T_make_sys_ADY

test.ASC_SOLVER.T_make_sys_ADY

This File has the sole purpose of creating the ASC DHARD Y Model that is then exported to the buzz repository.

pytest-html report

Functions

FBNSSS_DARM([return_name, return_scale, ...])
FBNSSS_Fit([gain, return_name, return_scale])
FBNSSS_Hand([return_name, return_scale, as_zpk])
FBNSsimpSS([return_name, lo_ord, ...])	This is a pytest needing documentation
FFlatSS([gain, return_name])	This is a pytest needing documentation
addSS([namespace, dt, sub, outSufx])	Create a state space model for an adder.
delayDrive([namespace, Sp, delay, order, ...])	This is a pytest needing documentation
delaySS([namespace, delay, order, numins, dt])	Create a state space model for the delay block.
nullSS([namespace, gain])	Create a state space model for the null block.
test_Make_ADY(folder)	This is the main function that is called to create the model for the ASC DHARD Y.

Details

FBNSSS_DARM(return_name=False, return_scale=False, as_zpk=False, normalize=True)

code

docstring

"""
"""

def FBNSSS_DARM(
    return_name=False,
    return_scale=False,
    as_zpk=False,
    normalize=True,
):

    if return_name:
        name = "FOM BNS (Hand Fit)"
        return name

    #BNS_zpk = file_io.load(fjoin('ExampleModels_DARMFOM/BNS_FOM_from_DARM.yml'))
    BNS_zpk = file_io.load(fjoin('ExampleModels_DARMFOM/BNS_FOM_IR.yml'))
    F_z = np.array(BNS_zpk["z"], dtype=np.complex128)
    F_p = np.array(BNS_zpk["p"], dtype=np.complex128)
    F_k = BNS_zpk["k"]

    F_zpk = SISO.zpk(F_z, F_p, F_k)
    F = F_zpk.asSS.mimo("FBNS.out", "FBNS.in")
    if not normalize:
        return F
    F, scale = ssutil.normalize_gain(F, norm=1, return_scale=True)
    if as_zpk:
        return F_zpk * scale
    if return_scale:
        return scale
    else:
        return F

pytest information

This code is wrapped in a pytest function using conventions detailed in pytest_conventions. The full name of this test, as known by the documentation, is:

test.ASC_SOLVER.T_make_sys_ADY.FBNSSS_DARM

The full name is useful when building documentation, to link a reference to this page using :func:`name`, or directly include it with an autofunction directive. The collapse nodes below show every instance of the test run. There may only be one, but if the test was run multiple times through pytest parametrizations, the list can be longer.

FBNSSS_Fit(gain=1, return_name=False, return_scale=False)

code

docstring

"""

"""

def FBNSSS_Fit(gain=1, return_name=False, return_scale = False):

    if return_name:
        name = "FOM BNS"
        return name

    if gain != 1:
        NotImplementedError("This function does not support gain scaling")

    BNS_zpk = file_io.load(fjoin('ExampleModels_newFOM/BNS_FOM.yml'))
    print(BNS_zpk['z'])
    F_z = np.array(BNS_zpk["z"], dtype=np.complex128)
    #BNS_zpk["p"].append(BNS_zpk["p"][-1])
    F_p = np.array(BNS_zpk["p"], dtype=np.complex128)
    F_k = BNS_zpk["k"]

    #print(len(F_z))
    #print(len(F_p))
    F_mod = SISO.zpk(F_z, F_p, F_k).asSS
    F_iod = {"FBNS.in": 0, "FBNS.out": 0}
    F = ssutil.wieldSS(F_mod.A, F_mod.B, F_mod.C, F_mod.D, iod=F_iod)
    F, scale = ssutil.normalize_gain(F, norm=1, return_scale=True)
    if return_scale:
        return scale
    else:
        return F

pytest information

This code is wrapped in a pytest function using conventions detailed in pytest_conventions. The full name of this test, as known by the documentation, is:

test.ASC_SOLVER.T_make_sys_ADY.FBNSSS_Fit

The full name is useful when building documentation, to link a reference to this page using :func:`name`, or directly include it with an autofunction directive. The collapse nodes below show every instance of the test run. There may only be one, but if the test was run multiple times through pytest parametrizations, the list can be longer.

FBNSSS_Hand(return_name=False, return_scale=False, as_zpk=False)

code

docstring

"""

"""

def FBNSSS_Hand(return_name=False, return_scale = False, as_zpk=False):

    if return_name:
        name = "FOM BNS (Hand Fit)"
        return name

    BNS_zpk = file_io.load(fjoin('ExampleModels_newFOM/BNS_FOM_handfit.yml'))
    F_z = np.array(BNS_zpk["z"], dtype=np.complex128)
    F_p = np.array(BNS_zpk["p"], dtype=np.complex128)
    F_k = BNS_zpk["k"]

    F_zpk = SISO.zpk(F_z, F_p, F_k)
    F = F_zpk.asSS.mimo("FBNS.out", "FBNS.in")
    F, scale = ssutil.normalize_gain(F, norm=1, return_scale=True)
    if as_zpk:
        return F_zpk * scale
    if return_scale:
        return scale
    else:
        return F

pytest information

This code is wrapped in a pytest function using conventions detailed in pytest_conventions. The full name of this test, as known by the documentation, is:

test.ASC_SOLVER.T_make_sys_ADY.FBNSSS_Hand

The full name is useful when building documentation, to link a reference to this page using :func:`name`, or directly include it with an autofunction directive. The collapse nodes below show every instance of the test run. There may only be one, but if the test was run multiple times through pytest parametrizations, the list can be longer.

FBNSsimpSS(return_name=False, lo_ord=4, return_scale=False, as_zpk=False)

This is a pytest needing documentation

code

def FBNSsimpSS(return_name=False, lo_ord=4, return_scale=False, as_zpk = False):
    if return_name:
        name = "FOM BNS Simple"
        return name

    BNS_zpk = file_io.load(fjoin('FOMs/FBNSsimp_FOM.yml'))
    print(BNS_zpk['z'])
    F_z = np.array(BNS_zpk["z"], dtype=np.complex128)
    F_p = np.array(BNS_zpk["p"], dtype=np.complex128)
    F_k = BNS_zpk["k"]

    F_zpk = SISO.zpk(F_z, F_p, F_k)
    F = F_zpk.asSS.mimo("FBNS.out", "FBNS.in")
    F, scale = ssutil.normalize_gain(F, norm=1, return_scale=True)
    if as_zpk:
        return F_zpk * scale
    if return_scale:
        return scale
    else:
        return F

pytest information

This code is wrapped in a pytest function using conventions detailed in pytest_conventions. The full name of this test, as known by the documentation, is:

test.ASC_SOLVER.T_make_sys_ADY.FBNSsimpSS

The full name is useful when building documentation, to link a reference to this page using :func:`name`, or directly include it with an autofunction directive. The collapse nodes below show every instance of the test run. There may only be one, but if the test was run multiple times through pytest parametrizations, the list can be longer.

