I used polyfit to find a fitline of a data set, but now I need to find the natural log of that fitline function and plot it. Here's what I have so far:
#Fit line for PD deg = 10 zn = np.polyfit(l_bins, l_hits, deg) l_pn = np.poly1d(zn) pylab.plot(l_bins, l_pn(l_bins), '-g') ln_list = [] for all in l_bins: ln_list.append(np.log(l_pn(all))) pylab.plot(l_bins, ln_list, '-b') Is there a better or more correct way to do this?
Edit
I would suggest using numpy.log as Roger Fan demonstrated below. Since you are already using numpy arrays, this will certainly outperform using map or a list comprehension.
Original answer
If you have a list of z-values, you can use map to perform some function to each value, in this case log (which is ln).
>>> x = range(1,10) >>> x [1, 2, 3, 4, 5, 6, 7, 8, 9] >>> from math import log >>> map(log, x) [0.0, 0.6931471805599453, 1.0986122886681098, 1.3862943611198906, 1.6094379124341003, 1.791759469228055, 1.9459101490553132, 2.0794415416798357, 2.1972245773362196] You could use any function, so you may use numpy.log if you prefer.
It seems that you just want the values for the originally provided bins. In that case, this is simpler and will be much faster.
ln_list = np.log(l_pn(l_bins)) Keep in mind that numpy functions will generally apply themselves element-wise to an array if it makes sense to do so.
log(x) is 10 based, while ln(x) is natural logarithm based.
Ok, talk is cheap, show the code:
import math x = 8 print math.log(x, math.e)