Matplotlib Curve With Arrow Ticks

I was wondering if it is possible to plot a curve in matplotlib with arrow ticks. Something like: from pylab import * y = linspace(0,10,0.01) x = cos(y) plot(x, y, '->') whic

Solution 1:

It is possible to use the same strategy as in matplotlib streamplot function. Based on the example already given by hitzg:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
import matplotlib.patches as mpatches

defadd_arrow_to_line2D(
    axes, line, arrow_locs=[0.2, 0.4, 0.6, 0.8],
    arrowstyle='-|>', arrowsize=1, transform=None):
    """
    Add arrows to a matplotlib.lines.Line2D at selected locations.

    Parameters:
    -----------
    axes: 
    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
    transform: a matplotlib transform instance, default to data coordinates

    Returns:
    --------
    arrows: list of arrows
    """ifnotisinstance(line, mlines.Line2D):
        raise ValueError("expected a matplotlib.lines.Line2D object")
    x, y = line.get_xdata(), line.get_ydata()

    arrow_kw = {
        "arrowstyle": arrowstyle,
        "mutation_scale": 10 * arrowsize,
    }

    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()
    ifisinstance(linewidth, np.ndarray):
        raise NotImplementedError("multiwidth lines not supported")
    else:
        arrow_kw['linewidth'] = linewidth

    if transform isNone:
        transform = axes.transData

    arrows = []
    for loc in arrow_locs:
        s = np.cumsum(np.sqrt(np.diff(x) ** 2 + np.diff(y) ** 2))
        n = np.searchsorted(s, s[-1] * loc)
        arrow_tail = (x[n], y[n])
        arrow_head = (np.mean(x[n:n + 2]), np.mean(y[n:n + 2]))
        p = mpatches.FancyArrowPatch(
            arrow_tail, arrow_head, transform=transform,
            **arrow_kw)
        axes.add_patch(p)
        arrows.append(p)
    return arrows


y = np.linspace(0, 100, 200)
x = np.cos(y/5.)

fig, ax = plt.subplots(1, 1)
# print the line and the markers in seperate steps
line, = ax.plot(x, y, 'k-')
add_arrow_to_line2D(ax, line, arrow_locs=np.linspace(0., 1., 200),
                    arrowstyle='->')

plt.show()

Also refer to this answer.

Solution 2:

Try this:

import numpy as np
import matplotlib.pyplot as plt

y = np.linspace(0,100,100)
x = np.cos(y/5.)

# use masked arrays
x1 = np.ma.masked_array(x[:-1], np.diff(x)>=0)
x2 = np.ma.masked_array(x[:-1], np.diff(x)<=0)

# print the line and the markers in seperate steps
plt.plot(x, y, 'k-')
plt.plot(x1, y[:-1], 'k<')
plt.plot(x2, y[:-1], 'k>')
plt.show()