import os
import pandas as pd
from datetime import timedelta,datetime

from lib.utilities import codebook
from lib.experiment_specs import study_config


from lib.data_helpers.builder_utils import BuilderUtils
from lib.data_helpers import test

from lib.utilities import serialize

class Gaming():
	gaming_dir = os.path.join("data","external","intermediate","PhoneDashboard","Gaming")
	events_file = os.path.join(gaming_dir,"Events")
	first_last_file = os.path.join(gaming_dir, "FirstLast")
	diagnosed_file = os.path.join(gaming_dir, "Diagnosed")
	diagnosed_test_file = diagnosed_file.replace("intermediate","intermediate_test")

	good_diag = ["Phone never shut off",
				 "Phone shut off",
				 "Phone shut off, even if d1<d0",
				 ]

	game_cols = ["AppCode", "AppRuntime", "ForegroundApp","BatteryLevel", "DeviceRuntime", "CreatedDatetime","CreatedEasternDatetime", "ScreenActive",
				 "CreatedDate", "Sequence","Zipfile","TimeZone"]


	""" - detects suspicious events of a granular dataframe that comes from an individual zipfile
	    - records the first and last pings in each granular data frame to scan first and last """
	@staticmethod
	def scan(df,file,first_last_bool = False):
		for col in Gaming.game_cols:
			if col in df.columns:
				df[f"Prev{col}"] = df[col].shift(1)
		keep_cols = [x + y for x in ["", "Prev"] for y in Gaming.game_cols]

		#Find all pings in which the previous runtime was greater than current runtime
		if first_last_bool == False:
			ev = df.loc[(df["PrevAppRuntime"]>df["AppRuntime"]) & (df["PrevAppCode"]==df["AppCode"]),keep_cols]

		else:
			# add suspicious obs from the the first_last df when the last reading from the previous zipfile
			# has a app runtime that is greater than the app runtime for the current zipfile
			ev = df.loc[(df["PrevAppRuntime"]>df["AppRuntime"])
						& (df["PrevAppCode"]==df["AppCode"])
						& (df["Sequence"]=="First")
						& (df["PrevSequence"]=="Last")
						& (df["PrevZipfile"]!=df["Zipfile"])
						, keep_cols]

		serialize.save_pickle(ev, os.path.join(Gaming.gaming_dir,"Granular",f"events{file}"))

	"""gets the first and last observation from each raw zipfile"""
	@staticmethod
	def get_first_last(df,file):
		first = df.groupby("AppCode").first()
		first["Sequence"] = "First"
		last = df.groupby("AppCode").last()
		last["Sequence"] = "Last"
		first_last_df = first.append(last).reset_index()
		first_last_df = first_last_df[Gaming.game_cols]
		serialize.save_pickle(first_last_df, os.path.join(Gaming.gaming_dir, "FirstLast", f"first_last_{file}"))

	""" assembles the events file, diagnoses gaming events, and summarizes blackouts on the user level by phase"""
	@staticmethod
	def process_gaming(error_margin, hour_use,raw_user_df):

		#don't run raw gaming detection pipeline during test...debug over notebooks if needed
		config_user_dict = serialize.open_yaml("config_user.yaml")
		if config_user_dict['local']['test']:
			diag_df = serialize.open_pickle(Gaming.diagnosed_file)

		else:
			Gaming._add_first_last_events()
			ev_df = Gaming._aggregate_events()
			diag_df = Gaming._diagnose_events(ev_df, error_margin, hour_use)

		#rehape all blackout events for main analysis
		game_user_df = Gaming._reshape_events(diag_df, raw_user_df)

		#reshape screen active blackout events for side analysis
		game_user_df_SA = Gaming._reshape_events(diag_df.loc[diag_df["PrevScreenActive"]==1], raw_user_df,"ActiveBlackoutsOverPhase")

		game_hour_df = Gaming._expand_gaming_df(diag_df,"GameHourDf")

		#game_hour_df_under_24 = Gaming._expand_gaming_df(diag_df.loc[diag_df["BlackoutHours"]<24],
		#												"GameHourDfUnder24")

		return game_user_df


	""" aggregate the first last observations, and then scan them. 
		We are scanning if the last reading from the previous zipfile
		has a app runtime that is greater than the app runtime for the next zipfile """
	@staticmethod
	def _add_first_last_events():
		fl_dir = os.path.join(Gaming.gaming_dir, "FirstLast")
		df = pd.concat([serialize.soft_df_open(os.path.join(fl_dir, x)) for x in os.listdir(fl_dir)])
		df = df.sort_values(by=["AppCode", "CreatedEasternDatetime"]).reset_index(drop=True)
		if datetime.now()>study_config.surveys["Baseline"]["Start"]:
			df = df.loc[df['CreatedDatetime']>study_config.surveys["Baseline"]["Start"]]
		df["PrevSequence"] = df["Sequence"].shift(1)
		Gaming.scan(df, "fl", first_last_bool=True)

