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Fix CI warnings#955

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Merged
murrayrm merged 4 commits intopython-control:mainfrommurrayrm:fixwarnings-23Dec2023
Jan 12, 2024
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4 changes: 2 additions & 2 deletionscontrol/flatsys/linflat.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -119,10 +119,10 @@ def forward(self, x, u, params):
x = np.reshape(x, (-1, 1))
u = np.reshape(u, (1, -1))
zflag = [np.zeros(self.nstates + 1)]
zflag[0][0] = self.Cf @ x
zflag[0][0] =(self.Cf @ x).item()
H = self.Cf # initial state transformation
for i in range(1, self.nstates + 1):
zflag[0][i] = H @ (self.A @ x + self.B @ u)
zflag[0][i] =(H @ (self.A @ x + self.B @ u)).item()
H = H @ self.A # derivative for next iteration
return zflag

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2 changes: 1 addition & 1 deletioncontrol/freqplot.py
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Expand Up@@ -473,7 +473,7 @@ def bode_plot(
if ax is None:
with plt.rc_context(_freqplot_rcParams):
ax_array = fig.subplots(nrows, ncols, squeeze=False)
fig.set_tight_layout(True)
fig.set_layout_engine('tight')
fig.align_labels()

# Set up default sharing of axis limits if not specified
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2 changes: 1 addition & 1 deletioncontrol/optimal.py
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Expand Up@@ -956,7 +956,7 @@ def solve_ocp(
transpose=None, return_states=True, print_summary=True, log=False,
**kwargs):

"""Compute the solution to an optimal control problem.
r"""Compute the solution to an optimal control problem.

The optimal trajectory (states and inputs) is computed so as to
approximately mimimize a cost function of the following form (for
Expand Down
8 changes: 7 additions & 1 deletioncontrol/robust.py
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Expand Up@@ -41,6 +41,7 @@

# External packages and modules
import numpy as np
import warnings
from .exception import *
from .statesp import StateSpace
from .statefbk import *
Expand DownExpand Up@@ -357,7 +358,12 @@ def augw(g, w1=None, w2=None, w3=None):
# output indices
oi = np.arange(1, 1 + now1 + now2 + now3 + ny)

p = connect(sysall, q, ii, oi)
# Filter out known warning due to use of connect
with warnings.catch_warnings():
warnings.filterwarnings(
'ignore', message="`connect`", category=DeprecationWarning)

p = connect(sysall, q, ii, oi)

return p

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6 changes: 5 additions & 1 deletioncontrol/sisotool.py
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Expand Up@@ -186,7 +186,11 @@ def _SisotoolUpdate(sys, fig, K, bode_plot_params, tvect=None):
sys_closed = append(sys, -K)
connects = [[1, 3],
[3, 1]]
sys_closed = connect(sys_closed, connects, 2, 2)
# Filter out known warning due to use of connect
with warnings.catch_warnings():
warnings.filterwarnings(
'ignore', message="`connect`", category=DeprecationWarning)
sys_closed = connect(sys_closed, connects, 2, 2)
if tvect is None:
tvect, yout = step_response(sys_closed, T_num=100)
else:
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4 changes: 2 additions & 2 deletionscontrol/stochsys.py
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Expand Up@@ -183,14 +183,14 @@ def lqe(*args, **kwargs):

# contributed by Sawyer B. Fuller <minster@uw.edu>
def dlqe(*args, **kwargs):
"""dlqe(A, G, C, QN, RN, [, N])
r"""dlqe(A, G, C, QN, RN, [, N])

Linear quadratic estimator design (Kalman filter) for discrete-time
systems. Given the system

.. math::

x[n+1] &= Ax[n] + Bu[n] + Gw[n] \\\\
x[n+1] &= Ax[n] + Bu[n] + Gw[n] \\
y[n] &= Cx[n] + Du[n] + v[n]

with unbiased process noise w and measurement noise v with covariances
Expand Down
19 changes: 11 additions & 8 deletionscontrol/tests/flatsys_test.py
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Expand Up@@ -194,14 +194,17 @@ def test_kinematic_car_ocp(
else:
initial_guess = None

