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Another take at #664 #665

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beb7bbe
try least-squares if regular solve returns nan
bnavigator Nov 4, 2021
88f62c4
DEBUG
bnavigator Nov 4, 2021
e050926
DEBUG
bnavigator Nov 4, 2021
c22d031
DEBUG2
bnavigator Nov 4, 2021
fb56d92
remove DEBUG
bnavigator Nov 4, 2021
5939d96
Revert "remove DEBUG"
bnavigator Nov 4, 2021
478fffb
no conda mkl in GHA
bnavigator Nov 4, 2021
cc44d3c
show np config
bnavigator Nov 4, 2021
8757bdb
cleanup
bnavigator Nov 4, 2021
a9ec2dc
Revert "no conda mkl in GHA"
bnavigator Nov 4, 2021
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3 changes: 3 additions & 0 deletions .github/workflows/python-package-conda.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -49,6 +49,9 @@ jobs:
# Use xvfb-run instead of pytest-xvfb to get proper mpl backend
# Use coverage instead of pytest-cov to get .coverage file
# See https://github.com/python-control/python-control/pull/504

python3 -c 'import numpy; numpy.show_config()'

xvfb-run --auto-servernum coverage run -m pytest control/tests

- name: Coveralls parallel
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18 changes: 10 additions & 8 deletions control/statesp.py
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Expand Up @@ -887,6 +887,8 @@ def horner(self, x, warn_infinite=True):
"""
# Make sure the argument is a 1D array of complex numbers
x_arr = np.atleast_1d(x).astype(complex, copy=False)
if len(x_arr.shape) > 1:
raise ValueError("input list must be 1D")

# return fast on systems with 0 or 1 state
if not config.defaults['statesp.use_numpy_matrix']:
Expand All @@ -908,21 +910,21 @@ def horner(self, x, warn_infinite=True):
# Fall back because either Slycot unavailable or cannot handle
# certain cases.

# Make sure that we are operating on a simple list
if len(x_arr.shape) > 1:
raise ValueError("input list must be 1D")

# Preallocate
out = empty((self.noutputs, self.ninputs, len(x_arr)),
dtype=complex)

# TODO: can this be vectorized?
for idx, x_idx in enumerate(x_arr):
try:
out[:, :, idx] = np.dot(
self.C,
solve(x_idx * eye(self.nstates) - self.A, self.B)) \
+ self.D
xI_A = x_idx * eye(self.nstates) - self.A
xI_A_inv = solve(xI_A, self.B)
# gh-664: xI_A did not raise singular matrix error,
print(f"DEBUG np.linalg.solve(\n,"
f"{xI_A}\n"
f"{self.B} = \n"
f"{xI_A_inv}")
out[:, :, idx] = np.dot(self.C, xI_A_inv) + self.D
except LinAlgError:
# Issue a warning messsage, for consistency with xferfcn
if warn_infinite:
Expand Down