"""Collection of Actions that deal linear time invariant (LTI) modelling tasks."""
from finesse.analysis.runners import (
run_fsig_sweep,
run_fsig_sweep2,
run_fsig_sweep3,
run_fsig_sweep4,
)
from finesse.solutions import BaseSolution
from finesse.components import DegreeOfFreedom
from finesse.analysis.actions.base import Action, names_to_nodes
import numpy as np
import logging
from finesse.components.node import NodeType
from finesse.exceptions import FinesseException
import collections.abc
from finesse.utilities.misc import deprecation_warning
from typing import List, Tuple
LOGGER = logging.getLogger(__name__)
[docs]class FrequencyResponseSolution(BaseSolution):
"""A solution from running a :class:`.FrequencyResponse` action on a model. This
solution contains the frequency vector and potentially multiple input and output
transfer function matrix.
Attributes
----------
f : array_like
Frequency vector [Hz]
inputs : array_like
The input names injected into for this analysis
outputs : array_like
The output names read out for this analysis
out : arrray_like[dtype=np.complex128]
A matrix of transfer functions for each input to every output over the
array of frequencies requested. Depending on which frequency response
action was run will decide what shape this output matrix actually is.
The shape of out is dependent on the analysis done:
- FrequencyResponse - (N_f, N_outputs, N_inputs)
- FrequencyResponse2 - (N_f, N_outputs, N_inputs, N_hom)
- FrequencyResponse3 - (N_f, N_outputs, N_inputs, N_hom, N_hom)
- FrequencyResponse4 - (N_f, N_outputs, N_inputs, N_hom)
type : type
Type of FrequencyResponse that was used to generate this solution
Examples
--------
Note that the name indexing below is only available when used with the
:class:`.FrequencyResponse` action, the other frequency-response actions
must be accessed using the `out` attribute.
Results from a `FrequencyResponseSolution` can be retrieved in two ways, first
through the `FrequencyResponseSolution.out` array or by name using
`[outputs, inputs]`. As an example we will create a fake solution:
>>> from finesse.analysis.actions.lti import FrequencyResponseSolution
>>> sol = FrequencyResponseSolution("name")
>>> sol.inputs = ("A", "B", "C")
>>> sol.outputs = ("D", "E", "F", "G")
>>> sol.out = np.random.rand(3, len(sol.outputs), len(sol.inputs))
The names will map to those provided in the `FrequencyResponse` action you
called to generate the solution.
The following will work to select single transfer functions between some input
and output by name:
>>> sol["D", "A"] # Select A -> D
>>> sol["D", "C"] # Select C -> D
>>> sol["F", "C"] # Select C -> F
Transfer function matrices can be extracted by providing multiple
>>> sol["F", ("C", "A")]
>>> sol[("F", "G"), ("C", "A")]
Slicing can also be used:
>>> sol["D", :] # Select all inputs to "D"
>>> sol["D", ::2] # Select every other input to "D"
>>> sol[:, "B"] # Select "B" to all outputs
>>> sol[1:, "B"] # Select "B" to all but the first output
"""
def __getitem__(self, key, *, reversed=False):
if isinstance(key, str) and self.name == key:
return self
elif (
isinstance(key, (str, bytes))
or not isinstance(key, collections.abc.Iterable)
or len(key) != 2
):
raise KeyError(
"""Provide 2 keys [output, input] to select a transfer function
for indexing this FrequencyResponseSolution, if you want to
select all of one input or output use a color `:`. Otherwise use the
`out` attribute to access the underlying data. Shape of `out` is:
- FrequencyResponse - (N_f, N_outputs, N_inputs)
- FrequencyResponse2 - (N_f, N_outputs, N_inputs, N_hom)
- FrequencyResponse3 - (N_f, N_outputs, N_inputs, N_hom, N_hom)
- FrequencyResponse4 - (N_f, N_outputs, N_inputs, N_hom)
"""
)
else:
o_idx, i_idx = self.outputs_inputs_indices(key[0], key[1])
return self.out[slice(None), o_idx, i_idx]
plot = plot_inputs # Default plot option
[docs] def plot_outputs(self, *outputs, axs=None, max_width=12, **kwargs):
"""Plot all transfer functions on a NxM grid with a max_width.
