缩放不变的角度标签

此示例说明如何创建比例不变角度注释。用圆弧标记线条之间或内部形状之间的角度通常很有用。虽然 Matplotlib 提供了一个Arc，但直接将其用于此类目的时的一个固有问题是，在数据空间中为圆形的弧不一定在显示空间中为圆形。此外，弧的半径通常最好在独立于实际数据坐标的坐标系中定义 - 至少如果您希望能够自由放大绘图而不会使注释增长到无穷大。

这需要一个解决方案，其中弧的中心在数据空间中定义，但其半径以物理单位（如点或像素）或作为 Axes 维度的比率来定义。下面的AngleAnnotation类提供了这样的解决方案。

下面的示例有两个目的：

• 它为在图形中容易绘制角度的问题提供了一个现成的解决方案。

• 它展示了如何对 Matplotlib 艺术家进行子类化以增强其功能，并提供有关如何使用 Matplotlib变换系统的动手示例。

如果主要对前者感兴趣，您可以复制下面的课程并跳转到使用部分。

AngleAnnotation 类

这里的基本思想是子类化Arc并将其变换设置为IdentityTransform，使弧的参数定义在像素空间中。然后我们覆盖, , ,并将它们设为Arc属性，耦合到每次访问属性时计算相应参数的内部方法，从而确保像素空间中的弧与输入点和大小保持同步。例如，每次圆弧的绘制方法都会查询它的 属性，而不是重新接收相同的数字，而是接收我们在子类中定义的方法的结果。此方法通过 Axes 变换将数据坐标中的中心转换为像素_centertheta1theta2widthheight_centerget_center_in_pixelsax.transData. 大小和角度以类似的方式计算，使得弧线在例如交互式缩放或平移时自动改变其形状。

此类的功能允许用文本注释弧。此文本Annotation存储在一个属性text中。由于弧的位置和半径仅在绘制时定义，我们需要相应地更新文本的位置。这是通过重新实现Arc' draw()方法以使其调用文本的更新方法来完成的。

弧线和文本将在实例化时添加到提供的 Axes 中：因此不必严格保留对它的引用。

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Arc
from matplotlib.transforms import IdentityTransform, TransformedBbox, Bbox


class AngleAnnotation(Arc):
    """
    Draws an arc between two vectors which appears circular in display space.
    """
    def __init__(self, xy, p1, p2, size=75, unit="points", ax=None,
                 text="", textposition="inside", text_kw=None, **kwargs):
        """
        Parameters
        ----------
        xy, p1, p2 : tuple or array of two floats
            Center position and two points. Angle annotation is drawn between
            the two vectors connecting *p1* and *p2* with *xy*, respectively.
            Units are data coordinates.

        size : float
            Diameter of the angle annotation in units specified by *unit*.

        unit : str
            One of the following strings to specify the unit of *size*:

            * "pixels": pixels
            * "points": points, use points instead of pixels to not have a
              dependence on the DPI
            * "axes width", "axes height": relative units of Axes width, height
            * "axes min", "axes max": minimum or maximum of relative Axes
              width, height

        ax : `matplotlib.axes.Axes`
            The Axes to add the angle annotation to.

        text : str
            The text to mark the angle with.

        textposition : {"inside", "outside", "edge"}
            Whether to show the text in- or outside the arc. "edge" can be used
            for custom positions anchored at the arc's edge.

        text_kw : dict
            Dictionary of arguments passed to the Annotation.

        **kwargs
            Further parameters are passed to `matplotlib.patches.Arc`. Use this
            to specify, color, linewidth etc. of the arc.

