Plot secondary axis with scale that is only related by index in the array to parent axis in matplotlib
Question:
Lets say I have:
x1 = [1,2,3,4,5,6,7,8,9,10]; x2 = [1,3,10,60,500,10000,70000,500000,1000000,10000000]; y =[1,2,3,4,5,6,7,8,9,10]
With matplotlib, how do I add x2 as a secondary axes (x1,y is the primary plot) when the only direct correlation between x1 and x2 is the index of the array? As in, there is no function to convert x1 to x2 (so I cannot use secondary_xaxis(pos, functions())? I’ve been trying secondary_xaxis for the past two days and just cannot get it to work. Also If i try to use set_xticks(x2) it bunches up the ticks but I want them directly corresponding to the x1 positions.
UPDATE:
my code looks like this:
self.x = [1,2,3,4,5,6,7,8,9,10]
self.y = [1,2,3,4,5,6,7,8,9,10]
x2 = [1,3,10,60,500,10000,70000,500000,1000000,10000000]
ax3 = ax.twiny()
ax.plot(self.x,self.y)
ax3.set_xticks(x2)
plt.show()
This gives the following graph. As you can see, the top ticks are not in line with where they should be (they should line up with the xticks on the bottom axis).
Answers:
From what I understand, you want the ticks of the top axis to align with the ticks at the bottom axis. For this, you need to define the amount of ticks on each axis and ensure they are the same. This is easily done by modifying the set_xticks() method for the ax object. Here is an example below
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots(ncols=1,nrows=1)
ax_o = ax.twiny()
N_ticks = 11
x1 = [1,2,3,4,5,6,7,8,9,10]
y = [1,2,3,4,5,6,7,8,9,10]
x2 = [1,3,10,60,500,10000,70000,500000,1000000,10000000]
ax.plot(x1,y) # Data for the bottom x-axis
ax.set_xticks(np.linspace(x1[0], x1[-1], N_ticks))
ax.set_xlim([x1[0], x1[-1]])
ax_o.plot(x2,y)# Data for the top x-axis
ax_o.set_xticks(x2)
ax_o.set_xticks(np.linspace(x2[0], x2[-1], N_ticks))
ax_o.set_xlim([x2[0], x2[-1]])
plt.grid() # To show they are aligned
plt.show()
Which gives
Hope this helps
Lets say I have:
x1 = [1,2,3,4,5,6,7,8,9,10]; x2 = [1,3,10,60,500,10000,70000,500000,1000000,10000000]; y =[1,2,3,4,5,6,7,8,9,10]
With matplotlib, how do I add x2 as a secondary axes (x1,y is the primary plot) when the only direct correlation between x1 and x2 is the index of the array? As in, there is no function to convert x1 to x2 (so I cannot use secondary_xaxis(pos, functions())? I’ve been trying secondary_xaxis for the past two days and just cannot get it to work. Also If i try to use set_xticks(x2) it bunches up the ticks but I want them directly corresponding to the x1 positions.
UPDATE:
my code looks like this:
self.x = [1,2,3,4,5,6,7,8,9,10]
self.y = [1,2,3,4,5,6,7,8,9,10]
x2 = [1,3,10,60,500,10000,70000,500000,1000000,10000000]
ax3 = ax.twiny()
ax.plot(self.x,self.y)
ax3.set_xticks(x2)
plt.show()
This gives the following graph. As you can see, the top ticks are not in line with where they should be (they should line up with the xticks on the bottom axis).
From what I understand, you want the ticks of the top axis to align with the ticks at the bottom axis. For this, you need to define the amount of ticks on each axis and ensure they are the same. This is easily done by modifying the set_xticks() method for the ax object. Here is an example below
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots(ncols=1,nrows=1)
ax_o = ax.twiny()
N_ticks = 11
x1 = [1,2,3,4,5,6,7,8,9,10]
y = [1,2,3,4,5,6,7,8,9,10]
x2 = [1,3,10,60,500,10000,70000,500000,1000000,10000000]
ax.plot(x1,y) # Data for the bottom x-axis
ax.set_xticks(np.linspace(x1[0], x1[-1], N_ticks))
ax.set_xlim([x1[0], x1[-1]])
ax_o.plot(x2,y)# Data for the top x-axis
ax_o.set_xticks(x2)
ax_o.set_xticks(np.linspace(x2[0], x2[-1], N_ticks))
ax_o.set_xlim([x2[0], x2[-1]])
plt.grid() # To show they are aligned
plt.show()
Which gives
Hope this helps