def test_without_pre_run(self):
runner = NetworkRunner()
components = [TheNeurons(), TheConnection(), TheSimulation()]
analyser = [TheMonitors()]
nm = NetworkManager(network_runner=runner, component_list=components,
analyser_list=analyser)
env = Environment(trajectory='Test_'+repr(time.time()).replace('.','_'),
filename=make_temp_dir(os.path.join(
'experiments',
'tests',
'briantests',
'HDF5',
'briantest.hdf5')),
file_title='test',
log_config=get_log_config(),
dynamic_imports=['pypet.brian2.parameter.BrianParameter',
BrianMonitorResult, BrianResult],
multiproc=True,
ncores=2)
traj = env.v_traj
nm.add_parameters(traj)
traj.f_explore({'v01': [11*mV, 13*mV]})
env.f_run(nm.run_network)
self.check_data(traj)
def main():
filename = os.path.join('hdf5', 'Clustered_Network.hdf5')
# If we pass a filename to the trajectory a new HDF5StorageService will
# be automatically created
traj = Trajectory(filename=filename,
dynamically_imported_classes=[BrianMonitorResult,
BrianParameter])
# Let's create and fake environment to enable logging:
env = Environment(traj, do_single_runs=False)
# Load the trajectory, but onyl laod the skeleton of the results
traj.f_load(index=-1, load_parameters=2, load_derived_parameters=2, load_results=1)
# Find the result instances related to the fano factor
fano_dict = traj.f_get_from_runs('mean_fano_factor', fast_access=False)
# Load the data of the fano factor results
ffs = fano_dict.values()
traj.f_load_items(ffs)
# Extract all values and R_ee values for each run
ffs_values = [x.f_get() for x in ffs]
Rees = traj.f_get('R_ee').f_get_range()
# Plot average fano factor as a function of R_ee
plt.plot(Rees, ffs_values)
plt.xlabel('R_ee')
plt.ylabel('Avg. Fano Factor')
plt.show()
# Finally disable logging and close all log-files
env.disable_logging()
def test_logging_stdout(self):
filename = 'teststdoutlog.hdf5'
filename = make_temp_dir(filename)
folder = make_temp_dir('logs')
env = Environment(trajectory=make_trajectory_name(self),
filename=filename, log_config=get_log_config(),
# log_levels=logging.CRITICAL, # needed for the test
log_stdout=('STDOUT', 50), #log_folder=folder
)
env.f_run(log_error)
traj = env.v_traj
path = get_log_path(traj)
mainstr = 'sTdOuTLoGGinG'
print(mainstr)
env.f_disable_logging()
mainfilename = os.path.join(path, 'LOG.txt')
with open(mainfilename, mode='r') as mainf:
full_text = mainf.read()
self.assertTrue(mainstr in full_text)
self.assertTrue('4444444' not in full_text)
self.assertTrue('DEBUG' not in full_text)
def main():
batch = get_batch()
filename = 'saga_%s.hdf5' % str(batch)
env = Environment(trajectory='Example_22_Euler_Integration_%s' % str(batch),
filename=filename,
file_title='Example_22_Euler_Integration',
comment='Go for Euler!',
overwrite_file=True,
multiproc=True, # Yes we can use multiprocessing within each batch!
ncores=4)
traj = env.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!')
# explore the trajectory
explore_batch(traj, batch)
# 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.run(euler_scheme, diff_lorenz)
def main():
batch = get_batch()
filename = 'saga_%s.hdf5' % str(batch)
env = Environment(
trajectory='Example_22_Euler_Integration_%s' % str(batch),
filename=filename,
file_title='Example_22_Euler_Integration',
comment='Go for Euler!',
overwrite_file=True,
multiproc=True, # Yes we can use multiprocessing within each batch!
ncores=4)
traj = env.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!')
# explore the trajectory
explore_batch(traj, batch)
# 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.run(euler_scheme, diff_lorenz)
def test_maximum_overview_size(self):
filename = make_temp_dir('maxisze.hdf5')
env = Environment(trajectory='Testmigrate', filename=filename,
log_config=get_log_config())
traj = env.v_trajectory
for irun in range(pypetconstants.HDF5_MAX_OVERVIEW_TABLE_LENGTH):
traj.f_add_parameter('f%d.x' % irun, 5)
traj.f_store()
store = ptcompat.open_file(filename, mode='r+')
table = ptcompat.get_child(store.root,traj.v_name).overview.parameters_overview
self.assertEquals(table.nrows, pypetconstants.HDF5_MAX_OVERVIEW_TABLE_LENGTH)
store.close()
for irun in range(pypetconstants.HDF5_MAX_OVERVIEW_TABLE_LENGTH,
2*pypetconstants.HDF5_MAX_OVERVIEW_TABLE_LENGTH):
traj.f_add_parameter('f%d.x' % irun, 5)
traj.f_store()
store = ptcompat.open_file(filename, mode='r+')
table = ptcompat.get_child(store.root,traj.v_name).overview.parameters_overview
self.assertEquals(table.nrows, pypetconstants.HDF5_MAX_OVERVIEW_TABLE_LENGTH)
store.close()
env.f_disable_logging()
def main():
env = Environment(trajectory='FiringRate',
comment='Experiment to measure the firing rate '
'of a leaky integrate and fire neuron. '
'Exploring different input currents, '
'as well as refractory periods',
add_time=False, # We don't want to add the current time to the name,
log_folder='./logs/',
log_level=logging.INFO,
log_stdout=True,
multiproc=True,
ncores=2, #My laptop has 2 cores ;-)
wrap_mode='QUEUE',
filename='./hdf5/', # We only pass a folder here, so the name is chosen
# automatically to be the same as the Trajectory
)
traj = env.v_trajectory
# Add parameters
add_parameters(traj)
# Let's explore
add_exploration(traj)
# Ad the postprocessing function
env.f_add_postprocessing(neuron_postproc)
# Run the experiment
env.f_run(run_neuron)
def test_without_pre_run(self):
runner = NetworkRunner()
components = [TheNeurons(), TheConnection(), TheSimulation()]
analyser = [TheMonitors()]
nm = NetworkManager(network_runner=runner,
component_list=components,
analyser_list=analyser)
env = Environment(
trajectory='Test_' + repr(time.time()).replace('.', '_'),
filename=make_temp_dir(
os.path.join('experiments', 'tests', 'briantests', 'HDF5',
'briantest.hdf5')),
file_title='test',
log_config=get_log_config(),
dynamic_imports=[
'pypet.brian2.parameter.BrianParameter', BrianMonitorResult,
BrianResult
],
multiproc=True,
ncores=2)
traj = env.v_traj
nm.add_parameters(traj)
traj.f_explore({'v01': [11 * mV, 13 * mV]})
env.f_run(nm.run_network)
self.check_data(traj)
def test_maximum_overview_size(self):
filename = make_temp_dir('maxisze.hdf5')
env = Environment(trajectory='Testmigrate', filename=filename,
log_config=get_log_config(), add_time=True)
traj = env.v_trajectory
for irun in range(pypetconstants.HDF5_MAX_OVERVIEW_TABLE_LENGTH):
traj.f_add_parameter('f%d.x' % irun, 5)
traj.f_store()
store = pt.open_file(filename, mode='r+')
table = store.root._f_get_child(traj.v_name).overview.parameters_overview
self.assertEquals(table.nrows, pypetconstants.HDF5_MAX_OVERVIEW_TABLE_LENGTH)
store.close()
for irun in range(pypetconstants.HDF5_MAX_OVERVIEW_TABLE_LENGTH,
2*pypetconstants.HDF5_MAX_OVERVIEW_TABLE_LENGTH):
traj.f_add_parameter('f%d.x' % irun, 5)
traj.f_store()
store = pt.open_file(filename, mode='r+')
table = store.root._f_get_child(traj.v_name).overview.parameters_overview
self.assertEquals(table.nrows, pypetconstants.HDF5_MAX_OVERVIEW_TABLE_LENGTH)
store.close()
env.f_disable_logging()
def main():
""" Main *boilerplate* function to start simulation """
# Now let's make use of logging
logger = logging.getLogger()
# Create folders for data and plots
folder = os.path.join(os.getcwd(), 'experiments', 'ca_patterns_pypet')
if not os.path.isdir(folder):
os.makedirs(folder)
filename = os.path.join(folder, 'all_patterns.hdf5')
# Create an environment
env = Environment(trajectory='cellular_automata',
multiproc=True,
ncores=4,
wrap_mode='QUEUE',
filename=filename,
overwrite_file=True)
# extract the trajectory
traj = env.traj
traj.v_lazy_adding = True
traj.par.ncells = 400, 'Number of cells'
traj.par.steps = 250, 'Number of timesteps'
traj.par.rule_number = 30, 'The ca rule'
traj.par.initial_name = 'random', 'The type of initial state'
traj.par.seed = 100042, 'RNG Seed'
# Explore
exp_dict = {'rule_number' : [10, 30, 90, 110, 184],
'initial_name' : ['single', 'random'],}
# # You can uncomment the ``exp_dict`` below to see that changing the
# # exploration scheme is now really easy:
# exp_dict = {'rule_number' : [10, 30, 90, 110, 184],
# 'ncells' : [100, 200, 300],
# 'seed': [333444555, 123456]}
exp_dict = cartesian_product(exp_dict)
traj.f_explore(exp_dict)
# Run the simulation
logger.info('Starting Simulation')
env.run(wrap_automaton)
# Load all data
traj.f_load(load_data=2)
logger.info('Printing data')
for idx, run_name in enumerate(traj.f_iter_runs()):
# Plot all patterns
filename = os.path.join(folder, make_filename(traj))
plot_pattern(traj.crun.pattern, traj.rule_number, filename)
progressbar(idx, len(traj), logger=logger)
