fcn, sys, pltinp, pltout, cmbsig, cmbtrc,

trpose, secsys, clear=True):

# Figure out the time range to use and check some special cases

if not isinstance(sys, ct.lti.LTI):

if fcn == ct.impulse_response:

pytest.skip("impulse response not implemented for nlsys")

# Nonlinear systems require explicit time limits

T = 10

timepts = np.linspace(0, T)

elif isinstance(sys, ct.TransferFunction) and fcn == ct.initial_response:

pytest.skip("initial response not tested for tf")

else:

# Linear systems figure things out on their own

T = None

timepts = np.linspace(0, 10) # for input_output_response

# Save up the keyword arguments

kwargs = dict(

plot_inputs=pltinp, plot_outputs=pltout, transpose=trpose,

overlay_signals=cmbsig, overlay_traces=cmbtrc)

# Create the response

if fcn is ct.input_output_response and \

not isinstance(sys, ct.NonlinearIOSystem):

# Skip transfer functions and other non-state space systems

return None

if fcn in [ct.input_output_response, ct.forced_response]:

U = np.zeros((sys.ninputs, timepts.size))

for i in range(sys.ninputs):

U[i] = np.cos(timepts * i + i)

args = [timepts, U]

elif fcn == ct.initial_response:

args = [T, np.ones(sys.nstates)] # T, X0

elif not isinstance(sys, ct.lti.LTI):

args = [T] # nonlinear systems require final time

else: # step, initial, impulse responses

args = []

# Create a new figure (in case previous one is of the same size) and plot

if not clear:

plt.figure()

response = fcn(sys, *args)

# Look for cases where there are no data to plot

if not pltout and (

pltinp is False or response.ninputs == 0 or

pltinp is None and response.plot_inputs is False):

with pytest.raises(ValueError, match=".* no data to plot"):

cplt = response.plot(**kwargs)

return None

elif not pltout and pltinp == 'overlay':

with pytest.raises(ValueError, match="can't overlay inputs"):

cplt = response.plot(**kwargs)

return None

elif pltinp in [True, 'overlay'] and response.ninputs == 0:

with pytest.raises(ValueError, match=".* but no inputs"):

cplt = response.plot(**kwargs)

return None

cplt = response.plot(**kwargs)

# Make sure all of the outputs are of the right type

nlines_plotted = 0

for ax_lines in np.nditer(cplt.lines, flags=["refs_ok"]):

for line in ax_lines.item():

assert isinstance(line, mpl.lines.Line2D)

nlines_plotted += 1

# Make sure number of plots is correct

if pltinp is None:

if fcn in [ct.forced_response, ct.input_output_response]:

pltinp = True

else:

pltinp = False

ntraces = max(1, response.ntraces)

nlines_expected = (response.ninputs if pltinp else 0) * ntraces + \

(response.noutputs if pltout else 0) * ntraces

assert nlines_plotted == nlines_expected

# Save the old axes to compare later

old_axes = plt.gcf().get_axes()

# Add additional data (and provide info in the title)

if secsys:

newsys = ct.rss(

sys.nstates, sys.noutputs, sys.ninputs, strictly_proper=True)

if fcn not in [ct.initial_response, ct.forced_response,

ct.input_output_response] and \

isinstance(sys, ct.lti.LTI):

# Reuse the previously computed time to make plots look nicer

fcn(newsys, *args, T=response.time[-1]).plot(**kwargs)

else:

# Compute and plot new response (time is one of the arguments)

fcn(newsys, *args).plot(**kwargs)

# Make sure we have the same axes

new_axes = plt.gcf().get_axes()

assert new_axes == old_axes

# Make sure every axes has more than one line

for ax in new_axes:

assert len(ax.get_lines()) > 1

# Update the title so we can see what is going on

fig = cplt.figure

fig.suptitle(

fig._suptitle._text +

f" [{sys.noutputs}x{sys.ninputs}, cs={cmbsig}, "

f"ct={cmbtrc}, pi={pltinp}, tr={trpose}]",

fontsize='small')

