python - 如何使用 matplotlib 自动设置轴绘制二维向量?
问题描述
我几乎写了一个绘制向量的代码:
a = [2,-1]
b = [1,2]
d = [5,2]
def plot_vectors(**kwargs):
M = []
for key, value in kwargs.items():
M.append(list([key]) + list(value))
ax = plt.axes()
ax.grid(b=True, which='major')
for i in range(len(M)):
l = 0
for j in range(1, len(M[i])):
l += M[i][j]**2
l = l**0.5
ax.text(M[i][1]/2, M[i][2]/2, f"{M[i][0]}={l:.2f}", size=14)
ax.plot([0,M[i][1]], [0,M[i][2]])
ax.set_aspect('equal', 'box')
plot_vectors(a=a, b=b, d=d)
主要思想不是直接设置,ax.set_xlim
而是使用ax.set_aspect('equal', 'box')
. 我没有找到如何使用ax.quiver
and来做到这一点ax.arrow
。任何人都可以建议如何在此处绘制箭头并将 y 轴值修改为如下所示:
我修改了代码以支持2D numpy
数组:
a = [2,-1]
b = [1,2]
d = [5,2]
def plot_vectors(**kwargs):
M = []
for key, value in kwargs.items():
if isinstance(value, np.ndarray):
value = value.tolist()
for i, v in enumerate(value):
if isinstance(v, np.ndarray):
value[i] = value[i].tolist()
if not isinstance(value[0], list):
value = [[0,0], value]
M.append([key] + value)
ax = plt.axes()
ax.grid(b=True, which='major')
for i in range(len(M)):
l = 0; pos = []
for j in range(0, len(M[i][1])):
pos.append(M[i][2][j] - M[i][1][j])
l += (pos[j])**2
pos[j] = pos[j] / 2 + M[i][1][j]
l = l**0.5
ax.plot([M[i][1][0], M[i][2][0]], [M[i][1][1], M[i][2][1]])
ax.text(pos[0], pos[1], f"{M[i][0]}={l:.2f}", size=14)
ax.set_aspect('equal', 'box')
plot_vectors(a=np.array(a), b=b, d=d, e=[d,np.array(b)])
我的尝试quiver
:
a = [2,-1]
b = [1,2]
d = [5,2]
def plot_vectors(**kwargs):
M = []
for key, value in kwargs.items():
if isinstance(value, np.ndarray):
value = value.tolist()
for i, v in enumerate(value):
if isinstance(v, np.ndarray):
value[i] = value[i].tolist()
if not isinstance(value[0], list):
value = [[0,0], value]
M.append([key] + value)
ax = plt.axes()
ax.grid(b=True, which='major')
print(M)
for i in range(len(M)):
l = 0; pos = []
for j in range(0, len(M[i][1])):
pos.append(M[i][2][j] - M[i][1][j])
l += (pos[j])**2
pos[j] = pos[j] / 2 + M[i][1][j]
l = l**0.5
ax.text(pos[0], pos[1], f"{M[i][0]}={l:.2f}", size=14)
x, y, u, v = zip(*[(i[1][0], i[1][1], i[2][0], i[2][1]) for i in M])
print(x, y, u, v)
ax.quiver(x, y, u, v, scale=1)
ax.set_aspect('equal', 'box')
plot_vectors(a=np.array(a), b=b, d=d, e=[d,np.array(b)])
返回:
[['a', [0, 0], [2, -1]], ['b', [0, 0], [1, 2]], ['d', [0, 0], [5, 2]],
['e', [5, 2], [1, 2]]]
(0, 0, 0, 5) (0, 0, 0, 2) (2, 1, 5, 1) (-1, 2, 2, 2)
问题:
e
未绘制矢量。- 如何为每个新向量分配视觉上不同的随机颜色?
a
未绘制部分矢量。- 如何从
y
轴中删除十进制值?
最后,在@ImportanceOfBeingErnest 的大力帮助下,我做了我想做的事:
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
import numpy as np
a = [2,-1]
b = [1,2]
d = [5,2]
def plot_vectors(**kwargs):
M = []
for key, value in kwargs.items():
if isinstance(value, np.ndarray):
value = value.tolist()
for i, v in enumerate(value):
if isinstance(v, np.ndarray):
value[i] = value[i].tolist()
if not isinstance(value[0], list):
value = [[0,0], value]
M.append([key] + value)
plt.figure(figsize=(12,12))
ax = plt.axes()
ax.grid(b=True, which='major')
ax.xaxis.set_major_locator(MaxNLocator(integer=True)); ax.yaxis.set_major_locator(MaxNLocator(integer=True))
ax.set_aspect('equal', 'box')
cmap = plt.get_cmap('nipy_spectral')
lc = np.linspace(0.03, 0.99, 20)
colors = cmap(np.insert(lc[::2], range(10), lc[::-2]))
for i in range(len(M)):
l = 0; pos = []
for j in range(0, len(M[i][1])):
pos.append(M[i][2][j] - M[i][1][j])
l += (pos[j])**2
pos[j] = pos[j] / 2 + M[i][1][j]
l = l**0.5
ax.text(pos[0], pos[1], f'{M[i][0]}={l:.2f}', size=18)
x, y, u, v = zip(*[(i[1][0], i[1][1], i[2][0] - i[1][0], i[2][1] - i[1][1]) for i in M])
ax.quiver(x, y, u, v, angles='xy', scale_units='xy', scale=1., color=colors[:len(M)])
ax.plot(np.array(x)+np.array(u), np.array(y)+np.array(v), np.array(x), np.array(y), visible=False)
plot_vectors(a=np.array(a), b=b, d=d, e=np.array([d,np.array(b)]), ab=[a,b])
解决方案
我想你会通过更努力地搜索stackoverflow来找到2.和4.的解决方案。真正的问题(1./3.)是箭头末端不参与自动缩放机制。当它们不在数据坐标中时,通常会出现这种情况,但如果它们不在数据坐标中,则可以预期它们会更改绘图的数据限制。
在任何情况下,一种解决方法是绘制一个plot
除了箭袋之外的不可见点,其中包含向量起点和终点的点:
ax.quiver(x, y, u, v, angles='xy', scale_units='xy', scale=1.)
ax.plot(np.array(x)+np.array(u), np.array(y)+np.array(v), np.array(x), np.array(y), visible=False)