diff --git a/.github/workflows/doctest.yml b/.github/workflows/doctest.yml
index 62638d104..49455a5c6 100644
--- a/.github/workflows/doctest.yml
+++ b/.github/workflows/doctest.yml
@@ -33,7 +33,6 @@ jobs:
- name: Run doctest
shell: bash -l {0}
env:
- PYTHON_CONTROL_ARRAY_AND_MATRIX: ${{ matrix.array-and-matrix }}
MPLBACKEND: ${{ matrix.mplbackend }}
working-directory: doc
run: |
diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml
index cea5e542f..04b46a466 100644
--- a/.github/workflows/python-package-conda.yml
+++ b/.github/workflows/python-package-conda.yml
@@ -9,7 +9,6 @@ jobs:
${{ matrix.slycot || 'no' }} Slycot;
${{ matrix.pandas || 'no' }} Pandas;
${{ matrix.cvxopt || 'no' }} CVXOPT
- ${{ matrix.array-and-matrix == 1 && '; array and matrix' || '' }}
${{ matrix.mplbackend && format('; {0}', matrix.mplbackend) }}
runs-on: ubuntu-latest
@@ -22,14 +21,12 @@ jobs:
pandas: [""]
cvxopt: ["", "conda"]
mplbackend: [""]
- array-and-matrix: [0]
include:
- python-version: '3.11'
slycot: conda
pandas: conda
cvxopt: conda
mplbackend: QtAgg
- array-and-matrix: 1
steps:
- uses: actions/checkout@v3
@@ -63,7 +60,6 @@ jobs:
- name: Test with pytest
shell: bash -l {0}
env:
- PYTHON_CONTROL_ARRAY_AND_MATRIX: ${{ matrix.array-and-matrix }}
MPLBACKEND: ${{ matrix.mplbackend }}
run: pytest -v --cov=control --cov-config=.coveragerc control/tests
diff --git a/control/config.py b/control/config.py
index f75bd52db..9009a30f3 100644
--- a/control/config.py
+++ b/control/config.py
@@ -14,7 +14,7 @@
__all__ = ['defaults', 'set_defaults', 'reset_defaults',
'use_matlab_defaults', 'use_fbs_defaults',
- 'use_legacy_defaults', 'use_numpy_matrix']
+ 'use_legacy_defaults']
# Package level default values
_control_defaults = {
@@ -202,7 +202,6 @@ def use_matlab_defaults():
The following conventions are used:
* Bode plots plot gain in dB, phase in degrees, frequency in
rad/sec, with grids
- * State space class and functions use Numpy matrix objects
Examples
--------
@@ -211,7 +210,6 @@ def use_matlab_defaults():
"""
set_defaults('freqplot', dB=True, deg=True, Hz=False, grid=True)
- set_defaults('statesp', use_numpy_matrix=True)
# Set defaults to match FBS (Astrom and Murray)
@@ -233,41 +231,6 @@ def use_fbs_defaults():
set_defaults('nyquist', mirror_style='--')
-# Decide whether to use numpy.matrix for state space operations
-def use_numpy_matrix(flag=True, warn=True):
- """Turn on/off use of Numpy `matrix` class for state space operations.
-
- Parameters
- ----------
- flag : bool
- If flag is `True` (default), use the deprecated Numpy
- `matrix` class to represent matrices in the `~control.StateSpace`
- class and functions. If flat is `False`, then matrices are
- represented by a 2D `ndarray` object.
-
- warn : bool
- If flag is `True` (default), issue a warning when turning on the use
- of the Numpy `matrix` class. Set `warn` to false to omit display of
- the warning message.
-
- Notes
- -----
- Prior to release 0.9.x, the default type for 2D arrays is the Numpy
- `matrix` class. Starting in release 0.9.0, the default type for state
- space operations is a 2D array.
-
- Examples
- --------
- >>> ct.use_numpy_matrix(True, False)
- >>> # do some legacy calculations using np.matrix
-
- """
- if flag and warn:
- warnings.warn("Return type numpy.matrix is deprecated.",
- stacklevel=2, category=DeprecationWarning)
- set_defaults('statesp', use_numpy_matrix=flag)
-
-
def use_legacy_defaults(version):
""" Sets the defaults to whatever they were in a given release.
@@ -331,7 +294,7 @@ def use_legacy_defaults(version):
# Version 0.9.0:
if major == 0 and minor < 9:
# switched to 'array' as default for state space objects
- set_defaults('statesp', use_numpy_matrix=True)
+ warnings.warn("NumPy matrix class no longer supported")
# switched to 0 (=continuous) as default timestep
set_defaults('control', default_dt=None)
diff --git a/control/iosys.py b/control/iosys.py
index 6c6458f35..00ed288fa 100644
--- a/control/iosys.py
+++ b/control/iosys.py
@@ -2227,8 +2227,6 @@ def ss(*args, **kwargs):
y[k] &= C x[k] + D u[k]
The matrices can be given as *array like* data types or strings.
- Everything that the constructor of :class:`numpy.matrix` accepts is
- permissible here too.
``ss(args, inputs=['u1', ..., 'up'], outputs=['y1', ..., 'yq'], states=['x1', ..., 'xn'])``
Create a system with named input, output, and state signals.
diff --git a/control/mateqn.py b/control/mateqn.py
index 1cf2e65d9..339f1a880 100644
--- a/control/mateqn.py
+++ b/control/mateqn.py
@@ -126,14 +126,9 @@ def lyap(A, Q, C=None, E=None, method=None):
Returns
-------
- X : 2D array (or matrix)
+ X : 2D array
Solution to the Lyapunov or Sylvester equation
- Notes
- -----
- The return type for 2D arrays depends on the default class set for
- state space operations. See :func:`~control.use_numpy_matrix`.
-
"""
# Decide what method to use
method = _slycot_or_scipy(method)
@@ -260,11 +255,6 @@ def dlyap(A, Q, C=None, E=None, method=None):
X : 2D array (or matrix)
Solution to the Lyapunov or Sylvester equation
- Notes
- -----
- The return type for 2D arrays depends on the default class set for
- state space operations. See :func:`~control.use_numpy_matrix`.
-
"""
# Decide what method to use
method = _slycot_or_scipy(method)
@@ -395,11 +385,6 @@ def care(A, B, Q, R=None, S=None, E=None, stabilizing=True, method=None,
G : 2D array (or matrix)
Gain matrix
- Notes
- -----
- The return type for 2D arrays depends on the default class set for
- state space operations. See :func:`~control.use_numpy_matrix`.
-
"""
# Decide what method to use
method = _slycot_or_scipy(method)
@@ -554,11 +539,6 @@ def dare(A, B, Q, R, S=None, E=None, stabilizing=True, method=None,
G : 2D array (or matrix)
Gain matrix
- Notes
- -----
- The return type for 2D arrays depends on the default class set for
- state space operations. See :func:`~control.use_numpy_matrix`.
-
"""
# Decide what method to use
method = _slycot_or_scipy(method)
diff --git a/control/matlab/wrappers.py b/control/matlab/wrappers.py
index e7d757248..d98dcabf0 100644
--- a/control/matlab/wrappers.py
+++ b/control/matlab/wrappers.py
@@ -48,7 +48,7 @@ def bode(*args, **kwargs):
--------
>>> from control.matlab import ss, bode
- >>> sys = ss("1. -2; 3. -4", "5.; 7", "6. 8", "9.")
+ >>> sys = ss([[1, -2], [3, -4]], [[5], [7]], [[6, 8]], 9)
>>> mag, phase, omega = bode(sys)
.. todo::
diff --git a/control/statefbk.py b/control/statefbk.py
index f98974199..50bad75c1 100644
--- a/control/statefbk.py
+++ b/control/statefbk.py
@@ -110,9 +110,6 @@ def place(A, B, p):
The algorithm will not place poles at the same location more
than rank(B) times.
