python-control
diff --git a/‎control/ctrlplot.py‎
Lines changed: 227 additions & 5 deletions b/‎control/ctrlplot.py‎
Lines changed: 227 additions & 5 deletions
@@ -5,13 +5,36 @@
 
 from os.path import commonprefix
 
+import matplotlib as mpl
 import matplotlib.pyplot as plt
 import numpy as np
 
 from . import config
 
 __all__ = ['suptitle', 'get_plot_axes']
 
+#
+# Style parameters
+#
+
+_ctrlplot_rcParams = mpl.rcParams.copy()
+_ctrlplot_rcParams.update({
+    'axes.labelsize': 'small',
+    'axes.titlesize': 'small',
+    'figure.titlesize': 'medium',
+    'legend.fontsize': 'x-small',
+    'xtick.labelsize': 'small',
+    'ytick.labelsize': 'small',
+})
+
+
+#
+# User functions
+#
+# The functions below can be used by users to modify ctrl plots or get
+# information about them.
+#
+
 
 def suptitle(
         title, fig=None, frame='axes', **kwargs):
@@ -35,7 +58,7 @@ def suptitle(
         Additional keywords (passed to matplotlib).
 
     """
-    rcParams = config._get_param('freqplot', 'rcParams', kwargs, pop=True)
+    rcParams = config._get_param('ctrlplot', 'rcParams', kwargs, pop=True)
 
     if fig is None:
         fig = plt.gcf()
@@ -61,10 +84,10 @@ def suptitle(
 def get_plot_axes(line_array):
     """Get a list of axes from an array of lines.
 
-    This function can be used to return the set of axes corresponding to
-    the line array that is returned by `time_response_plot`.  This is useful for
-    generating an axes array that can be passed to subsequent plotting
-    calls.
+    This function can be used to return the set of axes corresponding
+    to the line array that is returned by `time_response_plot`.  This
+    is useful for generating an axes array that can be passed to
+    subsequent plotting calls.
 
     Parameters
     ----------
@@ -89,6 +112,125 @@ def get_plot_axes(line_array):
 #
 # Utility functions
 #
+# These functions are used by plotting routines to provide a consistent way
+# of processing and displaing information.
+#
+
+
+def _process_ax_keyword(
+        axs, shape=(1, 1), rcParams=None, squeeze=False, clear_text=False):
+    """Utility function to process ax keyword to plotting commands.
+
+    This function processes the `ax` keyword to plotting commands.  If no
+    ax keyword is passed, the current figure is checked to see if it has
+    the correct shape.  If the shape matches the desired shape, then the
+    current figure and axes are returned.  Otherwise a new figure is
+    created with axes of the desired shape.
+
+    Legacy behavior: some of the older plotting commands use a axes label
+    to identify the proper axes for plotting.  This behavior is supported
+    through the use of the label keyword, but will only work if shape ==
+    (1, 1) and squeeze == True.
+
+    """
+    if axs is None:
+        fig = plt.gcf()         # get current figure (or create new one)
+        axs = fig.get_axes()
+
+        # Check to see if axes are the right shape; if not, create new figure
+        # Note: can't actually check the shape, just the total number of axes
+        if len(axs) != np.prod(shape):
+            with plt.rc_context(rcParams):
+                if len(axs) != 0:
+                    # Create a new figure
+                    fig, axs = plt.subplots(*shape, squeeze=False)
+                else:
+                    # Create new axes on (empty) figure
+                    axs = fig.subplots(*shape, squeeze=False)
+            fig.set_layout_engine('tight')
+            fig.align_labels()
+        else:
+            # Use the existing axes, properly reshaped
+            axs = np.asarray(axs).reshape(*shape)
+
+            if clear_text:
+                # Clear out any old text from the current figure
+                for text in fig.texts:
+                    text.set_visible(False)     # turn off the text
+                    del text                    # get rid of it completely
+    else:
+        try:
+            axs = np.asarray(axs).reshape(shape)
+        except ValueError:
+            raise ValueError(
+                "specified axes are not the right shape; "
+                f"got {axs.shape} but expecting {shape}")
+        fig = axs[0, 0].figure
+
+    # Process the squeeze keyword
+    if squeeze and shape == (1, 1):
+        axs = axs[0, 0]         # Just return the single axes object
+    elif squeeze:
+        axs = axs.squeeze()
+
+    return fig, axs
+
+
+# Turn label keyword into array indexed by trace, output, input
+# TODO: move to ctrlutil.py and update parameter names to reflect general use
+def _process_line_labels(label, ntraces, ninputs=0, noutputs=0):
+    if label is None:
+        return None
+
+    if isinstance(label, str):
+        label = [label] * ntraces          # single label for all traces
+
+    # Convert to an ndarray, if not done aleady
+    try:
+        line_labels = np.asarray(label)
+    except ValueError:
+        raise ValueError("label must be a string or array_like")
+
+    # Turn the data into a 3D array of appropriate shape
+    # TODO: allow more sophisticated broadcasting (and error checking)
+    try:
+        if ninputs > 0 and noutputs > 0:
+            if line_labels.ndim == 1 and line_labels.size == ntraces:
+                line_labels = line_labels.reshape(ntraces, 1, 1)
+                line_labels = np.broadcast_to(
+                    line_labels, (ntraces, ninputs, noutputs))
+            else:
+                line_labels = line_labels.reshape(ntraces, ninputs, noutputs)
+    except ValueError:
+        if line_labels.shape[0] != ntraces:
+            raise ValueError("number of labels must match number of traces")
+        else:
+            raise ValueError("labels must be given for each input/output pair")
+
+    return line_labels
+
+
+# Get labels for all lines in an axes
+def _get_line_labels(ax, use_color=True):
+    labels, lines = [], []
+    last_color, counter = None, 0       # label unknown systems
+    for i, line in enumerate(ax.get_lines()):
+        label = line.get_label()
+        if use_color and label.startswith("Unknown"):
+            label = f"Unknown-{counter}"
+            if last_color is None:
+                last_color = line.get_color()
+            elif last_color != line.get_color():
+                counter += 1
+                last_color = line.get_color()
+        elif label[0] == '_':
+            continue
+
+        if label not in labels:
+            lines.append(line)
+            labels.append(label)
+
+    return lines, labels
 
