"""Serial Action."""
import finesse.config
from finesse.solutions import BaseSolution
from .base import Action, convert_str_to_parameter
import logging
from tqdm.auto import tqdm
LOGGER = logging.getLogger(__name__)
[docs]class Series(Action):
"""A sequential series of actions to apply during a simulation.
Parameters
----------
actions : :class:`Action`
A collection of Actions to run in series
flatten : bool
When True, each action will be stored in the top level of the solution
tree. When False, each action will be a child of the previous solution
generated.
name : str
Optional name for the solution generated by these actions
"""
def __init__(self, *actions, flatten=True, name=None):
super().__init__(name or "series", True)
self.actions = actions
self.flatten = flatten
def _do(self, state):
rq = self.get_requests(state.model)
params = tuple(
convert_str_to_parameter(state.model, _) for _ in rq["changing_parameters"]
)
if state.sim is None:
state.build_model(params, rq["keep_nodes"])
if self.flatten:
first = BaseSolution(self.name)
else:
first = None
curr_sol = None
pbar = tqdm(
self.actions,
disable=len(self.actions) <= 1 or (not finesse.config.show_progress_bars),
leave=False,
)
for action in pbar:
pbar.set_description_str(self.name)
next_sol = state.apply(action)
if self.flatten and next_sol is not None:
first.add(next_sol)
else:
if next_sol and not curr_sol:
first = next_sol # need to return the first one
if next_sol:
if curr_sol:
curr_sol.add(next_sol)
curr_sol = next_sol
return first
def _requests(self, model, memo, first=True):
for action in self.actions:
action._requests(model, memo, False)
[docs]class ForSolution(BaseSolution):
"""Solution generated by a `For` Action.
Attributes
----------
values : array_like
Array of values that were iterated over
Examples
--------
A simple example printing some a varied laser power output.
>>> import finesse
>>> model = finesse.Model()
>>> model.parse('''
... l l1
... pd P l1.p1.o
... ''')
>>> sol = model.run('for(l1.P, [0, 1, 2, 3], print(l1.P))')
0.0 W
1.0 W
2.0 W
3.0 W
>>> print(sol.values)
[0, 1, 2, 3]
"""
pass
[docs]class For(Action):
"""An action changes a parameter value and runs a set of actions for each value in
the array. Essentially the same as `Series` combined with `Change`. The parameter
value is not reset to its original value once this action has finished.
Generates a :class:`ForSolution` output when run.
Examples
--------
A simple example printing some a varied laser power output.
>>> import finesse
>>> model = finesse.Model()
>>> model.parse('''
... l l1
... pd P l1.p1.o
... ''')
>>> sol = model.run('for(l1.P, [0, 1, 2, 3], print(l1.P))')
0.0 W
1.0 W
2.0 W
3.0 W
>>> print(sol.values)
[0, 1, 2, 3]
Parameters
----------
param : str|Parameter
Parameter to change in the model
values : array_like
Array of values to use for the parameter
*actions : tuple(Action)
Actions to run for each parameter value.
"""
def __init__(self, param, values, *actions):
super().__init__("for", True)
self.param = param
self.values = values
self.actions = actions
def _do(self, state):
rq = self.get_requests(state.model)
for_param = convert_str_to_parameter(state.model, self.param)
params = tuple(
[
for_param,
*(
convert_str_to_parameter(state.model, _)
for _ in rq["changing_parameters"]
),
]
)
if state.sim is None:
state.build_model(params, rq["keep_nodes"])
first = ForSolution(self.name)
first.values = self.values
curr_sol = None
pbar = tqdm(
self.actions,
disable=len(self.actions) <= 1 or (not finesse.config.show_progress_bars),
leave=False,
)
for for_param.value in self.values:
state.sim.update_all_parameter_values()
for action in pbar:
pbar.set_description_str(self.name)
next_sol = state.apply(action)
if next_sol is not None:
first.add(next_sol)
else:
if next_sol and not curr_sol:
first = next_sol # need to return the first one
if next_sol:
if curr_sol:
curr_sol.add(next_sol)
curr_sol = next_sol
return first
def _requests(self, model, memo, first=True):
for action in self.actions:
action._requests(model, memo, False)