if retcode!=None:
done.append(idx)
logging.info("{0} completed {1}".format(todo[idx][4], retcode))
done.reverse()
for d in done:
processes.pop(d)
todo.pop(d)
if len(processes)==0:
return True
time.sleep(interval)
return False
if __name__ == "__main__":
#logging.basicConfig(level=logging.INFO)
parser=DefaultArgumentParser(description="Quick look at an H5 file")
parser.add_argument("--file", dest="file", action="store",
default="rider.h5", help="data file to read")
parser.add_argument("--image", dest="image", action="store",
default="image.h5", help="summary data from file")
parser.add_function("report", "Build the report")
parser.add_function("lines", "Write the image of all realizations")
parser.add_function("binned", "Average daily prevalence")
parser.add_function("multiples", "Small multiples graph of one realization")
parser.add_function("summary", "Several summary graphs")
parser.add_function("generate", "Create graphs and report")
args=parser.parse_args()
pyfile=os.path.abspath(__file__)
filename=os.path.abspath(args.file)
for iri in it.product(range(1,res), range(1,res)):
ir=np.array(total_individual*np.array(iri)/res, dtype=np.int)
sir=[total_individual, ir[0], ir[1]]
fname="arr-{0}-{1}-{2}.h5".format(sir[0]-(ir[0]+ir[1]) , sir[1], sir[2])
to_run=['./sirexp', '-j','4', '--runcnt', str(run_cnt),
'-s', str(sum(sir)), '-i', str(sir[1]), '-r', str(sir[2]),
'--seed', str(seed), '--endtime', str(0.2), '--loglevel', 'warning',
'--beta1', str(0), '--datafile', fname]
logger.debug(to_run)
ret=subprocess.Popen(to_run)
result=ret.communicate()[0] # join the process
seed+=1
if __name__=='__main__':
parser=DefaultArgumentParser(description="Run sirexp many times")
parser.add_function("exp", "Explore initial conditions")
parser.add_function("time", "Time how long it takes to run.")
parser.add_argument("--res", dest="resolution", type=int, action="store",
default=10, help="How finely to subdivide the SIR space")
parser.add_argument("--runcnt", dest="runcnt", type=int, action="store",
default=10, help="How many times to run each simulation")
args=parser.parse_args()
if args.exp:
matrix_run(args.resolution, args.runcnt)
if args.time:
for individual_cnt in [5000, 10000, 20000, 50000, 100000, 200000,
500000, 1000000]:
elapsed=timing_run(individual_cnt, args.runcnt)
print("{0}\t{1}".format(individual_cnt, elapsed))
logger.info("Mean value of points {0}.".format(np.dot(x, y)/np.sum(y)))
logger.info("Gamma mean {0}".format(a*th))
cdf_to_5=scipy.stats.gamma.cdf(0.5, a=a, scale=th)
cdf_to_15=scipy.stats.gamma.cdf(1.5, a=a, scale=th)
cdf_to_25=scipy.stats.gamma.cdf(2.5, a=a, scale=th)
logger.info("Gamma {0}".format((cdf_to_5, cdf_to_15-cdf_to_5,
cdf_to_25-cdf_to_15)))
toplot="hazard"
if toplot=="survival":
plot_histogram(x, y, params[times_idx], names[times_idx])
elif toplot=="hazard":
plot_hazard(x, y, params[times_idx], names[times_idx])
if __name__ == "__main__":
parser=DefaultArgumentParser(description="Produces csv of total outbreak size")
parser.add_argument("--input", dest="infile", action="store",
default="naadsm.out", help="Input trace from NAADSM/SC")
parser.add_argument("--output", dest="outfile", action="store",
default="naadsm.h5", help="HDF5 file with events")
parser.add_function("multiple", "Copy all events to output file")
parser.add_function("showstates", "Take a look at state transitions")
parser.add_function("singlefarm", "One farm over and over.")
args=parser.parse_args()
if args.multiple:
initial=np.array([1])
allowed_transitions=read_multiple_naadsmsc(args.infile, args.outfile,
initial)
logger.info("allowed transitions are {0}.".format(allowed_transitions))
if args.singlefarm:
writer=csv.writer(csvfile, quoting=csv.QUOTE_MINIMAL)
writer.writerow(["trial", "value", "censored"])
idx=1
for oidx in range(len(observed)):
for i in range(observed[oidx]):
writer.writerow([idx, oidx, 1])
idx+=1
for oidx in range(len(censored)):
for i in range(censored[oidx]):
writer.writerow([idx, oidx, 0])
idx+=1
if __name__ == "__main__":
parser=DefaultArgumentParser(description="Finds residence time in states.")
