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fixes to unit tests so that they pass when the default array type is ndarray#433

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5 changes: 5 additions & 0 deletions.travis.yml
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Expand Up@@ -49,6 +49,11 @@ jobs:
services: xvfb
python: "3.8"
env: SCIPY=scipy SLYCOT=source
- name: "use numpy matrix"
dist: xenial
services: xvfb
python: "3.8"
env: SCIPY=scipy SLYCOT=source PYTHON_CONTROL_STATESPACE_ARRAY=1

# Exclude combinations that are very unlikely (and don't work)
exclude:
Expand Down
30 changes: 13 additions & 17 deletionscontrol/canonical.py
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Expand Up@@ -6,7 +6,8 @@
from .statesp import StateSpace
from .statefbk import ctrb, obsv

from numpy import zeros, shape, poly, iscomplex, hstack, dot, transpose
from numpy import zeros, zeros_like, shape, poly, iscomplex, vstack, hstack, dot, \
transpose, empty
from numpy.linalg import solve, matrix_rank, eig

__all__ = ['canonical_form', 'reachable_form', 'observable_form', 'modal_form',
Expand DownExpand Up@@ -70,9 +71,9 @@ def reachable_form(xsys):
zsys = StateSpace(xsys)

# Generate the system matrices for the desired canonical form
zsys.B =zeros(shape(xsys.B))
zsys.B =zeros_like(xsys.B)
zsys.B[0, 0] = 1.0
zsys.A =zeros(shape(xsys.A))
zsys.A =zeros_like(xsys.A)
Apoly = poly(xsys.A) # characteristic polynomial
for i in range(0, xsys.states):
zsys.A[0, i] = -Apoly[i+1] / Apoly[0]
Expand DownExpand Up@@ -124,9 +125,9 @@ def observable_form(xsys):
zsys = StateSpace(xsys)

# Generate the system matrices for the desired canonical form
zsys.C =zeros(shape(xsys.C))
zsys.C =zeros_like(xsys.C)
zsys.C[0, 0] = 1
zsys.A =zeros(shape(xsys.A))
zsys.A =zeros_like(xsys.A)
Apoly = poly(xsys.A) # characteristic polynomial
for i in range(0, xsys.states):
zsys.A[i, 0] = -Apoly[i+1] / Apoly[0]
Expand All@@ -144,7 +145,7 @@ def observable_form(xsys):
raise ValueError("Transformation matrix singular to working precision.")

# Finally, compute the output matrix
zsys.B = Tzx *xsys.B
zsys.B = Tzx.dot(xsys.B)

return zsys, Tzx

Expand DownExpand Up@@ -174,9 +175,9 @@ def modal_form(xsys):
# Calculate eigenvalues and matrix of eigenvectors Tzx,
eigval, eigvec = eig(xsys.A)

# Eigenvalues andaccording eigenvectors are not sorted,
# Eigenvalues andcorresponding eigenvectors are not sorted,
# thus modal transformation is ambiguous
#Sorting eigenvalues andrespectivevectorsby largest to smallest eigenvalue
#Sort eigenvalues and vectorsfrom largest to smallest eigenvalue
idx = eigval.argsort()[::-1]
eigval = eigval[idx]
eigvec = eigvec[:,idx]
Expand All@@ -189,23 +190,18 @@ def modal_form(xsys):

# Keep track of complex conjugates (need only one)
lst_conjugates = []
Tzx =None
Tzx =empty((0, xsys.A.shape[0])) # empty zero-height row matrix
for val, vec in zip(eigval, eigvec.T):
if iscomplex(val):
if val not in lst_conjugates:
lst_conjugates.append(val.conjugate())
if Tzx is not None:
Tzx = hstack((Tzx, hstack((vec.real.T, vec.imag.T))))
else:
Tzx = hstack((vec.real.T, vec.imag.T))
Tzx = vstack((Tzx, vec.real, vec.imag))
else:
# if conjugate has already been seen, skip this eigenvalue
lst_conjugates.remove(val)
else:
if Tzx is not None:
Tzx = hstack((Tzx, vec.real.T))
else:
Tzx = vec.real.T
Tzx = vstack((Tzx, vec.real))
Tzx = Tzx.T

