8000 center figures + small fixes · murrayrm/python-control@154a3c0 · GitHub
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center figures + small fixes
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‎control/frdata.py

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@@ -99,7 +99,8 @@ class constructor, using the `frd` factory function, or
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Other Parameters
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----------------
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plot_type : str, optional
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Set the type of plot to generate with `plot()` ('bode', 'nichols').
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Set the type of plot to generate with `~FrequencyResponseData.plot`
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('bode', 'nichols').
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title : str, optional
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Set the title to use when plotting.
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plot_magnitude, plot_phase : bool, optional
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# Plotting interface
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def plot(self, plot_type=None, *args, **kwargs):
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"""Plot the frequency response using a Bode plot.
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"""Plot the frequency response using Bode or singular values plot.
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Plot the frequency response using either a standard Bode plot
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(default) or using a singular values plot (by setting `plot_type`
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to 'svplot'). See `bode_plot` and `singular_values_plot` for more
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detailed descriptions.
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(``plot_type='bode'``, default) or a singular values plot
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(``plot_type='svplot'``). See `bode_plot` and
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`singular_values_plot` for more detailed descriptions.
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"""
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from .freqplot import bode_plot, singular_values_plot

‎control/nichols.py

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@@ -195,7 +195,7 @@ def nichols_grid(cl_mags=None, cl_phases=None, line_style='dotted', ax=None,
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:doc:`Matplotlib linestyle \
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<matplotlib:gallery/lines_bars_and_markers/linestyles>`.
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ax : matplotlib.axes.Axes, optional
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Axes to add grid to. If None, use `matplotlib.pyplot.gca()`.
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Axes to add grid to. If None, use `matplotlib.pyplot.gca`.
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label_cl_phases : bool, optional
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If True, closed-loop phase lines will be labelled.
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‎control/optimal.py

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@@ -86,7 +86,7 @@ class OptimalControlProblem():
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will be broadcast by extension of the time axis.
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log : bool, optional
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If True, turn on logging messages (using Python logging module).
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Use `python.logging.basicConfig` to enable logging output
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Use `logging.basicConfig` to enable logging output
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(e.g., to a file).
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Attributes
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`InputOutputSystem`.
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log : bool, optional
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If True, turn on logging messages (using Python logging module).
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Use `python.logging.basicConfig` to enable logging output
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Use `logging.basicConfig` to enable logging output
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(e.g., to a file).
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name : string, optional
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System name (used for specifying signals). If unspecified, a generic

‎doc/classes.rst

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@@ -33,7 +33,8 @@ some of the functions that can be used to convert objects from one class to
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another:
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.. image:: figures/classes.pdf
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:width: 800
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:width: 800
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:align: center
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Response and plotting classes

‎doc/conf.py

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{'scipy': ('https://docs.scipy.org/doc/scipy', None),
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'numpy': ('https://numpy.org/doc/stable', None),
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'matplotlib': ('https://matplotlib.org/stable/', None),
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'python': ('https://docs.python.org/3/', None),
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}
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# If this is True, todo and todolist produce output, else they produce nothing.

‎doc/config.rst

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@@ -159,7 +159,7 @@ System display parameters
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:value: True
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If True, show the input, output, and state count when using
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`iosys_repr()` and the 'eval' format. Otherwise, the input,
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`iosys_repr` and the 'eval' format. Otherwise, the input,
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output, and state values are repressed from the output unless
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non-generic signal names are present.
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‎doc/iosys.rst

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@@ -83,6 +83,7 @@ diagram:
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.. image:: figures/bdalg-feedback.png
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:width: 240
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:align: center
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By default a gain of -1 is applied at the output of the second system,
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so the dynamics illustrate above can be created using the command

‎doc/nlsys.rst

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@@ -51,7 +51,8 @@ To illustrate the creation of a nonlinear I/O system model, consider a
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simple model of a spring loaded arm driven by a motor:
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.. image:: figures/servomech-diagram.png
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:width: 240
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:width: 240
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:align: center
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The dynamics of this system can be modeling using the following code::
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‎doc/optimal.rst

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@@ -84,6 +84,8 @@ length. This process is then repeated, resulting in a sampled data feedback
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law. This approach is illustrated in the following figure:
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.. image:: figures/mpc-overview.png
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:width: 640
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:align: center
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Every :math:`\Delta T` seconds, an optimal control problem is solved over a
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:math:`T` second horizon, starting from the current state. The first
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yields
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.. image:: figures/steering-optimal.png
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:align: center
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An example showing the use of the optimal estimation problem and moving
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horizon estimation (MHE) is given in the :doc:`mhe-pvtol Jupyter

‎doc/phaseplot.rst

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@@ -19,6 +19,7 @@ The default method for generating a phase plane plot is to provide a
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ct.phase_plane_plot(sys, axis_limits, T)
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.. image:: figures/phaseplot-dampedosc-default.png
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:align: center
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By default, the plot includes streamlines generated from starting
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points on limits of the plot, with arrows showing the flow of the
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plt.ylabel(r"$\dot\theta$ [rad/sec]")
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.. image:: figures/phaseplot-invpend-meshgrid.png
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:align: center
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This figure shows several features of more complex phase plane plots:
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multiple equilibrium points are shown, with saddle points showing
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plt.gca().set_aspect('equal')
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.. image:: figures/phaseplot-oscillator-helpers.png
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:align: center
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The following helper functions are available:
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