# Finally disable logging and close all log-files
env.disable_logging()
def make_env(self, **kwargs):
self.mode.__dict__.update(kwargs)
filename = 'log_testing.hdf5'
self.filename = make_temp_dir(filename)
self.traj_name = make_trajectory_name(self)
self.env = Environment(trajectory=self.traj_name,
filename=self.filename, **self.mode.__dict__)
self.traj = self.env.v_traj
def main():
filename = os.path.join('hdf5', 'FiringRate.hdf5')
env = Environment(
trajectory='FiringRate',
comment='Experiment to measure the firing rate '
'of a leaky integrate and fire neuron. '
'Exploring different input currents, '
'as well as refractory periods',
add_time=False, # We don't want to add the current time to the name,
log_stdout=True,
log_config='DEFAULT',
multiproc=True,
ncores=4, #I think my laptop has 4 cores
git_repository='/home/pinolej/th',
wrap_mode='QUEUE',
filename=filename,
overwrite_file=True)
traj = env.trajectory
# Add parameters
add_parameters(traj)
# Let's explore
add_exploration(traj)
# Ad the postprocessing function
env.add_postprocessing(neuron_postproc)
# Run the experiment
env.run(run_neuron)
# Finally disable logging and close all log-files
env.disable_logging()
def main():
# pypet environment
env = Environment(trajectory=SIM_NAME,
comment="Experiment on density with binary covariates",
log_config=None,
multiproc=False,
ncores=1,
filename=SIM_PATH + "/results/",
overwrite_file=True)
traj = env.trajectory
# parameters (data generation)
traj.f_add_parameter("data.N", np.int64(500), "Number of nodes")
traj.f_add_parameter("data.K", np.int64(5),
"True number of latent components")
traj.f_add_parameter("data.p_cts", np.int64(0),
"Number of continuous covariates")
traj.f_add_parameter("data.p_bin", np.int64(0),
"Number of binary covariates")
traj.f_add_parameter("data.var_adj", np.float64(1.),
"True variance in the link Probit model")
traj.f_add_parameter("data.var_cov", np.float64(1.),
"True variance in the covariate model (cts and bin)")
traj.f_add_parameter("data.missing_rate", np.float64(0.1), "Missing rate")
traj.f_add_parameter("data.seed", np.int64(1), "Random seed")
traj.f_add_parameter("data.alpha_mean", np.float64(-1.85),
"Mean of the heterogeneity parameter")
# parameters (model)
traj.f_add_parameter("model.K", np.int64(5),
"Number of latent components in the model")
traj.f_add_parameter("model.adj_model", "Logistic", "Adjacency model")
traj.f_add_parameter("model.bin_model", "Logistic",
"Binary covariate model")
# parameters (fit)
traj.f_add_parameter("fit.n_iter", np.int64(20),
"Number of VEM iterations")
traj.f_add_parameter("fit.n_vmp", np.int64(5),
"Number of VMP iterations per E-step")
traj.f_add_parameter("fit.n_gd", np.int64(5),
"Number of GD iterations per M-step")
traj.f_add_parameter("fit.step_size", np.float64(0.01), "GD Step size")
# experiment
explore_dict = {
"data.alpha_mean":
np.array([-3.2, -2.8, -2.4, -2., -1.6, -1.2, -0.8, -0.4, 0.0, 0.4]),
"data.p_bin":
np.array([10, 100, 500]),
"data.seed":
np.arange(0, 100, 1)
}
experiment = cartesian_product(explore_dict, tuple(explore_dict.keys()))
traj.f_explore(experiment)
env.add_postprocessing(post_processing)
env.run(run)
env.disable_logging()
def get_runtime(length):
filename = os.path.join('tmp', 'hdf5', 'many_runs_improved.hdf5')
start = time.time()
with Environment(filename=filename,
log_levels=50,
report_progress=(2, 'progress', 50),
overwrite_file=True,
purge_duplicate_comments=False,
summary_tables=False,
small_overview_tables=False) as env:
traj = env.v_traj
traj.par.x = 0, 'parameter'
traj.f_explore({'x': range(length)})
max_run = 1000000000
for idx in range(len(traj)):
if idx > max_run:
traj.f_get_run_information(idx, copy=False)['completed'] = 1
env.f_run(job)
end = time.time()
dicts = [
traj.f_get_run_information(x)
for x in range(min(len(traj), max_run))
]
total = end - start
return total / float(len(traj)), total
def test_run():
global filename
np.random.seed()
trajname = 'profiling'
filename = make_temp_dir(os.path.join('hdf5', 'test%s.hdf5' % trajname))
env = Environment(trajectory=trajname, filename=filename,
file_title=trajname,
log_stdout=False,
results_per_run=5,
derived_parameters_per_run=5,
multiproc=False,
ncores=1,
wrap_mode='LOCK',
use_pool=False,
overwrite_file=True)
traj = env.v_trajectory
traj.v_standard_parameter=Parameter
## Create some parameters
param_dict={}
create_param_dict(param_dict)
### Add some parameter:
add_params(traj,param_dict)
#remember the trajectory and the environment
traj = traj
env = env
traj.f_add_parameter('TEST', 'test_run')
###Explore
explore(traj)
### Make a test run
simple_arg = -13
simple_kwarg= 13.0
env.f_run(simple_calculations,simple_arg,simple_kwarg=simple_kwarg)
size=os.path.getsize(filename)
size_in_mb = size/1000000.
print('Size is %sMB' % str(size_in_mb))
def test_fail_on_wrong_kwarg(self):
with self.assertRaises(ValueError):
filename = 'testsfail.hdf5'
env = Environment(filename=make_temp_dir(filename),
log_stdout=True,
log_config=get_log_config(),
logger_names=('STDERROR', 'STDOUT'),
foo='bar')
def test_run():
global filename
np.random.seed()
trajname = 'profiling'
filename = make_temp_dir(os.path.join('hdf5', 'test%s.hdf5' % trajname))
env = Environment(trajectory=trajname,
filename=filename,
file_title=trajname,
log_stdout=False,
results_per_run=5,
derived_parameters_per_run=5,
multiproc=False,
ncores=1,
wrap_mode='LOCK',
use_pool=False,
overwrite_file=True)
traj = env.v_trajectory
traj.v_standard_parameter = Parameter
## Create some parameters
param_dict = {}
create_param_dict(param_dict)
### Add some parameter:
add_params(traj, param_dict)
#remember the trajectory and the environment
traj = traj
env = env
traj.f_add_parameter('TEST', 'test_run')
###Explore
explore(traj)
### Make a test run
simple_arg = -13
simple_kwarg = 13.0
env.f_run(simple_calculations, simple_arg, simple_kwarg=simple_kwarg)
size = os.path.getsize(filename)
size_in_mb = size / 1000000.
print('Size is %sMB' % str(size_in_mb))
def test_parsing(self):
filename = make_temp_dir('config_test.hdf5')
env = Environment(filename=filename, config=self.parser)
traj = env.v_traj
self.assertTrue(traj.v_auto_load)
self.assertEqual(traj.v_storage_service.filename, filename)
self.assertEqual(traj.x, 42)
self.assertEqual(traj.f_get('y').v_comment, 'This is the second variable')
self.assertTrue(traj.testconfig)
self.assertTrue(env._logging_manager.log_config is not None)
self.assertTrue(env._logging_manager._sp_config is not None)
env.f_disable_logging()
def main():
filename = os.path.join('hdf5', 'FiringRate.hdf5')
env = Environment(trajectory='FiringRate',
comment='Experiment to measure the firing rate '
'of a leaky integrate and fire neuron. '
'Exploring different input currents, '
'as well as refractory periods',
add_time=False, # We don't want to add the current time to the name,
log_stdout=True,
log_config='DEFAULT',
multiproc=True,
ncores=2, #My laptop has 2 cores ;-)
wrap_mode='QUEUE',
filename=filename,
overwrite_file=True)
traj = env.trajectory
# Add parameters
add_parameters(traj)
# Let's explore
add_exploration(traj)
# Ad the postprocessing function
env.add_postprocessing(neuron_postproc)
# Run the experiment
env.run(run_neuron)
# Finally disable logging and close all log-files
env.disable_logging()
def main():
"""Main function to protect the *entry point* of the program.
If you want to use multiprocessing under Windows you need to wrap your
main code creating an environment into a function. Otherwise
the newly started child processes will re-execute the code and throw
errors (also see https://docs.python.org/2/library/multiprocessing.html#windows).
"""
# Create an environment that handles running.
# Let's enable multiprocessing with 2 workers.
filename = os.path.join('hdf5', 'example_04.hdf5')
env = Environment(trajectory='Example_04_MP',
filename=filename,
file_title='Example_04_MP',
log_stdout=True,
comment='Multiprocessing example!',
multiproc=True,
ncores=4,
use_pool=True, # Our runs are inexpensive we can get rid of overhead
# by using a pool
freeze_input=True, # We can avoid some
# overhead by freezing the input to the pool
wrap_mode=pypetconstants.WRAP_MODE_QUEUE,
graceful_exit=True, # We want to exit in a data friendly way
# that safes all results after hitting CTRL+C, try it ;-)
overwrite_file=True)
# Get the trajectory from the environment
traj = env.trajectory
# Add both parameters
traj.f_add_parameter('x', 1.0, comment='I am the first dimension!')
traj.f_add_parameter('y', 1.0, comment='I am the second dimension!')