Parameters
----------
outputs*,
Names of outputs for each subplot
axs : _type_, optional
Matplotlib axes to draw on
max_width : int, optional
Maximum number of subplots in width
show_unity : bool, optional
Plot a line where unity is
Returns
-------
figure, axes
Matplotlib figure and axes to plot on
"""
import matplotlib.pyplot as plt
if len(outputs) == 0:
outputs = self.outputs
outputs = np.atleast_1d(outputs)
if axs is None:
N = len(outputs)
W = min(5, max_width / N)
fig, axs = plt.subplots(
1, N, figsize=(W * N, 3.5), squeeze=False, sharey=True
)
else:
fig = plt.gcf()
if "label" not in kwargs:
kwargs["label"] = self.inputs
for i, output in enumerate(outputs):
axs[0, i].loglog(self.f, abs(self[output, :]), **kwargs)
axs[0, i].set_xlabel("Frequency [Hz]")
axs[0, i].set_title(output)
axs[0, i].legend()
axs[0, 0].set_ylabel("OUTPUT/DOF")
plt.tight_layout()
return fig, axs
# IMPORTANT: renaming this class impacts the katscript spec and should be avoided!
[docs]class FrequencyResponse(Action):
"""Computes the frequency response of a signal injected at various nodes to compute
transfer functions to multiple output nodes. Inputs and outputs should be electrical
or mechanical nodes. It does this in an efficient way by using the same model and
solving for multiple RHS input vectors.
This action does not alter the model state. This action will ignore any currently
definied signal generator elements in the model.
Produces an output transfer matrix from each input node to some readout output.
The shape of the output matrix is:
[frequencies, inputs, outputs]
To inject into optical nodes please see :class:`.FrequencyResponse2` and
:class:`.FrequencyResponse3`. To readout optical nodes please see
:class:`.FrequencyResponse3` and :class:`.FrequencyResponse4`.
Parameters
----------
f : array, double
Frequencies to compute the transfer functions over
inputs : iterable[str or Element]
Mechanical or electrical node to inject signal at
outputs : iterable[str or Element]
Mechanical or electrical nodes to measure output at
open_loop : bool, optional
Computes open loop transfer functions if the system has closed
name : str, optional
Solution name
Examples
--------
Here we measure a set of transfer functions from DARM and CARM
to four readouts for a particular `model`,
>>> sol = model.run(FrequencyResponse(np.geomspace(0.1, 50000, 100),
... ('DARM', 'CARM'),
... ('AS.DC', 'AS45.I', 'AS45.Q', 'REFL9.I'),
... ))
Single inputs and outputs can also be specified
>>> model.run(FrequencyResponse(np.geomspace(0.1, 50000, 100), 'DARM', AS.DC'))
The transfer functions can then be accessed like a 2D array by name,
the ordering of inputs to outputs does not matter.
>>> sol['DARM'] # DARM to all outputs
>>> sol['DARM', 'AS.DC'] # DARM to AS.DC
>>> sol['DARM', ('AS.DC', 'AS45.I')]
>>> sol['AS.DC'] # All inputs to AS.DC readout
"""
def __init__(
self, f, inputs, outputs, *, open_loop=False, name="frequency_response"
):
super().__init__(name)
inputs = np.atleast_1d(inputs)
outputs = np.atleast_1d(outputs)
if f is None:
raise FinesseException("A frequency vector must be provided")
try:
self.f = np.array(f, dtype=np.float64, copy=True)
except TypeError:
# If the f is a symbol...
self.f = np.array(f.eval(), dtype=np.float64, copy=True)
if self.f.size == 0:
raise FinesseException("Frequency vector has size 0")
if any(self.f <= 0):
raise FinesseException(
"Frequency vector must contain values greater than 0"
)
def process(x, input):
if isinstance(x, DegreeOfFreedom):
if input:
return x.AC.i.full_name
else:
return x.AC.o.full_name
elif isinstance(x, (str, np.str_)):
return x
else: # Try and get full_name
return x.full_name
self.inputs = list(process(i, True) for i in inputs)
self.outputs = list(process(o, False) for o in outputs)
self.open_loop = open_loop
def _do(self, state, fsig_independant_outputs=None, fsig_dependant_outputs=None):
input_node_indices = np.zeros(len(self.inputs), dtype=np.dtype("long"))
output_node_indices = np.zeros(len(self.outputs), dtype=np.dtype("long"))
# some signals will need to be scaled
input_scaling = np.ones(len(self.inputs), dtype=float)
output_scaling = np.ones(len(self.outputs), dtype=float)
for i, node in enumerate(
names_to_nodes(state.model, self.inputs, default_hints=("input",))
):
if node.type is NodeType.OPTICAL:
raise FinesseException(
f"Optical nodes ({node}) cannot be used with the FrequencyResponse action"
)
else:
# set scaling for mechanical input signals
if node.type is NodeType.MECHANICAL:
input_scaling[i] /= state.sim.model_settings.x_scale
input_node_indices[i] = state.sim.signal.node_id(node)
for i, node in enumerate(
names_to_nodes(state.model, self.outputs, default_hints=("output",))
):
if node.type is NodeType.OPTICAL:
raise FinesseException(
f"Optical nodes ({node}) cannot be used with the FrequencyResponse action"
)
else:
# set scaling for mechanical output signals
if node.type is NodeType.MECHANICAL:
output_scaling[i] *= state.sim.model_settings.x_scale
output_node_indices[i] = state.sim.signal.node_id(node)
sol = FrequencyResponseSolution(self.name)
sol.f = self.f
sol.inputs = self.inputs
sol.outputs = self.outputs
state.sim.run_carrier()
rtn = run_fsig_sweep(
state.sim,
self.f,
input_node_indices,
output_node_indices,
input_scaling,
output_scaling,
None,
self.open_loop,
(
tuple(fsig_independant_outputs)
if fsig_independant_outputs is not None
else None
),
(
tuple(fsig_dependant_outputs)
if fsig_dependant_outputs is not None
else None
),
)
if (fsig_dependant_outputs is not None) or (
fsig_independant_outputs is not None
):
sol.out = rtn[0]
sol.extra_outputs = rtn[1]
else:
sol.out = rtn
return sol
def _requests(self, model, memo, first=True):
memo["changing_parameters"].append("fsig.f")
memo["input_nodes"].extend((_, ("input",)) for _ in self.inputs)
memo["output_nodes"].extend((_, ("output",)) for _ in self.outputs)
# IMPORTANT: renaming this class impacts the katscript spec and should be avoided!
[docs]class FrequencyResponse2(Action):
"""Computes the frequency response of a signal injected at an optical port at a
particular optical frequency. This differs from :class:`.FrequencyResponse` in the
way the inputs and outputs are prescribed. For :class:`.FrequencyResponse2` you
specify optical input nodes and a signal output node.
This action does not alter the model state. This action will ignore any currently
definied signal generator elements in the model.
Produces an output transfer matrix from each HOM at a particular frequency and
optical node to some readout output. The shape of the output matrix is:
[frequencies, outputs, inputs, HOMs]
It should be noted that when exciting a lower signal sideband frequency it will
actually return the operator for propagating the conjugate of the lower sideband.
This is because FINESSE is internally solving for the conjugate of the lower sideband
to linearise non-linear optical effects.
To inject into mechanical and electrical nodes please see :class:`.FrequencyResponse`
and :class:`.FrequencyResponse4`. To readout optical nodes please see
:class:`.FrequencyResponse3` and :class:`.FrequencyResponse4`.