FFlatSS(gain=1, return_name=False)

This is a pytest needing documentation

code

def FFlatSS(gain=1, return_name=False):
    if return_name:
        name = "FOM Flat"
        return name

    return SISO.zpk([], [], gain).asSS.mimo("F2.out", "F2.in")

pytest information

This code is wrapped in a pytest function using conventions detailed in pytest_conventions. The full name of this test, as known by the documentation, is:

test.ASC_SOLVER.T_make_sys_ADY.FFlatSS

The full name is useful when building documentation, to link a reference to this page using :func:`name`, or directly include it with an autofunction directive. The collapse nodes below show every instance of the test run. There may only be one, but if the test was run multiple times through pytest parametrizations, the list can be longer.

addSS(namespace='T', dt=0.0001, sub=False, outSufx=None)

Create a state space model for an adder. It has two inputs and one output.

Parameters:

• namespace (string) – The namespace to use for the model i.e. the letter that comes at the beginning of the input and output names. Defaults to ‘T’.

• dt (float, optional) – The time step. Defaults to 1e-4.

• sub (bool, optional) – If true, the model will be a subtractor. Defaults to False.

Returns:

Returns the local name space for the function including the state space model as mod, input output dictionary as iod, and namespace.

Return type:

wield.bunch

code

docstring

"""Create a state space model for an adder. It has two inputs and one output.

Args:
    namespace (string): The namespace to use for the model i.e. the letter that comes at the beginning of the input and output names. Defaults to 'T'.
    dt (float, optional): The time step. Defaults to 1e-4.
    sub (bool, optional): If true, the model will be a subtractor. Defaults to False.

Returns:
    wield.bunch: Returns the local name space for the function including the state space model as `mod`, input output dictionary as `iod`, and namespace.
"""

def addSS(namespace='T', dt=1e-4, sub=False, outSufx = None):

    A = np.zeros([0,0]) # Construct the A state matrix
    B = np.zeros([2,0]) # Construct the B state matrix
    C = np.zeros([0,1]) # Construct the C state matrix
    if sub:
        D = np.array([[1, -1]]) # Construct the D state matrix for a subtractor
    else:
        D = np.array([[1, 1]]) # Construct the D state matrix
    if outSufx == None:
        iod = {namespace+'.in.1': 0, namespace+'.in.2': 1, namespace+'.out': 0} # Input output dictionary
    else:
        iod = {namespace+'.in.1': 0, namespace+'.in.2': 1, namespace+'.out.'+outSufx: 0} # Input output dictionary
    return ssutil.wieldSS(A, B, C, D, iod=iod)

pytest information

This code is wrapped in a pytest function using conventions detailed in pytest_conventions. The full name of this test, as known by the documentation, is:

test.ASC_SOLVER.T_make_sys_ADY.addSS

The full name is useful when building documentation, to link a reference to this page using :func:`name`, or directly include it with an autofunction directive. The collapse nodes below show every instance of the test run. There may only be one, but if the test was run multiple times through pytest parametrizations, the list can be longer.

delayDrive(namespace='D', Sp=None, delay=0.001, order=1, numins=1, dt=0.0001)

This is a pytest needing documentation

code

def delayDrive(namespace='D', Sp=None, delay=1e-3, order=1, numins=1, dt=1e-4):
    D = delaySS(namespace=namespace, delay=delay, order=order, numins=numins, dt=dt)

    if Sp is None or Sp == 1:
        #assert(False)
        return D

    #rename the input for pending merge
    D.iod[namespace + '.in.2'] = D.iod[namespace + '.in']
    del D.iod[namespace + '.in']

    #check that it only has 1 input and one output (THIS IS A HACKY WAY TO TEST)
    assert(len(Sp.iod) == 2)

    print(Sp.iod)
    Sp.iod.clear()
    Sp.iod[namespace + '.in'] = 0
    Sp.iod[namespace + '.out.1'] = 0
    print(Sp.iod)

    Dx = ssutil.multiconnect([Sp, D], [[namespace+'.in.2', namespace+'.out.1']])
    print(Dx.iod)
    return Dx

pytest information

This code is wrapped in a pytest function using conventions detailed in pytest_conventions. The full name of this test, as known by the documentation, is:

test.ASC_SOLVER.T_make_sys_ADY.delayDrive

The full name is useful when building documentation, to link a reference to this page using :func:`name`, or directly include it with an autofunction directive. The collapse nodes below show every instance of the test run. There may only be one, but if the test was run multiple times through pytest parametrizations, the list can be longer.

delaySS(namespace='D', delay=0.001, order=1, numins=1, dt=0.0001)

Create a state space model for the delay block. It has one input and one output.

Parameters:

• namespace (str, optional) – The namespace for the delay block. Defaults to ‘D’.

• delay (_type_, optional) – The delay in seconds. Defaults to 1e-3.

• order (int, optional) – Order of the filter. Defaults to 1.

• numins (int, optional) – Number of inputs. Defaults to 1.

• dt (_type_, optional) – The discretization time step. Defaults to 1e-4.

Returns:

Returns the local name space for the function including the state space model as mod, input output dictionary as iod, and namespace.

Return type:

wield.bunch

code

docstring

"""Create a state space model for the delay block. It has one input and one output.

Args:
    namespace (str, optional): The namespace for the delay block. Defaults to 'D'.
    delay (_type_, optional): The delay in seconds. Defaults to 1e-3.
    order (int, optional): Order of the filter. Defaults to 1.
    numins (int, optional): Number of inputs. Defaults to 1.
    dt (_type_, optional): The discretization time step. Defaults to 1e-4.