# Explore the parameters with a cartesian product, but we want to explore a bit more
traj.f_explore(cartesian_product({'x':[float(x) for x in range(20)],
'y':[float(y) for y in range(20)]}))
# Run the simulation
env.run(multiply)
# Finally disable logging and close all log-files
env.disable_logging()
def main():
env = Environment(trajectory='FiringRatePipeline',
comment='Experiment to measure the firing rate '
'of a leaky integrate and fire neuron. '
'Exploring different input currents, '
'as well as refractory periods',
add_time=False, # We don't want to add the current time to the name,
log_folder='./logs/',
log_level=logging.INFO,
log_stdout=True,
multiproc=True,
ncores=2, #My laptop has 2 cores ;-)
filename='./hdf5/', # We only pass a folder here, so the name is chosen
# automatically to be the same as the Trajectory
)
env.f_pipeline(mypipeline)
def make_env(self, **kwargs):
self.mode.__dict__.update(kwargs)
filename = 'log_testing.hdf5'
self.filename = make_temp_dir(filename)
self.traj_name = make_trajectory_name(self)
self.env = Environment(trajectory=self.traj_name,
filename=self.filename, **self.mode.__dict__)
self.traj = self.env.v_traj
def main():
filename = os.path.join('hdf5', 'FiringRate.hdf5')
env = Environment(trajectory='FiringRatePipeline',
comment='Experiment to measure the firing rate '
'of a leaky integrate and fire neuron. '
'Exploring different input currents, '
'as well as refractory periods',
add_time=False, # We don't want to add the current time to the name,
log_stdout=True,
multiproc=True,
ncores=2, #My laptop has 2 cores ;-)
filename=filename,
overwrite_file=True)
env.pipeline(mypipeline)
# Finally disable logging and close all log-files
env.disable_logging()
def test_net(self):
env = Environment(trajectory='Test_'+repr(time.time()).replace('.','_'),
filename=make_temp_dir(os.path.join(
'experiments',
'tests',
'briantests',
'HDF5',
'briantest.hdf5')),
file_title='test',
log_config=get_log_config(),
dynamic_imports=['pypet.brian2.parameter.Brian2Parameter',
Brian2MonitorResult],
multiproc=False)
traj = env.v_traj
traj.f_add_parameter(Brian2Parameter, 'v0', 0.0*mV,
comment='Input bias')
traj.f_explore({'v0': [11*mV, 13*mV, 15*mV]})
env.f_run(run_network)
self.get_data(traj)
def test_overwrite_annotations_and_results(self):
filename = make_temp_dir('overwrite.hdf5')
env = Environment(trajectory='testoverwrite',
filename=filename,
log_config=get_log_config(),
overwrite_file=True)
traj = env.v_traj
traj.f_add_parameter('grp.x', 5, comment='hi')
traj.grp.v_comment = 'hi'
traj.grp.v_annotations['a'] = 'b'
traj.f_store()
traj.f_remove_child('parameters', recursive=True)
traj.f_load(load_data=2)
self.assertTrue(traj.x == 5)
self.assertTrue(traj.grp.v_comment == 'hi')
self.assertTrue(traj.grp.v_annotations['a'] == 'b')
traj.f_get('x').f_unlock()
traj.grp.x = 22
traj.f_get('x').v_comment = 'hu'
traj.grp.v_annotations['a'] = 'c'
traj.grp.v_comment = 'hu'
traj.f_store_item(traj.f_get('x'), store_data=3)
traj.f_store_item(traj.grp, store_data=3)
traj.f_remove_child('parameters', recursive=True)
traj.f_load(load_data=2)
self.assertTrue(traj.x == 22)
self.assertTrue(traj.grp.v_comment == 'hu')
self.assertTrue(traj.grp.v_annotations['a'] == 'c')
env.f_disable_logging()
def test_hdf5_settings_and_context(self):
filename = make_temp_dir('hdfsettings.hdf5')
with Environment('testraj',
filename=filename,
add_time=True,
comment='',
dynamic_imports=None,
log_config=None,
multiproc=False,
ncores=3,
wrap_mode=pypetconstants.WRAP_MODE_LOCK,
continuable=False,
use_hdf5=True,
complevel=4,
complib='zlib',
shuffle=True,
fletcher32=True,
pandas_format='t',
pandas_append=True,
purge_duplicate_comments=True,
summary_tables=True,
small_overview_tables=True,
large_overview_tables=True,
results_per_run=19,
derived_parameters_per_run=17) as env:
traj = env.v_trajectory
traj.f_store()
hdf5file = pt.openFile(filename=filename)
table = hdf5file.root._f_getChild(traj.v_name)._f_getChild(
'overview')._f_getChild('hdf5_settings')
row = table[0]
self.assertTrue(row['complevel'] == 4)
self.assertTrue(row['complib'] == compat.tobytes('zlib'))
self.assertTrue(row['shuffle'])
self.assertTrue(row['fletcher32'])
self.assertTrue(row['pandas_format'] == compat.tobytes('t'))
for attr_name, table_name in HDF5StorageService.NAME_TABLE_MAPPING.items(
):
self.assertTrue(row[table_name])
self.assertTrue(row['purge_duplicate_comments'])
self.assertTrue(row['results_per_run'] == 19)
self.assertTrue(row['derived_parameters_per_run'] == 17)
hdf5file.close()
def test_net(self):
env = Environment(
trajectory='Test_' + repr(time.time()).replace('.', '_'),
filename=make_temp_dir(
os.path.join('experiments', 'tests', 'briantests', 'HDF5',
'briantest.hdf5')),
file_title='test',
log_config=get_log_config(),
dynamic_imports=[
'pypet.brian2.parameter.Brian2Parameter', Brian2MonitorResult
],
multiproc=False)
traj = env.v_traj
traj.f_add_parameter(Brian2Parameter,
'v0',
0.0 * mV,
comment='Input bias')
traj.f_explore({'v0': [11 * mV, 13 * mV, 15 * mV]})
env.f_run(run_network)
self.get_data(traj)
def fail_on_diff():
try:
Environment(trajectory='fail',
filename=os.path.join('fail',
'HDF5',),
file_title='failing',
git_repository='.', git_message='Im a message!',
git_fail=True)
raise RuntimeError('You should not be here!')
except GitDiffError as exc:
print('I expected the GitDiffError: `%s`' % repr(exc))
def test_overwrite_annotations_and_results(self):
filename = make_temp_dir('overwrite.hdf5')
env = Environment(trajectory='testoverwrite', filename=filename,
log_config=get_log_config())
traj = env.v_traj
traj.f_add_parameter('grp.x', 5, comment='hi')
traj.grp.v_comment='hi'
traj.grp.v_annotations['a'] = 'b'
traj.f_store()
traj.f_remove_child('parameters', recursive=True)
traj.f_load(load_data=2)
self.assertTrue(traj.x == 5)
self.assertTrue(traj.grp.v_comment == 'hi')
self.assertTrue(traj.grp.v_annotations['a'] == 'b')
traj.f_get('x').f_unlock()
traj.grp.x = 22
traj.f_get('x').v_comment='hu'
traj.grp.v_annotations['a'] = 'c'
traj.grp.v_comment = 'hu'
traj.f_store_item(traj.f_get('x'), store_data=3)
traj.f_store_item(traj.grp, store_data=3)
traj.f_remove_child('parameters', recursive=True)
traj.f_load(load_data=2)
self.assertTrue(traj.x == 22)
self.assertTrue(traj.grp.v_comment == 'hu')
self.assertTrue(traj.grp.v_annotations['a'] == 'c')
env.f_disable_logging()
def main(inputargs=None):
if inputargs is None:
inputargs = sys.argv[1:] if len(sys.argv) > 1 else ""
args = docopt(__doc__, argv=inputargs)
wavpath = path.join(modulePath, "resources", "tone_in_noise")
stimuli = [path.join(wavpath, i) for i in glob.glob(path.join(wavpath, "*.wav"))]
outfile = path.realpath(path.expanduser(args["--out"]))
env = Environment(trajectory='tone-in-noise',
filename=outfile,
overwrite_file=True,
file_title="Tone in noise at different SNR",
comment="some comment",
large_overview_tables="False",
# freeze_input=True,
# use_pool=True,
multiproc=True,
ncores=3,
graceful_exit=True,
#wrap_mode=pypetconstants.WRAP_MODE_QUEUE,
)
traj = env.trajectory
traj.f_add_parameter('periphery', 'verhulst', comment="which periphery was used")
traj.f_add_parameter('brainstem', 'nelsoncarney04', comment="which brainstem model was used")
traj.f_add_parameter('weighting', "--no-cf-weighting ", comment="weighted CFs")
traj.f_add_parameter('wavfile', '', comment="Which wav file to run")
traj.f_add_parameter('level', 80, comment="stimulus level, spl")
traj.f_add_parameter('neuropathy', "none", comment="")
parameter_dict = {
"periphery" : ['verhulst', 'zilany'],
"brainstem" : ['nelsoncarney04', 'carney2015'],
"weighting" : [cf_weighting, ""],
"wavfile" : stimuli,
"level" : [80],
"neuropathy": ["none", "moderate", "severe", "ls-moderate", "ls-severe"]
}
traj.f_explore(cartesian_product(parameter_dict))
env.run(tone_in_noise)
return 0
def main():
# Create an environment that handles running
filename = os.path.join('hdf5','example_18.hdf5')
env = Environment(trajectory='Multiplication',
filename=filename,
file_title='Example_18_Many_Runs',
overwrite_file=True,
comment='Contains many runs',
