def run_experiments():
logging.basicConfig(level = logging.INFO)
logfolder = os.path.join(tempfile.gettempdir(), TEMPDIR, 'logs')
pathfolder = os.path.join(tempfile.gettempdir(), TEMPDIR, 'hdf5')
exponents = np.arange(0, 8, 1)
res_per_run = 100
traj_names = []
filenames = []
runs = (np.ones(len(exponents))*2) ** exponents
for adx, nruns in enumerate(runs):
env = Environment(log_folder=logfolder, filename=pathfolder,
ncores=2, multiproc=True,
use_pool=True,
wrap_mode='QUEUE')
traj = env.v_trajectory
traj.f_add_parameter('res_per_run', res_per_run)
traj.f_add_parameter('trial', 0)
traj.f_explore({'trial': list(range(int(nruns)))})
env.f_run(add_data)
traj_names.append(traj.v_name)
filenames.append(traj.v_storage_service.filename)
return filenames, traj_names, pathfolder
def main():
# Let's be very verbose!
logging.basicConfig(level = logging.INFO)
# Let's do multiprocessing this time with a lock (which is default)
filename = os.path.join('hdf5', 'example_07.hdf5')
env = Environment(trajectory='Example_07_BRIAN',
filename=filename,
file_title='Example_07_Brian',
comment = 'Go Brian!',
dynamically_imported_classes=[BrianMonitorResult, BrianParameter],
multiproc=True,
wrap_mode='QUEUE',
ncores=2)
traj = env.v_trajectory
# 1st a) add the parameters
add_params(traj)
# 1st b) prepare, we want to explore the different network sizes and different tauw time scales
traj.f_explore(cartesian_product({traj.f_get('N').v_full_name:[50,60],
traj.f_get('tauw').v_full_name:[30*ms,40*ms]}))
# 2nd let's run our experiment
env.f_run(run_net)
# You can take a look at the results in the hdf5 file if you want!
# Finally disable logging and close all log-files
env.f_disable_logging()
def main():
try:
# Create an environment that handles running
env = Environment(trajectory='Example1_Quick_And_Not_So_Dirty',filename='experiments/example_01/HDF5/',
file_title='Example1_Quick_And_Not_So_Dirty', log_folder='experiments/example_01/LOGS/',
comment='The first example!',
complib='blosc',
small_overview_tables=False,
git_repository='./', git_message='Im a message!',
sumatra_project='./', sumatra_reason='Testing!')
# Get the trajectory from the environment
traj = env.v_trajectory
# Add both parameters
traj.f_add_parameter('x', 1, comment='Im the first dimension!')
traj.f_add_parameter('y', 1, comment='Im the second dimension!')
# Explore the parameters with a cartesian product:
traj.f_explore(cartesian_product({'x':[1,2,3], 'y':[6,7,8]}))
# Run the simulation
env.f_run(multiply)
print("Python git test successful")
# traj.f_expand({'x':[3,3],'y':[42,43]})
#
# env.f_run(multiply)
except Exception as e:
print(repr(e))
sys.exit(1)
def main():
# Let's be very verbose!
logging.basicConfig(level = logging.INFO)
# Let's do multiprocessing this time with a lock (which is default)
env = Environment(trajectory='Example_07_BRIAN',
filename='experiments/example_07/HDF5/example_07.hdf5',
file_title='Example_07_Euler_Integration',
log_folder='experiments/example_07/LOGS/',
comment = 'Go Brian!',
dynamically_imported_classes=[BrianMonitorResult, BrianParameter],
multiproc=True,
wrap_mode='QUEUE',
ncores=2)
traj = env.v_trajectory
# 1st a) add the parameters
add_params(traj)
# 1st b) prepare, we want to explore the different network sizes and different tauw time scales
traj.f_explore(cartesian_product({traj.f_get('N').v_full_name:[50,60],
traj.f_get('tauw').v_full_name:[30*ms,40*ms]}))
# 2nd let's run our experiment
env.f_run(run_net)
def main():
filename = os.path.join('hdf5', 'Clustered_Network.hdf5')
env = Environment(trajectory='Clustered_Network',
add_time=False,
filename=filename,
continuable=False,
lazy_debug=False,
multiproc=True,
ncores=2,
use_pool=False, # We cannot use a pool, our network cannot be pickled
wrap_mode='QUEUE',
overwrite_file=True)
#Get the trajectory container
traj = env.v_trajectory
# We introduce a `meta` parameter that we can use to easily rescale our network
scale = 0.5 # To obtain the results from the paper scale this to 1.0
# Be aware that your machine will need a lot of memory then!
traj.f_add_parameter('simulation.scale', scale,
comment='Meta parameter that can scale default settings. '
'Rescales number of neurons and connections strenghts, but '
'not the clustersize.')
# We create a Manager and pass all our components to the Manager.
# Note the order, CNNeuronGroups are scheduled before CNConnections,
# and the Fano Factor computation depends on the CNMonitorAnalysis
clustered_network_manager = NetworkManager(network_runner=CNNetworkRunner(),
component_list=(CNNeuronGroup(), CNConnections()),
analyser_list=(CNMonitorAnalysis(),CNFanoFactorComputer()))
# Add original parameters (but scaled according to `scale`)
clustered_network_manager.add_parameters(traj)
# We need `tolist` here since our parameter is a python float and not a
# numpy float.
explore_list = np.arange(1.0, 2.6, 0.2).tolist()
# Explore different values of `R_ee`
traj.f_explore({'R_ee' : explore_list})
# Pre-build network components
clustered_network_manager.pre_build(traj)
# Run the network simulation
traj.f_store() # Let's store the parameters already before the run
env.f_run(clustered_network_manager.run_network)
# Finally disable logging and close all log-files
env.f_disable_logging()
def main():
filename = os.path.join('hdf5', 'Clustered_Network.hdf5')
env = Environment(trajectory='Clustered_Network',
add_time=False,
filename=filename,
continuable=False,
lazy_debug=False,
multiproc=True,
ncores=2,
use_pool=False, # We cannot use a pool, our network cannot be pickled
wrap_mode='QUEUE',
overwrite_file=True)
#Get the trajectory container
traj = env.v_trajectory
# We introduce a `meta` parameter that we can use to easily rescale our network
scale = 1.0 # To obtain the results from the paper scale this to 1.0
# Be aware that your machine will need a lot of memory then!
traj.f_add_parameter('simulation.scale', scale,
comment='Meta parameter that can scale default settings. '
'Rescales number of neurons and connections strenghts, but '
'not the clustersize.')
