from datetime import timedelta

from pyparsing import col
from pyspark import SparkConf, SparkContext
from pyspark.sql import SparkSession
# from pyspark.sql import Window
from pyspark.sql import Window
from pyspark.sql.types import *
from pyspark.sql.functions import array, explode, lit, struct, window
from pyspark.sql import DataFrame
from typing import Iterable
from pyspark.sql import functions as f
import numpy as np

spark = SparkSession.builder.getOrCreate()

path = "turbine_power_data.xlsx"

# Loading data from excel, specifying required properties
df_excel = spark.read.format("com.crealytics.spark.excel") \
.option("header", "true") \
.option("dataAddress", "'power'!A7") \
.option("timestampFormat", "MM-dd-yyyy mm:ss") \
.option("treatEmptyValuesAsNulls", "false") \
.option("inferSchema", "true") \
.load(path)

# Removing all null and corrupted data
df_filtered = df_excel.na.drop()

# Specifying the data type of each column
df_filtered_refined = df_filtered.selectExpr(
"cast(time as timestamp) time",
"cast(power_turbine1 as double) power_turbine1",
"cast(power_turbine2 as double) power_turbine2",
"cast(power_turbine1 as double) power_turbine3")

# Melting the data frame from wide to long format
df_transformed = df_filtered_refined \
.selectExpr("time",
            "stack(3,'power_turbine1',power_turbine1,'power_turbine2',power_turbine2,'power_turbine3',power_turbine3)") \
.withColumnRenamed("col0", "turbine") \
.withColumnRenamed("col1", "power")

# Re-sampling and aggregating on 10 minutes interval
df_granularity = df_transformed.groupBy(window("time", "10 minute")).sum("power")
print(df_granularity.head(n=5))
df_granularity.printSchema()

df_selected = df_granularity.withColumn('time', f.col('window.end')) \
.withColumn('power', f.col('sum(power)'))
df_selected = df_selected.drop('window')
df_selected = df_selected.drop('sum(power)')
print(df_selected.head())
df_selected.printSchema()

"""
Moving Average class
to fit the simple averaging model
based on the specified window size.
"""
class MovingAverage:
def __init__(self, hrs):
   """
   Constructor taking initial moving average
   window duration in hours
   :param hrs:
   """
   self.hrs = hrs
   self.model = None

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