3.2 Using pNbody with scripts

Instead of using pNbody in the python interpreter, it is very usefull to use pNbody in scripts. Usually a python script begin by the line #!/usr/bin/env python and must be executable (chmod a+x file.py). As an example (slice.py), we show how to write a script that opens a gadget file, select gas and cut a thin slice (). The new files are saved using the extention .slice.

#!/usr/bin/env python

import sys
from pNbody import *

files = sys.argv[1:]

for file in files:
  print "slicing",file
  nb = Nbody(file,ftype='gadget')
  nb = nb.select('gas')  
  nb = nb.selectc((fabs(nb.pos[:,1])<1000))
  nb.rename(file+'.slice')
  nb.write()

You can run this script with the command

[lunix@lunix ~]$ ./scripts/slice.py gadget_z*0.dat

[lunix@lunix ~]$ python ./scripts/slice.py gadget_z*0.dat

In the current version of pNbody, scripts may also be used in parallel, if mpi and mpi4py is installed. In this case, run the script with a command like (depending of your mpi implementation):

[lunix@lunix ~]$ mpirun -np 2 slice.py gadget_z*0.dat

In this script only the processus of rank 0 open the file. It then broadcast the particles among all the other processors. The selection of gas and the slice are preformed by all processors. Finally, the nb.write() command will gather all particles and write the output file.

Instead of opening one file and writing another, one can ask that every processus open one file and write one. First, modify the previous script by adding the command nb.set_pio('yes') The script split.py demonstrate this capabilites.

#!/usr/bin/env python

import sys
from pNbody import *

files = sys.argv[1:]

for file in files:
  nb = Nbody(file,ftype='gadget')
  nb.set_pio('yes')	# enable parallel input/output
  nb.write()

and run

[lunix@lunix ~]$ mpirun -np 2  ./scripts/split.py gadget_z*0.dat

Every file is opened by the processus of rank 0, but now, during the nb.write command every processus writes its own file. The files have the same name than the name given in Nbody() with an extention .i where i corresponds to the processus rank.

Now that you have created two output, try to read them. In the script slice.py, add a pio='yes' in the object argument.

#!/usr/bin/env python

import sys
from pNbody import *

files = sys.argv[1:]

for file in files:
  print "slicing",file
  nb = Nbody(file,ftype='gadget',pio='yes')
  nb = nb.select('gas')  
  nb = nb.selectc((fabs(nb.pos[:,1])<1000))
  nb.rename(file+'.slice')
  nb.write()

and run :

[lunix@lunix ~]$ mpirun -np 2  ./scripts/slice2.py gadget_z*0.dat

Now, the script works fully in parallel. Every processus reads, writes and works on its own file, correspondig to a subset of the total number of particles.

Lets try two other scripts. The first one try to find the radial maximum distance among all particles and the center. It illustrate the difference between using max() wich gives the local maximum (maximum among particles of the node) and mpi.mpi_max() which gives the global maximum among all particles.

#!/usr/bin/env python

import sys
from pNbody import *

file = sys.argv[1]

nb = Nbody(file,ftype='gadget',pio='yes')
local_max  = max(nb.rxyz())
global_max = mpi.mpi_max(nb.rxyz())

print "proc %d local_max = %f global_max = %f"%(mpi.ThisTask,local_max,global_max)

When running it, you should get :

[lunix@lunix examples]$ mpirun -np 2 ./scripts/findmax.py gadget_z00.dat
proc 0 local_max = 12070.458008 global_max = 12757.492188
proc 1 local_max = 12757.492188 global_max = 12757.492188

which illustrate clearly the point. Finally, the latter script shows that even graphical functions support parallelisme. The script showmap.py illustrate this point by computing a map of the model :

#!/usr/bin/env python

import sys
from pNbody import *

file = sys.argv[1]

nb = Nbody(file,ftype='gadget',pio='yes')
nb.display(size=(10000,10000),shape=(256,256),palette='light')

When running :

[lunix@lunix examples]$ mpirun -np 2 ./scripts/showmap.py gadget_z00.dat

you get an image of the model.

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