# Load CSV file
options(width = 150)
#df <- read.csv("data.csv",col.names=c("DateTime", "DB", "RH","DP","P","WIND","WIND-DIR","SUN","RAIN","START"))

#df=remove_empty(df, which = c("rows"), cutoff = 1, quiet = TRUE)
#sum(is.na(df))
#df=df[complete.cases(df), ]
#nrow(df)
#print(df[54646:54650,])
#df=df[df$DB != FALSE, ] 
#df=na.omit(df)
# Print the first few rows of the data
library(psychrolib)
SetUnitSystem("SI")



#constants
SHC=1.026 # specif heat capciaty of air
LHE=2450 #latent heat of evap



SmallP=20
OccGs=80
OccGl=60


Area=27
Occupants=2
Qsp=Area*SmallP




P=2
A=18 # Area of room
H=3.7 #Height of room 
V=A*H #Volueme of room
S=361 # Surface area of external envelope
I=0.2


RM_DB=24
RM_RH=0.5
RM_G=GetHumRatioFromRelHum(RM_DB, RM_RH, P)

MIN_SUPPLY_DB=14

MaxQs=1.520 # peak
Qs=1.0

Ql=0.123


M=MaxQs/(SHC*(RM_DB-MIN_SUPPLY_DB))

SUPPLY_DB=(M*SHC*RM_DB-Qs)/(M*SHC)

print(M)
print(SUPPLY_DB)

P=101325
OA_DB=35
OA_WB=20
OA_G=GetHumRatioFromTWetBulb(OA_DB, OA_WB, P)


E_DB=0.9
HRS_S_DB=E_DB*RM_DB-OA_DB*(E_DB-1)

E_G=0.5
HRS_S_G=E_G*RM_G-OA_G*(E_G-1)

#print(HRS_S_G)
#print(HRS_S_DB)

CHW_F_DB=7
CHW_R_DB=12

CHW_AVE_DB=(CHW_F_DB+CHW_R_DB)/2
CHW_AVE_G=GetHumRatioFromTDewPoint(CHW_AVE_DB, P)

CF=0.85
CC_DB=CHW_AVE_DB+((HRS_S_DB-CHW_AVE_DB)*(1-CF))
CC_G=CHW_AVE_G+((HRS_S_G-CHW_AVE_G)*(1-CF))

FAN_GAIN_DB=1

SUPPLY_DB=CC_DB+FAN_GAIN_DB
SUPPLY_G=CC_G

RM_DB_RISE=RM_DB-SUPPLY_DB
RM_G_RISE=RM_G-SUPPLY_G

cat("room g rise",RM_G_RISE,"\n")

SHC=1.026 # specif heat capciaty of air
LHE=2450 #latent heat of evap

RR=(SHC*(RM_DB_RISE))/((SHC*(RM_DB_RISE))+(LHE*(RM_G_RISE)))

cat("Room ratio", RR, "\n")
cat("supply temp",SUPPLY_DB,"\n")




#y=RM_G_RISE/RM_DB_RISE
#print(y)

#print(CC_DB)
#print(CC_G)