# Generate the system matrices for the desired canonical form
zsys.A = solve(Tzx, xsys.A).dot(Tzx)
Expand Down
27 changes: 20 additions & 7 deletionscontrol/statesp.py
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Expand Up@@ -77,7 +77,10 @@


def _ssmatrix(data, axis=1):
"""Convert argument to a (possibly empty) state space matrix.
"""Convert argument to a (possibly empty) 2D state space matrix.

The axis keyword argument makes it convenient to specify that if the input
is a vector, it is a row (axis=1) or column (axis=0) vector.

Parameters
----------
Expand All@@ -94,8 +97,10 @@ def _ssmatrix(data, axis=1):
"""
# 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'] or isinstance(data, str):
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)
ndim = arr.ndim
Expand DownExpand Up@@ -195,12 +200,20 @@ def __init__(self, *args, **kw):
raise ValueError("Needs 1 or 4 arguments; received %i." % len(args))

# Process keyword arguments
remove_useless = kw.get('remove_useless', config.defaults['statesp.remove_useless_states'])
remove_useless = kw.get('remove_useless',
config.defaults['statesp.remove_useless_states'])

# Convert all matrices to standard form
A = _ssmatrix(A)
B = _ssmatrix(B, axis=0)
C = _ssmatrix(C, axis=1)
# if B is a 1D array, turn it into a column vector if it fits
if np.asarray(B).ndim == 1 and len(B) == A.shape[0]:
B = _ssmatrix(B, axis=0)
else:
B = _ssmatrix(B)
if np.asarray(C).ndim == 1 and len(C) == A.shape[0]:
C = _ssmatrix(C, axis=1)
else:
C = _ssmatrix(C, axis=0) #if this doesn't work, error below
if np.isscalar(D) and D == 0 and B.shape[1] > 0 and C.shape[0] > 0:
# If D is a scalar zero, broadcast it to the proper size
D = np.zeros((C.shape[0], B.shape[1]))
Expand DownExpand Up@@ -1240,8 +1253,8 @@ def _mimo2simo(sys, input, warn_conversion=False):
"Only input {i} is used." .format(i=input))
# $X = A*X + B*U
# Y = C*X + D*U
new_B = sys.B[:, input]
new_D = sys.D[:, input]
new_B = sys.B[:, input:input+1]
new_D = sys.D[:, input:input+1]
sys = StateSpace(sys.A, new_B, sys.C, new_D, sys.dt)

return sys
Expand Down
24 changes: 12 additions & 12 deletionscontrol/tests/canonical_test.py
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Expand Up@@ -22,13 +22,13 @@ def test_reachable_form(self):
D_true = 42.0

# Perform a coordinate transform with a random invertible matrix
T_true = np.matrix([[-0.27144004, -0.39933167, 0.75634684, 0.44135471],
T_true =np.array([[-0.27144004, -0.39933167, 0.75634684, 0.44135471],
[-0.74855725, -0.39136285, -0.18142339, -0.50356997],
[-0.40688007, 0.81416369, 0.38002113, -0.16483334],
[-0.44769516, 0.15654653, -0.50060858, 0.72419146]])
A = np.linalg.solve(T_true, A_true)*T_true
A = np.linalg.solve(T_true, A_true).dot(T_true)
B = np.linalg.solve(T_true, B_true)
C = C_true*T_true
C = C_true.dot(T_true)
D = D_true

# Create a state space system and convert it to the reachable canonical form
Expand DownExpand Up@@ -69,11 +69,11 @@ def test_modal_form(self):
D_true = 42.0