- The return type for 2D arrays depends on the default class set for
- state space operations. See :func:`~control.use_numpy_matrix`.
-
References
----------
.. [1] A.L. Tits and Y. Yang, "Globally convergent algorithms for robust
@@ -193,11 +190,6 @@ def place_varga(A, B, p, dtime=False, alpha=None):
[1] Varga A. "A Schur method for pole assignment." IEEE Trans. Automatic
Control, Vol. AC-26, pp. 517-519, 1981.
- Notes
- -----
- The return type for 2D arrays depends on the default class set for
- state space operations. See :func:`~control.use_numpy_matrix`.
-
Examples
--------
>>> A = [[-1, -1], [0, 1]]
@@ -279,10 +271,6 @@ def acker(A, B, poles):
K : 2D array (or matrix)
Gains such that A - B K has given eigenvalues
- Notes
- -----
- The return type for 2D arrays depends on the default class set for
- state space operations. See :func:`~control.use_numpy_matrix`.
"""
# Convert the inputs to matrices
a = _ssmatrix(A)
@@ -366,13 +354,10 @@ def lqr(*args, **kwargs):
Notes
-----
- 1. If the first argument is an LTI object, then this object will be used
- to define the dynamics and input matrices. Furthermore, if the LTI
- object corresponds to a discrete time system, the ``dlqr()`` function
- will be called.
-
- 2. The return type for 2D arrays depends on the default class set for
- state space operations. See :func:`~control.use_numpy_matrix`.
+ If the first argument is an LTI object, then this object will be used
+ to define the dynamics and input matrices. Furthermore, if the LTI
+ object corresponds to a discrete time system, the ``dlqr()`` function
+ will be called.
Examples
--------
@@ -514,11 +499,6 @@ def dlqr(*args, **kwargs):
--------
lqr, lqe, dlqe
- Notes
- -----
- The return type for 2D arrays depends on the default class set for
- state space operations. See :func:`~control.use_numpy_matrix`.
-
Examples
--------
>>> K, S, E = dlqr(dsys, Q, R, [N]) # doctest: +SKIP
@@ -971,11 +951,6 @@ def ctrb(A, B):
C : 2D array (or matrix)
Controllability matrix
- Notes
- -----
- The return type for 2D arrays depends on the default class set for
- state space operations. See :func:`~control.use_numpy_matrix`.
-
Examples
--------
>>> G = ct.tf2ss([1], [1, 2, 3])
@@ -1010,11 +985,6 @@ def obsv(A, C):
O : 2D array (or matrix)
Observability matrix
- Notes
- -----
- The return type for 2D arrays depends on the default class set for
- state space operations. See :func:`~control.use_numpy_matrix`.
-
Examples
--------
>>> G = ct.tf2ss([1], [1, 2, 3])
@@ -1063,11 +1033,6 @@ def gram(sys, type):
if slycot routine sb03md cannot be found
if slycot routine sb03od cannot be found
- Notes
- -----
- The return type for 2D arrays depends on the default class set for
- state space operations. See :func:`~control.use_numpy_matrix`.
-
Examples
--------
>>> G = ct.rss(4)
diff --git a/control/statesp.py b/control/statesp.py
index b2a3934a1..ae2c32c50 100644
--- a/control/statesp.py
+++ b/control/statesp.py
@@ -77,7 +77,6 @@
# Define module default parameter values
_statesp_defaults = {
- 'statesp.use_numpy_matrix': False, # False is default in 0.9.0 and above
'statesp.remove_useless_states': False,
'statesp.latex_num_format': '.3g',
'statesp.latex_repr_type': 'partitioned',
@@ -104,14 +103,8 @@ def _ssmatrix(data, axis=1):
arr : 2D array, with shape (0, 0) if a is empty
"""
- # Convert the data into an array or matrix, as configured
- # If data is passed as a string, use (deprecated?) matrix constructor
- if config.defaults['statesp.use_numpy_matrix']:
- arr = np.matrix(data, dtype=float)
- elif isinstance(data, str):
- arr = np.array(np.matrix(data, dtype=float))
- else:
- arr = np.array(data, dtype=float)
+ # Convert the data into an array
+ arr = np.array(data, dtype=float)
ndim = arr.ndim
shape = arr.shape
@@ -205,12 +198,7 @@ class StateSpace(LTI):
-----
The main data members in the ``StateSpace`` class are the A, B, C, and D
matrices. The class also keeps track of the number of states (i.e.,
- the size of A). The data format used to store state space matrices is
- set using the value of `config.defaults['use_numpy_matrix']`. If True
- (default), the state space elements are stored as `numpy.matrix` objects;
- otherwise they are `numpy.ndarray` objects. The
- :func:`~control.use_numpy_matrix` function can be used to set the storage
- type.
+ the size of A).
A discrete time system is created by specifying a nonzero 'timebase', dt
when the system is constructed:
@@ -358,10 +346,8 @@ def __init__(self, *args, init_namedio=True, **kwargs):
elif kwargs:
raise TypeError("unrecognized keyword(s): ", str(kwargs))
- # Reset shapes (may not be needed once np.matrix support is removed)
+ # Reset shape if system is static
if self._isstatic():
- # static gain
- # matrix's default "empty" shape is 1x0
A.shape = (0, 0)
B.shape = (0, self.ninputs)
C.shape = (self.noutputs, 0)
@@ -467,10 +453,6 @@ def _remove_useless_states(self):
"""
# Search for useless states and get indices of these states.
- #
- # Note: shape from np.where depends on whether we are storing state
- # space objects as np.matrix or np.array. Code below will work
- # correctly in either case.
ax1_A = np.where(~self.A.any(axis=1))[0]
ax1_B = np.where(~self.B.any(axis=1))[0]
ax0_A = np.where(~self.A.any(axis=0))[-1]
@@ -502,12 +484,11 @@ def __str__(self):
return string
# represent to implement a re-loadable version
- # TODO: remove the conversion to array when matrix is no longer used
def __repr__(self):
"""Print state-space system in loadable form."""
return "StateSpace({A}, {B}, {C}, {D}{dt})".format(
- A=asarray(self.A).__repr__(), B=asarray(self.B).__repr__(),
- C=asarray(self.C).__repr__(), D=asarray(self.D).__repr__(),
+ A=self.A.__repr__(), B=self.B.__repr__(),
+ C=self.C.__repr__(), D=self.D.__repr__(),
dt=(isdtime(self, strict=True) and ", {}".format(self.dt)) or '')
def _latex_partitioned_stateless(self):
@@ -930,18 +911,17 @@ def horner(self, x, warn_infinite=True):
x_arr = np.atleast_1d(x).astype(complex, copy=False)
# return fast on systems with 0 or 1 state
- if not config.defaults['statesp.use_numpy_matrix']:
- if self.nstates == 0:
- return self.D[:, :, np.newaxis] \
- * np.ones_like(x_arr, dtype=complex)
- if self.nstates == 1:
- with np.errstate(divide='ignore', invalid='ignore'):
- out = self.C[:, :, np.newaxis] \
- / (x_arr - self.A[0, 0]) \
- * self.B[:, :, np.newaxis] \
- + self.D[:, :, np.newaxis]
- out[np.isnan(out)] = complex(np.inf, np.nan)
- return out
+ if self.nstates == 0:
+ return self.D[:, :, np.newaxis] \
+ * np.ones_like(x_arr, dtype=complex)
+ elif self.nstates == 1:
+ with np.errstate(divide='ignore', invalid='ignore'):
+ out = self.C[:, :, np.newaxis] \
+ / (x_arr - self.A[0, 0]) \
+ * self.B[:, :, np.newaxis] \
+ + self.D[:, :, np.newaxis]
+ out[np.isnan(out)] = complex(np.inf, np.nan)
+ return out
try:
out = self.slycot_laub(x_arr)
diff --git a/control/stochsys.py b/control/stochsys.py
index 663b09ece..85e108336 100644
--- a/control/stochsys.py
+++ b/control/stochsys.py
@@ -87,9 +87,9 @@ def lqe(*args, **kwargs):
Returns
-------
- L : 2D array (or matrix)
+ L : 2D array
Kalman estimator gain
- P : 2D array (or matrix)
+ P : 2D array
Solution to Riccati equation
.. math::
@@ -101,13 +101,10 @@ def lqe(*args, **kwargs):
Notes
-----
- 1. If the first argument is an LTI object, then this object will be used
- to define the dynamics, noise and output matrices. Furthermore, if
- the LTI object corresponds to a discrete time system, the ``dlqe()``
- function will be called.
