T_ADY_make_sys

test.ASC_SOLVER.T_ADY_make_sys

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_Fit([gain, return_name, return_scale])

FBNSSS_Hand([return_name, return_scale])

FBNSSS_darm([return_name, return_scale])

FBNSsimpSS([return_name, lo_ord, return_scale])

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.

bode(sys[, axB, F_Hz, omega_limits, ...])

sys should be a wield.control.SISO object

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.

multi_bode(axB[, F_Hz, include_zp])

Plot multiple bode plots at once.

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.

transposeSys(sys, FOM_ns)

This is a pytest needing documentation

ws_tf2ss(z, p, k[, gain, mfactor, angular])

Takes a python control zpk and returns a python control statespace.

Details

FBNSSS_Fit(gain=1, return_name=False, return_scale=False)[source]
code
docstring
"""

"""

 1def FBNSSS_Fit(gain=1, return_name=False, return_scale = False):
 2
 3    if return_name:
 4        name = "FOM BNS"
 5        return name
 6
 7    if gain != 1:
 8        NotImplementedError("This function does not support gain scaling")
 9
10    BNS_zpk = file_io.load(fjoin('ExampleModels_newFOM/BNS_FOM.yml'))
11    print(BNS_zpk['z'])
12    F_z = np.array(BNS_zpk["z"], dtype=np.complex128)
13    #BNS_zpk["p"].append(BNS_zpk["p"][-1])
14    F_p = np.array(BNS_zpk["p"], dtype=np.complex128)
15    F_k = BNS_zpk["k"]
16
17    #print(len(F_z))
18    #print(len(F_p))
19    F_mod = SISO.zpk(F_z, F_p, F_k).asSS
20    F_iod = {"FBNS.in": 0, "FBNS.out": 0}
21    F = ssutil.wieldSS(F_mod.A, F_mod.B, F_mod.C, F_mod.D, iod=F_iod)
22    F, scale = ssutil.normalize_gain(F, norm=1, return_scale=True)
23    if return_scale:
24        return scale
25    else:
26        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_ADY_make_sys.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)[source]
code
docstring
"""

"""

 1def FBNSSS_Hand(return_name=False, return_scale = False):
 2
 3    if return_name:
 4        name = "FOM BNS (Hand Fit)"
 5        return name
 6
 7    BNS_zpk = file_io.load(fjoin('ExampleModels_newFOM/BNS_FOM_handfit.yml'))
 8    F_z = np.array(BNS_zpk["z"], dtype=np.complex128)
 9    F_p = np.array(BNS_zpk["p"], dtype=np.complex128)
10    F_k = BNS_zpk["k"]
11
12    F = SISO.zpk(F_z, F_p, F_k).asSS.mimo("FBNS.out", "FBNS.in")
13    F, scale = ssutil.normalize_gain(F, norm=1, return_scale=True)
14    if return_scale:
15        return scale
16    else:
17        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_ADY_make_sys.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.

FBNSSS_darm(return_name=False, return_scale=False)[source]
code
docstring
"""

"""

 1def FBNSSS_darm(return_name=False, return_scale = False):
 2
 3    if return_name:
 4        name = "FOM BNS (DARM"
 5        return name
 6
 7    BNS_zpk = file_io.load(fjoin('ExampleModels_DARMFOM/BNS_FOM_from_DARM.yml'))
 8    F_z = np.array(BNS_zpk["z"], dtype=np.complex128)
 9    F_p = np.array(BNS_zpk["p"], dtype=np.complex128)
10    F_k = BNS_zpk["k"]
11
12    F = SISO.zpk(F_z, F_p, F_k).asSS.mimo("FBNS.out", "FBNS.in")
13    F, scale = ssutil.normalize_gain(F, norm=1, return_scale=True)
14    if return_scale:
15        return scale
16    else:
17        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_ADY_make_sys.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.