Returns:
    wield.bunch: Returns the local name space for the function including the state space model as `mod`, input output dictionary as `iod`, and namespace.
"""

def delaySS(namespace='D', delay=1e-3, order=1, numins=1, dt=1e-4):

    if delay==0:
        print('Delay is zero, using nullSS')
        D_mod = nullSS().mod
    else:
        # take the poles of this normalized bessel filter (delay=1s)
        z, p, k = scipy.signal.besselap(order, norm="delay")

        # now rescale for desired delay
        roots = p / delay * 2
        if order % 2 == 0:
            k = 1
        else:
            k = -1


        D_mod = SISO.zpk(-roots.conjugate(), roots, k).ss
        # D_res = control.zpk(-roots.conjugate(), roots, k)
        # D_mod = control.tf2ss(D_res)

    B_blocked = D_mod.B
    D_blocked = D_mod.D
    for ii in range(numins-1):
        B_blocked = np.block([[B_blocked, D_mod.B]])
        D_blocked = np.block([[D_blocked, D_mod.D]])

    mod = control.ss(D_mod.A, B_blocked, D_mod.C, D_blocked) # Create the system
    mod_dis = control.c2d(mod, dt) # Discretize the system
    iod = dict({namespace+'.in': 0, namespace+'.out': 0}) # Input output dictionary
    for ii in range(numins-1):
        iod[namespace+'.in.'+str(ii+1)] = ii+1

    return Bunch(locals())

pytest information

This code is wrapped in a pytest function using conventions detailed in pytest_conventions. The full name of this test, as known by the documentation, is:

test.ASC_SOLVER.T_make_sys_ADY.delaySS

The full name is useful when building documentation, to link a reference to this page using :func:`name`, or directly include it with an autofunction directive. The collapse nodes below show every instance of the test run. There may only be one, but if the test was run multiple times through pytest parametrizations, the list can be longer.

nullSS(namespace='N', gain=1)

Create a state space model for the null block. It has one input and one output.

Parameters:

• namespace (str, optional) – The namespace to use for the model i.e. the letter that comes at the beginning of the input and output names. Defaults to ‘N’.

• gain (float, optional) – The gain of the null block. Defaults to 1.

Returns:

Returns Wield MIMO statespace

Return type:

MIMO Statespace

code

docstring

"""Create a state space model for the null block. It has one input and one output.

Args:
    namespace (str, optional): The namespace to use for the model i.e. the letter that comes at the beginning of the input and output names. Defaults to 'N'.
    gain (float, optional): The gain of the null block. Defaults to 1.

Returns:
    MIMO Statespace: Returns Wield MIMO statespace
"""

def nullSS(namespace='N', gain=1):


    A = np.zeros([0,0]) # Construct the A state matrix
    B = np.zeros([1,0]) # Construct the B state matrix
    C = np.zeros([0,1]) # Construct the C state matrix
    D = np.array([[gain]]) # Construct the D state matrix 
    iod = dict({namespace+'.in': 0, namespace+'.out': 0}) # Input output dictionary
    return ssutil.wieldSS(A, B, C, D, iod=iod)

pytest information

This code is wrapped in a pytest function using conventions detailed in pytest_conventions. The full name of this test, as known by the documentation, is:

test.ASC_SOLVER.T_make_sys_ADY.nullSS

The full name is useful when building documentation, to link a reference to this page using :func:`name`, or directly include it with an autofunction directive. The collapse nodes below show every instance of the test run. There may only be one, but if the test was run multiple times through pytest parametrizations, the list can be longer.

test_Make_ADY(folder)

This is the main function that is called to create the model for the ASC DHARD Y.

code

docstring

"""
This is the main function that is called to create the model for the ASC DHARD Y.
"""

@pytest.mark.parametrize(
    'folder', [
        'ExampleModels_working',
        'ExampleModels_working_F3',
        'ExampleModels_newFOM',
        'ExampleModels_newFOM_F3',
        'ExampleModels_DARMFOM',
        'ExampleModels_DARMFOM_F3',
    ]
)
@pytest.mark.gitlabCI
@pytest.mark.ADY
def test_Make_ADY(folder):

    use_old_balancing = False
    # not helpful it seems
    use_alt_balancing = False

    if use_old_balancing:
        from wield.control.utilities import algorithm_choice
        # This fix is needed since updating several wield.controls algorithms
        algorithm_choice.algorithm_adjust_default('zpk2ss', 'zpk2ss_chain_poly_nobal', 200)

    S = ssutil.loadSys(fjoin(folder, 'ADY_E_from_fit.mat'))
    S = S.balance(which='ABC')
    if use_old_balancing:
        S = ssutil.balance_sys_gain(S, verbose=True)
    if use_alt_balancing:
        S = ssutil.balance_sys_gain(S, method='bfsqrt', verbose=True)
        S = S.schur_form()

    O = ssutil.loadSys(fjoin(folder, 'ADY_M_from_fit.mat'))
    # O = nullSS(namespace='O', gain=5e-5)
    print('O.A: ', O.A)
    O = O.balance(which='ABC')
    if use_old_balancing:
        O = ssutil.balance_sys_gain(O, verbose=True)
    if use_alt_balancing:
        O = ssutil.balance_sys_gain(O, method='bfsqrt', verbose=True)
        O = O.schur_form()

    # TODO - make solver handle unobserved modes
    # can be tested by adding one here
    # the plant of the working folder has a nontrivial statespace for some reason even though
    # it has a flat response with no poles or zeros
    # this ensures an un-observable mode that the solver DOES NOT LIKE
    O = O.siso('O.out', 'O.in').asZPK.asSS.mimo('O.out', 'O.in')

    P_fname = 'ADY_plant_from_fit.mat'
    P = ssutil.loadSys(fjoin(folder, P_fname))
    P = P.balance(which='ABC')
    #if use_old_balancing:
    #    P = ssutil.balance_sys_gain(P, verbose=True)
    if use_alt_balancing:
        P = ssutil.balance_sys_gain(P, method='bfsqrt', verbose=True)
        P = P.schur_form()

    # print("plant shape:")
    # P.print_nonzero()

    # this rescale is still helping the solver (it shouldn't!)
    w2_rescale = 1e6
    if use_old_balancing:
        w2_rescale = 1e6