# Get rid of the figure to free up memory

if clear:

sys_2x3 = ct.rss(4, 2, 3)

sys_2x3b = ct.rss(4, 2, 3)

sys_3x2 = ct.rss(4, 3, 2)

sys_3x1 = ct.rss(4, 3, 1)

# Two plots of the same size leaves axes unchanged

cplt1 = ct.step_response(sys_2x3).plot()

cplt2 = ct.step_response(sys_2x3b).plot()

np.testing.assert_equal(cplt1.axes, cplt2.axes)

plt.close()

# Two plots of same net size leaves axes unchanged (unfortunately)

cplt1 = ct.step_response(sys_2x3).plot()

cplt2 = ct.step_response(sys_3x2).plot()

np.testing.assert_equal(

cplt1.axes.reshape(-1), cplt2.axes.reshape(-1))

plt.close()

# Plots of different shapes generate new plots

cplt1 = ct.step_response(sys_2x3).plot()

cplt2 = ct.step_response(sys_3x1).plot()

ax1_list = cplt1.axes.reshape(-1).tolist()

ax2_list = cplt2.axes.reshape(-1).tolist()

for ax in ax1_list:

assert ax not in ax2_list

plt.close()

# Passing a list of axes preserves those axes

cplt1 = ct.step_response(sys_2x3).plot()

cplt2 = ct.step_response(sys_3x1).plot()

cplt3 = ct.step_response(sys_2x3b).plot(ax=cplt1.axes)

np.testing.assert_equal(cplt1.axes, cplt3.axes)

plt.close()

# Sending an axes array of the wrong size raises exception

with pytest.raises(ValueError, match="not the right shape"):

cplt = ct.step_response(sys_2x3).plot()

ct.step_response(sys_3x1).plot(ax=cplt.axes)

sys_2x3 = ct.rss(4, 2, 3)

sys_2x3b = ct.rss(4, 2, 3)

sys_3x2 = ct.rss(4, 3, 2)

sys_mimo = ct.tf2ss(

[[[1], [0.1]], [[0.2], [1]]],

[[[1, 0.6, 1], [1, 1, 1]], [[1, 0.4, 1], [1, 2, 1]]], name="MIMO")

response = ct.step_response(sys_mimo)

response.plot(

legend_map=np.array([['center', 'upper right'],

[None, 'center right']]),

plot_inputs=True, overlay_signals=True, transpose=True,

sys_mimo = ct.rss(4, 2, 2)

timepts = np.linspace(0, 10, 100)

# Combine two responses with ntrace = 0

U = np.vstack([np.sin(timepts), np.cos(2*timepts)])

resp1 = ct.input_output_response(sys_mimo, timepts, U)

U = np.vstack([np.cos(2*timepts), np.sin(timepts)])

resp2 = ct.input_output_response(sys_mimo, timepts, U)

combresp1 = ct.combine_time_responses([resp1, resp2])

assert combresp1.ntraces == 2

np.testing.assert_equal(combresp1.y[:, 0, :], resp1.y)

np.testing.assert_equal(combresp1.y[:, 1, :], resp2.y)

# Combine two responses with ntrace != 0

resp3 = ct.step_response(sys_mimo, timepts)

resp4 = ct.step_response(sys_mimo, timepts)

combresp2 = ct.combine_time_responses([resp3, resp4])

assert combresp2.ntraces == resp3.ntraces + resp4.ntraces

np.testing.assert_equal(combresp2.y[:, 0:2, :], resp3.y)

np.testing.assert_equal(combresp2.y[:, 2:4, :], resp4.y)

