def runSimulation():
# extract the FMU to a temporary directory
unzipdir = fmpy.extract(fmu)
# read the model description
model_description = fmpy.read_model_description(unzipdir)
# instantiate the FMU
fmu_instance = fmpy.instantiate_fmu(unzipdir, model_description, 'CoSimulation')
cf = fun_lib.importCostFunction(dir = 'Combined\\')
params = readInitParams()
cost = 1e300
# shuffledParams = copy.
for iter in range(niter):
# todo paralelize this
cur_params = shuffleParams(params)
# reset the FMU instance instead of creating a new one
fmu_instance.reset()
result = fmpy.simulate_fmu(unzipdir,
stop_time=1,
start_values=cur_params,
model_description=model_description,
fmu_instance=fmu_instance,
fmi_call_logger=lambda s: print('[FMI] ' + s) )
var_set = {}
for name in result.dtype.names:
var_set[name] = result[name]
if DRAW_PLOTS:
var_set['__draw_plots'] = True
var_set['__plot_title'] = "Run %i" % (fun_lib.getRunNumber())
var_set['__saveFig_path'] = "%sFitFig_%03d.png" % (fun_lib.getSafeLogDir('Schedules'), fun_lib.getRunNumber())
objectives = cf.getObjectives(var_set, targetsFolder = r"../data/Valsalva/")
cur_cost = fun_lib.countTotalSumCost(objectives)
if cur_cost < cost:
# woohoo, better costs!
cost = cur_cost
params = cur_params
writeSchedule(params, cost, iter)
print(result)
# free the FMU instance and unload the shared library
fmu_instance.freeInstance()
# delete the temporary directory
shutil.rmtree(unzipdir, ignore_errors=True)
print('that is all, folks')
pass
def writeCost(objectives):
total_cost = fun_lib.countTotalSumCost(objectives)
# total_cost = sum(costs)
with open('dsout.out', 'w') as file:
file.write("banik pico\n")
file.write('f(x) =' + repr(total_cost))
print('Total costs: %s' % (total_cost))
def logOutput(objectives, var_set):
# log the output, if the log directory exists. exit otherwise
filepath = getLogFilePath(var_set)
run = fun_lib.getRunNumber()
with open(filepath, 'a') as file:
# prepare the line with value, cost value for this and percentage of total costs
total_cost = fun_lib.countTotalSumCost(objectives)
t = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
tail = " ,%03d,%s, %.6e" % (run, t, total_cost)
tc = fun_lib.countTotalSumCost(objectives)
if tc == 0:
# dvision by zero fix
tc = 1
string_seq = map(lambda o: logLine(o, tc), objectives)
file.write(', '.join(string_seq) + tail + '\n')
def runOptimization():
if os.path.exists(log_file):
# clear logfile
os.remove(log_file)
fmu_init = initFmus()
(params, cost) = readInitParams()
writeLog('Commencing first run..', -1)
for iter in range(niter):
# todo paralelize this
if cost is not None:
cur_params = shuffleParams(params)
else:
cur_params = params
try:
objectives = runSimulations(fmu_init, cur_params, iter)
cur_cost = fun_lib.countTotalSumCost(objectives)
if cost is None or cur_cost < cost:
# woohoo, better costs!
cost = cur_cost
params = cur_params
writeLog('Found better cost of %.6f' % cost, iter)
writeSchedule(params, cost, iter)
else:
writeLog('Rejecting cost %.6f' % cur_cost, iter)
except fun_lib.SimulationResultIncompleteError as e:
writeLog('Sim failed', iter)
writeSchedule(cur_params, 1e3, iter)
# clean up
for (fmu_instance, _, _, _) in fmu_init:
# free the FMU instance and unload the shared library
fmu_instance.freeInstance()
# delete the temporary directory
shutil.rmtree(fmu_instance.unzipDirectory, ignore_errors=True)
print('that is all, folks')
pass
def getObjectives(vars_set: dict):
""" Gets all objectives for our combined CF.
