From the numpy documentation on logarithms, I have found functions to take the logarithm with base e, 2, and 10:
import numpy as np
np.log(np.e**3) #3.0
np.log2(2**3) #3.0
np.log10(10**3) #3.0
However, how do I take the logarithm with base n (e.g. 42) in numpy?
If you have numpy 1.23 or later, you can use np.emath.logn:
import numpy as np
array = np.array([74088, 3111696]) # = [42^3, 42^4]
base = 42
exponent = np.emath.logn(base, array) # = [3, 4]
If your version of numpy is older:
To get the logarithm with a custom base using math.log:
import math
number = 74088 # = 42^3
base = 42
exponent = math.log(number, base) # = 3
To get the logarithm with a custom base using numpy.log:
import numpy as np
array = np.array([74088, 3111696]) # = [42^3, 42^4]
base = 42
exponent = np.log(array) / np.log(base) # = [3, 4]
Which uses the logarithm base change rule:
out, so when naively switching math.log(number, base) to np.log(array, base), you get confusing errors like TypeError: 'float' object does not support item assignment or TypeError: return arrays must be of ArrayType. Apparently all ufuncs have the out parameter, but if it were keyword-only, it'd be so much simpler.Numpy's base n logarithm function is np.emath.logn.
import numpy as np
arr = np.array([74088, 3111696]) # = [42^3, 42^4]
base = 42
np.emath.logn(base, arr) # array([3., 4.])
np.emath.logn(14, 14**3) # 3.0
Note that unlike math.log, the base is the first argument. Also, unlike math.log, it can handle negative numbers (returns a complex number).