# Mixture

combresp3 = ct.combine_time_responses([resp1, resp2, resp3])

assert combresp3.ntraces == resp3.ntraces + resp4.ntraces

np.testing.assert_equal(combresp3.y[:, 0, :], resp1.y)

np.testing.assert_equal(combresp3.y[:, 1, :], resp2.y)

np.testing.assert_equal(combresp3.y[:, 2:4, :], resp3.y)

assert combresp3.trace_types == [None, None] + resp3.trace_types

assert combresp3.trace_labels == \

[resp1.title, resp2.title] + resp3.trace_labels

# Rename the traces

labels = ["T1", "T2", "T3", "T4"]

combresp4 = ct.combine_time_responses(

[resp1, resp2, resp3], trace_labels=labels)

assert combresp4.trace_labels == labels

assert combresp4.trace_types == [None, None, 'step', 'step']

# Automatically generated trace label names and types

resp5 = ct.step_response(sys_mimo, timepts)

resp5.title = "test"

resp5.trace_labels = None

resp5.trace_types = None

combresp5 = ct.combine_time_responses([resp1, resp5])

assert combresp5.trace_labels == [resp1.title] + \

["test, trace 0", "test, trace 1"]

assert combresp5.trace_types == [None, None, None]

# ntraces = 0 with trace_types != None

# https://github.com/python-control/python-control/issues/1025

resp6 = ct.forced_response(sys_mimo, timepts, U)

combresp6 = ct.combine_time_responses([resp1, resp6])

assert combresp6.trace_types == [None, 'forced']

with pytest.raises(ValueError, match="must have the same number"):

resp = ct.step_response(ct.rss(4, 2, 3), timepts)

ct.combine_time_responses([resp1, resp])

with pytest.raises(ValueError, match="trace labels does not match"):

ct.combine_time_responses(

[resp1, resp2], trace_labels=["T1", "T2", "T3"])

with pytest.raises(ValueError, match="must have the same time"):

resp = ct.step_response(ct.rss(4, 2, 3), timepts/2)

sys1 = ct.rss(2, 2, 2, strictly_proper=True)

sys2 = ct.rss(2, 2, 2, strictly_proper=True)

# Figure out the expected shape of the system

match resp_fcn:

case ct.step_response | ct.impulse_response:

shape = (2, 2)

kwargs = {}

case ct.initial_response:

shape = (2, 1)

kwargs = {}

case ct.forced_response | ct.input_output_response:

shape = (4, 1) # outputs and inputs both plotted

T = np.linspace(0, 10)

U = [np.sin(T), np.cos(T)]

kwargs = {'T': T, 'U': U}

resp1 = resp_fcn(sys1, **kwargs)

resp2 = resp_fcn(sys2, **kwargs)

# Sequential plotting results in colors rotating

plt.figure()

cplt1 = resp1.plot()

cplt2 = resp2.plot()

assert cplt1.shape == shape # legacy access (OK here)

assert cplt2.shape == shape # legacy access (OK here)

for row in range(2): # just look at the outputs

for col in range(shape[1]):

assert cplt1.lines[row, col][0].get_color() == 'tab:blue'

assert cplt2.lines[row, col][0].get_color() == 'tab:orange'

plt.figure()

resp_combined = resp_fcn([sys1, sys2], **kwargs)

assert isinstance(resp_combined, ct.timeresp.TimeResponseList)

assert resp_combined[0].time[-1] == max(resp1.time[-1], resp2.time[-1])

assert resp_combined[1].time[-1] == max(resp1.time[-1], resp2.time[-1])

cplt = resp_combined.plot()

assert cplt.lines.shape == shape

for row in range(2): # just look at the outputs

for col in range(shape[1]):

assert cplt.lines[row, col][0].get_color() == 'tab:blue'

