I have a CSV file like below:
E Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Mean
1 0.7019 0.6734 0.6599 0.6511 0.701 0.6977 0.680833333
2 0.6421 0.6478 0.6095 0.608 0.6525 0.6285 0.6314
3 0.6039 0.6096 0.563 0.5539 0.6218 0.5716 0.5873
4 0.5564 0.5545 0.5138 0.4962 0.5781 0.5154 0.535733333
5 0.5056 0.4972 0.4704 0.4488 0.5245 0.4694 0.485983333
I'm trying to use find the row number where the final column has a value below a certain range. For example, below 0.6.
Using the above CSV file, I want to return 3 because E = 3 is the first row where Mean <= 0.60. If there is no value below 0.6 I want to return 0. I am in effect returning the value in the first column based on the final column.
I plan to initialize this number as a constant in gnuplot. How can this be done? I've tagged awk because I think it's related.
In case you want a gnuplot-only version... if you use a file remove the datablock and replace $Data by your filename in " ".
Edit: You can do it without a dummy table, it can be done shorter with stats (check help stats). Even shorter than the accepted solution (well, we are not at code golf here), but additionally platform-independent because it's gnuplot-only.
Furthermore, in case E could be any number, i.e. 0 as well, then it might be better
to first assign E = NaN and then compare E to NaN (see here: gnuplot: How to compare to NaN?).
Script:
### conditional extraction into a variable
reset session
$Data <<EOD
E Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Mean
1 0.7019 0.6734 0.6599 0.6511 0.701 0.6977 0.680833333
2 0.6421 0.6478 0.6095 0.608 0.6525 0.6285 0.6314
3 0.6039 0.6096 0.563 0.5539 0.6218 0.5716 0.5873
4 0.5564 0.5545 0.5138 0.4962 0.5781 0.5154 0.535733333
5 0.5056 0.4972 0.4704 0.4488 0.5245 0.4694 0.485983333
EOD
E = NaN
stats $Data u ($8<=0.6 && E!=E? E=$1 : 0) nooutput
print E
### end of script
Result:
3.0
Actually, OP wants to return E=0 if the condition was not met. Then the script would be like this:
E=0
stats $Data u ($8<=0.6 && E==0? E=$1 : 0) nooutput
Another awk. You could initialize the default return value to var ret in BEGIN but since it's 0 there is really no point as empty var+0 produces the same effect. If the threshold value of 0.6 is not met before the ENDis reached, that is returned. If it is met, exit invokes the END and ret is output:
$ awk '
NR>1 && $NF<0.6 { # final column has a value below a certain range
ret=$1 # I want to return 3 because E = 3
exit
}
END {
print ret+0
}' file
Output:
3
Something like this should do the trick:
awk 'NR>1 && $8<.6 {print $1;fnd=1;exit}END{if(!fnd){print 0}}' yourfile
I am trying to compile an .f95 fortran script so it can run on Ubuntu. The script is available here -> Link to zip file containing .f95 script
It compiles and runs fine when I switch over to Windows and compile using g95 compiler. The .exe file produced also runs fine in Ubuntu through wine.
However if I try to compile to make an Ubuntu file, it does not work properly. I don't get a compile error, but if I run the resultant file, either the program gets stuck in an infinite loop, or the output is all wrong. It's difficult for me to see where it is going wrong because I did not write the original code and only have a shaky understanding of Fortran, but it seems to be something to do with the numbers being calculated wrong leading to very large/small/inappropriately negative output (sorry to be so vague).
I am running 16.04 xenial ubuntu and gfortran 5.4.0.
