Job Accounting

In [1]:

    

import numpy as np
import networkx
from zephyr.Problem import SeisFDFDProblem


    

In [2]:

    

import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib
%matplotlib inline


    

In [3]:

    

cellSize    = 1             # m
freqs       = [2e2]         # Hz
density     = 2700          # units of density
Q           = np.inf        # can be inf
nx          = 164           # count
nz          = 264           # count
freeSurf    = [False, False, False, False] # t r b l
dims        = (nx,nz)       # tuple
nPML        = 32
rho         = np.fliplr(np.ones(dims) * density)
nfreq       = len(freqs)    # number of frequencies
nky         = 4            # number of y-directional plane-wave components
nsp         = nfreq * nky   # total number of 2D subproblems

velocity    = 2500          # m/s
vanom       = 500           # m/s
cPert       = np.zeros(dims)
cPert[(nx/2)-20:(nx/2)+20,(nz/2)-20:(nz/2)+20] = vanom
c           = np.fliplr(np.ones(dims) * velocity)
cFlat       = c
c          += np.fliplr(cPert)
cTrue       = c

srcs        = np.array([np.ones(101)*32, np.zeros(101), np.linspace(32, 232, 101)]).T
recs        = np.array([np.ones(101)*132, np.zeros(101), np.linspace(32, 232, 101)]).T
nsrc        = len(srcs)
nrec        = len(recs)
recmode     = 'fixed'

geom        = {
    'src':  srcs,
    'rec':  recs,
    'mode': 'fixed',
}

cache       = False
cacheDir    = '.'

# Base configuration for all subproblems
systemConfig = {
    'dx':   cellSize,       # m
    'dz':   cellSize,       # m
    'c':        c.T,        # m/s
    'rho':      rho.T,      # density
    'Q':        Q,          # can be inf
    'nx':       nx,         # count
    'nz':       nz,         # count
    'freeSurf': freeSurf,   # t r b l
    'nPML':     nPML,
    'geom':     geom,
    'cache':    cache,
    'cacheDir': cacheDir,
    'freqs':    freqs,
    'nky':      nky,
}


    

In [4]:

    

sp = SeisFDFDProblem(systemConfig)


    

In [5]:

    

jobs = sp.forwardAccumulate()


    

In [6]:

    

np.array([[job.ready() + (1. * (not job.successful()) if job.ready() else 0) for job in jobs['startJobs'][worker]] for worker in jobs['startJobs'].keys()])


    

    
Out[6]:





array([[ 1.],
       [ 1.],
       [ 1.],
       [ 1.]])

In [7]:

    

np.array([[job.ready() + (1. * (not job.successful()) if job.ready() else 0) for job in jobs['endJobs'][worker]] for worker in jobs['endJobs'].keys()])


    

    
Out[7]:





array([[0],
       [0],
       [0],
       [0]])

In [8]:

    

keys = jobs['systemJobs'].keys()
keys.sort()
[[system, jobs['systemJobs'][system][0].ready()] for system in keys]


    

    
Out[8]:





[[(0, 0), False], [(0, 1), False], [(0, 2), False], [(0, 3), False]]

In [9]:

    

jobs['startJobs'][0][0].r


    

In [10]:

    

jobs['systemJobs'][(0, 0)][0].r


    

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