# We create a Manager and pass all our components to the Manager.
# Note the order, CNNeuronGroups are scheduled before CNConnections,
# and the Fano Factor computation depends on the CNMonitorAnalysis
clustered_network_manager = NetworkManager(network_runner=CNNetworkRunner(),
component_list=(CNNeuronGroup(), CNConnections()),
analyser_list=(CNMonitorAnalysis(),CNFanoFactorComputer()))
# Add original parameters (but scaled according to `scale`)
clustered_network_manager.add_parameters(traj)
# We need `tolist` here since our parameter is a python float and not a
# numpy float.
explore_list = np.arange(1.0, 2.6, 0.2).tolist()
# Explore different values of `R_ee`
traj.f_explore({'R_ee' : explore_list})
# Pre-build network components
clustered_network_manager.pre_build(traj)
# Run the network simulation
traj.f_store() # Let's store the parameters already before the run
env.f_run(clustered_network_manager.run_network)
# Finally disable logging and close all log-files
env.f_disable_logging()
def profile_single_storing(profile_stroing=False, profile_loading=True):
logging.basicConfig(level = logging.INFO)
logfolder = os.path.join(tempfile.gettempdir(), TEMPDIR, 'logs')
pathfolder = os.path.join(tempfile.gettempdir(), TEMPDIR, 'hdf5')
res_per_run = 100
env = Environment(log_folder=logfolder, filename=pathfolder,
ncores=2, multiproc=False,
use_pool=True,
wrap_mode='QUEUE')
traj = env.v_trajectory
traj.f_add_parameter('res_per_run', res_per_run)
traj.f_add_parameter('trial', 0)
traj.f_explore({'trial':list(range(10))})
runexp = lambda : env.f_run(add_data)
if profile_stroing:
cProfile.runctx('runexp()', {'runexp': runexp},globals(), sort=1,
filename='store_stats.profile')
else:
runexp()
print('########################################################################')
traj = Trajectory(name=traj.v_name, add_time=False, filename= traj.v_storage_service.filename)
load = lambda : traj.f_load(load_parameters=2, load_results=1)
if profile_loading:
cProfile.runctx('load()', {'load': load},globals(), filename='load_stats.profile', sort=1)
class ResultSortTest(TrajectoryComparator):
def set_mode(self):
self.mode = 'LOCK'
self.multiproc = False
self.ncores = 1
self.use_pool=True
def setUp(self):
self.set_mode()
logging.basicConfig(level = logging.INFO)
self.filename = make_temp_file('experiments/tests/HDF5/test.hdf5')
self.logfolder = make_temp_file('experiments/tests/Log')
self.trajname = make_trajectory_name(self)
env = Environment(trajectory=self.trajname,filename=self.filename,
file_title=self.trajname, log_folder=self.logfolder,
log_stdout=False,
multiproc=self.multiproc,
wrap_mode=self.mode,
ncores=self.ncores,
use_pool=self.use_pool)
traj = env.v_trajectory
traj.v_standard_parameter=Parameter
traj.f_add_parameter('x',0)
traj.f_add_parameter('y',0)
self.env=env
self.traj=traj
def load_trajectory(self,trajectory_index=None,trajectory_name=None,as_new=False):
### Load The Trajectory and check if the values are still the same
newtraj = Trajectory()
newtraj.v_storage_service=HDF5StorageService(filename=self.filename)
newtraj.f_load(name=trajectory_name, load_derived_parameters=2,load_results=2,
index=trajectory_index, as_new=as_new)
return newtraj
def explore(self,traj):
self.explore_dict={'x':[0,1,2,3,4],'y':[1,1,2,2,3]}
traj.f_explore(self.explore_dict)
def expand(self,traj):
self.expand_dict={'x':[10,11,12,13],'y':[11,11,12,12]}
traj.f_expand(self.expand_dict)
def test_if_results_are_sorted_correctly(self):
###Explore
self.explore(self.traj)
self.env.f_run(multiply)
traj = self.traj
self.assertTrue(len(traj) == len(self.explore_dict.values()[0]))
self.traj.f_update_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
newtraj = self.load_trajectory(trajectory_name=self.traj.v_name,as_new=False)
self.traj.f_update_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.compare_trajectories(self.traj,newtraj)
def test_f_iter_runs(self):
###Explore
self.explore(self.traj)
self.env.f_run(multiply)
traj = self.traj
self.assertTrue(len(traj) == len(self.explore_dict.values()[0]))
self.traj.f_update_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
newtraj = self.load_trajectory(trajectory_name=self.traj.v_name,as_new=False)
self.traj.f_update_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
for idx, run_name in enumerate(self.traj.f_iter_runs()):
newtraj.v_as_run=run_name
self.traj.v_as_run == run_name
self.traj.v_idx = idx
newtraj.v_idx = idx
self.assertTrue('run_%08d' % (idx+1) not in traj)
self.assertTrue(newtraj.z==traj.x*traj.y,' z != x*y: %s != %s * %s' %
(str(newtraj.z),str(traj.x),str(traj.y)))
self.assertTrue(traj.v_idx == -1)
self.assertTrue(traj.v_as_run is None)
self.assertTrue(newtraj.v_idx == idx)
def test_expand(self):
###Explore
self.explore(self.traj)
print self.env.f_run(multiply)
traj = self.traj
self.assertTrue(len(traj) == len(self.explore_dict.values()[0]))
self.traj.f_update_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
traj_name = self.env.v_trajectory.v_name
del self.env
self.env = Environment(trajectory=self.traj,filename=self.filename,
file_title=self.trajname, log_folder=self.logfolder,
log_stdout=False)
self.traj = self.env.v_trajectory
self.traj.f_load(name=traj_name)
self.expand(self.traj)
self.env.f_run(multiply)
traj = self.traj
self.assertTrue(len(traj) == len(self.expand_dict.values()[0])+ len(self.explore_dict.values()[0]))
self.traj.f_update_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
newtraj = self.load_trajectory(trajectory_name=self.traj.v_name,as_new=False)
self.traj.f_update_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.compare_trajectories(self.traj,newtraj)
def test_expand_after_reload(self):
###Explore
self.explore(self.traj)
self.env.f_run(multiply)
traj = self.traj
self.assertTrue(len(traj) == len(self.explore_dict.values()[0]))
self.traj.f_update_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
self.expand(self.traj)
self.env.f_run(multiply)
traj = self.traj
self.assertTrue(len(traj) == len(self.expand_dict.values()[0])+ len(self.explore_dict.values()[0]))
self.traj.f_update_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
newtraj = self.load_trajectory(trajectory_name=self.traj.v_name,as_new=False)
self.traj.f_update_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.compare_trajectories(self.traj,newtraj)
def check_if_z_is_correct(self,traj):
for x in range(len(traj)):
traj.v_idx=x
self.assertTrue(traj.z==traj.x*traj.y,' z != x*y: %s != %s * %s' %
(str(traj.z),str(traj.x),str(traj.y)))
traj.v_idx=-1
class ResultSortTest(TrajectoryComparator):
tags = 'integration', 'hdf5', 'environment'
def set_mode(self):
self.mode = 'LOCK'
self.multiproc = False
self.ncores = 1
