python - Matplotlib:自定义函数，每次绘制图形时调用

Question

我想创建一个包含箭头的matplotlib图，其头部形状独立于数据坐标。这类似于 FancyArrowPatch ，但是当箭头长度小于头部长度时，头部会收缩以适合箭头的长度。

目前，我通过将宽度转换为显示坐标来设置箭头的长度，计算显示坐标中的箭头长度并将其转换回数据坐标来解决此问题。

只要轴的尺寸不改变，这种方法就可以很好地工作，这可能是由于set_xlim()而发生的。 , set_ylim()或tight_layout()例如。我想通过在绘图尺寸发生变化时重新绘制箭头来涵盖这些情况。目前我通过注册一个函数 on_draw(event) 来处理这个问题通过

axes.get_figure().canvas.mpl_connect("resize_event", on_draw)

但这仅适用于交互式后端。我还需要一个解决方案，将绘图保存为图像文件。还有其他地方可以注册我的回调函数吗？

编辑:这是我当前正在使用的代码:

def draw_adaptive_arrow(axes, x, y, dx, dy,
                        tail_width, head_width, head_ratio, draw_head=True,
                        shape="full", **kwargs):
    from matplotlib.patches import FancyArrow
    from matplotlib.transforms import Bbox

    arrow = None

    def on_draw(event=None):
        """
        Callback function that is called, every time the figure is resized
        Removes the current arrow and replaces it with an arrow with
        recalcualted head
        """
        nonlocal tail_width
        nonlocal head_width
        nonlocal arrow
        if arrow is not None:
            arrow.remove()
        # Create a head that looks equal, independent of the aspect
        # ratio
        # Hence, a transformation into display coordinates has to be
        # performed to fix the head width to length ratio
        # In this transformation only the height and width are
        # interesting, absolute coordinates are not needed
        # -> box origin at (0,0)
        arrow_box = Bbox([(0,0),(0,head_width)])
        arrow_box_display = axes.transData.transform_bbox(arrow_box)
        head_length_display = np.abs(arrow_box_display.height * head_ratio)
        arrow_box_display.x1 = arrow_box_display.x0 + head_length_display
        # Transfrom back to data coordinates for plotting
        arrow_box = axes.transData.inverted().transform_bbox(arrow_box_display)
        head_length = arrow_box.width
        if head_length > np.abs(dx):
            # If the head would be longer than the entire arrow,
            # only draw the arrow head with reduced length
            head_length = np.abs(dx)
        if not draw_head:
            head_length = 0
            head_width = tail_width
        arrow = FancyArrow(
            x, y, dx, dy,
            width=tail_width, head_width=head_width, head_length=head_length,
            length_includes_head=True, **kwargs)
        axes.add_patch(arrow)

    axes.get_figure().canvas.mpl_connect("resize_event", on_draw)



# Some place in the user code...

fig = plt.figure(figsize=(8.0, 3.0))
ax = fig.add_subplot(1,1,1)

# 90 degree tip
draw_adaptive_arrow(
    ax, 0, 0, 4, 0, tail_width=0.4, head_width=0.8, head_ratio=0.5
)
# Still 90 degree tip
draw_adaptive_arrow(
    ax, 5, 0, 2, 0, tail_width=0.4, head_width=0.8, head_ratio=0.5
)
# Smaller head, since otherwise head would be longer than entire arrow
draw_adaptive_arrow(
    ax, 8, 0, 0.5, 0, tail_width=0.4, head_width=0.8, head_ratio=0.5
)
ax.set_xlim(0,10)
ax.set_ylim(-1,1)

# Does not work in non-interactive backend
plt.savefig("test.pdf")
# But works in interactive backend
plt.show()

Answer 1

这是一个没有回调的解决方案。我从问题中接管了大部分算法，因为我不确定我是否理解箭头的要求。我很确定这可以简化，但这也超出了问题的重点。

因此，我们在这里对 FancyArrow 进行子类化，并让它将自身添加到坐标区中。然后，我们重写draw方法来计算所需的参数，然后——这在某种程度上是不寻常的，并且在其他情况下可能会失败——在draw方法中再次调用__init__。

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import FancyArrow
from matplotlib.transforms import Bbox

class MyArrow(FancyArrow):

    def __init__(self,  *args, **kwargs):
        self.ax = args[0]
        self.args = args[1:]
        self.kw = kwargs
        self.head_ratio = self.kw.pop("head_ratio", 1)
        self.draw_head = self.kw.pop("draw_head", True)
        self.kw.update(length_includes_head=True)
        super().__init__(*self.args,**self.kw)
        self.ax.add_patch(self)
        self.trans = self.get_transform()

    def draw(self, renderer):
        self.kw.update(transform = self.trans)

        arrow_box = Bbox([(0,0),(0,self.kw["head_width"])])
        arrow_box_display = self.ax.transData.transform_bbox(arrow_box)
        head_length_display = np.abs(arrow_box_display.height * self.head_ratio)
        arrow_box_display.x1 = arrow_box_display.x0 + head_length_display
        # Transfrom back to data coordinates for plotting
        arrow_box = self.ax.transData.inverted().transform_bbox(arrow_box_display)
        self.kw["head_length"] = arrow_box.width
        if self.kw["head_length"] > np.abs(self.args[2]):
            # If the head would be longer than the entire arrow,
            # only draw the arrow head with reduced length
            self.kw["head_length"] = np.abs(self.args[2])
        if not self.draw_head:
            self.kw["head_length"] = 0
            self.kw["head_width"] = self.kw["width"]    

        super().__init__(*self.args,**self.kw)
        self.set_clip_path(self.ax.patch)
        self.ax._update_patch_limits(self)
        super().draw(renderer)



fig = plt.figure(figsize=(8.0, 3.0))
ax = fig.add_subplot(1,1,1)

# 90 degree tip
MyArrow( ax, 0, 0, 4, 0, width=0.4, head_width=0.8, head_ratio=0.5 )

MyArrow( ax, 5, 0, 2, 0, width=0.4, head_width=0.8, head_ratio=0.5 )
# Smaller head, since otherwise head would be longer than entire arrow
MyArrow( ax, 8, 0, 0.5, 0, width=0.4, head_width=0.8, head_ratio=0.5 )
ax.set_xlim(0,10)
ax.set_ylim(-1,1)

# Does not work in non-interactive backend
plt.savefig("test.pdf")
# But works in interactive backend
plt.show()