multiproc=True,
use_pool=True,
freeze_input=True,
ncores=2,
wrap_mode='QUEUE')
# The environment has created a trajectory container for us
traj = env.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, yielding 2500 runs
traj.f_explore(cartesian_product({'x': range(50), 'y': range(50)}))
# Run the simulation
env.run(multiply)
# Disable logging
env.disable_logging()
# turn auto loading on, since results have not been loaded, yet
traj.v_auto_load = True
# Use the `v_idx` functionality
traj.v_idx = 2042
print('The result of run %d is: ' % traj.v_idx)
# Now we can rely on the wildcards
print(traj.res.crunset.crun.z)
traj.v_idx = -1
# Or we can use the shortcuts `rts_X` (run to set) and `r_X` to get particular results
print('The result of run %d is: ' % 2044)
print(traj.res.rts_2044.r_2044.z)
def test_throw_warning_if_old_kw_is_used(self):
pass
filename = make_temp_dir('hdfwarning.hdf5')
with warnings.catch_warnings(record=True) as w:
env = Environment(trajectory='test', filename=filename,
dynamically_imported_classes=[],
log_config=get_log_config())
with warnings.catch_warnings(record=True) as w:
traj = Trajectory(dynamically_imported_classes=[])
traj = env.v_trajectory
traj.f_store()
with warnings.catch_warnings(record=True) as w:
traj.f_load(dynamically_imported_classes=[])
env.f_disable_logging()
def test_logging_stdout(self):
filename = 'teststdoutlog.hdf5'
filename = make_temp_dir(filename)
folder = make_temp_dir('logs')
env = Environment(trajectory=make_trajectory_name(self),
filename=filename, log_config=get_log_config(),
# log_levels=logging.CRITICAL, # needed for the test
log_stdout=('STDOUT', 50), #log_folder=folder
)
env.f_run(log_error)
traj = env.v_traj
path = get_log_path(traj)
mainstr = 'sTdOuTLoGGinG'
print(mainstr)
env.f_disable_logging()
mainfilename = os.path.join(path, 'LOG.txt')
with open(mainfilename, mode='r') as mainf:
full_text = mainf.read()
self.assertTrue(mainstr in full_text)
self.assertTrue('4444444' not in full_text)
self.assertTrue('DEBUG' not in full_text)
def main(path, name, explore_dict):
comment = "\n".join(
["{}: {}".format(k, v) for k, v in explore_dict.items()])
# pypet environment
env = Environment(trajectory=name,
comment=comment,
log_config=None,
multiproc=False,
ncores=1,
filename=path + name + "/results/",
overwrite_file=True)
traj = env.trajectory
traj.f_add_parameter("path", path + name, "Path")
# parameters (data generation)
traj.f_add_parameter("data.N", np.int64(500), "Number of nodes")
traj.f_add_parameter("data.K", np.int64(5),
"True number of latent components")
traj.f_add_parameter("data.p_cts", np.int64(0),
"Number of continuous covariates")
traj.f_add_parameter("data.p_bin", np.int64(0),
"Number of binary covariates")
traj.f_add_parameter("data.var_cov", np.float64(1.),
"True variance in the covariate model (cts and bin)")
traj.f_add_parameter("data.missing_rate", np.float64(0.1), "Missing rate")
traj.f_add_parameter("data.seed", np.int64(1), "Random seed")
traj.f_add_parameter("data.center", np.int64(1), "Ego-network center")
traj.f_add_parameter("data.alpha_mean", np.float64(-1.85),
"Mean of the heterogeneity parameter")
# parameters (model)
traj.f_add_parameter("model.K", np.int64(5),
"Number of latent components in the model")
# parameters (fit)
traj.f_add_parameter("fit.algo", "MLE", "Inference algorithm")
traj.f_add_parameter("fit.max_iter", np.int64(500),
"Number of VEM iterations")
traj.f_add_parameter("fit.n_sample", np.int64(1),
"Number of samples for VIMC")
traj.f_add_parameter("fit.eps", np.float64(1.0e-6),
"convergence threshold")
traj.f_add_parameter("fit.lr", np.float64(0.01), "GD Step size")
# experiment
experiment = cartesian_product(explore_dict, tuple(explore_dict.keys()))
traj.f_explore(experiment)
env.add_postprocessing(post_processing)
env.run(run)
env.disable_logging()
def main():
# Create an environment that handles running
filename = os.path.join('hdf5', 'example_12.hdf5')
env = Environment(trajectory='Multiplication',
filename=filename,
file_title='Example_12_Sharing_Data',
overwrite_file=True,
comment='The first example!',
continuable=False, # We have shared data in terms of a multiprocessing list,
# so we CANNOT use the continue feature.
multiproc=True,
ncores=2)
# The environment has created a trajectory container for us
traj = env.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]}))
# We want a shared list where we can put all out results in. We use a manager for this:
result_list = mp.Manager().list()
# Let's make some space for potential results
result_list[:] =[0 for _dummy in range(len(traj))]
# Run the simulation
env.run(multiply, result_list)
# Now we want to store the final list as numpy array
traj.f_add_result('z', np.array(result_list))
# Finally let's print the result to see that it worked
print(traj.z)
#Disable logging and close all log-files
env.disable_logging()
def main(inputargs):
args = docopt(__doc__, argv=inputargs)
wavpath = path.join(modulePath, "resources", "tone_in_noise")
stimuli = [path.join(wavpath, i) for i in glob.glob(path.join(wavpath, "*.wav"))]
outfile = path.realpath(path.expanduser(args["--out"]))
env = Environment(trajectory='tone-in-noise',
filename=outfile,
overwrite_file=True,
file_title="Tone in noise at different SNR",
comment="some comment",
large_overview_tables="False",
# freeze_input=True,
# use_pool=True,
multiproc=True,
ncores=3,
graceful_exit=True,
#wrap_mode=pypetconstants.WRAP_MODE_QUEUE,
)
traj = env.trajectory
traj.f_add_parameter('periphery', 'verhulst', comment="which periphery was used")
traj.f_add_parameter('brainstem', 'nelsoncarney04', comment="which brainstem model was used")
traj.f_add_parameter('weighting', "--no-cf-weighting ", comment="weighted CFs")
traj.f_add_parameter('wavfile', '', comment="Which wav file to run")
traj.f_add_parameter('level', 80, comment="stimulus level, spl")
traj.f_add_parameter('neuropathy', "none", comment="")
parameter_dict = {
"periphery" : ['verhulst', 'zilany'],
"brainstem" : ['nelsoncarney04', 'carney2015'],
"weighting" : [cf_weighting, ""],
"wavfile" : stimuli,
"level" : [80],
"neuropathy": ["none", "moderate", "severe", "ls-moderate", "ls-severe"]
}
traj.f_explore(cartesian_product(parameter_dict))
env.run(tone_in_noise)
return 0
def main():
""" Main *boilerplate* function to start simulation """
# Now let's make use of logging
logger = logging.getLogger()
# Create folders for data and plots
folder = os.path.join(os.getcwd(), 'experiments', 'ca_patterns_pypet')
if not os.path.isdir(folder):
os.makedirs(folder)
filename = os.path.join(folder, 'all_patterns.hdf5')
# Create an environment
env = Environment(trajectory='cellular_automata',
multiproc=True,
ncores=4,
wrap_mode='QUEUE',
filename=filename,
overwrite_file=True)
# extract the trajectory
traj = env.traj
traj.par.ncells = Parameter('ncells', 400, 'Number of cells')
traj.par.steps = Parameter('steps', 250, 'Number of timesteps')
traj.par.rule_number = Parameter('rule_number', 30, 'The ca rule')
traj.par.initial_name = Parameter('initial_name', 'random',
'The type of initial state')
traj.par.seed = Parameter('seed', 100042, 'RNG Seed')
# Explore
exp_dict = {
'rule_number': [10, 30, 90, 110, 184],
'initial_name': ['single', 'random'],
}
# # You can uncomment the ``exp_dict`` below to see that changing the
# # exploration scheme is now really easy:
# exp_dict = {'rule_number' : [10, 30, 90, 110, 184],
# 'ncells' : [100, 200, 300],
# 'seed': [333444555, 123456]}
exp_dict = cartesian_product(exp_dict)
traj.f_explore(exp_dict)
# Run the simulation
logger.info('Starting Simulation')
env.run(wrap_automaton)
# Load all data
traj.f_load(load_data=2)
logger.info('Printing data')
for idx, run_name in enumerate(traj.f_iter_runs()):
# Plot all patterns
filename = os.path.join(folder, make_filename(traj))
plot_pattern(traj.crun.pattern, traj.rule_number, filename)
progressbar(idx, len(traj), logger=logger)
# Finally disable logging and close all log-files
env.disable_logging()
def main(fail=False):
try:
sumatra_project = '.'
if fail:
print('There better be not any diffs.')
# Create an environment that handles running
with Environment(trajectory='Example1_Quick_And_Not_So_Dirty',
filename=os.path.join(
'experiments',
'HDF5',
),
file_title='Example1_Quick_And_Not_So_Dirty',
comment='The first example!',
complib='blosc',
small_overview_tables=False,
git_repository='.',
git_message='Im a message!',
git_fail=fail,
sumatra_project=sumatra_project,
sumatra_reason='Testing!') as env:
# 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)
# Check that git information was added to the trajectory
assert 'config.git.hexsha' in traj
assert 'config.git.committed_date' in traj
assert 'config.git.message' in traj
assert 'config.git.name_rev' in traj
print("Python git test successful")
# traj.f_expand({'x':[3,3],'y':[42,43]})
#
# env.f_run(multiply)
except Exception as exc:
print(repr(exc))
sys.exit(1)
def get_runtime(length):
filename = os.path.join('tmp', 'hdf5', 'many_runs.hdf5')
with Environment(filename=filename,
log_levels=20,
report_progress=(0.0000002, 'progress', 50),
overwrite_file=True,
purge_duplicate_comments=False,
log_stdout=False,
summary_tables=False,
small_overview_tables=False) as env:
traj = env.v_traj
traj.par.f_apar('x', 0, 'parameter')
traj.f_explore({'x': range(length)})
max_run = 100
for idx in range(len(traj)):
if idx > max_run:
traj.f_get_run_information(idx, copy=False)['completed'] = 1
traj.f_store()
if not os.path.isdir('./tmp'):
os.mkdir('tmp')
graphviz = CustomOutput()
graphviz.output_file = './tmp/run_profile_storage_%d.png' % len(traj)
service_filter = GlobbingFilter(include=['*storageservice.*'])
config = Config(groups=True, verbose=True)
config.trace_filter = service_filter
print('RUN PROFILE')
with PyCallGraph(config=config, output=graphviz):
# start = time.time()
# env.f_run(job)
# end = time.time()
for irun in range(100):
traj._make_single_run(irun + len(traj) / 2)
# Measure start time
traj._set_start()
traj.f_ares('$set.$', 42, comment='A result')
traj._set_finish()
traj._store_final(store_data=2)
traj._finalize_run()
print('STARTING_to_PLOT')
print('DONE RUN PROFILE')
def test_hdf5_store_load_result(self):
traj_name = make_trajectory_name(self)
file_name = make_temp_dir(
os.path.join('brian2', 'tests', 'hdf5',
'test_%s.hdf5' % traj_name))
env = Environment(trajectory=traj_name,
filename=file_name,
log_config=get_log_config(),
dynamic_imports=[Brian2Result],
add_time=False,
storage_service=HDF5StorageService)
traj = env.v_trajectory
traj.v_standard_result = Brian2Result
traj.f_add_result('brian2.single.millivolts_single_a',
10 * mvolt,
comment='single value a')
traj.f_add_result('brian2.single.millivolts_single_c',
11 * mvolt,
comment='single value b')
traj.f_add_result('brian2.array.millivolts_array_a', [11, 12] * mvolt,
comment='array')
traj.f_add_result('mV1', 42.0 * mV)
# results can hold much more than a single data item:
traj.f_add_result('ampere1',
1 * mA,
44,
test=300 * mV,
test2=[1, 2, 3],
test3=np.array([1, 2, 3]) * mA,
comment='Result keeping track of many things')
traj.f_add_result('integer', 16)
traj.f_add_result('kHz05', 0.5 * kHz)
traj.f_add_result('nested_array',
np.array([[6., 7., 8.], [9., 10., 11.]]) * ms)
traj.f_add_result('b2a', np.array([1., 2.]) * mV)
traj.f_add_result('nounit',
Quantity(np.array([[6., 7., 8.], [9., 10., 11.]])))
traj.f_store()
traj2 = load_trajectory(filename=file_name,
name=traj_name,
dynamic_imports=[Brian2Result],
load_data=2)
self.compare_trajectories(traj, traj2)
def main(name,
explore_dict,
postprocess=False,
ncores=1,
testrun=False,
commit=None):
if not testrun:
if commit is None:
raise Exception("Non testrun needs a commit")
filename = os.path.join(os.getcwd(), 'data/', name + '.hdf5')
# if not the first run, tr2 will be merged later
label = 'tr1'
# if only post processing, can't use the same label
# (generates HDF5 error)
if postprocess:
label += '_postprocess-%.6d' % random.randint(0, 999999)
env = Environment(
trajectory=label,
add_time=False,
filename=filename,
continuable=False, # ??
lazy_debug=False, # ??
multiproc=True,
ncores=ncores,
use_pool=False, # likely not working w/ brian2
wrap_mode='QUEUE', # ??
overwrite_file=False)
tr = env.trajectory
add_params(tr)
if not testrun:
tr.f_add_parameter('mconfig.git.sha1', str(commit))
tr.f_add_parameter('mconfig.git.message', commit.message)
tr.f_explore(explore_dict)
def run_sim(tr):
try:
run_net(tr)
except TimeoutError:
print("Unable to plot, must run analysis manually")
post_process(tr)
if postprocess:
env.run(post_process)
else:
env.run(run_sim)
def main():
"""Main function to protect the *entry point* of the program.
If you want to use multiprocessing under Windows you need to wrap your
main code creating an environment into a function. Otherwise
the newly started child processes will re-execute the code and throw
errors (also see https://docs.python.org/2/library/multiprocessing.html#windows).
"""
# Create an environment that handles running.
# Let's enable multiprocessing with 2 workers.
filename = os.path.join('hdf5', 'example_04.hdf5')
env = Environment(
trajectory='Example_04_MP',
filename=filename,
file_title='Example_04_MP',
log_stdout=True,
comment='Multiprocessing example!',
multiproc=True,
ncores=4,
use_pool=True, # Our runs are inexpensive we can get rid of overhead
# by using a pool
freeze_input=True, # We can avoid some
# overhead by freezing the input to the pool
wrap_mode=pypetconstants.WRAP_MODE_QUEUE,
graceful_exit=True, # We want to exit in a data friendly way
# that safes all results after hitting CTRL+C, try it ;-)
overwrite_file=True)
# Get the trajectory from the environment
traj = env.trajectory
# Add both parameters
traj.f_add_parameter('x', 1.0, comment='I am the first dimension!')
traj.f_add_parameter('y', 1.0, comment='I am the second dimension!')
# Explore the parameters with a cartesian product, but we want to explore a bit more
traj.f_explore(
cartesian_product({
'x': [float(x) for x in range(20)],
'y': [float(y) for y in range(20)]
}))
# Run the simulation
env.run(multiply)
# Finally disable logging and close all log-files
env.disable_logging()
def main():
filename = os.path.join('tmp', 'hdf5', 'many_runs.hdf5')
with Environment(filename=filename,
log_levels=0,
report_progress=(2, 'progress', 50),
overwrite_file=True) as env:
traj = env.v_traj
traj.par.x = BrianParameter('', 0 * ms, 'parameter')
traj.f_explore({'x': [x * ms for x in range(1000)]})
traj.f_store()
# env.f_run(job)
dicts = [traj.f_get_run_information(x) for x in range(len(traj))]
runtimes = [
dic['finish_timestamp'] - dic['timestamp'] for dic in dicts
]
def setUp(self):
self.set_mode()
self.logfolder = make_temp_dir(os.path.join('experiments',
'tests',
'Log'))
random.seed()
self.trajname = make_trajectory_name(self)
self.filename = make_temp_dir(os.path.join('experiments',
'tests',
'HDF5',
'test%s.hdf5' % self.trajname))
env = Environment(trajectory=self.trajname, filename=self.filename,
file_title=self.trajname,
log_stdout=False,
port=self.url,
log_config=get_log_config(),
results_per_run=5,
derived_parameters_per_run=5,
multiproc=self.multiproc,
ncores=self.ncores,
wrap_mode=self.mode,
use_pool=self.use_pool,
fletcher32=self.fletcher32,
complevel=self.complevel,
complib=self.complib,
shuffle=self.shuffle,
pandas_append=self.pandas_append,
pandas_format=self.pandas_format,
encoding=self.encoding)
traj = env.v_trajectory
self.param_dict={}
create_param_dict(self.param_dict)
add_params(traj,self.param_dict)
self.traj = traj
self.env = env
def test_hdf5_store_load_parameter(self):
traj_name = make_trajectory_name(self)
file_name = make_temp_dir(
os.path.join('brian2', 'tests', 'hdf5',
'test_%s.hdf5' % traj_name))
env = Environment(trajectory=traj_name,
filename=file_name,
log_config=get_log_config(),
dynamic_imports=[Brian2Parameter],
add_time=False,
storage_service=HDF5StorageService)
traj = env.v_trajectory
traj.v_standard_parameter = Brian2Parameter
traj.f_add_parameter('brian2.single.millivolts',
10 * mvolt,
comment='single value')
#traj.f_add_parameter('brian2.array.millivolts', [11, 12]*mvolt, comment='array')
#traj.f_add_parameter('mV1', 42.0*mV)
#traj.f_add_parameter('ampere1', 1*mA)
#traj.f_add_parameter('integer', 16)
#traj.f_add_parameter('kHz05', 0.5*kHz)
#traj.f_add_parameter('nested_array', np.array([[6.,7.,8.],[9.,10.,11.]]) * ms)
#traj.f_add_parameter('b2a', np.array([1., 2.]) * mV)