-
- 2. The return type for 2D arrays depends on the default class set for
- state space operations. See :func:`~control.use_numpy_matrix`.
+ If the first argument is an LTI object, then this object will be used
+ to define the dynamics, noise and output matrices. Furthermore, if the
+ LTI object corresponds to a discrete time system, the ``dlqe()``
+ function will be called.
Examples
--------
@@ -224,9 +221,9 @@ def dlqe(*args, **kwargs):
Returns
-------
- L : 2D array (or matrix)
+ L : 2D array
Kalman estimator gain
- P : 2D array (or matrix)
+ P : 2D array
Solution to Riccati equation
.. math::
@@ -236,11 +233,6 @@ def dlqe(*args, **kwargs):
E : 1D array
Eigenvalues of estimator poles eig(A - L C)
- Notes
- -----
- The return type for 2D arrays depends on the default class set for
- state space operations. See :func:`~control.use_numpy_matrix`.
-
Examples
--------
>>> L, P, E = dlqe(A, G, C, QN, RN) # doctest: +SKIP
diff --git a/control/tests/config_test.py b/control/tests/config_test.py
index 15229139e..1547b7e22 100644
--- a/control/tests/config_test.py
+++ b/control/tests/config_test.py
@@ -242,15 +242,13 @@ def test_reset_defaults(self):
assert ct.config.defaults['freqplot.feature_periphery_decades'] == 1.0
def test_legacy_defaults(self):
- with pytest.deprecated_call():
+ with pytest.warns(UserWarning, match="NumPy matrix class no longer"):
ct.use_legacy_defaults('0.8.3')
- assert(isinstance(ct.ss(0, 0, 0, 1).D, np.matrix))
- ct.reset_defaults()
- assert isinstance(ct.ss(0, 0, 0, 1).D, np.ndarray)
- assert not isinstance(ct.ss(0, 0, 0, 1).D, np.matrix)
+ ct.reset_defaults()
- ct.use_legacy_defaults('0.8.4')
- assert ct.config.defaults['forced_response.return_x'] is True
+ with pytest.warns(UserWarning, match="NumPy matrix class no longer"):
+ ct.use_legacy_defaults('0.8.4')
+ assert ct.config.defaults['forced_response.return_x'] is True
ct.use_legacy_defaults('0.9.0')
assert isinstance(ct.ss(0, 0, 0, 1).D, np.ndarray)
diff --git a/control/tests/conftest.py b/control/tests/conftest.py
index b63db3e11..c5ab6cb86 100644
--- a/control/tests/conftest.py
+++ b/control/tests/conftest.py
@@ -13,24 +13,21 @@
# some common pytest marks. These can be used as test decorators or in
# pytest.param(marks=)
-slycotonly = pytest.mark.skipif(not control.exception.slycot_check(),
- reason="slycot not installed")
-cvxoptonly = pytest.mark.skipif(not control.exception.cvxopt_check(),
- reason="cvxopt not installed")
-matrixfilter = pytest.mark.filterwarnings("ignore:.*matrix subclass:"
- "PendingDeprecationWarning")
-matrixerrorfilter = pytest.mark.filterwarnings("error:.*matrix subclass:"
- "PendingDeprecationWarning")
+slycotonly = pytest.mark.skipif(
+ not control.exception.slycot_check(), reason="slycot not installed")
+cvxoptonly = pytest.mark.skipif(
+ not control.exception.cvxopt_check(), reason="cvxopt not installed")
@pytest.fixture(scope="session", autouse=True)
def control_defaults():
"""Make sure the testing session always starts with the defaults.
- This should be the first fixture initialized,
- so that all other fixtures see the general defaults (unless they set them
- themselves) even before importing control/__init__. Enforce this by adding
- it as an argument to all other session scoped fixtures.
+ This should be the first fixture initialized, so that all other
+ fixtures see the general defaults (unless they set them themselves)
+ even before importing control/__init__. Enforce this by adding it as an
+ argument to all other session scoped fixtures.
+
"""
control.reset_defaults()
the_defaults = control.config.defaults.copy()
@@ -39,63 +36,6 @@ def control_defaults():
assert control.config.defaults == the_defaults
-@pytest.fixture(scope="function", autouse=TEST_MATRIX_AND_ARRAY,
- params=[pytest.param("arrayout", marks=matrixerrorfilter),
- pytest.param("matrixout", marks=matrixfilter)])
-def matarrayout(request):
- """Switch the config to use np.ndarray and np.matrix as returns."""
- restore = control.config.defaults['statesp.use_numpy_matrix']
- control.use_numpy_matrix(request.param == "matrixout", warn=False)
- yield
- control.use_numpy_matrix(restore, warn=False)
-
-
-def ismatarrayout(obj):
- """Test if the returned object has the correct type as configured.
-
- note that isinstance(np.matrix(obj), np.ndarray) is True
- """
- use_matrix = control.config.defaults['statesp.use_numpy_matrix']
- return (isinstance(obj, np.ndarray)
- and isinstance(obj, np.matrix) == use_matrix)
-
-
-def asmatarrayout(obj):
- """Return a object according to the configured default."""
- use_matrix = control.config.defaults['statesp.use_numpy_matrix']
- matarray = np.asmatrix if use_matrix else np.asarray
- return matarray(obj)
-
-
-@contextmanager
-def check_deprecated_matrix():
- """Check that a call produces a deprecation warning because of np.matrix."""
- use_matrix = control.config.defaults['statesp.use_numpy_matrix']
- if use_matrix:
- with pytest.deprecated_call():
- try:
- yield
- finally:
- pass
- else:
- yield
-
-
-@pytest.fixture(scope="function",
- params=[p for p, usebydefault in
- [(pytest.param(np.array,
- id="arrayin"),
- True),
- (pytest.param(np.matrix,
- id="matrixin",
- marks=matrixfilter),
- False)]
- if usebydefault or TEST_MATRIX_AND_ARRAY])
-def matarrayin(request):
- """Use array and matrix to construct input data in tests."""
- return request.param
-
-
@pytest.fixture(scope="function")
def editsdefaults():
"""Make sure any changes to the defaults only last during a test."""
diff --git a/control/tests/iosys_test.py b/control/tests/iosys_test.py
index 4012770ba..1fe57d577 100644
--- a/control/tests/iosys_test.py
+++ b/control/tests/iosys_test.py
@@ -17,7 +17,6 @@
import control as ct
from control import iosys as ios
-from control.tests.conftest import matrixfilter
class TestIOSys:
@@ -253,10 +252,10 @@ def test_linearize_named_signals(self, kincar):
assert linearized_newnames.find_output('y') is None
# Test legacy version as well
- ct.use_legacy_defaults('0.8.4')
- ct.config.use_numpy_matrix(False) # np.matrix deprecated
- linearized = kincar.linearize([0, 0, 0], [0, 0], copy_names=True)
- assert linearized.name == kincar.name + '_linearized'
+ with pytest.warns(UserWarning, match="NumPy matrix class no longer"):
+ ct.use_legacy_defaults('0.8.4')
+ linearized = kincar.linearize([0, 0, 0], [0, 0], copy_names=True)
+ assert linearized.name == kincar.name + '_linearized'
def test_connect(self, tsys):
# Define a couple of (linear) systems to interconnection
@@ -1060,8 +1059,8 @@ def test_sys_naming_convention(self, tsys):
"""Enforce generic system names 'sys[i]' to be present when systems are
created without explicit names."""
