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4 changes: 2 additions & 2 deletions control/freqplot.py
Original file line number Diff line number Diff line change
Expand Up @@ -822,10 +822,10 @@ def default_frequency_range(syslist, Hz=None, number_of_samples=None,

# Set the range to be an order of magnitude beyond any features
if number_of_samples:
omega = sp.logspace(
omega = np.logspace(
lsp_min, lsp_max, num=number_of_samples, endpoint=True)
else:
omega = sp.logspace(lsp_min, lsp_max, endpoint=True)
omega = np.logspace(lsp_min, lsp_max, endpoint=True)
return omega


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31 changes: 17 additions & 14 deletions control/matlab/timeresp.py
Original file line number Diff line number Diff line change
Expand Up @@ -21,8 +21,9 @@ def step(sys, T=None, X0=0., input=0, output=None, return_x=False):
sys: StateSpace, or TransferFunction
LTI system to simulate

T: array-like object, optional
Time vector (argument is autocomputed if not given)
T: array-like or number, optional
Time vector, or simulation time duration if a number (time vector is
autocomputed if not given)

X0: array-like or number, optional
Initial condition (default = 0)
Expand Down Expand Up @@ -59,7 +60,7 @@ def step(sys, T=None, X0=0., input=0, output=None, return_x=False):
from ..timeresp import step_response

T, yout, xout = step_response(sys, T, X0, input, output,
transpose = True, return_x=True)
transpose=True, return_x=True)

if return_x:
return yout, T, xout
Expand All @@ -75,8 +76,9 @@ def stepinfo(sys, T=None, SettlingTimeThreshold=0.02, RiseTimeLimits=(0.1,0.9)):
sys: StateSpace, or TransferFunction
LTI system to simulate

T: array-like object, optional
Time vector (argument is autocomputed if not given)
T: array-like or number, optional
Time vector, or simulation time duration if a number (time vector is
autocomputed if not given)

SettlingTimeThreshold: float value, optional
Defines the error to compute settling time (default = 0.02)
Expand Down Expand Up @@ -127,9 +129,10 @@ def impulse(sys, T=None, X0=0., input=0, output=None, return_x=False):
sys: StateSpace, TransferFunction
LTI system to simulate

T: array-like object, optional
Time vector (argument is autocomputed if not given)

T: array-like or number, optional
Time vector, or simulation time duration if a number (time vector is
autocomputed if not given)

X0: array-like or number, optional
Initial condition (default = 0)

Expand Down Expand Up @@ -182,9 +185,10 @@ def initial(sys, T=None, X0=0., input=None, output=None, return_x=False):
sys: StateSpace, or TransferFunction
LTI system to simulate

T: array-like object, optional
Time vector (argument is autocomputed if not given)

T: array-like or number, optional
Time vector, or simulation time duration if a number (time vector is
autocomputed if not given)

X0: array-like object or number, optional
Initial condition (default = 0)

Expand Down Expand Up @@ -245,9 +249,8 @@ def lsim(sys, U=0., T=None, X0=0.):
If `U` is ``None`` or ``0``, a special algorithm is used. This special
algorithm is faster than the general algorithm, which is used otherwise.

T: array-like
Time steps at which the input is defined, numbers must be (strictly
monotonic) increasing.
T: array-like, optional for discrete LTI `sys`
Time steps at which the input is defined; values must be evenly spaced.

X0: array-like or number, optional
Initial condition (default = 0).
Expand Down
2 changes: 1 addition & 1 deletion control/rlocus.py
Original file line number Diff line number Diff line change
Expand Up @@ -50,7 +50,7 @@
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from scipy import array, poly1d, row_stack, zeros_like, real, imag
from numpy import array, poly1d, row_stack, zeros_like, real, imag
import scipy.signal # signal processing toolbox
import pylab # plotting routines
from .xferfcn import _convert_to_transfer_function
Expand Down
2 changes: 1 addition & 1 deletion control/sisotool.py
Original file line number Diff line number Diff line change
Expand Up @@ -136,7 +136,7 @@ def _SisotoolUpdate(sys,fig,K,bode_plot_params,tvect=None):
# Generate the step response and plot it
sys_closed = (K*sys).feedback(1)
if tvect is None:
tvect, yout = step_response(sys_closed)
tvect, yout = step_response(sys_closed, T_num=100)
else:
tvect, yout = step_response(sys_closed,tvect)
ax_step.plot(tvect, yout)
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4 changes: 2 additions & 2 deletions control/tests/sisotool_test.py
Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,7 @@ def test_sisotool(self):
assert_array_almost_equal(ax_rlocus.lines[2].get_data(),initial_point_2)

# Check the step response before moving the point
step_response_original = np.array([ 0., 0.02233651, 0.13118374, 0.33078542, 0.5907113, 0.87041549, 1.13038536, 1.33851053, 1.47374666, 1.52757114])
step_response_original = np.array([0. , 0.0217, 0.1281, 0.3237, 0.5797, 0.8566, 1.116 , 1.3261, 1.4659, 1.526 ])

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Why do these values need to be changed? The system is the same and so shouldn't the step response be the same?