# Perform a coordinate transform with a random invertible matrix
T_true = np.matrix([[-0.27144004, -0.39933167, 0.75634684, 0.44135471],
T_true =np.array([[-0.27144004, -0.39933167, 0.75634684, 0.44135471],
[-0.74855725, -0.39136285, -0.18142339, -0.50356997],
[-0.40688007, 0.81416369, 0.38002113, -0.16483334],
[-0.44769516, 0.15654653, -0.50060858, 0.72419146]])
A = np.linalg.solve(T_true, A_true)*T_true
A = np.linalg.solve(T_true, A_true).dot(T_true)
B = np.linalg.solve(T_true, B_true)
C = C_true*T_true
D = D_true
Expand All@@ -98,9 +98,9 @@ def test_modal_form(self):
C_true = np.array([[1, 0, 0, 1]])
D_true = np.array([[0]])

A = np.linalg.solve(T_true, A_true) *T_true
A = np.linalg.solve(T_true, A_true).dot(T_true)
B = np.linalg.solve(T_true, B_true)
C = C_true *T_true
C = C_true.dot(T_true)
D = D_true

# Create state space system and convert to modal canonical form
Expand DownExpand Up@@ -132,9 +132,9 @@ def test_modal_form(self):
C_true = np.array([[0, 1, 0, 1]])
D_true = np.array([[0]])

A = np.linalg.solve(T_true, A_true) *T_true
A = np.linalg.solve(T_true, A_true).dot(T_true)
B = np.linalg.solve(T_true, B_true)
C = C_true *T_true
C = C_true.dot(T_true)
D = D_true

# Create state space system and convert to modal canonical form
Expand DownExpand Up@@ -173,13 +173,13 @@ def test_observable_form(self):
D_true = 42.0

# Perform a coordinate transform with a random invertible matrix
T_true = np.matrix([[-0.27144004, -0.39933167, 0.75634684, 0.44135471],
T_true =np.array([[-0.27144004, -0.39933167, 0.75634684, 0.44135471],
[-0.74855725, -0.39136285, -0.18142339, -0.50356997],
[-0.40688007, 0.81416369, 0.38002113, -0.16483334],
[-0.44769516, 0.15654653, -0.50060858, 0.72419146]])
A = np.linalg.solve(T_true, A_true)*T_true
A = np.linalg.solve(T_true, A_true).dot(T_true)
B = np.linalg.solve(T_true, B_true)
C = C_true*T_true
C = C_true.dot(T_true)
D = D_true

# Create a state space system and convert it to the observable canonical form
Expand Down
13 changes: 13 additions & 0 deletionscontrol/tests/conftest.py
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@@ -0,0 +1,13 @@
# contest.py - pytest local plugins and fixtures

import control
import os

import pytest


@pytest.fixture(scope="session", autouse=True)
def use_numpy_ndarray():
"""Switch the config to use ndarray instead of matrix"""
if os.getenv("PYTHON_CONTROL_STATESPACE_ARRAY") == "1":
control.config.defaults['statesp.use_numpy_matrix'] = False
2 changes: 1 addition & 1 deletioncontrol/tests/discrete_test.py
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Expand Up@@ -353,7 +353,7 @@ def test_sample_ss(self):
for sys in (sys1, sys2):
for h in (0.1, 0.5, 1, 2):
Ad = I + h * sys.A
Bd = h * sys.B + 0.5 * h**2 * (sys.A * sys.B)
Bd = h * sys.B + 0.5 * h**2 *np.dot(sys.A, sys.B)
sysd = sample_system(sys, h, method='zoh')
np.testing.assert_array_almost_equal(sysd.A, Ad)
np.testing.assert_array_almost_equal(sysd.B, Bd)
Expand Down
10 changes: 5 additions & 5 deletionscontrol/tests/iosys_test.py
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Expand Up@@ -53,7 +53,7 @@ def test_linear_iosys(self):
for x, u in (([0, 0], 0), ([1, 0], 0), ([0, 1], 0), ([0, 0], 1)):
np.testing.assert_array_almost_equal(
np.reshape(iosys._rhs(0, x, u), (-1,1)),
linsys.A *np.reshape(x, (-1, 1)) + linsys.B * u)
np.dot(linsys.A,np.reshape(x, (-1, 1))) +np.dot(linsys.B, u))