Any help/thoughts appreciated this is driving me up the wall! Thanks
Code below for quick reference:
! Seed dispersal model of of Duman et al. (2015)
! Instructions and expample are found in:
! https://nicholas.duke.edu/people/faculty/katul/research.html
! in 'Library of Functions and Utilities'
! Author: Tomer Duman
! Version: Version 2
! Date: October 22, 2015
! References: Duman, T., Trakhtenbrot, A., Poggi, D.,
! Cassiani, M., Katul, G., Dissipation
! Intermittency Increases Long-Distance
! Dispersal of Heavy Particles in the Canopy
! Sublayer,
! accepcted to Boundary-Layer Meteorology
program LSmodel
implicit none
real :: sec,ran,gasdev ! random generator variables
real :: x,y,z,u,v,w,ut,vt,wt,t,dt ! simulation variables
real :: wg ! seed parametes
real :: Um,sigma_u,sigma_v,sigma_w,uw ! wind statistics variables
real :: dvaru_dz,dvarv_dz,dvarw_dz,duw_dz ! wind statistics variables
real :: dissip_m,TL ! vector over the range of ustars
real :: zs,zg,zmax ! release height & boundaries
real :: Ainv,C0inv ! inverse parameters
real :: C0,A,b,au,av,aw,dt_on_TL ! LS model parameters
real :: dz_max,dt_max ! time step limit
real :: CT,beta ! Crossing Trajectories correction
real :: C_chi,chi,TKE,T_chi,omega ! DI parameters
real :: a_ln,b_ln,sigma_chi,dissip_s ! DI parameters
real :: rhop,rho,r,g,gt,Re,AIP,Cd,nu ! IP parameters
real :: up,vp,wp,upt,vpt,wpt,vr,dt_ip,alpha ! IP parameters
integer :: seed ! random generator variables
integer :: pnum ! simulation parameters
integer :: i,j,jj,n,ii ! counting parameters
integer :: n_ip,IP=1 ! IP parameters
character(len=80) :: filename
real, allocatable,dimension(:) :: z_vec,Um_vec,sigma_u_vec,sigma_v_vec,sigma_w_vec,uw_vec
real, allocatable,dimension(:) :: dvaru_dz_vec,dvarv_dz_vec,dvarw_dz_vec,duw_dz_vec,dissip_m_vec
! setting the random generator seed
seed=7654321
sec=0.0
seed=seed+2*int(secnds(sec))
! input
open (21,file='input_parameters.txt')
read (21, *), x,C0,wg,zs,zg,beta,dt_on_TL,y,sigma_chi,C_chi,r,rhop,alpha,rho,nu
close(21)
pnum = int(x) ! number of released seeds
n = int(y) ! size of the input flow stats
wg = -1.0*wg ! seed terminal velocity [m/s]
!zs ! seed release height [m]
!C0 ! universal constant
!beta ! crossing trajectories parameter
!zg ! ground height [m]
!sigma_chi ! the standard deviation of chi (dissipation intermittency)
!C_chi ! constant for T_chi calc
!r ! particle radium [m] - set to 0 for no IP
!rhop ! particle dry density [kg/m^3]
!alpha ! drag parameter (Cd = 24/Re_p*(1+alpha*Re_p))
