class Simulation(object):
converged = False
def __init__(self, tardis_config):
self.tardis_config = tardis_config
self.runner = MontecarloRunner(self.tardis_config.montecarlo.seed,
tardis_config.spectrum.frequency,
tardis_config.supernova.get('distance',
None))
t_inner_lock_cycle = [False] * (tardis_config.montecarlo.
convergence_strategy.
lock_t_inner_cycles)
t_inner_lock_cycle[0] = True
self.t_inner_update = itertools.cycle(t_inner_lock_cycle)
def run_single_montecarlo(self, model, no_of_packets,
no_of_virtual_packets=0,last_run=False):
"""
Will do a single TARDIS iteration with the given model
Parameters
----------
model: ~tardis.model.Radial1DModel
no_of_packet: ~int
no_of_virtual_packets: ~int
default is 0 and switches of the virtual packet mode. Recommended
is 3.
Returns
-------
: None
"""
self.runner.run(model, no_of_packets,
no_of_virtual_packets=no_of_virtual_packets,
nthreads=self.tardis_config.montecarlo.nthreads,last_run=last_run)
(montecarlo_nu, montecarlo_energies, self.j_estimators,
self.nubar_estimators, last_line_interaction_in_id,
last_line_interaction_out_id, self.last_interaction_type,
self.last_line_interaction_shell_id) = self.runner.legacy_return()
if np.sum(montecarlo_energies < 0) == len(montecarlo_energies):
logger.critical("No r-packet escaped through the outer boundary.")
def calculate_emitted_luminosity(self):
"""
Returns
-------
"""
return self.runner.calculate_emitted_luminosity(
self.tardis_config.supernova.luminosity_nu_start,
self.tardis_config.supernova.luminosity_nu_end)
def calculate_reabsorbed_luminosity(self):
return self.runner.calculate_reabsorbed_luminosity(
self.tardis_config.supernova.luminosity_nu_start,
self.tardis_config.supernova.luminosity_nu_end)
def estimate_t_inner(self, input_t_inner, luminosity_requested,
t_inner_update_exponent=-0.5):
emitted_luminosity = self.calculate_emitted_luminosity()
luminosity_ratios = (
(emitted_luminosity / luminosity_requested).to(1).value)
return input_t_inner * luminosity_ratios ** t_inner_update_exponent
def get_convergence_status(self, t_rad, w, t_inner, estimated_t_rad, estimated_w,
estimated_t_inner):
convergence_section = self.tardis_config.montecarlo.convergence_strategy
no_of_shells = self.tardis_config.structure.no_of_shells
convergence_t_rad = (abs(t_rad - estimated_t_rad) /
estimated_t_rad).value
convergence_w = (abs(w - estimated_w) / estimated_w)
convergence_t_inner = (abs(t_inner - estimated_t_inner) /
estimated_t_inner).value
if convergence_section.type == 'specific':
fraction_t_rad_converged = (
np.count_nonzero(
convergence_t_rad < convergence_section.t_rad.threshold)
/ no_of_shells)
t_rad_converged = (
fraction_t_rad_converged > convergence_section.t_rad.threshold)
fraction_w_converged = (
np.count_nonzero(
convergence_w < convergence_section.w.threshold)
/ no_of_shells)
w_converged = (
fraction_w_converged > convergence_section.w.threshold)
t_inner_converged = (
convergence_t_inner < convergence_section.t_inner.threshold)
if np.all([t_rad_converged, w_converged, t_inner_converged]):
return True
else:
return False
else:
return False
def log_run_results(self, emitted_luminosity, absorbed_luminosity):
logger.info("Luminosity emitted = {0:.5e} "
"Luminosity absorbed = {1:.5e} "
"Luminosity requested = {2:.5e}".format(
emitted_luminosity, absorbed_luminosity,
self.tardis_config.supernova.luminosity_requested))
def log_plasma_state(self, t_rad, w, t_inner, next_t_rad, next_w,
next_t_inner, log_sampling=5):
"""
Logging the change of the plasma state
Parameters
----------
t_rad: ~astropy.units.Quanity
current t_rad
w: ~astropy.units.Quanity
current w
next_t_rad: ~astropy.units.Quanity
next t_rad
next_w: ~astropy.units.Quanity
next_w
log_sampling: ~int
the n-th shells to be plotted
Returns
-------
"""
plasma_state_log = pd.DataFrame(index=np.arange(len(t_rad)),
columns=['t_rad', 'next_t_rad',
'w', 'next_w'])
plasma_state_log['t_rad'] = t_rad