    S = (w2_rescale * S.siso('S.out', 'S.in')).mimo('S.out', 'S.in')
    O = (w2_rescale * O.siso('O.out', 'O.in')).mimo('O.out', 'O.in')

    if '_newFOM' in folder or '_newFOM_F3' in folder:
        FBNS_func = FBNSSS_Hand
        FBNS = FBNS_func()
        FBNS_zpk = FBNS_func(as_zpk=True)
        FBNS_scale = FBNS_func(return_scale=True)

    elif 'DARMFOM' in folder:
       FBNS_func = FBNSSS_DARM
       FBNS = FBNS_func()
       FBNS_zpk = FBNS_func(as_zpk=True)
       FBNS_scale = FBNS_func(return_scale=True)

    else:
        FBNS_func = FBNSsimpSS
        FBNS = FBNS_func()
        FBNS_zpk = FBNS_func(as_zpk=True)
        FBNS_scale = FBNS_func(return_scale=True)

        omega_limits = [1e-2, 1e4]
        axB = ssutil.bode(FBNS.siso('FBNS.out', 'FBNS.in'), omega_limits=omega_limits, label='Simple FBNS')
        axB = ssutil.bode(FBNSSS_Hand().siso('FBNS.out', 'FBNS.in'), omega_limits=omega_limits, axB=axB, label='Hand Fit FBNS')
        axB = ssutil.bode(FBNSSS_Fit().siso('FBNS.out', 'FBNS.in'), omega_limits=omega_limits, axB=axB, label='FBNS From Data')
        axB.save(tjoin('BNS_FOMS_Comp.pdf'))
        axB.save(tjoin('BNS_FOMS_Comp.png'))

    print("OBS STATESPACE-----------------------------------------------------------")
    print(O.print_nonzero())
    print("ZP: ", O.siso('O.out', 'O.in').asZPK.zeros, O.siso('O.out', 'O.in').asZPK.poles)
    print("/OBS STATESPACE-----------------------------------------------------------")

    print("SEI STATESPACE-----------------------------------------------------------")
    print(S.print_nonzero())
    print("/SEI STATESPACE-----------------------------------------------------------")

    print("FOM STATESPACE-----------------------------------------------------------")
    if use_old_balancing:
        FBNS = ssutil.balance_sys_gain(FBNS, verbose=True)
    print(FBNS.print_nonzero())
    print("/FOM STATESPACE-----------------------------------------------------------")

    FFlat = FFlatSS()
    if use_old_balancing:
        FFlat = ssutil.balance_sys_gain(FFlat, verbose=True)

    # Inject the coupling into the BNS FOM, normalize it to Hinf=1 and then collect its scale
    Coup = ssutil.loadSys(fjoin(folder, 'A2L_coupling_with_cal.mat')) # This is the Angle to length coupling it would be multiplied by the BNS FOM
    if use_old_balancing:
        Coup = ssutil.balance_sys_gain(Coup, verbose=True)
    if use_alt_balancing:
        Coup = ssutil.balance_sys_gain(Coup, method='bfsqrt', verbose=True)
        Coup = Coup.schur_form()
    # Coup = SISO.zpk([], [], 1e-14).asSS.mimo('COUP.out', 'COUP.in')

    FBNS_orig = FBNS
    FBNS = ssutil.multiconnect([Coup, FBNS], [['FBNS.in', 'COUP.out']])
    #FBNS = ssutil.truncate_io(FBNS, ['COUP.in'], ['FBNS.out', 'COUP.out']) #commented out to display DARM Spectrum
    FBNS = ssutil.rename_io(FBNS, 'COUP.out', 'FBNS.DARM.out') # to see DARM Spectrum
    FBNS = ssutil.rename_io(FBNS, 'FBNS.in', 'FBNS.noC.in') # add a bypass coup for the FBNS
    FBNS = ssutil.rename_io(FBNS, 'COUP.in', 'FBNS.in')

    FBNS_Trash, Coup_scale = ssutil.normalize_gain(ssutil.truncate_io(FBNS, ['FBNS.in'], ['FBNS.out']), norm=1, return_scale=True, method='fq_resp')
    del FBNS_Trash
    FBNS = ssutil.scale_io(FBNS, 'FBNS.out', Coup_scale)  # using this to scale preserves the DARM.out output
    # TODO, comment this for breakage test and  uncomment for typical operation
    #FBNS = ssutil.scale_io(FBNS, 'FBNS.noC.in', 1/Coup_scale)  # using this to scale preserves the DARM.out output
    FBNS = ssutil.scale_io(FBNS, 'FBNS.noC.in', 1/Coup_scale * 1/FBNS_scale)  # using this to scale preserves the DARM.out output

    omega_limits_noise = [1e-1, 1e3]
    axN = ssutil.bode(ssutil.asSISO(ssutil.truncate_io(FBNS, ['FBNS.noC.in'], ['FBNS.out'])), omega_limits=omega_limits_noise, label='FBNS Scaled')
    axN = ssutil.bode(ssutil.asSISO(FBNS_func(normalize=False)), axB=axN, omega_limits=omega_limits_noise, linestyle='--',label='Unscaled BNS')
    axN.save(tjoin('FBNS_bode_after_scl.png'))
    axN.save(tjoin('FBNS_bode_after_scl.pdf'))

    # FBNS_alt = Coup.siso('COUP.out', 'COUP.in') * FBNS.siso('FBNS.out', 'FBNS.in')

    print("FOM STATESPACE-with Coupling---------------------------------------------")
    print(FBNS.inputs)
    print(FBNS.print_nonzero())
    print("/FOM STATESPACE-with coupling---------------------------------------------")

