Finding partially feasible solution in matplotlib - matplotlib

Question

Consider the snippet:
# Original code was taken from https://benalexkeen.com/linear-programming-with-python-and-pulp-part-1/
import numpy as np
import matplotlib.pyplot as plt
# Constraints:
## 3x0 + 2x1 <= 5
## 4x0 - x1 <= 3
constraints = ["(5 - 3*x)/2.0", "- (3 - 4*x)/1.0"]
x = np.linspace(-10.5, 10.5, 2000)
### Taking x1 at RHS
### 3x0 + 2x1 <= 5
y1 = eval(constraints[0])
### 4x0 - x1 <= 3
y2 = eval(constraints[1])
# Make plot
plt.plot(x, y1, 'blue', label=r'$'+constraints[0]+'$')
plt.plot(x, y2, 'red', label=r'$'+constraints[1]+'$')
plt.xlim((-10, 10))
plt.ylim((-10, 10))
plt.xlabel(r'$x$')
plt.ylabel(r'$y$')
y3 = np.minimum(y1, y2) # Constraints of `<=` type
print (y3)
plt.plot(x, y3, 'green', label=r''+'overlap'+'')
#plt.fill_between(x, y1, y2, color='grey', alpha=0.5)
#plt.fill_between(x, y1, y3, color='pink', alpha=.5)
#plt.fill_between(x, y2, y3, color='yellow', alpha=.5)
plt.fill_between(x, y3, np.maximum(y1,y2), color='gray', alpha=.5)
#plt.fill_between(x, y2, y1, color='gray', alpha=.5)
#plt.fill_between(x, y1<=1, np.minimum(y1,y3), color='gray', alpha=.5)
#plt.fill_between(x, y3, y1, color='grey', alpha=0.5)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
What I am able to get is the common feasible solution and the non-feasible solution of the inequalities, such as:
What I actually wanted is to shade the partially feasible solutions individually (the upper and the lower triangles in the figure). Say, only the top triangle is shaded, rest is kept as is.
I tried multiple combinations (as can be seen from the code snippet), but could not achieve this. Any help would be appreciated.

Answer 1

You need to declare some upper and lower curves. Horizontal boundaries are good enough. Here y=-500 and y=500 are set up. A large number, i.e. 500 is needed.
y_ge_m500 = (x*0) - 500 #for lower-y bound
y_le_p500 = (x*0) + 500 #for upper-y bound
Then use
plt.fill_between(x, y_ge_m500, np.minimum(y1, y2), color='yellow', alpha=.5) #get lower ^
plt.fill_between(x, y_le_p500, np.maximum(y1, y2), color='pink', alpha=.5) #get Upper V
And you will get a plot similar to this: