将标签悬停在图表上时显示标签*具有高 y 轴值*、多条线和多轴

Question

我想从我使用 python 制作的图表中读取值，类似于将鼠标悬停在数据点上时数据点值在 Excel 图中的显示方式。在这里使用各种解决方案，我编写了以下代码来标记我将鼠标悬停在它们上面的点。

但是，当 y 轴值很高时，我似乎无法标记某些点（这是我对它为什么不起作用的假设），我也无法让框具有纯色背景以便我可以阅读坐标清晰（尝试将 alpha 设置为 1，但没有工作）。Here's a picture of how one such point shows up far away from the actual location and with the text blocked by the curves behind it. 奇怪的是，当所有 y 轴值都低于 1 时，代码工作正常。

from matplotlib import pyplot as plt
import numpy as np; np.random.seed(1)

x_data = list(range(0,30))
y1_data_a = np.sort(np.random.rand(30))
y1_data_b = np.sort(np.random.rand(30))
y1_data_c = [0.4 for point in x_data]
y2_data_a = [point**2 for point in x_data]
y2_data_b = [point*0.5 for point in y2_data_a]
y3_data = [(10/(point+1)) for point in x_data]

# #The code works fine with this data
# x_data = list(range(0,30))
# y1_data_a = np.sort(np.random.rand(30))
# y1_data_b = np.sort(np.random.rand(30))
# y1_data_c = [0.4 for point in x_data]
# y2_data_a = np.random.rand(30)
# y2_data_b = np.sort(np.random.rand(30))
# y3_data = np.sort(np.random.rand(30))[::-1]

fig, y1_axis = plt.subplots()
fig.subplots_adjust(right=0.75)

y2_axis = y1_axis.twinx()
y3_axis = y1_axis.twinx()

def make_patch_spines_invisible(ax):
    ax.set_frame_on(True)
    ax.patch.set_visible(False)
    for sp in ax.spines.values():
        sp.set_visible(False)

y3_axis.spines["right"].set_position(("axes", 1.2))
make_patch_spines_invisible(y3_axis)
y3_axis.spines["right"].set_visible(True)

plot1, = y1_axis.plot(x_data, y1_data_a, color="#000CFF", label="Temp1 (°C)")
plot2, = y1_axis.plot(x_data, y1_data_b, color="#FF5100", label="Temp2 (°C)")
plot3, = y1_axis.plot(x_data, y1_data_c, "r--", label="Critical Temp (°C)")

plot4, = y2_axis.plot(x_data, y2_data_a, color="#000000", label="Pressure1 (atm)")
plot5, = y2_axis.plot(x_data, y2_data_b, color="#17E111", label="Pressure2 (atm)")

plot6, = y3_axis.plot(x_data, y3_data, color="#D418DE", label="Volume (m3)")

y1_axis.set_xlabel("Time (hrs)")
y1_axis.set_ylabel("Temperature (°C)")
y2_axis.set_ylabel("Pressure (atm)")
y3_axis.set_ylabel("Volume (m3)")

y3_axis.yaxis.label.set_color(plot6.get_color())

tkw = dict(size=4, width=1.5)
y1_axis.tick_params(axis='y', **tkw)
y2_axis.tick_params(axis='y', **tkw)
y3_axis.tick_params(axis='y', colors=plot6.get_color(), **tkw)
y1_axis.tick_params(axis='x', **tkw)

lines = [plot1, plot2, plot4, plot5, plot6]

plt.title("Labeling data points for plots with Multiple Axes and Lines", fontdict=None, loc='center')

annot = y1_axis.annotate("", xy=(0, 0), xytext=(20, 20), textcoords="offset points",
                    bbox=dict(boxstyle="round", facecolor="#FFFFFF"),
                    arrowprops=dict(arrowstyle="->"))
annot.set_visible(False)


def update_annot(line, annot, ind):
    posx, posy = [line.get_xdata()[ind], line.get_ydata()[ind]]
    annot.xy = (posx, posy)
    text = f'{line.get_label()}: ({posx:.2f},{posy:.2f})'
    annot.set_text(text)
    annot.get_bbox_patch().set_alpha(1)


def hover(event):
    vis = annot.get_visible()
    if event.inaxes in [y1_axis, y2_axis, y3_axis]:
        for line in lines:
            cont, ind = line.contains(event)
            if cont:
                update_annot(line, annot, ind['ind'][0])
                annot.set_visible(True)
                fig.canvas.draw_idle()
            else:
                if vis:
                    annot.set_visible(False)
                    fig.canvas.draw_idle()


fig.canvas.mpl_connect("motion_notify_event", hover)
plt.show()

The ticker on the bottom right of the plot only seems to show the values based on the last axis that has been used.在这里搜索后，找到了3个解决方案，可以帮助显示一个包含点坐标的框：

1. Possible to make labels appear when hovering over a point in matplotlib?（没有完全使用此代码，因为它用于散点图，但从这里找到了其他解决方案）
2. Annotate lines of a plot with matplotlib when hover with mouse [duplicate]（用于将之前的解决方案应用于线条/曲线）
3. How to make labels appear when hovering over a point in multiple axis?（用于将标签应用于具有多个轴的图形）