FBNSsimpSS(return_name=False, lo_ord=4, return_scale=False)[source]

This is a pytest needing documentation

code
 1def FBNSsimpSS(return_name=False, lo_ord=4, return_scale=False):
 2    if return_name:
 3        name = "FOM BNS Simple"
 4        return name
 5
 6    BNS_zpk = file_io.load(fjoin('FOMs/FBNSsimp_FOM.yml'))
 7    print(BNS_zpk['z'])
 8    F_z = np.array(BNS_zpk["z"], dtype=np.complex128)
 9    F_p = np.array(BNS_zpk["p"], dtype=np.complex128)
10    F_k = BNS_zpk["k"]
11
12    F = SISO.zpk(F_z, F_p, F_k).asSS.mimo("FBNS.out", "FBNS.in")
13    F, scale = ssutil.normalize_gain(F, norm=1, return_scale=True)
14    if return_scale:
15        return scale
16    else:
17        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_ADY_make_sys.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)[source]

This is a pytest needing documentation

code
1def FFlatSS(gain=1, return_name=False):
2    if return_name:
3        name = "FOM Flat"
4        return name
5
6    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_ADY_make_sys.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)[source]

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.
"""

 1def addSS(namespace='T', dt=1e-4, sub=False, outSufx = None):
 2
 3    A = np.zeros([0,0]) # Construct the A state matrix
 4    B = np.zeros([2,0]) # Construct the B state matrix
 5    C = np.zeros([0,1]) # Construct the C state matrix
 6    if sub:
 7        D = np.array([[1, -1]]) # Construct the D state matrix for a subtractor
 8    else:
 9        D = np.array([[1, 1]]) # Construct the D state matrix
10    if outSufx == None:
11        iod = {namespace+'.in.1': 0, namespace+'.in.2': 1, namespace+'.out': 0} # Input output dictionary
12    else:
13        iod = {namespace+'.in.1': 0, namespace+'.in.2': 1, namespace+'.out.'+outSufx: 0} # Input output dictionary
14    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_ADY_make_sys.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.

bode(sys, axB=None, F_Hz=None, omega_limits=None, include_zp=True, label=None, **kwargs)[source]

sys should be a wield.control.SISO object

code
docstring
"""
sys should be a wield.control.SISO object
"""

 1def bode(
 2    sys,
 3    axB=None,
 4    F_Hz=None,
 5    omega_limits=None,
 6    include_zp = True,
 7    label=None,
 8    **kwargs
 9):
10
11    if axB is None:
12        axB = mplfigB(Nrows = 2)
13
14    if include_zp:
15        z, p = sys._zp
16        z = z[z.imag > 0]
17        p = p[p.imag > 0]
18        F_include = np.sort(np.concatenate(
19            [
20                z.imag,
21                p.imag,
22                z.imag - abs(z.real) - 1e-6,
23                z.imag + abs(z.real) + 1e-6,
24                p.imag - abs(p.real) - 1e-6,
25                p.imag + abs(p.real) + 1e-6,
26            ]
27        )) / (2 * np.pi)
28        F_include = F_include[F_include > 0]
29
30        if omega_limits is None:
31            omega_limits = (
32                (F_include[0] + 1e-6) / 3,
33                F_include[-1] * 3
34            )
35
36    if F_Hz is None:
37        F_Hz = np.geomspace(omega_limits[0], omega_limits[1], 1000)
38
39    if include_zp:
40        print("F_include", F_include)
41        F_Hz = np.sort(np.concatenate([F_Hz, F_include]))
42
43    fr = sys.fresponse(f=F_Hz)
44    axB.ax0.loglog(*fr.fplot_mag, label=label, **kwargs)
45    if hasattr(axB, 'ax1'):
46        axB.ax1.semilogx(*fr.fplot_deg225, label=label, **kwargs)
47    return axB
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_ADY_make_sys.bode

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)[source]

This is a pytest needing documentation

code
 1def delayDrive(namespace='D', Sp=None, delay=1e-3, order=1, numins=1, dt=1e-4):
 2    D = delaySS(namespace=namespace, delay=delay, order=order, numins=numins, dt=dt)
 3
 4    if Sp is None or Sp == 1:
 5        #assert(False)
 6        return D
 7
 8    #rename the input for pending merge
 9    D.iod[namespace + '.in.2'] = D.iod[namespace + '.in']
10    del D.iod[namespace + '.in']
11
12    #check that it only has 1 input and one output (THIS IS A HACKY WAY TO TEST)
13    assert(len(Sp.iod) == 2)
14
15    print(Sp.iod)
16    Sp.iod.clear()
17    Sp.iod[namespace + '.in'] = 0
18    Sp.iod[namespace + '.out.1'] = 0
19    print(Sp.iod)
20
21    Dx = ssutil.multiconnect([Sp, D], [[namespace+'.in.2', namespace+'.out.1']])
22    print(Dx.iod)
23    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_ADY_make_sys.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)[source]

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.
"""