    FBNS_scale = FBNS_scale * Coup_scale
    np.savetxt(fjoin(folder, 'FBNS_scale.txt'), [FBNS_scale])
    print('FBNS scale', FBNS_scale)

    current_range = np.loadtxt(fjoin(folder, 'FBNS_Current_range.txt'))  # Saved by the normalization in the make function
    io_scales = Bunch(
        FBNS = FBNS_scale,  # will need to apply this to the BNS FOM
        w2_rescale = w2_rescale,  # will need to divide by this on both FOMs
        # will need to be applied only to the Flat fom since the A2L_coupling_with_cal includes it as a factor
        ct2rad = 5.2e-11,  # Calibration from https://alog.ligo-wa.caltech.edu/aLOG/index.php?callRep=69551
        strain2m = 1/3995,  # Representing the 4km arm length. Strain is h = dL/L where L = 3995 m. The coupling is "I" is
        # in rad to displacement so we need to convert it to strain for the BNS FOM which is in strain
        current_range = current_range,
        coup_scale = Coup_scale,
    )
    io_scales.RMS_cal_F1 = 1/io_scales.FBNS * io_scales.strain2m / io_scales.w2_rescale
    io_scales.RMS_cal_F2 = io_scales.ct2rad / io_scales.w2_rescale
    file_io.save(fjoin(folder, 'io_scales.yml'), dict(io_scales))

    omega_limits = [1e-2, 1e4]

    omega_limits_noise = [1e-1, 1e3]
    axN = ssutil.bode(ssutil.asSISO(S), omega_limits=omega_limits_noise, label='Seismic')
    axN = ssutil.bode(ssutil.asSISO(O), axB=axN, omega_limits=omega_limits_noise, label='Meas. Noise')
    axN = ssutil.bode(ssutil.asSISO(P), axB=axN, omega_limits=omega_limits_noise, label='Plant')
    axN = ssutil.bode(ssutil.asSISO(S.siso('S.out', 'S.in') * P.siso('P.out', 'P.in')), axB=axN, omega_limits=omega_limits_noise, label='S*P')
    axN.save(tjoin('Noise_bode.png'))
    axN.save(tjoin('Noise_bode.pdf'))


    # control.bode(S.mod, dB=False, Hz=True, omega_limits=omega_limits, label='Seismic')
    # control.bode(O.mod, dB=False, Hz=True, omega_limits=omega_limits, label='Meas. Noise')
    # control.bode(P.mod, dB=False, Hz=True, omega_limits=omega_limits, label='Plant')
    # control.bode((S.siso('S.out', 'S.in') * P.siso('P.out', 'P.in')).mimo('out', 'in').mod, dB=False, Hz=True, omega_limits=omega_limits, label='S*P')
    # plt.legend()
    # plt.savefig(tjoin('Noise_bode.pdf'), bbox_inches='tight')
    # plt.savefig(tjoin('Noise_bode.png'), bbox_inches='tight', dpi=300)
    # plt.close()

    omega_limits_fom = np.array([1e-1, 1e5])*2*np.pi
    axFOM = ssutil.bode(FBNS.siso('FBNS.out', 'FBNS.in'), omega_limits=omega_limits_fom, label='FBNS')
    axFOM = ssutil.bode(FFlat.siso(iodutil.listoutputs(FFlat)[0], iodutil.listinputs(FFlat)[0]), axB=axFOM, omega_limits=omega_limits_fom, label='FFlat')
    axFOM.save(tjoin('FOM_bode.pdf'))
    axFOM.save(tjoin('FOM_bode.png'))
    # control.bode(FBNS.mod, dB=True, Hz=True, omega_limits=omega_limits, label='FBNS')
    # control.bode(FFlat.mod, dB=True, Hz=True, omega_limits=omega_limits, label='FFlat')
    # plt.legend()
    # plt.savefig(tjoin('FOM_bode.pdf'))
    # plt.savefig(tjoin('FOM_bode.png'))
    # plt.close()

    #make the SO
    #conlist =[['T_SO.in.1', 'O.out'], ['T_SO.in.2','S.out']]
    #T_SO = addSS(namespace='T_SO', sub=True)
    #SO = ssutil.multiconnect([S, O, T_SO], conlist)
    #SO = ssutil.truncate_io(SO, ['O.in', 'S.in'], ['T_SO.out', 'S.out'])
    #SO = ssutil.rename_io(SO, 'T_SO.out', 'O.out')

    #control.bode(SO.mod[SO.iod['S.out'], SO.iod['S.in']], dB=True, Hz=True, omega_limits=omega_limits, label='S to S')
    #control.bode(SO.mod[SO.iod['O.out'], SO.iod['O.in']], dB=True, Hz=True, omega_limits=omega_limits, label='O to O')
    #control.bode(SO.mod[SO.iod['O.out'], SO.iod['S.in']], dB=True, Hz=True, omega_limits=omega_limits, label='S to O', linestyle='--', linewidth=5)
    #control.bode(S.mod, dB=True, Hz=True, omega_limits=[1e-2, 1e3], label='Orig. S', linestyle='dotted', linewidth=4)
    #control.bode(O.mod, dB=True, Hz=True, omega_limits=[1e-2, 1e3], label='Orig. O', linestyle='dotted', linewidth=4)
    #plt.legend(fontsize=8)
    #plt.savefig(tjoin('SO_bode.pdf'))
    #plt.close()
    SPOFF_kw = {}
    if '_F3' in folder:
        extras = 'F3_withP'
        SPOFF_kw['include_F1'] = False
        FBNS_eff_in = 'F3.in'
        FBNS_eff_out = 'F3.out'
    else:
        extras = None
        FBNS_eff_in = 'FBNS.in'
        FBNS_eff_out = 'FBNS.out'

    # TODO, this will break the solve to use noC.in. but debugging breakage
    SPOFF = ssutil.makeSPOFF(
        S, P, O,
        FBNS,
        FFlat,
        diagnostic=True,
        extras=extras,
        balance=use_old_balancing,
        # F1in='FBNS.noC.in',
        F1in='FBNS.in',
        F1out='FBNS.out',
        **SPOFF_kw
    )
    SPOFF = SPOFF.topological_sort()
    SPOFF = SPOFF.balance()
    # SPOFF = SPOFF.schur_form()