- ct.config.use_legacy_defaults('0.8.4') # changed delims in 0.9.0
- ct.config.use_numpy_matrix(False) # np.matrix deprecated
+ with pytest.warns(UserWarning, match="NumPy matrix class no longer"):
+ ct.config.use_legacy_defaults('0.8.4') # changed delims in 0.9.0
# Create a system with a known ID
ct.namedio.NamedIOSystem._idCounter = 0
@@ -1128,8 +1127,8 @@ def test_signals_naming_convention_0_8_4(self, tsys):
output: 'y[i]'
"""
- ct.config.use_legacy_defaults('0.8.4') # changed delims in 0.9.0
- ct.config.use_numpy_matrix(False) # np.matrix deprecated
+ with pytest.warns(UserWarning, match="NumPy matrix class no longer"):
+ ct.config.use_legacy_defaults('0.8.4') # changed delims in 0.9.0
# Create a system with a known ID
ct.namedio.NamedIOSystem._idCounter = 0
@@ -1434,8 +1433,8 @@ def test_duplicates(self, tsys):
ios_series = nlios * nlios
# Nonduplicate objects
- ct.config.use_legacy_defaults('0.8.4') # changed delims in 0.9.0
- ct.config.use_numpy_matrix(False) # np.matrix deprecated
+ with pytest.warns(UserWarning, match="NumPy matrix class no longer"):
+ ct.config.use_legacy_defaults('0.8.4') # changed delims in 0.9.0
nlios1 = nlios.copy()
nlios2 = nlios.copy()
with pytest.warns(UserWarning, match="duplicate name"):
diff --git a/control/tests/modelsimp_test.py b/control/tests/modelsimp_test.py
index 0746e3fe2..49c2afd58 100644
--- a/control/tests/modelsimp_test.py
+++ b/control/tests/modelsimp_test.py
@@ -9,7 +9,7 @@
from control import StateSpace, forced_response, tf, rss, c2d
from control.exception import ControlMIMONotImplemented
-from control.tests.conftest import slycotonly, matarrayin
+from control.tests.conftest import slycotonly
from control.modelsimp import balred, hsvd, markov, modred
@@ -17,11 +17,11 @@ class TestModelsimp:
"""Test model reduction functions"""
@slycotonly
- def testHSVD(self, matarrayout, matarrayin):
- A = matarrayin([[1., -2.], [3., -4.]])
- B = matarrayin([[5.], [7.]])
- C = matarrayin([[6., 8.]])
- D = matarrayin([[9.]])
+ def testHSVD(self):
+ A = np.array([[1., -2.], [3., -4.]])
+ B = np.array([[5.], [7.]])
+ C = np.array([[6., 8.]])
+ D = np.array([[9.]])
sys = StateSpace(A, B, C, D)
hsv = hsvd(sys)
hsvtrue = np.array([24.42686, 0.5731395]) # from MATLAB
@@ -32,8 +32,8 @@ def testHSVD(self, matarrayout, matarrayin):
assert isinstance(hsv, np.ndarray)
assert not isinstance(hsv, np.matrix)
- def testMarkovSignature(self, matarrayout, matarrayin):
- U = matarrayin([[1., 1., 1., 1., 1.]])
+ def testMarkovSignature(self):
+ U = np.array([[1., 1., 1., 1., 1.]])
Y = U
m = 3
H = markov(Y, U, m, transpose=False)
@@ -111,17 +111,17 @@ def testMarkovResults(self, k, m, n):
# for k=5, m=n=10: 0.015 %
np.testing.assert_allclose(Mtrue, Mcomp, rtol=1e-6, atol=1e-8)
- def testModredMatchDC(self, matarrayin):
+ def testModredMatchDC(self):
#balanced realization computed in matlab for the transfer function:
# num = [1 11 45 32], den = [1 15 60 200 60]
- A = matarrayin(
+ A = np.array(
[[-1.958, -1.194, 1.824, -1.464],
[-1.194, -0.8344, 2.563, -1.351],
[-1.824, -2.563, -1.124, 2.704],
[-1.464, -1.351, -2.704, -11.08]])
- B = matarrayin([[-0.9057], [-0.4068], [-0.3263], [-0.3474]])
- C = matarrayin([[-0.9057, -0.4068, 0.3263, -0.3474]])
- D = matarrayin([[0.]])
+ B = np.array([[-0.9057], [-0.4068], [-0.3263], [-0.3474]])
+ C = np.array([[-0.9057, -0.4068, 0.3263, -0.3474]])
+ D = np.array([[0.]])
sys = StateSpace(A, B, C, D)
rsys = modred(sys,[2, 3],'matchdc')
Artrue = np.array([[-4.431, -4.552], [-4.552, -5.361]])
@@ -133,30 +133,30 @@ def testModredMatchDC(self, matarrayin):
np.testing.assert_array_almost_equal(rsys.C, Crtrue, decimal=3)
np.testing.assert_array_almost_equal(rsys.D, Drtrue, decimal=2)
- def testModredUnstable(self, matarrayin):
+ def testModredUnstable(self):
"""Check if an error is thrown when an unstable system is given"""
- A = matarrayin(
+ A = np.array(
[[4.5418, 3.3999, 5.0342, 4.3808],
[0.3890, 0.3599, 0.4195, 0.1760],
[-4.2117, -3.2395, -4.6760, -4.2180],
[0.0052, 0.0429, 0.0155, 0.2743]])
- B = matarrayin([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0]])
- C = matarrayin([[1.0, 2.0, 3.0, 4.0], [1.0, 2.0, 3.0, 4.0]])
- D = matarrayin([[0.0, 0.0], [0.0, 0.0]])
+ B = np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0]])
+ C = np.array([[1.0, 2.0, 3.0, 4.0], [1.0, 2.0, 3.0, 4.0]])
+ D = np.array([[0.0, 0.0], [0.0, 0.0]])
sys = StateSpace(A, B, C, D)
np.testing.assert_raises(ValueError, modred, sys, [2, 3])
- def testModredTruncate(self, matarrayin):
+ def testModredTruncate(self):
#balanced realization computed in matlab for the transfer function:
# num = [1 11 45 32], den = [1 15 60 200 60]
- A = matarrayin(
+ A = np.array(
[[-1.958, -1.194, 1.824, -1.464],
[-1.194, -0.8344, 2.563, -1.351],
[-1.824, -2.563, -1.124, 2.704],
[-1.464, -1.351, -2.704, -11.08]])
- B = matarrayin([[-0.9057], [-0.4068], [-0.3263], [-0.3474]])
- C = matarrayin([[-0.9057, -0.4068, 0.3263, -0.3474]])
- D = matarrayin([[0.]])
+ B = np.array([[-0.9057], [-0.4068], [-0.3263], [-0.3474]])
+ C = np.array([[-0.9057, -0.4068, 0.3263, -0.3474]])
+ D = np.array([[0.]])
sys = StateSpace(A, B, C, D)
rsys = modred(sys,[2, 3],'truncate')
Artrue = np.array([[-1.958, -1.194], [-1.194, -0.8344]])
@@ -170,18 +170,18 @@ def testModredTruncate(self, matarrayin):
@slycotonly
- def testBalredTruncate(self, matarrayin):
+ def testBalredTruncate(self):
# controlable canonical realization computed in matlab for the transfer
# function:
# num = [1 11 45 32], den = [1 15 60 200 60]
- A = matarrayin(
+ A = np.array(
[[-15., -7.5, -6.25, -1.875],
[8., 0., 0., 0.],
[0., 4., 0., 0.],
[0., 0., 1., 0.]])