 1def delaySS(namespace='D', delay=1e-3, order=1, numins=1, dt=1e-4):
 2
 3    if delay==0:
 4        print('Delay is zero, using nullSS')
 5        D_mod = nullSS().mod
 6    else:
 7        # take the poles of this normalized bessel filter (delay=1s)
 8        z, p, k = scipy.signal.besselap(order, norm="delay")
 9
10        # now rescale for desired delay
11        roots = p / delay * 2
12        if order % 2 == 0:
13            k = 1
14        else:
15            k = -1
16
17        #siso_rep = SISO.zpk(-roots.conjugate(), roots, k)
18        D_res = control.zpk(-roots.conjugate(), roots, k)
19        D_mod = control.tf2ss(D_res)
20
21    B_blocked = D_mod.B
22    D_blocked = D_mod.D
23    for ii in range(numins-1):
24        B_blocked = np.block([[B_blocked, D_mod.B]])
25        D_blocked = np.block([[D_blocked, D_mod.D]])
26
27    mod = control.ss(D_mod.A, B_blocked, D_mod.C, D_blocked) # Create the system
28    mod_dis = control.c2d(mod, dt) # Discretize the system
29    iod = dict({namespace+'.in': 0, namespace+'.out': 0}) # Input output dictionary
30    for ii in range(numins-1):
31        iod[namespace+'.in.'+str(ii+1)] = ii+1
32
33    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_ADY_make_sys.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.

multi_bode(axB, F_Hz=None, include_zp=True, **kwargs)[source]

Plot multiple bode plots at once.

The main advantage is that the scale of the axes can be better auto-determined to capture all known poles and zeros. The input arguments should all be dictionaries containing the bode kwargs.

the usage should be

the kwargs on the top call are passed into all sub calls

with multi_bode(axB=axB, **kw_cmn) as bode:

bode(sisoA, label=’A’, **kw) bode(sisoB, label=’B’, **kw) bode(sisoC, label=’C’, **kw)

axB.save(‘figname.pdf’)

nullSS(namespace='N', gain=1)[source]

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
"""

1def nullSS(namespace='N', gain=1):
2
3
4    A = np.zeros([0,0]) # Construct the A state matrix
5    B = np.zeros([1,0]) # Construct the B state matrix
6    C = np.zeros([0,1]) # Construct the C state matrix
7    D = np.array([[gain]]) # Construct the D state matrix 
8    iod = dict({namespace+'.in': 0, namespace+'.out': 0}) # Input output dictionary
9    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_ADY_make_sys.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)[source]

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.
"""