!rho ! fluid density [kg/m^3]
!nu ! fluid viscosity [m^2/s]
C0inv = 1.0/C0
g = 9.81
if (r==0.0) then
IP = 0
end if
! limiting parameters to prevent too big jumps in a time-step
dz_max = 0.1
dt_max = 0.1
open(unit=12,file='res.dat', form='formatted')
open(unit=13,file='res_traj.dat', form='formatted')
! allocate
allocate(z_vec(n))
allocate(Um_vec(n))
allocate(sigma_u_vec(n))
allocate(sigma_v_vec(n))
allocate(sigma_w_vec(n))
allocate(uw_vec(n))
allocate(dvaru_dz_vec(n))
allocate(dvarv_dz_vec(n))
allocate(dvarw_dz_vec(n))
allocate(duw_dz_vec(n))
allocate(dissip_m_vec(n))
! load normalized stats
open (22,file='input_flow.txt',form='formatted')
read (22,*) z_vec
read (22,*) Um_vec
read (22,*) sigma_u_vec
read (22,*) sigma_v_vec
read (22,*) sigma_w_vec
read (22,*) uw_vec
read (22,*) dvaru_dz_vec
read (22,*) dvarv_dz_vec
read (22,*) dvarw_dz_vec
read (22,*) duw_dz_vec
read (22,*) dissip_m_vec
close(22)
zmax = z_vec(n)
do i=1,pnum
t=0.0 ! initiate time and location
x=0.0
y=0.0
z=zs
do j=2,n ! interpolate
if ((z>=z_vec(j-1)).and.(z<=z_vec(j))) then
sigma_u=((sigma_u_vec(j-1)-sigma_u_vec(j))/(z_vec(j-1)-z_vec(j)))*(z-z_vec(j-1))+sigma_u_vec(j-1)
sigma_v=((sigma_v_vec(j-1)-sigma_v_vec(j))/(z_vec(j-1)-z_vec(j)))*(z-z_vec(j-1))+sigma_v_vec(j-1)
sigma_w=((sigma_w_vec(j-1)-sigma_w_vec(j))/(z_vec(j-1)-z_vec(j)))*(z-z_vec(j-1))+sigma_w_vec(j-1)
end if
end do
! velocity initiation
u=gasdev(seed)*sigma_u
v=gasdev(seed)*sigma_v
w=gasdev(seed)*sigma_w
chi=sigma_chi*gasdev(seed)-0.5*sigma_chi*sigma_chi
up=0.0 ! initiating particle velocity from rest
vp=0.0
wp=0.0
do ! time loop
do j=2,n ! interpolate
if ((z>=z_vec(j-1)).and.(z<=z_vec(j))) then
Um=((Um_vec(j-1)-Um_vec(j))/(z_vec(j-1)-z_vec(j)))*(z-z_vec(j-1))+Um_vec(j-1)
uw=((uw_vec(j-1)-uw_vec(j))/(z_vec(j-1)-z_vec(j)))*(z-z_vec(j-1))+uw_vec(j-1)
duw_dz=((duw_dz_vec(j-1)-duw_dz_vec(j))/(z_vec(j-1)-z_vec(j)))*(z-z_vec(j-1))+duw_dz_vec(j-1)
sigma_u=((sigma_u_vec(j-1)-sigma_u_vec(j))/(z_vec(j-1)-z_vec(j)))*(z-z_vec(j-1))+sigma_u_vec(j-1)
sigma_v=((sigma_v_vec(j-1)-sigma_v_vec(j))/(z_vec(j-1)-z_vec(j)))*(z-z_vec(j-1))+sigma_v_vec(j-1)
sigma_w=((sigma_w_vec(j-1)-sigma_w_vec(j))/(z_vec(j-1)-z_vec(j)))*(z-z_vec(j-1))+sigma_w_vec(j-1)
dvaru_dz=((dvaru_dz_vec(j-1)-dvaru_dz_vec(j))/(z_vec(j-1)-z_vec(j)))*(z-z_vec(j-1))+dvaru_dz_vec(j-1)
dvarv_dz=((dvarv_dz_vec(j-1)-dvarv_dz_vec(j))/(z_vec(j-1)-z_vec(j)))*(z-z_vec(j-1))+dvarv_dz_vec(j-1)
dvarw_dz=((dvarw_dz_vec(j-1)-dvarw_dz_vec(j))/(z_vec(j-1)-z_vec(j)))*(z-z_vec(j-1))+dvarw_dz_vec(j-1)
