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?
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6 Answers
1 Comment
Ravi Sankar Raju
For those who were wondering what np is, like myself "import numpy as np"
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
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qwr
what about loss of precision?
I usually do like this:
from numpy import log as ln Perhaps this can make you more comfortable.
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Friedrich -- Слава Україні
Even more consequent
from numpy import log as ln, log10 as log; but probably not so advisable.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 
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from numpy.lib.scimath import logn from math import e #using: x - var logn(e, x) 
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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 
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Alex Moore-Niemi
note
math.e exists and math.log takes the base 2nd. so math.log(10, math.e) is correct, while the above would actually return ~0.43...