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836 lines (705 loc) · 31.4 KB
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# ctrlplot_test.py - test out control plotting utilities
# RMM, 27 Jun 2024
import inspect
import itertools
import warnings
import matplotlib.pyplot as plt
import numpy as np
import pytest
import control as ct
# List of all plotting functions
resp_plot_fcns = [
# response function plotting function
(ct.frequency_response, ct.bode_plot),
(ct.frequency_response, ct.nichols_plot),
(ct.singular_values_response, ct.singular_values_plot),
(ct.gangof4_response, ct.gangof4_plot),
(ct.describing_function_response, ct.describing_function_plot),
(None, ct.phase_plane_plot),
(ct.pole_zero_map, ct.pole_zero_plot),
(ct.nyquist_response, ct.nyquist_plot),
(ct.root_locus_map, ct.root_locus_plot),
(ct.initial_response, ct.time_response_plot),
(ct.step_response, ct.time_response_plot),
(ct.impulse_response, ct.time_response_plot),
(ct.forced_response, ct.time_response_plot),
(ct.input_output_response, ct.time_response_plot),
]
nolabel_plot_fcns = [ct.describing_function_plot, ct.phase_plane_plot]
legacy_plot_fcns = [ct.gangof4_plot]
multiaxes_plot_fcns = [ct.bode_plot, ct.gangof4_plot, ct.time_response_plot]
deprecated_fcns = [ct.phase_plot]
# Utility function to make sure legends are OK
def assert_legend(cplt, expected_texts):
# Check to make sure the labels are OK in legend
legend = None
for ax in cplt.axes.flatten():
legend = ax.get_legend()
if legend is not None:
break
if expected_texts is None:
assert legend is None
else:
assert legend is not None
legend_texts = [entry.get_text() for entry in legend.get_texts()]
assert legend_texts == expected_texts
def setup_plot_arguments(resp_fcn, plot_fcn, compute_time_response=True):
# Create some systems to use
sys1 = ct.rss(2, 1, 1, strictly_proper=True, name="sys[1]")
sys1c = ct.rss(2, 1, 1, strictly_proper=True, name="sys[1]_C")
sys2 = ct.rss(2, 1, 1, strictly_proper=True, name="sys[2]")
# Set up arguments
kwargs = resp_kwargs = plot_kwargs = meth_kwargs = {}
argsc = None
match resp_fcn, plot_fcn:
case ct.describing_function_response, _:
sys1 = ct.tf([1], [1, 2, 2, 1], name="sys[1]")
sys2 = ct.tf([1.1], [1, 2, 2, 1], name="sys[2]")
F = ct.descfcn.saturation_nonlinearity(1)
amp = np.linspace(1, 4, 10)
args1 = (sys1, F, amp)
args2 = (sys2, F, amp)
resp_kwargs = plot_kwargs = {'refine': False}
case ct.gangof4_response, _:
args1 = (sys1, sys1c)
args2 = (sys2, sys1c)
default_labels = ["P=sys[1]", "P=sys[2]"]
case ct.frequency_response, ct.nichols_plot:
args1 = (sys1, None) # to allow *fmt in linestyle test
args2 = (sys2, )
meth_kwargs = {'plot_type': 'nichols'}
case ct.frequency_response, ct.bode_plot:
args1 = (sys1, None) # to allow *fmt in linestyle test
args2 = (sys2, )
case ct.singular_values_response, ct.singular_values_plot:
args1 = (sys1, None) # to allow *fmt in linestyle test
args2 = (sys2, )
case ct.root_locus_map, ct.root_locus_plot:
args1 = (sys1, )
args2 = (sys2, )
plot_kwargs = {'interactive': False}
case (ct.forced_response | ct.input_output_response, _):
timepts = np.linspace(1, 10)
U = np.sin(timepts)
if compute_time_response:
args1 = (resp_fcn(sys1, timepts, U), )
args2 = (resp_fcn(sys2, timepts, U), )
argsc = (resp_fcn([sys1, sys2], timepts, U), )
else:
args1 = (sys1, timepts, U)
args2 = (sys2, timepts, U)
argsc = None
case (ct.impulse_response | ct.initial_response | ct.step_response, _):
