Plotting Trig Functions and Help with Streamlining Code

[Dasiey12]

Hi
I am plotting a unit circle in Matlibplot. I am using a Jupiter Notebook to run the code. I am really looking for a way to shorten the code. I found the x and y coordinates for the angles 30, 45, 60, 90, and the rest of the angles that are always on the unit circle. I was wondering if there is a faster way to draw these lines? Is there away to find slope of each line and plot it?

Here is my code.

import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(1,1,1)
ax.spines['left'].set_position(('axes',.5))
ax.yaxis.set_ticks_position('left')
plt.ylim(-2,2)
ax.spines['bottom'].set_position(("axes", .5))
plt.xlim(-2,2)
x_theta_0 = [1, 0, -1]
y_theta_0 = [0,0,0]
x_theta_30 = [np.sqrt(3)/2, 0, -np.sqrt(3)/2]
y_theta_30 = [1/2, 0 , -1/2]
x_theta_45 = [np.sqrt(2)/2, 0, -np.sqrt(2)/2]
y_theta_45 = [np.sqrt(2)/2, 0, -np.sqrt(2)/2]
x_theta_60 = [1/2, 0, -1/2]
y_theta_60 = [np.sqrt(3)/2, 0, -np.sqrt(3)/2]
x_theta_90 = [0, 0, 0]
y_theta_90 = [1, 0, -1]
x_theta_120 = [-1/2, 0, 1/2]
y_theta_120 = [np.sqrt(3)/2, 0, -np.sqrt(3)/2]
x_theta_135 = [-np.sqrt(2)/2, 0, np.sqrt(2)/2]
y_theta_135 = [np.sqrt(2)/2, 0, -np.sqrt(2)/2]
x_theta_150 = [-np.sqrt(3)/2, 0, np.sqrt(3)/2]
y_theta_150 = [1/2, 0, -1/2]
x_tan_30 = [1, 1]
y_tan_30 = [0, np.sqrt(3)/3]
plt.plot(x_theta_0, y_theta_0)
plt.plot(x_theta_30, y_theta_30)
plt.plot(x_theta_45, y_theta_45)
plt.plot(x_theta_60, y_theta_60)
plt.plot(x_theta_90, y_theta_90)
plt.plot(x_theta_120, y_theta_120)
plt.plot(x_theta_135, y_theta_135)
plt.plot(x_theta_150, y_theta_150)
plt.plot(x_tan_30, y_tan_30)
c = plt.Circle((0,0), 1)
plt.gca().add_artist(c)

The image below is my output.

[Gribouillis]

Numpy knows trigonometric functions. No need to hard code their values

import matplotlib.pyplot as plt
import numpy as np

theta = np.deg2rad(np.array([30, 45, 60, 90, 120, 135, 150]))

x, y = np.cos(theta), np.sin(theta)

x = np.stack((-x, x)).T
y = np.stack((-y, y)).T

print(x)
print(y)

fig, ax = plt.subplots()
for u, v in zip(x, y):
    ax.plot(u, v)
    
ax.set(aspect='equal')
fig.savefig('foo.pdf')