	"""aggregates all the individual events in the granular directory"""
	@staticmethod
	def _aggregate_events():
		ev_dir = os.path.join(Gaming.gaming_dir, "Granular")
		ev_df = pd.concat([serialize.soft_df_open(os.path.join(ev_dir, x)) for x in os.listdir(ev_dir)])
		ev_df = ev_df.drop_duplicates(subset=["AppCode", "CreatedEasternDatetime"], keep='last').reset_index(drop=True)
		serialize.save_pickle(ev_df, Gaming.events_file)
		return ev_df


	""" estimates the runtime of the phone when the user was not tracking
		   - d0: the device runtime right before pennyworth stopped recording
		   - d1: the device runtime when PD returned to recording
		   - dd: difference in phone runtime (d1 - d0)
		   - td: difference in the timestamps associated with d0 and d1
		   - error_margin: number of hours that CreateDateTime or runtime stamps can deviate before error is flagged
		   - all variables have hour units
		   """

	@staticmethod
	def _diagnose_events(ev_df, error_margin, clean_hour_use):
		df = ev_df.sort_values(by = ['AppCode','CreatedEasternDatetime'])
		df = df.loc[df["PrevCreatedEasternDatetime"]>study_config.first_pull]

		if datetime.now()>study_config.surveys["Baseline"]["Start"]:
			df = df.loc[df['PrevCreatedDatetime']>study_config.surveys["Baseline"]["Start"]]

		df["CreatedEasternDatetimeDiffHours"] = (df["CreatedEasternDatetime"] - df["PrevCreatedEasternDatetime"]).apply(
			lambda x: round(x.days * 24 + x.seconds / (60 * 60), 2))

		for col in ["DeviceRuntime", "AppRuntime", "PrevDeviceRuntime", "PrevAppRuntime"]:
			df[f"{col}Hours"] = (df[f"{col}"] / (1000 * 60 * 60)).round(decimals=2)

		for col in ["DeviceRuntimeHours","AppRuntimeHours"]:
			df[col+"Diff"] = df[col]-df[f"Prev{col}"]

		ne_dict = df.to_dict(orient='index')
		day = clean_hour_use.groupby(["AppCode","CreatedDate"])["UseMinutes"].sum()
		day_dic = {k: day[k].to_dict() for k, v in day.groupby(level=0)}

		for key, val in ne_dict.items():

			d0 = val["PrevDeviceRuntimeHours"]
			d1 = val["DeviceRuntimeHours"]
			td = val["CreatedEasternDatetimeDiffHours"]
			date0 = val["PrevCreatedDatetime"]
			date1 = val["CreatedDatetime"]

			if val["AppCode"] in day_dic:
				app_dic = day_dic[val["AppCode"]]
			else:
				#this appcode has no use :/
				app_dic = {}

			# Remove false-positives due to data export lag.
			# ..i.e. drop an event if there is use in between the pings
			if (date0+timedelta(days=1)).date()<date1.date():
				next_day = date0+timedelta(days=1)
				while next_day < date1.date():
					if next_day in app_dic:
						ne_dict[key]['Diagnosis'] = "ERROR: FalsePositive"
						break
					else:
						next_day = next_day + timedelta(days=1)

			if date1<date0:
				ne_dict[key]['Diagnosis'] = "ERROR: Date1<Date0"

			#assert td>0
			elif d1 - d0 < 0:
				# indicates that phone shutdown:
				ne_dict[key]['Diagnosis'] = "Phone shut off"
				ne_dict[key]['BlackoutHoursLB'] = d1
				ne_dict[key]['BlackoutHoursUB'] = td

				if td + error_margin < d1:
					# Impossible, comment error
					ne_dict[key]['Diagnosis'] = "ERROR: td <d1 | d1-d0 <= 0 "

			else:
				if td >= d1:
					# if new runtime is less than or equal to time difference: phone had to have shut off,
					# even if new runtime is greater than old runtime
					ne_dict[key]['Diagnosis'] = f"Phone shut off, even if d1<d0"
					ne_dict[key]['BlackoutHoursLB'] = d1
					ne_dict[key]['BlackoutHoursUB'] = td

				else:
					# phone did not shut down, since d1>=d0 & d1>td
					ne_dict[key]['Diagnosis'] = f"Phone never shut off"
					ne_dict[key]['BlackoutHoursLB'] = td
					ne_dict[key]['BlackoutHoursUB'] = td