- B = matarrayin([[2.], [0.], [0.], [0.]])
- C = matarrayin([[0.5, 0.6875, 0.7031, 0.5]])
- D = matarrayin([[0.]])
+ B = np.array([[2.], [0.], [0.], [0.]])
+ C = np.array([[0.5, 0.6875, 0.7031, 0.5]])
+ D = np.array([[0.]])
sys = StateSpace(A, B, C, D)
orders = 2
@@ -211,18 +211,18 @@ def testBalredTruncate(self, matarrayin):
np.testing.assert_array_almost_equal(Dr, Drtrue, decimal=4)
@slycotonly
- def testBalredMatchDC(self, matarrayin):
+ def testBalredMatchDC(self):
# controlable canonical realization computed in matlab for the transfer
# function:
# num = [1 11 45 32], den = [1 15 60 200 60]
- A = matarrayin(
+ A = np.array(
[[-15., -7.5, -6.25, -1.875],
[8., 0., 0., 0.],
[0., 4., 0., 0.],
[0., 0., 1., 0.]])
- B = matarrayin([[2.], [0.], [0.], [0.]])
- C = matarrayin([[0.5, 0.6875, 0.7031, 0.5]])
- D = matarrayin([[0.]])
+ B = np.array([[2.], [0.], [0.], [0.]])
+ C = np.array([[0.5, 0.6875, 0.7031, 0.5]])
+ D = np.array([[0.]])
sys = StateSpace(A, B, C, D)
orders = 2
diff --git a/control/tests/statefbk_test.py b/control/tests/statefbk_test.py
index 951c817f1..dc72c0723 100644
--- a/control/tests/statefbk_test.py
+++ b/control/tests/statefbk_test.py
@@ -16,8 +16,7 @@
from control.mateqn import care, dare
from control.statefbk import (ctrb, obsv, place, place_varga, lqr, dlqr,
gram, acker)
-from control.tests.conftest import (slycotonly, check_deprecated_matrix,
- ismatarrayout, asmatarrayout)
+from control.tests.conftest import slycotonly
@pytest.fixture
@@ -36,48 +35,37 @@ class TestStatefbk:
# Set to True to print systems to the output.
debug = False
- def testCtrbSISO(self, matarrayin, matarrayout):
- A = matarrayin([[1., 2.], [3., 4.]])
- B = matarrayin([[5.], [7.]])
+ def testCtrbSISO(self):
+ A = np.array([[1., 2.], [3., 4.]])
+ B = np.array([[5.], [7.]])
Wctrue = np.array([[5., 19.], [7., 43.]])
-
- with check_deprecated_matrix():
- Wc = ctrb(A, B)
- assert ismatarrayout(Wc)
-
+ Wc = ctrb(A, B)
np.testing.assert_array_almost_equal(Wc, Wctrue)
- def testCtrbMIMO(self, matarrayin):
- A = matarrayin([[1., 2.], [3., 4.]])
- B = matarrayin([[5., 6.], [7., 8.]])
+ def testCtrbMIMO(self):
+ A = np.array([[1., 2.], [3., 4.]])
+ B = np.array([[5., 6.], [7., 8.]])
Wctrue = np.array([[5., 6., 19., 22.], [7., 8., 43., 50.]])
Wc = ctrb(A, B)
np.testing.assert_array_almost_equal(Wc, Wctrue)
- # Make sure default type values are correct
- assert ismatarrayout(Wc)
-
- def testObsvSISO(self, matarrayin):
- A = matarrayin([[1., 2.], [3., 4.]])
- C = matarrayin([[5., 7.]])
+ def testObsvSISO(self):
+ A = np.array([[1., 2.], [3., 4.]])
+ C = np.array([[5., 7.]])
Wotrue = np.array([[5., 7.], [26., 38.]])
Wo = obsv(A, C)
np.testing.assert_array_almost_equal(Wo, Wotrue)
- # Make sure default type values are correct
- assert ismatarrayout(Wo)
-
-
- def testObsvMIMO(self, matarrayin):
- A = matarrayin([[1., 2.], [3., 4.]])
- C = matarrayin([[5., 6.], [7., 8.]])
+ def testObsvMIMO(self):
+ A = np.array([[1., 2.], [3., 4.]])
+ C = np.array([[5., 6.], [7., 8.]])
Wotrue = np.array([[5., 6.], [7., 8.], [23., 34.], [31., 46.]])
Wo = obsv(A, C)
np.testing.assert_array_almost_equal(Wo, Wotrue)
- def testCtrbObsvDuality(self, matarrayin):
- A = matarrayin([[1.2, -2.3], [3.4, -4.5]])
- B = matarrayin([[5.8, 6.9], [8., 9.1]])
+ def testCtrbObsvDuality(self):
+ A = np.array([[1.2, -2.3], [3.4, -4.5]])
+ B = np.array([[5.8, 6.9], [8., 9.1]])
Wc = ctrb(A, B)
A = np.transpose(A)
C = np.transpose(B)
@@ -85,59 +73,55 @@ def testCtrbObsvDuality(self, matarrayin):
np.testing.assert_array_almost_equal(Wc,Wo)
@slycotonly
- def testGramWc(self, matarrayin, matarrayout):
- A = matarrayin([[1., -2.], [3., -4.]])
- B = matarrayin([[5., 6.], [7., 8.]])
- C = matarrayin([[4., 5.], [6., 7.]])
- D = matarrayin([[13., 14.], [15., 16.]])
+ def testGramWc(self):
+ A = np.array([[1., -2.], [3., -4.]])
+ B = np.array([[5., 6.], [7., 8.]])
+ C = np.array([[4., 5.], [6., 7.]])
+ D = np.array([[13., 14.], [15., 16.]])
sys = ss(A, B, C, D)
Wctrue = np.array([[18.5, 24.5], [24.5, 32.5]])
-
- with check_deprecated_matrix():
- Wc = gram(sys, 'c')
-
- assert ismatarrayout(Wc)
+ Wc = gram(sys, 'c')
np.testing.assert_array_almost_equal(Wc, Wctrue)
@slycotonly
- def testGramRc(self, matarrayin):
- A = matarrayin([[1., -2.], [3., -4.]])
- B = matarrayin([[5., 6.], [7., 8.]])
- C = matarrayin([[4., 5.], [6., 7.]])
- D = matarrayin([[13., 14.], [15., 16.]])
+ def testGramRc(self):
+ A = np.array([[1., -2.], [3., -4.]])
+ B = np.array([[5., 6.], [7., 8.]])
+ C = np.array([[4., 5.], [6., 7.]])
+ D = np.array([[13., 14.], [15., 16.]])
sys = ss(A, B, C, D)
Rctrue = np.array([[4.30116263, 5.6961343], [0., 0.23249528]])
Rc = gram(sys, 'cf')
np.testing.assert_array_almost_equal(Rc, Rctrue)
@slycotonly
- def testGramWo(self, matarrayin):
- A = matarrayin([[1., -2.], [3., -4.]])
- B = matarrayin([[5., 6.], [7., 8.]])
- C = matarrayin([[4., 5.], [6., 7.]])
- D = matarrayin([[13., 14.], [15., 16.]])
+ def testGramWo(self):
+ A = np.array([[1., -2.], [3., -4.]])
+ B = np.array([[5., 6.], [7., 8.]])
+ C = np.array([[4., 5.], [6., 7.]])