self.use_pool=True
self.log_stdout=False
self.freeze_input=False
self.use_scoop = False
self.log_config = True
self.port = None
self.graceful_exit = True
def tearDown(self):
self.env.f_disable_logging()
super(ResultSortTest, self).tearDown()
def setUp(self):
self.set_mode()
self.filename = make_temp_dir(os.path.join('experiments','tests','HDF5','sort_tests.hdf5'))
self.trajname = make_trajectory_name(self)
env = Environment(trajectory=self.trajname,filename=self.filename,
file_title=self.trajname,
log_stdout=self.log_stdout,
log_config=get_log_config() if self.log_config else None,
multiproc=self.multiproc,
wrap_mode=self.mode,
ncores=self.ncores,
use_pool=self.use_pool,
use_scoop=self.use_scoop,
port=self.port,
freeze_input=self.freeze_input,
graceful_exit=self.graceful_exit)
traj = env.v_trajectory
traj.v_standard_parameter=Parameter
traj.f_add_parameter('x',99)
traj.f_add_parameter('y',99)
self.env=env
self.traj=traj
def load_trajectory(self,trajectory_index=None,trajectory_name=None,as_new=False, how=2):
### Load The Trajectory and check if the values are still the same
newtraj = Trajectory()
newtraj.v_storage_service=HDF5StorageService(filename=self.filename)
newtraj.f_load(name=trajectory_name, index=trajectory_index, as_new=as_new,
load_derived_parameters=how, load_results=how)
return newtraj
def explore(self,traj):
self.explore_dict={'x':[-1,1,2,3,4],'y':[1,1,2,2,3]}
traj.f_explore(self.explore_dict)
def explore_cartesian(self,traj):
self.explore_dict=cartesian_product({'x':[-1,1,2,3,4, 5, 6],'y':[1,1,2,2,3,4,4]})
traj.f_explore(self.explore_dict)
def expand(self,traj):
self.expand_dict={'x':[10,11,12,13],'y':[11,11,12,12,13]}
with self.assertRaises(ValueError):
traj.f_expand(self.expand_dict)
self.expand_dict={'x':[10,11,12,13],'y':[11,11,12,12]}
traj.f_expand(self.expand_dict)
def test_if_results_are_sorted_correctly_manual_runs(self):
###Explore
self.explore(self.traj)
self.traj.f_store(only_init=True)
man_multiply = manual_run()(multiply_with_storing)
for idx in self.traj.f_iter_runs(yields='idx'):
self.assertTrue(isinstance(idx, int))
man_multiply(self.traj)
traj = self.traj
traj.f_store()
self.assertTrue(len(traj), 5)
self.assertTrue(len(traj) == len(list(self.explore_dict.values())[0]))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
newtraj = self.load_trajectory(trajectory_name=self.traj.v_name,as_new=False)
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.compare_trajectories(self.traj,newtraj)
def test_if_results_are_sorted_correctly_using_map(self):
###Explore
self.explore(self.traj)
args1=[10*x for x in range(len(self.traj))]
args2=[100*x for x in range(len(self.traj))]
args3=list(range(len(self.traj)))
results = self.env.f_run_map(multiply_args, args1, arg2=args2, arg3=args3)
self.assertEqual(len(results), len(self.traj))
traj = self.traj
self.assertTrue(len(traj) == len(list(self.explore_dict.values())[0]))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct_map(traj, args1, args2, args3)
for res in results:
self.assertEqual(len(res), 2)
self.assertTrue(isinstance(res[0], int))
self.assertTrue(isinstance(res[1], int))
idx = res[0]
self.assertEqual(self.traj.res.runs[idx].z, res[1])
newtraj = self.load_trajectory(trajectory_name=self.traj.v_name,as_new=False)
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.assertEqual(len(traj), 5)
self.compare_trajectories(self.traj,newtraj)
def test_if_results_are_sorted_correctly(self):
###Explore
self.explore(self.traj)
results = self.env.f_run(multiply)
self.are_results_in_order(results)
self.assertEqual(len(results), len(self.traj))
traj = self.traj
self.assertTrue(len(traj) == len(list(self.explore_dict.values())[0]))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
for res in results:
self.assertEqual(len(res), 2)
self.assertTrue(isinstance(res[0], int))
self.assertTrue(isinstance(res[1], int))
idx = res[0]
self.assertEqual(self.traj.res.runs[idx].z, res[1])
newtraj = self.load_trajectory(trajectory_name=self.traj.v_name,as_new=False)
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.compare_trajectories(self.traj,newtraj)
def test_graceful_exit(self):
###Explore
self.explore_cartesian(self.traj)
results = self.env.f_run(multiply_with_graceful_exit)
self.are_results_in_order(results)
self.assertFalse(self.traj.f_is_completed())
def test_f_iter_runs(self):
###Explore
self.explore(self.traj)
results = self.env.f_run(multiply)
self.are_results_in_order(results)
traj = self.traj
self.assertTrue(len(traj) == len(list(self.explore_dict.values())[0]))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
newtraj = self.load_trajectory(trajectory_name=self.traj.v_name,as_new=False)
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
for idx, run_name in enumerate(self.traj.f_iter_runs()):
newtraj.v_crun=run_name
self.traj.v_idx = idx
newtraj.v_idx = idx
nameset = set((x.v_name for x in traj.f_iter_nodes(predicate=(idx,))))
self.assertTrue('run_%08d' % (idx+1) not in nameset)
self.assertTrue('run_%08d' % idx in nameset)
self.assertTrue(traj.v_crun == run_name)
self.assertTrue(newtraj.crun.z==traj.x*traj.y,' z != x*y: %s != %s * %s' %
(str(newtraj.crun.z),str(traj.x),str(traj.y)))
for idx, traj in enumerate(self.traj.f_iter_runs(yields='self')):
run_name = traj.f_idx_to_run(idx)
self.assertTrue(traj is self.traj)
newtraj.v_crun=run_name
self.traj.v_idx = idx
newtraj.v_idx = idx
nameset = set((x.v_name for x in traj.f_iter_nodes(predicate=(idx,))))
self.assertTrue('run_%08d' % (idx+1) not in nameset)
self.assertTrue('run_%08d' % idx in nameset)
self.assertTrue(traj.v_crun == run_name)
self.assertTrue(newtraj.crun.z==traj.x*traj.y,' z != x*y: %s != %s * %s' %
(str(newtraj.crun.z),str(traj.x),str(traj.y)))
for idx, traj in enumerate(self.traj.f_iter_runs(yields='copy')):
run_name = traj.f_idx_to_run(idx)
self.assertTrue(traj is not self.traj)
newtraj.v_crun=run_name
self.traj.v_idx = idx
newtraj.v_idx = idx
nameset = set((x.v_name for x in traj.f_iter_nodes(predicate=(idx,))))
self.assertTrue('run_%08d' % (idx+1) not in nameset)
self.assertTrue('run_%08d' % idx in nameset)
self.assertTrue(traj.v_crun == run_name)
self.assertTrue(newtraj.crun.z==traj.x*traj.y,' z != x*y: %s != %s * %s' %
(str(newtraj.crun.z),str(traj.x),str(traj.y)))
traj = self.traj
self.assertTrue(traj.v_idx == -1)
self.assertTrue(traj.v_crun is None)
self.assertTrue(traj.v_crun_ == pypetconstants.RUN_NAME_DUMMY)
self.assertTrue(newtraj.v_idx == idx)
def test_f_iter_runs_auto_load(self):
###Explore
self.explore(self.traj)
results = self.env.f_run(multiply)
self.are_results_in_order(results)
traj = self.traj
self.assertTrue(len(traj) == len(list(self.explore_dict.values())[0]))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
