MemoryError with numpy arange

user_9703039 0
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I want to create an array of powers of 10 as a label for the y axis of a plot. I am using the plt.yticks() with matplotlib imported as plt but this does not matter here anyway. I have plots where as the y axis is varying from 1e3 to 1e15. Those are log plots. Matplotlib is automatically displaying those with ticks with 1e2 steps and I want to have a step of 10 instead (in order to be able to use the minorticks properly).

I want to use the plt.yticks(numpy.arange(1e3, 1e15, 10)) command as said but numpy.arange(1e3, 1e15, 10) result in a MemoryError. Isn't it supposed to output an array of length 13? Why does the memory gets full?

How to overpass this issue and not build the array manually?

I also tried using built-in range but it won't work with floats.

Thank you.

out-of-memory matplotlib numpy python
4 Answers

Try the logspace from NumPy as

plt.yticks(numpy.logspace(3, 15, 13))

Here you give the starting and the last exponent (powers of 10) and the number of data-points in between. If you print the above mesh, you get the following

array([1.e+03, 1.e+04, 1.e+05, 1.e+06, 1.e+07, 1.e+08, 1.e+09, 1.e+10,
   1.e+11, 1.e+12, 1.e+13, 1.e+14, 1.e+15])
user_49323160

You could also just do:

10. ** np.arange(3,16)

that decimal point is important, as without it you will overflow the default int32 dtype for integers

user_44277770

An alternative way to do this, rather than explicitly defining the tick positions, is to use a LogLocator from the matplotlib.ticker module, and manually increase the number of ticks (by default, it will try to set a nice-looking number of ticks; i.e. so it doesn't look too cramped).

In this example, I set the number of ticks to 13 on the Axes on the right (using numticks=13), and you can see this increases the number of ticks so there is one on each integer power of 10.

import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

# Create figure and axes
fig, (ax1, ax2) = plt.subplots(ncols=2)

# Make yscale logarithmic
ax1.set_yscale('log')
ax2.set_yscale('log')

# Set y limits
ax1.set_ylim(1e3, 1e15)
ax2.set_ylim(1e3, 1e15)

# On ax2, lets tell the locator how many ticks we want
ax2.yaxis.set_major_locator(ticker.LogLocator(numticks=13))

ax1.set_title('default ticks')
ax2.set_title('LogLocator with numticks=13')

plt.show()

enter image description here

EDIT:

To add minor ticks with this method, we can use another LogLocator, and this time set the subs option to say where we want minor ticks in each decade. Here I haven't set minor ticks on every 0.1 because it would be too cramped, so just done for a subset. Note that if you set minor ticks like this, you also need to turn off tick labels for the minor ticks, using a NullFormatter. 

ax2.yaxis.set_major_locator(ticker.LogLocator(numticks=13))
ax2.yaxis.set_minor_locator(ticker.LogLocator(subs=(0.2,0.4,0.6,0.8),numticks=13))
ax2.yaxis.set_minor_formatter(ticker.NullFormatter())

enter image description here

user_5880710

In this case the function logspace from numpy is more suitable. The answer to the example is np.logspace(3,15,num=15-3+1, endpoint=True)

user_97030390
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