plasma_state_log['next_t_rad'] = next_t_rad
plasma_state_log['w'] = w
plasma_state_log['next_w'] = next_w
plasma_state_log.index.name = 'Shell'
plasma_state_log = str(plasma_state_log[::log_sampling])
plasma_state_log = ''.join(['\t%s\n' % item for item in
plasma_state_log.split('\n')])
logger.info('Plasma stratification:\n%s\n', plasma_state_log)
logger.info('t_inner {0:.3f} -- next t_inner {1:.3f}'.format(
t_inner, next_t_inner))
@staticmethod
def damped_converge(value, estimated_value, damping_factor):
return value + damping_factor * (estimated_value - value)
def calculate_next_plasma_state(self, t_rad, w, t_inner,
estimated_w, estimated_t_rad,
estimated_t_inner):
convergence_strategy = (
self.tardis_config.montecarlo.convergence_strategy)
if (convergence_strategy.type == 'damped'
or convergence_strategy.type == 'specific'):
next_t_rad = self.damped_converge(
t_rad, estimated_t_rad,
convergence_strategy.t_rad.damping_constant)
next_w = self.damped_converge(
w, estimated_w, convergence_strategy.w.damping_constant)
next_t_inner = self.damped_converge(
t_inner, estimated_t_inner,
convergence_strategy.t_inner.damping_constant)
return next_t_rad, next_w, next_t_inner
else:
raise ValueError('Convergence strategy type is '
'neither damped nor specific '
'- input is {0}'.format(convergence_strategy.type))
def legacy_run_simulation(self, model, hdf_path_or_buf=None,
hdf_mode='full', hdf_last_only=True):
"""
Parameters
----------
model : tardis.model.Radial1DModel
hdf_path_or_buf : str, optional
A path to store the data of each simulation iteration
(the default value is None, which means that nothing
will be stored).
hdf_mode : {'full', 'input'}, optional
If 'full' all plasma properties will be stored to HDF,
if 'input' only input plasma properties will be stored.
hdf_last_only: bool, optional
If True, only the last iteration of the simulation will
be stored to the HDFStore.
Returns
-------
"""
if hdf_path_or_buf is not None:
if hdf_mode == 'full':
plasma_properties = None
elif hdf_mode == 'input':
plasma_properties = [Input]
else:
raise ValueError('hdf_mode must be "full" or "input"'
', not "{}"'.format(type(hdf_mode)))
start_time = time.time()
self.iterations_remaining = self.tardis_config.montecarlo.iterations
self.iterations_max_requested = self.tardis_config.montecarlo.iterations
self.iterations_executed = 0
converged = False
convergence_section = (
self.tardis_config.montecarlo.convergence_strategy)
while self.iterations_remaining > 1:
logger.info('Remaining run %d', self.iterations_remaining)
self.run_single_montecarlo(
model, self.tardis_config.montecarlo.no_of_packets)
self.log_run_results(self.calculate_emitted_luminosity(),
self.calculate_reabsorbed_luminosity())
self.iterations_executed += 1
self.iterations_remaining -= 1
estimated_t_rad, estimated_w = (
self.runner.calculate_radiationfield_properties())
estimated_t_inner = self.estimate_t_inner(
model.t_inner,
self.tardis_config.supernova.luminosity_requested)
converged = self.get_convergence_status(
model.t_rads, model.ws, model.t_inner, estimated_t_rad,
estimated_w, estimated_t_inner)
next_t_rad, next_w, next_t_inner = self.calculate_next_plasma_state(
model.t_rads, model.ws, model.t_inner,
estimated_w, estimated_t_rad, estimated_t_inner)
self.log_plasma_state(model.t_rads, model.ws, model.t_inner,
next_t_rad, next_w, next_t_inner)
model.t_rads = next_t_rad
model.ws = next_w
model.t_inner = next_t_inner
model.j_blue_estimators = self.runner.j_blue_estimator
model.calculate_j_blues(init_detailed_j_blues=False)
model.update_plasmas(initialize_nlte=False)
if hdf_path_or_buf is not None and not hdf_last_only:
self.to_hdf(model, hdf_path_or_buf,
'simulation{}'.format(self.iterations_executed),
plasma_properties)
# if switching into the hold iterations mode or out back to the normal one
# if it is in either of these modes already it will just stay there
if converged and not self.converged:
self.converged = True
# UMN - used to be 'hold_iterations_wrong' but this is
# currently not in the convergence_section namespace...
self.iterations_remaining = (
convergence_section["hold_iterations"])
elif not converged and self.converged:
# UMN Warning: the following two iterations attributes of the Simulation object don't exist
self.iterations_remaining = self.iterations_max_requested - self.iterations_executed
self.converged = False
else:
# either it is converged and the status of the simulation is
# converged OR it is not converged and the status of the
# simulation is not converged - Do nothing.
pass
if converged:
self.iterations_remaining = (
convergence_section["hold_iterations"])
#Finished second to last loop running one more time
logger.info('Doing last run')
if self.tardis_config.montecarlo.last_no_of_packets is not None:
no_of_packets = self.tardis_config.montecarlo.last_no_of_packets
else:
no_of_packets = self.tardis_config.montecarlo.no_of_packets
no_of_virtual_packets = (
self.tardis_config.montecarlo.no_of_virtual_packets)
self.run_single_montecarlo(model, no_of_packets, no_of_virtual_packets, last_run=True)
self.runner.legacy_update_spectrum(no_of_virtual_packets)
self.legacy_set_final_model_properties(model)
model.Edotlu_estimators = self.runner.Edotlu_estimator
#the following instructions, passing down information to the model are
#required for the gui
model.no_of_packets = no_of_packets
model.no_of_virtual_packets = no_of_virtual_packets
model.converged = converged
model.iterations_executed = self.iterations_executed
model.iterations_max_requested = self.iterations_max_requested
logger.info("Finished in {0:d} iterations and took {1:.2f} s".format(
self.iterations_executed, time.time()-start_time))
if hdf_path_or_buf is not None:
if hdf_last_only:
name = 'simulation'
else:
name = 'simulation{}'.format(self.iterations_executed)
self.to_hdf(model, hdf_path_or_buf, name, plasma_properties)
def legacy_set_final_model_properties(self, model):
"""Sets additional model properties to be compatible with old model design
The runner object is given to the model and other packet diagnostics are set.
Parameters
----------
model: ~tardis.model.Radial1DModel
Returns
-------
: None
"""
#pass the runner to the model
model.runner = self.runner
#TODO: pass packet diagnostic arrays
(montecarlo_nu, montecarlo_energies, model.j_estimators,
model.nubar_estimators, last_line_interaction_in_id,
last_line_interaction_out_id, model.last_interaction_type,
model.last_line_interaction_shell_id) = model.runner.legacy_return()
model.montecarlo_nu = self.runner.output_nu
model.montecarlo_luminosity = self.runner.packet_luminosity
model.last_line_interaction_in_id = model.atom_data.lines_index.index.values[last_line_interaction_in_id]
model.last_line_interaction_in_id = model.last_line_interaction_in_id[last_line_interaction_in_id != -1]
model.last_line_interaction_out_id = model.atom_data.lines_index.index.values[last_line_interaction_out_id]
model.last_line_interaction_out_id = model.last_line_interaction_out_id[last_line_interaction_out_id != -1]
model.last_line_interaction_angstrom = model.montecarlo_nu[last_line_interaction_in_id != -1].to('angstrom',
u.spectral())
# required for gui
model.current_no_of_packets = model.tardis_config.montecarlo.no_of_packets
def to_hdf(self, model, path_or_buf, path='', plasma_properties=None):
"""
Store the simulation to an HDF structure.
Parameters
----------
model : tardis.model.Radial1DModel
path_or_buf
Path or buffer to the HDF store
path : str
Path inside the HDF store to store the simulation
plasma_properties
`None` or a `PlasmaPropertyCollection` which will
be passed as the collection argument to the
plasma.to_hdf method.
