3.1. Python

In this tutorial

how can you integrate your code with UAL
how can you access data via UAL

Accessing data from UAL requires some modification to your code. In this part of tutorial, we will take a closer look on how to access IDS via UAL.

Warning

Stop here for a moment. Make sure you have followed the configuration setup before proceeding any further!

Configuration related tutorial is here -> Click me! <-

1.1.1. 3.1 Accessing data using Python

shell> python ./put_ids_array.py
shell> python ./put_ids_slices.py

putSlice()

ITM

#system libraries
import sys
from pylab import * 

# UAL library
import ual

# Create a new instance of database
itm_obj = ual.itm(14,4)
itm_obj.create()

cpo = itm_obj.equilibrium




# Filling fields with TIME-INDEPENDENT  data
cpo.codeparam.parameters = 'param'

#Save time independent fields
cpo.putNonTimed()





# ---- a loop ----
for i in range(0, 10):
	#Fill time-dependent fields 
	cpo.x.y = i
    
    #Do not forget time!!
    cpo.time = i 
    
    #Append this slice in the database
    cpo.putSlice() # <= Called inside the loop
# ---- a loop ----

#close the pulse file
itm_obj.close()

IMAS

#system libraries
import sys
import numpy

# UAL library
import imas

# Create a new instance of database
imas_obj = imas.ids(11, 22)
imas_obj.create()  # Create a new instance of database

ids = imas_obj.core_profiles

# Mandatory to define this property
ids.ids_properties.homogeneous_time = 1

# Filling fields with TIME-INDEPENDENT  data
ids.ids_properties.comment = 'Test IDS created by putSlice'

#Save time independent fields
ids.putNonTimed()

# Allocate all variables, time coordinate of size 1
ids.time.resize(1)
ids.x.y.resize(1)

# ---- a loop ----
for i in range(10):
       # Setting values of time depended data
       ids.x.y[0] = i 

       #Do not forget time!!
       ids.time[0] = i

       #Append this slice in the database
       ids.putSlice() # <= Called inside the loop
# ---- a loop ----

#close the pulse file
imas_obj.close()

4. XXX

put()

ITM

#system libraries
import sys
from pylab import * 

#UAL library
import ual

# Create a new instance of database
itm_obj = ual.itm(13,3)
itm_obj.create()




cpoArray = itm_obj.equilibriumArray

# allocate the CPO structures
cpoArray.resize(10)


# Filling fields with TIME-INDEPENDENT data
cpoArray[0].codeparam.parameters = 'param'

# ---- a loop ----
for i in range(10):
      #Fill time-dependent fields 
      cpoArray[i].x.y = i
    
	  #Do not forget time!!
      cpoArray[i].time = i
# ---- a loop ----

#Save data in the database
cpoArray.put() # <= Called outside the loop

#close the pulse file
itm_obj.close()

IMAS

#system libraries
import sys
import numpy

#UAL library
import imas

# Create a new instance of database
imas_obj = imas.ids(11, 22)
imas_obj.create()

# Mandatory to define this property
ids.ids_properties.homogeneous_time = 0

ids = imas_obj.core_profiles

# allocate the IDS structures
ids.x.y.resize(10)
ids.time.resize(10))

# Filling fields with TIME-INDEPENDENT  data
ids.ids_properties.comment = 'A test IDS created by PUT'

# ---- a loop ----
for i in range(10):
      #Fill time-dependent fields
      ids.x.y[i] = i 
	  
      #Do not forget time!!
      ids.time[i] = i
# ---- a loop ----

#Save data in the database
ids.put() # <= Called outside the loop

#close the pulse file
imas_obj.close()

VI related notice
I will use VI in every place where text files are modified. If you have any other text file editor of your choice - fell free to use it instead.

get() CPO vs get() IDS

ITM

#system libraries
import sys
import numpy

#UAL library
import ual

#Open the database
itm_obj = ual.itm(11,22)
itm_obj.open() 

cpoArray = itm_obj.pfsystemsArray

#Get data
cpoArray.get()

# SCALARS (!)
for i in range(len(cpoArray)):
    print 'Time SCALAR  =' + str(cpoArray[i].time) 
    print 'Value SCALAR =' + str(cpoArray[i].x.y)






#close the pulse file
itm_obj.close()