newtraj = Trajectory()
newtraj.v_storage_service=HDF5StorageService(filename=self.filename)
newtraj.f_load(name=self.traj.v_name, index=None, as_new=False, load_data=0)
newtraj.v_auto_load = True
newtraj.par.f_load_child('y', load_data=1)
for idx, run_name in enumerate(self.traj.f_iter_runs()):
newtraj.v_crun=run_name
self.traj.v_idx = idx
newtraj.v_idx = idx
nameset = set((x.v_name for x in traj.f_iter_nodes(predicate=(idx,))))
self.assertTrue('run_%08d' % (idx+1) not in nameset)
self.assertTrue('run_%08d' % idx in nameset)
self.assertTrue(traj.v_crun == run_name)
self.assertTrue(newtraj.res.runs.crun.z==newtraj.par.x*newtraj.par.y,' z != x*y: %s != %s * %s' %
(str(newtraj.crun.z),str(newtraj.x),str(newtraj.y)))
traj = self.traj
self.assertTrue(traj.v_idx == -1)
self.assertTrue(traj.v_crun is None)
self.assertTrue(traj.v_crun_ == pypetconstants.RUN_NAME_DUMMY)
self.assertTrue(newtraj.v_idx == idx)
def test_expand(self):
###Explore
self.explore(self.traj)
results = self.env.f_run(multiply)
self.are_results_in_order(results)
get_root_logger().info(results)
traj = self.traj
self.assertEqual(len(traj), len(list(list(self.explore_dict.values())[0])))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
traj_name = self.env.v_trajectory.v_name
del self.env
self.env = Environment(trajectory=self.traj,
log_stdout=False,
log_config=get_log_config())
self.traj = self.env.v_trajectory
self.traj.f_load(name=traj_name)
self.expand(self.traj)
results = self.env.f_run(multiply)
self.are_results_in_order(results)
traj = self.traj
self.assertTrue(len(traj) == len(list(self.expand_dict.values())[0])+
len(list(self.explore_dict.values())[0]))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
newtraj = self.load_trajectory(trajectory_name=self.traj.v_name,as_new=False)
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.compare_trajectories(self.traj,newtraj)
def test_expand_after_reload(self):
###Explore
self.explore(self.traj)
results = self.env.f_run(multiply)
self.are_results_in_order(results)
traj = self.traj
self.assertTrue(len(traj) == len(list(self.explore_dict.values())[0]))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
self.expand(self.traj)
self.env.f_run(multiply)
traj = self.traj
self.assertTrue(len(traj) == len(list(self.expand_dict.values())[0])+\
len(list(self.explore_dict.values())[0]))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
newtraj = self.load_trajectory(trajectory_name=self.traj.v_name,as_new=False)
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.compare_trajectories(self.traj,newtraj)
def check_if_z_is_correct_map(self,traj, args1, args2, args3):
for x, arg1, arg2, arg3 in zip(range(len(traj)), args1, args2, args3):
traj.v_idx=x
self.assertTrue(traj.crun.z==traj.x*traj.y+arg1+arg2+arg3,' z != x*y: %s != %s * %s' %
(str(traj.crun.z),str(traj.x),str(traj.y)))
traj.v_idx=-1
def check_if_z_is_correct(self,traj):
traj.v_shortcuts=False
for x in range(len(traj)):
traj.v_idx=x
z = traj.res.runs.crun.z
x = traj.par.x
y = traj.par.y
self.assertTrue(z==x*y,' z != x*y: %s != %s * %s' %
(str(z),str(x),str(y)))
traj.v_idx=-1
traj.v_shortcuts=True
class ResultSortTest(TrajectoryComparator):
tags = 'integration', 'hdf5', 'environment'
def set_mode(self):
self.mode = 'LOCK'
self.multiproc = False
self.ncores = 1
self.use_pool=True
self.log_stdout=False
self.freeze_input=False
self.use_scoop = False
self.log_config = True
self.port = None
def tearDown(self):
self.env.f_disable_logging()
super(ResultSortTest, self).tearDown()
def setUp(self):
self.set_mode()
self.filename = make_temp_dir(os.path.join('experiments','tests','HDF5','sort_tests.hdf5'))
self.trajname = make_trajectory_name(self)
env = Environment(trajectory=self.trajname,filename=self.filename,
file_title=self.trajname,
log_stdout=self.log_stdout,
log_config=get_log_config() if self.log_config else None,
multiproc=self.multiproc,
wrap_mode=self.mode,
ncores=self.ncores,
use_pool=self.use_pool,
use_scoop=self.use_scoop,
port=self.port,
freeze_input=self.freeze_input,)
traj = env.v_trajectory
traj.v_standard_parameter=Parameter
traj.f_add_parameter('x',0)
traj.f_add_parameter('y',0)
self.env=env
self.traj=traj
def load_trajectory(self,trajectory_index=None,trajectory_name=None,as_new=False):
### Load The Trajectory and check if the values are still the same
newtraj = Trajectory()
newtraj.v_storage_service=HDF5StorageService(filename=self.filename)
newtraj.f_load(name=trajectory_name, index=trajectory_index, as_new=as_new,
load_derived_parameters=2, load_results=2)
return newtraj
def explore(self,traj):
self.explore_dict={'x':[0,1,2,3,4],'y':[1,1,2,2,3]}
traj.f_explore(self.explore_dict)
def expand(self,traj):
self.expand_dict={'x':[10,11,12,13],'y':[11,11,12,12,13]}
with self.assertRaises(ValueError):
traj.f_expand(self.expand_dict)
self.expand_dict={'x':[10,11,12,13],'y':[11,11,12,12]}
traj.f_expand(self.expand_dict)
def test_if_results_are_sorted_correctly_manual_runs(self):
###Explore
self.explore(self.traj)
self.traj.f_store(only_init=True)
man_multiply = manual_run()(multiply_with_storing)
for idx in self.traj.f_iter_runs(yields='idx'):
self.assertTrue(isinstance(idx, int))
man_multiply(self.traj)
traj = self.traj
traj.f_store()
self.assertTrue(len(traj), 5)
self.assertTrue(len(traj) == len(compat.listvalues(self.explore_dict)[0]))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
newtraj = self.load_trajectory(trajectory_name=self.traj.v_name,as_new=False)
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.compare_trajectories(self.traj,newtraj)
def test_if_results_are_sorted_correctly_using_map(self):
###Explore
self.explore(self.traj)
args1=[10*x for x in range(len(self.traj))]
args2=[100*x for x in range(len(self.traj))]
args3=list(range(len(self.traj)))
results = self.env.f_run_map(multiply_args, args1, arg2=args2, arg3=args3)
self.assertEqual(len(results), len(self.traj))
traj = self.traj
self.assertTrue(len(traj) == len(compat.listvalues(self.explore_dict)[0]))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct_map(traj, args1, args2, args3)
for res in results:
self.assertEqual(len(res), 2)
self.assertTrue(isinstance(res[0], int))
self.assertTrue(isinstance(res[1], int))
idx = res[0]
self.assertEqual(self.traj.res.runs[idx].z, res[1])
newtraj = self.load_trajectory(trajectory_name=self.traj.v_name,as_new=False)
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.assertEqual(len(traj), 5)
self.compare_trajectories(self.traj,newtraj)
def test_if_results_are_sorted_correctly(self):
###Explore
self.explore(self.traj)
results = self.env.f_run(multiply)
self.are_results_in_order(results)