  1@pytest.mark.parametrize(
  2    'folder', [
  3        #'ExampleModels_rescale',
  4        'ExampleModels',
  5        'ExampleModels_working',
  6        'ExampleModels_working_F3',
  7        'ExampleModels_newFOM',
  8        'ExampleModels_newFOM_F3',
  9        'ExampleModels_DARMFOM',
 10    ]
 11)
 12@pytest.mark.gitlabCI
 13@pytest.mark.ADY
 14def test_Make_ADY(folder):
 15
 16
 17    # MUST be on the wield-control balancing branch for this to work!
 18    from wield.control.utilities import algorithm_choice
 19    if folder == 'ExampleModels_bal':
 20        # just needs to be below 100 to ensure default behavior
 21        algorithm_choice.algorithm_adjust_default('zpk2ss', 'zpk2ss_chain_poly_nobal', 90)
 22    else:
 23        # This fix is needed since updating several wield.controls algorithms
 24        algorithm_choice.algorithm_adjust_default('zpk2ss', 'zpk2ss_chain_poly_nobal', 200)
 25
 26    S = ssutil.loadSys(fjoin(folder, 'ADY_E_from_fit.mat'))
 27    O = ssutil.loadSys(fjoin(folder, 'ADY_M_from_fit.mat'))
 28    P_fname = 'ADY_plant_from_fit.mat'
 29    P = ssutil.loadSys(fjoin(folder, P_fname))
 30
 31    # w2_rescale = 2e5
 32    w2_rescale = 1e6
 33    S = (w2_rescale * S.siso('S.out', 'S.in')).mimo('S.out', 'S.in')
 34    O = (w2_rescale * O.siso('O.out', 'O.in')).mimo('O.out', 'O.in')
 35
 36    if '_newFOM' in folder or '_newFOM_F3' in folder:
 37        FBNS_func = FBNSSS_Hand
 38        FBNS = FBNS_func()
 39        FBNS_scale = FBNS_func(return_scale=True)
 40    if folder=='ExampleModels_DARMFOM':
 41        FBNS_func = FBNSSS_darm
 42        FBNS = FBNS_func()
 43        FBNS_scale = FBNS_func(return_scale=True)
 44    else:
 45        FBNS_func = FBNSsimpSS
 46        FBNS = FBNS_func()
 47        FBNS_scale = FBNS_func(return_scale=True)
 48
 49        omega_limits=[1e-2, 1e4]
 50        axB = ssutil.bode(FBNS.siso('FBNS.out', 'FBNS.in'), omega_limits=omega_limits, label='Simple FBNS')
 51        axB = ssutil.bode(FBNSSS_Hand().siso('FBNS.out', 'FBNS.in'), omega_limits=omega_limits, axB=axB, label='Hand Fit FBNS')
 52        axB = ssutil.bode(FBNSSS_Fit().siso('FBNS.out', 'FBNS.in'), omega_limits=omega_limits, axB=axB, label='FBNS From Data')
 53        axB.save(tjoin('BNS_FOMS_Comp.pdf'))
 54        axB.save(tjoin('BNS_FOMS_Comp.png'))
 55
 56    FFlat = FFlatSS()
 57
 58    # Inject the coupling into the BNS FOM, normalize it to Hinf=1 and then collect its scale
 59    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
 60    FBNS = ssutil.multiconnect([Coup, FBNS], [['FBNS.in', 'COUP.out']])
 61    # FBNS = ssutil.truncate_io(FBNS, ['COUP.in'], ['FBNS.out']) #commented out to display DARM Spectrum
 62    FBNS = ssutil.rename_io(FBNS, 'COUP.out', 'FBNS.DARM.out') # to see DARM Spectrum
 63    FBNS = ssutil.rename_io(FBNS, 'COUP.in', 'FBNS.in')
 64
 65    FBNS_Trash, Coup_scale = ssutil.normalize_gain(ssutil.truncate_io(FBNS, ['FBNS.in'], ['FBNS.out']), norm=1, return_scale=True)
 66    FBNS = ssutil.scale_io(FBNS, 'FBNS.out', Coup_scale) # using this to scale preserves the DARM.out output
 67
 68    FBNS_scale = FBNS_scale * Coup_scale
 69    np.savetxt(fjoin(folder, 'FBNS_scale.txt'), [FBNS_scale])
 70    print('FBNS scale', FBNS_scale)
 71
 72    current_range = np.loadtxt(fjoin(folder, 'FBNS_Current_range.txt')) # Saved by the normalization in the make function
 73    io_scales = Bunch(
 74        FBNS = FBNS_scale,  # will need to apply this to the BNS FOM
 75        w2_rescale = w2_rescale,  # will need to divide by this on both FOMs
 76        # will need to be applied only to the Flat fom since the A2L_coupling_with_cal includes it as a factor
 77        ct2rad = 5.2e-11,  # Calibration from https://alog.ligo-wa.caltech.edu/aLOG/index.php?callRep=69551
