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, '->')

which should come out with a curve made like this --->---->----> when x increases and like this ---<----<----< whenit decreases (and for y as well, of course). EDIT: Furthermore, the arrows should be inclined in the curve's direction (for example, 45 degrees for the y=x function)


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 def add_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 """ if not isinstance(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() if isinstance(linewidth, np.ndarray): raise NotImplementedError("multiwidth lines not supported") else: arrow_kw['linewidth'] = linewidth if transform is None: 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()

<img src=https://www.e-learn.cn/content/wangluowenzhang/"https://i.stack.imgur.com/r15M2.png" alt="enter image description here">

Also refer to this answer.


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()

<img src=https://www.e-learn.cn/content/wangluowenzhang/"https://i.stack.imgur.com/LnbYB.png" alt="Line with arrows">


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