dissip_m=((dissip_m_vec(j-1)-dissip_m_vec(j))/(z_vec(j-1)-z_vec(j)))*(z-z_vec(j-1))+dissip_m_vec(j-1)
end if
end do
CT = sqrt(1.0+beta*beta*wg*wg/sigma_w/sigma_w) ! crossing trajectories correction
TL = 2.0*sigma_w*sigma_w*C0inv/dissip_m/CT ! added CT effect
TKE = 0.5*(sigma_u*sigma_u+sigma_v*sigma_v+sigma_w*sigma_w)
! -------- Adding dissipation intermittency model --------
omega=dissip_m*CT/TKE ! added CT effect
T_chi=1.0/omega/C_chi
dt=min(dt_on_TL*TL,dt_on_TL*T_chi,dt_max)
a_ln = -(chi + 0.5*sigma_chi*sigma_chi)/T_chi
b_ln = sigma_chi*sqrt(2.0/T_chi)
chi = chi+a_ln*dt+b_ln*sqrt(dt)*gasdev(seed)
dissip_s = dissip_m*exp(chi)
! --------------------------------------------------------
A = 2.0*((sigma_u*sigma_u)*(sigma_w*sigma_w)- uw*uw)
Ainv = 1.0/A
b = sqrt(C0*dissip_s*CT) ! added CT effect
au = (b*b)*(uw*w - u*sigma_w*sigma_w)*Ainv + 0.5*duw_dz &
+ Ainv*(sigma_w*sigma_w*dvaru_dz*u*w - uw*dvaru_dz*w*w &
-uw*duw_dz*u*w + sigma_u*sigma_u*duw_dz*w*w)
av = (-(b*b)*v + dvarv_dz*v*w)/2.0/sigma_v/sigma_v
aw = (b*b)*(uw*u - w*sigma_u*sigma_u)*Ainv + 0.5*dvarw_dz &
+ Ainv*(sigma_w*sigma_w*duw_dz*u*w - uw*duw_dz*w*w &
-uw*dvarw_dz*u*w + sigma_u*sigma_u*dvarw_dz*w*w)
ut = u + au*dt + b*sqrt(dt)*gasdev(seed)
vt = v + av*dt + b*sqrt(dt)*gasdev(seed)
wt = w + aw*dt + b*sqrt(dt)*gasdev(seed)
u = ut
v = vt
w = wt
! -------- Adding IP model --------
if (IP==1) then
dt_ip = dt*0.01
n_ip = 100
upt = up
vpt = vp
wpt = wp
100 do ii=1,n_ip
vr = sqrt((u+Um-upt)*(u+Um-upt)+(v-vpt)*(v-vpt)+(w-wpt)*(w-wpt))
if (vr>1000.0) then
dt_ip = dt_ip*0.5
n_ip = n_ip*2
upt = up
vpt = vp
wpt = wp
goto 100
end if
Re = 2.0*r*vr/nu
Cd = 24.0*(1.0+alpha*Re)/Re
AIP = 3.0*rho*Cd/8.0/rhop/r
gt = g*(rhop - rho)/rhop
upt = upt + AIP*vr*(u+Um-upt)*dt_ip
vpt = vpt + AIP*vr*(v-vpt)*dt_ip
wpt = wpt + (AIP*vr*(w-wpt)-gt)*dt_ip
end do
up = upt
vp = vpt
wp = wpt
end if
! ----------------------------------
if (IP==0) then
up = Um+u
vp = v
wp = w+wg
end if
x = x + up*dt
y = y + vp*dt
z = z + wp*dt
if (i<50) then ! saving trajectories of 50 seeds
write(13,*) i,t,x,y,z
end if
if (z>zmax) then
exit
end if
if (z<zg) then
dt = (z-zg)/(w+wg)
z = z - (w+wg)*dt ! ensure that z = zg at landing
x = x - (u+Um)*dt
y = y - v*dt
write(12,*) i,x,y
exit
end if
dt_max = dz_max/abs(w+wg)
t = t+dt
end do
if (mod(i,100)==0) then
print *, 'wg = ',abs(wg),' zr = ',zs,' pp ',pnum-i
end if
end do
end program LSmodel
!***********************************************************************
! This function generates Gaussian Random Deviates from uniform deviates.