Returns
-------
None
"""
self.runner.to_hdf(path_or_buf, path)
model.to_hdf(path_or_buf, path, plasma_properties)
class Simulation(object):
converged = False
def __init__(self, tardis_config):
self.tardis_config = tardis_config
self.runner = MontecarloRunner(
self.tardis_config.montecarlo.seed,
tardis_config.spectrum.frequency,
tardis_config.supernova.get('distance', None))
t_inner_lock_cycle = [False] * (
tardis_config.montecarlo.convergence_strategy.lock_t_inner_cycles)
t_inner_lock_cycle[0] = True
self.t_inner_update = itertools.cycle(t_inner_lock_cycle)
def run_single_montecarlo(self,
model,
no_of_packets,
no_of_virtual_packets=0,
last_run=False):
"""
Will do a single TARDIS iteration with the given model
Parameters
----------
model: ~tardis.model.Radial1DModel
no_of_packet: ~int
no_of_virtual_packets: ~int
default is 0 and switches of the virtual packet mode. Recommended
is 3.
Returns
-------
: None
"""
self.runner.run(model,
no_of_packets,
no_of_virtual_packets=no_of_virtual_packets,
nthreads=self.tardis_config.montecarlo.nthreads,
last_run=last_run)
(montecarlo_nu, montecarlo_energies, self.j_estimators,
self.nubar_estimators, last_line_interaction_in_id,
last_line_interaction_out_id, self.last_interaction_type,
self.last_line_interaction_shell_id) = self.runner.legacy_return()
if np.sum(montecarlo_energies < 0) == len(montecarlo_energies):
logger.critical("No r-packet escaped through the outer boundary.")
def calculate_emitted_luminosity(self):
"""
Returns
-------
"""
return self.runner.calculate_emitted_luminosity(
self.tardis_config.supernova.luminosity_nu_start,
self.tardis_config.supernova.luminosity_nu_end)
def calculate_reabsorbed_luminosity(self):
return self.runner.calculate_reabsorbed_luminosity(
self.tardis_config.supernova.luminosity_nu_start,
self.tardis_config.supernova.luminosity_nu_end)
def estimate_t_inner(self,
input_t_inner,
luminosity_requested,
t_inner_update_exponent=-0.5):
emitted_luminosity = self.calculate_emitted_luminosity()
luminosity_ratios = ((emitted_luminosity /
luminosity_requested).to(1).value)
return input_t_inner * luminosity_ratios**t_inner_update_exponent
def get_convergence_status(self, t_rad, w, t_inner, estimated_t_rad,
estimated_w, estimated_t_inner):
convergence_section = self.tardis_config.montecarlo.convergence_strategy
no_of_shells = self.tardis_config.structure.no_of_shells
convergence_t_rad = (abs(t_rad - estimated_t_rad) /
estimated_t_rad).value
convergence_w = (abs(w - estimated_w) / estimated_w)
convergence_t_inner = (abs(t_inner - estimated_t_inner) /
estimated_t_inner).value
if convergence_section.type == 'specific':
fraction_t_rad_converged = (np.count_nonzero(
convergence_t_rad < convergence_section.t_rad.threshold) /
no_of_shells)
t_rad_converged = (fraction_t_rad_converged >
convergence_section.t_rad.threshold)
fraction_w_converged = (np.count_nonzero(
convergence_w < convergence_section.w.threshold) /
no_of_shells)
w_converged = (fraction_w_converged >
convergence_section.w.threshold)
t_inner_converged = (convergence_t_inner <
convergence_section.t_inner.threshold)
if np.all([t_rad_converged, w_converged, t_inner_converged]):
return True
else:
return False
else:
return False
def log_run_results(self, emitted_luminosity, absorbed_luminosity):
logger.info("Luminosity emitted = {0:.5e} "
"Luminosity absorbed = {1:.5e} "
"Luminosity requested = {2:.5e}".format(
emitted_luminosity, absorbed_luminosity,
self.tardis_config.supernova.luminosity_requested))
def log_plasma_state(self,
t_rad,
w,
t_inner,
next_t_rad,
next_w,
next_t_inner,
log_sampling=5):
"""
Logging the change of the plasma state
Parameters
----------
t_rad: ~astropy.units.Quanity
current t_rad
w: ~astropy.units.Quanity
current w
next_t_rad: ~astropy.units.Quanity
next t_rad
next_w: ~astropy.units.Quanity
next_w
log_sampling: ~int
the n-th shells to be plotted
Returns
-------
"""
plasma_state_log = pd.DataFrame(
index=np.arange(len(t_rad)),
columns=['t_rad', 'next_t_rad', 'w', 'next_w'])
