def compute(run_func, parameter_combinations, sample_size, save_path,
skipbadruns=False):
"""
Calls the 'run_func' for several 'parameter_combiantions' and
'sample_size's and provides a unique ID for each run to store the data at
the 'save_path' + ID
Parameters
----------
run_func : function
The function the executes the model for a given set of parameters.
The first P paramters need to fit to the parameter_combinations.
The last parameter of run_func has to be named filename
If all went well run_func needs to return '1', otherwise another number
parameter_combinations : list
A list of tuples of each parameter combination to compute
sample_size : int
The size of samples of each parameter combiantion
save_path : string
The path to the folder where to store the computed results
skipbadruns : bool (Default: False)
If you don't want this function to check for bad runs that shall be
recalculated, than set to "True". Possible reason: speed.
"""
# add "/" to save_path if it does not end already with a "/"
save_path += "/" if not save_path.endswith("/") else ""
def master():
if not os.path.exists(save_path):
os.makedirs(save_path)
elif skipbadruns:
badruns = []
else:
badruns = find_bad_runs(save_path)
# preprocessing - Obtaining the data
tocalc = []
for s in range(sample_size):
for p in parameter_combinations:
if not os.path.exists(save_path + _get_ID(p, s)) or\
_get_ID(p, s)[: -4] in badruns:
tocalc.append((p, s))
lentocalc = len(tocalc)
totallen = len(parameter_combinations) * sample_size
print ("%i%% already computed" % (100 - (float(lentocalc) * 100 /
totallen)))
print str(lentocalc) + " of " + str(totallen) +\
" single computations left"
n_nodes = mpi.size - 1
if n_nodes > 0:
jobs_per_node = int(lentocalc / n_nodes)
else:
jobs_per_node = lentocalc
n_nodes = 1
print "Splitting calculations to {} nodes.".format(n_nodes)
for i in range(n_nodes):
mpi.submit_call("slave", (tocalc[i*jobs_per_node:
(i+1)*jobs_per_node], i), module=__name__)
if lentocalc > (jobs_per_node * n_nodes):
mpi.submit_call("slave", (tocalc[(i+1)*jobs_per_node:], i),
module=__name__)
global slave # slave into the globe scope to work with mpi
def slave(slavecalcs, sid):
lentocalc = len(slavecalcs)
for i, (p, s) in enumerate(slavecalcs):
_progress_report(i, lentocalc, "computation {} ... ".format(sid))
exit_status = run_func(*p, filename=save_path + _get_ID(p, s))
if exit_status != 1: # Checking for expected execution
# TODO: proper logging
print "!!! " + _get_ID(p, s) + " returned with: " \
+ str(exit_status)
mpi.run(verbose=0, master_func=master)
mpi.submit_call("loop", (par1, pars2), id=index)
for i in xrange(nstep):
print i
a = mpi.get_result(id=i)
if i == 0:
a_alpha_max_thresh_geo = a
else:
a_alpha_max_thresh_geo = np.vstack([a_alpha_max_thresh_geo, a])
print a_alpha_max_thresh_geo
np.save('/save/a_alpha_max_thresh_geo_c_max=0.2', a_alpha_max_thresh_geo)
nstep = mpi.size # steps of parameter variation
print nstep
tmax = 600 # length of time series for diff. eq. solution
n_tm = 100 # length of initial condition vector
# boundaries
b_a = 0.21
b_m = 0.31
b_l = 0.59
# model parameters
main.c_max = 0.2
mpi.run()
def compute(run_func,
parameter_combinations,
sample_size,
save_path,
skipbadruns=False):
"""
Calls the 'run_func' for several 'parameter_combiantions' and
'sample_size's and provides a unique ID for each run to store the data at
the 'save_path' + ID
Parameters
----------
run_func : function
The function the executes the model for a given set of parameters.
The first P paramters need to fit to the parameter_combinations.
The last parameter of run_func has to be named filename
If all went well run_func needs to return '1', otherwise another number
parameter_combinations : list
A list of tuples of each parameter combination to compute
sample_size : int
The size of samples of each parameter combiantion
save_path : string
The path to the folder where to store the computed results
skipbadruns : bool (Default: False)
If you don't want this function to check for bad runs that shall be
recalculated, than set to "True". Possible reason: speed.
"""
# add "/" to save_path if it does not end already with a "/"
save_path += "/" if not save_path.endswith("/") else ""
def master():
if not os.path.exists(save_path):
os.makedirs(save_path)
elif skipbadruns:
badruns = []
else:
badruns = find_bad_runs(save_path)
# preprocessing - Obtaining the data
tocalc = []
for s in range(sample_size):
for p in parameter_combinations:
if not os.path.exists(save_path + _get_ID(p, s)) or\
_get_ID(p, s)[: -4] in badruns:
tocalc.append((p, s))
lentocalc = len(tocalc)
totallen = len(parameter_combinations) * sample_size
print("%i%% already computed" % (100 -
(float(lentocalc) * 100 / totallen)))
print str(lentocalc) + " of " + str(totallen) +\
" single computations left"
n_nodes = mpi.size - 1
if n_nodes > 0:
jobs_per_node = int(lentocalc / n_nodes)
else:
jobs_per_node = lentocalc
n_nodes = 1
print "Splitting calculations to {} nodes.".format(n_nodes)
for i in range(n_nodes):
mpi.submit_call(
"slave",
(tocalc[i * jobs_per_node:(i + 1) * jobs_per_node], i),
module=__name__)
if lentocalc > (jobs_per_node * n_nodes):
mpi.submit_call("slave", (tocalc[(i + 1) * jobs_per_node:], i),
module=__name__)
global slave # slave into the globe scope to work with mpi
def slave(slavecalcs, sid):
lentocalc = len(slavecalcs)
for i, (p, s) in enumerate(slavecalcs):
_progress_report(i, lentocalc, "computation {} ... ".format(sid))
exit_status = run_func(*p, filename=save_path + _get_ID(p, s))
if exit_status != 1: # Checking for expected execution
# TODO: proper logging
print "!!! " + _get_ID(p, s) + " returned with: " \
+ str(exit_status)
mpi.run(verbose=0, master_func=master)