.max_rows", None)
    pd.set_option("display.max_columns", None)

    # extracted_ers = pd.DataFrame(columns=["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"])

    # esm_df = clean_up_esm(preprocess_esm(esm_df))
    esm_preprocessed = clean_up_esm(preprocess_esm(esm_df))

    # Take only during work sessions
    # during_work = esm_df[esm_df["esm_trigger"].str.contains("during_work", na=False)]
    # esm_trigger_group = esm_df.groupby("esm_session").agg(pd.Series.mode)['esm_trigger'] # Get most frequent esm_trigger within particular session  
    # esm_filtered_sessions = list(esm_trigger_group[esm_trigger_group == 'during_work'].index) # Take only sessions that contains during work  

    # Take only ema_completed sessions responses
    classified = classify_sessions_by_completion_time(esm_preprocessed)
    esm_filtered_sessions = classified[classified["session_response"] == 'ema_completed'].reset_index()['esm_session']
    
    esm_df = esm_preprocessed[esm_preprocessed["esm_session"].isin(esm_filtered_sessions)]

    # Extract time-relevant information
    extracted_ers = esm_df.groupby(["device_id", "esm_session"])['timestamp'].apply(lambda x: math.ceil((x.max() - x.min()) / 1000)).reset_index() # in rounded up seconds
    extracted_ers = extracted_ers[extracted_ers["timestamp"] <= 15 * 60].reset_index(drop=True) # ensure that the longest duration of the questionnaire anwsering is 15 min 

    time_before_questionnaire = 30 * 60 # in seconds (30 minutes)

    extracted_ers["label"] = "straw_event_" + snakemake.params["pid"] + "_" + extracted_ers.index.astype(str).str.zfill(3)
    extracted_ers["event_timestamp"] = esm_df.groupby("esm_session")['timestamp'].min().reset_index()['timestamp']
    extracted_ers["length"] = (extracted_ers["timestamp"] + time_before_questionnaire).apply(lambda x: format_timestamp(x))
    extracted_ers["shift"] = time_before_questionnaire
    extracted_ers["shift"] = extracted_ers["shift"].apply(lambda x: format_timestamp(x))
    extracted_ers["shift_direction"] = -1 
    extracted_ers["device_id"] = device_id 

    return extracted_ers[["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"]]

if snakemake.params["stage"] == "extract": 
    esm_df = pd.read_csv(input_data_files['esm_raw_input'])

    with open(input_data_files['pid_file'], 'r') as stream:
        pid_file = yaml.load(stream, Loader=yaml.FullLoader)

    extracted_ers = extract_ers_from_file(esm_df, pid_file["PHONE"]["DEVICE_IDS"][0])

    extracted_ers.to_csv(snakemake.output[0], index=False)
elif snakemake.params["stage"] == "merge":

    input_data_files = dict(snakemake.input)
    straw_events = pd.DataFrame(columns=["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"])
    
    for input_file in input_data_files["ers_files"]:
        ers_df = pd.read_csv(input_file)
        straw_events = pd.concat([straw_events, ers_df], axis=0, ignore_index=True)

    straw_events.to_csv(snakemake.output[0], index=False)