self.assertEqual(len(results), len(self.traj))
traj = self.traj
self.assertTrue(len(traj) == len(compat.listvalues(self.explore_dict)[0]))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
for res in results:
self.assertEqual(len(res), 2)
self.assertTrue(isinstance(res[0], int))
self.assertTrue(isinstance(res[1], int))
idx = res[0]
self.assertEqual(self.traj.res.runs[idx].z, res[1])
newtraj = self.load_trajectory(trajectory_name=self.traj.v_name,as_new=False)
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.compare_trajectories(self.traj,newtraj)
def test_f_iter_runs(self):
###Explore
self.explore(self.traj)
results = self.env.f_run(multiply)
self.are_results_in_order(results)
traj = self.traj
self.assertTrue(len(traj) == len(compat.listvalues(self.explore_dict)[0]))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
newtraj = self.load_trajectory(trajectory_name=self.traj.v_name,as_new=False)
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
for idx, run_name in enumerate(self.traj.f_iter_runs()):
newtraj.v_as_run=run_name
self.traj.v_as_run == run_name
self.traj.v_idx = idx
newtraj.v_idx = idx
nameset = set((x.v_name for x in traj.f_iter_nodes(predicate=(idx,))))
self.assertTrue('run_%08d' % (idx+1) not in nameset)
self.assertTrue('run_%08d' % idx in nameset)
self.assertTrue(traj.v_crun == run_name)
self.assertTrue(newtraj.crun.z==traj.x*traj.y,' z != x*y: %s != %s * %s' %
(str(newtraj.crun.z),str(traj.x),str(traj.y)))
for idx, traj in enumerate(self.traj.f_iter_runs(yields='self')):
run_name = traj.f_idx_to_run(idx)
self.assertTrue(traj is self.traj)
newtraj.v_as_run=run_name
self.traj.v_as_run == run_name
self.traj.v_idx = idx
newtraj.v_idx = idx
nameset = set((x.v_name for x in traj.f_iter_nodes(predicate=(idx,))))
self.assertTrue('run_%08d' % (idx+1) not in nameset)
self.assertTrue('run_%08d' % idx in nameset)
self.assertTrue(traj.v_crun == run_name)
self.assertTrue(newtraj.crun.z==traj.x*traj.y,' z != x*y: %s != %s * %s' %
(str(newtraj.crun.z),str(traj.x),str(traj.y)))
for idx, traj in enumerate(self.traj.f_iter_runs(yields='copy')):
run_name = traj.f_idx_to_run(idx)
self.assertTrue(traj is not self.traj)
newtraj.v_as_run=run_name
self.traj.v_as_run == run_name
self.traj.v_idx = idx
newtraj.v_idx = idx
nameset = set((x.v_name for x in traj.f_iter_nodes(predicate=(idx,))))
self.assertTrue('run_%08d' % (idx+1) not in nameset)
self.assertTrue('run_%08d' % idx in nameset)
self.assertTrue(traj.v_crun == run_name)
self.assertTrue(newtraj.crun.z==traj.x*traj.y,' z != x*y: %s != %s * %s' %
(str(newtraj.crun.z),str(traj.x),str(traj.y)))
traj = self.traj
self.assertTrue(traj.v_idx == -1)
self.assertTrue(traj.v_crun is None)
self.assertTrue(traj.v_crun_ == pypetconstants.RUN_NAME_DUMMY)
self.assertTrue(newtraj.v_idx == idx)
def test_expand(self):
###Explore
self.explore(self.traj)
results = self.env.f_run(multiply)
self.are_results_in_order(results)
get_root_logger().info(results)
traj = self.traj
self.assertEqual(len(traj), len(list(compat.listvalues(self.explore_dict)[0])))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
traj_name = self.env.v_trajectory.v_name
del self.env
self.env = Environment(trajectory=self.traj,
log_stdout=False,
log_config=get_log_config())
self.traj = self.env.v_trajectory
self.traj.f_load(name=traj_name)
self.expand(self.traj)
results = self.env.f_run(multiply)
self.are_results_in_order(results)
traj = self.traj
self.assertTrue(len(traj) == len(compat.listvalues(self.expand_dict)[0])+ len(compat.listvalues(self.explore_dict)[0]))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
newtraj = self.load_trajectory(trajectory_name=self.traj.v_name,as_new=False)
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.compare_trajectories(self.traj,newtraj)
def test_expand_after_reload(self):
###Explore
self.explore(self.traj)
results = self.env.f_run(multiply)
self.are_results_in_order(results)
traj = self.traj
self.assertTrue(len(traj) == len(compat.listvalues(self.explore_dict)[0]))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
self.expand(self.traj)
self.env.f_run(multiply)
traj = self.traj
self.assertTrue(len(traj) == len(compat.listvalues(self.expand_dict)[0])+\
len(compat.listvalues(self.explore_dict)[0]))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
newtraj = self.load_trajectory(trajectory_name=self.traj.v_name,as_new=False)
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.compare_trajectories(self.traj,newtraj)
def check_if_z_is_correct_map(self,traj, args1, args2, args3):
for x, arg1, arg2, arg3 in zip(range(len(traj)), args1, args2, args3):
traj.v_idx=x
self.assertTrue(traj.crun.z==traj.x*traj.y+arg1+arg2+arg3,' z != x*y: %s != %s * %s' %
(str(traj.crun.z),str(traj.x),str(traj.y)))
traj.v_idx=-1
def check_if_z_is_correct(self,traj):
traj.v_shortcuts=False
for x in range(len(traj)):
traj.v_idx=x
z = traj.res.runs.crun.z
x = traj.par.x
y = traj.par.y
self.assertTrue(z==x*y,' z != x*y: %s != %s * %s' %
(str(z),str(x),str(y)))
traj.v_idx=-1
traj.v_shortcuts=True
traj2 = env2.v_trajectory
# Add both parameters
traj1.f_add_parameter('x', 1.0, comment='I am the first dimension!')
traj1.f_add_parameter('y', 1.0, comment='I am the second dimension!')
traj2.f_add_parameter('x', 1.0, comment='I am the first dimension!')
traj2.f_add_parameter('y', 1.0, comment='I am the second dimension!')
# Explore the parameters with a cartesian product for the first trajectory:
traj1.f_explore(cartesian_product({'x':[1.0,2.0,3.0,4.0], 'y':[6.0,7.0,8.0]}))
# Let's explore slightly differently for the second:
traj2.f_explore(cartesian_product({'x':[3.0,4.0,5.0,6.0], 'y':[7.0,8.0,9.0]}))
# Run the simulations with all parameter combinations
env1.f_run(multiply)
env2.f_run(multiply)
# Now we merge them together into traj1
# We want to remove duplicate entries
# like the parameter space point x=3.0, y=7.0.
# Several points have been explored by both trajectories and we need them only once.
# Therefore, we set remove_duplicates=True (Note this takes O(N1*N2)!).
# We also want to backup both trajectories, but we let the system choose the filename.
# Accordingly we choose backup_filename=True instead of providing a filename.
# We want to move the hdf5 nodes from one trajectory to the other.
# Thus we set move_nodes=True.
# Finally,we want to delete the other trajectory afterwards since we already have a backup.
traj1.f_merge(traj2,remove_duplicates=True,backup_filename=True,
move_nodes=True, delete_other_trajectory=True)
env = Environment(trajectory='Example08',filename='experiments/example_08/HDF5/example_08.hdf5',
file_title='Example08', log_folder='experiments/example_08/LOGS/',
comment='Another example!')