 
 # Utility function to make legend labels
@@ -160,3 +302,83 @@ def _find_axes_center(fig, axs):
         ylim = [min(ll[1], ylim[0]), max(ur[1], ylim[1])]
 
     return (np.sum(xlim)/2, np.sum(ylim)/2)
+
+
+# Internal function to add arrows to a curve
+def _add_arrows_to_line2D(
+        axes, line, arrow_locs=[0.2, 0.4, 0.6, 0.8],
+        arrowstyle='-|>', arrowsize=1, dir=1):
+    """
+    Add arrows to a matplotlib.lines.Line2D at selected locations.
+
+    Parameters:
+    -----------
+    axes: Axes object as returned by axes command (or gca)
+    line: Line2D object as returned by plot command
+    arrow_locs: list of locations where to insert arrows, % of total length
+    arrowstyle: style of the arrow
+    arrowsize: size of the arrow
+
+    Returns:
+    --------
+    arrows: list of arrows
+
+    Based on https://stackoverflow.com/questions/26911898/
+
+    """
+    # Get the coordinates of the line, in plot coordinates
+    if not isinstance(line, mpl.lines.Line2D):
+        raise ValueError("expected a matplotlib.lines.Line2D object")
+    x, y = line.get_xdata(), line.get_ydata()
+
+    # Determine the arrow properties
+    arrow_kw = {"arrowstyle": arrowstyle}
+
+    color = line.get_color()
+    use_multicolor_lines = isinstance(color, np.ndarray)
+    if use_multicolor_lines:
+        raise NotImplementedError("multicolor lines not supported")
+    else:
+        arrow_kw['color'] = color
+
+    linewidth = line.get_linewidth()
+    if isinstance(linewidth, np.ndarray):
+        raise NotImplementedError("multiwidth lines not supported")
+    else:
+        arrow_kw['linewidth'] = linewidth
+
+    # Figure out the size of the axes (length of diagonal)
+    xlim, ylim = axes.get_xlim(), axes.get_ylim()
+    ul, lr = np.array([xlim[0], ylim[0]]), np.array([xlim[1], ylim[1]])
+    diag = np.linalg.norm(ul - lr)
+
+    # Compute the arc length along the curve
+    s = np.cumsum(np.sqrt(np.diff(x) ** 2 + np.diff(y) ** 2))
+
+    # Truncate the number of arrows if the curve is short
+    # TODO: figure out a smarter way to do this
+    frac = min(s[-1] / diag, 1)
+    if len(arrow_locs) and frac < 0.05:
+        arrow_locs = []         # too short; no arrows at all
+    elif len(arrow_locs) and frac < 0.2:
+        arrow_locs = [0.5]      # single arrow in the middle
+
+    # Plot the arrows (and return list if patches)
+    arrows = []
+    for loc in arrow_locs:
+        n = np.searchsorted(s, s[-1] * loc)
+
+        if dir == 1 and n == 0:
+            # Move the arrow forward by one if it is at start of a segment
+            n = 1
+
+        # Place the head of the arrow at the desired location
+        arrow_head = [x[n], y[n]]
+        arrow_tail = [x[n - dir], y[n - dir]]
+
+        p = mpl.patches.FancyArrowPatch(
+            arrow_tail, arrow_head, transform=axes.transData, lw=0,
+            **arrow_kw)
+        axes.add_patch(p)
+        arrows.append(p)
+    return arrows