Parameters
----------
f : array, double
Frequencies to compute the transfer functions over
inputs : iterable[tuple[str or Node, Frequency]]
Optical node and frequency tuple to inject at. A symbolic refence to the
model's fsig.f parameter should always be used when defining a frequency to
look at.
outputs : iterable[str or Element]
Mechanical or electrical (signal)nodes to measure output to
name : str, optional
Solution name
Examples
--------
It is advisable to use always use a reference to the symbolic reference to
the signal frequency `model.fsig.f.ref` instead of a fixed number incase it
changes. This action will look for an initial frequency bin of X Hz to track
during the frequency response analysis. A symbolic reference will always
ensure the right bin is used, in cases such as looking at RF signal sidebands,
`10e6+model.fsig.f.ref` and `10e6-model.fsig.f.ref` will always look at the
upper and lower signal sideband around the +10MHz sideband.
>>> import finesse
>>> from finesse.analysis.actions import FrequencyResponse2
>>> model = finesse.script.parse('''
... l l1
... bs bs1 R=1 T=0 xbeta=1e-6 ybeta=1e-9
... readout_dc A
... link(l1, bs1, A)
... fsig(1)
... modes(maxtem=1)
... gauss g1 l1.p1.o w=1m Rc=inf
... ''')
>>> sol = model.run(
... FrequencyResponse2(
... [1, 10, 100],
... [
... ('bs1.p2.o', +model.fsig.f.ref),
... ('bs1.p2.o', -model.fsig.f.ref)
... ],
... ['A.DC']
... )
... )
"""
def __init__(self, f, inputs, outputs, *, name="frequency_response2"):
super().__init__(name)
if f is None:
raise FinesseException("A frequency vector must be provided")
try:
self.f = np.array(f, dtype=np.float64, copy=True)
except TypeError:
# If the f is a symbol...
self.f = np.array(f.eval(), dtype=np.float64, copy=True)
if self.f.size == 0:
raise FinesseException("Frequency vector has size 0")
if any(self.f <= 0):
raise FinesseException(
"Frequency vector must contain values greater than 0"
)
self.inputs = inputs
self.outputs = outputs
self.input_nodes = []
self.input_freqs = []
for node, freq in inputs:
self.input_nodes.append(node)
self.input_freqs.append(freq)
self.output_nodes = []
for node in outputs:
self.output_nodes.append(node)
def process_node(x, input):
if isinstance(x, DegreeOfFreedom):
if input:
return x.AC.i.full_name
else:
return x.AC.o.full_name
elif isinstance(x, (str, np.str_)):
return x
else: # Try and get full_name
return x.full_name
self.input_nodes = list(process_node(i, True) for i in self.input_nodes)
self.output_nodes = list(process_node(o, False) for o in self.output_nodes)
def _do(self, state, fsig_independant_outputs=None, fsig_dependant_outputs=None):
input_node_indices = np.zeros(len(self.input_nodes), dtype=np.dtype("long"))
input_freq_indices = np.zeros(len(self.input_nodes), dtype=np.dtype("long"))
output_node_indices = np.zeros(len(self.output_nodes), dtype=np.dtype("long"))
# some signals will need to be scaled
input_scaling = np.ones(len(self.input_nodes), dtype=float)
output_scaling = np.ones(len(self.output_nodes), dtype=float)
for i, (node, freq) in enumerate(
zip(
names_to_nodes(state.model, self.input_nodes, default_hints=("input",)),
self.input_freqs,
)
):
freq_obj = state.sim.signal.get_frequency_object(freq, node)
input_freq_indices[i] = freq_obj.index
if node.type is NodeType.OPTICAL:
input_node_indices[i] = state.sim.signal.node_id(node)
else:
if input_freq_indices[i] != 0:
raise FinesseException(
f"Input frequency for {node} should be the signal frequency"
)
# set scaling for mechanical input signals
if node.type is NodeType.MECHANICAL:
input_scaling[i] /= state.sim.model_settings.x_scale
input_node_indices[i] = state.sim.signal.node_id(node)
for i, node in enumerate(
names_to_nodes(state.model, self.output_nodes, default_hints=("output",))
):
if node.type is NodeType.OPTICAL:
raise FinesseException(