如何使框出现在具有高 y 轴值的图形中，以及如何使框出现在图形上方以便清晰阅读？提前致谢！

Answer 1

本质上，您的问题是您将注释创建为属于轴y1_axis。当您将鼠标悬停在某个点上时，您将注释的位置设置在 y1_axis 的数据坐标中，而不管该线是在该轴上还是在另一个轴上。

解决方案是不仅更新注解的坐标，还更新其transform，以正确地将点映射到正确的像素坐标。

注解的背景也是如此。由于您是在最底部的轴上创建它，因此注释位于这些轴中的线上方，但位于其他轴中的线下方。这里的解决方案是在最顶部的轴上创建注释。

(...)
# annotation should be on the top axis to avoid zorder problems
annot = fig.axes[-1].annotate("", xy=(0, 0), xytext=(20, 20), textcoords="offset points",
                    bbox=dict(boxstyle="round", facecolor="#FFFFFF"),
                    arrowprops=dict(arrowstyle="->"))
annot.set_visible(False)

(...)
def update_annot(line, annot, ind):
    posx, posy = [line.get_xdata()[ind], line.get_ydata()[ind]]
    annot.xycoords = line.axes.transData  # set the correct transform for that line
    annot.xy = (posx, posy)
    text = f'{line.get_label()}: ({posx:.2f},{posy:.2f})'
    annot.set_text(text)
    annot.get_bbox_patch().set_alpha(1)
(...)

完整代码：

from matplotlib import pyplot as plt
import numpy as np; np.random.seed(1)

x_data = list(range(0,30))
y1_data_a = np.sort(np.random.rand(30))
y1_data_b = np.sort(np.random.rand(30))
y1_data_c = [0.4 for point in x_data]
y2_data_a = [point**2 for point in x_data]
y2_data_b = [point*0.5 for point in y2_data_a]
y3_data = [(10/(point+1)) for point in x_data]

# #The code works fine with this data
# x_data = list(range(0,30))
# y1_data_a = np.sort(np.random.rand(30))
# y1_data_b = np.sort(np.random.rand(30))
# y1_data_c = [0.4 for point in x_data]
# y2_data_a = np.random.rand(30)
# y2_data_b = np.sort(np.random.rand(30))
# y3_data = np.sort(np.random.rand(30))[::-1]

fig, y1_axis = plt.subplots()
fig.subplots_adjust(right=0.75)

y2_axis = y1_axis.twinx()
y3_axis = y1_axis.twinx()

def make_patch_spines_invisible(ax):
    ax.set_frame_on(True)
    ax.patch.set_visible(False)
    for sp in ax.spines.values():
        sp.set_visible(False)

y3_axis.spines["right"].set_position(("axes", 1.2))
make_patch_spines_invisible(y3_axis)
y3_axis.spines["right"].set_visible(True)

plot1, = y1_axis.plot(x_data, y1_data_a, color="#000CFF", label="Temp1 (°C)")
plot2, = y1_axis.plot(x_data, y1_data_b, color="#FF5100", label="Temp2 (°C)")
plot3, = y1_axis.plot(x_data, y1_data_c, "r--", label="Critical Temp (°C)")

plot4, = y2_axis.plot(x_data, y2_data_a, color="#000000", label="Pressure1 (atm)")
plot5, = y2_axis.plot(x_data, y2_data_b, color="#17E111", label="Pressure2 (atm)")

plot6, = y3_axis.plot(x_data, y3_data, color="#D418DE", label="Volume (m3)")

y1_axis.set_xlabel("Time (hrs)")
y1_axis.set_ylabel("Temperature (°C)")
y2_axis.set_ylabel("Pressure (atm)")
y3_axis.set_ylabel("Volume (m3)")

y3_axis.yaxis.label.set_color(plot6.get_color())

tkw = dict(size=4, width=1.5)
y1_axis.tick_params(axis='y', **tkw)
y2_axis.tick_params(axis='y', **tkw)
y3_axis.tick_params(axis='y', colors=plot6.get_color(), **tkw)
y1_axis.tick_params(axis='x', **tkw)

lines = [plot1, plot2, plot4, plot5, plot6]

plt.title("Labeling data points for plots with Multiple Axes and Lines", fontdict=None, loc='center')

# annotation should be on the top axis to avoid zorder problems
annot = fig.axes[-1].annotate("", xy=(0, 0), xytext=(20, 20), textcoords="offset points",
                    bbox=dict(boxstyle="round", facecolor="#FFFFFF"),
                    arrowprops=dict(arrowstyle="->"))
annot.set_visible(False)


def update_annot(line, annot, ind):
    posx, posy = [line.get_xdata()[ind], line.get_ydata()[ind]]
    annot.xycoords = line.axes.transData
    annot.xy = (posx, posy)
    text = f'{line.get_label()}: ({posx:.2f},{posy:.2f})'
    annot.set_text(text)
    annot.get_bbox_patch().set_alpha(1)


def hover(event):
    vis = annot.get_visible()
    if event.inaxes in [y1_axis, y2_axis, y3_axis]:
        for line in lines:
            cont, ind = line.contains(event)
            if cont:
                update_annot(line, annot, ind['ind'][0])
                annot.set_visible(True)
                fig.canvas.draw_idle()
            else:
                if vis:
                    annot.set_visible(False)
                    fig.canvas.draw_idle()


fig.canvas.mpl_connect("motion_notify_event", hover)
plt.show()