 78        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
 79        current_range = current_range,
 80    )
 81    io_scales.RMS_cal_F1 = 1/io_scales.FBNS * io_scales.strain2m / io_scales.w2_rescale
 82    io_scales.RMS_cal_F2 = io_scales.ct2rad / io_scales.w2_rescale
 83    file_io.save(fjoin(folder, 'io_scales.yml'), dict(io_scales))
 84
 85    omega_limits=[1e-2, 1e4]
 86
 87    omega_limits_noise = [1e-1, 1e3]
 88    axN = ssutil.bode(ssutil.asSISO(S), omega_limits=omega_limits_noise, label='Seismic')
 89    axN = ssutil.bode(ssutil.asSISO(O), axB=axN, omega_limits=omega_limits_noise, label='Meas. Noise')
 90    axN = ssutil.bode(ssutil.asSISO(P), axB=axN, omega_limits=omega_limits_noise, label='Plant')
 91    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')
 92    axN.save(tjoin('Noise_bode.png'))
 93    axN.save(tjoin('Noise_bode.pdf'))
 94
 95
 96    # control.bode(S.mod, dB=False, Hz=True, omega_limits=omega_limits, label='Seismic')
 97    # control.bode(O.mod, dB=False, Hz=True, omega_limits=omega_limits, label='Meas. Noise')
 98    # control.bode(P.mod, dB=False, Hz=True, omega_limits=omega_limits, label='Plant')
 99    # 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')
100    # plt.legend()
101    # plt.savefig(tjoin('Noise_bode.pdf'), bbox_inches='tight')
102    # plt.savefig(tjoin('Noise_bode.png'), bbox_inches='tight', dpi=300)
103    # plt.close()
104
105    omega_limits_fom = np.array([1e-1, 1e5])*2*np.pi
106    axFOM = ssutil.bode(FBNS.siso('FBNS.out', 'FBNS.in'), omega_limits=omega_limits_fom, label='FBNS')
107    axFOM = ssutil.bode(FFlat.siso(iodutil.listoutputs(FFlat)[0], iodutil.listinputs(FFlat)[0]), axB=axFOM, omega_limits=omega_limits_fom, label='FFlat')
108    axFOM.save(tjoin('FOM_bode.pdf'))
109    axFOM.save(tjoin('FOM_bode.png'))
110    # control.bode(FBNS.mod, dB=True, Hz=True, omega_limits=omega_limits, label='FBNS')
111    # control.bode(FFlat.mod, dB=True, Hz=True, omega_limits=omega_limits, label='FFlat')
112    # plt.legend()
113    # plt.savefig(tjoin('FOM_bode.pdf'))
114    # plt.savefig(tjoin('FOM_bode.png'))
115    # plt.close()
116
117    #make the SO
118    #conlist =[['T_SO.in.1', 'O.out'], ['T_SO.in.2','S.out']]
119    #T_SO = addSS(namespace='T_SO', sub=True)
120    #SO = ssutil.multiconnect([S, O, T_SO], conlist)
121    #SO = ssutil.truncate_io(SO, ['O.in', 'S.in'], ['T_SO.out', 'S.out'])
122    #SO = ssutil.rename_io(SO, 'T_SO.out', 'O.out')
123
124    #control.bode(SO.mod[SO.iod['S.out'], SO.iod['S.in']], dB=True, Hz=True, omega_limits=omega_limits, label='S to S')
125    #control.bode(SO.mod[SO.iod['O.out'], SO.iod['O.in']], dB=True, Hz=True, omega_limits=omega_limits, label='O to O')
126    #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)
127    #control.bode(S.mod, dB=True, Hz=True, omega_limits=[1e-2, 1e3], label='Orig. S', linestyle='dotted', linewidth=4)
128    #control.bode(O.mod, dB=True, Hz=True, omega_limits=[1e-2, 1e3], label='Orig. O', linestyle='dotted', linewidth=4)
129    #plt.legend(fontsize=8)
130    #plt.savefig(tjoin('SO_bode.pdf'))
131    #plt.close()
132    if '_F3' in folder:
133        extras = 'F3_withP'
134    else:
135        extras = None
136
137    SPOFF = ssutil.makeSPOFF(S, P, O, FBNS, FFlat, diagnostic=True, extras=extras, balance=True)
138    #print('SPOFF', SPOFF.iod)
139    #SPOFF = ssutil.balance_sys_gain(SPOFF)
140    omega_limits=[1e-2, 1e3]
141
142    axB = mplfigB(Nrows=2)
143    F_Hz = np.geomspace(1e-2, 20, 1000)
144    with multi_bode(axB=axB, F_Hz = F_Hz) as bode:
145        bode(P['P.out', 'P.in'], label='P alone', linewidth=2, color='dodgerblue')
146        bode(SPOFF['P.out', 'P.in'], label='P to P in SPOFF', color='orange')
147        bode(SPOFF['P.out', 'S.in'], label='S to P', color='green')
