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735 lines (625 loc) · 28.8 KB
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# timeplot_test.py - test out time response plots
# RMM, 23 Jun 2023
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pytest
import control as ct
# Detailed test of (almost) all functionality
#
# The commented out rows lead to very long testing times => these should be
# used only for developmental testing and not day-to-day testing.
@pytest.mark.parametrize(
"sys", [
# ct.rss(1, 1, 1, strictly_proper=True, name="rss"),
ct.nlsys(
lambda t, x, u, params: -x + u, None,
inputs=1, outputs=1, states=1, name="nlsys"),
# ct.rss(2, 1, 2, strictly_proper=True, name="rss"),
ct.rss(2, 2, 1, strictly_proper=True, name="rss"),
# ct.drss(2, 2, 2, name="drss"),
# ct.rss(2, 2, 3, strictly_proper=True, name="rss"),
])
# @pytest.mark.parametrize("transpose", [False, True])
# @pytest.mark.parametrize("plot_inputs", [False, None, True, 'overlay'])
# @pytest.mark.parametrize("plot_outputs", [True, False])
# @pytest.mark.parametrize("overlay_signals", [False, True])
# @pytest.mark.parametrize("overlay_traces", [False, True])
# @pytest.mark.parametrize("second_system", [False, True])
# @pytest.mark.parametrize("fcn", [
# ct.step_response, ct.impulse_response, ct.initial_response,
# ct.forced_response])
@pytest.mark.parametrize( # combinatorial-style test (faster)
"fcn, pltinp, pltout, cmbsig, cmbtrc, trpose, secsys",
[(ct.step_response, False, True, False, False, False, False),
(ct.step_response, None, True, False, False, False, False),
(ct.step_response, True, True, False, False, False, False),
(ct.step_response, 'overlay', True, False, False, False, False),
(ct.step_response, 'overlay', True, True, False, False, False),
(ct.step_response, 'overlay', True, False, True, False, False),
(ct.step_response, 'overlay', True, False, False, True, False),
(ct.step_response, 'overlay', True, False, False, False, True),
(ct.step_response, False, False, False, False, False, False),
(ct.step_response, None, False, False, False, False, False),
(ct.step_response, 'overlay', False, False, False, False, False),
(ct.step_response, True, True, False, True, False, False),
(ct.step_response, True, True, False, False, False, True),
(ct.step_response, True, True, False, True, False, True),
(ct.step_response, True, True, True, False, True, True),
(ct.step_response, True, True, False, True, True, True),
(ct.impulse_response, False, True, True, False, False, False),
(ct.initial_response, None, True, False, False, False, False),
(ct.initial_response, False, True, False, False, False, False),
(ct.initial_response, True, True, False, False, False, False),
(ct.forced_response, True, True, False, False, False, False),
(ct.forced_response, None, True, False, False, False, False),
(ct.forced_response, False, True, False, False, False, False),
(ct.forced_response, True, True, True, False, False, False),
(ct.forced_response, True, True, True, True, False, False),
(ct.forced_response, True, True, True, True, True, False),
(ct.forced_response, True, True, True, True, True, True),
(ct.forced_response, 'overlay', True, True, True, False, True),
(ct.input_output_response,
True, True, False, False, False, False),
])
@pytest.mark.usefixtures('mplcleanup')
def test_response_plots(
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:
plt.clf()
@pytest.mark.usefixtures('mplcleanup')
def test_axes_setup():
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_3x1 = ct.rss(4, 3, 1)
@pytest.mark.slycot
@pytest.mark.usefixtures('mplcleanup')
def test_legend_map():
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,
title='MIMO step response with custom legend placement')
@pytest.mark.usefixtures('mplcleanup')
def test_combine_time_responses():
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)
ct.combine_time_responses([resp1, resp])
@pytest.mark.parametrize("resp_fcn", [
ct.step_response, ct.initial_response, ct.impulse_response,
ct.forced_response, ct.input_output_response])
@pytest.mark.usefixtures('mplcleanup')
def test_list_responses(resp_fcn):
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'
assert cplt.lines[row, col][1].get_color() == 'tab:orange'
@pytest.mark.parametrize("plot_inputs, shape", [
(None, (2, 1)), (True, (2, 1)), ('overlay', (1, 1)), (False, (1, 1))])
@pytest.mark.usefixtures('mplcleanup')
def test_time_response_plot_list(plot_inputs, shape):
# A list of responses is plotted like a TimeResponseList (gh-1171)
sys = ct.ss(ct.tf([1, 2], [3, 4, 5]))
timepts = np.linspace(0, 10)
resp1 = ct.input_output_response(sys, timepts, np.sin(timepts))
resp2 = ct.input_output_response(sys, timepts, np.cos(timepts))
cplt = ct.time_response_plot([resp1, resp2], plot_inputs=plot_inputs)
assert cplt.lines.shape == shape
plt.figure()
cplt_list = ct.TimeResponseList([resp1, resp2]).plot(
plot_inputs=plot_inputs)
assert cplt_list.lines.shape == shape
for lines, lines_list in zip(cplt.lines.flat, cplt_list.lines.flat):
assert len(lines) == len(lines_list)
for line, line_list in zip(lines, lines_list):
np.testing.assert_equal(line.get_ydata(), line_list.get_ydata())
@pytest.mark.slycot
@pytest.mark.usefixtures('mplcleanup')
def test_linestyles():
# 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() == '--'
assert lines[7].get_color() == 'green' and lines[7].get_linestyle() == '--'
@pytest.mark.parametrize("resp_fcn", [
ct.step_response, ct.initial_response, ct.impulse_response,
ct.forced_response, ct.input_output_response])
@pytest.mark.usefixtures('editsdefaults', 'mplcleanup')
def test_timeplot_trace_labels(resp_fcn):
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'
plt.close()
@pytest.mark.usefixtures('mplcleanup')
def test_full_label_override():
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]):
assert legend_text[i].get_text() == label
@pytest.mark.usefixtures('mplcleanup')
def test_relabel():
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)
assert cplt.axes[0, 0].get_ylabel() == 'y'
def test_errors():
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)"):
stepresp.plot(unknown=None)
def test_legend_customization():
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
plt.close()
if __name__ == "__main__":