        """
        self.ax = ax or plt.gca()
        self._xydata = xy  # in data coordinates
        self.vec1 = p1
        self.vec2 = p2
        self.size = size
        self.unit = unit
        self.textposition = textposition

        super().__init__(self._xydata, size, size, angle=0.0,
                         theta1=self.theta1, theta2=self.theta2, **kwargs)

        self.set_transform(IdentityTransform())
        self.ax.add_patch(self)

        self.kw = dict(ha="center", va="center",
                       xycoords=IdentityTransform(),
                       xytext=(0, 0), textcoords="offset points",
                       annotation_clip=True)
        self.kw.update(text_kw or {})
        self.text = ax.annotate(text, xy=self._center, **self.kw)

    def get_size(self):
        factor = 1.
        if self.unit == "points":
            factor = self.ax.figure.dpi / 72.
        elif self.unit[:4] == "axes":
            b = TransformedBbox(Bbox.unit(), self.ax.transAxes)
            dic = {"max": max(b.width, b.height),
                   "min": min(b.width, b.height),
                   "width": b.width, "height": b.height}
            factor = dic[self.unit[5:]]
        return self.size * factor

    def set_size(self, size):
        self.size = size

    def get_center_in_pixels(self):
        """return center in pixels"""
        return self.ax.transData.transform(self._xydata)

    def set_center(self, xy):
        """set center in data coordinates"""
        self._xydata = xy

    def get_theta(self, vec):
        vec_in_pixels = self.ax.transData.transform(vec) - self._center
        return np.rad2deg(np.arctan2(vec_in_pixels[1], vec_in_pixels[0]))

    def get_theta1(self):
        return self.get_theta(self.vec1)

    def get_theta2(self):
        return self.get_theta(self.vec2)

    def set_theta(self, angle):
        pass

    # Redefine attributes of the Arc to always give values in pixel space
    _center = property(get_center_in_pixels, set_center)
    theta1 = property(get_theta1, set_theta)
    theta2 = property(get_theta2, set_theta)
    width = property(get_size, set_size)
    height = property(get_size, set_size)

    # The following two methods are needed to update the text position.
    def draw(self, renderer):
        self.update_text()
        super().draw(renderer)

    def update_text(self):
        c = self._center
        s = self.get_size()
        angle_span = (self.theta2 - self.theta1) % 360
        angle = np.deg2rad(self.theta1 + angle_span / 2)
        r = s / 2
        if self.textposition == "inside":
            r = s / np.interp(angle_span, [60, 90, 135, 180],
                                          [3.3, 3.5, 3.8, 4])
        self.text.xy = c + r * np.array([np.cos(angle), np.sin(angle)])
        if self.textposition == "outside":
            def R90(a, r, w, h):
                if a < np.arctan(h/2/(r+w/2)):
                    return np.sqrt((r+w/2)**2 + (np.tan(a)*(r+w/2))**2)
                else:
                    c = np.sqrt((w/2)**2+(h/2)**2)
                    T = np.arcsin(c * np.cos(np.pi/2 - a + np.arcsin(h/2/c))/r)
                    xy = r * np.array([np.cos(a + T), np.sin(a + T)])
                    xy += np.array([w/2, h/2])
                    return np.sqrt(np.sum(xy**2))

            def R(a, r, w, h):
                aa = (a % (np.pi/4))*((a % (np.pi/2)) <= np.pi/4) + \
                     (np.pi/4 - (a % (np.pi/4)))*((a % (np.pi/2)) >= np.pi/4)
                return R90(aa, r, *[w, h][::int(np.sign(np.cos(2*a)))])

            bbox = self.text.get_window_extent()
            X = R(angle, r, bbox.width, bbox.height)
            trans = self.ax.figure.dpi_scale_trans.inverted()
            offs = trans.transform(((X-s/2), 0))[0] * 72
            self.text.set_position([offs*np.cos(angle), offs*np.sin(angle)])

用法

所需的参数AngleAnnotation是弧的中心、xy和两个点，使得弧分别跨越连接 p1和p2与xy的两个向量之间。这些是在数据坐标中给出的。进一步的参数是弧的大小及其单位。此外， 可以根据textposition的值指定一个文本，该文本将在弧内或弧外绘制。这些参数的用法如下所示。

fig, ax = plt.subplots()
fig.canvas.draw()  # Need to draw the figure to define renderer
ax.set_title("AngleLabel example")