148        bode(SPOFF['S.out', 'S.in'], label='S to S', linestyle='dotted', linewidth=4, color='green')
149        bode(SPOFF['P.out', 'SA.in'], linestyle='--', label='P Recombined', linewidth=4, color='dodgerblue')
150        bode(SPOFF['S.out', 'SA.in'], label='P that moved to S', color='dodgerblue', linestyle='dotted', linewidth=6)
151        bode(SPOFF['G.out', 'S.in'], label='Orig. S (from SPOFF)', color='red')
152        bode(S['S.out', 'S.in'], label='Orig. S alone', linestyle='dotted', linewidth=4, color='red')
153    axB.ax1.legend(fontsize=5)
154    axB.save(tjoin('SPOFF_diagnostic_bode.pdf'))
155    axB.save(tjoin('SPOFF_diagnostic_bode.png'))
156
157    axB = mplfigB(Nrows=2)
158    F_Hz = np.geomspace(1e-2, 20, 1000)
159    with multi_bode(axB=axB, F_Hz = F_Hz) as bode:
160        bode(sys=SPOFF['P.out', 'P.in'], label='P to P')
161        bode(sys=SPOFF['P.out', 'S.in'], label='S to P')
162        bode(sys=SPOFF['S.out', 'S.in'], label='S to S', linestyle=':', linewidth=2)
163        bode(sys=S['S.out', 'S.in'], label='S to S (orig)', linestyle=':', linewidth=2)
164        bode(sys=SPOFF['T.out', 'O.in'], label='O to Meas. Out')
165        bode(sys=SPOFF['T.out', 'S.in'], label='S to Meas. Out')
166
167    axB.ax1.legend(fontsize=5)
168    axB.save(tjoin('SPOFF_bode.pdf'))
169    axB.save(tjoin('SPOFF_bode.png'))
170
171    #print('SPOFF', SPOFF.iod)
172    #print(SPOFF['T.out', 'S.in']._zp[1])
173
174    #truncate_inputs = ['P.in', 'S.in', 'O.in', 'T.in.1', 'T.in.2', 'FBNS.in', 'F2.in', 'Zinf.in']
175    #truncate_outputs = ['P.out', 'O.out', 'S.out', 'T.out', 'FBNS.out', 'F2.out']
176    #SPOFF = ssutil.truncate_io(SPOFF, truncate_inputs, truncate_outputs)
177
178    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')
179    control.bode(SPOFF.mod[SPOFF.iod['FBNS.out'], SPOFF.iod['P.in']], dB=True, Hz=True, omega_limits=omega_limits, label='P to BNS FOM')
180    control.bode(SPOFF.mod[SPOFF.iod['P.out'], SPOFF.iod['S.in']], dB=True, Hz=True, omega_limits=omega_limits, label='S to P')
181    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)
182    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')
183    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')
184    plt.legend(fontsize=5)
185    plt.savefig(tjoin('SPOFF_all_bode.pdf'))
186    plt.savefig(tjoin('SPOFF_all_bode.png'))
187    plt.close()
188
189    SPOFF_bal = ssutil.balance_sys_gain(SPOFF)
190
191    axFOM_S = ssutil.bode(SPOFF.siso('FBNS.out', 'FBNS.in'), omega_limits=omega_limits_fom, label='FBNS', color='red')
192    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')
193    axFOM_S = ssutil.bode(SPOFF_bal.siso('FBNS.out', 'FBNS.in'), omega_limits=omega_limits_fom, axB=axFOM_S, label='FBNS SPOFF bal', color='green')
194    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')
195    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)
196    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)
197    axFOM_S.save(tjoin('FOM_bode_from_SPOFF.pdf'))
198    axFOM_S.save(tjoin('FOM_bode_from_SPOFF.png'))
199
200    #SPOFF = ssutil.balance_sys_gain(SPOFF)
201
202    P_load2 = ssutil.loadSys(fjoin(folder, P_fname))
203    assert(np.allclose(P_load2.A, P.A))
204    assert(np.allclose(P_load2.B, P.B))
205    assert(np.allclose(P_load2.C, P.C))
206    assert(np.allclose(P_load2.D, P.D))
207
208    ssutil.savesys(SPOFF, tjoin('ADY_Sanex.mat'))
209    ssutil.savesys(P, tjoin('ADY_plant.mat'))
210
211    ssutil.savesys(SPOFF, fjoin(folder, 'ADY_Sanex.mat'))
212    ssutil.savesys(P, fjoin(folder, 'ADY_plant.mat'))
213
214    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_ADY_make_sys.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: 0.000s
captured errors:
folder = 'ExampleModels_newFOM_F3'