plasma_state_log['t_rad'] = t_rad
plasma_state_log['next_t_rad'] = next_t_rad
plasma_state_log['w'] = w
plasma_state_log['next_w'] = next_w
plasma_state_log.index.name = 'Shell'
plasma_state_log = str(plasma_state_log[::log_sampling])
plasma_state_log = ''.join(
['\t%s\n' % item for item in plasma_state_log.split('\n')])
logger.info('Plasma stratification:\n%s\n', plasma_state_log)
logger.info('t_inner {0:.3f} -- next t_inner {1:.3f}'.format(
t_inner, next_t_inner))
@staticmethod
def damped_converge(value, estimated_value, damping_factor):
return value + damping_factor * (estimated_value - value)
def calculate_next_plasma_state(self, t_rad, w, t_inner, estimated_w,
estimated_t_rad, estimated_t_inner):
convergence_strategy = (
self.tardis_config.montecarlo.convergence_strategy)
if (convergence_strategy.type == 'damped'
or convergence_strategy.type == 'specific'):
next_t_rad = self.damped_converge(
t_rad, estimated_t_rad,
convergence_strategy.t_rad.damping_constant)
next_w = self.damped_converge(
w, estimated_w, convergence_strategy.w.damping_constant)
next_t_inner = self.damped_converge(
t_inner, estimated_t_inner,
convergence_strategy.t_inner.damping_constant)
return next_t_rad, next_w, next_t_inner
else:
raise ValueError('Convergence strategy type is '
'neither damped nor specific '
'- input is {0}'.format(
convergence_strategy.type))
def legacy_run_simulation(self,
model,
hdf_path_or_buf=None,
hdf_mode='full',
hdf_last_only=True):
"""
Parameters
----------
model : tardis.model.Radial1DModel
hdf_path_or_buf : str, optional
A path to store the data of each simulation iteration
(the default value is None, which means that nothing
will be stored).
hdf_mode : {'full', 'input'}, optional
If 'full' all plasma properties will be stored to HDF,
if 'input' only input plasma properties will be stored.
hdf_last_only: bool, optional
If True, only the last iteration of the simulation will
be stored to the HDFStore.
Returns
-------
"""
if hdf_path_or_buf is not None:
if hdf_mode == 'full':
plasma_properties = None
elif hdf_mode == 'input':
plasma_properties = [Input]
else:
raise ValueError('hdf_mode must be "full" or "input"'
', not "{}"'.format(type(hdf_mode)))
start_time = time.time()
self.iterations_remaining = self.tardis_config.montecarlo.iterations
self.iterations_max_requested = self.tardis_config.montecarlo.iterations
self.iterations_executed = 0
converged = False
convergence_section = (
self.tardis_config.montecarlo.convergence_strategy)
while self.iterations_remaining > 1:
logger.info('Remaining run %d', self.iterations_remaining)
self.run_single_montecarlo(
model, self.tardis_config.montecarlo.no_of_packets)
self.log_run_results(self.calculate_emitted_luminosity(),
self.calculate_reabsorbed_luminosity())
self.iterations_executed += 1
self.iterations_remaining -= 1
estimated_t_rad, estimated_w = (
self.runner.calculate_radiationfield_properties())
estimated_t_inner = self.estimate_t_inner(
model.t_inner,
self.tardis_config.supernova.luminosity_requested)
converged = self.get_convergence_status(model.t_rads, model.ws,
model.t_inner,
estimated_t_rad,
estimated_w,
estimated_t_inner)
next_t_rad, next_w, next_t_inner = self.calculate_next_plasma_state(
model.t_rads, model.ws, model.t_inner, estimated_w,
estimated_t_rad, estimated_t_inner)
self.log_plasma_state(model.t_rads, model.ws, model.t_inner,
next_t_rad, next_w, next_t_inner)
model.t_rads = next_t_rad
model.ws = next_w
model.t_inner = next_t_inner
model.j_blue_estimators = self.runner.j_blue_estimator
model.calculate_j_blues(init_detailed_j_blues=False)
model.update_plasmas(initialize_nlte=False)
if hdf_path_or_buf is not None and not hdf_last_only:
self.to_hdf(model, hdf_path_or_buf,
'simulation{}'.format(self.iterations_executed),
plasma_properties)
# if switching into the hold iterations mode or out back to the normal one
# if it is in either of these modes already it will just stay there
if converged and not self.converged:
self.converged = True
# UMN - used to be 'hold_iterations_wrong' but this is
# currently not in the convergence_section namespace...
self.iterations_remaining = (
convergence_section["hold_iterations"])
elif not converged and self.converged:
# UMN Warning: the following two iterations attributes of the Simulation object don't exist
self.iterations_remaining = self.iterations_max_requested - self.iterations_executed
self.converged = False
else:
# either it is converged and the status of the simulation is
# converged OR it is not converged and the status of the
# simulation is not converged - Do nothing.
pass
if converged:
self.iterations_remaining = (
convergence_section["hold_iterations"])
#Finished second to last loop running one more time
logger.info('Doing last run')
if self.tardis_config.montecarlo.last_no_of_packets is not None:
no_of_packets = self.tardis_config.montecarlo.last_no_of_packets
else:
no_of_packets = self.tardis_config.montecarlo.no_of_packets
no_of_virtual_packets = (
self.tardis_config.montecarlo.no_of_virtual_packets)
self.run_single_montecarlo(model,
no_of_packets,
no_of_virtual_packets,
last_run=True)
self.runner.legacy_update_spectrum(no_of_virtual_packets)
self.legacy_set_final_model_properties(model)
model.Edotlu_estimators = self.runner.Edotlu_estimator
#the following instructions, passing down information to the model are
#required for the gui
model.no_of_packets = no_of_packets
model.no_of_virtual_packets = no_of_virtual_packets
model.converged = converged
model.iterations_executed = self.iterations_executed
model.iterations_max_requested = self.iterations_max_requested
logger.info("Finished in {0:d} iterations and took {1:.2f} s".format(
self.iterations_executed,
time.time() - start_time))
if hdf_path_or_buf is not None:
if hdf_last_only:
name = 'simulation'
else:
name = 'simulation{}'.format(self.iterations_executed)
self.to_hdf(model, hdf_path_or_buf, name, plasma_properties)
def legacy_set_final_model_properties(self, model):
"""Sets additional model properties to be compatible with old model design
The runner object is given to the model and other packet diagnostics are set.
Parameters
----------
model: ~tardis.model.Radial1DModel
Returns
-------
: None
"""
#pass the runner to the model
model.runner = self.runner
#TODO: pass packet diagnostic arrays
(montecarlo_nu, montecarlo_energies, model.j_estimators,
model.nubar_estimators, last_line_interaction_in_id,
last_line_interaction_out_id, model.last_interaction_type,
model.last_line_interaction_shell_id) = model.runner.legacy_return()
model.montecarlo_nu = self.runner.output_nu
model.montecarlo_luminosity = self.runner.packet_luminosity
model.last_line_interaction_in_id = model.atom_data.lines_index.index.values[
last_line_interaction_in_id]
model.last_line_interaction_in_id = model.last_line_interaction_in_id[
last_line_interaction_in_id != -1]
model.last_line_interaction_out_id = model.atom_data.lines_index.index.values[
last_line_interaction_out_id]
model.last_line_interaction_out_id = model.last_line_interaction_out_id[
last_line_interaction_out_id != -1]
model.last_line_interaction_angstrom = model.montecarlo_nu[
last_line_interaction_in_id != -1].to('angstrom', u.spectral())
# required for gui
model.current_no_of_packets = model.tardis_config.montecarlo.no_of_packets
def to_hdf(self, model, path_or_buf, path='', plasma_properties=None):
"""
Store the simulation to an HDF structure.
Parameters
----------
model : tardis.model.Radial1DModel
path_or_buf
Path or buffer to the HDF store
path : str
Path inside the HDF store to store the simulation
plasma_properties
`None` or a `PlasmaPropertyCollection` which will
be passed as the collection argument to the
plasma.to_hdf method.
Returns
-------
None
"""
self.runner.to_hdf(path_or_buf, path)
model.to_hdf(path_or_buf, path, plasma_properties)