if compute_time_response:
args1 = (resp_fcn(sys1), )
args2 = (resp_fcn(sys2), )
argsc = (resp_fcn([sys1, sys2]), )
else:
args1 = (sys1, )
args2 = (sys2, )
argsc = ([sys1, sys2], )
case (None, ct.phase_plane_plot):
args1 = (sys1, )
args2 = (sys2, )
plot_kwargs = {'plot_streamlines': True}
case _, _:
args1 = (sys1, )
args2 = (sys2, )
return args1, args2, argsc, kwargs, meth_kwargs, plot_kwargs, resp_kwargs
# Make sure we didn't miss any plotting functions
def test_find_respplot_functions():
# Get the list of plotting functions
plot_fcns = {respplot[1] for respplot in resp_plot_fcns}
# Look through every object in the package
found = 0
for name, obj in inspect.getmembers(ct):
# Skip anything that is outside of this module
if inspect.getmodule(obj) is not None and \
not inspect.getmodule(obj).__name__.startswith('control'):
# Skip anything that isn't part of the control package
continue
# Only look for non-deprecated functions ending in 'plot'
if not inspect.isfunction(obj) or name[-4:] != 'plot' or \
obj in deprecated_fcns:
continue
# Make sure that we have this on our list of functions
assert obj in plot_fcns
found += 1
assert found == len(plot_fcns)
@pytest.mark.parametrize("resp_fcn, plot_fcn", resp_plot_fcns)
@pytest.mark.usefixtures('mplcleanup')
def test_plot_ax_processing(resp_fcn, plot_fcn):
# Set up arguments
args, _, _, kwargs, meth_kwargs, plot_kwargs, resp_kwargs = \
setup_plot_arguments(resp_fcn, plot_fcn, compute_time_response=False)
get_line_color = lambda cplt: cplt.lines.reshape(-1)[0][0].get_color()
match resp_fcn, plot_fcn:
case None, ct.phase_plane_plot:
get_line_color = None
warnings.warn("ct.phase_plane_plot returns nonstandard lines")
# Call the plot through the response function
if resp_fcn is not None:
resp = resp_fcn(*args, **kwargs, **resp_kwargs)
cplt1 = resp.plot(**kwargs, **meth_kwargs)
else:
# No response function available; just plot the data
cplt1 = plot_fcn(*args, **kwargs, **plot_kwargs)
assert isinstance(cplt1, ct.ControlPlot)
# Call the plot directly, plotting on top of previous plot
if plot_fcn == ct.time_response_plot:
# Can't call the time_response_plot() with system => reuse data
cplt2 = plot_fcn(resp, **kwargs, **plot_kwargs)
else:
cplt2 = plot_fcn(*args, **kwargs, **plot_kwargs)
assert isinstance(cplt2, ct.ControlPlot)
# Plot should have landed on top of previous plot, in different colors
assert cplt2.figure == cplt1.figure
assert np.all(cplt2.axes == cplt1.axes)
assert len(cplt2.lines[0]) == len(cplt1.lines[0])
if get_line_color is not None:
assert get_line_color(cplt2) != get_line_color(cplt1)
# Pass axes explicitly
if resp_fcn is not None:
cplt3 = resp.plot(**kwargs, **meth_kwargs, ax=cplt1.axes)
else:
cplt3 = plot_fcn(*args, **kwargs, **plot_kwargs, ax=cplt1.axes)
assert cplt3.figure == cplt1.figure
# Plot should have landed on top of previous plot, in different colors
assert np.all(cplt3.axes == cplt1.axes)
assert len(cplt3.lines[0]) == len(cplt1.lines[0])
if get_line_color is not None:
assert get_line_color(cplt3) != get_line_color(cplt1)
assert get_line_color(cplt3) != get_line_color(cplt2)
#
# Plot on a user-contructed figure
#
# Store modified properties from previous figure
cplt_titlesize = cplt3.figure._suptitle.get_fontsize()
cplt_labelsize = \
cplt3.axes.reshape(-1)[0].get_yticklabels()[0].get_fontsize()
# Set up some axes with a known title
fig, axs = plt.subplots(2, 3)