+ D = np.array([[13., 14.], [15., 16.]])
sys = ss(A, B, C, D)
Wotrue = np.array([[257.5, -94.5], [-94.5, 56.5]])
Wo = gram(sys, 'o')
np.testing.assert_array_almost_equal(Wo, Wotrue)
@slycotonly
- def testGramWo2(self, matarrayin):
- A = matarrayin([[1., -2.], [3., -4.]])
- B = matarrayin([[5.], [7.]])
- C = matarrayin([[6., 8.]])
- D = matarrayin([[9.]])
+ def testGramWo2(self):
+ A = np.array([[1., -2.], [3., -4.]])
+ B = np.array([[5.], [7.]])
+ C = np.array([[6., 8.]])
+ D = np.array([[9.]])
sys = ss(A,B,C,D)
Wotrue = np.array([[198., -72.], [-72., 44.]])
Wo = gram(sys, 'o')
np.testing.assert_array_almost_equal(Wo, Wotrue)
@slycotonly
- def testGramRo(self, matarrayin):
- A = matarrayin([[1., -2.], [3., -4.]])
- B = matarrayin([[5., 6.], [7., 8.]])
- C = matarrayin([[4., 5.], [6., 7.]])
- D = matarrayin([[13., 14.], [15., 16.]])
+ def testGramRo(self):
+ A = np.array([[1., -2.], [3., -4.]])
+ B = np.array([[5., 6.], [7., 8.]])
+ C = np.array([[4., 5.], [6., 7.]])
+ D = np.array([[13., 14.], [15., 16.]])
sys = ss(A, B, C, D)
Rotrue = np.array([[16.04680654, -5.8890222], [0., 4.67112593]])
Ro = gram(sys, 'of')
@@ -195,19 +179,18 @@ def checkPlaced(self, P_expected, P_placed):
P_placed.sort()
np.testing.assert_array_almost_equal(P_expected, P_placed)
- def testPlace(self, matarrayin):
+ def testPlace(self):
# Matrices shamelessly stolen from scipy example code.
- A = matarrayin([[1.380, -0.2077, 6.715, -5.676],
+ A = np.array([[1.380, -0.2077, 6.715, -5.676],
[-0.5814, -4.290, 0, 0.6750],
[1.067, 4.273, -6.654, 5.893],
[0.0480, 4.273, 1.343, -2.104]])
- B = matarrayin([[0, 5.679],
+ B = np.array([[0, 5.679],
[1.136, 1.136],
[0, 0],
[-3.146, 0]])
- P = matarrayin([-0.5 + 1j, -0.5 - 1j, -5.0566, -8.6659])
+ P = np.array([-0.5 + 1j, -0.5 - 1j, -5.0566, -8.6659])
K = place(A, B, P)
- assert ismatarrayout(K)
P_placed = np.linalg.eigvals(A - B @ K)
self.checkPlaced(P, P_placed)
@@ -219,17 +202,17 @@ def testPlace(self, matarrayin):
# Check that we get an error if we ask for too many poles in the same
# location. Here, rank(B) = 2, so lets place three at the same spot.
- P_repeated = matarrayin([-0.5, -0.5, -0.5, -8.6659])
+ P_repeated = np.array([-0.5, -0.5, -0.5, -8.6659])
with pytest.raises(ValueError):
place(A, B, P_repeated)
@slycotonly
- def testPlace_varga_continuous(self, matarrayin):
+ def testPlace_varga_continuous(self):
"""
Check that we can place eigenvalues for dtime=False
"""
- A = matarrayin([[1., -2.], [3., -4.]])
- B = matarrayin([[5.], [7.]])
+ A = np.array([[1., -2.], [3., -4.]])
+ B = np.array([[5.], [7.]])
P = [-2., -2.]
K = place_varga(A, B, P)
@@ -242,26 +225,26 @@ def testPlace_varga_continuous(self, matarrayin):
# Regression test against bug #177
# https://github.com/python-control/python-control/issues/177
- A = matarrayin([[0, 1], [100, 0]])
- B = matarrayin([[0], [1]])
- P = matarrayin([-20 + 10*1j, -20 - 10*1j])
+ A = np.array([[0, 1], [100, 0]])
+ B = np.array([[0], [1]])
+ P = np.array([-20 + 10*1j, -20 - 10*1j])
K = place_varga(A, B, P)
P_placed = np.linalg.eigvals(A - B @ K)
self.checkPlaced(P, P_placed)
@slycotonly
- def testPlace_varga_continuous_partial_eigs(self, matarrayin):
+ def testPlace_varga_continuous_partial_eigs(self):
"""
Check that we are able to use the alpha parameter to only place
a subset of the eigenvalues, for the continous time case.
"""
# A matrix has eigenvalues at s=-1, and s=-2. Choose alpha = -1.5
# and check that eigenvalue at s=-2 stays put.
- A = matarrayin([[1., -2.], [3., -4.]])
- B = matarrayin([[5.], [7.]])
+ A = np.array([[1., -2.], [3., -4.]])
+ B = np.array([[5.], [7.]])
- P = matarrayin([-3.])
+ P = np.array([-3.])
P_expected = np.array([-2.0, -3.0])
alpha = -1.5
K = place_varga(A, B, P, alpha=alpha)
@@ -271,30 +254,30 @@ def testPlace_varga_continuous_partial_eigs(self, matarrayin):
self.checkPlaced(P_expected, P_placed)
@slycotonly
- def testPlace_varga_discrete(self, matarrayin):
+ def testPlace_varga_discrete(self):
"""
Check that we can place poles using dtime=True (discrete time)
"""
- A = matarrayin([[1., 0], [0, 0.5]])
- B = matarrayin([[5.], [7.]])
+ A = np.array([[1., 0], [0, 0.5]])
+ B = np.array([[5.], [7.]])
- P = matarrayin([0.5, 0.5])
+ P = np.array([0.5, 0.5])
K = place_varga(A, B, P, dtime=True)
P_placed = np.linalg.eigvals(A - B @ K)
# No guarantee of the ordering, so sort them
self.checkPlaced(P, P_placed)
@slycotonly
- def testPlace_varga_discrete_partial_eigs(self, matarrayin):
+ def testPlace_varga_discrete_partial_eigs(self):
""""
Check that we can only assign a single eigenvalue in the discrete
time case.
"""
# A matrix has eigenvalues at 1.0 and 0.5. Set alpha = 0.51, and
# check that the eigenvalue at 0.5 is not moved.
- A = matarrayin([[1., 0], [0, 0.5]])
- B = matarrayin([[5.], [7.]])
- P = matarrayin([0.2, 0.6])
+ A = np.array([[1., 0], [0, 0.5]])
+ B = np.array([[5.], [7.]])
+ P = np.array([0.2, 0.6])
P_expected = np.array([0.5, 0.6])
alpha = 0.51
K = place_varga(A, B, P, dtime=True, alpha=alpha)
@@ -302,49 +285,49 @@ def testPlace_varga_discrete_partial_eigs(self, matarrayin):
self.checkPlaced(P_expected, P_placed)
def check_LQR(self, K, S, poles, Q, R):
- S_expected = asmatarrayout(np.sqrt(Q @ R))
- K_expected = asmatarrayout(S_expected / R)
+ S_expected = np.sqrt(Q @ R)
+ K_expected = S_expected / R
poles_expected = -np.squeeze(np.asarray(K_expected))
np.testing.assert_array_almost_equal(S, S_expected)
np.testing.assert_array_almost_equal(K, K_expected)
np.testing.assert_array_almost_equal(poles, poles_expected)
def check_DLQR(self, K, S, poles, Q, R):
- S_expected = asmatarrayout(Q)
- K_expected = asmatarrayout(0)
+ S_expected = Q
+ K_expected = 0
poles_expected = -np.squeeze(np.asarray(K_expected))
np.testing.assert_array_almost_equal(S, S_expected)
np.testing.assert_array_almost_equal(K, K_expected)
np.testing.assert_array_almost_equal(poles, poles_expected)
@pytest.mark.parametrize("method", [None, 'slycot', 'scipy'])
- def test_LQR_integrator(self, matarrayin, matarrayout, method):
+ def test_LQR_integrator(self, method):
if method == 'slycot' and not slycot_check():
return
- A, B, Q, R = (matarrayin([[X]]) for X in [0., 1., 10., 2.])