# Solve the optimal trajectory
traj_ocp = fs.solve_flat_ocp(
vehicle_flat, timepts, x0, u0,
cost=traj_cost, constraints=input_constraints,
terminal_cost=terminal_cost, basis=basis,
initial_guess=initial_guess,
minimize_kwargs={'method': method},
)
# Solve the optimal trajectory (allow warnings)
with warnings.catch_warnings():
warnings.filterwarnings(
'ignore', message="unable to solve", category=UserWarning)
traj_ocp = fs.solve_flat_ocp(
vehicle_flat, timepts, x0, u0,
cost=traj_cost, constraints=input_constraints,
terminal_cost=terminal_cost, basis=basis,
initial_guess=initial_guess,
minimize_kwargs={'method': method},
)
xd, ud = traj_ocp.eval(timepts)

if not traj_ocp.success:
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28 changes: 14 additions & 14 deletionscontrol/tests/freqresp_test.py
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Expand Up@@ -16,7 +16,8 @@
from control.statesp import StateSpace
from control.xferfcn import TransferFunction
from control.matlab import ss, tf, bode, rss
from control.freqplot import bode_plot, nyquist_plot, singular_values_plot
from control.freqplot import bode_plot, nyquist_plot, nyquist_response, \
singular_values_plot, singular_values_response
from control.tests.conftest import slycotonly

pytestmark = pytest.mark.usefixtures("mplcleanup")
Expand DownExpand Up@@ -97,19 +98,17 @@ def test_nyquist_basic(ss_siso):
tf_siso = tf(ss_siso)
nyquist_plot(ss_siso)
nyquist_plot(tf_siso)
count, contour = nyquist_plot(
tf_siso, plot=False, return_contour=True, omega_num=20)
assert len(contour) == 20
response = nyquist_response(tf_siso, omega_num=20)
assert len(response.contour) == 20

with pytest.warns(UserWarning, match="encirclements was a non-integer"):
count, contour = nyquist_plot(
tf_siso, plot=False, omega_limits=(1, 100), return_contour=True)
assert_allclose(contour[0], 1j)
assert_allclose(contour[-1], 100j)

count, contour = nyquist_plot(
tf_siso, plot=False, omega=np.logspace(-1, 1, 10), return_contour=True)
assert len(contour) == 10
response = nyquist_response(tf_siso, omega=np.logspace(-1, 1, 10))
assert len(response.contour) == 10


@pytest.mark.usefixtures("legacy_plot_signature")
Expand DownExpand Up@@ -200,7 +199,7 @@ def test_bode_margin(dB, maginfty1, maginfty2, gminv,
den = [1, 25, 100, 0]
sys = ctrl.tf(num, den)
plt.figure()
ctrl.bode_plot(sys,margins=True, dB=dB, deg=deg, Hz=Hz)
ctrl.bode_plot(sys,display_margins=True, dB=dB, deg=deg, Hz=Hz)
fig = plt.gcf()
allaxes = fig.get_axes()

Expand DownExpand Up@@ -655,21 +654,22 @@ def tsystem(request, ss_mimo_ct, ss_miso_ct, ss_simo_ct, ss_siso_ct, ss_mimo_dt)
def test_singular_values_plot(tsystem):
sys = tsystem.sys
for omega_ref, sigma_ref in zip(tsystem.omegas, tsystem.sigmas):
sigma, _ = singular_values_plot(sys, omega_ref, plot=False)
response = singular_values_response(sys, omega_ref)
sigma = np.real(response.fresp[:, 0, :])
np.testing.assert_almost_equal(sigma, sigma_ref)