# Check to make sure we can change line styles

sys_mimo = ct.tf2ss(

[[[1], [0.1]], [[0.2], [1]]],

[[[1, 0.6, 1], [1, 1, 1]], [[1, 0.4, 1], [1, 2, 1]]], name="MIMO")

cplt = ct.step_response(sys_mimo).plot('k--', plot_inputs=True)

for ax in np.nditer(cplt.lines, flags=["refs_ok"]):

for line in ax.item():

assert line.get_color() == 'k'

assert line.get_linestyle() == '--'

cplt = ct.step_response(sys_mimo).plot(

plot_inputs='overlay', overlay_signals=True, overlay_traces=True,

output_props=[{'color': c} for c in ['blue', 'orange']],

input_props=[{'color': c} for c in ['red', 'green']],

trace_props=[{'linestyle': s} for s in ['-', '--']])

assert cplt.lines.shape == (1, 1)

lines = cplt.lines[0, 0]

assert lines[0].get_color() == 'blue' and lines[0].get_linestyle() == '-'

assert lines[1].get_color() == 'orange' and lines[1].get_linestyle() == '-'

assert lines[2].get_color() == 'red' and lines[2].get_linestyle() == '-'

assert lines[3].get_color() == 'green' and lines[3].get_linestyle() == '-'

assert lines[4].get_color() == 'blue' and lines[4].get_linestyle() == '--'

assert lines[5].get_color() == 'orange' and lines[5].get_linestyle() == '--'

assert lines[6].get_color() == 'red' and lines[6].get_linestyle() == '--'

plt.close('all')

sys1 = ct.rss(2, 2, 2, strictly_proper=True, name='sys1')

sys2 = ct.rss(2, 2, 2, strictly_proper=True, name='sys2')

# Figure out the expected shape of the system

match resp_fcn:

case ct.step_response | ct.impulse_response:

kwargs = {}

case ct.initial_response:

kwargs = {}

case ct.forced_response | ct.input_output_response:

T = np.linspace(0, 10)

U = [np.sin(T), np.cos(T)]

kwargs = {'T': T, 'U': U}

# Use figure frame for suptitle to speed things up

ct.set_defaults('freqplot', title_frame='figure')

# Make sure default labels are as expected

cplt = resp_fcn([sys1, sys2], **kwargs).plot()

axs = cplt.axes

if axs.ndim == 1:

legend = axs[0].get_legend().get_texts()

else:

legend = axs[0, -1].get_legend().get_texts()

assert legend[0].get_text() == 'sys1'

assert legend[1].get_text() == 'sys2'

plt.close()

# Override labels all at once

cplt = resp_fcn([sys1, sys2], **kwargs).plot(label=['line1', 'line2'])

axs = cplt.axes

if axs.ndim == 1:

legend = axs[0].get_legend().get_texts()

else:

legend = axs[0, -1].get_legend().get_texts()

assert legend[0].get_text() == 'line1'

assert legend[1].get_text() == 'line2'

plt.close()

# Override labels one at a time

cplt = resp_fcn(sys1, **kwargs).plot(label='line1')

cplt = resp_fcn(sys2, **kwargs).plot(label='line2')

axs = cplt.axes

if axs.ndim == 1:

legend = axs[0].get_legend().get_texts()

else:

legend = axs[0, -1].get_legend().get_texts()

assert legend[0].get_text() == 'line1'

assert legend[1].get_text() == 'line2'

sys1 = ct.rss(2, 2, 2, strictly_proper=True, name='sys1')

sys2 = ct.rss(2, 2, 2, strictly_proper=True, name='sys2')

labels_2d = np.array([

["outsys1u1y1", "outsys1u1y2", "outsys1u2y1", "outsys1u2y2",

"outsys2u1y1", "outsys2u1y2", "outsys2u2y1", "outsys2u2y2"],

["inpsys1u1y1", "inpsys1u1y2", "inpsys1u2y1", "inpsys1u2y2",

"inpsys2u1y1", "inpsys2u1y2", "inpsys2u2y1", "inpsys2u2y2"]])

labels_4d = np.empty((2, 2, 2, 2), dtype=object)

for i, sys in enumerate(['sys1', 'sys2']):

for j, trace in enumerate(['u1', 'u2']):

