Matplotlib fill_between() 多行

Question

I am plotting four lines which form an area in between them, an area I am trying to fill. However, I can't quite figure it out due to the limited amount of arguments I can use to define the area.

Any help would be greatly appreciated.

import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 

#global values

sigma_ct_inf = 0
sigma_ct_0 = 0
sigma_c_inf = 30
sigma_c_0 = 12
beta = 0.8

#values T 70
A = 359000
Wb = 40830202.33
Wt = 72079066.94
Mmin = 701.17
Mmax = 978.52


#Magnel Diagram
e = range(0, 1001)

disqualities = [1, 2, 3, 4] #'t70_top_0', 't70_top_inf', 't70_bot_0', 't70_bot_inf'

for i in disqualities:
    if i == 1:
        t70_top_0 = pd.DataFrame(e, columns = ['x'])
        t70_top_0['y'] = ((t70_top_0.x - (Wt/A))/((Mmin*10**6) + sigma_ct_0 * Wt))*10**6

    elif i == 2:
        t70_bot_0 = pd.DataFrame(e, columns = ['x'])
        t70_bot_0['y'] = ((t70_bot_0.x + (Wb/A))/((Mmin*10**6) + sigma_c_0 * Wb))*10**6

    elif i == 3:
        t70_top_inf = pd.DataFrame(e, columns = ['x'])
        t70_top_inf['y'] = (((t70_top_inf.x - (Wt/A))*beta)/((Mmax*10**6) - sigma_c_inf * Wt))*10**6

    elif i == 4:
        t70_bot_inf = pd.DataFrame(e, columns = ['x'])
        t70_bot_inf['y'] = (((t70_bot_inf.x + (Wb/A))*beta)/((Mmax*10**6) - sigma_ct_inf * Wb))*10**6


fig, ax = plt.subplots()
ax.set_title('Magnel Diagram, T-70')
line1 = ax.plot(t70_top_0['x'], t70_top_0['y'], lw = 0.5, label = 'Top, t = 0')
line1 = ax.plot(t70_bot_0['x'], t70_bot_0['y'], lw = 0.5,label = 'Bottom, t = 0')
line1 = ax.plot(t70_top_inf['x'], t70_top_inf['y'], lw = 0.5,label = 'Top, t = \u221E')
line1 = ax.plot(t70_bot_inf['x'], t70_bot_inf['y'], lw = 0.5,label = 'Bottom, t = \u221E')

plt.fill_between(t70_top_inf['x'], t70_top_inf['y'], t70_bot_inf['y'], where=t70_top_inf['y']<t70_bot_inf['y'], color = 'r', alpha = 0.4)

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.ylabel('1/P0 [1/MN]')
plt.xlabel('Eccentricity [mm]')
plt.legend()
plt.show()

Answer 1

A new top curve can be defined as the minimum between the two existing top curves. And a new bottom curve as the maximum between the existing bottom curves.

The code below is somewhat simplified, using one common x-axis ('e'). The naming is the same as from the question, where bottom and top seem to be switched.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

#global values
sigma_ct_inf = 0
sigma_ct_0 = 0
sigma_c_inf = 30
sigma_c_0 = 12
beta = 0.8

#values T 70
A = 359000
Wb = 40830202.33
Wt = 72079066.94
Mmin = 701.17
Mmax = 978.52

#Magnel Diagram
e = np.arange(0, 1001)

t70_top_0 = pd.Series({'y': ((e - (Wt / A)) / ((Mmin * 10 ** 6) + sigma_ct_0 * Wt)) * 10 ** 6})
t70_bot_0 = pd.Series({'y': ((e + (Wb / A)) / ((Mmin * 10 ** 6) + sigma_c_0 * Wb)) * 10 ** 6})
t70_top_inf = pd.Series({'y': (((e - (Wt / A)) * beta) / ((Mmax * 10 ** 6) - sigma_c_inf * Wt)) * 10 ** 6})
t70_bot_inf = pd.Series({'y': (((e + (Wb / A)) * beta) / ((Mmax * 10 ** 6) - sigma_ct_inf * Wb)) * 10 ** 6})

bot = np.min([t70_bot_0['y'], t70_bot_inf['y']], axis=0)
top = np.max([t70_top_0['y'], t70_top_inf['y']], axis=0)

fig, ax = plt.subplots()
ax.set_title('Magnel Diagram, T-70')
ax.plot(e, t70_top_0['y'], lw=0.5, label='Top, t = 0')
ax.plot(e, t70_bot_0['y'], lw=0.5, label='Bottom, t = 0')
ax.plot(e, t70_top_inf['y'], lw=0.5, label='Top, t = \u221E')
ax.plot(e, t70_bot_inf['y'], lw=0.5, label='Bottom, t = \u221E')

# ax.fill_between(e, t70_top_inf['y'], t70_bot_inf['y'], where=t70_top_inf['y'] < t70_bot_inf['y'], color='r', alpha=0.1)
# ax.fill_between(e, t70_top_0['y'], t70_bot_0['y'], where=t70_top_0['y'] < t70_bot_0['y'], color='b', alpha=0.1)
ax.fill_between(e, bot, top, where=top < bot, color='dodgerblue', alpha=0.4)

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.ylabel('1/P0 [1/MN]')
plt.xlabel('Eccentricity [mm]')
plt.legend()
plt.show()