f"Optical nodes ({node}) cannot be used with the FrequencyResponse2 action"
)
else:
# set scaling for mechanical output signals
if node.type is NodeType.MECHANICAL:
output_scaling[i] *= state.sim.model_settings.x_scale
output_node_indices[i] = state.sim.signal.node_id(node)
sol = FrequencyResponseSolution(self.name)
sol.type = FrequencyResponse2
sol.f = self.f
sol.inputs = self.inputs
sol.outputs = self.outputs
state.sim.run_carrier()
rtn = run_fsig_sweep2(
state.sim,
self.f,
input_node_indices,
input_freq_indices,
output_node_indices,
input_scaling,
output_scaling,
None,
(
tuple(fsig_independant_outputs)
if fsig_independant_outputs is not None
else None
),
(
tuple(fsig_dependant_outputs)
if fsig_dependant_outputs is not None
else None
),
)
if (fsig_dependant_outputs is not None) or (
fsig_independant_outputs is not None
):
sol.out = rtn[0]
sol.extra_outputs = rtn[1]
else:
sol.out = rtn
return sol
def _requests(self, model, memo, first=True):
for freq in self.input_freqs:
try:
if model.fsig.f.ref not in freq.parameters():
raise IndexError()
except (AttributeError, IndexError): # catch if freq not a symbol
raise FinesseException(
f"{self} requires frequencies to be specified as a symbolic expression which must include `model.fsig.f.ref`, not {freq}."
)
memo["changing_parameters"].append("fsig.f")
memo["input_nodes"].extend((_, ("input",)) for _ in self.input_nodes)
memo["output_nodes"].extend((_, ("output",)) for _ in self.output_nodes)
# IMPORTANT: renaming this class impacts the katscript spec and should be avoided!
[docs]class FrequencyResponse3(Action):
"""Computes the frequency response of a signal injected at an optical port at a
particular optical frequency. This differs from :class:`.FrequencyResponse` in the
way the inputs and outputs are prescribed. For :class:`.FrequencyResponse3` you
specify optical input nodes and optical output nodes.
This action does not alter the model state. This action will ignore any currently
definied signal generator elements in the model.
Produces an output transfer matrix from each HOM at a particular frequency and
optical node to some other optical node and frequency. The shape of the output
matrix is:
[frequencies, outputs, inputs, HOMs, HOMs]
It should be noted that when exciting a lower signal sideband frequency it will
actually return the operator for propagating the conjugate of the lower sideband.
This is because FINESSE is internally solving for the conjugate of the lower sideband
to linearise non-linear optical effects.
To inject into mechanical and electrical nodes please see :class:`.FrequencyResponse`
and :class:`.FrequencyResponse4`. To readout mechanical and electrical nodes
please see :class:`.FrequencyResponse` and :class:`.FrequencyResponse2`.
Parameters
----------
f : array, double
Frequencies to compute the transfer functions over
inputs : iterable[tuple[str or Node, Frequency]]
Optical node and frequency tuple to inject at. A symbolic reference to the
model's fsig.f parameter should always be used when defining a frequency to
look at.
outputs : iterable[tuple[str or Node, Frequency]]
Optical node and frequency tuple to measure output at. A symbolic reference to the
model's fsig.f parameter should always be used when defining a frequency to
look at.
name : str, optional
Solution name
Examples
--------
It is advisable to use always use a reference to the symbolic reference to
the signal frequency `model.fsig.f.ref` instead of a fixed number incase it
changes. This action will look for an initial frequency bin of X Hz to track
during the frequency response analysis. A symbolic reference will always
ensure the right bin is used, in cases such as looking at RF signal sidebands,
`10e6+model.fsig.f.ref` and `10e6-model.fsig.f.ref` will always look at the
upper and lower signal sideband around the +10MHz sideband.