    #print('SPOFF', SPOFF.iod)
    #SPOFF = ssutil.balance_sys_gain(SPOFF)
    print("SPOFF STATESPACE-----------------------------------------------------------")
    try_block_diagonalize = False
    if try_block_diagonalize:
        SPOFF = SPOFF.schur_form()
        print(SPOFF.print_nonzero())

        sr = np.zeros(SPOFF.ss.Nstates, dtype=float)
        SPOFF_b2 = SPOFF.balance(which='A', Dr=sr)
        SPOFF_b2 = SPOFF

        from wield.control.ss_bare.ssprint import print_dense_nonzero_A, print_dense_nonzero, nz_int
        def flog2(arr):
            #arr = np.asarray(arr)
            #mant, exp = np.frexp(arr)
            exp = np.round(np.log2(abs(arr)))
            return exp
        sc = np.block([[flog2(sr)]])
        print("scaling?")
        print(print_dense_nonzero_A(sc, scaling=nz_int))
        Tr = np.zeros_like(SPOFF_b2.A)
        SPOFF_b2 = SPOFF_b2.block_diagonalize(condition_number=1e6, Tright=Tr)
        print("after balance2")
        print(SPOFF_b2.print_nonzero())
        print("Transfer Matrix")
        print(print_dense_nonzero_A(Tr))
        # SPOFF = SPOFF_b2
        print("POLES")
        print(SPOFF_b2.p)
    print("/SPOFF STATESPACE-----------------------------------------------------------")
    omega_limits=[1e-2, 1e3]

    axB = mplfigB(Nrows=2)
    F_Hz = np.geomspace(1e-2, 20, 1000)
    with SISObode.multi_bode(axB=axB, F_Hz = F_Hz) as bode:
        bode(P['P.out', 'P.in'], label='P alone', linewidth=2, color='dodgerblue')
        bode(SPOFF['P.out', 'P.in'], label='P to P in SPOFF', color='orange')
        bode(SPOFF['P.out', 'S.in'], label='S to P', color='green')
        bode(SPOFF['S.out', 'S.in'], label='S to S', linestyle='dotted', linewidth=4, color='green')
        bode(SPOFF['P.out', 'SA.in'], linestyle='--', label='P Recombined', linewidth=4, color='dodgerblue')
        bode(SPOFF['S.out', 'SA.in'], label='P that moved to S', color='dodgerblue', linestyle='dotted', linewidth=6)
        bode(SPOFF['G.out', 'S.in'], label='Orig. S (from SPOFF)', color='red')
        bode(S['S.out', 'S.in'], label='Orig. S alone', linestyle='dotted', linewidth=4, color='red')
    axB.ax1.legend(fontsize=5)
    axB.save(tjoin('SPOFF_diagnostic_bode.pdf'))
    axB.save(tjoin('SPOFF_diagnostic_bode.png'))

    axB = mplfigB(Nrows=2)
    F_Hz = np.geomspace(1e-2, 20, 1000)
    with SISObode.multi_bode(axB=axB, F_Hz = F_Hz) as bode:
        bode(sys=SPOFF['P.out', 'P.in'], label='P to P')
        bode(sys=SPOFF['P.out', 'S.in'], label='S to P')
        bode(sys=SPOFF['S.out', 'S.in'], label='S to S', linestyle=':', linewidth=2)
        bode(sys=S['S.out', 'S.in'], label='S to S (orig)', linestyle=':', linewidth=2)
        bode(sys=SPOFF['T.out', 'O.in'], label='O to Meas. Out')
        bode(sys=SPOFF['T.out', 'S.in'], label='S to Meas. Out')

    axB.ax1.legend(fontsize=5)
    axB.save(tjoin('SPOFF_bode.pdf'))
    axB.save(tjoin('SPOFF_bode.png'))

    #print('SPOFF', SPOFF.iod)
    #print(SPOFF['T.out', 'S.in']._zp[1])

    #truncate_inputs = ['P.in', 'S.in', 'O.in', 'T.in.1', 'T.in.2', 'FBNS.in', 'F2.in', 'Zinf.in']
    #truncate_outputs = ['P.out', 'O.out', 'S.out', 'T.out', 'FBNS.out', 'F2.out']
    #SPOFF = ssutil.truncate_io(SPOFF, truncate_inputs, truncate_outputs)

    control.bode(SPOFF.mod[SPOFF.iod['F2.out'], SPOFF.iod['P.in']], dB=True, Hz=True, omega_limits=omega_limits, label='P to Flat FOM')
    control.bode(SPOFF.mod[SPOFF.iod[FBNS_eff_out], SPOFF.iod['P.in']], dB=True, Hz=True, omega_limits=omega_limits, label='P to BNS FOM')
    control.bode(SPOFF.mod[SPOFF.iod['P.out'], SPOFF.iod['S.in']], dB=True, Hz=True, omega_limits=omega_limits, label='S to P')
    control.bode(SPOFF.mod[SPOFF.iod['S.out'], SPOFF.iod['S.in']], dB=True, Hz=True, omega_limits=omega_limits, label='S to S', linestyle='dotted', linewidth=4)
    control.bode(SPOFF.mod[SPOFF.iod['T.out'], SPOFF.iod['O.in']], dB=True, Hz=True, omega_limits=omega_limits, label='O to Meas. Out')
    control.bode(SPOFF.mod[SPOFF.iod['T.out'], SPOFF.iod['S.in']], dB=True, Hz=True, omega_limits=omega_limits, label='S to Meas. Out')
    plt.legend(fontsize=5)
    plt.savefig(tjoin('SPOFF_all_bode.pdf'))
    plt.savefig(tjoin('SPOFF_all_bode.png'))
    plt.close()

    # THIS MODIFIES THE ORIGINAL SYSTEM!!!!! WHAT - LEE
    #SPOFF_bal = ssutil.balance_sys_gain(SPOFF)

    axFOM_S = ssutil.bode(SPOFF.siso(FBNS_eff_out, FBNS_eff_in), omega_limits=omega_limits_fom, label='FBNS', color='red')
    axFOM_S = ssutil.bode(SPOFF.siso(iodutil.listoutputs(FFlat)[0], iodutil.listinputs(FFlat)[0]), axB=axFOM_S, omega_limits=omega_limits_fom, label='FFlat', color='dodgerblue')
    #axFOM_S = ssutil.bode(SPOFF_bal.siso(FBNS_eff_out, FBNS_eff_in), omega_limits=omega_limits_fom, axB=axFOM_S, label='FBNS SPOFF bal', color='green')
    #axFOM_S = ssutil.bode(SPOFF_bal.siso(iodutil.listoutputs(FFlat)[0], iodutil.listinputs(FFlat)[0]), axB=axFOM_S, omega_limits=omega_limits_fom, label='FFlat SPOFF bal', color='green')
    axFOM_S = ssutil.bode(FBNS.siso('FBNS.out', 'FBNS.in'), axB=axFOM_S, omega_limits=omega_limits_fom, label='FBNS orig.', linestyle='dotted', color='red', linewidth=4)
    axFOM_S = ssutil.bode(FBNS_orig.siso('FBNS.out', 'FBNS.in'), axB=axFOM_S, omega_limits=omega_limits_fom, label='FBNS orig.s', linestyle='dotted', color='purple', linewidth=4)
    axFOM_S = ssutil.bode(Coup.siso('COUP.out', 'COUP.in'), axB=axFOM_S, omega_limits=omega_limits_fom, label='FBNS orig.s', linestyle='-', color='magenta', linewidth=4)
    axFOM_S = ssutil.bode(FBNS_zpk, axB=axFOM_S, omega_limits=omega_limits_fom, label='FBNS orig_zpk.', linestyle='dotted', color='orange', linewidth=4)
    axFOM_S = ssutil.bode(FFlat.siso(iodutil.listoutputs(FFlat)[0], iodutil.listinputs(FFlat)[0]), axB=axFOM_S, omega_limits=omega_limits_fom, label='FFlat orig.', color='dodgerblue', linestyle='dotted', linewidth=4)
    axFOM_S.save(tjoin('FOM_bode_from_SPOFF.pdf'))
    axFOM_S.save(tjoin('FOM_bode_from_SPOFF.png'))