# Get the trajectory from the environment
traj = env.v_trajectory
# Add both parameters
traj.f_add_parameter('x', 1, comment='I am the first dimension!')
traj.f_add_parameter('y', 1, comment='I am the second dimension!')
# Explore the parameters with a cartesian product:
traj.f_explore(cartesian_product({'x':[1,2,3,4], 'y':[6,7,8]}))
# Run the simulation
env.f_run(multiply)
# We load all results
traj.f_load(load_results=pypetconstants.LOAD_DATA)
# And now we want to find som particular results, the ones where x was 2 or y was 8.
# Therefore, we use a lambda function
my_filter_predicate= lambda x,y: x==2 or y==8
# We can now use this lambda function to search for the run indexes associated with x==2 OR y==8.
# We need a list specifying the names of the parameters and the predicate to do this.
# Note that names need to be in the order as listed in the lambda function, here 'x' and 'y':
idx_iterator = traj.f_find_idx(['x','y'], my_filter_predicate)
# Now we can print the corresponding results:
print 'The run names and results for parameter combinations with x==2 or y==8:'
class LinkMergeTest(TrajectoryComparator):
tags = 'integration', 'hdf5', 'environment', 'links', 'merge'
def test_merge_with_linked_derived_parameter(self, disable_logging = True):
logging.basicConfig(level = logging.ERROR)
self.logfolder = make_temp_dir(os.path.join('experiments',
'tests',
'Log'))
random.seed()
self.trajname1 = 'T1'+ make_trajectory_name(self)
self.trajname2 = 'T2'+make_trajectory_name(self)
self.filename = make_temp_dir(os.path.join('experiments',
'tests',
'HDF5',
'test%s.hdf5' % self.trajname1))
self.env1 = Environment(trajectory=self.trajname1, filename=self.filename,
file_title=self.trajname1,
log_stdout=False, log_config=get_log_config())
self.env2 = Environment(trajectory=self.trajname2, filename=self.filename,
file_title=self.trajname2,
log_stdout=False, log_config=get_log_config())
self.traj1 = self.env1.v_trajectory
self.traj2 = self.env2.v_trajectory
create_link_params(self.traj1)
create_link_params(self.traj2)
explore_params(self.traj1)
explore_params2(self.traj2)
self.traj1.f_add_derived_parameter('test.$.gg', 42)
self.traj2.f_add_derived_parameter('test.$.gg', 44)
self.traj1.f_add_derived_parameter('test.hh.$', 111)
self.traj2.f_add_derived_parameter('test.hh.$', 53)
self.env1.f_run(dostuff_and_add_links)
self.env2.f_run(dostuff_and_add_links)
old_length = len(self.traj1)
self.traj1.f_merge(self.traj2, remove_duplicates=True)
self.traj1.f_load(load_data=2)
for run in self.traj1.f_get_run_names():
self.traj1.v_crun = run
idx = self.traj1.v_idx
param = self.traj1['test.crun.gg']
if idx < old_length:
self.assertTrue(param == 42)
else:
self.assertTrue(param == 44)
param = self.traj1['test.hh.crun']
if idx < old_length:
self.assertTrue(param == 111)
else:
self.assertTrue(param == 53)
self.assertTrue(len(self.traj1) > old_length)
for irun in range(len(self.traj1.f_get_run_names())):
self.assertTrue(self.traj1.res['r_%d' % irun] == self.traj1.paramB)
self.assertTrue(self.traj1.res.runs['r_%d' % irun].paraBL == self.traj1.paramB)
if disable_logging:
self.env1.f_disable_logging()
self.env2.f_disable_logging()
return old_length
def test_remerging(self):
prev_old_length = self.test_merge_with_linked_derived_parameter(disable_logging=False)
name = self.traj1
self.bfilename = make_temp_dir(os.path.join('experiments',
'tests',
'HDF5',
'backup_test%s.hdf5' % self.trajname1))
self.traj1.f_load(load_data=2)
self.traj1.f_backup(backup_filename=self.bfilename)
self.traj3 = load_trajectory(index=-1, filename=self.bfilename, load_all=2)
old_length = len(self.traj1)
self.traj1.f_merge(self.traj3, backup=False, remove_duplicates=False)
self.assertTrue(len(self.traj1) > old_length)
self.traj1.f_load(load_data=2)
for run in self.traj1.f_get_run_names():
self.traj1.v_crun = run
idx = self.traj1.v_idx
param = self.traj1['test.crun.gg']
if idx < prev_old_length or old_length <= idx < prev_old_length + old_length:
self.assertTrue(param == 42, '%s != 42' % str(param))
else:
self.assertTrue(param == 44, '%s != 44' % str(param))
param = self.traj1['test.hh.crun']
if idx < prev_old_length or old_length <= idx < prev_old_length + old_length:
self.assertTrue(param == 111, '%s != 111' % str(param))
else:
self.assertTrue(param == 53, '%s != 53' % str(param))
self.assertTrue(len(self.traj1)>old_length)
for irun in range(len(self.traj1.f_get_run_names())):
self.assertTrue(self.traj1.res.runs['r_%d' % irun].paraBL == self.traj1.paramB)
self.assertTrue(self.traj1.res['r_%d' % irun] == self.traj1.paramB)
self.env1.f_disable_logging()
self.env2.f_disable_logging()
class ResultSortTest(TrajectoryComparator):
tags = 'integration', 'hdf5', 'environment'
def set_mode(self):
self.mode = 'LOCK'
self.multiproc = False
self.ncores = 1
self.use_pool=True
self.log_stdout=False
self.freeze_pool_input=False
def tearDown(self):
self.env.f_disable_logging()
super(ResultSortTest, self).tearDown()
def setUp(self):
self.set_mode()
self.filename = make_temp_dir(os.path.join('experiments','tests','HDF5','test.hdf5'))
self.trajname = make_trajectory_name(self)
env = Environment(trajectory=self.trajname,filename=self.filename,
file_title=self.trajname,
log_stdout=self.log_stdout,
log_config=get_log_config(),
multiproc=self.multiproc,
wrap_mode=self.mode,
ncores=self.ncores,
use_pool=self.use_pool,
freeze_pool_input=self.freeze_pool_input,)
traj = env.v_trajectory
traj.v_standard_parameter=Parameter
traj.f_add_parameter('x',0)
traj.f_add_parameter('y',0)
self.env=env
self.traj=traj
def load_trajectory(self,trajectory_index=None,trajectory_name=None,as_new=False):
### Load The Trajectory and check if the values are still the same
newtraj = Trajectory()
newtraj.v_storage_service=HDF5StorageService(filename=self.filename)
newtraj.f_load(name=trajectory_name, index=trajectory_index, as_new=as_new,
load_derived_parameters=2, load_results=2)
return newtraj
def explore(self,traj):
self.explore_dict={'x':[0,1,2,3,4],'y':[1,1,2,2,3]}
traj.f_explore(self.explore_dict)
def expand(self,traj):
self.expand_dict={'x':[10,11,12,13],'y':[11,11,12,12,13]}
with self.assertRaises(ValueError):
traj.f_expand(self.expand_dict)
self.expand_dict={'x':[10,11,12,13],'y':[11,11,12,12]}
traj.f_expand(self.expand_dict)
def test_if_results_are_sorted_correctly(self):
###Explore
self.explore(self.traj)
self.env.f_run(multiply)
traj = self.traj
self.assertTrue(len(traj) == len(compat.listvalues(self.explore_dict)[0]))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
newtraj = self.load_trajectory(trajectory_name=self.traj.v_name,as_new=False)
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.compare_trajectories(self.traj,newtraj)
def test_f_iter_runs(self):
###Explore
self.explore(self.traj)
self.env.f_run(multiply)
traj = self.traj
self.assertTrue(len(traj) == len(compat.listvalues(self.explore_dict)[0]))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