>>> import finesse
>>> from finesse.analysis.actions import FrequencyResponse3
>>> model = finesse.script.parse('''
... l l1
... bs bs1 R=1 T=0 xbeta=1e-6 ybeta=1e-9
... readout_dc A
... link(l1, bs1, A)
... fsig(1)
... modes(maxtem=1)
... gauss g1 l1.p1.o w=1m Rc=inf
... ''')
>>> sol = model.run(
... FrequencyResponse3(
... [1, 10, 100],
... [
... ('bs1.p2.o', +model.fsig.f.ref),
... ('bs1.p2.o', -model.fsig.f.ref)
... ],
... [
... ('A.p1.i', +model.fsig.f.ref),
... ('A.p1.i', -model.fsig.f.ref)
... ]
... )
... )
"""
def __init__(self, f, inputs, outputs, *, name="frequency_response3"):
super().__init__(name)
if f is None:
raise FinesseException("A frequency vector must be provided")
try:
self.f = np.array(f, dtype=np.float64, copy=True)
except TypeError:
# If the f is a symbol...
self.f = np.array(f.eval(), dtype=np.float64, copy=True)
if self.f.size == 0:
raise FinesseException("Frequency vector has size 0")
if any(self.f <= 0):
raise FinesseException(
"Frequency vector must contain values greater than 0"
)
self.inputs = inputs
self.outputs = outputs
self.input_nodes = []
self.input_freqs = []
for node, freq in inputs:
self.input_nodes.append(node)
self.input_freqs.append(freq)
self.output_nodes = []
self.output_freqs = []
for node, freq in outputs:
self.output_nodes.append(node)
self.output_freqs.append(freq)
def process_node(x, input):
if isinstance(x, DegreeOfFreedom):
if input:
return x.AC.i.full_name
else:
return x.AC.o.full_name
elif isinstance(x, (str, np.str_)):
return x
else: # Try and get full_name
return x.full_name
self.input_nodes = list(process_node(i, True) for i in self.input_nodes)
self.output_nodes = list(process_node(o, False) for o in self.output_nodes)
def _do(self, state, fsig_independant_outputs=None, fsig_dependant_outputs=None):
input_node_indices = np.zeros(len(self.input_nodes), dtype=np.dtype("long"))
input_freq_indices = np.zeros(len(self.input_nodes), dtype=np.dtype("long"))
output_node_indices = np.zeros(len(self.output_nodes), dtype=np.dtype("long"))
output_freq_indices = np.zeros(len(self.output_nodes), dtype=np.dtype("long"))
# some signals will need to be scaled
input_scaling = np.ones(len(self.input_nodes), dtype=float)
output_scaling = np.ones(len(self.output_nodes), dtype=float)
for i, (node, freq) in enumerate(
zip(
names_to_nodes(state.model, self.input_nodes, default_hints=("input",)),
self.input_freqs,
)
):
if node.type is NodeType.OPTICAL:
freq_obj = state.sim.signal.get_frequency_object(freq, node)
input_freq_indices[i] = freq_obj.index
input_node_indices[i] = state.sim.signal.node_id(node)
else:
raise FinesseException(
f"Optical nodes ({node}) must be used with the FrequencyResponse3 action"
)
for i, (node, freq) in enumerate(
zip(
names_to_nodes(
state.model, self.output_nodes, default_hints=("output",)
),
self.output_freqs,
)
):
if node.type is NodeType.OPTICAL:
freq_obj = state.sim.signal.get_frequency_object(freq, node)
output_freq_indices[i] = freq_obj.index
output_node_indices[i] = state.sim.signal.node_id(node)
else:
raise FinesseException(
f"Optical nodes ({node}) must be used with the FrequencyResponse3 action"
)
sol = FrequencyResponseSolution(self.name)
sol.type = FrequencyResponse3
sol.f = self.f
sol.inputs = self.inputs