    @pytest.mark.parametrize(
        'folder', [
            #'ExampleModels_rescale',
            'ExampleModels',
            'ExampleModels_working',
            'ExampleModels_working_F3',
            'ExampleModels_newFOM',
            'ExampleModels_newFOM_F3',
            'ExampleModels_DARMFOM',
        ]
    )
    @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.
        """

        # MUST be on the wield-control balancing branch for this to work!
        from wield.control.utilities import algorithm_choice
        if folder == 'ExampleModels_bal':
            # just needs to be below 100 to ensure default behavior
            algorithm_choice.algorithm_adjust_default('zpk2ss', 'zpk2ss_chain_poly_nobal', 90)
        else:
            # This fix is needed since updating several wield.controls algorithms
>           algorithm_choice.algorithm_adjust_default('zpk2ss', 'zpk2ss_chain_poly_nobal', 200)
E           AttributeError: module 'wield.control.utilities.algorithm_choice' has no attribute 'algorithm_adjust_default'. Did you mean: 'algorithm_choices_defaults'?

/builds/buzz/test/ASC_SOLVER/T_ADY_make_sys.py:454: AttributeError
test_Make_ADY[ExampleModels]
output
status: failed
duration: 0.000s
captured errors:
folder = 'ExampleModels'

    @pytest.mark.parametrize(
        'folder', [
            #'ExampleModels_rescale',
            'ExampleModels',
            'ExampleModels_working',
            'ExampleModels_working_F3',
            'ExampleModels_newFOM',
            'ExampleModels_newFOM_F3',
            'ExampleModels_DARMFOM',
        ]
    )
    @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.
        """

        # MUST be on the wield-control balancing branch for this to work!
        from wield.control.utilities import algorithm_choice
        if folder == 'ExampleModels_bal':
            # just needs to be below 100 to ensure default behavior
            algorithm_choice.algorithm_adjust_default('zpk2ss', 'zpk2ss_chain_poly_nobal', 90)
        else:
            # This fix is needed since updating several wield.controls algorithms
>           algorithm_choice.algorithm_adjust_default('zpk2ss', 'zpk2ss_chain_poly_nobal', 200)
E           AttributeError: module 'wield.control.utilities.algorithm_choice' has no attribute 'algorithm_adjust_default'. Did you mean: 'algorithm_choices_defaults'?

/builds/buzz/test/ASC_SOLVER/T_ADY_make_sys.py:454: AttributeError
test_Make_ADY[ExampleModels_newFOM]
output
status: failed
duration: 0.000s
captured errors:
folder = 'ExampleModels_newFOM'

    @pytest.mark.parametrize(
        'folder', [
            #'ExampleModels_rescale',
            'ExampleModels',
            'ExampleModels_working',
            'ExampleModels_working_F3',
            'ExampleModels_newFOM',
            'ExampleModels_newFOM_F3',
            'ExampleModels_DARMFOM',
        ]
    )
    @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.
        """

        # MUST be on the wield-control balancing branch for this to work!
        from wield.control.utilities import algorithm_choice
        if folder == 'ExampleModels_bal':
            # just needs to be below 100 to ensure default behavior
            algorithm_choice.algorithm_adjust_default('zpk2ss', 'zpk2ss_chain_poly_nobal', 90)
        else:
            # This fix is needed since updating several wield.controls algorithms
>           algorithm_choice.algorithm_adjust_default('zpk2ss', 'zpk2ss_chain_poly_nobal', 200)
E           AttributeError: module 'wield.control.utilities.algorithm_choice' has no attribute 'algorithm_adjust_default'. Did you mean: 'algorithm_choices_defaults'?

/builds/buzz/test/ASC_SOLVER/T_ADY_make_sys.py:454: AttributeError
test_Make_ADY[ExampleModels_working_F3]
output
status: failed
duration: 0.000s
captured errors:
folder = 'ExampleModels_working_F3'

    @pytest.mark.parametrize(
        'folder', [
            #'ExampleModels_rescale',
            'ExampleModels',
            'ExampleModels_working',
            'ExampleModels_working_F3',
            'ExampleModels_newFOM',
            'ExampleModels_newFOM_F3',
            'ExampleModels_DARMFOM',
        ]
    )
    @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.
        """

        # MUST be on the wield-control balancing branch for this to work!
        from wield.control.utilities import algorithm_choice
        if folder == 'ExampleModels_bal':
            # just needs to be below 100 to ensure default behavior
            algorithm_choice.algorithm_adjust_default('zpk2ss', 'zpk2ss_chain_poly_nobal', 90)
        else:
            # This fix is needed since updating several wield.controls algorithms
>           algorithm_choice.algorithm_adjust_default('zpk2ss', 'zpk2ss_chain_poly_nobal', 200)
E           AttributeError: module 'wield.control.utilities.algorithm_choice' has no attribute 'algorithm_adjust_default'. Did you mean: 'algorithm_choices_defaults'?

