Fitting data with Python
I’ve recently become a heavy user of the numerical capabilities of Python. I’ve written about my experiments before, but now I’m writing production quality code with numpy and matplotlib.
The above is an actual plot that I created for some Hall
measurements I was doing. I was supposed for find functional
relationships between temperature and majority charge carriers, which
in my case were electrons because of the n-type doping. The simple
case was a least squares fit: scipy.optimize.leastsq to the
rescue. The more complicated part was solving a non-linear equation
for roots and then doing a least squares fit. The root-finding module
in scientific python provides lots of options.
At this point, I can confidently say that this environment has more
features than Octave.
Just today, I wanted to use the Fourier method on a differential
equation (plug: the advantages of which are here) and numerical
python with fft, fftshift and fftfreq are exact substitutes for
their Matlab equivalents. You can also put actual LaTeX equations on
plots, which is a major plus.
That is all.