sol.outputs = self.outputs
state.sim.run_carrier()
rtn = run_fsig_sweep3(
state.sim,
self.f,
input_node_indices,
input_freq_indices,
output_node_indices,
output_freq_indices,
input_scaling,
output_scaling,
None,
(
tuple(fsig_independant_outputs)
if fsig_independant_outputs is not None
else None
),
(
tuple(fsig_dependant_outputs)
if fsig_dependant_outputs is not None
else None
),
)
if (fsig_dependant_outputs is not None) or (
fsig_independant_outputs is not None
):
sol.out = rtn[0]
sol.extra_outputs = rtn[1]
else:
sol.out = rtn
return sol
def _requests(self, model, memo, first=True):
for flist in [self.input_freqs, self.output_freqs]:
for freq in flist:
try:
if model.fsig.f.ref not in freq.parameters():
raise IndexError()
except (AttributeError, IndexError): # catch if freq not a symbol
raise FinesseException(
f"{self} requires frequencies to be specified as a symbolic expression which must include `model.fsig.f.ref`, not {repr(freq)}."
)
memo["changing_parameters"].append("fsig.f")
memo["input_nodes"].extend((_, ("input",)) for _ in self.input_nodes)
memo["output_nodes"].extend((_, ("output",)) for _ in self.output_nodes)
[docs]class FrequencyResponse4(Action):
"""Computes the frequency response of a signal injected at an electrical or
mechanical port. This differs from :class:`.FrequencyResponse` in the way the inputs
and outputs are prescribed. For :class:`.FrequencyResponse4` you specify signal
input nodes and optical output nodes.
This action does not alter the model state. This action will ignore any currently
defined signal generator elements in the model.
Produces an output transfer matrix from each signal node to each HOM at a
particular frequency and optical node. The shape of the output
matrix is:
[frequencies, outputs, inputs, HOMs]
It should be noted that when exciting a lower signal sideband frequency it will
actually return the operator for propagating the conjugate of the lower sideband.
This is because FINESSE is internally solving for the conjugate of the lower sideband
to linearise non-linear optical effects.
To inject into optical nodes please see :class:`.FrequencyResponse2` and
:class:`.FrequencyResponse3`. To readout mechanical and electrical nodes
please see :class:`.FrequencyResponse` and :class:`.FrequencyResponse2`.
Parameters
----------
f : array, double
Frequencies to compute the transfer functions over
inputs : iterable[str or Element]
Mechanical or electrical node to inject signal at
outputs : iterable[tuple[str or Node, Frequency]]
Optical node and frequency tuple to measure output at. A symbolic reference to the
model's fsig.f parameter should always be used when defining a frequency to
look at.
name : str, optional
Solution name
Examples
--------
It is advisable to use always use a reference to the symbolic reference to
the signal frequency `model.fsig.f.ref` instead of a fixed number incase it
changes. This action will look for an initial frequency bin of X Hz to track
during the frequency response analysis. A symbolic reference will always
ensure the right bin is used, in cases such as looking at RF signal sidebands,
`10e6+model.fsig.f.ref` and `10e6-model.fsig.f.ref` will always look at the
upper and lower signal sideband around the +10MHz sideband.