title = "User-constructed figure"
plt.suptitle(title)
titlesize = fig._suptitle.get_fontsize()
assert titlesize != cplt_titlesize
labelsize = axs[0, 0].get_yticklabels()[0].get_fontsize()
assert labelsize != cplt_labelsize
# Figure out what to pass as the ax keyword
match resp_fcn, plot_fcn:
case _, ct.bode_plot:
ax = [axs[0, 1], axs[1, 1]]
case ct.gangof4_response, _:
ax = [axs[0, 1], axs[0, 2], axs[1, 1], axs[1, 2]]
case (ct.forced_response | ct.input_output_response, _):
ax = [axs[0, 1], axs[1, 1]]
case _, _:
ax = [axs[0, 1]]
# Call the plotting function, passing the axes
if resp_fcn is not None:
resp = resp_fcn(*args, **kwargs, **resp_kwargs)
cplt4 = resp.plot(**kwargs, **meth_kwargs, ax=ax)
else:
# No response function available; just plot the data
cplt4 = plot_fcn(*args, **kwargs, **plot_kwargs, ax=ax)
# Check to make sure original settings did not change
assert fig._suptitle.get_text() == title
assert fig._suptitle.get_fontsize() == titlesize
assert ax[0].get_yticklabels()[0].get_fontsize() == labelsize
# Make sure that docstring documents ax keyword
if plot_fcn not in legacy_plot_fcns:
if plot_fcn in multiaxes_plot_fcns:
assert "ax : array of `matplotlib.axes.Axes`, optional" \
in plot_fcn.__doc__
else:
assert "ax : `matplotlib.axes.Axes`, optional" in plot_fcn.__doc__
@pytest.mark.parametrize("resp_fcn, plot_fcn", resp_plot_fcns)
@pytest.mark.usefixtures('mplcleanup')
def test_plot_label_processing(resp_fcn, plot_fcn):
# Set up arguments
args1, args2, argsc, kwargs, meth_kwargs, plot_kwargs, resp_kwargs = \
setup_plot_arguments(resp_fcn, plot_fcn)
default_labels = ["sys[1]", "sys[2]"]
expected_labels = ["sys1_", "sys2_"]
match resp_fcn, plot_fcn:
case ct.gangof4_response, _:
default_labels = ["P=sys[1]", "P=sys[2]"]
if plot_fcn in nolabel_plot_fcns:
pytest.skip(f"labels not implemented for {plot_fcn}")
# Generate the first plot, with default labels
cplt1 = plot_fcn(*args1, **kwargs, **plot_kwargs)
assert isinstance(cplt1, ct.ControlPlot)
assert_legend(cplt1, None)
# Generate second plot with default labels
cplt2 = plot_fcn(*args2, **kwargs, **plot_kwargs)
assert isinstance(cplt2, ct.ControlPlot)
assert_legend(cplt2, default_labels)
plt.close()
# Generate both plots at the same time
if len(args1) == 1 and plot_fcn != ct.time_response_plot:
cplt = plot_fcn([*args1, *args2], **kwargs, **plot_kwargs)
assert isinstance(cplt, ct.ControlPlot)
assert_legend(cplt, default_labels)
elif len(args1) == 1 and plot_fcn == ct.time_response_plot:
# Use TimeResponseList.plot() to generate combined response
cplt = argsc[0].plot(**kwargs, **meth_kwargs)
assert isinstance(cplt, ct.ControlPlot)
assert_legend(cplt, default_labels)
plt.close()
# Generate plots sequentially, with updated labels
cplt1 = plot_fcn(
*args1, **kwargs, **plot_kwargs, label=expected_labels[0])
assert isinstance(cplt1, ct.ControlPlot)
assert_legend(cplt1, None)
cplt2 = plot_fcn(
*args2, **kwargs, **plot_kwargs, label=expected_labels[1])
assert isinstance(cplt2, ct.ControlPlot)
assert_legend(cplt2, expected_labels)
plt.close()
# Generate both plots at the same time, with updated labels
if len(args1) == 1 and plot_fcn != ct.time_response_plot:
cplt = plot_fcn(
[*args1, *args2], **kwargs, **plot_kwargs,
label=expected_labels)
assert isinstance(cplt, ct.ControlPlot)
assert_legend(cplt, expected_labels)
elif len(args1) == 1 and plot_fcn == ct.time_response_plot:
# Use TimeResponseList.plot() to generate combined response
cplt = argsc[0].plot(
**kwargs, **meth_kwargs, label=expected_labels)
assert isinstance(cplt, ct.ControlPlot)
assert_legend(cplt, expected_labels)
plt.close()
# Make sure that docstring documents label
if plot_fcn not in legacy_plot_fcns:
assert "label : str or array_like of str, optional" in plot_fcn.__doc__
@pytest.mark.parametrize("resp_fcn, plot_fcn", resp_plot_fcns)
@pytest.mark.usefixtures('mplcleanup')
def test_plot_linestyle_processing(resp_fcn, plot_fcn):
# Create some systems to use
sys1 = ct.rss(2, 1, 1, strictly_proper=True, name="sys[1]")
sys1c = ct.rss(4, 1, 1, strictly_proper=True, name="sys[1]_C")
sys2 = ct.rss(4, 1, 1, strictly_proper=True, name="sys[2]")
# Set up arguments
args1, args2, _, kwargs, meth_kwargs, plot_kwargs, resp_kwargs = \
setup_plot_arguments(resp_fcn, plot_fcn)
default_labels = ["sys[1]", "sys[2]"]
expected_labels = ["sys1_", "sys2_"]
match resp_fcn, plot_fcn:
case ct.gangof4_response, _:
default_labels = ["P=sys[1]", "P=sys[2]"]
# Set line color
cplt1 = plot_fcn(*args1, **kwargs, **plot_kwargs, color='r')
assert cplt1.lines.reshape(-1)[0][0].get_color() == 'r'
# Second plot, new line color
cplt2 = plot_fcn(*args2, **kwargs, **plot_kwargs, color='g')
assert cplt2.lines.reshape(-1)[0][0].get_color() == 'g'
# Make sure that docstring documents line properties
if plot_fcn not in legacy_plot_fcns:
assert "line properties" in plot_fcn.__doc__ or \
"color : matplotlib color spec, optional" in plot_fcn.__doc__
# Set other characteristics if documentation says we can
if "line properties" in plot_fcn.__doc__:
cplt = plot_fcn(*args1, **kwargs, **plot_kwargs, linewidth=5)
assert cplt.lines.reshape(-1)[0][0].get_linewidth() == 5
# If fmt string is allowed, use it to set line color and style
if "*fmt" in plot_fcn.__doc__:
cplt = plot_fcn(*args1, 'r--', **kwargs, **plot_kwargs)
assert cplt.lines.reshape(-1)[0][0].get_color() == 'r'
assert cplt.lines.reshape(-1)[0][0].get_linestyle() == '--'
@pytest.mark.parametrize("resp_fcn, plot_fcn", resp_plot_fcns)
@pytest.mark.usefixtures('mplcleanup')
def test_siso_plot_legend_processing(resp_fcn, plot_fcn):
# Set up arguments
args1, args2, argsc, kwargs, meth_kwargs, plot_kwargs, resp_kwargs = \
setup_plot_arguments(resp_fcn, plot_fcn)
default_labels = ["sys[1]", "sys[2]"]
match resp_fcn, plot_fcn:
case ct.gangof4_response, _:
# Multi-axes plot => test in next function
return
if plot_fcn in nolabel_plot_fcns:
# Make sure that using legend keywords generates an error
with pytest.raises(TypeError, match="unexpected|unrecognized"):
cplt = plot_fcn(*args1, legend_loc=None)
with pytest.raises(TypeError, match="unexpected|unrecognized"):
cplt = plot_fcn(*args1, legend_map=None)
with pytest.raises(TypeError, match="unexpected|unrecognized"):
cplt = plot_fcn(*args1, show_legend=None)
return
# Single system, with forced legend
cplt = plot_fcn(*args1, **kwargs, **plot_kwargs, show_legend=True)
assert_legend(cplt, default_labels[:1])
plt.close()
# Single system, with forced location
cplt = plot_fcn(*args1, **kwargs, **plot_kwargs, legend_loc=10)
assert cplt.axes[0, 0].get_legend()._loc == 10
plt.close()
# Generate two plots, but turn off legends
if len(args1) == 1 and plot_fcn != ct.time_response_plot:
cplt = plot_fcn(
[*args1, *args2], **kwargs, **plot_kwargs, show_legend=False)
assert_legend(cplt, None)