    #SPOFF = ssutil.balance_sys_gain(SPOFF)

    P_load2 = ssutil.loadSys(fjoin(folder, P_fname))
    # assert(np.allclose(P_load2.A, P.A))
    # assert(np.allclose(P_load2.B, P.B))
    # assert(np.allclose(P_load2.C, P.C))
    # assert(np.allclose(P_load2.D, P.D))

    print("SYS?")
    SPOFF.print_nonzero()

    ssutil.savesys(SPOFF, tjoin('ADY_Sanex.mat'))
    ssutil.savesys(P, tjoin('ADY_plant.mat'))

    ssutil.savesys(SPOFF, fjoin(folder, 'ADY_Sanex.mat'))
    ssutil.savesys(P, fjoin(folder, 'ADY_plant.mat'))

    return

pytest information

This code is wrapped in a pytest function using conventions detailed in pytest_conventions. The full name of this test, as known by the documentation, is:

test.ASC_SOLVER.T_make_sys_ADY.test_Make_ADY

The full name is useful when building documentation, to link a reference to this page using :func:`name`, or directly include it with an autofunction directive. The collapse nodes below show every instance of the test run. There may only be one, but if the test was run multiple times through pytest parametrizations, the list can be longer.

test_Make_ADY[ExampleModels_newFOM_F3]

output

status: failed
duration: 0.026s
captured errors:
folder = 'ExampleModels_newFOM_F3'

    @pytest.mark.parametrize(
        'folder', [
            'ExampleModels_working',
            'ExampleModels_working_F3',
            'ExampleModels_newFOM',
            'ExampleModels_newFOM_F3',
            'ExampleModels_DARMFOM',
            'ExampleModels_DARMFOM_F3',
        ]
    )
    @pytest.mark.gitlabCI
    @pytest.mark.ADY
    def test_Make_ADY(folder):
        """
        This is the main function that is called to create the model for the ASC DHARD Y.
        """
        use_old_balancing = False
        # not helpful it seems
        use_alt_balancing = False

        if use_old_balancing:
            from wield.control.utilities import algorithm_choice
            # This fix is needed since updating several wield.controls algorithms
            algorithm_choice.algorithm_adjust_default('zpk2ss', 'zpk2ss_chain_poly_nobal', 200)

        S = ssutil.loadSys(fjoin(folder, 'ADY_E_from_fit.mat'))
>       S = S.balance(which='ABC')

/builds/buzz/test/ASC_SOLVER/T_make_sys_ADY.py:286: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = MIMOStateSpace with 
 inputs ['S.in'], 
 outputs ['S.out'], 
 and 16 dimensional states
kwargs = {'which': 'ABC'}

    def balance(self, **kwargs):
        return self.__build_similar__(
>           ss=self.ss.balanceA(**kwargs),
        )
E       TypeError: BareStateSpace.balanceA() got an unexpected keyword argument 'which'

/wield/wield-control/src/wield/control/ss_bare/ss.py:1680: TypeError

• capture.txt

test_Make_ADY[ExampleModels_newFOM]

output

status: failed
duration: 0.002s
captured errors:
folder = 'ExampleModels_newFOM'

    @pytest.mark.parametrize(
        'folder', [
            'ExampleModels_working',
            'ExampleModels_working_F3',
            'ExampleModels_newFOM',
            'ExampleModels_newFOM_F3',
            'ExampleModels_DARMFOM',
            'ExampleModels_DARMFOM_F3',
        ]
    )
    @pytest.mark.gitlabCI
    @pytest.mark.ADY
    def test_Make_ADY(folder):
        """
        This is the main function that is called to create the model for the ASC DHARD Y.
        """
        use_old_balancing = False
        # not helpful it seems
        use_alt_balancing = False

        if use_old_balancing:
            from wield.control.utilities import algorithm_choice
            # This fix is needed since updating several wield.controls algorithms
            algorithm_choice.algorithm_adjust_default('zpk2ss', 'zpk2ss_chain_poly_nobal', 200)

        S = ssutil.loadSys(fjoin(folder, 'ADY_E_from_fit.mat'))
>       S = S.balance(which='ABC')

/builds/buzz/test/ASC_SOLVER/T_make_sys_ADY.py:286: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = MIMOStateSpace with 
 inputs ['S.in'], 
 outputs ['S.out'], 
 and 16 dimensional states
kwargs = {'which': 'ABC'}

    def balance(self, **kwargs):
        return self.__build_similar__(
>           ss=self.ss.balanceA(**kwargs),
        )
E       TypeError: BareStateSpace.balanceA() got an unexpected keyword argument 'which'

/wield/wield-control/src/wield/control/ss_bare/ss.py:1680: TypeError

• capture.txt

test_Make_ADY[ExampleModels_working_F3]

output

status: failed
duration: 0.018s
captured errors:
folder = 'ExampleModels_working_F3'

