def bcmd_proc(beta, n, params, queue, do_perturb, obj):
# create the input file
seq = obj.baseSeq[:]
seq += steps.abcParamSequence(obj.initnames, params)
seq += steps.makeSteadySequence(True)
seq += steps.abcAbsoluteSequence(obj.times,
obj.perturb(do_perturb),
obj.vars,
outhead=False)
input = os.path.join(obj.workdir, '%s_%d_%d.input' % (obj.name, n, beta))
steps.writeSequence(seq, input)
output = os.path.join(obj.workdir, '%s_%d_%d.out' % (obj.name, n, beta))
if obj.suppress:
# invoke the model
succ = abortable.call([obj.program, '-i', input, '-o', output],
stdout=obj.DEVNULL,
stderr=obj.DEVNULL,
timeout=obj.timeout)
else:
# create files to hold log values
stdoutname = os.path.join(obj.workdir,
'%s_%d_%d.stdout' % (obj.name, n, beta))
stderrname = os.path.join(obj.workdir,
'%s_%d_%d.stderr' % (obj.name, n, beta))
try:
f_out = open(stdoutname, 'w')
except IOError:
f_out = None
try:
f_err = open(stderrname, 'w')
except IOError:
f_err = None
# invoke the model
succ = abortable.call([obj.program, '-i', input, '-o', output],
stdout=f_out,
stderr=f_err,
timeout=obj.timeout)
if f_out: f_out.close()
if f_err: f_err.close()
# read the results
result = numpy.zeros([len(obj.times), obj.nspecies])
ii = 0
if succ:
with open(output, 'rb') as tabfile:
reader = csv.reader(tabfile, delimiter='\t')
for row in reader:
# first entry in each row is the RADAU5 return code
result[ii, :] = [float(x) for x in row[1:]]
ii += 1
else:
result[:] = float('nan')
# send them back to the master process
queue.put({'n': n, 'data': result})
def writeInput(self, id_beta, id_n, params, do_perturb=True):
seq = self.baseSeq[:]
names = self.initnames + self.fixnames
vals = numpy.concatenate((params, self.fixvals))
seq += steps.abcParamSequence(names, vals)
seq += steps.abcAbsoluteSequence(self.times, self.perturb(do_perturb), self.vars, outhead=False, steady=self.steady)
filename = os.path.join(self.workdir, '%s_%d_%d.input' % (self.name, id_n, id_beta))
steps.writeSequence(seq, filename)
return filename
def bcmd_proc (beta, n, params, queue, do_perturb, obj):
# create the input file
seq = obj.baseSeq[:]
seq += steps.abcParamSequence(obj.initnames, params)
seq += steps.makeSteadySequence(True)
seq += steps.abcAbsoluteSequence(obj.times, obj.perturb(do_perturb), obj.vars, outhead=False)
input = os.path.join(obj.workdir, '%s_%d_%d.input' % (obj.name, n, beta))
steps.writeSequence(seq, input)
output = os.path.join(obj.workdir, '%s_%d_%d.out' % (obj.name, n, beta))
if obj.suppress:
# invoke the model
succ = abortable.call([obj.program, '-i', input, '-o', output], stdout=obj.DEVNULL, stderr=obj.DEVNULL, timeout=obj.timeout )
else:
# create files to hold log values
stdoutname = os.path.join(obj.workdir, '%s_%d_%d.stdout' % (obj.name, n, beta))
stderrname = os.path.join(obj.workdir, '%s_%d_%d.stderr' % (obj.name, n, beta))
try: f_out = open(stdoutname, 'w')
except IOError: f_out = None
try: f_err = open(stderrname, 'w')
except IOError: f_err = None
# invoke the model
succ = abortable.call([obj.program, '-i', input, '-o', output], stdout=f_out, stderr=f_err, timeout=obj.timeout )
if f_out: f_out.close()
if f_err: f_err.close()
# read the results
result = numpy.zeros([len(obj.times), obj.nspecies])
ii = 0
if succ:
with open(output, 'rb') as tabfile:
reader = csv.reader(tabfile, delimiter='\t')
for row in reader:
# first entry in each row is the RADAU5 return code
result[ii, :] = [float(x) for x in row[1:]]
ii += 1
else:
result[:] = float('nan')
# send them back to the master process
queue.put({'n': n, 'data': result})
def writeInput(self, id_beta, id_n, params, do_perturb=True):
seq = self.baseSeq[:]
names = self.initnames + self.fixnames
vals = numpy.concatenate((params, self.fixvals))
seq += steps.abcParamSequence(names, vals)
seq += steps.abcAbsoluteSequence(self.times,
self.perturb(do_perturb),
self.vars,
outhead=False,
steady=self.steady)
filename = os.path.join(self.workdir,
'%s_%d_%d.input' % (self.name, id_n, id_beta))
steps.writeSequence(seq, filename)
return filename