elif len(args1) == 1 and plot_fcn == ct.time_response_plot:
# Use TimeResponseList.plot() to generate combined response
cplt = argsc[0].plot(**kwargs, **meth_kwargs, show_legend=False)
assert_legend(cplt, None)
plt.close()
# Make sure that docstring documents legend_loc, show_legend
assert "legend_loc : int or str, optional" in plot_fcn.__doc__
assert "show_legend : bool, optional" in plot_fcn.__doc__
# Make sure that single axes plots generate an error with legend_map
if plot_fcn not in multiaxes_plot_fcns:
with pytest.raises(TypeError, match="unexpected"):
cplt = plot_fcn(*args1, legend_map=False)
else:
assert "legend_map : array of str" in plot_fcn.__doc__
@pytest.mark.parametrize("resp_fcn, plot_fcn", resp_plot_fcns)
@pytest.mark.usefixtures('mplcleanup')
def test_mimo_plot_legend_processing(resp_fcn, plot_fcn):
# Generate the response that we will use for plotting
match resp_fcn, plot_fcn:
case ct.frequency_response, ct.bode_plot:
resp = ct.frequency_response([ct.rss(4, 2, 2), ct.rss(3, 2, 2)])
case ct.step_response, ct.time_response_plot:
resp = ct.step_response([ct.rss(4, 2, 2), ct.rss(3, 2, 2)])
case ct.gangof4_response, ct.gangof4_plot:
resp = ct.gangof4_response(ct.rss(4, 1, 1), ct.rss(3, 1, 1))
case _, ct.time_response_plot:
# Skip remaining time response plots to avoid duplicate tests
return
case _, _:
# Skip everything else that doesn't support multi-axes plots
assert plot_fcn not in multiaxes_plot_fcns
return
# Generate a standard plot with legend in the center
cplt1 = resp.plot(legend_loc=10)
assert cplt1.axes.ndim == 2
for legend_idx, ax in enumerate(cplt1.axes.flatten()):
if ax.get_legend() is not None:
break;
assert legend_idx != 0 # Make sure legend is not in first subplot
assert ax.get_legend()._loc == 10
plt.close()
# Regenerate the plot with no legend
cplt2 = resp.plot(show_legend=False)
for ax in cplt2.axes.flatten():
if ax.get_legend() is not None:
break;
assert ax.get_legend() is None
plt.close()
# Regenerate the plot with no legend in a different way
cplt2 = resp.plot(legend_loc=False)
for ax in cplt2.axes.flatten():
if ax.get_legend() is not None:
break;
assert ax.get_legend() is None
plt.close()
# Regenerate the plot with no legend in a different way
cplt2 = resp.plot(legend_map=False)
for ax in cplt2.axes.flatten():
if ax.get_legend() is not None:
break;
assert ax.get_legend() is None
plt.close()
# Put the legend in a different (first) subplot
legend_map = np.full(cplt2.shape, None, dtype=object)
legend_map[0, 0] = 5
legend_map[-1, -1] = 6
cplt3 = resp.plot(legend_map=legend_map)
assert cplt3.axes[0, 0].get_legend()._loc == 5
assert cplt3.axes[-1, -1].get_legend()._loc == 6
plt.close()
@pytest.mark.parametrize("resp_fcn, plot_fcn", resp_plot_fcns)
@pytest.mark.usefixtures('mplcleanup')
def test_plot_title_processing(resp_fcn, plot_fcn):
# Create some systems to use
sys1 = ct.rss(2, 1, 1, strictly_proper=True, name="sys[1]")
sys1c = ct.rss(4, 1, 1, strictly_proper=True, name="sys[1]_C")
sys2 = ct.rss(2, 1, 1, strictly_proper=True, name="sys[2]")
# Set up arguments
args1, args2, argsc, kwargs, meth_kwargs, plot_kwargs, resp_kwargs = \
setup_plot_arguments(resp_fcn, plot_fcn)
default_title = "sys[1], sys[2]"
expected_title = "sys1_, sys2_"
match resp_fcn, plot_fcn:
case ct.gangof4_response, _:
default_title = "P=sys[1], C=sys[1]_C, P=sys[2], C=sys[1]_C"
# Store the expected title prefix
match resp_fcn, plot_fcn:
case _, ct.bode_plot:
