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Commit 7321e8ab authored by Daniel Brown's avatar Daniel Brown

Making parakat more generic, can provide custom function to run now. IFO...

Making parakat more generic, can provide custom function to run now. IFO object now contains a lock drag function to run drags easier
parent 7325dd4f
...@@ -1184,6 +1184,84 @@ class IFO(object): ...@@ -1184,6 +1184,84 @@ class IFO(object):
print("New tunings") print("New tunings")
print(self.get_tunings()) print(self.get_tunings())
def lock_drag(self, N, *args, update_state=True, **kwargs):
Performs a "lock drag" to gradually change the state of the interferometer model
from one state to another. This is used when wanting to keep a model at a chosen
operating point but you want to explore how changes in various parameters affect
properties like power buildups, noise couplings, transfer functions, etc.
Essentially what this function does is slowly drag several parameters from some
initial value to some final one, whilst keeping the locks activated. The locks
must be setup correctly and enabled before this function is called.
Here we take some base model that has a mirror which we want to change the curvature
of whilst keeping the interferometer locked. In this example the radius of curvature
of ETMX will be changed from its current value to +10m in x and -10m in y.
>>> kat = base.deepcopy()
>>> kat.maxtem = 2
>>> kat.verbose = True
>>> drag = lock_drag(kat, 100,
>>> ('ETMX', 'Rcx', 10),
>>> ('ETMX', 'Rcy', -10)
>>> )
To apply the lock drag to the object you need to extract the `lock` outputs and apply
them to the model.
N : int
Number of points to do the lock drag over
update_state : bool
If True, the calling kat object's state will be updated with the
final value of the lock drag
*args : tuple(target, param, final_value)
target : Name of the component
param : Parameter to change
final_value : The relative change in value to do the lock drag over
**kwargs : dict
Keyword arguments are passed to the `**kwargs)` call.
if hasattr(self, 'xaxis'): self.xaxis.remove()
if hasattr(self, 'x2axis'): self.x2axis.remove()
kat = self.kat.deepcopy()
var dummy 0
xaxis dummy re lin 0 1 {N}
for i, (target, attr, final) in enumerate(args):
func LD{i} = ({final}) * $x1
put* {target} {attr} $LD{i}
drag =**kwargs)
if update_state:
self.apply_lock_drag(drag, -1, *args)
return drag
def apply_lock_drag(self, out, idx, *args):
for i, (target, attr, final) in enumerate(args):
p = [_ for _ in self.kat.components[target]._params if == attr]
if len(p) != 1:
raise Exception(f"Could not find parameter {attr} for component {target}")
p[0].value += final * out.x[idx]
self.apply_lock_feedback(out, idx)
def get_tuning_comps(self): def get_tuning_comps(self):
return self.__tuning_comps return self.__tuning_comps
...@@ -93,8 +93,10 @@ class parakat(object): ...@@ -93,8 +93,10 @@ class parakat(object):
self._results = [] self._results = []
self._run_count = 0 self._run_count = 0
def run(self, kat, **kwargs): def run(self, kat, func=None, *args, **kwargs):
self._results.append(self._lview.apply_async(_run, "".join(kat.generateKatScript()), os.getcwd(), **kwargs)) if func is None:
func = _run
self._results.append(self._lview.apply_async(func, "".join(kat.generateKatScript()), os.getcwd(), *args, **kwargs))
self._run_count += 1 self._run_count += 1
def getResults(self): def getResults(self):
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