newtraj = self.load_trajectory(trajectory_name=self.traj.v_name,as_new=False)
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
for idx, run_name in enumerate(self.traj.f_iter_runs()):
newtraj.v_as_run=run_name
self.traj.v_as_run == run_name
self.traj.v_idx = idx
newtraj.v_idx = idx
nameset = set((x.v_name for x in traj.f_iter_nodes(predicate=(idx,))))
self.assertTrue('run_%08d' % (idx+1) not in nameset)
self.assertTrue('run_%08d' % idx in nameset)
self.assertTrue(traj.v_crun == run_name)
self.assertTrue(newtraj.crun.z==traj.x*traj.y,' z != x*y: %s != %s * %s' %
(str(newtraj.crun.z),str(traj.x),str(traj.y)))
self.assertTrue(traj.v_idx == -1)
self.assertTrue(traj.v_crun is None)
self.assertTrue(traj.v_crun_ == pypetconstants.RUN_NAME_DUMMY)
self.assertTrue(newtraj.v_idx == idx)
def test_expand(self):
###Explore
self.explore(self.traj)
get_root_logger().info(self.env.f_run(multiply))
traj = self.traj
self.assertEqual(len(traj), len(list(compat.listvalues(self.explore_dict)[0])))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
traj_name = self.env.v_trajectory.v_name
del self.env
self.env = Environment(trajectory=self.traj,
log_stdout=False,
log_config=get_log_config())
self.traj = self.env.v_trajectory
self.traj.f_load(name=traj_name)
self.expand(self.traj)
self.env.f_run(multiply)
traj = self.traj
self.assertTrue(len(traj) == len(compat.listvalues(self.expand_dict)[0])+ len(compat.listvalues(self.explore_dict)[0]))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
newtraj = self.load_trajectory(trajectory_name=self.traj.v_name,as_new=False)
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.compare_trajectories(self.traj,newtraj)
def test_expand_after_reload(self):
###Explore
self.explore(self.traj)
self.env.f_run(multiply)
traj = self.traj
self.assertTrue(len(traj) == len(compat.listvalues(self.explore_dict)[0]))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
self.expand(self.traj)
self.env.f_run(multiply)
traj = self.traj
self.assertTrue(len(traj) == len(compat.listvalues(self.expand_dict)[0])+\
len(compat.listvalues(self.explore_dict)[0]))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
newtraj = self.load_trajectory(trajectory_name=self.traj.v_name,as_new=False)
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.compare_trajectories(self.traj,newtraj)
def check_if_z_is_correct(self,traj):
for x in range(len(traj)):
traj.v_idx=x
self.assertTrue(traj.crun.z==traj.x*traj.y,' z != x*y: %s != %s * %s' %
(str(traj.crun.z),str(traj.x),str(traj.y)))
traj.v_idx=-1
y.val = x**2
smurf = Result('','','','')
z = traj.f_add_result('Nada.Moo',smurf)
z.val = y()+1
print 'Dat wars'
#multip.log_to_stderr().setLevel(logging.INFO)
config['multiproc']=False
config['ncores']=2
env = Environment(trajectory='MyExperiment', filename='../experiments/env.hdf5',dynamicly_imported_classes=[BrianParameter])
traj = env.get_trajectory()
assert isinstance(traj, Trajectory)
par=traj.f_add_parameter('x',param_type=BrianParameter, value=3, unit = 'mV')
par.hui='buh'
print par()
print par.val
traj.f_explore(identity, {traj.x.gfn('value'):[1,2,3,4]})
env.f_run(test_run,to_print='test')
class LinkMergeTest(TrajectoryComparator):
tags = 'integration', 'hdf5', 'environment', 'links', 'merge'
def test_merge_with_linked_derived_parameter(self, disable_logging=True):
logging.basicConfig(level=logging.ERROR)
self.logfolder = make_temp_dir(
os.path.join('experiments', 'tests', 'Log'))
random.seed()
self.trajname1 = 'T1' + make_trajectory_name(self)
self.trajname2 = 'T2' + make_trajectory_name(self)
self.filename = make_temp_dir(
os.path.join('experiments', 'tests', 'HDF5',
'test%s.hdf5' % self.trajname1))
self.env1 = Environment(trajectory=self.trajname1,
filename=self.filename,
file_title=self.trajname1,
log_stdout=False,
log_config=get_log_config())
self.env2 = Environment(trajectory=self.trajname2,
filename=self.filename,
file_title=self.trajname2,
log_stdout=False,
log_config=get_log_config())
self.traj1 = self.env1.v_trajectory
self.traj2 = self.env2.v_trajectory
create_link_params(self.traj1)
create_link_params(self.traj2)
explore_params(self.traj1)
explore_params2(self.traj2)
self.traj1.f_add_derived_parameter('test.$.gg', 42)
self.traj2.f_add_derived_parameter('test.$.gg', 44)
self.traj1.f_add_derived_parameter('test.hh.$', 111)
self.traj2.f_add_derived_parameter('test.hh.$', 53)
self.env1.f_run(dostuff_and_add_links)
self.env2.f_run(dostuff_and_add_links)
old_length = len(self.traj1)
self.traj1.f_merge(self.traj2, remove_duplicates=True)
self.traj1.f_load(load_data=2)
for run in self.traj1.f_get_run_names():
self.traj1.v_crun = run
idx = self.traj1.v_idx
param = self.traj1['test.crun.gg']
if idx < old_length:
self.assertTrue(param == 42)
else:
self.assertTrue(param == 44)
param = self.traj1['test.hh.crun']
if idx < old_length:
self.assertTrue(param == 111)
else:
self.assertTrue(param == 53)
self.assertTrue(len(self.traj1) > old_length)
for irun in range(len(self.traj1.f_get_run_names())):
self.assertTrue(self.traj1.res['r_%d' % irun] == self.traj1.paramB)
self.assertTrue(
self.traj1.res.runs['r_%d' % irun].paraBL == self.traj1.paramB)
if disable_logging:
self.env1.f_disable_logging()
self.env2.f_disable_logging()
return old_length
def test_remerging(self):
prev_old_length = self.test_merge_with_linked_derived_parameter(
disable_logging=False)
name = self.traj1
self.bfilename = make_temp_dir(
os.path.join('experiments', 'tests', 'HDF5',
'backup_test%s.hdf5' % self.trajname1))
self.traj1.f_load(load_data=2)
self.traj1.f_backup(backup_filename=self.bfilename)
self.traj3 = load_trajectory(index=-1,
filename=self.bfilename,
load_all=2)
old_length = len(self.traj1)
self.traj1.f_merge(self.traj3, backup=False, remove_duplicates=False)
self.assertTrue(len(self.traj1) > old_length)
self.traj1.f_load(load_data=2)
for run in self.traj1.f_get_run_names():
self.traj1.v_crun = run
idx = self.traj1.v_idx
param = self.traj1['test.crun.gg']
if idx < prev_old_length or old_length <= idx < prev_old_length + old_length:
self.assertTrue(param == 42, '%s != 42' % str(param))
else:
self.assertTrue(param == 44, '%s != 44' % str(param))
param = self.traj1['test.hh.crun']
if idx < prev_old_length or old_length <= idx < prev_old_length + old_length:
self.assertTrue(param == 111, '%s != 111' % str(param))
else:
self.assertTrue(param == 53, '%s != 53' % str(param))
self.assertTrue(len(self.traj1) > old_length)
for irun in range(len(self.traj1.f_get_run_names())):
self.assertTrue(
self.traj1.res.runs['r_%d' % irun].paraBL == self.traj1.paramB)
self.assertTrue(self.traj1.res['r_%d' % irun] == self.traj1.paramB)
self.env1.f_disable_logging()
self.env2.f_disable_logging()
def main():
env = Environment(trajectory='Example_05_Euler_Integration',
filename='experiments/example_05/HDF5/example_05.hdf5',
file_title='Example_05_Euler_Integration',
log_folder='experiments/example_05/LOGS/',
comment = 'Go for Euler!')