title_prefix = "Bode plot for "
case _, ct.nichols_plot:
title_prefix = "Nichols plot for "
case _, ct.singular_values_plot:
title_prefix = "Singular values for "
case _, ct.gangof4_plot:
title_prefix = "Gang of Four for "
case _, ct.describing_function_plot:
title_prefix = "Nyquist plot for "
case _, ct.phase_plane_plot:
title_prefix = "Phase portrait for "
case _, ct.pole_zero_plot:
title_prefix = "Pole/zero plot for "
case _, ct.nyquist_plot:
title_prefix = "Nyquist plot for "
case _, ct.root_locus_plot:
title_prefix = "Root locus plot for "
case ct.initial_response, _:
title_prefix = "Initial response for "
case ct.step_response, _:
title_prefix = "Step response for "
case ct.impulse_response, _:
title_prefix = "Impulse response for "
case ct.forced_response, _:
title_prefix = "Forced response for "
case ct.input_output_response, _:
title_prefix = "Input/output response for "
case _:
raise RuntimeError(f"didn't recognize {resp_fnc}, {plot_fnc}")
# Generate the first plot, with default title
cplt1 = plot_fcn(*args1, **kwargs, **plot_kwargs)
assert cplt1.figure._suptitle._text.startswith(title_prefix)
# Skip functions not intended for sequential calling
if plot_fcn not in nolabel_plot_fcns:
# Generate second plot with default title
cplt2 = plot_fcn(*args2, **kwargs, **plot_kwargs)
assert cplt1.figure._suptitle._text == title_prefix + default_title
plt.close()
# Generate both plots at the same time
if len(args1) == 1 and plot_fcn != ct.time_response_plot:
cplt = plot_fcn([*args1, *args2], **kwargs, **plot_kwargs)
assert cplt.figure._suptitle._text == title_prefix + default_title
elif len(args1) == 1 and plot_fcn == ct.time_response_plot:
# Use TimeResponseList.plot() to generate combined response
cplt = argsc[0].plot(**kwargs, **meth_kwargs)
assert cplt.figure._suptitle._text == title_prefix + default_title
plt.close()
# Generate plots sequentially, with updated titles
cplt1 = plot_fcn(
*args1, **kwargs, **plot_kwargs, title="My first title")
cplt2 = plot_fcn(
*args2, **kwargs, **plot_kwargs, title="My new title")
assert cplt2.figure._suptitle._text == "My new title"
plt.close()
# Update using set_plot_title
cplt2.set_plot_title("Another title")
assert cplt2.figure._suptitle._text == "Another title"
plt.close()
# Generate the plots with no title
cplt = plot_fcn(
*args1, **kwargs, **plot_kwargs, title=False)
assert cplt.figure._suptitle == None
plt.close()
# Make sure that docstring documents title
if plot_fcn not in legacy_plot_fcns:
assert "title : str, optional" in plot_fcn.__doc__
@pytest.mark.parametrize("plot_fcn", multiaxes_plot_fcns)
@pytest.mark.usefixtures('mplcleanup')
def test_tickmark_label_processing(plot_fcn):
# Generate the response that we will use for plotting
top_row, bot_row = 0, -1
match plot_fcn:
case ct.bode_plot:
resp = ct.frequency_response(ct.rss(4, 2, 2))
top_row = 1
case ct.time_response_plot:
resp = ct.step_response(ct.rss(4, 2, 2))
case ct.gangof4_plot:
resp = ct.gangof4_response(ct.rss(4, 1, 1), ct.rss(3, 1, 1))
case _:
pytest.fail("unknown plot_fcn")
# Turn off axis sharing => all axes have ticklabels
cplt = resp.plot(sharex=False, sharey=False)
for i, j in itertools.product(
range(cplt.axes.shape[0]), range(cplt.axes.shape[1])):
assert len(cplt.axes[i, j].get_xticklabels()) > 0
assert len(cplt.axes[i, j].get_yticklabels()) > 0
plt.clf()
# Turn on axis sharing => only outer axes have ticklabels
cplt = resp.plot(sharex=True, sharey=True)