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The function in scipy that used to be used to generate the time vector, scipy.signal.ltisys._default_response_times(A, n) uses numpy.linspace without the keyword arg endpoint=False so it returns n+1 data points instead of n. The new function does, so it generates only n data points, which is consistent with how other functions in python-control seem to do it. (thus the slightly changed values). Let me know if you have a preference to go back to n+1 datapoints. My thinking is, if you ask for n datapoints, that ought to be how many you get.

assert_array_almost_equal(ax_step.lines[0].get_data()[1][:10],step_response_original)

bode_plot_params = {
Expand Down Expand Up @@ -59,7 +59,7 @@ def test_sisotool(self):
assert_array_almost_equal(ax_mag.lines[0].get_data()[1][10:20],bode_mag_moved)

# Check if the step response has changed
step_response_moved = np.array([[ 0., 0.02458187, 0.16529784 , 0.46602716 , 0.91012035 , 1.43364313, 1.93996334 , 2.3190105 , 2.47041552 , 2.32724853] ])
step_response_moved = np.array([0. , 0.0239, 0.161 , 0.4547, 0.8903, 1.407 , 1.9121, 2.2989, 2.4686, 2.353 ])

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Same question as above. Just want to make sure I understand the need for the change.

assert_array_almost_equal(ax_step.lines[0].get_data()[1][:10],step_response_moved)


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71 changes: 69 additions & 2 deletions control/tests/timeresp_test.py
Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@
import unittest
import numpy as np
from control.timeresp import *
from control.timeresp import _ideal_tfinal_and_dt, _default_time_vector
from control.statesp import *
from control.xferfcn import TransferFunction, _convert_to_transfer_function
from control.dtime import c2d
Expand Down Expand Up @@ -94,6 +95,7 @@ def test_step_response(self):
np.testing.assert_array_equal(Tc.shape, Td.shape)
np.testing.assert_array_equal(youtc.shape, youtd.shape)


# Recreate issue #374 ("Bug in step_response()")
def test_step_nostates(self):
# Continuous time, constant system
Expand Down Expand Up @@ -346,10 +348,75 @@ def test_step_robustness(self):
sys2 = TransferFunction(num, den2)

# Compute step response from input 1 to output 1, 2
t1, y1 = step_response(sys1, input=0)
t2, y2 = step_response(sys2, input=0)
t1, y1 = step_response(sys1, input=0, T_num=100)
t2, y2 = step_response(sys2, input=0, T_num=100)
np.testing.assert_array_almost_equal(y1, y2)

def test_auto_generated_time_vector(self):
# confirm a TF with a pole at p simulates for 7.0/p seconds
p = 0.5
np.testing.assert_array_almost_equal(
_ideal_tfinal_and_dt(TransferFunction(1, [1, .5]))[0],
(7/p))
np.testing.assert_array_almost_equal(
_ideal_tfinal_and_dt(TransferFunction(1, [1, .5]).sample(.1))[0],
(7/p))
# confirm a TF with poles at 0 and p simulates for 7.0/p seconds
np.testing.assert_array_almost_equal(
_ideal_tfinal_and_dt(TransferFunction(1, [1, .5, 0]))[0],
(7/p))
# confirm a TF with a natural frequency of wn rad/s gets a
# dt of 1/(7.0*wn)
wn = 10
np.testing.assert_array_almost_equal(
_ideal_tfinal_and_dt(TransferFunction(1, [1, 0, wn**2]))[1],
1/(7.0*wn))
zeta = .1
np.testing.assert_array_almost_equal(
_ideal_tfinal_and_dt(TransferFunction(1, [1, 2*zeta*wn, wn**2]))[1],
1/(7.0*wn))
# but a smapled one keeps its dt
np.testing.assert_array_almost_equal(
_ideal_tfinal_and_dt(TransferFunction(1, [1, 2*zeta*wn, wn**2]).sample(.1))[1],
.1)
np.testing.assert_array_almost_equal(
np.diff(initial_response(TransferFunction(1, [1, 2*zeta*wn, wn**2]).sample(.1))[0][0:2]),
.1)
np.testing.assert_array_almost_equal(
_ideal_tfinal_and_dt(TransferFunction(1, [1, 2*zeta*wn, wn**2]))[1],
1/(7.0*wn))
# TF with fast oscillations simulates only 5000 time steps even with long tfinal
self.assertEqual(5000,
len(_default_time_vector(TransferFunction(1, [1, 0, wn**2]),tfinal=100)))
# and simulates for 7.0/dt time steps
self.assertEqual(
len(_default_time_vector(TransferFunction(1, [1, 0, wn**2]))),
int(7.0/(1/(7.0*wn))))

sys = TransferFunction(1, [1, .5, 0])
sysdt = TransferFunction(1, [1, .5, 0], .1)
# test impose number of time steps
self.assertEqual(10, len(step_response(sys, T_num=10)[0]))
self.assertEqual(10, len(step_response(sysdt, T_num=10)[0]))
# test impose final time
np.testing.assert_array_almost_equal(
100,
step_response(sys, 100)[0][-1],
decimal=.5)
np.testing.assert_array_almost_equal(
100,
step_response(sysdt, 100)[0][-1],
decimal=.5)
np.testing.assert_array_almost_equal(
100,
impulse_response(sys, 100)[0][-1],
decimal=.5)
np.testing.assert_array_almost_equal(
100,
initial_response(sys, 100)[0][-1],
decimal=.5)


def test_time_vector(self):
"Unit test: https://github.com/python-control/python-control/issues/239"
# Discrete time simulations with specified time vectors
Expand Down
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