traj = env.v_trajectory
trajectory_name = traj.v_name
# 1st a) phase parameter addition
add_parameters(traj)
# 1st b) phase preparation
# We will add the differential equation (well, its source code only) as a derived parameter
traj.f_add_derived_parameter(FunctionParameter,'diff_eq', diff_lorenz,
comment='Source code of our equation!')
# We want to explore some initial conditions
traj.f_explore({'initial_conditions' : [
np.array([0.01,0.01,0.01]),
np.array([2.02,0.02,0.02]),
np.array([42.0,4.2,0.42])
]})
# 3 different conditions are enough for an illustrative example
# 2nd phase let's run the experiment
# We pass `euler_scheme` as our top-level simulation function and
# the Lorenz equation 'diff_lorenz' as an additional argument
env.f_run(euler_scheme, diff_lorenz)
# We don't have a 3rd phase of post-processing here
# 4th phase analysis.
# I would recommend to do post-processing completely independent from the simulation,
# but for simplicity let's do it here.
# Let's assume that we start all over again and load the entire trajectory new.
# Yet, there is an error within this approach, do you spot it?
del traj
traj = Trajectory(filename='experiments/example_05/HDF5/example_05.hdf5')
# We will only fully load parameters and derived parameters.
# Results will be loaded manually later on.
try:
# However, this will fail because our trajectory does not know how to
# build the FunctionParameter. You have seen this coming, right?
traj.f_load(name=trajectory_name,load_parameters=2,
load_derived_parameters=2,load_results=1)
except ImportError as e:
print 'That did\'nt work, I am sorry. %s ' % e.message
# Ok, let's try again but this time with adding our parameter to the imports
traj = Trajectory(filename='experiments/example_05/HDF5/example_05.hdf5',
dynamically_imported_classes=FunctionParameter)
# Now it works:
traj.f_load(name=trajectory_name,load_parameters=2,
load_derived_parameters=2,load_results=1)
#For the fun of it, let's print the source code
print '\n ---------- The source code of your function ---------- \n %s' % traj.diff_eq
# Let's get the exploration array:
initial_conditions_exploration_array = traj.f_get('initial_conditions').f_get_range()
# Now let's plot our simulated equations for the different initial conditions:
# We will iterate through the run names
for idx, run_name in enumerate(traj.f_get_run_names()):
#Get the result of run idx from the trajectory
euler_result = traj.results.f_get(run_name).euler_evolution
# Now we manually need to load the result. Actually the results are not so large and we
# could load them all at once. But for demonstration we do as if they were huge:
traj.f_load_item(euler_result)
euler_data = euler_result.data
#Plot fancy 3d plot
fig = plt.figure(idx)
ax = fig.gca(projection='3d')
x = euler_data[:,0]
y = euler_data[:,1]
z = euler_data[:,2]
ax.plot(x, y, z, label='Initial Conditions: %s' % str(initial_conditions_exploration_array[idx]))
plt.legend()
plt.show()
# Now we free the data again (because we assume its huuuuuuge):
del euler_data
euler_result.f_empty()
def main():
env = Environment(trajectory='Example_06_Euler_Integration',
filename='experiments/example_06/HDF5/example_06.hdf5',
file_title='Example_06_Euler_Integration',
log_folder='experiments/example_06/LOGS/',
comment = 'Go for Euler!')
traj = env.v_trajectory
# 1st a) phase parameter addition
# Remember we have some control flow in the `add_parameters` function, the default parameter
# set we choose is the `'diff_lorenz'` one, but we want to deviate from that and use the
# `'diff_roessler'`.
# In order to do that we can preset the corresponding name parameter to change the
# control flow:
traj.f_preset_parameter('diff_name', 'diff_roessler') # If you erase this line, you will get
# again the lorenz attractor
add_parameters(traj)
# 1st b) phase preparation
# Let's check which function we want to use
if traj.diff_name=='diff_lorenz':
diff_eq = diff_lorenz
elif traj.diff_name=='diff_roessler':
diff_eq = diff_roessler
else:
raise ValueError('I don\'t know what %s is.' % traj.diff_name)
# And add the source code of the function as a derived parameter.
traj.f_add_derived_parameter(FunctionParameter, 'diff_eq', diff_eq,
comment='Source code of our equation!')
# We want to explore some initial conditions
traj.f_explore({'initial_conditions' : [
np.array([0.01,0.01,0.01]),
np.array([2.02,0.02,0.02]),
np.array([42.0,4.2,0.42])
]})
# 3 different conditions are enough for now
# 2nd phase let's run the experiment
# We pass 'euler_scheme' as our top-level simulation function and
# the Roessler function as an additional argument
env.f_run(euler_scheme, diff_eq)
# Again no post-processing
# 4th phase analysis.
# I would recommend to do the analysis completely independent from the simulation
# but for simplicity let's do it here.
# We won't reload the trajectory this time but simply update the skeleton
traj.f_update_skeleton()
#For the fun of it, let's print the source code
print '\n ---------- The source code of your function ---------- \n %s' % traj.diff_eq
# Let's get the exploration array:
initial_conditions_exploration_array = traj.f_get('initial_conditions').f_get_range()
# Now let's plot our simulated equations for the different initial conditions.
# We will iterate through the run names
for idx, run_name in enumerate(traj.f_get_run_names()):
# Get the result of run idx from the trajectory
euler_result = traj.results.f_get(run_name).euler_evolution
# Now we manually need to load the result. Actually the results are not so large and we
# could load them all at once, but for demonstration we do as if they were huge:
traj.f_load_item(euler_result)
euler_data = euler_result.data
# Plot fancy 3d plot
fig = plt.figure(idx)
ax = fig.gca(projection='3d')
x = euler_data[:,0]
y = euler_data[:,1]
z = euler_data[:,2]
ax.plot(x, y, z, label='Initial Conditions: %s' % str(initial_conditions_exploration_array[idx]))
plt.legend()
plt.show()
# Now we free the data again (because we assume its huuuuuuge):
del euler_data
euler_result.f_empty()
