import pandas as pd
import numpy as np
import datetime

import math, sys, yaml

from esm_preprocess import preprocess_esm, clean_up_esm

input_data_files = dict(snakemake.input)

def extract_ers_from_file(esm_df, device_id): # TODO: kako se bodo pridobili device_id? Bo torej potreben tudi p0??.yaml?

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

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

    esm_df = 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  
    esm_df = esm_df[esm_df["esm_session"].isin(esm_filtered_sessions)]

    # Extract time-relevant information
    extracted_ers = esm_df.groupby("esm_session")['timestamp'].apply(lambda x: math.ceil((x.max() - x.min()) / 1000)).reset_index() # in rounded up seconds
    time_before_questionnaire = 30 * 60 # in seconds (30 minutes)

    extracted_ers["label"] = "straw_event_" + snakemake.params["pid"] + "_" + extracted_ers["esm_session"].astype(str)
    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: f"{x//3600}H {x % 3600 // 60}M {x % 60}S" if x//3600 > 0 else f"{x % 3600 // 60}M {x % 60}S")
    extracted_ers["shift"] = time_before_questionnaire
    extracted_ers["shift"] = extracted_ers["shift"].apply(lambda x: f"{x//3600}H {x % 3600 // 60}M {x % 60}S" if x//3600 > 0 else f"{x % 3600 // 60}M {x % 60}S") 
    extracted_ers["shift_direction"] = -1 
    extracted_ers["device_id"] = device_id 

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

# TODO: potrebno preveriti kako se izvaja iskanje prek device_id -> na tem temelji tudi proces ekstrahiranja ERS

if snakemake.params["stage"] == "extract": # TODO: najprej preveri ustreznost umeščenosti v RAPIDS pipelineu
    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)