for i, j in itertools.product(
range(cplt.axes.shape[0]), range(cplt.axes.shape[1])):
if i < cplt.axes.shape[0] - 1:
assert len(cplt.axes[i, j].get_xticklabels()) == 0
else:
assert len(cplt.axes[i, j].get_xticklabels()) > 0
if j > 0:
assert len(cplt.axes[i, j].get_yticklabels()) == 0
else:
assert len(cplt.axes[i, j].get_yticklabels()) > 0
@pytest.mark.parametrize("resp_fcn, plot_fcn", resp_plot_fcns)
@pytest.mark.usefixtures('mplcleanup', 'editsdefaults')
def test_rcParams(resp_fcn, plot_fcn):
# Create some systems to use
sys1 = ct.rss(2, 1, 1, strictly_proper=True, name="sys[1]")
sys1c = ct.rss(4, 1, 1, strictly_proper=True, name="sys[1]_C")
sys2 = ct.rss(2, 1, 1, strictly_proper=True, name="sys[2]")
# Set up arguments
args1, args2, argsc, kwargs, meth_kwargs, plot_kwargs, resp_kwargs = \
setup_plot_arguments(resp_fcn, plot_fcn)
default_title = "sys[1], sys[2]"
expected_title = "sys1_, sys2_"
match resp_fcn, plot_fcn:
case ct.gangof4_response, _:
default_title = "P=sys[1], C=sys[1]_C, P=sys[2], C=sys[1]_C"
# Create new set of rcParams
my_rcParams = {}
for key in ct.ctrlplot.rcParams:
match plt.rcParams[key]:
case 8 | 9 | 10:
my_rcParams[key] = plt.rcParams[key] + 1
case 'medium':
my_rcParams[key] = 11.5
case 'large':
my_rcParams[key] = 9.5
case _:
raise ValueError(f"unknown rcParam type for {key}")
checked_params = my_rcParams.copy() # make sure we check everything
# Generate a figure with the new rcParams
if plot_fcn not in nolabel_plot_fcns:
cplt = plot_fcn(
*args1, **kwargs, **plot_kwargs, rcParams=my_rcParams,
show_legend=True)
else:
cplt = plot_fcn(*args1, **kwargs, **plot_kwargs, rcParams=my_rcParams)
# Check lower left figure (should always have ticks, labels)
ax, fig = cplt.axes[-1, 0], cplt.figure
# Check to make sure new settings were used
assert ax.xaxis.get_label().get_fontsize() == my_rcParams['axes.labelsize']
assert ax.yaxis.get_label().get_fontsize() == my_rcParams['axes.labelsize']
checked_params.pop('axes.labelsize')
assert ax.title.get_fontsize() == my_rcParams['axes.titlesize']
checked_params.pop('axes.titlesize')
assert ax.get_xticklabels()[0].get_fontsize() == \
my_rcParams['xtick.labelsize']
checked_params.pop('xtick.labelsize')
assert ax.get_yticklabels()[0].get_fontsize() == \
my_rcParams['ytick.labelsize']
checked_params.pop('ytick.labelsize')
assert fig._suptitle.get_fontsize() == my_rcParams['figure.titlesize']
checked_params.pop('figure.titlesize')
if plot_fcn not in nolabel_plot_fcns:
for ax in cplt.axes.flatten():
legend = ax.get_legend()
if legend is not None:
break
assert legend is not None
assert legend.get_texts()[0].get_fontsize() == \
my_rcParams['legend.fontsize']
checked_params.pop('legend.fontsize')
# Make sure we checked everything
assert not checked_params
plt.close()
# Change the default rcParams
ct.ctrlplot.rcParams.update(my_rcParams)
if plot_fcn not in nolabel_plot_fcns:
cplt = plot_fcn(
*args1, **kwargs, **plot_kwargs, show_legend=True)
else:
cplt = plot_fcn(*args1, **kwargs, **plot_kwargs)
# Check everything
ax, fig = cplt.axes[-1, 0], cplt.figure
assert ax.xaxis.get_label().get_fontsize() == my_rcParams['axes.labelsize']
assert ax.yaxis.get_label().get_fontsize() == my_rcParams['axes.labelsize']
assert ax.title.get_fontsize() == my_rcParams['axes.titlesize']
assert ax.get_xticklabels()[0].get_fontsize() == \
my_rcParams['xtick.labelsize']