def test_singular_values_plot_mpl_base(ss_mimo_ct, ss_mimo_dt):
sys_ct = ss_mimo_ct.sys
sys_dt = ss_mimo_dt.sys
plt.figure()
singular_values_plot(sys_ct, plot=True)
singular_values_plot(sys_ct)
fig = plt.gcf()
allaxes = fig.get_axes()
assert(len(allaxes) == 1)
assert(allaxes[0].get_label() == 'control-sigma')
plt.figure()
singular_values_plot([sys_ct, sys_dt],plot=True,Hz=True, dB=True, grid=False)
singular_values_plot([sys_ct, sys_dt], Hz=True, dB=True, grid=False)
fig = plt.gcf()
allaxes = fig.get_axes()
assert(len(allaxes) == 1)
Expand All@@ -679,10 +679,10 @@ def test_singular_values_plot_mpl_base(ss_mimo_ct, ss_mimo_dt):
def test_singular_values_plot_mpl_superimpose_nyq(ss_mimo_ct, ss_mimo_dt):
sys_ct = ss_mimo_ct.sys
sys_dt = ss_mimo_dt.sys
omega_all = np.logspace(-3,2, 1000)
omega_all = np.logspace(-3,int(math.log10(2 * math.pi/sys_dt.dt)), 1000)
plt.figure()
singular_values_plot(sys_ct, omega_all, plot=True)
singular_values_plot(sys_dt, omega_all, plot=True)
singular_values_plot(sys_ct, omega_all)
singular_values_plot(sys_dt, omega_all)
fig = plt.gcf()
allaxes = fig.get_axes()
assert(len(allaxes) == 1)
Expand Down
15 changes: 9 additions & 6 deletionscontrol/tests/optimal_test.py
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Expand Up@@ -745,12 +745,15 @@ def vehicle_output(t, x, u, params):
initial_guess = (state_guess, input_guess)

# Solve the optimal control problem
result = opt.solve_ocp(
vehicle, timepts, x0, traj_cost, constraints,
terminal_cost=term_cost, initial_guess=initial_guess,
trajectory_method=method,
# minimize_method='COBYLA', # SLSQP',
)
with warnings.catch_warnings():
warnings.filterwarnings(
'ignore', message="unable to solve", category=UserWarning)
result = opt.solve_ocp(
vehicle, timepts, x0, traj_cost, constraints,
terminal_cost=term_cost, initial_guess=initial_guess,
trajectory_method=method,
# minimize_method='COBYLA', # SLSQP',
)

if fail == 'xfail':
assert not result.success
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3 changes: 1 addition & 2 deletionscontrol/tests/sisotool_test.py
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Expand Up@@ -79,8 +79,7 @@ def test_sisotool(self, tsys):
'omega_limits': None,
'omega_num': None,
'ax': np.array([[ax_mag], [ax_phase]]),
'margins': True,
'margin_info': True,
'display_margins': 'overlay',
}

# Check that the xaxes of the bode plot are shared before the rlocus click
Expand Down
2 changes: 1 addition & 1 deletioncontrol/timeplot.py
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Expand Up@@ -292,7 +292,7 @@ def time_response_plot(
if ax is None:
with plt.rc_context(timeplot_rcParams):
ax_array = fig.subplots(nrows, ncols, sharex=True, squeeze=False)
fig.set_tight_layout(True)
fig.set_layout_engine('tight')
fig.align_labels()

else:
Expand Down
2 changes: 1 addition & 1 deletioncontrol/timeresp.py
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Expand Up@@ -1084,7 +1084,7 @@ def forced_response(sys, T=None, U=0., X0=0., transpose=False,
if U.ndim == 1:
U = U.reshape(1, -1) # pylint: disable=E1103

# Algorithm: to integrate from time 0 to time dt, with linear
# Algorithm: to integrate from time 0 to time dt, with linear
# interpolation between inputs u(0) = u0 and u(dt) = u1, we solve
# xdot = A x + B u, x(0) = x0
# udot = (u1 - u0) / dt, u(0) = u0.
Expand Down
8 changes: 7 additions & 1 deletioncontrol/xferfcn.py
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Expand Up@@ -468,7 +468,13 @@ def __str__(self, var=None):
numstr = _tf_polynomial_to_string(self.num[no][ni], var=var)
denstr = _tf_polynomial_to_string(self.den[no][ni], var=var)
elif self.display_format == 'zpk':
z, p, k = tf2zpk(self.num[no][ni], self.den[no][ni])
num = self.num[no][ni]
if num.size == 1 and num.item() == 0:
# Catch a special case that SciPy doesn't handle
z, p, k = tf2zpk([1.], self.den[no][ni])
k = 0
else:
z, p, k = tf2zpk(self.num[no][ni], self.den[no][ni])
numstr = _tf_factorized_polynomial_to_string(
z, gain=k, var=var)
denstr = _tf_factorized_polynomial_to_string(p, var=var)
Expand Down
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