+ A, B, Q, R = (np.array([[X]]) for X in [0., 1., 10., 2.])
K, S, poles = lqr(A, B, Q, R, method=method)
self.check_LQR(K, S, poles, Q, R)
@pytest.mark.parametrize("method", [None, 'slycot', 'scipy'])
- def test_LQR_3args(self, matarrayin, matarrayout, method):
+ def test_LQR_3args(self, method):
if method == 'slycot' and not slycot_check():
return
sys = ss(0., 1., 1., 0.)
- Q, R = (matarrayin([[X]]) for X in [10., 2.])
+ Q, R = (np.array([[X]]) for X in [10., 2.])
K, S, poles = lqr(sys, Q, R, method=method)
self.check_LQR(K, S, poles, Q, R)
@pytest.mark.parametrize("method", [None, 'slycot', 'scipy'])
- def test_DLQR_3args(self, matarrayin, matarrayout, method):
+ def test_DLQR_3args(self, method):
if method == 'slycot' and not slycot_check():
return
dsys = ss(0., 1., 1., 0., .1)
- Q, R = (matarrayin([[X]]) for X in [10., 2.])
+ Q, R = (np.array([[X]]) for X in [10., 2.])
K, S, poles = dlqr(dsys, Q, R, method=method)
self.check_DLQR(K, S, poles, Q, R)
- def test_DLQR_4args(self, matarrayin, matarrayout):
- A, B, Q, R = (matarrayin([[X]]) for X in [0., 1., 10., 2.])
+ def test_DLQR_4args(self):
+ A, B, Q, R = (np.array([[X]]) for X in [0., 1., 10., 2.])
K, S, poles = dlqr(A, B, Q, R)
self.check_DLQR(K, S, poles, Q, R)
@@ -443,14 +426,14 @@ def testDLQR_warning(self):
with pytest.warns(UserWarning):
(K, S, E) = dlqr(A, B, Q, R, N)
- def test_care(self, matarrayin):
+ def test_care(self):
"""Test stabilizing and anti-stabilizing feedback, continuous"""
- A = matarrayin(np.diag([1, -1]))
- B = matarrayin(np.identity(2))
- Q = matarrayin(np.identity(2))
- R = matarrayin(np.identity(2))
- S = matarrayin(np.zeros((2, 2)))
- E = matarrayin(np.identity(2))
+ A = np.diag([1, -1])
+ B = np.identity(2)
+ Q = np.identity(2)
+ R = np.identity(2)
+ S = np.zeros((2, 2))
+ E = np.identity(2)
X, L, G = care(A, B, Q, R, S, E, stabilizing=True)
assert np.all(np.real(L) < 0)
@@ -465,14 +448,14 @@ def test_care(self, matarrayin):
@pytest.mark.parametrize(
"stabilizing",
[True, pytest.param(False, marks=slycotonly)])
- def test_dare(self, matarrayin, stabilizing):
+ def test_dare(self, stabilizing):
"""Test stabilizing and anti-stabilizing feedback, discrete"""
- A = matarrayin(np.diag([0.5, 2]))
- B = matarrayin(np.identity(2))
- Q = matarrayin(np.identity(2))
- R = matarrayin(np.identity(2))
- S = matarrayin(np.zeros((2, 2)))
- E = matarrayin(np.identity(2))
+ A = np.diag([0.5, 2])
+ B = np.identity(2)
+ Q = np.identity(2)
+ R = np.identity(2)
+ S = np.zeros((2, 2))
+ E = np.identity(2)
X, L, G = dare(A, B, Q, R, S, E, stabilizing=stabilizing)
sgn = {True: -1, False: 1}[stabilizing]
diff --git a/control/tests/statesp_test.py b/control/tests/statesp_test.py
index 1182674c1..83dc58b49 100644
--- a/control/tests/statesp_test.py
+++ b/control/tests/statesp_test.py
@@ -21,7 +21,7 @@
from control.statesp import StateSpace, _convert_to_statespace, tf2ss, \
_statesp_defaults, _rss_generate, linfnorm
from control.iosys import ss, rss, drss
-from control.tests.conftest import ismatarrayout, slycotonly
+from control.tests.conftest import slycotonly
from control.xferfcn import TransferFunction, ss2tf
@@ -196,17 +196,6 @@ def test_copy_constructor_nodt(self, sys322):
sys = StateSpace(sysin)
assert sys.dt is None
- def test_matlab_style_constructor(self):
- """Use (deprecated) matrix-style construction string"""
- with pytest.deprecated_call():
- sys = StateSpace("-1 1; 0 2", "0; 1", "1, 0", "0")
- assert sys.A.shape == (2, 2)
- assert sys.B.shape == (2, 1)
- assert sys.C.shape == (1, 2)
- assert sys.D.shape == (1, 1)
- for X in [sys.A, sys.B, sys.C, sys.D]:
- assert ismatarrayout(X)
-
def test_D_broadcast(self, sys623):
"""Test broadcast of D=0 to the right shape"""
# Giving D as a scalar 0 should broadcast to the right shape
@@ -1017,13 +1006,7 @@ def test_returnScipySignalLTI_error(self, mimoss):
class TestStateSpaceConfig:
"""Test the configuration of the StateSpace module"""
-
- @pytest.fixture
- def matarrayout(self):
- """Override autoused global fixture within this class"""
- pass
-
- def test_statespace_defaults(self, matarrayout):
+ def test_statespace_defaults(self):
"""Make sure the tests are run with the configured defaults"""
for k, v in _statesp_defaults.items():
assert defaults[k] == v, \
diff --git a/control/tests/stochsys_test.py b/control/tests/stochsys_test.py
index b2d90e2ab..91f4a1a08 100644
--- a/control/tests/stochsys_test.py
+++ b/control/tests/stochsys_test.py
@@ -3,7 +3,6 @@
import numpy as np
import pytest
-from control.tests.conftest import asmatarrayout
import control as ct
import control.optimal as opt
@@ -12,8 +11,8 @@
# Utility function to check LQE answer
def check_LQE(L, P, poles, G, QN, RN):
- P_expected = asmatarrayout(np.sqrt(G @ QN @ G @ RN))
- L_expected = asmatarrayout(P_expected / RN)
+ P_expected = np.sqrt(G @ QN @ G @ RN)
+ L_expected = P_expected / RN
poles_expected = -np.squeeze(np.asarray(L_expected))
np.testing.assert_almost_equal(P, P_expected)
np.testing.assert_almost_equal(L, L_expected)
@@ -21,19 +20,19 @@ def check_LQE(L, P, poles, G, QN, RN):
# Utility function to check discrete LQE solutions
def check_DLQE(L, P, poles, G, QN, RN):
- P_expected = asmatarrayout(G.dot(QN).dot(G))
- L_expected = asmatarrayout(0)
+ P_expected = G.dot(QN).dot(G)
+ L_expected = 0
poles_expected = -np.squeeze(np.asarray(L_expected))
np.testing.assert_almost_equal(P, P_expected)
np.testing.assert_almost_equal(L, L_expected)
np.testing.assert_almost_equal(poles, poles_expected)
@pytest.mark.parametrize("method", [None, 'slycot', 'scipy'])
-def test_LQE(matarrayin, method):
+def test_LQE(method):
if method == 'slycot' and not slycot_check():
return
- A, G, C, QN, RN = (matarrayin([[X]]) for X in [0., .1, 1., 10., 2.])
+ A, G, C, QN, RN = (np.array([[X]]) for X in [0., .1, 1., 10., 2.])
