Using numpy, how can I do the following:
ln(x)
Is it equivalent to:
np.log(x)
I apologise for such a seemingly trivial question, but my understanding of the difference between log and ln is that ln is logspace e?
6 Answers
Correct, np.log(x) is the Natural Log (base e log) of x.
For other bases, remember this law of logs: log-b(x) = log-k(x) / log-k(b) where log-b is the log in some arbitrary base b, and log-k is the log in base k, e.g.
here k = e
l = np.log(x) / np.log(100)
and l is the log-base-100 of x
I usually do like this:
from numpy import log as ln
Perhaps this can make you more comfortable.
Numpy seems to take a cue from MATLAB/Octave and uses log to be "log base e" or ln. Also like MATLAB/Octave, Numpy does not offer a logarithmic function for an arbitrary base.
If you find log confusing you can create your own object ln that refers to the numpy.log function:
>>> import numpy as np
>>> from math import e
>>> ln = np.log # assign the numpy log function to a new function called ln
>>> ln(e)
1.0
from numpy.lib.scimath import logn
from math import e
#using: x - var
logn(e, x)
You could simple just do the reverse by making the base of log to e.
import math
e = 2.718281
math.log(e, 10) = 2.302585093
ln(10) = 2.30258093