# We also need to check if explorations work with hdf5 store!
#explore_dict = {'ampere1': [1*mA, 2*mA, 3*mA],
# 'integer': [42,43,44],
# 'b2a': [np.array([1., 2.]) * mV, np.array([1., 4.]) * mV,
# np.array([1., 2.]) * mV]}
#traj.f_explore(explore_dict)
traj.f_store()
traj2 = load_trajectory(filename=file_name,
name=traj_name,
dynamic_imports=[Brian2Parameter],
load_data=2)
self.compare_trajectories(traj, traj2)
def get_runtime(length):
filename = os.path.join('tmp', 'hdf5', 'many_runs.hdf5')
with Environment(
filename=filename,
log_levels=50,
report_progress=(0.0002, 'progress', 50),
overwrite_file=True,
purge_duplicate_comments=False,
log_stdout=False,
multiproc=False,
ncores=2,
use_pool=True,
wrap_mode='PIPE', #freeze_input=True,
summary_tables=False,
small_overview_tables=False) as env:
traj = env.v_traj
traj.par.f_apar('x', 0, 'parameter')
traj.f_explore({'x': range(length)})
# traj.v_full_copy = False
max_run = 1000
for idx in range(len(traj)):
if idx > max_run:
traj.f_get_run_information(idx, copy=False)['completed'] = 1
start = time.time()
env.f_run(job)
end = time.time()
# dicts = [traj.f_get_run_information(x) for x in range(min(len(traj), max_run))]
total = end - start
return total / float(min(len(traj), max_run)), total / float(
min(len(traj), max_run)) * len(traj)
def main():
filename = os.path.join('hdf5', 'FiringRate.hdf5')
env = Environment(
trajectory='FiringRatePipeline',
comment='Experiment to measure the firing rate '
'of a leaky integrate and fire neuron. '
'Exploring different input currents, '
'as well as refractory periods',
add_time=False, # We don't want to add the current time to the name,
log_stdout=True,
multiproc=True,
ncores=2, #My laptop has 2 cores ;-)
filename=filename,
overwrite_file=True)
env.pipeline(mypipeline)
# Finally disable logging and close all log-files
env.disable_logging()
def main():
"""Main function to protect the *entry point* of the program.
If you want to use multiprocessing with SCOOP you need to wrap your
main code creating an environment into a function. Otherwise
the newly started child processes will re-execute the code and throw
errors (also see http://scoop.readthedocs.org/en/latest/usage.html#pitfalls).
"""
# Create an environment that handles running.
# Let's enable multiprocessing with scoop:
filename = os.path.join('hdf5', 'example_21.hdf5')
env = Environment(trajectory='Example_21_SCOOP',
filename=filename,
file_title='Example_21_SCOOP',
log_stdout=True,
comment='Multiprocessing example using SCOOP!',
multiproc=True,
freeze_input=True, # We want to save overhead and freeze input
use_scoop=True, # Yes we want SCOOP!
wrap_mode=pypetconstants.WRAP_MODE_LOCAL, # SCOOP only works with 'LOCAL'
# or 'NETLOCK' wrapping
overwrite_file=True)
# Get the trajectory from the environment
traj = env.trajectory
# Add both parameters
traj.f_add_parameter('x', 1.0, comment='I am the first dimension!')
traj.f_add_parameter('y', 1.0, comment='I am the second dimension!')
# Explore the parameters with a cartesian product, but we want to explore a bit more
traj.f_explore(cartesian_product({'x':[float(x) for x in range(20)],
'y':[float(y) for y in range(20)]}))
# Run the simulation
env.run(multiply)
# Let's check that all runs are completed!
assert traj.f_is_completed()
# Finally disable logging and close all log-files
env.disable_logging()
def main():
# Create an environment that handles running
filename = os.path.join('hdf5', 'example_12.hdf5')
env = Environment(
trajectory='Multiplication',
filename=filename,
file_title='Example_12_Sharing_Data',
overwrite_file=True,
comment='The first example!',
continuable=
False, # We have shared data in terms of a multiprocessing list,
# so we CANNOT use the continue feature.
multiproc=True,
ncores=2)
# The environment has created a trajectory container for us
traj = env.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]}))
# We want a shared list where we can put all out results in. We use a manager for this:
result_list = mp.Manager().list()
# Let's make some space for potential results
result_list[:] = [0 for _dummy in range(len(traj))]
# Run the simulation
env.run(multiply, result_list)
# Now we want to store the final list as numpy array
traj.f_add_result('z', np.array(result_list))
# Finally let's print the result to see that it worked
print(traj.z)
#Disable logging and close all log-files
env.disable_logging()
class LoggingTest(TrajectoryComparator):
tags = 'integration', 'environment', 'logging'
def setUp(self):
root = logging.getLogger()
for logger in itools.chain(root.manager.loggerDict.values(), [root]):
if hasattr(logger, 'handlers'):
for handler in logger.handlers:
if hasattr(handler, 'flush'):
handler.flush()
if hasattr(handler, 'close'):
handler.close()
logger.handlers = []
if hasattr(logger, 'setLevel'):
logger.setLevel(logging.NOTSET)
self.set_mode()
def tearDown(self):
super(LoggingTest, self).tearDown()
def set_mode(self):
self.mode = Dummy()
self.mode.wrap_mode = 'LOCK'
self.mode.multiproc = False
self.mode.ncores = 1
self.mode.use_pool=True
self.mode.pandas_format='fixed'
self.mode.pandas_append=False
self.mode.complib = 'blosc'
self.mode.complevel=9
self.mode.shuffle=True
self.mode.fletcher32 = False
self.mode.encoding = 'utf8'
self.mode.log_stdout=False
self.mode.log_config=get_log_config()
def make_env(self, **kwargs):
self.mode.__dict__.update(kwargs)
filename = 'log_testing.hdf5'
self.filename = make_temp_dir(filename)
self.traj_name = make_trajectory_name(self)
self.env = Environment(trajectory=self.traj_name,
filename=self.filename, **self.mode.__dict__)
self.traj = self.env.v_traj
def add_params(self, traj):
traj.v_lazy_adding = True
traj.par.p1 = 42, 'Hey'
traj.f_apar('g1.p2', 145, comment='Test')
def explore(self, traj):
traj.f_explore({'p1': range(7)})
@unittest.skipIf(platform.system() == 'Windows', 'Log file creation might fail under windows.')
def test_logfile_creation_normal(self):
# if not self.multiproc:
# return
self.make_env()
self.add_params(self.traj)
self.explore(self.traj)
self.env.f_run(log_wo_error_levels)
self.env.f_disable_logging()
traj = self.env.v_traj
log_path = get_log_path(traj)
if self.mode.multiproc:
if self.mode.use_pool:
length = self.mode.ncores * 2
else:
length = 2 * len(traj)
if self.mode.wrap_mode == 'LOCK':
length += 2
elif self.mode.wrap_mode == 'QUEUE':
length += 4
else:
raise RuntimeError('You shall not pass!')
else:
length = 2
file_list = [file for file in os.listdir(log_path)]
self.assertEqual(len(file_list), length) # assert that there are as many
# files as runs plus main.txt and errors and warnings
total_error_count = 0
total_store_count = 0
total_info_count = 0
total_retry_count = 0
for file in file_list:
with open(os.path.join(log_path, file), mode='r') as fh:
text = fh.read()
count = text.count('INFO_Test!')
total_info_count += count
error_count = text.count('ERROR_Test!')
total_error_count += error_count
store_count = text.count('STORE_Test!')
total_store_count += store_count
retry_count = text.count('Retry')
total_retry_count += retry_count
if 'LOG.txt' == file:
if self.mode.multiproc:
self.assertEqual(count,0)
self.assertEqual(store_count, 0)
else:
self.assertEqual(count, len(traj))
self.assertEqual(store_count, len(traj))
elif 'ERROR' in file:
full_path = os.path.join(log_path, file)
filesize = os.path.getsize(full_path)
with open(full_path) as fh:
text = fh.read()
if 'Retry' not in text:
self.assertEqual(filesize, 0)
elif 'Queue' in file:
self.assertEqual(store_count, len(traj))
elif 'LOG' in file:
if self.mode.multiproc and self.mode.use_pool:
self.assertGreaterEqual(count, 0, '%d < 1 for file %s' % (count, file))
else:
self.assertEqual(count, 1)
if self.mode.wrap_mode == 'QUEUE':
self.assertEqual(store_count, 0)
else:
self.assertEqual(store_count, 1)
else:
self.assertTrue(False, 'There`s a file in the log folder that does not '
'belong there: %s' % str(file))
self.assertEqual(total_store_count, len(traj))
self.assertEqual(total_error_count, 0)
self.assertEqual(total_info_count, len(traj))
self.assertLess(total_retry_count, len(traj))
def test_throw_error_when_specifying_config_and_old_method(self):
with self.assertRaises(ValueError):
self.make_env(log_config=None, logger_names='test')
def test_disable(self):
# if not self.multiproc:
# return
self.make_env(log_config=None)
traj = self.env.v_traj
log_path = get_log_path(traj)
self.assertFalse(os.path.isdir(log_path))
self.assertTrue(self.env._logging_manager._sp_config is None)
self.assertTrue(self.env._logging_manager._mp_config is None)
self.assertTrue(self.env._logging_manager.log_config is None)
self.add_params(self.traj)
self.explore(self.traj)
self.env.f_run(log_all_levels)
self.assertFalse(os.path.isdir(log_path))
self.assertTrue(self.env._logging_manager._sp_config is None)
self.assertTrue(self.env._logging_manager._mp_config is None)
self.assertTrue(self.env._logging_manager.log_config is None)
self.env.f_disable_logging()
# pypet_path = os.path.abspath(os.path.dirname(pypet.pypetlogging))
# init_path = os.path.join(pypet_path, 'logging')
# log_config = os.path.join(init_path, 'default.ini')
@unittest.skipIf(platform.system() == 'Windows', 'Log file creation might fail under windows.')