# Make sure that simulations also line up
T, U, X0 = self.T, self.U, self.X0
Expand DownExpand Up@@ -151,9 +151,9 @@ def test_nonlinear_iosys(self):

# Create a nonlinear system with the same dynamics
nlupd = lambda t, x, u, params: \
np.reshape(linsys.A *np.reshape(x, (-1, 1)) + linsys.B * u, (-1,))
np.reshape(np.dot(linsys.A,np.reshape(x, (-1, 1))) +np.dot(linsys.B, u), (-1,))
nlout = lambda t, x, u, params: \
np.reshape(linsys.C *np.reshape(x, (-1, 1)) + linsys.D * u, (-1,))
np.reshape(np.dot(linsys.C,np.reshape(x, (-1, 1))) +np.dot(linsys.D, u), (-1,))
nlsys = ios.NonlinearIOSystem(nlupd, nlout)

# Make sure that simulations also line up
Expand DownExpand Up@@ -747,8 +747,8 @@ def test_named_signals(self):
+ np.dot(self.mimo_linsys1.B, np.reshape(u, (-1, 1)))
).reshape(-1,),
outfcn = lambda t, x, u, params: np.array(
self.mimo_linsys1.C *np.reshape(x, (-1, 1)) \
+ self.mimo_linsys1.D *np.reshape(u, (-1, 1))
np.dot(self.mimo_linsys1.C,np.reshape(x, (-1, 1))) \
+np.dot(self.mimo_linsys1.D,np.reshape(u, (-1, 1)))
).reshape(-1,),
inputs = ('u[0]', 'u[1]'),
outputs = ('y[0]', 'y[1]'),
Expand Down
9 changes: 7 additions & 2 deletionscontrol/tests/statesp_array_test.py
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Expand Up@@ -13,6 +13,7 @@
from control.lti import evalfr
from control.exception import slycot_check
from control.config import use_numpy_matrix, reset_defaults
from control.config import defaults

class TestStateSpace(unittest.TestCase):
"""Tests for the StateSpace class."""
Expand DownExpand Up@@ -74,8 +75,12 @@ def test_matlab_style_constructor(self):
self.assertEqual(sys.B.shape, (2, 1))
self.assertEqual(sys.C.shape, (1, 2))
self.assertEqual(sys.D.shape, (1, 1))
for X in [sys.A, sys.B, sys.C, sys.D]:
self.assertTrue(isinstance(X, np.matrix))
if defaults['statesp.use_numpy_matrix']:
for X in [sys.A, sys.B, sys.C, sys.D]:
self.assertTrue(isinstance(X, np.matrix))
else:
for X in [sys.A, sys.B, sys.C, sys.D]:
self.assertTrue(isinstance(X, np.ndarray))

def test_pole(self):
"""Evaluate the poles of a MIMO system."""
Expand Down
4 changes: 2 additions & 2 deletionscontrol/tests/statesp_test.py
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Expand Up@@ -323,9 +323,9 @@ def test_array_access_ss(self):
np.testing.assert_array_almost_equal(sys1_11.A,
sys1.A)
np.testing.assert_array_almost_equal(sys1_11.B,
sys1.B[:, 1])
sys1.B[:, 1:2])
np.testing.assert_array_almost_equal(sys1_11.C,
sys1.C[0, :])
sys1.C[0:1, :])
np.testing.assert_array_almost_equal(sys1_11.D,
sys1.D[0, 1])

Expand Down
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