/
ole3.py
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/
ole3.py
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# Databricks notebook source
from pyspark.sql import SparkSession
# COMMAND ----------
from pyspark.sql.types import DateType
# COMMAND ----------
from pyspark.sql import functions as F
# COMMAND ----------
spark = SparkSession.builder.appName("INRIX3").getOrCreate()
# COMMAND ----------
dfsegment = spark.read.csv("/reactor/davami/INRIX_XDSegments_18_1.csv", inferSchema=True, header=True)
# COMMAND ----------
# COMMAND ----------
dfinrix = spark.read.csv("/reactor/davami/11-4-2018small.csv", inferSchema=True, header=True)
# COMMAND ----------
dfinrix.show()
# COMMAND ----------
dfdateadd = dfinrix.withColumn("Date",dfinrix["CentralTime"].cast(DateType()))
# COMMAND ----------
dfdate = dfdateadd.drop("SegmentClosed", "CentralTime", "Time")
# COMMAND ----------
dfspeed = dfdate.filter("Speed>0")
# COMMAND ----------
dfmile = dfspeed.join(dfsegment,dfspeed.Code == dfsegment.XDSegID).select(dfspeed["*"],dfsegment["Miles"])
# COMMAND ----------
dfdel = dfmile.withColumn("RefDel",(dfmile["Travel"]/60-dfmile["Miles"]/dfmile["Reference"])).withColumn("AvgDel",(dfmile["Travel"]/60-dfmile["Miles"]/dfmile["Average"]))
# COMMAND ----------
reffunc = (F.when(F.col("RefDel") < 0, 0).otherwise(F.col("RefDel")))
# COMMAND ----------
avgfunc = (F.when(F.col("AvgDel") < 0, 0).otherwise(F.col("AvgDel")))
# COMMAND ----------
dfdela = dfdel.withColumn("RefDelay",reffunc).withColumn("AvgDelay",avgfunc)
# COMMAND ----------
dfdelay = dfdela.drop("RefDel","AvgDel")
# COMMAND ----------
dftotalcount = dfdelay.groupby("Code", "Date").count().withColumnRenamed("count","TotalCount")
# COMMAND ----------
dffilt10 = dfdelay.filter(dfdelay.Score == 10)
# COMMAND ----------
dffilter10 = dffilt10.groupby("Code", "Date").count().withColumnRenamed("count","Count10")
# COMMAND ----------
dffilt20 = dfdelay.filter(dfdelay.Score == 20)
# COMMAND ----------
dffilter20 = dffilt20.groupby("Code","Date").agg({"Code":"Count","Speed":"avg","Average":"avg","Reference":"avg","Travel":"avg","RefDelay":"sum","AvgDelay":"sum"}).withColumnRenamed("avg(Speed)","Speed20").withColumnRenamed("avg(Average)","AvgSpeed20").withColumnRenamed("avg(Reference)","RefSpeed20").withColumnRenamed("avg(Travel)","Traveltime20").withColumnRenamed("sum(RefDelay)","RefDelay20").withColumnRenamed("sum(AvgDelay)","AvgDelay20").withColumnRenamed("count(Code)","Count20")
# COMMAND ----------
dffilt30 = dfdelay.filter(dfdelay.Score == 30)
# COMMAND ----------
dffilter30 = dffilt30.groupby("Code","Date").agg({"Code":"Count","C-Value":"avg","Speed":"avg","Average":"avg","Reference":"avg","Travel":"avg","RefDelay":"sum","AvgDelay":"sum"}).withColumnRenamed("avg(Speed)","Speed30").withColumnRenamed("avg(Average)","AvgSpeed30").withColumnRenamed("avg(Reference)","RefSpeed30").withColumnRenamed("avg(Travel)","Traveltime30").withColumnRenamed("sum(RefDelay)","RefDelay30").withColumnRenamed("sum(AvgDelay)","AvgDelay30").withColumnRenamed("count(Code)","Count30").withColumnRenamed("avg(C-Value)","Cvalue")
# COMMAND ----------
dfcodeo = dfsegment.select("XDSegID").withColumnRenamed("XDSegID","CodeO")
# COMMAND ----------
dfcode = dfcodeo.withColumn("Code",dfcodeo.CodeO.astype("Int")).drop("CodeO")
# COMMAND ----------
dftotalcountmix = dfcode.join(dftotalcount, on=["Code"], how="left")
# COMMAND ----------
dfjoin10 = dftotalcountmix.join(dffilter10, on=["Code","Date"], how="left")
# COMMAND ----------
dfjoin20 = dfjoin10.join(dffilter20, on=["Code","Date"], how="left")
# COMMAND ----------
dfjoin30 = dfjoin20.join(dffilter30, on=["Code","Date"], how="left")
# COMMAND ----------
# COMMAND ----------
from pyspark.sql.functions import substring, hour, minute
# COMMAND ----------
dfinrixm = dfinrix.filter("Speed<0.6 * Reference")
# COMMAND ----------
dftimestamp = dfinrixm.withColumn('NT', substring('CentralTime', 1, 19).astype("Timestamp")).drop("C-Value","SegmentClosed","Score","Speed","Average","Reference","Travel","Time")
# COMMAND ----------
dfmsm = dftimestamp.withColumn("msm",hour(dftimestamp.NT)*60 + minute(dftimestamp.NT)).drop("CentralTime")
# COMMAND ----------
# COMMAND ----------
import pyspark.sql.functions as F
import pyspark.sql.types as T
from pyspark.sql.functions import col
from pyspark.sql.types import IntegerType, ArrayType, StructType
# COMMAND ----------
dfb = dfmsm.sort("Code","msm")
# COMMAND ----------
dfc = dfb.groupBy("Code").agg({"msm":"collect_list"}).withColumnRenamed("collect_list(msm)","lowSpeed")
# COMMAND ----------
def calculate(mylist):
tduration = 0
duration = 0
avg = 0
bottleneck = 0
y = 0
x = 0
while x<len(mylist)-4 and y<len(mylist)-1 :
if mylist[x+4] == mylist[x]+4:
y = x + 4
print("MY LIST X = ",mylist[x])
print("X = ", x)
print ("Y = ", y)
while True:
if y==len(mylist)-1 or mylist[y+1] > mylist[y]+10:
bottleneck = bottleneck + 1
duration = mylist[y] - mylist[x] + 1
tduration = tduration + duration
avg = tduration/bottleneck
x = y + 1
print("MY LIST Y = " , mylist[y])
print("Duration = " , duration)
break
else:
y = y + 1
else:
x = x + 1
return bottleneck, avg
# COMMAND ----------
func = F.udf(lambda x: calculate(x), T.StructType(
[T.StructField("val1", T.IntegerType(), True),
T.StructField("val2", T.FloatType(), True)]))
# COMMAND ----------
dfd = dfc.withColumn('vals', func('lowSpeed'))
# COMMAND ----------
dfe = dfd.withColumn("BottleneckCount",col('vals.val1')).withColumn("AverageDuration",col('vals.val2')).drop("lowSpeed","vals")
# COMMAND ----------
dffinal1 = dfjoin30.join(dfe, on=["Code"], how="left")
# COMMAND ----------
dffinal2 = dffinal1.filter("TotalCount>5")
# COMMAND ----------
dffinal2.coalesce(1).write.csv("/reactor/davami/olefeed3/", header =True)
# COMMAND ----------
dffinal2.show()
# COMMAND ----------
dffinal2.show()