def test_logfile_creation_with_errors(self):
# if not self.multiproc:
# return
self.make_env()
self.add_params(self.traj)
self.explore(self.traj)
self.env.f_run(log_all_levels)
if self.mode.multiproc:
logging.getLogger('pypet.test').error('ttt')
self.env.f_disable_logging()
traj = self.env.v_traj
log_path = get_log_path(traj)
if self.mode.multiproc:
if self.mode.use_pool:
length = self.mode.ncores * 2
else:
length = 2 * len(traj)
if self.mode.wrap_mode == 'LOCK':
length += 2
elif self.mode.wrap_mode == 'QUEUE':
length += + 4
else:
raise RuntimeError('You shall not pass!')
else:
length = 2
file_list = [file for file in os.listdir(log_path)]
self.assertEqual(len(file_list), length) # assert that there are as many
# files as runs plus main.txt and errors and warnings
total_error_count = 0
total_store_count = 0
total_info_count = 0
total_retry_count = 0
for file in file_list:
with open(os.path.join(log_path, file), mode='r') as fh:
text = fh.read()
count = text.count('INFO_Test!')
total_info_count += count
error_count = text.count('ERROR_Test!')
total_error_count += error_count
store_count = text.count('STORE_Test!')
total_store_count += store_count
retry_count = text.count('Retry')
total_retry_count += retry_count
if 'LOG.txt' == file:
if self.mode.multiproc:
self.assertEqual(count,0)
self.assertEqual(store_count, 0)
else:
self.assertEqual(count, len(traj))
self.assertEqual(store_count, len(traj))
elif 'ERROR.txt' == file:
self.assertEqual(count, 0)
if self.mode.multiproc:
self.assertEqual(error_count,0)
self.assertEqual(store_count, 0)
else:
self.assertEqual(error_count, len(traj))
self.assertEqual(store_count, len(traj))
elif 'Queue' in file and 'ERROR' in file:
self.assertEqual(store_count, len(traj))
elif 'Queue' in file and 'LOG' in file:
self.assertEqual(store_count, len(traj))
elif 'LOG' in file:
if self.mode.multiproc and self.mode.use_pool:
self.assertGreaterEqual(count, 0)
self.assertGreaterEqual(error_count, 0)
else:
self.assertEqual(count, 1)
self.assertEqual(error_count, 1)
if self.mode.wrap_mode == 'QUEUE':
self.assertEqual(store_count, 0)
else:
self.assertEqual(store_count, 1)
elif 'ERROR' in file:
if self.mode.multiproc and self.mode.use_pool:
self.assertEqual(count, 0)
self.assertGreaterEqual(error_count, 1)
else:
self.assertEqual(count, 0)
self.assertEqual(error_count, 1)
if self.mode.wrap_mode == 'QUEUE':
self.assertEqual(store_count, 0)
else:
self.assertEqual(store_count, 1)
else:
self.assertTrue(False, 'There`s a file in the log folder that does not '
'belong there: %s' % str(file))
self.assertEqual(total_store_count, 2*len(traj))
self.assertEqual(total_error_count, 2*len(traj))
self.assertEqual(total_info_count, len(traj))
self.assertLess(total_retry_count, len(traj))
def test_file_renaming(self):
traj_name = 'test'
traj = Trajectory('test', add_time=False)
traj.f_add_parameter('x', 42)
traj.f_explore({'x': [1,2,3]})
rename_string = '$traj_$set_$run'
solution_1 = 'test_run_set_ALL_run_ALL'
solution_2 = 'test_run_set_00000_run_00000000'
renaming_1 = rename_log_file(rename_string, traj)
self.assertEqual(renaming_1, solution_1)
traj.v_idx = 0
renaming_2 = rename_log_file(rename_string, traj)
self.assertEqual(renaming_2, solution_2)
@unittest.skipIf(platform.system() == 'Windows', 'Log file creation might fail under windows.')
def test_logfile_old_way_creation_with_errors(self):
# if not self.multiproc:
# return
del self.mode.__dict__['log_config']
self.make_env(logger_names = ('','pypet'), log_level=logging.ERROR,
log_folder=make_temp_dir('logs'))
self.add_params(self.traj)
self.explore(self.traj)
self.env.f_run(log_all_levels)
if self.mode.multiproc:
logging.getLogger('pypet.test').error('ttt')
self.env.f_disable_logging()
traj = self.env.v_traj
log_path = get_log_path(traj)
if self.mode.multiproc:
if self.mode.use_pool:
length = self.mode.ncores * 2
else:
length = 2 * len(traj)
if self.mode.wrap_mode == 'LOCK':
length += 2
elif self.mode.wrap_mode == 'QUEUE':
length += 4
else:
raise RuntimeError('You shall not pass!')
else:
length = 2
file_list = [file for file in os.listdir(log_path)]
self.assertEqual(len(file_list), length) # assert that there are as many
# files as runs plus main.txt and errors and warnings
total_error_count = 0
total_store_count = 0
total_info_count = 0
total_retry_count = 0
for file in file_list:
with open(os.path.join(log_path, file), mode='r') as fh:
text = fh.read()
count = text.count('INFO_Test!')
total_info_count += count
error_count = text.count('ERROR_Test!')
total_error_count += error_count
store_count = text.count('STORE_Test!')
total_store_count += store_count
retry_count = text.count('Retry')
total_retry_count += retry_count
if 'LOG.txt' == file:
if self.mode.multiproc:
self.assertEqual(count,0)
self.assertEqual(store_count, 0)
else:
self.assertEqual(count, 0)
self.assertGreaterEqual(store_count, len(traj))
elif 'ERROR.txt' == file:
self.assertEqual(count, 0)
if self.mode.multiproc:
self.assertEqual(error_count,0)
self.assertEqual(store_count, 0)
else:
self.assertGreaterEqual(error_count, len(traj))
self.assertGreaterEqual(store_count, len(traj))
elif 'Queue' in file and 'ERROR' in file:
self.assertGreaterEqual(store_count, len(traj))
elif 'Queue' in file and 'LOG' in file:
self.assertGreaterEqual(store_count, len(traj))
elif 'LOG' in file:
if self.mode.multiproc and self.mode.use_pool:
self.assertEqual(count, 0)
self.assertGreaterEqual(error_count, 0)
else:
self.assertEqual(count, 0)
self.assertGreaterEqual(error_count, 1)
if self.mode.wrap_mode == 'QUEUE':
self.assertEqual(store_count, 0)
else:
self.assertGreaterEqual(store_count, 1)
elif 'ERROR' in file:
if self.mode.multiproc and self.mode.use_pool:
self.assertEqual(count, 0)
self.assertGreaterEqual(error_count, 0)
else:
self.assertEqual(count, 0)
self.assertGreaterEqual(error_count, 1)
if self.mode.wrap_mode == 'QUEUE':
self.assertEqual(store_count, 0)
else:
self.assertGreaterEqual(store_count, 1)
else:
self.assertTrue(False, 'There`s a file in the log folder that does not '
'belong there: %s' % str(file))
self.assertGreaterEqual(total_store_count, 2*len(traj))
self.assertGreaterEqual(total_error_count, 2*len(traj))
self.assertEqual(total_info_count, 0)
self.assertLess(total_retry_count, len(traj))
@unittest.skipIf(platform.system() == 'Windows', 'Log file creation might fail under windows.')
def test_logfile_old_way_disabling_mp_log(self):
# if not self.multiproc:
# return
del self.mode.__dict__['log_config']
self.make_env(logger_names = ('','pypet'), log_level=logging.ERROR,
log_folder=make_temp_dir('logs'), log_multiproc=False)
self.add_params(self.traj)
self.explore(self.traj)
self.env.f_run(log_all_levels)
if self.mode.multiproc:
logging.getLogger('pypet.test').error('ttt')
self.env.f_disable_logging()
traj = self.env.v_traj
log_path = get_log_path(traj)
# if self.mode.multiproc:
length = 2
file_list = [file for file in os.listdir(log_path)]