Trials:
valsalva sit
# valsalva supine
tilt 60
HR
brachial pressures
LV volumes (SV and EF)
max exercise"""
plt.close('all')
dpi = 100
fig, axs = plt.subplots(nrows=2,
ncols=2,
sharex=False,
sharey=False,
figsize=[1920 / dpi, 1080 / dpi],
dpi=dpi)
if '__root_path' not in vars_set:
vars_set['__root_path'] = '..\\..\\..\\'
if '__plot_title' in vars_set:
fig_title = vars_set['__plot_title']
# get rid of the key
del vars_set['__plot_title']
else:
fig_title = 'Debug run'
if '__saveFig_path' in vars_set and vars_set['__saveFig_path'] is not None:
saveFig_path = vars_set['__saveFig_path']
# prevent child cost functions to save figures
del vars_set['__saveFig_path']
else:
saveFig_path = None
if '__objectivesLog_path' in vars_set:
objectivesLog_path = vars_set['__objectivesLog_path']
else:
objectivesLog_path = None
def flat2gen(alist):
# https://stackoverflow.com/questions/3172930/flattening-mixed-lists-in-python-containing-iterables-and-noniterables
for item in alist:
if isinstance(item, Iterable):
for subitem in item:
yield subitem
else:
yield item
ax = list(flat2gen(axs))
axes_num = 0
# returns one objective per use case - i.e. per simulation or per cost function import
cost_objectives = []
# returns all objectives
all_objectives = []
def buildCostObjective(name, cost_func_folder, weight):
"""
the name is prefix in the large model as well
"""
nonlocal axes_num
nonlocal ax
# check if there are results for this particular use case
if len([v for v in vars_set.keys() if v.startswith(name + '.')]) == 0:
o = ObjectiveVar('IGNORING %s' % name,
value=0,
targetValue=0,
costFunctionType=CostFunctionType.Ignore)
cost_objectives.append(o)
all_objectives.append(o)
return
vars_set['__plot_axes'] = ax[axes_num]
axes_num = axes_num + 1
cf = importCostFunction(vars_set['__root_path'] + 'Identification\\' +
cost_func_folder)
# mapped_vars = mapVarSet(vars_set, mapping)
# filtering only vars relevant for that cost function, e.g. valsalva.brachial_pressure gives brachial_pressure
mapped_vars = filterVarSet(vars_set, name + '.')
objectives = cf.getObjectives(mapped_vars)
# normalize to variance type cost function
for o in objectives:
fun_lib.unifyCostFunc(o)
cost = fun_lib.countTotalWeightedCost(objectives)
costObjective = ObjectiveVar(
name,
value=cost,
costFunctionType=CostFunctionType.DistanceFromZero,
weight=weight)
# add it to the set
cost_objectives.append(costObjective)
# prepare to compare to all objectives in a log
def sumObjectives(o: ObjectiveVar):
o.name = name + '.' + o.name
o.weight = weight * o.weight
return o
all_objectives.extend(map(sumObjectives, objectives))
# they append inside
buildCostObjective('baseline', 'optimizeBaselineTriSegLumens', 1)
buildCostObjective('tilt', 'optimizeTilt', 1)
buildCostObjective('exercise', 'MaxExercise', 1)
buildCostObjective('valsalva', 'valsalva', 1)
# def plotObjectives():
# fignums = plt.get_fignums()
# axes = []
# for f in fignums:
# if len(plt.figure(f).axes) > 0:
# axes.append(plt.figure(f).axes)
# # # unpack axes
# # axes = list(f.axes for f in figs if f is not None and f.axes is not None and len(f.axes) > 0)
# plt.close('all')
# plt.figure()
# _, ax = plt.subplots(nrows = len(axes), sharex = True)
# ax.append(axes)
# ax.
# plotObjectives()
fun_lib.logObjectives(
vars_set['__LOG_ALL_OBJECTIVES_PATH'],
all_objectives,
vars_set['__SORT_COSTS_BY'],
compare_to_log_path=vars_set['__LOG_ALL_OBJECTIVES_PATH_BASE'])
fig.suptitle('%s costs %.6f' %
(fig_title, fun_lib.countTotalSumCost(cost_objectives)))
if saveFig_path is not None:
try:
plt.savefig(saveFig_path)
except PermissionError:
print('Writing the figure failed, permission denied')
if '__showPlots' in vars_set and vars_set['__showPlots']:
# and fun_lib.getRunNumber() == 0:
# zero means debug
plt.show()
# return cost_objectives
return all_objectives