>>> sol = model.run(
... FrequencyResponse(
... [1, 10, 100],
... [model.ETM.mech.z],
... [
... (model.ITM.p2.o, +model.fsig.f.ref),
... (model.ITM.p2.o, -model.fsig.f.ref)
... ]
... )
... )
"""
def __init__(self, f, inputs, outputs, *, name="frequency_response4"):
super().__init__(name)
if f is None:
raise FinesseException("A frequency vector must be provided")
try:
self.f = np.array(f, dtype=np.float64, copy=True)
except TypeError:
# If the f is a symbol...
self.f = np.array(f.eval(), dtype=np.float64, copy=True)
if self.f.size == 0:
raise FinesseException("Frequency vector has size 0")
if any(self.f <= 0):
raise FinesseException(
f"Frequency vector: {f} must contain values greater than 0"
)
self.inputs = inputs
self.outputs = outputs
self.input_nodes = []
for node in inputs:
self.input_nodes.append(node)
self.output_nodes = []
self.output_freqs = []
for node, freq in outputs:
self.output_nodes.append(node)
self.output_freqs.append(freq)
def process_node(x, input):
if isinstance(x, DegreeOfFreedom):
if input:
return x.AC.i.full_name
else:
return x.AC.o.full_name
elif isinstance(x, (str, np.str_)):
return x
else: # Try and get full_name
return x.full_name
self.input_nodes = list(process_node(i, True) for i in self.input_nodes)
self.output_nodes = list(process_node(o, False) for o in self.output_nodes)
def _do(self, state, fsig_independant_outputs=None, fsig_dependant_outputs=None):
input_node_indices = np.zeros(len(self.input_nodes), dtype=np.dtype("long"))
output_node_indices = np.zeros(len(self.output_nodes), dtype=np.dtype("long"))
output_freq_indices = np.zeros(len(self.output_nodes), dtype=np.dtype("long"))
# some signals will need to be scaled
input_scaling = np.ones(len(self.input_nodes), dtype=float)
output_scaling = np.ones(len(self.output_nodes), dtype=float)
for i, node in enumerate(
names_to_nodes(state.model, self.inputs, default_hints=("input",))
):
if node.type is NodeType.OPTICAL:
raise FinesseException(
f"This optical node ({node}) cannot be used with the FrequencyResponse4 action make sure you're using the correct input and outputs."
)
else:
# set scaling for mechanical input signals
if node.type is NodeType.MECHANICAL:
input_scaling[i] /= state.sim.model_settings.x_scale
input_node_indices[i] = state.sim.signal.node_id(node)
for i, (node, freq) in enumerate(
zip(
names_to_nodes(
state.model, self.output_nodes, default_hints=("output",)
),
self.output_freqs,
)
):
if node.type is NodeType.OPTICAL:
freq_obj = state.sim.signal.get_frequency_object(freq, node)
output_freq_indices[i] = freq_obj.index
output_node_indices[i] = state.sim.signal.node_id(node)
else:
raise FinesseException(
f"Optical nodes ({node}) must be used with the FrequencyResponse4 action"
)
sol = FrequencyResponseSolution(self.name)
sol.type = FrequencyResponse4
sol.f = self.f
sol.inputs = self.inputs
sol.outputs = self.outputs
state.sim.run_carrier()
rtn = run_fsig_sweep4(
state.sim,
self.f,
input_node_indices,
output_node_indices,
output_freq_indices,
input_scaling,
output_scaling,
None,
(
tuple(fsig_independant_outputs)
if fsig_independant_outputs is not None
else None
),
(
tuple(fsig_dependant_outputs)
if fsig_dependant_outputs is not None
else None
),
)
if (fsig_dependant_outputs is not None) or (
fsig_independant_outputs is not None
):
sol.out = rtn[0]
sol.extra_outputs = rtn[1]
else:
sol.out = rtn
return sol
def _requests(self, model, memo, first=True):
for freq in self.output_freqs:
try:
if model.fsig.f.ref not in freq.parameters():
raise IndexError()
except (AttributeError, IndexError): # catch if freq not a symbol
raise FinesseException(
f"{self} requires frequencies to be specified as a symbolic expression which must include `model.fsig.f.ref`, not {repr(freq)}."
)
memo["changing_parameters"].append("fsig.f")
memo["input_nodes"].extend((_, ("input",)) for _ in self.input_nodes)
memo["output_nodes"].extend((_, ("output",)) for _ in self.output_nodes)