Combining logarithm and linear scale in matplotlib

Question

Example here What is the difference between 'log' and 'symlog'? It shows well how the linear scale at the origin can be used with the log scale elsewhere. I want to go the other way around. I want to have a log scale of 1 to 100, and then linear! from 100 to 1000. What are my options? As in the picture above This attempt does not work.

    import matplotlib.pyplot as plt
    plt.figure()
    plt.errorbar(x, y, yerr=yerrors)
    plt.xscale('symlog', linthreshx= (100,1000))

The problem is that linthreshx is defined as a range (-x, x). Therefore, if x, if 5, we get a linear scale at (-5.5). One is limited to origin. I thought that just choosing a different range should work, but it is not. Any ideas?

Answer 1

From the response of user1318806 to cphlewis :

Thanks. Actually I need a combination of log + linear on the x axis , not y. But I assume that your code should be easily adaptable.

Hello! If you need a combination of log + linear along the x axis (with a picture from the Duncan Watts and CubeJockey code ):

import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np

# Numbers from -50 to 50, with 0.1 as step
xdomain = np.arange(-50,50, 0.1)

axMain = plt.subplot(111)
axMain.plot(np.sin(xdomain), xdomain)
axMain.set_xscale('linear')
axMain.set_xlim((0.5, 1.5))
axMain.spines['left'].set_visible(False)
axMain.yaxis.set_ticks_position('right')
axMain.yaxis.set_visible(False)


divider = make_axes_locatable(axMain)
axLin = divider.append_axes("left", size=2.0, pad=0, sharey=axMain)
axLin.set_xscale('log')
axLin.set_xlim((0.01, 0.5))
axLin.plot(np.sin(xdomain), xdomain)
axLin.spines['right'].set_visible(False)
axLin.yaxis.set_ticks_position('left')
plt.setp(axLin.get_xticklabels(), visible=True)

plt.title('Linear right, log left')

The above code gives:

(MISCELLANEOUS) Here is a very minor correction for the name and the absence of labels on the right side:

# Fix for: title + no tick marks on the right side of the plot
ax2 = axLin.twinx()
ax2.spines['left'].set_visible(False)
ax2.tick_params(axis='y',which='both',labelright='off')

Adding these lines will give you the following:

python matplotlib

Answer 2

, , log - `symlog ' - , , , axes_grid:

# linear and log axes for the same plot?
# starting with the histogram example from 
# http://matplotlib.org/mpl_toolkits/axes_grid/users/overview.html
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np

# Numbers from -50 to 50, with 0.1 as step
xdomain = np.arange(-50,50, 0.1)

axMain = plt.subplot(111)
axMain.plot(xdomain, np.sin(xdomain))
axMain.set_yscale('log')
axMain.set_ylim((0.01, 0.5))
divider = make_axes_locatable(axMain)
axLin = divider.append_axes("top", size=2.0, pad=0.02, sharex=axMain)
axLin.plot(xdomain, np.sin(xdomain))

axLin.set_xscale('linear')
axLin.set_ylim((0.5, 1.5))
plt.title('Linear above, log below')

plt.show()

Answer 3

This solution adds to cphlewis answer , so that a smooth transition occurs, and successive label markers appear on the chart. My change adds these three lines:

axLin.spines['bottom'].set_visible(False)

axLin.xaxis.set_ticks_position('top')

plt.setp(axLin.get_xticklabels(), visible=False)

Overall code

# linear and log axes for the same plot?
# starting with the histogram example from 
# http://matplotlib.org/mpl_toolkits/axes_grid/users/overview.html
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np

# Numbers from -50 to 50, with 0.1 as step
xdomain = np.arange(-50,50, 0.1)

axMain = plt.subplot(111)
axMain.plot(xdomain, np.sin(xdomain))
axMain.set_yscale('log')
axMain.set_ylim((0.01, 0.5))
axMain.spines['top'].set_visible(False)
axMain.xaxis.set_ticks_position('bottom')

divider = make_axes_locatable(axMain)
axLin = divider.append_axes("top", size=2.0, pad=0, sharex=axMain)
axLin.plot(xdomain, np.sin(xdomain))
axLin.set_xscale('linear')
axLin.set_ylim((0.5, 1.5))

# Removes bottom axis line
axLin.spines['bottom'].set_visible(False)
axLin.xaxis.set_ticks_position('top')
plt.setp(axLin.get_xticklabels(), visible=False)

plt.title('Linear above, log below')

plt.show()