assert ax.get_yticklabels()[0].get_fontsize() == \
my_rcParams['ytick.labelsize']
assert fig._suptitle.get_fontsize() == my_rcParams['figure.titlesize']
if plot_fcn not in nolabel_plot_fcns:
for ax in cplt.axes.flatten():
legend = ax.get_legend()
if legend is not None:
break
assert legend is not None
assert legend.get_texts()[0].get_fontsize() == \
my_rcParams['legend.fontsize']
plt.close()
# Make sure that resetting parameters works correctly
ct.reset_defaults()
for key in ct.ctrlplot.rcParams:
assert ct.defaults['ctrlplot.rcParams'][key] != my_rcParams[key]
assert ct.ctrlplot.rcParams[key] != my_rcParams[key]
def test_deprecation_warnings():
sys = ct.rss(2, 2, 2)
lines = ct.step_response(sys).plot(overlay_traces=True)
with pytest.warns(FutureWarning, match="deprecated"):
assert len(lines[0, 0]) == 2
cplt = ct.step_response(sys).plot()
with pytest.warns(FutureWarning, match="deprecated"):
axs = ct.get_plot_axes(cplt)
assert np.all(axs == cplt.axes)
with pytest.warns(FutureWarning, match="deprecated"):
axs = ct.get_plot_axes(cplt.lines)
assert np.all(axs == cplt.axes)
with pytest.warns(FutureWarning, match="deprecated"):
ct.suptitle("updated title")
assert cplt.figure._suptitle.get_text() == "updated title"
def test_ControlPlot_init():
sys = ct.rss(2, 2, 2)
cplt = ct.step_response(sys).plot()
# Create a ControlPlot from data, without the axes or figure
cplt_raw = ct.ControlPlot(cplt.lines)
assert np.all(cplt_raw.lines == cplt.lines)
assert np.all(cplt_raw.axes == cplt.axes)
assert cplt_raw.figure == cplt.figure
def test_pole_zero_subplots(savefig=False):
ax_array = ct.pole_zero_subplots(2, 1, grid=[True, False])
sys1 = ct.tf([1, 2], [1, 2, 3], name='sys1')
sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2')
ct.root_locus_plot([sys1, sys2], ax=ax_array[0, 0])
cplt = ct.root_locus_plot([sys1, sys2], ax=ax_array[1, 0])
with pytest.warns(UserWarning, match="Tight layout not applied"):
cplt.set_plot_title("Root locus plots (w/ specified axes)")
if savefig:
plt.savefig("ctrlplot-pole_zero_subplots.png")
# Single type of of grid for all axes
ax_array = ct.pole_zero_subplots(2, 2, grid='empty')
assert ax_array[0, 0].xaxis.get_label().get_text() == ''
# Discrete system grid
ax_array = ct.pole_zero_subplots(2, 2, grid=True, dt=1)
assert ax_array[0, 0].xaxis.get_label().get_text() == 'Real'
assert ax_array[0, 0].get_lines()[0].get_color() == 'grey'
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')
#
# Combination plot
#
P = ct.tf([0.02], [1, 0.1, 0.01]) # servomechanism
C1 = ct.tf([1, 1], [1, 0]) # unstable
L1 = P * C1
C2 = ct.tf([1, 0.05], [1, 0]) # stable
L2 = P * C2
plt.rcParams.update(ct.rcParams)
fig = plt.figure(figsize=[7, 4])
ax_mag = fig.add_subplot(2, 2, 1)
ax_phase = fig.add_subplot(2, 2, 3)
ax_nyquist = fig.add_subplot(1, 2, 2)
ct.bode_plot(
[L1, L2], ax=[ax_mag, ax_phase],
label=["$L_1$ (unstable)", "$L_2$ (unstable)"],
show_legend=False)
ax_mag.set_title("Bode plot for $L_1$, $L_2$")
ax_mag.tick_params(labelbottom=False)
fig.align_labels()
ct.nyquist_plot(L1, ax=ax_nyquist, label="$L_1$ (unstable)")
ct.nyquist_plot(
L2, ax=ax_nyquist, label="$L_2$ (stable)",
max_curve_magnitude=22, legend_loc='upper right')
ax_nyquist.set_title("Nyquist plot for $L_1$, $L_2$")
fig.suptitle("Loop analysis for servomechanism control design")
plt.tight_layout()
plt.savefig('ctrlplot-servomech.png')
plt.figure()
test_pole_zero_subplots(savefig=True)