#
# Interactive mode: generate plots for manual viewing
#
# Running this script in python (or better ipython) will show a
# collection of figures that should all look OK on the screeen.
#
# In interactive mode, turn on ipython interactive graphics
plt.ion()
# Start by clearing existing figures
plt.close('all')
# Define a set of systems to test
sys_siso = ct.tf2ss([1], [1, 2, 1], name="SISO")
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")
# Define and run a selected set of interesting tests
# def test_response_plots(
# fcn, sys, plot_inputs, plot_outputs, overlay_signals,
# overlay_traces, transpose, second_system, clear=True):
N, T, F = None, True, False
test_cases = [
# response fcn system in out cs ct tr ss
(ct.step_response, sys_siso, N, T, F, F, F, F), # 1
(ct.step_response, sys_siso, T, F, F, F, F, F), # 2
(ct.step_response, sys_siso, T, T, F, F, F, T), # 3
(ct.step_response, sys_siso, 'overlay', T, F, F, F, T), # 4
(ct.step_response, sys_mimo, F, T, F, F, F, F), # 5
(ct.step_response, sys_mimo, T, T, F, F, F, F), # 6
(ct.step_response, sys_mimo, 'overlay', T, F, F, F, F), # 7
(ct.step_response, sys_mimo, T, T, T, F, F, F), # 8
(ct.step_response, sys_mimo, T, T, T, T, F, F), # 9
(ct.step_response, sys_mimo, T, T, F, F, T, F), # 10
(ct.step_response, sys_mimo, T, T, T, F, T, F), # 11
(ct.step_response, sys_mimo, 'overlay', T, T, F, T, F), # 12
(ct.forced_response, sys_mimo, N, T, T, F, T, F), # 13
(ct.forced_response, sys_mimo, 'overlay', T, F, F, F, F), # 14
]
for args in test_cases:
test_response_plots(*args, clear=F)
#
# Run a few more special cases to show off capabilities (and save some
# of them for use in the documentation).
#
test_legend_map() # show ability to set legend location
# Basic step response
plt.figure()
ct.step_response(sys_mimo).plot()
plt.savefig('timeplot-mimo_step-default.png')
# Step response with plot_inputs, overlay_signals
plt.figure()
ct.step_response(sys_mimo).plot(
plot_inputs=True, overlay_signals=True,
title="Step response for 2x2 MIMO system " +
"[plot_inputs, overlay_signals]")
plt.savefig('timeplot-mimo_step-pi_cs.png')
# Input/output response with overlaid inputs, legend_map
plt.figure()
timepts = np.linspace(0, 10, 100)
U = np.vstack([np.sin(timepts), np.cos(2*timepts)])
ct.input_output_response(sys_mimo, timepts, U).plot(
plot_inputs='overlay',
legend_map=np.array([['lower right'], ['lower right']]),
title="I/O response for 2x2 MIMO system " +
"[plot_inputs='overlay', legend_map]")
plt.savefig('timeplot-mimo_ioresp-ov_lm.png')
# Multi-trace plot, transpose
plt.figure()
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)
ct.combine_time_responses(
[resp1, resp2], trace_labels=["Scenario #1", "Scenario #2"]).plot(
transpose=True,
title="I/O responses for 2x2 MIMO system, multiple traces "
"[transpose]")
plt.savefig('timeplot-mimo_ioresp-mt_tr.png')
plt.figure()
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 ['-', '--']])
plt.savefig('timeplot-mimo_step-linestyle.png')
sys1 = ct.rss(4, 2, 2)
sys2 = ct.rss(4, 2, 2)
resp_list = ct.step_response([sys1, sys2])
fig = plt.figure()
cplt = ct.combine_time_responses(
[ct.step_response(sys1, resp_list[0].time),
ct.step_response(sys2, resp_list[1].time)]
).plot(overlay_traces=True)
cplt.set_plot_title("[Combine] " + fig._suptitle._text)
fig = plt.figure()
ct.step_response(sys1).plot()
cplt = ct.step_response(sys2).plot()
cplt.set_plot_title("[Sequential] " + fig._suptitle._text)
fig = plt.figure()
ct.step_response(sys1).plot(color='b')
cplt = ct.step_response(sys2).plot(color='r')
cplt.set_plot_title("[Seq w/color] " + fig._suptitle._text)
fig = plt.figure()
cplt = ct.step_response([sys1, sys2]).plot()
cplt.set_plot_title("[List] " + fig._suptitle._text)