! The function is from Press et al. (1992 p. 280).
function gasdev(idum)
implicit none
integer :: idum, iset
real :: gasdev,fac, gset, rsq, v1, v2, ran
save :: iset, gset
data iset/0/
if (iset.eq.0) then
1 v1=2.*ran(idum)-1.
v2=2.*ran(idum)-1.
rsq=v1**2+v2**2
if (rsq.ge.1. .or. rsq .eq. 0) go to 1
fac =sqrt(-2.*log(rsq)/rsq)
gset=v1*fac
gasdev=v2*fac
iset=1
else
gasdev=gset
iset=0
end if
return
end function gasdev
!***********************************************************************
!uniform random generator between 0 and 1
function ran(idum)
implicit none
integer, parameter :: K4B=selected_int_kind(9)
integer(K4B), intent(inout) :: idum
real :: ran
integer(K4B), parameter :: IA=16807,IM=2147483647,IQ=127773,IR=2836
real, save :: am
integer(K4B), save :: ix=-1,iy=-1,k
if (idum <= 0 .or. iy < 0) then
am=nearest(1.0,-1.0)/IM
iy=ior(ieor(888889999,abs(idum)),1)
ix=ieor(777755555,abs(idum))
idum=abs(idum)+1
end if
ix=ieor(ix,ishft(ix,13))
ix=ieor(ix,ishft(ix,-17))
ix=ieor(ix,ishft(ix,5))
k=iy/IQ
iy=IA*(iy-k*IQ)-IR*k
if (iy < 0) iy=iy+IM
ran=am*ior(iand(IM,ieor(ix,iy)),1)
end function ran
The command I am using to compile in Ubuntu is
gfortran LSmodel.f95 -o LSmodel.o
There is no compile error, it compiles fine, but then on running the program afterwards the problems start.
I have included a typical expected output from running the program below (res.dat):
1 21.8583908 8.47351170
2 1.44100714 -8.78142548
3 1154.74109 -265.975677
4 8.41901588 2.71606803
5 84.5189209 -20.4699802
6 86.3176270 -18.4299908
7 133.826874 43.4905090
8 4.37516022 -2.50738835
9 1.31284332 -2.65105081
10 1.96412086 2.85013437
11 4.34823132 -3.83539009
12 40.1227837 -6.60268879
13 3.88699961 2.63749719
14 7.08872795 1.51467562
15 4.72280264 2.63384581
16 0.667112768 1.37209761
17 2.09094667 1.23296225
18 4.72298622 -1.43766475
19 1.04012501 -3.13314247
20 1.91324210 0.957163811
21 1.99065340 0.611227572
22 -2.09086251 -1.41756165
23 -1.46836996 -5.55722380
24 2.41403580 2.18929257E-02
25 3.96990728 -4.91323137
26 1.54687607 -0.527718127
27 8.24332428 -1.48289037
28 4.81600523 -8.87443924
29 2.39538932 0.323360980
30 192.294815 -36.7134209
31 24.6190643 21.7993126
32 -0.124062911 3.44432545
33 16.6237335 -8.54020786
34 50.0964355 -3.29175758
35 5.23409462 2.14592004
36 6.62141275 1.47515869
37 10.7572327 0.307090789
38 63.5973434 -47.7124138
39 74.9621201 2.11509633
40 4.46293068 -1.64074826
41 11.7773390 10.0654907
42 8.26941204 6.84578228
43 0.917451978 2.69560647
44 -2.21521306 15.0752039
45 8.18219483E-02 -2.06250334
46 0.279425710 -3.10328817
47 4.37736464 -1.37771702
48 -2.85058951 -1.79835165
49 5.08391476 2.68537569
50 -4.27199030 -0.642364025
Compiling your program with gfortran -Wall gives
test.f90:297:4:
function ran(idum)
1
Warning: 'ran' declared at (1) is also the name of an intrinsic.