L, P, poles = lqe(A, G, C, QN, RN, method=method)
check_LQE(L, P, poles, G, QN, RN)
@@ -80,11 +79,11 @@ def test_lqe_call_format(cdlqe):
L, P, E = cdlqe(sys_tf, Q, R)
@pytest.mark.parametrize("method", [None, 'slycot', 'scipy'])
-def test_DLQE(matarrayin, method):
+def test_DLQE(method):
if method == 'slycot' and not slycot_check():
return
- A, G, C, QN, RN = (matarrayin([[X]]) for X in [0., .1, 1., 10., 2.])
+ A, G, C, QN, RN = (np.array([[X]]) for X in [0., .1, 1., 10., 2.])
L, P, poles = dlqe(A, G, C, QN, RN, method=method)
check_DLQE(L, P, poles, G, QN, RN)
diff --git a/control/tests/timeresp_test.py b/control/tests/timeresp_test.py
index 124e16c1e..ccd808169 100644
--- a/control/tests/timeresp_test.py
+++ b/control/tests/timeresp_test.py
@@ -857,7 +857,7 @@ def test_default_timevector_functions_d(self, fun, dt):
initial_response,
forced_response])
@pytest.mark.parametrize("squeeze", [None, True, False])
- def test_time_vector(self, tsystem, fun, squeeze, matarrayout):
+ def test_time_vector(self, tsystem, fun, squeeze):
"""Test time vector handling and correct output convention
gh-239, gh-295
@@ -1130,8 +1130,8 @@ def test_squeeze_exception(self, fcn):
])
def test_squeeze_0_8_4(self, nstate, nout, ninp, squeeze, shape):
# Set defaults to match release 0.8.4
- ct.config.use_legacy_defaults('0.8.4')
- ct.config.use_numpy_matrix(False)
+ with pytest.warns(UserWarning, match="NumPy matrix class no longer"):
+ ct.config.use_legacy_defaults('0.8.4')
# Generate system, time, and input vectors
sys = ct.rss(nstate, nout, ninp, strictly_proper=True)
diff --git a/control/tests/xferfcn_test.py b/control/tests/xferfcn_test.py
index eec305807..fd1076db0 100644
--- a/control/tests/xferfcn_test.py
+++ b/control/tests/xferfcn_test.py
@@ -14,7 +14,7 @@
from control import defaults, reset_defaults, set_defaults
from control.statesp import _convert_to_statespace
from control.xferfcn import _convert_to_transfer_function
-from control.tests.conftest import slycotonly, matrixfilter
+from control.tests.conftest import slycotonly
class TestXferFcn:
@@ -749,20 +749,16 @@ def test_indexing(self):
np.testing.assert_array_almost_equal(sys.num[1][1], tm.num[1][2])
np.testing.assert_array_almost_equal(sys.den[1][1], tm.den[1][2])
- @pytest.mark.parametrize(
- "matarrayin",
- [pytest.param(np.array,
- id="arrayin",
- marks=[pytest.mark.skip(".__matmul__ not implemented")]),
- pytest.param(np.matrix,
- id="matrixin",
- marks=matrixfilter)],
- indirect=True)
- @pytest.mark.parametrize("X_, ij",
- [([[2., 0., ]], 0),
- ([[0., 2., ]], 1)])
- def test_matrix_array_multiply(self, matarrayin, X_, ij):
- """Test mulitplication of MIMO TF with matrix and matmul with array"""
+ @pytest.mark.parametrize("op", [
+ pytest.param('mul'),
+ pytest.param(
+ 'matmul', marks=pytest.mark.skip(".__matmul__ not implemented")),
+ ])
+ @pytest.mark.parametrize("X, ij",
+ [(np.array([[2., 0., ]]), 0),
+ (np.array([[0., 2., ]]), 1)])
+ def test_matrix_array_multiply(self, op, X, ij):
+ """Test mulitplication of MIMO TF with matrix"""
# 2 inputs, 2 outputs with prime zeros so they do not cancel
n = 2
p = [3, 5, 7, 11, 13, 17, 19, 23]
@@ -771,13 +767,12 @@ def test_matrix_array_multiply(self, matarrayin, X_, ij):
for i in range(n)],
[[[1, -1]] * n] * n)
- X = matarrayin(X_)
-
- if matarrayin is np.matrix:
+ if op == 'matmul':
+ XH = X @ H
+ elif op == 'mul':
XH = X * H
else:
- # XH = X @ H
- XH = np.matmul(X, H)
+ assert NotImplemented(f"unknown operator '{op}'")
XH = XH.minreal()
assert XH.ninputs == n
assert XH.noutputs == X.shape[0]
@@ -790,11 +785,12 @@ def test_matrix_array_multiply(self, matarrayin, X_, ij):
np.testing.assert_allclose(2. * H.num[ij][1], XH.num[0][1], rtol=1e-4)
np.testing.assert_allclose( H.den[ij][1], XH.den[0][1], rtol=1e-4)
- if matarrayin is np.matrix:
+ if op == 'matmul':
+ HXt = H @ X.T
+ elif op == 'mul':
HXt = H * X.T
else:
- # HXt = H @ X.T
- HXt = np.matmul(H, X.T)
+ assert NotImplemented(f"unknown operator '{op}'")
HXt = HXt.minreal()
assert HXt.ninputs == X.T.shape[1]
assert HXt.noutputs == n
diff --git a/control/xferfcn.py b/control/xferfcn.py
index 56006d697..7303d5e73 100644
--- a/control/xferfcn.py
+++ b/control/xferfcn.py
@@ -1640,8 +1640,8 @@ def tf(*args, **kwargs):
>>> G = (s + 1)/(s**2 + 2*s + 1)
>>> # Convert a StateSpace to a TransferFunction object.
- >>> sys_ss = ct.ss("1. -2; 3. -4", "5.; 7", "6. 8", "9.")
- >>> sys2 = ct.tf(sys1)
+ >>> sys_ss = ct.ss([[1, -2], [3, -4]], [[5], [7]], [[6, 8]], 9)
+ >>> sys_tf = ct.tf(sys_ss)
"""
@@ -1801,7 +1801,7 @@ def ss2tf(*args, **kwargs):
>>> sys1 = ct.ss2tf(A, B, C, D)
>>> sys_ss = ct.ss(A, B, C, D)
- >>> sys2 = ct.ss2tf(sys_ss)
+ >>> sys_tf = ct.ss2tf(sys_ss)
"""
diff --git a/doc/control.rst b/doc/control.rst
index 8dc8a09a4..ca46043db 100644
--- a/doc/control.rst
+++ b/doc/control.rst
@@ -197,6 +197,5 @@ Utility functions and conversions
unwrap
use_fbs_defaults
use_matlab_defaults
- use_numpy_matrix
diff --git a/doc/conventions.rst b/doc/conventions.rst
index 7c9c1ec6f..b5073b8ef 100644
--- a/doc/conventions.rst
+++ b/doc/conventions.rst
@@ -303,9 +303,6 @@ Selected variables that can be configured, along with their default values:
* freqplot.feature_periphery_decade (1.0): How many decades to include in
the frequency range on both sides of features (poles, zeros).
- * statesp.use_numpy_matrix (True): set the return type for state space
- matrices to `numpy.matrix` (verus numpy.ndarray)
-
* statesp.default_dt and xferfcn.default_dt (None): set the default value
of dt when constructing new LTI systems
@@ -322,5 +319,4 @@ Functions that can be used to set standard configurations:
reset_defaults
use_fbs_defaults
use_matlab_defaults
- use_numpy_matrix
use_legacy_defaults
pFad - Phonifier reborn
Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.
Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.
Alternative Proxies:
Alternative Proxy
pFad Proxy
pFad v3 Proxy
pFad v4 Proxy