/builds/buzz/test/ASC_SOLVER/T_ADY_make_sys.py:454: AttributeError
test_Make_ADY[ExampleModels_working]
output
status: failed
duration: 0.000s
captured errors:
folder = 'ExampleModels_working'

    @pytest.mark.parametrize(
        'folder', [
            #'ExampleModels_rescale',
            'ExampleModels',
            'ExampleModels_working',
            'ExampleModels_working_F3',
            'ExampleModels_newFOM',
            'ExampleModels_newFOM_F3',
            'ExampleModels_DARMFOM',
        ]
    )
    @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.
        """

        # MUST be on the wield-control balancing branch for this to work!
        from wield.control.utilities import algorithm_choice
        if folder == 'ExampleModels_bal':
            # just needs to be below 100 to ensure default behavior
            algorithm_choice.algorithm_adjust_default('zpk2ss', 'zpk2ss_chain_poly_nobal', 90)
        else:
            # This fix is needed since updating several wield.controls algorithms
>           algorithm_choice.algorithm_adjust_default('zpk2ss', 'zpk2ss_chain_poly_nobal', 200)
E           AttributeError: module 'wield.control.utilities.algorithm_choice' has no attribute 'algorithm_adjust_default'. Did you mean: 'algorithm_choices_defaults'?

/builds/buzz/test/ASC_SOLVER/T_ADY_make_sys.py:454: AttributeError
test_Make_ADY[ExampleModels_DARMFOM]
output
status: failed
duration: 0.000s
captured errors:
folder = 'ExampleModels_DARMFOM'

    @pytest.mark.parametrize(
        'folder', [
            #'ExampleModels_rescale',
            'ExampleModels',
            'ExampleModels_working',
            'ExampleModels_working_F3',
            'ExampleModels_newFOM',
            'ExampleModels_newFOM_F3',
            'ExampleModels_DARMFOM',
        ]
    )
    @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.
        """

        # MUST be on the wield-control balancing branch for this to work!
        from wield.control.utilities import algorithm_choice
        if folder == 'ExampleModels_bal':
            # just needs to be below 100 to ensure default behavior
            algorithm_choice.algorithm_adjust_default('zpk2ss', 'zpk2ss_chain_poly_nobal', 90)
        else:
            # This fix is needed since updating several wield.controls algorithms
>           algorithm_choice.algorithm_adjust_default('zpk2ss', 'zpk2ss_chain_poly_nobal', 200)
E           AttributeError: module 'wield.control.utilities.algorithm_choice' has no attribute 'algorithm_adjust_default'. Did you mean: 'algorithm_choices_defaults'?

/builds/buzz/test/ASC_SOLVER/T_ADY_make_sys.py:454: AttributeError
transposeSys(sys, FOM_ns)[source]

This is a pytest needing documentation

code
 1def transposeSys(sys, FOM_ns):
 2    # add Z_inf as an input
 3    sys_inf_in_badname = ssutil.duplicate_io(sys, 'T.in.1')
 4    sys_inf_in = ssutil.rename_io(sys_inf_in_badname, 'T.in.1.2', 'Zinf.in')
 5    #print(sys.iod)
 6
 7    inputs= ['D.in', 'O.in', 'S.in', 'Zinf.in']
 8    outputs_no_inf = [fns + '.out' for fns in FOM_ns] + ['T.out']
 9
10    sys_red = ssutil.truncate_io(sys_inf_in, inputs, outputs_no_inf)
11
12    # Transpose the system
13    sys_transpose = ssutil.transpose(sys_red)
14
15    # Reorder the inputs and outputs
16    sys_transpose = ssutil.reorder_io(sys_transpose, 
17        [fns + '.in' for fns in FOM_ns] + ['T.in'], [
18        'Zinf.out',
19        'S.out',
20        'O.out',
21        'D.out'
22    ])
23
24    return sys_transpose
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_ADY_make_sys.transposeSys

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.

ws_tf2ss(z, p, k, gain=1, mfactor=1, angular=True)[source]

Takes a python control zpk and returns a python control statespace. Uses more numerically stable wield computations to convert between.

code
docstring
"""
Takes a python control zpk and returns a python control statespace.
Uses more numerically stable wield computations to convert between.
"""

1def ws_tf2ss(z, p, k, gain=1, mfactor=1, angular=True):
2
3    ws_zpk = SISO.zpk(z, p, k * gain, angular=True, fiducial_rtol=1e-5, fiducial_atol=1e-10)
4    ws_SS = ws_zpk.asSS * mfactor
5    # print("ZPK: ", ws_SS.asZPK.z, ws_SS.asZPK.p)
6
7    # return control.ss(ws_SS.A.T, ws_SS.C.T, ws_SS.B.T, ws_SS.D.T)
8    return control.ss(ws_SS.A, ws_SS.B, ws_SS.C, ws_SS.D)
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_ADY_make_sys.ws_tf2ss

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.