It can only be called via an explicit interface or if declared
EXTERNAL. [-Wintrinsic-shadow]
which means that the user-defined routine ran() has the same name as intrinsic ran() in gfortran. So we need to declare it as an external routine (to tell the compiler that this is a user-defined one):
function gasdev(idum)
implicit none
integer :: idum, iset
real :: gasdev,fac, gset, rsq, v1, v2, ran
save :: iset, gset
data iset/0/
external ran !<--- here
...
It is necessary to include external ran in all routines that utilize the user-defined ran. (In this program, only the gasdev() routine is using it.) To avoid such interference, it is usually better to use a bit more specific name other than ran, rand, etc (for example, rand_uniform() or ranranran() might be good). It would also be very nice if the routine is included in a module to avoid such problems, so please search the net for how to use modules for more details (if necessary...)
I am studying for this great Coursera course https://www.coursera.org/learn/algorithmic-toolbox . On the fourth week, we have an assignment related to binary trees.
I think I did a good job. I created a binary search code that solves this problem using recursion in Python3. That's my code:
#python3
data_in_sequence = list(map(int,(input().split())))
data_in_keys = list(map(int,(input()).split()))
original_array = data_in_sequence[1:]
data_in_sequence = data_in_sequence[1:]
data_in_keys = data_in_keys[1:]
def binary_search(data_in_sequence,target):
answer = 0
sub_array = data_in_sequence
#print("sub_array",sub_array)
if not sub_array:
# print("sub_array",sub_array)
answer = -1
return answer
#print("target",target)
mid_point_index = (len(sub_array)//2)
#print("mid_point", sub_array[mid_point_index])
beg_point_index = 0
#print("beg_point_index",beg_point_index)
end_point_index = len(sub_array)-1
#print("end_point_index",end_point_index)
if sub_array[mid_point_index]==target:
#print ("final midpoint, ", sub_array[mid_point_index])
#print ("original_array",original_array)
#print("sub_array[mid_point_index]",sub_array[mid_point_index])
#print ("answer",answer)
answer = original_array.index(sub_array[mid_point_index])
return answer
elif target>sub_array[mid_point_index]:
#print("target num higher than current midpoint")
beg_point_index = mid_point_index+1
sub_array=sub_array[beg_point_index:]
end_point_index = len(sub_array)-1
#print("sub_array",sub_array)
return binary_search(sub_array,target)
elif target<sub_array[mid_point_index]:
#print("target num smaller than current midpoint")
sub_array = sub_array[:mid_point_index]
return binary_search(sub_array,target)
else:
return None
def bin_search_over_seq(data_in_sequence,data_in_keys):
final_output = ""
for key in data_in_keys:
final_output = final_output + " " + str(binary_search(data_in_sequence,key))
return final_output
print (bin_search_over_seq(data_in_sequence,data_in_keys))
I usually get the correct output. For instance, if I input:
5 1 5 8 12 13
5 8 1 23 1 11
I get the correct indexes of the sequences or (-1) if the term is not in sequence (first line):
2 0 -1 0 -1
However, my code does not pass on the expected running time.
Failed case #4/22: time limit exceeded (Time used: 13.47/10.00, memory used: 36696064/536870912.)
I think this happens not due to the implementation of my binary search (I think it is right). Actually, I think this happens due to some inneficieny in a peripheral part of the code. Like the way I am managing to output the final answer. However, the way I am presenting the final answer does not seem to be really "heavy"... I am lost.
