Matplotlib/Pylab - part of plot disappears after setting log scale

Question

I have a small code producing the following picture with this code:

Code 1:

hist, rhist = np.histogram(r, bins=40, range=(0, 0.25))
hist = -hist/np.trapz(rhist[:-1],hist)
plt.plot(rhist[:-1], hist)

Output of code 1: enter image description here

Then I try setting the plot to have a logarithmic Y axis so that I can recognise small peaks more clearly. This is the result.

Code 2:

hist, rhist = np.histogram(r, bins=40, range=(0, 0.25))
hist = -hist/np.trapz(rhist[:-1],hist)
plt.semilogy(rhist[:-1], hist)

Output of code 2: enter image description here

As you can see, part of my plot disappears. There are 40 bins, I can however only count about 15 in the new plot. Any help will be greatly appreciated. I am using Enthought Canopy of the latest version for academic use. E.

UPDATE: I did find a similar question here, old, dead and unanswered though.

will · Accepted Answer · 2013-07-15 23:50:58Z

1

I'm pretty sure it's just not plotting those values because they are zero.

Log(0) = -Infinity.

Plotting that is going to make your graph look pretty rubbish...

answered Jul 15, 2013 at 23:50

will

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1 Comment

erthy Over a year ago

Thank you. I did not even think about that and it is so obvious! :)

Community · Accepted Answer · 2017-05-23 10:28:59Z

1

Issue plt.yscale('symlog') at the end of your plotting. See here for a description of 'symlog'.

edited May 23, 2017 at 10:28

CommunityBot

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answered Jul 16, 2013 at 19:42

esmit

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Comments

user2304916 · Accepted Answer · 2013-07-15 23:58:19Z

0

A common visual trick to "display" zero in log scale is to use a very small value instead:

plt.semilogy(rhist[:-1], hist+1e-6)

In this case beaware of correct interpretation of plot though.

answered Jul 15, 2013 at 23:58

user2304916

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2 Comments

erthy Over a year ago

Good idea as well. I'd upvote you but I don't have the rep, haha.

will Over a year ago

If you're going to do this, then this really isn't how you should do it... You should use the nonposy argument. See this answer for more info.

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