IMAS

#system libraries
import sys
import numpy

#UAL library
import imas

#Open a database
imas_obj = imas.ids(11, 22)
imas_obj.open()  

ids = imas_obj.core_profiles

#Get data
ids.get()

# SCALARS (!)
for i in range(len(ids.time)):
    print 'Time   =' + str(ids.time[i]) 
    print 'Value  =' + str(ids.x.y[i])

# VECTORS (!)
print 'Time  = '  + str(ids.time)
print 'Value  = ' + str(ids.x.y)


#close the pulse file
imas_obj.close()

vi $TUTORIAL_DIR/ids_basics/python/put_ids.py

What you can see here is a simple code that stores particular IDS into MDSPlus database using UAL.

Let's check how to read these data in Fortran.

1.1.2. 3.2 Accessing data using Fortran

Exercise no. 4 - After this exercise you will:

know how to connect to UAL
know how to retrieve data from UAL
know how to prepare Makefile for your Fortran code

Exercise no. 4 (approx. 15 min)

In this exercise you will read IDS and print some data stored inside.

1. source ITMv1 script by invoking

source $ITMSCRIPTDIR/ITMv1 kepler test 4.10b > /dev/null

2. Change directory to a demo location for this exercise

cd $TUTORIAL_DIR/ids_basics/fortran

Handling IDSes: get() vs. getSlice()

$TUTORIAL_DIR/ids_basics/fortran/get_ids_array.f90

program diagnostic
    use imas_schemas
    use imas_routines
    implicit none

    integer :: idx, i, arraySize
    type (type_equilibrium), pointer :: eqArray(:) ! <= Array !!!



    call imas_open('IDS', 14, 4, idx)
    
    call imas_get(idx, 'equilibrium', eqArray)
    arraySize = size(eqArray)
    write (*,*) "Number of slices: ", arraySize
    
	do i=1, arraySize
    	write (*,*) "Time: ", eqArray(i)%time
        write (*,*) "Value of r: ", eqArray(i)%eqgeometry%boundary(1)%r(1)
        write (*,*) "Value of z: ", eqArray(i)%eqgeometry%boundary(1)%z(1)
    enddo
    call imas_close(idx)
end program

$TUTORIAL_DIR/ids_basics/fortran/get_ids_slices.f90

program diagnostic
    use imas_schemas
    use imas_routines  
    implicit none

    integer :: idx, i, arraySize 
    type (type_equilibrium) :: equilibrium
    real(IMAS_R8), pointer :: timeVector(:)
    real(IMAS_R8) :: time

    call imas_open('IDS', 14, 4, idx)
    
    call imas_get_times(idx, 'equilibrium',timeVector)
    
    arraySize = size(timeVector)
    write (*,*) "Number of slices: ", arraySize
    write (*,*) "Time vector: ", timeVector
    
	do i = 1, arraySize
        time = timeVector(i)
        call imas_get_slice(idx, 'equilibrium', equilibrium, time, 1)
            
    	write (*,*) "Time [", time, "]: ",  equilibrium%time
        write (*,*) "Value of r: ", equilibrium%eqgeometry%boundary(1)%r(1)
        write (*,*) "Value of z: ", equilibrium%eqgeometry%boundary(1)%z(1)
    enddo
    
	call imas_close(idx)
end program

4. Compile the code

shell> make clean
shell> make

5. Run the code

shell> ./get_ids_array.exe
shell> ./get_ids_slices.exe

6. You should see values that we have stored using Python based code.

1.2. 4. FC2K - Fortran Code to Kepler

It is possible to encapsulate Fortran/C++ code with Java code that represents Kepler actor. This way, you can easily incorporate your Fortran code with existing Kepler workflow. In order to make it happen you will have to:

Prepare Fortran code that has a subroutine to be called and is compiled as a library
Prepare FC2K based description of the actor
Recompile Kepler with newly created actor

After these steps are performed, you will have an access to Kepler actor that encapsulates your Fortran code.

All these topics will be covered in separate tutorial: 1.4 Using FC2K for embedding Fortran code into Kepler

Page tree

3.1. Python

1.1.1. 3.1 Accessing data using Python

1.1.2. 3.2 Accessing data using Fortran

1.2. 4. FC2K - Fortran Code to Kepler