Numpy linspace returns evenly spaced numbers over a specified interval. Numpy logspace return numbers spaced evenly on a log scale.
I don't understand why numpy logspace often returns values "out of range" from the bounds I set. Take numbers between 0.02
and 2.0
:
import numpy as np
print np.linspace(0.02, 2.0, num=20)
print np.logspace(0.02, 2.0, num=20)
The output for the first is:
[ 0.02 0.12421053 0.22842105 0.33263158 0.43684211 0.54105263
0.64526316 0.74947368 0.85368421 0.95789474 1.06210526 1.16631579
1.27052632 1.37473684 1.47894737 1.58315789 1.68736842 1.79157895
1.89578947 2. ]
That looks correct. However, the output for np.logspace()
is wrong:
[ 1.04712855 1.33109952 1.69208062 2.15095626 2.73427446
3.47578281 4.41838095 5.61660244 7.13976982 9.07600522
11.53732863 14.66613875 18.64345144 23.69937223 30.12640904
38.29639507 48.68200101 61.88408121 78.6664358 100. ]
Why does it output 1.047
to 100.0
?
2017 update: The numpy 1.12 includes a function that does exactly what the original question asked, i.e. returns a range between two values evenly sampled in log space.
The function is numpy.geomspace
>>> np.geomspace(0.02, 2.0, 20)
array([ 0.02 , 0.0254855 , 0.03247553, 0.04138276, 0.05273302,
0.06719637, 0.08562665, 0.1091119 , 0.13903856, 0.17717336,
0.22576758, 0.28768998, 0.36659614, 0.46714429, 0.59527029,
0.75853804, 0.96658605, 1.23169642, 1.56951994, 2. ])
logspace
computes its start and end points as base**start
and base**stop
respectively. The base
value can be specified, but is 10.0 by default.
For your example you have a start value of 10**0.02 == 1.047
and a stop value of 10**2 == 100
.
You could use the following parameters (calculated with np.log10
) instead:
>>> np.logspace(np.log10(0.02) , np.log10(2.0) , num=20)
array([ 0.02 , 0.0254855 , 0.03247553, 0.04138276, 0.05273302,
0.06719637, 0.08562665, 0.1091119 , 0.13903856, 0.17717336,
0.22576758, 0.28768998, 0.36659614, 0.46714429, 0.59527029,
0.75853804, 0.96658605, 1.23169642, 1.56951994, 2. ])
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