					if td + error_margin < d1 - d0:
						# Impossible, comment error
						ne_dict[key]['Diagnosis'] = "ERROR: if phone never shutoff, no way for td < d1-d0"



		df = pd.DataFrame.from_dict(ne_dict, orient='index')
		df["BlackoutHours"] = (df["BlackoutHoursLB"] + df["BlackoutHoursUB"])/2
		df = Gaming._diagnose_dups(df)
		serialize.save_pickle(df, Gaming.diagnosed_file)
		test.save_test_df(df,Gaming.diagnosed_test_file)

		return df

	@staticmethod
	def _diagnose_dups(df):
		df = df.sort_values(by=["AppCode", "PrevCreatedEasternDatetime"]).reset_index(drop=True)
		d_dict = df.to_dict(orient='index')
		for key, val in d_dict.items():
			if key + 1 not in d_dict:
				continue

			if d_dict[key]["AppCode"] != d_dict[key + 1]["AppCode"]:
				continue

			if d_dict[key]["CreatedEasternDatetime"] > d_dict[key + 1]["PrevCreatedEasternDatetime"]:
				d_dict[key]["Diagnosis"] = "Error: Another event starts before this event ends"

				#put an error on the other event if it is NOT embedded in the original event
				if d_dict[key]["CreatedEasternDatetime"] < d_dict[key + 1]["CreatedEasternDatetime"]:
					d_dict[key + 1]["Diagnosis"] = "Error: Another event ends after this event starts"

		df = pd.DataFrame.from_dict(d_dict, orient='index')
		return df

	"""
	Input: takes the diagnosed event level dataframe 
	Output: User level df that contains the total blackout period time by phase
	"""
	@staticmethod
	def _reshape_events(diag_df,raw_user,file_name = None):
		df = diag_df.loc[diag_df["Diagnosis"].isin(Gaming.good_diag)]
		df["CreatedDate"] = df["CreatedDatetime"].apply(lambda x: x.date())
		df = BuilderUtils.add_phase_label(df,
										raw_df_date = "CreatedDate",
										start_buffer = 0,
										end_buffer = -1,)

		# Replace Values of phase with the start survey code
		codes = [study_config.phases[x]["StartSurvey"]["Code"] for x in list(study_config.phases.keys())]
		rename_dic = dict(zip(list(study_config.phases.keys()), codes))
		df["Phase"] = df["Phase"].apply(lambda x: rename_dic[x] if x in rename_dic else x)


		df_s = df.groupby(["AppCode","Phase"])["BlackoutHours"].sum().reset_index()

		df_p = df_s.pivot_table(index=["AppCode"],
								values=["BlackoutHours"],
								columns=["Phase"],
								aggfunc='first')

		#Flatten Column Names (and rearange in correct order
		df_p.columns = ['_'.join(col[::-1]).strip() for col in df_p.columns.values]
		df_p = df_p.reset_index()

		#if file_name != None:
		#	serialize.save_pickle(df_p,os.path.join("data","external","intermediate","PhoneDashboard","Gaming",file_name))

		# We don't calculate blackouthours per day here because we use DaySet as the denomenator
		return df_p

	@staticmethod
	def _expand_gaming_df(diag,file_name):
		ex = diag.loc[diag["Diagnosis"].isin(Gaming.good_diag)]
		# Creates list of DatetimeHours that are in BlackoutPeriod
		ex["DatetimeHour"] = ex.apply(lambda x: Gaming.get_time_attributes(x, "Hour"), axis=1)

		# Expand the dataframe
		ex = ex.explode("DatetimeHour")
		ex["DatetimeHour"] = ex["DatetimeHour"].apply(lambda x: x.replace(minute=0, second=0, microsecond=0))
		ex["HourCount"] = ex.groupby(["AppCode", "CreatedDatetime"])["DatetimeHour"].transform('count')
		# Evenly divide the blackout period among the hours it occupied
		ex["BlackoutHours"] = ex["BlackoutHours"] / ex["HourCount"]

		# Compress onto the App-Hour Level (this compresses multiple blackout events that occured on the same datetime hour)
		ex = ex.groupby(["AppCode", "DatetimeHour"])["BlackoutHours"].sum().reset_index()

		config_user_dict = serialize.open_yaml("config_user.yaml")

		if config_user_dict['local']['test']==False:
			serialize.save_pickle(ex, os.path.join(Gaming.gaming_dir,file_name))
		return ex

	@staticmethod
	def get_time_attributes(df, kind):
		start = df["PrevCreatedDatetime"]
		end = df["CreatedDatetime"]
		if kind == "Hour":
			thing = [x for x in pd.date_range(start, end, freq="H")]
		else:
			thing = [x.weekday() for x in pd.date_range(start, end, freq="D")]
		return thing