parser.add_argument("--input", dest="infile", action="store",
default="naadsm.h5", help="Input HDF5 file with ensemble of events")
parser.add_argument("--id", dest="ID", action="store",
default="", help="Specify scenario ID label for output files")
args=parser.parse_args()
counts=BaseCounts()
foreach_dataset(args.infile, counts)
logger.info("Number of farms {0}.".format(counts.farm_cnt))
logger.info("Number of runs {0}.".format(counts.run_cnt))
logger.info("Largest number of days {0}.".format(counts.day_cnt))
tracking=Tracking(counts.farm_cnt, counts.run_cnt, counts.day_cnt)
foreach_dataset(args.infile, tracking)
def save_h5(openh5, events):
dset_idx=next_dset(openh5)
group=openh5.create_group("/trajectory/dset{0}".format(dset_idx))
event=group.create_dataset("Event", (len(events),), dtype="i")
whom=group.create_dataset("Who", (len(events),), dtype="i")
who=group.create_dataset("Whom", (len(events),), dtype="i")
when=group.create_dataset("When", (len(events),), dtype=np.float64)
for eidx in range(len(events)):
aevent, awhom, awho, aday=events[eidx]
event[eidx]=aevent
whom[eidx]=awhom
who[eidx]=awho
when[eidx]=aday
if __name__ == "__main__":
parser=DefaultArgumentParser(description="Produces HDF5 event file from trace data")
parser.add_argument("--input", dest="infile", action="store",
default="naadsm.out", help="Input trace from NAADSM")
parser.add_argument("--output", dest="outfile", action="store",
default="naadsm.h5", help="HDF5 file with events")
args=parser.parse_args()
allowed_transitions=read_multiple_naadsmsc(args.infile, args.outfile)
logger.info("allowed transitions are {0}.".format(allowed_transitions))
def eeid_long_behavior():
B=1/70
beta=400
mu=1/70
gamma=365/14
S=(mu+gamma)*B/(beta*mu)
I=(beta-mu-gamma)*B/(beta*(mu+gamma))
R=gamma*I/mu
print("EEID long time is\n\tS\t{0}\n\tI\t{1}\n\tR\t{2}".format(S, I, R))
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
parser=DefaultArgumentParser(description="Quick look at an H5 file")
parser.add_function("info", "Find what program made the file.")
parser.add_function("trajectory", "Plot the trajectory")
parser.add_function("dir", "List datasets")
parser.add_function("eeid", "verify eeid example values")
parser.add_argument("--file", dest="file", action="store",
default="sirexp.h5", help="data file to read")
args=parser.parse_args()
filename=args.file
f=h5py.File(filename, "r")
if args.info:
showproginfo(f)
if args.trajectory:
def write_totals(filename, outfile):
logger.info("Reading input {0}. Writing to {1}".format(filename, outfile))
totals=run_sizes(filename)
logger.info("Number of trajectories {0}, average size {1}".format(
len(totals), np.average(totals)))
logger.debug("Sizes are {0}".format(totals))
with open(outfile, 'w') as csvfile:
writer=csv.writer(csvfile, quoting=csv.QUOTE_MINIMAL)
writer.writerow(["trial", "outbreaksize"])
for i in range(len(totals)):
writer.writerow([i+1, totals[i]])
# X_plot=np.linspace(-5, 50, 1000)[:, np.newaxis]
# fig, ax=plt.subplots(1, 1)
# # Gaussian KDE
# kde=KernelDensity(kernel="gaussian", bandwidth=3).fit(totals)
# log_dens = kde.score_samples(X_plot)
# ax[0,0].fill(X_plot[:, 0], np.exp(log_dens), fc='#AAAFF')
if __name__ == '__main__':
parser=DefaultArgumentParser(description="Produces csv of total outbreak size")
parser.add_argument("--input", dest="infile", action="store",
default="run.h5", help="Input HDF5 file with ensemble of events")
parser.add_argument("--output", dest="outfile", action="store",
default="sizesc.csv", help="CSV output with sizes")
args=parser.parse_args()
write_totals(args.infile, args.outfile)