# Plot two crossing lines and label each angle between them with the above
# ``AngleAnnotation`` tool.
center = (4.5, 650)
p1 = [(2.5, 710), (6.0, 605)]
p2 = [(3.0, 275), (5.5, 900)]
line1, = ax.plot(*zip(*p1))
line2, = ax.plot(*zip(*p2))
point, = ax.plot(*center, marker="o")

am1 = AngleAnnotation(center, p1[1], p2[1], ax=ax, size=75, text=r"$\alpha$")
am2 = AngleAnnotation(center, p2[1], p1[0], ax=ax, size=35, text=r"$\beta$")
am3 = AngleAnnotation(center, p1[0], p2[0], ax=ax, size=75, text=r"$\gamma$")
am4 = AngleAnnotation(center, p2[0], p1[1], ax=ax, size=35, text=r"$\theta$")


# Showcase some styling options for the angle arc, as well as the text.
p = [(6.0, 400), (5.3, 410), (5.6, 300)]
ax.plot(*zip(*p))
am5 = AngleAnnotation(p[1], p[0], p[2], ax=ax, size=40, text=r"$\Phi$",
                      linestyle="--", color="gray", textposition="outside",
                      text_kw=dict(fontsize=16, color="gray"))

AngleLabel选项

textposition和unit关键字参数可用于修改文本标签的位置，如下所示：

# Helper function to draw angle easily.
def plot_angle(ax, pos, angle, length=0.95, acol="C0", **kwargs):
    vec2 = np.array([np.cos(np.deg2rad(angle)), np.sin(np.deg2rad(angle))])
    xy = np.c_[[length, 0], [0, 0], vec2*length].T + np.array(pos)
    ax.plot(*xy.T, color=acol)
    return AngleAnnotation(pos, xy[0], xy[2], ax=ax, **kwargs)


fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)
fig.suptitle("AngleLabel keyword arguments")
fig.canvas.draw()  # Need to draw the figure to define renderer

# Showcase different text positions.
ax1.margins(y=0.4)
ax1.set_title("textposition")
kw = dict(size=75, unit="points", text=r"$60°$")

am6 = plot_angle(ax1, (2.0, 0), 60, textposition="inside", **kw)
am7 = plot_angle(ax1, (3.5, 0), 60, textposition="outside", **kw)
am8 = plot_angle(ax1, (5.0, 0), 60, textposition="edge",
                 text_kw=dict(bbox=dict(boxstyle="round", fc="w")), **kw)
am9 = plot_angle(ax1, (6.5, 0), 60, textposition="edge",
                 text_kw=dict(xytext=(30, 20), arrowprops=dict(arrowstyle="->",
                              connectionstyle="arc3,rad=-0.2")), **kw)

for x, text in zip([2.0, 3.5, 5.0, 6.5], ['"inside"', '"outside"', '"edge"',
                                          '"edge", custom arrow']):
    ax1.annotate(text, xy=(x, 0), xycoords=ax1.get_xaxis_transform(),
                 bbox=dict(boxstyle="round", fc="w"), ha="left", fontsize=8,
                 annotation_clip=True)

# Showcase different size units. The effect of this can best be observed
# by interactively changing the figure size
ax2.margins(y=0.4)
ax2.set_title("unit")
kw = dict(text=r"$60°$", textposition="outside")

am10 = plot_angle(ax2, (2.0, 0), 60, size=50, unit="pixels", **kw)
am11 = plot_angle(ax2, (3.5, 0), 60, size=50, unit="points", **kw)
am12 = plot_angle(ax2, (5.0, 0), 60, size=0.25, unit="axes min", **kw)
am13 = plot_angle(ax2, (6.5, 0), 60, size=0.25, unit="axes max", **kw)

for x, text in zip([2.0, 3.5, 5.0, 6.5], ['"pixels"', '"points"',
                                          '"axes min"', '"axes max"']):
    ax2.annotate(text, xy=(x, 0), xycoords=ax2.get_xaxis_transform(),
                 bbox=dict(boxstyle="round", fc="w"), ha="left", fontsize=8,
                 annotation_clip=True)

plt.show()

参考

此示例中显示了以下函数、方法、类和模块的使用：

• matplotlib.patches.Arc

• matplotlib.axes.Axes.annotate/matplotlib.pyplot.annotate

• matplotlib.text.Annotation

• matplotlib.transforms.IdentityTransform

• matplotlib.transforms.TransformedBbox

• matplotlib.transforms.Bbox