A DDSCAT job, which corresponds to a single execution of DDSCAT, is controlled by a DDscat object. This is essentially a Settings object and Target object bound together.
Again, the simplest way to create a new job is by calling the class DDscat object with no arguments. This builds a new job, based on defaults. The Settings and Target that comprise the job are accesible as attributes.
>>> job = DDscat()
>>> job.settings
<ScatPy.core.Settings object at 0x10a6e4710>
>>> job.target
<ScatPy.targets.Target object at 0x10a6e4a10>
More typically you will construct Settings and Target first and then combine them into a DDscat job.
>>> s = Settings()
>>> s.NAMBIENT = 1.333
>>> t = targets.ELLIPSOID((0.5, 0.1, 0.1))
>>> job = DDscat(settings = s, target = t)
>>> job.settings.NAMBIENT
1.333
>>> job.target.directive
'ELLIPSOID'
It is possible to change the settings or target associated with a job afer creation by simple reassignment
>>> job = DDscat()
>>> job.target
<ScatPy.targets.Target object at 0x10b60bd50>
>>> new_t = targets.ELLIPSOID((0.1, 0.5, 0.5))
>>> job.target = new_t # Assign a new target
>>> job.target
<ScatPy.targets.ELLIPSOID object at 0x107998750>
DDscat jobs have features that are intended to simplify job management. For instance the class contains attributes for calculating various characteristics to determine the applicability of the DDA to a given simulation. For example it can calculate x and mkd defined in the DDSCAT user guide:
>>> job.target = targets.ELLIPSOID((0.5,0.1,0.1))
>>> job.mkd
array([ 0.82665329, 0.43637804, 0.45623888, 0.61425222, 0.64325583])
>>> job.x
array([ 3.68734716, 2.32885084, 1.70185254, 1.34085351, 1.10620415])
The entries correspond to the values at each of the wavelengths of the job. Note that this won’t work with generic targets since the number of dipoles they contain is unknown. The method info gives more information
>>> job.info()
Target: ELLIPSOID
N=6773
aeff=0.176058
d=0.015000
wave mkd x alpha beta
--------------------------------------------------------------
[ 0.3 0.82665329 3.68734716 0.21312368 4.45805261]
[ 0.475 0.43637804 2.32885084 0.4037311 5.33375841]
[ 0.65 0.45623888 1.70185254 0.38615602 3.72807117]
[ 0.825 0.61425222 1.34085351 0.2868193 2.18167051]
[ 1. 0.64325583 1.10620415 0.27388697 1.71872388]
Once you’ve made all the necessary adjustments to a job you can write it to file with the write command. This saves the ddscat.par file, and for a FROM_FILE target the shape.dat file as well:
>>> job = DDscat()
>>> job.target = targets.Helix(0.5, 0.2, 0.05, 0.02)
Generating Helix...
Done constructing sweep path...
>>> os.listdir('.') # Empty folder
[]
>>> job.write() # Write the job
>>> os.listdir('.') # Two new files
['ddscat.par', 'shape.dat']
>>> !head ddscat.par
===Generated by ScatPy (Thu Aug 1 17:33:41 2013)===
**** Preliminaries ****
NOTORQ
PBCGS2
GPFAFT
GKDLDR
NOTBIN
**** Initial Memory Allocation ****
100 100 100
**** Target Geometry and Composition ****
>>> !head shape.dat
FROM_FILE_Helix (0.500000, 0.200000, 0.050000, 0.020000, 0.015000)
344
1 0 0
0 1 0
1 1 1
34.66666667 4.66666667 4.66666667
J JX JY JZ ICOMPX,ICOMPY,ICOMPZ
1 1 6 2 1 1 1
2 1 7 2 1 1 1
3 1 7 3 1 1 1
You can read an old job from file with the fromfile command.
>>> job = DDscat()
>>> job.target = targets.Helix(0.5, 0.2, 0.05, 0.02)
Generating Helix...
Done constructing sweep path...
>>> job.write()
>>> job.info()
Target: FROM_FILE ( <class 'ScatPy.targets.Helix'> )
N=344
aeff=0.065200
d=0.015000
wave mkd x alpha beta
--------------------------------------------------------------
[ 0.3 0.82665329 1.36554647 0.07892674 1.65096416]
[ 0.475 0.43637804 0.8624504 0.14951496 1.97526695]
[ 0.65 0.45623888 0.63025222 0.14300633 1.38062792]
[ 0.825 0.61425222 0.49656235 0.10621866 0.80794466]
[ 1. 0.64325583 0.40966394 0.10142939 0.63650023]
>>>
>>> old_job = DDscat.fromfile('ddscat.par') # Read the old job
>>> old_job.info()
Target: FROM_FILE ( <class 'ScatPy.targets.Iso_FROM_FILE'> )
N=344
aeff=0.065200
d=0.015000
wave mkd x alpha beta
--------------------------------------------------------------
[ 0.3 0.82665277 1.36554561 0.07892674 1.65096416]
[ 0.475 0.43637777 0.86244986 0.14951496 1.97526695]
[ 0.65 0.45623859 0.63025182 0.14300633 1.38062792]
[ 0.825 0.61425183 0.49656204 0.10621866 0.80794466]
[ 1. 0.64325542 0.40966368 0.10142939 0.63650023]
If you have a local version of DDSCAT, you can run the job from within the Python session with calculate. This writes the settings and target to file and starts DDSCAt:
>>> job.calculate()
>>> old_job.calculate()
>DDSCAT using parameter file=
ddscat.par
>DDSCAT NUMPROC= 1
>DDSCAT --- DDSCAT 7.2.2 [12.06.05]
>DDSCAT Single-precision version
>REAPAR ===Generated
> ****
>REAPAR NOTORQ - do not compute torques
>REAPAR PBCGS2 - CCG Method
>REAPAR GPFAFT - using GPFA package from Clive Temperton
>REAPAR GKDLDR - Gutkowicz-Krusin &
.
...
.
>GETFML Q_abs = 2.0146E-02 Q_ext= 2.0152E-02
>SCAT 214 scattering directions used to calculate <cos>, etc.
>TIMEIT Timing results for: SCAT
>TIMEIT 0.006 = CPU time (sec)
>DDSCAT return from DDSCAT to calling program
ddscat main ckpt 1, NRFLD= 0
>DDSCAT normal termination, with no nearfield calculation
>>> os.listdir('.')
['ddscat.par', 'mtable', 'output.log', 'qtable', 'qtable2', 'shape.dat',
'target.out', 'w000r000.avg', 'w001r000.avg', 'w002r000.avg',
'w003r000.avg', 'w004r000.avg']