    @pytest.mark.parametrize(
        'folder', [
            'ExampleModels_working',
            'ExampleModels_working_F3',
            'ExampleModels_newFOM',
            'ExampleModels_newFOM_F3',
            'ExampleModels_DARMFOM',
            'ExampleModels_DARMFOM_F3',
        ]
    )
    @pytest.mark.gitlabCI
    @pytest.mark.ADY
    def test_Make_ADY(folder):
        """
        This is the main function that is called to create the model for the ASC DHARD Y.
        """
        use_old_balancing = False
        # not helpful it seems
        use_alt_balancing = False

        if use_old_balancing:
            from wield.control.utilities import algorithm_choice
            # This fix is needed since updating several wield.controls algorithms
            algorithm_choice.algorithm_adjust_default('zpk2ss', 'zpk2ss_chain_poly_nobal', 200)

        S = ssutil.loadSys(fjoin(folder, 'ADY_E_from_fit.mat'))
>       S = S.balance(which='ABC')

/builds/buzz/test/ASC_SOLVER/T_make_sys_ADY.py:286: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = MIMOStateSpace with 
 inputs ['S.in'], 
 outputs ['S.out'], 
 and 16 dimensional states
kwargs = {'which': 'ABC'}

    def balance(self, **kwargs):
        return self.__build_similar__(
>           ss=self.ss.balanceA(**kwargs),
        )
E       TypeError: BareStateSpace.balanceA() got an unexpected keyword argument 'which'

/wield/wield-control/src/wield/control/ss_bare/ss.py:1680: TypeError

• capture.txt

test_Make_ADY[ExampleModels_DARMFOM_F3]

output

status: failed
duration: 0.014s
captured errors:
folder = 'ExampleModels_DARMFOM_F3'

    @pytest.mark.parametrize(
        'folder', [
            'ExampleModels_working',
            'ExampleModels_working_F3',
            'ExampleModels_newFOM',
            'ExampleModels_newFOM_F3',
            'ExampleModels_DARMFOM',
            'ExampleModels_DARMFOM_F3',
        ]
    )
    @pytest.mark.gitlabCI
    @pytest.mark.ADY
    def test_Make_ADY(folder):
        """
        This is the main function that is called to create the model for the ASC DHARD Y.
        """
        use_old_balancing = False
        # not helpful it seems
        use_alt_balancing = False

        if use_old_balancing:
            from wield.control.utilities import algorithm_choice
            # This fix is needed since updating several wield.controls algorithms
            algorithm_choice.algorithm_adjust_default('zpk2ss', 'zpk2ss_chain_poly_nobal', 200)

        S = ssutil.loadSys(fjoin(folder, 'ADY_E_from_fit.mat'))
>       S = S.balance(which='ABC')

/builds/buzz/test/ASC_SOLVER/T_make_sys_ADY.py:286: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = MIMOStateSpace with 
 inputs ['S.in'], 
 outputs ['S.out'], 
 and 16 dimensional states
kwargs = {'which': 'ABC'}

    def balance(self, **kwargs):
        return self.__build_similar__(
>           ss=self.ss.balanceA(**kwargs),
        )
E       TypeError: BareStateSpace.balanceA() got an unexpected keyword argument 'which'

/wield/wield-control/src/wield/control/ss_bare/ss.py:1680: TypeError

• capture.txt

test_Make_ADY[ExampleModels_working]

output

status: failed
duration: 0.022s
captured errors:
folder = 'ExampleModels_working'

    @pytest.mark.parametrize(
        'folder', [
            'ExampleModels_working',
            'ExampleModels_working_F3',
            'ExampleModels_newFOM',
            'ExampleModels_newFOM_F3',
            'ExampleModels_DARMFOM',
            'ExampleModels_DARMFOM_F3',
        ]
    )
    @pytest.mark.gitlabCI
    @pytest.mark.ADY
    def test_Make_ADY(folder):
        """
        This is the main function that is called to create the model for the ASC DHARD Y.
        """
        use_old_balancing = False
        # not helpful it seems
        use_alt_balancing = False

        if use_old_balancing:
            from wield.control.utilities import algorithm_choice
            # This fix is needed since updating several wield.controls algorithms
            algorithm_choice.algorithm_adjust_default('zpk2ss', 'zpk2ss_chain_poly_nobal', 200)

        S = ssutil.loadSys(fjoin(folder, 'ADY_E_from_fit.mat'))
>       S = S.balance(which='ABC')

/builds/buzz/test/ASC_SOLVER/T_make_sys_ADY.py:286: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = MIMOStateSpace with 
 inputs ['S.in'], 
 outputs ['S.out'], 
 and 16 dimensional states
kwargs = {'which': 'ABC'}

    def balance(self, **kwargs):
        return self.__build_similar__(
>           ss=self.ss.balanceA(**kwargs),
        )
E       TypeError: BareStateSpace.balanceA() got an unexpected keyword argument 'which'

/wield/wield-control/src/wield/control/ss_bare/ss.py:1680: TypeError

• capture.txt

test_Make_ADY[ExampleModels_DARMFOM]

output

status: failed
duration: 0.017s
captured errors:
folder = 'ExampleModels_DARMFOM'

    @pytest.mark.parametrize(
        'folder', [
            'ExampleModels_working',
            'ExampleModels_working_F3',
            'ExampleModels_newFOM',
            'ExampleModels_newFOM_F3',
            'ExampleModels_DARMFOM',
            'ExampleModels_DARMFOM_F3',
        ]
    )
    @pytest.mark.gitlabCI
    @pytest.mark.ADY
    def test_Make_ADY(folder):
        """
        This is the main function that is called to create the model for the ASC DHARD Y.
        """
        use_old_balancing = False
        # not helpful it seems
        use_alt_balancing = False

        if use_old_balancing:
            from wield.control.utilities import algorithm_choice
            # This fix is needed since updating several wield.controls algorithms
            algorithm_choice.algorithm_adjust_default('zpk2ss', 'zpk2ss_chain_poly_nobal', 200)

        S = ssutil.loadSys(fjoin(folder, 'ADY_E_from_fit.mat'))
>       S = S.balance(which='ABC')

/builds/buzz/test/ASC_SOLVER/T_make_sys_ADY.py:286: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = MIMOStateSpace with 
 inputs ['S.in'], 
 outputs ['S.out'], 
 and 16 dimensional states
kwargs = {'which': 'ABC'}

    def balance(self, **kwargs):
        return self.__build_similar__(
>           ss=self.ss.balanceA(**kwargs),
        )
E       TypeError: BareStateSpace.balanceA() got an unexpected keyword argument 'which'

/wield/wield-control/src/wield/control/ss_bare/ss.py:1680: TypeError

• capture.txt