我正在尝试在极坐标图中标记节点。有 3 个被分割的“轴”，我已经弄清楚如何使用象限来选择要标记的节点。但是，我不知道如何在图的边缘对齐这些（即axis_maximum）。我花了几个小时试图弄清楚这一点。我最好的选择是用.在左边或右边，但这是一个固定的数字，当点太多时会变得混乱。而且，当有很多点时，这种方法太过脱离了情节的“循环”性质。我做了一些三角学计算出所有内容的长度，但这很难使用文本单元来实现，例如..
如果有人可以提供帮助，我们将不胜感激。我展示了下面的情节，然后用红色添加了我想要实现的内容。label模拟图中对应于name_node在for循环中。理想情况下，我想避免使用类似的字符.并且宁愿使用实际的matplotlib Line对象，以便我可以指定linestyle like : or -.
总而言之，我想做到以下几点：
name_node text.EDIT:
import numpy as np
from numpy import array # I don't like this but it's for loading in the pd.DataFrame
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'node_positions_normalized': {'iris_100': 200.0, 'iris_101': 600.0, 'iris_102': 1000.0, 'iris_0': 200.0, 'iris_1': 600.0, 'iris_2': 1000.0, 'iris_50': 200.0, 'iris_51': 600.0, 'iris_52': 1000.0}, 'theta': {'iris_100': array([5.42070629, 6.09846678]), 'iris_101': array([5.42070629, 6.09846678]), 'iris_102': array([5.42070629, 6.09846678]), 'iris_0': array([1.23191608, 1.90967657]), 'iris_1': array([1.23191608, 1.90967657]), 'iris_2': array([1.23191608, 1.90967657]), 'iris_50': array([3.32631118, 4.00407168]), 'iris_51': array([3.32631118, 4.00407168]), 'iris_52': array([3.32631118, 4.00407168])}})
axis_maximum = df["node_positions_normalized"].max()
thetas = np.unique(np.stack(df["theta"].values).ravel())
def pol2cart(rho, phi):
x = rho * np.cos(phi)
y = rho * np.sin(phi)
return(x, y)
def _get_quadrant_info(theta_representative):
# 0/360
if theta_representative == np.deg2rad(0):
quadrant = 0
# 90
if theta_representative == np.deg2rad(90):
quadrant = 90
# 180
if theta_representative == np.deg2rad(180):
quadrant = 180
# 270
if theta_representative == np.deg2rad(270):
quadrant = 270
# Quadrant 1
if np.deg2rad(0) < theta_representative < np.deg2rad(90):
quadrant = 1
# Quadrant 2
if np.deg2rad(90) < theta_representative < np.deg2rad(180):
quadrant = 2
# Quadrant 3
if np.deg2rad(180) < theta_representative < np.deg2rad(270):
quadrant = 3
# Quadrant 4
if np.deg2rad(270) < theta_representative < np.deg2rad(360):
quadrant = 4
return quadrant
with plt.style.context("seaborn-white"):
fig = plt.figure(figsize=(8,8))
ax = plt.subplot(111, polar=True)
ax_cartesian = fig.add_axes(ax.get_position(), frameon=False, polar=False)
ax_cartesian.set_xlim(-axis_maximum, axis_maximum)
ax_cartesian.set_ylim(-axis_maximum, axis_maximum)
# Draw axes
for theta in thetas:
ax.plot([theta,theta], [0,axis_maximum], color="black")
# Draw nodes
for name_node, data in df.iterrows():
r = data["node_positions_normalized"]
for theta in data["theta"]:
ax.scatter(theta, r, color="teal", s=150, edgecolor="black", linewidth=1, alpha=0.618)
# Draw node labels
quadrant = _get_quadrant_info(np.mean(data["theta"]))
# pad on the right and push label to left
if quadrant in {1,4}:
theta_anchor_padding = min(data["theta"])
# pad on left and push label to the right
if quadrant in {2,3}:
theta_anchor_padding = max(data["theta"])
# Plot
ax.text(
s=name_node,
x=theta_anchor_padding,
y=r,
horizontalalignment="center",
verticalalignment="center",
)
ax.set_rlim((0,axis_maximum))
# Convert polar to cartesian and plot on cartesian overlay?
xf, yf = pol2cart(theta_anchor_padding, r) #fig.transFigure.inverted().transform(ax.transData.transform((theta_anchor_padding, r)))
ax_cartesian.plot([xf, axis_maximum], [yf, yf])
您可以使用annotate代替text，这允许您独立于点坐标来指定文本坐标和文本坐标系。我们将文本放置在图形坐标中（0 to 1, see here了解详情）。从数据到图形坐标的转换很重要after the r已设置限制。
with plt.style.context("seaborn-white"):
fig = plt.figure(figsize=(8,8))
ax = plt.subplot(111, polar=True)
ax.set_rlim((0,axis_maximum))
ann_transf = ax.transData + fig.transFigure.inverted()
# Draw axes
for theta in thetas:
ax.plot([theta,theta], [0,axis_maximum], color="black")
# Draw nodes
for name_node, data in df.iterrows():
r = data["node_positions_normalized"]
for theta in data["theta"]:
ax.scatter(theta, r, color="teal", s=150, edgecolor="black", linewidth=1, alpha=0.618)
# Draw node labels
quadrant = _get_quadrant_info(np.mean(data["theta"]))
# pad on the right and push label to left
if quadrant in {1,4}:
theta_anchor_padding = min(data["theta"])
# pad on left and push label to the right
if quadrant in {2,3}:
theta_anchor_padding = max(data["theta"])
# Plot
_,y = ann_transf.transform((theta_anchor_padding, r))
ax.annotate(name_node,
(theta_anchor_padding,r),
(0.91 if quadrant in {1,4} else 0.01, y),
textcoords='figure fraction',
arrowprops=dict(arrowstyle='-', color='r'),
color='r',
verticalalignment='center'
)
