Skip to content

return frequency response for 0 and 1-state systems directly #663

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Nov 3, 2021
Merged

return frequency response for 0 and 1-state systems directly #663

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
433c136
return frequency response for 0 and 1-state systems directly
bnavigator Nov 2, 2021
5355b02
only return fast in array, not matrix
bnavigator Nov 2, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
24 changes: 19 additions & 5 deletions control/statesp.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -851,7 +851,7 @@ def slycot_laub(self, x):
# transformed state matrices, at, bt, ct.

# Start at the second frequency, already have the first.
for kk, x_kk in enumerate(x_arr[1:len(x_arr)]):
for kk, x_kk in enumerate(x_arr[1:]):
result = tb05ad(n, m, p, x_kk, at, bt, ct, job='NH')
# When job='NH', result = (g_i, hinvb, info)

Expand Down Expand Up @@ -885,15 +885,29 @@ def horner(self, x, warn_infinite=True):
Attempts to use Laub's method from Slycot library, with a
fall-back to python code.
"""
# Make sure the argument is a 1D array of complex numbers
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

try:
out = self.slycot_laub(x)
out = self.slycot_laub(x_arr)
except (ImportError, Exception):
# Fall back because either Slycot unavailable or cannot handle
# certain cases.

# Make sure the argument is a 1D array of complex numbers
x_arr = np.atleast_1d(x).astype(complex, copy=False)

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