Am I not seeing something? Is there another inefficiency I am not seeing? How can I solve this? Just trying to present the final result in a faster way?
if volt.isalpha() or res.isalpha() or amp.isalpha():
What did I do wrong here? I get an INVALID SYNTAX, I am using this for a calculator program I am making. It calculates voltage, resistance, and amperage. But thats the easy part, I am just trying to make it fool proof. I have 3 variables in the code (volt, amp, res) that are inputted by the user. I just wanna make sure that they don't type in anything stupid. Like letters for e.g. ...
try:
float(volt) >= 0 and float(res) >= 0 and float(amp) >= 0
print("")
print("You put a value for everything. You don't need the calculator.")
allowed = 0
if volt.isalpha() or res.isalpha() or amp.isalpha():
print("You typed in characters for one of the values, this calculator doesn't use letters.")
allowed = 0
def find_voltage(a,b): # V = I * R
voltage = a * b
return(voltage)`
You don't have an except block after try - it is required. Do something like:
try:
float(volt) >= 0 and float(res) >= 0 and float(amp) >= 0
print("")
print("You put a value for everything. You don't need the calculator.")
allowed = 0
except ValueError:
print("Oops, you messed up.")
Additionally, the line
float(volt) >= 0 and float(res) >= 0 and float(amp) >= 0
doesn't do anything. You'll need to assign it to a variable, then check the results of the variable - if True, do one thing, if False, do something else.
The following bit of SAS code is supposed to read from a dataset which contains a numeric variable called 'Radvalue'. Radvalue is the temperature of a radiator, and if a radiator is switched off but then its temperature increases by 2 or more it's a sign that it has come on, and if it is on but its temperature decreases by 2 or more it's a sign that it's gone off.
Radstate is a new variable in the dataset which indicates for every observation whether the radiator is on or off, and it's this I'm trying to fill in automatically for the whole dataset.
So I'm trying to use the LAG function, trying to initialise the first row, which doesn't have a dif_radvalue, and then trying to apply the algorithm I just described to row 2 onwards.
Any idea why the columns Radstate and l_radstate come out completely blank?
Thanks everso much!! Let me know if I haven't explained the problem clearly.
Data work.heating_algorithm_b;
Input ID Radvalue;
Datalines;
1 15.38
2 15.38
3 20.79
4 33.47
5 37.03
6 40.45
7 40.45
8 40.96
9 39.44
10 31.41
11 26.49
12 23.06
13 21.75
14 20.16
15 19.23
;
DATA temp.heating_algorithm_c;
SET temp.heating_algorithm_b;
DIF_Radvalue = Radvalue - lag(Radvalue);
l_Radstate = lag(Radstate);
if missing(dif_radvalue) then
do;
dif_radvalue = 0;
radstate = "off";
end;
else if l_Radstate = "off" & DIF_Radvalue > 2 then Radstate = "on";
else if l_Radstate = "on" & DIF_Radvalue < -2 then Radstate = "off";
else Radstate = l_Radstate;
run;
You were trying to perform the LAG function on a variable only existing in the output data set (RADSTATE). I replaced the LAG on RADSTATE with a RETAIN. Also, you were right to keep the LAG function outside any conditional logic...Try the below code.
Data work.heating_algorithm_b;
Input ID Radvalue;
Datalines;
1 15.38
2 15.38
3 20.79
4 33.47
5 37.03
6 40.45
7 40.45
8 40.96
9 39.44
10 31.41
11 26.49
12 23.06
13 21.75
14 20.16
15 19.23
;
DATA work.heating_algorithm_c;
length radstate $3;
retain radstate;
SET work.heating_algorithm_b;
old_radvalue=lag(radvalue);
if _n_=1 then do;
dif_radvalue=0;
radstate="off";
end;
else do;
DIF_Radvalue = Radvalue-Old_Radvalue;
if Radstate = "off" & DIF_Radvalue > 2 then Radstate = "on";
else if Radstate = "on" & DIF_Radvalue < -2 then Radstate = "off";
/* Else Radstate stays the same */
end;
run;
I have no SAS experience, but maybe you need a missing(l_Radstate) check to cover the first time through, maybe something like this:
if missing(l_Radstate) then
do; radstate = "off"; end;
I think that would only be needed if the Radvalue - lag(Radvalue) did not force DIF_Radvalue to be missing. If it does, I am not sure what would help...