self.assertEqual(len(file_list), length) # assert that there are as many
# files as runs plus main.txt and errors and warnings
# total_error_count = 0
# total_store_count = 0
# total_info_count = 0
# total_retry_count = 0
#
# for file in file_list:
# with open(os.path.join(log_path, file), mode='r') as fh:
# text = fh.read()
# count = text.count('INFO_Test!')
# total_info_count += count
# error_count = text.count('ERROR_Test!')
# total_error_count += error_count
# store_count = text.count('STORE_Test!')
# total_store_count += store_count
# retry_count = text.count('Retry')
# total_retry_count += retry_count
# if 'LOG.txt' == file:
# if self.mode.multiproc:
# self.assertEqual(count,0)
# self.assertEqual(store_count, 0)
# else:
# self.assertEqual(count, 0)
# self.assertGreaterEqual(store_count, len(traj))
# elif 'ERROR.txt' == file:
# self.assertEqual(count, 0)
# if self.mode.multiproc:
# self.assertEqual(error_count,0)
# self.assertEqual(store_count, 0)
# else:
# self.assertGreaterEqual(error_count, len(traj))
# self.assertGreaterEqual(store_count, len(traj))
#
# elif 'Queue' in file and 'ERROR' in file:
# self.assertGreaterEqual(store_count, len(traj))
# elif 'Queue' in file and 'LOG' in file:
# self.assertGreaterEqual(store_count, len(traj))
# elif 'LOG' in file:
# if self.mode.multiproc and self.mode.use_pool:
# self.assertEqual(count, 0)
# self.assertGreaterEqual(error_count, 0)
# else:
# self.assertEqual(count, 0)
# self.assertGreaterEqual(error_count, 1)
# if self.mode.wrap_mode == 'QUEUE':
# self.assertEqual(store_count, 0)
# else:
# self.assertGreaterEqual(store_count, 1)
# elif 'ERROR' in file:
# if self.mode.multiproc and self.mode.use_pool:
# self.assertEqual(count, 0)
# self.assertGreaterEqual(error_count, 0)
# else:
# self.assertEqual(count, 0)
# self.assertGreaterEqual(error_count, 1)
# if self.mode.wrap_mode == 'QUEUE':
# self.assertEqual(store_count, 0)
# else:
# self.assertGreaterEqual(store_count, 1)
# else:
# self.assertTrue(False, 'There`s a file in the log folder that does not '
# 'belong there: %s' % str(file))
# self.assertGreaterEqual(total_store_count, 2*len(traj))
# self.assertGreaterEqual(total_error_count, 2*len(traj))
# self.assertEqual(total_info_count, 0)
# self.assertLess(total_retry_count, len(traj))
@unittest.skipIf(platform.system() == 'Windows', 'Log file creation might fail under windows.')
def test_logging_stdout(self):
filename = 'teststdoutlog.hdf5'
filename = make_temp_dir(filename)
folder = make_temp_dir('logs')
env = Environment(trajectory=make_trajectory_name(self),
filename=filename, log_config=get_log_config(),
# log_levels=logging.CRITICAL, # needed for the test
log_stdout=('STDOUT', 50), #log_folder=folder
)
env.f_run(log_error)
traj = env.v_traj
path = get_log_path(traj)
mainstr = 'sTdOuTLoGGinG'
print(mainstr)
env.f_disable_logging()
mainfilename = os.path.join(path, 'LOG.txt')
with open(mainfilename, mode='r') as mainf:
full_text = mainf.read()
self.assertTrue(mainstr in full_text)
self.assertTrue('4444444' not in full_text)
self.assertTrue('DEBUG' not in full_text)
def test_logging_show_progress(self):
self.make_env(log_config=get_log_config(),
# log_folder=make_temp_dir('logs'),
# log_levels=logging.ERROR,
report_progress=(3, 'progress', 40))
self.add_params(self.traj)
self.explore(self.traj)
self.env.f_run(log_all_levels)
self.env.f_disable_logging()
traj = self.env.v_traj
path = get_log_path(traj)
mainfilename = os.path.join(path, 'LOG.txt')
with open(mainfilename, mode='r') as mainf:
full_text = mainf.read()
progress = 'PROGRESS: Finished'
self.assertTrue(progress in full_text)
bar = '[=='
self.assertTrue(bar in full_text)
def test_logging_show_progress_print(self):
self.make_env(log_config=get_log_config(), log_stdout=('prostdout', 50),
report_progress=(3, 'print'))
self.add_params(self.traj)
self.explore(self.traj)
self.env.f_run(log_all_levels)
self.env.f_disable_logging()
path = get_log_path(self.env.v_traj)
mainfilename = os.path.join(path, 'LOG.txt')
with open(mainfilename, mode='r') as mainf:
full_text = mainf.read()
progress = 'prostdout CRITICAL PROGRESS: Finished'
self.assertTrue(progress in full_text)
bar = '[=='
self.assertIn(bar, full_text)
from pypet import Environment, cartesian_product
# Let's reuse the simple multiplication example
def multiply(traj):
"""Sophisticated simulation of multiplication"""
z=traj.x*traj.y
traj.f_add_result('z',z=z, comment='I am the product of two reals!',)
# Create 2 environments that handle running
filename = os.path.join('hdf5', 'example_03.hdf5')
env1 = Environment(trajectory='Traj1',
filename=filename,
file_title='Example_03',
add_time=True, # Add the time of trajectory creation to its name
comment='I will be increased!')
env2 = Environment(trajectory='Traj2',
filename=filename,
file_title='Example_03', log_config=None, # One environment keeping log files
# is enough
add_time=True,
comment = 'I am going to be merged into some other trajectory!')
# Get the trajectories from the environment
traj1 = env1.trajectory
traj2 = env2.trajectory
# Add both parameters
def main():
filename = os.path.join('hdf5', 'example_06.hdf5')
env = Environment(trajectory='Example_06_Euler_Integration',
filename=filename,
file_title='Example_06_Euler_Integration',
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_load_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()
# Finally disable logging and close all log-files
env.f_disable_logging()
# Let's reuse the simple multiplication example
def multiply(traj):
"""Sophisticated simulation of multiplication"""
z=traj.x*traj.y
traj.f_add_result('z',z=z, comment='I am the product of two reals!')
# Create an environment that handles running.
# Let's enable multiprocessing with 2 workers.
env = Environment(trajectory='Example_04_MP',
filename='experiments/example_04/HDF5/example_04.hdf5',
file_title='Example_04_MP',
log_folder='experiments/example_04/LOGS/',
comment = 'Multiprocessing example!',
multiproc=True,
ncores=2,
use_pool=True,
wrap_mode=pypetconstants.WRAP_MODE_LOCK)
# Get the trajectory from the environment
traj = env.v_trajectory
# Add both parameters
traj.f_add_parameter('x', 1.0, comment='I am the first dimension!')
traj.f_add_parameter('y', 1.0, comment='I am the second dimension!')
# Explore the parameters with a cartesian product, but we want to explore a bit more
traj.f_explore(cartesian_product({'x':[float(x) for x in range(15)],
'y':[float(y) for y in range(15)]}))
import os # For path names working under Windows ans Linux
from pypet import Environment, cartesian_product
from pypet import pypetconstants
def multiply(traj):
"""Sophisticated simulation of multiplication"""
z=traj.x * traj.y
traj.f_add_result('z', z, comment='I am the product of two reals!')
# Create an environment that handles running
filename = os.path.join('hdf5', 'example_10.hdf5')
env = Environment(trajectory='Example10', filename=filename,
file_title='Example10',
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:
x_length = 12
y_length = 12
traj.f_explore(cartesian_product({'x': range(x_length), 'y': range(y_length)}))
# Run the simulation
the parameters in a particular combination,
it also serves as a container for results.
"""
z=traj.x*traj.y
result_list[traj.v_idx] = z
# Create an environment that handles running
env = Environment(trajectory='Multiplication',
filename='experiments/example_01/HDF5/example_01.hdf5',
file_title='Example_01_First_Steps',
log_folder='experiments/example_01/LOGS/',
comment='The first example!',
continuable=False, # We have shared data in terms of a multiprocessing list,
# so we CANNOT use the continue feature.
multiproc=True,
ncores=2)
# The environment has created a trajectory container for us
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]}))
def main():
filename = os.path.join('hdf5', 'example_05.hdf5')
env = Environment(trajectory='Example_05_Euler_Integration',
filename=filename,
file_title='Example_05_Euler_Integration',
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=filename)
# 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 ' % str(e))
# Ok, let's try again but this time with adding our parameter to the imports
traj = Trajectory(filename=filename,
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()
# You have to click through the images to stop the example_05 module!
# Finally disable logging and close all log-files
env.f_disable_logging()
Trajectory containing
the parameters in a particular combination,
it also serves as a container for results.
"""
z=traj.mylink1*traj.mylink2 # And again we now can also use the different names
# due to the creation of links
traj.res = Result('runs.$.z', z, 'Result of our simulation!')
# Create an environment that handles running
filename = os.path.join('hdf5','example_14.hdf5')
env = Environment(trajectory='Multiplication',
filename=filename,
file_title='Example_14_Links',
overwrite_file=True,
comment='How to use links')
# The environment has created a trajectory container for us
traj = env.trajectory
# Add both parameters
traj.v_lazy_adding = True
traj.par.x = 1, 'I am the first dimension!'
traj.par.y = 1, 'I am the second dimension!'
# Explore just two points
traj.f_explore({'x': [3, 4]})
# So far everything was as in the first example. However now we add links:
traj.f_add_parameter('location', 'mars', comment='location determining g-force')
def run_simulation(traj):
sim = FanSimulator(traj.N, vent_radius=traj.vent_radius, vmax=traj.vmax,
dt=traj.dt, location=traj.location)
sim.init_positions()
sim.init_velocities()
sim.incline_and_vary_jet(incline=traj.incline, jitter=traj.jitter)
sim.loop()
sim.plot(save=True, equal=False)
traj.f_add_result('positions', sim.positions, comment='End positions of particles')
traj.f_add_result('t', sim.t, comment='duration of flight')
env = Environment(trajectory='FanSimulation', filename='./pypet/',
large_overview_tables=True,
add_time=True,
multiproc=False,
ncores=6,
log_config='DEFAULT')
traj = env.v_trajectory
add_parameters(traj, dt=1e-2)
explore_dict = {'vent_radius':[0.1, 0.5, 1.0],
'vmax':[10, 50, 100],
'incline':[0.1, 1.0, 5.0]}
to_explore = cartesian_product(explore_dict)
traj.f_explore(to_explore)
env.f_run(run_simulation)