for k, out in enumerate(['y1', 'y2']):

labels_4d[i, j, k, 0] = "out" + sys + trace + out

labels_4d[i, j, k, 1] = "inp" + sys + trace + out

# Test 4D labels

cplt = ct.step_response([sys1, sys2]).plot(

overlay_signals=True, overlay_traces=True, plot_inputs=True,

label=labels_4d)

axs = cplt.axes

assert axs.shape == (2, 1)

legend_text = axs[0, 0].get_legend().get_texts()

for i, label in enumerate(labels_2d[0]):

assert legend_text[i].get_text() == label

legend_text = axs[1, 0].get_legend().get_texts()

for i, label in enumerate(labels_2d[1]):

assert legend_text[i].get_text() == label

# Test 2D labels

cplt = ct.step_response([sys1, sys2]).plot(

overlay_signals=True, overlay_traces=True, plot_inputs=True,

label=labels_2d)

axs = cplt.axes

assert axs.shape == (2, 1)

legend_text = axs[0, 0].get_legend().get_texts()

for i, label in enumerate(labels_2d[0]):

assert legend_text[i].get_text() == label

legend_text = axs[1, 0].get_legend().get_texts()

for i, label in enumerate(labels_2d[1]):

sys1 = ct.rss(2, inputs='u', outputs='y')

sys2 = ct.rss(1, 1, 1) # uses default i/o labels

# Generate a plot with specific labels

ct.step_response(sys1).plot()

# Generate a new plot, which overwrites labels

cplt = ct.step_response(sys2).plot()

ax = cplt.axes

assert ax[0, 0].get_ylabel() == 'y[0]'

# Regenerate the first plot

plt.figure()

ct.step_response(sys1).plot()

# Generate a new plt, without relabeling

with pytest.warns(FutureWarning, match="deprecated"):

cplt = ct.step_response(sys2).plot(relabel=False)

sys = ct.rss(2, 1, 1)

stepresp = ct.step_response(sys)

with pytest.raises(AttributeError,

match="(has no property|unexpected keyword)"):

stepresp.plot(unknown=None)

with pytest.raises(AttributeError,

match="(has no property|unexpected keyword)"):

ct.time_response_plot(stepresp, unknown=None)

with pytest.raises(ValueError, match="unrecognized value"):

stepresp.plot(plot_inputs='unknown')

for kw in ['input_props', 'output_props', 'trace_props']:

propkw = {kw: {'color': 'green'}}

with pytest.warns(UserWarning, match="ignored since fmt string"):

cplt = stepresp.plot('k-', **propkw)

assert cplt.lines[0, 0][0].get_color() == 'k'

# Make sure TimeResponseLists also work

stepresp = ct.step_response([sys, sys])

with pytest.raises(AttributeError,

match="(has no property|unexpected keyword)"):

sys = ct.rss(4, 2, 1, name='sys')

timepts = np.linspace(0, 10)

U = np.sin(timepts)

resp = ct.input_output_response(sys, timepts, U)

# Generic input/output plot

cplt = resp.plot(overlay_signals=True)

axs = cplt.axes

assert axs[0, 0].get_legend()._loc == 7 # center right

assert len(axs[0, 0].get_legend().get_texts()) == 2

assert axs[1, 0].get_legend() == None

plt.close()

# Hide legend

cplt = resp.plot(overlay_signals=True, show_legend=False)

axs = cplt.axes

assert axs[0, 0].get_legend() == None

assert axs[1, 0].get_legend() == None

plt.close()

# Put legend in both axes

cplt = resp.plot(

overlay_signals=True, legend_map=[['center left'], ['center right']])

axs = cplt.axes

assert axs[0, 0].get_legend()._loc == 6 # center left

assert len(axs[0, 0].get_legend().get_texts()) == 2

assert axs[1, 0].get_legend()._loc == 7 # center right

assert len(axs[1, 0].get_legend().get_texts()) == 1